Quantifying Greenhouse Gas Fluxes in 

Agriculture and Forestry: 

Methods for Entity-Scale Inventory 

 

Office of the 

Chief Economist

Climate Change 

Program Office

Technical 

Bulletin 1939

July 2014

United States Department of Agriculture

 

 i

Quantifying Greenhouse Gas Fluxes in Agriculture and

Forestry:		Methods for Entity‐Scale Inventory

Marlen Eve, Diana Pape, Mark Flugge, Rachel Steele, Derina Man, Marybeth Riley‐

Gilbert and Sarah Biggar, Editors.

USDA Technical Bulletin 1939

July 2014

Published by:

U.S. Department of Agriculture

Office of the Chief Economist

Washington, DC		20250

	 	
 

ii

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For	more	information	on	the	Quantifying Greenhouse Gas Fluxes in Agriculture and Forestry:		

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 iii

How to Obtain Copies:

You	may	electronically	download	this	document	from	the	U.S.	Department	of	Agriculture’s	website	

at:	http://www.usda.gov/oce/climate_change/estimation.htm		

Suggested Citation

Report Citation

Eve,	M.,	D.	Pape,	M.	Flugge,	R.	Steele,	D.	Man,	M.	Riley‐Gilbert,	and	S.	Biggar,	(Eds),	2014.		

Quantifying Greenhouse Gas Fluxes in Agriculture and Forestry:		Methods for Entity‐Scale Inventory.	

Technical	Bulletin	Number	1939.	Office	of	the	Chief	Economist,	U.S.	Department	of	Agriculture,	

Washington,	DC.	606	pages.	July	2014.	

Chapter Citations

Eve,	M.,	M.	Flugge,	D.	Pape,	2014.	Chapter	1:		Introduction.		In	Quantifying Greenhouse Gas Fluxes in

Agriculture and Forestry:		Methods for Entity‐Scale Inventory.	Technical	Bulletin	Number	1939.		

Office	of	the	Chief	Economist,	U.S.	Department	of	Agriculture,	Washington,	DC.	606	pages.		July	

2014.		Eve,	M.,	D.	Pape,	M.	Flugge,	R.	Steele,	D.	Man,	M.	Riley‐Gilbert,	and	S.	Biggar,	Eds.	

Eve,	M.,	M.	Flugge,	D.	Pape,	2014.	Chapter	2:	Considerations	When	Estimating	Agriculture	and	

Forestry	GHG	Emissions	and	Removals.		In	Quantifying Greenhouse Gas Fluxes in Agriculture and

Forestry:		Methods for Entity‐Scale Inventory.	Technical	Bulletin	Number	1939,	Office	of	the	Chief	

Economist,	U.S.	Department	of	Agriculture,	Washington,	DC.	606	pages.		July	2014.		Eve,	M.,	D.	Pape,	

M.	Flugge,	R.	Steele,	D.	Man,	M.	Riley‐Gilbert,	and	S.	Biggar,	Eds.	

Ogle,	S.M.,	P.R.	Adler,	F.J.	Breidt,	S.	Del	Grosso,	J.	Derner,	A.	Franzluebbers,	M.	Liebig,	B.	Linquist,	G.P.	

Robertson,	M.	Schoeneberger,	J.	Six,	C.	van	Kessel,	R.	Venterea,	T.	West,	2014.	Chapter	3:		

Quantifying	Greenhouse	Gas	Sources	and	Sinks	in	Cropland	and	Grazing	Land	Systems.		In	

Quantifying Greenhouse Gas Fluxes in Agriculture and Forestry:		Methods for Entity‐Scale Inventory.	

Technical	Bulletin	Number	1939,	Office	of	the	Chief	Economist,	U.S.	Department	of	Agriculture,	

Washington,	DC.	606	pages.		July	2014.		Eve,	M.,	D.	Pape,	M.	Flugge,	R.	Steele,	D.	Man,	M.	Riley‐

Gilbert,	and	S.	Biggar,	Eds.	

Ogle,	S.M.,	P.	Hunt,	C.	Trettin,	2014.	Chapter	4:		Quantifying	Greenhouse	Gas	Sources	and	Sinks	in	

Managed	Wetland	Systems.		In	Quantifying Greenhouse Gas Fluxes in Agriculture and Forestry:		

Methods for Entity‐Scale Inventory.	Technical	Bulletin	Number	1939,	Office	of	the	Chief	Economist,	

U.S.	Department	of	Agriculture,	Washington,	DC.	606	pages.		July	2014.		Eve,	M.,	D.	Pape,	M.	Flugge,	

R.	Steele,	D.	Man,	M.	Riley‐Gilbert,	and	S.	Biggar,	Eds.	

Powers,	W.,	B.	Auvermann,	N.A.	Cole,	C.	Gooch,	R.	Grant,	J.	Hatfield,	P.	Hunt,	K.	Johnson,	A.	Leytem,	

W.	Liao,	J.M.	Powell,	2014.	Chapter	5:		Quantifying	Greenhouse	Gas	Sources	and	Sinks	in	Animal	

Production	Systems.		In	Quantifying Greenhouse Gas Fluxes in Agriculture and Forestry:		Methods for

Entity‐Scale Inventory.	Technical	Bulletin	Number	1939,	Office	of	the	Chief	Economist,	U.S.	

Department	of	Agriculture,	Washington,	DC.	606	pages.		July	2014.		Eve,	M.,	D.	Pape,	M.	Flugge,	R.	

Steele,	D.	Man,	M.	Riley‐Gilbert,	and	S.	Biggar,	Eds.	
 

iv

Hoover,	C.,	R.	Birdsey,	B.	Goines,	P.	Lahm,	Gregg	Marland,	D.	Nowak,	S.	Prisley,	E.	Reinhardt,	K.	Skog,	

D.	Skole,	J.	Smith,	C.	Trettin,	C.	Woodall,	2014.	Chapter	6:		Quantifying	Greenhouse	Gas	Sources	and	

Sinks	in	Managed	Forest	Systems.		In	Quantifying Greenhouse Gas Fluxes in Agriculture and Forestry:		

Methods for Entity‐Scale Inventory.	Technical	Bulletin	Number	1939,	Office	of	the	Chief	Economist,	

U.S.	Department	of	Agriculture,	Washington,	DC.	606	pages.		July	2014.		Eve,	M.,	D.	Pape,	M.	Flugge,	

R.	Steele,	D.	Man,	M.	Riley‐Gilbert,	and	S.	Biggar,	Eds.	

Ogle,	S.M.,	2014.	Chapter	7:		Quantifying	Greenhouse	Gas	Sources	and	Sinks	from	Land	Use	Change.		

In	Quantifying Greenhouse Gas Fluxes in Agriculture and Forestry:		Methods for Entity‐Scale Inventory.	

Technical	Bulletin	Number	1939,	Office	of	the	Chief	Economist,	U.S.	Department	of	Agriculture,	

Washington,	DC.	606	pages.		July	2014.		Eve,	M.,	D.	Pape,	M.	Flugge,	R.	Steele,	D.	Man,	M.	Riley‐

Gilbert,	and	S.	Biggar,	Eds.	

Breidt,	F.J.,	Ogle,	S.M.,	Powers,	W.,	Hover,	C.,	2014.	Chapter	8:	Uncertainty	Assessment	for	

Quantifying	Greenhouse	Gas	Sources	and	Sinks.		In	Quantifying Greenhouse Gas Fluxes in Agriculture

and Forestry:		Methods for Entity‐Scale Inventory.	Technical	Bulletin	Number	1939,	Office	of	the	

Chief	Economist,	U.S.	Department	of	Agriculture,	Washington,	DC.	606	pages.		July	2014.		Eve,	M.,	D.	

Pape,	M.	Flugge,	R.	Steele,	D.	Man,	M.	Riley‐Gilbert,	and	S.	Biggar,	Eds.	

Acknowledgements:

The	Department	of	Agriculture	would	like	to	acknowledge	the	many	contributors	to	this	report,	

including	contractors,	university	researchers	and	Federal	Government	employees.		Marlen	D.	Eve,	

Ph.D.,	Environmental	Scientist	in	the	Climate	Change	Program	Office,	served	as	the	Project	Manager	

for	this	report.		He	provided	guidance	on	the	process	for	developing	the	report,	insights	on	the	level	

of	detail	provided	for	the	methods,	and	editorial	review	of	the	document.		William	Hohenstein,	

Director	for	the	Climate	Change	Program	Office,	provided	overall	direction	on	the	guiding	principles	

for	developing	the	methods	and	associated	report.	Specifically,	we	would	like	to	acknowledge	the	

team	at	ICF	International	that	played	a	key	role	in	coordinating	the	work	and	leading	the	

development	of	each	chapter	included	in	the	report.			

The	core	team	at	ICF	International	includes:	

Sarah	Biggar,	ICF	International	

Mark	Flugge,	ICF	International	

Derina	Man,	ICF	International	

Diana	Pape,	ICF	International	

Marybeth	Riley‐Gilbert,	ICF	International	

Rachel	Steele,	ICF	International	

Additionally,	we	would	like	to	acknowledge	the	tremendous	effort	put	in	to	the	report	by	our	

working	groups	and	expert	authors	for	each	chapter	of	the	report,	as	well	as	our	other	contributors,	

subject	matter	experts	and	scientific	reviewers.		USDA	recognizes	their	significant	investment	of	

time	and	expertise	and	appreciates	the	contribution	of	each	member.	
 

 v

Working Groups:

Croplands/Grazing Lands:

Stephen	M.	Ogle,	Colorado	State	University	(Lead	Author)	

Paul	R.	Adler,	USDA	Agricultural	Research	Service	

Jay	Breidt,	Colorado	State	University	

Stephen	Del	Grosso,	USDA	Agricultural	Research	Service	

Justin	Derner,	USDA	Agricultural	Research	Service	

Alan	Franzluebbers,	USDA	Agricultural	Research	Service	

Mark	Liebig,	USDA	Agricultural	Research	Service	

Bruce	Linquist,	University	of	California,	Davis	

Phil	Robertson,	Michigan	State	University	

Michele	Schoeneberger,	USDA	Forest	Service	

Johan	Six,	University	of	California,	Davis;	Swiss	Federal	Institute	of	Technology,	ETH‐Zurich	

Chris	van	Kessel,	University	of	California,	Davis	

Rod	Venterea,	USDA	Agricultural	Research	Service	

Tristram	West,	Pacific	Northwest	National	Laboratory	

Wetlands:

Stephen	M.	Ogle,	Colorado	State	University	(Lead	Author)	

Patrick	Hunt,	USDA	Agricultural	Research	Service	

Carl	Trettin,	USDA	Forest	Service	

Animal Agriculture:

Wendy	Powers,	Michigan	State	University	(Lead	Author)	

Brent	Auvermann,	Texas	A&M	University	

N.	Andy	Cole,	USDA	Agricultural	Research	Service	

Curt	Gooch,	Cornell	University	

Rich	Grant,	Purdue	University	

Jerry	Hatfield,	USDA	Agricultural	Research	Service	

Patrick	Hunt,	USDA	Agricultural	Research	Service	

Kristen	Johnson,	Washington	State	University	

April	Leytem,	USDA	Agricultural	Research	Service	

Wei	Liao,	Michigan	State	University	

J.	Mark	Powell,	USDA	Agricultural	Research	Service

Forestry:

Coeli	Hoover,	USDA	Forest	Service	(Lead	Author)	

Richard	Birdsey,	USDA	Forest	Service	(Co‐Lead	Author)	

Bruce	Goines,	USDA	Forest	Service	

Peter	Lahm,	USDA	Forest	Service	

Gregg	Marland,	Appalachian	State	University	

David	Nowak,	USDA	Forest	Service	

Stephen	Prisley,	Virginia	Polytechnic	Institute	and	State	University	
 

vi

Elizabeth	Reinhardt,	USDA	Forest	Service	

Ken	Skog,	USDA	Forest	Service	

David	Skole,	Michigan	State	University	

James	Smith,	USDA	Forest	Service	

Carl	Trettin,	USDA	Forest	Service	

Christopher	Woodall,	USDA	Forest	Service	

Additional Contributors

Mark	Easter,	Colorado	State	University	

Robert	Gleason,	U.S.	Geological	Survey	

J.	Boone	Kauffman,	Oregon	State	University	

Ernie	Marx,	Colorado	State	University	

Keith	Paustian,	Colorado	State	University		

Tom	Wirth,	U.S.	Environmental	Protection	Agency	

Andre‐Denis	Wright,	University	of	Vermont	

A	group	of	experts	were	convened	in	February	2012	to	review	the	soil	N2O	methods	in	the	

croplands/grazing	lands	section	of	the	Report.	

Soil N2O Workshop Organization Committee:

Stephen	M.	Ogle,	Colorado	State	University	(Co‐Chair)	

Phil	Robertson,	Michigan	State	University	(Co‐Chair)	

Steve	Del	Grosso,	USDA	Agricultural	Research	Service	

Johan	Six,	University	of	California,	Davis;	Swiss	Federal	Institute	of	Technology,	ETH‐Zurich	

Rod	Venterea,	USDA	Agricultural	Research	Service	

Soil N2O Workshop Participants:

Martin	Burger,	University	of	California,	Davis	

Raymond	Desjardins,	Agriculture	and	Agri‐Food	Canada	

Ron	Gehl,	North	Carolina	State	University	

Peter	Grace,	Queensland	University	of	Technology	

Peter	Groffman,	Cary	Institute	of	Ecosystem	Studies	

Ardell	Halvorson,	USDA	Agricultural	Research	Service	

William	Horwath,	University	of	California,	Davis	

Cesar	Izaurralde,	Joint	Global	Change	Research	Institute;	University	of	Maryland	

Changsheng	Li,	University	of	New	Hampshire	

Neville	Millar,	Michigan	State	University	

Keith	Paustian,	Colorado	State	University	

Philippe	Rochette,	Agriculture	and	Agri‐Food	Canada	

William	Salas,	Applied	Geosolutions	

Cliff	Snyder,	International	Plant	Nutrition	Institute	

Expert Reviewers

USDA	would	like	to	acknowledge	the	following	expert	reviewers,	who	reviewed	all	or	parts	of	the	

document	during	the	March	2013	Expert	Review:	
 

 vii

Bob	Abt,	North	Carolina	State	University	

Leon	Hartwell	Allen,	USDA	Agricultural	Research	Service	

Ben	Bond‐Lamberty,	Joint	Global	Change	Research	Institute	

Sandra	Brown,	Winrock	International	

David	Clay,	South	Dakota	State	University	

Steven	De	Gryze,	Terra	Global	Capital	

Pete	Epanchin,	AAAS	Fellow,	U.S.	Environmental	Protection	Agency	

Erin	Fitzgerald,	Innovation	Center	for	U.S.	Dairy	

Ron	Gehl,	North	Carolina	State	University	

Amrith	Gunasekara,	California	Department	of	Agriculture	

Noel	Gurwick,	Smithsonian	Environmental	Research	Center	

Linda	Heath,	USDA	Forest	Service	

William	Horwath,	University	of	California,	Davis	

Cesar	Izaurralde,	Joint	Global	Change	Research	Institute;	University	of	Maryland	

Jennifer	Jenkins,	U.S.	Environmental	Protection	Agency	

Kurt	Johnsen,	USDA	Forest	Service	

Ermias	Kebreab,	University	of	California,	Davis	

William	Lazarus,	University	of	Minnesota	

Deanne	Meyer,	University	of	California,	Davis	

Tim	Parkin,	USDA	Agricultural	Research	Service	

Charles	Rice,	Kansas	State	University	

Neil	Sampson,	The	Sampson	Group	

Karamat	Sistani,	USDA	Agricultural	Research	Service	

Cliff	Snyder,	International	Plant	Nutrition	Institute	

Brent	Sohngen,	Ohio	State	University	

Martha	Stevenson,	World	Wildlife	Fund	

Richard	Todd,	USDA	Agricultural	Research	Service	

Michele	Wander,	University	of	Illinois	

Tom	Wirth,	U.S.	Environmental	Protection	Agency	

Photo	credits	for	cover	and	each	chapter:		

 Executive	Summary:	Strips	and	shelterbelts:			USDA	NRCS;NRCSMT00001.tif	

http://photogallery.nrcs.usda.gov/netpub/server.np?find&catalog=catalog&template=detail.np&field=itemid&op=m

atches&value=4851&site=PhotoGallery

 Chapter	1:	Planting:		USDA	NRCS;	Jeff	Vanuga;	NRCSVA02001.tif	

http://photogallery.nrcs.usda.gov/netpub/server.np?find&catalog=catalog&template=detail.np&field=itemid&op=m

atches&value=6529&site=PhotoGallery

 Chapter	3:	Farmstead:		USDA	NRCS;	Tim	McCabe;	NRCSMD81005.tif	

http://photogallery.nrcs.usda.gov/netpub/server.np?find&catalog=catalog&template=detail.np&field=itemid&op=m

atches&value=4511&site=PhotoGallery

 Chapter	4:	Wetland;		USDA	NRCS;	Lynn	Betts; NRCSIA99470.tif		

http://photogallery.nrcs.usda.gov/netpub/server.np?find&catalog=catalog&template=detail.np&field=itemid&op=m

atches&value=3629&site=PhotoGallery

 Chapter	5:	Hogs:			USDA	NRCS;	Lynn	Betts;	NRCSIA99210.tif	

http://photogallery.nrcs.usda.gov/netpub/server.np?find&catalog=catalog&template=detail.np&field=itemid&op=m

atches&value=3115&site=PhotoGallery
 

viii

 Chapter	6: Forest:		USDA	NRCS;	NRCSNM02093	

http://photogallery.nrcs.usda.gov/netpub/server.np?find&catalog=catalog&template=detail.np&field=itemid&op=m

atches&value=5971&site=PhotoGallery

 Chapter	7:	Forest	HWP:		USDA	NRCS;	Jeff	Vanuga;	NRCSNM02093	

http://photogallery.nrcs.usda.gov/netpub/server.np?find&catalog=catalog&template=detail.np&field=itemid&op=m

atches&value=5471&site=PhotoGallery

 Chapter	8: Compost:		USDA	NRCS;	NRCSCA06008.tif	

http://photogallery.nrcs.usda.gov/netpub/server.np?find&catalog=catalog&template=detail.np&field=itemid&op=m

atches&value=961&site=PhotoGallery
 

 ix

Table of Contents

Executive Summary

1 Introduction ...............................................................................................................................................1‐3

1.1	 Overview	of	the	Report	...............................................................................................................................	1‐4	

1.2	 Report	Objectives	..........................................................................................................................................	1‐5	

1.3	 Process	for	the	Development	of	the	Methods	....................................................................................	1‐6	

1.4	 Contents	of	the	Report	................................................................................................................................	1‐9	

1.5	 Uses	and	Limitations	of	the	Report	......................................................................................................	1‐10	

Chapter	1	References	.............................................................................................................................................	1‐14	

2 Considerations When Estimating Agriculture and Forestry GHG Emissions and Removals		

	 	.........................................................................................................................................................................2‐3

2.1	 Scope	...................................................................................................................................................................	2‐3	

2.1.1	 Definition	of	Entity	........................................................................................................................	2‐3	

2.1.2	 Definition	of	System	Boundaries	.............................................................................................	2‐4	

2.2	 Review	of	Relevant	Current	Tools	and	Methods	............................................................................	2‐12	

2.3	 Selection	of	Most	Appropriate	Method	and	Mitigation	Practices	to	Include	......................	2‐13	

2.4	 Overview	of	Sectors	....................................................................................................................................	2‐14	

2.4.1	 Croplands	and	Grazing	Lands	.................................................................................................	2‐16	

2.4.2	 Wetlands	..........................................................................................................................................	2‐17	

2.4.3	 Animal	Production	.......................................................................................................................	2‐19	

2.4.4	 Forestry	............................................................................................................................................	2‐21	

2.5	 Land‐Use	Change	.........................................................................................................................................	2‐22	

2.6	 Uncertainty	....................................................................................................................................................	2‐23	

Chapter	2	References	.............................................................................................................................................	2‐24	

3 Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems.....	

	 	.........................................................................................................................................................................3‐4

3.1	 Overview	...........................................................................................................................................................	3‐5	

3.1.1	 Overview	of	Management	Practices	and	Resulting	GHG	Emissions	.........................	3‐6	

3.1.2	 System	Boundaries	and	Temporal	Scale	............................................................................	3‐10	

3.1.3	 Summary	of	Selected	Methods/Models	Sources	of	Data	.............................................	3‐10	

3.1.4	 Organization	of	Chapter/Roadmap	......................................................................................	3‐11	

3.2	 Cropland	Management	..............................................................................................................................	3‐12	

3.2.1	 Management	Influencing	GHG	Emissions	in	Upland	Systems	...................................	3‐12	

3.2.2	 Management	Influencing	GHG	Emissions	in	Flooded	Cropping	Systems	.............	3‐25	

3.2.3	 Land‐Use	Change	to	Cropland	................................................................................................	3‐28	

3.3	 Grazing	Land	Management	......................................................................................................................	3‐29	

3.3.1	 Management	Activity	Influencing	GHG	Emissions	.........................................................	3‐30	

3.3.2	 Land‐Use	Change	to	Grazing	Lands	......................................................................................	3‐36	

3.4	 Agroforestry	..................................................................................................................................................	3‐37	
 

x

3.4.1	 Carbon	Stocks	................................................................................................................................	3‐39	

3.4.2	 Nitrous	Oxide	.................................................................................................................................	3‐41	

3.4.3	 Methane	...........................................................................................................................................	3‐41	

3.4.4	 Management	Interactions	.........................................................................................................	3‐42	

3.5	 Estimation	Methods	...................................................................................................................................	3‐42	

3.5.1	 Biomass	Carbon	Stock	Changes	.............................................................................................	3‐43	

3.5.2	 Litter	Carbon	Stock	Changes	...................................................................................................	3‐49	

3.5.3	 Soil	Carbon	Stock	Changes	.......................................................................................................	3‐49	

3.5.4	 Soil	Nitrous	Oxide	........................................................................................................................	3‐58	

3.5.5	 Methane	Uptake	by	Soils	...........................................................................................................	3‐74	

3.5.6	 Methane	and	Nitrous	Oxide	from	Flooded	Rice	Cultivation	.......................................	3‐77	

3.5.7	 CO2	from	Liming	...........................................................................................................................	3‐83	

3.5.8	 Non‐CO2	Emissions	from	Biomass	Burning	......................................................................	3‐86	

3.5.9	 CO2	from	Urea	Fertilizer	Applications	.................................................................................	3‐90	

3.6	 Summary	of	Research	Gaps	for	Crop	and	Grazing	Land	Management	..................................	3‐92	

Appendix	3‐A:	Soil	N2O	Modeling	Framework	Specifications	...............................................................	3‐97	

3‐A.1	Description	of	Process‐Based	Models	.....................................................................	3‐99	

3‐A.2	Empirical	Scalars	for	Base	Emission	Rates.........................................................	3‐106	

3‐A.3	Practice‐Based	Scaling	Factors	................................................................................	3‐108	

Appendix	3‐B:	Guidance	for	Crops	Not	Included	in	the	DAYCENT	Model	....................................	3‐113	

Chapter	3	References	..........................................................................................................................................	3‐116	

4 Quantifying Greenhouse Gas Sources and Sinks in Managed Wetland Systems................4‐3

4.1	 Overview	...........................................................................................................................................................	4‐3	

4.1.1	 Overview	of	Management	Practices	and	Resulting	GHG	Emissions	.........................	4‐4	

4.1.2	 System	Boundaries	and	Temporal	Scale	..............................................................................	4‐7	

4.1.3	 Summary	of	Selected	Methods/Models	and	Sources	of	Data	......................................	4‐7	

4.1.4	 Organization	of	Chapter/Roadmap	........................................................................................	4‐8	

4.2	 Management	and	Restoration	of	Wetlands	........................................................................................	4‐8	

4.2.1	 Description	of	Wetland	Management	Practices	................................................................	4‐8	

4.2.2	 Land‐Use	Change	to	Wetlands	................................................................................................	4‐13	

4.3	 Estimation	Methods	...................................................................................................................................	4‐14	

4.3.1	 Biomass	Carbon	in	Wetlands	..................................................................................................	4‐14	

4.3.2	 Soil	C,	N2O,	and	CH4	in	Wetlands	............................................................................................	4‐17	

4.4	 Research	Gaps	for	Wetland	Management	.........................................................................................	4‐21	

Chapter	4	References	.............................................................................................................................................	4‐23	

5 Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems ..............5‐5

5.1	 Overview	...........................................................................................................................................................	5‐5	

5.1.1	 Overview	of	Management	Practices	and	Resulting	GHG	Emissions	.........................	5‐5	

5.1.2	 System	Boundaries	and	Temporal	Scale	............................................................................	5‐12	

5.1.3	 Summary	of	Selected	Methods/Models/Sources	of	Data	............................................	5‐12	
 

 xi

5.1.4	 Organization	of	Chapter/Roadmap	......................................................................................	5‐14	

5.2	 Animal	Production	Systems	....................................................................................................................	5‐18	

5.2.1	 Dairy	Production	Systems	........................................................................................................	5‐18	

5.2.2	 Beef	Production	Systems	..........................................................................................................	5‐22	

5.2.3	 Sheep	Production	Systems	.......................................................................................................	5‐25	

5.2.4	 Swine	Production	Systems	.......................................................................................................	5‐25	

5.2.5	 Poultry	Production	Systems	....................................................................................................	5‐28	

5.3	 Emissions	from	Enteric	Fermentation	and	Housing	.....................................................................	5‐30

5.3.1	 Enteric	Fermentation	and	Housing	Emissions	from	Dairy	Production	Systems	........		

	 	.............................................................................................................................................................	5‐31	

5.3.2	 Enteric	Fermentation	and	Housing	Emissions	from	Beef	Production	Systems	.	5‐44	

5.3.3	 Enteric	Fermentation	and	Housing	Emissions	from	Sheep	........................................	5‐52	

5.3.4	 Enteric	Fermentation	and	Housing	Emissions	from	Swine	Production	Systems	.......		

	 	.............................................................................................................................................................	5‐53	

5.3.5	 Housing	Emissions	from	Poultry	Production	Systems	.................................................	5‐60	

5.3.6	 Enteric	Fermentation	and	Housing	Emissions	from	Other	Animals	.......................	5‐64	

5.3.7	 Factors	Affecting	Enteric	Fermentation	Emissions	.......................................................	5‐66	

5.3.8	 Limitations	and	Uncertainty	in	Enteric	Fermentation	and	Housing	Emissions	

Estimates	.........................................................................................................................................	5‐73	

5.4	 Manure	Management	.................................................................................................................................	5‐75	

5.4.1	 Temporary	Stack	and	Long‐Term	Stockpile	.....................................................................	5‐77	

5.4.2	 Source:	U.S.	EPA	(2011).Composting	...................................................................................	5‐81	

5.4.3	 Aerobic	Lagoon	.............................................................................................................................	5‐85	

5.4.4	 Anaerobic	Lagoon,	Runoff	Holding	Pond,	Storage	Tanks	............................................	5‐86	

5.4.5	 Anaerobic	Digester	with	Biogas	Utilization	......................................................................	5‐91	

5.4.6	 Combined	Aerobic	Treatment	Systems	..............................................................................	5‐93	

5.4.7	 Sand‐Manure	Separation	..........................................................................................................	5‐94	

5.4.8	 Nutrient	Removal	.........................................................................................................................	5‐94	

5.4.9	 Solid–Liquid	Separation	............................................................................................................	5‐95	

5.4.10	 Constructed	Wetland	..................................................................................................................	5‐97	

5.4.11	 Thermo‐Chemical	Conversion	................................................................................................	5‐98	

5.4.12	 Limitations	and	Uncertainty	in	Manure	Management	Emissions	Estimates	......	5‐99	

5.5	 Research	Gaps	............................................................................................................................................	5‐105	

5.5.1	 Enteric	Fermentation	..............................................................................................................	5‐105	

5.5.2	 Manure	Management	...............................................................................................................	5‐106	

Appendix	5‐A:	Enteric	CH4	from	Feedlot	Cattle	–	Methane	Conversion	Factor	(Ym)	..............	5‐109	

Appendix	5‐B:	Feedstuffs	Composition	Table...........................................................................................	5‐113	

Appendix	5‐C:	Estimation	Methods	for	Ammonia	Emissions	from	Manure	Management	

Systems......................................................................................................................................	5‐123	

5‐C.1	 Method	for	Estimating	Ammonia	Emissions	Using	Equations	from	Integrated	

Farm	System	Model	................................................................................................................	5‐123	

5‐C.1.1	Rationale	for	Selected	Method	...............................................................................	5‐123	

5‐C.1.2	Activity	Data	..................................................................................................................	5‐123	
 

xii

5‐C.1.3	Ancillary	Data	...............................................................................................................	5‐124	

5‐C.2	 Method	for	Ammonia	Emissions	from	Temporary	Stack,	Long‐Term	Stockpile,	

Anaerobic	Lagoons/Runoff	Holding	Ponds/Storage	Tanks,	and	Aerobic	Lagoons

	........................................................................................................................................................	5‐124	

5‐C.3	 Method	for	Estimating	Ammonia	Emissions	from	Composting	Using	IPCC	Tier	2	

Equations	....................................................................................................................................	5‐128	

5‐C.3.1	Rationale	for	Selected	Method	...............................................................................	5‐128	

5‐C.3.2	Activity	Data	..................................................................................................................	5‐129	

5‐C.3.3	Ancillary	Data	...............................................................................................................	5‐129	

5‐C.4	 Method	for	Ammonia	Emissions	from	Composting	..................................................	5‐129	

5‐C.5	 Uncertainty	in	Ammonia	Emissions	Estimates	...........................................................	5‐129	

Appendix	5‐D:	Manure	Management	Systems	Shape	Factors	( )	...................................................	5‐131	

Appendix	5‐E:	Model	Review:	Review	of	Enteric	Fermentation	Models	.......................................	5‐134	

Chapter	5	References	..........................................................................................................................................	5‐139	

6 Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems ....................6‐4

6.1	 Overview	.........................................................................................................................................................	6‐5	

6.1.1	 Overview	of	Management	Practices	and	Resulting	GHG	Emissions	.........................	6‐6	

6.1.2	 System	Boundaries	and	Temporal	Scale	..............................................................................	6‐9	

6.1.3	 Summary	of	Selected	Methods/Models	..............................................................................	6‐10	

6.1.4	 Sources	of	Data	..............................................................................................................................	6‐11	

6.1.5	 Organization	of	Chapter/Roadmap	......................................................................................	6‐12	

6.2	 Forest	Carbon	Accounting	......................................................................................................................	6‐15	

6.2.1	 Description	of	Forest	Carbon	Accounting	..........................................................................	6‐15	

6.2.2	 Data	Collection	for	Forest	Carbon	Accounting	.................................................................	6‐23	

6.2.3	 Estimation	Methods	....................................................................................................................	6‐25	

6.2.4	 Limitations,	Uncertainty,	and	Research	Gaps	...................................................................	6‐28	

6.3	 Establishing,	Re‐establishing,	and	Clearing	Forests	....................................................................	6‐29	

6.3.1	 Description	.....................................................................................................................................	6‐29	

6.3.2	 Activity	Data	Collection	.............................................................................................................	6‐33	

6.3.3	 Estimation	Methods	....................................................................................................................	6‐34	

6.3.4	 Specific	Protocol	for	Computation	........................................................................................	6‐37	

6.3.5	 Actual	GHG	Removals	and	Emissions	by	Sources	and	Sinks	from	Forest	Clearing	...		

	 	.............................................................................................................................................................	6‐43	

6.3.6	 Limitations	and	Uncertainty	....................................................................................................	6‐44	

6.4	 Forest	Management	..................................................................................................................................	6‐45	

6.4.1	 Description	.....................................................................................................................................	6‐45	

6.4.2	 Activity	Data	...................................................................................................................................	6‐53	

6.4.3	 Management	Intensity	Categories	........................................................................................	6‐57	

6.4.4	 Estimation	Methods	....................................................................................................................	6‐64	

6.4.5	 Limitations	and	Uncertainty	....................................................................................................	6‐66	

6.5	 Harvested	Wood	Products	.....................................................................................................................	6‐66	

6.5.1	 General	Accounting	Issues	.......................................................................................................	6‐66	
 

 xiii

6.5.2	 Estimation	Methods	....................................................................................................................	6‐68	

6.5.3	 Activity	Data	Collection	.............................................................................................................	6‐69	

6.5.4	 Limitations,	Uncertainty,	and	Research	Gaps	...................................................................	6‐70	

6.6	 Urban	Forests	..............................................................................................................................................	6‐71	

6.6.1	 Description	.....................................................................................................................................	6‐71	

6.6.2	 Activity	Data	Collection	.............................................................................................................	6‐73	

6.6.3	 Estimation	Methods	....................................................................................................................	6‐74	

6.6.4	 Limitations	and	Uncertainty	....................................................................................................	6‐80	

6.7	 Natural	Disturbance	–	Wildfire	and	Prescribed	Fire	...................................................................	6‐82	

6.7.1	 Description	.....................................................................................................................................	6‐82	

6.7.2	 Activity	Data	Collection	.............................................................................................................	6‐82	

6.7.3	 Estimation	Methods	....................................................................................................................	6‐82	

6.7.4	 Limitations	and	Uncertainty	....................................................................................................	6‐87	

Appendix	6‐A:	Harvested	Wood	Products	Lookup	Tables	.....................................................................	6‐88

Chapter	6	References	..........................................................................................................................................	6‐107	

7 Quantifying Greenhouse Gas Sources and Sinks from Land‐Use Change .............................7‐3

7.1	 Overview	...........................................................................................................................................................	7‐3	

7.2	 Definitions	of	Land	Use	...............................................................................................................................	7‐4	

7.3	 Caveats	...............................................................................................................................................................	7‐6	

7.4	 Estimating	GHG	Flux	from	Land‐Use	Change	.....................................................................................	7‐6	

7.4.1	 Carbon	Pools	in	Live	Biomass,	Dead	Biomass,	and	Soil	Organic	Carbon	................	7‐8	

7.4.2	 Changes	in	Soil	Carbon	................................................................................................................	7‐8	

7.4.3	 Changes	in	other	GHG	emissions	...........................................................................................	7‐13	

Chapter	7	References	.............................................................................................................................................	7‐14	

8 Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks ...................8‐3

	 Components	and	Inputs	to	an	Entity‐Scale	Monte	Carlo	Uncertainty	Assessment	............	8‐4	

8.1.1	 Parameter	Uncertainty	................................................................................................................	8‐5	

8.1.2	 Sampling	Method	Uncertainty	..................................................................................................	8‐6	

8.1.3	 Large	Dataset	Uncertainty	..........................................................................................................	8‐9	

8.1.4	 Model	Uncertainty	.......................................................................................................................	8‐16	

	 Research	Gaps	...............................................................................................................................................	8‐20	

Appendix	8‐A:	Example	Output	File	from	FVS	Sampling	Uncertainty	Bootstrapping	Application	

FVSBoot	(as	provided	in	Gregg	and	Hummel,	2002)	................................................................................	8‐21	

Appendix	8‐B:	Uncertainty	Tables	....................................................................................................................	8‐22	

Chapter	8	References	.............................................................................................................................................	8‐55	
 

xiv

This	page	is	intentionally	left	blank.	
QUANTIFYING GREENHOUSE GAS FLUXES IN AGRICULTURE AND FORESTRY:

METHODS FOR ENTITY‐SCALE INVENTORY

Eve,	M.,	D.	Pape,	M.	Flugge,	R.	Steele,	D.	Man,	M.	Riley‐Gilbert,	and	S.	Biggar,	(Eds),	2014.		Quantifying

Greenhouse Gas Fluxes in Agriculture and Forestry:		Methods for Entity‐Scale Inventory.	Technical	Bulletin	

Number	1939.	Office	of	the	Chief	Economist,	U.S.	Department	of	Agriculture,	Washington,	DC.	606	pages.	July	

2014.	

Executive Summary

Background

Provisions	of	Section	2709	of	the	Food,	

Conservation,	and	Energy	Act	of	2008	direct	the	

U.S.	Department	of	Agriculture	(USDA)	to	prepare	

technical	guidelines	and	science‐based	methods	to	

measure	environmental	service	benefits	from	

conservation	and	land	management	activities,	

initially	focused	on	carbon.	The	methods	contained	

in	this	document	address	greenhouse	gas	(GHG)	

emissions	and	removals	from	agricultural	and	

forestry	activities.	

Through	the	development	of	this	report,	USDA	has	

prepared	two	primary	products:	

1. A	comprehensive	review	of	techniques	

currently	in	use	for	estimating	GHG	emissions	

and	removals	from	agricultural	and	forestry	

activities;	and	

2. A	technical	report	outlining	the	preferred	

science‐based	approach	and	specific	methods	

for	estimating	GHG	emissions	at	the	farm	or	

forest	scale	(i.e.,	this	document).	

Purpose of the Report

The	objective	for	this	report	is	to	create	a	standard	set	of	GHG	estimation	methods	for	use	by	USDA,	

landowners,	and	other	stakeholders	to	assist	them	in	evaluating	the	GHG	impacts	of	their	

management	decisions.	The	methods	presented	in	the	report	address	GHG	emissions	and	carbon	

sequestration	for	the	entire	entity	or	operation	and	also	provide	the	opportunity	to	assess	

individual	practices	or	management	decisions.	Therefore,	ease	of	use	is	critical.	

A	co‐objective	is	to	demonstrate	capacity	within	USDA,	establishing	a	standardized,	consensus	set	

of	methods	that	become	the	scientific	basis	for	entity‐scale	estimation	of	the	GHG	impacts	of	

landowner	management	decisions.	Therefore,	scientific	rigor	and	transparency	are	also	critical.	

Uses of the Report and Methods:

 Estimating	increases	and	

decreases	in	GHG	emissions	and	

carbon	sequestration	resulting	

from	current	and	future	

conservation	programs	and	

practices;	

 Providing	methods	suitable	for	

GHG	inventory	efforts	at	the	entity,	

farm,	or	forest	scale,	with	possible	

implications	for	regional	and	

national	scale	assessments	as	well;	

and	

 Estimating	increases	and	

decreases	in	GHG	emissions	and	

carbon	sequestration	associated	

with	changes	in	land	management.	
Executive Summary 

ES-2

Because	the	report	is	intended	as	a	means	of	evaluating	management	practices	across	the	full	scope	

of	the	farm,	ranch,	and	forest	management	system,	the	methods	in	the	report	need	to	be	as	

comprehensive	as	possible.	Research	and	data	gaps	exist	that	result	in	some	management	practices	

not	being	accounted	for	or	are	reflected	in	higher	levels	of	estimate	uncertainty.	Completeness	is	

important,	though,	and	the	report	attempts	to	identify	the	most	significant	research	gaps	and	data	

needs.		

This	report	will	be	used	within	USDA	and	by	farmers,	ranchers,	and	forest	landowners,	and	will	be	

made	publicly	available.	These	methods	are	designed	to:	

1. Provide	a	scientific	basis	for	methods	that	can	be	used	by	landowners	and	managers,	USDA,	

and	other	stakeholders	to	estimate	changes	in	GHG	emissions	and	removals	at	the	local	

entity	scale;	

2. Create	a	standard	set	of	GHG	quantification	guidelines	and	methods	for	use	by	stakeholders;	

3. Quantify	all	significant	emissions	and	removals	associated	with	specific	source	categories;	

4. Quantify	emissions	from	land‐use	change	and	carbon	sequestration	from	land	management	

practices	and	technologies;	and	

5. Support	the	development	of	entity‐,	farm‐,	or	forest‐scale	GHG	inventories	that	will	facilitate	

the	participation	of	landowners	in	public	and	private	environmental	market	registries	and	

reporting	systems.	

The	report	also	serves	as	input	into	the	development	of	a	USDA	GHG	Estimation	Tool.	The	report	

and	the	methods	are	not	intended	as	an	addition	to	or	replacement	of	any	current	Federal	GHG	

reporting	systems	or	requirements.	

Process for the Development of the Report

This	report	was	developed	by	three	author	teams	(i.e.,	working	groups)	under	the	direction	of	one	

lead	author	for	each	team	(plus	one	co‐lead	author	for	the	forestry	chapter).	The	lead	authors	were	

chosen	based	on	their	experience	with	GHG	inventories	and	accounting	methodologies	and	their	

professional	research	experience.	With	input	from	each	lead	author,	USDA	chose	8	to12	working	

group	members	per	team	to	write	the	report.	These	working	group	members	each	had	different	

backgrounds	that	fit	with	the	anticipated	content	of	the	document	and	also	had	experience	with	

GHG	accounting	and/or	field	research	that	was	unique	and	addressed	one	or	more	of	the	niche	

methods	that	were	essential	for	ensuring	the	comprehensiveness	of	the	methods	for	each	sector.	

The	author	teams	were	provided	with	a	preliminary	outline	of	their	chapters	and	with	two	

background	reports	developed	as	part	of	the	project.	One	background	report	was	an	analysis	of	the	

scientific	literature	related	to	rates	of	carbon	sequestration	or	emissions	reduction	resulting	from	

various	management	practices	and	technologies	(Denef	et	al.,	2011).	The	other	report	was	a	

compilation	of	all	of	the	available	tools,	protocols,	and	models,	with	basic	information	on	each	one	

(Denef	et	al.,	2012).	

The	methods	were	developed	according	to	several	criteria	in	order	to	maximize	their	usefulness.	In	

particular,	the	methods	must:	

1. Stand	on	their	own,	independent	of	any	other	accounting	system,	yet	maintain	consistency	

with	other	accounting	systems	to	the	maximum	extent	possible;	

2. Be	scalable	for	use	at	entity‐scale	sites	across	the	United	States,	with	applicability	at	county	

and/or	State	levels	as	well;	

3. Facilitate	use	by	USDA	in	assessing	the	performance	of	conservation	programs;	
 Executive Summary 

 ES-3 

4. Provide	a	broad	framework	to	assess	management	practices	to	evaluate	the	GHG	aspect	of	

production	sustainability;	

5. Maintain	maximum	applicability	for	use	in	environmental	markets,	including	possible	

future	Federal,	State,	or	local	GHG	offsets	initiatives;	

6. Be	scientifically	vetted	through	USDA,	U.S.	Government	and	academic	expert	review,	and	

public	comment;	

7. Provide	reliable,	real,	and	verifiable	estimates	of	onsite	GHG	emissions,	carbon	storage,	and	

carbon	sequestration	(the	methods	will	be	designed	so	that	over	time	they	can	be	applied	to	

quantify	onsite	GHG	reductions	and	increases	in	carbon	storage	due	to	conservation	and	

land	management	activities);	and	

8. Provide	a	basis	for	consistency	in	estimation	and	transparency	in	reporting.	

Development	of	the	report	has	been	iterative	as	various	drafts	of	the	document	have	been	put	

through	several	review	stages,	including	a	USDA	intra‐agency	technical	review,	a	Federal	

interagency	technical	review,	a	scientific	expert	review,	and	a	public	comment	period.	

Overview of Recommended GHG Estimation Methods in the Report

This	section	provides	an	overview	of	the	current	estimation	methods	or	approaches	an	entity	could	

use	to	estimate	GHG	emissions	and	sinks	on	his	or	her	property.	This	overview	is	followed	by	a	

summary	of	each	sector’s	proposed	methodologies	for	entity	GHG	estimations.	

There	are	several	approaches	that	a	farmer,	rancher,	or	forest	landowner	can	use	to	estimate	GHG	

emissions	at	an	entity	scale,	and	each	approach	gives	varying	accuracy	and	precision.	The	most	

accurate	way	of	estimating	emissions	is	through	direct	measurement,	which	often	requires	

expensive	equipment	or	techniques	that	are	not	feasible	for	a	single	landowner	or	manager.	On	the	

other	hand,	lookup	tables	and	estimation	equations	alone	often	do	not	adequately	represent	local	

variability	or	local	conditions.	This	report	attempts	to	delineate	methods	that	balance	user‐

friendliness,	data	requirements,	and	scientific	rigor	in	a	way	that	is	transparent	and	justified.	

The	following	approaches	were	considered	for	these	guidelines:	

 Basic	estimation	equations	(cf.,	IPCC	[Intergovernmental	Panel	on	Climate	Change]	Tier	

1)—involve	combinations	of	activity	data1	with	parameters	and	default	emission	factors.2

Any	default	parameters	or	default	emission	factors	(e.g.,	lookup	tables)	are	provided	in	the	

text,	or	if	substantial	in	length,	in	an	accompanying	compendium	of	data.	

 Models	(cf.,	IPCC	Tier	3)—use	combinations	of	activity	data	with	parameters	and	default	

emission	factors.	The	inputs	for	these	models	can	be	ancillary	data3	(e.g.,	temperature,	

precipitation,	elevation,	and	soil	nutrient	levels	that	may	be	pulled	from	an	underlying	

source),	biological	variables	(e.g.,	plant	diversity)	or	site‐specific	data	(e.g.,	number	of	acres,	

																																																													

1	Activity	data	is	defined	as	data	on	the	magnitude	of	human	activity	resulting	in	emissions	or	removals	taking	

place	during	a	given	period	of	time	(IPCC,	1997).	 2	Emission	factor	is	defined	as	a	coefficient	that	quantifies	the	emissions	or	removals	of	a	gas	per	unit	of	

activity.	Emission	factors	are	often	based	on	a	sample	of	measurement	data,	averaged	to	develop	a	

representative	rate	of	emission	for	a	given	activity	level	under	a	given	set	of	operating	conditions	(IPCC,	

2006).	

3	Ancillary	data	is	defined	as	additional	data	necessary	to	support	the	selection	of	activity data	and	emission

factors	for	the	estimation	and	characterization	of	emissions.	Data	on	soil,	crop	or	animal	types,	tree	species,	

operating	conditions,	and	geographical	location	are	examples	of	ancillary	data.	
Executive Summary 

ES-4

number	of	animals).	The	accuracy	of	the	models	is	dependent	on	the	robustness	of	the	

model	and	the	accuracy	of	the	inputs.	

 Field	measurements—actual	measurements	that	a	farmer	or	landowner	would	need	to	take

to	more	accurately	estimate	the	properties	of	the	soil,	forest, or	farm	or	to	estimate	actual

emissions.	Measuring	actual	emissions	on	the	land	requires	special	equipment	that

monitors	the	flow	of	gases	from	the	source	into	the	atmosphere.	This	equipment	is	not

readily	available	to	most	entities,	so	field	measurements	are	more	often	incorporated	into

other	methods	described	in	this	section	to	create	a	hybrid	approach.	A	field	measurement

such	as	a	sample	mean	tree	diameter	could	be	incorporated	into	other	models	or	equations

to	give	a	more	accurate	input.

 Inference	(cf.,	IPCC	Tier	2)—uses	State,	regional,	or	national	emissions/sequestration

factors	that	approximate	emissions/sequestration	per	unit	of	the	input.	The	input	data	is

then	multiplied	by	this	factor	to	determine	the	total	onsite	emissions.	This	factor	can	have

varying	degrees	of	accuracy	and	often	does	not	capture	the	mitigation	practices	on	the	farm

or	the	unique	soil	conditions,	climate,	livestock	diet,	livestock	genetics,	or	any	farm‐specific

characteristics,	although	they	can	be	developed	with	specific	soil	types,	livestock	categories,

or	climactic	regions.

 Hybrid	estimation	approach	(cf.,	IPCC	Tier	2	or	IPPC	Tier	3)—an	approach	that	uses	a

combination	of	the	approaches	described	above.	The	approach	often	uses	field

measurements	or	models	to	generate	inputs	used	for	an	inference‐based	approach	to

improve	the	accuracy	of	the	estimate.

The	types	of	approaches	that	the	authors	recommended	in	this	report	include	basic	estimation	

equations	with	default	emission	factors	(cf.,	IPCC	Tier	1);	geography‐,	crop‐,	livestock‐,	technology‐,	

or	practice‐specific	emission	factors	(cf.,	IPCC	Tier	2);	and	modified	IPCC/empirical	and/or	process‐

based	modeling	(cf.,	IPCC	Tier	2	or	IPCC	Tier	3).4	Table	ES‐1	categorizes	the	sources	of	emissions	

with	the	types	of	approaches	that	are	recommended	in	this	report.	

Table	ES‐2	summarizes	the	sources	of	agricultural	and	forestry	GHG	emissions	and	removals	

discussed	in	this	report,	the	recommended	method	for	estimating	emissions	and	removals	for	each	

source	category,	and	the	reference(s)	used	for	the	development	of	the	method.	

4	A	tier	represents	a	level	of	methodological	complexity.	Usually	three	tiers	are	provided.	Tier	1	is	the	basic	

method,	Tier	2	intermediate,	and	Tier	3	most	demanding	in	terms	of	complexity	and	data	requirements.	Tiers	

2	and	3	are	sometimes	referred	to	as	higher tier	methods	and	are	generally	considered	to	be	more	accurate	

(IPCC,	2006).	
Executive Summary

ES-5 

Table ES‐1: Summary of the Sources of Emissions and Types of Approaches in this Report

Source

Basic Estimation

Equation

(cf., IPCC Tier 1)

Inference

(cf., IPCC Tier 2)

Modified IPCC or

Empirical Model

(cf., IPCC Tier 2 or

IPPC Tier 3)

Processed‐Based

Model

(cf., IPPC Tier 3)

Croplands/Grazing Lands

 Direct	N2O

Emissions	from

Drainage	of

Organic	Soils

 CH4	Emissions

from	Rice

Cultivation

 CO2	from	Urea

Fertilizer

Application

 Soil	Organic	Carbon

Stocks	for	Organic

Soils

 CO2	from	Liming

 N2O	Emissions	from

Rice	Cultivation

 Non‐CO2	Emissions

from	Biomass

Burning

 Indirect	N2O

Emissions

 Biomass	Carbon

Stock	Changes

 CH4	Uptake	by	Soils

 Direct	N2O

Emissions	from

Mineral	Soils

 Soil	Organic	Carbon

Stocks	for	Mineral

Soils

Wet‐

lands

—	 — —  Biomass	Carbon

 Soil	C,	N2O,	and	CH4

Animal Production5

 Enteric	CH4	from

Swine

 Enteric	CH4	from

Other	Animals

(Goats,	American

Bison)

 CH4	from	Poultry

Housing

 CH4 from	Dairy

Cattle,	Beef	Cattle,

and	Swine	Housing

 CH4	and	N2Ofrom

Aerobic	Lagoons

 CH4	and	N2Ofrom

Temporary	Stack	and

Long‐Term	Stockpile

 CH4	and	N2Ofrom

Composting

 Enteric CH4 from

Dairy	Cattle,	Sheep,

Beef	Cow‐Calf,

Bulls,	Stockers,

Feedlot	Cattle

 CH4	from	Manure

from	Barn	Floors	–

Dairy	Cattle

 N2O	from	Dairy

Cattle,	Beef	Cattle,

Swine,	and	Poultry

Housing

 CH4	and	N2Ofrom

Anaerobic	Lagoon,

Runoff	Holding

Pond,	Storage

Tanks

 CH4	and	N2Ofrom

Combined	Aerobic

Treatment	Systems

 CH4	from

Anaerobic	Digester

—

Forestry

—	 —  Establishing,	Re‐

establishing,	and

Clearing	Forest

 Harvested	Wood

 Forest	Carbon

Accounting

 Forest	Management

 Urban	Forests

5	Ammonia	(NH3),	as	an	important	precursor	to	GHGs,	is	included	in	the	animal	production	systems	

discussion	where	necessary,	but	is	not	of	primary	focus.	If	readers	are	interested	in	more	technical	

information,	methods	for	estimating	NH3	emissions	can	be	found	in	Appendix	5‐C.	
Executive Summary 

ES-6

Source

Basic Estimation

Equation

(cf., IPCC Tier 1)

Inference

(cf., IPCC Tier 2)

Modified IPCC or

Empirical Model

(cf., IPCC Tier 2 or

IPPC Tier 3)

Processed‐Based

Model

(cf., IPPC Tier 3)

Products  Natural

Disturbance—

Wildfire	and

Prescribed	Fire

Land‐use Change

 Annual	Change	in

Carbon	Stocks	in

Dead	Wood	and

Litter	Due	to

Land	Conversion

 Change	in	Soil

Organic	Carbon

Stocks	for	Mineral

Soils

— — —

Organization of the Report

The	report	is	largely	organized	by	sector,	with	each	chapter	providing	an	overview	of	management	

practices	and	resulting	GHG	emissions	and	removals.	For	each	sector,	background	and	information	

on	management	practices	are	presented	first,	followed	by	the	detailed	methods	proposed	for	

estimating	emissions	and	removals	for	those	practices.	

 Chapter	1	provides	an	overview	of	the	report,	report	objectives,	contents	of	the	report,	and

uses	and	limitations	of	the	report.

 Chapter	2	describes	the	linkages	and	cross‐cutting	issues	relating	to	sector‐specific	and

entity‐scale	estimation	of	GHG	emissions	and	removals.

 Chapter	3	describes	the	GHG	emissions	from	crop	and	grazing	land	systems.	The	chapter

presents methods	for	estimating	the	influence	of	land	use	and	management	practices	on

GHG	emissions	(and	removals)	in	crop	and	grazing	land	systems.	Methods	are	described	for

estimating	biomass	and	soil	carbon	stocks	changes,	direct	and	indirect	soil	nitrous	oxide

(N2O)	emissions,	methane	(CH4)	and	N2O	emissions	from	wetland	rice,	CH4	uptake	in	soils,

carbon	dioxide	(CO2)	emissions	or	removals	from	liming,	non‐CO2	GHG	emissions	from

biomass	burning,	and	CO2	emissions	from	urea	fertilizer	application.

 Chapter	4	provides	guidance	for	estimation	of	carbon	stock	changes	and	CH4	and	N2O

emissions	from	actively	managed	wetlands.

 Chapter	5	describes	on‐farm	GHG	emissions	from	the	production	of	livestock	and	manure

management.	The	chapter	presents	GHG	estimation	methods	appropriate	to	the	production

of	each	common	livestock	sector	(beef,	dairy,	sheep,	swine,	and	poultry),	with	methods

related	to	manure	management	combined	for	all	livestock	types.

 Chapter	6	provides	guidance	on	estimating	carbon	sequestration	and	GHG	emissions	from

managed	forest	systems.	The	chapter	is	organized	to	provide	an	overview	of	the	elements	of

forest	carbon	accounting,	including	definitions	of	the	key	carbon	pools	and	basic	methods

for	their	estimation.
Executive Summary

ES-7 

 Chapter	7	provides	guidance	on	estimating	the	net	GHG	emissions	and	removals	resulting

from	changes	between	land	types—i.e.,	conversions	into	and	out	of	cropland,	wetland,

grazing	land,	or	forest	land—at	the	entity	scale.

 Chapter	8	presents	the	approach	for	accounting	for	the	uncertainty	in	the	estimated	net

emissions	based	on	the	methods	presented	in	this	report.	A	Monte	Carlo	approach	was

selected	as	the	method	for	estimating	the	uncertainty	around	the	outputs	from	the

methodologies	in	this	report	as	it	is	currently	the	most	comprehensive,	sound	method

available	to	assess	the	uncertainty	at	the	entity	scale.

Summary

In	developing	this	report,	the	authors	have	sought	to	outline	the	most	state‐of‐the	art	and	suitable	

science‐based	approaches	and	specific	methods	for	estimating	farm‐	or	forest‐scale	GHG	emissions	

(see	Table	ES‐2).	In	some	cases,	the	proposed	methods	have	not	previously	been	applied	in	

specifically	the	way	that	is	proposed.	For	example,	the	forestry	systems	chapter	describes	the	

integration	of	the	Forest	Vegetation	Simulator	(FVS)	within	other	estimation	tools	for	forest	carbon	

accounting.	This	application	of	FVS,	while	technically	sound,	will	require	additional	effort	to	

implement.	In	other	cases,	the	authors	have	proposed	new	methods	that	build	on	or	enhance	

previously	used	methods.	For	example,	a	new	hybrid	approach	is	proposed	for	estimating	direct	

soil	N2O	emissions	from	mineral	soils	on	croplands	and	grazing	lands.	The	hybrid	approach	uses	

models	to	derive	expected	emission	rates	at	the	typical	fertilization	rate	for	the	major	soil	textures,	

weather	patterns,	and	crop	rotation	systems	in	each	USDA	Land	Resource	Region	and	uses	a	meta‐

analysis	of	empirical	studies	to	develop	emission	scaling	factors	for	cropland	and	grazing	land	

systems.	The	method	also	applies	practice‐based	scaling	factors	derived	from	a	meta‐analysis	of	the	

most	recent	data.	This	hybrid	approach	is	the	result	of	a	workshop	held	in	February	2012	that	

convened	experts	on	N2O	emissions	from	croplands	in	order	to	develop	estimation	methods	that	

were	inclusive	and	best	met	the	objectives	of	USDA.	

In	addition	to	proposing	science‐based	methods,	the	authors	also	acknowledge	that	for	certain	

practices	and	technologies,	adequate	data	do	not	currently	exist	to	accurately	estimate	GHG	

emissions	and/or	carbon	sequestration.	In	each	sector	chapter,	the	authors	have	included	a	

discussion	of	research	gaps	or	priority	areas	for	future	data	collection	that	are	important	in	order	to	

improve	the	completeness	and	accuracy	of	the	estimation	methods	put	forth	in	this	report.	

Estimation	of	GHG	emissions	from	managed	wetland	systems	is	a	good	example.	While	a	method	is	

put	forward	that	reflects	the	best	currently	available	science,	the	authors	state	in	Section	4.3	that	

the	methods	for	these	lands	are	not	as	well	developed	as	for	other	sectors.	Later	in	that	same	

section	there	is	text	discussing	the	considerable	limitations	to	estimating	GHG	fluxes	from	these	

systems	and	the	large	levels	of	uncertainty	around	flux	estimates.	In	Section	4.4,	the	authors	outline	

a	significant	list	of	research	and	data	priorities	that	would	help	to	refine	and	strengthen	the	

estimation	methods.	

In	the	continual	effort	to	advance	the	science	and	improve	the	understanding	of	these	complex	and	

dynamic	systems,	this	report	provides	the	foundation	for	entity‐level	tools	to	quantify	the	GHG	

benefits	from	conservation	and	land	management	activities.	The	report	also	identifies	priorities	for	

future	effort	in	order	to	broaden	the	scope	of	entity‐scale	GHG	flux	estimation	and	reduce	

estimation	uncertainties.	
Executive Summary 

TableES-8

ES‐2: Summary of Source Categories, Recommended Methods, and Emission Factors in this Report

Source Methodology Approach Potential Management Practices Source of Emission Factors Improvements Compared to Other Greenhouse Gas Methodologies

Croplands/Grazing LandsBiomass	Carbon	Stock	Changes	

Herbaceous	biomass	is	estimated	with	an	empirical	method	using	entity	specific	data	as	input	into	the	IPCC6	equations	developed	by	

Lasco	et	al.	(2006)	and	Verchot	et	al.	(2006).	Woody	plant	growth	and	losses	in	agroforestry	or	perennial	tree	crops	are	estimated	with	a	simulation	model	(DAYCENT)	using	entity	input.	

Changes	in	the	estimated	biomass	carbon	stock	for	cropland	and	grazing	land	if	there	is	a	land‐use	change	or	a	change	in	the	crop	or	forage	species.	

U.S.‐specific	default	values	(West	et	al.,	2010)	are	used	for	estimating	biomass	carbon	for	annual	crops	and	grazing	lands.	The	IPCC	default	is	proposed	for	estimating	the	carbon	fraction	value.	Yield	in	units	of	dry	matter	can	be	estimated	by	the	entity,	or	average	values	from	USDA‐Natural	Agricultural	Statistics	Service	statistics	can	be	used.		

This	method	was	chosen	because	it	captures	the	influence	of	land‐use	change	and	changes	in	crop	or	forage	species	on	biomass	carbon	stocks	by	using	U.S.‐specific	default	values	where	entity	specific	data	are	not	available.	

6	IPCC	=	Intergovernmental	Panel	on	Climate	Change	
Executive Summary 

ES-9

Source Methodology Approach Potential Management Practices Source of Emission Factors Improvements Compared to Other Greenhouse Gas Methodologies

Soil	Organic	Carbon	stocks	for	Mineral	Soils	

The	DAYCENT	model	is	used	to	estimate	the	soil	organic	carbon	at	the	beginning	and	end	of	the	year	for	mineral	soils.	The	stocks	are	entered	into	the	IPCC	equations	developed	by	Lasco	et	al.	(2006)	and	Verchot	et	al.	(2006)	to	estimate	carbon	stock	changes.		

Addition	of	carbon	in	manure	and	other	organic	amendments;	tillage	intensity;	residue	management	(retention	in	field	without	incorporation;	retention	in	the	field	with	incorporation;	and	removal	with	harvest,	burning,	or	grazing);	influence	of	bare	and	vegetated	fallows;	irrigation	effects	on	decomposition	in	cropland	and	grazing	land	systems;	setting‐aside	cropland	from	production;	influence	of	fire	on	oxidation	of	soil	organic	matter;	and	woody	plant	encroachment,	agroforestry,	and	silvopasture	effects	on	carbon	inputs	and	outputs.

The	DAYCENT	model	(Parton	et	al.,	1987).	

DAYCENT	model	has	been	demonstrated	to	represent	the	dynamics	of	soil	organic	carbon	and	estimate	soil	organic	carbon	stock	change	in	cropland	and	grasslands	(Parton	et	al.,	1993).	There	have	been	uncertainties	noted	in	the	model	in	Ogle	et	al.	(2007).	The	model	captures	soil	moisture	dynamics,	plant production,	and	thermal	controls	on	net	primary	production	and	decomposition	with	a	time	step	of	a	month	or	less.

Soil	Organic	Carbon	Stocks	for	Organic	Soils	

CO2	emissions	from	drainage	of	

organic	soils	(i.e.,	histosols)	are	estimated	with	an	inference	method	(cf.,	IPCC	Tier	2)	using	the	IPCC	equation	developed	by	Aalde	et	al.	(2006)	and	region‐specific	emission	factors	from	Ogle	et	al.	(2003).		

Cropland	drainage	

Emission	factors	are	from	Ogle	et	al.	(2003)	and	are	region‐specific	based	on	typical	drainage	patterns	and	climatic	controls	(e.g.,	temperature/precipitation)	on	decomposition	rates.	

Uses	entity‐specific	annual	data	as	input	into	the	equation	used	in	the	U.S.	Inventory.	
Executive Summary 

ES-10

Source Methodology Approach Potential Management Practices Source of Emission Factors Improvements Compared to Other Greenhouse Gas Methodologies

Direct	N2O	Emissions	from	Mineral	Soils	

Direct	N2O	methods	are	estimated	with	a	hybrid	estimation	method.	For	major	commodity	crops,	(e.g.,	corn,	cotton,	alfalfa)	a	combination	of	experimental	data	and	process‐based	modeling	using	DAYCENT7	and	DE	nitrification‐decomposition	(DNDC)8	are	used	to	derive	expected	base	emission	rates	for	different	soil	texture	classes	in	each	USDA	Land	Resource	Region.	For	minor	commodity	crops	(e.g.,	barley,	oats,	peanuts)	and	in	cases	where	there	are	insufficient	empirical	data	to	derive	a	base	emission	rate,	the	base	emission	rate	is	based	on	the	IPCC	default	factor	(i.e.,	0.01)	multiplied	by	the	agronomic	nitrogen	input	(de	Klein	et	al.,	2006).	These	emission	rates	are	scaled	with	practice‐based	scaling	factors	to	estimate	the	influence	of	management	changes	such	as	application	of	nitrification	inhibitors	or	slow‐release	fertilizers.	

Nitrogen	application	to	crops.	In	addition,	specific	management	practices	are	included	as	scaling	factors	that	influence	a	portion	or	the	entire	pool	of	mineral	nitrogen.9	Management	practices	that	influence	a	portion	of	the	emission	rate	include:	 Use	of	slow	release

formulation	

 Nitrification	inhibitor

application	

 Manure	nitrogen

directly	deposited	on	pasture/	range/paddock

Management	practices	that	influence	the	entire	pool	of	mineral	nitrogen	include:		 Tillage

The	base	emission	factors	are	adjusted	by	scaling	factors	related	to	specific	crop	management	practices	that	are	derived	from	experimental	data.	

The	method	is	based	on	using	results	from	process‐based	models	and	measured	N2O	emissions	in	combination	with	scaling	factors	based	on	U.S.‐specific	empirical	data	on	a	seasonal	timescale.10

7	The	version	of	DAYCENT	coded	and	parameterized	for	the	most	recent	U.S.	national	GHG	inventory	(U.S.	EPA,	2013)	was	used	to	derive	expected	base	emission	rates.	8	DNDC9.5	compiled	on	Feb	25,	2013	was	used	to	derive	expected	base	emission	rates.	9	Practice‐based	emission	scaling	factors	(0	to	1)	are	used	to	adjust	the	portion	of	the	emission	rate	associated	with	slow	release	fertilizers,	nitrification	inhibitors,	and	

pasture/range/paddock	(PRP)	manure	nitrogen	additions.	The	slow‐release	fertilizer,	nitrification	inhibitor,	and	PRP	manure	scaling	factors	are	weighted	so	that	their	effect	is	only	on	the	amount	of	nitrogen	influenced	by	these	practices	relative	to	the	entire	pool	of	nitrogen	(i.e.,	the	amount	of	slow‐release	fertilizer,	fertilizer	with	nitrification	inhibitor	or	PRP	manure	nitrogen	added	to	the	soil).	In	contrast,	scaling	factors	for	tillage	are	used	to	scale	the	entire	emission	rate	under	the	assumption	that	this	practice	influences	the	entire	pool	of	mineral	nitrogen.		

10	A	full	description	of	the	method	is	included	in	Chapter	3	and	its	appendix.	Supplemental	data	outputs	from	the	model	runs	will	be	available	online	to	download.
Executive Summary 

ES-11

Source Methodology Approach Potential Management Practices Source of Emission Factors Improvements Compared to Other Greenhouse Gas Methodologies

Direct	N2O	

Emissions	from	Drainage	of	Organic	Soils	

Direct	N2O	emissions	from	

drainage	of	organic	soils,	i.e.,	histosols,	are	estimated	with	a	basic	estimation	equation	(cf.,	IPCC	Tier	1)	method	(de	Klein	et	al.,	2006).	

Drainage	of	organic	soils.	

Emission	rate	for	cropped	histosols	based	on	an	IPCC	Tier	1	emission	factor	of	0.008	tonnes	N2O‐nitrogen	ha‐

1			year‐1.		

Uses	entity	specific	annual	data	as	input	into	the	equation	used	in	the	USDA	Inventory	(USDA,	2011).	

Indirect	N2O	Emissions	

Indirect	soil	N2O	emissions	are	estimated	with	an	inference	(cf.,	IPCC	Tier	2)	based	on	IPCC	methodology	(de	Klein	et	al.,	2006).	

Irrigation.	

IPCC	defaults	are	used	for	estimating	the	proportion	of	nitrogen	that	is	subject	to	leaching,	runoff,	and	volatilization.	Where	cropping	systems	with	leguminous	and	non‐leguminous	winter	cover	crops	are	grown,	a	U.S.‐specific	emission	factor	is	provided.	

This	method	uses	entity‐specific	seasonal	data	on	nitrogen	management	practices.	

Methane	Uptake	by	Soils11

Methane	uptake	by	soil is	estimated	with	an	equation	that	uses	average	values	for	methane	oxidation	in	natural	vegetation—whether	grassland,	coniferous	forest,	or	deciduous	forest—attenuated	by	current	land	use	practices.	This	approach	is	an	empirical	model	(cf.,	IPCC	Tier	2	or	IPPC	Tier	3).	

Land	management	including	cultivation	for	crop	production,	grazing	in	grasslands,	forest	harvest,	grassland,	or	forest	fertilization.	

Annual	average	CH4	oxidation	

emissions	and	removals	are	from	the	data	set	used	by	Del	Grosso	et	al.	(2000).	

This	newly	developed	methodology	makes	use	of	recent	U.S.‐based	research	that	is	not	addressed	by	IPCC	or	the	U.S.	Inventory.	The	method	incorporates	entity	specific	annual	data.	

11	Methane	uptake	by	soils	is	a	natural	process	in	undisturbed	soils.		Processes	for	restoring	methanotrophic	activity	are	not	well	understood,	and	require	decades	to	

develop.		A	method	is	outlined	in	this	report,	but	additional	data	and	understanding	are	required	prior	to	use	or	implementation	in	quantification	tools.
Executive Summary 

ES-12

Source Methodology Approach Potential Management Practices Source of Emission Factors Improvements Compared to Other Greenhouse Gas Methodologies

Methane	and	Nitrous	Oxide	Emissions	from	Rice	Cultivation	

A	basic	estimation	equation	(cf.,	IPCC	Tier	1)	is	used	to	estimate	CH4,	and	an	inference	(cf.,	IPCC	Tier	2)	method	is	used	for	N2O	emissions	from	flooded	rice	production	(Akiyama	et	al.,	2005;	de	Klein	et	al.,	2006;	Lasco	et	al.,	2006;	USDA,	2011).	

CH4: scaling	factors	are	differentiated	by	hydrological	context	(e.g.,	irrigated,	rain	fed,	upland	(i.e.,	dry	soil)—all	rice	fields	in	the	United	States	are	irrigated),	cultivation	period	flooding	regime	(e.g.,	continuous,	multiple	aeration),	time	since	last	flooding	(prior	to	cultivation;	e.g.,	more	than	180	days,	less	than	30	days)	and	type	of	organic	amendment	(e.g.,	compost,	farmyard	manure).		N2O:	additions	from	mineral	fertilizers,	organic	amendments,	and	crop	residues.	

CH4:	the	baseline	emission	factor	or	typical	daily	rate	at	which	CH4	is	produced	per	unit	of	land	area	represents	fields	that	are	continuously	flooded	during	the	cultivation	period,	not	flooded	at	all	during	the	180	days	prior	to	cultivation	and	receive	no	organic	amendments.	CH4scaling	factors	to	account	for	water	regimes	come	from	Lasco	et	al.	(2006).		N2O: emission	factors	rely	on	Lasco	et	al.	(2006)	and	the	scaling	factor	to	account	for	drainage	effects;	comes	from	Akiyama	et	al.	(2005;	USDA,	2011).	

The	N2O	method	uses	the	IPCC	(2006)	equation	with	the	addition	of	a	scaling	factor	for	drainage	from	Akiyama	et	al.	(2005).	The	method	for	methane	emissions	uses	entity‐specific	annual	data	as	input	into	the	equation	and	is	consistent	with	U.S.	Inventory	method.		

CO2	from	

Liming	

An	inference	(cf.,	IPCC	Tier	2)method	is	used	to	estimate	CO2

emissions	from	application	of	carbonate	limes	(de	Klein	et	al.,	2006)	with	U.S.‐specific	emissions	factors	(adapted	from	West	and	McBride,	2005).	

The	amount	of	lime,	crushed	limestone,	or	dolomite	applied	to	soils.		

U.S.‐specific	emissions	factors	(West	and	McBride,	2005).	

Uses	U.S.‐specific	emission	factors	as	annual	input	into	the	IPCC	equation,	which	is	consistent	with	the	U.S.	Inventory.	

Non‐CO2Emissions	from	Biomass	Burning	

Non‐CO2	GHG	emissions	from	biomass	burning	of	grazing	land	vegetation	or	crop	residues	are	estimated	with	an	inference	(cf.,	IPCC	Tier	2)	method	(Aalde	et	al.,	2006).		

Area	burned.	

Emission	factors	are	from	values	in	the	IPCC	guidelines	(Aalde	et	al.,	2006)	and	West	et	al.	(2010)	for	the	residue:yield	ratios.		

Uses	entity‐specific	annual	data	as	input	into	the	IPCC	equation.	
Executive Summary 

ES-13

Source Methodology Approach Potential Management Practices Source of Emission Factors Improvements Compared to Other Greenhouse Gas Methodologies

CO2	from	Urea	

Fertilizer	Application	

CO2	emissions	from	application	of	

urea	or	urea‐based	fertilizers	to	soils	are	estimated	with	a	basic	estimation	equation	(cf.,	IPCC	Tier	

1) method	(de	Klein	et	al.,	2006).

The	amount	of	urea	

fertilizer	applied	to	soils.	

Emission	factors are	from	values	in	the	IPCC	guidelines	(de	Klein	et	al.,	2006).	This	method	assumes	that	the	source	of	CO2	used	to	

manufacture	urea	is	fossil	fuel	CO2	captured	during	NH3

manufacture.	

Uses	entity‐specific	annual	data	as	input	into	the	IPCC	equation,	which	is	used	for	the	U.S.	Inventory.	

WetlandsBiomass	Carbon	in	Wetlands	

Methods	for	estimating	forest	vegetation	and	shrub	and	grassland	vegetation	biomass	carbon	stocks	use	a	combination	of	the	Forest	Vegetation	Simulator	(FVS)	model	and	lookup	tables	for	dominant	shrub	and	grassland	vegetation	types	found	in	the	Cropland	and	Grazing	Land	Chapter.	If	there	is	a	land‐use	change,	methods	for	cropland	herbaceous	biomass	are	suggested.	

Forested Wetlands:	Same	as	those	

described	for	upland	forests	in	Section	6.2.3.	Shrub and Grassland Vegetation:	

Same	as	those	described	for	total	biomass	carbon	stock	changes	presented	in	the	Cropland	and	Grazing	Land	Chapter,	Section	3.5.1.	

Forest Wetlands:	Regional	

variants	are	available	for	FVS	that	allow	for	region‐specific	focus	on	species	and	forest	vegetation	communities.	The	driver	for	productivity	is	the	availability	of	site	index	curves,	and	the	regional	variants	include	many	wetland	tree	species.	However,	if	a	species‐specific	curve	is	not	available,	then	a	default	function	is	used	to	estimate	carbon	stock	changes.	Shrub and Grassland

Vegetation:	Same	as	the	Croplands	and	Grazing	Lands	Chapter,	Section	3.5.1.	

Uses	entity‐specific	seasonal	data.	No	IPCC	methodologies	currently	exist	for	this	source;	hence,	this	is	a	newly	developed	method.	

Soil	Carbon,	N2O,	and	CH4	in	Wetlands	

The	DeNitrification‐DeComposition	(DNDC)	process‐based	biogeochemical	model	is	the	method	used	for	estimating	soil	carbon,	N2O,	and	CH4emissions	from	wetlands.		

Vegetation	management,	water	management	regime,	soil	management,	fertilization	practices,	and	land‐use	history.		

Process	based	model	is	used;	hence,	no	emissions	factors	are	used	in	this	method.		

This	method	leverages	the	DNDC	model	to	simulate	soil	carbon,	N2O,	and	CH4	emissions	from	wetlands	on	a	seasonal	timescale.		
Executive Summary 

ES-14

Source Methodology Approach Potential Management Practices Source of Emission Factors Improvements Compared to Other Greenhouse Gas Methodologies

Animal Production SystemsEnteric FermentationMature	Dairy	Cows	

Mits3	equation	developed	by	Mills	et	al.	(2003)	and	further	utilized	by	DairyGEM	(Rotz	et	al.,	2011).	Mits3	equation	is	based	primarily	on	metabolizable	energy	intake.	Dry	matter	intake	(DMI),	starch,	acid	detergent	fiber,	crude	protein,	and	total	digestible	nutrients	provide	the	inputs	for	the	equation.	

Dietary changes:	increasing	DMI,	

using	fibrous	concentrate	rather	than	starch	concentrate,	feeding	rapidly	degraded	starch	(such	as	barley),	and	addition	of	dietary	fat.	Activity changes:	confining	currently	grazing	animals,	fewer	work	hours	per	day,	fewer	days	on	feed	prior	to	slaughter.	

Emission	factors	calculated	with	approach	developed	by	Mills	et	al.	(2003)	and	Rotz	et	al.	(2011).	

Use	of	the	DairyGEM/Mits3	equation	is	recommended	over	the	IPCC	Tier	2	equation	(2006)	because	it	has	proven	to	be	more	accurate,	in	general,	for	dairy	cows.	

Beef	Cow‐Calf	and	Bulls	

IPCC	Tier	2	approach	(2006).	The	calculation	considers	weight,	weight	gain,	mature	weight,	pregnancy,	lactation,	other	activity	(grazing,	confined,	daily	work),	and	the	energy	content	of	the	animals'	diets.	

Dietary changes: increasing	DMI,	using	fibrous	concentrate	rather	than	starch	concentrate,	feeding	rapidly	degraded	starch	(such	as	barley),	and	addition	of	dietary	fat.	Activity changes:	confining	currently	grazing	animals,	fewer	work	hours	per	day.	

Emission	factors	are	determined	with	the	IPCC	Tier	2	equation	(2006).	Methane	conversion	factor	(Ym)	based	on	animal‐specific	guidance	in	U.S.	EPA	(2013).	

The	equations	utilized	are	the	same	as	existing	inventory	methods;	however,	the	methods	utilize	farm‐specific	feed	types	and	utilize	monthly,	rather	than	annual,	level	data	(i.e.,	account	for	seasonal	variation	in	forage	quality).	

Stockers	

IPCC	Tier	2	approach	(2006).	The	calculation	considers	weight,	weight	gain,	mature	weight,	pregnancy,	lactation,	other	activity	(grazing,	confined,	daily	work),	and	the	energy	content	of	the	animals'	diets.	

Dietary changes: increasing	DMI,	

using	fibrous	concentrate	rather	than	starch	concentrate,	feeding	rapidly	degraded	starch	(such	as	barley),	and	addition	of	dietary	fat.		Activity changes:	confining	currently	grazing	animals,	fewer	work	hours	per	day,	fewer	days	on	feed	prior	to	slaughter.	

Emission	factors	are	determined	with	the	IPCC	Tier	2	equation	(2006)	on	an	entity‐by‐entity	basis.	Ym	based	on	animal‐specific	guidance	in	U.S.	EPA	(2013).	

The	equations	utilized	are	the	same	as	existing	inventory	methods;	however,	the	methods	utilize	farm‐specific	feed	types	and	utilize	monthly,	rather	than	annual,	level	data	(i.e.,	account	for	seasonal	variation	in	forage	quality).	
Executive Summary 

ES-15

Source Methodology Approach Potential Management Practices Source of Emission Factors Improvements Compared to Other Greenhouse Gas Methodologies

Feedlot	Cattle	

IPCC	Tier	2	approach	(2006).	The	calculation	considers	weight,	weight	gain,	mature	weight,	pregnancy,	lactation,	other	activity	(grazing,	confined,	daily	work),	and	the	energy	content	of	the	animals'	diets.	

Dietary changes: increasing	DMI,	using	fibrous	concentrate	rather	than	starch	concentrate,	feeding	rapidly	degraded	starch	(such	as	barley),	and	addition	of	dietary	fat.		Activity changes:	confining	currently	grazing	animals,	fewer	work	hours	per	day,	fewer	days	on	feed	prior	to	slaughter.	

Emission	factors	are	determined	with	the	IPCC	Tier	2	equation	(2006).	Ym	based	on	guidance	developed	by	Hales	(2012).	

The	calculation	considers	weight,	weight	gain,	mature	weight,	pregnancy,	lactation,	other	activity	(grazing,	confined,	daily	work),	and	the	energy	content	of	the	animals'	diets.	

Sheep	

Howden	equation	(Howden	et	al.,	1994),	based	on	dietary	DMI.		

Dietary	changes,	but	no	well‐developed	research	due	to	difficulty	of	obtaining	accurate	feed‐intake	estimates	for	grazing	sheep.	

The	equation	from	Howden	et	al.	(1994)	estimates	emissions	based	solely	on	DMI;	hence,	emission	factors	not	utilized.	

This	method	uses	actual	monthly	estimates	of	DMI,	rather	than	head	count,	as	utilized	by	the	IPCC	Tier	1	equation	(2006).		

Swine	 IPCC	Tier	1	approach	 (2006).	 None.	 Utilizes	IPCC	Tier	1	emission	 factor	(IPCC,	2006).	 None.	Other	Animals	(Goats,	American	Bison)	

IPCC	Tier	1	approach	for	American	bison	(based	on	buffalo,	modified	by	average	animal	weight)	and	goats	(IPCC,	2006).		

None.	 Utilizes	IPCC	Tier	1	emission	 factors(IPCC,	2006).	 None.	

HousingMethane	Emissions	from	Manure	on	Barn	Floors	for	Dairy	Cattle	

DairyGEM	(a	subset	of	the	Integrated	Farm	Systems	Model)	is	used	to	estimate	CH4	emissions.	

None.		

Empirical	relationship	as	provided	in	Chianese	et	al.	(Chianese	et	al.,	2009).			

Utilizes	climate	and	entity	characteristics.	

Methane	Emissions	from	Dairy	Cattle,	Beef	Cattle,	and	Swine	Housing		

IPCC	Tier	2	approach.	 Type	and	duration	of	manure	 storage.	 Utilizes	a	combination	of	IPCC	 and	U.S.	EPA	Inventory	 emission	factors.	 None.	
Executive Summary 

ES-16

Source Methodology Approach Potential Management Practices Source of Emission Factors Improvements Compared to Other Greenhouse Gas Methodologies

Nitrous	Oxide	Emissions	from	Dairy	Cattle,	Beef	Cattle,	Swine,	and	Poultry	Housing	

IPCC	Tier	2	approach,	using	American	Society	of	Agricultural	Engineers	(ASAE)	equations	to	estimate	nitrogen	excretion	and	default	values	for	ammonia	losses	to	account	for	nitrogen	balance.	

Animal	diets	and	type	of	manure	storage.		

Utilizes	IPCC	emission	factors	(IPCC,	2006)	and	ammonia	losses	from	Koelsh	and	Stowell	(2005).		

Uses	nitrogen	balance	approach	to	adjust	nitrogen	in	housing	to	account	for	ammonia	losses.	

Methane	Emissions	from	Poultry	Housing	

IPCC	Tier	1	approach.	 None.	

Utilizes	IPCC	emission	factors	that	vary	by	temperature	and	whether	manure	is	managed	as	dry	manure	or	as	a	liquid	(IPCC,	2006).		

Of	the	models	evaluated	for	poultry,	an	estimate	of	confidence	for	output	was	only	available	for	the	IPCC	Tier	1	approach.	Specific	to	estimates	of	poultry,	on	manure	CH4	emissions,	the	uncertainty	was	less	than	20%	(Little	et	al.,	2008).	

Manure Storage and TreatmentSolid	Manure	Storage	and	Treatment	‐	Temporary	Stack	and	Long‐Term	Stockpile	

Methane	Emissions	

IPCC	Tier	2	approach	using	IPCC	and	U.S.	EPA	Inventory	emission	factors,	utilizing	monthly	data	on	volatile	solids	and	dry	manure.	

Animal	diets.	

Utilizes	a	combination	of	IPCC	and	U.S.	EPA	Inventory	emission	factors.	

Uses	U.S.‐specific	emission	factors	and	takes	into	account	diet	characterization.	

Nitrous	Oxide	Emissions	

IPCC	Tier	2	approach	using	U.S.	EPA	Inventory	emission	factors	and	monthly	data	on	total	nitrogen,	and	dry	manure.	

Duration	of	manure	storage	and	animal	diets.	

Utilizes	emission	factors	from	U.S.	EPA	Inventory.	

Uses	U.S.‐specific	emission	factors	and	takes	into	account	diet	characterization.	

Manure	Storage	and	Treatment‐Composting	

Methane	Emissions	

IPCC	Tier	2	approach	utilizing	monthly	data	on	volatile	solids	and	dry	manure.	

Configuration	of	storage	unit	(e.g.,	composting	in‐vessel,	static	pile,	intensive	windrow,	passive	windrow)	and	animal	diets.	

Utilizes	emission	factors	from	IPCC.	

Takes	into	account	diet	and	climate	characteristics.	
Executive Summary 

ES-17

Source Methodology Approach Potential Management Practices Source of Emission Factors Improvements Compared to Other Greenhouse Gas Methodologies

Nitrous	Oxide	Emissions	

IPCC	Tier	2	approach	utilizing	data	on	total	initial	nitrogen	and	dry	manure.	

Manure	handling	(i.e.,	no	mix	or	active	mix)	and	animal	diets.	

Utilizes	emission	factors	from	IPCC.	

Takes	into	account	diet	and	climate	characteristics.	

Liquid	Manure	Storage	and	Treatment	–	Aerobic	Lagoon	

Methane	Emissions	

The	methane	correction	factor	for	aerobic	treatment	is	negligible	and	was	designated	as	0%	in	accordance	with	the	IPCC.	

Not	applicable.	 Utilizes	emission	factors	from	 IPCC.	 Not	estimated.	

Nitrous	Oxide	Emissions	 IPCC	Tier	2	method.	

Configuration	of	storage	(e.g.,	volume	of	lagoon),	natural	or	forced	aeration,	and	animal	diets.	

Utilizes	emission	factors	from	IPCC.	 None.		

Liquid	Manure	Storage	and	Treatment	–	Anaerobic	Lagoon,	Runoff	Holding	Pond,	Storage	Tanks	

Methane	Emissions	

Sommer	Model	(Sommer	et	al.,	2004)	is	used	with	degradable	and	non‐degradable	fractions	of	volatile	solids	from	Møller	et	al.	(2004).		

Configuration	of	storage	unit	(e.g.,	covered	or	uncovered	storage,	presence	or	absence	of	crust)	and	animal	diets.	

Parameters	for	estimation	from	Sommer	et	al.	(2004).	

Takes	into	account	diet	and	storage	temperature	characteristics.	

Nitrous	Oxide	Emissions	

Emissions	are	a	function	of	the	exposed	surface	area	and	U.S.‐based	emission	factors.	

Configuration	of	storage	unit	(e.g.,	surface	area	of	manure).	

Utilizes	emission	factors	from	Rotz	et	al.	(2011a).		

Utilizes	U.S.‐specific	emission	factors.	

Liquid	Manure	Storage	and	Treatment	–	Anaerobic	Digestion	with	Biogas	Utilization	

Methane	Emissions	

Leakage	from	anaerobic	digestion	system	is	estimated	using	IPCC	Tier	2	approach	and	system‐specific	emission	factors.		

Configuration	of	digester	(e.g.,	steel	or	lined	concrete	or	fiberglass	digesters)	and	animal	diets.		

Utilizes	emission	factors	from	CDM	(CDM,	2012).		

Takes	into	account	system	design	and	diets.	

Combined	Aerobic	Treatment	Systems	
Executive Summary 

ES-18

Source Methodology Approach Potential Management Practices Source of Emission Factors Improvements Compared to Other Greenhouse Gas Methodologies

Methane	Emissions	

Assumed	to	be	10	percent	of	the	emissions	resulting	from	method	to	estimate	emissions	from	Liquid	Manure	Storage	and	Treatment	–	Anaerobic	Lagoon,	Runoff	Holding	Pond,	Storage	Tanks.	

Configuration	of	storage	unit	(e.g.,	covered	or	uncovered	storage,	presence	or	absence	of	crust)	and	animal	diets.	

Parameters	for	estimation	from	Sommer	et	al.	(2004).	

Takes	into	account	diet	and	storage	temperature	characteristics.	

Nitrous	Oxide	Emissions	

Assumed	to	be	10	percent	ofthe	emissions	resulting	from	method	to	estimate	emissions	from	Liquid	Manure	Storage	and	Treatment	–	Anaerobic	Lagoon,	Runoff	Holding	Pond,	Storage	Tanks.	

Configuration	of	storage	unit	(e.g.,	surface	area	of	manure).	

Utilizes	emission	factors	from	Rotz	et	al.	(2011a).	 Uses	U.S.‐specific	emission	factors.	

Liquid	Manure	Storage	and	Treatment	–	Sand/Manure	Separation	

No	method	provided	as	GHG	emissions	are	negligible.	However,	resulting	volatile	solids,	total	nitrogen,	organic	nitrogen,	and	manure	temperature	of	the	separated	liquid	manure	should	be	measured	and	used	as	the	inputs	to	estimate	emissions	of	GHGs	for	subsequent	storage	and	treatment	operations.	

Not	applicable.	 Not	applicable.	 Not	applicable.	

Liquid	Manure	Storage	and	Treatment	–	Nutrient	Removal	

Not	estimated	due	to	limited	quantitative	information	on	GHGs	from	nitrogen	removal	processes.	

Not	applicable.	 Not	applicable.	 Not	applicable.	
Executive Summary 

ES-19

Source Methodology Approach Potential Management Practices Source of Emission Factors Improvements Compared to Other Greenhouse Gas Methodologies

Liquid	Manure	Storage	and	Treatment	–	Solid/Liquid	Separation	

No	method	provided	as	GHG	emissions	are	negligible.	Efficiency	factors	for	different	mechanical	solid‐liquid	separation	systems	provided.	However,	resulting	volatile	solids,	total	nitrogen,	organic	nitrogen,	and	manure	temperature	of	the	separated	liquid	manure	should	be	measured	and	used	as	the	inputs	to	estimate	emissions	of	GHGs	for	subsequent	storage	and	treatment	operations.	

Not	applicable.	 Not	applicable.	 Not	applicable.	

Liquid	Manure	Storage	and	Treatment	–	Constructed	Wetlands	GHG	Removals	

Currently	no	method	is	provided,	although	GHG	removals	are	noted	to	likely	be	greater	than	CH4	and	N2O	

emissions,	which	are	considered	negligible.	

Not	applicable.	 Not	applicable.	 Not	applicable.	

Solid	Manure	Storage	and	Treatment	–	Thermo‐Chemical	Conversion		

Not	estimated	as	CH4	and	N2O	emissions	considered	negligible.	

Not	applicable.	 Not	applicable.	 Not	applicable.	

Manure Application
Executive Summary 

ES-20

Source Methodology Approach Potential Management Practices Source of Emission Factors Improvements Compared to Other Greenhouse Gas Methodologies

Solid	Manure	Application	Systems	(manure	handling	prior	to	land	application)	

Not	estimated	due	to	limited	quantitative	information	on	GHGs	from	manure	mixing	and	removal	from	storage	systems	or	during	transport	to	fields	where	manure	is	land	applied.	

Not	applicable.	 Not	applicable.	 Not	applicable.	

Liquid	Manure	Application	Systems	(manure	handling	prior	to	land	application)	

No	method	is	provided	as	CH4	and	N2O	GHG	emissions	are	negligible;	however,	CO2emissions	would	result	from	the	operation	of	equipment.

Not	applicable.	 Not	applicable.	 Not	applicable.	

ForestryForest	Carbon		

Methods	include:	(1)	FVS	model	with	Fire	and	Fuels	Extension	module	(FVS‐FFE)	with	Jenkins	et	al.	(2003)	allometric	equations;	and	(2)	default	look	up	tables.	

FVS‐FFE	models	hundreds	of	management	practices	(thinning	from	below/above/evenly	through	a	stand,	thinning	with	species	preference,	conditional	thinning/planting/	regeneration,	piling	of	surface	fuels	and	prescribed	fires,	salvage	operations,	mastication	treatments,	insect/disease	management,	etc.)	

Allometric	equations	are	from	Jenkins	et	al.	(2003);	default	look	up	tables	from	Smith	et	al.	(2006).	

The	method	allows	large	landowners	to	estimate	base	year	carbon	stocks	from	field	surveys	and	repeat	the	field	survey	at	recommended	intervals	(e.g.,	5‐year,	10‐year)	depending	on	the	region/forest	type	group.	Small	landowners	estimate	carbon	stocks	from	lookup	tables	based	on	USDA	Forest	Inventory	and	Analysis	program	data,	which	is	comparable	to	other	GHG	methodologies	(e.g.,	Section	1605(b)	Guidance).		
Executive Summary 

ES-21

Source Methodology Approach Potential Management Practices Source of Emission Factors Improvements Compared to Other Greenhouse Gas Methodologies

Establishing,		Re‐establishing,	and	Clearing	Forest	

IPCC	equations	developed	by	Aalde	et	al.	(2006);	with	Jenkins	et	al.	(2003)	allometric	equations.	

Planting	trees	on	previously	unforested	lands;	replanting	trees	on	previously	forested	lands;	and	permanently	clearing	trees	from	forested	lands.	

Allometric	equations	are	from	Jenkins	et	al.	(2003)	

This	method	allows	large	landowners	to	estimate	base	year	carbon	stocks	from	field	surveys	and	repeat	the	field	survey	at	recommended	intervals	(e.g.,	5‐year,	10‐year)	depending	on	the	region/forest	type	group.	The	National	Inventory	Report	(NIR)	uses	a	carbon	stock	change	method,	which	explicitly	includes	the	establishment,	re‐establishment,	and	clearing	of	forests.	

Forest	Management	

Methods	include:	(1)	FVS‐

FFE	with	Jenkins	et	al.	(2003)	allometric	equations	and	(2)	default	lookup	tables	of	management	practice	scenarios.	

Stand	density	management;	site	preparation	techniques;	vegetation	control;	planting;	natural	regeneration;	fertilization;	selection	of	rotation	length;	harvesting	and	utilization	techniques;	fire	and	fuel	load	management;	reducing	risk	of	emissions	from	pests	and	disease;	short‐rotation	woody	crops.	

Default	lookup	tables	of	carbon	stocks	over	time	by	region,	forest	type	categories,	

including	species	group	(e.g.,	hardwood,	softwood,	mixed);	regeneration	(e.g.,	planted,	naturally	regenerated);	management	intensity	(e.g.,	low,	moderate,	high,	very	high)	and	site	productivity	(e.g.,	low,	high),	to	be	developed	as	a	supporting	product	using	FVS.	

This	method	provides	a	consistent	and	comparable	set	of	carbon	stocks	for	each	region,	forest	type	group,	management	intensity,	and	site	productivity	over	time,	under	management	scenarios	common	to	the	forest	types	and	management	intensities.	
Executive Summary 

ES-22

Source Methodology Approach Potential Management Practices Source of Emission Factors Improvements Compared to Other Greenhouse Gas Methodologies

Harvested	Wood	Products	

U.S.‐specific	harvested	wood	products	tables	developed	by	Skog	(2008),	taking	the	estimated	average	amount	of	harvested	wood	product	carbon	from	the	current	year’s	harvest	that	remains	stored	in	end	uses	and	landfills	over	the	next	100	years.	

The	approach	models	various	management	practices	including	the	disposition	of	each	primary	product	(e.g.,	lumber,	structural	panels)	to	major	end	uses	(e.g.,	percentage	of	product	going	to	residential	housing,	non‐residential	housing,	manufacturing	(furniture)),	and	percentage	going	to	exports;	with	decay	functions	indicating	how	quickly	products	go	out	of	use	for	each	end	use;	fraction	of	material	going	out	of	use	that	goes	to	landfills;	fraction	of	material	in	landfills	that	does	not	decay,	and	the	decay	rate	for	material	in	landfills	that	does	decay.	

WOODCARB	II	model	used	to	estimate	annual	change	in	carbon	stored	in	products	and	landfills	(Skog,	2008).	

Provides	a	method	that	is	suitable	to	count	the	average	amount	of	carbon	stored	in	products	in	use	and	in	landfills,	and	the	underlying	model	is	the	same	used	for	the	National	Inventory	Report	(NIR)	(i.e.,		The	NIR	also	uses	WOODCARB	II	model	to	estimate	annual	change	in	carbon	stored	in	products	and	landfills).	The	harvested	wood	product	tables	(Skog,	2008)	provide	annual	values	for	zero	to	10	years	after	production	and	5‐year	intervals	for	10	to	100	years	after	production.	

Urban	Forests	

Methods	include:	(1)	Field	Data	Method	using	i‐Tree	Eco	(formerly	UFORE)	model;	and	(2)	Aerial	Method	using	i‐Tree	Canopy	model	with	aerial	tree	cover	estimates	and	look	up	tables.	

Maintenance	(use	of	vehicles,	chain	saws,	etc.)	and	altering	building	energy	use	(use	of	trees	for	shading	and	wind	breaks);	quantitative	methods	for	estimating	emissions	from	these	management	practices	are	included	for	information	purposes	only.	

i‐Tree	Eco	model;	i‐Tree	Canopy	model.	

This	method	provides	a	range	of	options	dependent	on	the	data	availability	of	the	entities'	urban	forest	land.	The	NIR	uses	equations	based	on	look	up	tables	and	average	tree	canopy	values.	

Natural	Disturbance—Wildfire	and	Prescribed	Fire	

Methods	include:	(1)	First	Order	Fire	Effects	(FOFEM)	model	entering	measured	biomass;	and	(2)	FOFEM	model	using	default	values	generated	by	vegetation	type.	

Fire	and	fuel	load	management.	 FOFEM	(Reinhardt	et	al.,	 1997).	

This	method	provides	a	range	of	options	dependent	on	the	data	availability	of	the	entities'	disturbed	forest	land.	The	use	of	a	U.S.‐specific	fire	and	fuel	load	management	model	is	an	improvement	compared	to	the	NIR,	which	uses	equations	based	on	IPCC	(2006).	
Executive Summary 

ES-23

Source Methodology Approach Potential Management Practices Source of Emission Factors Improvements Compared to Other Greenhouse Gas Methodologies

Land‐use ChangeAnnual	Change	in	Carbon	Stocks	in	Dead	Wood	and	Litter	Due	to	Land	Conversion	

A	basic	estimation	equation(cf.,	IPCC	Tier	1)	is	used	to	estimate	change	in	carbon	stocks	in	dead	wood	and	litter	(Aalde	et	al.,	2006).	

Land	conversion.	 IPCC	2006	Guidelines	(Aalde	 et	al.,	2006).	 Uses	entity‐specific	annual	data	as	 input	into	the	equation	and	is	 consistent	with	IPCC	2006	guidance.

Change	in	Soil	Organic	Carbon	Stocks	for	Mineral	Soils	

The	methodologies	to	estimate	soil	carbon	stock	changes	for	organic	soils	and	mineral	soils	are	adopted	from	IPCC	(Aalde	et	al.,	2006)	and	are	a	basic	estimation	equation.	

Land	conversion.	 IPCC	2006	Guidelines	(Aalde	 et	al.,	2006).	 Uses	entity‐specific	annual	data	as	 input	into	the	equation	and	is	 consistent	with	IPCC	2006	guidance.

IPCC=	Intergovernmental	Panel	on	Climate	Change
Executive Summary 

ES-24

References

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to	multiple	land‐use	categories.	In	2006 IPCC Guidelines for National Greenhouse Gas

Inventories,	S.	Eggleston,	L.	Buendia,	K.	Miwa,	T.	Ngara	and	K.	Tanabe	(eds.).	Japan:	IGES.	

Akiyama,	H.,	K.	Yagi,	and	X.	Yan.	2005.	Direct	N2O	emissions	from	rice	paddy	fields:	Summary	of	

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to	assess	greenhouse	gas	reduction	strategies.	Trans. ASABE,	52:1313‐1323.	

de	Klein,	C.,	R.S.A.	Novoa,	S.	Ogle,	K.A.	Smith,	et	al.	2006.	Chapter	11:	N2O	emissions	from	managed	

soil,	and	CO2	emissions	from	lime	and	urea	application.	In	2006 IPCC guidelines for national

greenhouse gas inventories, Vol. 4: Agriculture, forestry and other land use,	S.	Eggleston,	L.	

Buendia,	K.	Miwa,	T.	Ngara	and	K.	Tanabe	(eds.).	Kanagawa,	Japan:	IGES.	

Del	Grosso,	S.,	W.	Parton,	A.	Mosier,	D.S.	Ojima,	et	al.	2000.	General	CH4	oxidation	model	and	

comparisons	of	CH4	oxidation	in	natural	and	managed	systems.	Global Biogeochemical

Cycles,	14:999‐1019.	

Denef,	K.,	S.	Archibeque,	and	K.	Paustian.	2011.	Greenhouse Gas Emissions from U.S. Agriculture and

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Hales,	K.E.,	N.A.	Cole,	and	J.C.	MacDonald.	2012.	Effects	of	corn	processing	method	and	dietary	

inclusion	of	wet	distillers	grain	with	solubles	on	energy	metabolism	and	enteric	methane	

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options	to	reduce	greenhouse	gas	emissions	from	tropical	grazing	systems.	Climatic Change,	

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IPCC.	1997.	Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories, Prepared by the

National Greenhouse Gas Inventories Programme.	Bracknell,	UK:	Intergovernmental	Panel	on	

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IPCC.	2006.	2006 IPCC Guidelines for National Greenhouse Gas Inventories, Prepared by the National

Greenhouse Gas Inventories Programme.	Edited	by	H.	S.	Eggleston,	L.	Buendia,	K.	Miwa,	T.	

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Jenkins,	J.C.,	D.C.	Chojnacky,	L.S.	Heath,	and	R.A.	Birdsey.	2003.	National‐Scale	Biomass	Estimators	

for	United	States	Tree	Species.	Forest Science,	49(1):12‐35.	

Lasco,	R.D.,	S.	Ogle,	J.	Raison,	L.	Verchot,	et	al.	2006.	Chapter	5:	Cropland.	In	2006 IPCC Guidelines for

National Greenhouse Gas Inventories,	S.	Eggleston,	L.	Buendia,	K.	Miwa,	T.	Ngara	and	K.	

Tanabe	(eds.).	Japan:	IGES,	IPCC	National	Greenhouse	Gas	Inventories	Program.	

Little,	S.,	J.	Linderman,	K.	MacLean,	and	H.	Janzen.	2008.	HOLOS – a tool to estimate an dreduce

greenhouse gases from farms. Methodology and algorithms for versions 1.1x:	Agriculture	and	
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afficher.do?id=1226606460726&lang=eng#s1.	

Mills,	J.A.N.,	E.	Kebreab,	C.M.	Yates,	L.A.	Crompton,	et	al.	2003.	Alternative	approaches	to	predicting	

methane	emissions	from	dairy	cows.	Journal of Animal Science,	81(12):3141‐3150.	

Møller,	H.B.,	S.G.	Sommer,	and	B.K.	Ahring.	2004.	Methane	productivity	of	manure,	straw	and	solid	

fractions	of	manure.	Biomass and Bioenergy,	26(5):485‐495.	

Ogle,	S.M.,	F.	Jay	Breidt,	M.D.	Eve,	and	K.	Paustian.	2003.	Uncertainty	in	estimating	land	use	and	

management	impacts	on	soil	organic	carbon	storage	for	US	agricultural	lands	between	1982	

and	1997.	Global Change Biology,	9(11):1521‐1542.	

Ogle,	S.M.,	F.J.	Breidt,	M.	Easter,	S.	Williams,	et	al.	2007.	Empirically	based	uncertainty	associated	

with	modeling	carbon	sequestration	in	soils.	Ecological Modelling,	205:453‐463.	

Parton,	W.J.,	D.S.	Schimel,	C.V.	Cole,	and	D.S.	Ojima.	1987.	Analysis	of	factors	controlling	soil	organic	

matter	levels	in	Great	Plains	grasslands.	Soil Science Society of America Journal,	51:1173‐

1179.	

Parton,	W.J.,	J.M.O.	Scurlock,	D.S.	Ojima,	T.G.	Gilmanov,	et	al.	1993.	Observations	and	Modeling	of	

Biomass	and	Soil	Organic	Matter	Dynamics	for	the	Grassland	Biome	Worldwide.	Global

Biogeochemical Cycles,	7(4):785‐809.	

Reinhardt,	E.D.,	R.E.	Keane,	and	J.K.	Brown.	1997.	First Order Fire Effects Model: FOFEM 4.0, User's

Guide.		

Rotz,	C.A.,	M.S.	Corson,	D.S.	Chianese,	F.	Montes,	et	al.	2011. Integrated farm system model: Reference

Manual.	University	Park,	PA:	U.S.	Department	of	Agriculture,	Agricultural	Research	Service.	

http://ars.usda.gov/SP2UserFiles/Place/19020000/ifsmreference.pdf.	

Skog,	K.E.	2008.	Sequestration	of	carbon	in	harvested	wood	products	for	the	United	States.	Forest

Products Journal,	58(6):56‐72.	

Smith,	J.E.,	L.S.	Heath,	K.E.	Skog,	and	R.A.	Birdsey.	2006.	Methods for calculating forest ecosystem and

harvested carbon with standard estimates for forest types of the United States.	Newtown	

Square,	PA:	US	Department	of	Agriculture,	Forest	Service,	Northern	Research	Station.		

Sommer,	S.G.,	S.O.	Petersen,	and	H.B.	Møller.	2004.	Algorithms	for	calculating	methane	and	nitrous	

oxide	emissions	from	manure	management.	Nutrient Cycling in Agroecosystems,	69:143‐154.	

U.S.	EPA.	2013.	Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990‐2011.	Washington,	DC:	

U.S.	Environmental	Protection	Agency.	

http://epa.gov/climatechange/emissions/usinventoryreport.html.	

USDA.	2011.	U.S. Agriculture and Forest Greenhouse Gas Inventory: 1990‐2008.	Washington,	DC:	U.S.	

Department	of	Agriculture.		

Verchot,	L.,	T.	Krug,	R.D.	Lasco,	S.	Ogle,	et	al.	2006.	Chapter	5:	Grassland.	In	2006 IPCC Guidelines for

National Greenhouse Gas Inventories,	S.	Eggleston,	L.	Buendia,	K.	Miwa,	T.	Ngara	and	D.	L.	

Tanaka	(eds.).	Japan:	IGES.	

West,	T.O.,	and	A.C.	McBride.	2005.	The	contribution	of	agricultural	lime	to	carbon	dioxide	

emissions	in	the	United	States:	dissolution,	transport,	and	net	emissions.	Agriculture,

Ecosystems & Environment,	108(2):145‐154.	

West,	T.O.,	C.C.	Brandt,	L.M.	Baskaran,	C.M.	Hellwinckel,	et	al.	2010.	Cropland	carbon	fluxes	in	the	

United	States:	increasing	geospatial	resolution	of	inventory‐based	carbon	accounting.	

Ecological Applications,	20:1074‐1086.	

	 	
Executive Summary 

ES-26

This	page	is	intentionally	left	blank.	
			

Authors:

Marlen	Eve,	U.S.	Department	of	Agriculture,	Office	of	the	Chief	Economist	

Mark	Flugge,	ICF	International	

Diana	Pape,	ICF	International	

Contents:

1 Introduction ...............................................................................................................................................1‐3

1.1	 Overview	of	the	Report	...............................................................................................................................	1‐4	

1.2	 Report	Objectives	..........................................................................................................................................	1‐5	

1.3	 Process	for	the	Development	of	the	Methods	....................................................................................	1‐6	

1.4	 Contents	of	the	Report	................................................................................................................................	1‐9	

1.5	 Uses	and	Limitations	of	the	Report	......................................................................................................	1‐10	

Chapter	1	References	.............................................................................................................................................	1‐14	

Suggested	Chapter	Citation:	Eve,	M.,	M.	Flugge,	D.	Pape,	2014.	Chapter	1:	Introduction.	In	

Quantifying Greenhouse Gas Fluxes in Agriculture and Forestry: Methods for Entity‐Scale Inventory.	

Technical	Bulletin	Number	1939.	Office	of	the	Chief	Economist,	U.S.	Department	of	Agriculture,	

Washington,	DC.	606	pages.	July	2014.	Eve,	M.,	D.	Pape,	M.	Flugge,	R.	Steele,	D.	Man,	M.	Riley‐Gilbert,	

and	S.	Biggar,	Eds.	

USDA	is	an	equal	opportunity	provider	and	employer.	

Chapter 1

Introduction 
Chapter 1: Introduction 

1-2

Acronyms, Chemical Formulae, and Units

C	 Carbon	

CH4 Methane

CO2 Carbon	dioxide

CO2‐eq	 Carbon	dioxide	equivalents

EO	 Executive	Order	

EPA	 U.S.	Environmental	Protection	Agency

GHG	 Greenhouse	gas	

ISO	 International	Organization	for	Standardization

LCA	 Life	cycle	assessment

LCI	 Life	cycle	inventory	

N2O	 Nitrous	Oxide	

USDA		 U.S.	Department	of	Agriculture
 Chapter 1: Introduction 

 1-3 

1 Introduction

In	2008,	agriculture	contributed	6.1	percent	of	the	total	greenhouse	gas	(GHG)	emissions	in	the	

United	States	(USDA,	2011).1	The	distribution	of	emissions	across	the	agriculture	sector	is

illustrated	in	Figure	1‐1.	In	addition,	forestry	

sequestered	enough	carbon	to	offset	about	

13	percent	of	total	U.S.	GHG	emissions	(USDA,	

2011).	Since	the	late	1990s,	the	U.S.	

Department	of	Agriculture	(USDA)	has	

analyzed	and	reported	GHG	emissions	and	

removals	via	national‐scale	inventories,	and	

field‐scale	measurement	of	these	fluxes	has	

been	done	for	decades	by	USDA	researchers.	

USDA	also	has	done	significant	work	in	the	

development	of	GHG	estimation	models	and	

tools	within	the	agriculture	and	forestry	

sectors.	

This	report	provides	methods	and	a	scientific	

basis	for	estimating	GHG	emissions	and	

sequestration	at	the	landowner,	land‐manager	

scale—entity	scale.	The	report	was	authored	

by	recognized	experts	from	across	USDA,	

other	U.S.	Government	agencies,	and	academia	

and	reflects	estimation	methods	that	balance	

scientific	rigor,	scale,	practicality,	and	

availability	of	data.	

This	chapter	provides	an	overview	of	the	

report	as	well	as	the	objectives	set	out	for	the	

project	and	the	process	used	in	developing	the	

report.	The	remainder	of	the	chapter	is	

organized	as	follows:	

 Overview	of	the	Report	

 Report	Objectives	

 Process	for	the	Development	of	the	Methods	

 Contents	of	the	Report	

 Uses	and	Limitations	of	the	Report	

 Chapter	1	References	

																																																													

1	Here	the	agriculture	sector	includes	GHG	emissions	and	removals	from	livestock,	grasslands,	croplands,	and	

energy	use	on	farms;	it	does	not	include	GHG	emissions	and	removals	from	industrial	processes	(e.g.,	

fertilizer	production)	or	from	off‐farm	energy	use	(e.g.,	transportation	fuels	used	in	exporting	commodity	

crops).	

a Cropland	soils	emissions	include	emissions	from	major	

crops;	non‐major	crops;	histosol	cultivation;	and	managed	

manure	that	accounts	for	the	loss	of	manure	nitrogen	

during	transport,	treatment,	and	storage,	including	

volatilization	and	leaching/runoff.	

Source:	USDA	(2011).	

Figure 1‐1: Agriculture Sources of

Greenhouse Gas Emissions in 2008a

Total Emissions = 502 million metric tons CO2 eq
Chapter 1: Introduction 

1-4

1.1 Overview of the Report

Under	provision	of	Section	2709	of	the	Food,	Conservation,	and	Energy	Act	of	2008,	USDA	has	been	

directed	to	“establish	technical	guidelines	that	outline	science‐based	methods	to	measure	the	

environmental	service	benefits	from	conservation	and	land	management	activities	in	order	to	

facilitate	the	participation	of	farmers,	ranchers,	and	forest	landowners	in	emerging	environmental	

services	markets.”	The	legislation	further	states	that	the	initial	emphasis	of	the	methods	

development	should	focus	on	GHG	emissions.	Agreement	on	that	set	of	methods	is	the	primary	

scope	and	purpose	for	this	report.	The	findings	in	this	report	provide	the	foundation	for	entity‐level	

tools	to	measure	the	GHG	benefits	from	conservation	and	land	management	activities.	

This	report	and	the	estimation	methods	are	not	intended	as	an	addition	to	or	replacement	of	any	

current	Federal	or	State	GHG	reporting	systems	or	requirements.	This	report	has	been	prepared	to	

outline	methods	to	calculate	direct	GHG	emissions	and	carbon	sequestration	from	agriculture	and	

forestry	processes	and	builds	upon	existing	inventory	efforts	such	as	U.S.	Environmental	Protection	

Agency	(EPA)	and	USDA’s	national	inventories	and	the	U.S.	Department	of	Energy’s	Voluntary	

Greenhouse	Gas	Reporting	Program	Section	1605(b)	Guidelines,	with	an	aim	of	providing	simple,	

transparent,	and	robust	inventory	and	reporting	methods.	

The	report	provides	technical	methods	for	estimating	and	reporting	GHGs	from	significant	

agriculture	and	forestry	sources	and	sinks.	These	methods	are	designed	to	quantify	significant	

emissions	and	sinks	associated	with	specific	source	categories	as	well	as	annual	reductions	in	those	

emissions	or	fluxes	in	carbon	storage	resulting	from	land‐use	change	and	land	management	

practices	and	technologies.	Therefore,	the	report	will	support	the	development	of	entity‐,	farm‐,	or	

forest‐scale	GHG	estimates	and	inventories.	

Because	the	report	is	intended	as	a	means	of	evaluating	management	practices	across	the	full	scope	

of	the	farm,	ranch,	and	forest	management	system,	the	methods	in	the	report	need	to	be	as	

comprehensive	as	possible.	Research	and	data	gaps	exist	that	result	in	some	management	practices	

not	being	accounted	for	or	are	reflected	in	higher	levels	of	estimate	uncertainty.	Completeness	is	

important,	though,	and	the	report	attempts	to	identify	the	most	significant	research	gaps	and	data	

needs.	

The	methods	were	developed	according	to	several	criteria	in	order	to	maximize	their	usefulness.	In	

particular,	the	methods	must:	

1. Stand	on	their	own,	independent	of	any	other	accounting	system,	yet	maintain	consistency

with	other	accounting	systems	to	the	maximum	extent	possible;

2. Be	scalable	for	use	at	entity‐scale	sites	across	the	United	States,	with	applicability	at	county

and/or	State	levels	as	well;

3. Facilitate	use	by	USDA	in	assessing	the	performance	of	conservation	programs;

4. Provide	a	broad	framework	to	assess	management	practices	to	evaluate	the	GHG	aspect	of

production	sustainability;

5. Maintain	maximum	applicability	for	use	in	environmental	markets,	including	possible

future	Federal,	State,	or	local	GHG	offsets	initiatives;

6. Be	scientifically	vetted	through	USDA,	U.S.	government,	and	academic	expert	review	and

public	comment;

7. Provide	reliable,	real,	and	verifiable	estimates	of	on‐site	GHG	emissions,	carbon	storage,	and

carbon	sequestration	(methods	will	be	designed	so	that	over	time	they	can	be	applied	to
 Chapter 1: Introduction 

 1-5 

quantify	on‐site	GHG	reductions	and	increases	in	carbon	storage	due	to	conservation	and	

land	management	activities);	and	

8. Provide	a	basis	for	consistency	in	estimation	and	transparency	in	reporting.	

1.2 Report Objectives

The	objectives	for	this	report	are	to	create	a	standard	set	of	GHG	estimation	methods	for	use	by	

USDA,	landowners,	and	other	stakeholders	and	to	serve	as	input	into	the	development	of	USDA	

estimation	tools.	The	methods	presented	in	the	report	address	GHG	emissions	and	carbon	removal	

for	the	entire	entity	or	operation	and	provide	the	opportunity	to	assess	individual	practices	or	

management	decisions.	

A	co‐objective	is	to	establish	consensus	on	a	standardized	set	of	methods	for	the	Department,	which	

become	the	scientific	basis	for	entity‐scale	estimation	of	the	GHG	impacts	of	landowner	

management	decisions.	Therefore,	scientific	rigor	and	transparency	are	also	critical.	

While	USDA	has	long	been	

involved	in	development	of	

GHG	inventories	and	

estimation	tools,	this	report	

brings	together	estimation	

approaches	from	all	

agriculture	and	land	

management	sectors	into	one	

place.	These	methods	are	

combined	in	such	a	way	that	

an	integrated	estimate	can	be	

derived	for	all	activities	within	

the	boundary	of	the	farm	and	

forest	management	operation.	

Figure	1‐2	shows	the	diversity	

of	activities	and	the	complexity	

of	estimating	GHG	emissions	

and	carbon	sequestration	

across	the	entire	management	

entity.	

Figure 1‐2: Conceptual Diagram of Activities Covered in

This Report

Combining	a	landowners’ crop,	livestock	and	forest	management	

activities	into	a	seamless	greenhouse	gas	estimate	for	the	entity.	

Source:	Eve	(2012).

Greenhouse Gas Mitigation Options and Costs for Agricultural Land and Animal Production within

the United States	covers	mitigation	practices	in	crop	production,	animal	production,	and	land	

retirement	systems	in	the	United	States.	This	report	reviews	available	scientific	methods	for	

estimating	GHG	sources	and	sinks	at	an	entity	level	and	recommends	particular	estimation	

methods	for	each	livestock	type	and	agriculture/forestry	practice.	To	estimate	the	costs,	USDA	

has	developed	another	report	that	estimates	the	implementation	costs,	GHG	mitigation	potential	

at	the	farm	level,	and	break‐even	prices	(i.e.,	GHG	incentive)	for	different	mitigation	practices	on	

a	farm	level.		

The	report	is	available	for	download	on	the	project	website	at:

http://www.usda.gov/oce/climate_change/mitigation_technologies/GHGMitigationProduction_

Cost.htm.
Chapter 1: Introduction 

1-6

1.3 Process for the Development of the Methods

This	report	was	developed	by	three	author	teams	(i.e.,	working	groups)	under	the	direction	of	one	

lead	author	for	each	team	(plus	one	co‐lead	author	for	the	forestry	chapter).	The	lead	authors	were	

chosen	based	on	their	experiences	with	GHG	inventories	and	accounting	methodologies	and	their	

professional	research	experiences.	With	input	from	each	lead	author,	USDA	chose	10	to	13	working	

group	members	per	team	to	write	the	report.	These	working	group	members	each	had	different	

backgrounds	that	fit	with	the	anticipated	content	of	the	document.	Members	also	had	experience	

with	GHG	accounting	and/or	field	research	that	was	unique	and	addressed	one	or	more	of	the	niche	

methods	that	were	essential	for	ensuring	the	comprehensiveness	of	the	methods	for	each	sector.	

The	author	teams	were	provided	with	a	preliminary	outline	of	a	chapter	and	two	background	

reports	developed	as	part	of	the	project.	One	background	report	was	an	analysis	of	the	scientific	

literature	related	to	rates	of	carbon	sequestration	or	emissions	reduction	resulting	from	various	

management	practices	and	technologies	(Denef	et	al.,	2011);	the	other	was	a	compilation	of	all	of	

the	available	tools,	protocols,	and	models	and	basic	information	on	each	one	(Denef	et	al.,	2012).	

Both	reports	are	available	for	download	on	the	project	website	at:	

http://usda.gov/oce/climate_change/estimation.htm.	

There	are	several	general	ways	to	estimate	GHG	emissions	and	sequestration	at	an	entity	scale,	and	

each	approach	gives	varying	accuracy	and	precision.	Typically,	the	most	accurate	way	to	estimate	

GHG	fluxes	is	through	direct	measurement,	which	often	requires	expensive	equipment	or	

techniques	that	are	not	feasible	for	a	single	landowner	or	manager.2

Lookup	tables	and	estimation	equations	can	be	much	simpler	to	implement	and	use,	but	when	used	

alone	may	not	adequately	represent	local	variability	or	local	conditions.	This	report	attempts	to	

delineate	methods	that	balance	user	friendliness,	data	requirements,	and	scientific	rigor	in	a	way	

that	is	transparent	and	justified.	

Figure	1‐3	illustrates	the	scope	of	the	GHG	emission	sources	and	removals	and	processes	in	

managed	ecosystems	that	these	methods	estimate.	

The	author	teams	considered	the	following	general	approaches	in	deriving	the	methods	for	this	

report:	

 Basic	estimation	equations	–	Involve	combinations	of	activity	data3	with	parameters	and

default	emission	factors.4	Any	default	parameters	or	default	emission	factors	(e.g.,	lookup

tables)	are	provided	in	the	text,	or	if	substantial	in	length,	in	an	accompanying	(or

referenced)	compendium	of	data.

2	Examples	include	intermittent	measurement	of	soil	organic	carbon	and	biomass	reserves.	Estimates	of	flux	

for	dynamic	process	measures	like	N2O	emissions	need	to	be	based	on	multiple	measures	taken	at	reasonable	

frequency.	Direct	measurement	may	work	for	comparative	analysis	but	must	be	extended	to	estimate	total	

emissions	using	assumptions	or	modeling	method.		

3	Activity	data	is	defined	as	data	on	the	magnitude	of	human	activity	resulting	in	emissions	or	removals	taking	

place	during	a	given	period	of	time	(IPCC,	1997).	 4	Emission	factor	is	defined	as	a	coefficient	that	quantifies	the	emissions	or	removals	of	a	gas	per	unit	of	

activity.	Emission	factors	are	often	based	on	a	sample	of	measurement	data,	averaged	to	develop	a	

representative	rate	of	emission	for	a	given	activity	level	under	a	given	set	of	operating	conditions	(IPCC,	

2006).	
Chapter 1: Introduction 

1-7 

Figure 1‐3: Greenhouse Gases Emission Sources/Removals and Processes in Managed

Ecosystems

Source:	Paustian	et	al.	(2006).	

NMVOC=	non‐methane	volatile	organic	compounds	

 Models	–	Use	combinations	of	activity	data	with	parameters	and	default	emission	factors.

The	inputs	for	these	models	can	be	ancillary	data5	(e.g.,	temperature,	precipitation,

elevation,	and	soil	nutrient	levels	that	may	be	pulled	from	an	underlying	source),	biological

variables	(e.g.,	plant	diversity),	or	site‐specific	data	(e.g.,	number	of	acres,	number	of

animals).	The	accuracy	of	the	process	model	is	dependent	on	the	robustness	of	the	model

and	the	accuracy	of	the	inputs.

 Field	measurements	–	Actual	measurements	that	a	farmer	or	landowner	would	need	to	take

to	more	accurately	estimate	the	properties	of	the	soil,	forest, or	farm	or	to	estimate	actual

emissions.	Measuring	actual	emissions	on	the	land	requires	special	equipment	that

monitors	the	flow	of	gases	from	the	source	into	the	atmosphere.	This	equipment	is	not

readily	available	to	most	entities,	so	more	often,	field	measurements	are	incorporated	into

other	methods	described	in	this	section	to	create	a	hybrid	approach.	A	field	measurement

such	as	a	sample	mean	tree	diameter	could	be	incorporated	into	other	models	or	equations

to	give	a	more	accurate	input.

 Inference	–	Uses	State,	regional,	or	national	emissions/sequestration	factors	that

approximate	emissions/sequestration	per	unit	of	the	input.	The	input	data	is	then

5	Ancillary	data	is	defined	as	additional	data	necessary	to	support	the	selection	of	activity data	and	emission

factors	for	the	estimation	and	characterization	of	emissions.	Data	on	soil,	crop	or	animal	types,	tree	species,	

operating	conditions,	and	geographical	location	are	examples	of	ancillary	data.	
Chapter 1: Introduction 

1-8

multiplied	by	this	factor	to	determine	the	total	onsite	emissions.	This	factor	can	have	

varying	degrees	of	accuracy	and	may	not	capture	the	mitigation	practices	on	the	farm	or	the	

unique	soil	conditions,	climate,	livestock	diet,	livestock	genetics,	or	any	farm‐specific	

characteristics,	unless	they	are	developed	for	specific	soil	types,	livestock	categories,	or	

climactic	regions,	etc.	

 Hybrid	estimation	approach	–	An	approach	that	uses	a	combination	of	the	approaches	

described	above.	The	approach	often	uses	field	measurements	or	models	to	generate	inputs	

used	for	an	inference‐based	approach	to	improve	the	accuracy	of	the	estimate.	

With	this	background,	and	evaluating	these	and	other	data	and	resources,	each	author	team	

developed	the	text	for	its	chapter.	Development	of	the	text	has	been	iterative	as	various	drafts	of	

the	document	have	been	put	through	several	review	stages.	The	review	process	for	the	report	of	

methods	consists	of:	

 USDA Technical Review.	USDA	performed	an	intra‐agency	review.	The	result	of	this	review	

was	a	series	of	comments	and	questions	for	the	lead	authors	and	their	working	groups.	

These	comments	were	received	by,	discussed	within,	and	responded	to	by	the	working	

groups	and	lead	authors.	For	example,	one	specific	outcome	of	this	review	process	was	a	

nitrous	oxide	(N2O)	Cropping	Practices	Workshop	consisting	of	20	experts	in	the	field	of	

N2O	emissions	from	croplands	and	grazing	lands.	The	workshop	was	convened	to	review	

the	methods	that	were	originally	proposed	by	the	working	group	and	to	determine	if	there	

was	a	more	scientifically	rigorous	method	to	quantifying	N2O	emissions	from	agricultural	

soils.	

 Inter‐agency Technical Review.	The	May	2012	version	of	the	report	was	circulated	for	

review	by	an	inter‐agency	group	of	GHG	emissions	and	inventory	experts.	The	reviewers	

included	over	50	members	from	nine	agencies	including	USDA,	U.S.	Department	of	Energy,	

U.S.	Department	of	the	Interior,	EPA,	U.S.	Department	of	State,	and	several	of	the	White	

House	Offices.	The	result	of	this	review	was	a	series	of	comments	and	questions	for	the	lead	

authors	and	their	working	groups.	These	comments	were	received	by,	discussed	within,	and	

responded	to	by	the	working	groups	and	lead	authors.	

 Scientific Expert Review.	Following	the	inter‐agency	review,	the	next	version	of	the	report	

was	reviewed	by	a	team	of	scientific	experts.	The	reviewers	were	chosen	based	on	

recognized	expertise,	experience	in	expert	reviews,	availability,	and	willingness	to	

participate.	Each	reviewer	was	asked	to	review	those	chapters	and/or	sections	of	the	report	

relating	to	his	or	her	expertise.	A	subset	of	the	group	of	expert	reviewers	was	asked	to	

review	the	report	in	its	entirety	and	provide	comments	specifically	regarding	issues	of	

consistency,	completeness,	and	accuracy.	Again,	the	lead	authors	and	author	teams	

responded	to	each	of	the	comments	posed	by	the	expert	panel	and	edited	the	document	as	

appropriate.	

 Public Comment Period.	Once	all	of	the	expert	comments	were	addressed	and	appropriate	

edits	were	made,	the	report	was	made	available	for	public	comment.	This	coincided	with	a	

final	review	by	USDA	and	other	Federal	agency	GHG	experts.	Comments	from	this	review	

were	assessed,	and	the	report	was	edited	as	necessary	prior	to	final	publication	of	the	

report.	
 Chapter 1: Introduction 

 1-9 

1.4 Contents of the Report

The	remainder	of	the	report	is	organized	by	sector.	For	each	sector,	background	and	information	on	

management	practices	are	presented	first,	followed	by	the	detailed	methods	proposed	for	

estimating	emissions	and	sequestration	for	those	practices.	Each	of	the	chapters	is	summarized	as	

follows:	

 Chapter 2: Considerations When Estimating Agriculture and Forestry GHG Emissions

and Removals.	Chapter	2	sets	the	context	for	the	methods,	including	linkages	and	cross‐

cutting	issues	that	span	the	sectors.	This	includes,	for	example,	definition	of	entity,	

definition	of	system	boundaries,	etc.

 Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing

Land Systems.	Chapter	3	describes	the	GHG	emissions	from	crop	and	grazing	land	systems.	

The	chapter	presents methods	for	estimating	the	influence	of	land	use	and	management

practices	on	GHG	emissions	(and	sinks)	in	crop	and	grazing	land	systems.	Methods	are	

described	for	estimating	biomass	and	soil	carbon	stocks	changes,	direct	and	indirect	soil	

N2O	emissions,	methane	(CH4)	and	N2O	emissions	from	wetland	rice,	CH4	uptake	in	soils,	

carbon	dioxide	(CO2)	emissions	or	sinks	from	liming,	non‐CO2	GHG	emissions	from	biomass	

burning,	and	CO2	emissions	from	urea	fertilizer	application.	

 Chapter 4: Quantifying Greenhouse Gas Sources and Sinks in Managed Wetland

Systems.	Chapter	4	provides	guidance	for	estimation	of	carbon	stock	changes,	CH4,	and	N2O	

emissions	from	actively	managed	wetlands.	

 Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production

Systems.	Chapter	5	describes	on‐farm	GHG	emissions	from	the	production	of	livestock	and	

manure	management.	The	chapter	presents	GHG	estimation	methods	appropriate	to	the	

production	of	each	common	livestock	sector	(i.e.,	beef,	dairy,	sheep,	swine,	and	poultry),	

with	methods	related	to	manure	management	combined	for	all	livestock	types.

 Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest

Systems.	Chapter	6	provides	guidance	on	estimating	carbon	sequestration	and	GHG	

emissions	for	the	forestry	sector.	The	chapter	is	organized	to	provide	an	overview	of	the	

elements	of	forest	carbon	accounting,	including	definitions	of	the	key	carbon	pools	and	

basic	methods	for	their	estimation.

How to Use the Report

In	order	to	accomplish	the	objectives	noted	above,	the	report	is	laid	out	by	broad	land‐use	sector	

(i.e.,	croplands	and	grazing	lands,	wetlands,	animal	production,	and	forestry).	Each	sector	

chapter	is	further	delineated	into	two	main	parts:	first	the	current	scientific	understanding	and	

available	data	for	estimating	GHG	fluxes	within	the	sector;	second,	the	methods	that	

demonstrate	the	current	best	approach	to	estimating	GHG	fluxes,	balancing	the	available	science	

and	data	with	the	criteria	and	considerations	mentioned	previously.	The	report	is	intended	to	be	

considered	in	its	entirety	with	contextual	information	provided	in	the	first	and	second	chapters	

as	background	to	the	content	presented	in	the	following	chapters.	The	authors	realize	that	many	

users	may	find	specific	chapters	or	sections	especially	valuable	or	useful;	therefore,	summarized	

contextual	information	is	also	included	at	the	beginning	of	each	chapter.	The	beginning	to	the	

croplands	and	grazing	lands,	wetlands,	animal	production,	and	forestry	chapters	include	tables	

that	summarize	the	methods	for	each	source	or	removal	of	GHG	emissions.	The	subsequent	

sections	in	the	report	are	organized	according	to	the	sources	mentioned	in	the	summary	table.	
Chapter 1: Introduction 

1-10

 Chapter 7: Quantifying Greenhouse Gas Sources and Sinks from Land‐Use Change.

Chapter	7	provides	guidance	on	estimating	the	net	GHG	flux	resulting	from	changes	

between	land	types—i.e.,	conversions	into	and	out	of	cropland,	wetland,	grazing	land,	or	

forestland—at	the	entity	scale.	

 Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and

Sinks.	Chapter	8	provides	a	framework	for	a	Monte	Carlo	assessment	of	estimation	

uncertainty.	

The	report	also	describes	methods	for	uncertainty	assessment	for	each	source	as	well	as	for	the	

estimate	in	total.	The	authors	recognize	that	for	some	sources,	current	data	are	not	complete	

enough	to	allow	for	a	reliable	statistical	estimate	of	uncertainty.	In	some	cases,	expert	judgment	

was	used	to	delineate	estimated	uncertainty	bounds.	In	other	cases,	the	report	simply	notes	that	

more	data	are	required	to	reliably	estimate	uncertainty.	Each	sector	chapter	of	the	report	contains	

a	section	on	uncertainty	and	limitations.	

The	authors	acknowledge	that	for	many	practices	and	technologies,	adequate	data	do	not	currently	

exist	to	accurately	estimate	GHG	emissions	and/or	carbon	sequestration.	For	each	sector,	the	

authors	have	included	a	discussion	of	research	gaps	or	priority	areas	for	future	data	collection	that	

are	important	in	order	to	improve	the	completeness	or	accuracy	of	the	estimation	methods	put	

forth	in	this	report.	

1.5 Uses and Limitations of the Report

Specific	potential	uses	of	the	methods	include	aiding:	

1. Landowners	and	other	stakeholders	in	quantifying	increases	and	decreases	in	GHG	

emissions	and	carbon	sequestration	associated	with	changes	in	land	management;	

2. USDA	in	understanding	GHG	and	carbon	sequestration	increases	and	decreases	resulting	

from	current	and	future	conservation	programs	and	practices;	and	

3. USDA	and	others	in	evaluating	and	improving	national	and	regional	GHG	inventory	efforts.	

The	report	will	provide	additional	cobenefits.	For	example,	the	report	may	provide	improved	

methods	for	voluntary	GHG	registries,	help	to	facilitate	regional	GHG	markets,	or	inform	existing	

and/or	future	GHG	reporting	programs	(e.g.,	sequestration/emissions	from	land	use	and	

agriculture	under	Executive	Order	[EO]	13514).6

These	methods	are	designed	to	provide	the	most	appropriate,	single,	accounting	method	for	

quantifying	GHG	emissions/sequestration	for	each	particular	source	category	(e.g.,	CH4	from	rice	

cultivation)	determined	from	the	activity	data,	published	emission	factors,	and	accounting	methods	

and	tools	typically	available	for	the	entity	scale.	These	methods	are	not	designed	to	provide	a	range	

of	emission/sequestration	accounting	options,	or	a	range	of	similar	options,	at	varying	levels	of	

complexity	(i.e.,	tiers)	for	each	particular	source	category.	That	said,	there	may	be	specific	instances	

(e.g.,	forest	carbon	stocks)	where	different	individual	options	might	be	specified	for	entities	within	

																																																													

6	It	should	be	noted	that	under	EO13514,	agency‐level	reporting	of	emissions	and	sequestration	as	a	result	of	land	

management	practices	is	not	required	at	this	time.	In	addition,	reporting	of	emissions	from	wildfire	management,	

prescribed	burning,	land	use,	and	land‐use	changes	is	not	required.	Agencies	choosing	to	report	activities	undertaken	to	

date	in	calculating	such	emissions	would	address	them	in	the	qualitative	portion	of	their	GHG	inventory.	Emissions	

resulting	from	manure	management	and	enteric	fermentation	when	the	animals	are	owned	by	the	Federal	agency	would	

be	reported	voluntarily	in	scope	1	at	this	time.	If	the	activities	take	place	on	Federal	land,	but	are	operated	by	others,	

these	emissions	may	be	voluntarily	reported	as	scope	3.	
Chapter 1: Introduction 

1-11 

source	categories	where	there	are	significantly	different	operational	scales	(e.g.,	commercial	forest	

plantations	versus	small	woodlots).	

This	report	is	designed	to	provide	GHG	accounting	methods	to	determine	actual	GHG	emissions	at	

the	entity	scale	(i.e.,	an	emissions	inventory)	and/or	to	quantify	the	emission	(or	emission	

reductions)	associated	with	an	existing	or	future	mitigation	practice/technology.	At	the	time	of	this	

writing,	the	United	States	does	not	have	a	national	policy	guiding	GHG	emissions	reduction,	

monitoring,	or	crediting	in	the	agriculture	and	forestry	sectors.	Presented	are	the	recommended	

methods	for	quantifying	GHG	emissions	and	emission	reductions.	The	report	is	not	intended	as	an	

accounting	framework	for	emission	reduction	crediting	or	trading—i.e.,	the	methods	do	not	

constitute	an	offset	protocol.	As	a	result,	this	report	does	not	provide	specific	guidance	on	critical	

policy	features	of	such	offset	protocols	including	additionality,	permanence,	and	leakage.	Any	

national	policy	would	provide	precise	definitions	of	these	terms,	and	then	the	methods	described	in	

this	report	would	be	adapted	to	conform	to	policy	standards	and	requirements.	

As	stated	above,	this	report	does	not	address	policy	issues	related	to	crediting	reductions	such	as	

permanence,	additionality,	or	leakage.	The	intended	purpose	is	simply	to	provide	a	quantitative	

estimate	of	what	is	occurring	under	a	given	set	of	practices	and	activities,	or	what	could	be	

expected	to	occur	given	a	change	in	management.	While	the	report	is	not	addressing	policy	issues,	it	

may	address	practical	concerns	around	GHG	estimation,	such	as	the	risk	of	reversal	if	management	

practices	revert	back	in	the	foreseeable	future.	For	example,	a	land	manager	must	understand	that	a	

change	in	management	that	results	in	soil	carbon	sequestration,	if	reversed,	will	lead	to	the	extra	

stored	carbon	likely	being	rereleased	to	the	atmosphere.	For	the	context	of	this	report,	we	are	most	

concerned	with	“what	the	atmosphere	sees”	or	what	the	long‐term	net	effect	is	to	GHG	levels	in	the	

atmosphere.	

The	source	categories	covered	in	the	report	are	specific	to	the	agriculture	and	forestry	sectors	(e.g.,	

croplands,	grazing	lands,	managed	wetlands,	animal	agriculture,	and	forestry).	The	report	does	not	

approach	emissions	from	these	sources	from	a	life‐cycle	perspective.	In	other	words,	the	report	

does	not	include	source	categories	that	are	associated	with	management	activities	related	to	

certain	agriculture	and	forestry	activities	(e.g.,	transportation,	fuel	use,	heating	fuel	use),	upstream	

production	(e.g.,	animal	feed	production,	fertilizer	manufacture),	or	downstream	(e.g.,	wastewater	

treatment,	pulp	and	paper	manufacture,	or	landfills).	As	a	result,	the	report	does	not	provide	GHG	

accounting	methods	for	sectors	including:	energy	and	industrial	processes	(e.g.,	fertilizer	

production).	

The	report	also	does	not	include	emissions	from	stationary	source	combustion	(e.g.,	burning	

heating	oil	or	natural	gas	to	heat	animal	housing)	or	mobile	source	combustion	(e.g.,	fuel	use	in	

vehicles)	at	this	time.	However,	where	there	are	obvious	changes	in	the	level	of	combustion	due	to	a	

change	in	practices,	that	change	is	qualitatively	discussed.	For	example,	a	shift	from	conventional	

tillage	to	no	till	can	result	in	a	large	reduction	in	fuel	consumption	because	of	fewer	trips	across	the	

field.	These	relationships	are	noted	qualitatively	in	the	report,	but	quantitative	methods	are	not	

proposed.	Methods	for	quantifying	emissions	from	stationary	or	mobile	combustions	are	available	

from	other	Federal	agencies.	

The	scope	of	this	report	is	assessing	the	impact	of	specific	decisions	made	by	the	farm	or	forest	

manager	within	the	confines	of	the	farm	or	forest	gate.	A	life‐cycle	perspective,	while	valuable,	is	

outside	the	scope	of	this	report.	A	life‐cycle	assessment	(LCA)	is	a	useful	tool	for	quantification	of	

environmental	impacts	and	benefits	on	a	basis	that	allows	for	analysis	of	environmental	burden	

shifting	and	trade‐offs	between	different	options.	LCAs	include	the	environmental	impact	of	

management	decisions	during	product	manufacturing	and	processing	of	raw	inputs	to,	as	well	as	

products	output	from,	the	farm	or	forest	system,	continuing	through	its	use	by	the	end	consumer.	
Chapter 1: Introduction 

1-12

The	methodologies	presented	in	this	report	do	not	constitute	an	LCA,	but	support	several	

components	of	LCAs.	For	example,	this	report	covers	emissions	(e.g.,	from	croplands)	that	could	be	

used	as	part	of	an	attribution	LCA	for	a	commodity	crop	product,	or	used	as	part	of	a	consequential	

LCA	studying	the	impacts	of	agricultural	policy	decisions	on	GHG	mitigation	potential.	

The	text	box	below	provides	further	information	on	LCAs	as	they	relate	to	quantifying	GHG	sources	

and	sinks	in	agriculture	and	forestry	systems,	including	background	information	on	the	purpose	of	

LCAs,	the	LCA	process,	the	interpretation	of	LCA	results,	and	current	LCA	efforts	by	USDA	and	other	

organizations	related	to	agriculture	and	forestry.	

Life Cycle Assessment

An	LCA	is	a	tool	for	addressing	the	environmental	aspects	(e.g.,	use	of	resources)	and	potential	

environmental	impacts	(e.g.,	global	warming	potential)	throughout	the	life‐cycle	of	a	product	or	

material.	When	applied	to	agriculture	and	forestry	products,	the	scope	of	an	LCA	would	likely	

include	upstream	impacts	from	extraction	and	production	of	material	inputs	(e.g.,	fuels,	fertilizers);	

the	environmental	impacts	of	management	decisions	during	crop,	livestock,	or	tree	growth	on	site;	

and	the	outputs	from	the	farm	or	forest	system,	including	the	downstream	impacts	from	use	and	

disposal	by	the	end	consumer.	The	accounting	boundary	of	GHG	emission	sources	and	sinks	

quantified	in	an	LCA	for	an	agricultural	or	forest	consumer	product	would	extend	beyond	the	

accounting	boundary	of	the	methodologies	presented	in	this	report.	For	example,	an	LCA	for	a	

grain	product	would	not	only	include	N2O	emissions	from	fertilizer	application,	but	also	other	

upstream	inputs	such	as	emissions	from	synthetic	fertilizer	production,	and	downstream	impacts	

such	as	emissions	from	grain	transportation	and	storage,	processing,	use,	and	disposal.		

The	International	Organization	for	Standardization	(ISO)	has	established	several	international	

standards	addressing	LCA,	including	ISO	14040	(ISO,	2006a)	describing	the	principles	and	

framework	for	LCAs,	ISO	14044	(ISO,	2006b)	addressing	LCA	requirements	and	guidelines,	and	ISO	

14048	(ISO,	2002)	presenting	a	standardized	LCA	data	documentation	format.a	As	defined	in	ISO	

14040	(ISO,	2006a),	the	LCA	development	process	includes	the	following	primary	steps:	defining	

the	goal	and	scope;	conducting	a	life‐cycle	inventory	(LCI)	analysis	by	gathering	data	and	

quantifying	all	relevant	inputs	and	outputs	of	the	product	system,	as	defined	in	the	scope	of	the	

study;	conducting	a	life‐cycle	impact	assessment	through	evaluation	of	the	significance	of	the	

environmental	impacts	defined	in	the	scope	of	the	study	and	determined	during	the	LCI	process;	

and	interpreting	the	results	(ISO,	2002;	2006a;	2006b).	USDA	has	several	initiatives	applying	LCAs	

to	agriculture	and	forestry.	

 USDA’s	National	Agricultural	Library	has	developed	the	LCA	Digital	Commons	Project,	a	

database	and	tool	intended	to	provide	LCI	data	for	use	in	LCAs	of	food,	biofuels,	and	other	

bio‐products.	The	database	currently	includes	data	on	inputs	(e.g.,	fertilizers)	and	outputs	

(e.g.,	air	emissions,	residues)	per	unit	of	field	crop	production	from	1996–2009	for	corn,	

cotton,	oats,	peanuts,	rice,	soybeans,	and	wheat	(durum,	spring,	and	winter)	in	States	

covered	by	the	USDA	Economic	Research	Service	annual	Agricultural	Resource	

Management	Survey.	Future	phases	of	this	work	will	include	the	addition	of	data	

representing	irrigation,	manure	management,	farm	equipment	operation,	crop	storage,	

transport,	and	production	of	mineral	and	organic	fertilizers,	herbicides,	insecticides,	and	

fungicides.		

(continued)
 Chapter 1: Introduction 

 1-13 

		 Life Cycle Assessment (continued)

 USDA	also	recently	worked	with	the	National	Cattlemen’s	Beef	Association	and	the	

chemical	company	BASF	in	the	development	of	an	eco‐efficiency	assessment	for	the	U.S.	

beef	industry	by	quantifying	life‐cycle	inputs	and	outputs	for	beef	production	over	time.	

The	process	involved	measuring	the	life‐cycle	environmental	impacts	and	life‐cycle	costs	

for	different	beef	production	processes	at	a	defined	level	of	output.	The	USDA	Agriculture	

Research	Service’s	Integrated	Farm	System	Model	was	used	to	estimate	environmental	

impacts	(e.g.,	air	emissions,	water	use,	abiotic	depletion	potential,	toxicity,	etc.)	based	on	

data	from	the	USDA’s	Roma	L.	Hruska	Meat	Animal	Research	Center	(Battagliese	et	al.,	

2013).	

Beyond	USDA,	other	LCAs	and	studies	related	to	quantifying	environmental	impacts	from	

agriculture	and	forestry	products	have	been	published.	Below	is	a	list	of	recent	studies,	projects,	or	

resources	that	use	LCAs	or	could	be	used	in	the	development	of	LCAs	to	evaluate	climate	impacts	

from	agriculture	and	forestry.	

 The	Innovation	Center	for	U.S.	Dairy	analyzed	fluid	milk,	cheese,	and	dairy	processing	and	

packaging.	These	data	have	recently	been	made	publicly	available	through	the	USDA’s	LCA	

Digital	Commons	database.b

 The	Innovation	Center	for	U.S.	Dairy	developed	the	FarmSmart	tool	that	compares	energy	

use,	GHG	emissions,	and	water	use	against	regional	and	national	averages.	The	tool	takes	

approximately	20	minutes	to	complete	and	will	have	enhanced	decision	support	features	

added	in	2014.c

 The	National	Pork	Board	funded	a	study	of	pork	products	conducted	by	researchers	at	the	

University	of	Arkansas.d

 The	United	Kingdom’s	Carbon	Trust	developed	a	“carbon	footprinting”	methodology	that	

has	been	used	by	the	grocery	chain	Tesco	to	determine	the	life‐cycle	GHG	impacts	of	many	

of	their	products.e

 The	United	Kingdom	Food	Climate	Research	Network	maintains	a	compendium	of	food	

LCAs.f

 Kumar	Venkat	of	CleanMetrics	Corp.	compared	12	organic	and	conventional	farming	

systems	from	a	life‐cycle	GHG	emissions	perspective	using	agricultural	production	data	

from	the	University	of	California‐Davis.g

 Field	to	Market	prepared	a	report	presenting	environmental	and	socioeconomic	indicators	

for	measuring	outcomes	from	on‐farm	agricultural	production	in	the	United	States.h

 A	coalition	of	food	industry	companies,	academic	organizations,	and	non‐governmental	

organizations	created	The	Cool	Farm	Tool,	a	GHG	calculator	designed	to	help	farmers	

reduce	emissions.i

(continued)

a	See	http://www.iso.org/iso/iso_catalogue/catalogue_tc/catalogue_tc_browse.htm?commid=54854.	 b	See		http://www.usdairy.com/sustainability/Greenhouse%20Gas%20Projects/Pages/Processingand	

PackagingLCA.aspx	and	http://www.lcacommons.gov/?q=node/16.	

c	See http://www.usdairy.com/FarmSmart/Pages/Home.aspx.	

d	See	http://www.pork.org/filelibrary/NPB%20Scan%20Final%20‐%20May%202011.pdf.	 e	See	http://www.carbontrust.com/our‐clients/t/tesco/.	 f

	See	http://www.fcrn.org.uk/research‐library/lca.	 g	Venkat,	K.	2012.	Comparison	of	Twelve	Organic	and	Conventional	Farming	Systems:	A	Life	Cycle	

Greenhouse	Gas	Emissions	Perspective.	Journal of Sustainable Agriculture	36	(6):	620‐649.	

h	See	http://www.fieldtomarket.org/report/national‐2/PNT_SummaryReport_A11.pdf.	 i

	See	http://www.coolfarmtool.org/Home.
Chapter 1: Introduction 

1-14

Finally,	the	methods	in	this	report	are	not	intended	as	a	sustainability	assessment.	Other	

environmental	services	and	cobenefits	are	not	addressed	by	these	methods.	Nor	are	potential	

tradeoffs	or	detriments	to	other	environmental	concerns	addressed	here.	The	methods	are	specific	

to	GHG	emissions	only,	and	sustainable	farm,	ranch,	or	forest	management	should	consider	the	GHG	

implications	of	management	in	tandem	with	other	environmental	concerns	such	as	water	quality,	

soil	health,	and	ecosystem	health.	

Chapter 1 References

Battagliese,	T.,	J.	Andrade,	I.	Schulze,	B.	Uhlman,	et	al.	2013.	Phase 1 More Sustainable Beef

Optimization Project.	

http://www.beefresearch.org/CMDocs/BeefResearch/Sustainability%20Completed%20Pr

oject%20Summaries/NCBA%20Phase%201%20Final%20Report_Amended%20with%20N

SF%20Verified%20Report_for_posting_draft%20WM_KS.pdf.	

Life Cycle Assessment (continued)

 The	National	Pork	Board	developed	a	predictive	model	that	provides	estimates	on	the	GHG

emissions,	water	consumption,	and	associated	costs	involved	in	sow	and	grow‐finish

production.	The	Pig	Production	Environmental	Footprint	Calculator	requires	fundamental

inputs	only	(herd	size,	feed	composition,	manure	handling	system,	farm	location,	barn	size,

characteristics	of	heating,	ventilation,	and	air	conditioning	system)	and	generates	an

annual	“cradle	to	gate”	estimate.j

 The	Technical	Working	Group	on	Agricultural	Greenhouse	Gases	has	published	three

editions	of	a	synthesis	of	literature	related	to	the	GHG	mitigation	potential	of	agricultural

land	management	in	the	United	States.k

 The	EPA	developed	and	maintains	the	Waste	Reduction	Model,	an	interactive	tool	that

calculates	and	totals	GHG	emissions	of	baseline	and	alternative	waste	management

practices	for	46	common	material	types,	including	food	waste,	yard	waste,	dimensional

lumber,	and	other	organic	materials.	EPA	is	currently	in	the	process	of	developing	detailed

food	waste	energy	and	emission	factors	to	quantify	the	life‐cycle	impacts	of	production	and

disposal	of	five	common	food	types—grains,	fruits	and	vegetables,	beef,	chicken,	and	dairy.l

There	are	many	potential	applications	for	LCA	results.	When	conducted	for	several	comparable	

agricultural	or	forest	products,	LCAs	can	allow	for	analysis	of	the	tradeoffs	between	yield	and	

environmental	impacts	between	different	production	processes	or	inputs.	For	example,	

comparing	LCA	results	for	grain	products	using	different	production	inputs	could	show	fewer	

life‐cycle	GHG	emissions	and	similar	yields	by	switching	to	a	different	fertilizer.	However,	there	

are	limitations	to	how	LCA	results	can	be	applied,	including	use	of	GHG	emissions	results	in	

annual	reporting	or	emission	inventories.	Since	LCAs	are	intended	to	quantify	the	

environmental	impacts	across	the	entire	product	life	cycle,	the	GHG	emissions	and	sinks	

frequently	occur	across	several	years	(and	several	source	categories)	and	are	therefore	not	

appropriate	for	use	in	applications	that	require	annual	emissions	data.	

j

	See	http://www.pork.org/Resources/1220/CarbonFootprintCalculatorHomepage.aspx#.Us7mGbSwWSo.		 k	See	http://nicholasinstitute.duke.edu/ecosystem/land/TAGGDLitRev#.Usbx9tJDuSp.			

l See http://www.epa.gov/warm.		
 Chapter 1: Introduction 

 1-15 

Denef,	K.,	S.	Archibeque,	and	K.	Paustian.	2011.	Greenhouse Gas Emissions from U.S. Agriculture and

Forestry: A Review of Emission Sources, Controlling Factors, and Mitigation Potential:	Interim	

report	to	USDA	under	Contract	#GS‐23F‐8182H.	

http://www.usda.gov/oce/climate_change/techguide/Denef_et_al_2011_Review_of_review

s_v1.0.pdf.	

Denef,	K.,	K.	Paustian,	S.	Archibeque,	S.	Biggar,	et	al.	2012.	Report of Greenhouse Gas Accounting

Tools for Agriculture and Forestry Sectors:	Interim	report	to	USDA	under	Contract	#GS‐23F‐

8182H.	

http://www.usda.gov/oce/climate_change/techguide/Denef_et_al_2011_Review_of_review

s_v1.0.pdf.	

Eve,	M.	2012.	Presentation	at	the	N2O	Cropping	Practices	Workshop.	February	7‐9,	2012,	Fort	

Collins,	CO.	

IPCC.	1997.	Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories, Prepared by the

National Greenhouse Gas Inventories Programme.	Bracknell,	UK:	Intergovernmental	Panel	on	

Climate	Change.	http://www.ipcc‐nggip.iges.or.jp/public/gl/invs1.html.	

IPCC.	2006.	2006 IPCC Guidelines for National Greenhouse Gas Inventories, Prepared by the National

Greenhouse Gas Inventories Programme.	Edited	by	H.	S.	Eggleston,	L.	Buendia,	K.	Miwa,	T.	

Ngara	and	K.	Tanabe.	Japan:	IGES.	http://www.ipcc‐

nggip.iges.or.jp/public/2006gl/vol4.html.	

ISO.	2002.	ISO 14048: Environmental management‐	Life cycle assessment‐	Data documentation

format:	International	Organization	for	Standardization.		

ISO.	2006a.	ISO 14040: Environmental management‐	Life cycle assessment‐	Principles and framework:	

International	Organization	for	Standardization.		

ISO.	2006b.	ISO 14044: Environmental management‐	Life cycle assessment‐	Requirements and

guidelines:	International	Organization	for	Standardization.		

Paustian,	K.,	N.H.	Ravindranath,	A.	Amstel,	and	M.	Gytarsky.	2006.	Chapter	1	‐	Introduction.	In	2006

IPCC Guidelines for National Greenhouse Gas Inventories, Prepared by the National

Greenhouse Gas Inventories Programme,	H.	S.	Eggleston,	L.	Buendia,	K.	Miwa,	T.	Ngara	and	K.	

Tanabe	(eds.).	Japan:	IGES.	

USDA.	2011.	U.S. Agriculture and Forest Greenhouse Gas Inventory: 1990‐2008.	Washington,	DC:	U.S.	

Department	of	Agriculture.		

	 	
Chapter 1: Introduction 

1-16

This	page	is	intentionally	left	blank.	
Authors:

Marlen	Eve,	U.S.	Department	of	Agriculture,	Office	of	the	Chief	Economist	

Mark	Flugge,	ICF	International	

Diana	Pape,	ICF	International	

Contents:

2 Considerations When Estimating Agriculture and Forestry GHG Emissions and Removals		

	 	.........................................................................................................................................................................2‐3

2.1	 Scope	...................................................................................................................................................................	2‐3	

2.1.1	 Definition	of	Entity	..........................................................................................................	2‐3	

2.1.2	 Definition	of	System	Boundaries	...............................................................................	2‐4	

2.2	 Review	of	Relevant	Current	Tools	and	Methods	............................................................................	2‐12	

2.3	 Selection	of	Most	Appropriate	Method	and	Mitigation	Practices	to	Include	......................	2‐13	

2.4	 Overview	of	Sectors	....................................................................................................................................	2‐14	

2.4.1	 Croplands	and	Grazing	Lands	...................................................................................	2‐16	

2.4.2	 Wetlands	............................................................................................................................	2‐17	

2.4.3	 Animal	Production	.........................................................................................................	2‐19	

2.4.4	 Forestry	..............................................................................................................................	2‐21	

2.5	 Land‐Use	Change	.........................................................................................................................................	2‐22	

2.6	 Uncertainty	....................................................................................................................................................	2‐23	

Chapter	2	References	.............................................................................................................................................	2‐24	

Suggested	Chapter	Citation:	Eve,	M.,	M.	Flugge,	D.	Pape,	2014.	Chapter	2:	Considerations	when	

Estimating	Agriculture	and	Forestry	GHG	Emissions	and	Removals.	In	Quantifying Greenhouse Gas

Fluxes in Agriculture and Forestry: Methods for Entity‐Scale Inventory.	Technical	Bulletin	Number	

1939.	Office	of	the	Chief	Economist,	U.S.	Department	of	Agriculture,	Washington,	DC.	606	pages.	July	

2014.	Eve,	M.,	D.	Pape,	M.	Flugge,	R.	Steele,	D.	Man,	M.	Riley‐Gilbert,	and	S.	Biggar,	Editors.	

USDA	is	an	equal	opportunity	provider	and	employer.	

Chapter 2

Considerations When Estimating 

Agriculture and Forestry GHG 

Emissions and Removals 
Chapter 2: Considerations When Estimating Agriculture and Forestry GHG Emissions and Removals 

2-2 

Acronyms, Chemical Formulae, and Units

CH4 Methane

CO	 Carbon	monoxide	

CO2 Carbon	dioxide

CO2‐eq	 Carbon	dioxide	equivalents

GHG	 Greenhouse	gas	

GWP	 Global	warming	potential

ha	 Hectares	

HWP	 Harvested	wood	products

IPCC	 Intergovernmental	Panel	on	Climate	Change

N2O	 Nitrous	oxide	

NH3	 Ammonia	

NO	 Nitric	oxide	

NOx	 Mono‐nitrogen	oxide		

NO2	 Nitrite	

NO3	 Nitrate	

PDF	 Probability	density	function

USDA		 U.S.	Department	of	Agriculture
Chapter 2: Considerations When Estimating Agriculture and Forestry GHG Emissions and Removals 

2-3

2 Considerations When Estimating Agriculture and Forestry GHG

Emissions and Removals

This	chapter	describes	the	linkages	and	cross‐cutting	issues	relating	to	sector‐specific	and	entity‐

scale	estimation	of	greenhouse	gas	(GHG)	sources	and	sinks.	In	particular,	this	chapter	describes	

the	common	elements	that	must	be	considered	both	within	an	emissions	sector	or	source	category	

as	well	as	across	sectors	or	source	categories	in	order	for	an	entity	to	report	accurate	GHG	

inventory	estimates.	

Chapter	2	is	organized	as	follows:	

 Scope

 Review	of	Relevant	Current	Tools	and	Methods

 Selection	of	Most	Appropriate	Method	and	Mitigation	Practices	to	Include

 Overview	of	Sectors

− Croplands	and	Grazing	Lands

− Wetlands

− Animal	Production

− Forestry

− Uncertainty

2.1 Scope

In	order	for	an	entity	to	accurately	inventory	its	direct	GHG	emissions	to	(and	removals	from)	the	

atmosphere	and	compare	emissions	and	removals	between	years,	practices,	or	entities,	it	is	

important	that	estimation	elements—e.g.,	definitions	of	entity	and	system	boundaries—are	

common	to	all	emission	sectors	and	source	categories.	These	common	elements	are	described	in	

more	detail	in	the	sections	that	follow	and	include:	

 Definition	of	Entity

 Definition	of	System	Boundaries:

− Physical	Boundaries

− Temporal	Boundaries

− Activity	Boundaries

− Material	Boundaries

2.1.1 Definition of Entity

The	definition	of	an	entity	will,	to	a	large	degree,	determine	the	(spatial)	bounds	of	the	estimation	

methodologies.	This	will	primarily	be	driven	by	what	data	a	landowner	chooses	to	input—i.e.,	the	

definition	will	be	user‐specific	and	primarily	depend	on	the	user’s	definition.1	However,	it	is	

anticipated	that	the	science‐based	methods	will	be	suitable	to	quantify	GHG	sources	and	sinks	at	a	

process	or	practice	scale.	The	methods	in	this	report	provide	an	integrated	assessment	of	the	net	

1	It	should	be	noted	that	the	definition	of	an	entity	used	in	this	report	is	not	a	policy	or	regulatory	definition,	

and	is	only	provided	to	help	the	land	manager	determine	what	practices	should	be	included	in	the	estimation.	
Chapter 2: Considerations When Estimating Agriculture and Forestry GHG Emissions and Removals 

2-4

GHG	emissions	for	an	entity,	all	lands	for	which	the	landowner	has	management	responsibility.	

They	also	provide	the	basis	for	an	integrated	tool	to	be	used	by	the	U.S.	Department	of	Agriculture	

(USDA)	as	well	as	by	individual	farmers,	ranchers,	forest	owners,	and	other	stakeholders	to	

evaluate	the	net	GHG	emissions	on	parcels	of	land	under	their	management.	So	while	the	entity	

would	be	defined	as	all	of	the	activities	occurring	on	all	tracts	of	land	under	the	management	

control	of	the	landowner,	the	report	describes	practice‐level	methodologies	that	can	be	summed	

collectively	to	arrive	at	an	estimate	for	the	entity.	The	definition	of	entity	applied	here	is	

intentionally	broad,	understanding	that	any	policy,	registry,	or	market	will	provide	its	own	

narrower	definition.	

2.1.2 Definition of System Boundaries

The	system	boundaries	should	include	the	GHG	emissions	and	carbon	sequestration	occurring	(or	

established)	onsite	for	the	source	category	and	management	practice	in	question.	For	example,	this	

report	does	not	address	indirect	land‐use	changes	occurring	offsite	or	biogenic	GHG	flux	related	to	

subsequent	use	of	agricultural	or	forestry	outputs	(e.g.,	food	processing,	pulp	and	paper	

manufacture,	biomass	combustion).	However,	certain	offsite	carbon	storage	considerations	(e.g.,	

flow	of	harvested	wood	into	harvested	wood	products	[HWPs])	have	been	considered	in	the	report	

to	maintain	consistency	with	national	inventory	efforts.	

Four	types	of	system	boundaries	are	important	for	consideration:	

 Physical	Boundaries

 Temporal	Boundaries

 Activity	Boundaries

 Material	Boundaries

2.1.2.1 Physical Boundaries

Physical	boundaries	(e.g.,	spatial,	sectoral)	address	the	area	and	the	management	to	be	considered	

in	the	reporting.	Setting	the	boundaries	for	which	emissions	and	sequestration	will	be	estimated	is	

more	difficult	than	it	first	seems.	Although	there	may	be	multiple	alternatives,	clarity	and	

consistency	are	important.	There	are	many	facets	to	consider.	One	factor	is	what	constitutes	an	

entity	or	a	farm/ranch/forest	operation;	another	is	what	operations	are	associated	with	that	entity.	

For	example,	does	the	use	of	fertilizer	on	a	farm	include	the	processes	of	manufacturing	and	

delivering	that	fertilizer?	Another	consideration	is	how	to	subdivide	that	larger	entity	into	the	

relevant	sectors	as	presented	in	the	individual	chapters	in	this	report.	For	example,	is	the	entity	

entirely	grazing	land	or	is	some	of	it	in	forest	management?	Finally,	there	may	be	questions	of	how	

to	associate	management	practices	to	the	most	relevant	categories	for	use	of	the	accounting	

guidelines	provided,	including	any	guidance	on	size	limits,	what	constitutes	management,	and	how	

to	address	changing	land	uses.	Definitions	are	an	important	part	of	setting	boundaries	and	will	be	

provided	here	as	well.	Examples	of		management	practices	(e.g.,	irrigation,	tillage,	or	residue	

management	for	croplands)	are	included	within	the	various	sector	descriptions	below	(i.e.,	

croplands	and	grazing	lands,	wetlands,	animal	production,	and	forestry);	when	considering	what	

constitutes	a	management	practice,	an	entity	should	note	that	in	the	context	of	these	guidelines,	a	

management	practice	refers	to	changes	in	the	management	of	agriculture,	animal,	or	forest	

production	that	impact	GHG	emissions	and	removals.	

The	objective	of	these	methods	is	to	provide	a	complete	estimation	of	GHG	emissions	and	carbon	

sequestration	within	the	boundaries	of	an	entity.	This	is	not	intended	as	a	life	cycle	analysis,	as	will	

be	further	explained	below	in	the	discussion	of	material	boundaries.	The	methods	are	designed	to	

be	applied	at	the	local	scale,	but	need	to	be	flexible	enough	to	be	valid	for	very	large	entities.	The	
Chapter 2: Considerations When Estimating Agriculture and Forestry GHG Emissions and Removals 

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methods	are	designed	to	estimate	fluxes	for	the	entirety	of	an	entity,	but	must	also	be	capable	of	

evaluating	a	single	practice	(e.g.,	project)	implemented	within	a	single	entity	or	aggregated	across	

multiple	entities.	

As	noted	in	Chapter	1,	the	definition	of	an	entity	can	be	complicated.	For	the	purposes	of	this	report,	

users	should	simply	delineate	the	spatial	extent	of	its	entity	as	the	land	area	that	is	under	their	

ownership	and/or	management	control	for	the	foreseeable	future.	This	is	a	generalized	application	

of	the	term	entity,	and	the	user	should	recognize	that	any	policy,	program,	or	contractual	

agreement	may	define	the	user’s	entity	differently	and	result	in	a	different	boundary	of	the	entity.	

Within	the	entity	boundary,	there	will	be	a	variety	of	land	uses	that	will	rely	on	methods	from	

various	chapters	in	this	report.	An	entity	should	be	subdivided	if	it	includes	different	categories	of	

land	use,	such	as	grazing	land	and	cropland,	but	the	entire	entity	should	fall	into	some	land‐use	

category.	No	rigid	lower	bound	is	specified	here	for	the	areal	extent	of	a	land‐use	categorization,	

but,	in	general,	areas	of	an	acre	or	more	merit	identification.

Within	the	boundaries	of	the	overall	entity,	areas	of	cropland	will	need	to	be	identified.	Beyond	just	

areas	producing	row	or	close‐grown	crops	or	hay,	cropland	also	includes	land	that	is	fallow	and	

areas	of	hay	and	pasture	that	are	managed	in	a	rotation	with	other	crops.	Wetlands	(including	

drained	wetlands	and	hydric	soils)	and	land	under	agroforestry	practices	where	the	predominant	

production	activity	is	cropping	should	also	be	considered	as	cropland	for	the	purposes	of	this	

report.	Finally,	areas	of	cropland	that	are	set	aside,	such	as	lands	in	the	Conservative	Reserve	

Program,	are	included	in	this	management	type.	The	methods	for	these	lands	are	included	in	

Chapter	3	of	this	report.	The	cropland	areas	should	be	delineated	as	fields	or	groups	of	fields	for	

which	the	basic	rotations	and	management	practices	are	all	similar.	

The	next	land	management	type	to	be	identified	is	grazing land.	This	is	land	that	is	used	primarily	

for	grazing	animals	and	not	as	part	of	a	rotation	with	other	crops.	This	portion	of	the	entity	will	

primarily	be	comprised	of	pastureland	(which	is	more	intensively	managed),	and	rangeland	(which	

is	typically	less	intensively	managed	and	usually	has	a	higher	proportion	of	native	species).	

Wetlands	(including	drained	wetlands	and	hydric	soils)	and	land	managed	as	agroforestry	should	

be	included	in	this	category	if	the	primary	use	of	the	tract	of	land	is	for	grazing	livestock.	There	will	

be	obvious	overlap	between	grazing	land	and	forestland	methods	where	the	land	matches	the	

definition	of	both	uses.	For	example,	if	any	active	management	is	focused	on	enhancing	tree	growth	

and	timber	production,	the	user	should	identify	these	areas	as	forestland	and	the	methods	will	need	

to	be	integrated	to	account	for	the	impact	of	grazing	management	on	the	forestland.	Grazing	lands	

should	be	delineated	as	contiguous	areas	that	are	under	a	similar	stocking	rate	and	set	of	

management	practices,	and	the	methods	for	grazing	lands	as	presented	in	Chapter	3	should	be	

followed.	In	addition,	the	GHG	estimation	methods	associated	with	the	grazing	animals	as	

presented	in	Chapter	5	should	be	followed.	Development	of	an	integrated	tool	that	follows	these	

methods	will	need	to	account	for	these	management	interactions.

Cropland:

A	land‐use	category	that	includes	areas	used	for	the	production	of	adapted	crops	for	harvest,	

including	both	cultivated	and	non‐cultivated	lands.	Cultivated	crops	include	row	crops	or	close‐

grown	crops	and	also	hay	or	pasture	in	rotation	with	cultivated	crops.	Non‐cultivated	cropland	

includes	continuous	hay,	perennial	crops	(e.g.,	orchards),	and	horticultural	cropland.	Cropland	

also	includes	land	with	alley	cropping	and	windbreaks,	as	well	as	lands	in	temporary	fallow	or	

enrolled	in	conservation	reserve	programs	(i.e.,	set‐asides).	Roads	through	cropland,	including	

interstate	highways,	State	highways,	other	paved	roads,	gravel	roads,	dirt	roads,	and	railroads	

are	excluded	from	cropland	area	estimates	and	are,	instead,	classified	as	settlements.	
Chapter 2: Considerations When Estimating Agriculture and Forestry GHG Emissions and Removals 

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Forestland	should	be	delineated	as	land	that	is	used	primarily	for	woody	biomass	production,	

whether	for	saw	wood,	pulp,	biofuels,	or	other	forest	or	woodland	related	industry,	or	land	that	is	

tree	covered	and	managed	for	recreational	or	conservation	purposes.	This	will	include	areas	of	

agroforestry	and	silvopasture	where	the	primary	management	objective	on	the	landscape	is	forest‐

related	production.	An	integrated	tool	would	need	to	be	flexible	enough	to	also	capture	the	impact	

of	the	additional	cropping	or	grazing	activities	occurring	on	the	parcel.	Similarly,	wetland	areas	that	

are	wooded	or	forested	and	managed	primarily	as	forests	and	woodlands	will	be	considered	in	this	

category.	Also,	because	harvesting	is	one	of	the	major	management	practices	in	forestland	and	

because	harvested	wood	moves	to	several	long‐term	carbon	pools	that	undergo	differing	rates	of	

decay,	it	is	important	that	the	methods	account	for	emissions	from	HWPs,	even	though	they	may	be	

moved	outside	of	the	boundary	of	the	farm/ranch/forest	operation.		

The	forestland	methods	are	presented	in	Chapter	6	of	this	report.	Tracts	of	forest	should	be	

delineated	such	that	any	given	tract	is	made	up	of	trees	of	a	similar	stand	age	and	species	mix,	and	

that	the	entire	tract	is	under	one	uniform	set	of	management	practices.	On	a	given	entity,	there	may	

be	trees	that	exist	outside	of	clearly	defined	forests,	such	as	orchards	and	vineyards,	farmstead	

shelterbelts	and	field	windbreaks,	and	agroforestry	practices.	Even	though	these	lands	may	not	

meet	the	definition	of	a	forest,	the	carbon	storage	in	the	trees	is	likely	significant.	In	some	cases	it	

may	be	useful	to	evaluate	individual	trees	or	small	stands	of	trees	(using	methods	presented	in	

Chapter	6).	In	other	cases,	the	estimation	may	require	a	blending	of	methods	such	as	cropland	

methods	from	Chapter	3	with	forest	methods	from	Chapter	6.	

Grazing Land:	

A	land‐use	category	on	which	the	plant	cover	is	composed	principally	of	grasses,	grass‐like	

plants,	forbs,	or	shrubs	suitable	for	grazing	and	browsing,	and	includes	both	pastures	and	native	

rangelands.	This	includes	areas	where	practices	such	as	clearing,	burning,	chaining,	and/or	

chemicals	are	applied	to	maintain	the	grass	vegetation.	Savannas,	some	wetlands	and	deserts,	

and	tundra	are	considered	grazing	land.	Woody	plant	communities	of	low	forbs	and	shrubs,	such	

as	mesquite,	chaparral,	mountain	shrub,	and	pinyon‐juniper,	are	also	classified	as	grazing	land	if	

they	do	not	meet	the	criteria	for	forest	land.	Grazing	land	includes	land	managed	with	

agroforestry	practices	such	as	silvopasture	and	windbreaks,	assuming	the	stand	or	woodlot	does	

not	meet	the	criteria	for	forest	land.	Roads	through	grazing	land,	including	interstate	highways,	

State	highways,	other	paved	roads,	gravel	roads,	dirt	roads,	and	railroads	are	excluded	from	

grazing	land	area	estimates	and	are,	instead,	classified	as	settlements.	

Forestland:

A	land‐use	category	that	includes	areas	at	least	120	ft	(36.6	m)	wide	and	1	acre	(0.4	ha)	in	size	

with	at	least	10	percent	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.	Forest	

land	includes	transition	zones,	such	as	areas	between	forest	and	non‐forest	lands	that	have	at	

least	10	percent	cover	(or	equivalent	stocking)	with	live	trees	and	forest	areas	adjacent	to	urban	

and	built‐up	lands.	Roadside,	streamside,	and	shelterbelt	strips	of	trees	must	have	a	crown	

width	of	at	least	120	ft	(36.6	m)	and	continuous	length	of	at	least	363	ft	(110.6	m)	to	qualify	as	

forest	land.	Unimproved	roads	and	trails,	streams,	and	clearings	in	forest	areas	are	classified	as	

forest	if	they	are	less	than	120	ft	(36.6	m)	wide	or	1	acre	(0.4	ha)	in	size;	otherwise	they	are	

excluded	from	forest	land	and	classified	as	settlements.	Tree‐covered	areas	in	agricultural	

production	settings,	such	as	fruit	orchards,	or	tree‐covered	areas	in	urban	settings,	such	as	city	

parks,	are	not	considered	forest	land	(Smith	et	al.,	2009).	
Chapter 2: Considerations When Estimating Agriculture and Forestry GHG Emissions and Removals 

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Wetland	areas	will	fall	into	one	of	two	categories:	managed	wetlands	or	natural,	unmanaged	

wetlands.	Many	wetland	areas	may	have	already	been	delineated	in	one	of	the	above	categories,	

and	their	management	will	be	captured	through	estimation	for	that	category.	If,	however,	there	are	

wetland	areas	that	have	not	already	been	included	in	the	cropland,	grazing	land,	or	forestland	

delineations	above,	those	should	be	identified	here.	A	naturally	occurring	wetland	that	does	not	

have	active	management	being	applied	in	order	to	increase	productivity	or	provide	other	

environmental	services	will	not	be	included	in	the	estimation	of	GHG	fluxes.	These	natural,	

unmanaged	wetlands	should	simply	be	included	in	the	category	of	“other	land”	as	defined	below.	

Any	wetland	areas	that	are	outside	the	boundaries	of	the	defined	areas	mentioned	above	and	where	

the	land	manager	is	actively	applying	management	decisions	in	order	to	enhance	productivity	or	

provide	environmental	services	should	be	delineated	as	a	managed	wetland	and	included.	This	

report	provides	estimation	methods	in	Chapter	4	for	emissions	from	palustrine	wetlands,2

influenced	by	a	variety	of	management	options	such	as	water	table	management,	timber	or	other	

plant	biomass	harvest,	and	wetlands	that	are	managed	with	fertilizer	applications.	Currently,	there	

are	insufficient	data	and	therefore,	the	GHG	fluxes	will	likely	not	be	included	in	an	entity’s	GHG	

estimation	until	adequate	data	exist	to	provide	that	estimation	with	a	reasonable	and	measurable	

level	of	uncertainty.	

Settlements	will	fall	into	two	broad	categories:	(1)	land	where	the	entity	manager	imposes	

management	decisions;	and	(2)	land	where	the	manager	does	not	regularly	impose	management	

decisions	that	impact	carbon	balances.	Examples	of	settlement	land	that	may	be	significant	from	a	

carbon	management	perspective	would	be	developed	livestock	feed	yards,	dairy	barns,	poultry	

houses,	manure	piles,	and	manure	or	runoff	lagoons.	Examples	of	developed	land	where	

management	is	not	of	concern	to	carbon	balances	is	homes,	yards,	driveways,	workshops,	roads,	

and	parking	areas.	For	purposes	of	the	GHG	flux	estimation,	only	the	areas	with	carbon	

management	implications	(e.g.,	animal	housing,	manure	waste	treatment	areas)	need	to	be	

identified	within	the	spatial	boundary	delineation.	These	livestock	and	manure	management	

methods	are	presented	in	Chapter	5	of	this	report.	The	remaining	settlement	lands	without	carbon	

management	implications	(e.g.,	roads	and	railroads)	can	simply	be	excluded	from	the	spatial	

boundaries	an	entity	chooses	to	account	for	within	the	settlement	land‐use	category.	

2	Palustrine	wetlands	are	nontidal	wetlands	that	are	primarily	composed	of	trees,	shrubs,	persistent	

emergent,	emergent	mosses	or	lichens,	and	all	wetlands	that	occur	in	tidal	areas	where	salinity	due	to	ocean‐

derived	salts	is	below	0.5	percent.	Palustrine	wetlands	must	have	an	area	less	than	20	acres,	not	have	active	

wave‐formed	or	bedrock	shoreline,	have	a	maximum	water	depth	of	less	than	2	m	[6.6	ft],	and	have	a	salinity	

less	than	0.5	percent	(USGS,	2006).	

Wetland:

A	land‐use	category	that	includes	land	with	hydric	soils,	native	or	adapted	hydrophytic	

vegetation,	and	a	hydrologic	regime	were	the	soil	is	saturated	during	the	growing	season	in	most	

years.	Wetland	vegetation	types	may	include	marshes,	grasslands	or	forests.	Wetlands	may	have	

water	levels	that	are	artificially	changed,	or	where	the	vegetation	composition	or	productivity	is	

manipulated.	These	lands	include	undrained	forested	wetlands,	grazed	woodlands	and	

grasslands,	impoundments	managed	for	wildlife,	and	lands	that	are	being	restored	following	

conversion	to	a	non‐wetland	condition	(typically	as	a	result	of	agricultural	drainage).	Provisions	

for	engineered	wetlands	including	storm	water	detention	ponds,	constructed	wetlands	for	water	

treatment,	and	farm	ponds	or	reservoirs	are	not	included.	Natural	lakes	and	streams	are	also	not	

included.	
Chapter 2: Considerations When Estimating Agriculture and Forestry GHG Emissions and Removals 

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Any	land	that	is	actively	managed	in	such	a	way	as	to	impact	biomass	growth	or	otherwise	impact	

production‐related	GHG	emissions	should	have	been	captured	within	the	spatial	boundaries	

defined	for	the	land‐use	categories	listed	above.	Any	remaining	land	should	be	categorized	as	other

lands	or	unmanaged land	and	will	not	be	considered	in	the	estimation	of	GHG	fluxes.	This	includes	

the	wetland	and	developed	areas	that	were	previously	noted	as	not	having	active	management—

i.e.,	unmanaged	wetlands	and	unmanaged	settlements.	It	also	includes	any	other	areas	within	the

entity	boundary	that	represent	barren,	mined,	abandoned,	or	otherwise	unmanaged	land—i.e.,	

other	land.	

Land‐cover change	is	simply	a	variation	from	year	to	year	in	what	is	growing	on	a	parcel	of	land,	

such	as	rotating	corn	and	soybean	crops,	and	is	not	considered	land‐use	change.	In	contrast,	land‐

use change	is	a	fundamental	shift	in	purpose	or	production	of	a	parcel,	such	as	a	shift	from	cropping	

to	forest	production	or	vice	versa.	Land‐use	change	needs	to	be	accounted	for	in	the	annual	GHG	

flux,	as	the	impact	(either	positive	or	negative)	on	biomass	and	soil	carbon	can	be	significant.	These	

land‐use	change	methods	are	presented	in	Chapter	7	of	this	report.	

Animal production	is	not	necessarily	a	spatially	defined	activity	within	the	entity,	but	has	to	be	

considered	as	part	of	the	physical	boundary	of	the	manager’s	operation.	There	are	three	main	areas	

that	need	to	be	considered	as	important	to	estimating	GHG	emissions	from	an	animal	production	

system:	methane	emissions	from	the	animals,	methane	and	nitrous	oxide	emissions	from	

management	of	manure,	and	any	emissions	impacts	related	to	animal	housing.	Animal	production	

in	the	chapter	is	discussed	by	animal	system	type,	including	beef,	dairy,	sheep,	swine,	and	poultry.	

The	collective	noun	for	a	group	of	animals	typically	varies	by	species,	but	for	the	purposes	of	this	

report,	we	will	refer	to	any	group	of	animals	of	the	same	animal	type	that	are	kept	together	under	a	

common	set	of	production	management	practices	as	a	herd.	Following	this	definition,	the	entity’s	

manager	may	have	several	distinctly	different	herds	that	make	up	the	entity.	GHG	emissions	from	

animal	production	will	vary	greatly	depending	upon	species	(digestive	processes),	growth	stage,	

diet,	and	manure	storage	and	management.	Timing	is	also	a	challenge	in	estimating	emissions	from	

the	animal	production	sector,	as	emissions	per	animal	change	dramatically	as	a	young	animal	grows	

and	matures,	as	feedlot	cattle	are	finished,	or	as	dairy	cows	cycle	between	gestating	and	lactating.	

In	some	cases,	it	will	likely	be	necessary	for	the	user	to	estimate	emissions	for	a	herd	using	average	

weight,	average	age,	and	other	representative	characteristics	to	represent	the	herd	population.	In	

other	cases,	it	will	be	necessary	to	generalize	by	seasons—manure	management	may	be	different	in	

winter	than	summer,	animal	feed	mixture	may	vary	by	season	or	by	animal	growth	stage.	Averaging	

Settlements:

A	land‐use	category	representing	developed	areas	consisting	of	units	of	0.25	acres	(0.1	ha)	or	

more	that	includes	residential,	industrial,	commercial,	and	institutional	land;	construction	sites;	

public	administrative	sites;	railroad	yards;	cemeteries;	airports;	golf	courses;	sanitary	landfills;	

sewage	treatment	plants;	water	control	structures	and	spillways;	parks	within	urban	and	built‐

up	areas;	and	highways,	railroads,	and	other	transportation	facilities.	Also	included	are	tracts	of	

less	than	10	acres	(4.05	ha)	that	may	meet	the	definitions	for	forest	land,	cropland,	grassland,	or	

other	land	but	are	completely	surrounded	by	urban	or	built‐up	land,	and	so	are	included	in	the	

settlement	category.	Rural	transportation	corridors	located	within	other	land	uses	(e.g.,	forest	

land,	cropland,	and	grassland)	are	also	included	in	settlements.	

Other Land:

A	land‐use	category	that	includes	bare	soil,	rock,	ice,	and	all	land	areas	that	do	not	fall	into	any	of	

the	other	five	land‐use	categories,	which	allows	the	total	of	identified	land	areas	to	match	the	

identified	land	base.	
Chapter 2: Considerations When Estimating Agriculture and Forestry GHG Emissions and Removals 

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and	generalizing	in	this	way	should	be	adequate	in	capturing	the	information	needed	to	provide	a	

reasonable	estimate	of	GHG	emissions	as	long	as	the	manager	applies	assumptions	consistently	

across	the	herds	and	throughout	the	time	under	consideration.	For	example,	assuming	an	average	

finish	weight	for	feeder	animals	in	the	herd	should	provide	a	reasonable	GHG	estimate	as	long	as	

the	assumed	weight	does	not	change	from	year	to	year,	unless	a	specific	management	decision	

(such	as	a	change	in	animal	diet)	results	in	an	actual	change	in	finishing	weight,	in	which	case	the	

change	in	averages	would	be	appropriate.	Specific	methods	for	animal	production	systems	are	

presented	in	Chapter	5	of	the	report.	In	some	cases,	such	as	manure	applied	to	cropland,	methods	

from	Chapter	3	will	be	utilized	as	well.	

Occasionally,	physical	boundaries	will	change	over	time.	Whether	a	portion	of	a	cropland	field	is	

converted	to	an	animal	feedlot,	shelterbelt	or	riparian	trees	are	planted	onto	former	cropland,	or	

abandoned	land	reverts	to	grazing	land	or	forestland,	these	changes	could	result	in	the	need	for	a	

new	delineation	of	parcel	boundaries	or	a	dissection	of	one	parcel	into	several	parcels	with	more	

than	one	management	strategy.	For	the	portion	of	the	parcel	where	this	change	has	occurred,	the	

land‐use	change	methods	(Chapter	7)	will	be	used	to	estimate	GHG	fluxes.	

Figure	2‐1	can	be	used	to	help	landowners	determine	the	land	use	category	for	their	land	area,	

according	to	the	definitions	above.	

2.1.2.2 Temporal Boundaries

Temporal	issues	include	such	considerations	as	the	frequency	of	the	reported	estimates,	the	

treatment	of	activities	that	occur	within	an	accounting	period	but	have	long‐term	implications	for	

carbon	balances	(e.g.,	changes	in	soil	carbon	following	a	change	in	tillage	practices),	and	how	to	

account	for	short‐term	management	or	short‐term	adjustments	to	long‐term	management	

decisions.	Also	significant	is	how	to	address	movement	of	spatial	boundaries	over	time	and	with	

land‐use	change.	This	section	will	attempt	to	resolve	some	of	these	temporal	issues	around	GHG	

emission	estimation	and	reporting.	

The	methods	reported	here	are	intended	to	provide	a	means	of	annual	accounting	and	reporting	of	

GHG	fluxes.	Annual	changes	in	some	emissions	are	easily	quantified,	but	for	others	it	is	much	more	

difficult.	Carbon	stored	in	trees,	for	example,	may	need	to	be	estimated	over	a	longer	period,	with	

the	change	then	converted	to	an	annualized	estimate.	

The	report	methodologies	assume	an	accounting	period	of	one	calendar	year	(e.g.,	365	days)	when	

estimating	annualized	emissions	in	a	particular	sector	or	source	category.	

Management	decisions	also	are	significant	to	the	accounting	time	horizon.	For	example,	a	forest	

management	plan	might	call	for	timber	harvest	or	thinning.	In	the	year	of	harvest,	the	annual	

accounting	will	reflect	a	loss	of	standing	live	and/or	standing	dead	carbon	stocks,	yet	in	the	longer	

term	management	strategy,	the	net	result	could	be	an	increase	in	total	carbon	stocks.	If	a	land	

manager	has	a	management	plan	that	prescribes	forest	thinning,	but	then	harvests	more	

aggressively	than	the	plan,	consideration	should	be	given	as	to	whether	this	constitutes	a	change	in	

forest	management,	which	would	be	discussed	in	the	forest	management	methods	(see	Chapter	6).	

There	are	also	times	when	management	has	to	take	corrective	action	or	temporarily	deviate	from	a	

long‐term	management	plan.	This	could	be	the	case	where	a	cropland	manager	has	adopted	a	no‐till	

management	strategy,	but	after	several	years	has	to	use	tillage	one	year	because	of	weather,	pests,	

or	other	extenuating	circumstances.	In	this	case,	the	methods	will	ideally	be	sensitive	enough	to	

capture	the	GHG	impact	of	the	deviation	from	the	management	plan.		
Chapter 2: Considerations When Estimating Agriculture and Forestry GHG Emissions and Removals 

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Figure 2‐1: Decision Tree for Determining Land‐Use Category for Land Areas
Chapter 2: Considerations When Estimating Agriculture and Forestry GHG Emissions and Removals 

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2.1.2.3 Activity Boundaries

It	is	important	to	distinguish	which	activities	within	an	entity	are	subject	to	accounting.	This	

accounting	system	is	focused	on	land‐based	activities	such	as	tillage	and	harvesting,	and	not	on	

emissions	of	GHGs	that	are	related	to	fossil	fuel	use.	Thus,	emissions	from	tractor	fuel	or	fuel	used	

for	crop	drying	are	not	counted,	nor	are	the	energy	inputs	required	to	manufacture	fertilizer	or	

farm	tools,	or	to	heat	farm	buildings—i.e.,	indirect	GHG	emissions	(see	Chapter	1).	However,	as	

mentioned	in	Chapter	1,	where	there	are	obvious	changes	in	the	level	of	combustion	due	to	a	

change	in	practices,	that	change	is	qualitatively	discussed.	For	example,	a	shift	from	conventional	

tillage	to	no	till	can	result	in	a	large	reduction	in	fuel	consumption	because	of	fewer	trips	across	the	

field.	These	relationships	are	noted	qualitatively	in	the	report,	but	quantitative	methods	are	not	

proposed.	Methods	for	quantifying	emissions	from	stationary	or	mobile	combustions	are	available	

from	other	Federal	agencies.	

As	previously	mentioned,	the	methods	in	this	report	do	not	constitute	a	life‐cycle	assessment	for	

two	primary	reasons.	First	the	activity	boundaries	do	not	include	emissions	from	fossil‐fuel	use.	

Second,	the	temporal	boundaries	are	focused	on	annual	reporting	and	do	not	encompass	the	range	

of	activities	such	as	capital	investment,	material	supplies,	and	disposal.	

2.1.2.4 Material Boundaries

Material	boundaries	include	the	GHGs	that	are	to	be	considered	in	the	estimation	and	should	also	

delineate	what	sources	of	those	gases	are	included	and	what	are	excluded.	Also	included	in	this	

section	is	a	discussion	of	the	global	warming	potentials	(GWPs)	used	throughout	the	report.	It	is	

important	to	determine	up	front	which	gases	are	included	and	which	are	not.	It	is	also	important	to	

determine	how	much	freedom	the	user	has	in	what	is	estimated	and	where	these	boundaries	lie	in	

order	to	ensure	that	a	change	in	management	that	reduces	emissions	in	one	sector	does	not	

inadvertently	cause	emissions	to	rise	outside	of	the	boundaries	being	reported.	

The	report	includes	estimation	methodologies	covering	the	GHG	emissions	from	the	croplands	and	

grazing	lands,	wetlands,	animal	production,	forestry,	and	land‐use	change	sectors.	Within	these	

sectors	and	source	categories,	emissions	and	removals	of	the	main	GHGs—carbon	dioxide	(CO2),	

methane	(CH4),	and	nitrous	oxide	(N2O)—are	accounted	for.	It	should	be	noted	that	carbon	

sequestration	(i.e.,	increases	in	carbon	stocks)	is	estimated	in	terms	of	carbon	dioxide	equivalents	

(CO2‐eq).	It	should	also	be	noted	that	the	animal	production	chapter	includes	discussion	of	

ammonia	(NH3),	as	this	is	an	important	precursor	to	N2O	emissions	from	manure	management.	

Estimating	NH3	emissions	is	beyond	the	scope	of	this	report—NH3	is	not	considered	a	GHG—but	

since	NH3	is	significant	as	a	precursor	to	N2O,	understanding	changes	in	NH3	emissions	resulting	

from	changes	in	management	is	important.	

Emissions	and	sequestration	values	are	presented	in	this	report	in	terms	of	the	mass	(not	volume)	

of	each	gas,	using	metric	units	(e.g.,	metric	tons	of	methane).	In	the	integrated	tool,	the	masses	of	

each	gas	will	be	converted	into	CO2	equivalent	units	using	the	GWPs	for	each	gas	in	the	

International	Panel	on	Climate	Change	(IPCC)	Second	Assessment	Report.	

A	GWP	is	an	index	used	to	compare	the	relative	radiative	forcing	of	different	gases	without	directly	

calculating	the	changes	in	atmospheric	conditions.	GWPs	are	calculated	as	the	ratio	of	the	radiative	

forcing	that	would	result	from	the	emissions	of	one	kilogram	of	a	GHG	to	that	from	the	emissions	of	

one	kilogram	of	CO2	over	a	defined	period	of	time,	such	as	100	years.	Emissions	in	terms	of	CO2

equivalents	(CO2‐eq)	are	estimated	by	multiplying	the	mass	of	a	particular	GHG	(e.g.,	CH4,	N2O)	by	

the	respective	GWP	for	that	particular	GHG.	The	GWPs	used	in	this	report	are	shown	in	Table	2‐1	

below.	
Chapter 2: Considerations When Estimating Agriculture and Forestry GHG Emissions and Removals 

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The	methods	in	this	report	focus	primarily	on	the	direct	emissions	resulting	from	management	

decisions	made	within	the	boundaries	of	the	entity—e.g.,	within	the	farm	and	forest	gate.	The	

indirect	emissions	related	to	inputs	into	the	

entity	are	not	considered.	The	reason	for	

this	is	that	those	emissions	would	likely	be	

reported	by	the	manufacturer	producing	the	

inputs.	If	one	were	conducting	a	full	life‐

cycle	assessment,	these	emissions	would	

need	to	be	included,	but	for	purposes	of	the	

emissions	being	estimated	here	we	focus	

primarily	on	the	emissions	resulting	within	

the	spatial	boundary	of	the	entity.	The	one	

notable	exception	that	is	accounted	for	is	

when	management	decisions	on	the	operation	have	a	specific	related	influence	on	emissions	

leaving	the	entity’s	boundary.	An	example	of	this	is	indirect	emissions	such	as	nitrogen	that	is	

applied	within	the	operation	but	then	carried	offsite	via	erosion	or	leaching	and	contributes	to	N2O	

emissions	offsite.	Another	example	to	consider	is	harvested	commodities.	In	the	case	of	grains	or	

other	agricultural	commodities,	the	product	is	assumed	to	be	consumed	within	a	relatively	short	

amount	of	time,	resulting	in	no	net	gain	or	loss	related	to	GHG	accounting.	HWPs	are	somewhat	

different,	as	much	of	that	harvest	will	end	up	in	long‐term	carbon	pools	either	as	structures,	

furniture,	or	other	wood	products,	or	in	landfills.	This	report	does	provide	a	discussion	of	N2O	

losses	that	result	from	erosion	and	leaching	of	fertilizer	nitrogen	and	the	carbon	pools	related	to	

the	fate	of	HWPs.	

2.2 Review of Relevant Current Tools and Methods

This	section	provides	an	overview	of	the	current	estimation	methods	or	approaches	an	entity	could	

use	to	estimate	GHG	emissions	and	sinks	on	their	property.	This	overview	is	followed	by	a	summary	

of	each	sector’s	proposed	methodologies	for	entity	GHG	estimations.	

There	are	several	approaches	that	a	farmer	or	landowner	can	use	to	estimate	GHG	emissions	at	an	

entity	scale,	and	each	approach	gives	varying	accuracy	and	precision.	The	most	accurate	way	of	

estimating	emissions	is	through	direct	measurement,	which	often	requires	expensive	equipment	or	

techniques	that	are	not	feasible	for	a	single	landowner	or	manager.	On	the	other	hand,	lookup	

tables	and	estimation	equations	alone	often	do	not	adequately	represent	local	variability	or	local	

conditions.	This	report	attempts	to	delineate	methods	that	balance	user‐friendliness,	data	

requirements,	and	scientific	rigor	in	a	way	that	is	transparent	and	justified.	

The	following	approaches	were	considered	for	these	guidelines:	

 Basic	estimation	equations	–	Involve	combinations	of	activity	data3	with	parameters	and	

default	emission	factors.	4	Any	default	parameters	or	default	emission	factors	(e.g.,	lookup	

tables)	are	provided	in	the	text,	or	if	substantial	in	length,	in	an	accompanying	compendium	

of	data.	

																																																													

3	Activity	data	are	data	on	the	magnitude	of	human	activity	resulting	in	emissions	or	removals	taking	place	

during	a	given	period	of	time	(IPCC,	1997).	

4	Emission	factor	is	defined	as	a	coefficient	that	quantifies	the	emissions	or	removals	of	a	gas	per	unit	activity.	

Emission	factors	are	often	based	on	a	sample	of	measurement	data,	averaged	to	develop	a	representative	rate	

of	emission	for	a	given	activity	level	under	a	given	set	of	operating	conditions	(IPCC,	2006).	

Table 2‐1: Global Warming Potentials Used in

the Report

Species Chemical

Formula

Lifetime

(years) GWPa

Carbon dioxide CO2 Variable	 1

Methane CH4 12±3	 21

Nitrous oxide N2O 120	 310 a GWPs	used	are	100‐year	time	horizon,	in	accordance	with	the	

IPCC	Second	Assessment	Report	(IPCC,	2007).	
Chapter 2: Considerations When Estimating Agriculture and Forestry GHG Emissions and Removals 

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 Models	–	Use	combinations	of	activity	data	with	parameters	and	default	emission	factors.

The	inputs	for	these	models	can	be	ancillary	data5	(e.g.,	temperature,	precipitation,

elevation,	and	soil	nutrient	levels	that	may	be	pulled	from	an	underlying	source),	biological

variables	(e.g.,	plant	diversity),	or	site‐specific	data	(e.g.,	number	of	acres,	number	of

animals).	The	accuracy	of	the	models	is	dependent	on	the	robustness	of	the	model	and	the

accuracy	of	the	inputs.

 Field	measurements	–	Actual	measurements	that	a	farmer	or	landowner	would	need	to	take

to	more	accurately	estimate	the	properties	of	the	soil,	forest,	or	farm	to	estimate	actual

emissions.	Measuring	actual	emissions	on	the	land	requires	special	equipment	that

monitors	the	flow	of	gases	from	the	source	into	the	atmosphere.	This	equipment	is	not

readily	available	to	most	entities,	so	more	often	field	measurements	are	incorporated	into

other	methods	described	in	this	section	to	create	a	hybrid	approach.	A	field	measurement

such	as	a	sample	mean	tree	diameter	could	be	incorporated	into	other	models	or	equations

to	give	a	more	accurate	input.

 Inference	–	Uses	State,	regional,	or	national	emissions/sequestration	factors	that

approximate	emissions/sequestration	per	unit	of	the	input.	The	input	data	is	then

multiplied	by	this	factor	to	determine	the	total	onsite	emissions.	This	factor	can	have

varying	degrees	of	accuracy	and	often	does	not	capture	the	mitigation	practices	on	the	farm

or	the	unique	soil	conditions,	climate,	livestock	diet,	livestock	genetics,	or	any	farm‐specific

characteristics,	although	they	can	be	developed	with	specific	soil	types,	livestock	categories,

or	climactic	regions.

 Hybrid	estimation	approaches	–	An	approach	that	uses	a	combination	of	the	approaches

described	above.	The	approach	often	uses	field	measurements	or	models	to	generate	inputs

used	for	an	inference‐based	approach	to	improve	the	accuracy	of	the	estimate.

2.3 Selection of Most Appropriate Method and Mitigation Practices to Include

In	drafting	the	report,	a	number	of	selection	criteria	were	considered	(e.g.,	transparency,	

consistency,	comparability,	completeness,	accuracy,	cost	effectiveness,	ease	of	use).	A	description	of	

each	appears	below:	

 Transparency	–	The	assumptions	and	methodologies	used	for	an	inventory	should	be

clearly	explained	to	facilitate	replication	and	assessment	of	the	inventory	by	users	of	the

reported	information.	The	transparency	of	inventories	is	fundamental	to	the	success	of	the

process	for	the	communication	and	consideration	of	information.

 Consistency	–	The	methods	used	to	generate	inventory	estimates	should	be	internally

consistent	in	all	its	elements	and	the	estimates	should	be	consistent	with	other	years.	An

inventory	is	consistent	if	the	same	methodologies	are	used	for	the	base	and	all	subsequent

years	and	if	consistent	data	sets	are	used	to	estimate	emissions	or	removals	from	sources	or

sinks.	Consistency	is	an	important	consideration	in	merging	differing	estimation	techniques

from	diverse	technologies	and	management	practices.

 Comparability	–	For	the	guidelines	to	be	comparable,	the	estimates	of	emissions	and

sequestration	being	reported	by	one	entity	are	comparable	to	the	estimates	being	reported

by	others.	For	this	purpose,	entities	should	use	common	methodologies	and	formats	for

5	Ancillary	data	are	additional	data	necessary	to	support	the	selection	of	activity data	and	emission factors	for	

the	estimation	and	characterization	of	emissions.	Data	on	soil,	crop	or	animal	types,	tree	species,	operating	

conditions,	and	geographical	location	are	examples	of	ancillary	data.	
Chapter 2: Considerations When Estimating Agriculture and Forestry GHG Emissions and Removals 

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estimating	and	reporting	inventories.	Consequently,	in	general,	the	methods	specify	one	

method	for	any	technology	or	management	practice	(i.e.,	methods	suggested	in	this	report	

do	not	allow	users	to	select	from	a	menu	of	methods).	

 Completeness	–	The	methods	must	account	for	all	sources	and	sinks,	as	well	as	all	GHGs	to	

the	greatest	extent	possible.	Completeness	also	means	full	coverage	of	sources	and	sinks	

under	the	control	of	the	entity.	Completeness	is	an	important	consideration	to	be	balanced	

with	ease	of	use	in	reporting	appropriately	for	an	entity	that	may	have	a	minor	activity	or	

an	activity	with	severely	limited	data	availability.	

 Accuracy	–	A	relative	measure	of	the	exactness	of	an	emission	or	removal	estimate.	

Estimates	should	be	accurate	in	the	sense	that	they	are	systematically	neither	over	nor	

under	true	emissions	or	removals,	as	far	as	can	be	judged,	and	that	uncertainties	are	

reduced	as	far	as	practicable.	

 Cost	effectiveness	–	A	measure	of	the	relative	costs	and	benefits	of	additional	efforts	to	

improve	inventory	estimates	or	reduce	uncertainty.	For	example	there	is	a	balance	between	

the	relative	costs	and	benefits	of	additional	efforts	to	reduce	uncertainty.	

 Ease	of	use	–	A	measure	of	the	complexity	of	the	user	interface	and	underlying	data	

requirements.	

The	working	groups	developed	the	following	selection	criteria	for	the	mitigation	practices	that	

could	be	included	in	the	methods:	

1. The	science	reflects	a	mechanistic	understanding	of	the	practice's	influence	on	an	emission	

source.	

2. Published	research	supports	a	reasonable	level	of	repeatability/consistency	(can	use	

international	studies	if	similar	management,	climate,	and	soils	as	U.S.	conditions).	

3. There	is	general	agreement	that	at	least	the	sign	and	range	of	responses	are	reasonably	well	

understood.	

4. There	is	consensus	of	the	authors	that	the	practice	can	be	adequately	included.	To	reach	

consensus,	the	authors	discussed	issues	such	as:	Would	leaving	a	mitigation	practice	out	

make	the	report	incomplete?	Is	there	strong	enough	evidence	that	the	method	will	hold	up	

for	this	practice	for	at	least	the	next	five	years?	

There	were	mitigation	practices	that	did	not	fulfill	these	criteria,	and	those	practices	were	cited	as	

areas	that	require	more	research	in	order	to	fully	understand	the	effect	of	changes	in	the	practice	to	

GHG	emissions.	These	research	gaps	are	intended	to	become	areas	that	USDA,	non‐governmental	

organizations,	universities,	and	other	research	institutions	will	consider	as	important	areas	to	focus	

agriculture	and	forestry	climate‐change	research	priorities.	Other	topics,	such	as	albedo	effects,	

were	not	considered.	Currently,	with	the	exception	of	urban	areas,	albedo	effects	are	highly	variable	

and	are	difficult	to	reliably	quantify.	

2.4 Overview of Sectors

This	report	covers	emissions	sources	and	sinks	from	croplands/grazing	lands,	managed	wetlands,	

animal	production	systems,	and	forestry,	along	with	changes	in	land	use.	Figure	2‐2	can	be	used	to	

help	landowners	determine	which	chapter	can	be	used	to	estimate	their	GHG	sources	and	sinks	

from	their	land.	
Chapter 2: Considerations When Estimating Agriculture and Forestry GHG Emissions and Removals 

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Figure 2‐2: Decision Tree for Determining Which Methods to Follow in This Report
Chapter 2: Considerations When Estimating Agriculture and Forestry GHG Emissions and Removals 

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Figure 2‐2: Decision Tree for Determining Which Methods to Follow in This Report

(continued)

The	following	sections	provide	an	overview	of	the	sectors	covered	in	this	report.	For	each	sector,	

the	emission	sources	and	sinks	are	introduced	as	well	as	the	management	practices	impacting	GHG	

emissions.	

2.4.1 Croplands and Grazing Lands

Croplands	include	all	systems	used	to	produce	food,	feed,	and	fiber	commodities,	in	addition	to	

feedstocks	for	bioenergy	production.	Most	U.S.	croplands	are	drylands	(irrigated	or	unirrigated);	

rice	and	a	few	other	crops	are	grown	in	wetlands.	Some	croplands	are	set	aside	in	the	Conservation	

Reserve	Program.	Croplands	also	include	agroforestry	systems	that	are	a	mixture	of	crops	and	

trees,	such	as	alley	cropping,	shelterbelts,	and	riparian	woodlots.	Grazing	lands	are	systems	that	are	

used	for	livestock	production	and	occur	primarily	on	grasslands.	Grasslands	are	composed	

principally	of	grasses,	grass‐like	plants,	forbs,	or	shrubs	suitable	for	grazing	and	browsing;	they	

include	both	pastures	and	native	rangelands	(EPA,	2011).	Savannas,	some	wetlands	and	deserts,	

and	tundra	can	be	considered	grazing	lands	if	used	for	livestock	production.	Grazing	land	systems	

include	managed	pastures	that	may	require	periodic	management	to	maintain	the	grass	vegetation	

and	native	rangelands	that	typically	require	limited	management	to	maintain.	
Chapter 2: Considerations When Estimating Agriculture and Forestry GHG Emissions and Removals 

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Cropland	and	grazing	lands	are	significant	sources	of	CO2,	N2O,	and	CH4	emissions	and	can	also	be	a	

sink	for	CO2	and	CH4	(U.S.	EPA,	2011).	N2O	emissions	from	soils	are	influenced	by	land	use	and	

management	activity,	particularly	nitrogen	application.	Land	use	and	management	also	influence	

carbon	stocks	in	biomass,	dead	biomass,	and	soil	pools.	Crop	and	grazing	land	systems	can	be	either	

a	source	or	sink	for	CO2,	depending	on	the	net	changes	in	these	carbon	pools.	The	main	influences	

on	nitrogen	use	efficiency	and	N2O	emissions	are	fertilizer	rate,	timing,	placement,	and	nitrogen	

source.	Tillage	intensity,	cropping	intensity,	and	the	use	of	crop	rotation	can	have	significant	effects	

on	soil	carbon	stocks.	

Other	management	activities	also	affect	GHG	emissions	from	soils.	Irrigation	can	impact	CH4	and	

N2O	emissions	as	well	as	carbon	stocks.	Burning	decreases	biomass	carbon	stocks	and	also	soil	

organic	carbon	stocks	due	to	decreased	carbon	input	to	the	soil	system.	Burning	will	also	lead	to	

emissions	of	CH4	and	N2O	and	other	gases	(CO,	NOx)	that	are	GHG	precursors.	CH4	can	be	removed	

from	the	atmosphere	through	the	process	of	methanotrophy	in	soils,	which	occurs	under	aerobic	

conditions	and	generally	in	undisturbed	soils.	CH4	is	produced	in	soils	through	the	process	of	

methanogenesis,	which	occurs	under	anaerobic	conditions	(e.g.,	wetland	soils	used	for	production	

of	rice).	Both	processes	are	driven	by	the	activity	of	micro‐organisms	in	soils,	but	the	rate	of	activity	

is	influenced	by	land	use	and	management.	

The	influence	of	crop	and	grazing	land	management	on	GHG	emissions	is	not	typically	the	simple	

sum	of	each	practice’s	effect.	The	influence	of	one	practice	can	depend	on	another	practice.	For	

example,	the	influence	of	tillage	on	soil	carbon	will	depend	on	residue	management.	The	influence	

of	nitrogen	fertilization	rates	can	depend	on	fertilizer	placement	and	timing.	Because	of	these	

interconnections,	estimating	GHG	emissions	from	crop	and	grazing	land	systems	will	depend	on	a	

complete	description	of	the	practices	used	in	the	operation,	as	well	as	ancillary	variables	such	as	

soil	characteristics	and	weather	or	climate	conditions.	It	is	also	important	to	note	that	trends	in	

GHG	emissions	associated	with	a	change	in	crop	and	grazing	land	management	can	be	reversed	if	

the	landowner	reverts	to	the	original	practice.	For	example,	a	farmer	might	switch	from	

conventional	tillage	to	no‐till	for	10	years	and	see	an	increase	in	soil	carbon	sequestration;	if,	

however,	the	farmer	then	reverts	to	conventional	tillage,	the	gains	in	soil	carbon	will	be	quickly	lost	

as	the	stored	soil	carbon	is	released	back	into	the	atmosphere	as	CO2,	negating	the	GHG	mitigation	

of	the	previous	10	years.	However,	reversals	will	not	negate	the	GHG	mitigation	for	CH4	or	N2O	that	

occurred	prior	to	the	reversion.	If	emissions	are	reduced	for	CH4	or	N2O,	the	emission	reduction	is	

permanent	and	cannot	be	changed	by	

subsequent	management	decisions.	

The	text	box,	Management	Practices	

Impacting	GHG	Emissions	from	Croplands	

and	Grazing	Lands,	lists	the	most	

significant	mitigation	practices	discussed	

in	Chapter	3.	Additional	mitigation	

practices	are	discussed	in	the	chapter,	but	

these	often	have	sparse	or	conflicting	

evidence	in	support	of	their	mitigation	

effects.	Therefore,	the	text	box	lists	the	

more	robustly	supported	practices.	

2.4.2 Wetlands

Wetlands	occur	across	the	United	States	

on	many	landforms,	particularly	in	floodplains	and	riparian	zones,	inland	lacustrine,	glaciated	

outwash,	and	coastal	plains.	The	National	Wetlands	Inventory	broadly	classifies	wetlands	into	five	

Management Practices Impacting GHG

Emissions from Croplands and Grazing Lands

 Nutrient	Management	(Synthetic	and

Organic)	

 Tillage	Practices

 Crop	Rotations	and	Cropping	Intensity

 Irrigation

 Residue	Management

 Set‐Aside/Reserve	Cropland

 Wetland	Rice	Cultivation

 Livestock	Grazing	Practices

 Forage	Options

 Silvopasture
Chapter 2: Considerations When Estimating Agriculture and Forestry GHG Emissions and Removals 

2-18

major	systems,	including	(1)	marine,	(2)	estuarine,	(3)	riverine,	(4)	lacustrine,	and	(5)	palustrine	

(Cowardin	et	al.,	1979).	These	systems	are	further	classified	by	major	vegetative	life	form.	For	

example,	forested	wetlands	are	often	classified	as	palustrine‐forested.	Similarly,	most	grassland	

wetlands	are	classified	as	palustrine	wetlands	with	emergent	vegetation	(e.g.,	grasses	and	sedges).	

Wetlands	also	vary	greatly	with	respect	to	groundwater	and	surface	water	interactions	that	directly	

influence	hydroperiod,	water	chemistry,	and	soils	(Cowardin	et	al.,	1979;	Winter	et	al.,	1998).	All	

these	factors	along	with	climate	and	land‐use	drivers	influence	overall	carbon	balance	and	GHG	

flux.	

Grassland	and	forested	wetlands	are	subject	to	a	wide	range	of	land	use	and	management	practices	

that	influence	the	carbon	balance	and	GHG	flux	(Faulkner	et	al.,	2011;	Gleason	et	al.,	2011).	For	

example,	forested	wetlands	may	be	subject	to	silvicultural	prescriptions	and	intensity	of	

management,	and	hence,	the	carbon	balance	and	GHG	emissions	should	be	evaluated	on	a	rotation	

basis.	In	contrast,	grassland	wetlands	may	be	grazed,	hayed,	or	directly	cultivated	to	produce	a	

harvestable	commodity.	All	these	manipulations	influence	the	overall	GHG	flux.	This	report	will	

focus	primarily	on	restoration	and	management	practices	associated	with	riverine	and	palustrine	

systems	in	forested,	grassland,	and	riparian	ecosystems;	although	other	major	wetlands	systems	

are	significant	in	the	global	carbon	cycle	(e.g.,	estuarine),	these	wetlands	systems	have	received	the	

most	attention	in	terms	of	implementation	of	restoration	and	management	practices	to	conserve	

wetlands	habitats	and	sustain	ecosystems	services	(Brinson	and	Eckles,	2011).	Wetlands	that	have	

been	drained	for	a	commodity	production,	such	as	annual	crops,	are	not	considered	wetlands	in	this	

guidance.	Therefore,	management	of	drained	wetlands	is	addressed	in	other	sections	of	the	

guidance,	such	as	in	Chapter	3.	

Wetland	emissions	are	largely	controlled	by	the	degree	of	water	saturation	as	well	as	climate	and	

nutrient	availability.	In	aerobic	conditions,	common	in	most	upland	wetland	ecosystems,	

decomposition	releases	of	CO2,	and	CH4	emissions	are	more	prevalent	in	anaerobic	conditions.	

Typically,	wetlands	are	a	source	of	CH4,	

with	estimated	global	emissions	of	55	to	

150	million	metric	tons	CH4	per	year	

(Blain	et	al.,	2006).	N2O	emissions	from	

wetlands	are	typically	low,	unless	an	

outside	source	of	nitrogen	is	entering	the	

wetland.	If	wetlands	are	drained,	N2O	

emissions	are	largely	controlled	by	the	

fertility	of	the	soil.	Wetland	drainage	

results	in	lower	CH4	emissions	and	an	

increase	in	CO2	emissions	due	to	oxidation	

of	soil	organic	matter	and	an	increase	in	

N2O	emissions	in	nutrient	rich	soil.	On	the	

other	hand,	the	creation	of	wetlands	

generates	higher	levels	of	CH4	and	lower	

levels	of	CO2	(Blain	et	al.,	2006).	

Biomass	carbon	can	change	significantly	

with	management	of	wetlands,	

particularly	in	peatlands,	forested	

wetlands,	or	changes	from	forest	to	wetlands	dominated	by	grasses	and	shrubs	or	open	water.	

Peatlands	cover	approximately	400	million	hectares	or	three	percent	of	the	global	land	surface,	

accounting	for	450	billion	metric	tons	of	stored	carbon	(Couwenbert,	2009).	Emissions	from	

peatland	degradation	and	fires	are	estimated	at	2	billion	metric	tons	of	CO2‐eq	per	year	(IPCC,	

Management Practices Impacting GHG

Emissions from Wetlands

 Silvicultural	Water	Table	Management

 Forest	Harvesting	Systems

 Forest	Regeneration	Systems

 Fertilization

 Conversion	to	Open	Wetland

 Forest	Type	Change

 Water	Quality	Management

 Wetland	Management	for	Waterfowl

 Constructed	Wetlands	for	Wastewater

Treatment

 Land‐Use	Change	to	Wetlands

 Actively	Restoring	Wetlands

 Actively	Restoring	Scrub‐Grass	Wetlands

 Constructing	Wetlands

 Passive	Restoration	of	Wetlands
Chapter 2: Considerations When Estimating Agriculture and Forestry GHG Emissions and Removals 

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2011).	In	forested	wetlands,	there	can	also	be	significant	carbon	in	dead	wood,	coarse	woody	

debris,	and	fine	litter.	Harvesting	practices	will	also	influence	the	carbon	stocks	in	wetlands	to	the	

extent	that	the	wood	is	collected	for	products,	fuel,	or	other	purposes.	Wetlands	are	also	a	source	of	

N2O	emissions,	primarily	because	of	nitrogen	runoff	and	leaching	into	groundwater	from	

agricultural	fields	and/or	livestock	facilities.	N2O	emissions	from	wetlands	due	to	nitrogen	inputs	

from	surrounding	fields	or	livestock	facilities	are	considered	an	indirect	emission	of	N2O	(de	Klein	

et	al.,	2006).	Direct	N2O	emissions	can	also	occur	if	management	practices	include	nitrogen	

fertilization	of	the	wetlands.	

The	text	box,	Management	Practices	Impacting	GHG	Emissions	from	Wetlands,	lists	the	

management	practices	in	wetlands	that	have	an	influence	on	GHG	emissions	(CH4	or	N2O)	or	carbon	

stock	changes,	and	will	be	covered	in	more	detail	later	in	the	report.	Individual	sections	will	deal	

with	different	types	of	wetlands	including	forested,	grassland,	and	constructed	wetlands	that	could	

occur	in	agricultural	and	forestry	operations.	The	methods	are	restricted	to	estimation	of	emissions	

on	palustrine	wetlands	that	are	influenced	by	a	variety	of	management	options	such	as	water	table	

management,	timber	or	other	plant	biomass	harvest,	and	wetlands	that	are	managed	with	fertilizer	

applications.	

2.4.3 Animal Production

GHG	emissions	from	animal	production	systems	consist	of	three	main	categories:	enteric	

fermentation,	housing,	and	manure	management.	The	three	categories	are	described	in	the	sections	

that	follow.	Discussion	about	enteric	fermentation	and	housing	are	addressed	together	in	this	

report.	

2.4.3.1 Enteric Fermentation and Housing

Enteric	fermentation	refers	to	the	methane	emissions	resulting	from	animal	digestive	processes,	

while	housing	emissions	refer	to	GHG	emissions	from	manure	that	is	stored	within	the	housing	

structure	(i.e.,	manure	stored	under	a	barn	floor).	GHG	emissions	arising	from	manure	stored	in	

housing	have	similar	emissions	to	manure	that	is	managed	in	stockpiles.	More	discussion	on	

housing	manure	emissions	can	be	found	in	Section	2.4.3.2	and	Chapter	5.	

For	enteric	fermentation,	CH4‐producing	micro‐organisms,	called	methanogens,	exist	in	the	

gastrointestinal	tract	of	many	animals.	Ruminant	animals	(hoofed	mammals)	that	have	three	or	

four	chambered	stomachs	(and	chew	cud	

as	a	part	of	the	digestive	process),	

produce	much	more	CH4	than	do	other	

animals	because	of	the	presence	and	

fermentative	capacity	of	the	rumen	(the	

first	stomach	in	a	ruminant	animal).	

In	the	rumen,	CH4	formation	is	a	disposal	

mechanism	by	which	excess	hydrogen	

from	the	anaerobic	fermentation	of	

dietary	carbohydrate	can	be	released.	

Control	of	hydrogen	ions	through	

methanogenesis	assists	in	maintenance	of	

an	efficient	microbial	fermentation	by	

reducing	the	partial	pressure	of	hydrogen	

to	levels	that	allow	normal	functioning	of	microbial	energy	transfer	enzymes	(Martin	et	al.,	2010).	

CH4	can	also	arise	from	hindgut	fermentation,	but	the	levels	associated	with	hindgut	fermentation	

are	much	lower	than	those	of	foregut	fermentation.	Although	animals	produce	CO2	through	

Management Practices Impacting GHG

Emissions from Enteric Fermentation and

Housing

 Dietary	Fat

 Grain	Source,	Grain	Processing,	Starch

Availability

 Feeding	Co‐Product	Ingredients

 Roughage	Concentration	and	Form

 Level	of	Intake

 Feed	Additives	and	Growth	Promoters

 Novel	Microorganisms	and	Their	Products

 Genetics
Chapter 2: Considerations When Estimating Agriculture and Forestry GHG Emissions and Removals 

2-20

respiration,	the	only	gas	of	concern	in	enteric	fermentation	processes	is	CH4.	In	field	studies,	

respiration	chambers	equipped	with	N2O	and	NH3	analyzers	have	confirmed	that	enteric	

fermentation	does	not	result	in	the	production	of	N2O	or	NH3	(Reynolds	et	al.,	2010).	

The	text	box,	Management	Practices	Impacting	GHG	Emissions	from	Enteric	Fermentation	and	

Housing,	lists	several	of	the	practices	that	can	modify	enteric	fermentation	emissions.	Most	of	the	

practices	relate	to	diet	composition.	These	practices	are	covered	in	greater	detail	in	Chapter	5.	

2.4.3.2 Manure Management

Storage	of	animal	manure	(dung	and	urine)	is	a	popular	management	practice	because	it	reduces	

the	need	to	buy	commercial	fertilizer,	allows	for	more	control	over	manure	application,	and	has	

lower	demands	on	farm	labor.	The	treatment	and	storage	of	manure	in	management	systems	

contributes	to	the	GHG	emissions	of	the	agricultural	sector.	Anaerobic	conditions,	as	found	in	many	

long‐term	storage	systems,	produce	CH4	through	anaerobic	decomposition.	N2O	is	produced	either	

directly,	as	part	of	the	nitrogen	cycle	through	nitrification	and	denitrification,	or	indirectly,	as	a	

result	of	volatilization	of	nitrogen	as	NH3	and	nitrogen	oxides	(NO,	NO2,	or	NO3)	and	runoff	during	

handling.	

Animal	manure	can	be	classified	as:	

 Slurry,	where	the	dry	matter	is	greater	than	10	percent;

 Solid,	where	the	dry	matter	is	greater	than	15	percent;	or

 Liquid,	where	the	dry	matter	is	lower	than	10	percent.

The	four	solid	manure	storage/treatment	practices	are:	(1)	temporary	stack;	(2)	long‐term	

stockpile;	(3)	composting;	and	(4)	thermo‐chemical	conversion.	The	eight	main	liquid	manure	

storage/treatment	practices	are:	(1)	

anaerobic	digestion;	(2)	nutrient	removal;	

(3)	anaerobic	lagoon/runoff	holding	

pond/storage	tanks;	(4)	aerobic	lagoon;	(5)	

constructed	wetland;	(6)	sand‐manure	

separation;	(7)	combined	aerobic	treatment	

system;	and	(8)	solid‐liquid	separation.	

Greater	analysis	of	each	of	these	systems	is	

provided	in	Chapter	5.	

The	magnitude	of	CH4	and	N2O	emissions	

that	result	from	animal	manure	is	dependent	largely	on	the	environmental	conditions	that	the	

manure	is	subjected	to.	CH4	is	emitted	when	oxygen	is	not	available	for	bacteria	to	decompose	

manure.	Storage	of	manure	in	ponds,	tanks,	or	pits,	as	is	typical	with	liquid/slurry	flushing	systems,	

promote	anaerobic	conditions	and	the	formation	of	CH4.	Storage	of	solid	manure	in	stacks	or	dry	

lots	or	deposition	of	manure	on	pasture,	range,	or	paddock	lands	tend	to	result	in	more	oxygen‐

available	conditions,	and	little	or	no	CH4	will	be	formed.	Other	factors	that	influence	CH4	generation	

include	the	ambient	temperature,	moisture	content,	residency	time,	and	manure	composition	

(which	is	dependent	on	the	diet	of	the	livestock,	growth	rate,	and	type	of	digestive	system)	(U.S.	

EPA,	2011).	

The	production	of	N2O	from	managed	livestock	manure	depends	on	the	composition	of	the	manure	

and	urine,	the	type	of	bacteria	involved,	the	oxygen	and	liquid	content	of	the	system,	and	the	

environment	for	the	manure	after	excretion	(U.S.	EPA,	2011).	N2O	occurs	when	the	manure	is	first	

subjected	to	aerobic	conditions	where	NH3	and	organic	nitrogen	are	converted	to	nitrates	and	

nitrites	(nitrification),	and	if	conditions	become	sufficiently	anaerobic,	the	nitrates	and	nitrites	can	

Management Practices Impacting GHG

Emissions from Manure Management

 Thermo‐Chemical	Conversion

 Anaerobic	Digestion

 Liquid	Manure	Storage	and	Treatment‐

Sand‐Manure	Separation

 Liquid	Manure	Storage	and	Treatment–

Solid‐Liquid	Separation
 Chapter 2: Considerations When Estimating Agriculture and Forestry GHG Emissions and Removals 

 2-21 

be	denitrified	(reduced	to	nitrogen	oxides	and	nitrogen	gas)	(Groffman	et	al.,	2000).	N2O	is	an	

intermediate	product	of	both	nitrification	and	denitrification	and	can	be	directly	emitted	from	soil	

as	a	result	of	either	of	these	processes.	Dry	waste	handling	systems	are	generally	oxygenated	but	

have	pockets	of	anaerobic	conditions	from	decomposition;	these	systems	have	conditions	that	are	

most	conducive	to	the	production	of	N2O	(USDA,	2011).	

Some	manure	management	systems	can	effectively	mitigate	the	release	of	GHG	emissions	from	

livestock	manure.	The	text	box,	Management	Practices	Impacting	GHG	Emissions	from	Manure	

Management,	lists	several	of	the	practices	that	can	modify	manure	management	emissions.	

2.4.4 Forestry

Forest	systems	represent	a	significant	opportunity	to	mitigate	GHGs	through	the	sequestration	and	

temporary	storage	of	forest	carbon	stocks.	Forests	remove	CO2	from	the	atmosphere	through	

photosynthesis	and	store	carbon	in	forest	biomass	(e.g.,	stems,	root,	bark,	leaves).	Respiration	

releases	CO2	to	the	atmosphere.	Net	forest	carbon	stocks	increase	over	time	when	carbon	

sequestration	during	photosynthesis	exceeds	carbon	released	during	respiration.	Other	GHGs	are	

also	exchanged	by	forest	ecosystems—e.g.,	CH4	from	microbial	communities	in	forest	soil	and	N2O	

from	fertilizer	use.	

Harvesting	forests	releases	some	sequestered	carbon	to	the	atmosphere,	while	the	remaining	

carbon	passes	in	HWPs,	the	fate	of	which	(e.g.,	combustion	for	energy,	manufacture	of	durable	

wood	products,	disposal	in	landfills)	determines	the	rate	at	which	the	carbon	is	returned	to	the	

atmosphere.	

There	are	many	forestry	activities	(i.e.,	

management	practices)	relevant	to	

reducing	GHG	emissions	and/or	increasing	

carbon	stocks	in	the	forestry	sector	

including	establishing	and/or	re‐

establishing	forest,	avoided	forest	clearing,	

and	forest	management.	More	information	

on	each	is	included	below.		

The	Chapter	6	describes	methods	for	the	

various	source	categories	contributing	to	

the	GHG	flux	from	forests.	These	source	

categories	include	forest	carbon	

accounting—e.g.,	live	trees,	understory,	

standing	dead,	down	dead	wood,	forest	

floor	or	litter,	forest	soil	organic	carbon—

establishing,	re‐establishing,	and	clearing	

forest,	forest	management,	HWPs,	urban	forestry,	and	natural	disturbances	(e.g.,	forest	fires).	This	

subsection	briefly	describes	these	source	categories.	Descriptions	of	the	current	tools	and	methods	

used	to	estimate	GHG	flux	from	these	source	categories	is	discussed	later	in	Chapter	6.	

Forest Carbon.	Accounting	for	forest	carbon	(i.e.,	forest	biomass)	typically	divides	the	forest	into	

forest	carbon	pools—e.g.,	live	trees,	understory,	standing	dead,	down	dead	wood,	forest	floor	or	

litter,	forest	soil	organic	carbon—the	definitions	for	which	are	developed	around	a	common	set	in	

use	by	a	number	of	publications,	which	are	further	outlined	in	Chapter	6.	The	methods	for	

estimating	the	key	forest	carbon	pools	are	well	developed	and	fairly	standard.	

Establishing, Re‐Establishing, and Clearing Forest. In	addition	to	forestland	remaining	forestland,	

there	are	three	distinct	processes	that	can	significantly	alter	forest	carbon	stocks,	and	are	termed:	

Management Practices Impacting Net GHG

Emissions from Forestry

 Establishing	and	Reestablishing	Forest	

 Avoiding	Clearing	Forest	

 Stand	Density	Management	

 Site	Preparation	Techniques	

 Vegetation	Control	

 Planting	

 Natural	Regeneration	

 Fertilization	

 Selection	of	Rotation	Length	

 Harvesting	and	Utilization	Techniques	

 Fire	and	Fuel	Load	Management	

 Reducing	the	Risk	of	Emissions	from	Natural	

Disturbances	

 Short	Rotation	Woody	Crops	
Chapter 2: Considerations When Estimating Agriculture and Forestry GHG Emissions and Removals 

2-22

forest	establishment	(i.e.,	afforestation),	forest	re‐establishment	(i.e.,	reforestation),	and	forest	

clearing	(i.e.,	deforestation).	Each	of	these	processes	alters	stocks	of	carbon	in	aboveground	and	

belowground	carbon	pools.	Establishment	involves	the	intentional	planting	(or	allowing	the	natural	

process	of	secondary	succession)	on	land	that	was	not	previously	forest.	Reestablishment	is	

returning	land	that	was	recently	forest	back	into	forest.	In	either	case,	establishing	forest	will	

generally	increase	the	carbon	stocks	in	aboveground	and	belowground	carbon	pools	over	time.	

Forest	clearing	is	the	removal	and/or	conversion	of	a	forest	system	into	another	land	cover	

(cropland,	grazing	land,	etc.)	and	is	the	most	significant	source	of	GHG	emissions	from	forests.

Forest Management. Forest	management	describes	the	range	of	practices	employed	by	landowners	

to	meet	their	objectives	(e.g.,	timber	production)	while	satisfying	biological,	economic,	and	social	

constraints.	A	number	of	the	practices	used	by	forest	managers	to	achieve	their	objectives	impact	

the	carbon	dynamics	in	forests	either	by	enhancing	forest	growth	or	accelerating	the	loss	of	forest	

carbon.	The	management	practices	include:	stand	density	management	(e.g.,	under	planting,	pre‐

commercial	and	commercial	thinning);	site	preparation	techniques	(e.g.,	mechanical	methods,	

chemical	application,	prescribed	burning);	vegetation	control;	planting	(e.g.,	planting	density,	

species	selection,	genetic	improvement);	natural	regeneration;	fertilization	(e.g.,	nitrogen	and	

phosphorous	fertilizer	application);	selection	of	rotation	lengths;	harvesting	and	utilization	

techniques;	fire	and	fuel	load	management;	reducing	the	risk	of	emissions	from	pests	and	disease;	

and	establishing	biomass	plantations	(i.e.,	short	rotation	woody	crops).	

Harvested Wood Products. A	proportion	of	the	wood	carbon	harvested	from	forests	ends	up	in	solid	

wood,	paper,	or	other	products,	which	are	collectively	known	as	HWPs.	The	carbon	contained	in	

these	products	can	remain	stored	for	years	or	decades	depending	on	the	end	use,	and	may	

eventually	be	combusted,	decay,	or	be	diverted	to	landfills.	

Urban Forestry. Urban	(or	urban	community)	forest	describes	the	population	of	trees	within	an	

urban	area.	Urban	trees	directly	store	atmospheric	carbon	as	woody	biomass	and	also	affect	local	

climate	(e.g.,	secondary	effects).	The	maintenance	of	urban	trees	also	affects	GHG	emissions	in	

urban	areas	(i.e.,	indirect	effects).	

Natural Disturbances.	Natural	disturbances	in	forest	systems	(e.g.,	forest	fires,	pests	and	disease,	

storms)	can	significantly	impact	forest	carbon	stocks	either	directly	in	the	case	of	combustion	from	

forest	fires	or	indirectly	by	converting	live	biomass	to	dead	or	converting	standing	trees	to	downed	

dead	wood	and	accelerating	decomposition.	

The	text	box,	Management	Practices	Impacting	Net	GHG	Emissions	from	Forestry,	lists	the	

management	practices	relevant	to	reducing	GHG	emissions	and/or	increasing	carbon	stocks	in	the	

forestry	sector	including	establishing	and/or	reestablishing	forest,	avoiding	forest	clearing,	and	

improving	forest	management.	

2.5 Land‐Use Change

Converting	land	parcels	from	one	land‐use	category	to	another	can	have	a	significant	effect	on	a	

parcel’s	carbon	stocks.	For	example,	carbon	stock	gains	can	be	realized	by	converting	cropland	soils	

to	wetlands	or	forestland,	while	carbon	stock	losses	often	result	from	a	conversion	from	forestlands	

to	grazing	lands.	A	land‐use	categorization	system	that	is	consistent	and	complete	(both	temporally	

and	spatially)	is	needed	in	order	to	assess	land	use	and	land‐use	change	status	within	an	entity’s	

boundaries.	All	of	the	land	within	an	entity’s	boundary	should	be	classified	according	to	the	

following	land‐use	types:	cropland,	grazing	land,	forestland,	wetland,	settlements	(e.g.,	residential	

and	commercial	buildings),	and	other	land	(e.g.,	bare	soil,	rock);	see	definitions	provided	above.	

Individual	parcel	areas	should	sum	to	the	total	land	area	before	and	after	land‐use	change.	
Chapter 2: Considerations When Estimating Agriculture and Forestry GHG Emissions and Removals 

2-23

In	many	cases,	the	methods	proposed	to	estimate	contributions	to	the	GHG	flux	resulting	from	land‐

use	change	are	the	same	as	those	used	to	estimate	carbon	stock	changes	in	the	individual	cropland	

and	grazing	land,	wetland,	and	forestry	chapters;	although,	in	specific	cases,	guidance	is	also	

provided	on	reconciling	carbon‐stock	estimates	between	discrete	data	sets	and	estimation	methods	

(e.g.,	reconciling	forest	soil	carbon	estimates	and	cropland	soil	carbon	estimates	for	land‐use	

change	from	forestland	to	cropland).	The	methods	for	quantifying	GHG	flux	from	land‐use	change	

are	intended	for	use	at	the	entity	scale	on	lands	managed	to	enhance	the	production	of	food,	feed,	

fiber,	and	renewable	energy.	Methods	are	currently	not	provided	for	estimating	emissions	from	

energy	used	when	converting	land	use	from	one	category	to	another.	Neither	are	methods	provided	

for	land‐use	change	from	settlements	or	the	“other	land”	category	to	cropland,	grazing	land,	

wetland	or	forestland.	The	methods	have	been	developed	for	U.S.	conditions	and	are	considered	

applicable	to	agricultural	and	forestry	production	systems	in	the	United	States.	This	subsection	

briefly	describes	the	source	categories	covered.	Further	descriptions	of	the	current	tools	and	

methods	used	to	estimate	GHG	flux	from	these	source	categories	are	discussed	later	in	Chapter	7.	

Annual Change in Carbon Stocks in Dead Wood and Litter Due to Land Conversion. Live	and	dead	

biomass	carbon	stocks	and	soil	organic	carbon	constitute	a	significant	carbon	sink	in	many	forest	

and	agricultural	lands.	Following	land‐use	conversion,	the	estimation	of	dead	biomass	carbon	stock	

changes	during	transition	periods	requires	that	the	area	subject	to	land‐use	change	on	the	entity’s	

operation	be	tracked	for	the	duration	of	a	20‐year	transition	period.	

Change in Soil Organic Carbon Stocks for Mineral Soils.	Soil	organic	carbon	stocks	are	influenced	by	

land‐use	change	(Aalde	et	al.,	2006)	due	to	changes	in	productivity	that	influence	carbon	inputs	and

to	changes	in	soil	management	that	influence	carbon	outputs	(Davidson	and	Ackerman,	1993;	Ogle	

et	al.,	2005;	Post	and	Kwon,	2000).	The	most	significant	changes	in	soil	organic	carbon	occur	with	

land‐use	change,	particularly	conversions	to	croplands,	due	to	changes	in	the	disturbance	regimes	

and	associated	effects	on	soil	aggregate	dynamics	(Six	et	al.,	2000).	

Specific	mitigation	practices	are	not	explicitly	described	in	Chapter	7;	however,	avoiding	land‐use	

conversions	that	result	in	significant	carbon	losses	could	mitigate	net	GHG	emissions	(e.g.,	avoiding	

the	conversion	of	forestlands	to	grazing	lands).	

2.6 Uncertainty

Quantifying	the	uncertainty	of	GHG	emissions	and	reductions	from	agriculture	and	forestry	

practices	is	an	important	aspect	of	decisionmaking	for	farmers	and	landowners	as	the	uncertainty	

range	for	each	GHG	estimate	communicates	our	level	of	confidence	that	the	estimate	reflects	the	

actual	balance	of	GHG	exchange	between	the	biosphere	and	the	atmosphere.	In	particular,	a	farm,	

ranch,	or	forest	landowner	may	be	more	inclined	to	invest	in	management	practices	that	reduce	net	

GHG	emissions	if	the	uncertainty	range	for	an	estimate	is	low,	meaning	that	higher	confidence	in	

the	estimates	exists.	This	report	presents	the	approach	for	accounting	for	the	uncertainty	in	the	

estimated	net	emissions	based	on	the	methods	presented	in	this	report.6	A	Monte	Carlo	approach	

6	The	IPCC	Good	Practice	Guidance	(IPCC,	2000)	recommends	two	approaches—Tier	1	and	Tier	2—for	

developing	quantitative	estimates	of	uncertainty	for	emissions	estimates	for	source	categories.	The	Tier	1	

method	uses	error	propagation	equations.	These	equations	combine	the	uncertainty	associated	with	the	

activity	data	and	the	uncertainty	associated	with	the	emission	(or	other)	factors.	This	approach	is	appropriate	

where	emissions	(or	removals)	are	estimated	as	the	product	of	activity	data	and	an	emission	factor	or	as	the	

sum	of	individual	sub‐source	category	values.	The	Tier	2	method	utilizes	the	Monte	Carlo	Stochastic	

Simulation	technique.	Using	this	technique,	an	estimate	of	emission	(or	removal)	for	a	particular	source	

category	is	generated	many	times	via	an	uncertainty	model,	resulting	in	an	approximate	PDF	for	the	estimate.	
Chapter 2: Considerations When Estimating Agriculture and Forestry GHG Emissions and Removals 

2-24

was	selected	as	the	method	for	estimating	the	uncertainty	around	the	outputs	from	the	

methodologies	in	this	report,	as	it	is	currently	the	most	comprehensive,	sound	method	available	to	

assess	the	uncertainty	at	the	entity	scale.	Limitations	and	data	gaps	exist;	however,	as	new	data	

become	available,	the	method	can	be	improved	over	time.	Implementation	of	a	Monte	Carlo	analysis	

is	complicated	and	requires	the	use	of	a	statistical	tool	to	produce	a	probability	density	function	

(PDF)7	around	the	GHG	emissions	estimate.8	From	the	probability	density	function,	the	uncertainty	

estimate	can	be	derived	and	reported.	

Chapter 2 References

Aalde,	H.,	P.	Gonzalez,	M.	Gytarski,	T.	Krug,	et	al.	2006.	Chapter	2:	Generic	methodologies	applicable	

to	multiple	land‐use	categories.	In	2006 IPCC Guidelines for National Greenhouse Gas

Inventories,	S.	Eggleston,	L.	Buendia,	K.	Miwa,	T.	Ngara	and	K.	Tanabe	(eds.).	Japan:	IGES.	

Blain,	D.,	C.	Row,	J.	Alm,	K.	Byrne,	et	al.	2006.	2006 IPCC Guidelines for National Greenhouse Gas

Inventories, Prepared by the National Greenhouse Gas Inventories Programme, Wetlands:

Volume 4, Chapter 7:	Intergovernmental	Panel	on	Climate	Change.	http://www.ipcc‐

nggip.iges.or.jp/public/2006gl/pdf/4_Volume4/V4_07_Ch7_Wetlands.pdf.	

Brinson,	M.M.,	and	S.D.	Eckles.	2011.	U.S.	Department	of	Agriculture	conservation	program	and	

practice	effects	on	wetland	ecosystem	services:	a	synthesis.	Ecological Applications,	

21(sp1):S116‐S127.	

Couwenbert,	J.	2009.	Emission factors for managed peat soils (organic soils, histosols). An analysis of

IPCC default values. .	Greifswald,	Germany:	Wetlands	International.		

Cowardin,	L.M.,	V.	Carter,	F.C.	Golet,	and	E.T.	LaRoe.	1979.	Classification	of	wetlands	and	deepwater	

habitats	of	the	United	States.	(FWS/OBS‐79/31).	

Davidson,	E.,	and	I.	Ackerman.	1993.	Changes	in	soil	carbon	inventories	following	cultivation	of	

previously	untilled	soils.	Biogeochemistry,	20(3):161‐193.	

de	Klein,	C.,	R.S.A.	Novoa,	S.	Ogle,	K.A.	Smith,	et	al.	2006.	Chapter	11:	N2O	emissions	from	managed	

soil,	and	CO2	emissions	from	lime	and	urea	application.	In	2006 IPCC guidelines for national

greenhouse gas inventories, Vol. 4: Agriculture, forestry and other land use,	S.	Eggleston,	L.	

Buendia,	K.	Miwa,	T.	Ngara	and	K.	Tanabe	(eds.).	Kanagawa,	Japan:	IGES.	

Faulkner,	S.,	W.	Barrow,	B.	Keeland,	S.	Walls,	et	al.	2011.	Effects	of	conservation	practices	on	

wetland	ecosystem	services	in	the	Mississippi	Alluvial	Valley.	Ecological Applications,	

21(sp1):S31‐S48.	

Gleason,	R.A.,	N.H.	Euliss,	B.A.	Tangen,	M.K.	Laubhan,	et	al.	2011.	USDA	conservation	program	and	

practice	effects	on	wetland	ecosystem	services	in	the	Prairie	Pothole	Region.	Ecological

Applications,	21(sp1):S65‐S81.	

Where	sufficient	and	reliable	uncertainty	data	for	the	input	variables	are	available,	the	Tier	2	method	is	the	

preferred	option.	

7	The	integral	of	a	PDF	over	a	given	interval	of	values	is	the	probability	for	a	random	variable	to	take	on	some	

value	in	the	interval.	That	is,	the	PDF	is	a	function	giving	probability	“densities”	and	its	integral	gives	

probabilities.	A	narrower	PDF	for	an	estimate	indicates	smaller	variance	around	the	central/most	likely	

value,	i.e.,	a	higher	probability	of	the	value	to	be	closer	to	the	central/most	likely	value.	The	uncertainty	for	

such	an	estimate	is	lower.	

8	Given	the	complexity	of	Monte	Carlo	analysis	and	the	necessity	for	a	tool,	the	approach	presented	here	is	not	

intended	for	development	by	a	landowner,	rather	it	is	intended	for	use	in	developing	a	tool	that	a	landowner	

would	use	to	assess	uncertainty	estimates.	
 Chapter 2: Considerations When Estimating Agriculture and Forestry GHG Emissions and Removals 

 2-25 

Groffman,	P.M.,	A.J.	Gold,	and	K.	Addy.	2000.	Nitrous	oxide	production	in	riparian	zones	and	its	

importance	to	national	emission	inventories.	Chemosphere: Global Change Science,	2(3):291‐

299.	

IPCC.	1997.	Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories, Prepared by the

National Greenhouse Gas Inventories Programme.	Bracknell,	UK:	Intergovernmental	Panel	on	

Climate	Change.	http://www.ipcc‐nggip.iges.or.jp/public/gl/invs1.html.	

IPCC.	2000.	Good Practice Guidance and Uncertainty Management in National Greenhouse Gas

Inventories.	http://www.ipcc‐nggip.iges.or.jp/public/gp/english/index.html.	

IPCC.	2006.	2006 IPCC Guidelines for National Greenhouse Gas Inventories, Prepared by the National

Greenhouse Gas Inventories Programme.	Edited	by	H.	S.	Eggleston,	L.	Buendia,	K.	Miwa,	T.	

Ngara	and	K.	Tanabe.	Japan:	IGES.	http://www.ipcc‐

nggip.iges.or.jp/public/2006gl/vol4.html.	

IPCC.	2007.	Contribution of Working Groups I, II and III to the Fourth Assessment Report of the

Intergovernmental Panel on Climate Change Core Writing Team.	Geneva,	Switzerland:	

Intergovernmental	Panel	on	Climate	Change.		

IPCC.	2011.	IPCC Expert Meeting on HWP, Wetlands and Soil N2O.	Geneval,	Switzerland.		

Martin,	C.,	D.P.	Morgavi,	and	M.	Doreau.	2010.	Methane	mitigation	in	ruminants:	from	microbe	to	

the	farm	scale.	Animal,	4:351‐365.	

Ogle,	S.M.,	F.J.	Breidt,	and	K.	Paustian.	2005.	Agricultural	management	impacts	on	soil	organic	

carbon	storage	under	moist	and	dry	climatic	conditions	of	temperate	and	tropical	regions.	

Biogeochemistry,	72(1):87–121.	

Post,	W.M.,	and	K.C.	Kwon.	2000.	Soil	Carbon	Sequestration	and	Land‐Use	Change:	Processes	and	

Potential.	Global Change Biology,	6:317‐327.	

Reynolds,	C.K.,	J.A.N.	Mills,	L.A.	Crompton,	D.I.	Givens,	et	al.	2010.	Ruminant	nutrition	regimes	to	

reduce	greenhouse	gas	emissions	in	dairy	cows.	In	Energy and Protein metabolism and

nutrition,	G.	M.	Crovetto	(ed.).	

Six,	J.,	E.T.	Elliot,	and	K.	Paustian.	2000.	Soil	macroaggregate	turnover	and	microaggregate	

formation:	a	mechanism	for	C	sequestration	under	no‐tillage	agriculture.	Soil Biol. Biochem.,	

32:2099‐2103.	

Smith,	W.B.,	P.D.	Miles,	C.H.	Perry,	and	S.A.	Pugh.	2009.	Forest Resources of the United States, 2007.	

Washington,	DC:	U.S.	Department	of	Agriculture	Forest	Service.	

U.S.	EPA.	2011.	Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990‐2009.	Washington,	DC:	

U.S.	Environmental	Protection	Agency.		

USDA.	2011.	U.S. Agriculture and Forest Greenhouse Gas Inventory: 1990‐2008.	Washington,	DC:	U.S.	

Department	of	Agriculture.		

Winter,	T.C.,	J.W.	Harvey,	and	O.L.	Franke.	1998.	Ground water and surface water: a single resource:	

U.S.	Geological	Survey.		

	 	
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Authors:		

Stephen	M.	Ogle,	Colorado	State	University	(Lead	Author)	

Paul	R.	Adler,	USDA	Agricultural	Research	Service	

Jay	Breidt,	Colorado	State	University	

Stephen	Del	Grosso,	USDA	Agricultural	Research	Service	

Justin	Derner,	USDA	Agricultural	Research	Service	

Alan	Franzluebbers,	USDA	Agricultural	Research	Service	

Mark	Liebig,	USDA	Agricultural	Research	Service	

Bruce	Linquist,	University	of	California,	Davis	

Phil	Robertson,	Michigan	State	University	

Michele	Schoeneberger,	USDA	Forest	Service	

Johan	Six,	University	of	California,	Davis;	Swiss	Federal	Institute	of	Technology,	ETH‐Zurich	

Chris	van	Kessel,	University	of	California,	Davis	

Rod	Venterea,	USDA	Agricultural	Research	Service	

Tristram	West,	Pacific	Northwest	National	Laboratory	

Contents:

3 Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems.....	

	 	.........................................................................................................................................................................3‐4

3.1	 Overview	...........................................................................................................................................................	3‐5	

3.1.1	 Overview	of	Management	Practices	and	Resulting	GHG	Emissions	...........	3‐6	

3.1.2	 System	Boundaries	and	Temporal	Scale	..............................................................	3‐10	

3.1.3	 Summary	of	Selected	Methods/Models	Sources	of	Data	...............................	3‐10	

3.1.4	 Organization	of	Chapter/Roadmap	........................................................................	3‐11	

3.2	 Cropland	Management	..............................................................................................................................	3‐12	

3.2.1	 Management	Influencing	GHG	Emissions	in	Upland	Systems	.....................	3‐12	

3.2.2	 Management	Influencing	GHG	Emissions	in	Flooded	Cropping	Systems	........		

	 	...............................................................................................................................................	3‐25	

3.2.3	 Land‐Use	Change	to	Cropland	..................................................................................	3‐28	

3.3	 Grazing	Land	Management	......................................................................................................................	3‐29	

3.3.1	 Management	Activity	Influencing	GHG	Emissions	...........................................	3‐30	

3.3.2	 Land‐Use	Change	to	Grazing	Lands	........................................................................	3‐36	

3.4	 Agroforestry	..................................................................................................................................................	3‐37	

3.4.1	 Carbon	Stocks	..................................................................................................................	3‐39	

3.4.2	 Nitrous	Oxide	...................................................................................................................	3‐41	

3.4.3	 Methane	.............................................................................................................................	3‐41	

3.4.4	 Management	Interactions	...........................................................................................	3‐42

Chapter 3

Quantifying Greenhouse Gas 

Sources and Sinks in Cropland 

and Grazing Land Systems 
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems 

3-2

3.5	 Estimation	Methods	...................................................................................................................................	3‐42	

3.5.1	 Biomass	Carbon	Stock	Changes	...............................................................................	3‐43	

3.5.2	 Litter	Carbon	Stock	Changes	.....................................................................................	3‐49	

3.5.3	 Soil	Carbon	Stock	Changes	.........................................................................................	3‐49

3.5.4	 Soil	Nitrous	Oxide	..........................................................................................................	3‐58	

3.5.5	 Methane	Uptake	by	Soils	.............................................................................................	3‐74	

3.5.6	 Methane	and	Nitrous	Oxide	from	Flooded	Rice	Cultivation	.........................	3‐77	

3.5.7	 CO2	from	Liming	.............................................................................................................	3‐83	

3.5.8	 Non‐CO2	Emissions	from	Biomass	Burning	........................................................	3‐86	

3.5.9	 CO2	from	Urea	Fertilizer	Applications	...................................................................	3‐90	

3.6	 Summary	of	Research	Gaps	for	Crop	and	Grazing	Land	Management	..................................	3‐92	

Appendix	3‐A:	Soil	N2O	Modeling	Framework	Specifications	...............................................................	3‐97	

3‐A.1	Description	of	Process‐Based	Models	.....................................................................	3‐99	

3‐A.2	Empirical	Scalars	for	Base	Emission	Rates.........................................................	3‐106	

3‐A.3	Practice‐Based	Scaling	Factors	................................................................................	3‐108	

Appendix	3‐B:	Guidance	for	Crops	Not	Included	in	the	DAYCENT	Model	....................................	3‐113	

Chapter	3	References	..........................................................................................................................................	3‐116	

Ogle,	S.M.,	P.R.	Adler,	F.J.	Breidt,	S.	Del	Grosso,	J.	Derner,	A.	Franzluebbers,	M.	Liebig,	B.	Linquist,	G.P.	

Robertson,	M.	Schoeneberger,	J.	Six,	C.	van	Kessel,	R.	Venterea,	T.	West,	2014.	Chapter	3:		

Quantifying	Greenhouse	Gas	Sources	and	Sinks	in	Cropland	and	Grazing	Land	Systems.		In	

Quantifying Greenhouse Gas Fluxes in Agriculture and Forestry:		Methods for Entity‐Scale Inventory.	

Technical	Bulletin	Number	1939.		Office	of	the	Chief	Economist,	U.S.	Department	of	Agriculture,	

Washington,	DC.	606	pages.		July	2014.		Eve,	M.,	D.	Pape,	M.	Flugge,	R.	Steele,	D.	Man,	M.	Riley‐

Gilbert,	and	S.	Biggar,	Eds.	

USDA	is	an	equal	opportunity	provider	and	employer.	
 Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

3-3

Acronyms, Chemical Formulae, and Units

C	 Carbon	

CH4	 Methane	

CO2	 Carbon	dioxide	

CO2‐eq	 Carbon	dioxide	equivalents

CRP	 Conservation	Reserve	Program

EPA	 U.S.	Environmental	Protection	Agency

GHG	 Greenhouse	gas	

H2CO3	 Carbonic	acid	

ha	 Hectare	

IPCC	 Intergovernmental	Panel	on	Climate	Change

K	 Potassium	

LRR	 Land	Resource	Region	

m	 Meter	

Mg	 Megagrams	

N	 Nitrogen	

N2	 Nitrogen	gas	

N2O	 Nitrous	Oxide	

NH4+	 Ammonium	

NO	 Nitric	oxide	

NO3‐

	 Nitrate	

NOx	 Mono‐nitrous	oxides	

NRCS	 Natural	Resources	Conservation	Service

NUE	 Nitrogen	use	efficiency	

O2	 Oxygen	

Pg	 Petagram	

PRISM	 Parameter‐Elevation	Regressions	on	Independent	Slopes	Model	

SOC	 Soil	organic	carbon	

SOM	 Soil	organic	matter	

SSURGO	 Soil	Survey	Geographic	Database

Tg	 Teragrams

USDA		 U.S.	Department	of	Agriculture
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems 

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Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

3-5 

3 Quantifying Greenhouse Gas Sources and Sinks in Cropland and

Grazing Land Systems

This	chapter	provides	methodologies	and	guidance	for	reporting	greenhouse	gas	(GHG)	emissions	

and	sinks	at	the	entity	scale	for	cropland	and	grazing	land	systems.	More	specifically,	it	focuses	on	

methods	for	land	used	for	the	production	of	crops	and	livestock	(i.e.,	grazing	lands).	Section	3.1	

provides	an	overview	of	cropland	and	grazing	land	systems	management	practices	and	resulting	

GHG	emissions,	system	boundaries	and	temporal	scale,	a	summary	of	the	selected	methods/models,	

sources	of	data,	and	a	roadmap	for	the	chapter.	Section	3.2	presents	the	various	management	

practices	that	influence	GHG	emissions	in	upland	and	wetland	cropping	systems	and	land‐use	

change	to	cropland.	Section	3.3	provides	a	similar	discussion	for	grazing	land	systems	and	land‐use	

change	to	grazing	systems.	Section	3.4	discusses	agroforestry,	and	Section	3.5	provides	the	

estimation	methods.	Finally,	Section	3.6	includes	a	summary	of	research	gaps	with	additional	

information	on	the	nitrous	oxide	(N2O)	methodology	and	supplemental	methodology	guidance	in	

the	Appendices.		

3.1 Overview

Cropland	and	grazing	land	systems	are	managed	in	a	variety	of	ways,	which	results	in	varying	

degrees	of	GHG	emissions	or	sinks.	Table	3‐1	provides	a	description	of	the	sources	of	emissions	or	

sinks	and	the	section	in	which	methodologies	are	provided	along	with	the	corresponding	GHGs.		

Table 3‐1: Overview of Cropland and Grazing Land Systems Sources and Associated

Greenhouse Gases

Source

Method for GHG

Estimation Description

CO2 N2O CH4

Biomass	and	

litter	carbon	

stock	changes	



Estimating herbaceous	biomass	carbon stock	during	changes	in	

land	use	is	necessary	to	account	for	the	influence	of	herbaceous	

plants	on	carbon	dioxide	(CO2)	uptake	from	the	atmosphere	and	

storage	in	the	terrestrial	biosphere	for	at	least	a	portion	of	the	

year	relative	to	the	biomass	carbon	and	associated	CO2	uptake	in	

the	previous	land	use	system.		Agroforestry	systems	also	have	a

longer	term	gain	or	loss	of	carbon	based	on	the	management	of	

trees	in	these	systems.

Soil	organic	

carbon	stocks	

for	mineral	soils



Soil	organic	carbon stocks	are	influenced	by	land	use	and	

management	in	cropland	and	grazing	land	systems,	as	well	as	

conversion	from	other	land	uses	into	these	systems	(Aalde	et	al.,	

2006).	Soil	organic	carbon	pools	can	be	modified	due	to	changes

in	carbon	inputs	and	outputs	(Paustian	et	al.,	1997).	

Soil	organic	

carbon	stocks	

for	organic	soils



Emissions	occur	in	organic	soils	following	drainage	due	to	the	

conversion	of	an	anaerobic	environment	with	a	high	water	table	

to	aerobic	conditions	(Armentano	and	Menges,	1986),	resulting	in	

a	significant	loss	of	carbon	to	the	atmosphere	(Ogle	et	al.,	2003).

Direct	and	

indirect	N2O	

emissions	from	

mineral	soils	



N2O	is	emitted	from	cropland	both	directly	and	indirectly.	Direct

emissions	are	fluxes	from	cropland	or	grazing	lands	where	there

are	nitrogen	additions	or	nitrogen	mineralized	from	soil	organic	

matter.	Indirect	emissions	occur	when	reactive	nitrogen	is	

volatilized	as	ammonia	(NH3)	or	nitrogen	oxide	(NOx),	or	

transported	via	surface	runoff	or	leaching	in	soluble	forms	from	

cropland	or	grazing	lands,	leading	to	N2O	emissions	in	another	

location.
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems 

3-6

Source

Method for GHG

Estimation Description

CO2 N2O CH4

Direct	N2O	

emissions	from	

drainage	of	

organic	soils	



Organic	soils (i.e.,	histosols) are	a	special	case	in	which	drainage	

leads	to	high	rates	of	nitrogen	mineralization	and	increased	N2O	

emissions.		The	method	assumes	that	organic	soils	have	a	

significant	organic	horizon	in	the	soil,	and	therefore,	the	main	

inputs	of	nitrogen	are	from	oxidation	of	organic	matter.		

Methane	uptake	

by	soils	



Agronomic	activity	universally	reduces	methanotrophy	in	arable	

soils	by	70%	or	more	(Mosier	et	al.,	1991;	Robertson	et	al.,	2000;	

Smith	et	al.,	2000).		Recovery	of	methane	(CH4) oxidation	upon	

abandonment	from	agriculture	is	slow,	taking	50	to	100	years	for	

the	development	of	even	50%	of	former	(original)	rates	(Levine	et	

al.,	2011).	

Methane	and	

N2O	emissions	

from	rice	

cultivation	

 

There	are	a	number	of	management	practices	that	affect	CH4 and	

N2O	emissions	from	rice	systems.	The	method	addresses	key	

practices	including	the	influence	of	water	management,	residue	

management	and	organic	amendments	on	CH4	emissions	from	

rice	(Lasco	et	al.,	2006;	Yan	et	al.,	2005)	and	associated	impacts	

on	N2O	emissions.

CO2	from	liming	 

Addition	of	lime	to	soils	is	typically	thought	to	generate	CO2

emissions	to	the	atmosphere	(de	Klein	et	al.,	2006).	However,	

prevailing	conditions	in	U.S.	agricultural	lands	lead	to	CO2	uptake	

because	the	majority	of	lime	is	dissolved	in	the	presence	of	

carbonic	acid	(H2CO3).	Therefore,	the	addition	of	lime	will	lead	to	

a	carbon	sink	in	the	majority	of	U.S.	cropland	and	grazing	land

systems.

Non‐CO2

emissions	from	

biomass	burning	

 

Biomass	burning	leads	to	emissions	of	CO2	as	well	as	other	GHGs	

or	precursors	to	GHGs	that	are	formed	later	through	additional	

chemical	reactions.	Note:	CO2	emissions	are	not	addressed	for	

crop	residues	or	grassland	burning,	because	the	carbon	is	re‐

absorbed	from	the	atmosphere	in	new	growth	of	crops	or	grasses	

within	an	annual	cycle.	

CO2	from	urea	

fertilizer	

application	



Urea	fertilizer	application	to	soils	contributes	CO2	emissions to	

the	atmosphere.	The	CO2	emitted	is	incorporated	into	the	urea	

during	the	manufacturing	process.	In	the	United	States,	the	source	

of	the	CO2	is	fossil	fuel	used	for	NH3	production.	The	CO2	captured	

during	NH3	production	is	included	in	the	manufacturer’s	

reporting	so	its	release	via	urea	fertilization	is	an	additional	CO2	

emission	to	the	atmosphere	and	is	included	in	the	farm‐scale	

entity	reporting.

3.1.1 Overview of Management Practices and Resulting GHG Emissions

Guidance	is	provided	in	this	section	for	reporting	of	GHG	emissions	associated	with	entity‐level	

fluxes	from	farm	and/or	livestock	operations.	The	guidance	focuses	on	methods	for	estimating	the	

influence	of	land	use	and	management	practices	on	GHG	emissions	(and	sinks)	in	crop	and	grazing	

land	systems.		Methods	are	described	for	estimating	biomass	and	soil	carbon	stock	changes,	soil	

N2O	emissions,	CH4	emissions	from	flooded	rice,	CH4	sinks	from	methanotrophic	activity,	CO2

emissions	or	sinks	from	liming,	biomass	burning	non‐CO2	GHG	emissions,	and	CO2	emissions	from	

urea	fertilizer	application	(see	Table	3‐2).		
 Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

 3-7 

Table 3‐2: Overview of Cropland and Grazing Land Systems Sources, Method and Section

Section Source Method

3.5.1‐

3.5.2	

Biomass	carbon	

stock	changes	

Herbaceous	biomass	is	estimated	with	an	IPCC	Tier	2	method	using	entity	

specific	data	as	input	into	the	IPCC	equations	developed	by	Lasco	et	al.	

(2006)	and	Verchot	et	al.	(2006).	Woody	plant	growth	and	losses	in	

agroforestry	or	perennial	tree	crops	are	estimated	with	an	IPCC	Tier	3	

method,	using	a	simulation	model	approach	with	entity	input.	

3.5.3	

Soil	organic	

carbon	stocks	for	

mineral	soils	

An	IPCC	Tier	3	method	is	used	to	estimate	the	SOC	at	the	beginning	and	

end	of	the	year	for	mineral	soils	with	the	DAYCENT	process‐based	model.	

The	stocks	are	entered	into	the	IPCC	equations	developed	by	Lasco	et	al.	

(2006),	Verchot	et	al.	(2006)	to	estimate	carbon	stock	changes.		

3.5.3	

Soil	organic	

carbon	stocks	for	

organic	soils	

CO2	emissions	from	drainage	of	organic	soils	(i.e.,	Histosols)	are	estimated	

with	an	IPCC	Tier	2	method	using	the	IPCC	equation	developed	by	Aalde	et	

al.	(2006)	and	region	specific	emission	factors	from	Ogle	et	al.	(2003).		

3.5.4	

Direct	N2O	

emissions	from	

mineral	soils	

The	direct	N2O	methods	are	estimated	with	an	IPCC	Tier	3	method.		For	

major	commodity	crops,	a	combination	of	experimental	data	and	process‐

based	modeling	using	the	DAYCENT1	model	and	DNDC2	(denitrification‐

decomposition)	are	used	to	derive	expected	base	emission	rates	for	

different	soil	texture	classes	in	each	U.S.	Department	of	Agriculture	Land	

Resource	Region.	For	minor	commodity	crops	and	in	cases	where	there	

are	insufficient	empirical	data	to	derive	a	base	emission	rate,	the	base	

emission	rate	is	based	on	the	IPCC	default	factor	multiplied	by	the	

nitrogen	input	(de	Klein	et	al.,	2006).	These	emission	rates	are	scaled	with	

practice‐based	scaling	factors	to	estimate	the	influence	of	management	

changes	such	as	application	of	nitrification	inhibitors	or	slow‐release	

fertilizers.	

Direct	N2O	

emissions	from	

drainage	of	

organic	soils	

Direct	N2O	emissions	from	drainage	of	organic	soils,	i.e.,	Histosols,	are	

estimated	with	the	IPCC	Tier	1	method	(de	Klein	et	al.,	2006).	

Indirect	N2O	

emissions	

Indirect	soil	N2O	emissions	are	estimated	with	the	IPCC	Tier	1	method	(de	

Klein	et	al.,	2006).	

3.5.5	 Methane	uptake	

by	soils	

Methane	uptake	by	soil	is	estimated	with	an	equation	that	uses	average	

values	for	methane	oxidation	in	natural	vegetation—whether	grassland,	

coniferous	forest,	or	deciduous	forest—attenuated	by	current	land	use	

practices.	This	approach	is	an	IPCC	Tier	3	method.	

3.5.6	

Methane	and	N2O		

emissions	from	

flooded	rice	

cultivation	

IPCC	Tier	1	methods	are	used	to	estimate	CH4	and	N2O	emissions	from	

flooded	rice	production	(de	Klein	et	al.,	2006;	Lasco	et	al.,	2006).	

																																																													

1	The	version	of	DAYCENT	coded	and	parameterized	for	the	most	recent	U.S.	national	GHG	inventory	(U.S.	

EPA,	2013)	was	used	to	derive	expected	base	emission	rates.	 2	DNDC	9.5	compiled	on	Feb	25,	2013	was	used	to	derive	expected	base	emission	rates.	
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems 

3-8

Section Source Method

3.5.7	 CO2	from	liming	

An	IPCC	Tier	2	method	is	used	to	estimate	CO2	emissions	from	application	

of	carbonate	limes	(de	Klein	et	al.,	2006)	with	U.S.‐specific	emissions	

factors	(adapted	from	West	and	McBride,	2005).	

3.5.8	

Non‐CO2

emissions	from	

biomass	burning	

Non‐CO2	GHG	emissions	from	biomass	burning	of	grazing	land	vegetation	

or	crop	residues	are	estimated	with	the	IPCC	Tier	2	method	(Aalde	et	al.,	

2006).		

3.5.9	

CO2	from	urea	

fertilizer	

application	

CO2	emissions	from	application	of	urea	or	urea‐based	fertilizers	to	soils	

are	estimated	with	the	IPCC	Tier	1	method	(de	Klein	et	al.,	2006).		

3.1.1.1 Description of Sector

Croplands	include	all	systems	used	to	produce	food,	feed,	and	fiber	commodities,	in	addition	to	

feedstocks	for	bioenergy	production.	Croplands	are	used	for	the	production	of	adapted	crops	for	

harvest	and	include	both	cultivated	and	non‐cultivated	crops	(U.S.	EPA,	2013).	Cultivated	crops	are	

typically	categorized	as	row	or	close‐grown	crops,	such	as	corn,	soybeans,	and	wheat.	Non‐

cultivated	crops	(or	those	occasionally	cultivated	to	replenish	the	crop)	include	hay,	perennial	

crops	(e.g.,	orchards	and	vineyards),	and	horticultural	crops.	The	majority	of	U.S.	cropland	is	in	

upland	systems	outside	of	wetlands	as	defined	in	Section	4.1.1,	Wetlands,	and	upland	cropping	

systems	(i.e.,	dry	land)	may	or	may	not	be	irrigated.	Rice	can	be	grown	on	natural	or	constructed	

wetlands,	but	we	will	refer	to	these	systems	as	flooded	rice	to	avoid	confusion	with	Chapter	4.	In	

addition,	wetlands	can	also	be	drained	for	crop	production,	which	again	is	considered	a	cropland	

because	the	principal	use	is	crop	production.	Some	croplands	are	set	aside	in	reserve,	such	as	lands	

enrolled	in	the	Conservation	Reserve	Program	(CRP).	Croplands	also	include	agroforestry	systems	

that	are	a	mixture	of	crops	and	trees,	such	as	alley	cropping,	shelterbelts,	and	riparian	buffers.		

Grazing	lands	are	systems	that	are	used	for	livestock	production,	and	occur	primarily	on	grasslands.	

Grasslands	are	composed	principally	of	grasses,	grass‐like	plants,	forbs,	or	shrubs	suitable	for	

grazing	and	browsing,	and	include	both	pastures	and	native	rangelands	(U.S.	EPA,	2013).	

Furthermore,	savannas,	some	wetlands	and	deserts,	and	tundra	can	be	considered	grazing	lands	in	

the	United	States	if	used	for	livestock	production.	Grazing	land	systems	include:	(1)	managed	

pastures	that	may	require	periodic	clearing,	burning,	chaining,	and/or	chemicals	to	maintain	the	

grass	vegetation;	and	(2)	native	rangelands	that	typically	require	limited	management	to	maintain	

but	may	be	degraded	if	overstocked	or	otherwise	overused.		

Crop	and	grazing	land	management	influences	GHG	emissions	(Smith	et	al.,	2008b),	which	can	be	

reduced	by	adopting	conservation	practices	(CAST,	2004;	2011).	Operators	of	cropland	systems	use	

a	variety	of	practices	that	have	implications	for	emissions,	such	as	nutrient	additions,	irrigation,	

liming	applications,	tillage	practices,	residue	management,	fallowing	fields,	forage	and	crop	

selection,	set‐asides	of	lands	in	reserve	programs,	erosion	control	practices,	water	table	

management	in	wetlands,	and	drainage	of	wetlands.	Operators	of	grazing	systems	also	have	a	

variety	of	management	options	that	influence	GHG	emissions,	such	as	stocking	rate,	forage	

selection,	use	of	prescribed	fires,	nutrient	applications,	wetland	drainage,	irrigation,	liming	

applications,	and	silvopastoral	practices.	

3.1.1.2 Resulting GHG Emissions

Cropland	and	grazing	lands	are	sources	of	N2O	and	CH4	emissions	and	have	a	large	potential	to	

sequester	carbon	with	changes	in	management	(Smith	et	al.,	2008b).	In	fact,	N2O	emissions	from	
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

3-9 

management	of	agricultural	soils	are	a	key	source	of	GHG	emissions	in	the	United	States	(U.S.	EPA,	

2013).	N2O	emissions	result	from	the	processes	of	nitrification	and	denitrification,	which	are	

influenced	by	land	use	and	management	activity.	Land	use	and	management	can	also	influence	

carbon	stocks	in	biomass,	dead	biomass,	and	soil	pools.	Carbon	stocks	can	be	enhanced	or	reduced	

depending	on	land	use	and	management	practices	(CAST,	2004;	IPCC,	2000;	Smith	et	al.,	2008b).	

Consequently,	crop	and	grazing	land	systems	can	be	either	a	source	or	sink	for	CO2,	depending	on	

the	net	changes	in	biomass,	dead	biomass,	and	soil	carbon.	Burning	biomass	is	a	practice	that	can	

initially	reduce	biomass	carbon	stock	but	can	provide	sufficient	stimulus	to	enhance	ensuing	

ecosystem	carbon	storage.	In	general	though,	burning	causes	a	decline	in	soil	organic	carbon	stocks	

due	to	loss	of	carbon	input	from	plant	litter	and	roots.	Burning	will	also	lead	to	non‐CO2	GHG	

emissions—CH4,	N2O,	and	other	aerosol	gases	(CO,	NOx)—that	can	be	later	converted	to	GHGs	in	the	

atmosphere	or	once	deposited	onto	soil.	

Soils	in	crop	and	grazing	land	systems	can	also	be	a	source	or	sink	for	CH4	depending	on	the	

conditions	and	management	of	soil.	CH4	can	be	removed	from	the	atmosphere	through	the	process	

of	methanotrophy	in	soils.	Methanotrophy	occurs	under	aerobic	conditions	and	is	common	in	most	

soils	that	do	not	have	standing	water.	In	contrast,	CH4	is	produced	in	soils	through	the	process	of	

methanogenesis,	which	occurs	under	anaerobic	conditions	(e.g.,	soils	with	standing	water	such	as	

soils	used	for	flooded	rice	production).	Both	of	these	processes	are	driven	by	the	activity	of	

microorganisms	in	soils,	and	their	rate	of	activity	is	influenced	by	land	use	and	management.	

3.1.1.3 Management interactions

The	influence	of	crop	and	grazing	land	management	on	GHG	emissions	is	not	typically	the	simple	

sum	of	each	practice’s	effect.	The	influence	of	one	practice	can	depend	on	another	practice.	For	

example,	the	influence	of	tillage	on	soil	carbon	will	depend	on	residue	management.	The	influence	

of	nitrogen	fertilization	rates	can	depend	on	the	application	of	nitrification	inhibitors.	A	variety	of	

examples	is	given	in	Section	3.2	and	Section	3.3.	Because	of	these	synergies,	estimating	GHG	

emissions	from	crop	and	grazing	land	systems	will	depend	on	a	complete	description	of	the	

practices	used	in	the	operation,	including	past	management	to	capture	legacy	effects	on	GHG	

emissions,	as	well	as	ancillary	variables	such	as	soil	characteristics	and	weather	or	climate	

conditions.	

3.1.1.4 Risk of Reversals

Any	trend	in	GHG	emissions	associated	with	a	change	in	crop	and	grazing	land	management	can	be	

reversed	if	the	operator	reverts	to	the	original	practice.	Reversals	will	not	negate	the	GHG	

mitigation	for	CH4	or	N2O	that	occurred	prior	to	the	reversion.	If	emissions	are	reduced	for	CH4	or	

N2O,	the	emission	reduction	is	permanent	and	cannot	be	changed	by	subsequent	management	

decisions.	

Reversals	can	occur	with	carbon	sequestration	in	biomass	and	soils.	CO2	can	be	removed	from	the	

atmosphere	through	crop	and	forage	production	and	sequestered	in	biomass	or	soils	following	the	

adoption	of	a	conservation	practice,	such	as	no‐till	(CAST,	2004;	USDA,	2011).	If	carbon	is	

increasing	in	the	biomass	or	soils,	then	the	practice	effectively	reduces	the	amount	of	CO2	in	the	

atmosphere.	However,	net	CO2	can	be	returned	to	the	atmosphere	if	there	is	a	reversion	in	

management	to	the	previous	practice	that	causes	a	decline	in	the	biomass	or	soil	carbon	stocks.		For	

example,	enrollment	of	land	in	the	CRP	has	increased	the	amount	of	carbon	in	soils	(i.e.,	increase	in	

soil	carbon	stock),	and	thus	mitigates	CO2	emissions	to	the	atmosphere	associated	with	other	

emissions	sources,	such	as	fossil	fuel	combustion	(USDA,	2011).	However,	tilling	former	CRP	lands	

will	lead	to	a	decline	in	soil	carbon	stocks,	thereby	reversing	the	trend	for	CO2	uptake	from	the	

atmosphere	and	leading	to	CO2	emission	to	the	atmosphere.	In	general,	GHG	emissions	involving	
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems 

3-10

carbon	stocks	in	biomass,	dead	biomass,	or	soils	can	be	considered	reversible,	depending	on	future	

management	decisions.	Consequently,	reversals	involving	carbon	stocks	not	only	affect	future	

emission	trends,	but	also	have	consequences	on	past	mitigation	efforts	by	returning	previously	

sequestered	CO2	to	the	atmosphere.		

3.1.2 System Boundaries and Temporal Scale

System	boundaries	are	defined	by	the	coverage,	extent,	and	resolution	of	the	estimation	methods.	

The	coverage	of	methods	in	this	guidance	can	be	used	to	estimate	GHG	emission	sources	that	occur	

on	farm	and	ranch	operations,	including	emissions	associated	with	biomass	carbon,	litter	carbon,	

and	soils	carbon	stock	changes;	CH4	and	N2O	fluxes	from	soils;	emissions	from	burning	of	biomass;	

and	CO2	fluxes	associated	with	urea	fertilization	and	addition	of	carbonate	limes.	GHG	emissions	

also	occur	with	production	of	management	inputs,	such	as	synthetic	fertilizers	and	pesticides,	and	

the	processing	of	food,	feed,	fiber,	and	bioenergy	feedstock	products	following	harvest;	but	

methods	are	not	provided	to	estimate	these	emissions.	Moreover,	emissions	from	energy	use,	

including	those	occurring	on	the	entity’s	operation,	are	not	addressed	in	the	methods.	

The	methods	provided	for	crop	and	grazing	land	systems	have	a	resolution	of	an	individual	parcel	

of	land	or	field	and	include	the	spatial	extent	of	all	fields	in	the	entity’s	operation.	Fields	are	areas	

used	to	produce	a	single	crop	or	rotation	of	crops,	or	to	raise	livestock	(i.e.,	pasture,	rangeland).	

Fields	are	often,	but	not	always,	divided	by	fences.	Emissions	are	estimated	for	each	individual	field	

that	is	used	for	cropland	and	grazing	land	on	the	operation,	and	then	the	emissions	are	added	

together	to	estimate	the	total	emissions	from	the	crop	and	grazing	land	systems	in	the	entity’s	

operation.	The	totals	are	then	combined	with	emissions	from	forest	and	livestock	to	determine	the	

overall	emissions	from	the	operation	based	on	the	methods	provided	in	this	guidance.	Emissions	

are	estimated	on	an	annual	basis	for	as	many	years	as	needed	for	GHG	emissions	reporting.		

3.1.3 Summary of Selected Methods/Models Sources of Data

The	Intergovernmental	Panel	on	Climate	Change	(IPCC)	(IPCC,	2006)	has	developed	a	system	of	

methodological	tiers	related	to	the	complexity	of	different	approaches	for	estimating	GHG	

emissions.	Tier	1	represents	the	simplest	methods,	using	default	equations	and	emission	factors	

provided	in	the	IPCC	guidance.	Tier	2	uses	default	methods,	but	emission	factors	that	are	specific	to	

different	regions.	Tier	3	uses	country‐specific	estimation	methods,	such	as	a	process‐based	model.	

The	methods	provided	in	this	report	range	from	the	simple	Tier	1	approaches	to	the	most	complex	

Tier	3	approaches.	Higher‐tier	methods	are	expected	to	reduce	uncertainties	in	the	emission	

estimates,	if	sufficient	activity	data	and	testing	are	available.	

Tier	1	methods	are	used	for	estimating	CO2	emissions	from	urea	fertilization,	CH4	emissions	from	

flooded	rice,	indirect	soil	N2O	emissions,	and	direct	soil	N2O	emissions	from	drained	organic	soils.	

These	methods	are	the	most	generalized	globally,	and	lack	ability	to	capture	specific	conditions	at	

local	sites,	and	consequently	have	more	uncertainty	for	estimating	emissions	from	an	entity’s	

operation.		Soil	N2O	emissions,	CO2	emissions	or	sinks	from	liming,	biomass	carbon	stock	changes,	

soil	carbon	stock	changes	for	drained	organic	soils,	and	biomass	burning	non‐CO2	GHG	emissions	all	

have	elements	of	Tier	2	methods,	but	may	rely	partly	on	emission	factors	provided	by	the	IPCC	

(2006).	These	methods	incorporate	some	information	about	conditions	specific	to	U.S.	agricultural	

systems	and	the	influence	on	emission	rates,	but	again	lack	specificity	for	local	site	conditions	in	

many	cases.	Soil	carbon	stock	changes	for	mineral	soils	are	estimated	using	a	Tier	3	method	with	a	

process‐based	simulation	model	(i.e.,	DAYCENT).	CH4	sinks	from	methanotrophic	activity	are	also	

estimated	with	a	Tier	3	method,	due	to	the	absence	of	IPCC	guidance	for	estimating	land	use	and	

management	effects	on	CH4	uptake	in	soils.	The	Tier	3	method	associated	with	soil	carbon	stock	

changes	in	mineral	soils	has	the	greatest	potential	for	estimating	the	influence	of	local	conditions	on	
 Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

 3-11 

GHG	emissions.	The	application	has	a	general	set	of	parameters	that	have	been	calibrated	across	a	

national	set	of	experiments.	However,	the	model	does	incorporate	drivers	associated	with	local	

conditions,	including	specific	management	practices,	soil	characteristics,	and	weather	patterns,	

providing	estimates	of	GHG	emissions	that	are	more	specific	to	the	entity’s	operation.	Future	

research	and	refinements	of	the	cropland	and	grazing	land	methods	will	likely	incorporate	more	

Tier	3	methods	in	the	future,	and	thus	provide	a	more	accurate	estimation	of	GHG	emissions	for	

entity	reporting.	

All	methods	include	a	range	of	data	sources	from	varying	levels	of	specificity	on	operation‐specific	

data	to	national	datasets.	Operation‐specific	data	will	need	to	be	collected	by	the	entity,	and	

generally	are	activity	data	related	to	the	farm	and	livestock	management	practices	(e.g.,	tillage	

practices,	grazing	practices,	fertilizer	usage).	National	datasets	are	recommended	for	ancillary	data	

requirements	that	are	used	in	methods,	such	as	climate	data	and	soil	characteristics.	However,	the	

entity	does	have	the	option	to	use	operation‐specific	data	for	climate	(i.e.,	weather	data)	and	soils.			

3.1.4 Organization of Chapter/Roadmap

The	croplands/grazing	lands	portion	of	this	report	is	organized	into	four	primary	sections.	Sections	

3.2	and	3.3	provide	a	description	of	management	impacts	on	GHG	emissions	in	crop	and	grazing	

land	systems.	Section	3.2	is	further	subdivided	into	sections	focused	on	upland	agriculture,	flooded	

management	for	crop	production,	and	the	influence	of	land‐use	change.	Section	3.3	is	subdivided	

into	a	general	description	of	management	practices	and	the	influence	of	land‐use	change.	The	first	

two	sections	provide	the	scientific	basis	for	how	management	practices	influence	GHG	emissions.	

These	two	sections	also	discuss	management	options	that	require	further	study.	Section	3.4	

provides	an	overview	of	agroforestry	systems.	A	general	description	of	the	various	GHG	emissions	

and	sinks	that	result	from	management	practices	and	potential	management	interactions	is	

provided	in	this	section.		

Section	3.5	describes	the	methods.	Each	method	includes	a	general	description	(including	equations	

and	factors	if	appropriate),	activity	data	requirements,	ancillary	data	requirements,	limitations	of	

the	method,	and	uncertainties	associated	with	the	estimation.	A	single	method	is	provided	for	each	

of	the	GHG	emission	sources	(and	sinks),	based	on	the	best	available	method	for	application	in	an	

operational	system	for	entity‐scale	reporting.	A	single	method	was	selected	to	ensure	consistency	

in	emission	estimation	by	all	reporting	entities.	More	advanced	approaches	may	be	adopted	in	the	

future	as	the	methods	mature.		

Section	3.6	provides	a	summary	of	research	gaps.	The	gaps	highlight	key	research	areas	that	

require	further	study	for	one	of	two	reasons.	The	first	reason	is	that	a	practice	lacks	sufficient	

evidence	or	a	clear	impact	on	GHG	emissions	based	on	existing	research.	This	gap	is	most	often	

related	to	a	lack	of	mechanistic	understanding	of	the	processes	influenced	by	the	practice.	These	

practices	may	be	included	in	future	revisions	to	the	methods	if	further	study	leads	to	a	consensus	

that	the	practice	has	an	impact	on	emissions.	The	second	reason	for	identifying	the	need	for	further	

study	is	that	the	practice	is	included	in	estimation	methods,	but	there	is	need	for	further	research	to	

reduce	uncertainty.	This	second	gap	may	involve	further	mechanistic	study,	but	could	also	require	

further	methods	of	development	or	refinement.		

Finally,	Appendix	3‐A	provides	a	more	comprehensive	description	of	the	soil	N2O	modeling	

framework	specifications.	This	appendix	includes	a	discussion	of	the	process‐based	models	used	in	

the	methodology;	the	empirical	scalars	for	the	base	emission	rates;	and	the	practice‐based	scaling	

factors.		Appendix	3‐B	provides	alternative	methodologies	in	cases	where	an	entity	is	managing	

crops	not	included	in	the	DAYCENT	model.		
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems 

3-12

3.2 Cropland Management

How	cropland	is	managed	can	have	a	significant	effect	on	GHG	emissions	and	removals.	This	section	

provides	a	summary	of	the	current	state	of	the	science	and	describes	how	management	practices	

drive	GHG	emissions	or	sinks	in	upland	cropland	systems.		

3.2.1 Management Influencing GHG Emissions in Upland Systems

The	cropland	management	practices	presented	below	focus	primarily	on	mitigation	potential	for	

soil	N2O,	CH4	emissions,	and	carbon	sequestration.	Each	subsection	describes	the	practice	and	the	

underlying	GHG	phenomenon	that	influence	mitigation	potential.			

3.2.1.1 Nutrient Management (Manufactured and Organic)

Nutrient	management	refers	to	the	addition	and	management	of	synthetic	and	organic	fertilizers	to	

cropland	soils,	primarily	to	augment	the	supply	of	nutrients	to	the	crop.	Nitrogen	is	generally	the	

most	important	nutrient	from	an	agronomic	standpoint,	because	it	is	usually	the	primary	nutrient	

limiting	crop	yields	and	often	must	be	added	more	frequently	and	in	greater	amounts	than	other	

nutrients	such	as	phosphorus	and	potassium	(ERS,	2011;	Robertson	and	Vitousek,	2009).	Nitrogen	

is	also	the	primary	nutrient	of	concern	with	regard	to	GHG	emissions,	because	once	fertilizer	

nitrogen	enters	the	soil	it	can	be	directly	converted	to	N2O	by	soil	biological	processes	and,	in	some	

cases,	chemical	reactions	(Firestone	and	Davidson,	1989;	Kool	et	al.,	2011;	Venterea,	2007).	While	

relatively	little	of	the	fertilizer	nitrogen	applied	is	converted	to	N2O,	these	emissions	are	generally	a	

large	component	of	the	total	GHG	budget	of	croplands	(e.g.,	Mosier	et	al.,	2005;	Robertson	et	al.,	

2000)	because	N2O	has	310	times	the	global	warming	potential	of	CO2	(IPCC,	2007).	Other	forms	of	

nitrogen	originating	from	fertilizers	may	also	be	lost	to	the	environment,	including	NH3,	nitric	oxide	

(NO),	and	nitrate	(NO3‐

).	Once	transported	to	downwind	or	downstream	ecosystems,	these	other	

nitrogen	species	can	be	converted	to	N2O;	such	emissions	are	referred	to	as	“indirect”	N2O	

emissions	(Beaulieu	et	al.,	2011;	de	Klein	et	al.,	2006).	

Nutrient	management	can	also	affect	GHG	emissions	other	than	N2O,	most	notably	the	

sequestration	of	carbon	upon	manure	addition	and	crop	residue	retention	or	addition.	The	addition	

of	organic	carbon	amendments,	such	as	manure	or	residues,	can	increase	soil	carbon	within	the	

boundaries	of	the	land	parcel	receiving	the	amendment	(Ogle	et	al.,	2005).		However,	soil	carbon	

losses	may	occur	from	the	source	field	(Schlesinger,	2000)	depending	on	the	management	

(Izaurralde	et	al.,	2001).	Manufactured	nitrogen	additions	can	also	lead	to	carbon	sequestration	

(Ladha	et	al.,	2011)	where	additions	lead	to	increased	residue	return	to	soil.	

Fertilizer	rate,	timing,	placement,	and	formulation	strongly	affect	N2O	fluxes.	In	general,	any	

practice	that	increases	crop	nitrogen	use	efficiency	(NUE)	would	be	expected	to	reduce	N2O	

emissions,	because	applied	nitrogen	that	is	taken	up	by	crops	or	cover	crops	is	not	available	to	the	

soil	processes	that	generate	N2O,	at	least	in	the	short	term;	this	also	may	prevent	nitrogen	leaching.	

Thus,	strategies	to	reduce	N2O	emissions	can	also	reduce	the	loss	of	NO3‐

	and	other	forms	of	

reactive	nitrogen	from	cropping	systems.	

However,	practices	that	improve	NUE	will	not	always	reduce	N2O	emissions.	Different	fertilizer	

formulations,	for	example,	can	result	in	different	N2O	emissions	irrespective	of	NUE	effects		(e.g.,	

Gagnon	and	Ziadi,	2010;	Gagnon	et	al.,	2011).	Likewise,	banded	fertilizer	placement	can	increase	

NUE	(e.g.,	Yadvinder‐Singh	et	al.,	1994)	but	also	can	increase	rather	than	decrease	N2O	emissions	

(e.g.,	Engel	et	al.,	2010),	and	tillage	management	can	also	increase	NUE	without	reducing	N2O	

emissions	(Grandy	et	al.,	2006).	Thus,	NUE	is	generally	important	but	not	by	itself	sufficient	to	

predict	or	manage	N2O	emissions.	Fertilizer	rate,	timing,	placement,	and	formulation	can	affect	NUE	

and	N2O	emissions	independently.		
 Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

 3-13 

Fertilizer Rate:	More	than	any	other	factor,	the	amount	of	nitrogen	fertilizer	applied	to	soil	affects	

the	amount	of	N2O	emitted;	in	many	cases	other	nitrogen‐use	strategies	(timing,	placement,	and	

formulation)	provide	their	benefit	by	effectively	reducing	fertilizer	nitrogen	available	in	the	soil.	In	

this	sense,	fertilizer	rate	integrates	the	effects	of	multiple	practices	and	is	the	basis	for	the	IPCC	Tier	

1	N2O	accounting	method	(de	Klein	et	al.,	2006),	whereby	N2O	emissions	are	assumed	to	be	a	simple	

fraction	of	nitrogen	inputs.	

Irrespective	of	other	practices,	however,	fertilizer	rate	itself	can	be	refined	to	reduce	N2O	emissions	

so	long	as	rates	are	not	reduced	to	the	point	that	yields	decline.	Otherwise	market	leakage—the	

need	to	make	up	yields	elsewhere	with	more	intensive	fertilizer	use	and	concomitant	N2O	loss—	

may	limit	the	benefit	of	reducing	local	fertilizer	rates.	The	question	then	becomes	whether	nitrogen	

fertilizer	rates	can	be	reduced	without	reducing	yields	in	a	particular	field.	At	least	for	corn,	recent	

changes	in	recommended	fertilizer	rates	for	many	Midwest	States	suggest	that	there	is	latitude	for	

reducing	fertilizer	nitrogen	rates	for	some	farmers.	Since	the	1970s,	most	fertilizer	nitrogen	

recommendations	have	been	based	on	yield	goals,	which	use	expected	maximum	yield	multiplied	

by	nitrogen	yield	factors	to	calculate	fertilizer	recommendations	(Stanford,	1973).		Preceding	

legume	crops,	manure	inputs,	and	soil	nitrogen	tests	are	then	used	to	further	refine	or	reduce	

recommended	nitrogen	application	rates	(Andraski	and	Bundy,	2002).	

An	alternative	to	the	yield‐goal	approach	is	the	Maximum	Return	to	Nitrogen	approach	(Sawyer	et	

al.,	2006),	whereby	the	rate	of	nitrogen	fertilizer	applied	is	based	on	the	maximum	fertilizer	rate	

that	generates	sufficient	additional	yield	to	justify	the	fertilizer	cost.	The	rates	are	determined	from	

crop	nitrogen	response	curves.	Typically	(but	not	always)	this	rate	is	significantly	less	than	that	

recommended	by	the	yield	goal	approach.	Maximum	Return	to	Nitrogen	calculators	for	corn	have	

been	adopted	in	at	least	seven	States	in	the	Midwest.	This	calculator	and	similar	decision	support	

tools	have	the	potential	for	reducing	the	amount	of	fertilizer	nitrogen	applied	to	crops	and	more	

precisely	match	crop	nitrogen	requirements,	without	affecting	the	net	returns	(Archer	et	al.,	2008),	

and	with	concomitant	decreases	in	N2O	emissions	(Millar	et	al.,	2010).		

Hundreds	of	fertilizer	addition	experiments	worldwide	have	shown	that	typically	0.5	to	3	percent	

of	nitrogen	added	to	soil	is	emitted	as	N2O	(Bouwman	et	al.,	2002;	Linquist	et	al.,	2011;	Stehfest	and	

Bouwman,	2006).	Site‐to‐site	variation	is	well	recognized	and	is	to	be	expected	based	on	soils,	

climate,	and	fertilizer	practices—including	rate.	Recent	evidence	suggests	that	emission	rates	may	

be	even	higher	at	nitrogen	input	levels	that	exceed	crop	demand	(Hoben	et	al.,	2011;	Ma	et	al.,	

2010;	McSwiney	and	Robertson,	2005;	Van	Groenigen	et	al.,	2010).	

Fertilizer Timing:	A	major	challenge	in	managing	nitrogen	fertilizer	for	crop	production	is	

synchronizing	nitrogen	availability	in	the	soil	with	the	crop’s	demand	for	nitrogen.	In	general,	crop	

demand	for	nitrogen	is	minimal	early	in	the	growing	season	and	increases	several	weeks	after	

planting.	

In	many	cases,	it	may	be	most	convenient	and/or	cost‐effective	for	the	producer	to	apply	nitrogen	

fertilizer	prior	to	planting	or	soon	after	plant	emergence.	In	many	parts	of	the	U.S.	Corn	Belt,	

however,	application	of	nitrogen	fertilizer	commonly	occurs	in	the	fall	prior	to	the	growing	season	

(Bierman	et	al.,	2011;	Ribaudo	et	al.,	2011).	In	the	absence	of	an	active	and	well‐developed	root	

system	to	utilize	the	fertilizer	nitrogen,	these	practices	increase	the	potential	for	soil	microbial	and	

chemical	processes	to	transform	the	applied	nitrogen	into	N2O	and	other	mobile	forms	such	as	NO3,	

which	can	contribute	to	indirect	N2O	emissions.		

Improving	the	synchrony	between	soil	nitrogen	availability	and	crop	nitrogen	demand	can	be	

achieved	by	switching	from	fall	to	spring	nitrogen	application;	applying	nitrogen	several	weeks	

after	planting	with	“sidedress”	fertilizer	applications	that	are	timed	to	coincide	with	plant	growth	

stages;	and	using	multiple	“split”	applications	distributed	in	time	over	the	growing	season.	Each	of	
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems 

3-14

these	strategies	has	the	potential	to	reduce	N2O	emissions,	but	this	is	not	always	the	case.	Switching	

from	fall	to	spring	nitrogen	fertilizer,	for	example,	has	been	shown	to	reduce	N2O	emissions	in	some	

cases	(Burton	et	al.,	2008a;	Hao	et	al.,	2001)	but	not	always	(Burton	et	al.,	2008a).	Similarly,	

switching	from	pre‐plant	to	post‐plant	applications	has	been	shown	to	reduce	N2O	emissions	in	

some	studies	(Matson	et	al.,	1998),	but	only	part	of	the	time	or	not	at	all	in	other	studies	(Burton	et	

al.,	2008b;	Phillips	et	al.,	2009;	Zebarth	et	al.,	2008b).	Some	studies	have	found	reduced	nitrate	

leaching,	which	implies	reduced	indirect	N2O	emissions,	with	fertilizer	application	later	in	the	

season	(e.g.,	Errebhi	et	al.,	1998).	

Fertilizer Placement:	The	manner	in	which	nitrogen	fertilizer	is	applied	to	soil	can	affect	its	

availability	for	crop	uptake	and	therefore	its	susceptibility	to	soil	transformation	and	N2O	

production.	Three	aspects	of	fertilizer	placement	are	significant	to	N2O	emissions:	(1)	broadcast	

application	versus	banding	within	the	crop	row;	(2)	the	soil	depth	to	which	nitrogen	is	applied;	and	

(3)	adding	fertilizer	uniformly	across	a	field	versus	applying	at	a	spatially	variable	rate.		

There	is	some	evidence	that	applying	nitrogen	fertilizer	in	narrow	bands	can	improve	crop	NUE	

(Malhi	and	Nyborg,	1985).	However,	banding	also	creates	zones	of	highly	concentrated	soil	

nitrogen,	which	can	increase	N2O	production	compared	with	broadcast	applications	(Engel	et	al.,	

2010).	Other	studies	have	found	no	differences	in	N2O	emissions	in	broadcast	versus	banded	

applications	(Burton	et	al.,	2008a;	Sehy	et	al.,	2003).	Direct	comparisons	of	application	depth	effects	

on	N2O	emissions	have	also	shown	inconsistent	results	(e.g.,	Breitenbeck	and	Bremner,	1986b;	

Drury	et	al.,	2006;	Fujinuma	et	al.,	2011;	Hosen	et	al.,	2002;	Liu	et	al.,	2006).	However,	variable	rate	

application	uses	different	nitrogen	rates	for	different	areas	of	field,	based	on	expected	variations	in	

crop	nitrogen	demand.	This	is	a	new	technique	that	appears	promising	based	on	its	ability	to	

substantially	improve	fertilizer	use	efficiency	at	the	field	scale	(Mamo	et	al.,	2003;	Scharf	et	al.,	

2005),	and	at	least	one	early	study	has	shown	reduced	N2O	emissions	when	nitrogen	rate	was	

varied	to	match	crop	yield	potential	(Sehy	et	al.,	2003).	

Fertilizer Formulation and Additives:	The	most	commonly	used	forms	of	synthetic	nitrogen	fertilizer	

in	the	United	States	include	anhydrous	ammonia	(35	percent	of	total	use),	urea	(24	percent),	and	

liquid	solutions,	including	urea	ammonium	nitrate	(29	percent)	(ERS,	2011).	Available	evidence	

suggests	that	N2O	emissions	following	applications	of	anhydrous	ammonia	are	greater	than	

emissions	following	broadcast	urea,	although	in	some	studies	this	may	be	partly	due	to	fertilizer	

placement.	In	five	studies,	anhydrous	ammonia	resulted	in	40	to	200	percent	greater	N2O	emissions	

compared	with	broadcast	urea	(Breitenbeck	and	Bremner,	1986a;	Fujinuma	et	al.,	2011;	Thornton	

et	al.,	1996;	Venterea	et	al.,	2005).	One	study	(Burton	et	al.,	2008a)	found	no	difference	in	N2O	

emissions	between	anhydrous	ammonia	and	broadcast	urea	when	both	were	applied	at	a	lower	

rate	(80	kg	N	ha‐1	year‐1)	compared	with	the	other	studies	(≥	120	kg	N	ha‐1).	Consequently,	there	

may	be	a	threshold	in	the	application	rate	before	there	is	a	significant	effect	on	emissions.		

The	chemical	form	of	nitrogen	fertilizer	influences	losses	of	nitrogen	from	three	major	pathways:	

surface	volatilization,	soil	microbial	processes,	and	NO3‐

	leaching.	All	fertilizers	are	susceptible	to	

denitrification	once	nitrified	to	(or	applied	as)	NO3‐

.	Ammonium‐based	fertilizers,	including	

anhydrous	ammonia,	urea,	and	organic	sources	such	as	manure,	are	also	susceptible	to	N2O	loss	

during	nitrification.	Urea,	anhydrous	ammonia,	and	manure	are	additionally	susceptible	to	surface	

volatilization	as	NH3	under	some	conditions.	Volatilized	NH3	and	leached	NO3‐

	contribute	to	indirect	

N2O	loss.	

Chemical	additives	have	been	developed	to	release	fertilizer	nitrogen	into	the	soil	more	gradually	

and	to	delay	the	nitrification	of	nitrogen	from	ammonium	(NH4+)	to	NO3‐

	in	order	to	improve	the	

synchrony	between	crop	nitrogen	demand	and	soil	nitrogen	availability.	Polymer‐coated	urea	

slowly	releases	nitrogen	with	increasing	soil	temperature	and	water,	and	is	intended	to	make	
 Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

 3-15 

nitrogen	supply	more	synchronous	with	plant	nitrogen	demand	and	reduce	nitrogen	losses.	Effects	

on	N2O	production,	however,	appear	mixed,	with	some	studies	showing	reduced	N2O	for	polymer‐

coated	urea	(e.g.,	Hyatt	et	al.,	2010)	and	others	showing	no	impact	or	even	higher	emissions	

(Venterea	et	al.,	2011a).	A	recent	meta‐analysis	of	13	studies	of	mostly	volcanic	and	wetland‐

derived	soils	found	that	polymer‐coated	urea	reduced	N2O	emissions	by	35	percent	on	average	

compared	with	conventional	fertilizers,	but	results	are	difficult	to	generalize	because	most	of	the	

soils	included	in	the	analysis	were	not	typical	for	U.S.	cropping	systems	(Akiyama	et	al.,	2010).		

Fertilizers	formulated	with	nitrification	inhibitors	can	potentially	reduce	emissions	from	

nitrification	and	denitrification,	as	well	as	NO3‐

	leaching.	Some	U.S.	field	studies	show	substantial	

reductions	in	N2O	emissions	when	fertilizers	with	nitrification	inhibitors	are	added	compared	with	

conventional	fertilizers	(e.g.,	Halvorson	et	al.,	2010a),	while	others	show	little	or	no	impact		(e.g.,	

Parkin	and	Hatfield,	2010a).	A	meta‐analysis	of	some	28	studies	worldwide	reported	an	average	

reduction	of	38	percent	(Akiyama	et	al.,	2010),	but	again	results	are	difficult	to	generalize	due	to	

the	small	sample	size	and		soils	that	are	not	typical	of	U.S.	cropping	systems.	

One	reason	the	impacts	of	fertilizers	designed	to	reduce	emissions	are	inconsistent	is	that	the	form	

of	nitrogen	applied	interacts	with	other	factors	to	control	nitrogen	losses.	Among	these	factors	is	

weather,	which	directly	affects	the	processes	that	lead	to	gaseous	nitrogen	losses	and	NO3‐

	leaching,	

and	indirectly	affects	these	processes	by	controlling	plant	nitrogen	uptake.	Soil	properties	such	as	

texture	and	hydraulic	status	are	also	important.	In	general,	nitrification	is	important	in	well‐aerated	

soils,	while	denitrification	is	more	important	in	poorly	drained	soils.	The	nitrogen	source	also	

interacts	with	other	management	practices.	For	example,	polymer‐coated	urea	substantially	

reduced	N2O	emissions	under	no‐till	but	not	full	till	cultivation	for	irrigated	corn	in	Colorado	

(Halvorson	et	al.,	2010a).	

Organic Fertilizer Effects on N2O Emissions:	Land	application	of	animal	manure	has	been	related	to	

N2O	emissions.	Mosier	et	al.	(1998)	and	Petersen	(1999)	measured	increases	in	N2O	emissions	with	

manure	application.	Kaiser	and	Ruser	(2000)	measured	annual	emissions	of	the	added	nitrogen	in	

slurry	ranging	from	0.74	to	2.86	percent,	and	De	Klein	et	al.	(2001)	found	that	annual	N2O‐N	losses	

ranged	from	zero	to	five	percent	of	the	organic	nitrogen	applied	to	soils.	Others	(e.g.,	Barton	and	

Schipper,	2001)	found	N2O	emissions	following	the	addition	of	manure	slurries	exceeded	emissions	

from	an	equivalent	amount	of	manufactured	N,	likely	due	to	the	slurry’s	creating	enhanced	

conditions	for	denitrification.	However,	GHG	emissions	also	occur	if	manure	is	managed	in	pits,	

lagoons,	or	solid	storage.	

Injection	of	manure	is	a	common	practice	to	avoid	surface	runoff	and	reduce	objectionable	odors	

from	manure	application.	Both	Flessa	and	Besse	(2000)and	Wulf	et	al.	(2002)	suggested	that	

injection	of	swine	manure	would	create	more	favorable	conditions	for	N2O	and	CH4	formation	

because	of	the	reduced	aeration	within	the	soil.	However,	Dendooven	et	al.	(1998)	did	not	find	

differences	in	either	N2O	or	CH4	emissions	from	injected	or	surface‐applied	swine	slurry	onto	a

loamy	soil.	These	findings	suggest	that	the	rate,	timing,	placement,	and	formulation	of	manure	is	

important	to	N2O	production,	similar	to	manufactured	nitrogen	fertilizer,	but	there	is	a	need	for	

additional	research.			 	

CO2 Emissions Generated from Urea Fertilizer Applications:	Unlike	other	nitrogen	fertilizers,	urea	

results	in	the	direct	production	of	CO2	in	addition	to	whatever	N2O	might	be	subsequently	produced	

by	microbes	(de	Klein	et	al.,	2006).		Since	urea	is	20	percent	C,	every	metric	ton	of	urea	applied	to	

soil	results	in	the	direct	emission	of	20	kg	CO2‐C;	alternatively,	every	kilogram	of	nitrogen	applied	

as	urea	results	in	the	direct	emissions	of	0.43	kg	CO2‐C.	Urea	is	manufactured	by	reacting	NH3	and	

CO2	to	form	ammonium	carbamate,	which	is	then	dehydrated	to	form	urea	prills.	In	the	United	
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States	the	CO2	in	urea	is	captured	from	the	fossil	fuel	used	to	manufacture	NH3,	so	the	soil	CO2

produced	represents	a	fossil	fuel	emission.		

Management System Interactions:	Nitrogen	management	practices	can	interact	with	other	cropland

management	components	in	regulating	GHG	emissions.	As	emphasized	above,	any	factor	that	

affects	crop	NUE	has	the	potential	to	affect	N2O	emissions.	Therefore,	optimizing	other	practices—

including	tillage	and	the	management	of	soil	pH,	pests,	irrigation,	drainage,	and	other	factors—will	

tend	to	increase	nitrogen	fertilizer	uptake	by	the	crop	and	therefore	reduce	N2O	emissions.	For	this	

reason,	nutrient	management	effects	on	GHG	emissions	should	be	considered	in	the	context	of	the	

entire	set	of	cropland	management	practices.	For	example,	there	is	evidence	that	fertilizer	

placement	can	interact	with	tillage	management	in	controlling	N2O	emissions	(Venterea	et	al.,	

2005),	and	that	inadequate	management	of	other	nutrients	(e.g.,	phosphorus	and	potassium)	can	

reduce	NUE	(Snyder	et	al.,	2009).	Efforts	to	minimize	or	remediate	water	quality	impacts	of	nitrate	

transport	from	farm	to	aquatic	systems	may	also	reduce	indirect	N2O	emissions.	For	example,	the	

use	of	subsurface	bioreactors	to	remove	nitrate	from	drainage	water	has	beneficial	impacts	on	

indirect	N2O.	However,	to	date	these	bioreactors	have	not	been	implemented	at	large	(field)	scales	

and	there	are	also	questions	about	release	of	N2O	and	CH4	during	the	treatment	process	that	need	

to	be	answered	before	their	net	effect	on	GHGs	can	be	assessed	(Elgood	et	al.,	2010).	Also,	

environmental	and	climate	factors,	which	are	generally	not	under	management	control,	may	affect	

N2O	emissions;	for	example,	nitrogen	fertilizer	applied	just	before	large	rainfall	events	can	

stimulate	increased	emissions	(Li	et	al.,	1992).	

3.2.1.2 Tillage Practices

Different	tillage	practices	are	generally	classified	into	one	of	three	categories:	full	tillage,	reduced	

tillage,	or	no	tillage.	Tillage	intensity	is	based	on	implements,	number	of	passes,	and	the	percentage	

of	surface	and	depth	of	tillage	disturbance.	Tools	are	available	to	determine	tillage	intensity	(e.g.,	

the	STIR	Model;	see	USDA	NRCS,	2008).	No‐tillage	practices	are	characterized	by	the	use	of	seed	

drills	and	fertilizer	or	pesticide	applicators	with	no	additional	tillage	events	or	implements.	Surface	

residues	are	not	incorporated	into	the	soil	when	following	no‐tillage	practices,	and	there	is	limited	

disturbance	to	the	soil	profile;	consequently	no‐tillage	management	increases	soil	cover	and	

improves	aggregate	stability	(Six	et	al.,	2000).	In	contrast,	examples	of	full	tillage	(often	referred	to	

as	conventional	tillage)	include	one	or	more	passes	with	the	following	tillage	implements:	

moldboard	plow,	disk	plow,	disk	chisel,	twisted	point	chisel	plow,	heavy	duty	offset	disk,	subsoil	

chisel	plow,	and	bedder	or	disk	ripper.	Systems	are	also	classified	as	full	tillage	if	there	are	two	or	

more	passes	with	one	of	the	following	implements:	chisel	plow,	single	disk,	tandem	disk,	offset	disk‐

light	duty,	one‐way	disk,	heavy‐duty	cultivator,	ridge	till,	or	rototiller.	Systems	with	other	tillage	

practices,	such	as	a	single	pass	with	a	ridge	till	implement,	mulch	till,	or	chisel	plow,	lead	to	

intermediate	disturbance	of	the	soil	and	are	classified	as	reduced	tillage.		

Changes	in	tillage	practices	can	influence	vertical	distribution	of	carbon	in	the	soil	profile	and	total	

soil	carbon	stocks	(Paustian	et	al.,	1997).	Historically,	full	tillage	has	resulted	in	the	reduction	of	soil	

carbon	stocks	(Lal	et	al.,	2004).	A	synthesis	of	previous	analyses	estimated	that	long‐term	full	

tillage	can	decrease	soil	carbon	stocks	by	30	percent	(Ogle	et	al.,	2005;	West	et	al.,	2004).	Changing	

from	full	tillage	to	no	tillage	can	reverse	historic	losses	of	soil	C.	No‐tillage	practices	can	lead	to	

accumulation	of	soil	carbon	in	the	upper	soil	profile	(0	to	30	cm),	with	little	to	no	change	in	the	

lower	soil	profile	(30	to	60	cm)	(Syswerda	et	al.,	2011).	The	opposite,	a	decrease	in	the	upper	soil	

horizon	with	an	increase	in	the	lower	soil	horizon,	can	sometimes	occur	with	a	change	from	no	

tillage	to	full	tillage	(Baker	et	al.,	2007).	However,	changes	in	the	lower	soil	profile	tend	to	be	more	

variable,	thereby	requiring	a	larger	sample	size	to	detect	significant	differences	(Kravchenko	and	

Robertson,	2011).	A	reduction	in	carbon	input	associated	with	the	influence	of	no‐till	management	

on	crop	production	may	also	lead	to	losses	of	soil	carbon,	particularly	in	cooler	and	wetter	climates	
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(Ogle	et	al.,	2012).		However,	while	differences	in	the	response	of	soil	carbon	stocks	to	tillage	occur	

among	field	experiments,	comprehensive	analyses	of	available	field	data	indicate	that,	on	average,	

soil	carbon	stocks	increase	with	a	change	from	full	tillage	to	reduced	tillage	or	no	tillage,	especially	

with	long‐term	adoption	of	no	tillage	(Ogle	et	al.,	2005;	Six	et	al.,	2004;	West	and	Marland,	2002).		

Decreased	tillage	intensity	increases	soil	carbon	because	of	reduced	disturbance	to	soil	aggregates,	

reduced	exposure	of	soil	organic	matter	to	weathering	processes,	and	decreased	decomposition	

rates	(Paustian	et	al.,	2000).	The	extent	to	which	soil	carbon	accumulation	occurs	after	a	reduction	

in	tillage	intensity	is	determined	by	the	history	of	land	management,	soil	attributes,	regional	

climate,	and	current	carbon	stocks	(West	and	Six,	2007).	In	general,	greater	soil	carbon	

accumulation	will	be	observed	in	C‐poor	soils	(i.e.,	due	to	long‐term	cultivation)	with	a	clayey	

texture	under	high	biomass	cropping	systems	in	temperate	humid	and	warm	climates	

(Franzluebbers	and	Steiner,	2002;	Plante	et	al.,	2006;	Six	et	al.,	2004).	In	some	cases,	intermittent	

tillage,	during	long‐term	reduced	or	no	tillage,	is	needed	to	reduce	soil	compaction,	for	weed	

control,	or	to	reduce	pests	or	pathogens.	While	intermittent	tillage	can	cause	a	decrease	in	soil		

stocks,	up	to	80	percent	of	soil		gains	from	no‐tillage	practices	can	be	maintained	when	

implementing	no	tillage	with	intermittent	tillage	(Conant	et	al.,	2007;	Venterea	et	al.,	2006).		

The	effect	of	tillage	management	changes	on	soil	N2O	emissions	is	variable	and	not	fully	

understood.	Increases	(Rochette,	2008),	decreases	(Mosier	et	al.,	2006),	and	no	changes	(Grandy	et	

al.,	2006;	Lemke	et	al.,	1998)	in	soil	N2O	emissions	have	been	observed.	However,	those	differences	

are	not	totally	random	and	past	meta‐analyses	have	concluded	that	climate	regime,	duration	of	

practice,	and	nitrogen	fertilizer	placement	have	influenced	tillage	effects	on	N2O	emissions	(Six	et	

al.,	2004;	van	Kessel	et	al.,	2012).	Other	variables	such	as	soil	texture	may	also	be	important.	

Regional	climate	has	also	been	identified	as	a	major	driver	for	the	change	in	N2O	emissions	with	

adoption	of	no‐tillage	practices,	with	emissions	increasing	in	humid	climates	and	decreasing	in	dry	

climates	(Six	et	al.,	2004).	However,	time	since	adoption	of	no	tillage	might	also	play	a	role	with	

higher	emissions	initially	after	adoption	of	no	tillage	in	both	humid	and	dry	climates,	but	over	time	

emissions	from	no‐tillage	systems	may	decline	in	humid	climates	relative	to	previous	emissions	

from	full	tillage	systems.	Nevertheless,	various	field	studies	have	shown	mixed	results,	both	

supporting	and	contradicting	the	finding.	Studies	in	drier	climates	of	the	Great	Plains	have	shown	a	

decrease	in	emissions	even	when	no‐tillage	practices	had	been	adopted	for	less	than	10	years	

(Kessavalou	et	al.,	1998;	Mosier	et	al.,	2006).	Long‐term	no	tillage	in	moist	climates	of	Minnesota	

and	Canada	led	to	both	higher	and	lower	emissions	of	N2O	(Drury	et	al.,	2006;	Venterea	et	al.,	

2005).		

Another	important	factor	influencing	N2O	emissions	under	no	tillage,	and	one	that	farmers	can	

actively	manage,	is	fertilizer	placement	(van	Kessel	et	al.,	2012).	Venterea	et	al.	(2005)	found	that	

when	nitrogen	fertilizer	was	placed	on	the	surface,	N2O	emissions	were	greater	under	no	tillage	

than	full	tillage,	but	the	reverse	was	found	when	nitrogen	fertilizer	was	placed	below	10	

centimeters.	Fertilizer	placement	in	general	has	been	found	to	have	differing	results	on	N2O	

emissions,	as	discussed	in	Section	3.2.1.1.	However,	the	findings	of	Venterea	et	al.	(2005)	as	well	as	

other	studies	(e.g.,	Groffman,	1985;	Venterea	and	Stanenas,	2008)	indicate	that	deeper	nitrogen	

placement	tends	to	decrease	N2O	emissions	when	accompanying	no‐till	or	reduced‐tillage	practices,	

at	least	relative	to	full	tillage	cropping	systems	at	the	same	location.	The	conflicting	results	

associated	with	N2O	emissions	from	fertilizer	applications	may	be	partly	explained	by	the	tillage	

practice.	

In	addition,	Lemke	et	al.	(1998)	determined	that	soil	clay	content	explained	92	percent	of	the	

variation	in	N2O	emissions	between	full	tillage	and	no	tillage	across	multiple	sites	in	Alberta.	

Similarly,	Burford	et	al.	(1981)	found	that	emissions	from	no‐tillage	practices	were	greater	than	
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from	full	tillage	on	soils	with	higher	clay	contents	at	a	study	site	in	the	United	Kingdom.	It	is	argued	

that	soils	with	higher	clay	contents	have	higher	moisture	content	and	therefore	have	a	greater	

potential	for	increased	N2O	emissions	under	no	tillage.	Indeed,	Rochette	(2008)	attributed	higher	

rates	of	N2O	flux	from	minimum	versus	standard	tillage	to	greater	soil	compaction,	poor	soil	

drainage,	reduced	gas	diffusivity,	and	air‐filled	porosity	in	high	clay	soils.		

3.2.1.3 Crop Rotations, Cover Crops, and Cropping Intensity

Crop	rotation	refers	to	the	sequence	of	crops	planted	in	a	field,	within	or	across	years.	Crop	

rotations	vary	by	location	and	growing	region,	and	may	be	practiced	for	a	variety	of	reasons	such	as	

improved	economic	returns,	pest	management,	disease	control,	nutrient	management	and	water	

availability.	A	simple	rotation	may	be	a	sequence	of	corn	and	soybeans	that	is	repeated	over	time,	

while	more	complex	rotations	might	include	perennial	crops	such	as	alfalfa	with	corn	and	

sunflower	rotation	over	five	years,	with	three	years	of	alfalfa	and	one	year	each	of	corn	and	

sunflower.	The	actual	rotations	can	also	vary	from	a	strict	order	to	the	sequence,	particularly	in	

response	to	market	demand,	i.e.,	opportunistic	rotations.	Rotations	with	high	biomass‐yielding	

crops	or	perennial	hay	crops	or	grass	cover	can	increase	soil	carbon	stocks	(Ogle	et	al.,	2005).	

Cropping	intensity	can	vary	across	years,	due	to	variations	in	fallow	frequency	and	use	of	multiple	

growing	seasons	with	more	than	one	crop	planted	and	harvested	in	a	single	year.	For	example,	in	

semi‐arid	environments,	crop	rotations	often	include	a	year‐long	fallow	period	in	order	to	increase	

the	amount	of	water	stored	in	the	soil	profile	for	the	subsequent	crop.	This	limits	the	amount	of	

organic	matter	input	to	the	soil,	and	with	the	severe	water	limitation,	these	cropping	systems	

produce	small	amounts	of	biomass,	leading	to	a	reduction	in	soil	carbon	stocks	(Doran	et	al.,	1998).	

Consequently,	intensifying	crop	production	by	reducing	fallow	frequency,	which	will	generally	

involve	adoption	of	no‐tillage	practices,	will	increase	carbon	input	across	the	whole	rotation	and	

possibly	the	amount	of	soil	organic	carbon	(Sherrod	et	al.,	2003;	2005).		

Winter	cover	crops	can	also	be	used	to	provide	plant	cover	outside	of	the	normal	growing	season.	

Prior	to	planting	the	following	summer	crop,	the	cover	crop	is	either	left	to	decompose	as	a	green	

cover	or	harvested	for	forage.	In	general,	the	inclusion	of	a	cover	crop	in	a	crop	rotation	will	lead	to	

an	increase	in	soil	carbon	due	to	the	increased	carbon	input	derived	from	the	cover	crop	(Kong	et	

al.,	2005),	especially	cover	crop	roots	(Kong	and	Six,	2010).	Cover	crops	can	also	be	used	effectively	

for	nitrogen	management.	In	the	fall	and	spring	they	can	capture	soil	nitrogen	that	would	otherwise	

be	transformed	directly	to	N2O	by	soil	microbes	or	leach	to	groundwater	and	contribute	to	indirect	

N2O	emissions	(i.e.,	offsite	emissions	due	to	nitrogen	losses	from	the	site).	Additionally,	when	killed	

prior	to	planting	the	main	crop,	their	decomposition	can	provide	nitrogen	that	will	displace	some	

portion	of	crop	fertilization	requirements	(whether	manufactured	or	organic).	Therefore,	cover	

crops	can	reduce	indirect	N2O	emissions	and	possibly	offset	fertilization	rates.	However,	there	are	

no	studies	demonstrating	that	adding	nitrogen	to	soils	in	cover	crops	rather	than	through	

fertilization	will	reduce	direct	N2O	emissions.	In	the	future,	cover	crop	biomass	may	also	be	

harvested	for	cellulosic	ethanol	feedstock,	leaving	roots	to	enhance	soil	carbon	stocks	similar	to	

perennial	plants	grown	in	rotation	(Ogle	et	al.,	2005).	

The	effects	of	crop	rotation	and	intensity	on	soil	organic	carbon	can	also	interact	with	other	

management	practices,	such	as	residue	management,	tillage,	and	irrigation	(Eghball	et	al.,	1994).	

Consequently,	management	interactions	among	practices	including	tillage	and	irrigation	will	be	

important	in	determining	the	influence	of	crop	rotations	on	GHG	emissions.	Additionally,	crop	

selection	as	a	component	of	crop	rotation	can	have	a	major	effect	on	N2O	emissions	(Cavigelli	and	

Parkin,	2012)	insofar	as	crops	can	vary	in	their	nitrogen	use	efficiencies	and	nitrogen	fertilizer	

needs.	This	is	particularly	the	case	when	long‐lived	perennial	crops	are	substituted	for	annual	crops	

in	forage	or	cellulosic	biofuel	cropping	systems	(Robertson	et	al.,	2011).	
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3.2.1.4 Irrigation

Types	of	irrigation	systems	include	surface	or	flood	irrigation,	(micro‐)	sprinkler	irrigation,	

subsurface	drip	irrigation,	and	subirrigation.	In	general,	irrigation	increases	soil	water	content,	

evapotranspiration	rates,	and	relative	humidity;	decreases	soil	and	air	temperatures;	and	can	lead	

to	increased	regional	precipitation	(Lobell	and	Bonfils,	2008;	Pielke	et	al.,	2007).	These	changes	

affect	important	processes	such	as	plant	growth	rates	and	soil	microbial	activity	that	control	net	

GHG	fluxes.		

As	soil	water	content	approaches	saturation,	oxygen	(O2)	diffusion	is	inhibited,	resulting	in	

anaerobic	conditions	that	can	enhance	CH4	emissions	(Chan	and	Parkin,	2001;	Delgado	et	al.,	1996),	

or	at	least	reduce	the	CH4	sink	strength	of	otherwise	aerobic	soils	(Livesley	et	al.,	2010).	Saturated	

conditions	also	enhance	denitrification	rates	and	potentially	N2O	emissions	(Delgado	et	al.,	1996;	

Jambert	et	al.,	1997;	Livesley	et	al.,	2010),	but	note	that	peak	N2O	emissions	from	denitrification	

often	occur	at	water	contents	lower	than	saturation	because	when	O2	is	extremely	limiting,	N2O	is	

likely	to	be	further	reduced	to	N2	before	diffusing	from	the	soil	surface	to	the	atmosphere	

(Davidson,	1991;	Dunfield	et	al.,	1995).	Furthermore,	nitrification	rates	peak	at	approximately	50	

percent	of	saturation,	and	water	contents	close	to	field	capacity	(60	to	70	percent	of	saturation)	are	

expected	to	support	maximum	total	N2O	emission	rates	(Davidson,	1991).	In	addition,	irrigation	can	

increase	indirect	N2O	emissions	by	enhancing	NO3‐

	leaching	and	runoff	if	more	water	is	added	than	

is	evaporated	(Gehl	et	al.,	2005;	Spalding	et	al.,	2001).	

Wetting	of	dry	soils	typically	increases	CO2	emissions	(Fierer	and	Schimel,	2002).	However,	

irrigation	also	increases	plant	growth	rates	and,	therefore,	soil	organic	carbon	levels	typically	

increase	after	upland	cropping	is	converted	to	irrigated	cropping,	although	loss	of	soil	carbon	from	

erosion	can	also	increase	under	irrigation	(Follett,	2001;	Lal	et	al.,	1998).	Furthermore,	irrigation	

can	affect	inorganic	carbon	levels,	but	current	available	data	show	contrasting	results	(Blanco‐

Canqui	et	al.,	2010;	Denef	et	al.,	2008;	Entry	et	al.,	2004).		

Flood and Surface Irrigation:	Flood	irrigation	involves	flooding	the	entire	field	with	water.	Under	

continuously	flooded	conditions,	soils	are	highly	anoxic,	thus	facilitating	high	methanogenesis	and	

denitrification	rates	(Mosier	et	al.,	2004).	However,	high	denitrification	rates	do	not	necessarily	

imply	high	N2O	emissions	because	the	extremely	anoxic	conditions	facilitate	further	reduction	of	

N2O	to	N2	before	it	is	emitted	from	the	soil	(Mahmood	et	al.,	2008).	This	is	supported	by	

observations	showing	higher	N2O	emissions	from	intermittent	compared	to	continuously	flooded	

rice	systems	(Katayanagi	et	al.,	2012;	Xu	et	al.,	2012),	although	it	remains	difficult	to	predict	the	

relative	portion	of	denitrified	nitrogen	that	is	emitted	as	N2O	relative	to	N2.	

Surface	irrigation	also	involves	supplying	large	amounts	of	water	to	the	surface	of	soils,	but	in	this	

case	the	water	is	added	through	furrows	adjacent	to	crop	beds.	These	systems	are	often	not	very	

efficient,	because	water	losses	from	evaporation	and	seepage	can	be	large.	The	impact	of	furrow	

irrigation	on	GHG	emissions	depends	on	how	often	and	the	extent	to	which	furrows	are	filled	with	

water.	Wetting	and	drying	cycles	are	likely	to	emit	large	pulses	of	NO	and	N2O	(Davidson,	1992),	as	

well	as	CO2	(Fierer	and	Schimel,	2002).	Spatial	variability	can	also	be	high,	such	as	the	higher	N2O	

emissions	from	furrows	compared	with	beds	that	have	been	observed	for	irrigated	cotton	cropping	

(Grace	et	al.,	2010).	In	addition,	micro	to	landscape	scale	heterogeneity	in	environmental	

conditions,	due	to	topography	and	other	factors,	contribute	to	multiscale	variability	in	N2O	

emissions	(Hénault	et	al.,	2012;	Yates	et	al.,	2006).	This	spatial	and	temporal	heterogeneity	in	

environmental	conditions	and	flux	rates	makes	it	very	difficult	to	quantify	GHG	fluxes	from	these	

types	of	systems	with	high	levels	of	accuracy	and	precision.		

Sprinkler Systems:	Sprinkler	systems	deliver	water	to	vegetation	and	the	soil	from	above	the	surface	

using	overhead	sprinklers	or	guns.	This	is	usually	more	efficient	than	surface	irrigation,	but	
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evaporative	losses	from	water	intercepted	by	vegetation,	litter,	and	the	soil	surface	can	still	be	

substantial.	During	and	shortly	after	irrigation	events,	soil	may	become	saturated	and	emit	pulses	

of	N2O,	but	because	the	soil	is	not	continuously	saturated,	N2O	emissions	are	expected	to	be	lower	

compared	with	surface	irrigation	(Nelson	and	Terry,	1996).	Both	N2O	emissions	and	soil	carbon	

levels	are	expected	to	increase	with	sprinkler	irrigation	compared	with	upland	cropping.		

Surface and Subsurface Drip Irrigation:	Surface	drip	irrigation	supplies	water	from	drip	lines	placed	

adjacent	to	crop	rows.	Evaporative	losses	should	be	less	compared	with	above‐surface	sprinkler	

systems,	because	less	water	is	intercepted	by	growing	vegetation.	However,	evaporative	losses	can	

still	occur	to	the	extent	that	surface	litter	and	soil	layers	absorb	water	from	the	drip	sprinkler.	The	

impacts	of	surface	drip	irrigation	on	GHG	fluxes	are	expected	to	be	similar	to	those	of	sprinkler	

systems,	although	there	is	early	evidence	that	both	surface	and	subsurface	drip	irrigation	leads	to	

less	emissions	of	CH4	and	N2O	(Kallenbach	et	al.,	2010;	Kennedy	et	al.,	2013).	

Subsurface	drip	irrigation	targets	water	delivery	to	the	root	zone	using	buried	pipes	and	tubing.	

These	systems	can	be	very	efficient,	because	water	is	concentrated	in	the	root	zone	at	a	slow,	steady	

rate,	hence	minimizing	or	eliminating	evaporation	losses	and	avoiding	saturation	of	the	whole	soil	

profile.	Consequently,	these	systems	are	not	expected	to	be	large	CH4	sources	(Del	Grosso	et	al.,	

2000a).	Soil	water	content	has	less	temporal	variation	with	subsurface	drip	irrigation	compared	

with	sprinkler	and	surface	systems,	so	pulses	of	N2O	and	CO2	emissions	are	also	expected	to	be	of	

smaller	magnitude	(Kallenbach	et	al.,	2010).	Similarly,	subsurface	drip	irrigation/fertigation	of	high	

values	crops,	such	as	tomatoes,	has	been	shown	to	reduce	N2O	emissions	compared	with	furrow	

irrigation	(Kennedy	et	al.,	2013).	

Subirrigation:	Subirrigation	is	used	in	areas	with	relatively	high	water	tables	and	involves	

artificially	raising	the	water	table	to	allow	the	soil	to	be	moistened	from	below	the	root	zone.	

Because	water	is	supplied	to	roots	from	below,	evaporation	losses	are	not	enhanced	as	they	would	

be	with	surface	irrigation	systems.	This	system	can	decrease	NO3‐

	leaching	(Elmi	et	al.,	2003)	but	

may	increase	N2O	losses	from	denitrification	(Munoz	et	al.,	2005).	

Management Interactions:	Irrigation	systems	interact	with	other	crop	management	strategies	such	

as	changes	in	crop	rotation,	cropping	intensity,	tillage,	and	fertilizer	amount	to	control	net	GHG	

fluxes.	Irrigation	tends	to	amplify	the	effects	of	these	factors	on	N2O	and	CH4	emissions	at	the	same	

time	as	the	practices	increase	crop	yields	and	soil	carbon	stocks.	However,	the	response	of	soil	

carbon	to	irrigation	is	complex	and	driven	by	interacting	factors.	When	water	and	nutrient	stress	

are	reduced	through	irrigation	and	fertilization,	the	portion	of	total	plant	production	allocated	

below	ground	can	decrease,	but	absolute	below	ground	production	and	soil	organic	carbon	can	

increase	(Bhat	et	al.,	2007).		However	not	all	experiments	show	increased	soil	carbon	with	

irrigation	(Denef	et	al.,	2008).	Consequently,	the	irrigation	benefits	of	increased	yields	and	potential	

carbon	storage	may	be	counter‐balanced	with	the	increased	N2O	and	CH4	fluxes.	

However,	there	are	also	options	for	limiting	emissions,	particularly	with	fertilization.	Fertigation	

adds	nutrients	to	the	irrigation	system	to	deliver	water	along	with	soluble	nutrients	to	the	root	

zone.	These	systems	have	the	potential	to	be	very	efficient	from	both	nutrient	and	water	use	

perspectives	(Spalding	et	al.,	2001),	because	the	slow	and	timed	supply	of	nutrients	and	water	is	

more	synchronous	with	plant	demand	and	they	are	concentrated	in	the	root	zone.	Consequently,	

N2O	and	other	nitrogen	losses	are	minimized	while	plant	growth,	carbon	inputs,	and	carbon	

sequestration	can	be	maximized.	Similarly,	CH4	emissions	are	minimized	because	soil	saturation	is	

avoided.	
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3.2.1.5 Erosion Control

Soil	erosion	processes	include	soil	detachment,	transport,	and	deposition.	Soil	erosion	can	

potentially	reduce	soil	carbon	stocks	and	increase	net	carbon	flux	to	the	atmosphere	through	

decreased	plant	productivity	and	subsequent	decreased	organic	matter	input	to	soil	and	increased	

decomposition	of	the	eroded	soil	fraction	(Lal,	2003).	However,	soil	erosion	can	also	potentially	

increase	net	soil	carbon	stocks	and	decrease	net	carbon	flux	to	the	atmosphere	through	dynamic	

replacement	of	soil	carbon	on	eroded	landscapes	and	decreased	decomposition	rates	in	zones	of	

soil	deposition	(Harden	et	al.,	1999;	Stallard,	1998).	

Lal	(2003)	estimated	that	20	percent	of	carbon	in	eroded	soil	is	emitted	to	the	atmosphere,	due	to	

oxidation	of	soil	organic	carbon	following	the	disruption	of	soil	aggregates	caused	by	detachment	

and	transport.	However,	in	an	analysis	of	1,400	soil	profiles,	Van	Oost	et	al.	(2007)	found	negligible	

carbon	loss	as	a	direct	result	of	soil	detachment	and	transport.	At	sites	where	the	transported	soil	

was	deposited,	there	was	a	slight	(~one	percent)	decrease	in	soil	carbon	decomposition	rates,	

resulting	in	slightly	higher	soil	carbon	accumulation.	More	importantly,	it	was	found	that	on	

average,	25	percent	of	eroded	carbon	was	replaced	on	the	eroded	sites	over	a	50‐year	period	

(Harden	et	al.,	2008).	The	combination	of	these	findings	supports	an	approximate	26	percent	sink	

capacity	of	eroded	soil	(Van	Oost	et	al.,	2007).		

The	accumulation	of	soil	carbon	on	eroded	locations	within	landscapes	is	referred	to	as	dynamic	

replacement	(Harden	et	al.,	1999).	Dynamic	replacement	occurs	as	a	result	of	soil	carbon	building	

toward	a	steady	state	of	soil	carbon	content,	constrained	by	soil	type	and	climate	(West	and	Six,	

2007).	Steady	state	occurs	when	soil	carbon	accumulation	equals	soil	carbon	losses.	Both	Van	Oost	

et	al.	(2007)	and	Lal	and	Pimentel	(2008)	note	that	the	dynamic	replacement	rate	may	be	low	in	

areas	with	lower	cropland	production	inputs.	For	example,	dynamic	replacement	may	be	low	in	

crop	systems	with	low	residue	production,	such	as	cotton	and	tobacco	in	the	United	States,	which	

have	lower	carbon	accumulation	rates	than	high	residue	inputs	crops	(Ogle	et	al.,	2005).	

Note	that	while	water	erosion	can	generate	a	small	carbon	sink,	the	benefit	of	a	carbon	sink	is	offset	

by	other	negative	impacts	from	soil	erosion.	For	example,	soil	erosion	can	result	in	water	pollution	

due	to	sediment	loading,	air	pollution	from	airborne	particulate	matter	(PM10),	and	decreased	soil	

fertility	resulting	in	subsequent	yield	declines.	

3.2.1.6 Management of Drained Wetlands

Drainage	of	wetlands	effectively	creates	an	upland	cropping	system	by	lowering	water	tables	with	

tiles	or	ditches	to	produce	annual	crops.	The	most	obvious	effect	of	wetland	drainage	is	increased	

oxidation	and	tillage	of	soils.	For	example,	conversion	of	native	wetlands	and	grasslands	into	

cropland	has	been	shown	to	deplete	native	soil	carbon	stocks	by	20	to	more	than	50	percent	(Blank	

and	Fosberg,	1989;	Euliss	et	al.,	2006;	Mann,	1986).	In	turn,	CO2	emissions	increase	with	higher	

decomposition	rates,	particularly	in	organic	soils,	i.e.,	Histosols	(Allen,	2012;	Armentano	and	

Menges,	1986).	Loss	of	the	organic	layer	has	caused	tremendous	subsidence	in	U.S.	croplands	

(Stephens	et	al.,	1984)	such	as	the	Florida	Everglades	(Shih	et	al.	1998)	and	the	California	Delta	

region	(Broadbent,	1960;	Weir,	1950),	where	rates	vary	from	0.46	to	2.3	cm	year‐1	(Deverel	and	

Rojstaczer,	1996;	Deverel	et	al.,	1998;	Rojstaczer	and	Deverel,	1995).	Similar	subsidence	rates	have	

also	occurred	in	other	regions	such	as	the	Florida	Everglades.	

Manipulation	of	water	levels	can	have	multiple	effects	on	nutrient	cycling	in	wetlands.	Drainage	

also	may	result	in	more	optimal	soil	moisture	conditions	(e.g.,	40	to	60%	water‐filled	pore	space)	

that	enhance	formation	of	N2O	as	a	byproduct	of	nitrification	and	denitrification	reactions

(Davidson	et	al.,	2000).	Drainage	increases	nitrogen	mineralization	rates	with	conversion	from	

anaerobic	to	aerobic	conditions	and	enhances	N2O	emissions	(Duxbury	et	al.,	1982;	Kasimir‐
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3-22

Klemedtsson	et	al.,	1997).	In	contrast,	drainage	decreases	CH4	emissions	by	reducing	the	frequency	

and	duration	of	soil	saturation	required	for	CH4	production	as	well	as	enhancing	frequency	of	

methanotrophic	activity	(Dorr	et	al.,	1993;	Gleason	et	al.,	2009;	Phillips	and	Beeri,	2008).	However,	

in	situations	where	wetlands	are	in	a	crop	production,	but	not	directly	drained,	CH4	production	can	

actually	be	enhanced	due	to	increased	runoff	from	adjacent	croplands	or	consolidation	drainage,	

which	increases	water	depth	and	hydroperiods	(Gleason	et	al.,	2009).	

Managing	the	water	table	by	raising	the	depth	of	drainage	to	the	extent	possible	has	been	an	

effective	measure	to	reduce	loss	of	CO2	and	other	GHGs	from	drained	organic	soils	(Jongedyk	et	al.,	

1950;	Shih	et	al.,	1998).	Recent	research	suggests	that	even	periodic	flooding	of	organic	soils	that	

are	drained	for	crop	production	may	be	effective	in	reducing	CO2	emissions	(Morris	et	al.,	2004).	

There	is	limited	information	on	the	effect	of	drainage	in	mineral	soils	with	a	high	water	table	(i.e.,	

hydric	soils),	but	the	influence	on	GHG	emissions	is	likely	less	significant	than	in	drained	organic	

soils.	It	is	important	to	note	that	wetlands	are	afforded	some	protection	by	laws	(e.g.,	Clean	Water	

Act)	and	conservation	programs	that	recognize	the	importance	of	wetlands,	such	as	for	wildlife	

habitat,	and	provide	agricultural	producers	incentives	to	avoid	draining	wetlands	(e.g.,	the	

“Swampbuster”	provision	of	the	Food	Security	Act).		

3.2.1.7 Lime Amendments

Agricultural	lime	consists	primarily	of	crushed	limestone	(CaCO3)	and	dolomite	(CaMg(CO3)2)	in	

varying	proportions.	Agricultural	lime,	hereinafter	referred	to	as	lime,	is	applied	to	soils	to	decrease	

soil	acidity.	Lime	is	commonly	applied	to	agricultural	lands	where	nitrogenous	fertilizers	are	

continuously	used	and	where	precipitation	exceeds	evapotranspiration.	

The	application	of	lime	to	soils	can	create	a	sink	or	source	of	CO2	to	the	atmosphere	(Hamilton	et	al.,	

2007),	depending	on	the	strength	of	the	weathering	agent.	Weathering	of	lime	by	carbonic	acid	

(H2CO3),	formed	when	CO2	is	dissolved	in	water,	results	in	the	uptake	of	one	mole	of	CO2	for	every	

mole	of	lime‐derived	carbon	dissolved	(Eq.	1).	Carbonic	acid	weathering	produces	bicarbonate	

(HCO3‐)	that	contributes	to	alkalinity	in	groundwater,	streams,	and	rivers	(Oh	and	Raymond,	2006;	

Raymond	et	al.,	2008).	Alternatively,	when	lime	reacts	with	the	stronger	nitric	acid	(HNO3),	which	is	

produced	when	nitrifying	bacteria	convert	NH4+	based	fertilizer	and	other	sources	of	NH4+	to	nitrate	

(NO3‐

),	carbon	in	lime	is	dissolved	and	released	directly	to	the	atmosphere	(Eq.	2).	

CaCO3	+	H2O	+	CO2	=	Ca2+	+	2HCO3‐

	 	 	 	 	 Eq.	1	

CaCO3	+	2HNO3	=	Ca2+	+	2NO3‐

	+	H2O	+	CO2	 	 	 	 Eq.	2	

Field	measurements	and	modeling	analyses	indicate	that	more	lime	is	dissolved	by	carbonic	acid	

than	by	nitric	acid.	For	example,	West	and	McBride	(2005)	estimated	that	62	percent	of	lime	was	

dissolved	by	carbonic	acid	weathering,	Hamilton	et	al.	(2007)	estimated	75	to	88	percent,	and	Oh	

and	Raymond	(2006)	estimated	66	percent.	Biasi	et	al.	(2008)	used	chamber	flux	measurements	to	

estimate	15	percent	loss	of	lime‐derived	carbon	by	dissolution	with	strong	acids	and	inferred	that	

85	percent	is	dissolved	by	carbonic	acid.		

West	and	McBride	(2005)	also	estimated	the	precipitation	of	HCO3‐

	back	to	CaCO3	once	HCO3‐

reaches	the	ocean,	thereby	releasing	CO2	to	the	atmosphere.	However,	the	long	time	period	(many	

decades	to	centuries)	over	which	precipitation	would	occur	in	the	ocean	(Hamilton	et	al.,	2007)	

effectively	results	in	carbon	sequestration	for	annual	accounting	purposes.		

Current	consensus	of	leached	drainage	samples,	stream	gauge	data,	and	mass	balance	modeling	

indicates	that	about	66	percent	of	carbon	in	applied	lime	is	essentially	transferred	from	one	long‐

lived	pool	(CaCO3	in	geologic	formations)	to	another	(HCO3‐

	in	oceans),	and	is	therefore	not	counted	

as	new	sequestration.	However,	the	atmospheric	CO2	newly	captured	by	this	process	does	
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 3-23 

represent	sequestration	when	corrected	for	the	33	percent	released	to	the	atmosphere	as	CO2;	this	

results	in	a	net	33	percent	sink	strength	per	carbon	in	lime.	This	estimate	is	similar	to	that	of	Oh	

and	Raymond	(2006)	and	West	and	McBride	(2005),	and	is	within	the	range	of	Hamilton	et	al.	

(2007).	While	lime	can	increase	soil	carbon	via	effects	on	soil	microbial	activity	(Fornara	et	al.,	

2011),	in	most	soils	liming	has	no	direct	carbon	effect	(Page	et	al.,	2009).		

3.2.1.8 Residue Management

Crop	residues	are	the	residual	remaining	after	harvest	of	the	economic	part	of	the	crop.	The	amount	

of	crop	residue	varies	with	the	crop	and	the	harvest	operation	method.	For	example,	cotton	harvest	

contributes	very	little	aboveground	residue	to	the	soil	due	to	the	plant’s	low	leaf	area	index	and	

small	amount	of	plant	material	after	leaf	drop.	Soybean	and	other	legume	crops	also	have	small	

amounts	of	aboveground	residue	that	rapidly	decompose	because	of	low	C:N	ratios.	In	contrast,	

crops	like	corn	can	leave	substantial	amounts	of	residue	on	the	soil	surface	unless	the	whole	plant	

is	harvested	for	silage	or	the	residue	is	collected	for	bedding	or	other	purposes.	

Aboveground	residue	management	entails	five	potential	strategies:	(1)	leave	the	residue	on	the	soil	

surface	to	decay	and	be	incorporated	into	the	soil	(requires	no‐till	management);	(2)	incorporate	

the	residue	into	the	soil	via	tillage;	(3)	remove	the	residue	through	a	harvesting	operation	(i.e.,	

silage	or	cellulosic	biomass	harvest);	(4)	allow	livestock	to	graze	on	the	residue;	or	(5)	burn	the	

residue.	Each	of	these	management	practices	has	the	potential	to	affect	GHG	emissions.	Leaving	

crop	residue	on	the	surface	and	incorporating	it	into	the	soil	after	decay	by	microorganisms	affects	

CO2	release	from	the	soil	due	to	the	enhanced	biological	activity,	and	potentially	increases	N2O	

emissions	through	an	alteration	of	the	nitrogen	balance	in	the	soil.	A	similar	process	occurs	when	

residue	is	incorporated	into	the	soil	via	tillage.	Note	that	tillage	also	causes	reductions	in	soil	

carbon	stocks,	and	additional	CO2	is	released	through	burning	fuel	to	run	tillage	equipment.	

Harvesting	the	residue	releases	CO2	from	burning	fuel	in	the	engines	linked	with	the	harvesting	

process,	although	residue	harvested	for	biofuel	production	may	create	net	fossil	fuel	offset	credits.	

Burning	crop	residues	in	the	field	releases	CO2,	CH4,	and	N2O	(as	well	as	CO	and	NOx)	emissions	to	

the	atmosphere.	In	general,	but	not	always,	residue	removal	reduces	soil	carbon	stocks	(Gregg	and	

Izaurralde,	2010;	Wilhelm	et	al.,	2007).	

Management	interactions	are	also	important	when	considering	the	influence	of	residue	

management	on	GHG	emissions.	For	example,	the	influence	of	residue	management	on	soil	organic	

carbon	will	be	affected	by	the	tillage	practices	(Malhi	et	al.,	2006).	

3.2.1.9 Set‐Aside/Reserve Cropland

The	1985	Farm	Bill	established	the	Conservation	Reserve	Program	(CRP)	to	pay	producers	to	

convert	highly	erodible	cropland	or	other	environmentally	sensitive	agricultural	areas	into	

vegetative	cover.	These	areas	could	be	converted	into	grassland,	native	bunchgrasses,	pollinator	

habitat,	shelterbelts,	filter	or	buffer	strips,	or	riparian	buffers.	Areas	are	removed	from	production	

and	seeded	with	annual	and	perennial	species	to	form	a	cover	that	would	be	undisturbed	for	a	

minimum	of	10	years.	In	return,	producers	or	landowners	received	a	payment	for	enrolling	these	

land	areas	into	the	CRP.	Throughout	the	agricultural	history	of	the	United	States,	there	have	been	

times	in	which	agricultural	lands	were	set	aside	to	reduce	agricultural	surpluses;	however,	the	time	

period	of	removal	was	typically	short‐term	(one	to	two	years)	and	maintained	in	a	weed‐free	state.		

The	primary	aims	of	CRP	are	to	decrease	erosion,	restore	wildlife	habitat,	and	safeguard	ground	

and	surface	water	quality.	An	important	ancillary	aim	is	carbon	capture:	CRP	lands	sequester	

carbon	in	soil	and	long‐lived	plants,	and	thus	represent	a	valuable	mitigation	opportunity.	In	a	

meta‐analysis	of	paired	soils,	Ogle	et	al.	(2005)	found	that	20	years	of	set‐aside	resulted	in	

temperate	region	soils’	accumulating	82	to	93	percent	of	the	carbon	levels	under	original	native	
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vegetation,	on	average.	Post	and	Kwon	(2000)	concluded	from	a	global	meta‐analysis	that,	on	

average,	soil	carbon	sequestration	rates	on	land	converted	from	agricultural	production	to	

grassland	is	33	g	C	m‐2	year‐1.	At	39	paired	CRP‐crop	sites	in	Wisconsin,	Kucharik	(2007)	found	

sequestration	rates	of	50	g	C	m‐2	year‐1	on	Mollisols	and	44	g	C	m‐2	y‐1	on	Alfisols.	Follett	et	al.	

(2009)	estimate	that	CRP	soils	sequester	~50	g	C	m‐2	year‐1	on	average.	The	Council	for	Agricultural	

Science	and	Technology	(2011)	estimates	that	CRP	lands	are	currently	responsible	for	6.3	Tg	of	soil	

carbon	sequestration	per	year.	Gebhart	et	al.	(1994)	reported	a	mean	18.8	percent	increase	on	five	

CRP	sites	during	a	six‐year	period.	However,	there	are	studies	showing	little	or	no	increase	in	C,	

leading	to	uncertainty	in	the	effect	of	set‐aside	land	in	a	reserve	program	(Jelinski	and	Kucharik,	

2009;	Karlen	et	al.,	1999;	Reeder	et	al.,	1998).	For	example,	Karlen	et	al.	(1999)	compared	CRP	land	

with	perennial	grasses	to	cropland	across	five	States	and	found	that	only	one	site	of	the	five	showed	

a	significant	difference	in	total	organic	carbon	content	in	the	soil	after	being	in	CRP.		

Increases	in	soil	carbon	resulting	from	setting	aside	cropland	in	CRP	can	be	reversed	by	converting	

these	lands	back	into	production.	Gilley	et	al.	(1997)	found	that	the	positive	changes	in	CRP	land	

disappeared	immediately	when	the	soils	were	tilled	upon	conversion	back	into	crop	production.	

However,	many	studies	indicate	that	if	land	under	CRP	is	returned	to	cultivation,	some	or	all	of	the	

soil	carbon	can	potentially	be	retained	if	the	land	is	cultivated	with	no‐till	practices	(Bowman	and	

Anderson,	2002;	Dao	et	al.,	2002;	Olson	et	al.,	2005).	In	addition	to	changes	in	soil	carbon	stocks,	

changes	will	also	occur	in	N2O	emissions	depending	on	the	nutrient	management	practices.	Gelfand	

et	al.	(2011)	measured	a	net	carbon	cost	of	10.6	Mg	CO2‐eq	ha‐1	(289	g	C‐eq	m‐2)	for	the	first	year	of	

no‐till	soybeans	following	20	years	of	CRP	grassland,	and	a	significant	portion	of	the	net	emission	

was	due	to	N2O	produced	in	the	conversion	year.		

3.2.1.10 Biochar

Biochar	is	a	soil	amendment	that	is	promoted	for	its	ability	to	improve	crop	production	and	

sequester	carbon	in	soils	(Atkinson	et	al.,	2010;	Lehmann,	2007a;	2007b).	Biochar	is	charcoal	

produced	when	wood	or	other	plant	biomass	is	burned	under	low‐oxygen	conditions,	known	as	

pyrolysis.	When	applied	to	soils,	biochar	can	persist	for	long	periods	of	time;	its	chemical	structure	

makes	it	resistant	to	microbial	attack	under	most	soil	conditions.	However,	its	persistence	can	vary	

greatly	for	reasons	not	yet	completely	understood.	Biochar	is	a	common	component	of	most	U.S.	

agricultural	soils	(Skjemstad	et	al.,	2002),	left	from	fires	that	occurred	prior	to	conversion	of	the	

original	forest	or	prairie.	Adding	biochar	to	soils	has	been	proposed	as	a	way	to	sequester	carbon	

(Lehmann,	2007a)	because	of	this	potential	to	persist	for	centuries	(Kimetu	and	Lehmann,	2010;	

Nguyen	et	al.,	2008).	But	biochar’s	longevity	in	soil	depends	on	a	number	of	factors	including	

pyrolysis	conditions	(e.g.,	pyrolysis	temperature)	and	the	chemical	composition	of	the	biochar	

feedstock	(Spokas,	2010).	Climate	and	soil	factors	such	as	mineralogy	and	pre‐existing	organic	

matter	content	also	affect	biochar’s	persistence	in	soil.		

An	additional	benefit	of	biochar	is	its	positive	effects	on	agricultural	soil	fertility	(Atkinson	et	al.,	

2010;	Laird	et	al.,	2010),	largely	by	providing	advantages	similar	to	other	forms	of	soil	organic	

matter:	improved	soil	structure,	water	holding	capacity,	and	cation‐exchange	capacity.	Biochar	has	

also	been	shown	to	reduce	soil	N2O	emissions	in	some	laboratory	studies,	but	the	small	number	of

field	trials	so	far	reported	have	documented	no	significant	effects	under	field	conditions	(e.g.,	

Scheer	et	al.,	2011).		

It	is	too	early	to	know	if	promising	results	from	laboratory	and	short‐term	field	experiments	can	be	

generalized	to	long‐term	field	conditions.	Biochar	soil	additions	may	be	a	future	source	of	carbon	

credits	for	pyrolysis	waste	if	long‐term	field	experiments	confirm	results	from	shorter	term	studies.	

The	climate	advantage	of	adding	biochar	to	soil	is	less	clear,	however,	relative	to	other	potential	

uses	of	plant	biomass.	Life	cycle	analyses	(e.g.,	Roberts	et	al.,	2010)	suggest	that	biochar	may	
 Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

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increase	or	decrease	net	emissions	depending	on	alternative	uses	of		the	original	biomass	and	life	

cycle	system	boundaries.	Furthermore,	if	the	biomass	(or	biochar)	was	burned	directly	for	energy	

then	the	source	of	displaced	energy	must	also	be	considered	(Roberts	et	al.,	2010).	Nevertheless,	

both	the	sequestration	and	N2O	suppression	potential	of	biochar	merit	further	study.		

3.2.2 Management Influencing GHG Emissions in Flooded Cropping Systems

There	are	a	variety	of	flooded	cropping	systems	in	the	United	States,	including	systems	for	rice,	wild	

rice,	cranberries,	and	taro.	Apart	from	rice,	these	systems	are	relatively	minor	(specialty	crops)	and	

there	is	little	to	no	research	or	information	on	their	GHG	emissions.	Rice	systems	emit	both	CH4	and	

N2O;	however,	many	reports	show	an	inverse	relationship	between	CH4	and	N2O	during	the	rice	

cropping	season,	with	CH4	occurring	under	anaerobic	conditions	and	N2O	emissions	occurring	

under	aerobic	conditions	(Zou	et	al.,	2005).	Therefore,	to	accurately	determine	a	mitigation	strategy	

one	needs	to	consider	the	net	cumulative	effect	of	GHG	emissions	by	evaluating	both	CH4	and	N2O.	

Water	and	residue	management	have	received	the	most	attention	in	terms	of	offering	possibilities	

for	mitigating	CH4	emissions.	Other	mitigation	options	have	also	been	examined	and	show	promise	

(e.g.,	Feng	et	al.,	2013;	Linquist	et	al.,	2012;	Majumdar,	2003;	Wassmann	and	Pathak,	2007;	Yagi	et	

al.,	1997)	and	further	research	is	required	in	many	areas	before	these	options	can	be	scaled	up.	The	

intent	here	is	not	to	provide	a	review	of	the	literature	but	to	provide	a	brief	overview	of	some	

factors	affecting	GHG	emissions	from	flooded	rice	systems.		

3.2.2.1 Water Management in Flooded Rice

In	the	United	States,	rice	is	planted	in	one	of	two	ways:	(1)	water	seeded,	where	seeds	are	sown	by	

airplane	in	flooded	fields;	or	(2)	dry‐seeded,	where	seeds	are	drilled	or	broadcast	(then	

incorporated)	into	dry	fields.	Water	seeding	is	the	predominant	practice	in	California	and	parts	of	

Louisiana,	while	dry	seeding	is	predominant	in	much	of	the	southern	United	States	(e.g.,	Arkansas,	

Mississippi,	Missouri,	and	Texas).	Water	management	varies	between	these	two	established	

practices.	In	water‐seeded	rice,	the	fields	are	typically	flooded	for	the	entire	season.	However,	in	

Louisiana,	the	field	may	be	drained	with	a	pinpoint	flood	system	(three	to	five	days)	or	with	a	

delayed	flood	(up	to	20	days)	after	seeding.	In	dry‐seeded	rice,	rainfall	or	flush	irrigation	events	are	

relied	upon	during	the	first	three	to	five	weeks	of	establishment	and	then	flooded	for	the	rest	of	the	

season.	In	all	cases,	fields	are	typically	drained	a	few	weeks	before	harvest	to	allow	the	soil	to	dry	

out	enough	to	support	harvest	equipment.	Further	details	of	U.S.	rice	production	systems	can	be	

found	in	Snyder	and	Slaton	(2001)	and	Street	and	Bollich	(2003).	

Midseason	drain	or	intermittent	irrigation	is	a	strategy	to	mitigate	CH4	emissions.	This	practice	

results	in	aerobic	conditions	that	are	unfavorable	for	methanogens.	However,	such	conditions	are	

favorable	for	N2O	emissions	(e.g.,	Zou	et	al.,	2005).	Most	studies	report	that	midseason	drains	

significantly	decrease	CH4	emissions	but	increase	N2O	emissions	relative	to	continuous	flooding.	

Regardless,	net	GHG	emissions	in	rice	systems	are	usually	decreased	with	midseason	drain	despite	

the	increase	in	N2O.	Wassman	et	al.	(2000)	reported	that	CH4	emission	reductions	ranged	from		

seven	percent	to	80	percent.	The	reduction	in	CH4	emissions	depends	on	the	number	of	drainage	

events	during	the	cropping	season	and	on	other	management	factors	and	soil	properties.	Yan	et	al.	

(2005)	reported	that	CH4	fluxes	from	rice	fields	with	single	and	multiple	drainage	events	were	

reduced	by	60	percent	and	52	percent	compared	to	continuously	flooded	rice	fields.	This	practice	

has	not	been	widely	evaluated	in	the	United	States,	and	it	may	be	difficult	to	drain	and	re‐flood	the	

large	relatively	flat	parcels	of	land	that	are	commonly	used	for	rice	production	in	the	United	States.	

Furthermore,	such	practices	can	lead	to	increased	weed	and	disease	pressure	along	with	lower	

yields	and	grain	quality.	
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Soil	carbon	stocks	are	also	influenced	by	water	management.	For	example,	carbon	stocks	in	Chinese	

rice	systems	are	higher	than	in	upland	crops,	presumably	due	to	the	accumulation	of	carbon	under	

the	flooded	conditions	(Pan	et	al.,	2010;	Wu,	2011).	It	remains	unknown	if	efforts	to	mitigate	CH4

emissions	in	the	United	States	using	intermittent	flooding	will	lead	to	a	reduction	in	soil	carbon	

stocks.		

The	use	of	midseason	drainage	has	been	shown	to	delay	harvest	in	California.	Therefore,	in	climates	

with	a	short	growing	season,	the	use	of	a	midseason	drain	will	increase	risk	of	crop	failure,	and	

therefore	will	be	a	less	appealing	alternative	to	growers.	

3.2.2.2 Residue Management

Straw	management	has	a	large	impact	on	CH4	production.	Straw	additions,	particularly	those	with	a	

high	carbon	to	nitrogen	ratio,	increase	CH4	emissions	but	have	the	potential	to	reduce	N2O	

emissions	(e.g.,	Zou	et	al.,	2005).	This	reduction	in	N2O	may	be	due	to	increased	nitrogen	

immobilization	or	more	effective	conversion	to	N2.	Low	carbon	to	nitrogen	organic	materials	tend	

to	increase	N2O	emissions	(Kaewpradit	et	al.,	2008).		Yan	et	al.	(2005)	reported	that	the	timing	of	

straw	application	is	also	an	important	factor.		For	example,	applying	rice	straw	before	transplanting	

increased	CH4	emissions	by	2.1	times,	while	applying	rice	straw	in	the	previous	season	increased	

CH4	emissions	by	0.8	times.	Several	studies	have	demonstrated	that	composting	rice	straw	prior	to	

incorporation	reduces	CH4	emissions	(Wassmann	et	al.,	2000);	however,	this	requires	additional	

energy	to	collect	the	straw	and	then	spread	it	back	on	the	field	after	composting.		

In	contrast	to	the	potential	for	reducing	CH4	emissions	with	removal	of	rice	straw,	there	is	also	the	

potential	to	reduce	soil	carbon	stocks	due	to	less	carbon	input	to	soils.	Other	nutrients	(particularly	

K)	are	removed	in	large	amounts	with	residues,	and	these	need	to	be	replaced	to	maintain	the	

productivity	of	the	system.		

3.2.2.3 Organic Amendments

Various	organic	amendments	can	be	applied	to	rice	fields,	including	farmyard	manure	specialty	

mixes	of	organic	fertilizers,	and	green	manures	(e.g.,	cover	crops).	Based	on	a	meta‐analysis	by	

Linquist	et	al.	(2012),	livestock	manure	increases	CH4	emissions	by	26	percent	and	green	manures	

increased	CH4	by	192	percent.	Neither	manure	source	had	a	significant	effect	on	N2O	emissions.	

Few	studies	have	evaluated	the	influence	of	different	manure	storage	and	processing	techniques	on	

CH4	emissions.	One	example	is	a	study	by	Wassman	et	al.	(2000),	who	found	that	fermentation	of	

farmyard	manure	prior	to	application	can	reduce	CH4	emissions.	Farmyard	manure	will	also	

influence	soil	carbon	stock	and	soil	N2O	emissions.	

3.2.2.4 Varieties, Ratoon Cropping, and Fallow Management

Seasonal	CH4	(Lindau	et	al.,	1995)	and	N2O	(Chen‐Ching,	1996)	emissions	are	affected	by	rice	

variety.	The	cause	of	varietal	differences	vary	but	may	be	due	to	gas	transport	through	arenchyma	

cells,	different	rooting	structures,	or	differences	among	varieties	in	terms	of	root	exudates	

(Wassmann	and	Aulakh,	2000).	Identifying	the	mechanisms	for	varietal	differences	may	enable	

breeding	programs	to	select	varieties	that	have	lower	CH4	emissions.		

In	some	States,	the	climate	allows	re‐sprouting	of	a	second,	or	ratoon	crop,	that	grows	from	the	

stubble	of	the	first	crop	after	harvesting.	Ratoon	crop	yields	are	smaller	than	the	first	crop,	but	can	

add	substantially	to	the	overall	annual	yield,	thereby	reducing	costs	of	production	per	unit.	In	

addition,	it	takes	fewer	resources	and	less	time	to	grow	a	ratoon	crop	than	to	grow	the	first	crop.	

However,	ratooning	has	higher	CH4	emission	rates	(about	two	to	three	times	higher)	than	the	first	

crop,	because	the	straw	from	the	first	crop	remains	in	the	field	under	anaerobic	conditions	during	

the	ratoon	period	rather	than	the	field	being	drained	so	that	the	stubble	can	decay	aerobically	
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(Lindau	et	al.,	1995).	Therefore,	the	amount	of	CH4	producing	organic	material	(i.e.,	material	

available	for	anaerobic	decomposition)	is	considerably	higher	than	with	the	primary	crop.	

Management	of	rice	fields	during	the	winter	has	a	significant	effect	on	annual	GHG	emissions.	For	

example,	in	California,	legislation	in	the	1990s	has	limited	the	burning	of	rice	straw	to	a	maximum	

of	25	percent	of	an	area,	although	in	reality	only	about	10	percent	of	rice	production	fields	are	

burned.	Currently,	rice	straw	is	incorporated	after	harvest	on	about	85	percent	of	the	rice	

production	fields	in	California,	and	in	these	fields	about	half	are	intentionally	flooded	to	facilitate	

straw	decomposition,	although	this	value	can	vary	widely	from	year	to	year.	Winter	flooding	has	

increased	annual	CH4	emissions	(Devito	et	al.,	2000),	but	it	has	also	increased	the	quality	of	habitat	

for	overwintering	waterfowl	in	the	Pacific	Flyway.	Rice	straw	is	baled	and	removed	on	about	five	

percent	of	the	area.	

3.2.2.5 Nitrification and Urease Inhibitors in Flooded Rice

Nitrification	inhibitors	prevent	or	slow	the	conversion	of	NH4+	to	NO3‐

	and	thus	reduce	N2O	

emissions	from	nitrification	and	subsequent	denitrification.	In	a	meta‐analysis	of	these	products,	

Akiyama	et	al.	(2010)	found	that	in	rice	systems	the	use	of	nitrification	inhibitors	on	average	

reduced	N2O	emissions	by	30	percent,	although	some	products	were	more	effective	than	others.	

Certain	nitrification	inhibitors	(i.e.,	dicyandiamide,	thiosulfate,	and	encapsulated	calcium	carbide)	

can	mitigate	both	CH4	and	N2O	emissions.	Reduced	CH4	emissions	using	dicyandiamide	was	

attributed	to	a	higher	redox	potential,	lower	pH,	lower	Fe2+,	and	lower	readily	mineralizable	carbon	

content	(Bharati	et	al.,	2000).		

Urease	inhibitors,	such	as	hydroquinone,	slow	the	microbial	conversion	of	urea	to	NH4+,	thus	

reducing	the	amount	of	nitrogen	available	for	nitrification	and	dentrification.	Both	CH4	and	N2O	

emissions	were	reduced	with	the	use	of	hydroquinone	(Boeckx	et	al.,	2005).	It	is	suggested	that	

urease	inhibitors	mitigate	CH4	emission	by	inhibiting	the	methanogenic	fermentation	of	acetate	

(Wang	et	al.,	1991).	Furthermore,	a	combination	of	a	urease	inhibitor	(hydroquinone)	and	a	

nitrification	inhibitor	(dicyandiamide)	was	shown	to	result	in	lower	GHG	emissions	compared	with	

using	only	one	of	the	products	(Boeckx	et	al.,	2005).	See	Section	3.2.1.1	for	more	information	on	

nitrification	and	urease	inhibitors.	

3.2.2.6 Fertilizer Placement in Flooded Rice

Incorporating/injecting	or	placing	fertilizer	deep	into	the	soil	has	been	shown	in	some	studies	to	

reduce	both	CH4	(Wassmann	et	al.,	2000)	and	N2O	(Keerthisinghe	et	al.,	1995)	emissions.	While	

much	of	a	flooded	rice	field’s	soil	is	anaerobic,	the	floodwater	and	top	few	centimeters	of	soil	

typically	remain	aerobic	while	soil	below	five	centimeters	exists	in	an	anaerobic,	reduced	state	

(Keeney	and	Sahrawat,	1986).	Thus	mineral	nitrogen	in	the	top	few	centimeters	of	soil	may	

undergo	nitrification	and	denitrification,	which	can	lead	to	N2O	emissions;	but	mineral	nitrogen	in	

lower	soil	depths	will	remain	as	ammonium.	In	contrast,	nitrogen	fertilizer	that	is	applied	to	the	soil	

surface	(either	preseason	or	midseason)	tends	be	more	susceptible	to	losses	either	from	ammonia	

volatilization	or	more	rapid	nitrification‐denitrification	processes	(Griggs	et	al.,	2007).	By	placing	

nitrogen	into	anaerobic	soil	layers,	it	is	better	protected	from	losses	and	remains	available	for	crop	

nitrogen	uptake	(Linquist	et	al.,	2009).	The	effect	of	deep	fertilizer	placement	on	CH4	reduction	

remains	uncertain.	See	Section	3.2.1.1	for	more	information	on	fertilizer	placement.		

3.2.2.7 Sulfur Products

Sulfur‐containing	fertilizers	(i.e.,	ammonium	sulfate,	calcium	sulfate,	phosphogypsum,	and	single	

super	phosphate)	reduce	CH4	emissions	(Lindau	et	al.,	1998).	The	magnitude	of	CH4	reduction	is	

dependent	on	fertilization	rate	with	averages	between	208	and	992	kg	S	ha‐1,	reducing	CH4
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emissions	by	28	percent	and	53	percent,	respectively	(Linquist	et	al.,	2012).	At	low	levels	of	sulfur	

fertilization,	which	are	common	in	recommended	rates,	the	effect	on	CH4	emissions	will	be	limited	

(Linquist	et	al.,	2012).	Sulfur	mitigates	CH4	emissions	in	two	ways.	First,	SO4	additions	to	soil	add	

electron	acceptors,	thus	slowing	soil	reduction	(Majumdar,	2003).	Second,	the	product	of	SO4

reduction	(H2S)	may	inhibit	methanogenic	bacteria	and	thus	depress	CH4	production.	

Unfortunately,	most	studies	have	not	examined	the	effect	on	N2O	emissions.	

3.2.3 Land‐Use Change to Cropland

Conversion	from	one	land‐use	category	(e.g.,	forestland,	wetlands)	to	cropland	can	have	significant	

effects	on	the	GHG	emissions	and	removals	associated	with	the	land	under	conversion.	When	land	is	

converted	to	cropland,	there	is	often	a	loss	of	carbon,	an	increase	in	N2O	and	CH4	emissions,	a	

reduction	in	CH4	oxidation,	and	if	biomass	is	burned,	an	increase	in	non‐CO2	GHG	emissions.	A	

number	of	variables	influence	the	direction	and	magnitude	of	the	emissions	and	sinks	including	

prior	land	use,	climate,	and	management.	The	influence	of	land‐use	change	on	carbon,	nitrogen,	

methane,	and	non‐CO2	GHGs	are	discussed	below.		

3.2.3.1 Influence on Carbon Stocks

Land‐use	conversion	to	cropland	can	have	significant	effects	on	biomass,	litter,	and	soil	carbon	

(IPCC,	2000).	Houghton	et	al.	(1999)	estimated	that	land	clearance	in	the	United	States	has	led	to	a	

loss	of	27	Pg		C	to	the	atmosphere	since	the	1700s,	although	recently	some	carbon	has	been	

restored	with	conversion	of	cropland	back	to	other	uses	and	also	improved	soil	management	(U.S.	

EPA,	2010).	Clearing	forest	leads	to	a	large	loss	of	aboveground	and	belowground	biomass	and	

litter	C;	grassland	conversion	can	also	reduce	the	amount	of	carbon	in	these	pools,	but	to	a	lesser	

extent	than	forest	conversion	because	grasslands	have	less	biomass.	Soil	carbon	losses	can	be	

significant	with	conversion	to	cultivated	crop	management	(Davidson	and	Ackerman,	1993),	with	

relative	losses	in	temperate	regions	from	20	to	30	percent	on	average	(Ogle	et	al.,	2005).		

Ultimately,	the	net	influence	of	land	conversion	will	depend	on	the	previous	land	use,	vegetation	

composition,	and	management,	and	the	resulting	cropland	system	and	its	associated	vegetation	

composition	and	management.	For	example,	conversion	of	grassland	to	tree	crops,	such	as	

orchards,	may	lead	to	gains	in	carbon	relative	to	the	grassland	due	to	accumulation	of	carbon	in	

woody	biomass.		

3.2.3.2 Influence on Soil Nitrous Oxide

The	conversion	of	land	to	cropland	generally	accelerates	nitrogen	cycling,	with	subsequent	effects	

on	N2O	and	CH4	fluxes.	Soil	nitrogen	availability	is	the	factor	that	most	often	limits	soil	N2O	

emissions	(see	Section	3.2.1.1),	so	any	practice	that	increases	the	concentration	of	inorganic	

nitrogen	in	soil	is	likely	to	also	accelerate	N2O	emissions.	As	noted	above,	land‐use	change	typically	

results	in	faster	soil	organic	matter	turnover	and	associated	nitrogen	mineralization,	which	means	

that	even	in	the	absence	of	nitrogen	fertilizer,	soil	N2O	fluxes	will	be	higher	on	converted	land.	

Additional	nitrogen	from	fertilizers,	whether	synthetic	or	organic,	or	from	planted	legumes	will	

further	enhance	N2O	fluxes,	as	will	tillage—insofar	as	tillage	stimulates	nitrogen	mineralization.	

The	conversion	of	unmanaged	land	to	cellulosic	biofuel	production	may	avoid	additional	GHG	

loading	if	care	is	taken	to	avoid	soil	carbon	oxidation	and	excess	soil	nitrogen	availability	

(Robertson	et	al.,	2011).	This	might	occur,	for	example,	if	existing	perennial	vegetation	were	

harvested	for	feedstock	or	when	new	perennial	grasses	were	direct‐seeded	into	an	otherwise	

undisturbed	soil	profile,	and	when	no	or	minimal	nitrogen	inputs	are	used.	Although	the	current	

market	for	cellulosic	biomass	is	nascent	at	best,	as	it	develops	in	response	to	legislative	mandates	

and	energy	demand	there	will	be	pressure	to	convert	lands	now	unmanaged	into	biofuel	cropping	
 Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

 3-29 

systems.	Minimizing	the	GHG	impact	of	these	conversions	will	be	crucial	for	avoiding	long‐term	

carbon	debt	that	will	otherwise	lead	to	carbon	sources	rather	than	carbon	sinks,	irrespective	of	

their	capacity	to	generate	fossil	fuel	offset	credits	(Fargione	et	al.,	2008;	Gelfand	et	al.,	2011;	Pineiro	

et	al.,	2009).	

3.2.3.3 Influence on Methanotrophic Activity

Methanotrophic	bacteria	capable	of	consuming	(oxidizing)	atmospheric	CH4	are	found	in	most	

aerobic	soils.	CH4	uptake	in	soils	is	globally	important;	the	size	of	the	soil	sink	is	the	same	

magnitude	as	the	atmospheric	increase	in	CH4	(IPCC,	2001),	suggesting	that	significant	changes	in	

the	strength	of	the	soil	sink	could	significantly	affect	atmospheric	CH4	concentrations	if	uptake	

declines	due	to	land	use	and	management.	In	unmanaged	upland	ecosystems,	CH4	uptake	is	

controlled	largely	by	the	rate	at	which	it	diffuses	to	the	soil	microsites	inhabited	by	active	

methanotrophs.	Diffusion	is	regulated	by	physical	factors—principally	moisture	but	also	

temperature,	soil	structure,	and	the	concentration	of	CH4	in	the	soil.	

Agricultural	management	typically	diminishes	soil	CH4	oxidation	by	70	percent	or	more	(Mosier	et	

al.,	1991;	Robertson	et	al.,	2000;	Smith	et	al.,	2000)	for	at	least	as	long	as	the	soil	is	farmed.	The	

mechanism	for	this	suppression	is	not	well	understood;	likely	it	is	related	to	nitrogen	availability	as	

affected	by	enhanced	nitrogen	mineralization,	fertilizer,	and	other	nitrogen	inputs	(Steudler	et	al.,	

1989;	Suwanwaree	and	Robertson,	2005).	NH4+	is	known	to	competitively	inhibit	methane	

monooxygenase,	the	principal	enzyme	responsible	for	oxidation	at	atmospheric	concentrations.	

Microbial	diversity	also	seems	to	play	an	important	role	(Levine	et	al.,	2011).	

There	are	no	known	agronomic	practices	that	promote	soil	CH4	oxidation;	although	a	better	

understanding	of	the	mechanisms	responsible	for	its	suppression	may	eventually	suggest	

mitigation	opportunities.	To	date,	recovery	of	significant	CH4	oxidation	capacity	following	

agricultural	management	has	only	been	documented	decades	after	conversion	to	forest	or	

grassland;	complete	recovery	appears	to	take	a	century	or	longer	(Robertson	et	al.,	2000;	Smith	et	

al.,	2000).		

3.2.3.4 Non‐CO2 GHG Emissions from Burning

Burning	can	be	conducted	on	lands	in	preparation	for	cultivation	to	facilitate	access	for	equipment,	

remove	standing	accumulated	biomass,	and	provide	organic	material	(ash)	for	incorporation	into	

soils.	Burning	of	the	biomass	can	be	an	important	source	of	non‐CO2	GHGs	(N2O,	CH4)	as	well	as	

precursors	to	GHG	formation	(CO,	NOx)	following	additional	chemical	reactions	in	the	atmosphere	

or	soils.	More	information	on	burning	of	grazing	lands	vegetation	can	be	found	in	Section	3.3.1.5,	

and	burning	of	the	remaining	biomass	with	clearing	of	forest	can	be	found	in	Section	6.4.1.9.	

3.3 Grazing Land Management

Rangelands	are	defined	as	land	on	which	the	climax	or	potential	plant	cover	is	composed	

principally	of	native	grasses,	grass‐like	plants,	forbs	or	shrubs	suitable	for	grazing	and	browsing,	

and	introduced	forage	species	managed	for	grazing	and	browsing.	Conversely,	pasturelands	

represent	land	managed	primarily	for	the	production	of	introduced	forage	plants	for	livestock	

grazing,	with	management	consisting	of	fertilization,	weed	control,	irrigation,	reseeding	or	

renovation,	and	control	of	grazing	(USDA,	2009).	How	grazing	lands	are	managed	influences	the	

potential	for	carbon	sequestration	or	GHG	emissions.	The	paragraphs	below	highlight	some	of	the	

key	management	practices	and	their	associated	GHG	emissions	and	removals	summarizing	the	

current	state	of	the	science.		
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3.3.1 Management Activity Influencing GHG Emissions

Soil	organic	carbon	dominates	the	terrestrial	carbon	pool	in	grazing	lands.	Aboveground	carbon	is	<	

five	percent	of	the	total	ecosystem	carbon	pool	in	most	non‐woody	plant	dominated	ecosystems,	

but	up	to	25	percent	in	grassland‐shrubland	ecosystems.	Grazing	lands	can	be	carbon	sinks,	with	

rates	of	soil	organic	carbon	sequestration	up	to	0.5	Mg	C	ha‐1	year‐1	for	rangelands	(Derner	and	

Schuman,	2007;	Liebig	et	al.,	2010)	and	1.4	Mg	C	ha‐1	year‐1	for	pastures	(Franzluebbers,	2005;	

2010a).	Actual	rates	are	often	less	than	these	apparent	maximal	rates	of	soil	organic	carbon	

sequestration	due	to	management,	climate,	weather,	and	other	environmental	constraints.	

Potentially	high	rates	of	soil	organic	carbon	accumulation	are	predicted	in	newly	established	

pastures	and	restoration	of	degraded	rangelands,	while	improper	management	and	drought	can	

result	in	significant	carbon	releases.	Due	to	the	large	land	area,	the	movement	of	carbon	into	and	

out	of	the	soil	reservoir	in	grazing	land	can	be	an	important	component	of	the	global	carbon	cycle.	

In	addition	to	soil	organic	C,	a	large	pool	of	soil	inorganic	carbon	occurs	as	carbonates	in	semi‐arid	

and	arid	rangeland	soils	that	can	lead	to	either	sequestration	or	release	of	CO2	(Emmerich,	2003).	

However,	the	direction	and	magnitude	of	soil	inorganic	carbon	stocks	are	currently	poorly	

understood	(Follett	et	al.,	2001;	Liebig	et	al.,	2006;	Svejcar	et	al.,	2008).	

Two	important	management	factors	that	control	the	fate	of	soil	organic	carbon	in	grazing	lands	are	

long‐term	changes	in	production	and	quality	of	aboveground	and	belowground	biomass	that	can	

alter	the	quantity	of	nitrogen	available	and	the	C‐to‐N	ratio	of	soil	organic	matter	(Pineiro	et	al.,	

2010),	and	grazing‐induced	effects	on	vegetation	composition,	which	can	be	as	important	as	the	

direct	impact	of	grazing	(e.g.,	grazing	intensity)	on	soil	organic	carbon	sequestration	(Derner	and	

Schuman,	2007).	The	rate	of	soil	organic	carbon	sequestration	can	be	linear	for	decades	

(Franzluebbers	et	al.,	2012),	but	eventually	diminishes	to	a	steady‐state	level	with	no	further	

change	in	the	stock	following	several	decades	of	a	management	practice	(Derner	and	Schuman,	

2007).	Additional	positive	changes	in	management	or	inputs	are	often	needed	to	sequester	

additional	soil	organic	carbon	(Conant	et	al.,	2001),	but	negative	changes	in	management	causing	

loss	of	soil	structure	and	surface	litter	cover	can	lead	to	erosion	and	loss	of	productivity	resulting	in	

a	decline	in	soil	organic	carbon	(Pineiro	et	al.,	2010).	

Methane	flux	from	grazing	lands	is	controlled	by	the	balance	of	enteric	and	manure	emissions	from	

ruminant	animals	and	uptake	of	CH4	by	soil.	(Emissions	and	methods	for	estimating	CH4	emissions	

from	ruminants	are	discussed	further	in	Section	5.3).	In	the	western	United	States,	grasslands	have	

greater	CH4	uptake	by	soil	than	do	neighboring	croplands	(Liebig	et	al.,	2005),	probably	due	to	

greater	surface	soil	organic	matter	that	promotes	the	growth	of	methanotrophic	bacteria.	In	an	

assessment	of	GHG	emissions	from	three	grazing	land	systems	in	North	Dakota,	enteric	emissions	of	

CH4	from	grazing	cattle	were	three	to	nine	times	greater	(on	a	CO2	equivalent	basis)	than	CH4

uptake	by	soil	(Liebig	et	al.,	2010).	With	CH4	emissions	directly	tied	to	number	of	cattle,	fertilized	

grasslands	are	often	a	net	carbon	source	due	to	enhanced	CH4	emission	from	cattle	and	potentially	

greater	N2O	emissions,	while	unfertilized	grasslands	are	often	a	net	carbon	sink	(Luo	et	al.,	2010;	

Tunney	et	al.,	2010).	

3.3.1.1 Livestock Grazing Practices

Livestock	grazing	practices	(i.e.,	stocking	rate	and	grazing	method)	are	summarized	below	along	

with	data	on	the	influence	these	practices	have	on	GHG	emissions	and	removals.	

Stocking Rate:	Stocking	rate	is	the	number	of	animals	per	management	unit	utilized	over	a	specified	

time	period,	e.g.,	number	of	steers	per	acre	per	month.	Based	on	published	studies,	responses	of	soil	

organic	carbon	to	stocking	rate	and	grazing	intensity	have	been	variable,	despite	grazing	either	

causing	an	increase	or	having	little	effect	on	the	more	commonly	measured	property	of	soil	bulk	

density	(Greenwood	and	McKenzie,	2001;	Schuman	et	al.,	1999).	In	northern	mixed‐grass	prairie,	
 Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

 3-31 

soil	organic	carbon	has	increased	in	grazed	compared	with	ungrazed	areas,	partly	resulting	from	

increasing	dominance	of	shallow‐rooted,	grazing‐resistant	species,	such	as	blue	grama	(Bouteloua

gracilis),	which	incorporates	a	larger	amount	of	root	mass	in	the	upper	soil	profile	than	the	mid‐

grass	species	that	it	replaces	during	grazing	(Derner	et	al.,	2006).	Further	research	is	needed	to	

determine	the	extent	of	different	root	distributions	on	total	carbon	storage	in	an	entire	soil	profile.	

Increasing	stocking	rate	beyond	an	optimum	for	achieving	maximum	livestock	production	per	unit	

land	area	(Bement,	1969;	Dunn	et	al.,	2010)	would	be	expected	to	result	in	a	loss	of	soil	organic	

carbon	due	to	reduced	plant	vigor	and	root	distribution	in	the	soil	profile.	With	suboptimal	stocking	

rate,	vigor	of	pasture	forages	may	decline	as	plant	residues	develop	a	thick	litter	layer	at	the	soil	

surface.	However,	in	semi‐arid	regions,	the	high	UV	light	intensity	may	significantly	reduce	litter	on	

the	soil	surface	through	photochemical	decomposition	processes,	regardless	of	grazing	intensity	

(Brandt	et	al.,	2010).	Vegetation	composition	shifts	that	change	the	quantity	and	quality	of	plant	

material	produced	can	influence	the	amount	of	carbon	inputs	to	soils.	In	managed	pastures,	it	has	

been	shown	that	soil	organic	carbon	can	be	optimized	with	a	moderate	stocking	rate	compared	

with	no	grazing	or	heavy,	continuous	grazing	(Franzluebbers,	2010b).	An	optimized	stocking	rate	

for	a	particular	region	(climatic	conditions),	vegetation	composition,	and	soil	type	is	thought	to	

maximize	the	amount	of	soil	organic	carbon	sequestered.		

Limited	evidence	shows	that	grazing	at	moderate	levels	can	further	increase	environmental	

benefits	over	those	of	grassland	establishment	alone,	in	addition	to	providing	an	important	

economic	return	to	producers.	If	soil	organic	carbon	were	to	decline	with	overgrazing,	there	would	

also	be	a	decline	in	animal	productivity	due	to	lack	of	forage.		Therefore,	a	negative	relationship	

between	soil	organic	carbon	storage	and	animal	productivity	is	likely	when	grazing	intensity	

exceeds	a	moderate	level.		This	response	is	likely	modified	under	moderate	grazing	pressure	due	to	

the	fact	that	greater	animal	product	per	head	can	be	achieved	with	lower	GHG	emissions.	Limiting	

the	effect	of	high	stocking	rate	on	soil	organic	carbon	levels	may	be	achievable	with	high	nitrogen	

fertilizer	inputs,	an	outcome	with	an	uncertain	carbon	footprint	relative	to	GHG	intensity.	Stocking	

rate	and	fertilizer	nitrogen	input	interactions	need	to	be	quantified	to	accurately	assess	total	GHG	

intensity.	Some	evidence	in	the	humid	United	States	suggests	that	overgrazing	can	lead	to	increased	

soil	erosion	and	a	reduction	in	soil	quality.	Literature	from	other	regions	has	also	shown	increasing	

soil	erosion	and	declining	soil	quality	with	excessive	stocking	rates.	While	evidence	is	lacking,	an	

assumption	is	that	soil	organic	carbon	follows	this	same	positive	response	to	moderate	grazing	and	

negative	response	to	overgrazing.		

Emissions	of	N2O	from	grazing	lands	are	affected	by	grazing,	but	net	flux	can	be	increased	or	

decreased,	depending	on	stocking	rate,	grazing	system,	and	season	(Allard	et	al.,	2007).	Stocking	

rate	had	little	influence	on	N2O	emissions	from	mixed‐grass	prairie	in	North	Dakota	(Liebig	et	al.,	

2010).	While	elevated	N2O	emissions	may	be	expected	under	increased	stocking	rate,	Wolf	et	al.	

(2010)	suggested	that	grazing	can	counteract	potential	N‐induced	emissions	on	rangelands	by	

reducing	surface	biomass,	resulting	in	more	extreme	soil	temperatures,	lower	soil	moisture,	and	

corresponding	inhibition	of	microbial	activity	responsible	for	N2O	emissions.	If	grazing	intensity	on	

pastures	were	viewed	as	a	fertilizer	effect	with	increasing	animal	manure	deposition,	then	N2O	flux	

from	a	grazing	effect	does	not	behave	in	the	same	manner	as	manufactured	nitrogen	fertilizer	

inputs.	Interactions	between	stocking	rate	and	nitrogen	fertilizer	inputs	have	not	been	quantified,	

despite	such	diversity	in	management	likely	occurs	among	producers.		Stocking	rate	and	manure	

and	fertilizer	nitrogen	inputs	are	areas	requiring	further	research	to	better	understand	the	complex	

set	of	controlling	factors	in	addition	to	soil	texture	and	environmental	conditions	on	N2O	emissions	

in	grazing	lands.	On	rangelands,	the	abundance	of	N‐fixing	legumes	in	the	plant	community	

becomes	more	critical	for	increasing	SOC,	particularly	since	fertilizer	additions	and	manure	are	not	

as	significant	for	returning	nitrogen	to	the	soil	compared	to	pasture	systems.	This	is	an	area	

requiring	further	research	to	better	understand	the	controlling	factors	on	N2O	emissions.	
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Grazing Method:	Grazing	methods	vary	based	on	producer	goals	and	the	type	of	forage	available	

(Scheaffer	et	al.,	2009).	Two	distinct	grazing	methods,	continuous	and	rotational	grazing,	represent	

the	prevalent	methods	employed	on	grazing	lands	in	the	United	States	to	manage	the	

livestock.	Continuous	grazing	allows	animals	to	freely	move	and	have	full	access	to	a	grazing	area,	

whereas	rotational	grazing	is	more	controlled,	involving	movement	of	animals	based	on	monitoring	

forage	condition,	such	as	plant	height,	between	two	or	more	paddocks	subdivided	from	a	larger	

grazing	area.	Rotational	grazing	terminology	has	been	confused	with	terms	such	as	holistic	grazing,	

planned	grazing,	prescribed	grazing,	and	management‐intensive	grazing,	which	continue	to	be	used	

with	multiple	and	ambiguous	meanings	despite	attempts	to	standardize	definitions	(SRM,	1998).		

Terms	to	define	intentions	of	rotational	grazing	systems	include	rest‐rotation,	deferred‐rotation,	

high‐intensity‐short‐duration,	and	season‐long	grazing	(Briske	et	al.,	2008;	Briske	et	al.,	2011).	

Here	we	define	rotational	grazing	as	the	movement	of	livestock	between	two	or	more	subunits	of	

grazing	land	such	that	alternating	periods	of	grazing	and	no	grazing	(‘rest’)	occur	within	a	single	

growing	season	(Heitschmidt	and	Taylor,	1991).		

Rotational	grazing	limits	plants	from	reaching	reproductive	stages	in	which	forage	quality	rapidly	

declines.	This	contrasts	with	continuous	grazing	in	which	there	is	more	selective	grazing	of	the	

highest	quality	forages.	As	such,	forage	quality	may	be	maintained	at	a	high	level	longer	into	the	

growing	season.	Therefore,	rotational	stocking	in	the	humid	United	States	could	provide	more	

uniform	forage	consumption	across	pastures	and	allow	sufficient	rest	to	forage	species	between	

grazing	events	to	promote	greater	production.	Pastures	with	greater	plant	production	via	an	

improved	stocking	method	would	be	expected	to	have	lower	soil	erosion	and	greater	soil	organic	

carbon	storage.	Although	these	expectations	seem	intuitive,	there	are	limited	data	in	the	scientific	

literature	to	support	them.	Two	studies	have	suggested	an	increase	in	soil	organic	carbon	with	

rotational	grazing	compared	with	continual	season‐long	grazing	(Conant	et	al.,	2003;	Teague	et	al.,	

2010),	and	another	study	found	no	difference	between	systems	(Manley	et	al.,	1995).	Since	

rotational	grazing	data	are	mostly	available	for	rangeland	and	few	studies	conducted	on	pastures,	

there	is	not	enough	evidence	to	evaluate	how	rotational	grazing	might	affect	soil	organic	carbon	in	

pastures.	Given	that	the	preponderance	of	evidence	suggests	that	rotational	grazing	does	not	

influence	vegetation	production	in	rangelands	(Briske	et	al.,	2008),	changes	in	soil	organic	carbon	

with	rotational	grazing	would	be	expected	only	if	substantial	vegetation	change	occurred	

independently	from	stocking	rate.	Rangelands	typically	have	a	much	higher	diversity	and	multiple	

growth	patterns	of	forbs,	cool‐season	and	warm‐season	grasses,	which	would	result	in	a	smaller	

influence	of	stocking	method	on	vegetation	phenology	(i.e.,	keeping	forage	in	a	vegetative	rather	

than	a	reproductive	state)	than	would	occur	in	monoculture	or	simple	mixtures	of	forages	in	

pastures.	Much	more	research	on	grazing	method	is	needed,	due	to	the	high	adoption	rate	and	

promotion	of	the	benefits	of	improved	grazing	methods	for	soil	organic	carbon	sequestration	by	

producers	and	agricultural	advisors	(Beetz	and	Rhinehart,	2010).	

3.3.1.2 Forage Options

Cool‐	and	warm‐season	forages	have	growth	activity	at	different	times	of	the	year,	thereby	affecting	

when	root	and	litter	carbon	inputs	are	supplied	to	soil.	Depending	on	environmental	growing	

conditions	(i.e.,	relatively	short,	cool,	and	wet	summer	with	long,	cold	winter	versus	long,	hot,	and	

dry	summer	with	mild,	wet	winter),	the	performance	of	cool‐	versus	warm‐season	forages	will	vary	

across	regions.	In	the	southeastern	United	States,	perennial	cool‐season	forages	(e.g.,	tall	fescue)	

have	produced	greater	soil	organic	carbon	than	warm‐season	forage	(e.g.,	bermudagrass)	in	grazing	

land	systems,	despite	the	more	vigorous	growing	habit	of	bermudagrass	(Franzluebbers	et	al.,	

2000).	This	result	is	likely	due	to	the	opportunities	of	forages	for	growth	and	the	balance	of	water	

in	soil	that	remains	for	microbial	decomposition	of	organic	matter.	
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 3-33 

Timing	of	forage	grazing	can	affect	plant	productivity,	wildlife	habitat,	and	compaction	of	soil.	Each	

of	these	effects	can,	in	turn,	affect	soil	organic	carbon	sequestration	and	GHG	emissions.	The	

capacity	of	soil	to	withstand	compaction	forces	of	animal	treading,	resulting	in	significant	

deformation,	destabilization,	loss	of	infiltration	capacity,	and	soil	organic	carbon	sequestration,	can	

be	exceeded—especially	under	wet	conditions	(Bilotta	et	al.,	2007).	Soil	saturation	during	winter	

and	spring	lead	to	severe	effects	from	animal	trampling.	In	northern	latitudes	and	rangelands	of	the	

western	United	States	subject	to	freeze‐thaw	cycles,	sandy	and	loamy	soils	are	less	likely	to	be	

affected	by	the	negative	impacts	of	compaction.	Intuitively,	deferring	grazing	to	periods	of	limited	

active	forage	growth	(e.g.,	winter	and	spring)	might	contribute	to	increased	soil	compaction.	

However,	allowing	forage	to	accumulate	to	full	canopy	prior	to	grazing	might	be	beneficial	to	

controlling	erosion	by	providing	a	longer	period	of	forage	and	residue	cover.	Grazing	of	winter	

cover	crops	may	also	be	an	effective	farm‐diversity	strategy,	but	the	effects	on	soil	erosion	control	

and	soil	condition	need	to	be	quantified.	Wildlife	management	guidelines	on	rangeland	suggest	

longer‐term	(>	one	year)	rest	to	accumulate	vegetation	structure	for	certain	birds	needing	habitat.	

Timing	of	grazing	could	be	a	critical	factor	in	controlling	compaction,	susceptibility	to	erosion,	and	

soil	organic	carbon	sequestration,	so	the	sequence	of	when	pastures	are	grazed	should	be	rotated	

among	years	to	ensure	that	plant	communities	are	not	always	grazed	at	the	same	time	to	ensure	

greater	community	sustainability.	

Organic	matter‐rich	surface	soil	absorbs	compactive	forces	of	grazing	much	like	a	sponge,	in	which	

soil	often	rebounds	in	volume	once	forces	are	removed.	However,	effects	of	winter	grazing	of	

deferred	growth	may	be	different	in	colder	than	in	warmer	regions:	frozen	soil	may	avoid	

compaction,	but	nutrient	runoff	may	become	more	important	(Clark	et	al.,	2004).	In	the	southern	

United	States,	perennial	cool‐season	grasses	are	often	grazed	during	late	winter	and	throughout	

spring	during	typically	wet	conditions,	but	due	to	active	forage	growth,	soil	can	also	dry	quickly	and	

trampling	may	not	always	cause	damage.	In	Georgia,	soil	organic	carbon	was	greater	under	long‐

term	stands	of	cool‐season	tall	fescue	(typically	grazed	in	spring	and	autumn)	than	under	warm‐

season	bermudagrass	(typically	grazed	in	summer)	(Franzluebbers	et	al.,	2000).	

In	the	southeastern	United	States,	annual	cool‐season	forages	are	often	planted	as	a	cover	crop	

following	summer	crops	or	sod‐seeded	into	perennial	grass	pastures.	This	practice	can	enhance	

forage	production	and	should	increase	soil	organic	C,	although	limited	data	are	available	to	support	

this	conclusion.	In	an	integrated	crop/livestock	system	in	the	southeastern	United	States,	there	was	

a	limited	effect	of	grazing	annual	cover	crops	on	soil	organic	C,	either	in	the	summer	or	winter	

compared	with	ungrazed	cover	crops	(Franzluebbers	and	Stuedemann,	2009).	

3.3.1.3 Irrigation

Water	is	a	limiting	factor	in	the	ability	of	plants	to	fix	carbon	and	subsequently	produce	the	carbon	

input	necessary	to	accumulate	soil	organic	C.	It	is	also	a	factor	limiting	decomposition	of	soil	

organic	C.	While	the	extent	of	irrigation	in	grazing	lands	is	limited,	where	it	occurs	there	are	

consequences	for	soil	organic	carbon	storage.	For	example,	some	productive	meadows	in	the	

western	United	States	are	irrigated.	How	irrigation	affects	soil	organic	carbon	will	depend	on	the	

quantity,	frequency,	and	timing	of	irrigation	events.	Irrigation	only	at	peak	plant	growth	stages	will	

likely	cause	a	much	greater	positive	impact	on	forage	carbon	fixation	than	a	negative	impact	on	soil	

organic	carbon	decomposition.	In	the	same	manner,	irrigation	quantity,	frequency,	and	timing	will	

likely	affect	N2O	and	CH4	emissions,	although	pulsed	responses	of	these	GHGs	could	likely	be	much	

more	dramatic.	Unfortunately,	there	are	only	limited	studies	on	these	potential	impacts.	See	Section	

3.2.1.4	for	more	information	on	irrigation	methods.	

In	a	comparison	of	agricultural	systems	with	surrounding	arid	and	semi‐arid	natural	vegetation,	

Entry	et	al.	(2002)	found	that	soil	organic	carbon	was	greater	in	irrigated	agricultural	systems	due	
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems 

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to	enhanced	productivity.	Emission	of	N2O	from	irrigated	systems	occurs	following	closely	timed	

irrigation	and	nitrogen	fertilizer	applications	in	cropland	conditions,	and	this	would	be	expected	

under	grazing	lands	as	well,	but	there	are	few	data	available	(Liebig	et	al.,	2006;	Liebig	et	al.,	2012).		

3.3.1.4 Nutrient Management (Synthetic and Organic)

Fertilizers	are	often	applied	to	pastures,	due	to	the	high	yield	response	with	adequate	precipitation,	

but	are	less	common	in	western	rangelands	due	to	inconsistent	yield	response	and	risky	cost‐

effectiveness	with	limited	and	variable	precipitation.	Nitrogen	availability	in	soil	determines	to	a	

large	extent	the	emissions	of	N2O.	Grazing	lands	typically	have	lower	nitrogen	availability	in	soil	

than	croplands,	and	therefore	have	lower	N2O	emissions	(Liebig	et	al.,	2005).	However,	application	

of	fertilizer	nitrogen	to	rangeland	has	been	found	to	consistently	stimulate	N2O	emissions	(Flechard	

et	al.,	2007).	Liebig	et	al.	(2010)	observed	two‐fold	greater	N2O	emissions	from	fertilized	crested	

wheatgrass	compared	with	unfertilized	mixed‐grass	prairie.	Addition	of	fertilizer	nitrogen	to	

pasture	in	Michigan	had	a	negligible	effect	on	N2O	emissions	(Ambus	and	Robertson,	2006),	

whereas	application	of	poultry	manure	on	a	bermudagrass	pasture	in	Arkansas	increased	N2O	

emissions	by	45	percent	compared	with	pasture	without	manure;	N2O	flux	and	soil	nitrate	

dynamics	were	positively	associated	(Sauer	et	al.,	2009).	A	strategy	to	reduce	soil	nitrate	by	

interseeding	annual	ryegrass	on	manure‐amended	soil	decreased	N2O	emissions	by	50	percent.	

Similar	to	cropland,	reducing	soil	nitrate	to	low	levels	during	periods	of	low	root	activity	and	high	

levels	during	periods	of	high	root	activity	will	generally	enhance	plant	nitrogen	uptake	and	reduce	

N2O	emissions.	Application	of	composted	green	waste	could	sequester	C,	but	this	research	topic	has	

not	been	fully	evaluated.	A	significant	increase	in	soil	organic	carbon	has	only	been	demonstrated	at	

one	of	two	sites	in	California	(Ryals	et	al.,	2014).		From	model	simulations,	compost	application	has	

been	shown	to	reduce	the	overall	GHG	emission	on	CO2	equivalent	basis,	by	sequestering	carbon	

and	reducing	N2O	emissions,	while	manure	slurry	and	inorganic	fertilizer	applications	led	to	net	

GHG	emissions	on	CO2	equivalent	basis	(DeLonge	et	al.,	2013).	For	more	information	on	

management	options	associated	with	fertilization	practices,	see	Section	3.2.1.1.	

3.3.1.5 Prescribed Fires

Burning	has	the	potential	to	alter	soil	organic	carbon	through	effects	on	photosynthesis,	soil,	and	

canopy	respiration,	and	through	species	changes,	in	addition	to	stabilizing	or	increasing	livestock	

gains,	improving	habitat	diversity,	and	reducing	fuel	loads	(Boutton	et	al.,	2009;	Toombs	et	al.,	

2010).	Although	carbon	loss	from	burning	grazing	lands	is	a	minor	component	of	the	annual	carbon	

emissions,	burning	rangelands	with	a	significant	woody	aboveground	plant	biomass	can	result	in	

substantial	immediate	ecosystem	carbon	loss	(Bremer	and	Ham,	2010;	Rau	et	al.,	2010).	However,	

prescribed	burning	of	grazing	lands	could	also	affect	long‐lived	char	that	accumulates	in	soil,	and	

therefore	would	influence	soil	carbon	stocks.	Burning	also	leads	to	non‐CO2	GHG	emissions,	which	

can	be	significant	due	to	the	higher	global	warming	potential	of	these	gases	compared	with	CO2

(IPCC,	2006).	For	more	information	on	non‐CO2	GHG	emissions	from	burning,	see	Section	3.2.3.4.	

3.3.1.6 Erosion Control

Riparian	buffers	can	be	a	significant	sink	for	excess	nutrients	running	off	neighboring	grazing	lands.	

The	fate	of	nutrients	is	dependent	on	the	flow	characteristics	and	type	of	vegetation.	Excess	nitrate	

in	saturated	soil	of	riparian	areas	can	lead	to	significant	N2O	emissions—although	these	emissions	

are	typically	treated	as	indirect,	with	the	emissions	associated	with	the	field	or	livestock	facility	

that	is	contributing	the	excess	nutrients	(See	Section	3.2.1.1).	Transport	of	soluble	carbon	into	

riparian	areas	could	also	enhance	CH4	emissions	from	saturated	soil.	
 Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

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3.3.1.7 Management of Drained Wetlands

Drainage	of	wetland	or	hydric	soils	that	are	used	for	grazing	has	implications	for	soil	organic	

carbon	and	GHG	emissions,	similar	to	drainage	for	crop	production.	The	water	regime	and	plant	

communities	are	significantly	altered	and	soils	are	modified	from	anaerobic	to	aerobic	conditions.	

Increasing	oxygen	in	soil	will	cause	organic	matter	to	decompose	more	rapidly	than	under	

saturated	conditions,	resulting	in	release	of	CO2	(Eagle	et	al.,	2010;	Franzluebbers	and	Steiner,	

2002;	IPCC,	2006;	Liebig	et	al.,	2012).	Large	emissions	of	CO2	result	from	drainage	of	wetlands	

(Allen,	2007;	2012),	and	drainage	can	also	increase	nitrogen	mineralization	and	enhance	N2O	

emissions	directly	(IPCC,	2006).	Emissions	of	CH4	are	reduced	considerably	with	drainage,	but	this	

impact	is	often	not	considered	in	estimation	of	GHG	emissions	(IPCC,	2006).	A	large	proportion	of	

grassland	wetlands	have	been	directly	drained	or	modified	to	enhance	agricultural	production	

(Dahl	and	Johnson,	1991),	and	many	other	wetlands	are	indirectly	affected	by	subsurface	tile	drains	

and	agricultural	practices	in	surrounding	catchments.	See	Section	3.2.1.6	for	more	information	

about	management	of	drained	soils.	

3.3.1.8 Lime Amendments

Lime	amendments	are	needed	when	soil	pH	is	low	(e.g.,	pH<5)	to	enhance	productivity	and	support	

balanced	nutrient	levels	in	grazing	land	soils.	Typical	liming	materials	in	grazing	lands	are	calcitic	

limestone	(CaCO3),	dolomitic	limestone	(CaMg(CO3)2),	and	confined	livestock	manure,	particularly	

poultry	litter,	which	has	liming	activity	from	lime	additive	to	the	feed	ration.	When	carbonate	lime	

is	applied	to	soil	it	dissolves	in	solution	over	time,	with	the	cation	and	carbonate	dissociating.	There	

is	potential	for	releasing	CO2	to	the	atmosphere	depending	on	whether	the	lime	reacts	with	

carbonic	or	nitric	acid	in	the	soil	solution.	The	enhanced	plant	nutrient	offered	by	liming	can	have	a	

net	positive	effect	on	the	carbon	balance	for	an	extended	period	of	time.	See	Section	3.2.1.7	for	

more	information	on	lime	and	the	consequences	for	GHG	emissions.	

3.3.1.9 Woody Plant Encroachment

Woody	plant	encroachment3	leads	to	carbon	accumulation	in	above‐ground	and	root	biomass	and	

may	increase	overall	ecosystem	carbon	storage,	but	can	degrade	agricultural	productivity	of	

grazing	land	(McClaran	et	al.,	2008).	Over	the	past	century	in	western	rangelands,	soil	organic	

carbon	has	increased	in	near‐surface	soils	with	woody	plant	encroachment	(Boutton	et	al.,	2009;	

Creamer	et	al.,	2011;	Liao	et	al.,	2006;	Liebig	et	al.,	2012).	Removal	of	woody	plants	by	fire	or	other	

mechanisms	depletes	these	shallow,	relatively	susceptible	soil	organic	carbon	stores	associated	

with	encroachment	(Neff	et	al.,	2009;	Rau	et	al.,	2010);	but	does	not	have	an	effect	on	SOC	or	total	

nitrogen	stocks	at	depths	of	>20	cm	(Dai	et	al.,	2006).	Regardless,	removal	of	the	woody	plants	will	

cause	a	decline	in	aboveground	biomass	carbon	stocks	(Rau	et	al.,	2010).	

In	a	summary	of	research	on	CH4	emissions	from	grazing	lands,	Liebig	et	al.	(2012)	reported	CH4

uptake	under	mesquite,	but	net	CH4	production	under	grassland	and	dead	mesquite	stumps.	

Methane	uptake	under	mesquite	was	associated	with	reduced	soil	bulk	density	and	increased	soil	

moisture	(McLain	and	Martens,	2006),	as	well	as	greater	nitrogen	accrual/accumulation	associated	

in	the	area	around	mesquite	plants	(10	meters)	(Boutton	and	Liao,	2010;	Liao	et	al.,	2006;	Liu	et	al.,	

2010).	Methane	uptake	under	mesquite	was	also	associated	with	altered	soil	microbial	

communities	(Hollister	et	al.,	2010;	Liao	and	Boutton,	2008),	which	can	affect	NOx	andN2O	rates,	

while	CH4	production	from	grassland	and	woody	detritus	was	likely	caused	by	termite	activity.	The	

																																																													

3	Woody	encroachment	will	eventually	lead	to	a	transition	from	grazing	land	to	a	forest.	See	Chapter7:	Land	

Use	Change	for	definition	of	forestland	to	determine	when	woody	encroachment	will	lead	to	a	transition	to	

forestland.	
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems 

3-36

role	of	mesquite	to	fix	N,	thereby	altering	nitrogen	dynamics,	resulted	in	N2O	emissions	under	

mesquite	canopy	four‐fold	greater	than	under	grasses	or	woody	detritus	(McLain	et	al.,	2008).	

3.3.2 Land‐Use Change to Grazing Lands

Land‐use	conversion	to	grazing	lands	influences	the	carbon	stocks	and	GHG	emissions	of	a	parcel.	

Prior	land	use,	climate,	soil	type,	and	management	practices	are	just	a	few	of	the	factors	influencing	

the	magnitude	and	direction	of	GHG	emissions	and	removals	resulting	from	a	land‐use	conversion	

to	grazing	lands.	The	paragraphs	below	summarize	the	current	state	of	the	science	on	the	influence	

of	a	land‐use	conversion	on	carbon	stocks,	soil	N2O,	CH4,	and	non‐CO2	GHGs	resulting	from	biomass	

burning.		

3.3.2.1 Influence on Carbon Stocks

Establishment	of	pastures	on	previous	cropland	helps	reduce	soil	erosion	and	improves	soil	quality	

(Singer	et	al.,	2009).	There	is	substantial	evidence	that	establishment	of	pastures	leads	to	

significant	soil	organic	carbon	sequestration.	The	rate	of	accumulation	across	a	number	of	studies	

averaged	0.84	Mg	C	ha‐1	year‐1	(Franzluebbers,	2010a).	Literature	is	inadequate	to	determine	

whether	forage	composition	or	soil	type	have	a	discernible	influence	on	soil	organic	carbon	stock	

(see	Section	3.3.1.2).	The	quantity	of	forage	produced	and	the	quantity	of	residues	from	surface	

litter	and	root	biomass	are	likely	key	determinants	of	soil	organic	carbon	accumulation.	These	

quantities	can	be	influenced	by	factors	such	as	forage	mixture,	climatic	conditions,	soil	type,	

inherent	soil	fertility,	fertilizer	application,	and	liming.	

3.3.2.2 Influence on Soil Nitrous Oxide

Depending	upon	previous	land	use,	grassland	establishment	may	or	may	not	affect	net	N2O	

emissions	during	land‐use	change.	In	general,	emissions	of	N2O	are	controlled	by	soil	nitrogen	

availability	with	additional	influence	of	soil	oxygen	and	soluble	carbon	availability.	If	the	previous	

land	use	was	for	example,	a	nutrient‐limited	forest,	converted	subsequently	to	high‐fertility	

pasture,	then	N2O	emissions	would	likely	increase.	If	the	previous	land	use	was	nutrient‐rich	

cropland	converted	to	pasture,	then	N2O	emissions	would	likely	decline	due	to	greater	opportunity	

for	perennial	forage	species	to	assimilate	available	soil	nitrogen	and	thus	reduce	opportunities	for	

soil	nitrogen	transformations	to	N2O.	This	is	an	area	requiring	further	research	to	obtain	

quantitative	responses,	however.	

3.3.2.3 Influence on Methanotrophic Activity

Land‐use	change	to	grazing	land,	particularly	from	forestland,	may	involve	fertilization	to	enhance	

forage	production.	Nitrogen	fertilization	causes	a	reduction	of	methanotrophic	activity	in	soils	and	

therefore	reduces	the	uptake	of	CH4	from	the	atmosphere	(Ambus	and	Robertson,	2006).	See	

Section	3.2.3.3	for	more	information	on	the	impact	of	land‐use	change	on	methanotrophic	activity.	

3.3.2.4 Non‐CO2 GHG Emissions from Burning

Biomass	burning	in	grazing	land	can	be	an	important	source	of	GHGs	(CO2,	N2O,	CH4)	(Aalde	et	al.,	

2006;	Andreae	and	Merlet,	2001;	Badarinath	et	al.,	2009;	IPCC,	2006).	While	conversion	of	cropland	

to	grazing	land	rarely	involves	burning,	conversion	of	forest	to	grazing	land	can	involve	burning	of	

the	wood	and/or	slash	left	from	land	clearing.	The	effect	on	GHG	emissions	from	biomass	burning	is	

discussed	further	in	the	cropland	section	(Section	3.2.3.4)	and	in	the	forestland	section	(Section	

6.4.1.9).	
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3.4 Agroforestry

Agroforestry	represents	a	unique	case	within	GHG	accounting,	encompassing	both	forest	and	

agricultural	components,	along	with	many	combinations	of	their	respective	management	activities	

(Table	3‐1	and	Table	3‐2).	Agroforestry	is	defined	within	the	United	States	as	an	“intensive	land‐use	

management	that	optimizes	the	benefits	(physical,	biological,	ecological,	economic,	and	social)	from	

biophysical	interactions	created	when	trees	and/or	shrubs	are	deliberately	combined	with	crops	

and/or	livestock”	(Gold	and	Garrett,	2009).	Another	way	of	looking	at	agroforestry	is	as	a	set	of	

tree‐based4	conservation/production	practices	combined	into	bigger	agricultural	operations,	

providing	forest‐derived	functions	and	interacting	with	agriculture‐derived	functions	in	support	of	

agricultural	land	use.	While	providing	many	other	services	(see	Table	3‐3),	agroforestry	can	

contribute	to	carbon	sequestration,	GHG	mitigation,	and	adaptation	to	shifting	climate	(CAST,	2011;	

IPCC,	2000;	Morgan	et	al.,	2010;	Verchot	et	al.,	2007).		

Table 3‐3: Six Categories of Agroforestry Practices Practiced in the United States

Practice Descriptiona Benefitsb

Alley	cropping	

Trees	or	shrubs	planted	in	

sets	of	single	or	multiple	

rows	with	agronomic,	

horticultural	crops,	or	

forages	produced	in	the	

alleys	between	the	sets	of	

woody	plants	that	produce	

additional	products	

 Produce	annual	and	higher‐value	but	longer‐term

crops	for	diversification	of	income

 Enhance	microclimate	conditions	to	improve	crop	or

forage	quality	and	quantity

 Reduce	surface	water	runoff	and	erosion

 Improve	soil	quality	by	increasing	utilization	and

cycling	of	nutrients

 Alter	subsurface	water	quantity	or	water	table

depths

 Enhance	wildlife	and	beneficial	insect	habitat

 Decrease	offsite	movement	of	nutrients	or	chemicals

 Increase	carbon	storage	in	plant	biomass	and	soils

 Improve	air	quality

Forest	farming	

(also	called	multi‐

story	cropping)	

Existing	or	planted	stands	

of	trees	or	shrubs	that	are	

managed	as	an	overstory	

with	an	understory	of	

woody	and/or	non‐woody	

plants	that	are	grown	for	a	

variety	for	products	

 Improve	crop	diversity	by	growing	mixed	but

compatible	crops	having	different	heights	on	the

same	area

 Improve	soil	quality	by	increasing	utilization	and

cycling	of	nutrient	and	maintaining	or	increasing	soil

organic	matter

 Increase	net	carbon	storage	in	plant	biomass	and

soil

Riparian	forest	

buffersc		

(combines	Natural	

Resources	

Conservation	

Service	Practice	

Standards:	

Riparian	Forest	

Buffer	and	Filter	

Strip)	

A	combination	of	trees,	

shrubs,	and	grasses	

established	on	the	banks	of	

streams,	rivers,	wetlands,	

and	lakes	

 Decrease	offsite	movement	of	nutrients	or	chemicals

 Stabilize	streambanks

 Enhance	aquatic	and	terrestrial	habitats

 Provide	economic	diversification	either	through

plant	production	or	recreational	fees

 Increase	carbon	storage	in	plant	biomass	and	soils

4	Also	referred	to	as	trees‐outside‐forests,	the	term	“tree”	here	includes	both	tree	and	shrubs	(Bellefontaine	et	

al.,	2002).	
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3-38

Practice Descriptiona Benefitsb

Silvopasture	

Trees	combined	with	

pasture	and	livestock	

production	

 Provide	diversification	of	crops	in	time	and	space

 Produce	annual	and	higher‐value	but	longer‐term	

crops	

 Decrease	offsite	movement	of	nutrients	or	chemicals	

Windbreaks		

(also	referred	to	as	

shelterbelts)	

Linear	plantings	of	trees	

and	shrubs	to	form	barriers	

to	reduce	wind	speed	(	may	

be	specifically	referred	to	

as	crop	or	field	windbreak,	

livestock	windbreak,	living	

snowfence,	or	farmstead	

windbreak,	depending	on	

the	primary	use)	

 Control	wind	erosion

 Protect	wind‐sensitive	crops	

 Enhance	crop	yields		

 Reduce	animal	stress	and	mortality	

 Serve	as	a	barrier	to	dust,	odor,	and	pesticide	drift	

 Conserve	energy		

 Provide	snow	management	benefits	to	keep	roads	

open	or	harvest	moisture	

Special	

applications	

Use	of	agroforestry	

technologies	to	help	solve	

special	concerns,	such	as	

disposal	of	animal	wastes	

or	filtering	irrigation	

tailwater,	while	producing	

a	short‐	or	long‐rotation	

woody	crop	

 Treat	municipal	and	agricultural	wastes	

 Treat	stormwater		

 Use	in	center	pivot	corner	plantings	

 Produce	biofeedstock	

 Reduce	impacts	of	flooding	

 Decrease	offsite	movement	of	nutrients	or	chemicals	

Source:	USDA	Natural	Resources	Conservation	Service	(2012).		 a	Descriptions	follow	USDA	Natural	Resources	Conservation	Service	Conservation	Practices	Standards.		 b	All	agroforestry	plantings	add	increased	diversity	within	the	agricultural	landscape.	As	such,	they	will	improve	wildlife	

habitat	and	generally	are	designed	or	managed	with	this	as	a	secondary	benefit.	 c	Riparian	forest	buffer	refers	to	the	planted	practice.	This	category	does	not	include	naturally	established	riparian	forests.	

In	the	United	States,	five	main	categories	of	agroforestry	practices	are	recognized:	alley	cropping,	

forest	farming,	riparian	forest	buffers,	silvopasture,	and	windbreaks.	There	is	an	emerging	sixth	

category	of	special	applications	or	adaptations	of	these	practices	(Table	3‐3).	These	practices	are	

treated	within	the	cropland	and	grazing	land	system	section	with	the	exception	of	forest	farming.		

Forest	farming	(also	referred	to	as	multi‐story	cropping	within	USDA	Natural	Resources	

Conservation	Service	Practice	Standards)	involves	the	manipulation	of	existing	forest	canopy	cover	

in	order	to	produce	high‐value	non‐timber	(i.e.,	food,	floral,	medicinal,	and	craft)	products	in	the	

understory,	thus	maintaining	land	use	as	forest.	As	such,	GHG	accounting	in	forest	farming	practices	

will	need	to	be	treated	within	the	methods	and	approaches	presented	in	Section	6.2	and	Section	6.4.	

The	many	services	derived	from	agroforestry	practices	can	extend	well	beyond	the	small	parcel	or	

amount	of	land	they	physically	occupy	within	the	agricultural	landscape	(Bellefontaine	et	al.,	2002;	

Garrett,	2009).	The	use	of	agroforestry	technologies	are	important	components	at	the	

rural/community	interface,	as	well	as	within	urban	settings	to	address	emerging	needs	such	as	

stormwater	treatment,	recreation	or	green	space,	and	feedstock	production	(Schoeneberger	et	al.,	

2001).	Although	agroforestry	is	categorized	into	these	practices,	each	agroforestry	planting,	even	

within	a	practice,	potentially	represents	a	unique	case	of	species	selection,	arrangement,	placement	

within	other	practices	and	the	larger	landscape,	and	use	of	management	activities,	depending	on	

landowner	objectives.	Agroforestry	plantings	are	therefore	more	of	a	“designer	landscape	feature”	

than	a	standardized	and	easily	described	practice	(Mize	et	al.,	2008)	within	GHG	accounting	

activities.	

Silvopasture	provides	a	good	illustration	of	this	complexity	in	agroforestry	systems.	Silvopasture	is	

the	deliberate	combination	of	three	components—	trees,	forage,	and	livestock—along	with	the	

range	of	their	respective	management	activities.	Studies	demonstrate	a	higher	carbon	sequestration	

potential	in	silvopasture	compared	with	forest	or	pasture	alone	(Haile	et	al.,	2010;	Nair	et	al.,	2007;	
 Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

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Sharrow	and	Ismail,	2004).	Much	of	this	new	carbon	is	in	the	woody	biomass,	but	soil	carbon	also	

has	the	potential	to	increase	as	a	consequence	of	carbon	inputs	from	the	trees,	which	over	time	

extend	further	into	the	forage	component	(Peichl	et	al.,	2006),	as	well	as	management	of	the	forage	

and	of	the	livestock	(see	Franzluebbers	and	Stuedemann,	2009;	Karki	et	al.,	2009).	Management	

activities	within	a	silvopasture	may	include	fertilization,	liming,	cultivation,	and	harvesting	of	the	

forage	crop	(in	some	years);	periodic	harvesting	of	pine	needles	for	pine	straw;	incorporation	of	

pruned	woody	material	into	the	forage	component;	and	different	grazing	intensities	and	rotations.	

The	frequency	and	intensity	of	management	activities	and	inputs	from	all	three	components	can	

vary	significantly	from	year	to	year,	which	makes	accounting	for	the	sequestered	carbon	in	a	

silvopasture	operation	challenging.			

Rates	and	amounts	of	GHG	emissions	within	each	agroforestry	planting	will	vary	depending	on	

prior	land	management	and	current	conditions	(i.e.,	site,	climate),	as	well	as	by	stand	development.	

These	rates	and	amounts	will	also	be	dependent	on	landowners’	decisions	that	determine	planting	

design,	as	well	as	management	activities—agricultural,	forestry,	and	grazing—used	over	the	

lifetime	of	an	agroforestry	system	(Table	3‐4).		

Table 3‐4:		Management Activities5 and Other Factors Within Agroforestry Practices That

May Alter Carbon Sequestration and GHG Emission Amounts

Practice Management Activities

Windbreaks	

 Establishment	disturbance	to	soil	during	site	preparation

 Deposition	of	wind‐	and	water‐transported	sediments,	nutrients,	and	other	agricultural	

chemicals	into	the	planting	

 Windbreak	renovation	(removal	of	dead	and	dying	trees	over	time)	

Riparian	

forest	buffers	

 Establishment	disturbance	to	soil	during	site	preparation

 Deposition	of	wind‐	and	water‐transported	sediments,	nutrients,	and	other	agricultural	

chemicals	into	the	planting	

 Harvesting	of	herbaceous	materials	planted	in	Zone	3	(zone	closest	to	crop/grazing	

system)	and	of	woody	materials	planted	in	Zone	2	(middle	zone)	

Alley	

cropping	

 Establishment	disturbance	to	soil	during	site	preparation

 Weed	control	(mechanical	or	chemical)	

 Pruning,	thinning,	and	harvesting	of	woody	material	(amount	and	frequency	vary	

greatly	depending	on	short‐	and	long‐term	objective	of	practice)	

 Fertilization	for	alley	crop	and	occasionally	needed	for	trees	in	rows	

 Tillage	in	alleys	(frequency	and	intensity)	

 Crop	species	used	in	alley	production	

 Complex	harvesting	schedules	stratified	in	space	and	time	

Silvopasture	

 Establishment	disturbance	to	soil	during	site	preparation	

 Weed	control	(mechanical	or	chemical)	

 Pruning,	thinning,	and	harvesting	of	woody	material	(amount	and	frequency	vary	

greatly	depending	on	short‐	and	long‐term	objective	of	practice)	

 Fertilization	of	forage	component	

 Tillage	in	forage	component	(frequency	and	intensity)	

 Crop	species	used	in	forage	component	

 Grazing	management	(timing,	intensity,	frequency)	

 Complex	harvesting	schedules	stratified	in	space	and	time	

3.4.1 Carbon Stocks

Agroforestry’s	potential	for	sequestering	large	amounts	of	carbon	per	unit	area	is	well	recognized	

(Dixon	et	al.,	1994;	Kumar	and	Nair,	2011;	Nair	et	al.,	2010),	with	sequestration	rates	being	greater	

																																																													

5	Forest	Farming	is	not	included	in	these	considerations.	
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than	many	of	the	other	agricultural	options	(IPCC,	2000).	Carbon	is	sequestered	directly	into	the	

woody	biomass	and	soil.	Indirectly,	agroforestry	practices	can	alter	carbon	cycling	by	enhancing	

crop	and	forage	production	(up	to	15	H—height	of	trees—distance	from	the	windbreak)	and	

trapping	wind‐blown	and	runoff	erosion	(Brandle	et	al.,	2009).	Lack	of	data	limits	accounting	of	

these	other	carbon	fluxes	impacted	by	the	addition	of	trees	and	is	beyond	the	scope	of	this	effort.			

Woody Biomass:	The	majority	of	new	carbon	contributed	to	a	site	by	agroforestry	will	be	from	the	

production	of	woody	biomass,	with	the	larger	contribution	being	from	the	aboveground	woody	

biomass,	as	generally	observed	in	forest	establishment	plantings	(Nui	and	Duiker,	2006).	The	more	

open	environment	created	in	agroforestry	plantings	results	in	the	trees	having	different	growth	

forms	than	encountered	under	forest	conditions—e.g.,	greater	branch	production	(Zhou,	1999)	and	

specific	gravity	(Zhou	et	al.,	2011)—which	will	need	to	be	taken	into	account	when	estimating	the	

aboveground	woody	biomass.		

The	belowground	biomass	pool	in	agroforestry	plantings	will	also	be	a	significant	portion	of	new	

carbon	added	to	the	site.	However,	measuring,	estimating,	and/or	verifying	this	component	is	very	

difficult	and	expensive.	The	contributions	from	root	biomass	can	be	estimated	using	various	

approaches	that	rely	on	knowing	the	aboveground	portion.		

Forest Products and Other Removed Materials:	Windbreaks	and	riparian	forest	buffers	are	planted	

for	purposes	that	require	the	trees	to	be	in	place	for	the	targeted	function(s)	(i.e.,	alteration	of	

microclimate;	interception	of	sediments,	nutrients,	and	chemicals).	Windbreak	renovation	(removal	

of	dead	trees	and	replanting)	is	recommended	to	maintain	microclimate	benefits	(Brandle	et	al.,	

2009).	Periodic	harvesting	of	plant	materials	in	the	herbaceous	zone	(adjacent	to	crop	field)	and	

middle	woody	zone	is	also	recommended	in	riparian	forest	buffers	to	maintain	higher	rates	of	

nutrient	uptake	and	therefore	water	quality	services	(Dosskey	et	al.,	2010).	More	innovative	and	

diversified	planting	designs	that	incorporate	bioenergy	feedstocks	are	being	considered	for	both	of	

these	practices,	which	would	increase	levels	of	harvesting	within	these	systems.	In	the	case	of	

riparian	forest	buffers,	harvesting	of	the	herbaceous	and	woody	middle	zone	for	bioenergy	

feedstocks	would	serve	to	replenish	a	higher	nutrient	uptake	rate	and	thus	water	quality	services,	

as	well	as	provide	an	additional	income	stream	(Schoeneberger	et	al.,	2008).	Many	alley	cropping	

and	silvopasture	systems	are	managed	for	high‐value	veneer	and	saw‐timber.	These	trees,	along	

with	some	special	applications	of	agroforestry	technologies,	are	also	being	investigated	for	their	use	

in	producing	bioenergy	feedstocks.	For	these	plantings,	removal	or	harvesting	of	aboveground	

woody	material	can	occur	as	early	as	three	years	to	75	years	or	more,	depending	on	the	product.	

Harvested	materials	can	also	include	stem‐pruning,	generally	up	to	15	feet	over	several	years	to	

attain	a	clean	bole,	to	periodic	thinning	in	order	to	maintain	a	canopy	cover	that	is	optimal	for	the	

growth	of	the	tree	as	well	as	the	crop	being	grown	in	the	alleys.	The	material	may	be	left	onsite	to	

create	wildlife	habitat,	chopped	and	incorporated	into	the	soil,	or	taken	off‐site	and	burned.		

Soil:	Studies	have	documented	that	U.S.	agroforestry	practices	generally	have	greater	soil	carbon	

stocks	(under	the	whole	practice,	which	may	vary	from	just	under	a	windbreak	to	under	the	whole	

tree/crop	system,	such	as	alley	cropping)	when	compared	with	that	in	conventional	agricultural	

and	grazing	practices	(Nair	et	al.,	2010).	However,	estimating	change	or	flux	in	soil	carbon	stocks	in	

agroforestry	plantings	is	challenging	due	to	its	inherently	high	spatial	and	temporal	variability.	For	

instance,	Sharrow	and	Ismail	(2004)	found	variability	of	soil	carbon	to	be	two	to	three	times	

greater	in	a	non‐grazed	silvopasture	system	than	in	the	adjacent	forest	or	pasture	alone.	

Soil	carbon	can	increase	in	agroforestry	systems	due	to	added	carbon	inputs	from	the	trees,	the	

elimination	of	carbon	loss	due	to	annual	cropping	activities	(i.e.,	conservation	tillage),	and	

potentially	the	addition	of	carbon	through	other	agricultural	management	activities,	such	as	

incorporation	of	different	crops,	cover	crops,	residue	management,	and	fertilization	regimes.	
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 3-41 

Changes	in	soil	carbon	stocks	have	been	estimated	in	a	number	of	forest	establishment	plots	from	

the	Midwest,	and	were	found	to	vary	from	‐0.07	to	0.58	Mg	C	ha‐1	year‐1	and	‐0.85	to	0.56	Mg	C	ha‐1

year‐1	in	deciduous	and	coniferous	plots,	respectively.	Paul	et	al.	(2003)	attributed	the	variation	to	

the	impact	and	variable	recovery	from	tree	planting,	but	also	mentioned	the	possibility	that	

variation	may	be	due	to	the	use	of	present‐day	cropping	fields	as	the	carbon	baseline	for	

comparison.	Many	agroforestry	studies	are	reporting	comparable	rates	of	soil	sequestration	(see	

Nair	et	al.,	2010).	Results	from	temperate	agroforestry	studies	indicate,	especially	for	alleys	

receiving	high	level	of	organic	matter	input	from	the	trees,	that	it	may	be	several	years	before	

significantly	measurable	carbon	differences	are	detectable	between	the	agroforestry	planting	and	

traditional	sole	cropping	system	(Peichl	et	al.,	2006;	Udawatta	et	al.,	2009).	The	amount	and	

duration	of	soil	organic	matter	accumulation	in	agricultural	soils	with	agroforestry	management	

will	depend	on	the	degree	to	which	prior	soil	carbon	stocks	are	depleted.	In	addition,	it	will	depend	

on	the	soils	in	general,	climate,	placement	within	a	landscape,	type	of	vegetation,	and	most	

importantly,	by	the	additional	management	activities	employed	in	the	mixed	tree/agricultural	

system	(Table	3‐4).	

Note	that	carbon	increases	from	nitrogen	inputs	may	be	offset	through	enhanced	N2O	emissions,	

depending	on	a	number	of	factors	(see	Section	6.4.1.6).	Many	agroforestry	plantings,	such	as	

windbreaks	and	riparian	forest	buffers,	are	purposefully	designed	to	intercept	soil	in	wind	erosion	

and	surface	runoff,	which	is	another	addition	of	carbon	to	this	pool	(Sauer	et	al.,	2007).	Deposition	

of	sediment	will	influence	cycling	of	both	elements	and	therefore	net	GHG	values	(McCarty	and	

Ritchie,	2002;	Sudmeyer	and	Scott,	2002).	We	currently	lack	the	understanding	and	data	needed	for	

adequately	modeling	and	therefore	predicting	these	intra‐	and	inter‐soil	carbon	transfers	from	

erosion	and	deposition.	

3.4.2 Nitrous Oxide

Data	on	direct	N2O	emissions	in	agroforestry	plantings	are	sparse.	The	few	studies	to‐date	found	

reduced	N2O	emissions	in	afforested	plots	that	were	older	than	five	years	(Allen	et	al.,	2009),	under	

windbreaks	(Ryskowski	and	Kedziora,	2007)	and	riparian	forest	buffers	(Kim,	2008).	Alley	

cropping	systems	reduced	N2O	emissions	by	0.7	kg	ha‐1	year‐1	compared	with	the	annual	cropping	

systems	with	no	tree	cover	(Thevathasan	and	Gordon,	2004).	These	studies	suggest	the	trees	can	

act	as	a	“nitrogen‐safety	net”	in	the	system,	taking	up	the	“extra”	nitrogen	that	might	otherwise	

result	in	N2O	emissions.	In	addition,	reduced	nitrogen	leaching	has	been	documented	within	

agroforestry	plantings	compared	with	the	annual	cropping	system	with	no	tree	cover	(Allen	et	al.,	

2004;	Lopez‐Diaz	et	al.,	2011;	Nair	et	al.,	2007).	The	reduced	leaching	implies	that	less	nitrogen	is	

available	for	indirect	soil	N2O	emissions,	which	could	be	beneficial	in	those	agroforestry	plantings	

requiring	fertilization	(i.e.,	alley	cropping	and	silvopasture	systems)	or	that	receive	large	inputs	of	

nitrogen	through	surface	and	subsurface	runoff	(i.e.,	riparian	forest	buffers).	As	many	agroforestry	

plantings	are	purposefully	designed	and	planted	to	provide	tighter	nutrient	cycling	capabilities	as	a	

means	to	protect	water	quality	(Olson	et	al.,	2000),	the	capability	and	capacity	of	these	systems	to	

reduce	N2O	emissions	in	agricultural	systems	warrants	further	study	to	determine	whether	and	

how	it	should	be	accounted	for	in	GHG	accounting	methods.		

3.4.3 Methane

Very	little	research	has	been	done	to	determine	whether	the	establishment	of	agroforestry	

plantings	can	lead	to	any	change	in	CH4	sinks	or	sources	in	soils	due	to	changes	in	methanotrophy	

or	methanogenesis,	respectively.	Kim	et	al.	(2010)	did	not	find	any	evidence	in	established	riparian	

forest	buffers	in	Iowa	(seven	to	17	years	old)	that	CH4	flux	differed	from	neighboring	crop	fields.	

Riparian	forest	buffers	could	potentially	serve	as	a	CH4	emitter	given	the	periodic	flooding	that	may	

occur	within	these	plantings.	However,	riparian	forest	buffers	established	on	agricultural	lands	may	
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems 

3-42

not	be	significant	emitters	of	CH4	because	the	hydrological	connections	within	these	landscapes	

have	been	decoupled.	This	indicates	use	of	riparian	forest	(naturally	occurring)	derived	data	may	

result	in	overestimating	sink/source	capacity	of	riparian	forest	buffers.	In	general,	there	is	

insufficient	data	to	model	and	predict	methane	fluxes	in	agroforestry	at	this	time.		

3.4.4 Management Interactions

Agroforestry	practices	can	indirectly	alter	carbon	cycling	by	enhancing	crop	and	forage	production	

and	trapping	windblown	and	surface	runoff	sediments.	Examining	the	carbon	potential	of	

windbreaks	in	the	Great	Plains,	Brandle	et	al.	(1992)	estimated	indirect	carbon	benefits	could	

potentially	be	double	the	amount	of	the	carbon	sequestered	in	the	wood.	Although	projects	to	

examine	indirect	carbon	benefits	from	several	of	the	agroforestry	practices	are	ongoing,	we	

currently	lack	the	ability	to	model	or	predict	these	impacts.	

3.5 Estimation Methods

This	section	provides	methods	for	estimating	GHG	emissions	from	cropland	and	grazing	land	

systems	on	an	entity’s	land.	The	methods	are	applied	for	both	land	remaining	in	cropland	or	grazing	

lands,	as	well	as	land‐use	change	to	cropland	or	grazing	lands.	The	methods	provided	are	for	

estimating	the	emission	levels	for	a	given	year	on	a	parcel	of	land.	A	parcel	is	a	field	in	the	entity’s	

operation	with	uniform	management.	If	management	varies	across	the	field,	then	the	field	should	

be	subdivided	into	separate	parcels	for	estimating	the	emissions.	

Trends	across	years	or	comparisons	to	baselines	can	be	made	using	the	annual	emission	estimates.	

Guidance	is	not	given	here	on	how	to	develop	baselines	or	subsequent	trends	for	emission	

estimation.	The	level	of	emissions	for	carbon	stocks	is	based	on	estimating	the	change	in	stock	from	

the	beginning	and	end	of	the	year,	while	the	level	of	emissions	for	N2O	and	CH4	are	based	on	

estimating	the	total	annual	emissions.	Methods	are	also	provided	for	estimating	total	emissions	of	

precursor	gases	emitted	during	biomass	burning,	as	well	as	nitrogen	compounds	that	are	

volatilized	or	subject	to	leaching	and	runoff	from	an	entity’s	cropland	or	grazing	land	that	are	later	

converted	into	GHGs.		

The	methods	range	in	complexity	for	the	different	emission	source	categories	according	to	the	state	

of	the	science	and	prior	method	development.	Simple	methods	are	selected	for	several	of	the	

emission	or	carbon	stock	change	source	categories;	because	the	more	complex	methods	are	not	

fully	developed	for	operational	accounting	of	emissions	or	the	simple	methods	provide	a	

reasonably	accurate	and	precise	result.	Although	simplicity	may	be	preferred	for	transparency	in	

estimation,	some	of	the	methods	use	more	complex	approaches,	such	as	process‐based	simulation	

models,	because	these	methods	greatly	improve	the	accuracy	and/or	precision	of	the	result.	
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3.5.1 Biomass Carbon Stock Changes		

3.5.1.1 Rationale for Selected Method

Both	IPCC	(2006)	and	the	U.S.	Environmental	Protection	Agency	(2011)	consider	herbaceous	

biomass	carbon	stocks	to	be	ephemeral,	and	recognize	that	there	are	no	net	emissions	to	the	

atmosphere	following	crop	growth	and	senescence	during	one	annual	crop	cycle	(West	et	al.,	2011).	

However,	with	respect	to	changes	in	land	use	(e.g.,	forest	to	cropland),	the	IPCC	(Lasco	et	al.,	2006)	

recommends	that	cropland	biomass	be	counted	in	the	year	that	land	conversion	occurs,	and	the	

same	assumption	also	applies	for	grassland	(Verchot	et	al.,	2006).		According	to	the	IPCC,	

accounting	for	the	herbaceous	biomass	carbon	stock	during	changes	in	land	use	is	necessary	to	

account	for	the	influence	of	herbaceous	plants	on	CO2	uptake	from	the	atmosphere	and	storage	in	

the	terrestrial	biosphere.	However,	this	method	does	not	recognize	changes	in	herbaceous	biomass	

that	occur	with	changes	in	crop	rotations,	nor	does	it	recognize	long‐term	increases	in	annual	crop	

yields.	The	method	is	a	considered	a	Tier	2	method	as	defined	by	the	IPCC	because	it	incorporates	

factors	that	are	based	on	U.S.	specific	data.	

Agroforestry,	along	with	other	woody	vegetation	in	croplands,	such	as	orchards	and	vineyards,	

sequester	significant	amounts	of	new	carbon	within	long‐lived	biomass	over	time	with	tree	growth.		

Methods	for	estimating	the	aboveground	woody	and	whole	tree	biomass	for	trees	growing	under	

forest	conditions	are	described	in	the	Forestry	Section	of	this	report.	However,	these	methods,	

developed	from	forest‐derived	(i.e.,	greater	canopy	closure)	conditions,	do	not	accurately	reflect	

conditions	encountered	in	agroforestry	or	woody	crops.	Trees	growing	under	windbreak	and	other	

linear‐type	plantings	have	been	documented	to	differ	from	forest‐grown	trees	in	terms	of	

architecture	and	properties,	such	as	crown:trunk	allocation	(Zhou,	1999),	specific	gravity	(Zhou	et	

al.,	2011),	and	taper	(Zhou	et	al.,	in	review).		Moreover,	the	Forest	Inventory	and	Analysis	program	

of	the	USDA	Forest	Service	and	National	Resource	Inventory	of	the	USDA	Natural	Resources	

Conservation	Service	do	not	collect	agroforestry	or	woody	crop	data	through	their	surveys	(Perry	

et	al.,	2005).	Therefore,	a	Tier	3	method	using	process‐based	models	is	a	viable	alternative	for	

estimating	the	carbon	stock	changes	associated	with	agroforestry	and	woody	crops	without	direct	

measurement	through	a	survey.	Specifically,	the	DAYCENT	model	has	been	parameterized	to	

simulate	tree	growth	and	has	been	adopted	for	estimating	woody	biomass	carbon	for	agroforestry	

and	woody	crops.	

Method for Estimating Biomass Carbon Stock Changes

 A	modified	version	of	the	methodology	developed	by	the	IPCC	(Lasco	et	al.,	2006;	Verchot	

et	al.,	2006)	has	been	adopted	for	entity‐scale	estimation	of	herbaceous	and	woody	

biomass	stock	changes	associated	with	land	use.		

 The	DAYCENT	process‐based	simulation	model	or	the	traditional	forest	inventory	

approaches	are	used	to	estimate	carbon	for	aboveground	biomass	for	agroforestry.		

 U.S.	specific	default	values	(West	et	al.,	2010)	are	used	for	estimating	biomass	carbon	for	

annual	crops	and	grazing	lands.	The	IPCC	default	is	used	for	estimating	the	carbon	

fraction	value.	Yield	in	units	of	dry	matter	can	be	estimated	by	the	entity	or	average	

values	from	USDA‐National	Agricultural	Statistics	Service	statistics	can	be	used.		

 This	method	was	chosen	because	it	captures	the	influence	of	land‐use	change	on	crop	or	

forage	species	on	biomass	carbon	stocks	by	using	U.S.	specific	default	values	where	entity	

specific	activity	data	are	not	available	and	a	process‐based	simulation	model	for	

agroforestry	systems.		
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems 

3-44

3.5.1.2 Description of Method

A	modified	version	of	the	methodology	developed	by	the	IPCC	(Lasco	et	al.,	2006;	Verchot	et	al.,	

2006)	has	been	adopted	for	entity‐scale	reporting	in	the	United	States	of	herbaceous	and	woody	

biomass	stock	changes	associated	with	land	use	change.	The	method	consists	of	estimating	the	

mean	annual	biomass	stock	for	a	cropland	or	grazing	lands	following	a	land	use	change,	which	can	

be	averaged	across	years	for	a	crop	or	rotation.		This	method	only	addresses	a	change	in	the	

herbaceous	biomass	carbon	stocks	in	the	year	following	a	land‐use	change,	consistent	with	the	IPCC	

methods	(Lasco	et	al.,	2006;	Verchot	et	al.,	2006).	In	contrast,	carbon	stock	change	in	woody	

biomass	is	estimated	every	year.		

Use	Equation	3‐1	to	estimate	the	total	biomass	carbon	stock	change	for	a	land	parcel	over	a	year:	

Herbaceous Biomass: Estimate	the	mean	annual	herbaceous	biomass	stock	in	a	land	parcel	for	

cropland	or	grazing	land	following	a	land	use	change	with	the	following	equation:	

The	mean	annual	biomass	stock	is	intended	to	represent	the	time	period	following	harvest	where	

no	crop	exists	and	both	litter	and	roots	are	decomposing	quickly	(Gill	et	al.,	2002),	and	the	time	

period	during	the	growing	season	where	biomass	continues	to	grow	until	it	reaches	peak	annual	

biomass.	The	average	of	zero	biomass	and	peak	biomass	(e.g.,	peak	biomass	divided	by	two)	is	

considered	representative	of	the	mean	annual	carbon	stock	(i.e.,	Yf	=	0.5).		

Equation	3‐3	is	used	to	estimate	the	peak	aboveground	biomass	in	a	land	parcel	from	harvest	yield	

data	in	croplands	or	peak	forage	yields	in	grazing	lands.	

Equation 3‐1: Total Biomass Carbon Stock Change

ΔCBiomass = (Ht + Wt) – (Ht‐1 + Wt‐1)

Where:	

ΔCBiomass		=	Total	change	in	biomass	carbon	stock	(metric	tons	CO2‐eq	year‐1)	

H		 =	Mean	annual	herbaceous	biomass	(metric	tons	CO2‐eq	year‐1)	

W		 =	Mean	annual	woody	biomass	(metric	tons	CO2‐eq	year‐1)	

t		 =	Current	year	stocks	

t‐1	 =	Previous	year’s	stocks	

Equation 3‐2: Mean Annual Herbaceous Biomass Carbon Stock

H = [Hpeak + (Hpeak × R:S)] × A × CO2MW / Yf

Where:	

H		 =	Mean	annual	herbaceous	biomass	carbon	stock	(metric	tons	CO2‐eq	year‐1)	

Hpeak		 =	Annual	peak	aboveground	biomass	(metric	tons	C	ha‐1	year‐1)	

R:S		 =	Root‐shoot	ratio	(unitless)	

A		 =	Area	of	land	parcel	(ha)	

CO2MW		=	Ratio	of	molecular	weight	of	CO2	to	carbon	=	44/12	

			(metric	tons	CO2	(metric	tons	C)‐1)	

Yf	 =	Approximate	fraction	of	calendar	year	representing	the	growing	season (unitless)	
 Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

 3-45 

		

This	method	captures	the	influence	of	land‐use	change	and	changes	in	crop	or	forage	species	on	

biomass	carbon	stocks.	Therefore,	crop	harvest	or	peak	forage	yields	should	be	averaged	across	

years	as	long	as	the	same	forage	species,	crop	or	rotation	of	crops	are	grown.	The	harvest	index	is	

set	to	one	for	grazing	lands.	

Peak	forage	estimates	for	grazing	lands	can	be	estimated	using	the	biomass	clipping	method.6	This	

method	is	destructive	with	the	removal	of	forage	samples	from	the	field.	Non‐destructive	methods	

can	also	be	used	including	the	comparative	yield	method	for	rangelands7,	or	the	robel	pole	method	

on	rangelands	or	pastures	(Harmoney	et	al.,	1997;	Vermeire	et	al.,	2002).		Any	sampling	that	is	

done,	whether	destructive	or	non‐destructive,	should	occur	at	locations	that	are	representative	of	

the	land	parcel.	If	sampling	the	forage	is	not	feasible,	default	forage	production	values	are	provided	

by	the	Natural	Resources	Conservation	Service	in	Ecological	Site	Descriptions	(ESDs).8		After	

identifying	the	appropriate	ESD,	the	entity	would	select	the	plant	community	that	is	representative	

of	the	parcel.	These	values	represent	total	production	for	the	site	so	Yf	in	Equation	3‐2	would	be	set	

to	1	if	the	aboveground	forage	production	is	obtained	from	an	ESD.	

Woody Biomass:	The	largest	amount	of	carbon	captured	by	agroforestry	systems	is	in	woody	

biomass,	with	the	majority	occurring	in	the	aboveground	biomass.	Woody	crops	also	gain	carbon	as	

they	grow.	This	method	also	addresses	carbon	removals	through	harvest	or	other	events	that	

remove	tree	biomass.	

The	methods	to	estimate	biomass	carbon	in	a	land	parcel	for	the	more‐open	growth	of	agroforestry	

systems	and	woody	crops	(Wt	and	Wt‐1	in	Equation	3‐1)	are	based	on	DAYCENT	model	simulations	

and	growth	functions	for	agroforestry.	Agroforestry	practices	are	based	on	the	Natural	Resources	

Conservation	Service	agroforestry	practice	standards,	which	are	provided	in	a	pick	list.	For	woody	

crops,	the	DAYCENT	model	simulates	the	influence	of	common	management	practices	on	biomass	

stocks,	including	irrigation,	fertilization,	organic	matter	amendments,	groundcover	management,	

																																																													

6	See	section	15,	“Standing	Biomass”			

http://www.nrisurvey.org/nrcs/Grazingland/2011/instructions/instruction.htm 7	See	section	13,	“Dry	Weight	Rank”	

http://www.nrisurvey.org/nrcs/Grazingland/2011/instructions/instruction.htm 8	See	ESDs	https://esis.sc.egov.usda.gov/

Equation 3‐3: Aboveground Herbaceous Biomass Carbon Stock

Hpeak = (Ydm / HI) × C

Where:	

Hpeak		=	Annual	peak	aboveground	herbaceous	biomass	carbon	stock	

		 	 (metric	tons	C	ha‐1	year‐1)	

Ydm		 =	Crop	harvest	or	forage	yield,	corrected	for	dry	matter	content	

				 	 (metric	tons	biomass	ha‐1	year‐1)	

	 =	Y	x	DM	

Y	 =	Crop	harvest	or	forage	yield	(metric	tons	biomass	ha‐1	year‐1)	

DM	 =	Dry	matter	content	of	harvested	crop	biomass	or	forage	(dimensionless)	

HI		 =	Harvest	Index	(dimensionless)	

	C		 =	Carbon	fraction	of	aboveground	biomass	(dimensionless)	
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems 

3-46

pruning	of	branches,	thinning	of	young	fruit,	and	harvest	and	removal	of	mature	fruit.	Given	the	

practice,	DAYCENT	simulates	changes	in	woody	biomass	carbon	stocks	for	the	reporting	period.	

For	agroforestry	systems	where	the	entity	has	measured	tree	parameters,	an	empirical	model	is	

provided	to	more	precisely	estimate	woody	biomass	carbon	growth	increment	for	the	year	

(Merwin	and	Townsend,	2007;	Merwin	et	al.,	2009).		The	empirical	model	uses	an	individual	tree	

growth	equations	based	on	Lessard	(2000)	and	Lessard	et	al.	(2001).		Carbon	pools	are	then	

derived	from	diameter‐based	allometric	equations	that	predict	total	aboveground	biomass	

components	for	10	broad	species	groups	in	the	United	States.	(Jenkins	et	al.,	2003;	2004).	Both	

published	and	unpublished	data	for	the	U.S.	Forest	Service	Forest	Inventory	and	Analysis	program	

were	used	to	develop	the	growth	increment	model.			

In	addition,	harvested	woody	products	associated	with	agroforestry	are	estimated	using	the	

approaches	described	in	the	Forestry	Chapter	(Section	6.5).	Woody	products	may	be	harvested	

from	silvopasture,	alley	cropping,	and	other	agroforestry	practices,	providing	a	variety	of	products	

such	as	veneer,	saw	timber,	and	bioenergy	feedstocks.	

3.5.1.3 Activity Data

Activity	and	related	data	needed	to	estimate	biomass	carbon	for	annual	crops	and	grazing	lands	(as	

applicable)	include:	

 Crop	type,	cropland	area,	and	harvest	indices;		

 Type	of	forage,	grazing	area,	and	peak	forage	yield	data;	

 Total	aboveground	yield	of	crop	or	peak	forage	yield	for	grazing	lands	(metric	tons	biomass	

per	ha);	

 Root:shoot	ratios;	

 Carbon	fractions;	and		

 Dry	matter	content	of	forage	and	harvested	crop	biomass	to	estimate	dry	matter	content.	

If	the	entity	does	not	provide	values,	default	values	for	moisture	content,	residue‐yield	ratios,	and	

root:shoot	ratios	are	provided	in	Table	3‐5.	A	general	default	value	for	crop	carbon	fraction	is	0.45.	

In	some	years,	the	entity	may	not	harvest	the	crop	due	to	drought,	pest	outbreaks	or	other	reasons	

for	crop	failure.	In	those	cases,	the	entity	should	provide	the	average	yield	that	they	have	harvested	

in	the	past,	and	an	approximate	percentage	of	average	crop	growth	that	occurred	in	the	year.	The	

yield	is	estimated	based	on	multiplying	the	average	crop	yield	by	the	percentage	of	crop	growth	

obtained	prior	to	crop	loss.	Peak	forage	yields	will	vary	from	year	to	year,	but	can	be	based	on	a	

five‐year	average.	

Table 3‐5: Representative Dry Matter Content of Harvested Crop Biomass, Harvest Index,

and Root:Shoot Ratios for Various Crops .a		

Crop Dry Matter

Content Harvest Index Root:ShootRatio

Food crops

Barley 0.865	(3.8%) 0.46	(18.7%) 0.11	(90.7%)

Beans 0.84	(3.3%) 0.46	(18.7%) 0.08	(89.7%)

Corn	grain 0.86	(1.9%) 0.53	(15.0%) 0.18	(97.3%)

Corn	silage 0.74	(1.9%) 0.95	(3.3%) 0.18	(97.1%)

Cotton 0.92	(1.4%) 0.40	(20.0%) 0.17	(44.0%)

Millet 0.90	(1.9%) 0.46	(17.6%) 0.25	(91.1%)

Oats 0.865	(1.9%) 0.52	(18.7%) 0.40	(90.9%)

Peanuts 0.91	(1.9%) 0.40	(16.6%) 0.07	(12.4%)

Potatoes 0.20	(9.3%) 0.50	(20.0%) 0.07	(44.1%)
 Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

 3-47 

Crop Dry Matter

Content Harvest Index Root:ShootRatio

Rice 0.91	(1.6%) 0.42	(28.1%) 0.22	(13.2%)

Rye 0.90	(1.9%) 0.50	(18.7%) 0.14	(90.1%)

Sorghum	grain 0.86	(1.9%) 0.44	(14.8%) 0.18	(97.2%)

Sorghum	silage 0.74	(1.9%) 0.95	(3.3%) 0.18(97.2%)

Soybean 0.875	(1.7%) 0.42	(16.7%) 0.19	(89.8%)

Sugarbeets 0.15	(12.4%) 0.40	(24.1%) 0.43	(43.9%)

Sugarcane 0.258	(11.6%) 0.75	(6.4%) 0.18	(37.4%)

Sunflower 0.91	(1.9%) 0.27	(11.1%) 0.06	(44.0%)

Tobacco 0.80	(1.9%) 0.60	(3.3%) 0.80	(44.0%)

Wheat 0.865	(3.8%) 0.39	(17.7%) 0.20	(86.2%)

Forage and Fodder crops

Alfalfa	hay 0.87	(1.8%) 0.95	(3.3%) 0.87	(21.8%)

Non‐legume	hay 0.87	(1.8%) 0.95	(3.3%) 0.87	(21.8%)

Nitrogen‐fixing	

forages	 0.35	(3.3%) 0.95	(3.3%)	 1.1	(21.2%)

Non‐nitrogen‐fixing	

forages	 0.35	(3.3%) 0.95	(3.3%)	 1.5	(21.2%)

Perennial	grasses	 0.35	(3.3%) 0.95	(3.3%)	 1.5	(21.2%)

Grass‐clover	mixtures	 0.35	(3.3%) 0.95	(3.3%)	 1.5	(21.2%)

Source:	Revised	from	West	et	al.	(2010).	

a	Uncertainty	is	expressed	on	a	percentage	basis	as	half	of	the	95%	confidence	interval.	

Activity	data	for	estimating	carbon	in	aboveground	biomass	for	agroforestry	will	entail	the	

collection	of	some	level	of	inventory	of	trees	associated	with	the	agroforestry	practice.	Simplified	

inventory	approaches	requiring	a	minimum	of	work	by	the	landowner	have	been	developed	by	the	

USDA	Natural	Resources	Conservation	Service	and	the	Colorado	State	University	Natural	Resource	

Ecological	Laboratory	(USDA,	2012),	which	are	largely	based	on	methods	described	in	the	Natural	

Resources	Conservation	Service	National	Forest	Handbook	(USDA	NRCS,	2004).	The	specific	

activity	data	requirements	include:	

 Species	of	trees	and	number	by	age	of	diameter	class	for	each	agroforestry	practice;	and	

 Diameter	at	breast	height	for	a	subsample	of	trees	using	one	of	three	sampling	methods	that	

capture	the	spacing	arrangements	and	densities	within	the	different	practices	(i.e.,	row	type	

plantings,	woodlot‐like	plantings,	and	riparian	forest	buffers).	

3.5.1.4 Ancillary Data		

No	ancillary	data	are	needed	for	this	method.	

3.5.1.5 Model Output

Model	output	is	generated	for	the	change	in	biomass	carbon	stocks.	This	change	is	determined	

based	on	subtracting	the	total	biomass	carbon	stock	in	the	previous	year	from	the	total	stock	in	the	

current	year,	which	will	include	both	herbaceous	and	woody	biomass.	The	herbaceous	stocks	will	

represent	mean	estimates	over	years	if	the	same	forages,	crop,	or	rotation	of	crops	are	grown,	and	

is	only	estimated	for	a	land	use	change.	The	approach	for	estimating	biomass	carbon	for	wetlands	

and	forestlands	are	described	in	Sections	4.3.1	and	6.2.1,	respectively.		

Emissions	intensity	is	also	estimated	based	on	the	amount	of	emissions	per	unit	of	yield	for	crops	in	

cropland	systems,	or	of	animal	products	in	grazing	systems.	Note	that	the	biomass	change	is	based	

solely	on	woody	plant	growth	except	in	a	year	following	a	land‐use	change.		
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems 

3-48

The	emissions	intensity	is	estimated	with	the	following	equation:	

3.5.1.6 Limitations and Uncertainty

Uncertainty	in	herbaceous	carbon	stock	changes	will	result	from	lack	of	precision	in	crop	or	forage	

yields,	residue‐yield	ratios,	root‐shoot	ratios,	and	carbon	fractions,	as	well	as	the	uncertainties	

associated	with	estimating	the	biomass	carbon	stocks	for	the	other	land	uses.	Emissions	intensity	

will	also	include	uncertainty	in	the	total	yield	for	the	crop,	meat,	or	milk	product.	This	herbaceous	

biomass	method	is	based	on	the	assumption	that	half	of	the	crop	harvest	yields	or	peak	forage	

amounts	provide	an	accurate	estimate	of	the	mean	annual	carbon	stock	in	cropland	or	grazing	

lands.	This	assumption	warrants	further	study,	and	the	method	may	need	to	be	refined	in	the	

future.			

Uncertainties	in	model	parameters	are	combined	using	a	Monte	Carlo	simulation	approach.	

Uncertainty	is	assumed	to	be	minor	for	the	management	activity	data	provided	by	the	entity.	Table	

3‐6	provides	the	relative	uncertainty	for	the	DAYCENT	model	and	the	carbon	fraction	of	biomass.	

Table 3‐6: Available Uncertainty Data for Biomass Carbon Stock Changes

Parameter Mean Units Relative Uncertainty Distribution Data Source Low (%) High (%)

DAYCENT	(empirical	

uncertainty)	 NS	 Various	 NS	 NS	 Normal	 Ogle	et	al.	(2007);	

EPA	(2013)	

Carbon	fraction	of	

aboveground	biomass	 0.45	 Fraction	 11	 11	 Normal	 IPCC	(1997)	

NS	=	Not	Shown.	Data	are	not	shown	for	parameters	that	have	100’s	to	1000’s	of	values	(denoted	as	NS).	

The	uncertainty	differs	whether	it	is	herbaceous	biomass	or	trees.	Uncertainty	associated	with	

estimating	carbon	in	live	trees	is	influenced	by	a	number	of	factors,	including	sampling	and	

measurement	error	and	error	associated	with	regression	models	(see	Melson	et	al.	2011;	further	

discussion	in	Forestry	Section).	Estimating	carbon	in	agroforestry	trees,	especially	for	young	

seedlings	and	saplings	(up	to	10	years	or	so	depending	on	species	and	growing	conditions)	remains	

highly	uncertain	particularly	since	traditional	forestry‐derived	equations	have	been	shown	to	

underestimate	whole‐tree	biomass	in	agroforestry	systems	and	requires	additional	field	work	to	

further	document	biomass	carbon	allocation	differences.		Melson	et	al.(2011)	noted	in	their	forest‐

based	work	that	estimation	of	live‐tree	carbon	was	sensitive	to	model	selection	(with	model‐

selection	error	of	potentially	20	to	40	percent),	and	that	model	selection	could	be	improved	by	

matching	tree	form	to	existing	equations	for	use	in	the	models.	On‐going	work	comparing	

agroforestry‐derived	equations	with	a	variety	of	forest‐derived	equations	in	the	Great	Plains	region	

indicate	uncertainty	could	be	reduced	through	use	of	a	correction	factor.	Currently	belowground	

biomass/C	estimates	are	calculated		using	two	approaches:	root:shoot	ratios	(see	Birdsey,	1992),	

Equation 3‐4: Emissions Intensity of Biomass Carbon Stock Change		

EIBiomassC =	ΔCBiomass/Y

Where:	

EIBiomassC		 =	Emissions	intensity	(metric	tons	CO2	per	metric	ton	dry	matter	crop	yield,	metric	

tons	CO2	per	kg	carcass	yield,	or	metric	tons	CO2	per	kg	fluid	milk	yield)	

ΔCBiomass		 =	Change	in	biomass	stock	in	CO2	equivalents	(metric	tons	CO2‐eq	year‐1)	

Y		 =	Total	yield	of	crop	(metric	tons	dry	matter	crop	yield),	meat	(kg	carcass	yield)	or	

milk	production	(kg	fluid	milk	yield)	
 Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

 3-49 

and	aboveground	density	allometry	(Cairns	et	al.,	1997),	both	with	large	uncertainties	due	to	lack	of	

data.	The	full	set	of	probability	distributions	have	not	been	developed	for	the	agroforestry	method,	

and	so	will	require	further	research	before	uncertainty	can	be	estimated.	See	Chapter	6,	Forestry,	

for	further	discussion	of	uncertainty	of	tree	volume	and	biomass	equations.		

3.5.2 Litter Carbon Stock Changes

Litter	in	herbaceous	biomass	decomposes	mostly	over	a	one‐year	period.		However	the	influence	of	

litter	carbon	stocks	on	atmospheric	CO2	is	assumed	to	be	insignificant	after	addressing	the	changes	

in	biomass	and	subsequent	influence	on	soil	carbon	stocks.	Further	methods	development	may	be	

possible	in	the	future,	given	this	potential	limitation	to	the	methods	in	this	report.	For	cropland	or	

grazing	land	systems	with	trees,	coarse	woody	debris	and	litter	carbon	should	be	estimated	based	

on	forest	methods	(See	Section	6.2.2.4	and	6.2.2.5).	The	loss	of	litter	and	coarse	woody	debris	with	

conversion	from	forestland	to	cropland	and	grazing	land	is	also	addressed	in	Section	6.	3.	

3.5.3 Soil Carbon Stock Changes

3.5.3.1 Rationale for Selected Method

SOC	stocks	are	influenced	by	land	use	and	management	in	cropland	and	grazing	land	systems,	as	

well	as	conversion	from	other	land	uses	into	these	systems	(Aalde	et	al.,	2006).	SOC	pools	can	be	

modified	due	to	changes	in	carbon	inputs	and	outputs	(Paustian	et	al.,	1997).	Carbon	inputs	will	

change	over	time	due	to	interannual	variability	and	longer	term	trends	in	net	primary	production,	

as	well	as	differences	in	carbon	removals	from	harvesting	and	residue	management	practices.	

External	carbon	inputs	will	also	have	an	influence	on	the	SOC	stocks,	such	as	manure,	compost,	

sewage	sludge,	wood	chips,	and	biochar	amendments.	Carbon	outputs	will	change	due	to	

interannual	variability	and	longer	term	trends	in	microbial	decomposition	rates.	In	addition,	

erosion	and	deposition	contribute	to	changes	in	SOC	stocks	associated	with	crop	and	grazing	land	

soils.	Recent	studies	(Harden	et	al.,	2008;	Van	Oost	et	al.,	2007)	provide	evidence	that	the	majority	

of	carbon	in	eroded	soils	is	dynamically	replaced,	compensating	for	the	losses,	and	at	least	some	of	

the	carbon	transported	from	the	site	is	deposited	at	the	edge	of	fields,	downslope,	or	in	rivers.	In	all	

cases,	SOC	is	moved	from	one	location	to	another	under	the	assumption	that	only	a	portion	of	the	

Method for Estimating Soil Carbon Stock Changes

Mineral	soils:		

 The	DAYCENT	process‐based	simulation	model	estimates	the	soil	organic	carbon	(SOC)	at	

the	beginning	and	end	of	the	year.	These	inputs	are	entered	into	the	IPCC	equation	to	

estimate	carbon	stock	changes	in	mineral	soils	developed	by	Lasco	et	al.	(2006),	and	

Verchot	et	al.	(2006).	

 This	method	was	chosen	because	the	DAYCENT	model	has	been	demonstrated	to	

represent	the	dynamics	of	soil	organic	carbon	and	estimate	soil	organic	carbon	stock	

change	in	U.S.	cropland	and	grasslands	(Parton	et	al.,	1993),	and	uncertainties	have	been	

quantified	(Ogle	et	al.	(2007).	The	model	captures	soil	moisture	dynamics,	plant	

production,	and	thermal	controls	on	net	primary	production	and	decomposition	with	a	

time	step	of	a	month	or	less.		

Organic	Soils:		

 IPCC	equation	developed	by	Aalde	et	al.	(2006;	USDA,	2011)	using	region	specific	

emission	factors	from	Ogle	et	al.	(2003).		

 This	method	was	chosen	because	it	is	the	only	readily	available	model	for	estimating	soil	

carbon	stock	changes	from	organic	soils.		
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems 

3-50

carbon	in	transport	is	lost	to	the	atmosphere.	This	assumption	may	have	significant	variation	due	to	

the	diversity	of	environmental	conditions	in	which	eroded	carbon	is	transported	and	subsequently	

resides.	Other	environmental	drivers	will	also	influence	carbon	dynamics	in	soils,	particularly	

weather	and	soil	characteristics.	

Process‐based	models,	which	are	considered	an	IPCC	Tier	3	methodology,	have	been	developed	and	

sufficiently	evaluated	for	application	in	an	operational	tool	to	estimate	SOC	stock	changes	in	

mineral	soils.	The	DAYCENT	process‐based	model	(Parton	et	al.,	1987;	Parton,	1998)	has	been	

selected	because	it	is	well‐tested	for	estimating	soil	carbon	dynamics	in	cropland	and	grazing	land	

systems	(Parton	et	al.,	1993)	and	is	also	used	in	the	U.S.	national	GHG	inventory	(Ogle	et	al.,	2010;	

U.S.	EPA,	2011).	Del	Grosso	et	al.	(2011)	demonstrated	the	reduction	in	uncertainty	associated	with	

the	more	advanced	approach	using	the	DAYCENT	model	compared	to	the	lower	tier	methods.	The	

DAYCENT	model	simulates	plant	production	by	representing	long‐term	effects	of	land	use	and	

management	on	net	primary	production	(NPP),	as	influenced	by	selection	of	crops	and	forage	

grasses.	The	influence	of	management	practices	on	NPP	are	also	simulated,	including	mineral	

fertilization,	organic	amendments,	irrigation	and	fertigation,	liming,	green	manures	and	cover	

crops,	cropping	intensity,	hay	or	pasture	in	rotation	with	annual	crops,	grazing	intensity	and	

stocking	rate,	and	bare	fallow.	Nutrient	and	moisture	dynamics	are	influenced	by	soil	

characteristics,	such	as	soil	texture.	The	method	addresses	interannual	variability	due	to	annual	

changes	in	management	and	the	effect	of	weather	on	NPP.		

In	the	DAYCENT	model,	three	soil	organic	carbon	pools	are	included	representing	active,	slow,	and	

passive	soil	organic	matter,	which	have	different	turnover	times.	It	is	generally	considered	that	the	

active	carbon	pool	is	microbial	biomass	and	associated	metabolites	having	a	rapid	turnover	

(months	to	years),	the	slow	carbon	pool	has	intermediate	stability	and	turnover	times	(decades),	

and	the	passive	carbon	pool	represents	highly	processed	and	humified	decomposition	products	

with	longer	turnover	times	(centuries).	However,	these	pools	are	kinetically	defined	and	do	not	

necessarily	represent	explicit	fractions	of	soil	organic	carbon	that	can	be	isolated.	Soil	texture,	

temperature,	moisture	availability,	aeration,	burning,	and	other	factors	are	represented	in	the	

simulations	that	influence	the	decomposition	and	loss	of	carbon	from	these	pools.		

The	model	simulates	management	practices	influencing	soil	organic	carbon	pools.	These	practices	

include	addition	of	carbon	in	manure	and	other	organic	amendments,	such	as	compost,	wood	chips,	

and	biochar;	tillage	intensity;	residue	management	(retention	of	residues	in	field	without	

incorporation,	retention	in	the	field	with	incorporation,	and	removal	with	harvest,	burning,	or	

grazing).	The	influence	of	bare	and	vegetated	fallows	is	represented,	in	addition	to	irrigation	effects	

on	decomposition	in	cropland	and	grazing	land	systems.	The	model	can	also	simulate	setting‐aside	

cropland	from	production;	the	influence	of	fire	on	oxidation	of	soil	organic	matter;	and	woody	plant	

encroachment,	agroforestry,	and	silvopasture	effects	on	carbon	inputs	and	outputs.	

A	water/soil	moisture	submodel	(e.g.,	Parton	et	al.,	1987)	is	used	to	represent	the	influence	of	

weather,	irrigation,	crop	type,	and	management	on	soil	moisture	dynamics.	This	impact	is	

particularly	important	because	moisture	tends	to	be	a	more	proximal	factor	controlling	soil	organic	

carbon	dynamics,	which,	in	turn,	is	influenced	by	land	use	and	management	activity.	For	example,	

irrigation	influences	plant	production	and	carbon	inputs	because	of	the	modification	to	the	

moisture	regime.	

The	modeled	estimates	from	DAYCENT	are	combined	with	measurement	data	from	a	monitoring	

network	to	formally	evaluate	uncertainty.	This	approach	leverages	the	scalability	of	the	model	

while	providing	an	underlying	measurement‐basis	for	the	method	(Conant	et	al.,	2011;	Ogle	et	al.,	

2007).	
 Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

 3-51 

Erosion	and	deposition	influence	soil	organic	carbon	stocks	(Izaurralde	et	al.,	2007)	and	therefore	

are	represented	in	the	method,	although	there	is	uncertainty	in	the	net	effect	on	CO2	exchange	

between	the	biosphere	and	atmosphere.	Moreover,	there	is	also	some	risk	of	double‐counting	

carbon	as	it	is	transferred	across	ownership	boundaries,	in	terms	of	who	receives	credit	for	the	

eroded	carbon	in	their	accounting.	Regardless,	erosion	clearly	has	an	impact	on	carbon	stocks	in	a	

field,	which	can	be	estimated	with	reasonable	accuracy	using	erosion	calculators,	such	as	the	

Revised	Universal	Soil	Loss	Equation,	Version	2	(RUSLE2)	for	water	erosion	(USDA,	2003)	and	

Wind	Erosion	Prediction	System	(WEPS)	for	wind	erosion	(USDA,	2004).	Therefore,	the	current	

method	will	include	an	estimate	of	erosion‐related	carbon	loss	from	a	field,	but	neither	the	fate	of	

eroded	C,	nor	the	deposition	of	carbon	from	other	areas	onto	a	land	parcel,	will	be	estimated.		As	

more	studies	are	conducted,	carbon	transport	and	deposition	can	be	incorporated	in	future	

versions	of	the	method.				

Drainage	of	organic	soils	for	crop	production	leads	to	net	annual	emissions	due	to	increased	

decomposition	of	the	organic	matter	after	lowering	the	water	table	and	creating	aerobic	conditions	

in	the	upper	layers	of	the	soil	(Allen,	2012;	Armentano	and	Menges,	1986).	There	has	been	less	

evaluation	of	process‐based	models	for	organic	soils,	particularly	the	simulation	of	water	table	

dynamics	throughout	the	year,	which	will	influence	the	emission	rate.	Consequently,	the	approach	

is	based	on	more	simplistic	emission	factor	approach	developed	by	the	IPCC	(Aalde	et	al.,	2006).		

The	method	incorporates	U.S.	emission	rates	associated	with	region‐specific	drainage	patterns	

(Ogle	et	al.,	2003),	so	it	is	a	Tier	2	method	as	defined	by	the	IPCC.	

3.5.3.2 Description of Method

The	method	representing	the	influence	of	land	use	and	management	on	SOC	and	associated	CO2	flux	

to	the	atmosphere	is	estimated	with	a	carbon	stock	change	approach	(Aalde	et	al.,	2006).	For	

mineral	soils,	the	method	will	require	estimates	of	carbon	stocks	at	the	beginning	and	end	of	the	

year	in	order	to	estimate	the	annual	change	using	the	equation	below.	In	contrast,	carbon	stock	

changes	in	organic	soils	(i.e.,	Histosols)	will	address	only	the	emissions	occurring	with	drainage,	

which	is	the	typical	situation	in	cropland.	Emissions	occur	in	organic	soils	following	drainage	due	to	

the	conversion	of	an	anaerobic	environment	with	a	high	water	table	to	aerobic	conditions	

(Armentano	and	Menges,	1986),	resulting	in	a	significant	loss	of	carbon	to	the	atmosphere	(Ogle	et	

al.,	2003).	Recent	data	on	subsidence	were	used	to	derive	these	estimates	(e.g.,	Shih	et	al.,	1998).	

Mineral Soils:	The	model	to	estimate	changes	in	SOC	stocks	for	mineral	soils	has	been	adapted	from	

the	method	developed	by	IPCC	(Aalde	et	al.,	2006).	The	annual	change	in	stocks	to	a	30	centimeter	

depth	for	a	land	parcel	is	estimated	using	the	following	equation:	

Equation 3‐5: Change in Soil Organic Carbon Stocks for Mineral Soils

ΔCMineral = [(SOCt	‐	SOCt‐1)/ t]× A×CO2MW

Where:	

ΔCMineral		 =	Annual	change	in	mineral	soil	organic	carbon	stock	(metric	tons	CO2‐eq	year‐1)	

SOCt		 =	Soil	organic	carbon	stock	at	the	end	of	the	year	(metric	tons	C	ha‐1)	

SOCt‐1		 =	Soil	organic	carbon	stock	at	the	beginning	of	the	year	(metric	tons	C	ha‐1)	

t	 =	1	year	

A		 =	Area	of	parcel	(ha)	

CO2MW		 =	Ratio	of	molecular	weight	of	CO2	to	carbon	

	 =	44/12	(metric	tons	CO2	(metric	tons	C)‐1)	
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems 

3-52

The	DAYCENT	model	is	used	to	simulate	the	SOC	stocks	at	the	beginning	and	end	of	each	year	for	

Equation	3‐5	based	on	recent	management	practices	for	a	land	parcel.	Initial	values	for	DAYCENT	

are	needed	for	the	SOCt‐1	and	are	based	on	a	simulation	of	historical	management	to	provide	

accurate	stocks	and	distribution	of	organic	carbon	among	the	pools	represented	in	the	model	

(active,	slow,	and	passive	soil	organic	matter	pools).	Each	pool	has	a	different	turnover	rate	

(representing	the	heterogeneous	nature	of	soil	organic	matter),	and	the	amount	of	carbon	in	each	

pool	at	any	point	in	time	influences	the	forward	trajectory	of	the	total	soil	organic	carbon	storage	

(Parton	et	al.,	1987).	By	simulating	the	historical	land	use,	the	distributions	of	carbon	in	active,	

slow,	and	passive	pools	are	estimated	in	an	unbiased	way.	

Three	steps	are	required	to	estimate	the	initial	values.	The	first	step	involves	running	the	model	to	

a	steady‐state	condition	(e.g.,	equilibrium)	under	native	vegetation,	historical	climate	data,	and	the	

soil	physical	attributes	for	the	land	parcel.	The	second	step	is	to	simulate	period	of	time	from	the	

1800’s	to	1980	and	1980	to	2000.	The	entity	is	provided	a	list	of	options	for	selecting	the	practices	

that	best	match	the	land	management	for	the	parcel.	From	2000	to	the	initial	year	for	reporting,	the	

entity	enters	more	specific	data	on	crops	planted,	tillage	practices,	fertilization	practices,	irrigation,	

and	other	management	activity	(See	Section	3.5.3.3	for	more	information).	The	simulated	carbon	

stock	at	the	end	of	the	simulation	provides	the	initial	baseline	value	(SOCt‐1).	

The	stock	at	the	end	of	a	year	(SOCt)	is	estimated	by	the	DAYCENT	model	based	on	simulating	

management	activity	during	the	specific	year.	The	entity	provides	the	management	activity	for	the	

land	parcel,	including	crops	planted,	tillage	practices,	fertilization	practices,	irrigation	and	other	

management	activity	data	(See	Section	3.5.3.3	for	more	information).		The	change	in	SOC	stocks	are	

estimated	for	additional	years	by	using	the	ending	stock	from	the	previous	year	as	the	initial	SOC	

stock	(SOCt‐1)	and	then	simulating	the	management	for	another	year	to	produce	the	stock	at	the	end	

of	the	next	year	(SOCt).		

Eroded	carbon	is	estimated	with	the	RUSLE2	for	water	erosion	(USDA,	2003)	and	WEPS	for	wind	

erosion	(USDA,	2004).	Neither	the	deposition	of	carbon	on	the	site	nor	the	fate	of	eroded	carbon	is	

in	this	version	of	the	USDA	methods.	The	eroded	carbon	estimate	is	reported	separately	to	account	

for	uncertainty	associated	with	the	potential	effect	of	erosion	on	SOC	stocks,	and	may	be	used	as	a	

discount	for	the	SOC	stock	changes	estimate	with	Equation	3‐5.			

The	DAYCENT	model	is	not	able	to	estimate	soil	organic	carbon	stocks	in	mineral	soils	for	all	crops.	

In	instances	where	a	crop	is	not	estimated	by	the	DAYCENT	model,	the	method	developed	by	the	

IPCC	(2006)	(i.e.,	a	Tier	1	methodology)	may	be	used	(See	Appendix	3‐B).	

Organic Soils:	The	methodology	for	estimating	soil	carbon	stock	changes	in	drained	organic	soils	

has	been	adopted	from	IPCC	(Aalde	et	al.,	2006).	The	method	applies	to	Histosols	and	soils	that	

have	high	organic	matter	content	and	developed	under	saturated,	anaerobic	conditions	for	at	least	

part	of	the	year,	which	includes	Histels,	Historthels,	and	Histoturbels.	The	following	equation	is	

used	to	estimate	emissions	from	a	land	parcel:	
 Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

 3-53 

Emission	factors	have	been	adopted	from	Ogle	et	al.	(2003)	and	are	region‐specific,	based	on	typical	

drainage	patterns	and	climatic	controls	on	decomposition	rates;	these	rates	are	also	used	in	the	U.S.	

national	GHG	inventory	(U.S.	EPA,	2011).	Drained	cropland	soils	lose	carbon	at	a	rate	of	11±2.5	

metric	tons	C	ha‐1	year‐1	in	cool	temperate	regions,	14±2.5	metric	tons	C	ha‐1	year‐1	in	warm	

temperate	regions,	and	14±3.3	metric	tons	C	ha‐1	year‐1	in	subtropical	climate	regions.	Organic	soils	

in	grazing	lands	are	typically	not	drained	to	the	depth	of	cropland	systems,	and	therefore	the	

emission	factors	are	only	25	percent	of	the	cropland	values	(Ogle	et	al.,	2003).	

3.5.3.3 Activity Data

The	activity	data	requirements	vary	between	mineral	soils	and	organic	soils.	Mineral	soils	require	

the	following	activity	data	for	croplands:	

 Area	of	land	parcel	(i.e.,	field);	

 Crop	selection	and	rotation	sequence;		

 Planting	and	harvesting	dates;		

 Residue	management,	including	amount	harvested,	burned,	grazed,	or	left	in	the	field;		

 Irrigation	method,	application	rate,	and	timing	of	water	applications;	

 Mineral	fertilizer	type,	application	rate,	and	timing	of	application(s);	

 Lime	amendment	type,	application	rate,	and	timing	of	application(s);	

 Organic	amendment	type,	application	rate,	and	timing	of	application(s);		

 Tillage	implements,	dates	of	operation,	and	number	of	passes	in	each	operation	(which	can	

be	used	to	determine	tillage	intensity	with	the	STIR	Model	(USDA	NRCS,	2008));	

 Use	of	drainage	practices	and	depth	of	drainage	(commonly	in	hydric	soils);	and	

 Cover	crop	types,	planting,	and	harvesting	dates	(if	applicable).	

The	method	for	grazing	land	on	mineral	soils	requires	the	following	management	activity	data:	

 Area	of	land	parcel	(i.e.,	field);	

 Plant	species	composition;		

 Periods	of	grazing	during	the	year;		

 Animal	type,	class,	and	size	used	for	grazing;	

 Stocking	rates	and	methods;	

 Irrigation	method,	application	rate,	and	timing	of	water	applications;	

 Mineral	fertilizer	type,	application	rate,	and	timing	of	application(s);	

 Lime	amendment	type,	application	rate,	and	timing	of	application(s);	

 Organic	amendment	type,	application	rate,	and	timing	of	application(s);	

 Pasture/Range/Paddock	(PRP)	N	excreted	directly	onto	land	by	livestock	(i.e.,	manure	that	

is	not	managed);	

Equation 3‐6: Change in Soil Organic Carbon Stocks for Organic Soils

ΔCOrganic = A × EF × CO2MW

Where:	

ΔCOrganic		=	Annual	CO2	emissions	from	drained	organic	soils	in	crop	and	grazing	lands

	 	 	(metric	tons	CO2‐eq	year‐1)	

A		 =	Area	of	drained	organic	soils	(ha)	

EF		 =	Emission	factor	(metric	tons	C	ha‐1	year‐1)		

CO2MW		=	Ratio	of	molecular	weight	of	CO2	to	C	(=	44/12)	(metric	tons	CO2	(metric	tons	C)‐1)	
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems 

3-54

 Use	of	drainage	practices	and	depth	of	drainage	(commonly	in	hydric	soils);		

 Level	of	woody	plant	encroachment;	and	

 Total	yield	of	crop	(metric	tons	dry	matter	crop	yield	year‐1),	meat	(kg	carcass	yield	year‐1)	

or	milk	(kg	fluid	milk	year‐1).		

Longer‐term	history	of	site	management	will	be	used	to	simulate	initial	soil	organic	carbon	stocks	

for	the	crop	or	grazing	system.	In	order	to	estimate	the	initial	values,	the	entity	will	need	to	provide	

management	activity	data	for	the	past	three	decades.	A	list	of	management	systems	will	be	

provided.	The	entity	will	also	provide	the	previous	land	use	and	year	of	conversion	if	a	land‐use	

change	occurred	during	the	past	three	decades.	Historical	data	for	activity	from	more	than	three	

decades	in	the	past	will	be	represented	based	on	national	agricultural	statistics	using	enterprise	

budgets	and	census	data	for	various	regions	in	the	country.	However,	an	entity	can	provide	the	

longer	term	history	if	it	is	known.	Data	on	the	carbon	and	nitrogen	content	of	organic	amendments	

will	also	be	needed	from	the	entity,	although	defaults	are	provided	below	if	the	entity	does	not	have	

this	information.	Pasture/Range/Paddock	(PRP)	manure	N	input	is	the	N	excreted	directly	onto	

land	by	livestock,	and	the	manure	is	not	collected	or	managed	(de	Klein	et	al.,	2006).		The	amount	of	

PRP	manure	N	is	estimated	with	the	livestock	methods	(See	Chapter	5,	Section	5.3.2	Enteric

Fermentation and Housing Emissions from Beef Production Systems)	and	assumed	to	be	split	with	

50%	of	the	N	in	urine	and	the	other	50%	of	the	N	in	solids.	

Table 3‐7: Nitrogen and Carbon Fractions of Common Organic Fertilizers – Midpoint and

Range (Percent by Weight)

Organic Fertilizer % Na % C

Poultry	manure	 2.25%	(1.5‐3) 8.75%	(7‐10.5)b

Pig,	horse,	cow	manure	 0.45%	(0.3‐0.6) 5.1%	(3.4–6.8)c

Green	manure	 3.25%	(1.5‐5) 42%d

Compost	 1.25%	(0.5‐2) 16%	(12‐20)e

Seaweed	meal	 2.5%	(2‐3) 27%f

Sewage	sludge	 3%	(1‐5) 11.7%	(3.9‐19.5)b

Fish	waste	 7%	(4‐10) 24.3%	(14.6‐34)g

Blood	 11%	(10‐12) 35.2%	(32‐38.4)h

Human	urine/night	soil 1.25%	(1‐1.5) 9.5%	(9‐10)i

a	Hue,	N.V.	Organic Fertilizers in Sustainable Agriculture Retrieved	from	

http://www.ctahr.hawaii.edu/huen/hue_organic.htm.		 b	USDA.	1992.	Agricultural	Waste	Characteristics.	Chapter	4.	In	Animal	Waste	Management	Field	Handbook:	

Natural	Resources	Conservation	Service,	United	States	Department	of	Agriculture.		 c	EPA,	2013.	Inventory	of	U.S.	Greenhouse	Gas	Emissions	and	Sinks:	1990‐2011.	Weighted	U.S.	average	

carbon:nitrogen	ratio	for	manure	available	for	application.	

d	Assumes	dry	matter	is	42%	carbon.	

e	A1	Organics.	Compost Classification, Specification and Resource Manual.	

http://www.a1organics.com/CLSP/CLASS%20MANUAL%20‐%20COLORADO.pdf

f	http://www.naorganics.com/en/science_analysis.asp.	North	Atlantic	Organics.		

g	Hartz,	T.K.	and	P.R.	Johnstone.	2006.	Nitrogen	available	from	high‐nitrogen‐containing	organic	fertilizers.	

HortTechnology	16:39‐42.		

h	Sonon,	D,	et	al.	2012.	Mineralization	of	high‐N	organic	fertilizers.	Clemson	University.		 i	Polprasert,	C.	2007.	Organic	Waste	Recycling:	Technology	and	Management.	IWA	Publishing.		

The	method	for	organic	soils	requires	the	following	activity	data	for	croplands	and	grazing	lands:	

 Area	of	land	parcel	(i.e.,	field);	and	

 Total	yield	of	crop	(metric	tons	dry	matter	crop	yield	year‐1),	meat	(kg	carcass	yield	year‐1)	

or	milk	(kg	fluid	milk	year‐1).	
 Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

 3-55 

3.5.3.4 Ancillary Data

Ancillary	data	for	the	mineral	soil	method	include	historical	weather	patterns	and	soil	

characteristics.	Weather	data	may	be	based	on	national	datasets	such	as	the	Parameter‐Elevation	

Regressions	on	Independent	Slopes	Model	(PRISM)	data	(Daly	et	al.,	2008).	Soil	characteristics	may	

also	be	based	on	national	datasets	such	as	the	Soil	Survey	Geographic	Database	(SSURGO)	(Soil	

Survey	Staff,	2011).	However,	there	will	also	be	an	option	for	entities	to	substitute	soils	data	

collected	from	the	specific	field.	The	erosion	model	will	also	require	ancillary	data	on	topography	

(i.e.,	slope),	length	of	field	and	row	orientation,	crop	canopy	height,	diversions,	surface	residue	

cover,	and	soil	texture.	

No	ancillary	data	are	needed	for	the	method	to	estimate	emissions	from	drainage	of	organic	soils.	

3.5.3.5 Model Output

Model	output	is	generated	for	the	quantity	of	emissions	and	emissions	intensity.	The	change	in	

mineral	soil	organic	carbon	stocks	is	estimated	based	on	stock	changes	over	five‐year	time	periods	

in	order	to	manage	uncertainty.	Uncertainties	in	the	model‐based	estimates	are	about	three	times	

larger	for	annual	estimates	in	change	rate	compared	with	five‐year	blocks	(Compare	U.S.	

Environmental	Protection	Agency	(2009)	and	(2010)).	Uncertainties	are	larger	at	the	finer	time	

scale	because	there	is	large	variability	in	measurements	of	soil	carbon	stock	changes	at	annual	time	

scales,	and	this	variability	is	incorporated	into	the	model	uncertainty	using	the	empirically	based	

method	(Ogle	et	al.,	2007).	In	addition,	trends	in	soil	organic	carbon	will	be	estimated	for	the	30	

previous	years	of	history	and	the	reporting	period.	

Emissions	intensity	is	based	on	the	amount	of	emissions	per	unit	of	yield	for	crops	in	cropland	

systems	or	animal	products	in	grazing	systems.	The	emissions	intensity	is	estimated	with	the	

following	equation:	

3.5.3.6 Limitations and Uncertainty

Uncertainties	in	the	mineral	soil	methods	include	imprecision	and	bias	in	the	process‐based	model	

parameters	and	algorithms,	in	addition	to	uncertainties	in	the	activity	and	ancillary	data.	

Uncertainty	in	the	parameterization	and	algorithms	will	be	quantified	with	an	empirically	based	

approach,	as	used	in	the	U.S.	national	GHG	inventory	(Ogle	et	al.,	2007;	U.S.	EPA,	2011).	The	method	

combines	modeling	and	measurements	to	provide	an	estimate	of	SOC	stock	changes	for	entity	scale	

reporting	(Conant	et	al.,	2011).	Measurements	of	carbon	stock	changes	are	expected	to	be	based	on	

Equation 3‐7: Emissions Intensity of Soil Organic Carbon Stock Change

EISoilC = (ΔCMineral +	ΔCOrganic)/Y

Where:	

EISoilC		 =	Emissions	intensity	(metric	tons	CO2	per	metric	ton	dry	matter	crop	yield,	metric	

tons	CO2	per	kg	carcass	yield,	metric	tons	CO2	per	kg	fluid	milk	yield)	

ΔCMineral		 =	Annual	CO2	equivalent	emissions	from	soil	organic	carbon	change	in	mineral	soils	

(metric	tons	CO2‐eq	year‐1)	

ΔCOrganic		 =	Annual	CO2	equivalent	emissions	from	soil	organic	carbon	change	in	organic	soils,	

Histosols	(metric	tons	CO2‐eq	year‐1)	

Y		 =	Total	yield	of	crop	(metric	tons	dry	matter	crop	yield	year‐1),	meat	(kg	carcass	

yield	year‐1)	or	milk	production	(kg	fluid	milk	yield	year‐1)	
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems 

3-56

a	national	soil	monitoring	network	(Spencer	et	al.,	2011).	The	network	should	include	samples	from	

different	regions	of	the	country	and	soil	types	that	are	used	for	crop	production	or	grazing,	and	a	

random	sampling	of	the	management	systems	in	each	of	the	regions.	The	sampling	plots	will	need	

to	be	designed	for	resampling	over	time	in	order	to	evaluate	the	modeled	changes	in	SOC	stocks	

(Conant	et	al.,	2003).	Uncertainties	in	national	datasets	for	weather	will	be	based	on	information	

included	with	the	dataset,	while	uncertainties	in	the	SSURGO	should	be	quantified	using	the	

underlying	field	data	that	form	the	basis	for	the	mapping	exercise,	or	an	independent	accuracy	

assessment	of	the	map	product.	Other	input	data	is	assumed	to	be	known	by	the	entity,	such	as	the	

crop	plants,	yields,	tillage,	and	residue	management	practices.

The	limitations	of	the	mineral	soil	carbon	method	include	no	assessment	of	the	effect	of	land	use	

and	management	in	sub‐surface	layers	of	the	soil	profile	(below	30	centimeters),	no	assessment	of	

the	location	of	transport	and	deposition	of	eroded	C,	and	limited	data	to	assess	uncertainty	in	the	

parameters	and	algorithms	using	the	empirically	based	method.	For	agroforestry,	the	DAYCENT	

model	has	been	used	in	the	COMET‐Farm	voluntary	carbon	reporting	tool	to	simulate	soil	organic	

carbon	stock	changes.	However,	there	are	several	unknowns	with	the	use	of	the	DAYCENT	model	

for	estimating	soil	organic	carbon	stock	changes	in	agroforestry,	including	whether	the	model	is	

able	to	take	into	account	the	interactions	occurring	between	woody	and	herbaceous	vegetation	and	

respective	management	activities.	Oelbermann	and	Voroney	(2011)	evaluated	the	use	of	the	

Century	model,	the	monthly	time‐step	version	of	the	DAYCENT	model,	to	predict	soil	organic	

carbon	in	temperate	and	tropical	alley	cropping	systems	that	were	13	and	19	years	old,	

respectively.	They	found	that	the	model	underestimated	the	levels	of	soil	organic	carbon	compared	

with	measured	values.	With	more	testing,	the	methods	may	be	revised	in	the	future	to	use	the	

DAYCENT	model	for	the	purposes	of	estimating	soil	organic	carbon	stock	changes	in	agroforestry	

systems.	

Biochar	research	has	been	an	area	of	rapid	development	over	the	past	few	years,	but	there	are	still	

uncertainties.	Biochar	is	a	product	of	combusted	biomass	that	has	a	variety	of	chemical	structures	

depending	on	the	biomass	and	pyrolysis	method,	and	the	variation	has	implications	for	the	stability	

of	the	carbon	in	the	soil	(Spokas,	2010).	Biochar	can	have	concomitant	impacts	on	emissions	of	

other	GHGs	such	as	CH4	and	N2O	(Cayuela	et	al.,	2010;	Malghani	et	al.,	2013;	Yu	et	al.,	2013),	

although	some	studies	have	shown	no	effect	(Case	et	al.,	2013;	Clough	et	al.,	2010).	Soil	

amendments	with	biochar	may	also	prime	the	decomposition	of	the	native	soil	organic	matter	

although	the	CO2	emissions	from	priming	appear	to	be	considerably	smaller	than	the	carbon	added	

in	the	biochar	(Stewart	et	al.,	2013;	Woolf	and	Lehmann,	2012).	Other	research	suggests	that	there	

may	even	be	“negative”	priming	leading	to	a	reduction	in	heterotrophic	respiration	(Case	et	al.,	

2013).	Furthermore,	the	temporal	duration	of	the	GHG	mitigation	potential	of	biochar	is	also	

uncertain	but	appears	to	be	of	a	short	term	nature	(Spokas,	2013).	The	influence	of	biochar	on	

emissions	and	priming	needs	more	research	before	the	full	effect	of	biochar	on	carbon	

sequestration	and	GHG	emissions	can	be	incorporated	into	models	and	GHG	reporting	frameworks.		

Microbial	degradation	of	biochar	can	occur	over	time	scales	ranging	from	as	little	as	a	few	decades	

to	1000s	of	years	(Spokas,	2010).	In	the	technical	methods,	biochar	is	treated	as	a	high	carbon	to	

low	nitrogen	amendment	in	the	DAYCENT	model	framework,	but	with	a	conservative	residence	

time	of	the	carbon	from	decades	to	a	century.	These	methods	can	be	further	refined	in	the	future	as	

the	different	types	and	residence	times	of	biochar	are	further	resolved.	

The	method	for	organic	soils	also	has	limitations,	particularly	the	inability	to	estimate	the	effect	of	

mitigation	measures	such	as	water	table	management	because	emission	factors	are	set	for	each	

climate	region	(i.e.,	currently	scaling	factors	are	not	available	to	revise	the	emission	factors	for	

water	table	management).	Only	complete	restoration	of	the	wetland	with	no	further	drainage	can	

be	addressed	with	the	method	(i.e.,	assumes	no	further	emissions	of	CO2).	However,	if	crop	
 Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

 3-57 

production	is	maintained	on	the	land	parcel,	the	most	practical	method	for	reducing	emissions	is	to	

raise	the	water	table	to	near	the	rooting	depth	of	the	crop	during	the	growing	season	and		then	not	

draining	the	soil	during	the	non‐growing	season	(Jongedyk	et	al.,	1950;	Shih	et	al.,	1998),	or	

possibly	managing	the	system	with	periodic	flooding	(Morris	et	al.,	2004).	

For	all	systems	there	is	additional	uncertainty	associated	with	climate	change.	Modeled	output	for	

any	given	location	assumes	temperature	and	precipitation	similar	to	that	of	the	past	30	years,	the	

period	for	which	historical	weather	is	used	to	simulate	soil	organic	carbon	dynamics.	Expected	

changes	in	temperature,	precipitation,	and	extreme	events	such	as	droughts,	floods,	and	heat	waves	

will	add	further	uncertainty	to	estimates	of	soil	organic	carbon	stock	change.		

While	there	is	considerable	evidence	and	mechanistic	understanding	about	the	influence	of	land	

use	and	management	on	SOC,	there	is	less	known	about	the	effect	on	soil	inorganic	C.	Consequently,	

there	is	uncertainty	associated	with	land	use	and	management	impacts	on	soil	inorganic	carbon	

stocks,	which	cannot	be	quantified.	Current	methods	do	not	include	impacts	on	inorganic	C,	but	this	

may	be	added	in	the	future	as	more	studies	are	conducted	and	methods	are	developed.	

Uncertainties	in	model	parameters	and	structure	are	combined	using	a	Monte	Carlo	simulation	

approach.	Uncertainty	is	assumed	to	be	minor	for	the	management	activity	data	provided	by	the	

entity.		Table	3‐8	provides	the	probability	distribution	functions	associated	with	the	mineral	and	

organic	soils	methods.		

Table 3‐8: Available Uncertainty Data for Soil Organic Carbon Stock Change			

Parameter Mean Units

Relative

Uncertainty Distribution Data Source Low

(%)

High

(%)

DAYCENT	(empirical	

uncertainty)	 NS	 Various	 NS	 NS	 Normal	

Ogle	et	al.	

(2007);	EPA	

(2013)	

Emission	factor	for	cropland	in	

cool	temperate	regions	 11	 metric	tons	C	ha‐1	year‐1 45	 45	 Normal	 Ogle	et	al.	(2003)	

Emission	factor	for	cropland	in	

warm	temperate	regions	 14	 metric	tons	C	

ha‐1	year‐1 35	 35	 Normal	 Ogle	et	al.	

(2003)	

Emission	factor	for	cropland	in	

subtropical	regions	 14	 metric	tons	C	ha‐1	year‐1 46	 46	 Normal	 Ogle	et	al.	(2003)	

Emission	factor	for	grazing	land	

in	cool	temperate	regions	 2.8	 metric	tons	C	

ha‐1	year‐1 45	 45	 Normal	 Ogle	et	al.	

(2003)	

Emission	factor	for	grazing	land	

in	warm	temperate	regions	 3.5	 metric	tons	C	ha‐1	year‐1 35	 35	 Normal	 Ogle	et	al.	(2003)	

Emission	factor	for	grazing	land	

in	subtropical	regions	 3.5	 metric	tons	C	

ha‐1	year‐1 46	 46	 Normal	 Ogle	et	al.	

(2003)	

NS	=	Not	Shown.	Data	are	not	shown	for	parameters	that	have	100’s	to	1000’s	of	values	(denoted	as	NS).	
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems 

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3.5.4 Soil Nitrous Oxide		

3.5.4.1 Rationale for Selected Method		

N2O	fluxes	are	notoriously	difficult	to	measure	because	of	the	labor	required	to	sample	emissions,	

combined	with	high	spatial	and	temporal	variability.	Agronomic	practices	that	affect	N2O	fluxes	in	

one	soil,	climate,	or	site‐year	may	have	little	or	no	measurable	effect	in	others.	Consequently,	

considerable	care	is	required	to	ensure	that	methods	to	estimate	changes	in	emissions	for	a	

particular	cropping	practice	are	accurate	and	robust	for	the	geographic	region	for	which	they	are	

proposed,	or	are	sufficiently	generalizable	to	be	accurate	in	aggregate.	

De	Klein	et	al.	(2006)	provide	three	estimation	strategies	for	direct	N2O	emissions	from	cropland.	

Two	are	based	on	emission	factors,	the	proportion	of	nitrogen	added	to	a	crop	that	becomes	N2O.	

Tier	1	is	based	on	a	near‐universal	emission	factor,	applicable	globally	without	regard	to	

geography,	cropping	practice,	or	fertilizer	placement,	timing,	or	formulation.	Tier	2	methods	utilize	

Method for Estimating Soil Direct N2O Emissions

Mineral Soils

 The	method	is	based	on	using	results	from	process‐based	models	and	measured	N2O	

emissions	in	combination	with	scaling	factors	based	on	U.S.	specific	empirical	data	on	a	

seasonal	timescale.	

 Process‐based	modeling	(an	ensemble	approach	using	DAYCENT	and	DNDC)	combined	

with	field	data	analysis	are	used	to	derive	base	emission	rates	for	the	major	cropping	

systems	and	dominant	soil	texture	classes	in	each	USDA	Land	Resource	Region.	In	cases	

where	there	are	insufficient	empirical	data	to	derive	a	base	emission	rate,	the	base	

emission	rate	is	based	on	the	IPCC	default	factor.	

The	base	emission	factors	are	adjusted	by	scaling	factors	related	to	specific	crop	

management	practices	that	are	derived	from	experimental	data.		

Organic Soils

 Direct	N2O	emissions	from	drainage	of	organic	soils	uses	the	IPCC	equations	developed	in	

de	Klein	et	al.,	(2006).	The	method	for	organic	soils	assumes	that	there	is	still	a	significant	

organic	horizon	in	the	soil,	and	therefore,	there	are	substantial	inputs	of	nitrogen	from	

oxidation	of	organic	matter.			 	

 The	emission	rate	for	drained	organic	soils	is	based	on	IPCC	Tier	1	emission	factor	(0.008	

metric	tons	N2O‐N	ha‐1	year‐1).		

 This	method	relies	on	entity	specific	activity	data	as	input	into	the	equations.	

Method for Estimating Soil Indirect N2O Emissions

 This	method	uses	the	IPCC	equation	for	indirect	soil	N2O	(de	Klein	et	al.,	2006).	

 IPCC	defaults	are	used	for	estimating	the	proportion	of	nitrogen	that	is	subject	to	

leaching,	runoff,	and	volatilization.	In	land	parcels	where	the	precipitation	plus	irrigation	

water	input	is	less	than	80	percent	of	the	potential	evapotranspiration,	nitrogen	leaching	

and	runoff	are	considered	negligible	and	no	indirect	N2O	emissions	are	estimated	from	

leaching	and	runoff.		

 This	method	uses	entity	specific	seasonal	data	on	nitrogen	management	practices.	
 Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

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geographic,	crop,	or	practice‐specific	emission	factors	where	field	tests	show	that	a	factor	different	

from	the	one	percent	Tier	1	factor	is	warranted.	At	present	there	is	only	one	Tier	2	example	in	the	

primary	literature	that	is	specific	to	conditions	in	the	United	States,	and	it	is	for	corn	in	the	North	

Central	region	(Millar	et	al.,	2010).		This	method	has	been	incorporated	into	several	N2O	reduction	

protocols	(Verified	Carbon	Standard,	American	Carbon	Registry,	and	Climate	Action	Reserve).	The	

third	option	for	estimating	direct	N2O	emissions,	or	Tier	3,	is	a	measurement	or	process‐based	

modeling	approach.	In	this	case,	emissions	are	monitored	specifically	for	the	entity’s	field	by	

deploying	instruments	in	a	measurement	system	or	by	gathering	the	information	specific	to	the	

field	conditions	to	simulate	N2O	emissions	with	a	process‐based	model.	This	third	option	is	the	

most	precise,	but	requires	more	resources	and	sufficient	testing	prior	to	implementation.	

In	Section	3.2.1.1,	several	practices	are	discussed	that	have	been	shown	to	reduce	N2O	emissions	in	

field	experiments.	However,	many	of	the	experiments	have	been	conducted	for	a	limited	number	of	

specific	cropping	systems	and	regions.	Consequently,	there	are	no	mitigation	practices	for	which	

emission	reductions	have	been	quantified	under	all	conditions	in	the	United	States.	Nevertheless,	

for	many	practices	there	is	sufficient	knowledge	at	the	cropping	system	and	regional	levels	to	

establish	that	adoption	will	reduce	soil	N2O	emissions.		

Process‐based	simulation	models	use	knowledge	of	C,	N,	and	water	processes	(among	others)	to	

predict	ecosystem	responses	to	climate	and	other	environmental	factors,	including	crop	and	

grazing	land	management	(see	soil	carbon	methodology	in	Section	3.5.3).	N2O	fluxes	can	be	

predicted	using	simulation	models	(Chen	et	al.,	2008;	Del	Grosso	et	al.,	2010).	A	key	advantage	of	

simulation	models	is	that	they	are	generalizable	to	a	wide	variety	of	soils,	climates,	and	cropping	

systems,	allowing	factors	to	interact	in	complex	ways	that	may	be	difficult	to	predict	with	less	

sophisticated	approaches.	However,	a	disadvantage	is	that	complexity	can	limit	their	transparency,	

and	at	present	there	are	still	substantial	data	gaps	that	limit	our	ability	to	fully	test	available	models	

for	their	sensitivity	to	different	management	practices	across	various	regions	and	crops	in	the	

United	States.	

To	overcome	these	challenges,	a	hybrid	approach	that	utilizes	process‐based	simulation	models	and	

field	data	was	developed	to	estimate	N2O	emissions.	The	method	uses	a	base	emission	rate	

associated	with	the	typical	amount	of	nitrogen	applied,	and	then	adjustments	are	applied	via	

scaling	factors	to	account	for	management	practices	that	affect	N2O	emissions.	This	approach	is	a	

Tier	3	method	as	defined	by	the	IPCC.		

Base	emission	rates	are	estimated	for	each	dominant	crop	and	three	soil	texture	classes	(coarse,	

medium,	fine)	within	a	climatic	region	using	process‐based	simulation	modeling.	The	factors	are	

developed	at	the	scale	of	USDA	Land	Resource	Regions	(LRR).	Field	data	indicate	that	N2O	

emissions	generally	increase	as	the	amount	of	applied	nitrogen	increases,	especially	when	nitrogen	

application	rates	exceed	crop	uptake	rates	(Hoben	et	al.,	2011;	Kim	et	al.,	2013;	McSwiney	and	

Robertson,	2005;	Shcherbak	et	al.,	in	press)	Research	data	from	field	experiments	were	compiled	

and	used	to	adjust	the	emission	rates	for	nitrogen	fertilizer	application	rates	that	exceeded	the	

typical	nitrogen	application	rate	for	the	crop	in	a	land	resource	region.	For	crops	where	sufficient	

data	are	not	available	to	simulate	the	base	emission	rate	with	a	process‐based	model,	the	standard	

IPCC	Tier	1	emission	factor	is	applied.	In	addition,	for	land	parcels	that	have	a	mix	of	crops	where	

only	some	can	be	simulated,	the	standard	IPCC	Tier	1	approach	should	also	be	applied.	

Emissions	are	affected	by	specific	farm	management	practices	such	as	reducing	tillage	intensity;	

adding	nitrification	inhibitors,	or	changing	how,	when	and	where	nitrogen	fertilizers	are	applied.	

To	account	for	the	effect	of	management	practices	on	N2O	emission,	scaling	factors	were	developed	

to	adjust	the	base	emission	rates.	The	scaling	factors	were	estimated	from	available	research	data	

(See	Appendix	3‐A	for	more	information).	Management	practices	other	than	those	included	in	the	
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems 

3-60

equation	may	also	mitigate	N2O	emissions,	but	there	are	not	currently	sufficient	data	to	create	

generalized	scaling	factors.	Additional	data	may	lead	to	their	inclusion	in	future	updates	to	the	

method.			

This	method	incorporates	more	information	than	a	method	based	solely	on	the	IPCC	model.	It	

provides	a	transparent	and	science‐based	means	of	estimating	annualized	N2O	emissions	from	crop	

and	grazing	lands,	and	it	facilitates	the	estimation	of	uncertainty.	For	N2O	emissions	from	crop	and	

grazing	lands,	an	IPCC	Tier	1	approach	is	only	sensitive	to	nitrogen	application	rate,	and	therefore	

does	not	reflect	the	full	suite	of	factors	that	are	known	to	influence	N2O	emissions	including	climate,	

soils,	crops,	and	management	practices	that	range	from	tillage	to	cover	crops	to	fertilizer	timing,	

placement,	formulation,	and	additives.		Dynamic	process	models	as	embodied	in	the	IPCC	Tier	3	

approach	can,	in	concept,	account	for	most	of	these	factors	but	to	date	have	not	been	sufficiently	

evaluated	for	many	U.S.	locations,	crops,	and	management	practices.	This	report	takes	a	hybrid	

approach	that	represents	the	best	available	science	at	the	time	of	publication:	dynamic	process	

models	to	estimate	baseline	N2O	emissions	for	those	crops	and	locations	sufficiently	evaluated,	then	

scaled	by	management	practices	to	the	extent	supported	by	available	research	results.		Initial	

testing	indicates	that	this	method	is	more	sensitive	to	U.S.	nutrient	management	practices	than	the	

IPCC	Tier	1	approach.		The	authors	anticipate	publication	of	an	addendum	that	will	provide	test	

results	and	suggest	further	tuning	of	the	method.	Over	time,	as	dynamic	process	models	are	further	

developed	and	tested.	The	method	will	likely	migrate	towards	an	exclusive	Tier	3	approach	to	

better	account	for	management	effects	given	the	local	variables	and	conditions.	In	the	interim,	in	

addition	to	providing	best‐available	and	reliable	estimates	of	N2O	emissions	from	crop	and	grazing	

lands,	the	method	outlined	here	is	expected	to	set	a	research	agenda	that	provides	for	broader	

evaluation	of	environmental	conditions	and	management	practices	influencing	N2O	emissions	as	

well	as	further	development	of	models	to	more	accurately	estimate	emissions.	

Offsite	or	indirect	N2O	emissions,	which	occur	when	reactive	nitrogen	escapes	to	downwind	or	

downstream	ecosystems	where	favorable	conditions	for	N2O	production	exist,	are	even	more	

difficult	to	estimate	than	direct	emissions	because	there	is	uncertainty	in	both	the	amount	of	

reactive	nitrogen	that	escapes	and	the	portion	of	this	nitrogen	that	is	converted	to	N2O.		Ideally,	

fluxes	of	volatile	and	soluble	reactive	nitrogen	leaving	the	entity’s	parcel	of	land	would	be	

combined	with	atmospheric	transport	and	hydrologic	models	to	simulate	the	fate	of	reactive	N.	At	

present	there	are	no	linked	modeling	approaches	sufficiently	tested	to	be	used	in	an	operational	

framework.	Consequently,	the	indirect	N2O	emissions	are	based	on	the	IPCC	Tier	1	method	(de	

Klein	et	al.,	2006).	

Similarly,	direct	N2O	emissions	from	drainage	of	organic	soils	are	based	on	the	IPCC	Tier	1	methods	

(de	Klein	et	al.,	2006).	Although	research	is	ongoing	to	provide	improved	emission	factors	and	

methods	for	estimating	N2O	emissions	from	drainage	of	organic	soils	(Allen,	2012),	more	testing	

will	be	needed	before	incorporating	them	into	an	operational	method.	Future	revisions	to	these	

methods	will	need	to	consider	advancements	and	revise	the	methods	accordingly.	

3.5.4.2 Description of Method

N2O	is	emitted	from	cropland	both	directly	and	indirectly.	Direct	emissions	are	fluxes	from	

cropland	or	grazing	lands	where	there	are	nitrogen	additions	or	nitrogen	mineralized	from	soil	

organic	matter.	Indirect	emissions	occur	when	reactive	nitrogen	is	volatilized	as	NH3	or	NOx	or	

transported	via	surface	runoff	or	leaching	in	soluble	forms	from	cropland	or	grazing	lands,	leading	

to	N2O	emissions	in	another	location.	
 Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

 3-61 

Direct N2O Emissions

Mineral Soils:		Total	direct	N2O	emissions	from	mineral	soils	are	estimated	for	a	land	parcel	using	

Equation	3‐8.	

The	practice‐scaled	emission	rate	for	the	parcel	of	land	(ERp)	is	estimated	using	Equation	3‐9.	

Equation 3‐8: Direct Soil N2O Emissions from Mineral Soils

N2ODirect = ERp × A × N2OMW × N2OGWP

Where:	

N2ODirect		=	Total	direct	soil	N2O	emission	for	parcel	of	land	(metric	tons	CO2‐eq	year‐1)	

ERp		 =	Practice‐scaled	emission	rate	for	land	parcel	(metric	tons	N2O‐N	ha‐1	year‐1)	

A		 =	Area	of	parcel	of	land	(ha)	

N2OMW		 =	Ratio	of	molecular	weights	of	N2O	to	N2O‐N	

	 =	44/28	(metric	tons	N2O	(metric	tons	N2O‐N)‐1)	

N2OGWP	 =	Global	warming	potential	for	N2O	(metric	tons	CO2‐eq	(metric	tons	N2O)‐1)	
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems 

3-62

a	A	difference	arises	in	the	ERb	estimation	of	PRP	manure	N	input	and	the	actual	PRP	manure	N	input	because	a	typical	

rate	of	N	input	was	assumed	in	the	DAYCENT	and	DNDC	simulations	for	the	ERb	calculation	(See	Textbox	3‐1	and	

Appendix	3‐A).	

b	Emission	factors	from	de	Klein	et	al.	(2006).	

In	this	equation,	the	base	emission	rate	(ERb)	varies	by	the	amount	of	nitrogen	input	to	the	soil.	The	

rate	may	also	vary	for	different	crop	and	grazing	land	systems	by	LRR	to	capture	variation	in	

climate,	and	by	texture	class	in	order	to	represent	the	influence	of	soil	heterogeneity	on	N2O	

emissions.	More	information	about	base	emission	rates	is	given	in	Text	box	3‐1.	

Practice‐based	emission	scaling	factors	(0	to	1)	are	used	to	adjust	the	portion	of	the	emission	rate	

associated	with	slow	release	fertilizers	(Ssr),	nitrification	inhibitors	(Sinh),	and	

pasture/range/paddock	(PRP)	manure	nitrogen	additions	(Sprp,cps).	The	slow‐release	fertilizer,	

Equation 3‐9: Practice‐Scaled Soil N2O Emission Rate for Mineral Soils

ERp =			[ERb		+		(ΔNprp * EFprp)]		x		{1 +		[Ssr x (Nsr/Ni )]}		x		{1		+		[Sinh x (Ninb/Ni)]}			x		(1		+

Still)		 x		{1 – [Nresidr/ (Ni + Nresidr)]}

Where:	

ERp	 =	Practice‐scaled	emission	rate	for	land	parcel	(metric	tons	N2O‐N	ha‐1	year‐1)	

ERb		 =	Base	emission	rate	for	crop	or	grazing	land	that	varies	based	on	nitrogen	input	

rate	from	mineral	fertilizer,	organic	amendments,	residues,	and	additional	

mineralization	with	land‐use	change	or	tillage	change		

	 	 (metric	tons	N2O‐N	ha‐1	year‐1)	

ΔNprp	 =	Difference	in	PRP	manure	N	excretiona	between	the	PRP	manure	N	excretion	

based	on	entity	activity	data	(NPRPe)	and	PRP	manure	N	excretion	for	the	base	

emission	rate	(NPRPb)	(metric	tons	N)	

	 =	NPRPe	‐	NPRPb

EFprp	 =	Emission	factor	for	PRP	manure	N	input	to	soils,	0.02	metric	tons	N2O‐N	ha‐1

year‐1	(metric	tons	N)‐1	for	cattle,	poultry	and	swine,	and	0.01	metric	tons	N2O‐N	

(metric	tons	N)‐1	for	other	livestockb			

Ni			 	 =	Nitrogen	inputs,	including	mineral	fertilizer,	organic	amendments,	PRP	manure	

N,	residues,	and	SOM	mineralization	(See	Equation	3‐11)		

	 (metric	tons	N	ha‐1	year‐1)		

Ssr		 =	Scaling	factor	for	slow‐release	fertilizers,	0	where	no	effect	(dimensionless)	

Nsr			 =	Nitrogen	in	slow‐release	nitrogen	fertilizer	applied	to	the	parcel	of	land		

	 	 (metric	tons	N	ha‐1	year‐1)	

Sinh		 =	Scaling	factor	for	nitrification	inhibitors,	0	where	no	effect	(dimensionless)	

Ninh	 =	Nitrogen	in	nitrogen	fertilizer	with	inhibitor	applied	to	the	parcel	of	land	

	 	 (metric	tons	N	ha‐1	year‐1)	

Still		 =	Scaling	factor	for	no‐tillage,	0	except	for	NT	(dimensionless)	

Nresidr =	N	removed	through	collection,	grazing,	harvesting	or	burning	of	aboveground	

residues	(metric	tons	N	ha‐1	year‐1).	Estimate	using	Equation	3‐10	for	results	

generated	with	DAYCENT	and	DNDC	models	with	the	exception	of	hay	crops.		No	

calculation	is	needed	for	results	generated	by	the	IPCC	method	or	for	results	

associated	with	hay	crops	generated	by	DAYCENT	and	DNDC	(set	value	to	0).
 Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

 3-63 

nitrification	inhibitor	and	PRP	manure	scaling	factors	are	weighted	so	that	their	effect	is	only	on	the	

amount	of	nitrogen	influenced	by	these	practices	relative	to	the	entire	pool	of	nitrogen	(i.e.,	the	

amount	of	slow‐release	fertilizer,	fertilizer	with	nitrification	inhibitor	or	PRP	manure	nitrogen	

added	to	the	soil).	In	contrast,	scaling	factors	for	tillage	(Still)	are	used	to	scale	the	entire	emission	

rate	under	the	assumption	that	this	practice	influences	the	entire	pool	of	mineral	nitrogen	inputs	

(i.e.,	Ni).		

Table 3‐9: Scaling Factors for Nitrogen Management Practices

Management Practice Nitrogen Management

Factor

Factor (Proportional

Change in Emissions) Source

Slow‐release	fertilizer	use	 Ssr ‐0.21	(‐0.12	to	‐0.30)	 See	Appendix	3‐A

Manure	nitrogen	directly	

deposited	on	

pasture/range/paddock	

Sprp,cps +0.5		(0.33	to	0.67)	 IPCC	(2006)	

Nitrification	inhibitor	use	 Sinh	–	semi	arid/arid	climate ‐0.38	(‐0.21	to	‐0.51)	 See	Appendix	3‐A

Nitrification	inhibitor	use	 Sinh	– mesic/wet	climate ‐0.40	(‐0.24	to	‐0.52)	 See	Appendix	3‐A

Tillage	

Still	–	semi	arid/arid	climate

(<	10	years	following	no‐till	

adoption)	

0.38	(0.04	to	0.72)	 van	Kessel	et	al.	(2012),	

Six	et	al.	(2004)	

Tillage	

Still	–	semi	arid/arid	climate

(≥	10	years	following	no‐till	

adoption)	

‐0.33	(‐0.16	to	‐0.5)	 van	Kessel	et	al.	(2012),	

Six	et	al.	(2004)	

Equation 3‐10: Aboveground Residue N Removal

For Crops:

Nresidr = [((Ydm / HI) – Ydm) x Rr) x Na]

For Grazing Forage:

Nresidr = [Ydm x (Fr + Rr) x Na]

Where:

Nresidr		=	N	removed	through	collection,	grazing,	harvesting	or	burning	of	aboveground	

residues	(metric	tons	N	ha‐1	year‐1)	

Ydm		 =	Crop	harvest	or	forage	yield,	corrected	for	moisture	content		

	 	 (metric	tons	biomass	ha‐1	year	‐1)	

	 =	Y	x	DM	

Y	 =	Crop	harvest	or	total	forage	yield	(metric	tons	biomass	ha‐1	year	‐1)	

DM		 =	Dry	matter	content	of	harvested	biomass	(dimensionless)	

HI		 =	Harvest	Index	(dimensionless)	

Fr	 =	Proportion	of	live	forage	removed	by	grazing	animals	(dimensionless)	

Rr								 =	Proportion	of	crop/forage	residue	removed	due	to	harvest,	burning	or	grazing	

(dimensionless)		

Na		 =	Nitrogen	fraction	of	aboveground	residue	biomass	for	the	crop	or	forage		

	 	 (metric	tons	N	(metric	tons	biomass)‐1)	
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems 

3-64

Management Practice Nitrogen Management

Factor

Factor (Proportional

Change in Emissions) Source

Tillage	

Still	–	mesic/wet	climate

(<	10	years	following	no‐till	

adoption)	

‐0.015	(‐0.16	to	0.16)	 van	Kessel	et	al.	(2012),	

Six	et	al.	(2004)	

Tillage	

Still	–	mesic/wet	climate

(≥	10	years	following	no‐till	

adoption)	

‐0.09	(‐0.19	to	0.01)	 van	Kessel	et	al.	(2012),	

Six	et	al.	(2004)	

Note:	See	Appendix	3‐A	for	further	explanation	on	the	practices	included	in	the	soil	N2O	method	and	the	

sources	of	data	that	were	used	to	derive	the	base	emission	rates	and	scaling	factors	for	the	management	

practices.			
 Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

 3-65 

Text box 3‐1: Base Emission Rate for Direct Soil N2O Emissions from Mineral Soils

The	base	emission	rate	is	a	crop	or	grazing	land	specific	estimate	that	varies	based	on	the	

total	mineral	nitrogen	input	to	the	soil.	There	are	two	methods	used	to	estimate	the	base	

emission	rate.	The	first	method	uses	a	combination	of	process‐based	modeling	and	

measurement	data	to	estimates	N2O	base	emission	rates	by	land	resource	region,	major	crop	

type,	and	soil	texture	class.	The	second	method	uses	the	default	IPCC	emission	factor	of		one	

percent	(de	Klein	et	al.,	2006),	multiplying	this	value	by	the	total	nitrogen	input	(See	

Equation	3‐11)	to	estimate	the	base	emission	rate.	The	second	approach	is	used	for	crops	that	

are	not	included	in	the	process‐based	modeling	analysis.		

The	remainder	of	this	box	describes	the	first	method.	The	equation	for	the	first	method,	

combining	the	modeling	and	measurement	data,	is	given	below:	

ERb = ER0 + (EFtypical + (SEF ×	ΔNf)) × Nf

ERb		 =	Base	emission	rate	(metric	tons	N2O‐N	ha‐1	year‐1)	

ER0	 =	Emission	rate	modeled	at	0	level	of	nitrogen	input	(Nt	=0)		

	 			(metric	tons	N2O‐N	ha‐1	year‐1)	

EFtypical	 =	Emission	factor	for	the	typical	fertilization	rate	

																				(metric	tons	N2O‐N	(metric	tons	N)‐1)	

	 =	(ERtypical	–	ER0)/	Ntf	

ERtypical			=	Emission	rate	for	the	typical	case	modeled (metric	tons	N2O‐N	ha‐1	year‐1)	

SEF		 =	Base	EF	scalar;		

	 				for	ΔNf	>	zero:	SEF	=	0.0274	for	all	non‐grassland	crops,		

	SEF	=	0.117	for	grasslands;	

	 	 	for	ΔNf		<	zero	(less	than	or	the	same	as	typical	fertilizer	rates):	SEF	=	0;	

	 	 	((metric	tons	N2O‐N	(metric	tons	N)‐2)	ha	year)	

ΔNf	 =	Nf	‐	Ntf	(metric	tons	N	ha‐1	year‐1)	

Nf	 =	Actual	nitrogen	fertilizer	rate,	including	synthetic	and	organic	

			(metric	tons	N	ha‐1	year‐1)	

Ntf	 =	Typical	nitrogen	fertilizer	rate	(metric	tons	N	ha‐1	year‐1)		

Process‐based	models	were	used	to	simulate	N2O	emissions	at	the	typical	nitrogen	

fertilization	rate	for	major	commodity	crops	according	to	the	USDA	Agricultural	Resource	

Management	Survey	data	(ERtypical),	in	addition	to	a	zero	rate	application	(ER0).	The	N2O	

emission	at	the	typical	rate	of	fertilization	for	major	commodity	crops	are	produced	for	

coarse,	medium,	and	fine	textured	soils	in	each	land	resource	region.	The	emission	factor	

(EFtypical)	for	fertilization	rates	greater	than	the	typical	rate	for	the	crop	or	grass	are	scaled	

according	to	the	trend	in	measured	soil	N2O	data	across	a	range	of	fertilization	rates	based	on	

experimental	data.	The	change	in	the	emission	factor	between	the	typical	nitrogen	

fertilization	rate	and	a	higher	rate	was	averaged	to	derive	an	emission	factor	scalar	or	rate	of	

change	per	unit	of	additional	N.		The	scalar	is	multiplied	by	the	additional	nitrogen	to	derive	

an	adjustment	to	the	emission	factor	(SEF	×	ΔNf)	that	is	then	added	to	the	emission	factor	

derived	for	the	typical	fertilizer	rate	(EFtypical).	No	scaling	is	done	for	the	case	where	ΔNf		≤	

zero,	i.e.,	where	the	fertilization	rate	is	equal	to	or	less	than	the	typical	rate	of	nitrogen	

application.	In	this	case	SEF	=	0	such	that	SEF	×	ΔNf	=	0.	The	resulting	emission	factor	is	

multiplied	by	the	actual	fertilizer	rate	(Nf)	and	added	to	the	emission	rate	at	the	0	level	of	

nitrogen	fertilization	(ER0)	to	derive	the	base	emission	rate	(ERb).	
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems 

3-66

Nitrogen	inputs	are	estimated	with	the	following	equation:	

a	The	approach	for	estimating	nitrogen	mineralization	inputs	is	consistent	with	the	U.S.	National	Inventory	(U.S.	EPA,	

2013).	

b	Pasture/Range/Paddock	(PRP)	manure	N	is	a	term	utilized	by	the	IPCC	(de	Klein	et	al.,	2006)	for	the	N	excreted	directly	

onto	land	by	livestock,	and	the	manure	is	not	collected	or	managed.		The	total	PRP	manure	N	is	estimated	with	methods	

from	Chapter	5,	and	assumed	to	be	split	with	50%	of	the	N	in	urine	and	50%	of	the	N	in	solids.		

The	total	N	mineralization	is	estimated	from	the	DAYCENT	mineral	soil	C	method	in	aggregate	for	

manure	amendments	(Nman),	compost	(Ncom),	residues	(Nres),	soil	organic	matter	(Nsmin)	and	solids	

associated	with	PRP	manure,	and	is	used	to	approximate	these	N	inputs	in	Equation	3‐11.		This	

approach	creates	a	linkage	between	the	mineral	soil	C	method	(See	Section	3.5.3.2)	and	the	N2O	

method,	ensuring	consistency	in	treatment	of	N.	In	instances	where	crops	cannot	be	estimated	by	

the	DAYCENT	mineral	soil	C	method,	the	method	from	the	IPCC	guidelines	(Aalde	et	al.,	2006)	can	

be	used	to	estimate	the	N	inputs	from	mineralization	with	the	exception	of	Nsmin,	which	is	set	to	0	

(See	Appendix	3‐B).		

Organic Soils:	The	method	for	organic	soils	includes	Histosols	and	soils	that	have	high	organic	

matter	content	and	developed	under	saturated,	anaerobic	conditions	for	at	least	part	of	the	year,	

which	includes	Histels,	Historthels,	Histoturbels.	The	method	assumes	that	there	is	a	significant	

organic	horizon	in	the	soil,	and	therefore,	major	inputs	of	nitrogen	are	from	oxidation	of	organic	

matter	rather	than	external	inputs	from	synthetic	and	organic	fertilizers.	If	these	assumptions	are	

not	true,	then	the	entity	should	use	the	mineral	soil	method	to	estimate	the	N2O	emissions.	Total	

direct	N2O	emissions	from	drained	organic	soils	are	estimated	for	individual	parcels	of	land	(i.e.,	

fields)	with	the	following	equation:		

Equation 3‐11: Nitrogen Inputsa

Ni = Nsfert + Nman + Ncomp + Nresid + Nsmin + Nprp

Where:	

Ni		 =	Nitrogen	inputs,	including	mineral	fertilizer,	organic	amendments,	PRP	manure	N,	

residues,	and	SOM	mineralization	

									 	 (metric	tons	N	ha‐1	year‐1)	

Nsfert		 =	Nitrogen	in	synthetic	fertilizer	applied	to	a	parcel	of	land		

	 (metric	tons	N	ha‐1	year‐1)	

Nman		 =	Nitrogen	mineralization	from	manure	amendments	(or	sewage	sludge)	applied	to	

a	parcel	of	land	(metric	tons	N	ha‐1	year‐1)	

Ncomp		 =	Nitrogen	mineralization	from	compost	applied	to	a	parcel	of	land	

	 	 (metric	tons	N	ha‐1	year‐1)	

Nresid		 =	Nitrogen	mineralization	from	crop	and	cover	crop	residues	above	and	

belowground	that	are	left	on	the	parcel	of	land	following	senescence	(i.e.,	not	

collected,	grazed,	or	burned)	(metric	tons	N	ha‐1	year‐1)		

		Nsmin		=	Nitrogen	inputs	from	soil	organic	matter	mineralization	as	estimated	by	the	

DAYCENT	mineral	soil	C	method	(See	Section	3.5.3.2)	(metric	tons	N	ha‐1	year‐1).	

Value	set	to	0	for	crops	that	are	not	estimated	with	the	DAYCENT	mineral	soil	C	

method.	

Nprp		 =	Nitrogen	in	urine	and	mineralization	from	solids	associated	with	manure	in	

pasture/range/paddock	(PRP)	(metric	tons	N	ha‐1	year‐1)b
 Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

 3-67 

Indirect N2O Emissions: The	method	to	estimate	indirect	N2O	emissions	for	mineral	soils	has	been	

adopted	from	the	approach	developed	by	IPCC	(de	Klein	et	al.,	2006).	The	following	equation	is	

used	to	estimate	the	total	indirect	N2O	emissions	associated	with	nitrogen	volatilization	and	

nitrogen	leaching	and	runoff	from	the	land	parcel:	

The	following	equation	is	used	to	estimate	the	indirect	emissions	associated	with	nitrogen	

volatilization	from	the	land	parcel:	

Equation 3‐12: Direct N2O Emissions from Drainage of Organic Soils (Histosols)

N2OORGANIC = AOS × EROS		

Where:	

N2OORGANIC		 =	Direct	soil	N2O	emission	from	drainage	of	organic	soils	

	 			(metric	tons	N2O‐N	year‐1)	

Aos		 =	Area	of	organic	soils	drained	on	a	parcel	of	land	(ha)	

EROS		 =	Emission	rate	for	cropped	Histosols,		

	 				IPCC	Tier	1	EROS	=	0.008	metric	tons	N2O‐N	ha‐1	year‐1

Equation 3‐13: Total Indirect Soil N2O Emissions from Mineral Soils

N2OIndirect = (N2OVol + N2OLeach) × N2OMW × N2OGWP

Where:	

N2OIndirect		=	Indirect	soil	N2O	emission	(metric	tons	CO2‐eq	year‐1)	

N2OVol		 =	N2O	emitted	by	ecosystem	receiving	volatilized	nitrogen	

	 			(metric	tons	N2O‐N	year‐1)	

N2OLeach		 =	N2O	emitted	by	ecosystem	receiving	leached	and	runoff	nitrogen	

	 	 (metric	tons	N2O‐N	year‐1)	

N2OMW		 =	Ratio	of	molecular	weights	of	N2O	to	N2O‐N	=	44/28	

	 	 (metric	tons	N2O	(metric	tons	N2O‐N)‐1)	

N2OGWP		 =	Global	warming	potential	for	N2O	(metric	tons	CO2‐eq	(metric	tons	N2O)‐1)	
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems 

3-68

The	IPCC	defaults	are	used	for	FRSN and FRON.

The	following	equation	is	used	to	estimate	the	indirect	N2O	emissions	associated	with	leaching	or	

overland	flow	of	reactive	nitrogen	that	is	transported	from	the	land	parcel	(i.e.,	field):	

The	fraction	of	nitrogen	that	is	leached	from	a	profile	will	vary	depending	on	the	level	of	

precipitation	and	irrigation	water	applied	in	the	field.	In	land	parcels	(i.e.,	fields)	where	the	

precipitation	plus	irrigation	water	input	is	less	than	80	percent	of	the	potential	evapotranspiration,	

nitrogen	leaching	and	runoff	are	considered	negligible	and	no	indirect	N2O	emissions	are	estimated	

(U.S.	EPA,	2011).	IPCC	default	fractions	are	used	for EFleach	and	FRleach where	no	cover	crops	are	

present.	Where	winter	cover	crops	precede	the	cash	crop,	FRleach is	further	adjusted	to	account	for	

cover	crop	effects	on	nitrate	leaching.	In	a	meta‐analysis	of	36	geographically	distributed	field	

Equation 3‐14: Indirect Soil N2O Emissions from Mineral Soils —Volatilization

N2OVol = [(FSN × FRSN) + (FON × FRON)] × EFVOL

Where:	

N2OVol		 =	Indirect	soil	N2O	emitted	by	ecosystem	receiving	volatilized	nitrogen	

	 				(metric	tons	N2O‐N	year‐1)	

FSN		 =	Synthetic	nitrogen	fertilizer	applied	(metric	tons	N	year‐1)	

FRSN		 =	Fraction	of	NSN	that	volatilizes	as	NH3	and	NOx.	IPCC	default	Tier	1	=	0.10	

	 				(metric	tons	N	(metric	ton	Nsfert)‐1)	

FON		 =	Nitrogen	fertilizer	applied	of	organic	origin	including	manure,	sewage	sludge,	

compost	and	other	organic	amendments	(metric	tons	N	year‐1)	

FRON		 =	Fraction	or	proportion	of	FON	that	volatilizes	as	NH3	and	NOx.	IPCC	default	Tier	1	=	

0.2	(metric	tons	N	(metric	ton	NON)‐1)	

EFVOL		 =	Emission	factor	for	volatilized	nitrogen	or	proportion	of	nitrogen	volatilized	as	NH3

and	NOx	that	is	transformed	to	N2O	in	receiving	ecosystem;	IPCC	Tier	1	EF	=	0.01	

(metric	tons	N2O‐N	(metric	ton	N)‐1)	

Equation 3‐15: Indirect Soil N2O Emissions from Mineral Soils —Leaching and Runoff

N2Oleach = (Ni × FRleach) × EFleach

Where:	

N2Oleach		 =	Indirect	soil	N2O	emitted	by	ecosystem	receiving	leached	and	runoff	nitrogen	

(metric	tons	N2O‐N	year‐1)	

Ni			 	 	 =	Nitrogen	inputs,	including	mineral	fertilizer,	organic	amendments,	PRP	manure	N,	

residues,	and	SOM	mineralization	(metric	tons	N	ha‐1	year‐1)	(See	Equation	3‐11)	

FRleach		 =	Fraction	or	proportion	of	Ni	that	leaches	or	runs	off.	IPCC	default	Tier	1	=	0.30	

except	a)	where	irrigation+precipitation	is	less	than	80%	of	potential	

evapotranspiration	(metric	tons	N	(metric	ton	N)‐1)	FRleach	=	0;	and	b)	cropping	

systems	with	leguminous	or	non‐leguminous	winter	cover	crops,	for	leguminous	

cover	crops,	FRleach	=	0.18,	and	for	non‐leguminous	cover	crops,	FRleach	=	0.09.	

EFleach		 =	Emission	factor	for	leached	and	runoff	nitrogen	or	proportion	of	leached	and	

runoff	nitrogen	that	is	transformed	to	N2O	in	receiving	ecosystem;	IPCC	Tier	1	EF	

	 =	0.0075	(metric	tons	N2O‐N	(metric	ton	N)‐1)
 Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

 3-69 

studies,	Tonitto	et	al.	(2006)	found	a	40	percent	and	70	percent	reduction	in	nitrate	leaching	with	

the	use	of	legume	and	non‐legume	cover	crops,	respectively.	Accordingly,	FRleach,	is	reduced	to	0.18	

for	legume	cover	crops	(0.3	×	(1‐0.4);	or	18%	of	total	nitrogen	inputs)	and	0.09	for	non‐legume	

cover	crops	(0.3	×	(1‐0.7);	or	nine	percent	of	total	nitrogen	inputs).	

3.5.4.3 Activity Data

Calculating	emissions	requires	the	following	activity	data	for	croplands:	

 Area	of	land	parcel	(i.e.,	field);	

 Prior‐year	crop	type,	dry	matter	yields,	and	residue‐yield	ratios	to	calculate	crop	residue	

nitrogen	input,	including	cover	crop	(if	present);		

 Residue	management,	including	amount	harvested,	burned,	grazed,	or	left	in	the	field;		

 Synthetic	fertilizer	type	(chemical	formulation)	and	coatings	(if	present);	

 Synthetic	and	organic	fertilizer	application	rate,	application	method	(broadcast,	banded,	or	

injected,	including	depth	of	injection),	timing	of	application(s);	

 Type	of	nitrification	inhibitor	applications	(if	used);	

 Tillage	implements,	dates	of	operation,	and	number	of	passes	in	each	operation	(which	can	

be	used	to	determine	tillage	intensity	with	the	STIR	Model),	(USDA	NRCS,	2008);		

 Irrigation	method,	application	rate	and	timing	of	applications;		

 Total	dry	matter	yield	of	crop	(metric	tons	dry	matter	year‐1),	dry	matter	content	of	yield,	

and	harvest	index;	and	

 Cover	crop	types,	planting,	and	harvesting	dates	(if	applicable).	

The	method	for	grazing	land	requires	the	following	management	activity	data:	

 Area	of	land	parcel	(i.e.,	field);	

 Prior‐year	grass	type	and	dry	matter	production	to	calculate	grass	nitrogen	input;		

 Synthetic	fertilizer	type	(chemical	formulation)	and	coatings	(if	present);	

 Organic	amendment	types	and	timing;	

 Synthetic	and	organic	amendment	application	rate,	application	method	(broadcast,	banded,	

or	injected,	including	depth	of	injection),	timing	of	application(s);	

 Pasture/range/paddock	(PRP)	N	excreted	directly	onto	land	by	livestock	(i.e.,	manure	that	

is	not	managed);	

 Type	of	nitrification	inhibitor	applications	(if	used);	

 Tillage	implements,	dates	of	operation,	and	number	of	passes	in	each	operation	which	can	

be	used	to	determine	tillage	intensity	with	the	STIR	Model,	(USDA	NRCS,	2008);	

 Irrigation	method,	application	rate,	and	timing	of	applications;	

 Periods	of	grazing	during	the	year;		

 Animal	type,	class,	and	size	used	for	grazing;		

 Stocking	rates	and	methods;	and	

 Total	yield	of	meat	(kg	carcass	yield	year‐1)	or	milk	(kg	fluid	milk	year‐1).	

Crop	yields	are	provided	by	the	entity	for	the	crop	system,	or	peak	forage	amounts	for	grazing	

systems.	In	some	years,	the	entity	may	not	harvest	the	crop	due	to	drought,	pest	outbreaks,	or	other	

reasons	for	crop	failure.		In	those	cases,	the	entity	should	provide	the	average	yield	that	they	have	

harvested	in	the	past	five	years,	and	an	approximate	percentage	of	crop	growth	that	occurred	prior	

to	crop	failure.	The	yield	is	estimated	based	on	multiplying	the	average	crop	yield	by	the	percentage	

of	crop	growth	obtained	prior	to	failure.		

	To	calculate	the	amount	of	synthetic	fertilizer	nitrogen	applied	to	soils,	the	type	of	fertilizer	applied	

and	its	nitrogen	content	are	required.	Table	3‐10	provides	nitrogen	content	information	for	

common	types	of	synthetic	fertilizers.	
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems 

3-70

Pasture/range/paddock	(PRP)	manure	N	input	

is	the	N	excreted	directly	onto	land	by	livestock,	

and	the	manure	is	not	collected	or	managed	(de	

Klein	et	al.,	2006).		The	amount	of	PRP	manure	

N	is	estimated	with	the	livestock	methods	(See	

Chapter	5),	and	assumed	to	be	split	with	50%	of	

the	N	in	urine	and	the	other	50%	of	the	N	in	

solids.			

3.5.4.4 Ancillary Data		

Ancillary	data	for	estimating	direct	soil	N2O	

emissions	from	mineral	soils	include	land	

resource	region,	soil	texture,	and	climate	

variables.	Land	resource	region	can	be	

identified	based	on	the	geographic	coordinates	

of	the	field.	Soil	data	are	available	from	national	

datasets	such	as	SSURGO	(Soil	Survey	Staff,	

2011),	and	average	growing	season	

precipitation	and	evapotranspiration	data	are	available	from	national	weather	datasets	such	as	

PRISM	(Daly	et	al.,	2008).		These	data	are	used	by	the	models	to	determine	base	emission	rates.	

3.5.4.5 Model Output

N2O	emissions	are	expressed	both	as	the	quantity	of	emissions	and	as	emissions	intensity—

emissions	per	unit	yield,	e.g.,	g	N2O	per	Mg	grain	or	animal	product.	Reducing	the	emissions	

intensity	can	be	assumed	to	avoid	emissions	from	indirect	land‐use	change.	In	contrast,	if	the	

emissions	intensity	increases	due	to	a	loss	of	yield,	then	there	is	potential	for	additional	land	to	be	

converted	into	agriculture	to	make	up	for	a	yield	loss.	

3.5.4.6 Limitations and Uncertainty		

The	primary	limitation	of	N2O	estimation	models	is	that	they	depend	on	surrogate	measures	that	

will	not	allow	fluxes	for	a	particular	location	or	time	to	be	predicted	precisely.	Nevertheless,	while	

it	may	be	decades,	if	ever,	before	annual	rates	of	N2O	emissions	from	a	specific	field	can	be	

measured	with	great	certainty	and	for	low	cost,	average	estimates	for	similar	cropping	systems	and	

landscapes	will	converge	as	estimates	aggregate	to	larger	areas.	

Table 3‐10: Nitrogen Fraction of Common

Synthetic Fertilizers (percent by weight)

Synthetic Fertilizer %	N

Ammonium	nitrate	(NH4NO3)	 33.5%

Ammonium	nitrate	limestone		 20.5%

Ammonium	sulfate 20.75%

Anhydrous	ammonia 82%

Aqua	ammonia 22.5%

Calcium	cyanamide	(CaCN2)	 21%

Calcium	ammonia	nitrate 27.0%

Diammonium	phosphate 18%

Monoammonium	phosphate	 11%

Potassium	nitrate	(KNO3) 13%

Sodium	nitrate	(NaNO3) 16%

Urea	CO(NH2)2 45%

Source:	Fertilizer	101	(2011).

Equation 3‐16: Soil N2O Emissions Intensity

EIN2O = (N2ODirect + N2OIndirect) / Y

Where:	

EIN2O		 =	N2O	emissions	intensity	

	 	 (metric	tons	CO2‐eq	per	metric	ton	dry	matter	crop	yield	or	kg	carcass	or	kg	fluid	

milk)	

N2ODirect		 =	Total	direct	soil	N2O	emission	(metric	tons	CO2‐eq	year‐1)	(See	Equation	3‐8)	

N2OIndirect	=	Total	indirect	soil	N2O	emission	(metric	tons	CO2‐eq	year‐1)	(See	Equation	3‐13)	

Y		 =	Total	yield	of	crop	(metric	tons	dry	matter	crop	yield	year‐1),	meat	(kg	carcass	

yield	year‐1),	or	milk	production	(kg	fluid	milk	yield	year‐1)	
 Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

 3-71 

Limitations	in	the	method	also	occur	due	to:	

 Lack	of	knowledge	of	how	different	practices	affect	fluxes	in	some	regions	and	cropping	

systems.	

 Lack	of	knowledge	about	how	some	of	the	management	practices	interact	with	each	other	

and	with	soil	and	climate	factors	to	affect	the	fundamental	processes	driving	N2O	

emissions—e.g.,	nitrification,	denitrification,	gas	diffusion,	etc.—and	incorporation	of	these	

effects	into	process	models.	

 Limited	number	of	data	sets	currently	available	to	test	the	efficacy	of	practices	to	mitigate	

fluxes	and	to	evaluate	process‐based	models.		

 Limited	number	of	datasets	with	more	than	two	fertilizer	rates	to	estimate	the	scalars	for	

emission	factors	associated	with	the	base	emission	rates,	particularly	the	possibility	for	

non‐linear	scalars.	

 The	mineral	soils	method	assumes	a	one	percent	emission	factor	for	indirect	N2O	emissions	

from	volatilized	nitrogen	and	0.75	percent	emission	factor	for	leached	NO3‐

.	However,	there	

is	evidence	that	the	EF	for	NO3‐

	leaching	varies	from	0.75%,	depending	on	the	type	of	

waterway	(Beaulieu	et	al.,	2011)	and	it	is	also	likely	that	the	soil	N2O	emissions	from	

atmospheric	deposition	of	nitrogen	will	vary	depending	on	the	nitrogen	status	of	the	

receiving	ecosystem.		

 The	fraction	of	nitrogen	that	is	volatilized	(assumed	to	be	10	percent	for	inorganic	nitrogen	

sources	and	20	percent	for	organic	nitrogen	sources	in	Equation	3‐15)	is	very	uncertain.		

Likewise,	the	fraction	of	nitrogen	that	is	leached	from	a	profile	or	runs	off	is	highly	

uncertain	(assumed	to	be	30	percent	of	all	nitrogen	sources	except	where	precipitation	plus	

irrigation	is	less	than	80	percent	of	potential	evapotranspiration;	U.S.	Environmental	

Protection	Agency,	2011).	Experiments	suggest	that	gross	generalizations	are	not	valid	and	

that	many	practices	can	reduce	both	volatilized	nitrogen	and	the	nitrogen	that	is	lost	by	

leaching	and	runoff.9			

 Climate	change	will	affect	model	output	insofar	as	baseline	N2O	estimates	are	simulated	for	

any	given	location	using	temperature	and	precipitation	distributions	for	the	past	30	years.	

Expected	changes	in	temperature,	precipitation,	and	extreme	events	such	as	droughts,	

floods,	and	heat	waves	will	add	further	uncertainty	to	estimates	of	all	N2O	emissions	and	

potentially	interact	with	scaling	factors.		Crop	nitrogen	management	may	further	change	

with	climate	change	(Robertson,	2013).	

Uncertainties	in	model	parameters	are	combined	using	a	Monte	Carlo	simulation	approach.	

Uncertainty	is	assumed	to	be	minor	for	the	management	activity	data	provided	by	the	entity	Table	

3‐11	provides	the	probability	distribution	functions	to	estimate	uncertainty	in	the	direct	and	

indirect	soil	N2O	emissions.	Data	are	not	shown	for	DNDC	and	DAYCENT	output	that	are	delineated	

by	LRR,	soil	type,	and	climate.	

																																																													

9	The	IPCC	factors	assume	that	the	maximum	aboveground	nitrogen	recovery	by	crops	is	50	to	60	percent.	

However,	rates	of	nitrogen	recovery	can	be	significantly	higher	with	best	practices.	
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems 

3-72

Table 3‐11: Available Uncertainty Data for Direct and Indirect N2O Emissions

Parameter Estimated

Value Units

Effective

Lower

Limit

Effective

Upper

Limit

Distribution Data Source

Typical	direct	N2O	

emission	rate	and	0‐

level	input	rate	

from	process‐based	

model		

NS	 Various	 NS	 NS	 Multiple	

distributions	

DAYCENT,	

DNDC	

Scaling	factor	for	

slow‐release	

fertilizers	

‐0.21	

Proportional	

Change	in	

Emissions	

‐0.30	 ‐0.12	 Normal	 Appendix	3‐

A	

Scaling	factor	for	

PRP	manure	N	 +0.5	

Proportional	

Change	in	

Emissions	

0.33	 0.67	 Normal	 Appendix	3‐

A	

Scaling	factor	

nitrification	

inhibitors	–	semi‐

arid/arid	climate	

‐0.38	

Proportional	

Change	in	

Emissions	

‐0.51	 ‐0.21	 Normal	 Appendix	3‐

A	

Scaling	factor	

nitrification	

inhibitors	–	mesic	

climate	

‐0.40	

Proportional	

Change	in	

Emissions	

‐0.52	 ‐0.24	 Normal	 Appendix	3‐

A	

Scaling	factor	for	

no‐till,	semi‐

arid/arid	climate,	

<10	years	

0.38	

Proportional	

Change	in	

Emissions	

0.04	 0.72	 Normal	

van	Kessel	et	

al.	(2012),	

Six	et	al.	

(2004)	

Scaling	factor	for	

no‐till,	semi‐

arid/arid	climate,	

≥10	years	

‐0.33	

Proportional	

Change	in	

Emissions	

‐0.5	 ‐0.16	 Normal	

van	Kessel	et	

al.	(2012),	

Six	et	al.	

(2004)	

Scaling	factor	for	

no‐till,		mesic/wet	

climate,	<10	years	

		

‐0.015	

Proportional	

Change	in	

Emissions	

‐0.16		 0.16	 Normal	

van	Kessel	et	

al.	(2012),	

Six	et	al.	

(2004)	

Scaling	factor	for	

no‐till,	mesic/wet	

climate,	≥10	years	

‐0.09	

Proportional	

Change	in	

Emissions	

‐0.19	 0.01	 Normal	

van	Kessel	et	

al.	(2012),	

Six	et	al.	

(2004)	

Base	EF	scalar	–	

cropland	for	non‐

grassland	crops	

0.0274	

(metric	tons	

N2O‐N	(metric	

tons	N)‐2)	ha	

year

Normal	 Appendix	3‐

A	

Base	EF	scalar	–	for	

grasslands	 0.117	

(metric	tons	

N2O‐N	(metric	

tons	N)‐2)	ha	

year

	 	 Normal	 Appendix	3‐

A	

Emission	rate	for	

cropped	Histosols	 0.008	

metric	tons	

N2O‐N	ha‐1		

year‐1

0.002	 0.024	 Uniform	 IPCC	(2006)

Fraction	of	

synthetic	nitrogen	

(NSN)	that	volatilizes	

as	NH3	and	NOx

0.1	

metric	tons	N	

(metric	ton	

Nsfert)‐1	

0.03	 0.3	 Uniform	 IPCC	(2006)
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

3-73 

Parameter Estimated

Value Units

Effective

Lower

Limit

Effective

Upper

Limit

Distribution Data Source

Fraction	of	nitrogen	

in	organic	

amendments	(FON)	

that	volatilizes	as	

NH3	and	NOx

0.2	

metric	tons	N	

(metric	ton	

NON)‐1	

0.05	 0.5	 Uniform	 IPCC	(2006)

Emission	factor	for	

volatilized	nitrogen	

as	NH3	and	NOx	that	

is	transformed	to	

N2O.	

0.01	

metric	tons	

N2O‐N	(metric	

ton	N)‐1	

0.002	 0.05	 Uniform	 IPCC	(2006)

Fraction	of	Nt	that	

leaches	or	runs	off	

except	in	systems	

with	cover	crops	

0.3	 metric	tons	N

(metric	ton	N)‐1 0.1	 0.8	 Uniform	 IPCC	(2006)

Fraction	of	Nt	that	

leaches	or	runs	off	

with	a	leguminous	

cover	crop	

0.18	 metric	tons	N	

(metric	ton	N)‐1 0.14	 0.26	 Log‐Normal	 Tonitto	et	al.	

(2006)	

Fraction	of	Nt	that	

leaches	or	runs	off	

with	non‐

leguminous	cover	

crop	

0.09	 metric	tons	N	

(metric	ton	N)‐1 0.06	 0.15	 Log‐Normal	 Tonitto	et	al.	(2006)	

Emission	factor	for	

leached	and	runoff	

nitrogen	that	is	

transformed	to	N2O	

0.0075	 metric	tons	N	

(metric	ton	N)‐1 0.0005	 0.025	 Uniform	 IPCC	(2006)

NS	=	Not	Shown.	Data	are	not	shown	for	parameters	that	have	100’s	to	1000’s	of	values	(denoted	as	NS).	Data	are	

provided	in	supplementary	material	available	online.	
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems 

3-74

3.5.5 Methane Uptake by Soils

3.5.5.1 Rationale for Selected Method		

There	are	no	agronomic	practices	known	to	enhance	CH4	uptake	(oxidation)	in	croplands,	other	

than	in	wetlands	converted	to	flooded	rice	(discussed	in	Section	3.2.2).	Agronomic	activity	

universally	reduces	methanotrophy	in	arable	soils	by	70	percent	or	more	(Mosier	et	al.,	1991;	

Robertson	et	al.,	2000;	Smith	et	al.,	2000).		Recovery	of	CH4	oxidation	upon	abandonment	from	

agriculture	is	slow,	probably	taking	50	to	100	years	for	the	development	of	even	50	percent	of	

former	(original)	rates	(Levine	et	al.,	2011).	No	recovery	has	been	documented	for	CRP	grasslands	

or	perennial	biofuel	crops	to	date.	There	are	currently	no	models	for	quantifying	CH4	oxidation	

recovery	other	than	rate	of	reversion	to	natural	vegetation,	so	this	is	a	Tier	3	method	as	defined	by	

the	IPCC.	

3.5.5.2 Description of Method

The	model	is	based	on	average	values	for	methane	oxidation	in	natural	vegetation—whether	

grassland,	coniferous	forest,	or	deciduous	forest—attenuated	by	current	land	use	practices.	

Average	values	are	from	the	data	set	used	by	Del	Grosso	et	al.	(2000a),	who	reported	average	fluxes	

(±	standard	deviation)	for	temperate	and	tropical	grassland	soils	of	3.2±1.9	kg	CH4	ha‐1	year‐1;	for	

coniferous	forest	soils,	2.8±1.4	kg	CH4	ha‐1	year‐1;	and	for	deciduous	forest	soils,	11.8±5	kg	CH4	ha‐1

year‐1.	Management	reduces	potential	(historic)	oxidation	to	30	percent	of	original	rates	based	on	

available	data	(Del	Grosso	et	al.,	2000a;	Mosier	et	al.,	1991;	Robertson	et	al.,	2000;	Smith	et	al.,	

2000)	as	noted	in	Sections	3.2.3.3	and	3.3.2.3.	Recovery	of	oxidation	is	assumed	to	occur	over	the	

period	required	for	ecological	succession	to	restore	original	vegetation	(Del	Grosso	et	al.,	2000a;	

Mosier	et	al.,	1991;	Robertson	et	al.,	2000;	Smith	et	al.,	2000),	which	is	approximated	at	100	years	

after	abandonment	from	agriculture	or	forest	harvest.	Recovery	is	assumed	to	occur	at	a	linear	rate	

(Smith	et	al.,	2000)	such	that	successional	forests	and	grasslands	will	consume	CH4	at	a	rate	that	is	

between	30	and	100	percent	of	the	original	oxidation	capacity	between	the	initial	year	of	

abandonment	until	year	100.	The	following	equation	is	used	to	estimate	methane	oxidation	for	a	

land	parcel:	

Method for Estimating Methane Uptake by Soil

 Methane	uptake	by	soil	uses	an	equation	based	on	average	values	for	methane	oxidation

in	natural	vegetation—whether	grassland,	coniferous	forest,	or	deciduous	forest—

attenuated	by	current	land	use	practices.

 Annual	average	CH4	oxidation	fluxes	are	from	the	data	set	used	by	Del	Grosso	et	al.

(2000a)	who	reviewed	average	fluxes	from	grassland	and	agricultural	soils,	coniferous

forest	soils,	and	deciduous	forest	soils.	Management	reduces	potential	(historic)

oxidation	to	30	percent	of	original	rates	based	on	available	data	(Del	Grosso	et	al.,	2000a;

Mosier	et	al.,	1991;	Robertson	et	al.,	2000;	Smith	et	al.,	2000).	Kuchler	potential

vegetation	maps	can	be	used	to	determine	the	natural	vegetation	across	the	United	States

if	the	entity	does	not	have	information	for	land	parcels	in	operation.

 This	newly	developed	methodology	makes	use	of	recent	U.S.‐based	research	that	is	not

addressed	by	IPCC	or	the	U.S.	Inventory.	The	method	incorporates	entity	specific	annual

data	such	as	current	management	of	the	land	parcel,	cultivation	for	crop	production,

grazing	activity,	recently	harvested	forests,	or	fertilized	grasslands	or	forests.
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

3-75 

3.5.5.3 Activity Data

This	method	requires	land	use	and	type	of	vegetation	for	the	past	80	years.	Kuchler	potential	

vegetation	maps	can	be	used	to	determine	the	natural	vegetation	across	the	United	States	

(grassland,	coniferous	forest,	or	deciduous	forest)	if	the	entity	does	not	have	this	information	for	

land	parcels	in	the	operation.	The	entity	will	need	to	identify	if	the	current	management	of	the	land	

parcel	includes	cultivation	for	crop	production,	grazing	in	grasslands,	recently	harvested	forests,	or	

fertilized	grasslands	or	forests.	Assuming	the	parcel	of	land	is	not	under	cultivation,	fertilized,	

grazed	grasslands,	or	recently	harvested	forest,	the	entity	will	need	to	provide	the	time	since	the	

land	has	been	managed	with	one	of	these	practices.		

3.5.5.4 Ancillary Data		

No	ancillary	data	are	required	for	this	method.	

3.5.5.5 Model Output

The	model	provides	a	value	for	diminished	CH4	oxidation	capacity.	The	change	in	CH4	oxidation	

capacity	will	be	negative,	and	so	there	is	no	potential	for	increased	CH4	oxidation	with	this	method.	

Unlike	other	methods	in	this	section,	the	emissions	intensity	is	not	relevant	for	this	method.		

3.5.5.6 Limitations and Uncertainty		

 Lack	of	precision	in	knowledge	of	prior	land	use.

 Uncertainties	associated	with	estimating	CH4	oxidation	rates	prior	to	conversion	(PCH4	in

Equation	3‐17).	In	a	review	of	available	data,	Del	Grosso	et	al.	(2000a)	noted	annual	CH4

oxidation	rates	of	<1.8	kg	CH4	ha‐1	year‐1	for	grassland	and	agricultural	soils,	1.4	to	4.1	kg

CH4	ha‐1	year‐1	for	coniferous	and	tropical	forest	soils,	and	5.3	to	12	kg	CH4	ha‐1	year‐1	for

deciduous	forest	soils.

Equation 3‐17: Methane (CH4) Oxidation

CH4SoilOxidation = (PCH4 × AF) × SF × A × CH4GWP		

Where:	

CH4SoilOxidation		=	CH4	oxidation	in	soils	(metric	tons	CO2‐eq	year‐1)	

PCH4		 =	Potential	CH4	oxidation	based	on	historic	natural	vegetation;	grasslands	=	

3.2;	coniferous	forests	=	2.8,	deciduous	forests	=	11.8	(kg	CH4	ha‐1	year‐1)	

AF	 =	CH4	oxidation	attenuation	factor;	cropland	including	set‐aside	(CRP)	

grassland,	grazing	land,	and	fertilized	or	recently	harvested	forests	=	0.30;	

natural	vegetation,	0‐100	years	after	abandonment	of	agricultural	

production	or	timber	harvest	=	0.3	+	(0.007	×	years	since	abandonment);	

>100	years	post‐management	or	never	used	for	agricultural	management	or	

timber	harvest	=	1.0	

SF		 =	Scaling	factor,	1/1000	(metric	tons	kg‐1)	

A	 =	Area	(ha)	

CH4GWP	 =	Global	warming	potential	of	CH4	(metric	tons	CO2‐eq	(metric	tons	CH4)‐1)	
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems 

3-76

 Uncertainty	associated	with	the	attenuation	factor.	In	a	review	of	temperate	region

comparisons	of	paired	sites	in	natural	vegetation	vs.	agricultural	management,	Smith	et	al.

(2000)	found	that	agricultural	conversion	to	cropland	or	pasture	reduced	oxidation	by	71

percent	on	average.

Uncertainties	in	model	parameters	are	combined	using	a	Monte	Carlo	simulation	approach.	

Uncertainty	is	assumed	to	be	minor	for	the	management	activity	data	provided	by	the	entity,	

although	this	may	not	be	the	case	if	there	is	limited	knowledge	about	land‐use	change.	Table	3‐12	

provides	the	probability	distribution	functions	associated	with	estimating	uncertainty	in	methane	

oxidation.	

Table 3‐12 Available Uncertainty Data for Methane Oxidation		

Parameter Estimated

Value

Effective

Lower

Limit

Effective

Upper

Limit

Distribution Data Source

CH4	oxidation	rates	prior	to	

conversion	(PCH4)	grasslands	(kg	

CH4	ha‐1	year‐1)	

3.2		 0	 6.9	 Normal	 Del	Grosso	et	

al.	(2000a)	

CH4	oxidation	rates	prior	to	

conversion	(PCH4)	coniferous	

forests	(kg	CH4	ha‐1	year‐1)	

2.8	 0.1	 5.5	 Normal	 Del	Grosso	et	

al.	(2000a)	

CH4	oxidation	rates	prior	to	

conversion	(PCH4)	deciduous	

Forests	(kg	CH4	ha‐1	year‐1)	

11.8	 1.9	 21.6	 Normal	 Del	Grosso	et	

al.	(2000a)	

CH4	oxidation	attenuation	factor:	

cropland	including	set‐aside	(CRP)	

grassland,	grazing	land,	and	

fertilized	or	recently	harvested	

forests	

0.30	 0.07	 1	 Log‐Normal	 Smith	et	al.	

(2000)	

CH4	oxidation	attenuation	factor:	

natural	vegetation,	0‐100	years	

after	abandonment	of	agricultural	

production	or	timber	harvest		

0.3	+	(0.007	×	

years	since	

abandonment)	

0.07	+	

(0.007	×	

years	

since	

abandon

ment)	

1	 Log‐Normal	 Smith	et	al.	

(2000)	

CH4	oxidation	attenuation	factor:		

>100	years	post‐management	or	

never	used	for	agricultural	

management	or	timber	harvest	

1	 0.07	 1	 Log‐Normal	 Smith	et	al.	

(2000)	
 Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

 3-77 

3.5.6 Methane and Nitrous Oxide from Flooded Rice Cultivation

3.5.6.1 Rationale for Selected Method

There	are	a	number	of	possibilities	for	estimating	GHG	emissions	from	flooded	rice	systems.	

Process	based	models	are	being	developed	to	quantify	GHG	emissions,	such	as	the	DNDC	(e.g.,	

Zhang	et	al.,	2011)	and	DAYCENT	models	(Cheng	et	al.,	2013).	While,	these	models	have	been	

evaluated	for	various	regions	and	countries	in	Asia,	they	have	not	been	sufficiently	evaluated	for	

U.S.	rice	systems,	which	are	significantly	different	from	those	found	in	Asia	(establishment	

practices,	residue	management,	water	management,	and	varieties).	Therefore,	the	selected	method	

is	based	on	the	IPCC	Tier	1	methodology.		While	the	IPCC	methodology	has	also	been	largely	

developed	from	Asian	rice	studies,	it	is	more	transparent	and	uncertainties	can	be	derived	in	the	

emissions	estimates.	It	is	anticipated	that	the	process‐based	models	may	be	further	tested	and	

calibrated	in	the	near	future	for	U.S.	conditions	and	possibly	used	in	a	future	version	of	these	

methods.	

Several	management	practices	have	the	potential	to	influence	CH4	and	N2O	emissions	from	flooded	

rice	systems.	However,	there	are	currently	not	enough	data	available	to	quantitatively	account	for	

(or	establish	scaling	factors	for)	the	effects	of	all	of	these	management	practices.	There	is	sufficient	

information	to	account	for	the	influence	of	water	management,	residue	management,	and	organic	

amendments	on	CH4	emissions	from	flooded	rice	(Lasco	et	al.,	2006;	Yan	et	al.,	2005).			

3.5.6.2 Description of Method

Methane:	The	methodology	assumes	a	baseline	emission	factor	or	“typical”	daily	rate	at	which	CH4

is	produced	per	unit	of	land	area.	This	baseline	factor	represents	fields	that	are	continuously	

flooded	during	the	cultivation	period,	not	flooded	at	all	during	the	180	days	prior	to	cultivation,	and	

receive	no	organic	amendments.	Differences	between	the	baseline	scenario	and	other	scenarios	are	

accounted	for	by	the	use	of	scaling	factors	that	are	used	to	adjust	the	baseline	emission	factor	for	

Method for Estimating Methane and N2O Emissions from Rice Cultivation

 IPCC	equations	developed	by	Lasco	et	al.	(2006)	for	CH4	and	de	Klein	et	al.	(2006)	for	N2O.		

‐ The	baseline	emission	factor	or	typical	daily	rate	at	which	CH4	is	produced	per	

unit	of	land	area	represents	fields	that	are	continuously	flooded	during	the	

cultivation	period,	not	flooded	at	all	during	the	180	days	prior	to	cultivation,	and	

receive	no	organic	amendments.	Differences	between	the	baseline	continuously	

flooded	fields	without	organic	amendments	are	accounted	for	by	scaling	factors	

(e.g.,	water	regime	adjustments	(pre‐and	during	the	cultivation	period),	or	

organic	amendments).	CH4	scaling	factors	to	account	for	water	regimes	and	

organic	amendments	come	from	Lasco	et	al.	(2006).			

‐ N2O	emission	factors	rely	on	Lasco	et	al.	(2006),	and	the	scaling	factor	to	account	

for	drainage	effects	comes	from	Akiyama	et	al.	(2005;	USDA,	2011).			

 This	method	uses	the	IPCC	(2006)	equations	with	the	addition	of	a	scaling	factor	for	

estimating	N2O	emissions	from	drainage	(Akiyama	et	al.,	2005;	U.S.	EPA,	2011).	The	

method	for	methane	emissions	uses	entity	specific	seasonal	parcel	data	as	input	into	the	

IPCC	equation.	

 This	method	was	chosen	to	minimize	uncertainty.	Process	models	were	considered,	but	

not	chosen	for	this	method	due	to	a	need	for	further	research	on	U.S.	rice	cultivation	

conditions	and	practices.	
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems 

3-78

the	effects	of	water	management	(occurring	both	before	and	during	the	cultivation	period)	and	the	

amount	of	organic	amendments.	The	rate	at	which	CH4	is	emitted	depends	on	water	

flooding/drainage	regimes	and	on	rates	and	types	of	organic	amendments	applied	to	the	soil.	As	

such,	scaling	factors	for	a	broad	range	of	scenarios	are	provided	with	this	methodology.		The	factors	

are	differentiated	by	hydrological	context	(e.g.,	irrigated,	rainfed,	upland—all	rice	fields	in	the	

United	States	are	irrigated),	cultivation	period	flooding	regime	(e.g.,	continuous,	multiple	aeration),	

time	since	last	flooding	(prior	to	cultivation;	e.g.,	over	180	days,	under	30	days)	and	type	of	organic	

amendment	(e.g.,	compost,	farm	yard	manure).		

The	following	equation	has	been	adopted	from	the	methodology	developed	by	the	IPCC	to	estimate	

CH4	emissions	from	a	land	parcel	(Lasco	et	al.,	2006):	

The	daily	emission	factor	is	estimated	based	on	the	conditions	(i,	j,	k,	etc.)	that	influence	CH4

emissions	for	flooded	rice	production,	including	the	ecosystem	type,	water	regime,	and	organic	

amendment	rate.	As	more	data	become	available,	additional	conditions	that	influence	CH4

emissions	may	be	added.	The	“i"	in	the	equations	below	represents	the	specific	scenario	or	“other	

conditions”	that	can	significantly	influence	CH4	emissions	on	a	parcel.	In	the	future,	additional	

scenarios	with	factors	that	affect	CH4	emissions	may	be	included	as	the	relationship	between	these	

conditions	becomes	clear.	The	following	equation	is	used	to	estimate	the	daily	emission	factor	for	a	

land	parcel:	

Equation 3‐18: Flooded Rice Methane Emissions

CH4Rice = CH4GWP ×	Σi j k (EFi j k x ti j k xAi j kx 10‐3)

Where:	

CH4Rice		 =	Annual	methane	emissions	from	rice	cultivation	(metric	tons	CO2‐eq	year‐1)	

EFijk		 =	A	daily	emission	factor	for	i,	j,	and	k	conditions	(kg	CH4	ha‐1	day‐1)	

tijk		 =	Cultivation	period	of	rice	for	i,	j,	and	k	conditions	(days)	

Aijk		 =	Annual	harvested	area	of	rice	for	i,	j,	and	k	conditions	(ha	year‐1)	

CH4GWP		 =	Global	warming	potential	for	CH4	(metric	tons	CO2‐eq	(metric	tons	CH4)‐1)	

i,	j,	and	k		 =	Represent	different	ecosystems,	water	regimes,	type	and	amount	of	organic	

amendments,	soil	type,	rice	cultivar,	sulfate	containing	amendments,	and	other	

conditions	under	which	CH4	emissions	from	rice	may	vary.
 Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

 3-79 

The	scaling	factor	for	organic	amendments	to	a	land	parcel	is	estimated	using	the	following	

equation:	

The	scaling	factors	for	Equation	3‐19	and	Equation	3‐20	are	from	Lasco	et	al.	(2006)	and	shown	

below.	

Table 3‐13: Rice Water Regime Emission Scaling Factors (During Cultivation Period)

Water Regime During the Cultivation Period (assumes irrigated) SFw

Continuously	flooded	 1

Intermittently	flooded	–	single	aeration	 0.6

Intermittently	flooded	–	multiple	aeration 0.52

Source:	Lasco	et	al.	(2006),	Table	5.12.	

Table 3‐14: Rice Water Regime Emission Scaling Factors (Before Cultivation Period)

Water Regime Before the Cultivation Period SFp

Non	flooded	pre‐season	<	180	days	 1

Non	flooded	pre‐season	>	180	days	 0.68

Flooded	pre‐season	>	30	days	 1.9

Source:	Lasco	et	al.	(2006),	Table	5.13.	

Equation 3‐19: Flooded Rice Methane Emission Factor

EFi = EFc x SFw x SFp x SFo x SFs,r

Where:	

EFi		=	adjusted	daily	emission	factor	for	a	particular	harvested	area		(kg	CH4	ha‐1	day‐1)	

EFc		=	baseline	emission	factor	for	continuously	flooded	fields	without	organic	amendments	

(kg	CH4	ha‐1	day‐1)	

SFw	=	scaling	factor	to	account	for	the	differences	in	water	regime	during	the	cultivation	

period	(from	Lasco	et	al.	2006,	Table	5.12)	(unitless)	

SFp		=	scaling	factor	to	account	for	the	differences	in	water	regime	in	the	pre‐season	before	

the	cultivation	period	(from	Lasco	et	al.	2006,	Equation	5.3	and	Table	5.14)	(unitless)	

SFo		=	scaling	factor	should	vary	for	both	type	and	amount	of	organic	amendment	applied	

(Equation	3‐20)	(unitless)	

SFs,r	=	scaling	factor	for	soil	type,	rice	cultivar,	etc.,	if	available	

Equation 3‐20: Organic Amendments Scaling Factor

SFo = (1 + (ROAi x CFOAi))0.59

Where:	

SFo		 =	scaling	factor	for	both	type	and	amount	of	organic	amendment	

ROAi				=	rate	of	application	of	organic	amendment(s)	(metric	tons	ha‐1)	

CFOAi		 =	conversion	factor	for	organic	amendments	(from	Lasco	et	al.	2006,	Table	5.14)	

(unitless)		
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems 

3-80

Table 3‐15: Rice Organic Amendment Emission Scaling Factors; adapted from Lasco et al.

(2006)

Organic Amendments CFOA

Straw	incorporated	shortly	(<30	days)	before	cultivation 1

Straw	incorporated	long	(>30	days)	before	cultivation 0.29

Compost	 0.05

Farm	yard	manure	 0.14

Green	manure	 0.50

Source:	Lasco	et	al.	(2006),	Table	5.14.	

Soil N2O:	The	IPCC	methodology	(de	Klein	et	al.,	2006)	has	been	adapted	to	estimate	direct	N2O	

emissions	from	rice	fields.	The	emission	factor	for	rice	soils	accounts	for	nitrogen	additions	from	

mineral	fertilizers,	organic	amendments,	and	crop	residues.	Note	that	an	effect	of	nitrogen	

mineralized	from	mineral	soil	as	a	result	of	loss	of	soil	carbon	is	not	included	in	this	equation.	

Flooded	rice	cultivation	leads	to	minimal	losses	of	soil	carbon	due	to	periodic	flooding,	which	is	the	

default	assumption	with	the	IPCC	method	(Lasco	et	al.,	2006),	and	therefore	it	is	not	necessary	to	

include	the	effect	of	enhanced	nitrogen	mineralization	from	loss	of	soil	C.		

The	following	equation	is	used	to	estimate	the	soil	N2O	emissions	from	a	parcel	of	land:	

The	emission	factor	and	SFD	factors	are	based	on	research	conducted	by	Akiyama	et	al.	(2005).		The	

IPCC	(2006)	does	not	account	for	differences	in	water	management,	and	uses	an	emission	factor	of	

0.3,	but	Akiyama	et	al.	(2005)	provide	further	disaggregation	of	the	emission	factors	based	on	

water	management.	Therefore,	the	selected	emission	factor	value	is	0.0022	based	on	Akiyama	et	al.	

(2005),	and	the	scaling	factors	are	0	for	continuously	flooded	rice	and	0.59	for	aerated	systems	(i.e.,	

drainage	events	during	the	growing	season).			

Indirect N2O Emissions:	For	indirect	N2O	emissions	from	flooded	rice,	the	same	method	is	used	as	

described	in	Section		3.5.4.2,	by	applying	Equation	3‐13,	Total	Indirect	Soil	N2O	Emissions	from	

Mineral	Soils;	Equation	3‐14,	Indirect	Soil	N2O	Emissions	from	Mineral	Soils	—Volatilization;	and	

Equation	3‐15,	Indirect	Soil	N2O	Emissions	from	Mineral	Soils	—Leaching	and	Runoff.		In	the	latter	

Equation 3‐21: Direct Soil N2O Emissions from flooded Rice

N2ORice = Nt × EF × (1 + SFD) × N2OMW × N2OGWP

Where:	

N2ORice		=	Direct	emissions	of	N2O	from	soils	in	flooded	rice	production	systems		

				(metric	tons	CO2‐eq	year‐1)	

Nt			 =	Total	nitrogen	inputs	from	all	agronomic	sources:	mineral	fertilizer,	organic	

amendments,	residues,	and	additional	mineralization	from	land‐use	change	or	

tillage	change	(metric	tons	N	year‐1)		

EF		 =	Emission	factor	or	proportion	of	Nt	transformed	to	N2O	(kg	N2O‐N	(kg	N)‐1)	

SFD		 =	Scaling	factor	to	account	for	drainage	effects;	0	for	continuously	flooded	

(dimensionless)	

N2OMW		=	Ratio	of	molecular	weights	of	N2O	to	N2O‐N	

=	44/28	(metric	tons	N2O	(metric	tons	N2O‐N)‐1)	

N2OGWP		=	Global	warming	potential	for	N2O	(metric	tons	CO2‐eq	(metric	tons	N2O)‐1)	
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

3-81 

two	equations,	use	the	IPCC	default	fractions	for	FRSN,	FRON,	and	FRleach,	which	are	provided	in	the	

equation	boxes.	

3.5.6.3 Activity Data

The	activity	and	related	data	requirements	for	this	method	include:	

 Harvested	area	(ha);

 Cultivation	period	in	days;

 Water	management	practices	throughout	the	year	(e.g.,	aeration	or	not);

 Organic	matter	amendment	(including	residue)	rate;

 Organic	fertilizer	N;

 Fertilizer	nitrogen	management	(rate);

 Type	of	fertilizer(s)	applied	(qualitative);

 Crop	residue	N;	and

 Crop	yield,	metric	tons	dry	matter	crop	yield	year‐1.

3.5.6.4 Ancillary Data

	No	ancillary	data	are	needed	for	this	method.	

3.5.6.5 Model Output

Model	output	is	the	combined	emissions	of	CH4	and	N2O	in	CO2	equivalents,	expressed	on	an	area	

basis.	The	intensity	of	CH4	emissions	and	nitrous	oxide	(i.e.,	emissions	per	unit	of	land	area	

cultivated)	is	related	to	the	quantity	of	crops	grown	and	can	be	estimated	with	the	following	

equation:	

3.5.6.6 Limitations and Uncertainty

This	method	has	several	limitations	that	will	potentially	create	bias	or	imprecision	in	the	results.	

Currently,	scaling	factors	account	only	for	water	and	organic	matter	management	and	do	not	

account	for	other	mitigation	options.	As	indicated	earlier	there	are	other	management	

opportunities	that	may	reduce	emissions,	but	further	research	is	required	in	these	areas.	Baseline	

emissions	are	highly	variable,	but	this	methodology	provides	only	one	factor	value	representing	the	

baseline	emissions.	In	addition,	the	methodology	assumes	a	period	of	drainage;	however,	drain	

events	(even	those	of	similar	duration)	can	vary	markedly	based	on	soil	and	climatic	conditions,	

from	dry	and	cracking	on	the	surface	to	saturated	at	the	end	of	a	drainage	event.	The	influence	of	

drainage	on	the	soil	saturation	is	not	addressed	with	the	current	method.	In	addition,	there	is	

currently	insufficient	information	to	develop	a	method	for	the	use	of	sulfur	products	as	

amendments;	future	guidance	may	be	updated	with	a	method	for	this	practice.	

Equation 3‐22: Flooded Rice Combined Methane and Nitrous Oxide Emissions Intensity

EI = (CH4Rice + N2ORice)/Y

Where:	

EI		 =	Emissions	intensity	(metric	tons	CO2‐eq	per	metric	tons	dry	matter	crop	yield)	

CH4Rice		=	Annual	methane	emissions	from	rice	cultivation	(metric	tons	CO2‐eq	year‐1)	

N2ORice		=	Direct	emissions	of	N2O	from	soils	in	flooded	rice	production	systems		

			(metric	tons	CO2‐eq‐year‐1)	

Y		 =	Total	yield	of	crop	(metric	tons	dry	matter	crop	yield	year‐1)	
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems 

3-82

CH4	emissions	are	the	result	of	a	number	of	interacting	biological	processes,	which	by	nature	vary	

spatially	and	temporally.	The	greatest	amount	of	uncertainty	is	the	baseline	emission	factor.	When	

using	this	methodology,	the	emission	factor	is	an	average	emission	factor	for	continuously	flooded	

rice	systems	that	have	not	been	flooded	the	180	days	prior	to	cultivation	and	have	not	received	

organic	amendments.	In	the	case	of	CH4	emissions	from	rice	cultivation,	the	uncertainty	ranges	of	

Tier	1	values	(emission	and	scaling	factors)	are	adopted	directly	from	Lasco	et	al.	(2006).	Ranges	

are	defined	as	the	standard	deviation	about	the	mean,	indicating	the	uncertainty	associated	with	a	

given	default	value	for	this	source	category.		

Uncertainties	in	model	parameters	are	combined	using	a	Monte	Carlo	simulation	approach.	

Uncertainty	is	assumed	to	be	minor	for	the	management	activity	data	provided	by	the	entity.		Table	

3‐16	provides	the	probability	distribution	functions	associated	with	estimating	uncertainty	in	

methane	and	N2O	emissions	from	rice	cultivation.	

Table 3‐16: Available Uncertainty Data for Methane, Direct and Indirect N2O Emissions

Methane from Flooded Rice Cultivation

Parameter Abbreviation/

Symbol

Estimated

Value

Effective

Lower

Limit

Effective

Upper

Limit

Distribution Data

Source

Baseline	emission	

factor	for	

continuously	

flooded	fields	

without	organic	

amendments	

EFc	 1.3	 0.8	 2.2	 Uniform	 IPCC	

(2006)

Water	regime	during	

the	cultivation	

period	–	Scaling	

factor	

SFw	for	

continuously	

flooded	

1	 0.79	 1.26	 Uniform	 IPCC	

(2006)

Water	regime	during	

the	cultivation	

period	–	Scaling	

factor	

SFw	for	single	

aeration	 0.6	 0.46	 0.8	 Uniform	 IPCC	(2006)

Water	regime	during	

the	cultivation	

period	–	Scaling	

factor	

SFw	for	multiple	

aerations	 0.52	 0.41	 0.66	 Uniform	 IPCC	(2006)

Water	regime	before	

the	cultivation	

period	–	Scaling	

factor	

SFp	for	non‐flooded	

pre‐season	<180	

days	

1	 0.88	 1.14	 Uniform	 IPCC	

(2006)

Water	regime	before	

the	cultivation	

period	–	Scaling	

factor	

SFp	for	non‐flooded	

pre‐season	>	180	

days	

0.68	 0.58	 0.8	 Uniform	 IPCC	

(2006)

Water	regime	before	

the	cultivation	

period	–	Scaling	

factor	

SFp	for	flooded	pre‐

season	>	30	days	 1.9	 1.65	 2.18	 Uniform	 IPCC	(2006)

Organic	amendment	

conversion	factor	

CFOAi	for	straw	

incorporation	less	

than	30	days	before	

cultivation	

1	 0.97	 1.04	 Uniform	 IPCC	

(2006)
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

3-83 

Methane from Flooded Rice Cultivation (continued)

Parameter Abbreviation/

Symbol

Estimated

Value

Effective

Lower

Limit

Effective

Upper Limit Distribution DataSource

Organic	

amendment	

conversion	factor	

CFOAi	for	

straw	

incorporation	

more	than	30	

days	before	

cultivation	

0.29	 0.2	 0.4	 Uniform	 IPCC	

(2006)	

Organic	

amendment	

conversion	factor	

CFOAi	for	

compost	 0.05	 0.01	 0.08	 Uniform	 IPCC	(2006)	

Organic	

amendment	

conversion	factor	

CFOAi	for	farm	

yard	manure	 0.14	 0.07	 0.2	 Uniform	 IPCC	(2006)	

Organic	

amendment	

conversion	factor	

CFOAi	for	

green	manure	

0.5	 0.3	 0.6	 Uniform	 IPCC	

(2006)	

N2O from Flooded Rice

Parameter Abbreviation/

Symbol Mean

Relative

Uncertainty

Low (%)

Relative

Uncertainty

High (%)

Distribution Data

Source

Emission	factor	or	

proportion	of	Nt

transformed	to	

N2O		

EF	 0.0022	 0.24%	 0.24%	 Normal	 Akiyama	et	

al.	(2005)

Scaling	factor	to	

account	for	

drainage	effects	

SFD	for	aerated	

systems	 0.59	 0.35%	 0.35%	 Normal	 Akiyama	et	al.	(2005)

3.5.7 CO2 from Liming

3.5.7.1 Rationale for Selected Method

Addition	of	lime	to	soils	is	typically	thought	to	generate	CO2	emissions	to	the	atmosphere	(de	Klein	

et	al.,	2006).	However,	prevailing	conditions	in	U.S.	agricultural	lands	lead	to	CO2	uptake	because	

the	majority	of	lime	is	dissolved	in	the	presence	of	carbonic	acid	(H2CO3).	Therefore,	the	addition	of	

lime	leads	to	a	carbon	sink	in	the	majority	of	U.S.	cropland	and	grazing	land	systems.	Whether	

liming	contributes	to	a	sink	or	source	depends	on	the	pathways	of	dissolution	and	rates	of	

bicarbonate	leaching.	The	emissions	factor	provided	in	this	guidance	has	been	estimated	from	a	

Method for Estimating CO2 Emissions from Liming

 This	method	uses	the	IPCC	equation	(de	Klein	et	al.,	2006)	with	U.S.	specific	emissions

factors.

 Entity	specific	annual	parcel	data	as	input	into	the	IPCC	equation	(e.g.,	the	amount	of	lime,

crushed	limestone,	or	dolomite	applied	to	soils).

 This	method	was	selected	as	it	was	the	only	readily	available	model	for	estimating	CO2

emissions	from	liming.
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems 

3-84

review	of	existing	models	and	mass	balance	analyses	conducted	for	the	application	of	lime	in	the	

United	States	and	is	a	Tier	2	method	as	defined	by	the	IPCC.	

Since	crushed	limestone	(CaCO3)	contains	12	percent	C,	an	application	of	1,000	kg	CaCO3	places	120	

kg	C	on	the	soil	surface.	It	is	assumed	that	two‐thirds	of	this	(80	kg)	is	acidified	to	HCO3‐	and	

leached	to	the	ocean	where	it	will	be	sequestered	for	decades	to	centuries	(Oh	and	Raymond,	

2006).	Because	this	transfer	represents	a	movement	from	one	long‐term	pool	(geologic	formations)	

to	another	(ocean),	this	carbon	transfer	does	not	represent	a	net	uptake	of	CO2	from	the	

atmosphere.	However,	with	this	transfer,	there	is	80	kg	C	of	atmospheric	CO2	uptake	into	soils.	The	

uptake	of	CO2	from	the	atmosphere,	after	subtracting	the	one‐third	of	carbon	in	the	lime	that	is	

acidified	directly	to	CO2	(40	kg	C),	yields	a	total	net	CO2	uptake	of	40	kg	C	per	1,000	kg	CaCO3

applied.	This	results	in	a	carbon	coefficient	or	emission	factor	of	40/1000	=	‐0.04	kg	C	per	kg	CaCO3.	

This	equates	to	a	carbon	sink	(40	kg	C	sequestered/120	kg	C	×	100).	Dolomite	contains	only	slightly	

more	carbon	than	does	CaCO3	(13	percent	vs.	12	percent)	so	the	factors	are	essentially	the	same.		

The	emission	factor	is	country‐specific	based	on	a	revision	of	the	estimates	proposed	in	West	and	

McBride	(2005),	which	are	currently	used	in	the	U.S.	National	GHG	Inventory	(U.S.	EPA,	2011).	The	

underlying	difference	with	the	earlier	emission	factor	from	West	and	McBride	(2005)	is	that	the	

revised	value	assumes	that	the	amount	of	bicarbonate	carried	into	rivers	has	a	long	turnover	time	

and	is	essentially	not	returned	to	the	atmosphere	over	decadal	to	century	time	scales.	

3.5.7.2 Description of Method

The	model	to	estimate	CO2	emissions	from	liming	has	been	adapted	from	methods	developed	by	the	

IPCC	(de	Klein	et	al.,	2006),	with	refinement	in	the	emission	factors	based	on	conditions	in	U.S.	

agricultural	lands.	The	following	equation	is	used	to	estimate	emissions	from	carbonate	lime	

additions	to	a	land	parcel:	

3.5.7.3 Activity Data

The	method	requires	data	on	the	amount	of	lime	(crushed	limestone	or	dolomite)	applied	to	soils.	

3.5.7.4 Ancillary Data		

No	ancillary	data	are	needed	in	order	to	apply	the	method.	

Equation 3‐23: Change in Soil Carbon Stocks from Lime Application

ΔCLime = M × EF × CO2MW

Where:	

ΔCLime		 =	Annual	change	in	soil	carbon	stocks	from	lime	application	(metric	tons	CO2‐eq)	

M	 =	Annual	application	of	lime	as	crushed	limestone	or	dolomite	

	 (metric	tons	of	crushed	limestone	or	dolomite	year‐1)

EF		 =	Metric	ton	CO2	emissions	per	metric	ton	of	lime	‐0.04	

		(metric	ton	carbon	(metric	ton	lime)‐1)	

CO2MW		 =	Ratio	of	molecular	weight	of	CO2	to	carbon	(44/12)	(metric	tons	CO2	(metric	tons	C)‐1)
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

3-85 

3.5.7.5 Model Output

Model	output	is	generated	on	both	an	absolute	quantity	of	emissions	and	emissions	intensity.	The	

latter	is	based	on	the	amount	of	emissions	per	unit	of	yield	for	crops	in	cropland	systems	or	grazing	

systems.	The	emissions	intensity	is	estimated	with	the	following	equation:	

Yields	are	based	on	the	total	amount	of	product	from	the	land	managed	with	lime	application.		

3.5.7.6 Limitations and Uncertainty		

Limitations	include	variation	in	soil	carbon	emissions	due	to	soil	pH	and	rate	of	nitrogen	fertilizer	

application,	which	influence	the	chemical	pathway	of	lime	dissolution	(Hamilton	et	al.,	2007;	West	

and	McBride,	2005).	More	specifically,	the	EF	will	not	accurately	capture	the	result	of	lime	

dissolution	in	the	presence	of	stronger	nitric	acid	(HNO3),	which	is	produced	when	nitrifying	

bacteria	convert	ammonium	(NH4+)	based	fertilizer	and	other	sources	of	NH4+	to	nitrate	(NO3‐

).	

Uncertainties	in	the	lime	emissions	methods	include	imprecision	at	the	farm	scale,	because	the	

method	of	estimation	is	based	on	stream‐gauge	data	that	are	collected	at	the	watershed	scale.	

Uncertainties	in	model	parameters	are	combined	using	a	Monte	Carlo	simulation	approach.	

Uncertainty	is	assumed	to	be	minor	for	the	management	activity	data	provided	by	the	entity.	Table	

3‐17	provides	the	probability	distribution	functions	associated	with	CO2	emissions	per	metric	ton	

of	lime	applied.	

Table 3‐17: Available Uncertainty Data for CO2 from Liming

Parameter Mean

Relative

Uncertainty

Low (%)

Relative

Uncertainty

High (%)

Distribution Data Source

Emissions	factor	(metric	

ton	CO2	emissions	per	

metric	ton	of	lime)	

‐0.04	 46%	 46%	 Normal	

Adapted	from	

West	and	

McBride	(2005)

Equation 3‐24: Emissions Intensity from Lime Application

EI =	ΔCLime/Y

Where:	

EI		 =	Emissions	intensity	(metric	tons	CO2	per	metric	ton	dry	matter	crop	yield)	

ΔCLime	 =	Annual	change	in	soil	carbon	stocks	from	lime	application	(metric	tons	CO2)	

Y		 =	Total	yield	of	crop	(metric	tons	dry	matter	crop	yield	year‐1),	meat	(kg	carcass	yield	

year‐1),	or	milk	production	(kg	fluid	milk	yield	year‐1)	
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems 

3-86

3.5.8 Non‐CO2 Emissions from Biomass Burning

3.5.8.1 Rationale for Selected Method

Non‐CO2	GHG	emissions	from	biomass	burning	include	CH4	and	N2O.	CO	and	NOx	are	also	emitted	

and	are	precursors	that	are	later	converted	into	GHGs	following	additional	reactions	(i.e.,	release	of	

these	gases	leads	to	GHG	formation).	CO2	is	also	emitted	but	not	addressed	for	crop	residues	or	

grassland	burning	because	the	carbon	is	reabsorbed	from	the	atmosphere	in	new	growth	of	crops	

or	grasses	within	an	annual	cycle.			

There	has	been	limited	development	and	testing	of	process‐based	approaches	for	estimating	non‐

CO2	GHG	emission	from	biomass	burning.	Moreover,	country‐specific	data	are	limited	on	the	

amount	of	non‐CO2	GHG	emissions.	Therefore,	this	guidance	has	adopted	the	IPCC	Tier	1	method	as	

described	by	Aalde	et	al.	(2006).	

3.5.8.2 Description of Method

The	model	to	estimate	non‐CO2	GHG	emissions	and	precursors	has	been	adapted	from	methods	

developed	by	IPCC	(Aalde	et	al.,	2006).	The	following	equation	is	used	to	estimate	emissions	due	to	

burning	biomass	on	a	parcel	of	land:	

Combustion	efficiency,	as	defined	in	IPCC	(2006)	combines	the	proportion	of	biomass	that	is	

actually	burned	in	a	fire	with	the	amount	of	carbon	released	as	a	proportion	of	the	total	carbon	in	

the	burned	biomass.	The	mass	of	the	fuel	combusted	includes	live	and	dead	biomass	(i.e.,	dead	

Method for Estimating Non‐CO2 Emissions from Biomass Burning

 The	method	uses	the	IPCC	equation	and	emission	factors	developed	by	Aalde	et	al.

(2006).

 Entity	specific	annual	parcel	data	(e.g.,	area	burned	for	croplands	and	grazing	land;	crop

type	and	harvest	yield	data;	residue‐yield	ratios	(West	et	al.,	2010);	type	of	forage,

grazing	area,	and	amount	of	biomass	before	the	fire	in	grazing	lands	that	are	burned;	and

combustion	efficiency)	are	inputs	to	the	IPCC	equation.

 This	method	was	selected	as	it	was	the	only	readily	available	model	for	estimating	non‐

CO2	emissions	from	biomass	burning.

Equation 3‐25: GHG Emissions from Biomass Burning

GHGBiomassBurning = A × M × C × EF × 10‐3 × GHGGWP		

Where:	

GHGBiomassBurning		=	Annual	emissions	of	GHG	or	precursor	due	to	biomass	burning

			(metric	tons	of	CO2‐eq	year‐1)	

A		 =	Area	burned	(ha)	

M	 =	Mass	of	fuel	available	for	combustion	(metric	tons	dry	matter	ha‐1	year‐1)	

C		 =	Combustion	efficiency,	dimensionless	

EF		 =	Emission	factor	(g	GHG	(kg	of	burned	biomass)‐1)	

GHGGWP		 =	Global	warming	potential	for	each	GHG	

			(metric	tons	CO2‐eq	(metric	tons	GHG)‐1)	
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

3-87 

biomass	includes	plant	residues	in	grazing	and	cropland	systems)	and	is	approximated	for	a	land	

parcel	with	the	following	equation:	

Peak	aboveground	biomass	is	estimated	with	Equation	3‐3	for	crops	and	grass	vegetation.	For	

croplands	that	are	burned	following	harvest,	the	residue	mass	is	estimated	by	subtracting	the	

harvest	index	(HI)	from	one	and	converting	to	a	percentage,	which	is	the	residual	biomass	left	in	

the	field.	Default	harvest	indices	are	given	in	Table	3‐5.	The	estimated	mass	of	fuel	for	grazing	

systems	based	on	Equation	3‐3	does	not	include	the	dead	biomass.	If	there	is	significant	residual	

litter	in	grazing	systems,	then	

multiply	the	mass	of	fuel	by	two	as	

a	conservative	estimate	of	the	total	

live	and	dead	biomass	on	the	land	

parcel.	Alternatively,	entities	may	

enter	an	estimate	for	the	

proportion	of	residual	litter	mass	

relative	to	the	live	biomass,	instead	

of	using	two,	which	doubles	the	

mass	of	fuel.	A	summary	of	

emission	factors	by	land	use	

category	is	provided	in	Table	3‐18.	

3.5.8.1 Activity Data

The	following	activity	and	related	data	are	needed	to	apply	the	method:	

 Area	burned	for	croplands	and	grazing	land;

 Crop	type	and	harvest	yield	data	for	crops	grown

in	fields	with	residue	burning	management;

 Residue:	yield	ratios	(optional);

 Type	of	forage,	grazing	area,	and	amount	of

biomass	before	the	fire	in	grazing	lands	that	are

burned;	and

 Combustion	efficiency	(optional).

A	list	of	default	combustion	efficiencies	is	provided	for	

residues	and	forages	(Table	3‐19	and	Table	3‐20),	but	the	

entity	can	provide	value	specific	to	their	operation.	

Default	dry	matter	contents	and	residue‐yield	ratios	are	

provided	in	Table	3‐5,	but	can	also	be	entered	by	the	

entity	if	the	information	is	available.			

Table 3‐18: Emission Factors for Biomass Burning

Land‐Use Category CO	 CH4 N2O NOx

(g kg‐1)

Grassland	burning 65	 2.3	 0.21 3.9	

Cropland	residue	 92	 2.7	 0.07 2.5	

Forest	biomass	(with	conversion	to	

cropland	or	grazing	lands)	 107	 4.7	 0.26 3.0	

Source:	Aalde	et	al.	(2006).

Table 3‐19: Default Combustion

Efficiencies for Selected Crops

Crop Combustion	

Efficiency	(C)	

Corn 0.88	x	0.93	=	0.82

Cotton 0.88	x	0.93	=	0.82

Lentils 0.88	x	0.93	=	0.82

Rice 0.88	x	0.93	=	0.82

Soybeans 0.88	x	0.93	=	0.82

Sugarcane 0.68	x	0.81	=	0.55

Wheat 0.88	x	0.93	=	0.82

Source:	EPA	(2013),	Table	6‐25.

Equation 3‐26: Mass of Fuel

M = (Hpeak/C) × (D/100)

Where:	

M		 =	Mass	of	fuel	available	for	combustion	(metric	tons	dry	matter	ha‐1	year‐1)	

Hpeak	 =	Annual	peak	aboveground	herbaceous	biomass	carbon	stock	

			(metric	tons	C	ha‐1	year‐1)	

C			 =	Carbon	fraction	of	aboveground	biomass	(dimensionless)	

D		 =	Percentage	of	biomass	present	at	the	stage	of	burning	relative	to	peak	(%)	
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems 

3-88

In	some	years,	the	entity	may	not	

harvest	the	crop	due	to	drought,	pest	

outbreaks,	or	other	reasons	for	crop	

failure.		In	those	cases,	the	entity	

should	provide	the	average	yield	that	

it	has	harvested	in	the	past,	and	an	

approximate	percentage	of	average	

crop	growth	that	occurred	prior	to	

burning.		The	yield	is	estimated	

based	on	multiplying	the	average	

crop	yield	by	the	percentage	of	crop	

growth	obtained	prior	to	burning.	

3.5.8.2 Ancillary Data		

No	ancillary	data	are	needed	in	order	

to	apply	the	method.	

3.5.8.3 Model Output

Model	output	is	generated	on	both	

an	absolute	quantity	of	emissions	

and	emissions	intensity.	The	latter	is	

based	on	the	amount	of	emissions	

per	unit	of	yield	for	crops	in	

cropland	systems	or	animal	products	

in	grazing	systems.	The	emissions	

intensity	is	estimated	with	the	

following	equation:	

Yields	are	based	on	the	total	amount	

of	product	from	the	land	managed	

with	burning.		

Table 3‐20: Default Combustion Efficiencies for Select

Vegetation Types

Vegetation Type Combustion

Efficiency (C)

Boreal Forest (all) 0.34

				Wildfire	 0.40

				Crown	fire	 0.43

				Surface	fire	 0.15

				Post	logging	slash	burn	 0.33

				Land	clearing	fire	 0.59

Temperate Forest (all) 0.45

				Post	logging	slash	burn	 0.62

				Felled	and	burned	(land‐clearing	fire)	 0.51

Shrublands (all) 0.72

				Shrubland	(general)	 0.95

				Calluna health	 0.71

				Fynbos	 0.61

Savanna woodlands (early dry season

burns) (all) 0.40	

				Savanna	woodland	(early)	 0.22

				Savanna	parkland	(early)	 0.73

Savanna woodlands (mid/late dry

season burns) (all) 0.74	

				Savanna	woodland	(mid/late)	 0.72

				Savanna	parkland	(mid/late)	 0.82

				Tropical	savanna	 0.73

				Other	savanna	woodlands	 0.68

Savanna grasslands (early dry season

burns) (all) 0.74	

				Tropical/sub‐tropical	grassland	 0.74

Savanna Grasslands/Pastures (mid/late

dry season burns) (all) 0.77	

				Tropical/sub‐tropical	grassland	 0.92

				Tropical	pasture	 0.35

				Savanna	 0.86

Source:	Aalde	et	al.	(2006),	Table	2.4	(C	×	M)	and	Table	2.6	(C)

Equation 3‐27: Biomass Burning Emissions Intensity

EI = GHGBiomassBurning/Y

Where:	

EI		 =	Emissions	intensity	(metric	tons	CO2	per	metric	ton	dry	matter	crop	yield,	

metric	tons	CO2	per	kg	carcass	yield,	metric	tons	CO2	per	kg	fluid	milk	yield)	

GHGBiomassBurning		=	Annual	CO2	equivalent	emissions	from	burning	(metric	tons	CO2‐eq	year‐1)	

Y		 =	Total	yield	of	crop	(metric	tons	dry	matter	crop	yield	year‐1),	meat	(kg	

carcass	yield	year‐1),	or	milk	production	(kg	fluid	milk	yield	year‐1)	
 Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

 3-89 

3.5.8.4 Limitations and Uncertainty

Uncertainty	in	the	emission	estimates	is	attributed	to	imprecision	in	carbon	fractions,	dry	matter	

contents,	harvest	indices,	combustion	efficiencies,	and	the	emission	factors.	Uncertainties	in	model	

parameters	are	combined	using	a	Monte	Carlo	simulation	approach.	Uncertainty	is	assumed	to	be	

minor	for	the	crop	yields,	peak	forage,	and	relative	amount	of	crop	or	forage	growth	compared	to	

the	peak	production.	However,	these	values	are	likely	to	have	some	level	of	uncertainty,	and	

methods	will	need	to	be	refined	in	the	future	to	better	address	these	uncertainties,	particularly	the	

mass	of	fuel	in	grazing	lands.	Table	3‐21	provides	the	probability	distribution	functions	for	

estimating	uncertainty	in	non‐CO2	emissions	from	biomass	burning.	

Table 3‐21: Available Uncertainty Data for Non‐CO2 Emissions from Biomass Burning

Parameter Mean

Relative

Uncertainty

Low (%)

Relative

Uncertainty

High (%)

Distribution Data Source

CH4 EF for grassland

(g CH4 kg ‐1) 2.3	 8%	 8%	 Normal	 IPCC	(2006)	

CH4 EF for crop residue

(g CH4 kg ‐1) 2.7	 50%	 50%	 Normal	 IPCC	(2006)	

N2O EF for grassland

(g N20 kg ‐1) 0.21	 93%	 93%	 Normal	 IPCC	(2006)	

N2O EF for crop residue

(g N20 kg ‐1) 0.07	 50%	 50%	 Normal	 IPCC	(2006)	

Combustion efficiency for

shrublands 0.72	 68%	 68%	 Normal	 IPCC	(2006)	

Combustion efficiency		for

grasslands with early

season burns

0.74	 50%	 50%	 Normal	 IPCC	(2006)	

Combustion efficiency for

grasslands with mid to late

season burns

0.77	 66%	 66%	 Normal	 IPCC	(2006)	

Combustion efficiency for

small grains 0.9	 50%	 50%	 Normal	

Expert	

Assessment	by	

authors	

Combustion efficiency for

large grain and other crop

residues

0.8	 50%	 50%	 Normal	

Expert	

Assessment	by	

authors	

Combustion efficiency

Boreal forest (all) 0.34	 102%	 102%	 Normal	 IPCC	(2006)	

Wildfire	 0.40	 340% 340% Normal	 IPCC	(2006)

Crown	fire	 0.43	 104% 104% Normal	 IPCC	(2006)

Surface	fire	 0.15	 96% 96% Normal	 IPCC	(2006)

Post	logging	slash	burn	 0.33	 130% 130% Normal	 IPCC	(2006)

Combustion efficiency

Temperate forest (all) 0.45	 51%	 51%	 Normal	 IPCC	(2006)	

					Post	logging	slash	burn	 0.62	 264% 264% Normal	 IPCC	(2006)

Combustion efficiency

Shrublands (all) 0.72	 147%	 147%	 Normal	 IPCC	(2006)	

					Calluna	health	 0.71	 121% 121% Normal	 IPCC	(2006)

					Fynbos	 0.61	 195% 195% Normal	 IPCC	(2006)

Combustion efficiency

Savanna woodlands (early

dry season burns) (all)

0.40	 93%	 93%	 Normal	 IPCC	(2006)	
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems 

3-90

Parameter Mean

Relative

Uncertainty

Low (%)

Relative

Uncertainty

High (%)

Distribution Data Source

Combustion efficiency

Savanna woodlands

(mid/late dry season

burns) (all)

0.74	 99%	 99%	 Normal	 IPCC	(2006)	

					Savanna	woodland	

(mid/late)	 0.72	 270%	 270%	 Normal	 IPCC	(2006)	

					Tropical	savanna	 0.73	 598% 598% Normal	 IPCC	(2006)

					Other	savanna	woodlands	 0.68	 931% 931% Normal	 IPCC	(2006)

Combustion efficiency

Savanna grasslands (early

dry season burns) (all)

0.74	 183%	 183%	 Normal	 IPCC	(2006)	

					Tropical/sub‐tropical	

grassland	

0.74	 270%	 270%	 Normal	 IPCC	(2006)	

					Tropical/sub‐tropical	

grassland	

0.92	 151%	 151%	 Normal	 IPCC	(2006)	

					Tropical	pasture	 0.35	 427% 427% Normal	 IPCC	(2006)

					Savanna	 0.86	 85% 85% Normal	 IPCC	(2006)

3.5.9 CO2 from Urea Fertilizer Applications

3.5.9.1 Rationale for Selected Method

Urea	fertilizer	application	to	soils	contributes	CO2	emissions	to	the	atmosphere.	The	source	of	the	

CO2	that	is	incorporated	into	the	urea	during	the	fertilizer	production	process	is	from	fossil	fuel	

sources	in	the	U.S.	fertilizer	plants.	The	CO2	captured	during	the	production	process	is	considered	

an	emissions	removal	in	the	manufacturer’s	reporting	so	its	release	following	urea	fertilization	on	

soils	is	included	in	the	farm‐scale	entity	reporting.	If	manufacturers	do	not	estimate	CO2	capture	

during	urea	production	and	include	the	recaptured	CO2	as	an	emission,	there	is	no	need	for	a	farm‐

scale	entity	to	report	release.	

The	Tier	1	method	has	been	adopted	from	the	IPCC	(de	Klein	et	al.,	2006).	No	other	methods	have	

been	developed	or	tested	sufficiently	for	an	operational	system.	

3.5.9.2 Description of Method

The	model	to	estimate	CO2	emissions	from	urea	application	has	been	adopted	from	the	

methodology	developed	by	the	IPCC	and	uses	the	IPCC	default	emission	factor	(de	Klein	et	al.,	

2006).	The	following	equation	is	used	to	estimate	the	CO2	emission	from	a	land	parcel	where	urea‐

based	fertilizers	have	been	applied:	

Method for Estimating CO2 Emissions from Urea Fertilizer Application

 This	method	uses	IPCC	equation	and	emission	factors	developed	by	de	Klein	et	al.	(2006).	

 This	method	uses	entity	specific	annual	parcel	data	as	input	into	the	IPCC	equation	(e.g.,	

the	amount	of	urea	fertilizer	applied	to	soils).	

 This	method	assumes	that	the	source	of	CO2	used	to	manufacture	urea	is	fossil	fuel	CO2

captured	during	NH3	manufacture.	
 Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

 3-91 

3.5.9.3 Activity Data

This	method	requires	data	on	the	amount	of	urea	fertilizer	applied	to	soils.	

3.5.9.4 Ancillary Data		

No	ancillary	data	are	needed	in	order	to	apply	the	method.	

3.5.9.5 Model Output

Model	output	is	generated	on	both	an	absolute	quantity	of	emissions	and	emissions	intensity.	The	

latter	is	based	on	the	amount	of	emissions	per	unit	of	yield	for	crops	in	cropland	systems	or	animal	

products	in	grazing	systems.	The	emissions	intensity	is	estimated	with	the	following	equation:	

Yields	are	based	on	the	total	amount	of	product	from	the	land	managed	with	urea	application.		

3.5.9.6 Limitations and Uncertainty

Urea	(CO(NH2)2)	is	converted	into	ammonium	and	CO2	in	the	presence	of	water	and	the	enzyme	

urease.	The	CO2	will	dissolve	in	water	to	form	carbonate,	bicarbonate,	and	carbonic	acid	as	a	

function	of	soil	pH	and	temperature.	Some	of	the	bicarbonate	may	be	transferred	to	groundwater,	

waterways,	and	eventually	the	ocean,	and	therefore	reduce	the	CO2	emissions	to	the	atmosphere	

(de	Klein	et	al.,	2006;	Hamilton	et	al.,	2007)).	However,	there	is	insufficient	information	available	to	

include	this	possibility	in	the	urea	method,	so	it	is	assumed	that	any	increase	in	bicarbonate	will	

lead	to	production	of	CO2.	

Equation 3‐28: CO2 Emissions from Urea Fertilization

CUrea = M × EF × CO2MW		

Where:	

CUrea		 =	Annual	release	of	carbon	from	urea	added	to	soil	(metric	tons	CO2‐eq	year‐1)	

M		 =	Annual	amount	of	urea	fertilization	(metric	tons	urea	year‐1)	

EF		 =	Emission	factor	or	proportion	of	carbon	in	urea,	0.20	

	 	 (metric	ton	C	(metric	ton	urea)‐1)	

	CO2MW		=	Ratio	of	molecular	weight	of	CO2	to	carbon	(44/12)	

	 			(metric	tons	CO2	(metric	tons	C)‐1)	

Equation 3‐29: Emissions Intensity from Urea Fertilization

EIUrea = CUrea/Y

Where:	

EIUrea	=	Emissions	intensity	(metric	tons	CO2	per	metric	ton	dry	matter	crop	yield,	metric	tons	

CO2	per	kg	carcass	yield,	metric	tons	CO2	per	kg	fluid	milk	yield)	

CUrea		 =	Annual	change	in	soil	carbon	stocks	due	to	urea	application	(metric	tons	CO2	year‐1)	

Y		 =	Total	yield	of	crop	(metric	tons	dry	matter	crop	yield	year‐1),	meat	(kg	carcass	yield	

year‐1),	or	milk	production	(kg	fluid	milk	yield	year‐1)	
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems 

3-92

Uncertainty	is	assumed	to	be	minor	for	the	management	activity	data	provided	by	the	entity,	

although	this	may	not	be	the	case	if	there	is	limited	knowledge	about	land	use	history	for	individual	

parcels.	Uncertainty	may	also	exist	in	the	emission	factor,	assuming	that	some	of	the	bicarbonate	is	

not	converted	to	CO2.	However,	the	method	assumes	all	CO2	is	emitted	because	uncertainty	

estimates	are	not	available	for	this	emission	factor.	Therefore,	no	uncertainty	is	estimated	for	this	

source	of	GHG	emissions	based	on	this	conservative	assumption	that	all	CO2	is	emitted.	

3.6 Summary of Research Gaps for Crop and Grazing Land Management

This	section	discusses	research	gaps	associated	with	cropland	and	grazing	land	management	

impacts	on	soil	carbon	stock	changes	and	GHG	emissions.	The	list	is	not	necessarily	exhaustive,	but	

highlights	some	key	gaps	that	will	need	further	research	before	there	is	sufficient	evidence	for	

additional	criteria	to	be	included	in	the	methodology.	In	general,	the	majority	of	prior	experimental	

efforts	have	focused	on	components	of	GHGs,	but	few	studies	have	been	conducted	on	total	GHG	

budgets	to	include	CO2,	N2O,	and	CH4	in	combination,	which	is	needed	to	quantify	interacting	effects	

on	the	net	emissions	of	these	gases	(Liebig	et	al.,	2010).		In	addition,	limited	research	has	been	

conducted	to	address	the	influence	of	catastrophic	weather	events	on	GHG	emissions,	such	as	major	

floods,	tornadoes,	and	hurricanes.	

Carbon Stocks:10	The	following	processes	and	practices	require	further	study	to	improve	the	

fundamental	understanding	or	fill	data	gaps	in	the	carbon	inventory	methods.	In	particular,	

deficiencies	in	understanding	continue	to	undermine	the	development	of	robust	estimates	of	net	

GHG	emissions	in	rangelands	and	pastures.	Such	deficiencies	stem	from	a	lack	of	measurements	

across	the	major	grassland	ecoregions,	as	well	as	limitations	associated	with	basic	understanding	of	

mechanistic	processes	related	to	GHG	fluxes.	There	are	also	major	gaps	with	respect	to	

agroforestry,	woody	plant	encroachment,	and	perennial	woody	crop	systems.		

 More	data	on	allometric	relationships	for	agroforestry,	woody	plant	encroachment,	and	

perennial	woody	crop	systems,	such	as	orchards.	

 Improved	ability	to	quantify	the	influence	of	agroforestry,	woody	plant	encroachment,	and	

perennial	woody	crops	on	soil	organic	carbon	stocks,	including	optimal	density	of	trees,	the	

type	of	trees,	and	the	landscape	position	of	silvopasture	systems.	

 Improved	mechanistic	understanding	and	ability	to	quantify	the	fate	of	carbon	with	

transport	and	sedimentation	following	erosion	events.	

 Field	estimates	of	the	amount	of	carbon	added	to	soils	through	dynamic	replacement	on	

erodible	lands.	

 Improved	mechanistic	understanding	of	carbon	dynamics	in	the	subsoil	horizons.	

 Further	study	on	the	effect	of	irrigation	on	plant	production	and	decomposition	to	quantify	

the	net	effect	on	soil	organic	carbon	stocks.	

 Further	research	on	the	variation	in	types	and	residence	times	of	biochar	amendments,	in	

addition	to	biochar	impact	on	other	GHG	emissions,	priming	of	soil	organic	matter	

decomposition,	and	the	overall	physical	breakdown	and	disintegration	of	biochar	over	time	

(Jaffé	et	al.,	2013).		

 Data	on	long‐term	responses	of	soil	organic	carbon	to	variation	in	stocking	rate,	grazing	

method	(i.e.,	continuous,	rotational,	short‐duration	rotational,	and	ultra‐high	stocking	

density),	and	vegetation	composition	(i.e.,	forb	and	grass	mixtures,	cool‐	and	warm‐season	

grass	mixtures,	grass	and	legume	mixtures,	grass	and	woody	mixtures,	and	plant	

architecture	types),	and	whether	these	responses	are	mediated	by	different	soils	types,	

climatic	conditions,	botanical	composition,	grazing	method	used,	fertilizer	regime,	etc.	

																																																													

10	Except	agroforestry	carbon	stock	changes,	which	are	covered	later	in	this	section.	
 Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

 3-93 

 Further	study	to	address	mitigation	of	GHGs	in	arid	rangelands,	particularly	in	shrublands,	

including	interactions	between	management	and	environmental	conditions	(Ingram	et	al.,	

2008).		Additional	data	collection	and	model	improvement	are	also	needed	in	arid	

rangelands,	as	uncertainty	is	extremely	large	for	the	soil	carbon	sequestration	estimates	

associated	with	reduced	stocking	rates	and	seeding	of	legumes	(Brown	et	al.,	2010;	Brown,	

2010).	Our	basic	knowledge	of	carbon	sequestration	and	GHG	mitigation	in	arid	and	

semiarid	environments	is	limited,	and	the	effect	of	management	is	relatively	understudied.	

 Need	for	life‐cycle	assessment	of	grazing	systems	with	particular	attention	to	balance	of	soil	

organic	carbon,	N2O	emissions	from	soil,	and	CH4	emissions	from	ruminants	and	soil,	

depending	on	stocking	rate,	stocking	method,	forage	type	associated	with	quality	of	intake,	

and	environmental	conditions	of	grazing	system.	

 Data	from	adaptive	management	approaches	to	inform	understanding	of	soil	organic	carbon	

sequestration	and	GHG	emissions	under	different	grazing	management	strategies.	This	

approach	could	help	strengthen	conservation‐oriented	programs	to	obtain	greater	impact	

for	reducing	GHG	emissions	and	sequestering	soil	organic	C.	

 Additional	field	experiments	and	data	on	soil	carbon	emissions	resulting	from	the	combined	

application	of	lime	and	nitrogen	fertilizers.	

Soil Nitrous Oxide Emissions:	The	following	practices	have,	in	some	studies,	significantly	affected	

N2O	emissions,	but	require	additional	research	in	side‐by‐side	comparison	studies	across	different	

soil	types	and	climate,	especially	for	extensively	grown	row	crops	that	receive	high	levels	of	

nitrogen	fertilizers	(corn	and	wheat	in	particular):	

 Effects	of	split	or	delayed	nitrogen	applications	on	lowering	N2O	fluxes	and	on	increasing	

NUE	to	provide	equivalent	yields	at	lower	total	nitrogen	input.		

 Capacity	of	spatially	precise	fertilizer	application	technology	(variable	rate	applicators)	to	

lower	N2O	fluxes	(both	direct	and	indirect)	and	increase	NUE.	

 Effects	of	banded	nitrogen	fertilizer	applications,	shown	in	some	studies	to	increase	NUE	

and	in	others	to	increase	N2O	emissions.	

 The	generalizability	of	higher	N2O	EFs	and	nitrate	loss	at	nitrogen	fertilizer	rates	greater	

than	crop	needs	(i.e.,	at	rates	greater	than	those	recommended	by	Maximum	Return	to	

Nitrogen	approaches).	

 The	generalizability	of	different	fertilizer	formulations	on	N2O	emissions,	in	particular	for	

urea	vs.	anhydrous	ammonia	vs.	injected	solutions.	

 The	generalizability	of	coated	fertilizers	such	as	polymer	coated	urea,	urease	inhibitors,	

biochar	additions,	and	nitrification	inhibitors	for	lowering	N2O	emissions	and	nitrate	loss.	

 More	research	on	the	responses	of	soil	N2O	emissions	to	variations	in	stocking	rates,	grazing	

methods	(continuous,	rotational,	short‐duration	rotational,	and	ultra‐high	stocking	density),	

and	vegetation	composition	(forb	and	grass	mixtures,	cool‐	and	warm‐season	grass	

mixtures,	grass	and	legume	mixtures,	grass	and	woody	mixtures,	and	plant	architecture	

types),	both	individually	and	in	combinations.	

 The	potential	for	mobile	water	and	shelter	sources	in	pastures	to	reduce	N2O	emissions	by	

allowing	for	a	more	even	distribution	of	manure.	

 Influence	of	crop	residue	harvesting	on	N2O	emissions,	as	well	as	soil	organic	carbon	stocks,	

given	the	interest	in	using	crop	residues	as	a	feedstock	for	bioenergy	production.	

 Influence	of	cover	crops	on	N2O	emissions,	including	effects	of	plant	type	(e.g.,	legume	vs.	

nonlegume)	and	residue	management	(e.g.,	harvested	vs.	incorporated).	

 Influence	of	manure	and	compost	on	N2O	emissions	insofar	as	effects	may	differ	from	

synthetic	nitrogen	inputs	with	respect	to	rate,	timing,	placement,	and	form	of	organic	

nitrogen	added	(e.g.,	liquid	vs.	dry	manure	vs.	compost	with	different	C:N	ratios).	
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems 

3-94

 Improved	quantification	of	spatial	and	temporal	variation	of	N2O	emissions	in	different	

cropping	systems	and	landscapes	to	provide	a	more	accurate	assessment	of	seasonal	and	

annual	emissions	across	whole	fields.	

 Improved	estimates	of	indirect	emissions,	and	in	particular	the	percentage	of		nitrogen	that	

is	lost	from	a	field	through	volatilization	or	leaching/runoff,	and	later	converted	to	N2O	in	

downstream	and	downwind	ecosystems.		Additional	study	on	practices	that	can	reduce	

nitrate	losses	as	well	as	practices	that	can	reduce	NH3	and	NOx	losses.	

Research	is	also	needed	to	improve	modeling	and	empirical	quantification	of	soil	N2O	emissions	in	

order	to	provide	estimates	of	N2O	fluxes	that	integrate	across	multiple	management	practices	

simultaneously:	

 Further	development	and	validation	of	quantitative	simulation	models	capable	of	accurately	

predicting	N2O	fluxes	in	response	to	differing	management	practices,	with	particular	

respect	to	rate,	timing,	placement,	and	formulation	of	added	fertilizers,	both	synthetic	and	

organic;	tillage	type	and	intensity;	and	residue	management.	

 More	data	regarding	seasonal	and	annual	N2O	emissions,	including	emissions	during	the	

non‐growing	season	and	in	particular	winter	and	freeze‐thaw	periods.	

 Better	knowledge	of	fluxes	across	all	Land	Resource	Regions	(LRRs)	concentrated	especially	

in	those	areas	and	cropping	and	grazed	systems	expected	to	contribute	most	to	local	and	

regional	N2O	fluxes,	with	side‐by‐side	comparisons	of	different	management	practices.	

 Development	of	standardized	methodologies	and	creation	of	new	technologies	for	rapid	

assessment	of	N2O	fluxes	in	the	field.	

 An	improved	understanding	of	the	sources	of	N2O	in	cropped	soils	(e.g.,	nitrification	vs.	

denitrification)	and	consequences	for	feedbacks	among	adaptive	management,	soil	physical	

and	biological	attributes,	and	SOC	dynamics.	

 Development	of	a	set	of	geographically	stratified	test	sites	at	which	factors	known	to	affect	

agronomic	N2O	emissions	could	be	tested	in	the	context	of	different	management	systems.	

This	would	provide	a	robust	empirical	dataset	for	establishing	Tier	2	and	3	models.		

Flooded Rice Production Emissions:	The	primary	research	gap	is	the	limited	amount	of	research	

conducted	in	the	United	States	on	GHG	from	rice	systems.	Therefore,	most	of	the	current	

conclusions	about	management	influences	on	rice	CH4	emissions	are	based	on	Asian	studies	where	

rice	is	transplanted	as	opposed	to	direct	seeded.	This	may	be	problematic	because	water	is	

managed	differently	in	Asian	transplanted	flooded	rice	systems	during	the	establishment	period	

than	in	U.S.	systems.	Until	recently,	no	studies	evaluated	seasonal	or	annual	N2O	emissions	from	

rice	systems	in	the	United	States	(Adviento‐Borbe	et	al.,	2007;	Pittelkow	et	al.,	2013).	In	the	United	

States,	much	of	the	research	on	GHG	emissions	comes	from	Louisiana,	Texas,	and	California.	

Lindau’s	lab	conducted	onstation	research	in	Louisiana	to	evaluate	CH4	emissions	(e.g.,	Lindau	et	al.,	

1995;	Lindau	et	al.,	1998).	Sass’s	group	also	evaluated	CH4	emissions	on	experimental	stations	in	

Texas	(e.g.,	Huang	et	al.,	1997;	Sass	et	al.,	1994).	In	California,	various	researcher	groups	(e.g.,	

Bossio	et	al.,	1999;	Fitzgerald	et	al.,	2000)	have	been	conducting	research	both	onstation	and	

offstation	and	have	recently	also	included	N2O	measurements	(Adviento‐Borbe	et	al.,	2007;	

Pittelkow	et	al.,	2013).	

The	following	practices	have	in	some	studies	significantly	affected	CH4	or	N2O	emissions	but	require	

further	side‐by‐side	comparisons	with	experimental	designs	across	different	soil	types	and	climates	

within	the	United	States.	

 Water	management	practices	(in	particular	midseason	drains	or	intermittent	irrigation)	are	

often	suggested	as	viable	options	to	mitigate	CH4	emissions.	While	data	support	this	

conclusion,	these	management	practices	have	not	been	widely	tested	in	the	United	States.	In	
 Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

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studies	where	the	soil	has	been	drained	during	the	season,	investigators	have	reported	

delayed	crop	maturation	(a	problem	in	temperate	climates	with	relatively	short	growing	

seasons),	reduced	yields	and	grain	quality,	and	increased	weed	and	disease	pressure.	

Therefore,	although	midseason	drainage	is	mentioned	as	a	mitigation	option,	more	research	

is	required	before	it	is	recommended	for	use	in	U.S.	rice	systems.	

 Returning	rice	straw	to	soil	often	results	in	increased	CH4	emissions,	but	the	removal	of	

straw	requires	energy	and	time.	Further	compounding	the	problem	is	that	there	are	

relatively	few	uses	for	rice	straw.	The	removal	of	rice	straw	also	removes	nutrients	which	

would	need	to	be	replaced.	Of	particular	concern	is	potassium,	as	rice	straw	contains	an	

average	of	1.4	percent	of	potassium.	Therefore,	it	is	possible	to	remove	more	than	100	

kg/ha	of	potassium	through	removal	of	rice	straw,	which	will	need	to	be	replaced	in	order	

to	maintain	a	sustainable	cropping	system.		

 In	California,	farmers	typically	incorporate	rice	straw	and	flood	to	facilitate	straw	

decomposition	during	the	winter.	This	practice	increases	CH4	emissions	from	rice	fields	

during	the	winter	and	the	following	growing	season.	However,	it	has	also	significantly	

improved	habitat	for	overwintering	waterfowl	in	the	Pacific	Flyway.	Fitzgerald	et	al.	(2000)	

reported	that	up	to	half	of	the	annual	CH4	emissions	occurred	during	the	winter	fallow	

period	when	straw	was	incorporated	and	flooded.	Recent	studies	suggest	that	50	percent	

may	be	a	high	estimate	and	that	further	research	is	needed	(Adviento‐Borbe	et	al.,	2007;	

Pittelkow	et	al.,	2013).	

 While	many	studies	have	shown	varietal	differences	in	how	much	CH4	is	emitted,	these	

studies	are	all	relatively	old	and	many	of	the	varieties	are	no	longer	widely	used.	Further	

research	on	current	varieties	needs	to	be	conducted.	

 Limited	data	on	nitrogen	placement	suggests	that	deep	placement	of	fertilizer	reduces	CH4

emissions,	but	more	research	is	needed	to	confirm	the	findings.

 Side‐by‐side	comparisons	with	experimental	designs	are	needed	of	wet‐	and	dry‐seeded	

rice	to	evaluate	their	influence	on	CH4	and	N2O	emissions.	These	are	the	two	most	common	

rice	establishment	practices	in	the	United	States.	

 Some	studies	from	China	suggest	that	more	carbon	is	sequestered	in	rice	systems	than	in	

upland	(aerobic)	systems,	but	this	has	not	been	evaluated	in	the	United	States.	

Agroforestry:	A	sufficient	database	for	developing	the	methods	to	readily	measure	and/or	model	the	

various	GHG	impacts	of	agroforestry	is	currently	lacking.	Full	GHG	monitoring	and	accounting	in	

agroforestry	will	require	a	mix	of	methodologies	from	among	the	GHG	accounting	frameworks	

because	of	the	diversity	in	uses	associated	with	agroforestry	systems.	The	following	research	gaps	

are	highlighted.	

 Assessment	of	approaches	for	estimating	woody	biomass	in	agroforestry	plantings,	which	

includes	comparison	of	existing	equations	and	lookup	tables	with	agroforestry‐generated	

volume	and	biomass	equations	to	determine	best	approach	for	estimating	carbon	in	the	

woody	biomass	of	agroforestry	plantings.	

 Development	of	effective	strategies	for	measuring/monitoring	carbon	sequestration	and	

GHG	emissions	in	soil	and	woody	components.	

 Effect	of	different	species	mixtures	and	combinations	of	management	activities	on	soil	

carbon	sequestration	and	minimizing	total	GHG	emissions.	

 Impact	of	management	options	and	environment	interactions	on	carbon	sequestration	and	

total	GHG	emissions	within	agroforestry	systems.	

 Development	of	tools	relevant	to	the	inventory/measurement/estimation	of	these	“trees	

outside	of	forests.”		In	addition,	testing	the	validity	of	current	carbon	accounting	tools	(e.g.,	

DAYCENT,	HOLOS)	in	providing	accurate	estimates	of	carbon	sequestered	in	the	woody	

biomass	of	agroforestry	plantings.	
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems 

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 Understanding	soil	carbon	dynamics	in	agroforestry	systems,	along	with	the	impact	of	soil	

erosion,	transport	and	deposition	on	carbon	stocks.		

 Developing	inventory	methodologies	(such	as	the	use	of	Light	Detection	and	Ranging)	to	

establish	a	cost‐effective	national	agroforestry	inventory	compatible	for	inclusion	with	

current	inventories	contributing	to	regional/national	GHG	assessments.			

 Developing	standardized	experimental	procedures,	measurement,	and	monitoring	

protocols,	such	as	those	being	developed	through	the	Greenhouse	Gas	Reduction	through	

Agricultural	Carbon	Enhancement	network	(GRACEnet)11	to	agroforestry	practices	with	the	

standardized	measurement	and	monitoring	for	agricultural	N2O	emissions.	

Methane Oxidation in Soils:	Soil	CH4	oxidation	is	known	to	decrease	by	~70	percent	upon	

conversion	of	longstanding	natural	vegetation	to	crop	and	pastureland	(see	Section	3.5.5).	CH4

oxidation	rates	for	soils	under	natural	vegetation	are	not	well	known	for	all	climates	and	soils,	so	

additional	measurements	would	be	useful.	As	with	N2O,	the	further	development	and	validation	of	

quantitative	simulation	models	capable	of	accurately	predicting	CH4	fluxes	would	also	be	helpful	for	

better	generalizing	effects	and	for	future	inclusion	of	factors	that	may	be	discovered	to	restore	

oxidation	in	cropped	soils.	There	is	also	limited	research	on	the	effect	of	grazing	land	management	

on	CH4	oxidation	although	variation	in	stocking	rates,	grazing	methods,	and	associated	practices	

may	have	an	influence	on	this	process.	

Inorganic Soil Carbon: The	effect	of	management	on	soil	inorganic	carbon	dynamics	and	exchange	of	

CO2	with	the	atmosphere	is	also	in	need	of	further	research.	The	following	list	is	a	brief	summary	of	

some	of	the	key	gaps	identified	for	quantification	of	GHG	emissions:	

 When	inorganic	carbon	is	added	to	soil	as	agricultural	lime	or	as	a	breakdown	product	of	

urea,	part	of	the	inorganic	carbon	becomes	bicarbonate.	Improved	understanding	of	the	fate	

of	this	bicarbonate	in	different	soils	and	landscapes	would	help	to	better	characterize	the	

presence	and	strength	of	the	resulting	bicarbonate	CO2	sink.		

 Improved	quantification	of	emissions	or	uptake	of	atmospheric	CO2	with	addition	of	

carbonate	limes	to	soils	will	require	methods	to	determine	the	dominance	of	weathering	

due	to	carbonic	acid	(H2CO3)	vs.	the	stronger	nitric	acid	(HNO3)	in	cropland	and	grazing	

land	soils.		

 Improved	mechanistic	understanding	and	quantification	of	inorganic	carbon	dynamics	are	

needed	in	irrigated	systems,	as	well	as	in	nonirrigated	systems—particularly	in	arid	and	

semiarid	regions.		

																																																													

11	GRACEnet	is	a	research	program	initiated	by	USDA	Agricultural	Research	Service	to	“identify	and	further	

develop	agricultural	practices	that	will	enhance	carbon	sequestration	in	soils,	promote	sustainability,	and	

provide	a	sound	scientific	basis	for	carbon	credits	and	trading	programs”	(USDA	ARS,	2013).	
 Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

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Appendix 3‐A: Soil N2O Modeling Framework Specifications

Soil	N2O	emissions	are	estimated	using	a	combination	of	process‐based	modeling,	empirical	scalars	

based	on	experimental	data,	and	scaling	factors	for	practices	influencing	the	N2O	emissions	as	

represented	in	the	base	emission	rates	(Section	3.5.4.1,	Equations	3‐8	and	3‐9,	and	Text	box	3‐1).		

This	appendix	provides	more	information	about	the	process‐based	models,	in	addition	to	the	

derivation	of	empirical	scalars	and	the	practice‐based	scaling	factors.		

DAYCENT	and	DNDC	models	were	used	to	estimate	N2O	emissions	for	the	typical	fertilizer	rate	and	

a	0‐level	nitrogen	fertilization	rate	associated	with	major	crops	in	each	USDA	LRR.	Crops	simulated	

are	listed	in	Table	3‐A.	1;	base	emission	rates	for	other	crops	(e.g.,	sugar	cane,	millet,	rye)	were	

estimated	using	the	Tier	1	emission	factor	(one	percent	of	nitrogen	inputs).	To	estimate	emission	

factors	from	the	model	output,	the	N2O	emissions	at	the	0‐level	addition	was	subtracted	from	the	

N2O	emission	for	the	typical	fertilization	rate.	The	difference	was	then	divided	by	the	synthetic	

agronomic	nitrogen	input	to	estimate	the	emission	factor	at	the	typical	rate	of	fertilization.	Scalars	

were	used	to	scale	the	N2O	emissions	for	fertilization	rates	that	were	greater	than	the	typical	rate.	

The	scalars	were	derived	from	empirical	data	based	on	the	change	in	emission	factors	across	a	

range	of	fertilization	rates.	See	Text	box	3‐1	for	more	information	about	how	the	resulting	emission	

factors	were	used	to	estimate	base	emission	rates	for	the	direct	soil	N2O	method.	

Meta‐analyses	were	used	to	derive	practice‐based	scaling	factors	from	experimental	data.	The	

scaling	factors	were	used	to	adjust	the	base	emission	rates	for	specific	practices	that	influence	soil	

N2O	emissions.		The	scaling	factors	included	the	effect	of	nitrification	inhibitors	(Sinh),	slow‐release	

fertilizers	(SSR),	pasture/range/paddock	manure	(SPRP),	and	tillage	(Still).		The	resulting	scaling	

factors	are	used	in	Equation	3‐9	to	scale	the	base	emission	rates	for	land	parcels	managed	with	

these	practices.	

Figure	3‐A.1	provides	an	overview	of	the	decisions	and	steps	involved	in	estimating	N2O	emissions	

from	mineral	soils.
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Figure 3‐A.1: Decision Tree for Estimating N2O Emissions from Mineral Soils
 Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

 3-99 

3‐A.1 Description of Process‐Based Models		

DAYCENT12	is	a	general	terrestrial	biogeochemical	model	that	simulates	carbon	and	nitrogen	

transformations	involved	in	primary	productivity,	decomposition	and	nutrient	dynamics	(Del	

Grosso	et	al.,	2000b;	Parton	et	al.,	2001).	The	model	also	simulates	heat	and	water	fluxes	vertically	

through	the	soil	profile	(one‐dimensional).	Lateral	flow	of	water	is	not	simulated	except	that	

overland	runoff	occurs	when	rainfall	events	of	sufficient	magnitude	occur	given	the	permeability	of	

the	surface	soil	layer.	Key	submodels	include	plant	growth	with	dynamic	carbon	allocation	among	

plant	components,	soil	organic	matter	decomposition	and	nutrient	mineralization,	and	N2O	

emissions	from	nitrification	and	denitrification.	Plant	growth	is	controlled	by	nutrient	availability,	

soil	water	and	temperature,	and	vegetation	type	specific	parameters	controlling	maximum	plant	

growth	rates,	maximum/minimum	C:N	ratios	of	biomass	components,	and	phenology.	

Decomposition	of	senesced	plant	material	and	soil	organic	matter	is	controlled	by	the	quality	and	

quantity	of	litter	inputs,	soil	texture,	water,	and	temperature.	N2O	emissions	are	controlled	by	soil	

NH4	and	NO3,	water	content,	temperature,	gas	diffusivity,	and	labile	carbon	availability.	Land	

management/disturbance	events	such	as	cultivation,	water	and	nutrient	additions,	fire,	and	

grazing,	can	be	readily	implemented	in	the	model.	The	model	has	been	applied	to	simulate	soil	GHG	

fluxes	at	scales	ranging	from	plots	to	regions	to	the	globe	(Del	Grosso	et	al.,	2010;	Del	Grosso	et	al.,	

2005;	Del	Grosso	et	al.,	2009).	The	ability	of	DAYCENT	to	simulate	crop	yields,	SOM,	N2O	emissions,	

and	NO3	leaching	has	been	tested	against	a	variety	of	field	experiments	in	cropland	and	grassland	in	

the	United	States	(Del	Grosso	et	al.,	2005;	Del	Grosso	et	al.,	2008a;	Del	Grosso	et	al.,	2008b).			

DNDC13	is	a	process‐based	biogeochemical	model	that	is	used	to		predict	plant	growth	and	

production,	carbon	and	nitrogen	balance,	and	generation	and	emission	of	soil‐borne	trace	gases	by	

																																																													

12	The	version	of	DAYCENT	coded	and	parameterized	for	the	U.S.	National	GHG	inventory	(U.S.	EPA,	2013)	

was	used	to	derive	expected	base	emission	rates.	

13	DNDC	9.5	compiled	on	Feb.	25,	2013,	was	used	to	derive	expected	base	emission	rates.	
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems 

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means	of	simulating	carbon	and	nitrogen	dynamics	in	natural	and	agricultural	ecosystems	(Li	et	al.,	

2000;	Miehle	et	al.,	2006;	Stang	et	al.,	2000)	and	forested	wetlands	(Zhang	et	al.,	2002).	The	model	

integrates	decomposition,	nitrification‐denitrification,	photosynthesis	and	hydrothermal	balance	

with	the	ecosystem.	These	components	are	mainly	driven	by	environmental	factors,	including	

climate,	soil,	vegetation,	and	management	practices.	The	model	has	been	tested	and	used	for	

estimating	GHG	emissions	from	forested	ecosystems	in	a	wide	range	of	climatic	regions,	including	

boreal,	temperate,	subtropical,	and	tropical	(Kesik	et	al.,	2006;	Kiese	et	al.,	2005;	Kurbatova	et	al.,	

2008;	Li	et	al.,	2004;	Stang	et	al.,	2000;	Zhang	et	al.,	2002),	and	similarly	for	grasslands	and	

cultivated	wetlands	(Giltrap	et	al.,	2010;	Rafique	et	al.,	2011).	

Model	inputs,	for	both	models,	include	the	weather	data,14	soil	characteristics,	and	management	

data	for	these	simulations.	A	total	of	1,200	samples	were	drawn	for	cropland	site	simulations	and	

another	1,200	samples	for	grassland	site	simulations.	The	sample	number	was	originally	

determined	from	a	plan	to	select	three	soil	types	from	20	counties	dominated	by	agriculture	in	each	

of	20	LRRs	(3	x	20	x	20	=	1,200).	The	emission	rates	that	were	produced	by	both	models	will	be	

available	online	in	supplementary	material	files.	An	example	of	the	rates	for	corn,	winter	wheat,	and	

grass	are	given	in	Figure	3‐A.	2.		

Figure 3‐A. 2: Example of Median Base Emission Rates for Corn, Winter Wheat, and Grass

Production in Land Resource Regions with Coarse, Medium, and Fine Textured Soils

Table	3‐A.	1	provides	the	2.5,	50,	and	97.5	percentile	base	emission	rates	for	each	crop,	LRR,	and	

soil	texture	combination.	Emission	rates	are	kgN2O‐N	per	ha	when	crops	are	fertilized	at	typical	

nitrogen	rates.	

																																																													

14	The	models	used	DAYMET	weather	for	the	centroid	of	grassland/cropland	in	each	county.	
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Table 3‐A. 1 Base Emission Rate (kg N2O‐N ha‐1) Percentiles by Land Resource Region (LRR),

Crop, and Soil Texture at Typical Nitrogen Fertilizer Rates			

LRR Crop Soil Group Emission Rate

(25th Percentile)

Emission Rate

(50th Percentile)

Emission Rate

(97.5th		Percentile)

A Grass	 Coarse	 0.02 0.56	 5.28

A Grass	 Medium	 0.41 1.20	 3.86

A Grass	 Fine	 0.49 1.34	 5.30

A Tomato	 Coarse	 0.04 1.08	 4.83

A Tomato	 Medium	 0.28 1.69	 8.31

A Tomato	 Fine	 0.49 2.09	 15.73

A Wheat,	Spring	 Coarse	 0.03 0.61	 3.53

A Wheat,	Spring	 Medium	 0.16 1.00	 2.87

A Wheat,	Spring	 Fine	 0.40 1.32	 3.50

A Wheat,	Winter	 Coarse	 0.05 0.55	 4.00

A Wheat,	Winter	 Medium	 0.19 0.91	 2.99

A Wheat,	Winter	 Fine	 0.35 1.21	 2.77

B Grass	 Coarse	 0.01 0.40	 5.25

B Grass	 Medium	 0.02 0.45	 5.41

B Grass	 Fine	 0.05 0.74	 8.20

B Pea	 Coarse	 0.00 0.36	 2.43

B Pea	 Medium	 0.00 0.61	 3.80

B Pea	 Fine	 0.02 0.53	 3.02

B Wheat,	Spring	 Coarse	 0.00 0.49	 2.71

B Wheat,	Spring	 Medium	 0.01 0.80	 4.43

B Wheat,	Spring	 Fine	 0.04 0.87	 3.56

B Wheat,	Winter	 Coarse	 0.00 0.40	 2.05

B Wheat,	Winter	 Medium	 0.01 0.54	 3.58

B Wheat,	Winter	 Fine	 0.04 0.75	 3.72

C Alfalfa	 Coarse	 0.01 0.58	 0.99

C Alfalfa	 Medium	 0.01 0.66	 1.60

C Alfalfa	 Fine	 0.00 0.86	 2.25

C Corn	 Coarse	 0.21 0.78	 3.00

C Corn	 Medium	 0.27 0.93	 8.23

C Corn	 Fine	 0.60 1.60	 12.96

C Grass	 Coarse	 0.05 0.32	 1.17

C Grass	 Medium	 0.08 0.36	 1.37

C Grass	 Fine	 0.07 0.42	 1.16

C Rice	 Coarse	 0.04 0.63	 1.34

C Rice	 Medium	 0.03 0.70	 2.19

C Rice	 Fine	 0.02 0.95	 7.50

C Safflower	 Coarse	 0.17 0.89	 2.86

C Safflower	 Medium	 0.38 1.15	 7.46

C Safflower	 Fine	 0.56 2.09	 12.92

C Sunflower	 Coarse	 0.07 0.58	 2.13

C Sunflower	 Medium	 0.15 0.73	 6.45

C Sunflower	 Fine	 0.29 1.37	 9.16

C Tomato	 Coarse	 0.48 1.15	 2.90

C Tomato	 Medium	 0.57 1.21	 8.01

C Tomato	 Fine	 0.79 2.25	 18.94

C Wheat,	Winter	 Coarse	 0.05 0.86	 1.81

C Wheat,	Winter	 Medium	 0.06 0.96	 3.30

C Wheat,	Winter	 Fine	 0.15 1.47	 5.08
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LRR Crop Soil Group Emission Rate

(25th Percentile)

Emission Rate

(50th Percentile)

Emission Rate

(97.5th		Percentile)

D Alfalfa	 Coarse	 0.01 0.55	 1.47

D Alfalfa	 Medium	 0.01 0.49	 2.91

D Alfalfa	 Fine	 0.01 0.67	 4.79

D Corn	 Coarse	 0.20 0.85	 2.03

D Corn	 Medium	 0.26 0.87	 3.28

D Corn	 Fine	 0.30 1.32	 5.99

D Cotton	 Coarse	 0.01 1.04	 2.53

D Cotton	 Medium	 0.02 0.97	 3.37

D Cotton	 Fine	 0.09 1.63	 5.68

D Grass	 Coarse	 0.02 0.39	 3.14

D Grass	 Medium	 0.02 0.46	 6.27

D Grass	 Fine	 0.05 0.55	 6.91

D Wheat,	Winter	 Coarse	 0.00 0.35	 1.27

D Wheat,	Winter	 Medium	 0.00 0.36	 2.21

D Wheat,	Winter	 Fine	 0.04 0.56	 5.10

E Grass	 Coarse	 0.01 0.46	 7.35

E Grass	 Medium	 0.02 0.63	 8.00

E Grass	 Fine	 0.12 0.66	 5.52

E Wheat,	Spring	 Coarse	 0.02 0.59	 2.46

E Wheat,	Spring	 Medium	 0.05 0.70	 4.67

E Wheat,	Spring	 Fine	 0.07 0.87	 2.92

E Wheat,	Winter	 Coarse	 0.02 0.39	 1.97

E Wheat,	Winter	 Medium	 0.06 0.53	 4.80

E Wheat,	Winter	 Fine	 0.10 0.63	 2.89

F Corn	 Coarse	 0.28 0.76	 1.57

F Corn	 Medium	 0.36 0.92	 2.92

F Corn	 Fine	 0.45 1.29	 4.92

F Grass	 Coarse	 0.12 0.57	 2.80

F Grass	 Medium	 0.15 0.66	 2.69

F Grass	 Fine	 0.16 0.80	 3.52

F Soybean	 Coarse	 0.20 0.95	 3.26

F Soybean	 Medium	 0.26 1.05	 3.23

F Soybean	 Fine	 0.29 1.48	 4.40

F Wheat,	Spring	 Coarse	 0.10 0.69	 1.85

F Wheat,	Spring	 Medium	 0.11 0.93	 2.92

F Wheat,	Spring	 Fine	 0.12 1.19	 4.90

F Wheat,	Winter	 Coarse	 0.14 0.85	 3.17

F Wheat,	Winter	 Medium	 0.19 1.03	 6.43

F Wheat,	Winter	 Fine	 0.18 1.41	 11.05

G Corn	 Coarse	 0.11 0.69	 1.88

G Corn	 Medium	 0.16 0.90	 3.41

G Corn	 Fine	 0.23 1.62	 6.59

G Grass	 Coarse	 0.09 0.55	 1.85

G Grass	 Medium	 0.09 0.54	 1.92

G Grass	 Fine	 0.18 0.91	 3.67

G Wheat,	Winter	 Coarse	 0.08 0.49	 1.64

G Wheat,	Winter	 Medium	 0.09 0.64	 2.05

G Wheat,	Winter	 Fine	 0.10 0.91	 4.43

H Corn	 Coarse	 0.31 0.92	 5.62

H Corn	 Medium	 0.62 1.49	 11.03

H Corn	 Fine	 0.81 2.67	 20.40
 Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

 3-103 

LRR Crop Soil Group Emission Rate

(25th Percentile)

Emission Rate

(50th Percentile)

Emission Rate

(97.5th		Percentile)

H Cotton	 Coarse	 0.14 0.70	 2.28

H Cotton	 Medium	 0.18 1.17	 4.38

H Cotton	 Fine	 0.41 1.55	 8.88

H Grass	 Coarse	 0.30 0.88	 2.53

H Grass	 Medium	 0.29 0.95	 3.53

H Grass	 Fine	 0.57 1.64	 4.34

H Wheat,	Winter	 Coarse	 0.15 0.65	 2.29

H Wheat,	Winter	 Medium	 0.21 0.99	 3.81

H Wheat,	Winter	 Fine	 0.32 1.30	 9.16

I Cotton	 Coarse	 0.25 0.63	 4.38

I Cotton	 Medium	 0.23 0.63	 8.15

I Cotton	 Fine	 0.34 1.27	 8.70

I Grass	 Coarse	 0.36 1.02	 4.24

I Grass	 Medium	 0.42 1.09	 5.49

I Grass	 Fine	 0.56 1.90	 5.27

I Sorghum	 Coarse	 0.34 0.78	 5.69

I Sorghum	 Medium	 0.31 0.79	 8.75

I Sorghum	 Fine	 0.43 1.60	 9.35

I Wheat,	Spring	 Coarse	 0.38 0.78	 6.87

I Wheat,	Spring	 Medium	 0.41 0.82	 12.28

I Wheat,	Spring	 Fine	 0.60 1.60	 15.24

I Wheat,	Winter	 Coarse	 0.19 0.43	 4.66

I Wheat,	Winter	 Medium	 0.20 0.58	 6.57

I Wheat,	Winter	 Fine	 0.22 1.06	 7.75

J Corn	 Coarse	 0.48 1.10	 4.33

J Corn	 Medium	 0.61 1.54	 7.48

J Corn	 Fine	 0.71 2.63	 17.71

J Grass	 Coarse	 0.48 1.41	 3.95

J Grass	 Medium	 0.61 1.86	 5.13

J Grass	 Fine	 0.69 2.41	 5.77

J Sorghum	 Coarse	 0.35 0.90	 3.81

J Sorghum	 Medium	 0.47 1.31	 6.67

J Sorghum	 Fine	 0.52 1.96	 14.66

J Wheat,	Spring	 Coarse	 0.37 0.89	 3.65

J Wheat,	Spring	 Medium	 0.48 1.30	 5.93

J Wheat,	Spring	 Fine	 0.72 2.31	 13.76

J Wheat,	Winter	 Coarse	 0.24 0.80	 3.30

J Wheat,	Winter	 Medium	 0.33 1.02	 5.63

J Wheat,	Winter	 Fine	 0.32 1.13	 11.65

K Alfalfa	 Coarse	 0.16 0.90	 2.35

K Alfalfa	 Medium	 0.28 1.39	 2.95

K Alfalfa	 Fine	 0.16 1.25	 2.96

K Corn	 Coarse	 0.40 1.14	 2.41

K Corn	 Medium	 0.72 1.75	 4.57

K Corn	 Fine	 0.45 1.81	 5.27

K Grass	 Coarse	 0.35 1.07	 3.77

K Grass	 Medium	 0.56 1.45	 4.17

K Grass	 Fine	 0.35 1.54	 5.64

K Soybean	 Coarse	 0.26 0.94	 2.07

K Soybean	 Medium	 0.57 1.37	 2.80

K Soybean	 Fine	 0.37 1.43	 3.35
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems 

3-104

LRR Crop Soil Group Emission Rate

(25th Percentile)

Emission Rate

(50th Percentile)

Emission Rate

(97.5th		Percentile)

K Wheat,	Spring	 Coarse	 0.35 1.04	 2.33

K Wheat,	Spring	 Medium	 0.77 1.65	 4.58

K Wheat,	Spring	 Fine	 0.46 1.79	 5.19

L Corn	 Coarse	 0.41 1.42	 3.31

L Corn	 Medium	 0.63 1.97	 5.92

L Corn	 Fine	 1.36 3.09	 15.09

L Grass	 Coarse	 0.47 1.39	 6.01

L Grass	 Medium	 0.56 1.82	 7.02

L Grass	 Fine	 0.63 2.08	 6.61

L Soybean	 Coarse	 0.31 1.29	 2.45

L Soybean	 Medium	 0.45 1.66	 3.10

L Soybean	 Fine	 0.95 2.31	 6.22

L Wheat,	Winter	 Coarse	 0.44 1.65	 3.14

L Wheat,	Winter	 Medium	 0.54 1.97	 3.34

L Wheat,	Winter	 Fine	 1.06 2.75	 8.73

M Corn	 Coarse	 0.55 1.51	 4.33

M Corn	 Medium	 0.87 2.28	 11.87

M Corn	 Fine	 0.99 2.76	 15.46

M Grass	 Coarse	 0.49 1.31	 4.06

M Grass	 Medium	 0.68 1.91	 4.97

M Grass	 Fine	 0.65 1.94	 5.19

M Soybean	 Coarse	 0.41 1.29	 2.66

M Soybean	 Medium	 0.71 1.86	 5.03

M Soybean	 Fine	 0.78 2.08	 7.52

M Wheat,	Winter	 Coarse	 0.55 1.62	 2.91

M Wheat,	Winter	 Medium	 0.85 2.16	 5.17

M Wheat,	Winter	 Fine	 0.84 2.45	 7.72

N Corn	 Coarse	 0.60 1.48	 12.11

N Corn	 Medium	 0.76 2.11	 19.17

N Corn	 Fine	 1.14 2.80	 32.82

N Grass	 Coarse	 0.42 1.64	 3.94

N Grass	 Medium	 0.57 2.08	 5.03

N Grass	 Fine	 0.91 2.61	 5.95

N Soybean	 Coarse	 0.58 1.31	 4.04

N Soybean	 Medium	 0.73 1.80	 5.24

N Soybean	 Fine	 1.00 2.07	 11.18

O Corn	 Coarse	 0.60 1.55	 4.52

O Corn	 Medium	 0.67 2.14	 9.63

O Corn	 Fine	 1.07 3.08	 24.03

O Cotton	 Coarse	 0.51 1.19	 4.95

O Cotton	 Medium	 0.61 1.84	 14.76

O Cotton	 Fine	 0.99 3.24	 25.42

O Grass	 Coarse	 0.39 1.70	 3.92

O Grass	 Medium	 0.44 2.24	 7.03

O Grass	 Fine	 0.76 2.81	 7.97

O Rice	 Coarse	 0.52 1.11	 5.15

O Rice	 Medium	 0.73 1.29	 9.18

O Rice	 Fine	 1.00 2.45	 11.14

O Soybean	 Coarse	 0.53 1.22	 3.73

O Soybean	 Medium	 0.55 1.66	 6.67

O Soybean	 Fine	 0.86 2.18	 14.83
 Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

 3-105 

LRR Crop Soil Group Emission Rate

(25th Percentile)

Emission Rate

(50th Percentile)

Emission Rate

(97.5th		Percentile)

P Corn	 Coarse	 0.43 0.93	 4.56

P Corn	 Medium	 0.60 1.85	 12.27

P Corn	 Fine	 0.76 2.23	 27.80

P Cotton	 Coarse	 0.37 0.81	 4.04

P Cotton	 Medium	 0.63 1.68	 10.68

P Cotton	 Fine	 0.73 2.18	 20.32

P Grass	 Coarse	 0.29 1.26	 4.30

P Grass	 Medium	 0.41 1.95	 5.44

P Grass	 Fine	 0.50 2.79	 7.47

P Soybean	 Coarse	 0.36 0.80	 2.98

P Soybean	 Medium	 0.56 1.65	 5.62

P Soybean	 Fine	 0.67 1.72	 12.55

R Alfalfa	 Coarse	 0.09 1.35	 3.01

R Alfalfa	 Medium	 0.26 1.63	 3.10

R Alfalfa	 Fine	 0.25 1.85	 3.61

R Corn	 Coarse	 0.25 1.35	 2.84

R Corn	 Medium	 0.51 1.81	 4.92

R Corn	 Fine	 0.53 2.25	 4.97

R Grass	 Coarse	 0.30 1.77	 7.53

R Grass	 Medium	 0.49 1.96	 7.25

R Grass	 Fine	 0.56 2.82	 9.59

R Soybean	 Coarse	 0.20 1.24	 2.69

R Soybean	 Medium	 0.45 1.62	 3.06

R Soybean	 Fine	 0.41 1.95	 3.80

S Alfalfa	 Coarse	 0.16 1.03	 2.23

S Alfalfa	 Medium	 0.36 1.54	 2.99

S Alfalfa	 Fine	 0.44 1.53	 3.44

S Corn	 Coarse	 0.44 1.14	 2.84

S Corn	 Medium	 0.86 1.81	 6.89

S Corn	 Fine	 0.97 2.20	 12.36

S Grass	 Coarse	 0.60 1.37	 3.02

S Grass	 Medium	 0.77 1.85	 4.99

S Grass	 Fine	 0.93 2.35	 6.43

S Soybean	 Coarse	 0.39 1.04	 1.66

S Soybean	 Medium	 0.77 1.59	 3.48

S Soybean	 Fine	 0.89 1.78	 4.72

T Corn	 Coarse	 0.45 0.92	 5.78

T Corn	 Medium	 0.48 1.15	 11.08

T Corn	 Fine	 0.63 2.76	 24.52

T Grass	 Coarse	 0.33 1.05	 4.89

T Grass	 Medium	 0.41 1.23	 8.49

T Grass	 Fine	 0.50 2.32	 9.65

T Soybean	 Coarse	 0.40 0.81	 4.06

T Soybean	 Medium	 0.48 0.98	 8.03

T Soybean	 Fine	 0.50 1.79	 17.49

T Wheat,	Winter	 Coarse	 0.33 0.81	 4.89

T Wheat,	Winter	 Medium	 0.36 1.10	 8.05

T Wheat,	Winter	 Fine	 0.46 2.72	 17.87

U Corn	 Coarse	 0.36 0.64	 2.64

U Corn	 Medium	 0.34 0.66	 4.67

U Corn	 Fine	 0.47 1.18	 14.76
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems 

3-106

LRR Crop Soil Group Emission Rate

(25th Percentile)

Emission Rate

(50th Percentile)

Emission Rate

(97.5th		Percentile)

U Grass	 Coarse	 0.33 0.99	 4.74

U Grass	 Medium	 0.35 0.79	 4.09

U Grass	 Fine	 0.39 1.72	 5.90

U Potato	 Coarse	 0.57 0.82	 2.53

U Potato	 Medium	 0.63 1.05	 13.93

U Potato	 Fine	 0.79 1.53	 13.88

U Wheat,	Spring	 Coarse	 0.23 0.55	 2.08

U Wheat,	Spring	 Medium	 0.30 0.54	 5.11

U Wheat,	Spring	 Fine	 0.32 0.84	 10.58

3‐A.2 Empirical Scalars for Base Emission Rates

As	described	in	Text	box	3‐1,	the	base	emission	rate	modeled	by	DAYCENT	and	DNDC	is	used	to	

calculate	an	emission	factor	for	the	typical	fertilizer	case	that	is	then	scaled	to	reflect	the	increase	in	

emission	factor	with	increasing	nitrogen	inputs	(SEF	in	Text	box	3‐1).	To	calculate	SEF	a	meta‐

analysis	was	performed	using	data	from	all	field	studies	in	the	literature	where	at	least	three	

different	levels	of	nitrogen	input,	including	a	zero	nitrogen	rate,	were	applied	to	the	same	crop	at	

the	same	site	during	the	same	growing	season.	Emission	factors	were	calculated	as	the	difference	

between	the	N2O	fluxes	at	0N	and	at	xN	divided	by	the	N2O	flux	at	0N.	The	null	hypothesis	was	that	

emission	factors	will	be	constant	across	different	nitrogen	rates.	

A	total	of	44	data	sets	that	meet	the	base	criteria	were	identified.	From	each	data	set,	slopes	for	

each	fertilizer	addition	interval	were	calculated	and	compared	to	the	slope	of	the	first	interval	(0N	

to	the	first	nitrogen	addition	level).	The	value	of	the	slope	is	a	measure	of	how	much	the	emission	

factor	changes	per	additional	unit	of	nitrogen	fertilizer	input	(kg	N	ha‐1)	for	a	given	study	site	year.	

Thus,	the	slope	measures	the	degree	of	nonlinearity	of	the	emission	factor.	The	slope	is	zero	if	the	

emission	factor	is	constant,	as	assumed	by	the	IPCC	Tier	1	method.	A	positive	slope	indicates	that	

the	total	emission	function	is	convex	with	respect	to	total	nitrogen	input,	i.e.,	that	the	unit	of	flux	

increase	(the	emission	factor)	is	greater	with	each	successive	unit	of	nitrogen	input.	Uncertainty	

was	quantified	with	a	confidence	interval	obtained	by	performing	a	bootstrap	analysis	(n=100,000)	

on	the	original	slopes.	

There	were	sufficient	data	to	analyze	five	different	sub‐categories:	corn,	grassland,	other	crops,	

clay‐textured	soils,	and	other‐textured	soils.	The	mean	slope	was	significantly	greater	than	zero	for	

all	analyzed	categories	but	only	the	grassland	category	was	significantly	different	from	the	others.	

Thus	in	the	ERb	equation	in	Text	box	3‐1	there	are	two	values	for	SEF,	one	for	grasslands	and	

another	for	all	other	crops.		

The	studies	used	in	the	meta‐analysis	are	provided	below.						

Breitenbeck,	G.A.,	and	J.M.	Bremner.	1986.	Effects	of	rate	and	depth	of	fertilizer	application	on	

emission	of	nitrous	oxide	from	soil	fertilized	with	anhydrous	ammonia.	Biology and fertility

of soils,	2(4):201‐204.	

Cardenas,	L.M.,	R.	Thorman,	N.	Ashlee,	M.	Butler,	et	al.	2010.	Quantifying	annual	N2O	emission	fluxes	

from	grazed	grassland	under	a	range	of	inorganic	fertiliser	nitrogen	inputs.	Agriculture,

Ecosystems and Environment,	136:218‐226.	

Chang,	C.,	C.M.	Cho,	and	D.H.	Janzen.	1998.	Nitrous	oxide	emission	from	long‐term	manured	soils.	

Soil Science Society America Journal,	62:677‐682.	

Ding,	W.,	Y.	Cai,	X.	Cai,	K.	Yagi,	et	al.	2007.	Nitrous	oxide	emissions	from	an	intensively	cultivated	

maize‐wheat	rotation	in	soil	in	the	North	China	Plain.	Science and the Total Environment,	

373.	
 Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

 3-107 

Halvorson,	A.D.,	S.J.	Del	Grosso,	and	C.A.	Reule.	2008.	Nitrogen,	Tillage,	and	Crop	Rotation	Effects	on	

Nitrous	Oxide	Emissions	from	Irrigated	Cropping	Systems.	Journal of Environmental Quality,	

37(4):1337‐1344.	

Hoben,	J.P.,	R.J.	Gehl,	N.	Millar,	P.R.	Grace,	et	al.	2011.	Nonlinear	nitrous	oxide	(N2O)	response	to	

nitrogen	fertilizer	in	on‐farm	corn	crops	of	the	US	Midwest.	Global Change Biology,	

17(2):1140‐1152.	

Kaiser,	E.A.,	K.	Kohrs,	M.	Kücke,	E.	Schnug,	et	al.	1998.	Nitrous	oxide	release	from	arable	soil:	

importance	of	N‐fertilization,	crops	and	temporal	variation.	Soil Biology and Biochemistry,	

30:1553‐1563.	

Kammann,	C.,	L.	Grünhage,	C.	Müller,	S.	Jacobi,	and	H.‐J.	Jäger.	1998.	Seasonal	variability	and	

mitigation	options	for	N2O	emissions	from	differently	managed	grasslands.	Environmental

Pollution,	102(S1):179‐186.	

Kim,	D.‐G.,	G.	Hernandez‐Ramirez,	and	D.	Giltrap.	2013.	Linear	and	nonlinear	dependency	of	direct	

nitrous	oxide	emissions	on	fertilizer	nitrogen	input:	a	meta‐analysis.	Agriculture, Ecosystems

and Environment,	168:53‐65.	

Letica,	S.A.,	C.A.M.	de	Klein,	C.J.	Hoogendoorn,	R.W.	Tillman,	et	al.	2010.	Short‐term	measurement	of	

N2O	emissions	from	sheep‐grazed	pasture	receiving	increasing	rates	of	fertiliser	nitrogen	in	

Otago,	New	Zealand.	Animal Production Science,	50:17‐24.	

Lin,	S.,	J.	Iqbal,	R.	Hu,	J.	Wu,	et	al.	2011.	Nitrous	oxide	emissions	from	rape	field	as	affected	by	

nitrogen	fertilizer	management:	a	case	study	in	central	China.	Atmospheric Environment,	

45:1775‐1779.	

Ma,	B.L.,	T.Y.	Wu,	N.	Tremblay,	W.	Deen,	et	al.	2010.	Nitrous	oxide	fluxes	from	corn	fields:	on‐farm	

assessment	of	the	amount	and	timing	of	nitrogen	fertilizer.	Global Change Biology,	

16(1):156‐170.	

McSwiney,	C.P.,	and	G.P.	Robertson.	2005.	Nonlinear	response	of	N2O	flux	to	incremental	fertilizer	

addition	in	a	continuous	maize	(Zea	mays	L.)	cropping	system.	Global Change Biology,	

11(10):1712‐1719.	

Mosier,	A.R.,	A.D.	Halvorson,	C.A.	Reule,	and	X.J.	Liu.	2006.	Net	global	warming	potential	and	

greenhouse	gas	intensity	in	irrigated	cropping	systems	in	northeastern	Colorado.	Journal of

Environmental Quality,	35(4):1584‐1598.	

Signor,	D.,	C.E.P.	Cerri,	and	R.	Conant.	2013.	N2O	emissions	due	to	nitrogen	fertilizer	applications	in	

two	regions	of	sugarcane	cultivation	in	Brazil.	Environmental Research Letters,	8(1):015013.	

Song,	C.,	and	J.	Zhang.	2009.	Effects	of	soil	moisture,	temperature,	and	nitrogen	fertilization	on	soil	

respiration	and	nitrous	oxide	emission	during	maize	growth	period	in	northeast	China.	Acta

Agriculturae Scandinavia,	59:97‐106.	

van	Groenigen,	J.W.,	G.J.	Kasper,	G.L.	Velthof,	A.	van	den	Pol‐van	Dasselar,	et	al.	2004.	Nitrous	oxide	

emissions	from	silage	maize	fields	under	different	mineral	nitrogen	fertilizer	and	slurry	

applications.	Plant and Soil,	263.	

Velthof,	G.L.,	O.	Oenema,	R.	Postma,	and	M.L.	Van	Beusichem.	1997.	Effects	of	type	and	amount	of	

applied	nitrogen	fertilizer	on	nitrous	oxide	fluxes	from	intensively	managed	grassland.	

Nutrient Cycling in Agroecosystems,	46:257‐267.	

Zebarth,	B.J.,	P.	Rochette,	and	D.L.	Burton.	2008.	N2O	emissions	from	spring	barley	production	as	

influenced	by	fertilizer	nitrogen	rate.	Canadian Journal of Soil Science,	88:197‐205.	

Zhang,	J.,	and	X.	Han.	2008.	N2O	emission	from	the	semi‐arid	ecosystem	under	mineral	fertilizer	

(urea	and	superphosphate)	and	increased	precipitation	in	northern	China.	Atmospheric

Environment,	42:291‐302.	
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems 

3-108

3‐A.3 Practice‐Based Scaling Factors

Data	were	analyzed	to	derive	scaling	factors	for	the	following	practices:	nitrogen	fertilizer	

placement,	nitrification	inhibitors,	no‐till	management,	and	slow‐release	fertilizers.	Practices	were	

included	if	there	was	sufficient	evidence	from	field	experiments	to	suggest	that	the	practice	

influenced	N2O	emissions,	or	for	which	a	previous	meta‐analysis	had	been	conducted	and	shown	

that	the	practice	had	an	effect	on	N2O	emissions	(i.e.,	no‐till	management;	van	Kessel	et	al.,	2012).	

All	practices	were	found	to	have	a	significant	effect	on	N2O	emission	with	the	exception	of	nitrogen	

placement.	The	scaling	factors	are	provided	in	Table	3‐9.	

Documentation	for	the	no‐till	scaling	factor	can	be	found	in	van	Kessel	et	al.	Scaling	factors	for	

nitrification	inhibitors	were	derived	using	a	linear	mixed‐effect	modeling	approach	(Pinheiro	and	

Bates,	2000),	similar	to	the	method	used	by	Ogle	et	al.	(2005)	to	derive	factors	that	were	used	in	the	

2006	IPCC	Guidelines	(IPCC,	2006).	Variances	associated	with	individual	experimental	results	were	

not	taken	into	consideration	in	the	meta‐analyses	because	many	studies	did	not	provide	this	

information.		A	goal	for	future	analyses	supporting	the	USDA	methods	will	be	to	include	variances,	

under	the	assumption	that	studies	will	report	this	information	in	future	publications.	Covariates	

were	included	in	the	analysis	to	determine	if	the	practice	had	a	different	effect	depending	on	the	

land	use,	climate,	soil	type,	water	management,	tillage	practice,	or	crop	type.	Covariates	were	

retained	in	the	model	if	the	variable	was	significant	at	an	alpha	level	of	0.05.	For	other	scaling	

factors,	there	were	insufficient	data	to	use	the	linear	mixed‐effect	modeling	approach,	and	so	

average	differences	between	the	control	and	treatments	were	estimated	from	the	studies	to	

estimate	a	scaling	factor.	The	resulting	estimates	were	evaluated	for	statistical	significant	from	a	

value	of	0	(or	no	effect)	using	an	alpha	level	of	0.05.	A	95	percent	confidence	interval	was	derived	

for	each	scaling	factor	and	provided	in	Table	3‐6	as	an	upper	and	lower	bound	on	the	estimated	

factor.			

The	studies	used	in	each	meta‐analysis	are	provided	below.	

Nitrogen Fertilizer Placement:			

Burton,	D.L.,	X.	Li,	and	C.A.	Grant.	2008.	Influence	of	fertilizer	nitrogen	source	and	management	

practice	on	N2O	emissions	from	two	Black	Chernozemic	soils.	Canadian Journal of Soil

Science,	88:219‐227.	

Drury,	C.F.,	W.D.	Reynolds,	C.S.	Tan,	T.W.	Welacky,	et	al.	2006.	Emissions	of	Nitrous	Oxide	and	

Carbon	Dioxide.	Soil Science Society of America Journal,	70(2):570‐581.	

Engel,	R.,	D.L.	Liang,	R.	Wallander,	and	A.	Bembenek.	2010.	Influence	of	Urea	Fertilizer	Placement	

on	Nitrous	Oxide	Production	from	a	Silt	Loam	Soil.	Journal of Environmental Quality,	

39(1):115‐125.	

Halvorson,	A.D.,	and	S.J.	Del	Grosso.	2013.	Nitrogen	placement	and	source	effects	on	nitrous	oxide	

emissions	and	yields	of	irrigated	corn.	Journal of Environmental Quality,	42(In	press).	

Hou,	A.X.,	and	H.	Tsuruta.	2003.	Nitrous	oxide	and	nitric	oxide	fluxes	from	an	upland	field	in	Japan:	

effect	of	urea	type,	placement,	and	crop	residues.	Nutrient Cycling in Agroecosystems,	

65:191‐200.	

Hultgreen,	G.,	and	P.	Leduc.	2003.	The effect of nitrogen fertilizer placement, formulation, timing, and

rate on greenhouse gas emissions and agronomic performance:	Agriculture	Agri‐Food	

Canada,	Prairie	Agricultural	Machinery	Institute.		

Liu,	X.J.,	A.R.	Mosier,	A.D.	Halvorson,	and	F.S.	Zhang.	2005.	Tillage	and	nitrogen	application	effects	

on	nitrous	and	nitric	oxide	emissions	from	irrigated	corn	fields.	Plant and Soil,	276:235‐249.	

Maharjan,	B.,	and	R.T.	Venterea.	In	review.	Nitrite	dynamics	explain	fertilizer	management	effects	

on	nitrous	oxide	emissions	in	maize.	Submitted to Soil Biology and Biochemistry.	

Zebarth,	B.J.,	P.	Rochette,	D.L.	Burton,	and	M.	Price.	2008.	Effect	of	fertilizer	nitrogen	management	

on	N2O	emissions	in	commercial	corn	fields.	Canadian Journal of Soil Science,	88:189‐195.	
 Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

 3-109 

Nitrification Inhibitors:			

Akiyama,	H.,	H.	Tsuruta,	and	T.	Watanabe.	2000.	N2O	and	No	Emissions	from	Soils	after	the	

Application	of	Different	Chemical	Fertilizers.	Chemosphere‐Global Change Science,	2(3):313‐

320.	

Ball,	B.C.,	K.C.	Cameron,	H.J.	Di,	and	S.	Moore.	2012.	Effects	of	Trampling	of	a	Wet	Dairy	Pasture	Soil	

on	Soil	Porosity	and	on	Mitigation	of	Nitrous	Oxide	Emissions	by	a	Nitrification	Inhibitor,	

Dicyandiamide.	Soil Use and Management,	28(2):194‐201.	

Bremner,	J.M.,	G.A.	Breitenbeck,	and	A.M.	Blackmer.	1981.	Effect	of	Nitrapyrin	on	Emission	of	

Nitrous	Oxide	from	Soil	Fertilized	with	Anhydrous	Ammonia.	Geophysical Research Letters,	

8(4):353‐356.	

Bronson,	K.F.,	A.R.	Mosier,	and	S.R.	Bishnoi.	1992.	Nitrous	Oxide	Emissions	in	Irrigated	Corn	as	

Affected	by	Nitrification	Inhibitors.	Soil Science Society of America Journal,	56(1):161‐165.	

Cui,	M.,	X.C.	Sun,	C.X.	Hu,	H.J.	Di,	et	al.	2011.	Effective	Mitigation	of	Nitrate	Leaching	and	Nitrous	

Oxide	Emissions	in	Intensive	Vegetable	Production	Systems	Using	a	Nitrification	Inhibitor,	

Dicyandiamide.	Journal of Soils and Sediments,	11(5):722‐730.	

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Inhibitor	Dicyandiamide	(Dcd)	Does	Not	Alter	Its	Effectiveness	in	Reducing	N2O	Emissions	

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Delgado,	J.A.,	and	A.R.	Mosier.	1996.	Mitigation	Alternatives	to	Decrease	Nitrous	Oxides	Emissions	

and	Urea‐Nitrogen	Loss	and	Their	Effect	on	Methane	Flux.	Journal of Environmental Quality,	

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Dittert,	K.,	R.	Bol,	R.	King,	D.	Chadwick,	et	al.	2001.	Use	of	a	novel	nitrification	inhibitor	to	reduce	

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Dobbie,	K.E.,	and	K.A.	Smith.	2003.	Impact	of	Different	Forms	of	N	Fertilizer	on	N2O	Emissions	from	

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Ghosh,	S.,	D.	Majumdar,	and	M.C.	Jain.	2003.	Methane	and	Nitrous	Oxide	Emissions	from	an	Irrigated	

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Halvorson,	A.D.,	S.J.	Del	Grosso,	and	C.P.	Jantalia.	2011.	Nitrogen	Source	Effects	on	Soil	Nitrous	Oxide	

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Soil Biology and Biochemistry,	38:2782‐2793.	
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Venterea,	R.T.,	B.	Maharjan,	and	M.S.	Dolan.	2011.	Fertilizer	source	and	tillage	effects	on	yield‐

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Burton,	D.L.,	X.	Li,	and	C.A.	Grant.	2008.	Influence	of	fertilizer	nitrogen	source	and	management	

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Science,	88:219‐227.	

Cheng,	W.,	Y.	Nakajima,	S.	Sudo,	H.	Akiyama,	et	al.	2002.	N2O	and	NO	emissions	from	a	field	of	

Chinese	cabbage	as	influenced	by	band	application	of	urea	or	controlled‐release	urea	

fertilizers.	Nutrient Cycling in Agroecosystems,	63:231‐238.	

Chu,	H.Y.,	Y.	Hosen,	and	K.	Yagi.	2007.	NO,	N2O,	CH4	and	fluxes	in	winter	barley	field	of	Japanese	

Andisol	as	affected	by	N	fertilizer	management.	Soil Biology & Biochemistry,	39:330‐339.	

Delgado,	J.A.,	and	A.R.	Mosier.	1996.	Mitigation	Alternatives	to	Decrease	Nitrous	Oxides	Emissions	

and	Urea‐Nitrogen	Loss	and	Their	Effect	on	Methane	Flux.	Journal of Environmental Quality,	

25(5):1105‐1111.	

Dobbie,	K.E.,	and	K.A.	Smith.	2003.	Impact	of	Different	Forms	of	N	Fertilizer	on	N2O	Emissions	from	

Intensive	Grassland.	Nutrient Cycling in Agroecosystems,	67(1):37‐46.	

Hadi,	A.,	O.	Jumadi,	K.	Inubushi,	and	K.	Yagi.	2008.	Mitigation	Options	for	N2O	Emission	from	a	Corn	

Field	in	Kalimantan,	Indonesia.	Soil science and plant nutrition,	54(4):644‐649.	

Halvorson,	A.D.,	S.J.	Del	Grosso,	and	F.	Alluvione.	2010a.	Nitrogen	Source	Effects	on	Nitrous	Oxide	

Emissions	from	Irrigated	No‐Till	Corn.	Journal of Environmental Quality,	39(5):1554‐1562.	

Halvorson,	A.D.,	S.J.	Del	Grosso,	and	F.	Alluvione.	2010b.	Tillage	and	Inorganic	Nitrogen	Source	

Effects	on	Nitrous	Oxide	Emissions	from	Irrigated	Cropping	Systems.	Soil Science Society of

America Journal,	74(2):436‐445.	

Halvorson,	A.D.,	S.J.	Del	Grosso,	and	C.P.	Jantalia.	2011.	Nitrogen	Source	Effects	on	Soil	Nitrous	Oxide	

Emissions	from	Strip‐Till	Corn.	Journal of Environmental Quality,	40(6):1775‐1786.	

Halvorson,	A.D.,	and	S.J.D.	Grosso.	2012.	Nitrogen	Source	and	Placement	Effects	on	Soil	Nitrous	

Oxide	Emissions	from	No‐Till	Corn.	Journal of Environmental Quality,	41(5):1349‐1360.	

Halvorson,	A.D.,	and	S.J.	Del	Grosso.	2013.	Nitrogen	placement	and	source	effects	on	nitrous	oxide	

emissions	and	yields	of	irrigated	corn.	Journal of Environmental Quality,	42(In	press).	

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Appendix 3‐B: Guidance for Crops Not Included in the DAYCENT Model

The	DAYCENT	model	is	recommended	for	use	in	estimating	Soil	Carbon	Stock	Changes	(Section	

3.5.3),	and	was	used	(along	with	the	DNDC	model)	to	generate	base	emission	rates	for	Equation	3‐9	

(See	Appendix	3‐A	for	a	discussion	of	how	models	were	used	to	estimate	N2O	emissions	from	

mineral	soils).	In	addition,	nitrogen	mineralized	from	soil	organic	matter	(Nmin);	additional	nitrogen	

inputs	from	a	change	in	soil	organic	matter	mineralization	due	to	a	land‐use	change	or	tillage	

change	(Ndmin);	nitrogen	mineralization	from	organic	amendments	(e.g.,	manure,	sewage	sludge,	

compost);	and	nitrogen	mineralization	from	crop,	grass,	and	cover	crop	residues	(Nresid)	are	

generated	by	the	DAYCENT	model.	

The	DAYCENT	model	is	not	used	to	generate	estimates	for	all	crops	grown	in	the	United	States.	The	

DAYCENT	model	is	currently	used	to	estimate	SOC	stocks	for	the	following	crops	and	sectors:	

agroforestry,	almond,	alfalfa,	windbreak,	woodlot,	sorghum,	spring	wheat,	winter	wheat,	woodlot—

softwoods,	woodlot—hardwoods,	clover,	cotton,	dryland	beans,	corn,	oats,	millet,	grass‐clover	

pasture,	grass,	peas,	potato,	sugar	beets,	sunflower,	soybean,	sugar	cane,	peanut,	tobacco,	upland	

rice,	windbreak	three‐row,	and	walnut.	These	crops	represent	90	percent	of	the	crops	grown	in	the	

United	States,	and	more	crops	are	tested	and	added	to	the	DAYCENT	model‐based	assessment	on	a	

regular	basis.	

However,	if	an	entity	is	managing	a	crop	that	is	not	included	in	the	DAYCENT	list	of	crops,	the	2006	

IPCC	Guidelines	may	be	used	to	estimate	emissions	or	sinks	for	the	sources	listed	above.	This	

approach	is	consistent	with	the	U.S.	Environmental	Protection	Agency	National	Inventory	Report	

(U.S.	Environmental	Protection	Agency,	2013),	and	a	complete	discussion	of	this	alternative	

methodology	in	provided	in	Annex	3	(Section	3.12)	of	the	National	Inventory	Report.	15	Specifically,	

the	National	Inventory	Report	uses	a	combination	of	Tier	1,	2,	and	3	approaches	to	estimate	direct	

and	indirect	N2O	emissions	and	soil	changes	in	agricultural	soils.	This	report	follows	the	same	

approach	for	the	crops	not	included	in	the	DAYCENT	model	when	estimating	soil	carbon	stock	

changes	and	direct	N2O	emissions	(See	Table	3‐B‐	1).		

Table 3‐B‐	1 Alternative Methodologies for Crops Not Included in the DAYCENT Model

Source Tier 1 Tier 2

Soil	carbon	stock	changes	 IPCC	2006	Guidelines	(See	

Chapter	5,	Section	5.2.3.3)

Direct	N2O	emissions	from	mineral	soils	

for	the	crops	NOT	estimated	by	the	

DAYCENT	model	

IPCC	2006	Guidelines	with	

management	based	scaling	factors	

(See	Section	3.5.4)	

Nsmin,		 Not	estimated

Nitrogen	inputs	from	organic	

amendments	(Nman and	Ncomp)	

IPCC	2006	Guidelines	(See	

Chapter	11	Section	

11.2.1.1)	

Nresid	 	 Equation	3‐B‐1	Residue	nitrogen	

(See	below)	

																																																													

15	See	U.S.	Environmental	Protection	Agency,	National	GHG	Inventory	Annex	3:		

http://www.epa.gov/climatechange/Downloads/ghgemissions/US‐GHG‐Inventory‐2013‐Annex‐3‐

Additional‐Source‐or‐Sink‐Categories.pdf
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Default	values	for	dry	matter	content,	root:shoot	ratio	and	harvest	index	are	provided	in	Table	3‐5	

in	Section	3.5.1.2.	Default	values	from	the	IPCC	guidelines	values	are	provided	in	Table	3‐B‐2	for	the	

nitrogen	content	of	aboveground	and	belowground	residues	in	major	crop	types	and	individual	

crops.		

Equation 3‐B‐1: Residue N

For Crops:

Nresid = [((Ydm / HI) – Ydm) x (1 – Rr) x Na] + [(Ydm / HI) x R:S x Nb]

For Grazing Forage:

Nresid = [Ydm x (1 – Fr – Rr) x Na] + [Ydm x R:S x Nb]

Where:

Nresid		 =	Nitrogen	in	residues	above	and	belowground	on	the	parcel	of	land		

	 			(metric	tons	N	year‐1	ha‐1)	

Ydm		 =	Crop	harvest	or	forage	yield,	corrected	for	moisture	content	

	 	 (metric	tons	biomass	ha‐1)	

	 =	Y	x	DM	

Y	 =	Crop	harvest	or	total	forage	yield	(metric	tons	biomass	ha‐1)	

DM		 =	Dry	matter	content	of	harvested	biomass	(dimensionless)	

HI		 =	Harvest	Index	(dimensionless)	

Fr	 =	Proportion	of	live	forage	removed	by	grazing	animals	(dimensionless)	

Rr								 =	Proportion	of	crop/forage	residue	removed	due	to	harvest,	burning	or	grazing	

(dimensionless)		

Na		 =	Nitrogen	fraction	of	aboveground	residue	biomass	for	the	crop	or	forage	

(dimensionless)	

Nb		 =	Nitrogen	fraction	of	belowground	residue	biomass	for	the	crop	or	forage	

(dimensionless)	

R:S		 =	Root‐shoot	ratio	(unitless)	
 Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

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Table 3‐B‐2: Nitrogen Content of Aboveground and Belowground Residues of Major and

Individual Crops

Crop

Nitrogen Content of

Aboveground Residues

(kg N (kg dm)‐1)

Nitrogen Content of

Belowground Residues

(kg N (kg dm)‐1)

Major crop types

Grains		 0.006 0.009	

Beans and pulses		 0.008 0.008	

Grass‐clover mixtures		 0.025 0.016	

Nitrogen‐fixing forages		 0.027 0.022	

Non‐nitrogen‐fixing forages		 0.015 0.012	

Perennial grasses		 0.015 0.012	

Root crops, other		 0.016 0.014	

Tubers		 0.019 0.014	

Individual crops

Alfalfa 0.027 0.019	

Barley 0.007 0.014	

Dry bean		 0.01 0.01	

Maize		 0.006 0.007	

Millet		 0.007 NA	

Non‐legume hay 0.015 0.012	

Oats		 0.007 0.008	

Peanut (w/pod)		 0.016 NA	

Potato		 0.019 0.014	

Rice		 0.007 NA	

Rye 0.005 0.011	

Sorghum		 0.007 0.006	

Soybean 0.008 0.008	

Spring wheat		 0.006 0.009	

Wheat 0.006 0.009	

Winter wheat		 0.006 0.009	

Source:	de	Klein	(2006).
Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems 

3-116

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Authors:		

Stephen	M.	Ogle,	Colorado	State	University	(Lead	Author)	

Patrick	Hunt,	USDA	Agricultural	Research	Service	

Carl	Trettin,	USDA	Forest	Service	

Contents:

4 Quantifying Greenhouse Gas Sources and Sinks in Managed Wetland Systems................4‐3

4.1	 Overview	...........................................................................................................................................................	4‐3	

4.1.1	 Overview	of	Management	Practices	and	Resulting	GHG	Emissions	...........	4‐4	

4.1.2	 System	Boundaries	and	Temporal	Scale	................................................................	4‐7	

4.1.3	 Summary	of	Selected	Methods/Models	and	Sources	of	Data	........................	4‐7	

4.1.4	 Organization	of	Chapter/Roadmap	..........................................................................	4‐8	

4.2	 Management	and	Restoration	of	Wetlands	........................................................................................	4‐8	

4.2.1	 Description	of	Wetland	Management	Practices	..................................................	4‐8	

4.2.2	 Land‐Use	Change	to	Wetlands	..................................................................................	4‐13	

4.3	 Estimation	Methods	...................................................................................................................................	4‐14	

4.3.1	 Biomass	Carbon	in	Wetlands	....................................................................................	4‐14	

4.3.2	 Soil	C,	N2O,	and	CH4	in	Wetlands	..............................................................................	4‐17	

4.4	 Research	Gaps	for	Wetland	Management	.........................................................................................	4‐21	

Chapter	4	References	.............................................................................................................................................	4‐23	

Suggested	Chapter	Citation:		Ogle,	S.M.,	P.	Hunt,	C.	Trettin,	2014.	Chapter	4:		Quantifying	

Greenhouse	Gas	Sources	and	Sinks	in	Managed	Wetland	Systems.		In	Quantifying Greenhouse Gas

Fluxes in Agriculture and Forestry:		Methods for Entity‐Scale Inventory.	Technical	Bulletin	Number	

1939.		Office	of	the	Chief	Economist,	U.S.	Department	of	Agriculture,	Washington,	DC.	606	pages.		

July	2014.		Eve,	M.,	D.	Pape,	M.	Flugge,	R.	Steele,	D.	Man,	M.	Riley‐Gilbert,	and	S.	Biggar,	Eds.	

USDA	is	an	equal	opportunity	provider	and	employer.	

Chapter 4

Quantifying Greenhouse Gas 

Sources and Sinks in Managed 

Wetland Systems 
Chapter 4: Quantifying Greenhouse Gas Sources and Sinks in Managed Wetland Systems 

4-2

Acronyms, Chemical Formulae, and Units

C	 Carbon	

CH4	 Methane	

CO2	 Carbon	dioxide	

CO2‐eq	 Carbon	dioxide	equivalents

DNDC	 Denitrification‐Decomposition

EPA	 Environmental	Protection	Agency

FVS	 Forest	Vegetation	Simulator

GHG	 Greenhouse	gas	

ha	 Hectare	

IPCC	 Intergovernmental	Panel	on	Climate	Change

N	 Nitrogen	

N2O	 Nitrous	oxide	

NOx	 Mono‐nitrogen	oxides	

NRCS	 USDA	Natural	Resources	Conservation	Service

P	 Phosphorous	

SOC	 Soil	organic	carbon	

Tg	 Teragrams	

USDA		 U.S.	Department	of	Agriculture

USDA‐ARS	 U.S.	Department	of	Agriculture, Agricultural	Research	Service
 Chapter 4: Quantifying Greenhouse Gas Sources and Sinks in Managed Wetland Systems 

 4-3 

4 Quantifying Greenhouse Gas Sources and Sinks in Managed Wetland

Systems

This	chapter	provides	methodologies	and	guidance	for	reporting	greenhouse	gas	(GHG)	emissions	

and	sinks	at	the	entity	scale	for	managed	wetland	systems.	More	specifically,	it	focuses	on	methods	

for	managed	palustrine	wetlands.1	Section	4.1	provides	an	overview	of	wetland	systems	and	

resulting	GHG	emissions,	system	boundaries	and	temporal	scale,	a	summary	of	the	selected	

methods/models,	sources	of	data,	and	a	roadmap	for	the	chapter.	Section	4.2	presents	the	various	

management	practices	that	influence	GHG	emissions	in	wetland	systems	and	land‐use	change	to	

wetlands.	Section	4.3	provides	the	estimation	methods	for	biomass	carbon	in	wetlands	and	for	soil	

carbon,	N2O,	and	CH4	emissions	and	sinks.	Finally,	Section	4.4	includes	a	discussion	of	research	gaps	

in	wetland	management.	

4.1 Overview		

Wetlands	occur	across	most	landforms,	existing	as	natural	unmanaged	and	managed	lands,	

restored	lands	following	conversion	from	another	use	(typically	agriculture),	and	as	constructed	

systems	for	water	treatment,	such	as	anaerobic	lagoons.		All	wetlands	sequester	carbon	and	are	a	

source	of	GHGs.	Table	4‐1	provides	a	description	of	the	sources	of	emissions	or	sinks	and	the	gases	

estimated	in	the	methodology.		

Table 4‐1: Overview of Wetland Systems Sources and Associated Greenhouse Gases

Source

Method for GHG

Estimation Description

CO2 N2O CH4

Biomass	

carbon		

 	 	

Provisions	for	estimating	aboveground	biomass	for	wetland	forests	and	

above	and	belowground	biomass	and	carbon	are	included	for	shrub	and	

grass	wetlands	in	this	chapter.	Aboveground	biomass	for	forested	

wetlands	and	shrub	and	grass	wetlands	includes	live	vegetation,	trees,	

shrubs,	and	grasses,	standing	dead	wood	(dead	biomass),	and	down	

dead	organic	matter—litter	layer	(dead	biomass).		

Soil	C,	N2O,	

and	CH4	in	

wetlands	

  

The	production	and	consumption	of	carbon in	wetland‐dominated	

landscapes	are	important	for	estimating	the	contribution	of	GHGs,	

including	CO2,	CH4,	and	N2O	emitted	from	those	areas	to	the	atmosphere.	

The	generation	and	emission	of	GHGs	from	wetland‐dominated	

landscapes	are	closely	related	to	inherent	biogeochemical	processes,	

which	also	regulate	the	carbon	balance	(Rose	and	Crumpton,	2006).	

However,	those	processes	are	highly	influenced	by	the	land	use,

vegetation,	soil	organisms,	chemical	and	physical	soil	properties,	

geomorphology,	and	climate	(Smemo	and	Yavitt,	2006).		

																																																													

1	Palustrine	wetlands	include	non‐tidal	and	tidal	wetlands	that	are	primarily	composed	of	trees,	shrubs,	

persistent	emergent,	emergent	mosses,	or	lichens,	where	salinity	due	to	ocean‐derived	salts	is	below	0.5	‰	

(parts	per	thousand).	Palustrine	wetlands	also	include	those	wetlands	lacking	vegetation	that	have	the	

following	four	characteristics:	(1)	are	less	than	20	acres;	(2)	do	not	have	active	wave‐formed	or	bedrock	

shorelines;	(3)	have	a	maximum	water	depth	of	less	than	6.5	ft.	at	low	water;	and	(4)	have	a	salinity	due	to	

ocean‐derived	salts	less	than	0.5%	(Stedman	and	Dahl,	2008).	
Chapter 4: Quantifying Greenhouse Gas Sources and Sinks in Managed Wetland Systems 

4-4

4.1.1 Overview of Management Practices and Resulting GHG Emissions

This	chapter	provides	methods	for	estimating	carbon	stock	changes	and	CH4	and	N2O	emissions	

from	naturally	occurring	wetlands2	and	restored	wetlands	on	previously	converted	wetland	sites.	

Constructed	wetlands	for	water	treatment,	including	detention	ponds,	are	engineered	systems	that	

are	beyond	the	scope	considered	here	because	they	have	specific	design	criteria	for	influent	and	

effluent	loads.	In	addition,	the	methods	are	restricted	to	estimation	of	emissions	on	palustrine	

wetlands	that	are	influenced	by	a	variety	of	management	options	such	as	water	table	management,	

timber,	or	other	plant	biomass	harvest,	and	wetlands	that	are	managed	with	fertilizer	applications.	

The	methods	are	based	on	established	principles	and	represent	the	best	available	science	for	

estimating	changes	in	carbon	stocks	and	GHG	fluxes	associated	with	wetland	management	

activities.	However,	given	the	wide	diversity	of	wetlands	types	and	the	variety	of	management	

regimes,	the	basis	for	the	methods	provided	in	this	section	are	not	as	well‐developed	as	other	

sections	in	this	guidance	(i.e.,	Cropland	and	Grazing	Lands,	Animal	Production,	and	Forestry	

Methods).	Table	4‐2	provides	a	summary	of	the	methods	and	their	corresponding	section	for	the	

sources	of	emissions	estimated	in	this	report.		

Table 4‐2: Overview of Wetland Systems Sources, Method, and Section

Section Source Method

4.3.1	 Biomass	

carbon		

Methods	for	estimating	forest	vegetation	and	shrub	and	grassland	vegetation	

biomass	carbon	stocks	use	a	combination	of	the	Forest	Vegetation	Simulator	(FVS)	

model	and	lookup	tables	for	dominant	shrub	and	grassland	vegetation	types	found	

in	Chapter	3,	Cropland,	and	Grazing	Land.	If	there	is	a	land‐use	change	to	

agricultural	use,	methods	for	cropland	herbaceous	biomass	are	provided	in	

Chapter	3.	

4.3.2	

Soil	C,	N2O,	

and	CH4	in	

wetlands	

The	Denitrification‐Decomposition (DNDC) process‐based	biogeochemical	model	is	

the	method	used	for	estimating	soil	C,	N2O,	and	CH4	emissions	from	wetlands.	

DNDC	simulates	the	soil		carbon	and	nitrogen	balance	and	generates	emissions	of	

soil‐borne	trace	gases	by	simulating	carbon	and	nitrogen	dynamics	in	natural	and	

agricultural	ecosystems	(Li	et	al.,	2000;	Miehle	et	al.,	2006;	Stang	et	al.,	2000)	and	

forested	wetlands	(Dai	et	al.,	2011;	Zhang	et	al.,	2002),	using	plant	growth	

estimated	as	described	in	Section	4.3.1.	

4.1.1.1 Description of Sector

The	National	Wetlands	Inventory	broadly	classifies	wetlands	into	five	major	systems:	(1)	marine,	

(2)	estuarine,	(3)	riverine,	(4)	lacustrine,	and	(5)	palustrine	(Cowardin	et	al.,	1979).		Four	of	those	

systems	(marine,	estuarine,	riverine,	and	lacustrine)	are	open‐water	bodies	and	not	considered	

within	the	methods	described	in	this	guidance.		Palustrine	wetlands	encompass	the	wetland	types	

occurring	on	the	land	and	are	further	classified	by	major	vegetative	life	form	and	wetness	or	

flooding	regime.	Common	palustrine	wetlands	are	illustrated	in	Figure	4‐1.	For	example,	forested	

wetlands	are	often	classified	as	palustrine—forested.	Similarly,	most	grass	wetlands	are	classified	

as	palustrine—emergent,	reflecting	emergent	vegetation	(e.g.,	grasses	and	sedges).	Wetlands	also	

vary	greatly	with	respect	to	groundwater	and	surface	water	interactions	that	directly	influence	

																																																													

2	Wetlands	are	defined	in	Chapter	7,	Land	Use	Change.	Wetlands	that	are	converted	to	a	non‐wetland	status	

should	be	considered	in	the	appropriate	chapter	(e.g.,	Cropland	and	Grazing	Lands,	Animal	Production,	and	

Forestry	Methods).	
 Chapter 4: Quantifying Greenhouse Gas Sources and Sinks in Managed Wetland Systems 

 4-5 

hydroperiod	(i.e.,	the	length	of	time	and	portion	of	the	year	the	wetland	holds	water),	water	

chemistry,	and	soils	(Cowardin	et	al.,	1979;	Winter	et	al.,	1998).		All	these	factors	along	with	climate	

and	land	use	drivers	influence	the	overall	carbon	balance	and	GHG	fluxes.					

Figure 4‐1: Palustrine Wetland Classes Based on Vegetation and Flooding Regime

Source: Cowardin et al. (1979).

Grassland	and	forested	wetlands	are	subject	to	a	wide	range	of	land	use	and	management	practices	

that	influence	the	carbon	balance	and	GHG	flux	(Faulkner	et	al.,	2011;	Gleason	et	al.,	2011).		For	

example,	forested	wetlands	may	be	subject	to	silvicultural	prescriptions	with	varying	intensities	of	

management	through	the	stand	rotation;	hence,	the	carbon	balance	and	GHG	emissions	should	be	

evaluated	on	a	rotation	basis,	which	could	range	from	20	to	more	than	50	years.	In	contrast,	grass	

wetlands	may	be	grazed,	hayed,	or	directly	cultivated	to	produce	a	harvestable	commodity	

annually.	While	each	management	practice	may	influence	carbon	sequestration	and	GHG	fluxes,	the	

effect	is	dependent	on	vegetation,	soil,	hydrology,	climatological	conditions,	and	the	management	

prescriptions.	This	section	focuses	on	restoration	and	management	practices	associated	with	

palustrine	wetlands	that	are	typically	forested	or	grassland.		

4.1.1.2 Resulting GHG Emissions

GHG	emissions	from	wetlands	are	largely	controlled	by	water	table	depth	and	duration	as	well	as	

climate	and	nutrient	availability.	Under	aerobic	soil	conditions,	which	are	common	in	most	upland	

ecosystems,	organic	matter	decomposition	releases	CO2,	and	atmospheric	CH4	can	be	oxidized	in	

the	surface	soil	layer	(Trettin	et	al.,	2006).	In	contrast,	the	anaerobic	soils	that	characterize	

wetlands	can	produce	CH4	(depending	on	the	water	table	position)	in	addition	to	emitting	CO2.		

Accordingly,	wetlands	are	an	inherent	source	of	CH4,	with	globally	estimated	emissions	of	55	to	150	

teragrams	(Tg)	of	CH4	per	year	(Blain	et	al.,	2006).			

	To	accommodate	entity‐scale	reporting	in	the	United	States	for	agricultural	and	forestry	

operations,	Tier	2	and	3	methods	address	palustrine	wetlands	containing	both	organic	and	mineral	

hydric	soils.	These	wetlands	may	be	influenced	by	agricultural	and	forestry	management,	and	

methods	are	currently	available	for	both	types	of	management.	This	chapter	provides	

methodologies	for	the	following	wetland	source	categories:		
Chapter 4: Quantifying Greenhouse Gas Sources and Sinks in Managed Wetland Systems 

4-6

1. Biomass	carbon	in	forested,	shrub,	and	grass	wetlands;	

2. Soil	carbon	sinks	in	wetlands;	and	

3. N2O	and	CH4	emissions	in	wetlands.	

Biomass	carbon	can	change	significantly	with	management	of	wetlands,	particularly	in	forested	

wetlands,	changes	from	forest	to	wetlands	dominated	by	grasses	and	shrubs,	or	open	water.		In	

forested	wetlands,	there	can	also	be	significant	carbon	in	dead	wood,	coarse	woody	debris,	and	fine	

litter.		Harvesting	practices	will	also	influence	the	carbon	stocks	in	wetlands	to	the	extent	the	wood	

is	collected	for	products,	fuel,	or	other	purposes.	

Wetlands	are	also	a	source	of	soil	N2O	emissions,	primarily	because	of	nitrogen	runoff	from	

adjoining	uplands	and	leaching	into	groundwater	from	agricultural	fields	and/or	animal	production	

facilities.		N2O	emissions	from	wetlands	due	to	nitrogen	inputs	from	surrounding	fields	or	animal	

production	are	considered	indirect	emissions	of	N2O	(de	Klein	et	al.,	2006).	Methodologies	for	

estimating	indirect	N2O	are	provided	in	the	respective	source	chapter	(i.e.,	Chapter	3,	Cropland	and	

Grazing	Lands,	or	Chapter	5,	Animal	Production).	However,	direct	N2O	emissions	occur	in	wetlands	

if	management	practices	include	nitrogen	fertilization,	hence,	guidance	is	provided	for	this	source	

of	emissions.		

4.1.1.3 Risk of Reversals

Wetlands	inherently	accumulate	carbon	in	the	soils	due	to	anaerobic	conditions,	and	they	are	

natural	sources	of	CO2	and	CH4	to	the	atmosphere.	Management	may	alter	conditions	that	affect

both	the	pools	and	fluxes.	For	example,	accumulated	soil	carbon	can	be	returned	to	the	atmosphere	

if	the	wetland	is	drained	(Armentano	and	Menges,	1986).	In	contrast,	silvicultural	water	

management	in	wetlands	can	lead	to	higher	biomass	production,	which	may	partially	offset	

increased	soil	organic	matter	oxidation.	Conversely,	the	soil	carbon	pool	in	converted	wetlands	is	

typically	lower	than	the	unmanaged	soil,	and	restoring	wetland	conditions	may	increase	carbon	

storage	over	time	if	inherent	hydric	soil	conditions	are	maintained	with	consistent	organic	matter	

inputs.	

Reversals	of	emission	trends	can	occur	if	a	manager	reverts	to	a	prior	condition	or	an	earlier	

practice.	For	example,	an	entity	may	decide	to	return	a	wetland	that	had	been	drained	and	cropped	

back	to	a	forested	wetland	condition.	Another	common	example	would	be	if	a	restored	forested	

wetland	is	reverted	back	to	agriculture.	These	reversals	do	not	negate	the	mitigation	of	CH4	or	N2O	

emissions	to	the	atmosphere	that	had	occurred	previously,	to	the	extent	that	wetland	restoration	or	

change	in	management	can	reduce	or	change	these	emissions.	Correspondingly,	the	starting	point	

from	the	reversion	will	determine	the	effect	on	carbon	sequestration	and	GHG	flux.	For	example,	in	

a	restored	forested	wetland,	reversion	of	the	site	to	crop	production	would	return	carbon	

sequestered	during	the	restoration	period	to	the	atmosphere	over	time.	

There	is	a	trade‐off	in	CH4	and	N2O	emissions	with	management	of	the	water	table	position.		

Wetlands	with	anaerobic	soil	conditions	that	are	persistent	near	the	surface	for	a	longer	period	

during	the	year	will	tend	to	have	higher	CH4	emissions	and	lower	emissions	of	N2O.		N2O	emissions	

are	greatly	reduced	if	soils	are	saturated	because	there	is	little	inherent	nitrification,	and	

denitrification	will	lead	to	N2	production	(Davidson	et	al.,	2000).		For	example,	restoration	of	

wetlands	will	normally	lead	to	a	higher	water	table	for	a	longer	period	of	the	year,	and	thus	

contribute	to	higher	emissions	of	CH4	but	lower	emissions	of	N2O.	These	trends	can	be	reversed	if	

the	water	table	is	lowered	through	management	or	drought,	which	will	tend	to	enhance	N2O	

emissions	if	there	is	a	source	of	nitrate,	while	reducing	emissions	of	CH4.	Figure	4‐2	provides	an	

illustration	of	the	carbon	cycle	typically	found	in	wetland	forest	and	grassland	wetlands	and	

represents	the	scope	of	the	methods	presented	in	this	guidance.
 Chapter 4: Quantifying Greenhouse Gas Sources and Sinks in Managed Wetland Systems 

 4-7 

Figure 4‐2: Carbon Cycle for Forest and Grassland Wetlands

Source:	Trettin	and	Jurgensen	(2003).	

4.1.2 System Boundaries and Temporal Scale

System	boundaries	are	defined	by	the	coverage,	extent,	and	resolution	of	the	estimation	methods.		

The	location	of	the	wetlands	may	be	approximated	by	use	of	the	National	Wetlands	Inventory,3	the	

location	of	hydric	soils	as	conveyed	by	the	NRCS	soils	map,	or	through	direct	delineation	of	

wetlands.	The	coverage	of	the	methods	can	be	used	to	estimate	a	variety	of	emission	sources,	

including	emissions	associated	with	biomass	C,	litter	C,	and	soils	carbon	stock	changes	and	CO2,	CH4,

and	N2O	fluxes	from	soils.	System	boundaries	are	also	defined	by	the	extent	and	resolution	of	the	

estimation	method.	The	methods	provided	for	wetlands	have	a	spatial	extent	that	would	include	all	

wetlands	in	the	entity’s	operation,	with	estimation	occurring	at	the	resolution	of	an	individual	

wetland.	Emissions	are	estimated	on	an	annual	basis	for	as	many	years	as	needed	for	GHG	

emissions	reporting.		

4.1.3 Summary of Selected Methods/Models and Sources of Data

The	IPCC	(2006)	has	developed	a	system	of	methodological	tiers	for	estimating	GHG	emissions.	Tier	

1	represents	the	simplest	methods	using	default	equations	and	factors	provided	in	the	IPCC	

guidance.	Tier	2	uses	default	methods	but	emission	factors	that	are	specific	to	different	regions.	

Tier	3	utilizes	a	region‐specific	estimation	method,	such	as	a	process‐based	model.	Higher	tier	

methods	are	expected	to	reduce	uncertainties	in	the	emission	estimates	if	there	is	sufficient	

information	and	testing	to	develop	these	methods.	In	this	guidance,	biomass,	litter,	and	soil	carbon	

stock	changes,	in	addition	to	soil	N2O	and	CH4	emissions,	are	estimated	using	Tier	2	and	3	methods.	

The	data	required	to	apply	these	methods	range	from	basic	information	on	soils,	vegetation,	

weather,	land	use,	and	management	history	to	data	on	fertilization	rates	or	drainage	conditions.		

While	some	of	these	data	are	operation‐specific	and	must	be	provided	by	the	entity,	other	data	can	

be	obtained	from	national	databases,	such	as	weather	data	and	soil	characteristics.	

																																																													

3	See	National	Wetlands	Inventory	http://www.fws.gov/wetlands/.	
Chapter 4: Quantifying Greenhouse Gas Sources and Sinks in Managed Wetland Systems 

4-8

4.1.4 Organization of Chapter/Roadmap

The	wetlands	section	of	this	report	is	organized	into	three	primary	sections.	Section	4.2	provides	a	

description	of	wetland	management	effects	on	GHG	emissions,	elaborating	on	the	scientific	basis	for	

how	various	practices	influence	GHG	emissions.	Section	4.3	provides	a	rationale	for	the	selected	

method,	a	description	of	the	method,	including	a	general	description	(with	equations	and	factors),	

activity	data	requirements,	ancillary	data	requirements,	limitations	of	the	method,	and	

uncertainties	associated	with	the	estimation.	A	single	method	is	provided	for	each	source	presented	

in	this	chapter	(i.e.,	biomass	carbon	in	forested,	shrub,	and	grass	wetlands;	soil	carbon	and	CH4	in	

wetlands;	and	direct	N2O	emissions	in	wetlands).	A	single	method	was	selected	to	ensure	

consistency	in	emission	estimation	by	all	reporting	entities,	and	the	selected	method	is	considered	

the	best	option	among	possibilities	for	entity‐scale	reporting.	Methods	may	be	refined	in	the	future	

as	they	are	further	developed.	The	last	section	provides	a	summary	of	selected	research	gaps.			

4.2 Management and Restoration of Wetlands

How	wetlands	are	managed	can	have	a	significant	effect	on	GHG	emissions	and	sinks,	which	are	

primarily	influenced	by	the	degree	of	water	saturation,	climate,	and	nutrient	availability.	In	a	

majority	of	wetlands,	90	percent	of	carbon	in	gross	primary	production	is	returned	to	the	

atmosphere	through	decay,	and	the	remaining	10	percent	accumulates	in	the	bottom	of	the	water	

body	accumulating	on	previously	deposited	materials	(Blain	et	al.,	2006).	Management	of	the	water	

table	within	a	wetland	will	result	in	both	lower	CH4	emissions	due	to	decreased	production	and	

oxidation	of	CH4	produced	in	the	subsoil	and	an	increase	in	CO2	emissions	due	to	increased	

oxidation	of	soil	organic	matter.	N2O	emissions	from	wetlands	are	typically	low,	unless	an	

anthropogenic	source	of	nitrogen	enters	the	wetland.	In	drained	wetlands,	N2O	emissions	are	

largely	controlled	by	the	fertility	of	the	soil	and	water	management	regime.	In	contrast,	restored	

and	constructed	wetlands	generate	higher	levels	of	CH4	and	lower	levels	of	CO2	because	of	the	

change	in	a	water	table	depth	(Blain	et	al.,	2006).	

4.2.1 Description of Wetland Management Practices

This	section	provides	a	description	of	management	practices	in	wetlands	that	influence	GHG	

emissions	(CH4	or	N2O)	or	carbon	stocks.	Individual	sections	deal	with	forested	and	grass	wetlands	

that	could	occur	in	agricultural	and	forestry	operations.	It	is	important	to	note	that	drainage	of	

wetlands	for	commodity	production,	such	as	annual	crops,	or	for	other	purposes	are	not	considered	

wetlands	in	these	guidelines.	Methods	for	drained	wetlands	can	be	found	in	Chapter	3,	Croplands	

and	Grazing	Lands,	or	Chapter	6,	Forest	Lands,	depending	on	the	land	use	after	drainage	of	the	

wetland.	

4.2.1.1 Silvicultural Water Table Management

Silvicultural	water	management	systems	are	principally	used	to	regulate	the	water	table	depth	in	

order	to	reduce	soil	disturbance	associated	with	harvesting	operations	and	alleviate	stress	from	

saturated	soil	conditions	on	artificially	regenerated	plantations.	The	silvicultural	water	

management	system	should	not	eliminate	the	wetland	conditions	of	the	site.	

Silvicultural	water	management	systems	affect	the	carbon	balance	and	GHG	emissions	from	the	site	

(Bridgham	et	al.,	2006).	Typically	organic	matter	decomposition	is	enhanced	with	the	imposition	of	

a	drainage	system,	CH4	emissions	are	reduced,	and	N2O	emissions	may	increase	(Li	et	al.,	2004).	

Carbon	sequestration	in	biomass	may	be	enhanced	on	sites	with	silvicultural	drainage	systems	due	

to	increased	tree	productivity	(Minkkinen	and	Laine,	1998).	
 Chapter 4: Quantifying Greenhouse Gas Sources and Sinks in Managed Wetland Systems 

 4-9 

4.2.1.2 Forest Harvesting Systems

There	are	two	general	types	of	systems	used	to	harvest	trees	from	forested	wetlands:	partial	

cutting	and	clear	cutting.	A	partial	cut	involves	the	removal	of	selected	trees	from	the	stand.	The	

number	of	trees	removed	or	the	residual	density	of	the	stand	will	depend	on	the	stand	type,	species,	

intended	product(s),	and	stand	age.	The	amount	of	tree	biomass	removed	during	the	partial	cut	

may	also	vary;	tops	may	be	left	onsite	if	only	logs	are	removed,	or	they	may	be	concentrated	in	a	

landing	if	whole‐tree	harvesting	is	used.	With	the	latter	system,	the	tops	may	also	be	utilized	and	

removed	from	the	site.	Partial	cutting	is	typically	used	in	riparian	zones	and	sites	that	are	managed	

for	solid	wood	products.	Clear	cutting	results	in	the	removal	of	all	overstory	trees	from	the	site.	

Clear	cutting	is	typically	used	on	natural	stands	occurring	in	floodplains	of	the	southeastern	coastal	

plain	and	lacustrine	and	outwash	plains	of	the	upper	Midwest.	Clear	cutting	is	also	the	typical	

system	employed	to	harvest	conifer	and	hardwood	plantations.	

Partial	cutting	affects	the	carbon	balance	of	the	site	by	direct	removal	of	biomass;	increased	

biomass	on	the	forest	floor,	which	is	then	subject	to	decay	processes;	and	increased	growth	of	the	

remaining	trees	for	several	years.	Decomposition	of	dead	biomass	within	the	stand	may	be	

accelerated	temporarily	due	to	the	changes	in	ambient	conditions	and	the	added	residue	from	the	

harvest.			

Clear	cutting	affects	carbon	stocks	of	the	site	by	directly	removing	the	biomass;	increasing	amounts	

of	biomass	added	to	the	forest	floor;	altering	the	carbon	sequestration	for	several	years,	depending	

on	the	type	of	regeneration;	and	altering	the	rate	of	organic	matter	decomposition	in	the	forest	

floor	and	soil	(Lockaby	et	al.,	1999).	Clear	cutting	affects	the	ambient	conditions	of	the	site	because	

of	the	removal	of	the	overstory	vegetation.	It	also	alters	the	water	balance	of	the	wetland	due	to	the	

reduction	in	evapotranspiration	following	harvesting.	Typically,	as	a	result	of	lower	

evapotranspiration,	the	water	table	rises,	and	the	site	will	exhibit	longer	periods	of	saturation.	This	

change	in	the	water	table	position	has	direct	effects	on	the	production	of	CH4	and	N2O	and	

subsequent	fluxes	to	the	atmosphere	(Li	et	al.,	2004).	

4.2.1.3 Forest Regeneration Systems		

There	are	two	basic	forest	regeneration	systems,	characterized	as	(a)	natural	regeneration,	and	(b)	

artificial	regeneration.	Natural	regeneration,	as	the	name	implies,	relies	upon	regeneration	of	the	

trees	from	seed	or	sprouts	that	are	left	by	harvested	trees.	Natural	regeneration	is	used	in	both	

partial‐cut	and	clear‐cut	harvest	systems.	Natural	regeneration	will	lead	to	even‐aged	stands	of	

shade‐intolerant	or	early	successional	communities,	typically	in	floodplains	in	the	southeastern	

United	States	and	the	coniferous	plains	of	the	upper	Midwest.		

Artificial	regeneration	results	from	planting	seedlings	on	a	prepared	site.	The	site	preparation	

practices	may	involve	removal	of	the	harvest	residue	biomass,	mechanical	scarification	and/or	the	

application	of	herbicide	to	temporarily	reduce	weed	competition	with	seedlings,	and	the	creation	of	

planting	beds.	

The	effect	of	the	forest	regeneration	system	on	carbon	stocks	and	trace	GHG	emissions	depends	on	

the	type	of	harvesting	system	that	was	used	(Lockaby	et	al.,	1999;	Trettin	et	al.,	1995).	The	

combination	of	partial	cutting	and	natural	regeneration	has	little	additive	effect	because	the	extent	

of	regeneration	is	typically	quite	low	following	a	partial	cut	that	removes	less	than	half	of	the	basal	

area.	Carbon	stocks	following	clear‐cut	harvesting	with	natural	regeneration	is	affected	by	the	rate	

of	growth	of	the	regeneration,	changes	in	ambient	conditions,	and	changes	in	the	soil	water	regime.		

Those	factors	also	affect	artificial	regeneration	systems;	additionally,	the	type	and	extent	of	site	

preparation	also	affects	the	carbon	stocks.	
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4.2.1.4 Fertilization

Fertilization	is	used	primarily	in	forested	wetlands,	such	as	tree	plantations,	to	enhance	growth	

(Albaugh	et	al.,	2004).	Grass	wetlands	also	receive	fertilizer	as	a	result	of	adjacent	agricultural	

activities,	and	when	dry	conditions	permit,	are	directly	tilled,	planted,	and	fertilized.	Nitrogen	is	the	

most	commonly	applied	fertilizer,	and	increased	nitrogen	inputs	are	known	to	increase	emissions	

of	N2O		(Bedard‐Haughn	et	al.,	2006;	Davidson	et	al.,	2000;	Gleason	et	al.,	2009;	Merbach	et	al.,	

2002;	Phillips	and	Beeri,	2008;	Thornton	and	Valente,	1996).	Nitrogen	fertilizers	will	also	enhance	

N2O	emissions	both	directly	on	the	site	and	indirectly	if	nitrogen	is	lost	from	the	site	as	nitrate	in	

groundwater	or	runoff,	as	well	as	volatilization	of	nitrogen	as	ammonia	or	NOx.	The	indirect	losses	

will	contribute	to	N2O	emissions	at	other	sites.			

The	effect	of	fertilization	on	carbon	stocks	is	principally	realized	through	changes	in	tree	growth	

rates.	The	effect	would	result	from	nitrogen	fertilizers,	but	phosphorus	may	also	be	applied	in	the	

southeastern	United	States.				

4.2.1.5 Conversion to Open‐Water Wetland

The	conversion	of	wetland	to	open	water	occurs	primarily	as	a	result	of	beaver	impoundments	and	

to	a	lesser	degree	improperly	installed	roads	or	other	artificial	embankments	through	a	wetland	

that	impedes	natural	drainage.	The	conversion	to	open	water	significantly	reduces	carbon	

sequestration	through	plant	growth,	because	uptake	is	limited	to	submerged	aquatic	vegetation.		

The	higher	water	table	for	a	longer	period	of	the	year	will	also	tend	to	increase	CH4	flux.		

4.2.1.6 Forest Type Change

Changing	a	managed	forest	to	a	characteristic	native	condition	is	also	considered	a	form	of	

restoration.	The	effect	of	the	restoration	activities	on	the	carbon	stocks	and	CH4	emissions	depends	

on	the	extent	of	the	hydrologic	modifications	that	were	employed	in	the	previous	silvicultural	

system.	The	two	most	common	situations	are	a	site	that	has	been	managed	for	a	particular	species	

or	product	without	hydrologic	modification;	the	other	common	situation	is	where	the	site	has	been	

managed	for	plantation	forestry	and	the	hydrology	and	vegetation	have	been	extensively	modified.		

4.2.1.7 Water Quality Management

Riparian	zones	along	streams,	rivers,	and	lakes	may	be	managed	to	protect	water	quality	by	

mitigating	nonpoint	source	pollution	(Balestrini	et	al.,	2011;	Chaubey	et	al.,	2010).4	Pollutants	are	

removed	by	physical	filtration,	chemical	adsorption,	plant	uptake,	and	microbial	transformations	

(Abu‐Zreig	et	al.,	2003;	Borin	et	al.,	2005).5	However,	riparian	buffers	are	limited	in	their	

adsorption	capacities	for	some	constituents,	which	may	then	flow	into	waterways.	The	buffer	zone	

size	and	configuration	varies	according	to	runoff	patterns	of	the	site,	phosphorus/nitrogen	inputs,	

hydrologic	connectivity,	organic	carbon,	mineral	content,	and	oxidative/reductive	state	(Abu‐Zreig	

et	al.,	2003;	Hoffmann	et	al.,	2009;	Novak	et	al.,	2002;	Young	and	Briggs,	2008).		

Riparian	buffer	zones	are	comprised	of	native	and	non‐native	vegetation	or	may	also	contain	

cultivated	plants	in	some	cases.	Management	activities	of	the	native	vegetation	buffer	zones	are	

typically	constrained	or	limited	to	small	removals.	In	the	case	of	forest	riparian	buffers,	a	selective‐

																																																													

4	Additional	references	include	(Cho	et	al.,	2010;	Flite	et	al.,	2001;	Hoffmann	et	al.,	2009;	Hunt	et	al.,	2004;	Lee	

et	al.,	2004;	Lowrance	et	al.,	2007;	Montreuil	et	al.,	2010;	Peterjohn	and	Correll,	1984;	Ranalli	and	Macalady,	

2010;	Schoonover	et	al.,	2005;	Tabacchi	et	al.,	1998;	Young	and	Briggs,	2008).	

5	Additional	references	include	(Dillaha	et	al.,	1989;	Dillaha	et	al.,	1988;	Hoffmann	et	al.,	2009;	Jordan	et	al.,	

2003;	Kelly	et	al.,	2007;	Novak	et	al.,	2002;	Vellidis	et	al.,	2003;	Young	and	Briggs,	2008).	
 Chapter 4: Quantifying Greenhouse Gas Sources and Sinks in Managed Wetland Systems 

 4-11 

harvest	regime	would	be	used	that	influences	both	carbon	stocks	and	GHG	emissions.	In	mixed	

buffers	(i.e.,	grass	strips	followed	by	forest),	the	management	of	the	cultivated	buffer	would	largely	

determine	the	effect	of	the	practice,	which	will	be	analogous	to	hay	cultivation.	Riparian	zones	may	

contain	a	mosaic	of	hydric	(wetland)	and	non‐hydric	soils;	accordingly,	the	distribution	of	soil	types	

is	important	for	assessing	the	effect	of	the	management	activity.				

Whereas	riparian	buffers	occupy	low	landscape	positions	and	are	typically	wet,	they	are	often	very	

effective	in	removing	nitrogen	via	denitrification	(Ambus,	1991;	Davis	et	al.,	2008;	Dodla	et	al.,	

2008;	Hill	et	al.,	2000;	Hunt	et	al.,	2007;	Jordan	et	al.,	1998;	Roobroeck	et	al.,	2010;	Smith	et	al.,	

2006;	Stone	et	al.,	1998;	Woodward	et	al.,	2009),	which	leads	to	indirect	N2O	emissions	(Jetten,	

2008).	Denitrification	in	riparian	buffers	is	often	spatially	uneven	because	riparian	buffers	vary	

considerably	in	their	size	and	landscape	positions	as	well	as	their	soil,	vegetative,	and	hydrological	

conditions	(Bowden	et	al.,	1992;	Bruland	and	MacKenzie,	2010;	Flite	et	al.,	2001;	Hill	et	al.,	2000).	

Studies	have	suggested	that	N2O	emissions	in	riparian	zones	were	not	a	significant	“pollution‐

swapping	phenomenon”	(Dhondt	et	al.,	2004;	Kim	et	al.,	2009a;	Kim	et	al.,	2009b).	Significant	

emissions	are	likely	to	be	limited	to	spatial	and	temporal	hot	spots	(Groffman	et	al.,	2000;	Hunt	et	

al.,	2007;	Kim	et	al.,	2009b).	Moreover,	some	riparian	wetland	systems	can	serve	as	sinks	for	

nitrogen	(Roobroeck	et	al.,	2010).	While	many	factors	affect	the	microbial	production	of	N2O,	one	of	

the	most	dominating	factors	is	the	carbon	to	nitrogen	ratio;	larger	ratios	generally	have	low	N2O	

emissions	because	nitrogen	is	immobilized	in	the	soil	organic	matter	(Hunt	et	al.,	2007;	

Klemedtsson	et	al.,	2005).	However,	it	is	important	to	note	that	indirect	N2O	emissions	are	

attributed	to	the	source	of	the	nitrogen,	which	can	be	a	neighboring	field	or	livestock	facility;	so	the	

methods	to	estimate	indirect	N2O	emissions	are	provided	in	other	sections	of	this	report	(i.e.,	

Chapter	3,	Cropland	and	Grazing	Lands,	or	Chapter	5,	Animal	Production).	

Riparian	buffers	can	serve	as	both	sources	and	sinks	of	CH4	(Hopfensperger	et	al.,	2009;	Soosaar	et	

al.,	2011).	Their	hydrology	and	biogeochemical	characteristics	exhibit	significant	influence	on	the	

net	CH4	emission.	These	characteristics	include	water	table	position,	temperature,	

oxidative/reductive	potential,	and	plant	community	compositions	(Pennock	et	al.,	2010;	Whalen,	

2005).	Moreover,	N2O	emissions	from	denitrification	can	be	significantly	influenced	by	

methanotrophs	(Costa	et	al.,	2000;	Knowles,	2005;	Modin	et	al.,	2007;	Osaka	et	al.,	2008).	

Similar	buffers	exist	for	grass	wetlands,	either	as	part	of	a	conservation	program	or	as	a	naturally	

occurring	area	around	a	wetland	where	moist‐soil	conditions	prevent	tillage.	Grass	buffers	reduce	

runoff	and	intercept	sediments	that	would	affect	water	quality	by	increasing	turbidity	and	inputs	of	

fertilizers	and	agrichemicals.	Moreover,	planting	the	entire	catchment	with	grass	can	reduce	CH4

emissions	by	decreasing	the	artificially	high	water	levels	and	extended	hydroperiods	that	often	are	

associated	with	cropland	sites	(Euliss	Jr	and	Mushet,	1996;	Gleason	et	al.,	2009;	van	der	Kamp	et	al.,	

2003).	

4.2.1.8 Wetland Management for Waterfowl

Wetlands	may	be	managed	for	waterfowl	habitat.	Activities	that	are	specific	to	wetland	waterfowl	

management	have	direct	influences	on	carbon	stocks	and	GHG	emissions,	including	regulation	of	

the	water	regime,	specifically	depth	and	duration	of	inundation,	as	well	as	planting	and	cultivation	

of	crops	for	food	and	habitat.	Water	regimes	imposed	for	waterfowl	management	may	be	different	

than	the	natural	water	table	cycle	of	the	site.	Accordingly,	changing	the	water	table	alters	the	

periods	of	soil	aeration	and	saturation	influencing	rates	of	CH4	and	N2O,	as	well	as	carbon	stock	

changes	in	timber	stands	and	other	wetland	vegetation.	Cultivating	crops	in	wetlands	managed	for	

waterfowl	will	also	influence	carbon	stocks	and	N2O	emissions	based	on	selection	of	crops	and/or	

rotation	practice,	tillage,	liming,	and	nutrient	management.	
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4.2.1.9 Constructed Wetlands for Wastewater Treatment, Sediment Capture, and

Drainage Water Abatement

Constructed	wetlands	are	engineered	systems	for	wastewater	treatment,	capture	of	sediments,	and	

drainage	water	abatement	in	agricultural	and	forestry	operations	(Chen	et	al.,	2011;	Elgood	et	al.,	

2010).	Surface‐flow	and	subsurface	flow	systems	are	the	two	principal	types	of	constructed	

wetlands	(Kadlec	and	Knight,	1996).	The	principal	difference	between	these	two	types	of	

constructed	wetlands	is	the	water	flow	path.	In	the	case	of	the	subsurface	flow	wetlands,	all	the	

water	flows	are	beneath	the	soil	surface;	the	surface‐flow	systems	have	flow	both	above	and	within	

the	soil.			

The	subsurface	wetlands	typically	consist	of	wetland	plants	growing	in	a	bed	of	highly	porous	

media	such	as	gravel	or	wood	chips	that	have	a	water	table	from	one	to	two	meters	above	the	soil	

surface	with	a	rectangular	shape.	There	is	lack	of	agreement	about	the	relative	impact	of	microbial	

and	plant	processes	in	the	function	of	subsurface	wetlands	including	GHG	emissions.	However,	

plants	and	microbes	are	typically	interdependently	involved	in	the	processes	that	contribute	to	

emissions	(Faubert	et	al.,	2010;	Lu	et	al.,	2010;	Picek	et	al.,	2007;	Tanner	and	Headley,	2011;	Wang	

et	al.,	2008;	Zhu	et	al.,	2007).	While	the	microbial	community	drives	the	biogeochemical	processes	

that	specifically	emit	GHGs	(Dodla	et	al.,	2008;	Faulwetter	et	al.,	2009;	Hunt	et	al.,	2003;	Tanner	et	

al.,	1997;	Zhu	et	al.,	2010),	the	plant	community	modifies	the	environmental	conditions	

contributing	to	emission	rates,	including		transporting	oxygen	into	the	depth	of	the	wetlands,	

providing	root	surfaces	for	rhizosphere	reactions,	and	venting	gases	to	the	atmosphere.	The	plant	

processes	are	significantly	impacted	by	plant	community	composition	and	weather	conditions	

(Stein	et	al.,	2006;	Stein	and	Hook,	2005;	Taylor	et	al.,	2010;	Towler	et	al.,	2004;	Wang	et	al.,	2008;	

Zhu	et	al.,	2007).	

Surface	flow	wetlands	have	a	much	more	direct	exchange	of	oxygen	and	GHGs	with	the	atmosphere.		

They	can	be	variable	in	shape	and	are	generally	less	than	0.5	meters	in	depth.	Surface	wetlands	

minimize	clogging	problems,	but	they	can	have	a	significant	loss	of	treatment	as	a	result	of	channel	

flow.	They	are	typically	designed	for	either	carbon	or	nitrogen	removal	(Stein	et	al.,	2006;	Stein	et	

al.,	2007;	Stone	et	al.,	2002;	Stone	et	al.,	2004),	including	the	prevention	of	excessive	ammonia	

emissions	(Poach	et	al.,	2004;	Poach	et	al.,	2002).	

Constructed	wetlands	are	typically	created	in	upland	settings	(e.g.,	non‐wetland);	accordingly,	the	

site	assumes	the	same	biogeochemical	processes	that	are	inherent	to	natural	wetlands.	Carbon	

stocks	and	GHG	emissions	are	affected	by	the	type	and	quantity	of	effluent	being	treated,	the	type	of	

vegetation	in	the	wetland	cells,	and	management	of	the	hydrologic	regimes	within	the	cells.	The	

management	of	CH4	and	N2O	from	constructed	wetlands	is	somewhat	similar	to	managing	GHG

emissions	from	wetland	rice	systems	(Fey	et	al.,	1999;	Freeman	et	al.,	1997;	Johansson	et	al.,	2003;	

Maltais‐Landry	et	al.,	2009;	Mander	et	al.,	2005a;	Mander	et	al.,	2005b;	Picek	et	al.,	2007;	Tanner	et	

al.,	1997;	Teiter	and	Mander,	2005;	Wu	et	al.,	2009).	Of	particular	importance	is	the	maintenance	of	

wetland	oxidative/reductive	potentials	that	are	sufficiently	positive	to	avoid	CH4	production	(Insam	

and	Wett,	2008;	Seo	and	DeLaune,	2010;	Tanner	et	al.,	1997).	This	requires	higher	levels	of	oxygen	

and	lower	levels	of	available	carbon.	The	management	of	N2O	emissions	is	complicated	by	the	fact	

that	nitrates	are	often	present	in	the	wastewaters	or	drainage	waters,	and	so	GHG	emissions	can	be	

reduced	in	the	constructed	wetlands	if	N2	gas	is	emitted	instead	of	N2O.	Complete	denitrification	to	

N2	gas	requires	higher	carbon/nitrogen	ratios	(Hunt	et	al.,	2007;	Hwang	et	al.,	2006;	Klemedtsson	

et	al.,	2005).		Thus,	there	is	an	important	balance	between	sufficient	carbon	for	complete	

denitrification	and	copious	carbon	that	drives	wetlands	into	the	low	redox	conditions	associated	

with	CH4	production.	
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 4-13 

This	section	is	included	for	completeness,	but	no	method	for	constructed	wetlands	is	provided	in	

this	section.	Section	5.4.10	in	Chapter	5,	Animal	Agriculture,	provides	a	qualitative	discussion	of	

estimating	emissions	from	liquid	manure	storage	and	treatment‐constructed	wetlands.	However,	

Chapter	5	does	not	provide	methods	to	estimate	greenhouse	gas	emissions	from	constructed	

wetlands.			

4.2.2 Land‐Use Change to Wetlands

Conversion	of	land	to	wetlands	may	involve	restoring	agricultural	land	into	a	functioning	wetland.		

However,	wetlands	can	be	restored	from	previously	drained	forest	or	grasslands,	and	the	change	

tends	to	vary	for	different	regions	of	the	United	States.	Wetlands	can	also	be	constructed	in	any	

location	for	wastewater	treatment.	The	original	conversion	of	wetlands	to	another	use	typically	

involves	an	alteration	of	the	natural	wetland	hydrology.	Chapter	7,	Land	Use	Change,	addresses	this	

type	of	conversion.	Restoration	of	wetlands	entails	reestablishment	of	the	requisite	hydrology	to	

support	forest,	scrub‐shrub,	sedge,	or	emergent	wetland	plant	communities	and	occurs	in	

floodplains,	riparian	zones,	depressions,	and	slopes	and	valleys.			

4.2.2.1 Actively Restoring Wetlands

The	effect	of	restoring	both	forested	and	grass	wetlands	will	lead	to	carbon	sequestration	and	CH4

emissions	that	would	be	characteristic	for	that	wetland	type.	However,	the	extent	to	which	carbon	

sequestration,	organic	matter	turnover,	and	gas	fluxes	return	to	rates	typical	for	the	wetland	type	

depends	on	many	factors,	particularly	the	degree	of	alteration,	time	since	restoration,	hydrology,	

and	development	of	the	vegetation.	In	general,	restored	sites	will	be	carbon	sinks	due	to	

sequestration	in	the	developing	biomass	(e.g.,	forest	stand)	and	soils	(Euliss	Jr	et	al.,	2008).	Soil	

carbon	is	expected	to	increase	slowly	in	forested	settings	and	somewhat	more	rapidly	in	grassland	

sites	(Gleason	et	al.,	2009);	however,	the	extent	and	rates	of	change	are	uncertain.	Reestablishment	

of	the	wetland	hydrology	will	also	alter	the	CH4	flux	from	the	restored	site	since	hydrologic	

modifications	for	other	land	uses	will	typically	involve	drainage	or	diversions.	Raising	the	water	

table	and	increasing	the	period	of	time	that	the	soil	surface	is	covered	with	water	will	increase	CH4

production.	However,	many	restored	grassland	sites	are	not	directly	drained,	and	reestablishment	

of	grasses	in	the	catchment	can	shorten	the	hydroperiod	(Van	Der	Kamp	et	al.,	1999;	Voldseth	et	al.,	

2007),	thus	reducing	CH4	production.	

Conversion	of	scrub‐shrub	wetlands	typically	involves	drainage	to	a	non‐wetland	state,	and	the	

imposition	of	cultivation	or	other	practices	depending	on	the	land	use.	Accordingly,	the	restoration	

of	prior‐converted	scrub‐shrub	wetlands	typically	involves	reestablishment	of	the	natural	wetland	

hydrology	and	selective	planting	to	establish	native	vegetation.	The	development	of	the	

characteristic	wetland	hydrology	is	the	principal	factor	affecting	the	carbon	stocks	and	GHG	

emissions	from	the	site	following	conversion,	but	the	type	of	vegetation	and	time	since	

establishment	will	also	have	some	influence.	

4.2.2.2 Created Wetlands

Created	wetlands	are	engineered	into	non‐wetland	or	upland	sites.	Typical	examples	include	

mitigation	sites,	anaerobic	lagoons	(See	Section	5.4.10	in	Chapter	5,	Animal	Agriculture)	on	

livestock	operations,	and	storm	water	detention	basins.	The	principal	activity	affecting	the	carbon	

stocks	and	GHG	emissions	is	the	imposition	of	a	hydrologic	regime	that	induces	hydric	soil	

properties	and	supports	hydrophytic	plants,	in	addition	to	clearing	of	the	previous	vegetation	that	

may	lead	to	a	change	in	biomass	carbon	stocks.			
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4.2.2.3 Passive Restoration of Wetlands

Allowing	an	area	to	regenerate	through	natural	succession	is	also	considered	a	form	of	restoration.		

The	effect	of	the	restoration	activities	on	the	carbon	stocks	and	CH4	emissions	depends	on	whether	

there	was	hydrologic	remediation	and	the	degree	of	vegetation	change	over	time.	

4.3 Estimation Methods

Section	4.3.1	provides	methods	for	estimating	live	and	dead	biomass	in	forested,	shrub,	and	

grassland	wetlands.	Section	4.3.2	provides	methods	for	estimating	soil	C,	N2O,	and	CH4	emissions	

from	managed	naturally	occurring	wetlands.		

4.3.1 Biomass Carbon in Wetlands

4.3.1.1 Rationale for Selected Method

Various	approaches	are	used	for	estimating	tree	biomass	carbon,	but	ultimately	each	relies	on	

allometric	relationships	developed	from	a	characteristic	subset	of	trees.	The	Forest	Vegetation	

Simulator	(FVS)	has	been	selected	as	the	method	to	estimate	tree	biomass.	FVS	is	model‐based	

approach	that	is	specific	to	U.S.	conditions	and	a	Tier	3	method	as	defined	by	the	IPCC.		The	

simulator	is	the	most	complete	model	in	the	United	States	to	estimate	tree	biomass.	Regional	

versions	of	FVS	have	been	refined	based	on	large	databases	developed	from	many	years	of	data	

collection	on	forest	stands	throughout	the	United	States,	thereby	providing	improved	estimates	

while	requiring	few	input	parameters	from	the	user.			

Both	IPCC	(2006)	and	EPA	(2011)	consider	herbaceous	biomass	carbon	stocks	to	be	ephemeral,	

and	recognize	that	there	are	no	net	emissions	to	the	atmosphere	following	growth	and	senescence.	

However,	with	respect	to	changes	in	land	use	(e.g.,	forest	to	cropland),	the	IPCC	(Lasco	et	al.,	2006)	

recommends	that	grazing	land	biomass	be	counted	in	the	year	that	land	conversion	occurs	(Verchot	

et	al.,	2006).		According	to	the	IPCC,	accounting	for	the	herbaceous	biomass	carbon	stock	during	

changes	in	land	use	is	necessary	to	account	for	the	influence	of	herbaceous	plants	on	CO2	uptake	

from	the	atmosphere	and	storage	in	the	terrestrial	biosphere.	The	method	is	considered	a	Tier	2	

method	as	defined	by	the	IPCC	because	it	incorporates	factors	that	are	based	on	U.S.	specific	data.	

The	methods	presented	in	this	section	are	based	on	the	following	definitions.	

 Live vegetation biomass:	Live	vegetation	includes	trees,	shrubs,	and	grasses.	The	tree	carbon	

pool	includes	aboveground	and	belowground	carbon	mass	of	live	trees,	as	defined	in	

Section	6.2.3.1,	and	the	aboveground	biomass	of	the	forest	understory	is	defined	in	Section	

6.2.3.2.	The	methods	to	estimate	full‐tree	and	aboveground	biomass	for	trees	greater	than	

one	inch	in	diameter	at	breast	height	are	based	on	the	models	provided	in	the	forest	section.	

Method for Estimating Live and Dead Biomass Carbon in Wetlands

 Methods	for	estimating	forest	vegetation	and	shrub	and	grassland	vegetation	biomass	

carbon	stocks	use	a	combination	of	the	Forest	Vegetation	Simulator	model	and	the	

biomass	carbon	stock	changes	method	in	Section	3.5.1	of	Chapter	3,	Cropland	and	Grazing	

Land.	If	there	is	a	land‐use	change	to	agricultural	use,	methods	for	cropland	herbaceous	

biomass	are	provided	in	Chapter	3.		

 These	methods	were	chosen	because	they	offer	the	most	consistent	approach	within	the	

context	of	this	report.
 Chapter 4: Quantifying Greenhouse Gas Sources and Sinks in Managed Wetland Systems 

 4-15 

The	forest	understory	vegetation	includes	all	biomass	of	undergrowth	plants	in	a	forest,	

including	woody	shrubs	and	trees	less	than	one	inch	in	diameter	at	breast	height.			

 Standing dead wood (dead biomass):	The	carbon	pool	of	standing	deadwood	in	a	forested	

wetland	is	defined	and	estimated	according	to	the	methods	in	Section	6.2.3.3	of	Chapter	6,	

Forestry.						

 Down dead organic matter—litter layer (dead biomass):	Down	dead	organic	matter	includes	

the	litter	layer	composed	of	small	pieces	of	dead	wood,	branches,	leaves,	and	roots	in	

various	stages	of	decay.	This	layer	is	typically	designated	as	the	organic	layer	of	the	soil.		

This	pool	also	includes	logs	in	various	stages	of	decay	that	lie	on	the	soil	surface	(e.g.,	

Section	6.2.3.4,	down‐dead	wood,	and	Section	6.2.3.5,	forest	floor	or	litter).	

4.3.1.2 Description of Method

Provisions	for	estimating	aboveground	biomass	for	wetland	forests	and	above	and	belowground	

biomass	and	carbon	are	included	for	shrub	and	grass	wetlands	in	this	section.	Since	the	vegetative	

cover	on	wetlands	may	vary	from	natural	communities	to	agricultural	crops,	cross‐references	are	

made	to	ensure	congruity	with	Section	3.5.1	of	Chapter	3,	Croplands,	and	Grazing	Lands,	and	

Section	6.2.3	of	Chapter	6,	Forestry.				

Forest vegetation:	Biomass	carbon	stocks	are	estimated	for	forests	in	wetlands	using	the	methods	

described	in	Section	6.2.3	of	Chapter	6,	Forestry.	The	approach	uses	the	FVS,	which	is	a	system	of	

growth	and	yield	models	that	estimate	growth	and	yield	for	U.S.	forests.	FVS	is	an	individual	tree	

model	and	can	estimate	biomass	carbon	stock	change	for	nearly	any	type	of	forest	stand.	The	Fire	

and	Fuels	Extension	to	FVS	can	be	used	to	generate	reports	of	all	live	and	dead	biomass	carbon	

pools	in	addition	to	harvested	wood	products.	Regional	variants	are	available	for	FVS	that	allow	for	

region‐specific	focus	on	species	and	forest	vegetation	communities.	The	driver	for	productivity	is	

the	availability	of	site	index	curves,6	and	the	regional	variants	include	many	wetland	tree	species.		

Regional	variants	of	FVS	may	also	provide	provisions	for	refining	the	basis	for	estimating	

productivity	by	classifying	the	area	of	interest	into	ecological	units,	habitat	type,	or	plant	

associations.	However,	if	a	species‐specific	curve	is	not	available,	then	a	default	function	is	used	to	

estimate	carbon	stock	changes.	

Grassland vegetation:	The	change	in	carbon	stock	for	grass	wetlands	is	generally	small	unless	there	

are	drought	conditions	or	the	area	is	actively	managed.	In	cases	where	reporting	is	required,	

biomass	carbon	stock	changes	can	be	estimated	following	a	land	use	change	using	the	method	in	

Section	3.5.1	of	Chapter	3,	Croplands	and	Grazing	Lands.	There	are	no	methods	currently	available	

to	estimate	the	shrub	cover.		

4.3.1.3 Activity Data

Forested wetlands:	The	data	and	requirements	for	estimating	the	changes	in	carbon	stocks	in	

wetland	forests	are	the	same	as	those	described	for	upland	forests	in	Section	6.2.3.			

Grassland vegetation: The	data	and	requirements	for	estimating	the	changes	in	carbon	stocks	in	

grassland	vegetation	are	the	same	as	those	described	for	total	biomass	carbon	stock	changes	

presented	in	the	Croplands/Grazing	Lands	Sections	3.5.1.		

																																																													

6	Site	index	is	the	measure	of	a	forest’s	potential	productivity.	The	height	of	the	dominant	or	co‐dominant	

trees	at	a	specified	age	in	a	stand	are	calculated	in	an	equation	that	uses	the	tree’s	height	and	age.	Site	index	

equations	differ	by	tree	species	and	region.	Site	index	curves	are	constructed	by	using	the	tree	heights	at	a	

base	age	and	an	equation	is	derived	from	the	curves	to	estimate	the	site	index	when	an	individual	tree’s	age	is	

not	the	same	as	the	base	age	(Hanson	et	al.,	2002).		
Chapter 4: Quantifying Greenhouse Gas Sources and Sinks in Managed Wetland Systems 

4-16

4.3.1.4 Model Output

Change	in	aboveground	carbon	pools	associated	with	wetland	forests	are	provided	for	live	

vegetation,	standing	dead	biomass,	and	down	dead	biomass.	Change	in	live	biomass	carbon	is	also	

provided	for	belowground	biomass.	The	units	of	reporting	are	metric	tonnes	ha‐1	CO2‐eq.	

4.3.1.5 Limitations and Uncertainty

Estimates	of	the	forest	biomass	carbon	pools	in	wetlands	are	constrained	by	limited	data	on	

productivity	response	to	management	and	are	sensitive	to	the	wide	array	of	characteristic	

vegetative	communities	and	soil	types.	Although	FVS	is	the	most	inclusive	model	available,	many	

results	for	wetlands	will	still	be	based	on	default	model	functions,	because	there	is	limited	data	on	

the	growth	of		specific	wetland	species	under	particular	management	regimes.	Accordingly,	the	

results	will	provide	a	relative	basis	for	tracking	changes	over	time	in	biomass	carbon.	Table	4‐3	

summarizes	additional	limitations	in	the	current	approach.			

Table 4‐3: Key Limitations to Estimating Biomass Carbon Pools in Forest Wetland Vegetation

Consideration Limitation

Ratio for belowground

biomass

A	ratio	is	used to	estimate	belowground	biomass	in	upland	and	wetland	forests	

based	on	aboveground	biomass.	While	a	common	ratio	will	provide	a	basis	for	

estimating	relative	change,	it	will	likely	over	or	underestimate	actual	stocks	in	

many	wetlands.			

Response to

management or

climatic conditions

Wetland	vegetation	is	known	to	respond	to	management	practices,	soil, and	

climatic	conditions.	Those	relationships	are	not	necessarily	reflected	in	FVS	

because	there	is	insufficient	basis	for	generalized	assessment	purposes.	For	

example,	in	response	to	dynamic	water‐level	fluctuations	during	wet	and	dry	

cycles,	wetlands	often	exhibit	major	intra	and	interannual	shifts	in	vegetative	

structure,	ranging	from	open	water	to	emergent	herbaceous	vegetation.	

Correspondingly,	the	altered	site	conditions	under	the	management	regime	

and	the	genetic	quality	of	the	planted	trees	may	exhibit	responses	that	are	not	

captured	by	the	existing	allometric	relationships	in	FVS.	

This	shrub	and	grassland	method	is	based	on	the	assumptions	found	in	Chapter	3,	Cropland	and	

Grazing	Land.	Essentially,	the	method	assumes	that	half	of	the	crop	biomass	at	harvest	or	peak	

forage/shrub	biomass	provides	an	accurate	estimate	of	the	mean	annual	carbon	stock.	This	

assumption	warrants	further	study,	and	the	method	may	need	to	be	refined	in	the	future.	

Major	sources	of	uncertainty	include	belowground	biomass,	vegetation	response	to	management,	

and	hydrologic	regime	(e.g.,	seasonal	hydroperiod).	Uncertainty	in	herbaceous	carbon	stock	

changes	will	result	from	lack	of	precision	in	crop	or	forage	yields,	residue‐yield	ratios,	root‐shoot	

ratios,	and	carbon	and	carbon	fractions,	as	well	as	the	uncertainties	associated	with	estimating	the	

biomass	carbon	stocks	for	the	other	land	uses.		

Measurement,	sampling,	and	regression/modeling	errors	are	all	part	of	the	estimation	process	in	

FVS.	Some	similar	measure	of	the	representativeness	of	selected	forest	inventory	and	analysis	plots	

to	the	entities’	forests	is	needed.	Uncertainties	about	carbon	conversion	factors	are	also	significant	

in	some	cases.	
 Chapter 4: Quantifying Greenhouse Gas Sources and Sinks in Managed Wetland Systems 

 4-17 

4.3.2 Soil C, N2O, and CH4 in Wetlands

4.3.2.1 Rationale of Method

The	production	and	consumption	of	carbon	in	wetland‐dominated	landscapes	are	important	for	

estimating	the	contribution	of	GHGs,	including	CO2,	CH4,	and	N2O	emitted	from	those	areas	to	the	

atmosphere.	The	generation	and	emission	of	GHGs	from	wetland‐dominated	landscapes	are	closely	

related	to	inherent	biogeochemical	processes	that	also	regulate	the	carbon	balance	(Rose	and	

Crumpton,	2006).		However,	those	processes	are	highly	influenced	by	the	land	use,	vegetation,	soil	

organisms,	chemical	and	physical	soil	properties,	geomorphology,	and	climate	(Smemo	and	Yavitt,	

2006).			

Given	this	complexity,	a	process‐based	modeling	approach	is	desirable	because	these	approaches	

typically	account	for	more	of	the	variability	than	simpler	emission	factor	methods	(IPCC,	2006).	

However,	few	process‐based	models	have	been	tested	sufficiently	to	be	used	for	operational	

reporting	of	GHG	emissions.	One	of	the	more	widely	tested	models	for	estimating	GHG	fluxes	from	

wetlands	is	the	DNDC	model.	DNDC	is	a	process‐based	biogeochemical	model	that	is	used	to		

predict	plant	growth	and	production,	carbon	and	nitrogen	balance,	and	generation	and	emission	of	

soil‐borne	trace	gases	by	means	of	simulating	carbon	and	nitrogen	dynamics	in	natural	and	

agricultural	ecosystems	(Li	et	al.,	2000;	Miehle	et	al.,	2006;	Stang	et	al.,	2000)	and	forested	wetlands	

(Zhang	et	al.,	2002).	The	model	is	designed	to	explicitly	consider	anaerobic	biogeochemical	

processes,	which	are	fundamental	to	addressing	soil	carbon	dynamics	and	trace	GHG	dynamics	in	

wetlands	(Trettin	et	al.,	2001).	It	integrates	decomposition,	nitrification–denitrification,	

photosynthesis,	and	hydro‐thermal	balance	within	the	ecosystem.	These	components	are	mainly	

driven	by	environmental	factors,	including	climate,	soil,	vegetation,	and	management	practices.	

DNDC	has	been	tested	and	used	for	estimating	GHG	emissions	from	forested	ecosystems	in	a	wide	

range	of	climatic	regions,	including	boreal,	temperate,	subtropical,	and	tropical	(Kesik	et	al.,	2006;	

Kiese	et	al.,	2005;	Kurbatova	et	al.,	2008;	Li	et	al.,	2004;	Stang	et	al.,	2000;	Zhang	et	al.,	2002),	and	

similarly	for	grasslands	and	cultivated	wetlands	(Giltrap	et	al.,	2010;	Rafique	et	al.,	2011).			

4.3.2.2 Description of Method

The	method	consists	of	using	the	process‐based	model—DNDC—to	estimate	the	changes	in	soil	

organic	carbon	(SOC)	stocks,	CH4,	and	N2O	emissions,	based	on	the	standing	biomass	and	plant	

growth	that	are	provided	by	the	vegetation	method	outlined	above	(Section	4.3.1),	wetland	

characteristics,	and	the	planned	management	activities.	The	model	simulates	SOC	stocks,	CH4,	and	

N2O	emissions	at	the	beginning	of	the	reporting	period	based	on	an	assessment	of	initial	conditions	

at	the	site;	then	the	model	simulates	the	reporting	period	based	on	the	current/recent	management	

activity	and	any	changes	in	the	wetland	conditions.	This	information	characterizes	the	physical	and	

chemical	soil	properties	that	in	turn	interact	with	the	climatic	regime,	management	practices,	and	

Method for Estimating Soil C, N2O and CH4 in Wetlands

 The	DNDC	process‐based	biogeochemical	model	is	the	method	used	for	estimating	soil	

C,	N2O,	and	CH4	emissions	from	wetlands.	

 DNDC	predicts	soil	carbon	and	nitrogen	balance	and	generation	and	emission	of	soil‐

borne	trace	gases	by	simulating	carbon	and	nitrogen	dynamics	in	natural	and	

agricultural	ecosystems	(Li	et	al.,	2000;	Miehle	et	al.,	2006;	Stang	et	al.,	2000)	and	

forested	wetlands	(Dai	et	al.,	2011;	Zhang	et	al.,	2002),	using	plant	growth	estimated	as	

described	in	Section	4.3.1.		
Chapter 4: Quantifying Greenhouse Gas Sources and Sinks in Managed Wetland Systems 

4-18

the	vegetation	response.	The	reported	emissions	for	the	land	parcel	must	reflect	the	total	for	the	

entire	land	area.	Accordingly,	the	per‐unit	area	emission	rates	from	DNDC	are	expanded	based	on	

the	total	wetland	area	for	the	land	parcel	to	estimate	total	emissions.			

Equation	4‐1	is	used	to	estimate	SOC	stock	changes	from	a	parcel	of	land	in	a	wetland:	

Equation	4‐2	is	used	to	estimate	CH4	emissions	from	a	parcel	of	land	in	a	wetland:	

N2O	emissions	are	estimated	for	a	land	parcel	in	a	wetland	using	Equation	4‐3:	

Equation 4‐1: Change in Soil Organic Carbon Stocks for Wetlands

ΔCSoil = (SOCt	‐	SOCt‐1) x AxCO2MW

Where:	

ΔCSoil		 =	Annual	change	in	mineral	soil	organic	carbon	stock	(metric	tons	CO2‐eq	year‐1)	

SOCt		 =	Soil	organic	carbon	stock	at	the	end	of	the	year	(metric	tons	C	ha‐1)	

SOCt‐1		 =	Soil	organic	carbon	stock	at	the	beginning	of	the	year	(metric	tons	C	ha‐1)	

A		 =	Area	of	parcel	(ha)	

CO2MW		 =	Ratio	of	molecular	weight	of	CO2	to	C	=	44/12	(metric	tons	CO2	(metric	tons	C)‐1)	

Equation 4‐2: Methane Emissions from Wetlands

CH4 = ER x A xCH4MW x CH4GWP

Where:	

CH4	 =	Total	CH4	emissions	from	the	land	parcel	(metric	tons	CO2‐eq	year‐1)	

ER		 =	Emission	rate	on	a	per	unit	wetland	area	(metric	tons	CH4	ha‐1	year‐1)	

A	 =	Area	(ha)	

CO2MW		 =	Ratio	of	molecular	weight	of	CH4	to	C	=	16/12	(metric	tons	CH4	(metric	tons	C)‐1)	

CH4GWP	 =	Global	warming	potential	of	CH4

Equation 4‐3: Nitrous Oxide Emissions from Wetlands

N2O= ER x A xCO2MW x CH4GWP

Where:	

N2O	 =	Total	N2O	emissions	from	the	land	parcel	(metric	tons	CO2‐eq	year‐1)	

ER		 =	Emission	rate	on	a	per	unit	land	area	(metric	tons	N2O	ha‐1	year‐1)	

A	 =	Area	(ha)	

CO2MW		 =	Ratio	of	molecular	weight	of	N2O	to	N	=	44/28	

	(metric	tons	N2O	(	metric	tons	N2O‐N)‐1)	

CH4GWP	 =	Global	warming	potential	of	N2O	
 Chapter 4: Quantifying Greenhouse Gas Sources and Sinks in Managed Wetland Systems 

 4-19 

To	estimate	the	SOC	stock	changes,	CH4,	and	N2O	emissions,	DNDC	requires	a	considerable	amount	

of	information	to	characterize	the	plant	production	(Section	4.3.1),	wetland	characteristics,	and	

management	activities.	The	initial	step	in	applying	the	method	is	to	parameterize	DNDC	using	the	

baseline	soil	conditions,	along	with	the	corresponding	forest	or	grassland	conditions.	For	example,	

if	a	forest	plantation	is	to	be	harvested	and	regenerated	during	the	reporting	period,	the	initial	

conditions	should	reflect	the	pre‐harvest	conditions.	Based	on	the	initial	conditions,	the	model	

simulates	baseline	fluxes	and	the	SOC	stock	prior	to	the	reporting	period	for	the	entity.		

Subsequently,	the	entity	specifies	the	type	of	management	activity(s)	changes	that	occurred	during	

the	reporting	period	(if	any	occurred).	Provisions	are	available	to	have	multiple	management	

activities	on	a	single	tract	if	there	were	mixed	activities.	Climatic	factors,	especially	precipitation,	

can	affect	carbon	turnover	and	wetland	conditions.	Consequently,	weather	data	are	a	key	input	to	

DNDC,	and	will	be	provided	from	a	climatological	data	set.	

The	simulation	output	at	the	end	of	each	year	is	used	to	estimate	change	in	SOC	stocks	and	the	total	

amount	of	CH4	and	N2O	emissions	for	the	year.		Annual	changes	in	SOC	can	be	estimated	based	on	

the	difference	between	years,	and	the	total	change	in	emissions	can	be	estimated	by	combining	the	

changes	in	SOC	pools	with	the	annual	CH4	and	N2O	flux.			

4.3.2.3 Activity Data

Activity	data	for	the	application	of	DNDC	are	summarized	in	Table	4‐4.	Vegetation	management	

information	affects	the	amount	of	organic	matter	that	is	available	for	decomposition	processes.		

Water	management	information	conveys	how	the	drainage	system	affects	the	soil	water	table	

dynamic	as	compared	to	an	undrained	condition.	The	soil	tillage	information	is	used	to	convey	

when	the	surface	soil	is	disturbed	or	its	elevation	changed	because	of	the	associated	effects	on	

decomposition.	The	fertilization	information	is	needed	because	the	addition	of	nitrogen	greatly	

affects	decomposition	and	N2O	production.	In	addition,	land	use	history	influences	the	amount	of	

soil	organic	carbon.	If	an	entity	is	composed	of	different	wetland	types,	it	is	recommended	that	

separate	estimates	be	prepared	because	the	carbon	turnover	rate	and	GHG	emissions	can	vary	

widely	depending	on	hydric	soil	properties	and	the	type	of	vegetation.	

Table 4‐4: Activity Data for Application of DNDC

Category Management Practice Data

Vegetation

management

Grazing	or	management	events	should	be	

included	to	capture	the	influence	on	

carbon	input	to	soils	and	subsequent	

effects	on	the	soil	carbon	stocks.	

 Harvesting:	date,	harvest,	or	cut	fraction

 Understory	thinning	or	chopping:	date,	

chopped	fraction	

 Prescribed	fire:	date,	proportion	of	forest	

floor,	and	understory	consumed	

 Tree	planting:	date,	species,	density	

Water

management

regime

Water	table	response	to	the	drainage	

system,	daily	data.	

 Drainage	system:	date,	controlled	water	

table	elevation	

Soil

management

Application	of	soil	amendments	or	site	

preparation	practices	for	tree	planting.	  Type	of	site	preparation	

Fertilization

practices

Applications	of	mineral	or	organic	

nitrogen	fertilizers	will	be	needed	to	

simulate	the	effect	on	N2O	emissions.	

 Fertilization	frequency,	date,	application	

rate	(N,	P	kg	ha‐1)	

Land use

history

Summary	of	land	use	practices	over	the

past	5 years.	For	assessing	if	prior	use	

affects	parameterization.	The	time	since	a	

change	in	land	management	practice	for	

assessing	effects	on	decomposition.	

 Fertilization	regimes,	drainage	regimes,	

cropping,	or	forest	management	history	
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4.3.2.4 Ancillary Data		

The	DNDC	model	requires	relatively	detailed	information	about	the	site	(Table	4‐5).	While	default	

values	are	available	for	most	parameters,	some	entity‐specific	data	are	needed	to	produce	

reasonable	estimates.	Most	of	the	required	soils	input	data	are	available	from	the	national	soils	data	

base.7	Similarly,	climate	data	are	available	from	the	National	Climate	Data	Center.8		

Table 4‐5: Input Information Needed for the Application of DNDC		

Category Data

Climate Daily	maximum	and	minimum	temperature,	daily	rainfall; nitrogen	deposition	in	rainfall,

or	use	default	value.	

Vegetation Standing	biomass	and	biomass	and	detrital	inputs	provided	in	Section	4.3.1;	belowground

biomass	estimated	based	on	aboveground	biomass.	

Soil

Hydraulic	parameters	and	physical	and	chemical	components, including	thickness;	layers;

hydraulic	conductivity;	porosity;	field	capacity;	wilting	point;	carbon	content;	pH;	organic	

matter	fractions;	content	of	stone,	sand,	silt,	and	clay;	and	bulk	density	for	major	soil	

layers.	

Hydrology Water	table	below	surface	as	daily	input	or	starting	position	and	DNDC	can	estimate	GHG	

emissions	and	sinks	using	empirical	functions.	

4.3.2.5 Model Output

Model	output	includes	annual	estimates	of	CH4,	N2O	emissions,	and	changes	in	soil	organic	carbon	

stocks.	The	units	of	reporting	are	metric	tons	CO2‐eq	ha‐1.	

4.3.2.6 Limitations and Uncertainty

The	models	to	estimate	carbon	sequestration	in	vegetation	are	robust	with	respect	to	species	and	

community	composition.	However,	uncertainties	may	be	higher	than	for	uplands	because	of	limited	

background	information.	The	merit	of	the	recommended	approach	is	that	it	ensures	consistency	for	

estimating	changes	in	the	vegetative	carbon	pool	among	land	types	and	uses	by	using	common	

methods	as	described	in	Section	4.3.1.	However,	this	approach	complicates	the	application	of	DNDC	

for	estimating	changes	in	soil	carbon	pools	and	fluxes	because	it	contains	provisions	for	

sequestering	carbon	in	crops,	grasslands,	and	forest	vegetation.	Accordingly,	DNDC	would	have	to	

undergo	substantial	revisions	to	accommodate	the	vegetative	component	as	an	input	variable	

because	the	vegetation	growth	functions	are	integral	with	the	consideration	of	hydrologic	

processes	(especially	evapotranspiration)	and	biogeochemical	processes.	The	DNDC	model	could	

be	used	as	a	stand‐alone	tool	for	wetlands,	but	unfortunately,	the	production	or	carbon	

sequestration	functions	have	not	been	validated	for	many	of	the	wetland	plant	communities.			

The	availability	of	water	table	data	is	essential	to	modeling	the	carbon	cycle	in	wetland	soils.	Since	

the	lack	of	site‐specific	water	table	data	for	a	sufficient	period	is	likely	a	constraint	for	most	entities,	

an	approach	incorporating	a	hydrologic	module	or	look‐up	table	is	needed.	Hydrologic	models	that	

provide	information	on	water	table	dynamics	are	inherently	complex,	but	they	can	be	effective	(Dai.	

et	al.,	2010).	Accordingly,	the	development	of	characteristic	water	table	conditions	for	a	range	of	

climatological	and	soil	settings	would	be	a	viable	approach	that	can	also	incorporate	water	

management	effects	(e.g.,	Skaggs	et	al.,	2011).				

																																																													

7 See	National	Cooperative	Soil	Survey	Soil	Characterization	data http://soils.usda.gov/survey/nscd/.

8 See	NOAA	National	Climatic	Data	Center	http://www.ncdc.noaa.gov/.
 Chapter 4: Quantifying Greenhouse Gas Sources and Sinks in Managed Wetland Systems 

 4-21 

Tidal	freshwater	forested	wetlands,	which	occur	to	a	limited	extent	along	the	Atlantic,	Gulf,	and	

Pacific	coasts,	are	a	special	case.	The	tidal	influence	on	water	table	dynamics	can	make	

characterizing	the	water	table	regime	of	such	sites	more	difficult.	For	DNDC	to	simulate	the	carbon	

dynamics	would	require	detailed	data	on	daily	water	table	dynamics,	and	such	detailed	data	are	

unavailable.							

While	the	effects	of	the	various	management	regimes	on	soil	carbon	pools	and	GHG	fluxes	have	not	

been	widely	studied,	this	is	more	of	a	consideration	with	respect	to	uncertainties	in	the	estimates	as	

opposed	to	a	limitation	to	its	application.	The	DNDC	framework	is	robust	because	it	is	a	process‐

based	model	that	has	been	validated	in	a	wide	variety	of	wetland	types	and	soils.	However,	it	has	

not	been	extensively	tested	on	Histosols	or	peat	soils,	especially	with	respect	to	changes	in	soil	

carbon	stocks.	The	model	was	validated	successfully	for	estimating	CH4	from	micotopographic	

positions	in	a	peatland	(Zhang	et	al.,	2002),	but	additional	work	is	needed	to	better	address	the	

wide	array	of	managed	Histosols	that	exist	across	the	country.	

Similarly,	this	method	is	not	applicable	to	constructed	wetlands,	impoundments,	or	shallow	

reservoir	systems	that	have	extended	periods	of	ponding;	those	sites	would	tend	to	have	dynamics	

more	similar	to	a	lake	or	pond	as	opposed	to	a	terrestrial	ecosystem.		

With	respect	to	the	forest	model,	accuracy	of	the	estimates	is	dependent	on	applicability	of	the	

available	site	index	curves.	While	the	general	curves	are	available	for	all	species,	they	may	not	

accurately	represent	the	site	or	the	entity’s	management	regime.	Provisions	are	included	within	

FVS	for	customizing	the	tree	site	index	curves,	which	could	be	important	for	an	entity	especially	if	

genetically‐improved	planting	stock	and	fertilization	regimes	are	employed.	

Detrital	organic	matter	is	the	source	for	decomposition	processes.	The	effect	of	vegetation	on	

wetland	carbon	dynamics	is	promulgated	through	the	amount	of	organic	matter	and	the	water	

regime	(e.g.,	evapotranspiration).	Accordingly,	the	accuracy	of	the	vegetation	productivity	and	

turnover	will	affect	the	estimates	of	the	soil	carbon	pools	and	GHG	flux.			

Water	table	position	is	the	most	critical	factor	affecting	CH4	and	N2O	flux	from	the	wetland	soil	

(Trettin	et	al.,	2006).	Accordingly,	considerations	to	improve	that	estimate	as	discussed	in	Section	

4.3.2	will	improve	the	estimates	of	GHG	emissions	from	the	soil.	There	are	other	uncertainties	in	

the	activity	and	ancillary	data,	as	well	as	model	structure	that	can	create	bias	and	imprecision	in	the	

resulting	estimates.	Wetlands	typically	exist	in	a	mosaic	with	upland	forests,	grasslands,	and	

cultivated	lands.	Accordingly,	the	accuracy	of	partitioning	the	entity	into	upland	(agriculture,	

forest)	and	wetlands	will	affect	the	accuracy	of	the	estimates.			

4.4 Research Gaps for Wetland Management

Wetland	management,	and	its	influence	on	GHG	emissions,	is	not	as	well	studied	as	some	of	the	

other	management	practices	in	this	report,	such	as	tillage	in	croplands	or	forest	harvesting	

practices	in	uplands.		There	is	the	potential	for	improving	the	estimation	of	GHG	emissions	

associated	with	different	management	practices	in	the	future	if	there	are	monitoring	activities	and	

studies	to	fill	information	gaps.	A	select	number	of	information	needs	and	research	gaps	are	

identified	here.			

 The	2013	Supplement	to	the	2006	Intergovernmental	Panel	on	Climate	Change	(IPCC)	

Guidelines	provide	new	guidance	for	estimating	emissions	from	drained	inland	organic	

soils,	rewetted	organic	soils,	coastal	wetlands,	inland	wetland	mineral	soils,	and	constructed	

wetlands	for	wastewater	treatment	(Blain	et	al.,	2013).		These	newly	developed	guidelines	

will	be	compared	to	the	technical	methods	provided	in	this	report.	
Chapter 4: Quantifying Greenhouse Gas Sources and Sinks in Managed Wetland Systems 

4-22

 Water	table	position	is	the	principal	factor	affecting	carbon	dynamics	in	wetlands;	

unfortunately	there	is	a	lack	of	long‐term	data,	which	is	needed	to	characterize	the	water	

table	response	to	a	management	regime	and	to	provide	a	basis	for	validating	assessment	

tools.	Establishment	of	a	network	of	water	table	monitoring	sites	within	selected	USDA	

Forest	Service	experimental	forests	and	ranges	and	USDA‐Agricultural	Research	Service	

(ARS)	experiment	stations	could	provide	the	continuity	in	measurements	and	linkages	with	

common	management	practices	to	represent	the	major	soil	and	climatic	condition	in	the	

United	States.	

 Improving	modeling	capabilities	that	integrate	surrounding	areas	with	the	wetlands	that	

receive	surface	and	subsurface	drainage	waters	will	allow	for	modeling	the	flows	of	

nutrients	and	organic	matter	into	wetlands	and	subsequent	losses	to	other	wetlands	

beyond	the	entity’s	operation.	This	type	of	assessment	framework	is	used	in	several	

established	spatially‐explicit	hydrologic	models;	the	need	is	to	integrate	the	

biogeochemistry.	Linked	models	can	be	used	at	present;	but	development	of	a	functionally‐

integrated	system	is	needed	to	support	broad‐based	applications.	

 There	is	a	need,	generally,	for	improved	information	on	biomass	production	and	allocation	

in	managed	wetlands.	These	data	could	be	obtained	through	a	coordinated	monitoring	

program	employing	USDA‐Forest	Service	experimental	forests	and	ranges,	USDA‐ARS	

experiment	stations,	and	U.S.	Department	of	the	Interior	wildlife	refuges	to	monitor	

production	of	key	species	or	vegetation	types	in	association	with	common	management	

prescriptions.	There	is	also	need	for	more	detailed	mechanistic	research	to	provide	

information	on	energy,	water,	and	GHG	dynamics	on	selected	managed	sites;	this	

information	is	critical	for	validating	process‐based	models.	

 Field‐based	studies	are	needed	to	develop	more	complete	databases	that	provide	ancillary	

data	for	GHG	estimation,	particularity	CH4	emissions	for	DNDC	or	similar	process‐based	

models,	rather	than	relying	on	entity	input,	which	will	likely	be	challenging.	A	key	attribute	

of	this	work	should	be	the	consideration	of	the	inherent	spatial	and	temporal	variability	

within	a	site.	

 Further	quantification	of	the	controlling	and	threshold	parameters	and	associated	

uncertainty	within	DNDC	or	similar	process‐based	models	to	estimate	trace	gas	emissions	is	

warranted.	This	work	could	also	suggest	a	path	towards	development	of	an	assessment	tool	

that	was	not	reliant	on	a	wide	array	of	parameters	to	effectively	simulate	the	GHG	dynamics	

of	the	site.	

 A	more	robust	and	extensive	database	on	GHG	emissions	from	freshwater	tidal	(salinity	<	

0.5	‰)	palustrine	wetlands	is	needed	to	more	fully	understand	the	drivers	of	emissions,	in	

addition	to	providing	a	more	complete	dataset	for	parameterization	and	evaluation	of	

process‐based	models.			

 Studies	on	individual	sites	and	meta‐analyses	of	existing	data	are	needed	to	fully	evaluate	

the	net	GHG	flux	for	CH4,	N2O,	and	soil	carbon.	Most	studies	only	consider	one	of	the	GHGs	

and	may	mask	some	of	the	differences	in	fluxes	among	the	GHGs	associated	with	a	

management	activity.	

 Constructed	wetlands	are	discussed	qualitatively	in	Section	5.4.10	of	Chapter	5,	Animal	

Production	Systems	for	Liquid	Manure	Storage	and	Treatment	in	Constructed	Wetlands.	

More	research	is	needed	in	this	area	to	accurately	estimate	emissions	from	constructed	

wetlands.		

This	list	is	not	exhaustive	but	is	intended	to	provide	some	direction	for	improving	the	estimation	

methods	for	GHG	emission	from	wetlands.	
 Chapter 4: Quantifying Greenhouse Gas Sources and Sinks in Managed Wetland Systems 

 4-23 

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Winter,	T.C.,	J.W.	Harvey,	and	O.L.	Franke.	1998.	Ground water and surface water: a single resource:	

U.S.	Geological	Survey.		

Woodward,	K.B.,	C.S.	Fellows,	C.L.	Conway,	and	H.M.	Hunter.	2009.	Nitrate	removal,	denitrification	

and	nitrous	oxide	production	in	the	riparian	zone	of	an	ephemeral	stream.	Soil Biology and

Biochemistry,	41(4):671‐680.	

Wu,	J.,	J.	Zhang,	W.L.	Jia,	H.J.	Xie,	et	al.	2009.	Nitrous	oxide	fluxes	of	constructed	wetlands	to	treat	

sewage	wastewater.	Huanjing Kexue/Environmental Science,	30(11):3146‐3151.	

Young,	E.O.,	and	R.D.	Briggs.	2008.	Phosphorus	concentrations	in	soil	and	subsurface	water:	a	field	

study	among	cropland	and	riparian	buffers.	J Environ Qual,	37(1):69‐78.	

Zhang,	Y.,	C.	Li,	C.C.	Trettin,	and	G.	Sun.	2002.	An	integrated	model	of	soil,	hydrology	and	vegetation	

for	carbon	dynamics	in	wetland	ecosystems.	Global Biogeochemical Cycles,	16:1‐17.	

Zhu,	G.,	M.S.M.	Jetten,	P.	Kuschk,	K.F.	Ettwig,	et	al.	2010.	Potential	roles	of	anaerobic	ammonium	and	

methane	oxidation	in	the	nitrogen	cycle	of	wetland	ecosystems.	Applied Microbiology and

Biotechnology,	86(4):1043‐1055.	

Zhu,	N.,	P.	An,	B.	Krishnakumar,	L.	Zhao,	et	al.	2007.	Effect	of	plant	harvest	on	methane	emission	

from	two	constructed	wetlands	designed	for	the	treatment	of	wastewater.	Journal of

Environmental Management,	85(4):936‐943.	
Chapter 4: Quantifying Greenhouse Gas Sources and Sinks in Managed Wetland Systems 

4-30

This	page	is	intentionally	left	blank.	
Authors:		

Wendy	Powers,	Michigan	State	University	(Lead	Author)	

Brent	Auvermann,	Texas	A&M	University	

N.	Andy	Cole,	USDA	Agricultural	Research	Service	

Curt	Gooch,	Cornell	University	

Rich	Grant,	Purdue	University	

Jerry	Hatfield,	USDA	Agricultural	Research	Service	

Patrick	Hunt,	USDA	Agricultural	Research	Service	

Kristen	Johnson,	Washington	State	University	

April	Leytem,	USDA	Agricultural	Research	Service	

Wei	Liao,	Michigan	State	University	

J.	Mark	Powell,	USDA	Agricultural	Research	Service

Contents:

5 Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems ..............5‐5

5.1	 Overview	...........................................................................................................................................................	5‐5	

5.1.1	 Overview	of	Management	Practices	and	Resulting	GHG	Emissions	.........................	5‐5	

5.1.2	 System	Boundaries	and	Temporal	Scale	............................................................................	5‐12	

5.1.3	 Summary	of	Selected	Methods/Models/Sources	of	Data	...........................................	5‐12	

5.1.4	 Organization	of	Chapter/Roadmap	......................................................................................	5‐14	

5.2	 Animal	Production	Systems	....................................................................................................................	5‐18	

5.2.1	 Dairy	Production	Systems	........................................................................................................	5‐18	

5.2.2	 Beef	Production	Systems	..........................................................................................................	5‐22	

5.2.3	 Sheep	Production	Systems.......................................................................................................	5‐25	

5.2.4	 Swine	Production	Systems	......................................................................................................	5‐25	

5.2.5	 Poultry	Production	Systems	....................................................................................................	5‐28	

5.3	 Emissions	from	Enteric	Fermentation	and	Housing	.....................................................................	5‐30

5.3.1	 Enteric	Fermentation	and	Housing	Emissions	from	Dairy	Production	Systems	.......		

	.............................................................................................................................................................	5‐31	

5.3.2	 Enteric	Fermentation	and	Housing	Emissions	from	Beef	Production	Systems	.	5‐44	

5.3.3	 Enteric	Fermentation	and	Housing	Emissions	from	Sheep	.......................................	5‐52	

5.3.4	 Enteric	Fermentation	and	Housing	Emissions	from	Swine	Production	Systems	......		

	.............................................................................................................................................................	5‐53	

5.3.5	 Housing	Emissions	from	Poultry	Production	Systems	................................................	5‐60	

5.3.6	 Enteric	Fermentation	and	Housing	Emissions	from	Other	Animals	......................	5‐64	

5.3.7	 Factors	Affecting	Enteric	Fermentation	Emissions	.......................................................	5‐66	

5.3.8	 Limitations	and	Uncertainty	in	Enteric	Fermentation	and	Housing	Emissions	

Estimates	.........................................................................................................................................................	5‐73	

5.4	 Manure	Management	.................................................................................................................................	5‐75	

Chapter 5

Quantifying Greenhouse Gas 

Sources and Sinks in Animal 

Production Systems 
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-2 

5.4.1	 Temporary	Stack	and	Long‐Term	Stockpile	.....................................................................	5‐77	

5.4.2	 Source:	U.S.	EPA	(2011).Composting	..................................................................................	5‐81	

5.4.3	 Aerobic	Lagoon	.............................................................................................................................	5‐85	

5.4.4	 Anaerobic	Lagoon,	Runoff	Holding	Pond,	Storage	Tanks............................................	5‐86	

5.4.5	 Anaerobic	Digester	with	Biogas	Utilization	......................................................................	5‐91	

5.4.6	 Combined	Aerobic	Treatment	Systems	..............................................................................	5‐93	

5.4.7	 Sand‐Manure	Separation	..........................................................................................................	5‐94	

5.4.8	 Nutrient	Removal	........................................................................................................................	5‐94	

5.4.9	 Solid–Liquid	Separation	............................................................................................................	5‐95	

5.4.10	 Constructed	Wetland	.................................................................................................................	5‐97	

5.4.11	 Thermo‐Chemical	Conversion	................................................................................................	5‐98	

5.4.12	 Limitations	and	Uncertainty	in	Manure	Management	Emissions	Estimates	......	5‐99	

5.5	 Research	Gaps	............................................................................................................................................	5‐105	

5.5.1	 Enteric	Fermentation	..............................................................................................................	5‐105	

5.5.2	 Manure	Management	..............................................................................................................	5‐106	

Appendix	5‐A:	Enteric	CH4	from	Feedlot	Cattle	–	Methane	Conversion	Factor	(Ym)	..............	5‐109	

Appendix	5‐B:	Feedstuffs	Composition	Table...........................................................................................	5‐113	

Appendix	5‐C:	Estimation	Methods	for	Ammonia	Emissions	from	Manure	Management	

Systems	.....................................................................................................................................................................	5‐123	

5‐C.1	 Method	for	Estimating	Ammonia	Emissions	Using	Equations	from	Integrated	

Farm	System	Model	...............................................................................................................................	5‐123	

5‐C.1.1	Rationale	for	Selected	Method	...............................................................................	5‐123	

5‐C.1.2	Activity	Data	..................................................................................................................	5‐123	

5‐C.1.3	Ancillary	Data	...............................................................................................................	5‐124	

5‐C.2	 Method	for	Ammonia	Emissions	from	Temporary	Stack,	Long‐Term	Stockpile,	

Anaerobic	Lagoons/Runoff	Holding	Ponds/Storage	Tanks,	and	Aerobic	Lagoons	.....	5‐124	

5‐C.3	 Method	for	Estimating	Ammonia	Emissions	from	Composting	Using	IPCC	Tier	2	

Equations....................................................................................................................................................	5‐128	

5‐C.3.1	Rationale	for	Selected	Method	...............................................................................	5‐128	

5‐C.3.2	Activity	Data	..................................................................................................................	5‐129	

5‐C.3.3	Ancillary	Data	...............................................................................................................	5‐129	

5‐C.4	 Method	for	Ammonia	Emissions	from	Composting	..................................................	5‐129	

5‐C.5	 Uncertainty	in	Ammonia	Emissions	Estimates	...........................................................	5‐129	

Appendix	5‐D:	Manure	Management	Systems	Shape	Factors	( )	...................................................	5‐131	

Appendix	5‐E:	Model	Review:	Review	of	Enteric	Fermentation	Models	.......................................	5‐134	

Chapter	5	References	..........................................................................................................................................	5‐139	

Suggested	Chapter	Citation:	Powers,	W.,	B.	Auvermann,	A.	Cole,	C.	Gooch,	R.	Grant,	J.	Hatfield,	P.	

Hunt,	K.	Johnson,	A.	Leytem,	W.	Liao,	J.	M.	Powell,	2014.	Chapter	5:	Quantifying	Greenhouse	Gas	

Sources	and	Sinks	in	Animal	Production	Systems.	In	Quantifying Greenhouse Gas Fluxes in

Agriculture and Forestry: Methods for Entity‐Scale Inventory.	Technical	Bulletin	Number	1939,	Office	

of	the	Chief	Economist,	U.S.	Department	of	Agriculture,	Washington.	DC.	606	pages.	July	2014.	Eve,	

M.,	D.	Pape,	M.	Flugge,	R.	Steele,	D.	Man,	M.	Riley‐Gilbert,	and	S.	Biggar,	Eds.	

USDA	is	an	equal	opportunity	provider	and	employer.	

	 	
 Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

 5-3 

Acronyms, Chemical Formulae, and Units

AA	 Amino	acids	

AD	 Anaerobic	digestion

ADF	 Acid	detergent	fiber

AGP	 Antibiotic	growth	promoters

ASABE	 American	Society	of	Agricultural and	Biological Engineers

B0	 Maximum	methane	production	capacities

bLS	 backward	Lagrangian	stochastic	

BNR	 Biological	nitrogen	removal

BW	 Body	weight

CH4	 Methane	

CNCPS	 Cornell	Net	Carbohydrate	and	Protein	System	

CO2‐eq	 Carbon	dioxide	equivalents

CP	 Crude	protein	

CSTR	 Continuous	stirred	tank	reactor

DDGS	 Dried	distillers	grains	with	solubles

DE	 Digestible	energy	

DFM	 Direct	fed	microbials

DGS	 Distillers	grains	with	solubles

DIP	 Dietary	crude	protein

DMI	 Dry	matter	intake	

DRC	 Dry‐rolled	corn	

EF	 Emission	factor	

g	 Grams	

Gg	 Gigagrams	

GEI	 Gross	energy	intake

GHG	 Greenhouse	gas	

HCW	 Hot	carcass	weight

HMC	 High‐moisture	corn

IFSM	 Integrated	Farm	System	Model

kcal	 Kilocalorie	

kg	 Kilograms	

lb(s)	 Pound(s)	

LCA	 Life	cycle	analysis	

LU	 Livestock	unit

m	 Meters	

MCF	 Methane	conversion	factor

ME	 Metabolizable	energy

mg	 Milligram	

MGA	 Melengestrol	acetate

MJ	 Millijoules	

NE	 Net	energy	

Nex	 Nitrogen	excreted	

N	 Nitrogen	

N2O	 Nitrous	oxide	

NDF	 Neutral	detergent	fiber

NFC	 Non‐fiber	carbohydrate

NH3	 Ammonia	

NPN	 Non‐protein	nitrogen
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-4 

NSP	 Non‐starch	polysaccharide

O2	 Oxygen	

OM	 Organic	matter	

ppb	 parts	per	billion	

ppm	 parts	per	million	

RDP	 Ruminal	degradable	protein

RFI	 Residual	feed	intake

RMSPE	 Residual	mean	square	prediction	error	

SF6	 Sulfur	hexafluoride

SFC	 Steam‐flaked	corn	

TAN	 Total	ammoniacal	nitrogen

TDN	 Total	digestible	nutrients

TKN	 Total	Kjeldahl	nitrogen

TMR	 Total	mixed	ration	

UASB	 Upflow	anaerobic	sludge	blanket

UP	 Unprocessed		

U.S.	EPA	 U.S.	Environmental	Protection	Agency

VFA	 Volatile	fatty	acids	

VS	 Volatile	solids	

WDGS	 Wet	distillers	grains	with	solubles

Ym	 Methane	conversion	factor,	percent	of	gross	energy	in	feed	converted	

to	methane	

	 	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-5 

5 Quantifying Greenhouse Gas Sources and Sinks in Animal Production

Systems		

This	chapter	provides	guidance	for	reporting	greenhouse	gas	(GHG)	emissions	associated	with	

entity‐level	fluxes	from	animal	production	systems.	In	particular,	it	focuses	on	methods	for	

estimating	emissions	from	beef	cattle	(cow‐calf,	stocker,	and	feedlot	systems),	dairy	cattle,	sheep,	

swine,	and	poultry	(layers,	broilers,	and	turkey).	Information	provided	is	based	on	available	data	at	

the	time	of	writing.	In	many	cases	systems	are	oversimplified	because	of	limited	data	availability.	It	

is	expected	that	more	data	will	become	available	over	time.	This	chapter	provides	insight	into	the	

current	state	of	the	science	and	serves	as	a	starting	point	for	future	assessments.	

 Section	5.1	summarizes	animal	management	practices	and	the	resulting	GHG	emissions.

 Section	5.2	presents	an	overview	of	each	production	system	and	a	general	discussion	of

common	management	systems	and	practices.		

 Section	5.3	describes	the	methods	for	estimating	GHG	emissions	from	enteric	fermentation

and	housing	(enteric	fermentation	being	a	much	more	significant	emissions	source	than

housing).		

 Section	5.4	describes	methods	for	estimating	GHGs	from	manure	management	systems.

 Section	5.5	identifies	research	gaps	that	exist	for	quantifying	GHGs	from	animal	production

systems.	The	intent	of	identifying	research	gaps	is	to	highlight	where	improvements	in

knowledge	can	best	improve	the	usefulness	of	this	document	at	farm‐,	regional‐,	and

industry‐scales.

5.1 Overview

This	section	summarizes	the	key	practices	in	animal	management	and	the	resulting	GHG	emissions	

that	are	discussed	in	detail	in	this	chapter.	The	agricultural	practices	discussed	include	those	

required	to	breed	and	house	livestock,	including	the	management	of	resultant	livestock	waste.	

Emissions	considered	here	include	those	from	enteric	fermentation	(resulting	from	livestock	

digestive	processes),	livestock	waste	in	housing	areas,	and	livestock	waste	managed	in	systems	

(such	as	stockpiles,	lagoons,	digesters,	solid	separation,	and	others).	Options	for	management	

changes	that	may	result	in	changes	in	GHG	emissions	are	also	discussed.	

5.1.1 Overview of Management Practices and Resulting GHG Emissions

Animal	production	systems	include	agricultural	practices	that	involve	breeding	and	rearing	

livestock	for	meat,	eggs,	dairy,	and	other	animal	products	such	as	leather,	wool,	fur,	and	industrial	

Ammonia Emissions in Animal Production Systems

Ammonia	(NH3),	although	not	a	GHG,	is	emitted	in	large	quantities	from	animal	housing	and	

manure	management	systems	and	is	an	indirect	precursor	to	nitrous	oxide	(N2O)	emissions	

as	well	as	an	environmental	concern.	Inside	barns	and	housing	units,	NH3	is	considered	an	

indoor	air	quality	concern	because	it	can	have	a	negative	impact	on	animal	health	and	

production.	Volatilized	ammonia	can	react	with	other	compounds	in	the	air	to	form	

particulate	matter	with	a	diameter	of	2.5	microns.	This	fine	particulate	matter	can	penetrate	

into	the	lungs,	causing	respiratory	and	cardiovascular	problems,	and	contribute	to	the	

formation	of	haze.		

Information	about	ammonia	has	been	included	in	this	chapter	and	proposed	quantification	

methods	are	presented	in	Appendix	5‐C.	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-6 

products	like	glue	or	oils.	Farmers	and	other	facility	owners	raise	animals	in	either	confined,	semi‐

confinement,	or	unconfined	spaces;	the	practices	used	to	raise	them	are	dependent	on	animal	type,	

region,	land	availability,	and	individual	preferences	(e.g.,	conventional	or	“organic”	standards).	

Regardless	of	the	conditions	in	which	animals	are	raised	and	housed,	they	produce	GHG	emissions.	

The	magnitude	of	emissions	depends	primarily	on	the	quality	of	the	diet,	the	animals’	requirements	

and	intake	(e.g.,	grazing,	pregnant,	lactating,	performing	work),	and	the	types	of	systems	in	place	to	

manage	manure.	The	primary	source	of	methane	(CH4)	emissions	from	animal	production	systems	

is	enteric fermentation,	which	is	a	result	of	bacterial	fermentation	during	digestion	of	feed	in	

ruminant	animals.	The	second	largest	source	of	emissions	from	animal	production	systems	is	from	

the	management	of	livestock	manure.		Methane	emissions	also	occur	from	the	digestive	processes	

in	monogastric	animals;	however,	the	quantity	is	significantly	less	than	these	other	two	sources.	For	

simplicity,	in	the	report,	the	term	enteric	fermentation	refers	to	emissions	from	the	digestive	

process	of	both	ruminant	and	monogastric	animals.		

Manure	management	is	the	collection,	storage,	transfer,	and	treatment	of	animal	urine	and	feces.	

Storage	of	animal	manure	has	become	increasingly	popular	as	it	allows	synchronization	of	land	

application	of	manure	nutrients	with	crop	needs,	reduces	the	need	for	purchased	commercial	

fertilizer,	and	reduces	potential	for	soil	compaction	due	to	poor	timing	of	manure	application.	

Depending	on	the	storage	and	treatment	practices,	manure	management	has	the	added	benefit	of	

reducing	air	and	water	pollution.	However,	manure	stored	in	anaerobic	conditions	results	in	the	

production	and	potential	release	of	GHGs	and	odors.	Greenhouse	gas	emissions	from	three	solid	

manure	storage/treatment	practices	(temporary	stack	and	long‐term	stockpile,	composting,	and	

thermo‐chemical	conversion)	and	eight	liquid	manure	storage/treatment	practices	(aerobic	lagoon,	

anaerobic	lagoon/runoff	holding	pond/storage	tanks,	anaerobic	digestion,	combined	aerobic	

treatment	system,	sand‐manure	separation,	nutrient	removal,	solid‐liquid	separation,	and	

constructed	wetland)	are	considered	in	the	report.		

Figure	5‐1	provides	an	overview	of	the	connections	between	feed,	animals,	manure,	and	GHG	

emissions	in	an	animal	production	system.	At	the	top	of	the	conceptual	model,	livestock	are	fed	a	

variety	of	diets.	Ruminant	animals	eat	feedstuffs	and,	through	fermentation	by	the	ruminal	

microbes,	CH4	is	produced.	Poultry	and	swine,	although	they	do	not	release	a	significant	amount	of	

CH4	through	enteric	fermentation,	deposit	manure	into	bedding,	and	upon	manure	decomposition,	

may	release	nitrous	oxide	(N2O),	CH4	and	ammonia	(NH3)	into	the	atmosphere.	Methodology	to	

estimate	emissions	from	bedding	and	dry	manure	in	housing	is	similar	to,	and	often	parallel	to,	the	

method	described	for	dry	manure	handling	and	storage	systems.	Manure	from	grazing	livestock	is	

left	on	fields	or	paddocks,	and	the	manure	may	be	collected	to	be	treated	and	stored.	Manure	that	

has	been	collected	and	stored	can	be	applied	to	croplands.	GHG	emissions	from	grazing	lands	and	

croplands	are	addressed	in	Chapter	3,	Quantifying	Greenhouse	Gas	Sources	and	Sinks	in	Cropland	

and	Grazing	Land	Systems.	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-7 

Figure 5‐1: Connections Between Feed, Animals, Manure, and GHG for Animal Agriculture
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-8 

5.1.1.1 Resultant GHG Emissions

For	this	report,	methods	are	categorized	according	to	those	from	enteric	fermentation,	housing,	and	

manure	management	systems.	The	housing	discussion	includes	emissions	from	manure	deposited	

in	the	housing	unit	and	manure	that	is	managed	inside	those	areas	(such	as	interior	stockpiles).	

Manure	management	includes	emissions	from	managed,	treated,	and	stored	manure.1

Enteric Fermentation and Housing Emissions

Methane‐producing	microorganisms,	called	methanogens,	

exist	in	the	gastrointestinal	tract	of	many	animals.	

However,	the	volume	of	CH4	emitted	by	ruminants	is	

vastly	different	from	that	of	other	animals	because	of	the	

presence	and	fermentative	capacity	of	the	rumen.	In	the	

rumen,	CH4	formation	is	a	disposal	mechanism	by	which	

excess	hydrogen	from	the	anaerobic	fermentation	of	

dietary	carbohydrate	can	be	released.	Control	of	

hydrogen	ions	through	methanogenesis	assists	in	

maintenance	of	efficient	microbial	fermentation	by	

reducing	the	partial	pressure	of	hydrogen	to	levels	that	

allow	normal	functioning	of	microbial	energy	transfer	

enzymes	(Morgavi	et	al.,	2010).	The	only	GHG	of	concern	

resulting	from	enteric	fermentation	is	CH4.	Respiration	chambers	equipped	with	N2O	analyzers	

indicate	that	enteric	fermentation	does	not	result	in	the	production	of	N2O	(Reynolds	et	al.,	2010).	

Methane	can	also	arise	from	hindgut	fermentation,	but	the	levels	associated	with	hindgut	

fermentation	are	much	lower	than	those	of	foregut	fermentation.		

Because	the	magnitude	of	enteric	emissions	is	so	great	and,	therefore,	a	significant	contributor	to	

many	countries’	GHG	emissions,	decades	of	research	have	gone	into	characterizing,	understanding,	

and	attempting	to	mitigate	enteric	CH4	emissions.	A	fundamental	challenge	in	this	type	of	research	

has	been	the	measurement	of	these	emissions.	

Methane,	N2O,	carbon	dioxide	(CO2),	and	NH3	are	produced	from	livestock	feces	and	urine,	and	

some	gaseous	forms	are	emitted	soon	after	manure	excretion.	In	dry‐lot	situations,	feces	and	urine	

are	deposited	on	the	pen	surface	and	are	mixed	via	animal	hoof	action.	Microorganisms	in	the	feces	

or	underlying	soil	metabolize	nutrients	in	the	manure	to	produce	GHGs.	In	feedlots,	where	manure	

is	normally	cleaned	from	pens	once	or	twice	per	year,	distinctive,	hard‐packed	layers	of	manure	

and	soil	may	develop	that	produce	microenvironments	favorable	to	oxidative	and	reductive	

processes	(Woodbury	et	al.,	2001;	Cole	et	al.,	2009b).	Periods	of	rainfall	or	dry	conditions	may	alter	

the	microbial	and	chemical	nature	of	the	pen	surface.	Production	of	CH4	and	N2O	occur	in	the	

underlying	manure/soil	layers	and	in	water‐saturated	areas	where	oxygen	is	limited,	such	as	wet	

areas	of	the	pen	around	water	troughs	and	depressions	that	collect	rain	water	and	snow	melt.	In	

contrast,	most	NH3	produced	in	the	pen	probably	comes	from	fresh	urine	spots	on	the	pen	surface.	

To	date,	few	measurements	of	GHG	emissions	from	feedlot	or	dry‐lot	pen	surfaces	have	been	made.		

Runoff	from	dry‐lot	and	feedlot	pens	is	normally	collected	in	retention	ponds	(more	typical	in	

feedlots),	or	lagoons	(more	common	in	dairies).	In	some	cases,	runoff	may	undergo	partial	removal	

of	suspended	solids	in	settling	basins	(feedlots	and	dairies)	or	in	mechanical	separators	(dairies	

only)	that	parallels	treatment	of	manure	collected	in	these	same	systems.	Losses	of	GHGs	and	NH3

1	Emissions	from	manure	deposited	on	grazing	lands	are	addressed	in	Chapter	3:	Croplands	and	Grazing	

Lands.	

Background: Ruminants

Ruminants	are	animals	that	have	

four‐chambered	stomachs,	which	

allow	for	easier	digestion	of	high‐

fiber,	hard‐to‐digest	feedstuffs.	They	

include:	

 Cattle

 Goats

 Sheep

 Deer

 American	Bison
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-9 

from	these	facilities	depend	upon	climatic	factors	and	the	oxidative‐reductive	potential,	pH,	and	

chemistry	of	the	effluent	in	the	pond	or	lagoon.	A	limited	number	of	studies	have	measured	GHG	or	

NH3	emissions	from	retention	ponds	or	lagoons.		

Manure Management

Manure	is	managed	in	a	wide	variety	of	systems.	The	resulting	GHG	emissions	differ	by	GHG	and	

magnitude	of	emissions	per	quantity	of	manure.	Table	5‐1	provides	an	overview	of	the	liquid	and	

solid	manure	systems	considered	in	this	report	and	the	resulting	GHGs.	

Table 5‐1: Overview of Manure Management Systems and Associated Greenhouse Gases

Storage and

Treatment

Practices

Estimation

Method Description

CH4 N2O NH3 a

Solid Manure

Temporary	and	

long‐term	

storage	

  

Manure	may	be	stored	temporarily	for	a	few	weeks	to	avoid	land	

application	during	unfavorable	weather	or	it	can	be	stored	for	

several	months.		

Composting	   

Composting	involves	the	controlled	aerobic	decomposition	of	

organic	material	and	can	occur	in	different	forms.	Estimation	

methods	are	provided	for	in	vessel,	static	pile,	intensive	windrow,	

and	passive	windrow	composting.	

Thermo‐

chemical	

conversion	

Thermo‐chemical	conversion	involves	the	combustion	of	animal	

waste,	converting	CH4	to	CO2.	Pyrolysis/gasification	is	one	method	

that	has	received	much	interest.	No	method	is	provided	as	GHGs	

are	considered	negligible.		

Liquid Manure

Aerobic	lagoon	    Aerobic	lagoons	involve	the	biological	oxidation	of	manure	as	a

liquid	with	natural	or	forced	aeration.	

Anaerobic	

lagoon/runoff	

holding	

ponds/storage	

tanks	

  

Anaerobic	lagoons	are	earthen	basins	that	provide	an	

environment	for	anaerobic	digestion	and	storage	of	animal	waste.	

Lagoons	may	be	covered	or	uncovered	and	have	a	crust	or	no	

crust	formation.	Runoff	and	holding	ponds	are	constructed	to	

capture	and	store	runoff	from	feedlots	and	dry‐lots.	In	some	cases	

wash	water	from	dairy	parlors	may	be	stored	in	holding	ponds.	

Storage	tanks	typically	store	slurry	or	wastewater	that	was	

scraped	or	pumped	from	housing	systems.	

Combined	

aerobic	

treatment	

system	

  

This	process	involves	removing	solids	using	flocculation	and	then	

composting	the	solid	stream	and	aerating	the	liquid	stream	of	

manure.		

Anaerobic	

digester	



Anaerobic	digesters	are	manure	treatment	systems	designed	to	

maximize	conversion	of	organic	wastes	into	biogas.	These	can	

range	from	covered	anaerobic	lagoons	to	highly	engineered	

systems.	Methane	gas	leakage	is	the	main	source	of	GHG	

emissions;	NH3	and	N2O	leakage	is	negligible.	

Sand–manure	

separation	

Manure	is	separated	from	sand	and	bedding	by	mechanical	and	

sedimentation	separation.	No	method	is	provided	as	emissions	

are	negligible.	Separated	liquids	and	solids	could	be	inputs	into	

other	storage	systems.	

Nutrient	removal	

There	are	four	main	nitrogen	removal	approaches:	biological	

nitrogen	removal,	Anammox	(i.e.,	anaerobic	ammonium	

oxidation),	NH3	stripping,	ion	exchange,	and	struvite	

crystallization.	No	method	is	provided	due	to	limited	quantitative	

information	on	GHG	generation	from	nutrient	removal	systems.	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-10 

Storage and

Treatment

Practices

Estimation

Method Description

CH4 N2O NH3 a

Solid–liquid	

separation	

Mechanical	separation	of	liquids	and	solids	through	screens,

centrifuges,	pressing,	filtration,	or	microscreening.	Separated

liquids	and	solids	could	be	inputs	into	other	storage	systems.	

Constructed	

wetland	

Typically	consist	of	wetland	plants	growing	in	a	bed	of	highly	

porous	media.	No	method	is	provided	as	emissions	are	negligible;	

GHG	sinks	are	noted	to	likely	be	greater	than	emissions.	 a	Although	NH3	is	considered	in	this	chapter	as	an	important	precursor	to	particulate	formulation	(affecting	radiation	

balance)	and	GHGs	and	is	a	key	element	of	discussion,	NH3	itself	is	not	a	GHG.	Therefore,	methods	for	estimating	NH3

emissions	are	provided	in	Appendix	5‐C.	

An	entity	can	reduce	its	GHG	emissions	from	manure	by	utilizing	alternative	treatment	options	

and/or	management	systems.	Anaerobic	digesters	do	not	reduce	the	amount	of	CH4	released	but	

offer	an	option	to	capture	and	convert	the	CH4	to	CO2	and	energy	through	combustion.	Digesters	

offer	both	CH4	reductions	as	well	as	GHG	avoidance	by	reducing	an	entity’s	electricity	demand.

5.1.1.2 Management Interactions

Table	5‐2	depicts	the	key	types	of	information	desired	for	estimating	GHG	emissions	from	an	

animal	production	facility.	This	table	illustrates	the	attributes	of	a	system	that	have	the	greatest	

influence	over	emissions	within	each	component.	A	number	of	existing	models	can	be	used	to	

estimate	GHG	emissions	that	utilize	the	key	activity	data	indicated	in	Table	5‐2.			

Table 5‐2: Desired Activity and Ancillary Data for Estimating GHG Emissions from Animal

Production Systems

General

Category Specific Data

Cattle

Sheep Swine Poultry Goats Amer.

Bison Cow–

calf Stockers Feedlot Dairy

Animal	

Characteristics	

Body	weight	 ● ● ● ● ● ● ● ● ●

Body	condition	score	 ● ● ● ●

Stage	of	production	(dry,	

lactating,	pregnant)	 ● ● ●

Dieta

ry	

FactoDiet	intake	(or	factors	that	

can	be	used	to	predict	

intake)	

● ● ● ● ● ● ● ● ●

Combined Aerobic Treatment Compared to Anaerobic Lagoons

A	combined	aerobic	treatment	system	involves	the	treatment	of	a	manure	stream	with	

flocculants	to	remove	the	majority	of	solids	from	the	stream.	The	solids	portion	is	composted	

while	the	remaining	liquid	is	transferred	to	a	storage	tank	where	it	is	aerated.	Methane	is	

avoided	by	aerobically	treating	the	solids	via	composting	while	NH3	in	the	wastewater	is	

avoided	via	nitrification.	The	GHGs	resulting	from	a	combined	aerobic	treatment	are	only	10	

percent	of	what	would	be	emitted	from	an	anaerobic	lagoon,	thus	combined	aerobic	

treatments	represent	a	potential	mitigation	option	for	entities.
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-11 

General

Category Specific Data

Cattle

Sheep Swine Poultry Goats Amer.

Bison Cow–

calf Stockers Feedlot Dairy

Type	of	forage	(conserved	

or	grazed,	pasture	

composition,	stage	of	plant	

growth)	

● ● ● ● ● ●

Diet	dry	matter	intake,	

crude	protein,	neutral	

detergent	fiber,	acid	

detergent	fiber,	non‐

structural	carbohydrates,	

fiber,	fat,	energy	content	

● ● ● ● ● ● ● ● ●

Diet	digestibility	and/or	

rate	of	passage	 ● ● ● ● ● ●

Degradability	of	

carbohydrates	and	proteins	 ● ● ●

Supplementation	practices	

– type	(e.g.,	grains,	protein,

liquid,	dry	blocks,	non‐

protein	nitrogen)	and	

quantity	

● ● ● ● ●

Supplemental	or	diet	

ionophore	concentration	 ● ● ● ●

Dietary	beta‐agonists	 ● ●

Nutrient	

Excretion:	

Quantity	 Carbon,	nitrogen,	and	

volatile	solids	 ● ● ● ● ● ● ● ● ●

Other	

Animal	

Factors	 Growth	promoting	

implants	 ● ●

Manure	Management	Factors	

Animal	management	

regimen	used	to	spread	

manure	over	pasture	to	

reduce	concentration	near	

water	or	feed	sources	

● ● ● ● ● ●

Soil	type	 ● ● ● ● ● ● ● ● ●

Practices	to	control	runoff	

from	pastures/lots/fields	 ● ● ● ● ● ● ● ● ●

If	housed,	the	length	of	

time	they	are	housed,	

animal	concentration,	

manure	handling	

procedures	

● ● ● ● ● ● ● ● ●

Type	of	manure	

collection/storage	system	 ● ● ● ● ●

Frequency	of	manure	

collections	and	

composition	

● ● ● ● ●

Bedding/litter	use	and	

source	 ● ● ● ● ●
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-12 

5.1.2 System Boundaries and Temporal Scale

System	boundaries	are	defined	by	the	coverage,	

extent,	and	resolution	of	the	estimation	methods.	The	

methods	in	this	report	can	be	used	to	estimate	GHG	

emission	sources	that	occur	within	the	production	

area	of	an	animal	production	system,	including	the	

animals,	animal	housing,	and	manure	handling,	

treatment,	and	storage.	Methane	emissions	from	

enteric	fermentation,	as	well	as	the	CH4	and	N2O	

emissions	from	manure	management	systems	or	

manure	stored	in	housing,	are	considered	in	this	

report.	Ammonia,	while	not	a	GHG,	is	a	precursor	to	

N2O	formation	and	is,	therefore,	included,	primarily	in	

Appendix	5‐C.	The	act	of	transporting	manure	to	the	

field	for	land	application	is	included	in	the	production	

area	boundary,	but	emissions	from	vehicle	transport	are	not	included	in	the	scope	of	this	report	as	

there	are	many	variables	that	would	determine	emissions	from	vehicles	(age	of	vehicle,	type,	fuel	

efficiency,	idle	time),	and	they	are	not	direct	agricultural	emissions	and	could	instead	be	considered	

part	of	the	transport	sector	(off‐road).	Additionally,	this	report	does	not	encompass	a	full	life	cycle	

analysis	(LCA)	of	GHG	emissions	from	animal	production	systems.	The	adjacent	text	box	

summarizes	several	studies	on	LCAs	for	animal	production	systems;	however,	they	are	not	utilized	

in	this	report.	Emissions	that	result	following	manure	application	are	addressed	separately	in	

Chapter	3,	Quantifying	Greenhouse	Gas	Sources	and	Sinks	in	Cropland	and	Grazing	Land	Systems.		

For	emissions	from	animal	production	systems,	the	methods	provided	have	a	resolution	of	

individual	herds	within	an	entity’s	operation.	A	herd	is	defined	as	a	group	of	animals	that	are	the	

same	species,	graze	on	the	same	parcel	of	land	(same	diet	composition),	and	utilize	the	same	

manure	management	systems.	Emissions	are	estimated	for	each	individual	herd	within	an	

operation	and	then	added	together	to	estimate	the	total	animal	production	emissions	for	an	entity.	

The	animal	production	totals	are	then	combined	with	emissions	from	croplands,	grazing	lands,	and	

forestry	to	determine	the	overall	emissions	from	the	operation	based	on	the	methods	provided	in	

this	document.	Emissions	are	estimated	on	an	annual	basis.		

5.1.3 Summary of Selected Methods/Models/Sources of Data

The	Intergovernmental	Panel	on	Climate	Change	(IPCC,	2006)	has	developed	a	system	of	

methodological	tiers	related	to	the	complexity	of	different	approaches	for	estimating	GHG	

emissions.	Tier	1	represents	the	simplest	methods,	using	default	equations	and	emission	factors	

provided	in	the	IPCC	guidance.	Tier	2	uses	default	methods,	but	emission	factors	that	are	specific	to	

different	regions.	Tier	3	uses	country‐specific	estimation	methods,	such	as	a	process‐based	model.	

The	methods	provided	in	this	report	range	from	the	simple	Tier	1	approaches	to	the	most	complex	

Tier	3	approaches.	Higher‐tier	methods	are	expected	to	reduce	uncertainties	in	the	emission	

estimates,	if	sufficient	activity	data	and	testing	are	available.	

Estimating	CH4	emissions	from	enteric	fermentation	in	swine,	goats,	American	bison,	llamas,	

alpacas,	and	managed	wildlife	use	Tier	1	methods.	Enteric	emissions	from	sheep	are	estimated	

using	the	Howden	equation	(Howden	et	al.,	1994),	and	emissions	from	dairy	production	systems	

are	estimated	using	the	Mitscherlich	3	(Mits3)	equation	(Mills	et	al.,	2003)	as	provided	in	the	Dairy	

Gas	Emissions	Model	(DairyGEM)	(Rotz	et	al.,	2011a).	Emissions	from	beef	cows	are	estimated	

using	the	IPCC	Tier	2	approach.	Emissions	from	feedlots	are	estimated	using	a	modification	of	the	

IPCC	Tier	2	approach.		

Qualitative Discussion on Manure

Sources

Estimation	methods	are	not	available	for	

some	sources.	Qualitative	discussion	is	

provided	for:	

 Sand‐Manure	Separation

 Nutrient	Removal

 Solid‐Liquid	Separation

 Constructed	Wetlands

 Thermo‐chemical	Conversion
 Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

 5-13 

For	manure	management,	the	IPCC	Tier	2	methodology	is	used	for	CH4	emissions	from	temporary	

stack	and	long‐term	stockpile,	CH4	and	N2O	emissions	from	composting,	and	N2O	emissions	from	

aerobic	lagoons.	The	Sommer	model	is	used	to	estimate	CH4	emissions	from	anaerobic	lagoons.		

All	methods	include	a	range	of	data	sources	from	operation‐specific	data	to	national	datasets.	

Operation‐specific	data	will	need	to	be	collected	by	the	entity	and	generally	are	activity	data	related	

to	the	farm	and	livestock	management	practices	(e.g.,	dietary	information,	volatile	solids	content	of	

manure).	National	datasets	are	recommended	for	ancillary	data	requirements,	such	as	climate	data	

and	soil	characteristics.		

A	summary	of	proposed	methods	and	models	for	estimating	GHG	emissions	from	animal	production	

systems	is	provided	in	Table	5‐3.	

Life Cycle Analysis of Cattle Production Systems

Peters	et	al.	(2010)	reported	that	the	estimated	carbon	footprint	of	cattle	production	systems	

around	the	world	ranged	from	8.4	kg	of	CO2‐eq	(kg	HCW)‐1	(HCW=hot	carcass	weight)	in	an	

African	pastoral	system	to	25.5	kg	CO2‐eq	(kg	HCW)‐1	in	an	intensive	Japanese	grain	feeding	

system.	Five	North	American	studies	(Verge	et	al.	(2008)	and	Beauchemin	et	al.	(Sweeten,	

2004;	2010)	in	Canada,	Pelletier	et	al.	(2010)	and	Lupo	et	al.	(2013)	in	the	U.S.	Midwest,	and	

Stackhouse	et	al.	(2012)	and	Stackhouse‐Lawson	et	al.	(2012)	in	California)	estimated	the	

carbon	footprint	of	various	beef	cattle	production	systems:	The	carbon	footprint	for	the	total	

beef	production	systems	ranged	from	10.4	to	19.2	kg	CO2‐eq	(kg	final	body	weight)	‐1	(or	16.7	

to	32.5	kg	CO2‐eq	(kg	HCW)‐1).	Sixty	four	to	80	percent	of	the	total	CO2‐eq	was	produced	in	

the	cow‐calf	sector	of	production;	whereas	8	to	20	percent	of	CO2‐eq	was	produced	in	the	

stocker	phase,	and	only	12	to	16	percent	was	produced	during	the	finishing	phase.	The	

majority	(55	to	63	percent)	of	the	total	CO2‐eq	was	enteric	CH4,	18	to	23	percent	was	manure	

N2O,	and	14	to	24	percent	was	from	fossil	energy	use	and	secondary	emissions.	

In	general,	the	daily	carbon	footprint	was	greater	during	the	grazing	(stocker)	phase	than	

during	the	feedlot	finishing	phase.	Both	Pelletier	et	al.	(2010)	and	Stackhouse	et	al.	(2012)	

reported	that	the	carbon	footprint	was	slightly	lower	for	calves	that	were	weaned	and	went	

directly	to	the	feedlot	(21.1	and	23.0	kg	CO2‐eq	(kg	HCW)‐1	or	2,382	and	3,493	kg	head‐1,	

respectively)	than	for	cattle	that	went	through	a	stocker	grazing	phase	before	entering	the	

feedlot	(22.6	and	26.1	kg	CO2‐eq	(kg	HCW)‐1	or	2,904	and	4,522	kg	CO2‐eq	head‐1,	

respectively).	Pelletier	et	al.	(2010)	and	Lupo	et	al.	(2013)	both	reported	that	the	carbon	

footprint	of	grass‐finished	cattle	was	greater	than	for	calves	that	were	weaned	and	went	

directly	to	the	feedlot.	These	differences	are	due	in	part	to	slower	weight	gain	and	lighter	

final	body	weights	and	carcass	weights	of	grass‐fed	cattle	than	cattle	finished	on	grain‐	and	

byproduct‐based	diets	in	the	feedlot.	

Most	LCAs	assume	that	carbon	sequestration	is	minimal	in	established,	unfertilized	pastures.	

Phetteplace	et	al.	(2001)	and	Liebig	et	al.	(2010)	suggested	there	may	be	some	small	net	

carbon	sequestration,	in	established	native	pastures.	However,	Liebig	et	al.	(2010)	noted	that	

fertilized,	improved	pastures	had	net	CO2‐eq	emissions;	primarily	because	of	increased	losses	

of	N2O	from	fertilizer	nitrogen.	Lupo	et	al.	(2013)	noted	that	the	assumed	carbon	

sequestration	of	pastures	(equilibrium	vs.	net	sequestration)	affected	the	carbon	footprint	of	

grass‐finished	cattle;	however,	regardless	of	the	carbon	sequestration	assumption,	grass‐

finished	cattle	had	a	greater	carbon	footprint	than	grain‐finished	cattle.	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-14 

Table 5‐3: Summary of Sources and Proposed GHG Estimation Methods for Animal

Production Systems

Section Source Method

Animal Production Systems, Including Enteric Fermentation and Housing Emissions

5.3.1.2	 Dairy	Cattle	 Mits3	equation; ASABE	Standard	D384.2	and	IPCC	Tier	2	(housing)

5.3.2.2	 Beef	Cattle	 Modified	IPCC	Tier	2 (enteric	and	housing);	ASABE	Standard	D384.2	

(housing)	

5.3.3.2	 Sheep	 Howden	equation	for	grazing	sheep	(Howden et	al.,	1994)	and	Blaxter	and	

Clapperton	(1965)	for	feedlot	sheep	

5.3.4.2	 Swine	 IPCC	Tier	1 (enteric	methane);	ASABE	Standard	D384.2	and	IPCC	Tier	2	

(housing)	

5.3.5.2	 Poultry	 IPCC	Tier	1;	ASABE	Standard	D384.2	and	IPCC	Tier	2	(housing)

5.3.6.1	 Goats	 IPCC	Tier	1

5.3.6.2	 American	Bison,	

Llamas,	Alpacas,	and	

Managed	Wildlife	

IPCC	Tier	1

Manure Storage and Treatment

Temporary Stack & Long‐Term Stockpile

5.4.1.2	 Methane		 IPCC	Tier	2	using	U.S.	EPA	Inventory	emission	factors	(EFs)	and	diet	

characterization	

5.4.1.4	 Nitrous	Oxide		 IPCC	Tier	2	using	U.S.‐based	EFs	and	monthly	data	

Composting

5.4.2.2	 Methane		 IPCC	Tier	2	with	monthly	data

5.4.2.4	 Nitrous	Oxide		 IPCC	Tier	2

Aerobic Lagoon

5.4.3.2	 Methane		 Methane	Conversion	Factor for	aerobic	treatment	is	negligible	and	was	

designated	as	0%	in	accordance	with	IPCC	

5.4.3.4	 Nitrous	Oxide		 IPCC	Tier	2	using	IPCC	EFs

Anaerobic Lagoon, Runoff Holding Pond, Storage Tanks

5.4.4.2	 Methane		 Sommer	model	based	on fractions	of	volatile	solids	(Møller	et	al.,	2004)

5.4.4.4	 Nitrous	Oxide		 Function	of	the	exposed	surface	area	and	U.S.‐based	emission	factors

Anaerobic Digestion

5.4.5.2	 Methane		 IPCC	Tier	2	using	Clean	Development	Mechanism	EFs	for	digester	types	to	

estimate	CH4	leakage	from	digesters	

Combined Aerobic Treatment Systems

5.4.6.2	

5.4.6.2	

Methane		

Nitrous	Oxide	

10%	of	emissions	from	estimation	of	liquid	manure	storage	and	treatment	

–	anaerobic	lagoon,	runoff	holding	pond,	storage	tanks	

Other Treatment Methods

5.4.7	 Sand–Manure	

Separation	

No	method	provided	because GHG	emissions	are	negligible	

5.4.8	 Nutrient	Removal	 Not	estimated	due	to	limited	quantitative	information	

5.4.9	 Solid	Liquid	

Separation	

No	method	provided	because GHG	emissions	are	negligible	

5.4.10	 Constructed	Wetland	 No	method	provided	because	emissions	are	negligible;	GHG	sinks	are	

noted	to	likely	be	greater	than	emissions	

5.4.11	 Thermo‐chemical	

Conversion	

No	method	provided	as	GHG	emissions	are	negligible	

5.1.4 Organization of Chapter/Roadmap

The	remainder	of	this	chapter	is	organized	into	four	primary	sections,	as	illustrated	in	Figure	5‐2.	

Section	5.2	provides	overviews	of	dairy	cattle,	beef	cattle,	sheep,	swine,	and	poultry	production	
 Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

 5-15 

systems	and	provides	information	on	diet	and	housing.	Section	5.3	provides	the	methods	for	

estimating	GHGs	from	housing,	primarily	focusing	on	GHGs	from	enteric	fermentation.	Methods	are	

also	provided	for	all	the	species	described	in	Section	5.2,	plus	additional	animal	types	(i.e.,	goats,	

American	bison,	llamas,	alpacas,	and	managed	wildlife).	Section	5.4	provides	the	methodology	for	

estimating	emissions	from	different	manure	management	systems.	Methodology	is	provided	to	

estimate	CH4	and	N2Ofrom	temporary	stack	and	long‐term	stockpiles,	composting,	aerobic	lagoons,	

anaerobic	lagoons,	and	combined	aerobic	treatment	systems.	Section	5.4	also	provides	methods	for	

estimating	CH4	from	anaerobic	digestion.	A	qualitative	discussion	is	provided	for	sand‐manure	

separation,	nutrient	removal,	solid‐liquid	separation,	constructed	wetlands,	or	thermo‐chemical	

conversion.	Section	5.5	presents	research	gaps	for	both	enteric	fermentation	and	manure	

management.	

There	are	five	appendices	to	the	animal	production	systems	chapter	of	this	report.	Appendix	5‐A	

provides	Ym	adjustment	factors	for	calculating	enteric	CH4	from	feedlot	cattle.	Appendix	5‐B	

provides	nutritional	information	about	animal	feedstuffs	(Ewan,	1989;	Preston,	2013).	Appendix	5‐

C	discusses	available	methodologies	for	estimating	NH3	emissions	from	animal	production	systems.	

Appendix	5‐D	describes	the	shape	factors	and	related	equations	that	can	be	applied	in	Appendix	5‐C	

to	more	accurately	estimate	emissions	from	manure	stockpiles	that	are	shaped	differently	(as	

surface	area	partially	determines	emissions).	Appendix	5‐E	provides	a	detailed	review	of	models	

evaluated	for	suitability	for	estimating	emissions	from	animal	production	systems.	
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Figure 5‐2: Animal Production Systems Road Map
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Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

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5.2 Animal Production Systems

This	section	provides	discussion	on	the	production	systems	for	beef	and	dairy	cattle,	sheep,	swine,	

and	poultry.	This	provides	the	background	necessary	for	understanding	Section	5.3,	which	covers	

GHG	emissions	from	animal	production	systems.	

5.2.1 Dairy Production Systems

5.2.1.1 Overview of Dairy Production Systems

The	U.S.	dairy	production	system	is	comprised	of	several	key	processes	for	dairy	cattle,	their	

manure,	and	their	end	products	(meat,	dairy)	as	depicted	in	Figure	5‐3.	This	conceptual	model	

provides	an	overview	of	the	typical	dairy	system,	following	cattle	from	birth	to	slaughter	and	

following	manure	from	the	animal	through	a	management	system.	Manure	is	produced	during	each	

stage,	and	depending	on	the	location,	is	managed	differently.	The	management	of	the	resultant	

manure	has	implications	on	the	quantity	of	GHG	emissions	and	sinks;	the	key	practices	are	

discussed	in	detail	below.	The	estimation	methods	in	this	chapter	include	discussions	for	emissions	

from	enteric	fermentation,	housing,	and	manure	management	and	are	not	a	full	LCA.	

The	U.S.	dairy	industry	is	composed	primarily	of	four	major	segments	of	production:	1)	calf	rearing;	

2)	replacement	heifers;	3)	lactating	cows;	and	4)	nonlactating	(dry)	cows.	The	U.S.	dairy	cattle	

population	in	2012	consisted	of	approximately	9.2	million	milk	cows	and	first	calf	heifers	and	

approximately	4.6	million	replacement	heifers.	The	majority	of	dairy	cattle	in	the	United	States	are	

Holstein	(Holstein‐Friesian),	followed	by	Jersey,	with	smaller	numbers	of	Guernsey,	Brown	Swiss,	

and	Ayrshire.	Over	the	last	65	years	there	have	been	dramatic	increases	in	milk	production	per	

animal,	due	to	changes	in	herd	management,	nutrition,	composition,	and	breeding	programs.	

Present‐day	dairy	herds	are	dominated	by	Holstein	cows	(90	percent)	as	opposed	to	a	mix	of	the	

five	most	common	breeds	(Jersey,	Guernsey,	Ayrshire,	Brown	Swiss,	and	Holstein)	as	was	common	

in	the	1940s.	With	a	change	in	breed	dominance	and	enhanced	genetics,	the	typical	milk	production	

per	cow	has	increased	from	2,074	to	9,193	kg	of	milk	per	year (Capper	et	al.,	2009).		

5.2.1.2 Diets for Dairy Cattle

Cows	in	intensive	dairy	production	systems	are	fed	diets	that	reflect	regionally	available	feeds	and	

typically	contain	between	40	and	60	percent	concentrates,	such	as	feed	grains,	protein	

supplements,	and	byproducts	such	as	distiller’s	grains.	Typical	diets	include	corn	silage,	alfalfa	or	

grass	silage,	alfalfa	hay,	ground	or	high‐moisture	shelled	corn,	soybean	meal,	fuzzy	whole	

cottonseed,	and	often	byproduct	feeds	(e.g.,	corn	gluten,	distiller’s	grains,	soybean	hulls,	citrus	pulp,	

beet	pulp).	Byproduct	feeds	may	make	up	a	large	portion	of	the	diet	composition,	providing	key	

nutrients	and	a	means	of	disposal	for	otherwise	landfilled	ingredients.	Proximity	to	crop	processing	

plants	and	industries	may	dictate	the	availability	of	byproduct	feeds	by	region.		

Growing Heifers

Diets	for	growing	heifers	are	formulated	based	on	growth	rate	and	stage	of	rumen	development.	

Diets	range	from	liquid	diets	(e.g.,	milk	or	milk	replacer)	in	newborn	calves	to	pelleted	complete	

feeds	in	the	growing	calf	(e.g.,	calf	starter)	to	diets	that	are	similar	to	that	offered	to	lactating	cows	

as	the	cows	grow	and	rumens	develop.	Roughage	content	of	the	diet	increases	as	the	rumen	

develops	with	hay	or	silage	often	offered	in	conjunction	with	a	calf	starter	during	a	transition	

period.	Following	that	transition,	typical	feeds	include	those	listed	above.	Feeds	are	often	mixed	

together	in	a	mixer	and	fed	as	a	Total	Mixed	Ration	(TMR).	In	some	cases,	feed	not	consumed	by	the	

lactating	herd	is	fed	to	growing	heifers	when	the	rumen	is	fully	developed	(>	9	months	of	age).		
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Figure 5‐3 Conceptual Model of Dairy Systems in the United States
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Lactating Cows

Diets	for	lactating	cows	are	formulated	by	target	milk	production	or	stage	of	lactation,	which	

reflects	the	differences	in	energy	and	protein	required	for	different	amounts	of	milk	produced.	Peak	

lactation	occurs	about	60	days	after	calving,	and	production	slowly	declines	over	the	next	several	

months.	Feedstuffs	are	commonly	blended	together	in	a	mixer	and	fed	as	a	TMR.		

Dry Cows

Dry	cow	diets	are	often	formulated	into	two	stages:	far‐off	dry	and	close‐up	dry.	During	the	far‐off	

dry	period,	cows	are	fed	diets	with	high	forage	content	(>60%)	using	ingredients	similar	to	that	fed	

to	the	lactating	herd.	As	dry	cows	approach	calving,	energy	content	of	the	diet	increases	by	

decreasing	forage	to	include	more	concentrate	feeds	and	mineral	formulation	changes	in	order	to	

avoid	pre‐	and	post‐partum	metabolic	disorders	that	often	center	around	calcium	mobilization	as	

the	cow	begins	to	lactate.	Feedstuffs	are	commonly	blended	together	in	a	mixer	and	fed	as	a	TMR	

5.2.1.3 Dairy Housing and Manure Handling

Two	general	dairy	farm	types	can	be	distinguished	in	the	United	States:	confinement	feeding	

systems	(including	barns	and	dry‐lots)	and	pasture‐based	systems	(USDA,	2004a).	Typical	housing	

systems	for	confinement	feeding	operations	include	tie	stall	barns,	freestall	barns,	freestall	barns	

with	drylot	access,	and	drylots.	Drylot	systems	consist	of	housing	animals	in	pens	similar	to	beef	

cattle	feedlots,	but	at	a	lower	stocking	density.	In	pasture‐based	systems,	cattle	graze	pasture	for	

periods	of	time,	based	on	feed	availability	and	environmental	conditions,	and	are	housed	in	barns	

and	fed	stored	feed	when	pasture	is	not	available.	The	dairy	cattle	lifecycle	production	phase	is	

generally	divided	into	three	segments:	growing	animals	(calves	and	replacement	heifers),	lactating	

mature	cows,	and	dry	mature	cows.	Nutrient	needs,	and	therefore	diets,	and	intake	are	very	

different	between	the	different	lifecycle	phases:	growing	cattle	(calves	and	heifers),	lactating	cows,	

and	dry	cows.	Housing	and	manure	management	systems	vary	considerably	throughout	the	

country	and	can	differ	in	a	region	and	by	the	size	of	the	herd.	In	cases	where	housing	and	manure	

management	varies	by	animal	group	(e.g.,	heifers,	dry,	and	lactating	cows),	estimates	of	GHG	

emissions	from	one	group	are	not	applicable	to	other	groups.	When	housing	and	manure	

management	are	similar	between	groups	(e.g.,	all	cattle	on	dry‐lots),	diet	and	intake	adjustment	

factors	can	be	used	to	compare	GHG	emissions	for	the	different	groups.	

With	the	exception	of	calves,	replacement	heifers	and	dry	cows	may	be	housed	and	managed	in	

similar	ways	as	lactating	cows.	When	this	is	the	case,	much	of	the	discussion	is	relevant	to	the	three	

groups.	In	cases	where	the	lactating	herd	is	managed	in	confinement	but	replacement	and	dry	

animals	are	managed	on	pasture	or	in	dry‐lots,	emissions	from	lactating	cattle	are	not	applicable	

not	only	due	to	differences	in	diet	and	intake	but	also	due	to	housing	differences.	There	are	no	

readily	available	studies	that	have	focused	strictly	on	emissions	from	dairy	calf	management	and	

housing.	Summarized	below	are	key	characteristics	of	difference	in	housing	by	life	cycle	phase	of	a	

dairy	cow.	

 Growing (calves and replacement heifers).	Following	birth,	calves	are	usually	removed	

from	the	cow	within	a	few	hours	and	are	typically	reared	on	milk	or	milk	replacer	in	calf	

hutches	or	barns	for	three	to	seven	weeks	until	weaning.	Female	calves	(replacement	

heifers)	are	typically	moved	to	group	housing	(e.g.,	super	hutches,	transition	barns,	open	

housing,	or	pasture)	until	they	reach	appropriate	breeding	weight	at	about	14	to	15	months	

of	age.	Some	replacements	are	contract‐reared	by	heifer	growers	or	sold.	Following	

breeding,	heifers	are	often	raised	in	lots,	pasture,	or	barns	until	they	are	ready	to	calve.	

Manure	in	group	housing	may	be	handled	as	a	solid	(bedded	pack	or	compost	barn)	or	as	a	

slurry,	similar	to	that	described	below	for	lactating	cows	in	freestall	barns.	
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 Lactating Cows. Heifers	typically	have	their	first	calf	at	about	23	to	24	months	of	age,	after	

which	they	join	the	production	herd.	A	cow	typically	remains	in	the	herd	until	about	five	

years	of	age,	although	many	cows	are	capable	of	remaining	productive	in	the	herd	for	12	to	

15	years.	Each	period	of	production	or	lactation	lasts	for	11	to	14	months	or	longer	and	

spans	the	time	period	from	calving	to	dry‐off,	which	is	when	milking	is	terminated	about	40	

to	60	days	before	the	next	anticipated	calving.	Thus,	cows	are	bred	while	they	are	producing	

milk,	usually	beginning	at	about	60	days	after	calving,	to	maintain	a	yearly	calving	schedule.	

Following	the	35	to	60‐day	dry	period,	the	cow	calves	again,	and	the	lactation	cycle	begins	

anew.	Cows	average	about	2.8	lactations,	although	many	remain	productive	considerably	

longer	(Hare	et	al.,	2006).		

Lactating	cows	may	be	housed	in	tie	stall	(stanchion)	barns,	which	limit	the	cows’	mobility	

because	the	cows	are	tethered,	fed,	and	milked	in	the	stalls.	A	gutter	is	used	to	remove	the	

manure	by	a	barn	cleaner,	which	typically	places	the	manure	directly	into	a	manure	

spreader	or	in	a	temporary	storage	pile.	Freestall	barns	allow	the	cows	to	move	freely	in	

and	out	of	stalls,	and	the	cows	are	moved	to	a	separate	area	(milking	center	or	parlor)	for	

milking.	Manure	typically	accumulates	in	alleyways	and	is	removed	via	scraping,	

vacuuming,	or	flushing	with	either	clean	or	recirculated	water.	Some	freestall	barns	have	

slotted	floors	with	long‐term	manure	storage	below	the	floors.	Manure	is	generally	worked	

naturally	through	the	slots	by	the	cows’	feet	and	with	assistance	via	mechanical	scraping	

equipment.	Dairy	facilities	may	also	use	pastures	and	dry‐lots	to	house	lactating	cows.	Lots	

are	scraped	periodically,	as	are	pastures	occasionally,	and	the	solid	manure	is	collected.	

Although	not	prevalent,	some	dairy	facilities	may	house	lactating	cows	in	bedded	pack	or	

compost	barns,	again	handling	manure	as	a	solid	material.	

 Dry Cows. Much	like	growing	cows,	housing	options	for	dry	cows	are	the	same	as	described	

above	for	lactating	cows.	The	key	determinant	is	management	preference	for	the	farm	

owner	and/or	facility	availability.

Manure	and	soiled	bedding	from	barns	can	be	handled	in	a	number	of	ways.	Manure	can	be	

removed	from	the	barns	mechanically	and	directly	loaded	into	manure	spreaders,	although	this	is	

not	common	on	medium	and	large	farms.	Manure	can	also	be	processed	in	an	anaerobic	digester	

where	bacteria	can	break	down	manure	to	produce	biogas	that	can	be	flared	or	captured	for	energy	

purposes	prior	to	storage	of	digester	effluent.	When	manure	has	a	lower	solids	content,	it	may	be	

stored	in	a	tank	or	pit	as	a	slurry,	or	transported	to	a	solid‐liquid	separation	system	with	the	liquid	

fraction	conveyed	(pumped	or	by	gravity)	to	a	long‐term	storage	pond,	while	the	solids	can	be	

dewatered	naturally	and	reused	as	bedding,	composted,	land‐applied,	and/or	sold.	In	dry‐lot	

systems,	the	manure	in	the	pens	is	typically	stacked	and	following	storage	is	either	land‐applied	or	

composted.	Lot	runoff	and	milking	parlor	wash	water	is	pumped	to	a	storage	pond.	There	are	some	

dry‐lot	dairies	that	use	a	flush	system	to	clean	manure	from	alleyways	behind	the	feed	bunks;	this	

washwater	is	eventually	stored	in	a	wastewater	pond.	Open	freestall	dairies	have	a	combination	of	

barns	with	exercise	yards	between	the	barns,	and	therefore	manure	is	handled	similarly	as	in	a	

traditional	freestall	barn	and	dry‐lot	production	system.	Wastewater	from	milking	centers	(manure,	

clean‐in‐place	water,	and	floor	washdown	water)	is	typically	combined	with	barn	manure	destined	

for	long‐term	storage,	and	may	go	through	a	solid‐liquid	separation	process	first.	In	pasture‐based	

systems,	manure	is	deposited	directly	onto	the	pasture	and	therefore	not	intensively	managed,	but	

may	accumulate	in	areas	where	animals	tend	to	congregate	(e.g.,	watering	areas,	shade).	
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5.2.2 Beef Production Systems

5.2.2.1 Overview of Beef Production Systems

The	U.S.	beef	production	system	is	comprised	of	several	key	components	for	beef	cattle,	their	waste,	

and	their	end	products,	as	depicted	in	Figure	5‐4.	This	conceptual	model	provides	an	overview	of	

the	typical	beef	processing	systems,	following	the	segments	of	the	beef	cattle	industry	(i.e.,	cow‐calf,	

stocker,	feeder/finisher,	and	packer)	from	birth	to	slaughter	and	following	waste	from	the	animal	

through	a	management	system.	Waste	is	produced	during	each	stage	of	activity	occurring	in	the	

system,	and	depending	on	the	location,	is	managed	differently.	

Of	the	90	million	beef	cattle	in	the	United	States,	approximately	50	million	are	mature	cows	and	

their	calves	on	cow‐calf	operations	(USDA	NASS,	2012),	which	range	in	size	from	a	few	cows	to	

several	thousand	cows.	These	operations	are	normally	based	on	forages,	either	improved	pastures	

or	native	range,	and	vary	in	size	from	a	few	acres	to	hundreds	of	sections.	Typically,	when	calves	

are	150	to	220	days	of	age	they	are	weaned	and	moved	to	pasture	for	periods	of	60	to	200	days	(the	

stocker	phase),	although	some	may	move	directly	to	a	feedlot.	The	pastures	may	be	native	range,	

improved	perennial	pastures,	or	annuals	such	as	wheat	pasture,	forage‐sorghums,	and	crop	

residues	such	as	corn	stalks.	After	the	stocker	phase,	calves	normally	move	to	feedlots	where	they	

are	fed	grain‐	and	byproduct‐based	diets	for	110	to	160	days,	until	they	are	ready	for	harvest.	In	

addition,	steers	and	cull	heifers	from	dairy	operations	are	also	fed.	Approximately	23	million	cattle	

are	fed	in	feedlots	annually	in	the	United	States.	Feedlots	range	in	size	from	a	few	hundred	head	to	

more	than	100,000	head	capacity.		

5.2.2.2 Diet Information for Beef Cattle

Cow‐Calf and Bulls

Grazing	pastures	may	be	native	range,	improved	perennial	pastures,	or	annuals	such	as	wheat	

pasture,	forage‐sorghums,	and	crop	residues	such	as	corn	stalks.	Beef	cows	and	bulls	are	typically	

fed	supplemental	feeds	during	times	when	pasture	or	range	forage	does	not	meet	their	nutritional	

requirements,	usually	in	winter.	A	recent	survey	of	the	beef	cow‐calf	industry	found	that	74	percent	

of	operations	fed	a	protein	supplement	and	51	percent	fed	an	energy	supplement	(USDA,	2010).	

Overall	protein	was	supplemented	for	an	average	of	173	days	(SE=9.6)	and	energy	for	162	days	

(SE=12.7),	but	this	was	highly	variable	across	regions	of	the	country.	Ninety‐seven	percent	of	

operations	in	the	survey	supplemented	the	cow	herd	with	roughage	for	an	average	of	154	days	

(SE=7.0).	The	protein	supplements	were	reported	as	plant	protein	or	urea‐based.	Corn	was	

reported	as	the	primary	energy	supplement.	The	amount	of	supplement	fed	per	head	per	day	was	

not	included	in	the	report.	

Stockers

Stockers	graze	forage,	including	wheat	pasture,	improved	pastures,	range,	and	crop	residues.	

Stocker	cattle	may	also	receive	supplemental	protein	or	energy	feeds	to	increase	performance	

and/or	extend	pasture	forage.	Supplements	may	or	may	not	contain	an	ionophore.	Some	stocker	

calves	may	be	implanted	with	a	growth	promoting	implant;	others	are	not.		
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Figure 5‐4 Conceptual Model of Beef Production Systems in the United States
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Feedlot

Cattle	typically	enter	feedyards	between	the	ages	of	100	and	350	days	weighing	200	to	350	kg,	and	

go	to	slaughter	weighing	between	500	to	700	kg.	They	are	fed	high‐concentrate	or	high‐byproduct	

diets	for	100	to	200	days.	Of	the	cattle	fed,	approximately	55	percent	are	beef	steers,	25	to	30	

percent	are	beef	heifers,	and	12	to	20	percent	are	dairy	steers	and	heifers.	The	vast	majority	of	

cattle	fed	are	beef	breeds	of	British	or	Continental	breeding.	However,	many	cattle	with	Brahman	

genetics	are	also	fed,	mostly	in	the	southern	plains.	In	areas	with	a	significant	dairy	industry,	steers	

and	heifers	of	dairy	breeding	(mostly	Holstein)	are	also	fed.	

Typical	feedlot	diets	contain	high	concentrations	of	grain	(75	percent	or	more)	and/or	byproducts	

such	as	distillers	grains	and	gluten	feed.	They	are	normally	balanced	for	protein,	energy,	vitamins,	

and	minerals	(Vasconcelos	and	Galyean,	2007).	Because	many	byproducts	contain	high	

concentrations	of	protein	and	minerals	such	as	phosphorus	and	sulfur,	when	these	byproducts	are	

fed,	dietary	concentrations	of	protein	and	some	minerals	may	exceed	animal	requirements.	Feeding	

of	ionophores	such	as	monensin	is	common	in	the	United	States,	as	is	the	use	of	growth‐promoting	

implants.	The	diets	fed	in	feedyards	tend	to	differ	between	the	northern	and	southern	plains.	

Finishing	diets	based	on	dry‐rolled	corn	(DRC)	and	high‐moisture	corn	(HMC)	dominate	in	the	

North,	whereas	diets	based	on	steam‐flaked	corn	(SFC)	dominate	in	the	South.	The	use	of	

bioethanol	co‐products	such	as	distiller’s	grains	and	corn‐milling	co‐products	such	as	corn	gluten	

feed	in	finishing	diets	is	greater	in	the	northern	plains	because	of	the	greater	availability	of	these	

co‐products,	but	their	use	is	increasing	in	the	southern	plains.	

5.2.2.3 Beef Cattle Housing and Manure Handling

Cow‐Calf and Bulls

Cow	herds	and	replacement	heifers	are	most	often	housed	on	pasture.	Feces	and	urine	are	

deposited	on	pastures	and	rangeland	and	may	be	concentrated	in	areas	in	which	feeding	or	

watering	takes	place.		

Stockers

Stockers	are	usually	housed	on	pasture	and	thus	no	manure	handling	is	used	and	GHG	emissions	

are	a	part	of	the	croplands	section	(see	Chapter	3,	Quantifying	Greenhouse	Gas	Sources	and	Sinks	in	

Cropland	and	Grazing	Land	Systems).	Calves	to	be	used	as	stockers	can	be	housed	for	short	periods	

of	time	in	dry‐lots.		

Feedlot

Housing	and	manure	management	at	most	beef	cattle	feeding	operations	differ	greatly	from	those	

used	in	other	livestock	species,	with	the	vast	majority	being	finished	in	dry‐lot	pens	with	soil	

surfaces.	Manure	is	normally	deposited	on	the	pen	surface	and	scraped	from	the	pens	after	each	

group	of	cattle	goes	to	market.	Part	of	the	manure	may	be	stacked	in	the	pen	to	provide	mounds	

that	improve	pen	drainage	and	assure	that	cattle	have	a	dry	place	to	lie	after	rains.	Manure	

removed	from	the	pen	may	be	immediately	applied	to	fields	near	the	feedlot,	stockpiled	for	later	

use,	or	composted	in	windrows.	Manure	scraped	from	the	pens	normally	has	a	moisture	content	of	

30	to	50	percent	and	may	contain	some	soil	from	the	pen.	Because	the	manure	may	remain	in	the	

pen	or	in	stockpiles	for	several	months	before	it	is	applied	to	the	field,	a	portion	of	the	nitrogen	and	

carbon	may	be	lost	before	the	manure	is	collected	or	applied	to	land.	Runoff	from	pens	is	normally	

collected	in	retention	ponds.	Settling	basins	may	be	used	to	limit	the	quantity	of	manure	solids	and	

soil	particles	that	reach	the	retention	pond.	

In	the	Northern	United	States,	and	in	areas	with	high	rainfall,	cattle	may	be	fed	in	naturally	

ventilated	barns	with	slotted	floors	for	collection	of	urine	and	feces	or	in	deep‐bedded	barns	with	

concrete	floors	in	which	the	manure	and	bedding	(normally	straw	or	stalks)	are	allowed	to	
 Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

 5-25 

accumulate	during	the	feeding	period	(Spiehs	et	al.,	2011).	Adding	bedding	will	increase	the	

quantity	of	carbon	(and	possibly	nitrogen)	available	to	be	metabolized	by	microbes	in	the	pen.	

These	facilities	are	characterized	by	the	absence	of	runoff	control	systems.		

5.2.3 Sheep Production Systems

5.2.3.1 Overview of Sheep Production Systems

There	are	81,000	sheep	and	lamb	operations	in	the	United	States,	with	an	inventory	of	5.53	million	

sheep	and	lambs	as	of	January	1,	2011	(USDA	NASS,	2011).	Most	breeding	flocks	are	small	and	

consist	of	less	than	100	head	of	ewes.	The	lamb	feeding	industry	is	also	diverse	in	size,	with	small	

feedlots	located	throughout	the	farm	flock	areas	and	large	feeding	operations	located	in	close	

proximity	to	local	grain	production	capacity	(Shiflett,	2011).	

5.2.3.2 Diets, Housing, and Manure Handling for Sheep

Lambing	season	may	occur	at	various	times	during	the	year,	depending	on	production	objectives,	

feed	resources,	environmental	conditions,	and	market	targets.	When	lambing	occurs,	January	

through	March,	ewes	are	generally	housed	in	bedded	barns.	Bedding	is	removed	and	spread	after	

animals	are	turned	out	on	pasture.	Ewes	are	generally	bred	on	pasture	in	September	through	

November	and,	depending	on	weather,	will	be	moved	into	barns	prior	to	lambing—or	earlier	as	

forage	availability	and	weather	dictate.	Diets	consist	of	pasture	or	grazing	crop	residue	from	spring	

turnout	through	early‐	and	mid‐gestation.	When	grazed	forage	is	no	longer	available,	ewes	are	

housed	or	moved	to	dry‐lots	and	fed	hay	and/or	hay	and	grain	diets	as	gestation	requirements	

dictate.	The	primary	forage	source	is	alfalfa,	and	corn	is	the	predominant	grain.	Diets	range	from	

100	percent	hay	to	60:40	percent	forage:concentrate	while	lactating.	Most	lambs	are	weaned	at	

approximately	90	days	and	41	kg	and	sent	to	feedlots	for	finishing.	

Pasture	lambing	is	another	farm	flock	production	system	that	is	used	to	maximize	nutrients	

provided	by	grazed	forages.	In	this	case	the	ewe	is	bred	in	November	or	December	to	lamb	on	

pasture	in	April	or	May.	Lambs	are	weaned	at	approximately	120	days	and	32	kg	and	may	be	sent	to	

the	feedlot	or	finished	on	grass.	Ewes	are	not	fed	grain,	and	harvested	forage	is	provided	only	when	

growing	seasons	and	weather	dictate.	These	flocks	will	be	housed	in	bedded	barns	in	areas	

requiring	protection	from	winter	weather	conditions.	Range	production	systems	include	lambing	in	

April	or	May,	where	most	(and	in	some	cases	all)	diets	are	provided	by	grazed	forages.	

Supplementation	with	harvested	feeds	or	grains	is	usually	in	response	to	unpredictable	weather	

and	environmental	conditions.	

Most	lambs	are	finished	in	feedlots	and	fed	diets	containing	85	to	90	percent	grain.	Length	of	

feeding	periods	will	range	from	weeks	to	months	depending	on	in‐weights	and	time	required	to	

reach	final	weight	(industry	average	final	weight	=	61	kg).	Sheep	feedlots	are	primarily	dry‐lots,	

and	manure	is	scraped	from	the	pens	similarly	to	beef	cattle	feedlots.		

5.2.4 Swine Production Systems

5.2.4.1 Overview of Swine Production Systems

The	conceptual	model	(Figure	5‐5)	of	the	U.S.	swine	production	system	provides	an	overview	of	

typical	production	systems,	following	animals	from	birth	to	harvest	and	following	manure	from	the	

animal	through	a	management	system.	Manure	is	produced	during	each	stage	of	production	

occurring	in	the	system,	and	depending	on	the	location,	is	managed	differently.	This	has	

implications	on	the	quantity	of	GHG	emissions	and	sinks,	some	of	which	are	discussed	in	detail	in	

the	emissions	discussion	section	(Section	5.3.4).	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

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Figure 5‐5: Conceptual Model of Swine Production Systems in the United States
 Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

 5-27 

Swine	production	in	the	United	States	remains	important	to	both	the	nation’s	diet	and	economy	

(Davies,	2011),	with	significant	levels	of	consumption,	imports,	and	exports.	According	to	the	U.S.	

Department	of	Agriculture’s	National	Agricultural	Statistics	Service,	the	2011	population	was	nearly	

66	million	head	(USDA	NASS,	2012).		

Swine	are	predominantly	grown	with	production	of	pork	occurring	in	a	two‐stage	or	three‐stage	

system:	

 Stage	1:	Sow	operation,	piglets	leave	at	weaning.	

 Stage	2	(optional):	Nursery	operation,	weaning	(10	days	of	age/17	lbs)	to	42	days	of	age/45	

lbs.	

 Stage	3:	Several	options:	

− A	finishing	operation	(16‐week	production	site	where	piglets	are	delivered	from	a	

nursery	site	at	approximately	42	days	of	age/	45	lbs	and	stay	until	154	days	of	age	(22	

weeks)	or		

− A	wean‐to‐finish	operation	(24‐week	production	site	where	pigs	are	delivered	at	

weaning	directly	from	a	sow	operation	(10	days	of	age/17	lbs)	and	stay	until	178	days	

of	age	(25.5	weeks)).	

The	manure	management	systems	associated	with	these	production	operations	all	have	the	basic	

elements	of	collection,	storage,	treatment,	transport,	and	utilization.	Most	swine	facilities	handle	

manure	as	a	slurry	either	within	the	building	(deep	pit	finishing	barns	or	shallow	pit	nursery,	

gestation	or	finishing	barns)	or	in	outside	storage	(pull‐plug	systems	for	nurseries,	sows,	or	

finishing	pigs).	Collection	and	storage	is	generally	accomplished	by	storage	of	the	waste	under	the	

facility,	discharge	to	a	separate	storage	tank,	or	flushing	to	an	anaerobic	lagoon.	In	the	case	of	in‐

house	manure	storage,	little	water	is	added	to	the	storage	structure,	and	anaerobic	conditions	

prevail	with	little	biological	processing	of	manure	taking	place.	Outside	storage	structures	that	

contain	slurry	with	little	dilution	water	offer	minimal	biological	treatment	as	well.	However,	lagoon	

systems	where	manure	is	flushed	from	housing	and	additional	dilution	water	is	added	offer	more	

treatment.	Dry	systems	or	deep‐bedded	systems	exist	to	a	much	lesser	extent,	primarily	for	sow	or	

finishing	production.	In	these	cases	bedding	material,	often	straw,	is	provided	and	manure	plus	

bedding	is	handled	as	solid	material,	sometimes	composted.	

In	the	Midwest,	the	system	of	moving	stored	swine	waste	to	crop	fields	is	well	defined	and	

understood	(Hatfield	and	Pfeiffer,	2005;	Malone	et	al.,	2007;	Jarecki	et	al.,	2008;	Vanotti	et	al.,	2008;	

Brooks	and	McLaughlin,	2009;	Jarecki	et	al.,	2009;	Agnew	et	al.,	2010;	Cambardella	et	al.,	2010;	

Lovanh	et	al.,	2010).	Yet	these	systems	continue	to	evolve	to	address	both	old	and	new	issues,	such	

as	frozen	ground,	application	timing,	and	emissions	associated	with	soil	application	via	new	

equipment.	All	of	the	manure	management	systems	result	in	GHG	emissions,	but	they	vary	in	terms	

of	point	and	non‐point	sources.	

5.2.4.2 Diet Information for Swine

The	swine	industry	feeds	primarily	a	corn‐soybean	meal	based	diet.	Dried	distillers	grains	with	

solubles	(DDGS)	are	often	fed	to	both	sows	and	finishing	pigs	and,	as	availability	of	this	feed	

increases,	the	amount	fed	increases	to	as	much	as	40	percent	of	diet	dry	matter	intake	(DMI).	

Similarly,	when	synthetic	amino	acid	sources	price	competitively	with	feed	protein	sources,	the	

number	of	synthetic	amino	acids	included	in	finishing	pig	diets	increases.	Two	(lysine	and	

methionine)	or	more	(threonine,	perhaps	tryptophan)	synthetic	amino	acids	are	fed	commonly	

today	with	the	benefit	of	reducing	total	nitrogen	fed,	and	therefore	excreted,	by	swine.	
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5.2.4.3 Swine Housing and Manure Handling

Most	commercially‐raised	finishing	swine	are	housed	indoors	to	provide	a	biosecure	environment	

and	reduce	disease	pressures.	Manure	is	handled	as	slurry	with	little	or	no	bedding	added	to	the	

system	and	minimal	addition	of	water.	A	small	but	growing	portion	of	the	commercial	swine	

industry	house	both	finishing	pigs	and	sows	in	hoop	barns.	In	these	cases,	bedding	material,	often	

straw,	is	provided,	and	manure	plus	bedding	is	handled	as	solid	material.	

5.2.5 Poultry Production Systems

5.2.5.1 Overview of Poultry Production Systems

The	U.S.	poultry	production	system	is	comprised	of	several	key	processes	for	poultry,	their	

manure/litter,	and	their	end	products	(meat,	eggs)	as	depicted	in	Figure	5‐6.	

The	figure	provides	an	overview	of	the	typical	production	systems,	following	both	the	layer	and	

broiler	phases.	This	conceptual	model	provides	an	overview	of	the	typical	poultry	

production	systems,	following	birds	from	birth	to	slaughter	and	following	manure	from	the	animal	

through	a	management	system.	Manure	is	produced	during	each	stage	of	activities	occurring	in	the	

system,	and	depending	on	the	location,	is	managed	differently.	The	emissions	from	manure	

management	are	discussed	in	detail	in	Section	5.3.		

The	U.S.	poultry	industry	is	the	world's	largest	producer	and	second	largest	exporter	of	poultry	

meat.	The	U.S.	is	also	a	major	egg	producer.	The	poultry	and	egg	industry	is	a	major	feed	grain	user,	

accounting	for	approximately	45.4	billion	kg	(100	billion	lbs)	of	feed	yearly.		

The	egg	incubation	period	for	a	chicken	is	21	days.	Following	hatch,	broiler	chickens	are	reared	for	

42	to	49	days	(six	to	seven	flocks	per	year),	depending	upon	the	market	intent	(e.g.,	roasters).	U.S.	

egg	operations	produce	more	than	90	billion	eggs	annually.	More	than	75	percent	of	egg	production	

is	for	human	consumption	(the	table‐egg	market).	The	remainder	of	production	is	for	the	hatching	

market.	These	eggs	are	hatched	to	provide	replacement	birds	for	the	egg‐laying	flocks	and	to	

produce	broiler	chicks	for	grow‐out	operations.	Following	a	16	to	22	week	growth	period,	hens	

start	laying	eggs.		

The	U.S.	turkey	industry	produces	more	than	one‐quarter	of	a	billion	birds	annually,	with	the	live	

weight	of	each	bird	averaging	more	than	25	lbs.	The	egg	incubation	period	for	a	turkey	is	28	days.	

Following	hatch,	turkey	poults	are	reared	for	15	to	22	weeks	(one	to	three	flocks	per	year)	

depending	on	the	market	intent	(e.g.,	roasters).		

5.2.5.2 Diet and Growth Information for Poultry

Diets	for	meat	birds	consist	largely	of	corn	and	soybean	meal	(commonly	85	to	92	percent	of	the	

diet);	however,	alternate	ingredients	such	as	dried	distillers	grains	with	solubles	(DDGS)	and	other	

co‐products,	and	synthetic	amino	acids	are	increasingly	used.	Hen	diets	are	most	commonly	

composed	of	corn	and	soybean	meal.	Other	ingredients,	such	as	DDGS,	may	be	included	(rarely	

more	than	20	percent	of	the	diet).	Ingredient	variability	is	largely	in	sources	of	supplemental	

energy,	minerals,	and	additives	to	improve	animal	health	and	performance.	Diets	are	formulated	

based	on	growth	rate	and	egg	production	and	fed	as	either	a	mash	or	a	pellet.	Bone	strength	is	an	

important	characteristic	of	meat	bird	quality	therefore	provision	of	minerals	such	as	calcium	and	

phosphorus	are	carefully	considered	when	diets	are	formulated.	Similarly,	eggshell	quality	is	key	

for	laying	hens,	and	as	a	result,	calcium	utilization	is	a	key	element	in	diet	formulation.		
 Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

 5-29 

Figure 5‐6: Conceptual Model of Poultry Production Systems in the United States

	 	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-30 

Poultry	breeds	change	rapidly,	demonstrating	improved	production	efficiency,	and	as	such,	diets	

are	increasingly	dense	with	energy	and	protein.	These	changes	are	due	to	a	combination	of	genetics	

and	management,	including	diet	formulation.2	While	diet	and	genetic	influences	were	considered	in	

a	study	by	Havenstein	et	al.	(2007),	the	results	suggest	that	the	diet	changes	that	occurred	between	

1966	and	2003	interacted	with	other	factors	(flock	age,	ambient	temperature)	to	influence	bird	

growth.	Some	estimate	that	85	percent	of	the	improvement	in	the	growth	rate	of	broiler	chickens	is	

attributable	to	genetics	(Havenstein	et	al.,	2003).3		

In	the	United	States	there	is	no	ban,	at	present,	on	use	of	antibiotic	growth	promoters	(AGPs)	in	

poultry	production	(meat	birds).	However,	the	trend	is	toward	consumers	wanting	products	that	

have	not	used	AGP.	Finding	replacements	for	AGP	will	likely	involve	the	use	of	multiple	products	in	

the	diet,	each	with	some	of	the	benefits	of	AGP,	and	management	changes	will	play	a	key	role	in	

maintaining	animal	productivity	in	their	absence.	It	is	unlikely	that	a	single	replacement	will	be	

found	that	will	prove	to	be	economically	viable	(Dibner	and	Richards,	2005).		

5.2.5.3 Poultry Housing and Manure Handling

The	vast	majority	of	the	industry	raises	birds	on	litter	in	mechanically	ventilated	or	naturally	

ventilated	houses.	Reuse	of	litter	and	number	of	flocks	grown	on	the	same	litter	is	variable	across	

the	country,	and	can	range	from	as	low	as	a	single	flock	to	as	many	as	18	flocks	on	the	same	litter	

source.	Litter	dry	matter	content	can	vary	from	40	to	80	percent,	depending	on	management.	

Laying	hen	and	pullet	housing	types	range	from	high‐rise	houses	where	hens	are	in	cages	and	

manure	accumulates	in	a	basement	under	the	cages	and	is	removed	annually,	to	a	manure‐belt	

house	where	hens	are	in	cages	and	manure	is	removed	daily	or	more	frequently	from	the	basement	

to	an	external	shed	and	stacked	before	periodic	removal	for	land	application	(once	or	twice	per	

year),	to	aviaries	where	hens	are	raised	on	litter	(in	large	rooms	as	opposed	to	cages)	that	is	

removed	from	the	aviary	annually	or	more	frequently.	When	manure	is	removed	from	the	house	it	

may	be	immediately	applied	to	fields,	stockpiled,	or	composted.	Moisture	content	may	vary	from	80	

percent	moisture	down	to	20	percent	moisture	(aviaries).		

5.3 Emissions from Enteric Fermentation and Housing

Emissions	from	animal	production	systems	include	those	from	both	enteric	fermentation	and	from	

animal	housing	(including	animal	manure	in	housing	areas	that	may	ultimately	be	flushed	or	

scraped	and	then	transported	to	an	external	manure	management	system).	The	production	of	GHGs	

in	livestock	systems	originates	from	a	variety	of	sources,	including	directly	from	the	animals	

themselves;	manure	in	lots	and	barns;	stockpiled	and	composting	manures;	manure	slurries	or	

waters	in	tanks,	pits,	lagoons,	retention	ponds,	settling	cells,	etc.;	and	from	soils	after	manure	

application.	Emissions	from	these	sources	depend	on	animal	size	and	age,	diet,	manure	production,	

handling	and	storage	system,	lot	surface	and	soil	characteristics,	and	ambient	weather	conditions	

(i.e.,	temperature,	wind,	humidity,	and	precipitation).	For	each	animal	type,	this	section	summarizes	

																																																													

2	Havenstein et	al.	(2007)	compared	1966	strains	to	2003	strains	and	observed	a	20	percent	better	

cumulative	feed	conversion	ratio	in	the	2003	tom	turkey	fed	a	2003	diet	relative	to	a	1966	tom	fed	a	diet	

typical	of	1966.	Feed	efficiency	to	11	kg	bodyweight	was	approximately	50	percent	better	(2.13	at	98	days	of	

age	in	2003	toms,	compared	with	4.21	at	196	days	for	1966	toms).		

3	Havenstein	et	al.	(2003)	compared	the	1957	Athens‐Canadian	Randombred	Control	strain	and	the	2001	

Ross	308	strain	of	broilers	when	fed	representative	1957	and	2001	diets.	The	42‐day	feed	conversions	for	the	

Ross	308	birds	fed	the	2001	and	1957	feeds	were	1.62	and	1.92,	respectively	(with	average	bodyweight	of	

2,672	and	2,126	g).	The	42‐day	feed	conversions	for	the	Athens‐Canadian	Randombred	Control	were	2.14	and	

2.34	(average	bodyweight	of	578	and	539	g,	respectively).	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

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the	current	understanding	of	enteric	fermentation	and	livestock	housing	emissions	and	presents	

recommended	models	for	estimating	such	emissions,	including	the	rationale	for	selecting	methods.	

Actual	field	measurements	of	GHGs	from	enteric	fermentation	over	the	past	several	decades	have	

been	instrumental	in	improving	our	understanding	of	the	underlying	science	and	the	resulting	

models	presented	in	this	section.	For	dairy	animals,	most	of	the	emissions	estimates	available	

represent	the	lactating	animal.	The	equations	for	growing	beef	animals	are	likely	appropriate	for	

growing	dairy	animals	if	diet	composition	is	considered.	The	text	boxes	on	the	following	pages	

summarize	several	of	the	key	techniques	that	have	been	used	in	measurement	studies	for	both	

individual	animals	and	groups	of	animals.	Further	studies	of	this	type	will	be	needed	to	address	

research	gaps	in	Section	5.5.		

This	section	provides	the	recommended	method	for	estimating	GHGs	from	enteric	fermentation	

and	applicable	housing	emissions.	Quantitative	methods	are	provided	for	dairy,	beef,	sheep,	swine,	

poultry,	and	other	animals	(i.e.,	goats,	American	bison,	llama,	alpacas,	and	managed	wildlife).	For	

each	section,	background	information	is	provided	on	the	range	of	emissions	and	existing	models	for	

estimating	emissions	and	the	rationale	for	the	method	selected.	For	estimating	emissions	from	

enteric	fermentation,	the	activity	data	is	the	same	for	all	animal	types.	Ancillary	data	includes	the	

properties	of	the	diets	(e.g.,	crude	protein	(CP),	digestible	energy	(DE),	neutral	detergent	fiber	

(NDF)).	For	simplicity,	activity	data	and	ancillary	data	are	listed	in	Table	5‐2	and	are	not	repeated	

below	for	each	animal	type.		

5.3.1 Enteric Fermentation and Housing Emissions from Dairy Production Systems		

Although	the	dairy	industry	is	primarily	composed	of	three	livestock	types	[growing	(i.e.,	calves,	

replacement	heifers),	lactating	cows,	and	dry	cows],	most	of	the	limited	emissions	research	

conducted	to	date	has	been	targeted	at	lactating	cows,	which	typically	produce	at	least	50	percent	

more	enteric	CH4	per	head	than	other	dairy	cattle.	Few	emissions	data	exist	for	calves,	heifers,	and	

dry	cows.	Therefore,	the	discussion	here	focuses	primarily	on	lactating	cows.		

Data	needed	to	estimate	emissions	include	housing	system	(pasture,	barn	type,	dry‐lot),	animal	

characteristics	(breed,	body	weight,	growth	potential,	stage	of	lactation,	milking	frequency,	and	

milk	production)	and	population,	dietary	information	(DMI,	dietary	CP—also	NDF,	fat,	DE,	

metabolizable	energy	(ME),	net	energy	(NE),	nutrient	excretion	(N,	C,	and	volatile	solids),	use	of	

recombinant	bovine	somatotropin,	use	of	monensin,	type	of	manure	handling	system,	frequency	of	

manure	removal,	type	of	bedding,	and	manure	characteristics	(total	ammonium	nitrogen,	pH).	

Enteric Fermentation

Enteric	CH4	production	varies	with	production	stage	in	dairy	cattle,	with	the	highest	rates	being	

produced	by	lactating	cows	(Table	5‐4).	This	table	illustrates,	conceptually,	the	observed	variation	

in	cattle	at	different	stages	of	maturity	and	activity,	but	it	is	not	intended	to	provide	a	depiction	of	

absolute	differences.	There	are	many	factors	that	affect	enteric	CH4	production,	and	therefore	

altering	dairy	cattle	diets	could	have	an	impact	on	enteric	CH4	production.	For	an	in‐depth	

discussion	of	dietary	effects	on	enteric	CH4	production,	see	Section	5.3.7	(Factors Affecting Enteric

Fermentation Emissions).	However,	the	results	in	Table	5‐4	clearly	illustrate	the	difference	in	

enteric	emissions;	in	particular,	emissions	from	dairy	cattle	are	relatively	higher	than	those	from	

growing	(i.e.,	heifers)	and	dry	cattle.		

Table 5‐4: Examples of CH4 Emissions Measured in Dairy Cattle

Animal Type CH4 Emission Method Used to

Measure Emissions Reference

Dairy	cattle	 260	g	animal‐1	day‐1 Calculated	Blaxter	and	

Clapperton	 Crutzen	et	al.	(1986)	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-32 

Animal Type CH4 Emission Method Used to

Measure Emissions Reference

Heifer	6‐24	month	 140	g	LU‐1	day‐1 See	above

Dairy	cattle,	dry	period	 139	g	LU‐1	day‐1 Respiration	calorimetry Holter	&	Young	(1992)	 Dairy	cattle,	lactating	 268	g	LU‐1	day‐1 See	above

Dairy	cattle	 257	g	LU‐1	day‐1	 Respiration	calorimetry	 Kirchgessner	et	al.	

(1991)	

Dairy	cattle,	lactating	 429	g	animal‐1 day‐1 Wind	tunnel Sun	et	al.	(2008)	 Dairy	cattle,	dry	period	 290	g	animal‐1 day‐1 Wind	tunnel

Dairy	cattle,	lactating	 538	–	648	g	animal‐1	day‐1	 Respiration	calorimetry	 Aguerre	et	al.	(2011)	

LU,	livestock	unit	=	500	kg	

Methods for Measuring CH4 Emissions from Enteric Fermentation

Individual Animals

The	standard	method	of	measuring	CH4	emissions	from	ruminants	is	by	respiration	

calorimetry	chambers.	Other	techniques,	including	head	boxes,	internal	tracers,	

micrometeorology,	isotope	dilution,	and	polyethylene	tunnels,	have	been	used	(Kebreab	et	

al.,	2006;	Harper	et	al.,	2011).	Several	new	technologies	have	been	developed	to	measure	

individual	animal	emissions.	To	address	the	difficulty	in	measuring	enteric	CH4	from	many	

animals	on	pasture,	alternate	methods	are	sought.	As	one	example,	Goopy	et	al.	(2011)	has	

proposed	a	portable	static	chamber	method	to	measure	daily	CH4	production.	Until	validated,	

results	using	alternate	methods	should	be	viewed	with	caution.	

A	variety	of	respiration	chambers	have	been	developed	to	measure	enteric	CH4	losses	and/or	

total	energy	metabolism	of	the	animal.	In	general,	air	is	pulled	from	the	chamber	at	a	known	

rate	and	replaced	with	outside	air.	Flow	of	air	and	concentrations	of	CH4,	CO2,	and	oxygen	

(O2)	in	the	air	entering	and	leaving	the	chamber	are	measured	to	determine	total	CO2	and	

CH4	production	and	O2	consumption.	When	properly	calibrated	and	used,	respiration	

chambers	give	highly	accurate,	precise	measurements.	However,	they	are	expensive	to	build	

and	operate,	and	require	significant	knowledge,	skill,	and	labor.		

Feed	intake	and	production	are	usually	depressed	in	animals	in	chambers	and	the	

measurements	do	not	necessarily	reflect	intake	and	production	from	typical	commercial	

operations.	This	limitation	can	be	partially	overcome	by	feeding	animals	at	different	levels	of	

intake	and	measuring	the	effects	of	intake	level.	Head	boxes	use	the	same	principles	as	

respiration	calorimetry,	and	have	many	of	the	same	limitations.	In‐barn	chambers	using	

drop‐down	curtains	have	been	used	to	measure,	at	relatively	low	cost,	emissions	of	NH3,	CH4,

and	other	gasses	from	groups	of	dairy	cows	(Powell	et	al.,	2007;	Powell	et	al.,	2008;	Aguerre	

et	al.,	2011).		

Internal	tracer	techniques	such	as	the	sulfur	hexafluoride	(SF6)	tracer	method	(Johnson	et	al.,	

1994)	were	developed	to	allow	measurements	from	free‐ranging	animals,	such	as	those	

managed	under	pasture	situations,	or	when	real‐world	levels	of	feed	intake	are	needed.	The	

limitations	to	this	method	are	the	need	for	trained	animals,	the	need	for	larger	sample	sizes	

(compared	with	chambers)	to	detect	the	influence	of	mitigation	techniques,	and	concerns	

about	inconsistent	releases	of	tracer	gas	from	SF6	permeation	tubes	manufactured	for	large	

release	rates.	Additionally,	the	SF6	technique	generally	results	in	emission	estimates	that	are	

lower	than	chamber	measurements;	possibly	because	the	SF6	method	does	not	measure	all	

lower	gut	CH4	production	(McGinn	et	al.,	2006).	The	advantages	and	shortcomings	of	the	SF6

method	have	been	recently	reviewed	(Lassey	et	al.,	2011).	
 Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

 5-33 

Methods for Measuring CH4 Emissions from Enteric Fermentation

Group of Animals

Micrometeorology	methods	have	been	used	extensively	to	measure	CH4	and	NH3	emissions	

from	pastures,	whole	feed	yards,	or	portions	of	the	feed	yard	(pens,	retention	ponds,	manure	

stockpiles,	etc.).	These	methods	have	been	reviewed	(Fowler	et	al.,	2001;	Flesch	et	al.,	2005;	

Harper	et	al.,	2011).	Lauback	et	al.	(2008)	compared	the	SF6	method	with	three	

micrometeorological	methods	(integrated	horizontal	flux,	flux	gradient,	and	backward	

Lagrangian	stochastic	(bLS))	using	steers	grazing	paddocks.	In	general,	the	

micrometeorological	methods	gave	higher	CH4	measurements	than	the	SF6	method,	with	the	

difference	being	greater	when	animals	were	within	22	meters	of	the	CH4	sampler.	This	effect	

was	especially	true	for	the	flux	gradient	method.	The	lower	values	for	the	SF6	method	could	be	

due	in	part	to	the	fact	that	the	SF6	method	does	not	measure	emissions	from	the	lower	gut	or	

from	fermentation	of	feces	on	the	paddock	surface.		

Tomkins	et	al.	(2011)	compared	enteric	CH4	emissions	of	steers	on	pasture	using	the	bLS	

method	and	respiration	chambers.	Emissions	estimated	using	the	bLS	model	were	slightly	

greater	than	with	respiration	chambers	(136.1	vs.	114.3	g	head	daily‐1).	However	emissions	per	

gram	of	DMI	were	similar	(29.7	vs.	30.1	g	CH4	kg	DMI‐1,	respectively),	suggesting	that	the	bLS	

model	may	be	suitable	for	estimating	enteric	emissions.	

Most	dispersion	models	and	micrometeorological	methods	assume	that	emissions	are	

uniformly	distributed	over	the	source	area.	In	some	cases,	such	as	for	individual	cattle	in	a	pen	

or	field,	this	is	not	true.	Therefore,	McGinn	et	al.	(2011)	developed	a	method	that	used	a	point‐

source	dispersion	model	and	atmospheric	CH4	concentrations	measured	using	multiple	open‐

path	lasers	to	measure	CH4	emissions	from	a	paddock	containing	18	cattle.	Measured	enteric	

CH4	emissions	were	similar	to	values	measured	using	other	techniques.	However,	recoveries	of	

known	CH4	releases	averaged	only	77	percent	using	this	method.	The	method	gave	more	

reliable	measurements	during	the	daytime	when	atmospheric	conditions	were	unstable	than	at	

night	when	atmospheric	conditions	were	stable.	

Methods for Measuring Emissions from Manure

Estimating	emissions	from	large	open	source	areas	typically	associated	with	both	dairy	and	

beef	cattle	production	is	very	challenging,	due	to	the	inability	to	contain	and	measure	the	

source	area.	Instruments	and	techniques	to	measure	ambient	atmospheric	gases	from	these	

large	source	areas	(i.e.,	dry‐lot	beef	and	dairy	cattle	yards,	freestall	dairies	with	naturally	

ventilated	curtain	sidewall	barns,	and	grazing	land)	must	be	able	to	detect	lower	

concentrations	than	those	encountered	in	typical	enclosed	confined	animal	production	

systems,	because	of	the	low	concentrations	and	high	variability	resulting	from	high	and	

variable	ventilation	rates.	A	larger	challenge	with	measuring	emissions	from	open	facilities	is	

the	ability	to	estimate	airflow	due	to	the	lack	of	a	defined,	constant	air	inlet	and	air	outlet.	

Reported	background	NH3	concentrations	typically	range	from	<1.3	to	53.3	parts	per	billion	

(ppb)	(Todd	et	al.,	2005),	background	atmospheric	N2O	concentrations	near	feedyards	average	

about	319	ppb	(Michal	et	al.,	2010),	and	background	CH4	concentrations	typically	run	in	the	

area	of	1,780	ppb	(Michal	et	al.,	2010).	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-34 

Methods for Measuring Emissions from Manure (Continued)

Numerous	factors	can	affect	atmospheric	concentrations	of	NH3	and	GHG	near	livestock	

operations	including	sampling	height,	atmospheric	stability,	wind	speed,	background	

concentrations,	stocking	density,	sampling	site,	sampling	time,	temperature,	and	wind	

direction	(fetch).	Average	daily	NH3	concentrations	measured	at	a	variety	of	similar	

source	areas	ranged	from	approximately	100	to	2,000	μg	m‐3.	Measured	maximum	

concentrations	rarely	exceed	2,000	μg	m‐3.	Ammonia	concentrations	decrease	rapidly	

downwind	of	source	areas	(Miner,	1975),	approaching	background	concentrations	in	less	

than	800	meters	(McGinn	et	al.,	2003;	Sweeten,	2004).	

Atmospheric	CH4	concentrations	measured	at	feedlots	and	dry	lot	dairies	have	ranged	

from	3.3	to	4.7	parts	per	million	(ppm)	(Michal	et	al.,	2010),	and	from	background	

(approximately	1.78	ppm)	to	6.20	ppm	(Bjorneberg	et	al.,	2009),	respectively.	Nitrous	

oxide	concentrations	measured	at	feedlots	ranged	from	319	ppb	(background)	to	443	ppb	

and	averaged	396	±	16	ppb	(Michal	et	al.,	2010).	Nitrous	oxide	concentrations	were	

highest	following	a	rainfall	event.	After	a	rain,	CH4	concentrations	averaged	3.7	±	0.1	ppm.	

At	dry‐lot	dairies,	median	N2O	concentrations	ranged	from	314	ppb	to	330	ppb,	which	are	

very	close	to	global	background	values	(Bjorneberg	et	al.,	2009).		

Small	flux	chambers	and	wind	tunnels	have	been	used	to	estimate	emissions	of	NH3,	CH4,	

and	N2O	from	farmlands,	pastures,	pen	surfaces,	lagoons,	and	retention	ponds	

(Hutchinson	and	Mosier,	1981;	Venterea	et	al.,	2009;	Venterea,	2010;	Harper	et	al.,	2011;	

Hristov	et	al.,	2011).	In	general,	chambers	alter	the	microenvironment	of	the	surface	and	

may	alter	emissions.	Thus,	the	accuracy	of	these	methods	for	determining	emission	

factors	for	some	gases	(especially	NH3)	has	been	questioned	(Gao	and	Yates,	1998;	

Harper,	2005;	Venterea	et	al.,	2009;	Parker	et	al.,	2010;	Venterea,	2010;	Harper	et	al.,	

2011).	Measures	of	NH3	emissions	using	flux	chambers	and	wind	tunnels	are	highly	

dependent	upon	air	flow	and	air	turnover	rates	in	the	chamber	(Cole	et	al.,	2007b;	Parker	

et	al.,	2010).	Based	on	the	conventional	two‐film	model	used	to	describe	volatilization	

from	a	solute‐solvent	mixture	(Parker	et	al.,	2010),	many	gaseous	emissions	are	

controlled	by	the	gas	film	above	the	liquid	or	the	upper	portion	of	the	liquid	(liquid	film)	

defined	by	the	Henry’s	law	constant.	If	volatilization	is	inhibited	by	high	concentrations	in	

the	gas	phase	(i.e.,	gas‐film	controlled),	increases	in	gaseous	concentration—such	as	with	

flux	chambers—will	lead	to	significant	underestimation	of	true	flux.	Venterea	(2010)	

reported	that	emissions	of	N2O	estimated	using	static	chambers	were	underestimated	by	

approximately	three	to	38	percent,	depending	upon	soil	water	content,	type	of	regression	

performed	(linear	vs.	quadratic	vs.	nonlinear),	and	other	factors.	The	percentage	of	

underestimation	tended	to	be	greater	with	dry	soils,	probably	because	N2O	flux	is	lower	

when	soils	are	dry.	Sommer	et	al.	(2004)	reported	that	GHG	emissions	from	compost	

stockpiles	measured	using	static	chambers	were	only	12	to	22	percent	of	values	

measured	using	the	integrated	horizontal	flux	method.		

Because	of	these	factors,	flux	chambers	should	be	used	to	examine	relative	differences,	

rather	than	emission	factors	of	NH3,	CH4,	and	N2O	emissions	from	pen	surfaces,	lagoons,	

retention	ponds,	manure	stockpiles,	or	compost	windrows.	In	addition,	the	surface	of	

pastures	and	feedlot	pens	is	temporally	and	spatially	heterogeneous,	with	dry	areas,	areas	

with	fresh	feces,	and	areas	with	urine	of	different	ages	(Woodbury	et	al.,	2001;	Cole	et	al.,	

2009a;	Cole	et	al.,	2009b).	To	adequately	represent	the	surface,	the	number	of	chamber	

measurements	required	(estimated	as	the	coefficient	of	variation	squared/100:	

Kienbusch,	1986)	can	be	very	large	(i.e.,	one	chamber/quare	meter:	Cole	et	al.,	2007b).
 Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

 5-35 

Housing		

There	are	a	wide	variety	of	dairy	cattle	housing	systems	due	to	variations	in	herd	size	and	regional	

practices.	In	the	northeastern	United	States,	herd	size	tends	to	be	smaller	and	cattle	are	housed	in	

freestall	and	tie‐stall	barns	and	on	pasture;	in	the	western	part	of	the	country,	herd	sizes	tend	to	be	

larger	and	animals	are	housed	in	freestall	barns	or	dry‐lots	with	few	producers	using	pasture‐based	

systems.	These	differences	in	housing	can	lead	to	differences	in	both	GHG	and	NH3	emissions.	

Examples	of	reported	emissions	from	varying	housing	systems	are	presented	in	Table	5‐5.		

Table 5‐5: Examples of Reported On‐Farm Emission Estimates for CH4, N2O, and NH3 from a

Variety of Dairy Cattle Housing Systems

Housing Country Emissions (g cow‐1 d‐1) Reference CH4 N2O NH3

Barn	 Germany	 402 64.8 Saha	et	al.	(2014)	

Tie	stall	barn	 Austria	 170‐232a 0.14‐1.2a 4‐7.4a Amon	et	al.	(2001)	

Barn	 Germany	 256 1.8 14.4 Jungbluth	et	al.	(2001)	

Dry‐lot	 U.S.	 41‐140 Cassel	et	al.	(2005)	

Hardstanding	 UK	 0.03b 0.01 11 Ellis	et	al.	(2001)	

Open‐freestall	 U.S.	 410 22 80 Leytem	et	al.	(2013)	

Tie	stall	barn	 Canada	 390 Kinsman	et	al.	(1995)	

Pasture	 NZ	 300‐427 Laubach	&	Kelliher	(2005)	

Dry‐lot	 U.S.	 490 10 130 Leytem	et	al.	(2011)	

Standoff	pad	 NZ	 1.66b 0.03 Luo	&	Saggar	(2008)	

Barn	 Denmark	 256 1.2 16 Zhang	et	al.	(Zhang	et	al.,	2005)	

Dry‐lot	 China	 397 37 Zhu	et	al.	(Zhu	et	al.,	2014)	

Barn	 Sweden	 216‐312a 21‐27a Ngwabie	et	al.	(2009)	

Barn	 Germany	 464 45 92.4 Samer	et	al.	(Samer	et	al.,	2011)	

Pasture	 Uruguay	 372 Dini	et	al.	(Dini	et	al.,	2012)	

*Denotes	measurements	in	g	LU‐1	d‐1,	where	a	LU	(livestock	unit)	=	500	kg.	

†Measurements	do	not	include	enteric	CH4	production.	

Variations	in	emissions	from	housing	are	due	to	factors	such	as	temperature,	diet	composition,	

water	consumption,	ventilation	flow	rates,	type	of	manure	handling	systems,	manure	removal	

frequency,	feces,	and	urine	characteristics	(i.e.,	pH	and	total	ammoniacal	nitrogen	(TAN)),	and	type	

of	bedding	used.	Although	differences	can	be	great	between	emission	rates,	there	are	some	

emission	characteristics	that	are	consistent	across	most	studies.	Many	studies	have	reported	strong	

diel	trends	in	emissions	of	CH4	and	NH3,	with	emissions	tending	to	be	lower	in	the	late	evening	and	

early	morning	and	then	higher	throughout	the	day	till	early	evening	(Amon	et	al.,	2001;	Cassel	et	al.,	

2005;	Powell	et	al.,	2008;	Sun	et	al.,	2008;	Bjorneberg	et	al.,	2009;	Flesch	et	al.,	2009;	Ngwabie	et	al.,	

2009;	Aguerre	et	al.,	2011;	Leytem	et	al.,	2011).	This	strong	diel	trend	in	emissions	can	be	

associated	with	wind	speed	and	temperature,	as	winds	tend	to	be	light	in	the	late	evening	and	early	

morning	and	then,	in	most	instances,	steadily	increase	throughout	the	day	to	reach	a	peak	in	the	

late	afternoon.	Temperature	also	increases	from	early	morning	to	late	afternoon,	and	then	

decreases	again.	Additionally,	cattle	activity	tends	to	increase	from	morning	to	late	afternoon	as	

animals	wake	and	begin	to	eat,	drink,	ruminate,	defecate,	and	urinate.	As	these	activities	increase,	

one	would	expect	an	increase	in	CH4	(and	NH3)	emissions.	There	are	also	seasonal	trends	in	

emissions,	the	most	prominent	being	in	NH3	emissions,	with	the	lowest	rates	in	winter	compared	

with	the	other	seasons	(Amon	et	al.,	2001;	Powell	et	al.,	2008;	Bjorneberg	et	al.,	2009;	Flesch	et	al.,	

2009;	Aguerre	et	al.,	2011;	Leytem	et	al.,	2011).	Powell	et	al.	(2008),	Flesch	et	al.	(2009),	and	

Aguerre	et	al.	(2011)	reported	that	barn	emissions	of	NH3	in	Wisconsin	were	lowest	in	winter,	with	

winter	rates	about	one‐half	to	one‐third	lower	than	those	in	the	spring	and	summer,	which	was	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-36 

Ammonia Emissions in Dairy Cattle Housing

As	mentioned	earlier,	ammonia	is	not	a	greenhouse	gas,	however,	ammonia	emissions	are	

estimated	as	part	of	the	nitrogen	balance	approach.	Emissions	of	NH3	from	dairy	cattle	housing	

systems	have	been	strongly	linked	to	dietary	nitrogen	intake,	as	this	affects	the	amount	of	urea	

nitrogen	excreted	in	urine.	Of	the	nitrogen	in	the	total	crude	protein	(CP)	typically	consumed	

by	a	dairy	cow	on	commercial	dairy	farms,	20	to	35	percent	is	secreted	in	milk	and	the	

remaining	nitrogen	from	CP	is	excreted	about	equally	in	feces	and	urine.	Feed	nitrogen	

(N=CP÷6.25)	use	efficiency	(percentage	of	feed	nitrogen	secreted	as	milk	nitrogen)	and	the	

50:50	fecal	nitrogen:urinary	nitrogen	excretion	ratio	can	be	influenced	greatly,	however,	by	

what	is	fed	to	the	cow.	Feeding	nitrogen	in	excess	of	nutritional	requirements	has	very	few	

significant	impacts	on	milk	production	or	quality;	it	decreases	feed	nitrogen	use	efficiency	and	

increases	the	relative	amount	of	urea	nitrogen	excreted	in	urine.	The	urea	nitrogen	contained	

in	cow	urine	(which	is	55	to	80	percent	of	the	nitrogen	contained	in	urine,	depending	on	

concentrations	of	CP	in	the	ration)	is	the	major	source	of	NH3	emission	from	dairy	farms.	Urea	

is	produced	when	nitrogen‐rich	proteins	and/or	non‐protein	nitrogen	sources	break	down	

(mainly	in	the	cow	rumen),	forming	NH3	gas	that	may	be	used	by	ruminal	microbes	to	produce	

microbial	proteins	or	can	be	absorbed	through	the	ruminal	wall	to	the	blood	stream.	In	the	

kidney,	blood	NH3	from	the	digestive	tract	or	tissue	metabolism	is	eventually	converted	to	urea	

before	being	excreted	in	the	urine.	Urease	enzymes,	which	are	present	in	feces	and	soil,	rapidly	

convert	excreted	urea	to	ammonium,	which	can	be	hydrolyzed	quickly	into	NH3	gas	and	lost	to	

the	atmosphere.	Thus,	the	increase	in	urea	nitrogen	excretion	due	to	excessive	ration	CP	

increases	NH3	emissions	during	the	collection,	storage,	and	land	application	of	manure	(Rotz,	

2004;	Misselbrook	et	al.,	2005;	Powell	et	al.,	2008;	Arriaga	et	al.,	2010).	

Paul	et	al.	(1998)	examined	the	effects	of	altering	dietary	CP	on	NH3	losses	from	dairy	cows.	

They	reported	that	NH3	emissions	during	the	first	24	hours	following	manure	excretion	were	

38	and	23	percent	of	the	total	manure	nitrogen	from	diets	with	16.4	and	12.3	percent	CP	

concentrations,	respectively,	and	22	and	15	percent	of	total	manure	nitrogen	from	diets	

containing	18.3	and	15.3	percent	dietary	CP,	respectively.	Misselbrook	et	al.	(Misselbrook	et	al.,	

2005)	reported	that	reducing	dietary	CP	content	resulted	in	less	total	nitrogen	excretion	and	a	

smaller	proportion	of	the	excreted	nitrogen	being	present	in	urine;	urine	nitrogen	

concentration	was	90	percent	greater	for	the	high‐CP	than	the	low‐CP	diet.	

However,	Li	et	al.	(2009)	found	no	effect	of	lowering	dietary	CP	in	lactating	dairy	cattle	on	NH3

emissions	from	the	floor	of	a	naturally	ventilated	freestall	dairy	barn	at	low	and	moderate	

temperatures	(0	to	20°C).	This	lack	of	response	to	CP	is	likely	due	to	the	fact	that	urease	

activity	is	negligible	at	temperatures	below	10°C	(Bluteau	et	al.,	2009).	Factors	that	are	

essential	in	determining	NH3	emissions	are	manure	or	urine	pH	and	the	total	ammoniacal	

nitrogen	content,	both	of	which	are	related	to	the	dietary	CP	level.		

The	majority	of	NH3	emissions	from	housing	systems	are	due	to	the	volatilization	of	NH3	from	

urine	deposition.	As	discussed	above,	nitrogen	intake	drives	the	amount	of	urea	that	is	

excreted	in	the	urine.	As	this	urine	is	deposited	on	barn	floors,	pastures,	or	dry‐lots,	it	mixes	

with	urease	from	either	feces	or	soil	and	is	then	hydrolyzed	to	ammonium	and,	via	effects	of	

pH,	converted	to	NH3	and	lost	to	the	atmosphere.	The	loss	of	NH3	happens	rapidly,	with	most	

NH3	losses	occurring	within	24	hours	following	deposition.	Therefore,	estimation	of	NH3

emissions	needs	to	take	into	account	the	amount	of	urea	generated	by	the	cow,	pH	(urine,	

manure,	or	soil),	temperature,	and	air	flow	over	the	source.	Strategies	that	reduce	nitrogen	

excretion	will	be	very	beneficial	in	reducing	NH3	emissions	from	housing/pasture	systems.		
 Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

 5-37 

attributed	to	cold	winter	temperatures.	In	general,	N2O	emissions	from	housing	were	found	to	be	

low	and	showed	no	discernible	diel	or	seasonal	trends	(Bjorneberg	et	al.,	2009;	Ngwabie	et	al.,	

2009;	Adviento‐Borbe	et	al.,	2010;	Leytem	et	al.,	2011),	suggesting	that	these	emissions	from	this	

sector	of	the	production	system	are	of	relatively	little	concern.	There	are	consistent	reports	of	both	

diel	and	seasonal	variations	in	both	CH4	and	NH3	emissions,	so	it	is	imperative	that	these	factors	be	

captured	in	any	estimation	of	emissions	for	a	given	production	system.	

Emissions	of	CH4	are	dominated	by	enteric	fermentation	in	housing/pasture	systems.	Amon	et	al.	

(2001)	examined	CH4	emissions	from	a	tie‐stall	dairy	barn	in	Austria	using	either	a	slurry‐based	

system	or	straw‐based	system.	In	both	systems,	about	80	percent	of	the	net	CH4	emissions	were	

due	to	enteric	fermentation,	with	the	remaining	amount	coming	from	the	manure.	Sun	et	al.	(2008)	

measured	CH4	emission	from	dairy	cows	and	fresh	manure	in	chambers,	and	reported	that	fresh	

manure	alone	did	not	produce	noticeable	CH4	fluxes.	In	some	dairy	production	systems,	manure	is	

removed	from	the	animal	housing	area	frequently;	therefore,	CH4	emissions	from	animal	housing	

areas	of	a	dairy	can	be	largely	attributed	to	enteric	emissions.		

N2O	emissions	tend	to	be	negligible	from	both	animals	and	fresh	manure.	The	majority	of	N2O	

emissions	result	from	manure	storage,	pasture,	and	land	application	of	manures.	Therefore,	the	

main	sources	of	N2O	emissions	from	animal	housing	would	be	from	dry‐lot	dairies	and	stand‐off	

pads,	because	there	is	potential	for	deposited	nitrogen	to	be	nitrified	and	denitrified	under	wet	

conditions	and	lost	as	N2O.	Luo	and	Saggar	(2008)	measured	N2O	and	CH4	emissions	from	a	dairy	

farm	stand‐off	pad	in	New	Zealand	and	reported	N2O	fluxes	from	0	to	3	g	N2O‐N	day‐1,	which	they	

attributed	to	the	concentrations	of	water	and	nitrate	in	the	pad	materials.	Overall,	only	54	g	of	N2O‐

N	was	emitted	from	the	pad	over	the	time	of	use,	representing	~0.01	percent	of	the	excreta	

nitrogen	deposited	on	the	pad.	

While	there	have	been	overall	improvements	in	milk	production	with	breeding	programs,	there	is	

no	evidence	that	any	breed	of	dairy	cow	produces	less	enteric	CH4.	Münger	and	Kreuzer	(Münger	

and	Kreuzer,	2008)	measured	enteric	CH4	production	from	Holstein,	Simmental,	and	Jersey	cows	

and	found	no	persistent	differences	in	CH4	yields,	with	average	enteric	CH4	being	approximately	

25g	CH4	kg	DMI‐1.		

5.3.1.1 Method for Estimating Emissions from Dairy Production Systems

		

Method for Estimating CH4 Emissions from Enteric Fermentation in Dairy Cows

 Mills	et	al.	(2003)	developed	a	series	of	submodels	to	estimate	enteric	CH4	emissions	from	dairy	

and	beef	cattle.	The	optimal	model	appeared	to	be	a	nonlinear	Mits3	equation,	which	is	utilized	by	

the	DairyGEM	Model	(a	subset	of	IFSM)	(Rotz	et	al.,	2011b)	and	is	shown	in	Equation	5‐1	(Mits3	

equation)	is	based	primarily	on	metabolizable	energy	intake,	acid	detergent	fiber	(ADF),	and	starch	

content	of	diet.	

 Data	sources	are	user	input	on	dietary	intake,	as	well	as	dietary	data	from	the	Feedstuffs	

Composition	Table	(Ewan,	1989;	Preston,	2013).	

 Use	of	the	DairyGEM/Mits3	equation	is	recommended	over	the	IPCC	Tier	2	equation	(IPCC,	2006)	

because	it	has	proven	to	be	more	accurate,	in	general,	for	dairy	cows.	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-38 

The	Emax	is	constant	for	all	animals	at	45.98	MJ/head/day.	The	shape	parameter	“c”	is	calculated	

from	the	dietary	non‐fiber	carbohydrate	(NFC)	to	acid	detergent	fiber	(ADF)	ratio	in	Equation	5‐2.	

Mills	et	al.	(2003)	noted	that	nonlinear	models	have	two	advantages	over	linear	models:	1)	a	

maximum	emission	is	set;	and	2)	it	is	explainable	from	a	biological	sense.	The	feedstuff	

characteristics	needed	to	calculate	emissions	from	dairy	cattle	are	included	in	the	example	below	

(Ewan,	1989;	Preston,	2013).	The	full	table	can	be	found	in	Appendix	5‐B.	

Table 5‐6: Example Feedstuffs Tablea

Feedstuff DM

%

Energy Protein Fiber

EE

%

ASH

%

Ca

%

P

%

K

%

Cl

%

S

%

Zn

TDN ppm

%

NEm NEg NEl 

(Mcal/cwt.)

DE

(% of

GE)*

CP

%

UIP

%

CF

%

ADF

%

NDF

%

eNDF

%

Alfalfa	

Cubes x91 57	 57	 25	 57	 18	 30	 29	 36	 46	 40	 2.0 11	 1.30 0.23	 1.9	 0.37	 0.33 20	

Alfalfa	

dehydrated	

17%	CP

92	 61	 62	 31	 61	 65.16	 19	 60	 26	 34	 45	 6	 3.0 11	 1.42 0.25	 2.5	 0.45	 0.28 21	

Alfalfa	

fresh 24	 61	 62	 31	 61	 62.54	 19	 18	 27	 34	 46	 41	 3.0 9	 1.35 0.27	 2.6	 0.40	 0.29 18	

Source:	Preston	(2013).	

Equation 5‐1: Non‐Linear Mits3 Equation

ࡴ࡯ ૝ൌ ሺ۳ܠ܉ܕ െ ሾ۳ܠ܉ܕ expିୡ୶ሿሻ ൈ ૙. ૙૚ૡ	

Where:	

CH4	 =	Enteric	methane	emissions	per	day	(kg	CH4	head‐1	day‐1)	

Emax		 =	Maximum	possible	CH4	emissions	(MJ	head‐1	day‐1)	

c		 =	Shape	parameter	determining	emission	change	with	increasing	metabolizable	

energy	intake	(see	Equation	5‐2)	

x	 =	Metabolizable	energy	intake	(MJ	head‐1	day‐1)	

0.018			=	Conversion	of	MJ	to	kg	of	CH4	(kg	CH4	MJ‐1)	

Equation 5‐2: Calculating Shape Parameter

܋ ൌ െ૙. ૙૙૚૚ ൈ

۴۱ۼ

૞૝૙૙ .૙ ൅ ۲۴ۯ

Where:	

c	 =	Shape	parameter	determining	emission	change	with	increasing	metabolizable	

energy	intake	(unitless)	

NFC		 =	[(100‐NDF	+	CP	+	EE)/100]	x	DMI	(kg	head‐1	day‐1)	

DMI		 =	Dry	matter	intake	(kg	dry	feed	animal‐1	day‐1)	

ADF		 =	Acid	Detergent	Fiber	(kg	head‐1	day‐1)	

NDF		 =	Neutral	Detergent	Fiber	(%)	

CP		 =	Crude	Protein	(%)	

EE		 =	Ether	extract	(%)	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-39 

a	Column	headings:	

DM =	Dry	matter GE = Gross	energy ASH	 =	Ash

TDN	 =	Total	digestible	nutrients CP =Crude	protein Ca =	Calcium

NEm	 =	Net	energy	for	maintenance UIP = Undegradable	intake	protein P =	Phosphorous

NEg	 =	Net	energy	for	growth CF = Crude	fiber K =	Potassium

NEl	 =	Net	energy	for	lactation ADF = Acid	detergent	fiber Cl =	Chlorine

Mcal	 = Megacalories NDF = Neutral	detergent	fiber S =	Sulfur

cwt	 =	Centum	weight	(hundredweight) eNDF = effective	neutral	detergent	fiber Zn =	Zinc

DE	 =Digestible	energy EE = Ether	extract ppm =	parts	per	million

Methane	Emissions	from	Dairy	Cows’	Housing

The	DairyGEM	Model	(Rotz	et	al.,	2011a)	calculates	CH4	emissions	from	barn	floors	using	an	

empirical	model	developed	from	three	freestall	barns	(Chianese	et	al.,	2009c).	

When	manure	is	allowed to	accumulate	as	a	stockpile,	on	a	dry‐lot,	or	in	a	pit	below the	animal	

confinement, the	DairyGEM	model uses the	IPCC	(2006)	Tier	2	method to	estimate	CH4	emissions

(Equation	5‐4).	This	is	the	same	equation	used	for	estimating emissions	from	manure	that	is	

managed	outside	of	housing	(see	Section	5.4.1 Temporary Stack and Long‐Term Stockpile	and	

5.4.2 Composting for details).

Method for Estimating Dairy Cows’ GHG Emissions from Housing

Methane	

 The	DairyGEM	Model	(a	subset	of	IFSM)	(Rotz	et	al.,	2011a)	calculates	CH4	emissions

from	housing	surfaces.

 DairyGEM	uses	the	IPCC	(2006)	Tier	2	method	to	estimate	CH4	emissions	when	manure

is	allowed	to	accumulate	in	the	housing.

Nitrous	Oxide		

 Nitrogen	excreted	estimated	using	equations	provided	in	ASABE	D384.2.

 IPCC	(2006)	Tier	2	approach	for	N2O	emissions	from	manure	in	housing.

Equation 5‐3: Calculating CH4 Emissions from Barn Floors (Chianese et al., 2009c)

۱۶૝ ൌ ܠ܉ܕ	ሺ૙. ૙, ૙. ૚૜܂ሻ ൈ

ܖܚ܉܊ۯ

૚૙૙૙

Where:	

CH4								=	Methane	emissions	per	day	(kg	CH4	head‐1	day‐1)	

T		 	 =	Barn	temperature	( ̊C)	

Abarn		 =	Area	of	the	barn	floor	covered	with	manure	(m2)	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-40 

The	maximum	CH4	producing	capacity	(B0)	for	manure	varies	by	animal	category	and	is	provided	in	

Table	5‐19.	The	CH4	conversion	factors	(MCF)	for	manure	deposited	on	a	dry‐lot,	stored	in	a	deep	

pit,	or	from	cattle	bedding	can	be	found	in	Table	5‐7.	The	MCFs	for	manure	stored	as	a	stockpile	are	

provided	in	Table	5‐20	through	Table	5‐22.	The	MCFs	for	manure	composted	within	housing	are	

provided	in	Table	5‐24.		

Table 5‐7: Methane Conversion Factors for Dry‐Lots, Pit Storage Below Animal Confinement,

and Cattle/Swine Bedding

Temperature Dry‐Lot

Pit Storage Below

Animal Confinement and

Cattle/Swine Deep

Bedding

< 1 month > 1 month

Cool	

≤10	°C	

1%	 3%	

17%

11	°C	 19%

12	°C	 20%

13	°C	 22%

14	°C	 25%

Temperate	

15	°C	

1.5%	 3%	

27%

16	°C	 29%

17	°C	 32%

18	°C	 35%

19	°C	 39%

20	°C	 42%

21	°C	 46%

22	°C	 50%

23	°C	 55%

24	°C	 60%

25	°C	 65%

Warm	

26	°C	

2%	 30%	

71%

27	°C	 78%

≥28	°C	 80%

Source:	IPCC	(2006).	

Equation 5‐4: IPCC Tier 2 Approach for Estimating CH4 Emissions in Housing

۳۱۶૝ ൌ ܕ ൈ ܁܄ ൈ ۰૙ ൈ ૙. ૟ૠ ൈ

۱۴ۻ

૚૙૙

Where:

ECH4	 =	CH4	emissions	per	day	(kg	CH4	day‐1)	

m		 =	Total	dry	manure	per	day	a	(kg	dry	manure	day‐1)	

VS		 =	Volatile	solids	(kg	VS	(kg	dry	manure)‐1)		

B0	 =	Maximum	CH4	producing	capacity	for	manure	(m3	CH4	(kg	VS)‐1)	

MCF	 =	CH4	conversion	factor	for	the	manure	management	system	(%)		

0.67		 =	Conversion	factor	of	m3	CH4	to	kg	CH4
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-41 

The	Sommer	model	is	used	to	estimate	emissions	from	any	liquid	manure	(less	than	10	percent	dry	

matter)	stored	in	housing.	The	estimation	method	for	liquid	manure	can	be	found	in	Section	5.4.4	

Anaerobic Lagoon, Runoff Holding Pond, Storage Tanks.	

Nitrous	Oxide	Emissions	from	Dairy	Cows’	Housing	

To	estimate	nitrogen	losses	from	housing,	the	amount	of	nitrogen	excreted	(Nex)	by	each	animal	

category	is	first	estimated.	Equation	5‐5,	Equation	5‐6,	and	Equation	5‐7	are	the	equations	

recommended	by	the	American	Society	of	Agricultural	and	Biological	Engineers	(ASABE)	for	

estimating	Nex.	

Some	of	the	nitrogen	excreted	is	volatilized	as	NH3,	hence,	the	estimation	of	NH3	losses	is	necessary	

to	estimate	N2O	emissions	using	a	nitrogen	balance	approach.	The	NH3	lost	from	manure	in	housing	

is	estimated	as	a	fraction	of	Nex,	Koelsch	and	Stowell	(2005)	provide	estimates	on	the	typical	NH3

loss	from	different	housing	facilities	and	animal	species	as	a	fraction	of	Nex	(see	Table	5‐8).	A	range	

Equation 5‐5: ASABE Approach for Estimating Nitrogen Excretion from Lactating Cows

ܠ܍ۼ ൌ ሺۻܑܓܔ ൈ ૛. ૜૙૜ሻ ൅ ሺ۲۷ۻ ൈ ૙. ૚૞ૢሻ ൅ ሺ۲ۻ۷ ൈ ۱۱۾ ൈ ૠ૙. ૚૜ૡሻ ൅ ሺ۰܅ ൈ ૙. ૚ૢ૜ሻ

െ ૞૟. ૟૜૛	

Where:	

Nex	 =	Total	nitrogen	excretion	per	animal	per	day	(g	N	animal‐1	day‐1)	

Milk		 =	Milk	production	per	animal	per	day (kg	milk	animal‐1	day‐1)	

DIM	 =	Days	in	milk	(days)	

DMI	 =	Dry	matter	intake	(kg	animal‐1	day‐1)	

CCP		 =	Concentration	of	crude	protein	of	total	ration	(g	crude	protein	(g	dry	feed)‐1)	

BW	 =	Average	live	body	weight	(kg)	

Equation 5‐6: ASABE Approach for Estimating Nitrogen Excretion from Dry Cows

ܠ܍ۼ ൌ ሺ۲ۻ۷ ൈ ૚૛. ૠ૝ૠሻ ൅ ሺ۱۱۾ ൈ ૚૟૙૟. ૛ૢ૙ሻ െ ૚૚ૠ. ૞૙૙

Where:	

Nex	 =	Total	nitrogen	excretion	per	animal	per	day	(g	N	animal‐1	day‐1)	

DMI	 =	Dry	matter	intake	(kg	dry	feed	animal‐1day‐1)	

CCP		 =	Concentration	of	crude	protein	of	total	ration	(g	crude	protein	(g	dry	feed)	‐1)	

Equation 5‐7: ASABE Approach for Estimating Nitrogen Excretion from Heifers

ܠ܍ۼ ൌ ሺ۲ۻ۷ ൈ ۱۱۾ ൈ ૠૡ. ૜ૢ૙ሻ ൅ ૞૚. ૜૞૙

Where:	

Nex	 =	Total	nitrogen	excretion	per	animal	per	day	(g	N	animal‐1	day‐1)	

DMI	 =	Dry	matter	intake	(kg	dry	feed	animal‐1day‐1)	

CCP		 =	Concentration	of	crude	protein	of	total	ration	(g	crude	protein	(g	dry	feed)	‐1)	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-42 

of	values	has	been	provided	for	each	facility	type;	the	lower	values	should	be	used	during	the	

winter,	the	higher	values	should	be	used	during	the	summer,	and	intermediate	values	should	be	

used	for	the	spring	and	autumn.		

Table 5‐8: Typical Ammonia Losses from Dairy Housing Facilities (Percent of Nex)

Facility Description % Loss Facility Description % Loss

Open	dirt	lots	(cool,	humid	region)		 15	‐ 30	 Roofed	facility	(shallow	pit	under	floor)		 10	‐ 20	

Open	dirt	lots	(hot,	arid	region)	 30	‐ 45 Roofed	facility	(bedded	pack)	 20	‐ 40	

Roofed	facility	(flushed	or	scraped)		

Roofed	facility	(daily	scrape	and	haul)	 5	‐	15		 Roofed	facility	(deep	pit	under	floor	‐	

includes	storage	loss)		 30	‐	40	

Source:	Koelsh	and	Stowell	(2005).	

N2O	is	lost	from	the	excreted	nitrogen.	A	quantitative	method	for	estimating	N2O	emissions	from	

solid	manure	is	the	IPCC	Tier	2	approach,	which	is	also	used	for	the	U.S.	Greenhouse	Gas	Inventory	

(Equation	5‐8).	This	estimation	method	is	the	same	as	the	method	present	in	the	Temporary Stack

and Long‐Term Stockpile	and	the	

Composting	sections	(See	Sections	5.4.1	and	0).	This	equation	will	over‐estimate	the	emissions	

from	animal	housing	if	some	of	the	nitrogen	excreted	is	managed	outside	of	housing	(i.e.,	the	

equation	accounts	for	nitrogen	loss	due	to	NH3	emissions	but	does	not	account	for	the	quantity	of	

nitrogen	that	is	managed	in	manure	management	systems).		

For	manure	in	deep	pits,	dry‐lots,	or	mixed	with	bedding,	the	emission	factors	are	provided	in	Table	

5‐9.	The	N2O	emission	factors	for	manure	in	housing	that	is	stored	in	a	stockpile	are	provided	in	

Table	5‐23.	The	emission	factors	for	manure	that	is	composted	within	a	housing	area	are	provided	

in	Table	5‐25.		

Table 5‐9: N2O Emission Factors for Manure Stored in Housing

Category N2O Emission Factor (kg N2O‐N/ kg N)

Cattle	and	Swine	Deep	Bedding	(Active	Mix) 0.07	

Cattle	and	Swine	Deep	Bedding	(No	Mix) 0.01	

Pit	Storage	Below	Animal	Confinements 0.002	

Dry‐Lot	 0.02	

Source:	IPCC	(2006).	

Equation 5‐8: IPCC Tier 2 Approach for Estimating N2O Emissions from Housing

ൈ ۽૛ۼ۳۴ ൈ ሻ૚૙૙/ܛܛܗܔ	૜۶ۼ	% െ૚ ሺ ൈ ܠ܍ۼൈܖൌ ܏ܖܑܛܝܗܐ,۽૛ۼ۳

૝૝

૛ૡ

ൈ

૚

૚૙૙૙

Where:	

EN2O,	housing	 =	Nitrous	oxide	emissions	from	housing	per	day	(kg	N2O	day‐1)	

N	 =	Number	of	head	of	livestock	species	(animal)	

Nex	 =	Total	nitrogen	excretion	per	animal	per	day	(g	N	animal‐1	day‐1)	

%NH3	loss		 =	Percent	of	Nex	lost	as	NH3	in	animal	housing	‐	see	Table	5‐8	

EFN2O	 =	N2O	emission	factor	for	manure	in	housing	(kg	N2O‐N	kg	N‐1)	

ସସ

ଶ଼

=	Conversion	of	N2O‐N	emissions	to	N2O	emissions	

ଵ

ଵ଴଴଴ =	Conversion	of	grams	to	kilograms	
 Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

 5-43 

The	remaining	nitrogen	excreted	that	is	not	lost	as	N2O	or	volatilized	as	NH3	in	housing	then	enters	

manure	storage	and	treatment.	If	data	are	not	available	to	track	the	nitrogen	that	is	transferred	

along	with	the	manure‐to‐manure	storage	and	treatment,	the	nitrogen	can	be	estimated	as	

described	in	Equation	5‐9.	However,	this	equation	is	overestimating	the	nitrogen	transferring	to	

manure	storage	and	treatment	as	some	nitrogen	will	be	lost	in	housing.	This	remaining	total	

nitrogen	value	is	an	input	into	the	N2O	equations	for	manure	stored	or	treated.	

The	DairyGEM	Model	provides	daily	estimates;	users	can	refer	to	that	model	for	a	more	in‐depth	

analysis	of	their	emissions.	

5.3.1.2 Rationale for Selected Method for Estimating Emissions from Dairy

Production Systems

There	are	a	variety	of	methods	and	models	available	to	estimate	emissions	from	dairy	production	

systems,	ranging	from	simple	carbon	footprint	models	to	highly	complex	process‐based	models	for	

the	determination	of	NH3	and	GHG	emissions.	The	IPCC	Tier	1	methodology	provides	a	simplistic	

method	used	for	country	inventory	purposes.	When	additional	data	are	available,	there	are	a	series	

of	equations	that	can	be	used	to	develop	IPCC	Tier	2	estimates.	The	data	used	for	these	estimates	

are	typically	easily	obtainable	from	the	production	facility	or	available	in	a	lookup	table.	While	

these	methods	provide	estimates	for	emissions	that	may	be	suitable	for	a	rough	determination	of	

emissions	inventories,	they	are	in	some	cases	based	on	very	limited	data	and	may	not	be	very	

representative	of	emissions	at	the	farm	level.	The	development	of	process‐based	models	has	

provided	a	way	to	obtain	a	more	detailed	analysis	of	emissions	at	the	farm	scale.		

A	wide	variety	of	models	applicable	to	dairy	production	facilities	were	identified	and	evaluated,	

including:	Carbon	Accounting	for	Land	Managers;	Climate	Friendly	Food	Carbon	Calculator;	Cool	

Farm	Tool;	CPLAN;	DairyGEM;	Dairy	Wise;	Farming	Enterprise	GHG	Calculator;	Farm	GHG;	Holos;	

Integrated	Farm	System	Model	(IFSM);	Manure	And	Nutrient	Reduction	Estimator	(MANURE);	

Manure	DeNitrification‐DeComposition	(Manure	DNDC);	OVERSEER;	and	SIMS	Dairy.		

These	models	were	evaluated	to	determine	their	suitability	for	use	to	determine	emissions	

estimates	for	dairy	production	facilities	in	the	United	States.	Eleven	criteria	were	used	to	identify	

models	that	could	be	used	to	estimate	CH4	from	enteric	CH4	production	and	CH4,	N2O,	and	NH3	from	

animal	housing	systems.	Two	of	the	criteria	were	considered	critical:	the	model	had	to	be	relevant	

to	U.S.	climate	and	dairy	production	systems	and	it	had	to	be	publically	available.	If	the	models	met	

these	two	criteria	they	were	further	ranked	based	on	the	remaining	nine	criteria.	Four	of	the	

models	considered	met	the	critical	criteria:	DairyGEM,	IFSM,	Cool	Farm	Tool,	and	MANURE.	

Although	DairyGEM	is	a	subset	of	IFSM,	it	was	included	separately	because	DairyGEM	only	

Equation 5‐9: Total Nitrogen Entering Manure Storage and Treatment

܉ܚܗܜܛۼ܂܏܍ ൌܖൈܠ܍ۼ ൈ ሺ૚ െ %	ۼ۶	૜ܛܛܗܔ/૙૙૚ሻ ൈ

૚

૚૙૙૙

Where:	

TNstorage						 =	Total	nitrogen	entering	manure	storage	(kg	N	day‐1)	

N	 =	Number	of	head	of	livestock	species	(animal)	

Nex	 =	Total	nitrogen	excretion	per	animal	per	day	(g	N	animal‐1	day‐1)	

%NH3	loss	 =	Percent	of	Nex	lost	as	NH3	in	animal	housing	‐	see	Table	5‐8	

ଵ

ଵ଴଴଴		 =	Conversion	of	grams	to	kilograms	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-44 

estimates	emissions	from	the	animal	housing	and	manure	storage	area.	Therefore,	it	is	less	

cumbersome	to	use	and	requires	fewer	inputs.		

Out	of	these	four	models,	DairyGEM	had	the	most	flexibility	for	describing	the	production	system	

and	met	all	of	the	specified	criteria.	In	addition,	this	model	implements	emission	estimate	

methodologies	that	are	advanced	beyond	the	IPCC	Tier	2	determinations.	It	models	CH4	emissions	

from	enteric	fermentation	and	manure	management	and	the	nitrogen	balance	associated	with	

nitrogen	excreted	in	manure.	The	underlying	methods	in	the	DairyGEM	model	are	recommended	

for	determining	CH4	emissions	from	enteric	fermentation	and	housing	systems	for	dairy	cattle	(see	

further	discussion	in	Appendix	5‐E,	Table	5‐E‐1,	and	subsequent	relevant	text).	The	estimates	

generated	from	this	model	could	then	be	modified	to	account	for	mitigation	strategies	that	could	

alter	the	emissions	currently	being	generated	on‐farm.	Some	mitigation	strategies	are	already	

embedded	in	the	model,	such	as	alternative	feeding,	manure	handling/storage,	and	the	use	of	

bovine	somatotropin,	while	others	could	be	used	by	developing	a	table	with	modifiers	based	on	

literature	values	to	determine	how	on‐farm	emissions	could	change	with	the	implementation	of	

these	strategies.	For	N2O	emissions,	a	nitrogen	balance	approach	(based	on	the	concepts	in	

DairyGEM)	using	nitrogen	excretion	equations	from	ASABE	Standard	D384.2	is	recommended.	The	

use	of	the	ASABE	equations	takes	into	account	the	impact	of	dietary	changes	on	nitrogen	excretion.	

5.3.2 Enteric Fermentation and Housing Emissions from Beef Production Systems

Because	of	differences	in	the	diets,	animal	physiological	state	and	age,	and	manure	handling,	the	

proportions	and	sources	of	GHGs	differ	among	the	cow‐calf,	stocker,	and	finishing	segments	of	the	

beef	cattle	industry.	A	primary	source	of	GHGs	from	the	beef	cattle	industry	is	enteric	CH4,	

produced	primarily	in	the	rumen,	although	some	CH4	is	also	produced	in	the	lower	gut.	In	addition,	

CH4	and	N2O	may	be	produced	from	feces	and	urine	on	pastures	and	feedlot	pen	surfaces.	Emissions	

Model Evaluation Criteria for Dairy Production Systems

1. The	model	is	based	on	well‐established,	scientifically	sound	relationships	among	farm

management	inputs,	emissions	outputs	(process‐based/mass‐balance	model

preferable).

2. The	model	is	relevant	to	U.S.	climate	and	dairy	production	systems.

3. The	model	can	estimate	CH4,	and	N2O,	and	NH3	emissions	from	dairy	housing	systems

(including	enteric	CH4	production).

4. There	is	flexibility	in	the	model	to	describe	the	production	system	(animals,	feed,

housing,	and	in‐house	manure	management).

5. The	model	is	easy	to	use	and	is	designed	to	use	easily	obtainable	farm	information	to

determine	emissions	estimates.

6. Model	emission	estimates	for	both	enteric	CH4	production	and	emissions	associated

with	cattle	housing	are	easily	captured.

7. The	model	includes	some	mitigation	strategies	for	reducing	emissions	and	produces

realistic	changes	in	emissions	values	when	these	changes	are	made	within	the

production	system.

8. There	is	transparency	in	the	model	calculations,	and	technical	guidelines	are	available

to	elaborate	the	methodologies	used	to	obtain	the	emissions	estimates.

9. The	model	has	been	tested/validated	with	on‐farm	data.

10. The	model	works	reliably	(little	to	no	errors	or	program	crashes).

11. The	model	is	publicly	available	and	accessible.
 Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

 5-45 

from	housing	and	manure	handling	(prior	to	entering	a	management	system)	are	discussed,	and	

equations	for	stockpiled	manure	(Section	5.4)	can	be	applied	for	emission	estimation.	

Phetteplace	et	al.	(2001)	estimated	GHG	emissions	from	simulated	beef	and	dairy4	systems	in	the	

United	States	using	modifications	of	the	IPCC	(1997)	methodology.	The	systems	were	comprised	of	

a	base	herd	of	mature	cows	plus	calves	and	replacements,	stocker	calves,	a	feedlot,	and	a	dairy	with	

100	lactating	cows.	They	also	evaluated	emissions	from	calves	that	went	through	the	entire	cow‐

calf,	stocker	and	feedlot	system	(cow‐calf	to	feedlot).	Greenhouse	gas	emissions	head‐1	(CO2‐eq)	

from	Phetteplace	et	al.	(2001)	are	presented	in	Table	5‐10	(with	the	exception	of	the	dairy	herd).	

Table 5‐10: Simulated GHG Emissions for Ruminant Systems (kg CO2‐eq/head/year)		

Item Cow‐calf Stocker Feedlot Cow‐calf Through

Feedlot

Dietary	TDN,	%	 62	 57 88 62	

GHG (kg CO2‐eq/head/year)

Enteric	CH4 1,140 1,725 743 1,167	

Manure	CH4	 34	 48 12 34	

Total	CH4 1,175 1,773 755 1,201	

N2O	 1,487 1,721 1,294 1,490	

CO2 127 380 1,245 252	

Total	CO2‐eq	 2,788 3,874 3,294 2,944	

Source:	Phetteplace	et	al.	(2001).	

Elsewhere,	Beauchemin	et	al.	(2010)	used	the	Holos	model	(Little	et	al.,	2008)	to	conduct	a	life‐

cycle	assessment	of	beef	production	in	western	Canada.	Of	total	CO2‐eq,	63	percent	was	from	

enteric	CH4.

5	These	are	very	similar	to	values	reported	by	the	U.S.	Department	of	Agriculture	

(2004b).	Sixty‐one	percent	of	CO2‐eq	emissions	were	from	the	cow‐calf	herd,	19	percent	were	from

replacement	heifers,	eight	percent	were	from	backgrounding	operations,	and	12	percent	were	from	

feedlots.	Seventy	nine	percent	of	enteric	CH4	losses	were	from	the	cow	herd,	three	percent	from	

bulls,	two	percent	from	calves,	seven	percent	from	backgrounders,	and	nine	percent	from	feedlots.	

N2O	contributions	(CO2‐eq)	as	a	percent	of	total	GHG	emissions	were	as	follows:	feedlot	manure	–	

two	percent,	feedlot	soil	–	two	percent,	cow‐calf	herd	soil	–	two	percent,	and	cow‐calf	herd	manure	

–	20	percent.	

Cow‐Calf and Bulls		

There	is	no	evidence	that	any	breed	of	beef	cow	produces	less	enteric	CH4	than	another.	There	are	a	

few	reports	suggesting	that	efficient	cattle	(those	selected	for	feed	efficiency	or	residual	feed	intake	

(RFI))	may	produce	less	enteric	CH4	(Nkrumah	et	al.,	2006;	Hegarty	et	al.,	2007).	However,	Freetly

and	Brown‐Brandl	(2013)	reported	that	cattle	with	greater	feed	efficiency	actually	produced	more	

CH4;	thus	raising	some	questions	about	the	genetic	factors	associated	with	feed	efficiency	and	CH4

emissions.	It	is	unclear	whether	the	changes	observed	are	a	result	of	altered	feed	intake	or	are	

associated	with	a	change	in	altered	ruminal	microbial	population.	Additionally,	recent	information	

indicates	that	there	is	an	interaction	between	diet	quality	and	feed	efficiency	on	enteric	CH4

emissions,	where	efficient	cows	produce	less	CH4	when	grazing	high‐quality	pasture	but	not	when	

grazing	poor‐quality	forage	(Jones	et	al.,	2011).	Residual	feed	intake	is	moderately	heritable—(0.28	

to	0.58;	Moore	et	al.,	2009),	thus	it	might	be	possible	to	genetically	select	for	animals	with	lower	

enteric	CH4	production.	An	examination	of	the	value	for	selection	for	low	enteric	CH4	production	

has	been	conducted	with	sheep	in	New	Zealand	and	Australia.	Simulations	using	published	data	

																																																													

4	Discussion	of	emissions	from	dairy	production	systems	can	be	found	in	Section	5.3.1.	

5	5%	of	emissions	were	from	manure	CH4,	23%	from	manure	N2O,	4%	from	soil	N2O,	and	5%	from	energy	CO2.
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-46 

indicate	that	without	accurate	feed	intake	information	and	a	method	by	which	many	animals	can	be	

screened	for	CH4	emissions,	selection	for	lower	enteric	CH4	production	is	not	likely	to	be	

economically	viable	(Cottle	et	al.,	2011).		

Measurement	of	enteric	CH4	from	grazing	cattle	has	been	conducted	primarily	from	animals	grazing	

improved	pastures	using	micrometeorological	methods	and	tracer	techniques.	Lassey	(2007)	

summarized	much	of	the	CH4	emissions	data	that	had	been	collected	using	the	SF6	tracer	technique.	

Intake	was	either	calculated	from	a	requirements	model	or	from	use	of	markers	(Cr2O3	or	Yb2O3).	

Estimated	forage	digestibility	(in	vitro)	ranged	from	48.7	to	83	percent,	which	resulted	in	estimated	

CH4	conversion	factors	[i.e.,	enteric	CH4	as	a	percentage	of	gross	energy	intake	(GEI)]	ranging	from	

3.7	to	9.5	percent.	The	mean	Ym	from	all	of	the	studies	was	6.25	and	agrees	reasonably	well	with	

that	used	by	IPCC	(2006)	for	cattle	on	pasture.	Methane	emissions	from	cows	grazing	improved	

pasture,	Kentucky	fescue,	and	Bermuda	grass	in	the	southern	United	States	were	reported	by	

Pavao‐Zuckerman	et	al.	(1999)	and	DeRamus	et	al.	(2003).	In	both	of	these	studies	significant	

reductions	in	enteric	CH4	unit‐1	of	animalweight	gain	resulted	from	the	implementation	of	best	

management	practices	designed	to	improve	pasture	quality.		

Enteric	emissions	estimates	can	be	made	using	micrometeorological	methods	and	tracer	

techniques.	One	report	in	which	CH4	emissions	were	measured	from	beef	cows	grazing	native	range	

in	October	and	May	illustrated	a	large	variation	in	enteric	emissions.	In	October,	when	cows	were	

losing	BW,	they	produced	87	g	CH4	head	daily‐1,	and	on	the	same	pasture	in	May	they	produced	252	

g	CH4	head	daily‐1	(Olson	et	al.,	2000).	Westberg	et	al.	(2001)	measured	CH4	from	cows	grazing	the	

same	pasture	across	seasons	and	found	similar	results,	with	higher	CH4	emissions	from	cows	

grazing	lush	spring	growth	and	the	lowest	emissions	from	grazing	stockpiled	fall	pasture.	These	

differences	are	attributable	to	differences	in	both	DMI	and	forage	quality.	In	general,	as	forage	

quality	increases,	DMI	also	increases.	Some	"rules	of	thumb"	for	DMI	on	pasture	include	the	

following:		

 Poor	quality	pasture	‐	DMI	=	1	to	1.75	percent	of	body	weight;

 Medium	quality	forage	‐	DMI	=	1.75	to	2.25	percent	of	body	weight;

 High	quality	forage	DMI	=	2.25	to	3	percent	of	body	weight.

Stockers

Enteric	CH4	emissions	of	stockers	while	grazing	have	been	measured	by	Laubach	et	al.	(2008),	

Tomkins	et	al.	(2011),	McGinn	et	al.	(2011),	and	Boadi	et	al.	(2002),	using	a	variety	of	techniques	

including	the	SF6	tracer,	and	several	micrometeorological	approaches.	The	same	factors	that	affect	

CH4	emissions	from	grazing	beef	cows	are	important	in	stocker	cattle.	Those	factors	are	level	of	

feed	intake,	digestibility	of	forage	consumed,	supplementation,	and	the	chemical	composition	of	the	

plants	consumed.	Enteric	emissions	estimates	can	be	made	using	micrometeorological	methods	or,	

tracer	techniques	or	can	be	predicted	from	IPCC	Tier	2	methods	(see	enteric	discussion).	Critical	

variables	include	measurements	or	estimations	of	feed	intake	and	feed	quality	(chemical	

composition).	Many	of	the	equations	currently	available	may	not	accurately	predict	measured	

enteric	emissions	from	grazing	cattle	(Tomkins	et	al.,	2011).		

Feedlot

Most	estimates	of	enteric	methane	emission	from	finishing	beef	cattle	are	based	on	work	using	

animals	confined	to	respiration	chambers,	although	a	few	studies	have	used	micrometeorological	

methods	in	open	feedlots.	Enteric	CH4	losses	from	finishing	beef	cattle	normally	range	from	50	to	

200	L	head‐1	daily	(Johnson	and	Johnson,	1995;	McGinn	et	al.,	2004;	Beauchemin	et	al.,	2008;	Loh	et	

al.,	2008;	Hales	et	al.,	2012;	2013;	Hales	et	al.,	2014;	Todd	et	al.,	2014a;	Todd	et	al.,	2014b).	In	most	

studies	in	the	U.S.,	diets	have	been	based	on	DRC	or	SFC;	whereas	most	studies	in	Canada	the	diets	

are	based	on	barley.	The	IPCC	Tier	2	(2006)	enteric	CH4	conversion	factor	(Ym)	for	feedlot	cattle	is	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-47 

3	±	1	percent	of	GEI.	There	are	few	studies	that	have	measured	emissions	of	CH4	and	N2O	from	

feedlot	pen	surfaces	and	runoff	control	structures.	The	primary	factors	that	control	enteric	methane	

emissions	in	feedlot	cattle	are	feed	intake,	grain	type,	grain	processing	method,	dietary	roughage	

concentration	and	characteristics,	and	dietary	fat	concentration.		

5.3.2.1 Method for Estimating Emissions from Beef Production Systems

a	Calculated	using		Eqn10.3	in	IPCC	(2006)	based	on	body	weight	(“BW”).		 b	Calculated	using	Eqn	10.4	in	IPCC	(2006)	based	on	NEa	and	feeding	situation.	 c	Calculated	using	Eqn	10.8	in	IPCC	(2006)	based	on	milk	production	(“milk”)	and	milk	fat	(“fat”).	 d	Calculated	using	Eqn	10.11	in	IPCC	(2006)	based	on	information	on	daily	hours	of	work	(“work”).	

e	Calculated	using	Eqn	10.13	in	IPCC	(2006)	based	on	NEm	and	pregnancy	status.	 f		Calculated	using	Eqn	10.14	in	IPCC	(2006)	based	on	DE.	

g	Calculated	using	Eqn	10.13	in	IPCC	(2006)	based	on	body	weight,		mature	weight	(“MW”),		and	daily	weight	gain	(“WG”).	 h	Calculated	using	Eqn	10.15	in	IPCC	(2006)	based	on	DE.	

Method for Estimating CH4 Emissions from Enteric Fermentation in Beef Cattle

 IPCC	Tier	2	approach,	with	some	adjustment	factors,	based	on	diet,	animal	weight,

pregnancy/lactation,	activity	(IPCC,	2006).

 Data	sources	are	user	inputs	on	dietary	intake,	lactation	and	pregnancy	rates,	animal

weight,	housing,	and	the	Feedstuffs	Composition	Table	(Ewan,	1989;	Preston,	2013).

 Although	the	equations	utilized	are	the	same	as	existing	inventory	methods,	the	method

takes	into	account	a	large	database	of	feed	types	(found	in	Appendix	5‐B,	Feedstuff

Composition	Table),	as	well	as	reporting	at	the	monthly,	rather	than	annual,	temporal

scale.

Equation 5‐10: IPCC Tier 2 Equation for Calculating Gross Energy Requirements for Beef

Cattle

GE ൌ ൦

൬

NE୫ ൅ NEୟ ൅ NE୪ ൅ NE୵୭୰୩ ൅ NE୮

REM ൰ ൅ ൬

NE୥

REG൰

DE%

100 ൪

Where:	

GE		 =	Gross	energy	(MJ	day‐1)	

NEm		 =	Net	energy	required	by	the	animal	for	maintenance	(MJ	day‐1)a

NEa		 =	Net	energy	for	animal	activity	(MJ	day‐1)b

NEl		 =	Net	energy	for	lactation	(MJ	day‐1)c

NEwork	=	Net	energy	for	work	(MJ	day‐1)d

NEp		 =	Net	energy	required	for	pregnancy	(MJ	day‐1)e

REM	 =	Ratio	of	net	energy	available	in	a	diet	for	maintenance	to	digestible	energy	

consumedf

NEg		 =	Net	energy	needed	for	growth	(MJ	day‐1)g

REG	 =	Ratio	of	net	energy	available	for	growth	in	a	diet	to	digestible	energy	consumedh

DE		 =	Digestible	energy	expressed	as	a	percent	of	gross	energy	(%)	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-48 

The	DE	ultimately	used	in	the	IPCC	Tier	2	equation	(in	Equation	5‐11)	will	be	weighted	based	on	

portion	of	total	feed	intake	from	a	particular	feed	type.	The	DE	data	for	particular	feedstuffs	can	be	

found	in	Appendix	5‐B.	The	recommended	Ym	for	beef	replacement	heifers,	steer	stockers,	heifer	

stockers,	beef	cows,	and	bulls	is	6.5	percent	for	all	regions	of	the	country.	For	feedlot	cattle,	the	

IPCC	(2006)	Ym	of	3	percent	is	adjusted	based	on	diets.	All	feedlot	cattle	initially	start	with	a	

baseline	Ym	of	three	percent	(IPCC,	2006).The	correction	factors	to	Ym	for	feedlot	cattle	for	

different	scenarios	are	provided	below	(see	Appendix	5‐A	for	additional	details).	The	Ym	used	for	

calculating	emissions	for	these	cattle	is	modified	based	on	animal	diets,	as	indicated	in	Table	5‐11.		

Table 5‐11: Determination of Adjusted Methane Conversion Factor (Ym) for Feedlot Cattle

Variable Ym (as a % of GEI)

Baseline Ym (IPCC, 2006) 3%

Ionophore in diet (Tedeschi et al., 2003; Guan et al., 2006):

 Yes	 No	change

 No	 Increase	Ym	by	4%

(Ym=	3%	x	1.04=	3.12%	of	GEI)

Fat Content (Zinn and Shen, 1996; Beauchemin et al., 2008; Martin et al., 2010) (For	each	percent	of	

added	fat	(as	supplemental	fat	or	in	byproducts	such	as	distillers	grain	that	contain	about	10	percent	fat),	

decrease	by	four	percent	to	a	maximum	of	a	16	percent	decrease)

 1%	supplemental	fat	 Decrease	Ym	by	4%

(Ym	=	3%	x	0.96	=	2.88%)

 2%	supplemental	fat	 Decrease	Ym	by	8%

(Ym	=	3%	x	0.92	=	2.76%)

 Four	or	higher	added	fat	content	 Decrease	Ym	by	16%

	(Ym=	3%	x	0.84=2.52%)

Grain Type (Beauchemin and McGinn, 2005; Archibeque et al., 2006; Hales et al., 2012):

 Grain	in	animal	diet	is	steam	flaked	(SF)	or	high	moisture	(HM) No	Change

 Grain	in	animal	diet	is	unprocessed	(UP)	or	dry	rolled	(DR)		 Increase	Ym	20%

	(Ym	=	3%	x	1.2	=	3.6%)

 Grain	in	diet	is	barley	rather	than	corn	or	sorghum	 Increase	Ym	30%

(Ym	=	3%	x	1.3	=	3.9)	

Grain Concentration (see Appendix 5‐A for details and references):

 Diet	contains	more	than	60	percent	grain No	Change

 Diet	contains	45	to	60	percent	grain	 Increase	Ym	10%

	(Ym=	3%	x	1.1	=	3.3%)

 Diet	contains	less	than	45	percent	grain	 Increase	Ym	40%	

(Ym	=	3%	x	1.40	=	4.2%)

Equation 5‐11: IPCC Tier 2 Equation for Calculating Enteric CH4 Emissions from Beef

Cattle

ൌ ܜܑܕ۳ܡ܉۲

૙૙૚/ܕ܇ ൈ ۵۳

૞૞. ૟૞

Where:	

DayEmit	 =	Emission	factor	(kg	CH4	head‐1	day‐1)	

GE		 	 =	Gross	energy	intake	(MJ	head‐1	day‐1)	

Ym		 	 =	CH4	conversion	factor,	which	is	the	fraction	of	GE	in	feed	converted	to	CH4	(%)		

55.65		 	 =	A	factor	for	the	energy	content	of	methane	(MJ	kg	CH4‐1)		
 Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

 5-49 

Emissions from Feedlot Pen Surfaces

There	are	few,	if	any,	studies	that	have	measured	CH4	or	N2O	emissions	from	beef	cattle	feedlot	pen	

surfaces	and	retention	ponds.	The	study	of	Todd	et	al.	(2014a;	2014b)	suggests	there	is	little	CH4

production	from	feedlot	pen	surfaces.	The	use	of	the	IPCC	(2006)	methodologies	is	recommended	

to	estimate	emissions	from	feedlot	pens	and	retention	ponds.		

In	order	to	estimate	CH4	emissions	from	beef	feedlot	pen	surfaces,	the	quantity	of	volatile	solids	

excreted	is	first	estimated.	These	can	be	estimated	by	lab	testing	samples	from	the	facility	or	using	

values	from	the	ASABE	Standard	D384.2	(ASABE,	2005).6	CH4	emissions	from	the	pen	surface	can	

be	estimated	using	the	IPCC	(2006)	Tier	2	approach	as	outlined	in	section	5.4.1.2.	For	cattle	

feedlots,	a	maximum	CH4	production	capacity	(B0)	of	0.33	m3/	kg	volatile	solids	is	assumed	(Table	

5‐19)	and	the	CH4	conversion	factor	for	pen	surfaces	ranges	from	1	to	2	percent	of	B0,	depending	

upon	environmental	temperature	(Table	5‐20).	Once	manure	is	scraped	from	the	pens	and	

removed,	the	methods	described	in	section	5.4.1	can	be	used	to	estimate	CH4	emission	from	manure	

that	is	composted	or	stored	in	stockpiles.		

In	order	to	estimate	N2O	emissions	from	the	pen	surfaces	of	beef	feedlots	the	quantity	of	nitrogen	

excreted	on	to	the	pen	surface	must	be	known.	This	can	be	estimated	using	Equation	5‐12	from	the	

ASABE	Standard	D384.2.	For	a	beef	feedlot,	a	default	value	of	0.069	kg	of	N	kg	dry	manure‐1	can	be	

used	if	Nex	is	not	calculated.	

																																																													

6	Volatile	solids	values	can	be	estimated	from	equations	(1)	or	(2)	in	section	4.3.1	of	ASABE	D384.2.	Default	

volatile	solids	values	are	also	presented	in	Table	5‐32	of	this	document.	

Method for Estimating Beef Cattle GHG Emissions from Housing

Methane	

 The	IPCC	(2006)	Tier	2	method	can	be	used	to	estimate	CH4	emissions	when	manure	is	

allowed	to	accumulate	on	feedlot	pen	surfaces	as	described	below.		

Nitrous	Oxide		

 Nitrogen	excreted	estimated	using	equations	provided	in	ASABE	D384.2.		

 IPCC	(2006)	Tier	2	approach	for	N2O	emissions	from	manure	in	housing.	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-50 

Some	of	the	nitrogen	excreted	is	volatilized	as	NH3,	hence,	the	estimation	of	NH3	losses	is	necessary	

to	estimate	N2O	emissions	using	a	nitrogen	balance	approach.	The	NH3	lost	from	manure	in	housing	

is	estimated	as	a	fraction	of	Nex.	Koelsch	and	Stowell	(2005)	provide	estimates	on	the	typical	NH3

loss	from	different	housing	facilities	as	a	fraction	of	Nex	(see	Table	5‐12).	A	range	of	values	has	been	

provided	for	each	facility	type;	the	lower	values	should	be	used	during	the	winter,	the	higher	values	

should	be	used	during	the	summer,	and	intermediate	values	should	be	used	for	the	spring	and	

autumn.	

Table 5‐12: Typical Ammonia Losses from Beef Cattle Housing Facilities Expressed as a

Percent of Nex

Facility Description % Loss Facility Description % Loss

Open	dirt	lots	(cool,	humid	region	)		 30	–	45	 Roofed	facility	(bedded	pack)		 20	‐	40	

Open	dirt	lots	(hot,	arid	region)	 40	–	60	 Roofed	facility	(deep	pit	under	floor,	

including	storage	loss)		 30	‐	40	

Source:	Koelsh	and	Stowell	(2005).	

An	alternative	approach	is	to	use	the	equation	of	Todd	et	al.	(2013)	which	calculates	monthly	

feedlot	NH3	emissions	as	a	function	of	dietary	crude	protein	and	average	monthly	temperature	

(Equation	5‐13).	

Equation 5‐12: ASABE Approach for Estimating Nitrogen Excretion from Beef Cattle

ܠ۴۽۲ ൈ ܠି۾۱۱ ൈ ܠ۷ۻ۲ ෍ ൌ ܠ܍ۼ

૟. ૛૞

ܖ

૚ୀܠ

െ ሾ૝૚. ૛ ൈ ሺ۰܅۴ െ ۰܅۷ሻሿ

൅ ቈ૙. ૛૝૜ ൈ ۲۽۴܂ ൈ ൤

۷܅۰ ൅ ۴܅۰

૛ ൨

૙.ૠ૞

܅܀܁ ൤ ൈ

۰܅۴ ൈ ૙. ૢ૟൨

૙.ૠ૞

ൈ ൤

۷܅۰ െ ۴܅۰

܂۴۽۲

൨

૚.૙ૢૠ቉	

Where:	

Nex	 =	Total	nitrogen	excretion	per	animal	per	day	(g	N	animal‐1	day‐1)	

DMIx	 =	Dry	Matter	Intake	for	ration	x	(kg	dry	feed	animal‐1day‐1)	

CCP‐x		 =	Concentration	of	crude	protein	of	total	ration	(g	crude	protein	g	dry	feed‐1)	

DOF୶	=	Days	on	feed	for	an	individual	ration	(days)	

BW୊	 =	Live	body	weight	at	finish	of	feeding	period	(kg)	

BW୍	 =	Live	body	weight	at	the	start	of	feeding	period	(kg)	

DOFT	=	Total	days	on	feed	from	start	to	finish	of	feeding	periods	(days)

SRW	 =	Standard	reference	weight	for	expected	final	body	fat	(kg)	

x		 =	Ration	number	

n		 =	Total	number	of	rations	fed	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-51 

N2O	emissions	are	calculated	using	the	IPCC	(2006)	Tier	2	method	and	dry‐lot	emission	factors	

described	in	Equation	5‐8	and	Table	5‐9.	The	quantity	of	nitrogen	that	leaves	the	feedlot	pens	in	

manure	can	then	be	calculated	using	Equation	5‐9.	N2O‐N	losses	from	manure	collected	and	

removed	from	the	pens	can	be	determined	from	manure	nitrogen	using	Equation	5‐27	and	

Equation	5‐29	and	the	emission	factors	in	Table	5‐23	and	Table	5‐25	found	in	Section	5.4	Manure	

Management.	NH3	losses	from	manure	nitrogen	removed	from	the	pens	can	be	calculated	as	

described	in	Appendix	5‐C.1	and	5‐C.3.		

5.3.2.2 Rationale for Selected Method for Estimating Emissions from Beef

Production Systems

Cow‐Calf, Bulls, and Stockers

The	most	appropriate	predictions	available	for	entity	scale	estimation	are	IPCC	Tier	2	methods	for	

grazing	cattle.	Critical	variables	that	are	important	to	define	in	order	to	generate	prediction	

methods	include	measurements	or	estimations	of	feed	intake	and	feed	quality	(chemical	

composition)	for	pasture	or	rangelands.	If	the	intake	is	not	known,	intake	prediction	

equations/models	such	as	NRC	(2000)	can	be	used.	The	NRC	(2000)	provides	an	equation	for	the	

calculation	of	DMI	for	grazing	beef	cows	and	for	stocker	cattle:	NEm	intake	=	SBW0.75	*	(0.04997	*	

NEm2	+	0.04631)	where	NEm	is	the	estimated	Mcal	kg‐1	of	the	pasture,	and	SBW	is	the	average	

shrunk	body	weight	for	the	period	of	grazing	(kg).	The	requirement	for	knowledge	of	the	NEm	

concentration	of	the	pasture	may	limit	the	usefulness	of	the	prediction	in	some	situations.		

In	situations	in	which	the	herd	is	housed	in	a	dry‐lot	or	barn	facility,	emission	factors	for	CH4	and	

N2O	associated	with	pen	surfaces,	manure	storage,	and	animal	movement/manure	disturbance	

would	be	appropriate.	

Feedlot

Ellis	et	al.,	(2009)	reported	that	several	equations	appeared	to	be	good	predictors	of	enteric	CH4

losses	from	feedlot	cattle	based	on	Canadian	studies.	However,	many	of	those	equations	tend	to	

greatly	overestimate	enteric	losses	when	compared	with	data	from	cattle	fed	a	typical	southern	

plains	finishing	diet	(Hales	et	al.,	2012;	2013;	Todd	et	al.,	2014a;	Todd	et	al.,	2014b).	Although	

Kebreab	et	al.	(2008)	reported	that	MOLLY	and	IPCC	Tier	2	(2006)	gave	predicted	values	similar	to	

measured	values	with	feedlot	cattle,	there	was	a	large	variability	in	individual	animals	with	errors	

of	75	percent	or	greater.	Kebreab	et	al.	(2008)	noted	the	average	Ym	(MJ	enteric	CH4	MJ	GEI‐1)	for	

feedlot	cattle	based	on	experimental	data	was	3.88	percent	of	GEI	(range	3.36	to	4.56),	which	was	

higher	than	the	IPCC	(2006)	value	of	3.0	percent	and	the	recently	obtained	values	with	typical	

finishing	diets	of	2.85	percent	(Hales	et	al.,	2012;	2013).		

Equation 5‐13: Monthly Beef Feedlot NH3 Emissions as a Function of Dietary Crude

Protein and Monthly Temperature

ܖۺሺۼ۶૜ሻ ൌ ૡ. ૡ૛ െ ૚૟૛ૢ ൤

૚

܂

൨ ൅ ૙. ૚૙ૡ ൈ ۱۾

Where:	

NH3		 =	NH3	emission	from	housing	per	day	(g	NH3	head‐1	day‐1)	

T	 =	Average	monthly	temperature	(K)	

CP	 =	Dietary	crude	protein	as	a	fraction	of	dry	matter	(%)	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-52 

Currently,	IPCC	Tier	2	may	be	the	most	useful	methodology	for	prediction	of	enteric	emissions	from	

feedlot	beef	cattle.	Unfortunately,	the	Tier	2	method	does	not	allow	for	estimating	changes	in	

enteric	emissions	related	to	changes	in	diet	or	management.		

Therefore,	a	modified	IPCC	(2006)	method	is	recommended	to	estimate	enteric	CH4	emissions	from	

beef	cattle	fed	high	concentrate	finishing	diets.	The	CH4	conversion	factor	(Ym)	will	be	adjusted	by	

factors	in	the	animals’	diets	as	described	in	Section	5.3.2.1.	A	baseline	scenario	based	on	typical	U.S.	

beef	cattle	feeding	conditions	is	established,	and	the	Ym	values	are	adjusted	based	on	published	

research.	Emission	values	are	modified	using	correction	factors	that	are	based	on	changes	in	animal	

management	and	feeding	conditions	from	the	baseline	scenario.		

5.3.3 Enteric Fermentation and Housing Emissions from Sheep

GHG	emissions	associated	with	sheep	production	include	enteric	CH4	emissions,	manure	and	

bedding	emissions,	and	emissions	associated	with	grazing	and	manure	application	to	land.	

The	New	Zealand	Ministry	for	the	Environment	(2010)	estimated	that	sheep	younger	than	a	year	of	

age	emit	5.1	percent	of	GEI	as	enteric	CH4,	and	adult	sheep	emit	6.3	percent	of	their	GEI	as	CH4.	

These	emission	factors,	when	combined	with	population	estimates,	result	in	baseline	enteric	

emissions	of	11.60	kg	CH4	head‐1	year‐1.	Sheep	are	also	estimated	to	deposit	15.9	kg	N	head‐1	year‐1.		

Lassey	(2007)	summarized	the	enteric	emissions	measurements	from	grazing	sheep	trials	from	

New	Zealand	and	Australia	in	which	the	SF6	tracer	technique	was	used.	Forage	characteristics	

ranged	from	lush	(in	vitro	digestibility	estimate	of	82	percent)	to	poor	quality	(called	“dead,”	with	

an	in	vitro	digestibility	of	54	percent).	Intake	was	measured	using	complete	fecal	collection	or	a	

marker	(n‐alkane).	Enteric	CH4	emissions	ranged	from	11.7	g	day‐1	for	sheep	fed	forage	of	higher	

quality	(6.9	percent	of	GEI)	to	35.2	g	day‐1	for	sheep	fed	forage	of	lower	quality	(6.3	percent	of	GEI).	

The	average	enteric	emissions	were	5.39	percent	of	GEI,	or	23.5	g	day‐1.	In	general,	lower	forage	

quality	resulted	in	a	greater	amount	of	CH4	emitted	as	a	proportion	of	the	energy	intake	than	did	

higher	forage	quality.		

New	Zealand	pastures	grazed	by	sheep	had	elevated	N2O	emissions	(7.4	g	N2O‐N	ha‐1	day‐1	vs.	3.4g	

N2O‐N	ha‐1	day‐1)	compared	with	control,	but	significantly	less	than	that	observed	when	cattle	

grazed	(32.0	g	N2O‐N	ha‐1	day‐1)	(Saggar	et	al.,	2007).	The	data	were	used	to	evaluate	the	NZ‐DNDC	

model,	a	process‐based	New	Zealand	whole	farm	model.	To	our	knowledge	there	are	no	published	

estimates	of	GHG	emission	from	sheep	manure	systems.		

5.3.3.1 Method for Estimating Emissions from Sheep

Method for Estimating Enteric Fermentation CH4 Emissions from Sheep

 Howden	equation	(Howden	et	al.,	1994),	based	on	dietary	DMI.

 The	equation	from	Howden	et	al.	(1994)	estimates	emissions	based	solely	on	DMI;	hence,

emission	factors	not	utilized.
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-53 

The	dry	matter	data	for	particular	feedstuffs	can	be	obtained	from	Appendix	5‐B.	

No	emissions	estimation	methods	have	been	provided	for	housing	as	most	sheep	are	kept	on	

pasture	and	minimal	emissions	are	expected.		

5.3.3.2 Rationale for Selecting Method for Estimating Emissions from Sheep

Howden	et	al.	(1994)	generated	an	equation	from	which	to	predict	CH4	emissions	from	sheep.	

Equation	5‐7	resulted	from	a	linear	extrapolation	of	DMI	to	emissions.	It	has	since	been	evaluated	

and	found	to	be	robust	enough	to	be	the	equation	used	in	the	Australian	National	Greenhouse	Gas	

Inventory.	Klein	and	Wright	(2006)	measured	CH4	from	sheep	in	respiration	chambers	and	

compared	their	results	to	the	Howden	et	al.	(1994)	equation.	Actual	CH4	averaged	1.1	g	head‐1	(SE	±	

0.05)	and	predicted	CH4	was	1.1	g	head‐1	(SE	±	0.02).	A	potential	concern	regarding	the	Howden	

equation	is	that	much	of	the	data	included	in	the	analysis	was	based	on	tropical	forages.	

Nonetheless,	when	intake	data	are	available,	the	Howden	equation	presents	the	best	method	by	

which	to	estimate	sheep	enteric	emissions.	When	intake	is	not	available,	the	IPCC	Tier	2	method	of	

estimation	should	be	used.	Emissions	from	feedlot	sheep	should	use	the	Ym	values	from	Blaxter	

and	Clapperton’s	original	paper	(1965)	in	which	they	measured	CH4	emissions	from	sheep	with	

respiration	calorimetry	chambers.	Sheep	fed	highly	digestible	diets	at	three	times	maintenance	

produced	35	percent	less	CH4	(kcal	100K	kcal	of	feed	energy‐1)	than	those	fed	similar	diets	at	

maintenance;	thus,	a	reduced	Ym	value	is	warranted.	The	equation	is	CH4	=	1.3	+	[0.112	×	(%	

digestibility/100)]	+	[ME	intake/maintenance	ME	requirement]	×	[2.37	‐	0.050	×	

(%digestibility/100)].		

5.3.4 Enteric Fermentation and Housing Emissions from Swine Production Systems

Sources	of	GHG	emissions	include	enteric	fermentation;	manure	stored	within	the	animal	housing,	

whether	it	is	stored	as	a	liquid	or	mixed	with	bedding;	emissions	that	occur	during	the	transport	of	

manure	to	an	external	manure	storage	structure;	the	outside	manure	storage	structure;	emissions	

that	occur	during	transport	of	manure	to	the	field;	and	emissions	following	land	application	of	

manure.	Because	GHG	mitigation	has	not	been	a	focus	of	U.S.	research	for	the	swine	industry	nor	a	

high	priority	for	swine	producers,	data	are	not	readily	available	to	identify	the	magnitude	of	each	of	

the	above	points	of	emission	within	a	farm.	However,	emissions	of	CH4	are	expected	to	occur	

primarily	during	manure	storage,	and	emissions	of	N2O	are	expected	to	predominate	following	land	

application	of	manure.7	Often	manure	is	stored	underneath	the	pig	housing	in	a	deep	pit.	For	this	

reason,	emissions	discussion	in	this	section	includes	in‐house	manure	storage	and	comparison	of	

in‐house	manure	storage	systems	with	systems	that	store	manure	externally.	Because	swine	feeds	

are	dry,	emissions	of	GHG	from	feed	storage	areas	are	believed	to	be	negligible.	

7	Greenhouse	gas	emissions	resulting	following	land	application	are	addressed	separately	in	the	sections	on	

Chapter	3:	Croplands	and	Grazing	Lands.	

Equation 5‐14: Equation for Enteric Fermentation Emissions from Sheep (Howden et al.,

1994)

۱۶૝ ൌ ۷܍ܓ܉ܜܖ ൈ ૙. ૙૚ૡૡ ൅ ૙. ૙૙૚૞ૡ	

Where:	

CH4	 =	Methane	emissions	(kg	CH4	head‐1	day‐1)	

Intake	 =	Dry	Matter	Intake	(kg	head‐1	day‐1)	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-54 

Greenhouse	gas	emission	data	from	swine	facilities	is	somewhat	limited.	Liu	et	al.	(2011a)	reported	

that	grow/finish	pigs	emitted	42	to	79	mg	CH4	kg	BW‐1	daily	from	chambers	where	pigs	were	

housed	with	manure.	Daily	emissions	of	N2O	ranged	from	11.4	to	12.4	mg	N2O	kg	BW‐1	(Li	et	al.,	

2011).	These	values	are	somewhat	higher	than	data	used	by	Verge	et	al.	(2009)	in	calculating	GHG	

emissions	from	Canadian	pork	production	(43	mg	CH4	kg	BW‐1	and	4	mg	N2O	kg	BW‐1).	Philippe	et	

al.	(2007)	observed	GHG	emissions	in	the	range	reported	by	Li	et	al.	(2011)	though	their	

observations	were	in	European	deep	litter	and	slatted	floor	systems.	The	reported	gaseous	

emissions	from	pigs	raised	on	the	slatted	floor	and	on	the	deep	litter	were,	respectively,	0.54	and	

1.11	g	pig‐1	day‐1	for	N2O,	and	16.3	and	16.0	g	pig‐1	day‐1	for	CH4.	

Liu	et	al.	(2011a)	conducted	a	meta‐analysis	to	identify	factors	that	contribute	to	GHG	emissions	

from	swine	production.	Findings,	shown	in	Table	5‐13,	illustrate	that	type	of	emission	source	

(swine	buildings	or	manure	storage	facilities)	was	not	significant	for	CH4	and	N2O	emissions.	Swine	

category	(stage	of	production)	and	geographic	location	was	significant	for	both	of	the	GHG	gases.	

Neither	temperature	nor	size	of	operation	was	significant	in	the	overall	analysis.	

Within	the	meta‐analysis,	Liu	et	al.	(2011a)	

found	that	swine	buildings	with	straw‐flow	

systems	generated	the	lowest	CH4	and	N2O	

emissions	of	systems	compared,	while	pit	

systems	generated	the	highest	CH4	emissions,	

and	bedding	systems	generated	the	highest	

N2O	emissions.	Emissions	from	lagoons	and	

slurry	storage	basin/tanks	were	compared;	

lagoons	generated	significantly	higher	N2O	

emissions	than	slurry	storage	basin/tanks,	

while	CH4	emissions	were	not	different.	Straw‐

based	bedding	resulted	in	numerically	higher	CH4	but	lower	N2O	emissions	when	compared	with	

sawdust	or	corn	stalk	bedding	systems.	Liu	et	al.	(2011a)	observed	an	increasing	trend	for	CH4

emissions	as	manure	removal	frequency	decreased	(P	=	0.13).	Deep	pits	and	pits	flushed	using	

lagoon	effluent	also	generated	relatively	high	CH4	emissions.	Results	for	N2O	emissions	showed	

very	high	uncertainties	(P	=	0.49).	Deep	pits	and	pits	with	manure	removed	every	three	or	four	

months	had	relatively	higher	N2O	emissions.	A	summary	of	other	findings	from	the	meta‐analysis

conducted	by	Liu	et	al.	(2011a)	showed	that	CH4	emissions	from	slurry	storage	facilities	without	

covers	were	significantly	higher	than	from	those	with	covers.		

The	highest	CH4	emissions	were	from	farrowing	swine,	and	were	significantly	higher	than	those	

from	finishing	and	nursery	swine.	Compared	with	farrowing	swine,	the	gestating	swine	had	

significantly	lower	CH4	emissions.	The	highest	N2O	emissions	were	from	gestating	swine	and	were	

significantly	higher	than	those	from	finishing	swine.	

North	American	studies	reported	significantly	higher	CH4	emissions	from	swine	operations	than	

European	and	Asian	studies	(Liu	et	al.,	2011a)	.	This	is	probably	due	to	the	different	prevailing	

manure	handling	systems	and	different	manure	handling	practices	in	different	regions.	Emissions	

of	CH4	from	lagoons	and	manure	storage	facilities	increased	with	increasing	temperature.	For	swine	

buildings,	temperature	was	not	a	significant	factor.	

Table 5‐13: P Values of Main Effects on GHG

Emissions from Swine Operations

Cause of Variation CH4 (n=76) N2O

(n=53)

Emission	source	 0.94	 0.93

Swine	category 0.05	 <0.01

Geographic	region 0.04	 0.02

Temperature 0.20	 0.95

Size	of	operation	 0.89	 0.24

Source:	Liu	et	al.	(2011a).
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

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5.3.4.1 Method for Estimating Emissions from Swine Production Systems

Methane Emissions from Swine Housing

The	IPCC	(2006)	Tier	2	equation	is	used	to	estimate	CH4	emissions	when	manure	is	allowed	to	

accumulate	in	a	pit	below	the	animal	confinement.	The	estimation	method	is	provided	in	Equation	

5‐4.	The	maximum	CH4	producing	capacity	for	swine	is	provided	in	Table	5‐19.	The	MCFs	for	

manure	stored	in	a	deep	pit	or	from	swine	bedding	is	provided	in	Table	5‐7.		

Nitrous Oxide Emissions from Swine Housing

To	estimate	nitrogen	losses	from	swine	housing,	the	amount	of	nitrogen	excreted	(Nex)	for	each	

animal	classes	are	first	estimated.	Equation	5‐16	describes	the	relationship	between	nitrogen	

intake,	retention,	and	excretion	for	swine.	Equation	5‐17,	Equation	5‐18,	Equation	5‐19,	and	

Equation	5‐20	provide	the	methods	for	estimating	the	nitrogen	intake	and	retention	for	the	

different	swine	classes	as	recommended	by	the	ASABE.		

Method for Estimating Enteric Fermentation CH4 Emissions from Swine

 IPCC	Tier	1	approach,	using	U.S.	emission	factor	of	1.5	kg	CH4/head/year.	(IPCC,	2006).

 Sole	data	source	is	the	IPCC	Tier	I	emission	factor	for	swine.	User	input	is	total	number

of	head,	regardless	of	class	or	weight.

Equation 5‐15: Equation for Enteric Fermentation Emissions from Swine (IPCC, 2006)

	૚૚૝૙૙ .૙ ൈ ܖܗܑܜ܉ܔܝܘܗ۾ ൌ૝ ۱۶

Where:	

CH4	 	 =	Methane	emissions	per	day	(kg	CH4	day‐1)	

Population		 =	Number	of	swine	(head)	

0.00411	 =	Daily	CH4	emissions	from	each	animal	(kg	head‐1	day‐1)	

Method for Estimating Swine GHG Emissions from Housing

Methane	

 The	IPCC	(2006)	Tier	2	method	is	used	to	estimate	CH4	emissions	when	manure	is

allowed	to	accumulate	below	the	animal	confinement	as	described	below.

Nitrous	Oxide		

 Nitrogen	intake,	retention,	and	excretion	estimated	using	equations	provided	in	ASABE

D384.2.

 IPCC	(2006)	Tier	2	approach	for	N2O	from	manure	in	housing.
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

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Equation 5‐16: ASABE Approach for Estimating Nitrogen Excretion from Swine

ܖܗܑܜܖ܍ܜ܍܀ۼ െ ܍ܓ܉ܜܖܑۼ ൌ ܠ܍ۼ

Where:	

Nex	 =	Total	nitrogen	excretion	per	animal	(g	animal‐1)	

Nintake	 =	Nitrogen	intake	per	finished	animal	(g	animal‐1)	

Nretention	 =	Nitrogen	retained	per	finished	animal	(g	animal‐1)	

Equation 5‐17: ASABE Approach for Estimating Nitrogen Excretion from Grow‐Finish

Pigs

ൈ ۾۱۱ ൈ ۲۴۷۵ۯ ൌ ܍ܓ܉ܜܖܑۼ

۴۵۽۲

૟૛૞ 		

۴۾ۺ۴۴ ൈ ۴۾۲ ൈ ۴܅۰ ൌ ܖܗܑܜܖ܍ܜ܍ܚۼ

૚૞ૢ. ૝ െ ۰܅۷ ൈ ሾ۲۾۴ െ 	૙. ૙૞ ൈ ሺ۰܅۴ െ ۰܅۷ሻሿ

ൈ

۴۴۾ۺ۴૙ ൅ .૙ ૠ ൈ ሺ۰܅۴ െ ۰܅۷ሻ

૚૞ૢ. ૝

Where:	

Nintake	 =	Nitrogen	intake	per	finished	animal	(g	animal‐1)	

Nretention	 =	Nitrogen	retained	per	finished	animal	(g	animal‐1)	

ADFIG	 =	Average	daily	feed	intake	over	finishing	period	(g	day‐1)	

CCP		 =	Concentration	of	crude	protein	of	total	(wet)	ration	(%)	

DOFG	 =	Days	on	feed	to	finish	animal	(grow‐finish	phase)	(days)	

BW୊	 =	Final	(market)	body	weight	(kg)	

DPF	 =	Average	dressing	percent	(yield)	at	final	weight	(%)	

BW୍	 =	Initial	body	weight	(kg)	

FFLPF	 =	Average	fat‐free	lean	percentage	at	final	weight	(%)
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

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a	Recommended	values	are:	350	g	day‐1	for	high	lean	growth	capacity	pigs;	325	g	day‐1	for	high‐moderate	lean	growth	

capacity	pigs;	and	300	g	day‐1	for	moderate	lean	growth	capacity	pigs.		

a	Assumed	to	be	115	days.	

b	Recommended	value	from	ASABE	is	19.205	kg.	

Equation 5‐18: ASABE Approach for Estimating Nitrogen Excretion from Weaning Pigs

ൈ ۾۱۱ ൈ ۲۴۷۵ۯ ൌ ܍ܓ܉ܜܖܑۼ

ۼ۴۽۲

૟૛૞

ሻሿۼ۷ି܅۰	 ൅ ۼ۴ି܅۰ሺ ൈ ૠ ૚૜.૙ሾ ૚ ൅ ൈ	 ۵۵ۺ۴۴ ൈ ۼ۴۽۲ ൌ ܖܗܑܜܖ܍ܜ܍ܚۼ

૚૛૞. ૡ

Where:	

Nintake	 =	Nitrogen	intake	per	finished	animal	(g	animal‐1)	

Nretention	 =	Nitrogen	retained	per	finished	animal	(g	animal‐1)	

ADFIG	 =	Average	daily	feed	intake	over	finishing	period	(g	day‐1)	

CCP		 =	Concentration	of	crude	protein	of	total	(wet)	ration	(%)	

DOFN	 =	Days	on	feed	to	finish	animal	(nursery	phase)	(days)	

FFLGG	 =	Average	fat‐free	lean	gain	from	20	to	120kg	(g	day‐1)	a

BW୊ି୒	 =	Final	body	weight	in	nursery	phase	(kg)	

BW୍ି୒	 =	Initial	body	weight	in	nursery	phase	(kg)	

Equation 5‐19: ASABE Approach for Estimating Nitrogen Excretion from Gestating Sows

ۺ۵ ൬ ൈ ۾۱۱ ൈ ܁۲۴۷ۯ ൌ ܍ܓ܉ܜܖܑۼ

૟૛૞൰

ܜܖ܍ܜ܍܀ۼܑܖܗ ൌ ሺ۵܂ۺ۵ ൈ ૜૟. ૡሻ ൅ 	 ሺۺ۷܂܂۳܀ 	ൈ ૜ૢ. ૚ሻ

Where:	

Nintake	 =	Nitrogen	intake	per	finished	animal	(g	animal‐1)	

Nretention	 =	Nitrogen	retained	per	finished	animal	(g	animal‐1)	

ADFIS	 =	Average	daily	feed	intake	during	gestation	(g	day‐1)	

CCP		 =	Concentration	of	crude	protein	(%)	

GL	 =	Gestation	period	length	(days)a	

GLTG	 =	Gestation	lean	tissue	gain	(kg)b	

LITTER	 =	Number	of	pigs	in	litter	(head)	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

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a	Recommended	value	from	ASABE	is	‐4.20	kg.	

Some	of	the	nitrogen	excreted	is	volatilized	as	NH3,	hence,	the	estimation	of	NH3	losses	is	necessary	

to	estimate	N2O	emissions	using	a	nitrogen	balance	approach.	The	NH3	lost	from	manure	in	housing	

is	estimated	as	a	fraction	of	Nex	according	to	the	ranges	provided	in	Table	5‐14.	A	range	of	values	

has	been	provided	for	each	facility	type;	the	lower	values	should	be	used	during	the	winter,	the	

higher	values	should	be	used	during	the	summer,	and	intermediate	values	should	be	used	for	the	

spring	and	autumn.	

Table 5‐14: Typical Ammonia Losses from Swine Housing Facilities (Percent of Nex)

Facility Description % Loss Facility Description % Loss

Roofed	facility	(flushed	or	scraped)		

Roofed	facility	(daily	scrape	and	haul)	 5	‐	15		 Roofed	facility	(bedded	pack)		 20	‐	40	

Roofed	facility	(shallow	pit	under	floor)		 10	‐	20	 Roofed	facility	(deep	pit	under	floor	‐	

includes	storage	loss)		 30	‐	40	

Source:	Koelsh	and	Stowell	(2005).	

The	IPCC	(2006)	Tier	2	approach	is	used	for	N2O	emissions	from	manure	stored	in	housing.	The	

estimation	method	is	provided	in	Equation	5‐8.	The	N2O	emission	factors	can	be	found	in	Table	5‐9.		

The	remaining	nitrogen	excreted	that	is	not	lost	as	N2O	or	volatilized	as	NH3	in	housing	then	enters	

manure	storage	and	treatment.	If	data	are	not	available	to	track	the	nitrogen	that	is	transferred	

along	with	the	manure	to	manure	storage	and	treatment,	the	nitrogen	can	be	estimated	as	

described	in	Equation	5‐9.	However,	this	equation	is	overestimating	the	nitrogen	transferring	to	

manure	storage	and	treatment	as	some	nitrogen	will	be	lost	in	housing.	This	remaining	total	

nitrogen	value	is	an	input	into	the	N2O	equations	for	manure	stored	or	treated.	

N2O‐N	losses	from	manure	collected	and	removed	from	housing	can	be	determined	from	manure	

nitrogen	using	equations	from	Section	5.4	Manure	Management	for	the	appropriate	manure	

management	system.	NH3	losses	from	manure	nitrogen	removed	from	housing	can	be	calculated	

using	the	methodology	presented	in	Appendix	5‐C.1	and	5‐C.3.	

Equation 5‐20: ASABE Approach for Estimating Nitrogen Excretion from Lactating Sows

ۺۺ ൬ ൈ ۾۱۱ ൈ ܂۱ۯۺ۲۴۷ۯ ൌ ܍ܓ܉ܜܖܑۼ

૟૛૞൰

ܜܖ܍ܜ܍܀ۼܑܖܗ ൌ ሺ૜ૡ. ૟ ൈ ܂ۺۺ۵ሻ ൅ ሺ܅܅ۺ۳ۼۯ 	ൈ ૜૛ሻ െ ሺ܅ۺ۰۷܂܀۶ ൈ ૜૟. ૡሻ

Where:	

Nintake	 =	Nitrogen	intake	per	finished	animal	(g	animal‐1)	

Nretention	 =	Nitrogen	retained	per	finished	animal	(g	animal‐1)	

ADFILACT	 =	Average	daily	feed	intake	during	lactation	(g	day‐1)	

CCP		 =	Concentration	of	crude	protein	(%)	

LL	 =	Lactation	length	(days	to	weaning)	(days)	

LLTG	 =	Lactation	lean	tissue	gain	(kg)a	

LWEAN	 =	Litter	weight	at	weaning	(kg)	

LWBIRTH	 =	Litter	weight	at	birth	(kg)	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-59 

5.3.4.2 Rationale for Selecting Method for Estimating Emissions from Swine

Miles	et	al.	(2006)	suggest	that	a	robust	model	for	enteric	and	housing	emissions	would	include	

factors	such	as	house	management,	animal	size	and	age,	pH,	and	manure	moisture.	Due	to	the	

current	data	limitations,	an	NH3	and	GHG	estimation	model	should	minimally	include	number	of	

animals,	excreta	moisture	content,	diet	protein	and	fiber	content,	and	excreta	pH.	The	challenge	is	

that	these	criteria	may	not	be	readily	available	to	the	farm	manager.	

Liu	et	al.	(2011a)	compared	literature	values	with	IPCC	values	and	concluded	that	the	variation	of	

the	measured	CH4	and	N2O	housing	emission	rates	has	not	been	adequately	captured	by	the	IPCC	

approaches.	For	CH4	emissions,	the	differences	between	the	IPCC‐estimated	emission	rates	and	

measured	values	were	significantly	influenced	by	type	of	emission	source,	geographic	region,	and	

measurement	methods.	Larger	differences	between	estimated	and	measured	CH4	emission	rates	

were	observed	in	North	American	studies	than	in	European	studies.	In	North	American	studies,	the	

results	of	meta‐analysis	indicated	an	overestimation	by	the	IPCC	approaches	for	CH4	emissions	

from	lagoons	(pooled	relative	difference:	‐33.9%;	95%	CI:	‐66.8%	to	‐0.01%),	and	the	discrepancy	

between	the	IPCC‐estimated	emissions	and	the	measured	values	occurred	mainly	at	lower	

temperatures.	In	European	studies,	the	results	indicated	an	overestimation	of	the	IPCC	approaches	

in	swine	buildings	with	pit	systems.	For	N2O	emissions	from	swine	operations,	an	overall	

underestimation	of	the	IPCC	approaches	was	observed	in	European	studies	but	not	in	North	

American	studies.	In	European	studies,	the	pooled	N2O	emission	factors	for	swine	buildings	with	pit	

systems	was	1.6%	(95%	CI,	0.6%	to	2.7%),	while	the	IPCC	default	emission	factor	for	pit	systems	is	

0.2%.	Larger	uncertainties	were	observed	for	measured	N2O	emissions	from	bedding	systems	and	

from	straw	flow	systems.	

In	order	to	consider	an	alternative	to	the	IPCC	approach,	a	wide	variety	of	models	applicable	to	

swine	production	facilities	were	identified	and	evaluated,	including	1)	CAR	Livestock,	2)	Manure	

And	Nutrient	Reduction	Estimator	(MANURE),	3)	COOL	Farm	Tool,	4)	Carbon	Accounting	for	Land	

Model Evaluation Criteria for Swine Production Systems

1. The	model	is	based	on	well‐established	scientifically	sound	relationships	between	farm

management	inputs	and	emissions	outputs	(process‐based	model	or	mass‐balance	model

preferable);

2. The	model	is	relevant	to	U.S.	climate	and	swine	production	systems;

3. The	model	can	estimate	CH4,	N2O,	and	NH3	emissions	from	enteric	fermentation	and

swine	housing	systems;

4. There	is	flexibility	in	the	model	to	describe	the	production	system	(animals,	feed,

housing,	and	in‐house	manure	management);

5. The	model	is	easy	to	use	and	is	designed	to	use	easily	obtainable	farm	information	to

determine	emissions	estimates;

6. The	model	includes	some	mitigation	strategies	for	reducing	emissions,	and	produces

realistic	changes	in	emissions	values	when	these	changes	are	made	within	the	production

system;

7. There	is	transparency	in	the	model	calculations,	and	technical	guidelines	are	available	to

elaborate	the	methodologies	used	to	obtain	the	emissions	estimates;

8. The	model	has	been	tested/validated	with	on‐farm	data;

9. The	model	works	reliably	(little	to	no	errors	or	program	crashes);	and

10. The	model	is	publicly	available	and	accessible.
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

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Managers,	5)	Farming	Enterprise	GHG	Calculator,	6)	CPLAN,	and	7)	Holos.	These	models	were	

evaluated	by	10	criteria	(see	box)	to	determine	their	suitability	for	use	in	determining	emissions	

estimates	for	swine	production	facilities	in	the	United	States.	Two	of	these	criteria	were	considered	

to	be	critical,	in	that	if	they	were	not	met	by	the	model,	they	could	not	be	considered	for	use	(i.e.,	

the	model	had	to	be	relevant	to	U.S.	climate	and	swine	production	systems	and	had	to	be	publicly	

available).	

The	Holos	model	considered	diet	(standard,	low	crude	protein,	or	high‐digestibility	feeds)	and	

manure	handling	options	(anaerobic	digestion,	covered	or	uncovered	slurry	storage,	deep	pit,	or	

solid	storage).	In	addition,	the	Holos	model	provided	an	estimate	of	uncertainty	for	the	model	

output.	The	MANURE	model	(WRI,	2009)	collected	the	most	comprehensive	data	and	allowed	for	

easy	comparison	of	the	impact	of	changes	in	manure	handling	and	use	on	emissions	of	NH3,	N2O	

(direct	and	indirect),	and	CH4.	On	the	animal	side,	MANURE	based	its	calculations	solely	on	animal	

numbers;	feeding	was	not	considered.	The	other	models	considered,	while	meeting	minimum	

criteria,	lacked	any	improvements	over	the	IPCC	approach.	Consequently,	the	IPCC	method	was	

selected	(i.e.,	Holos	utilizes	the	IPCC	Tier	1	approach	for	housing)	with	nitrogen	excretion	estimated	

using	ASABE	equations	that	account	for	diets.	

5.3.5 Housing Emissions from Poultry Production Systems

Meat Birds

Greenhouse	gas	emissions	within	the	farm	boundary	of	a	broiler	chicken	farm	will	originate	almost	

exclusively	from	the	animal	housing,	which	also	serves	as	the	storage	location	for	manure.	Liu	et	al.	

(2011a)	reported	that	for	a	20‐week	grow‐out	of	turkeys	on	litter,	average	daily	N2O	emissions	

were	0.045	g	(kg	bodyweight)‐1,	and	daily	CH4	emissions	were	0.08	g	(kg	bodyweight)‐1.	Emission	

sources	external	to	the	housing	include	GHG	emissions	from	farm	vehicles.	If	a	house	is	cleaned	or	

decaked	(removal	of	the	top,	crusted	portion	of	the	litter)	and	stored	on	the	farm,	GHG	and	NH3

production	and	emissions	could	occur;	Appendix	5‐C	provides	further	discussion	on	NH3	emissions	

from	housing.	Practices	to	decake	and	the	timing	of	land	application	of	cake	and	litter	vary	from	site	

to	site	and	may	or	may	not	include	further	composting.		

Laying Hens

Greenhouse	gas	emissions	within	the	farm	boundary	of	an	egg	farm	may	originate	from	the	housing	

or	the	manure	storage	location.	Emission	sources	external	to	the	housing	include	GHG	emissions	

from	farm	vehicles.	External	to	the	farm	itself,	GHG	emissions	result	from	land	application	of	litter	

or	stockpiling	of	the	litter	in	fields	prior	to	land	application.		

Laying	hen	housing	systems	without	litter	would	likely	exhibit	greater	emissions	than	litter	

systems,	but	comparison	of	estimates	are	sparse.	Laying	hen	houses	typically	store	excreta	in	a	

basement	or	may	move	excreta	out	of	the	house	frequently	(daily	or	more	often);	this	would	

relocate	emissions	to	a	storage	shed	rather	than	change	the	cumulative	emissions	unless	some	form	

of	processing	(drying)	took	place	prior	to	storage.	Li	et	al.		(2010)	reported	daily	CH4	emissions	of	

39.3	to	45.4	mg	hen‐1	and	N2O	emissions	of	58.6	mg	hen‐1	(hen	bodyweight	average	=	1.9	kg)	in	a	

basement‐type	system.	This	compares	to	a	litter	system	for	a	20‐week	grow‐out	of	turkeys	where	

average	daily	N2O	emissions	were	0.045	g	kg‐1	bodyweight	and	daily	CH4	emissions	were	0.08	g	kg‐1

bodyweight	(Liu	et	al.,	2011a).	Based	on	the	comparison	of	these	two	studies,	differences	in	GHG	

emissions	from	dry	litter	systems	and	wetter,	stacked	laying	hen	systems	would	be	expected.	

Management	practices	to	reduce	litter	moisture	offer	the	most	promise	for	reducing	emissions	of	

CH4	and	N2O.	Quantitative	estimates	of	how	emissions	vary	with	litter	moisture	are	not	available,	

but	would	likely	follow	similar	dynamics	as	soil	moisture	content.	Reuse	of	litter	and	decaking	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-61 

procedures	might	also	be	used	as	strategies	to	reduce	emissions	in	the	future.	However,	data	are	

not	available	at	present	to	use	as	part	of	a	systems	model.	

Ammonia Emissions in Poultry Housing

As	mentioned	earlier,	ammonia	is	not	a	greenhouse	gas,	however,	ammonia	emissions	are	

estimated	as	part	of	the	nitrogen	balance	approach.	Meat	birds	are	typically	raised	in	litter	

systems.	Litter	temperature,	pH,	and	moisture,	along	with	the	ammonium	content	and	house	

ventilation	rate	are	recognized	as	major	factors	controlling	NH3	loss	from	broiler	litter	(Elliot	and	

Collins,	1982;	Carr	et	al.,	1990;	Moore	et	al.,	2010).	There	are	seasonal	variations	in	emissions,	with	

losses	tending	to	be	greater	in	summer	(warmer	months)	than	in	winter	(Coufal	et	al.,	2006).	Bird	

age/size	can	affect	litter	temperature,	which	may	influence	seasonal	effects	on	emissions	(Miles	et	

al.,	2008).	In	addition,	the	formation	of	cake	in	the	house	can	have	a	large	impact	on	emissions.	

Miles	et	al.	(2008)	reported	that	extremely	caked	areas	of	the	house	had	virtually	no	fluxes	of	NH3.	

Areas	of	litter	where	anaerobic	conditions	develop	suppress	NH3	formation	and	release	(Carr	et	al.,	

1990).	Moore	et	al.	(2011)	determined	that	NH3	emissions	from	broiler	houses	averaged	37.5	g	

bird‐1,	or	14.5	g	kg	bird	marketed‐1	(50‐day	old	birds).	The	same	authors	estimated	that	of	the	total	

nitrogen	output	from	typical	broiler	houses,	approximately	22	percent	can	be	associated	with	NH3

emissions,	56	percent	from	harvested	birds,	and	21	percent	from	litter	plus	cake.	The	addition	of	

aluminum	sulfate	(alum)	at	a	rate	equivalent	to	five	to	10	percent	by	weight	(alum/manure)	

reduces	NH3	emission	from	broiler	houses	by	70	percent	(Moore	et	al.,	2000)	and	results	in	heaver	

birds,	better	feed	conversion,	and	lower	mortality	(Moore,	2013).	Emissions	of	N2O	and	CH4	are	

dependent	upon	litter	conditions	that	favor	an	anaerobic	environment.	Limited	data	are	available	

documenting	litter	moisture	content	effects	on	N2O	and	CH4	emissions.	Miles	et	al.	(2011)	

demonstrated	that	incremental	increases	in	litter	moisture	content	increased	NH3	volatilization.	

Similarly,	Cabrera	and	Chiang	(1994)	demonstrated	a	range	in	NH3	volatilization	of	32	percent	to	

139	percent	of	initial	ammonium	content	as	litter	water	content	increased.	Litter	temperature	is	

another	factor	that	may	influence	GHG	emissions.	Miles	et	al.	(2006)	demonstrated	that	litter	

temperature	affected	NH3	flux,	but	the	study	did	not	measure	other	gases.	Miles	et	al.	(2011)	

observed	that	organic	bedding	materials	generated	the	least	amount	of	NH3	at	the	original	

moisture	content	when	compared	with	the	inorganic	materials.	The	influence	of	bedding	material	

at	increased	moisture	levels	was	not	clear	across	the	treatments	tested.	But	the	findings	suggest	

that	choice	of	bedding	material	may	also	influence	N2O	and/or	CH4	emissions	and	could	potentially	

be	used	as	a	mitigation	strategy.		
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-62 

5.3.5.1 Method for Estimating Emissions from Poultry Production Systems

Nitrous Oxide and Ammonia Emissions from Poultry Housing

To	estimate	nitrogen	losses	from	housing,	the	amount	of	nitrogen	excreted	(Nex)	by	each	animal	

category	is	first	estimated.	Equation	5‐22	and	Equation	5‐23	are	the	equations	recommended	by	

the	American	Society	of	Agricultural	Engineers	(ASABE)	for	estimating	Nex	from	broilers,	turkeys,	

ducks,	and	laying	hens.	

Method for Estimating Emissions from Poultry Production Systems

Methane	

 IPCC	Tier	1	approach,	utilizing	barn	capacity	and	manure	CH4	emissions	factors	per

poultry	type.

 IPCC	emission	factor	for	poultry	enteric	CH4	production	is	0.	Emissions	from	hindgut

fermentation	are	small	and	generally	considered	part	of	housing	emissions.

Nitrous	Oxide		

 Nitrogen	excretion	estimated	using	equations	provided	in	ASABE	D384.2.

 IPCC	(2006)	Tier	2	approach	for	N2O	from	manure	in	housing.

Equation 5‐21: Methane Emissions from Poultry Housing (IPCC, 2006)

	ܡܜܑ܋܉ܘ܉۱_ܖܚ܉۰ ൈ ܍ܜ܉܀ ൌ૝ ۱۶

Where:	

CH4	 =	Methane	emissions	per	year	(kg	CH4	year‐1)	

Rate		 =	Manure	methane	emissions	(kg	CH4	head‐1	year‐1)	

Barn_Capacity		=	Capacity	of	barn	(head)	

Equation 5‐22: ASABE Approach for Estimating Nitrogen Excretion from Broilers,

Turkeys, and Ducks

ൈ ܠ۴۷൤ ෍ ൌ ܠ܍ۼ

ܠି۾۱۱

૟. ૛૞൨

ܖ

૚ୀܠ

ൈ ሺ૚ െ ܀ۼ۴ሻ

Where:	

Nex	 =	Total	nitrogen	excretion	per	finished	animal	(g	N	(finished	animal)‐1)	

FIx		 =	Feed	intake per	phase	(g	feed	(finished	animal)‐1)	

CCP‐X	 =	Concentration	of	crude	protein	of	total	ration	in	each	phase

	(g	crude	protein	(g	(wet)	feed)‐1)		

NRF	 =	Retention	factor	for	nitrogen	(fraction)	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-63 

a	Default	egg	weight	is	60	g	for	light	layer	strains	and	63	g	for	heavy	layer	strains.		 b	Default	fraction	is	0.80.	

The	NH3	lost	from	manure	for	meat	and	egg‐producing	birds	is	estimated	as	a	fraction	of	Nex.	

Koelsch	and	Stowell	(2005)	provide	estimates	on	the	typical	NH3	loss	from	different	housing	

facilities	as	a	fraction	of	Nex	(see	Table	5‐15).	A	range	of	values	has	been	provided	for	each	facility	

type;	the	lower	values	should	be	used	during	the	winter,	the	higher	values	should	be	used	during	

the	summer,	and	intermediate	values	should	be	used	for	the	spring	and	autumn.	

Table 5‐15: Typical Ammonia Losses from Poultry Housing Facilities (Percent of Nex)

Facility Description Applicable

Species % Loss Facility Description ApplicableSpecies % Loss

Roofed	facility	(litter)		

Meat	

producing	

birds		

25	‐	50	

Roofed	facility	(stacked	

manure	under	floor	‐

includes	storage	loss)		

Egg‐producing	

birds		 25	‐	50	

Source:	Koelsh	and	Stowell	(2005).	

N2O can also be lost from the excreted nitrogen. A quantitative method for estimating N2O 

emissions from solid manure is the IPCC Tier 2 approach, which is also used for the U.S. Greenhouse 

Gas Inventory (Equation 5-8). This estimation method is the same as the method present in the 

Temporary Stack and Long-Term Stockpile and the Composting sections (see sections 5.4.1 and 

5.4.2 for more details). The N2O emission factors for poultry manure in housing is 0.001 (kg N2O-N/ 

kg N) for poultry manure with or without bedding IPCC (2006). 

The	remaining	nitrogen	excreted	that	is	not	volatilized	as	NH3	or	lost	as	N2O	in	housing	then	enters	

manure	storage	and	treatment.	If	data	are	not	available	to	track	the	nitrogen	that	is	transferred	

along	with	the	manure	to	manure	storage	and	treatment,	the	nitrogen	can	be	estimated	as	

described	in	Equation	5‐9.	However,	this	equation	is	overestimating	the	nitrogen	transferring	to	

manure	storage	and	treatment	as	some	nitrogen	will	be	lost	in	housing.	This	remaining	total	

nitrogen	value	is	an	input	into	the	N2O	equations	for	manure	stored	or	treated.	

5.3.5.2 Rationale for Selecting Method for Estimating Emissions from Poultry

Production Systems

Miles	et	al.	(2006)	suggest	that	a	robust	model	would	include	factors	such	as	house	management,	

bird	size	and	age,	cake	management,	pH,	and	litter	moisture.	Due	to	current	data	limitations,	an	NH3

and	GHG	estimation	model	should	minimally	include	number	of	animals,	litter/excreta	moisture	

content,	dietary	protein	and	fiber	content,	and	litter/excreta	pH.	A	variety	of	models	applicable	to	

Equation 5‐23: ASABE Approach for Estimating Nitrogen Excretion from Laying Hens

ܠ܍ۼ ൌ ൬۴۷ ൈ

۾۱۱

૟. ૛૞൰ െ ൫૙. ૙૚ૡ૛ ൈ ۳܏܏ܜܟ ൈ ۳܏܏ܗܚܘ൯

Where:	

Nex	 =	Total	nitrogen	excretion	per	animal	per	day	(g	N	animal‐1	day‐1)	

FI	 =	Feed	intake (g	feed	(finished	animal)‐1)	

CCP		 =	Concentration	of	crude	protein	of	total	ration	(g	crude	protein	(g	(wet)	feed)‐1)		

Egg୵୲	 =	Egg	weighta	(g)	

Egg୮୰୭	 =	Fraction	of	eggs	produced	each	dayb	(eggs	hen‐1	day‐1)	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-64 

poultry	production	facilities	were	identified	and	evaluated,	including	Carbon	Accounting	for	Land	

Managers;	CFF	Carbon	Calculator;	CPLAN;	and	4)	Holos.	These	models	were	evaluated	with	respect	

to	10	criteria	(see	box)	to	determine	their	suitability	for	use	in	determining	emissions	estimates	for	

poultry	production	facilities	in	the	United	States.		

Two	of	these	criteria	were	considered	to	be	critical,	in	that	if	they	were	not	met	by	the	model,	they	

could	not	be	considered	for	use	(i.e.,	the	model	had	to	be	relevant	to	U.S.	climate	and	poultry	

production	systems	and	had	to	be	publicly	available).	The	Holos	model	did	consider	wet	or	dry	

manure	handling	for	laying	hen	operations.	For	all	poultry	types,	the	Carbon	Accounting	for	Land	

Managers	model	requested	information	related	to	burning	of	manure	and	time	birds	spend	in	a	

free‐range	system.	This	information	was	then	used	to	calculate	the	mass	of	manure	available	for	

direct	and	indirect	emissions.	No	model	requested	information	on	diet	or	in‐house	litter	

management	practices.	For	CH4	emissions,	only	the	Holos	model	provided	an	estimate	of	confidence	

of	model	output.	Specific	to	estimates	of	poultry	manure	CH4	emissions,	the	model	had	an	

uncertainty	under	20	percent	for	broilers,	turkeys,	layers	in	wet	manure	handling	systems,	and	

layers	in	dry	manure	handling	systems.	Consequently,	the	IPCC	method	was	selected	(i.e.,	Holos	

utilizes	the	IPCC	Tier	1	approach	for	housing).	For	N2O	emissions,	the	IPCC	Tier	2	was	used	with	

nitrogen	excretion	estimated	using	ASABE	equations	that	account	for	diets.	

5.3.6 Enteric Fermentation and Housing Emissions from Other Animals

Although	the	majority	of	emissions	from	livestock	in	the	United	States	are	from	cattle,	sheep,	swine,	

and	poultry,	emissions	from	other	animals	can	also	be	important	to	consider,	particularly	at	the	

entity	level.	Overall,	populations	of	the	animals	discussed	in	this	section	(goats,	American	bison,	

llamas,	alpacas,	and	managed	wildlife)	are	much	fewer	than	those	of	the	animals	discussed	in	prior	

sections.	However,	the	availability	of	research	on	emissions	from	these	animals	allows	us	to	explore	

them	at	least	at	an	introductory	level.	At	the	entity	level,	populations	of	these	animals	may	be	

significant	enough	to	warrant	calculating	their	emissions.	This	report	recommends	methods	for	

estimating	CH4	emissions	from	goats	and	American	bison	(Equation	5‐24	and	Equation	5‐25).	

Model Evaluation Criteria for Poultry Production Systems

1. The	model	is	based	on	well‐established	scientifically	sound	relationships	between	

farm	management	inputs	and	emissions	outputs	(process‐based	model	or	mass‐

balance	model	preferable).	

2. The	model	is	relevant	to	U.S.	climate	and	production	systems.	

3. The	model	can	estimate	CH4,	N2O,	and	NH3	emissions	from	poultry	housing	systems.	

4. There	is	flexibility	in	the	model	to	describe	the	production	system	(animals,	feed,	

housing,	and	in‐house	manure	management).	

5. The	model	is	easy	to	use	and	is	designed	to	use	easily	obtainable	farm	information	to	

determine	emissions	estimates.	

6. The	model	includes	some	mitigation	strategies	for	reducing	emissions	and	produces	

realistic	changes	in	emissions	values	when	these	changes	are	made	within	the	

production	system.	

7. There	is	transparency	in	the	model	calculations,	and	technical	guidelines	are	available	

to	elaborate	the	methodologies	used	to	obtain	the	emissions	estimates.	

8. The	model	has	been	tested/validated	with	on‐farm	data.	

9. The	model	works	reliably	(little	to	no	errors	or	program	crashes).	

10. The	model	is	publicly	available	and	accessible.	
 Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

 5-65 

5.3.6.1 Goats

Enteric	emissions	from	goat	production	systems	were	estimated	by	U.S.	EPA	(U.S.	EPA,	2011)	using	

IPCC	(2006)	methods	to	be	16	Gg	CH4	(of	a	total	of	6,655	Gg).	Emissions	of	manure	CH4	and	N2O	

from	goat	production	were	made	using	IPCC	(2006)	methods.	Goats	were	associated	with	1	Gg	of	

manure	CH4	(of	a	total	of	2,356	Gg)	and	less	than	0.5	Gg	of	N2O.		

The	impact	of	diet	on	Japanese	goat	enteric	CH4	emissions	was	measured	by	Bhatta	et	al.	(2007).	

Goats	fed	a	range	of	diets	from	100	percent	forage	to	80	percent	concentrate	produced	from	16.4	to	

22	g	CH4	day‐1	(5.0	to	8.2	percent	of	GEI).	

The	IPCC	(2006)	Tier	1	equation,	presented	in	Equation	5‐24,	for	estimating	enteric	fermentation	

emissions	from	goats	is	the	best	option	for	calculating	emissions	at	the	entity	level.	

5.3.6.2 American Bison, Llamas, Alpacas, and Managed Wildlife

Galbraith	et	al.	(1998)	measured	enteric	CH4	from	growing	bison	(n=5),	wapiti	(n=5),	and	white‐

tailed	deer	(n=8)	fed	alfalfa	pellets	in	the	winter‐spring	(February‐March)	and	spring	(April‐May)	

using	respiration	calorimetry	chambers.	The	bison	produced	an	average	of	86.4	g	day‐1	(6.6	percent	

GEI),	the	wapiti,	62.1	g	day‐1	(5.2	percent	GEI),	and	the	deer	23.6	g	day‐1	CH4	(3.3	percent	GEI).	

Using	a	detailed	method	of	calculation	to	estimate	historical	bison	emissions,	Kelliher	and	Clark	

(2010)	estimated	that	grazing	bison	would	produce	72	kg	CH4	year‐1	or	197g	CH4	day‐1.	Hristov	

(2012)	estimated	present	day	bison	produce	21	g	CH4	(kg	DMI)‐1	day‐1,	eat	approximately	12.8	kg	

DM	day‐1,	and	produce	268	g	CH4	day‐1.	The	differences	between	these	estimates	are	differences	in	

animal	weights,	DMI,	limited	measurements	of	bison	emissions,	and	assumed	CH4	conversion	

factors.	The	U.S.	EPA	uses	IPCC	Tier	1	methodologies	to	estimate	bison	emissions,	and	currently	

Tier	1	is	the	best	option	to	estimate	enteric	emissions.	

The	IPCC	(2006)	Tier	1	equation	for	estimating	enteric	fermentation	emissions	from	American	

bison	is	based	on	the	emission	factor	for	buffalo	and	has	been	modified	as	recommended	by	IPCC	to	

account	for	average	weight	as	seen	in	Equation	5‐25.	

Equation 5‐24: Tier 1 Equation for Calculating Methane Emissions from Goats

۱۶૝ ൌ ܘܗ۾ ൈ ۳۴۵

Where:	

CH4	 =	Methane	emissions	per	day	(kg	CH4	day‐1)	

Pop	 =	Population	of	goats	(head)	

EFG		 =	Emission	factor	for	goats	(0.0137	kg	CH4	head‐1	day‐1)	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-66 

The	New	Zealand	Ministry	for	the	Environment	(2010)	uses	a	factor	of	6.4	percent	of	GEI	to	predict	

enteric	CH4	emissions	from	farmed	red	deer	and	projects	an	emission	rate	per	year	of	23.7	kg	CH4

head‐1 year‐1.	Deer	are	also	estimated	to	excrete	31.0	kg	Nhead‐1	year‐1	contributing	toward	N2O	

production.	The	values	used	to	make	these	calculations	are	from	measurements	of	deer	CH4

emissions	using	the	SF6	tracer	method.	Elk,	white‐tailed,	and	mule	deer	enteric	emissions	were	

estimated	by	Hristov	(2012)	to	be	86.4,	16,	17	g	CH4 head‐1 day‐1	respectively.	IPCC	Tier	1	is	the	

recommended	method	by	which	these	emissions	should	be	estimated.	

Adult	llamas	fed	oat	hay	in	a	study	designed	to	define	energy	requirements	were	found	to	lose	7.1	

percent	of	GEI	as	enteric	CH4	(Carmean	et	al.,	1992).	Pinares‐Patino	et	al.	(2003)	compared	enteric	

CH4	emissions	measured	with	respiration	calorimetry	chambers	from	alpaca	and	sheep	fed	alfalfa	

diets	and	found	the	alpaca	produced	14.9	g	CH4	day‐1	(5.1	percent	of	GEI)	and	the	sheep	produced	

18.8	g	CH4	day‐1	(4.7	percent	of	GEI).	When	grazing	a	perennial	ryegrass/white	clover	pasture,	the	

alpaca	produced	22.6g	CH4	day‐1	(9.4	percent	GEI)	compared	to	31.1	g	CH4	day‐1	(7.5	percent	GEI)	

for	sheep.	The	authors	attribute	the	high	conversion	of	GEI	to	CH4	from	the	alpaca	to	grazing	

selectivity	on	pasture;	the	alpaca	were	observed	to	select	“more	structural	plant	parts.”		

5.3.7 Factors Affecting Enteric Fermentation Emissions

A	number	of	factors	may	influence	enteric	fermentation	and	resulting	CH4	emissions.	A	thorough	

review	of	such	factors	is	outside	the	scope	of	this	document,	but	key	factors	have	been	reviewed	by	

others	(Monteny	et	al.;	(2006),	Beauchemin	et	al.;	(2008),	Eckard	et	al.;	(2010),	and	Martin	et	al.;	

(2010))	and	are	discussed	briefly	below.		

Benchaar	et	al.	(2001)	used	the	rumen	digestion	model	of	Dijkstra	et	al.	(1992),	as	modified	by	

Benchaar	et	al.	(1998),	and	the	CH4	prediction	system	of	Baldwin	(1995)	to	estimate	the	effects	of

dietary	modifications	on	the	enteric	CH4	production	of	a	500	kg	dairy	cow.	The	model	predicted	

enteric	CH4	production	based	on	a	ruminal	H	balance.	Inputs	into	the	model	included	the	following:	

daily	DMI;	chemical	composition	of	the	diet;	solubility	and	degradability	of	protein	and	starch	in	the	

diet;	degradation	rates	of	protein,	starch,	and	NDF;	ruminal	volume;	and	fractional	passage	rates	of	

solids	and	liquid	fractions	from	the	rumen.	Values	modified	in	the	simulations	were	DMI,	dietary	

forage,	concentrate	ratio,	starch	availability	(barley	vs.	corn),	stage	of	maturity	of	forage,	form	of	

forage	(hay	or	silage),	particle	size	of	alfalfa,	and	ammonization	of	cereal	straw.	The	modeled	effects	

of	dietary	changes	on	enteric	CH4	emissions	in	diets	fed	to	dairy	cows	are	presented	in	Table	5‐16.		

Equation 5‐25: Tier 1 Equation for Calculating Methane Emissions from American

Bison

۰ۯ۳۴	 ൈ	 ܘܗ۾ ൌ૝ ۱۶

Where:	

CH4	 =	Methane	emissions	per	day	(kg	CH4	head‐1	day‐1)	

Pop	 =	Population	of	American	bison	(head)	

EFAB		 =	Emission	factor	for	American	bison	(kg	CH4	head‐1	day‐1)	

EFAB	is	the	IPCC	emission	factor	for	buffalo	(0.15	kg	CH4	head‐1	day‐1),	adjusted	for	American	

bison	based	on	the	ratio	of	live	weights	of	American	bison	(513	kg)	to	buffalo	(300	kg)	to	the	

0.75	power.	

۳۴ۯ۰ ൌ ૞૞ ܓ܏ ൈ ൬

܏ܓ ૜૚૞

൰܏ܓ ૙૙૜

૙.ૠ૞
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-67 

There	are	many	factors	that	affect	enteric	CH4	emissions	but	the	most	critical	factors	are	the	level	of	

dry	matter	intake,	the	composition	of	the	diet,	and	the	digestibility	of	the	dry	matter,	as	illustrated	

in	Table	5‐16.	

Table 5‐16: Summary of Effects of Various Dietary Strategies on Enteric CH4 Production in

Dairy Cows using Modeled Simulations		

Strategy CH4Variation

(per unit of GEI)

CH4Variation

(per unit of DE)

Increasing	DMI	 ‐9	to	‐23% ‐7	to	‐17%

Increasing	concentrate	proportion	in	the	diet	 ‐31% ‐40%

Switching	from	fibrous	concentrate	to	starchy	concentrate ‐24% ‐22%

Increased	forage	maturity +15% ‐15%

Alfalfa	vs.	timothy	hay +28% ‐21%

Method	of	forage	preservation	(ensiled	vs	dried) ‐32% ‐28%

Increased	forage	processing	(smaller	particle	size) ‐21% ‐13%

Ammoniated	treatment	of	poor	quality	forage(straw)a x	5 x	2

Protein	supplementation	of	poor	quality	forage	(straw) ×	3 ×	1.5

Source:	Benchaar	et	al.,	(2001),	Table	12.	

a	Effects	are	due	to	significant	increase	in	hay	digestibility	with	no	change	in	DM	intake.	

Dietary Fat:	Many	studies	have	demonstrated	that	supplemental	fat	can	decrease	enteric	CH4

emissions	in	ruminants.	In	a	review	of	studies,	Beauchemin	et	al.	(2008)	noted	that	enteric	CH4

emissions	(g	[kg	DMI]‐1)	decreased	by	approximately	5.6	percent	for	each	one	percent	increase	in	

fat	added	to	the	diet.	In	a	larger	review,	Martin	et	al.	(2010)	reported	a	decrease	of	3.8	percent	(g	

[kg	DMI]‐1)	with	each	one	percent	addition	of	fat.	Lovett	et	al.	(2003)	reported	that	total	daily	

emissions	decreased	from	0.19	to	0.12	kg	CH4	head‐1	(reported	as	260	to	172	L	CH4	head‐1)	(6.6	and	

4.8	percent	of	GEI)	from	steers	fed	diets	containing	0	or	350	g	of	coconut	oil,	respectively.	This	

effect	was	consistent	regardless	of	dietary	forage	concentration	(65,	40,	and	10	percent	of	DM).	

Although	added	fat	may	reduce	enteric	CH4	emissions,	ruminants	have	a	low	tolerance	for	dietary	

fat.	Thus,	total	fat	level	in	the	diet	must	be	maintained	below	eight	percent	of	dietary	DM.	Some	

sources	of	fat	appear	to	have	some	protection	against	biohydrogenation	by	ruminal	microbes	and	

thus	may	be	better	tolerated	(Corrigan	et	al.,	2009;	Vander	Pol	et	al.,	2009).	

Grain Source, Grain Processing, Starch Availability: Grain	source	and	grain	processing	method	can	

also	affect	enteric	CH4	losses.	In	general,	the	greater	the	ruminal	starch	digestibility,	the	lower	the	

enteric	CH4	emissions.	At	constant	energy	intake	(2	x	maintenance),	Hales	et	al.	(2012)	reported	

approximately	20	percent	lower	(2.5	vs.	3.0	percent	of	GEI)	enteric	CH4	emission	in	cattle	fed	

typical	high‐concentrate	(75	percent	corn)	steam	flaked	corn	(SFC)	based	finishing	diets	than	in	

steers	fed	dry‐rolled	corn	(DRC)	based	finishing	diets.	Based	on	the	rumen	stoichiometry	of	Wolin	

(1960),	Zinn	and	Barajas	(1997)	estimated	that	CH4	production	per	unit	of	glucose	equivalent	

fermented	in	the	rumen	also	decreased	with	more	intensive	grain	processing	(i.e.,	coarse,	medium,	

or	fine	flakes).	Similar	responses	were	noted	with	the	feeding	of	high‐moisture	corn	compared	with	

DRC	(Archibeque	et	al.,	2006).	Somewhat	in	contrast,	Beauchemin	and	McGinn	(2005)	reported	

lower	enteric	CH4	emissions	from	feedlot	cattle	fed	DRC‐based	diets	(2.81	percent	of	GEI)	than	from	

cattle	fed	steam‐rolled	barley‐based	diets	(4.03	percent	of	GEI),	possibly	the	result	of	lower	ruminal	

pH	on	the	corn‐based	diet	(5.7	vs.	6.2,	respectively;	(Van	Kessel	and	Russell,	1996)	and/or	higher	

NDF	in	the	barley	diet.	Enteric	CH4	emissions	were	38	percent	(barley)	to	65	percent	(corn)	lower	

in	high‐concentrate	(nine	percent	silage)	finishing	diets	than	on	grower	(70	percent	silage)	diets.	
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Feeding Coproduct Ingredients: Distillers	grains	with	solubles	(DGS)	and	other	coproducts	of	the	

milling	and	ethanol	industries	are	widely	used	as	cattle	feeds.	The	effects	of	feeding	30	to	35	

percent	DGS	(DM	basis)	on	enteric	CH4	emission	have	been	variable,	ranging	from	a	significant	

decrease	of	25	to	30	percent	(McGinn	et	al.,	2009)	to	no	effect	(Hales	et	al.,	2012),	to	an	increase	

(Hales	et	al.,	2013).	These	differing	results	were	probably	due	to	differences	in	forage	and	fat	

intake.	In	the	study	by	McGinn	et	al.	(2009)	the	diet	contained	65	percent	silage,	and	dietary	fat	

intake	increased	by	approximately	three	percentage	units8	when	dried	DGS	were	added	to	the	diet.	

In	contrast,	Hales	et	al.	(2012;	2013)	fed	diets	that	contained	only	10	percent	forage	and	were	equal	

in	total	fat	concentration.	

Roughage Concentration and Form: The	concentration	and	form	of	roughage	in	the	diet	will	affect	

both	enteric	and	manure	CH4	production	(Hales	et	al.,	2014).	Using	a	ruminal	volatile	fatty	acids	

(VFA)	stoichiometry	model,	Dijkstra	et	al.	(2007)	suggested	that	CH4	losses	from	carbohydrates	

substrates	(g	kg‐1	substrate)	in	a	concentrate	diet	with	ruminal	pH	variation	and	a	pH	of	6.5	were	

2.11,	3.18,	3.38,	and	3.10	for	starch,	soluble	sugars,	hemicellulose,	and	cellulose,	respectively.	

Similarly,	with	dairy	cows,	Moe	and	Tyrrell	(1979)	reported	that	enteric	CH4	production	per	unit	

carbohydrate	digested	was	three	times	greater	for	cellulose	than	for	hemicellulose.	Aguerre	et	al.	

(2011)	found	that	lactating	dairy	cattle	emitted	more	CH4	when	the	forage:concentrate	ratio	was	

changed	from	47:53	to	68:32,	0.54	kg	CH4	day‐1	vs.	0.65	kg	CH4	day‐1	respectively.	

In	general,	as	the	concentration	of	forage	in	the	diet	increases,	enteric	CH4	production	increases	and	

the	quantity	of	volatile	solids	excreted	increases.	Using	a	micrometeorological	mass	difference	

method,	Harper	et	al.	(1999)	reported	CH4	emissions	of	230	g	animal‐1	daily	(7.7	to	8.1	percent	of	

GEI)	in	feeder	cattle	on	pasture,	but	only	70	g	head‐1	daily	(1.9	to	2.2	percent	of	GEI)	in	cattle	fed	

high‐concentrate	diets.	Measured	CH4	losses	for	pasture	cattle	were	higher	than	values	predicted	

using	the	IPCC	(1997;	2006)	CH4	conversion	factors	(MCF	or	Ym),	or	Australian	methodology	

(NGGIC,	1996).	In	contrast,	measured	CH4	losses	for	feedlot	cattle	were	about	67	percent	of	those	

estimated	using	the	IPCC	(2006)	Ym	of	three	percent	of	GEI	or	the	Australian	methodology	(NGGIC,	

1996),	but	were	similar	to	values	reported	by	Branine	and	Johnson	(1990),	Blaxter	and	Wainman	

(1964),	and	Hales	et	al.	(2012;	2013;	Hales	et	al.,	2014).	

Enteric	fermentation	of	tropical	grasses	and	legumes	may	also	be	different	than	predicted	by	IPCC	

or	national	GHG	inventory	methods.	Kennedy	and	Charmley	(2012)	measured	enteric	CH4

production	of	cattle	fed	Australian	tropical	grasses	and	legumes	to	be	5.0	to	7.2	percent	of	GE	intake	

which	is	similar	to	IPCC	(2006)	Tier	2	estimates	(5.5	to	7.5	percent	of	GE	intake)	of	cattle	fed	forage	

diets	but	somewhat	lower	than	the	Australian	National	Greenhouse	Accounts	National	Inventory	

Report	(2007)	of	8.7	to	9.6	percent	of	GE	intake.		

Blaxter	and	Wainman	(1964)	compared	the	effects	of	feeding	diets	with	six	varying	hay:	flaked	corn	

ratios	(100:0,	80:20,	60:40,	40:60,	20:80,	5:95)	on	enteric	CH4	emissions	when	fed	at	two	times	the	

maintenance	level	of	intake.	CH4	emissions	as	a	percentage	of	GEI	increased	slightly	between	the	

100:0	diet	(7.44	percent)	and	the	60:40	diet	(8.17	percent),	then	decreased	to	the	5:95	diet	(3.4	

percent).		

In	Ireland,	Lovett	et	al.	(2003)	reported	total	daily	enteric	CH4	emissions	of	0.15,	0.19,	and	0.12	kg	

head‐1	(reported	as	207,	270,	and	170	L	head‐1)	for	heifers	fed	diets	containing	65,	40,	and	10	

percent	forage	(the	remainder	as	concentrate),	respectively.	As	a	percentage	of	GEI,	losses	were	6.1,	

6.6,	and	4.4	percent,	respectively.	

8	The	term	“percentage	units”	in	this	document	is	used	to	refer	to	changes	in	diets	or	emissions	that	are	not	proportional	

to	their	baselines.	For	example,	a	reduction	in	emissions	from	three	percent	to	one	percent	is	a	2	“percentage	unit”	

reduction	or	a	67 percent reduction.		
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Using	steers	fed	all‐forage	diets,	Ominski	et	al.	(2006)	reported	that,	within	the	range	of	forage	

qualities	tested	(alfalfa‐grass	silage	containing	61,	53,	51,	or	46	percent	NDF,	DM	basis),	enteric	CH4

emissions	of	steers,	as	a	percentage	of	GEI,	were	not	significantly	affected	by	NDF	content	(5.1	to	

5.9	percent),	although	daily	CH4	production	tended	to	be	highest	for	the	53	percent	NDF	diet	(0.12,	

0.15,	0.13,	and	0.14	kg	head‐1	day‐1,	respectively).	Similarly,	using	grazing	sheep,	Milano	and	Clark	

(2008)	reported	no	effect	of	forage	quality	(rye	grass	–	52	or	47	percent	NDF,	77	or	67	percent	

organic	matter	[OM]	digestibility)	on	enteric	CH4	emissions.	

Although	dietary	forage	quality	may	sometimes	not	affect	enteric	CH4	emissions,	it	will	affect	forage	

digestibility	and	thus	fecal	excretion	of	volatile	solids.	Thus,	feeding	more	digestible	forages	or	

concentrates	may	decrease	GHG	emissions	from	manure.	

Level of Intake:	Blaxter	and	Wainman	(1964)	compared	the	effects	of	feeding	six	diets	at	two	levels	

of	intake.	Enteric	CH4	emissions,	as	a	percent	of	GEI,	were	23	percent	greater	in	steers	fed	at	

maintenance	than	in	steers	fed	at	2X	maintenance	(8.1	vs.	6.6	percent	of	GEI,	respectively).	

However,	in	a	study	evaluating	emissions	from	cattle	fed	ryegrass	diets,	Milano	and	Clark	(2008)	

reported	that	as	DMI	increased	from	0.75	percent	of	maintenance	to	2X	maintenance,	enteric	CH4

emissions	(g	day‐1)	increased	linearly	(r2	=	0.80	to	0.84).	Emissions	as	a	percentage	of	GEI	were	not	

affected	by	DMI,	and	ranged	from	4.9	to	9.5	percent	of	GEI	(15.9	to	30.4	g	[kg	DMI]‐1).	

Using	a	high‐forage	(70	percent	barley	silage)	or	medium‐forage	(30	percent	silage)	diet	fed	at	

levels	from	1X	to	approximately	1.8X	maintenance,	Beauchemin	and	McGinn	(2006b)	noted	that	

enteric	CH4	emissions,	as	a	percent	of	GEI,	decreased	by	approximately	0.77	percentage	units	for	

each	unit	increase	in	feed	intake	(expressed	as	level	of	feed	intake	above	maintenance).	This	was	

less	than	the	estimate	using	the	Blaxter	and	Clapperton	(1965)	equation	(0.93	to	1.28	percent	

percentage	units)	or	the	1.6	percentage	units	suggested	by	Johnson	and	Johnson	(1995).		

Feed Additives and Growth Promoters: Cooprider	et	al.	(2011)	noted	that	the	daily	CH4	and	manure	

N2O	production	of	cattle	fed	through	a	“natural”	program	with	no	use	of	antibiotics,	ionophores,	or	

growth	promoters	were	similar	to	cattle	fed	in	more	traditional	systems	that	used	anabolic	

implants	and	diets	that	contained	ionophores	and	beta‐agonists.	However,	typical	cattle	had	greater	

average	daily	weight	gain	(1.85	vs.	1.35	kg	day‐1)	and	thus	took	42	fewer	days	to	reach	the	same	

end	point	(596	kg	body	weight	[BW]).	Thus,	overall,	cattle	fed	using	modern	growth	technologies	

had	31	percent	lower	GHG	emissions	per	head.	CH4	emissions	kg	of	BW	gain‐1	was	1.1	kg	greater	for	

the	“natural”	cattle	(5.02	vs.	3.92	CO2‐eq	kg	BW	gain‐1)	than	the	traditional	cattle.	

Monensin	decreases	enteric	CH4	emissions	in	finishing	cattle	by	10	to	25	percent	(Tedeschi	et	al.,	

2003;	McGinn	et	al.,	2004).	However,	in	feedlot	cattle	the	effects	appear	to	be	transitory,	lasting	for	

30	days	or	less	(Guan	et	al.,	2006).	In	contrast,	Odongo	et	al.	(2007)	reported	that	monensin	(24	

ppm)	in	dairy	diets	decreased	enteric	CH4	by	seven	to	nine	percent	for	up	to	six	months.	Waghorn	

et	al.	(2008)	found	no	effect	of	monensin	controlled‐release	capsules	on	CH4	production	of	pasture‐

fed	dairy	cows,	and	Hamilton	et	al.	(2010)	also	found	no	change	in	enteric	CH4	production	from	

monensin	fed	to	dairy	cows	offered	a	total	mixed	ration.		

A	number	of	studies	have	demonstrated	that	a	variety	of	halogenated	analogues	have	the	potential	

to	dramatically	decrease	ruminal	CH4	production	(Johnson,	1972;	Trei	et	al.,	1972;	Johnson,	1974;	

Cole	and	McCroskey,	1975;	Tomkins	and	Hunter,	2004;	Tomkins	et	al.,	2009).	In	general	the	effect	

was	greater	in	cattle	fed	high‐forage	diets	than	in	cattle	fed	high‐concentrate	diets.	When	CH4	losses	

were	dramatically	reduced,	a	significant	quantity	of	hydrogen	could	be	lost	(one	to	two	percent	of	

GEI)	via	eructation,	suggesting	an	alternative	electron	sink	is	also	needed.	In	general,	the	

compounds	did	not	improve	production	efficiency	significantly.	In	addition,	the	potential	toxicity	of	

these	compounds	made	them	impractical	for	routine	use.		
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A	number	of	nitrocompounds	(nitropropanol,	nitroethane,	nitroethanol)	have	also	significantly	

decreased	ruminal	CH4	production	in	vitro	(Anderson	et	al.,	2003),	with	a	concomitant	increase	in	

hydrogen	production/release.	The	effect	appeared	to	be	enhanced	when	a	nitrate	reducing	

bacterium	was	added	to	the	culture	(Anderson	and	Rasmussen,	1998).	

Several	studies	have	suggested	that	feeding	of	condensed	tannins	can	decrease	enteric	CH4

production	by	13	to	16	percent;	either	through	a	direct	toxic	effect	on	ruminal	methanogens	or	

indirectly	via	a	decrease	in	feed	intake	and	diet	digestibility	(Eckard	et	al.,	2010).	Tannins	may	also	

shift	nitrogen	excretion	away	from	urine	to	feces	and	inhibit	urease	activity	in	feces,	which	could	

potentially	decrease	NH3	and	N2O	emissions	from	manure	(Powell	et	al.,	2009;	Powell	et	al.,	2011).		

Feeding	yeast	cultures,	enzymes,	dicarboxylic	acids	(fumarate,	malate,	acrylate),	and	plant	

secondary	compounds,	such	as	saponins,	may	decrease	enteric	CH4	emissions	under	some	feeding	

conditions	(McGinn	et	al.,	2004;	Beauchemin	and	McGinn,	2006a;	Ungerfeld	et	al.,	2007;	

Beauchemin	et	al.,	2008;	Eckard	et	al.,	2010;	Martin	et	al.,	2010).		

Novel Microorganisms and their Products: Klieve	and	Hegarty	(1999)	noted	that	enteric	CH4

production	may	be	biocontrolled	directly	by	use	of	viruses	and	bacteriocins.	Lee	et	al.	(2002)	

reported	that	a	bacteriocin	(Bovicin	HC5)	from	Streptococcus bovis reduced	in	vitro	CH4	production	

by	up	to	50	percent.	It	appeared,	that	in	contrast	to	results	with	monensin,	the	ruminal	

microorganisms	did	not	adapt	to	the	bacteriocin.	

Australian	researchers	have	suggested	that	vaccinating	against	methanogens	can	decrease	CH4

emissions.	However,	the	results	have	not	been	consistent	(Wright	et	al.,	2004;	Eckard	et	al.,	2010)	

because	efficacy	is	dependent	on	the	specific	methanogen	population	and	that	is	dependent	on	diet,	

location,	and	other	factors.

Genetics: As	previously	noted,	several	studies	have	suggested	that	cattle	selected	for	lower	RFI	(i.e.,	

increased	feed	use	efficiency)	tend	to	have	lower	ruminal	enteric	CH4	production	(Nkrumah	et	al.,	

2006;	Hegarty	et	al.,	2007),	although	the	effect	may	depend	on	stage	of	production	(lactation	vs.	dry	

and	pregnant)	and/or	quality	of	the	diet	consumed	(Jones	et	al.,	2011).	RFI	is	moderately	heritable	

(0.28	to	0.58)	(Moore	et	al.,	2009),	thus	it	might	be	possible	to	genetically	select	for	animals	with	

lower	enteric	CH4	production.	However,	Freetly	and	Brown‐Brandl	(2013)	found	higher	CH4

emissions	from	more	efficient	animals.	Thus,	more	information	is	needed	to	define	under	what	

conditions	CH4	emissions	are	related	to	feed	efficiency	or	to	genetics.	

Factors Affecting Emissions from Sheep

Sheep,	like	cattle,	are	ruminant	animals	and	thus	the	same	dietary	factors	will	positively	or	

negatively	affect	emissions	from	enteric	fermentation.		

Factors Affecting Emissions from Swine

Dietary	modifications	can	effectively	reduce	nitrogen	excretions	and	mitigate	air	emissions	

(especially	NH3,	a	precursor	for	N2O)	from	livestock	operations	(Sutton	et	al.,	1996;	Canh	et	al.,

1998b).	Feeding	strategies	to	reduce	nitrogen	excretions	include	reduced	CP	diets	supplemented	

with	synthetic	amino	acids	(AA)	(Panetta	et	al.,	2006),	and	modifying	the	dietary	electrolytes	to	

reduce	urinary	pH	(Canh	et	al.,	1998a).	In	both	hog	and	poultry	operations,	reductions	in	NH3

emissions	have	been	reported	by	supplementing	with	AA	and	reducing	CP	in	diets.		

Reducing	dietary	CP	content	has	been	shown	to	be	an	effective	way	to	reduce	the	amount	of	

nitrogen	excreted	(Lenis,	1993;	Hartung	and	Phillips,	1994).	This	can	be	achieved	without	any	

negative	effect	on	animal	performance	by	supplementing	with	an	improved	synthetic	AA	balance,	

resulting	in	a	reduction	of	excess	CP	excreted	(Canh	et	al.,	1998b;	Ferket	et	al.,	2002).	In	U.S.‐type	

diets	(corn‐soybean	meal	based)	the	most	limiting	amino	acids	are	Lysine,	Methionine,	Threonine,	
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and	Tryptophan,	followed	by	Isoleucine,	Valine,	and	Histidine	(Outor‐Monteiro	et	al.,	2010).	Sutton	

et	al.	(1996)	reported	that	nitrogen	excretion	decreased	by	28	percent	when	diet	CP	content	

decreased	from	13	percent	to	10	percent	(corn‐soybean	meal)	for	growing‐finishing	pig	diets	

supplemented	with	Lys,	Met,	Thr,	and	Trp.	Several	studies	reported	reductions	in	nitrogen	

excretion	and	subsequent	decreases	in	NH3	emissions	in	non‐ruminants	(swine	and	poultry)	

(Hartung	and	Phillips,	1994;	Canh	et	al.,	1997;	Canh	et	al.,	1998a;	Canh	et	al.,	1998b;	Hayes	et	al.,	

2004).	Powers	et	al.	(2007)	observed	that,	as	a	result	of	feeding	reduced	CP	diets	with	increased	

amounts	of	synthetic	AA,	NH3	emissions	were	reduced	by	22	percent	(three	AA)	and	48	percent

(five	AA)	compared	with	the	control	diet	containing	only	one	AA,	and	diet	had	no	effect	on	pig	

performance.	

Canh	et	al.	(1998b)	and	Ndegwa	et	al.	(2008)	reported	that	some	nitrogen	excretion	could	be	

shifted	from	urine	to	feces	by	increasing	dietary	fiber	content,	or	by	reducing	dietary	nitrogen	

content,	with	no	significant	differences	in	animal	performance	or	growth.	Urinary	nitrogen	is	

predominantly	inorganic	in	nature	and	fecal	nitrogen	is	mostly	organic.	The	conversion	of	urea	

from	urine	to	NH3	is	a	fast	process,	while	conversion	of	organic	nitrogen	to	volatile	NH3	in	feces	is	a	

slow	process.		

The	reduction	in	NH3	emission	associated	with	lower	CP	diets	not	only	comes	from	reduction	in	

nitrogen	excretion,	but	also	from	lower	manure	pH.	Portejoie	et	al.	(2004)	reported	that	slurry	pH	

decreased	by	1.3	units	when	dietary	CP	decreased	from	20	to	12	percent,	and	slurry	from	pigs	fed	

the	lower	CP	diet	had	a	higher	DM	content	and	lower	TAN	and	TKN	contents.	Le	et	al.	(2008),	Hanni	

et	al.	(2007),	and	Canh	et	al.	(1998b)	also	reported	that	lower	manure	pH	resulted	from	feeding	

lower	CP	in	diets.	It	should	be	noted	that	water	intake	was	often	restricted	in	earlier	studies.	

Aarnink	and	Verstegen	(2007)	summarized	four	dietary	strategies	to	reduce	NH3	emissions:	1)	

lowering	CP	intake	in	combination	with	the	addition	of	limiting	AA;	2)	shifting	nitrogen	excretion	

from	urine	to	feces	by	including	fermentable	carbohydrates	in	the	diet;	3)	lowering	urinary	pH	with	

the	addition	of	acidifying	salts	to	the	diet;	and	4)	lowering	feces	pH	with	the	inclusion	of	

fermentable	carbohydrates	in	the	diet.	They	claimed	that	by	combining	these	strategies,	NH3

emissions	in	growing‐finishing	pigs	could	be	reduced	by	a	total	of	70	percent.	To	reduce	odor	from	

pig	manure,	Le	et	al.	(2007)	suggest	that	dietary	sulfur‐containing	AA	should	be	minimized	to	just	

meet	the	recommended	requirements.	

Current	research	has	concentrated	on	farm	production	efficiency	and	reducing	NH3	emissions;	little	

has	focused	on	GHG	emissions	mitigation	(Bhatti	et	al.,	2005).	Ball	and	Möhn	(2003)	showed	that	

low	CP	diets	can	reduce	total	GHG	emissions	from	growing	pigs	by	25	to	30	percent	(directly	from	

the	animals	as	well	as	from	the	manure	after	excretion)	and	from	sows	by	10	to	15	percent.	Atakora	

et	al.	(2003)	reported	a	27.3	percent	decrease	in	CH4	emissions	in	pigs	fed	16	percent	CP	

(supplemented	with	AA)	diets,	compared	with	19.0	percent	CP	diets.	Atakora	et	al.	(2004)	reported	

that	the	CO2	equivalents	emitted	by	finishing	pigs	and	sows	fed	wheat‐barley‐canola	diets	were	

reduced	by	14.3	to	16.5	percent	when	feeding	the	reduced	CP,	AA‐supplemented	diets,	and	were	

similar	for	finishing	pigs	and	sows.	The	reduction	was	only	7.5	percent	when	feeding	the	corn‐

soybean	meal‐based	reduced	CP	diet.	Misselbrook	et	al.	(1998)	found	that	CH4	emissions	during	

storage	were	less	at	low	than	at	a	high	dietary	CP	content.	The	emission	of	CH4	was	significantly	

related	to	content	of	dry	matter,	total	carbon,	and	VFA	in	the	manure.	Misselbrook	et	al.	(1998)	

claimed	that	the	50	percent	reduction	in	CH4	emission	from	the	slurry	observed	when	pigs	were	fed	

the	lower	CP	diet	was	probably	the	result	of	the	reduced	volatile	fatty	acids	(VFA)	content	of	the	

slurry,	and	CH4	emissions	were	more	closely	related	to	VFA	content	than	to	total	carbon	content.	

There	appears	to	be	a	close	relationship	between	fermentable	carbohydrates	in	the	diet	and	CH4

production	(Kirchgessner	et	al.,	1991).	Manure	pH	also	influences	CH4	production.	Kim	et	al.	(2004)	

noted	a	14	percent	reduction	in	CH4	emission	when	ideal	pH	was	reduced	one	unit	through	addition	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

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of	acidogenic	calcium	and	phosphorus	sources	to	pig	diets.	Increasing	fermentable	carbohydrate	

levels	in	the	diet	to	lower	the	pH	of	manure,	with	the	goal	of	reducing	NH3	emissions,	might	

increase	CH4	production	(Aarnink	and	Verstegen,	2007).	Canh	et	al.	(1998a)	observed	that	for	each	

100‐g	increase	in	the	intake	of	dietary	non‐starch	polysaccharide	(NSP),	the	slurry	pH	decreased	by	

approximately	0.12	units	and	the	NH3	emission	from	slurry	decreased	by	5.4	percent	when	dietary	

NSP	ranged	from	150	to	340	g	NSP	kg	DM‐1.		

Feeding	of	dried	distillers	grains	with	solubles	(DDGS)	has	become	common	practice	in	the	swine	

industry.	Li	et	al.	(2011)	demonstrated	that	feeding	diets	containing	20	percent	DDGS	increased	

emissions	of	CH4	but	not	N2O	when	compared	to	control	diets	without	DDGS.	Observed	increases	in	

CH4	emissions	approximated	18	percent.	Ammonia	emissions	resulting	from	feeding	20	percent	

DDGS	were	either	higher	or	lower	than	diets	without	DDGS,	depending	on	the	form	of	trace	

minerals	included	in	the	diet.	Diets	including	inorganic	forms	of	trace	minerals	had	seven	percent	

greater	NH3	emissions,	while	feeding	organic	forms	of	trace	minerals	decreased	NH3	emissions	

almost	20	percent	compared	to	control	diets	(Liu	et	al.,	2011a).	

In	a	recent	meta‐analysis,	Liu	et	al.	(2011a)	used	32	data	points	in	a	subgroup	of	studies	that	

included	diet	CP	information	to	analyze	the	effect	of	diet	CP	on	GHG	emissions.	Three	factors	(diet	

CP,	geographic	region,	and	swine	production	phase)	were	considered	in	the	regression	analysis.	

Diet	CP	was	not	a	significant	factor.	Emissions	of	CH4	are	positively	correlated	with	diet	crude	

protein	in	swine	production,	most	significantly	for	lagoon	and	slurry	storage	systems	(Liu	et	al.,	

2011a).	Clark	et	al.	(2005)	determined	that	reducing	dietary	CP	may	actually	increase	CH4

emissions,	so	results	are	varied.	It	had	been	expected	that	a	lower	CP	diet	may	result	in	lower	

nitrogen	excretion,	and	thus	might	be	able	to	reduce	N2O	emissions	from	manure.	However,	this	

hypothesis	was	not	supported	by	the	results	of	the	meta‐analysis.	

Diet	formulation	at	each	stage	of	the	life	cycle	influences	nutrients	excreted	in	manure,	as	well	as	

emissions	that	result	from	that	manure	during	storage	and	potentially	following	land	application.	

From	a	modeling	perspective,	the	focus	needs	to	be	on	management	factors,	including	diet	

formulation	and	manure	handling	practices.	

Feed	efficiency	improvements	can	reduce	emissions	throughout	the	entire	food	production	cycle	by	

reducing	the	amount	of	feed	needed	for	meat	production,	thereby	reducing	inputs	into	feed	

production	as	well	as	reducing	manure	nutrients	that	must	be	managed.	Feed	efficiency	is	the	

product	of	genetics	and	environment	(management).	Genetic	differences	are	difficult	to	assess,	

because	this	information	is	retained	by	companies.	Genetic	improvements	are	not	insignificant	over	

time	and	may	in	fact	be	a	larger	contributor	to	gains	than	management.	However,	from	a	modeling	

perspective,	the	focus	needs	to	be	on	management	factors,	including	diet	formulation	and	in‐house	

manure/litter	practices.	Feed	efficiency	could	be	a	model	component	in	the	future	once	more	data	

on	the	impacts	of	feed	efficiency	on	GHG	emissions	are	available.		

Factors Affecting Emissions from Meat Birds

Emissions	of	both	N2O	and	NH3	can	be	restricted	by	reducing	the	litter	nitrogen	content	through	

diet	modification.	Ferguson	et	al.	(1998a;	1998b)	fed	reduced	dietary	protein	diets	to	broiler	

chickens.	Although	performance	was	hindered	in	both	studies,	NH3	concentration	and	litter	

nitrogen	content	were	reduced	significantly	as	a	result	of	the	low‐protein	diets.	Applegate	et	al.	

(2008)	reported	similar	litter	nitrogen	effects	when	turkey	toms	were	fed	reduced‐protein	diets.	No	

performance	differences	were	observed.	These	diets	were	then	fed	to	turkey	toms	by	Liu	et	al.	

(2011a),	who	observed	a	12	percent	reduction	in	NH3	emissions	as	a	result	of	reducing	cumulative	

nitrogen	intake	by	9	percent.	Feeding	specific	AA	allowed	for	similar	nitrogen	intakes	across	

treatments,	but	reduced	NH3	emissions	by	25	percent	(Liu	et	al.,	2011a)	and	nitrogen	in	litter	by	12	

percent	(Liu	et	al.,	2011b),	because	nitrogen	was	better	utilized	by	the	birds.	Across	all	diets,	N2O	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

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emissions	made	up	less	than	one	percent	of	nitrogen	output	(Liu	et	al.,	2011b),	suggesting	that	

reducing	dietary	nitrogen	may	have	less	influence	on	N2O	emissions	than	other	factors.	

Factors Affecting Emissions from Laying Hens

Diet	factors	can	alter	air	emissions	from	laying	hen	facilities.	Much	of	the	work	to	date	has	focused	

on	reducing	NH3	emissions.	Roberts	et	al.	(2007)	showed	that	inclusion	of	dietary	corn	DDGS,	

wheat	middlings,	or	soy	hulls	lowered	the	seven‐day	cumulative	manure	NH3	emission	from	3.9	g	

kg	of	dry	manure‐1	for	the	control	to	1.9,	2.1,	and	2.3	g	kg	of	dry	manure‐1,	respectively;	it	also	

lowered	the	daily	NH3	emission	rate.	Reducing	the	CP	content	by	one	percent	had	no	measurable	

effect	on	NH3	emission.	Wu‐Haan	et	al.	(2007b)	fed	a	reduced‐emissions	diet	containing	6.9	percent	

of	a	CaSO4‐zeolite	mixture	and	slightly	reduced	protein	to	21‐,	38‐,	and	59‐week‐old	Hy‐Line	W‐36	

hens;	they	observed	that	daily	NH3	emissions	from	hens	fed	the	reduced‐emissions	diets	(185.5,	

312.2,	and	333.5	mg	bird‐1)	were	less	than	emissions	from	hens	fed	the	control	diet	(255.1,	560.6,	

and	616.3	mg	bird‐1)	for	trials	1,	2,	and	3,	respectively.	Total	nitrogen	excretion	from	hens	fed	the	

control	and	reduced‐protein	diets	was	not	different	(Wu‐Haan	et	al.,	2007a).	Because	of	the	

acidifying	nature	of	the	diets,	the	mass	of	nitrogen	remaining	in	excreta	following	a	three‐week	

storage	period	was	less	from	hens	fed	the	control	diet	than	from	hens	fed	the	reduced‐protein	diet	

(Wu‐Haan	et	al.,	2007a).	Li	et	al.	(2010)	found	that	feeding	corn	DDGS	decreased	the	mass	of	NH3

emitted	daily	by	80	mg	hen‐1(592	vs.	512	mg	hen‐1	day‐1	for	zero	percent	and	20	percent	DDGS,	

respectively),	and	by	14	percent	per	egg	produced,	and	daily	CH4	emissions	by	13	to	15	percent	

(39.3	vs.	45.4	mg	hen‐1	day‐1;	and	0.70	vs.	0.82	mg	g	egg‐1	day‐1).		

5.3.8 Limitations and Uncertainty in Enteric Fermentation and Housing Emissions

Estimates

At	the	entity	level,	uncertainty	in	enteric	CH4	production	in	cattle	typically	results	from,	lack	of	

precision	in	estimating	energy	intake,	feed	type	and	intake,	characteristics	of	particular	feedstuffs	

(i.e.,	acid	detergent	fiber,	starch,	etc.),	DE,	maximum	possible	CH4	emissions,	CH4	conversion	factors	

(Ym),	synergies	or	countereffects	between	mitigation	options,	and	net	energy	expenditure	by	the	

animal.	The	assumptions	about	implications	of	dietary	changes	on	enteric	CH4	production	are	based	

on	literature	values	(including	empirical	field	studies)	and	may	not	be	indicative	of	true	changes	in	

emissions	for	particular	animal	types,	as	this	will	vary	depending	on	an	individual	animal’s	health,	

management	practices,	animal	activities,	and	baseline	diet.	For	swine,	goats,	American	Bison,	

llamas,	alpacas,	and	managed	wildlife,	the	recommended	estimation	methods	for	emissions	from	

enteric	fermentation	are	based	on	the	IPCC	Tier	1	approach,	which	has	an	uncertainty	of	30	to	50	

percent.	

Methane	emissions	from	dairy	cattle	housing	areas	are	estimated	using	equations	from	DairyGEM	

(IFSM).	In	predicting	emissions,	uncertainty	will	result	from	a	lack	of	precision	in	estimating	

excreted	volatile	solids	and	nitrogen	excreted,	pH,	temperature,	air	velocity,	and	surface	area	of	

exposed	manure,	bedding	pack,	CH4	conversion	factors	(MCFs),	and	maximum	CH4‐producing	

capacity	for	manures.	Comparison	of	modeled	values	with	on‐farm	evaluations	has	found	the	model	

predicts	on‐farm	emissions	within	five	to	20	percent	(unpublished	data).	

Methane	emissions	from	poultry	housing	areas	are	estimated	using	the	IPCC	Tier	1	method.	

Uncertainty	in	predictions	of	emissions	result	from	a	lack	of	precision	in	estimating	feed	intake,	

nitrogen	excreted	and	volatile	solids,	MCF,	volatilization	fraction,	and	in	some	instances	emission	

factors	that	were	chosen	in	the	model.	Unfortunately	there	is	a	lack	of	published	information	

related	to	GHG	emissions	from	poultry	and	to	the	best	of	our	knowledge	this	model	has	not	been	

validated/tested	using	on‐farm	data.	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

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Much	of	the	published	uncertainty	information	in	inventory	guidance,	such	as	IPCC	Good	Practice	

Guidance	(IPCC,	2000)	and	in	the	U.S.	National	GHG	Inventory	(U.S.	EPA,	2013),	focus	on	

uncertainties	present	in	calculating	inventories	at	the	regional	or	national	scale,	many	of	which	do	

not	translate	to	the	entity	level.	Some	of	the	sources	of	uncertainty	at	the	regional	or	national	scale	

included	variability	in	native	vegetation	eaten	by	grazing	animals,	assumptions	about	the	types	of	

feed	farmers	provide	for	animals	(including	the	practice	of	including	nutritional	supplements),	

management	practices	such	as	housing	options	and	daily	animal	activity,	average	animal	weights,	

and	animal	populations.	The	quantity	of	uncertainty	at	larger	scales	is	difficult	to	define,	dependent	

on	both	the	accuracy	in	reporting	practices	and	experts’	understanding	of	the	implications	of	

management	practices	and	the	accuracy	of	particular	estimation	methodologies.	Consistent	

improvement	in	reporting	practices	can	help	remove	some	of	this	uncertainty.	

Available	default	values	and	uncertainty	information	is	included	in	Table	5‐17.	

Table 5‐17: Available Uncertainty Data for Emissions from Housing and Enteric

Fermentation

Parameter

Abbreviation/Symbol

Data Input

Unit

Estimated Value

Relative uncertainty

Low (%)

Relative uncertainty

High (%)

Effective Lower Limit

Effective Upper Limit

Data Source

Daily	Milk	Production	 Milk	

kg	

milk/animal/d

ay	

	 3%	 5%	 	 	 Expert	Assessment	

Supplemental	Fat	(feedlot)	 S.Fat	 Percent	 2	 4	 Expert	Assessment	

Maximum	daily	emissions	for	dairy	cows	 Emax	 MJ/head	 45.98	 	 	 	 	 Mills	et	al.	(2003)	

Typical	Ammonia	Losses	from	Dairy	Housing	

Facilities	–Open	dirt	lots	(cool,	humid	region)		

NH3

loss	 Percent	of	Nex 15%	 30%	 Koelsh	and	Stowell	(2005)	

Typical	Ammonia	Losses	from	Dairy	Housing	

Facilities	–Open	dirt	lots	(hot,	arid	region)	

NH3

loss	 Percent	of	Nex	 	 	 	 30%	 45%	 Koelsh	and	Stowell	

(2005)	

Typical	Ammonia	Losses	from	Dairy	Housing	

Facilities	–Roofed	facility	(flushed	or	scraped)		

Roofed	facility	(daily	scrape	and	haul)	

NH3

loss	 Percent	of	Nex 5%	 15%	 Koelsh	and	Stowell	(2005)	

Typical	Ammonia	Losses	from	Dairy	Housing	

Facilities	–Roofed	facility	(shallow	pit	under	

floor)		

NH3

loss	 Percent	of	Nex	 	 	 	 10%	 20%	 Koelsh	and	Stowell	

(2005)	

Typical	Ammonia	Losses	from	Dairy	Housing	

Facilities	–Roofed	facility	(bedded	pack)	

NH3

loss	 Percent	of	Nex 20%	 40%	 Koelsh	and	Stowell	(2005)	

Typical	Ammonia	Losses	from	Dairy	Housing	

Facilities	–Roofed	facility	(deep	pit	under	floor,	

includes	storage	loss)	

NH3

loss	 Percent	of	Nex	 	 	 	 30%	 40%	 Koelsh	and	Stowell	

(2005)	

Typical	Ammonia	Losses	from	Beef	Housing	

Facilities	–	Open	dirt	lots	(cool,	humid	region)	

NH3

loss	 Percent	of	Nex 30%	 45%	 Koelsh	and	Stowell	(2005)	

Typical	Ammonia	Losses	from	Beef	Housing	

Facilities	–	Open	dirt	lots	(hot,	arid	region)	

NH3

loss	 Percent	of	Nex	 	 	 	 40%	 60%	 Koelsh	and	Stowell	

(2005)	

Typical	Ammonia	Losses	from	Beef	Housing	

Facilities	–Roofed	facility	(bedded	pack)	

NH3

loss	 Percent	of	Nex 20%	 40%	 Koelsh	and	Stowell	(2005)	

Typical	Ammonia	Losses	from	Beef	Housing	

Facilities	–Roofed	facility	(deep	pit	under	floor,	

includes	storage	loss)	

NH3

loss	 Percent	of	Nex	 	 	 	 30%	 40%	 Koelsh	and	Stowell	

(2005)	

Typical	Ammonia	Losses	from	Swine	Housing	

Facilities	–Roofed	facility	(flushed	or	scraped)	

Roofed	facility	(daily	scrape	and	haul)		

%NH3

loss	 Percent	of	Nex 5%	 15%	 Koelsh	and	Stowell	(2005)	
 Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

 5-75 

Parameter

Abbreviation/Symbol

Data Input

Unit

Estimated Value

Relative uncertainty

Low (%)

Relative uncertainty

High (%)

Effective Lower Limit

Effective Upper Limit

Data Source

Typical	Ammonia	Losses	from	Swine	Housing	

Facilities	–Roofed	facility	(shallow	pit	under	

floor)	

%NH3

loss	 Percent	of	Nex	 	 	 	 10%	 20%	 Koelsh	and	Stowell	

(2005)	

Typical	Ammonia	Losses	from	Swine	Housing	

Facilities	–Roofed	facility	(bedded	pack)		

%NH3

loss	 Percent	of	Nex 20%	 40%	 Koelsh	and	Stowell	(2005)	

Typical	Ammonia	Losses	from	Swine	Housing	

Facilities	–Roofed	facility	(deep	pit	under	floor,	

includes	storage	loss)		

%NH3

loss	 Percent	of	Nex	 	 	 	 30%	 40%	 Koelsh	and	Stowell	

(2005)	

Typical	Ammonia	Losses	from	Poultry	Housing	–

Roofed	facility	(litter)	(Meat	Producing	birds)	

%NH3

loss	 Percent	of	Nex 25%	 50%	 Koelsh	and	Stowell	(2005)	

Typical	Ammonia	Losses	from	Poultry	Housing	–

Roofed	facility	(stacked	manure	under	floor,	

includes	storage	loss)	(Egg‐producing	birds)	

%NH3

loss	 Percent	of	Nex	 	 	 	 25%	 50%	 Koelsh	and	Stowell	

(2005)	

Methane	Emissions	from	Goats	–	Emission	factor	

for	goats	 EFG

kg	

CH4/head/day	 0.0137 IPCC	(2006)	

5.4 Manure Management

Use	of	manure	as	a	source	of	plant	nutrients	reduces	the	need	for	purchased	commercial	fertilizer.	

Manure	storage	allows	for	manure	applications	to	land	to	be	synchronized	with	crop	cultural	needs.	

This	practice	reduces	the	potential	for	soil	compaction	due	to	poor	timing	of	manure	application	

(wet	soil	conditions)	and	makes	more	efficient	use	of	farm	labor.	Many	animal	manure	storage	or	

treatment	structures	create	anaerobic	conditions	that	result	in	the	production	and	release	of	GHGs	

and	odors.	Manure	that	is	recycled	to	the	land	base	can	have	potential	negative	effects	on	water	

quality	(both	surface	and	ground	water).		

Manure	storage	and	treatment,	as	a	component	of	manure	management	systems,	plays	a	critical	

role	in	GHG	emissions.	At	the	entity	level,	various	manure	storage	and	treatment	approaches	will	

lead	to	different	amounts	of	GHG	emission.	Animal	manure	can	be	classified	into	two	categories	

based	on	their	physical	properties:	solid,	defined	as	dry	matter	above	15	percent;	and	liquid,	defined	

as	dry	matter	of	less	than	15	percent	(including	liquid	manure	with	a	dry	matter	of	less	than	10	

percent	and	slurry	manure	with	a	dry	matter	between	10	and	15	percent).	Three	solid	manure	

storage/treatment	practices	(temporary	stack/long‐term	stockpile,	composting,	and	thermo‐

chemical	conversion)	and	eight	liquid	manure	storage/treatment	practices	(aerobic	lagoon,	

anaerobic	lagoon/runoff	holding	pond/storage	tanks,	anaerobic	digestion,	combined	aerobic	

treatment	system,	sand‐manure	separation,	nutrient	removal,	solid‐liquid	separation,	and	

constructed	wetland)	were	evaluated	and	the	emission	estimation	methods	are	presented.	At	the	

farm	entity	level,	several	practices	are	often	strategically	combined	to	treat	manure.	In	order	to	

provide	tools	to	evaluate	these	scenarios,	activity	data	(i.e.,	mass	flow	data	and	chemical	and	

physical	characteristics	of	influent	and	effluent,	environmental	temperature,	pH,	and	total	nitrogen)	

from	individual	practices	will	be	used	to	link	practices	in	the	combined	system	for	individual	farm	

entities.	A	schematic	structure	of	possible	combinations	of	manure	storage	and	treatment	practices	

at	the	entity	level	is	presented	in	Figure	5‐7.	As	illustrated	in	the	figure, manure	can	be	handled	as	a	

solid	or	liquid. For	each	stream,	the	manure	can	be	applied	directly	to	land,	stored,	or	treated	before	

storage	or	land	application.	In	some	practices,	solids	are	separated	from	the	liquid	manure	stream	

and	treated	using	a	solids	handling	system.		
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

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Figure 5‐7: Schematic Structure of Possible Combination of Manure Storage and Treatment

Practices

Note:	Individual	practices	could	be	combined	together	to	treat	manure	based	on	the	need	at	the	entity	level.

Each	manure	management	practice	is	described	as	an	individual	unit	practice	in	this	document.	The	

references	for	estimation	of	GHG	emission	for	individual	practice	are	listed	in	Table	5‐18.		

Table 5‐18: List of Individual Manure Storage and Treatment Practices

Section Storage and Treatment Practices Major References for GHG

Estimation

Solid manure

5.4.1	 Temporary	and	long‐term	storage	 IPCC	(2006);	U.S.	EPA	(2011)	

0	 Composting	 IPCC	(2006);	U.S.	EPA	(2011)	

Liquid manure

5.4.3	 Aerobic	lagoon	 IPCC	(2006);	U.S.	EPA	(2011)	

5.4.4	 Anaerobic	lagoon/runoff	holding	ponds/storage	

tanks	 Sommer	et	al.	(2004)	

5.4.5	 Anaerobic	digestion	with	biogas	utilization	 IPCC	(2006);	CDM	(2012)	
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Section Storage and Treatment Practices Major References for GHG

Estimation

5.4.6	 Combined	aerobic	treatment	system	 Vanotti	et	al.	(2008)	

5.4.7	 Sand–manure	separation	

5.4.8	 Nutrient	removal	

5.4.9	 Solid–liquid	separation	 Ford	and	Fleming	(2002)	

5.4.10	 Constructed	wetland	 Stein	et	al.	(2006;	2007b)	

Stone	et	al.	(2002;	2004)	

5.4.11	 Thermo‐chemical	conversion	

The	remainder	of	this	section	presents	the	method	for	estimating	GHGs	from	the	sources	listed	in	

Table	5‐18.	For	each	source	of	GHGs	with	an	estimation	method,	the	following	information	is	

provided:		

 Overview of the GHG Source and the Resulting GHGs.	This	section	provides	an	overview

of	manure	management	technology,	the	resulting	GHG	emissions,	and	the	methodology

proposed	for	estimating	the	emissions.

 Rationale for Selected Method.	This	section	presents	the	reasoning	for	the	selection	of	the

method	recommended	in	this	report.

 Activity Data.	This	section	lists	the	activity	data	required	for	estimating	GHGs	at	the	entity

level.

 Ancillary Data.	This	section	lists	ancillary	data	such	as	CH4	conversion	factors	(MCF)	and

maximum	CH4	production	capacity	(B0).

 Method.	This	section	provides	detailed	descriptions,	including	equations	for	the	selected

methods.

 For	each	source	of	GHGs	without	an	estimation	method,	a	qualitative	overview	is	provided.

Methods	for	estimating	NH3	emissions	are	provided	in	Appendix	5‐C.

5.4.1 Temporary Stack and Long‐Term Stockpile

5.4.1.1 Overview of Temporary Stack and Long‐Term Stockpiles		

Management	methods	for	stored	manure	are	differentiated	by	the	length	of	time	they	are	stockpiled	

(i.e.,	temporary	stack	and	long‐term	storage).	Temporary	stack	is	a	short‐term	manure	storage	

method	that	is	used	to	temporarily	hold	solid	manure	when	bad	weather	prohibits	land	application,	

and/or	when	there	is	limited	availability	of	cropland	for	manure	application.	With	temporary	stack,	

Method for Estimating Emissions from Manure Storage and Treatment – Temporary

Stack and Long‐Term Stockpile

Methane	

 IPCC	Tier	2	approach	using	IPCC	and	U.S.	EPA	Inventory	emission	factors,	utilizing

monthly	data	on	volatile	solids	and	dry	manure.	Volatile	solids	content	can	be	obtained

from	sampling	and	lab	testing.

 Method	is	only	readily	available	method.

Nitrous	Oxide	

 IPCC	Tier	2	approach	using	U.S.‐based	emission	factors	and	monthly	data	on	volatile

solids,	total	nitrogen,	and	dry	manure.

 No	specific	models	exist;	method	is	the	only	readily	available	method.
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

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the	manure	is	removed	and	applied	to	land	within	a	few	weeks	of	piling.	Temporary	storage	is	not	a	

preferred	method	to	store	manure	because	it	requires	the	manure	to	be	handled	twice.	

Long‐term	storage	is	a	permanent	manure	storage	method	in	which	solid	manure	is	piled	on	a	

confined	area	or	stored	in	a	deep	pit	for	longer	than	six	months.	In	low‐rainfall	areas,	the	stockpile	

can	be	piled	on	the	field	with	the	installation	of	nutrient	runoff	control.	In	higher	rainfall	areas,	a	

concrete	pad	and	wall	are	constructed	to	store	solid	manure	and	prevent	nutrient	runoff	from	

heavy	rain.	

Greenhouse	gases	generated	from	both	storage	methods	have	a	pattern	similar	to	that	of	enteric	

fermentation.	Carbon	and	nitrogen	compounds	in	manure	are	broken	down	by	microbes	to	CH4,	

and	N2O.	The	main	factors	influencing	GHG	emissions	from	storage	are	temperature	and	storage	

time.	Due	to	the	longer	storage	time,	long‐term	stockpile	solid	manure	storage	generates	a	

significant	amount	of	GHGs.	Temporary	stack,	as	a	short‐term	manure	storage	method,	generates	

less	GHGs	than	the	long‐term	stockpile	solid	storage.	However,	it	is	still	necessary	to	quantitatively	

delineate	the	emissions	in	order	to	assist	livestock	and	poultry	farms	in	evaluating	their	manure	

management	operations.	Temporary	stack	and	long‐term	stockpiles	of	manure	also	produce	NH3;	

proposed	methods	to	estimate	NH3	emissions	are	presented	in	Appendix	5‐C.	

The	IPCC	Tier	2	methodology	is	provided	for	estimating	CH4	emissions	from	temporary	stacks	or	

long‐term	stockpiles.	This	methodology	uses	a	combination	of	IPCC	and	country‐specific	emission	

factors	from	the	U.S.	EPA	GHG	Inventory.	The	amount	of	manure,	volatile	solids	content,	and	

temperature	are	specific	to	the	entity.	The	method	for	calculating	N2O	emissions	is	the	same	as	the	

equation	presented	in	the	U.S.	GHG	Inventory.		

Rationale for Selected Method

The	IPCC	equations	are	the	only	available	methods	for	estimating	CH4,	and	N2O	emissions	from	

temporary	stack	and	long‐term	stockpiles.	These	methodologies	best	describe	the	quantitative	

relationship	among	activity	data	at	the	entity	level.	

Activity Data

In	order	to	estimate	the	daily	CH4	emissions,	the	following	information	is	needed:9

 Animal	type	

 Total	dry	manure	

 Volatile	solids	of	dry	manure10

 Temperatures	(local	ambient	temperature	and	manure	temperature)

In	order	to	estimate	the	daily	N2O	emission,	the	following	information	is	needed:	

 Total	dry	manure	

 Total	nitrogen	content	of	the	manure	

The	total	nitrogen	content	of	the	manure	entering	storage	systems	can	be	estimated	according	to	

the	nitrogen	balance	method	as	described	in	Equation	5‐9:	Total	Nitrogen	Entering	Manure	Storage	

and	Treatment	The	fraction	of	nitrogen	excreted	by	an	animal	that	is	not	emitted	as	a	gas	is	the	

portion	that	enters	storage.		

																																																													

9	Although	daily	estimates	for	the	activity	data	are	optimal,	tracking	this	level	of	detail	would	be	burdensome.	Annual	

estimates	don’t	allow	for	seasonal	variation	in	diets	and	climate.	Consequently,	disaggregation	of	the	data	by	season	or	by	

periods	of	major	shifts	in	animal	population	is	suggested.		

10	Volatile	solids,	total	nitrogen	content,	and	ammonia‐nitrogen	content	should	be	obtained	through	sampling	and	lab	

testing.
 Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

 5-79 

Ancillary Data

The	ancillary	data	used	to	estimate	CH4	emission	for	temporary	storage	and	long	term	stockpiles	

are:	maximum	CH4	producing	capacities	(B0)	and	MCFs.	The	B0	values	for	solid	manure	storage	are	

obtained	from	the	IPCC	and	listed	in	Table	5‐19.	Methane	conversion	factors	for	different	manure	

management	systems	(including	temporary	storage	of	solid	manure)	are	also	obtained	from	the	

IPCC	and	listed	in	Table	5‐20	and	5‐16.		

The	ancillary	data	used	to	estimate	N2O	emissions	for	temporary	storage	and	long	term	stockpiles	

are	the	N2O	emission	factors	for	solid	manure	storage	systems	are	presented	in	Table	5‐23	(U.S.	

EPA,	2011).		

5.4.1.2 Method

Methane Emissions from Temporary Stack and Long‐Term Stockpile

The	Tier	2	approach	by	the	IPCC	model	is	recommended	to	estimate	CH4	emissions	and	is	described	

in	Equation	5‐26	(IPCC,	2006).	Daily	CH4	emission	is	estimated	as	a	function	of	the	volatile	solids	in	

manure	placed	into	the	storage	and	the	animal‐specific	MCF.		

a	Dry	manure	refers	to	material	remaining	after	removal	of	water.	It	is	determined	through	the	evaporation	of	water	from	

the	manure	sample	at	103‐105°C.	Forced	air	oven	is	the	most	common	equipment	to	measure	the	dry	matter.	

Table 5‐19: Maximum CH4 Producing Capacities (B0) from Different Animals		

Animal

Maximum CH4

Producing Capacity

(B0)

(m3/kg VS)

Animal

Maximum CH4

Producing Capacity

(B0)

(m3/kg VS)

Beef	replacement	heifers	 0.33b Breeding	swine 0.48	

Dairy	replacement	heifers	 0.17b Layer	(dry) 0.39	

Mature	beef	cows	 0.33b Layer	(wet) 0.39	

Steers	(>500	lbs)	 0.33b Broiler 0.36	

Stockers	(All)	 0.17b Turkey 0.36	

Cattle	on	feed	 0.33b Duck 0.36	

Dairy	cow	 0.24b Sheep 0.19b

Cattle	 0.19b Feedlot	sheep 0.36b

Buffalo	 0.1a Goat 0.17b

Market	swine	 0.48	 Horse 0.3	

Mule/Ass 0.33	 a	There	are	no	data	for	North	America	region;	the	data	from	Western	Europe	are	used	to	calculate	the	estimation.	 b	Numbers	are	from	the	EPA	U.S.	Inventory:	1990‐2009	(U.S.	EPA,	2011).	Other	numbers	are	from	IPCC	(2006).		

Equation 5‐26: IPCC Tier 2 Approach for Estimating CH4 Emissions

۳۱۶૝ ൌ ܕ ൈ ܁܄ ൈ ۰૙ ൈ ૙. ૟ૠ ൈ

۱۴ۻ

૚૙૙

Where:	

ECH4	 =	CH4	emissions	per	day	(kg	CH4	day‐1)	

m		 =	Total	dry	manure	per	day	a	(kg	dry	manure	day‐1)	

VS		 =	Volatile	solids	(kg	VS	(kg	dry	manure)‐1)		

B0	 =	Maximum	CH4	producing	capacity	for	manure	(m3	CH4	(kg	VS)‐1)	

MCF	 =	CH4	conversion	factor	for	the	manure	management	system	(%)		

0.67		 =	Conversion	factor	of	m3	CH4 to	kg	CH4
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-80 

Table 5‐20: Methane Conversion Factors for Temporary Storage of Solid Manure from

Different Animals

Animal Methane Conversion Factor (%)

Temp = 10‐14°C Temp = 15‐25°C Temp = 26‐28°C

Dairy	cow	 1 1.5 2	

Cattle	 1 1.5 2	

Buffalo	 1 1.5 2	

Market	swine	 1 1.5 2	

Breeding	swine	 1 1.5 2	

Layer	(dry)	 1.5 1.5 1.5

Broiler	 1.5 1.5 1.5

Turkey 1.5 1.5 1.5

Duck	 1 1.5 2	

Sheep	 1 1.5 2	

Goat	 1 1.5 2	

Horse	 1 1.5 2	

Mule/Ass	 1 1.5 2	

Source:	IPCC	(2006).	

Table 5‐21: Methane Conversion Factors for Long‐Term Stock Storage of Solid Manure from

Different Animals

Animal Methane Conversion Factor (%)

Temp = 10‐14°C Temp = 15‐25°C Temp = 26‐28°C

Dairy	cow	 2 4 5	

Cattle	 2 4 5	

Buffalo	 2 4 5	

Market	swine	 2 4 5	

Breeding	swine	 2 4 5	

Layer	(dry)	 1.5 1.5 1.5

Broiler	 1.5 1.5 1.5

Turkey 1.5 1.5 1.5

Duck	 1 1.5 2	

Sheep	 1 1.5 2	

Goat	 1 1.5 2	

Horse	 1 1.5 2	

Mule/Ass	 1 1.5 2	

Source:	IPCC	(2006).	

Table 5‐22: Methane Conversion Factors for Long‐Term Storage of Slurry Manure from

Buffalo

Temperature (°C) Methane Conversion

Factor (%) Temperature (°C) Methane ConversionFactor (%)

10	 17 20 42

11	 19 21 46

12	 20 22 50

13	 22 23 55

14	 25 24 60

15	 27 25 65

16	 29 26 71

17	 32 27 78

18	 35 28 80

19	 39 	

Source:	IPCC	(2006).	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-81 

Nitrous Oxide Emissions from Temporary Stack and Long‐Term Stockpile

Nitrous	oxide	emissions	are	dependent	on	nitrification	and	denitrification.	Manure	storage	is	one	of	

the	main	sources	of	U.S.	overall	N2O	emissions.	The	only	quantitative	method	for	estimating	N2O	

emissions	from	solid	manure	is	the	IPCC	Tier	2	approach,	which	is	also	used	for	the	U.S.	Inventory.	

This	approach	is	based	on	the	use	of	emission	factors	from	the	most	recent	IPCC	Guidelines	and	

total	nitrogen	values	are	estimated	according	to	Equation	5‐9.	Equation	5‐27	presents	the	equation	

to	estimate	the	N2O	emissions	for	solid	manure.		

a	Dry	manure	refers	to	material	remaining	after	removal	of	water.	It	is	determined	through	the	evaporation	of	water	from	

the	manure	sample	at	103‐105°C.	Forced	air	oven	is	the	most	common	equipment	to	measure	the	dry	matter.	

Table 5‐23: N2O Emission Factors for Solid Manure Storage		

Type of Storage N2O Emission Factor (kg N2O‐N/kg N)

Temporary	storage	of	solid/slurry	manure	 0.005	

Long‐term	storage	of	solid	manure	 0.002	

Long‐term	storage	of	slurry	manure	 0.005	

Source: U.S. EPA (2011).

5.4.2 Composting

5.4.2.1 Overview of Composting		

Equation 5‐27: IPCC Tier 2 Approach for Estimating N2O Emissions

ൈ ۼ܂ ൈ ۽૛ۼ۳۴ ൈ ܕ ൌ ۽૛ۼ۳

૝૝

૛ૡ

Where:	

EN2O	 =	Nitrous	oxide	emission	per	day	(kg	N2O	day‐1)	

m		 =	Total	dry	manure	per	daya	(kg	dry	manure	day‐1)	

EFN2O	=	N2O	emission	factor	(kg	N2O‐N	kg	N‐1)	

TN		 =	Total	nitrogen	at	a	given	day	(kg	N	(kg	dry	manure)‐1)	

ସସ

ଶ଼

	 =	Conversion	of	N2O‐N	emissions	to	N2O	emissions	

Method for Estimating Emissions from Manure Storage and Treatment –Composting

Methane	

 IPCC	Tier	2	approach,	utilizing	monthly	data	on	volatile	solids	and	dry	manure.	Volatile

solids	content	can	be	obtained	from	sampling	and	lab	testing.

 Method	is	the	only	readily	available	method.

Nitrous	Oxide	

 IPCC	Tier	2	approach,	utilizing	data	on	a	nitrous	oxide	emission	factor,	total	initial

nitrogen,	and	dry	manure.

 Method	depends	on	whether	the	system	is	in	vessel,	static	pile,	intensive	windrow,	or

passive	windrow.

 Method	is	only	readily	available	method.
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-82 

Composting	is	the	controlled	aerobic	decomposition	of	organic	material	into	a	stable,	humus‐like	

product	(USDA	NRCS,	2007).	Animal	manure	may	be	composted	in	a	variety	of	different	systems,	

including	in‐vessel	systems,	windrows,	or	static	piles.	In‐vessel	systems	handle	compost	in	a	closed	

system	such	as	a	rotary	drum	or	box	that	incorporates	regular	movement	to	ensure	proper	

aeration.	The	largest	composting	operations	divide	up	the	compost	into	long	heaps	for	windrow	

composting	or	into	one	large	pile	for	aerated	static	pile	composting.	In	the	former	method,	proper	

oxygen	flow	can	be	maintained	via	manual	turning	or	pipe	systems,	whereas	in	the	latter	method,	it	

is	maintained	through	pipe	systems.	Composting	has	become	a	popular	method	in	some	regions	to	

decrease	the	volume	and	weight	of	livestock	manure	and	to	produce	a	product	that	is	often	more	

acceptable	to	farmers	as	a	fertilizer.	During	a	100‐	to	120‐day	composting	period,	the	weight	and	

volume	of	manure	may	be	decreased	by	15	to	70	percent	(Eghball	et	al.,	1997;	Inbar	et	al.,	1993;	

Lopez‐Real	&	Baptista,	1996).	Furthermore,	the	heat	generated	through	the	composting	process	can	

kill	parasites,	pathogens,	and	weed	seeds	found	in	animal	waste,	creating	a	safer	product	for	crop	

application.	

The	quantity	of	GHG	emissions	is	affected	by	the	composting	method	employed.	Hao	et	al.	(2001)	

reported	that	GHG	emissions	from	cattle	manure	compost	increased	about	twofold	when	the	

compost	was	actively	composted	rather	than	passively	composted	in	windrows.	Active	windrows	

were	turned	six	times	(days	14,	21,	29,	50,	70,	and	84).	Passive	windrows	were	never	turned,	but	

air	was	introduced	into	the	windrows	by	a	series	of	open‐ended	perforated	steel	pipes.	To	the	

extent	that	the	rate	of	GHG	formation	depends	on	oxygen	saturation	in	the	pore	space,	aeration	

method	(i.e.,	forced‐air	vs.	passive/convective)	and	rate	(or	turning	frequency)	will	affect	the	

magnitude	of	GHG	emissions	during	the	composting	process.	

Eghball	et	al.	(1997)	reported	that	19	to	45	percent	of	the	nitrogen	present	in	manure	was	lost	

during	composting,	with	the	majority	of	this	presumably	as	NH3.	Using	changes	in	the	

nitrogen:phosphorus	ratio	of	feedlot	manure	that	was	placed	in	compost	windrows	and	the	

nitrogen:phosphorus	ratio	of	“finished”	compost,	Cole	et	al.	(2011)	estimated	that	10	to	20	percent	

of	nitrogen	was	lost	during	composting.	The	U.S.	EPA	currently	assumes	that	one	to	10	percent	of	

nitrogen	entering	compost	systems	is	lost	as	N2O	(IPCC,	2006;	U.S.	EPA,	2009).	

The	IPCC	Tier	2	methodology	is	provided	for	estimating	CH4	and	N2O	emissions	from	composting.	

This	methodology	uses	country‐specific	emission	factors	from	the	U.S.	EPA	GHG	Inventory.	The	

amount	of	manure,	volatile	solids	content,	and	temperature	are	specific	to	the	entity.	The	GHG	

estimation	method	for	manure	composting	does	not	consider	other	organic	carbon	sources	that	

might	be	added	into	manure	composting.		

Rationale for Selected Method

The	IPCC	equations	are	the	only	available	methods	for	estimating	CH4	and	N2O	emissions	from	

composting.	These	methodologies	best	describe	the	quantitative	relationship	amongst	activity	data	

at	the	entity	level.	

5.4.2.2 Activity Data

In	order	to	estimate	the	daily	CH4	emissions,	the	following	information	is	needed:		

 Animal	type	

 Total	dry	manure	

 Volatile	solids	of	dry	manure	

 Temperatures	(local	ambient	temperature	and	manure	temperature)

In	order	to	estimate	the	daily	N2O	emissions,	the	following	information	is	needed:		

 Total	dry	manure	in	the	storage	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-83 

 Total	nitrogen	in	manure

The	total	nitrogen	content	of	the	manure	entering	storage	systems	can	be	estimated	according	to	

the	nitrogen	balance	method	as	described	in	Equation	5‐9:	Total	Nitrogen	Entering	Manure	Storage	

and	Treatment	The	fraction	of	nitrogen	excreted	by	an	animal	that	is	not	emitted	as	a	gas	is	the	

portion	that	enters	storage.		

5.4.2.3 Ancillary Data

The	ancillary	data	used	to	estimate	CH4	emissions	for	manure	composting	are:	maximum	CH4

producing	capacities	(B0)	and	MCFs.	The	B0	values	are	obtained	from	the	IPCC	(2006)	and	listed	in	

Table	5‐19.	The	MCF	values	are	obtained	from	EPA	(U.S.	EPA,	2011)	and	listed	in	Table	5‐24.		

The	ancillary	data	used	to	estimate	N2O	emission	for	manure	composting	are	the	N2O	emission	

factors	(Table	5‐25).		

5.4.2.4 Method

Methane Emissions from Composting

The	Tier	2	approach	in	the	IPCC	model	is	adapted	with	country‐specific	factors	to	estimate	CH4

emissions	from	composting	of	solid	manure.	Daily	CH4	emissions	are	estimated	as	a	function	of	the	

volatile	solids	in	manure	placed	into	the	storage	and	the	MCF.	

a	Dry	manure	refers	to	material	remaining	after	removal	of	water.	It	is	determined	through	the	evaporation	of	water	from	

the	manure	sample	at	103‐105°C.	Forced	air	oven	is	the	most	common	equipment	to	measure	the	dry	matter.

The	B0	values	for	composting	solid	manure	are	obtained	from	the	IPCC	(2006)	and	are	listed	in	

Table	5‐19.	Methane	conversion	factors	for	different	approaches	of	composting	solid	manure	are	

obtained	from	IPCC	(2006).		

Equation 5‐28: IPCC Tier 2 Approach for Calculating Methane Emissions from

Composting Solid Manure

۳۱۶૝ ൌ ܕ ൈ ܁܄ ൈ ۰૙ ൈ ૙. ૟ૠ ൈ

۱۴ۻ

૚૙૙

Where:	

ECH4	 =	Methane	emissions	per	day	(kg	CH4	day‐1)	

m		 =	Total	dry	manurea	(kg	dry	manure	day‐1)	

VS		 =	Volatile	solids	(kg	VS	(kg	dry	manure)‐1)	

B0	 =	Maximum	CH4	producing	capacity	for	manure	(m3	CH4	(kg	VS)‐1)	(see	Table	5‐24)	

MCF	 =	Methane	conversion	factor	for	the	manure	management	system	(%)	

0.67		 =	Conversion	factor	of	m3	CH4	to	kg	CH4
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-84 

Table 5‐24: Methane Conversion Factors for Composting Solid Manure

Animal

Methane Conversion Factor (%)

Cool Climate Temperate

Climate Warm Climate

Manure	composting	–	in	vessel	 0.5	 0.5	 0.5	

Manure	composting	–	static	pile	 0.5	 0.5	 0.5	

Manure	composting	–	intensive	

windrow	 0.5	 1	 1.5	

Manure	composting	–	passive	windrow	 0.5	 1	 1.5	

Source:	IPCC	(2006).	

Nitrous Oxide Emissions from Composting

A	Tier	2	IPCC	model	is	adapted	to	estimate	N2O	emissions	from	composting	of	solid	manure.	

Equation	5‐29	presents	the	equation	for	estimating	N2O	emissions	from	composting	of	solid	

manure.	Emission	factors	for	different	composting	methods	are	listed	in	Table	5‐25	and	total	

nitrogen	is	estimated	according	to	Equation	5‐9.11		

a	Dry	manure	refers	to	material	remaining	after	removal	of	water.	It	is	determined	through	the	evaporation	of	water	from	

the	manure	sample	at	103‐105°C.	Forced	air	oven	is	the	most	common	equipment	to	measure	the	dry	matter.	

Table 5‐25: N2O Conversion Factors (EFN2O) for Composting Solid Manure

Category N2O Emission Factor (kg N2O‐N/ kg TN)

Cattle	and	Swine	Deep	Bedding	(Active	Mix)	 0.07	

Cattle	and	Swine	Deep	Bedding	(No	Mix)	 0.01	

Pit	Storage	Below	Animal	Confinements	 0.002	

Source:	IPCC	(2006).	

																																																													

11	Some	studies	have	been	conducted	on	the	rate	of	N2O	emissions	for	swine	(Fukummoto	et	al.,	2003;	Szanto	

et	al.,	2006)	but	this	data	is	limited	and	further	research	is	necessary.	See	Section	0	Research	Gaps	for	further	

discussion.		

Equation 5‐29: IPCC Tier 2 Approach for Estimating N2O Emissions from Composting of

Solid Manure

ൈ ۼ܂ ൈ 	۽૛ۼ۳۴ ൈ ܕ ൌ ۽૛ۼ۳

૝૝

૛ૡ

Where:	

EN2O	 =	Nitrous	oxide	emissions	per	day	(kg	N2O	day‐1)	

m		 =	Total	dry	manurea	(kg	day‐1)	

EFN2O	=	N2O	emission	(loss)	relative	to	total	nitrogen	in	manure	(kg	N2O‐N	(kg	TN)‐1)	

TN		 =	Total	nitrogen	in	the	initial	(fresh)	manure	(kg	TN	(kg	dry	manure)‐1)	

ସସ

ଶ଼

	 =	Conversion	of	N2O‐N	emissions	to	N2O	emissions	
 Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

 5-85 

5.4.3 Aerobic Lagoon

5.4.3.1 Overview of Aerobic Lagoons		

Aerobic	lagoons	are	man‐made	outdoor	basins	that	hold	animal	wastes.	The	aerobic	treatment	of	

manure	involves	the	biological	oxidation	of	manure	as	a	liquid,	with	either	forced	or	natural	

aeration.	Natural	aeration	is	limited	to	aerobic	lagoons	with	photosynthesis	and	is	consequently	

shallow	to	allow	for	oxygen	transfer	and	light	penetration.	These	systems	become	anoxic	during	

low‐sunlight	periods.	Due	to	the	depth	limitation,	naturally	aerated	aerobic	lagoons	have	large	

surface	area	requirements	and	are	impractical	for	large	operations.		

The	IPCC	Tier	2	methodology	is	provided	for	estimating	CH4	and	N2O	emissions	from	aerobic	

lagoons.	This	methodology	uses	a	combination	of	IPCC	and	country‐specific	emission	factors	from	

the	U.S.	EPA	GHG	Inventory.	Aerobic	conditions	result	in	the	oxidation	of	carbon	to	CO2,	not	the	

reduction	of	carbon	to	CH4,	thus	CH4	emissions	from	aerobic	lagoons	is	considered	negligible	and	is

designated	as	zero	in	accordance	with	IPCC.	The	method	for	calculating	N2O	emissions	accounts	for	

the	volume	of	the	lagoon	as	well	as	the	total	nitrogen	content	of	the	manure.		

5.4.3.2 Rationale for Selected Methods

The	IPCC	equations	are	the	only	available	methods	for	estimating	CH4,	and	N2O	emissions	from	

aerobic	lagoons.	These	methodologies	best	describe	the	quantitative	relationship	among	activity	

data	at	the	entity	level.	

5.4.3.3 Activity Data

No	activity	data	are	needed	(MCF=0)	for	the	estimation	of	CH4	gas	emissions.		

In	order	to	estimate	the	daily	N2O	emissions,	the	following	information	is	needed:	

 Surface	area	of	lagoon	

 Volume	of	the	material	in	the	lagoon	

 Total	nitrogen	content	of	the	manure	

The	total	nitrogen	content	of	the	manure	entering	storage	systems	can	be	estimated	according	to	

the	nitrogen	balance	method	as	described	in	Equation	5‐9.	The	fraction	of	nitrogen	excreted	by	an	

animal	that	is	not	emitted	as	a	gas	is	the	portion	that	enters	storage.		

5.4.3.4 Ancillary Data

The	ancillary	data	used	to	estimate	N2O	emissions	for	aerobic	lagoon	are	N2O	emission	factors	(U.S.	

EPA,	2011).		

Method for Estimating Emissions from Manure Storage and Treatment – Aerobic

Lagoon

Methane	

 The	MCF	for	aerobic	treatment	is	negligible	and	is	designated	as	zero	percent	in	

accordance	with	the	IPCC	Guidance.	

Nitrous	Oxide	

 IPCC	Tier	2	method	utilizing	IPCC	emission	factors.	

 Method	takes	into	account	the	volume	of	the	lagoon	and	the	total	nitrogen	content	of	

the	manure.	

 Method	is	the	only	readily	available	method.	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-86 

5.4.3.5 Method

Methane Emissions from Aerobic Lagoon

The	MCF	for	aerobic	treatment	is	negligible	and	was	designated	as	zero	percent	in	accordance	with	

the	IPCC	(2006).	The	solids	from	the	bottom	of	the	lagoon	have	significant	volatile	solids	and	B0	

associated	with	livestock	type;	the	characteristics	of	the	solids	should	be	measured	and	used	as	the	

inputs	to	estimate	emissions	of	GHGs	for	subsequent	storage	and	treatment	operations.	

Nitrous Oxide Emissions from Aerobic Lagoon

The	Tier	2	approach	in	the	IPCC	model	is	adapted	to	

estimate	N2O	emissions	from	aerobic	lagoons.	The	

N2O	conversion	factors	for	different	aeration	system	

are	listed	in	Table	5‐26.	The	estimation	method	for	

N2O	emissions	is	provided	in	Equation	5‐30.	

5.4.4 Anaerobic Lagoon, Runoff Holding Pond, Storage Tanks

5.4.4.1 Overview of Anaerobic Lagoons, Runoff Holding Ponds, and Storage Tanks

Table 5‐26: N2O Conversion Factors

(EFN2O) for Aerobic Lagoons

Aeration Type N2O Conversion Factor

(kg N2O‐N/kg N)

Natural	aeration 0.01

Forced	aeration 0.005

Source:	IPCC	(2006).

Equation 5‐30: Calculating N2O emissions from Aerobic Lagoons

ൈ ۼ܂ ൈ ۽૛ۼ۳۴ ൈ ܄ ൌ ۽૛ۼ۳

૝૝

૛ૡ

Where:	

EN2O		 =	Nitrous	oxide	emissions	per	day	(kg	N2O	day‐1)	

V	 =	Total	volume	of	the	lagoon	liquid	(m3	day‐1)	

EFN2O		=	Nitrous	oxide	emission	(loss)	relative	to	total	nitrogen	in	the	lagoon	liquid		

	 				(kg	N2O‐N	(kg	TN)‐1)	

TN		 =	Total	nitrogen	in	the	lagoon	liquid	(kg	TN	m‐3)	 	

ସସ

ଶ଼

	 =	Conversion	of	N2O‐N	emissions	to	N2O	emissions	

Method for Estimating Emissions from Manure Storage and Treatment – Anaerobic

Lagoons, Runoff Holding Ponds, Storage Tanks

Methane	

 Sommer	model	(Sommer	et	al.,	2004)	is	used	with	degradable	and	nondegradable	

fractions	of	volatile	solids	from	Møller	et	al.	(2004).	

 This	method	was	selected	as	it	accounts	for	manure	temperature	and	total	volatile	

solids	content	of	manure.	Volatile	solids	content	can	be	obtained	from	sampling	and	lab	

testing.	

Nitrous	Oxide	

 Emissions	are	a	function	of	the	exposed	surface	area	and	U.S.‐based	emission	factors.	

 Method	is	the	only	readily	available	option.		
 Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

 5-87 

The	most	frequently	used	liquid	manure	storage	systems	are	anaerobic	lagoons	(in	the	Southern	

portion	of	the	United	States),	earthen	or	earthen‐lined	storages	(in	the	Northern	portion	of	the	

country),	runoff	holding	ponds,	and	above‐grade	storage	tanks.	Anaerobic	lagoons	are	earthen	

basins	that	provide	an	environment	for	anaerobic	digestion	and	storage	of	animal	waste.	Both	the	

American	Society	of	Agricultural	and	Biological	Engineers	and	U.S.	Department	of	Agriculture	

Natural	Resources	Conservation	Service	have	engineering	design	standards	for	construction	and	

operation	of	anaerobic	lagoons.	In	most	feedlots	a	holding	pond	is	constructed	to	collect	runoff	for	

short‐term	storage.	Storage	tanks	range	from	lower‐cost	earthen	basins	to	higher‐cost,	glass‐lined	

steel	tanks.	The	manure	that	enters	these	systems	is	usually	diluted	with	flush	water,	water	wasted	

at	stalls,	and	rainwater.		

All	of	these	storage	systems	(without	aeration)	are	biologically‐anaerobic	lagoons,	which	mean	that	

they	have	similar	potential,	as	with	enteric	fermentation,	to	produce	CH4	and	N2O.	Due	to	the	large	

quantity	of	liquid	manure	produced	in	the	United	States,	liquid	manure	storage	can	be	a	major	

source	of	GHG	emissions	from	animal	operations.	In	terms	of	estimation	of	GHG	emission	from	

anaerobic	lagoon/runoff	holding	pond/storage	tanks,	these	storage	systems	are	classified	into	four	

categories:	1)	covered	storage	with	a	crust	formed	on	the	surface;	2)	covered	storage	without	a	

crust	formed	on	the	surface;	3)	uncovered	storage	with	a	crust	formed	on	the	surface;	and	4)	

uncovered	storage	without	a	crust	formed	on	the	surface.		

The	algorithms	for	calculating	CH4	emissions	described	by	Sommer	et	al.	(2004)	are	recommended	

for	estimating	emissions	at	the	entity‐level.	The	model	considers	volatile	solids	to	be	the	main	

factor	influencing	emissions	from	manure	and	relates	emissions	to	the	content	of	degradable	

volatile	solids.	Nitrous	oxide	is	estimated	as	a	function	of	the	exposed	surface	area	of	the	manure	

storage	and	whether	a	crust	is	present	on	the	surface.		

Rationale for Selected Methods

The	Sommer	algorithms	link	carbon	turnover,	volatile	solids,	temperature,	and	storage	time	to	CH4

emissions	estimates	and	is	the	best	available	method	for	estimating	CH4	emissions	at	the	entity	

level.	The	method	provided	for	N2O	is	the	only	available	method	for	estimating	emissions.	These	

methodologies	best	describe	the	quantitative	relationship	among	activity	data	at	the	entity	level.	

5.4.4.2 Activity Data

In	order	to	estimate	the	daily	CH4	emissions,	the	following	information	is	needed:	

 Animal	type		

 Total	dry	manure		

 Volatile	solids	in	the	storage	

 Temperatures	(local	ambient	temperature	and	manure	temperature)

In	order	to	estimate	the	N2O	emission,	the	following	information	is	needed:	

 Total	dry	manure	

 Total	nitrogen	content	of	the	manure	

 The	exposed	surface	area	of	the	manure	storage	

The	total	nitrogen	content	of	the	manure	entering	storage	systems	can	be	estimated	according	to	

the	nitrogen	balance	method	as	described	in	Equation	5‐9.	The	fraction	of	nitrogen	excreted	by	an	

animal	that	is	not	emitted	as	a	gas	is	the	portion	that	enters	storage.		

5.4.4.3 Ancillary Data

The	ancillary	data	used	to	estimate	CH4	emissions	for	anaerobic	lagoons,	runoff	holding	ponds,	and	

storage	tanks	are	the	maximum	CH4	producing	capacities	(B0),	potential	CH4	yield	(ECH4,	pot),	rate	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-88 

correcting	factors	(b1	and	b2),	Arrhenius	constant	(A),	activation	energy	(E),	gas	constant	(r),	and	

collection	efficiency	(η)	for	liquid	manure	storage	from	different	animals.	These	data	are	available	

from	the	IPCC	(2006)	and	Sommer	et	al.	(2004)	and	are	listed	in	Table	5‐27.	

The	ancillary	data	used	to	estimate	N2O	emissions	for	anaerobic	lagoons,	runoff	holding	ponds,	and	

storage	tanks	is	the	N2O	emission	factor	from	Table	5‐29	(U.S.	EPA,	2011).	

5.4.4.4 Method

Methane Emissions from Anaerobic Lagoons, Runoff Holding Ponds, Storage Tanks

The	Sommer	model	(Sommer	et	al.,	2004)	is	used	as	the	estimation	method	for	CH4	emission	(Rotz	

et	al.,	2011b).	Daily	CH4	emissions	are	estimated	as	a	function	of	manure	temperature	and	the	

volatile	solids	in	manure	placed	into	liquid	storages.	The	parameters	for	the	estimation	are	listed	in	

Table	5‐28.	

a	Dry	manure	refers	to	material	remaining	after	removal	of	water.	It	is	determined	through	the	evaporation	of	water	from	

the	manure	sample	at	103‐105°C.	Forced	air	oven	is	the	most	common	equipment	to	measure	the	dry	matter.	

The	degradable	fraction	of	the	volatile	solids	is	dependent	on	the	potential	CH4	yield	and	the	

maximum	CH4	producing	capacities	and	can	be	calculated	using	Equation	5‐32.	The	fraction	of	

nondegradable	volatile	solids	(material	that	is	not	broken	down	by	microorganisms)	is	calculated	

from	the	total	volatile	solids	content	and	degradable	fraction	of	the	volatile	solids,	as	described	by	

Equation	5‐33.	The	B0	values	are	obtained	from	the	IPCC	(2006)	and	are	listed	in	Table	5‐19.		

Equation 5‐31: Using the Sommer Model to Calculate Daily CH4 Emissions		

۳۱۶૝ ൌ ܕ ൈ ૙. ૙૛૝ ൈ ሺ܌܁܄ ൈ ܊ ൅ ૚܌ܖ܁܄ ൈ ܊૛ሻ ൈ ܖܔ܍ሺۯሻି	 ۳

܂܀ ൈ ሺ૚ െ િሻ	

Where:	

ECH4	 =	Methane	emission	per	day	(kg	CH4	day‐1)	

m		 =	Total	dry	manure	per	day	(kg	dry	manure	day‐1)a	

0.024		 =	Dimensionless	factor	to	modify	the	Sommer	model	based	on	VS	

VSd	and	VSnd	 =	Degradable	and	nondegradable	VS	in	the	manure,	respectively	

	 	 (kg	(kg	dry	manure)‐1)	

b1	and	b2	 =	Rate	correcting	factors	(dimensionless)	

A		 =	Arrhenius	parameter	(g	CH4	(kg	VS)‐1	hr‐1)	

E		 =	Activation	energy	(J	mol‐1)	

R		 =	Gas	constant	(J	K‐1	mol‐1)	

T		 =	Storage	temperature	(K)	

η		 =	Collection	efficiency	of	different	liquid	storage	categories	
 Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

 5-89 

The	collection	efficiency	(η)	depends	on	different	liquid	storage	categories	of:	1)	covered	

storage	with	a	crust	formed	on	the	surface;	2)	covered	storage	without	a	crust	formed	on	the	

surface;	3)	uncovered	storage	with	a	crust	formed	on	the	surface;	and	4)	uncovered	storage	without	

a	crust	formed	on	the	surface.	A	crust	allows	air	and	CH4	to	be	retained	on	the	surface	of	the	

manure	storage	and	increases	the	potential	for	oxidation	of	CH4	(Hansen	et	al.,	2009;	Nielsen	et	al.,	

2010).	When	a	crust	does	not	form,	CH4	is	directly	emitted	without	rapid	oxidation.	For	cattle	slurry	

and	pig	slurry,	degradable	and	nondegradable	volatile	solids	(as	a	fraction	of	VST)	are	given	in	Table	

5‐28.	

Table 5‐27: Parameters for Estimating CH4 Emission from Liquid Manure Storage		

Parameters Cattle Swine

Arrhenius	constant	(ln(A))	–	g	CH4	(kg	VS)‐1	hr‐1 43.33	 43.21	

Activation	energy	(E)	–	J	mol‐1 1.127×105 1.127×105

Gas	constant	(R)	–	J	K‐1	mol‐1 8.314	 8.314	

Rate	correction	factor	for	VSd	(b1)	 1	 1	

Rate	correction	factor	for	VSnd	(b2)	 0.01	 0.01	

Potential	methane	yield	of	the	manure	(ECH4,	pot)	(kg	CH4/	kg	VS)	 0.48	 0.50	

Collection	

efficiency	(η)	

Covered	storage	with	a	crust	form	on	the	surfacea	 1	 1	

Covered	storage	without	a	crust	form	on	the	surfacea 1	 1	

Uncovered	storage	with	a	crust	form	on	the	surfaceb	 0	 0	

Uncovered	storage	without	a	crust	form	on	the	surfacec ‐0.4	 ‐0.4	

Source:	Sommer	et	al.	(2004)	and	IPCC	(2006).

a	CH4	gas	from	covered	storage	with	a	crust	form	on	the	surface	is	collected	and	flared.		 b	Uncovered	storage	with	a	crust	form	on	the	surface	is	used	for	the	derivation	of	Equation	5‐22.		 c	The	emission	for	uncovered	storage	without	a	crust	is	40	percent	greater	than	uncovered	storage	with	a	crust,	so	the	

collection	efficiency	for	this	case	is	‐40	percent.		

Equation 5‐32: Calculating the Degradable Fraction of the Volatile Solids

ൈ ܂܁܄ 	 ൌ ܌܁܄

۰૙

ܜܗܘ,૝۳۱۶

Where:	

VSd		 =	Degradable	VS	fractions	in	the	manure	on	a	given	day	(kg	(kg	dry	manure)‐1)	

VST	 =	Volatile	solids	content	in	the	storage	on	a	given	day	(kg	(kg	dry	manure)‐1)	

B0	 =	Maximum	CH4	producing	capacities	(kg	CH4	(kg	VS)‐1)	

ECH4,	pot	 =	Potential	CH4	yield	of	the	manure	(kg	CH4	(kg	VS)‐1)	

Equation 5‐33: Calculating the Non‐Degradable Fraction of the Volatile Solids

܌܁܄ െ ܂܁܄ ൌ ܌ܖ܁܄

Where:	

VSd	and	VSnd	 =	Degradable	and	nondegradable	VS	fractions	in	the	manure	on	a	given	day	

	 	 	(kg	(kg	dry	manure)‐1),	respectively	

VST	 =	Volatile	solids	content	in	the	storage	on	a	given	day	

	 	 	(kg	(kg	dry	manure)‐1)	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-90 

Table 5‐28: Degradable and Nondegradable Volatile Solids for Cattle and Swine Manure

Type of Manure VSd/VST VSnd/VST

Cattle	liquid	manure	 0.46 0.54

Swine	liquid	manure	 0.89 0.11

Source:	Møller	et	al.	(2004).	

Nitrous Oxide Emissions from Anaerobic Lagoon, Runoff Holding Pond, Storage Tanks

Nitrous	oxide	emissions	from	liquid	manure	storage	typically	represent	a	relatively	small	portion	of	

the	N2O	emissions	from	farms.	Most	studies	indicate	the	criticality	of	the	crust	for	the	formation	and	

emission	of	N2O	(Petersen	and	Sommer,	2011).	Therefore,	N2O	emissions	from	liquid	manure	

storage	are	estimated	as	a	function	of	the	exposed	surface	area	of	the	manure	storage	and	the	

presence	of	a	crust	on	the	surface.		

The	emission	factor	of	N2O	is	dependent	on	crust	formation	on	the	liquid	storage.	The	crust	allows	

air	to	be	retained	on	the	surface	of	the	manure	storage	and	increases	the	potential	for	nitrification	

and	denitrification	(Hansen	et	al.,	2009;	Nielsen	et	al.,	2010).	When	a	crust	does	not	form,	oxygen	is	

not	retained	on	the	liquid	surface	with	nitrogenous	compounds,	and	therefore	no	N2O	is	formed	and	

emitted.	The	emission	factors	of	N2O	for	different	liquid	storage	methods	are	listed	in	Table	5‐29.	

Table 5‐29: Emission Factor of N2O for Liquid Storage with Different Crust Formation

Type of Liquid Storage EFN2O,man (g N2O/m2/day)

Uncovered	liquid	manure	with	crust	 0.8	

Uncovered	liquid	manure	without	crust	 0	

Covered	liquid	manure	 0	

Source:	Rotz	et	al.	(2011a).	

Equation 5‐34: Calculating N2O Emissions from Liquid Manure Storage

ൈ ۽૛ۼ۳۴ ൌ ۽૛ۼ۳

܍܋܉܎ܚܝܛۯ

૚૙૙૙

Where:	

EN2O	 =	Nitrous	oxide	emissions	per	day	(kg	N2O	day‐1)		

EFN2O		 =	Emission	rate	of	N2O	(g	N2O	m‐2	day‐1)	

Asurface	 =	Exposed	surface	area	of	the	manure	storage	(m2)	

1,000			 =	Conversion	factor	for	grams	to	kilograms	ቀ ଵ	୩୥

ଵ଴଴଴ ୥ቁ
 Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

 5-91 

5.4.5 Anaerobic Digester with Biogas Utilization

5.4.5.1 Overview of Anaerobic Digester with Biogas Utilization		

One	of	the	most	commonly	discussed	waste	management	alternatives	for	GHG	reduction	and	

energy	generation	is	anaerobic	digestion.	Anaerobic	digestion	is	a	natural,	biological	conversion	

process	that	has	been	proven	effective	at	converting	wet	organic	wastes	into	biogas	(approximately	

60	percent	CH4	and	40	percent	CO2).	Biogas	can	be	used	as	a	fuel	source	for	engine‐generator	sets,	

producing	relatively	clean	electricity	while	also	reducing	some	of	the	environmental	concerns	

associated	with	manure.	The	digester	can	be	as	simple	as	a	covered	anaerobic	lagoon	(Gould‐Wells	

and	Williams,	2004)	or	as	sophisticated	as	thermophilic	or	media	matrix	(attached	growth)	

digesters	(Cantrell	et	al.,	2008a).	There	are	a	wide	variety	of	anaerobic	digestion	configurations,	

such	as	continuous	stirred	tank	reactor	(CSTR),	covered	lagoon,	plug‐flow,	temperature	phased,	

upflow	anaerobic	sludge	blanket	(UASB),	packed‐bed,	and	fixed	film.	The	digestion	is	also	

categorized	based	on	culture	temperature:	thermophilic	digestion	in	which	manure	is	fermented	at	

a	temperature	of	around	55°C,	or	mesophilic	digestion	at	a	temperature	of	around	35°C.	Among	

these	technologies,	CSTR,	plug‐flow,	and	covered	lagoon,	all	under	mesophilic	conditions,	are	the	

most	often‐used	methods.		

During	anaerobic	digestion,	a	group	of	microbes	work	together	to	convert	organic	matter	into	CH4,	

CO2,	and	other	simple	molecules.	The	main	advantages	of	applying	anaerobic	digestion	to	animal	

manures	are	odor	reduction,	electricity	generation,	and	the	reduction	of	GHG	emissions	and	

manure‐borne	pathogens.	Anaerobic	digestion	is	also	an	excellent	pre‐treatment	process	for	

subsequent	manure	treatment	to	remove	organic	matter	and	concentrate	phosphorus.	Considering	

the	small	amount	of	N2O	existing	in	biogas,	N2O	emissions	are	not	estimated	for	the	anaerobic	

digestion	of	liquid	manure.		

The	challenges	associated	with	anaerobic	digestion	relate	to	initial	capital	cost,	operation,	and	

maintenance	and	other	gases	that	may	be	generated	(e.g.,	nitric	oxides).	The	economics	relate	to	

access	to	the	electrical	grid	and	sufficient	green‐electricity	offsets	to	make	the	operation	profitable.	

Profitable	conditions	are	relatively	scarce.	Finally,	the	digester	sludge	must	be	managed.	Another	

conversion	alternative	with	energy	creation	potential	is	thermochemical	conversion	(Cantrell	et	al.,	

2008a).	Systems	that	use	thermochemical	conversions	to	syngases,	bio‐oil,	and	biochar	for	

electricity	and	fuel	are	emerging,	but	are	not	yet	established.		

Since	an	anaerobic	digestion	system	converts	organic	carbon	in	manure	into	CH4	and	subsequently	

combusts	CH4	into	CO2,	the	GHG	emissions	from	manure	anaerobic	digestion	operation	are	mainly	

Method for Estimating Emissions from Manure Storage and Treatment – Anaerobic

Digester with Biogas Utilization

Methane	

 IPCC	Tier	2	using	Clean	Development	Mechanism	EFs	for	digester	types	to	estimate	CH4

leakage	from	digesters.		

 Anaerobic	digester	systems	convert	organic	matter	in	manure	into	CH4	and	

subsequently	combust	CH4	into	CO2.	

 Gas	leakage	from	digesters	is	the	main	source	of	GHG	emission.	

 Leakage	of	CH4	from	the	anaerobic	digester	system	is	estimated.	

Nitrous	Oxide	

 N2O	leakage	from	digesters	is	fairly	small	and	negligible.		
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-92 

from	the	leakage	of	digesters.	The	leakage	of	CH4	can	be	estimated	based	on	the	IPCC	Tier	2	

approach	in	combination	with	technology‐specific	emission	factors.

5.4.5.2 Rationale for Selected Method

The	IPCC	equation	is	the	only	available	method	for	estimating	CH4	emission	from	digesters.	This	

methodology	best	describes	the	quantitative	relationship	among	activity	data	at	the	entity	level	and	

takes	into	account	the	specific	technology	employed.	

5.4.5.3 Activity Data

In	order	to	estimate	the	CH4	leakage	from	anaerobic	digestion,	the	following	information	is	needed:	

 Animal	type	

 Total	dry	manure	into	the	digester	

 Volatile	solids	in	the	manure	

 Digester	temperatures	

5.4.5.4 Ancillary Data

Ancillary	data	for	anaerobic	digestion	effluent	are	needed	for	further	estimation	of	CH4	and	N2O	

emissions	from	post‐treatment	approaches	such	as	aerobic	or	anaerobic	lagoons,	nutrient	removal	

operations,	etc.	Thus,	the	necessary	data	for	the	effluent	include	effluent	flow	rate,	total	solids,	

volatile	solids,	chemical	oxygen	demand,	effluent	temperature,	environmental	temperature,	

liquid/solid	separation	methods,	and	total	nitrogen.		

5.4.5.5 Method

Equation	5‐35	describes	the	IPCC	Tier	2	approach	for	estimating	CH4	emissions	for	anaerobic	

digesters.	The	CH4	generated	from	digesters	is	assumed	to	be	flared	or	used	as	a	biogas;	the	only	

emissions	from	digesters	are	from	system	leakage.	

The	B0	values	are	obtained	from	the	IPCC	(2006)	and	are	listed	in	Table	5‐19.	The	emission	factors	

for	the	amount	of	CH4	leakage	by	technology	are	listed	in	Table	5‐30.		

Equation 5‐35: IPCC Tier 2 Approach for Estimating CH4 Emissions

۳۱۶૝ ൌ ܕ ൈ ܁܄ ൈ ۰૙ ൈ ૙. ૟ૠ ൈ

܍܏܉ܓ܉܍ܔ,૝۳۴۱۶

૚૙૙

Where:	

ECH4	 =	CH4	emissions	per	day	(kg	CH4	day‐1)	

m		 =	Total	dry	manure	per	day	(kg	day‐1)	

VS		 =	Volatile	solids	(kg	VS	(kg	dry	manure)‐1)	

B0	 =	Maximum	CH4	producing	capacity	for	manure	from	different	animal	

	 	 (m3	CH4	(kg	VS)‐1)

0.67		 =	Conversion	factor	from	weight	to	volume	of	methane	(kg	CH4	m‐3)	

EFCH4,	leakage	=	Emission	factor	for	the	fraction	of	CH4	produced	that	leaks	from	the	anaerobic	

digester	(%)	
 Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

 5-93 

Table 5‐30: Emission Factors for the Fraction of Methane Leaking from Digesters

Digester Configurations EFCH4, leakage (%)

Digesters	with	steel	or	lined	concrete	or	fiberglass	digesters	with	a	gas	holding	

system	(egg	shaped	digesters)	and	monolithic	construction	 2.8	

UASB	type	digesters	with	floating	gas	holders	and	no	external	water	seal 5	

Digesters	with	unlined	concrete/ferrocement/brick	masonry	arched	type	gas	

holding	section;	monolithic	fixed	dome	digesters	 10	

Other	digester	configurations	 10	

Source:	CDM	(2012).	

5.4.6 Combined Aerobic Treatment Systems

Dealing	with	the	total	treatment	of	wastewater	from	either	swine	or	dairy	is	complex,	because	the	

liquid	and	solid	phases	must	be	treated.	In	municipal	sewage	treatment	systems,	the	wastewater	is	

very	dilute	so	the	treatment	of	the	biochemical	oxygen	demand	by	aeration	is	a	fundamental	

process.	In	contrast,	the	solids	content	of	livestock	wastewater	is	quite	high,	as	is	the	biochemical	

oxygen	demand.	Consequently,	the	cost	of	stabilizing	the	biochemical	oxygen	demand	with	aeration	

has	proven	to	be	uneconomical.	A	successful	solution	to	this	problem	was	developed	by	Vanotti	et	

al.	(2007),	who	used	polyacrylamide	flocculation	to	remove	more	than	90	percent	of	the	solids	

(Vanotti	and	Hunt,	1999;	Vanotti	et	al.,	2002).	The	solid	fraction	was	then	composted	(Vanotti,	

2006).	The	remaining	liquid	was	transferred	to	a	separated	water	tank	where	it	was	subsequently	

aerated	(Vanotti	and	Hunt,	2000;	Vanotti	et	al.,	2007;	Vanotti	and	Szogi,	2008).	During	these	two	

phases	of	treatment,	more	than	90	percent	of	the	GHG	emissions	from	standard	anaerobic	lagoon	

treatment	were	avoided	(Vanotti	et	al.,	2008).	The	avoidance	was	achieved	by	aerobic	treatment	of	

the	solids	via	composting	and	nitrification/denitrification	in	the	liquid	effluent.		

After	nitrification/denitrification,	the	treated	effluent	moves	to	the	settling	tank	and	subsequently	

into	the	phosphorus	treatment	chamber.	Here	the	wastewater,	which	has	low	alkalinity,	is	amended	

with	liquid	lime,	and	the	pH	is	raised	to	approximately	10.	In	the	presence	of	high	pH	and	calcium,	

the	phosphorus	is	precipitated	and	the	pathogens	are	killed	(Vanotti	et	al.,	2003;	Vanotti	et	al.,	

2005;	Vanotti	et	al.,	2009).	The	treated	wastewater	is	then	recycled	into	the	houses.	This	process	

provides	a	healthier	environment	for	the	pigs	(Vanotti	et	al.,	2009).	The	system	must	be	operated	to	

ensure	proper	and	timely	flushing	of	the	house.	The	polyacrylamide	addition	and	the	solids	

separation	units	must	be	operated	properly.	Aeration	of	the	nitrification	tank	must	be	maintained,	

as	must	the	addition	of	liquid	lime.	The	pumps	that	maintain	the	internal	recycling	must	also	be	

maintained	and	operated	correctly.	This	system	is	the	only	treatment	system	to	meet	and	be	

certified	for	expansion	of	swine	production	in	North	Carolina.	

Method for Estimating Emissions from Combined Aerobic Treatment Systems

 Method	is	to	utilize	10	percent	of	the	emissions	resulting	from	estimation	of	emissions	

from	Liquid	Manure	Storage	and	Treatment	–	Anaerobic	Lagoons,	Runoff	Holding	

Ponds,	and	Storage	Tanks.	

 Method	based	on	research	findings	that	systems	avoid	90	percent	of	the	GHG	emissions	

from	standard	anaerobic	lagoon	treatment.	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-94 

To	estimate	emissions	for	combined	aerobic	treatment	systems,	the	methodology	for	anaerobic	

lagoons,	runoff	holding	ponds,	and	storage	tanks	is	applied	to	the	system.	Gas	emissions	of	CH4	and	

N2O	are	estimated	using	10	percent	of	the	values	for	emissions	from	anaerobic	lagoon	treatment.	

5.4.7 Sand‐Manure Separation

Sand	is	one	of	the	standard	materials	for	dairy	cow	bedding.	It	provides	superior	cow	comfort,	

environment	for	udder	health	(and	consequently	better	milk	quality),	and	traction	when	compared	

with	organic	bedding	materials.	Sand	separation	systems	can	be	classified	as	mechanical	separation	

and	sedimentation	separation.	Sedimentation	separation	uses	dilution	water	and	gravity	to	allow	

sand	to	passively	settle	in	sand	traps.	Due	to	the	high	organic	material	content	contained	in	the	

settled	sand,	the	sand	recovered	from	the	sand	trap	needs	to	be	drained	multiple	times	and	dried	

prior	to	reuse.	Mechanical	sand‐manure	separation	systems	use	recycled	liquid	manure	and	

aeration	to	suspend	manure	solids,	settle	sand	at	the	bottom	of	the	separator,	and	recover	the	sand	

using	a	heavy	duty	auger.	Sand	is	generally	discharged	with	less	than	two	percent	organic	matter.	

The	mechanically	separated	sand	can	be	reused	for	bedding.		

Since	sand‐manure	separation	is	relatively	quick	(compared	with	other	storage	and	treatment	

methods),	GHG	emissions	from	the	operation	are	minimal.	The	process	of	separating	sand	and	

manure	is	not	assumed	to	contribute	to	GHG	emissions.	After	sand‐manure	separation,	the	

separated	liquid	manure	is	treated	as	the	influent	for	the	next	step	of	storage	and	treatment	

operations.	The	various	storage	and	treatment	operation	options	are	shown	in	Figure	5‐7.	The	

parameters	of	volatile	solids,	total	nitrogen,	organic	nitrogen,	and	manure	temperature	of	the	

separated	liquid	manure	should	be	measured,	and	used	as	the	inputs	to	estimate	emissions	of	

GHGs.

5.4.8 Nutrient Removal

Nitrogen	and	phosphorus	are	the	primary	elements	that	cause	eutrophication	in	surface	waters.	

With	increased	Federal,	State	and	local	attention	on	non‐point	waste	sources,	more	and	more	

animal	operations	will	likely	use	nutrient	removal	approaches	to	treat	liquid	manure	before	land	

application	and	other	uses.	Compared	to	phosphorus,	nitrogen	in	manure	contributes	to	N2O	

emission;	removing	it	can	significantly	alleviate	emissions.	Nitrogen	in	manure	comprises	NH3,	

particulate	organic	nitrogen,	and	soluble	organic	nitrogen.	Five	main	nitrogen	removal	

Method for Estimating Emissions from Liquid Manure Storage and Treatment –

Sand/Manure Separation

 No	method	is	provided	as	GHG	emissions	are	negligible	from	the	sand/manure	

separation	process.	However,	resulting	volatile	solids,	total	nitrogen,	organic	nitrogen,	

and	manure	temperature	of	the	separated	liquid	manure	should	be	measured	and	

used	as	the	inputs	to	estimate	emissions	of	GHGs	for	subsequent	storage	and	

treatment	operations.	

Method for Estimating Emissions from Liquid Manure Storage and Treatment –

Nutrient Removal

 Not	estimated	due	to	limited	quantitative	information	on	GHGs	from	nitrogen	removal	

processes.	
 Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

 5-95 

approaches—Biological	Nitrogen	Removal	(BNR),	Anamox,	NH3	stripping,	ion	exchange,	and	

struvite	crystallization—have	been	applied	for	municipal	and	industrial	wastewater,	as	well	as	for	

animal	waste	streams.	Because	N2O	originates	from	nitrogen	sources,	quantification	of	nitrogen	

removal	is	important	to	estimate	emissions	from	animal	manure.	

Because	most	nitrogen	removal	methods	for	liquid	manure	are	currently	in	the	research	and	

development	stage,	very	little	quantitative	information	is	available	on	the	nitrogen	removal	

methods	mentioned	above	for	animal	manure	under	different	operation	conditions.	The	suggested	

estimation	method	is	to	consider	the	liquid	manure	after	nutrient	removal	as	the	influent	for	

storage	and	treatment	approaches	that	entities	will	use	to	further	treat	liquid	manure.	

Measurements	of	volatile	solids,	total	nitrogen,	organic	nitrogen,	and	manure	temperature	of	the	

treated	liquid	manure	are	needed	to	estimate	CH4	and	N2O	emissions.	

5.4.9 Solid–Liquid Separation

Solid–liquid	manure	separation	has	been	used	widely	by	dairy	farms.	One	purpose	of	solid–liquid	

separation	is	to	physically	separate	and	remove	the	larger	solids	from	liquid	manure	in	order	to	

store	and	treat	them	separately.	The	available	commercial	methods	include	gravity	sedimentation	

and	mechanical	separation	(with	or	without	coagulation	flocculation).	Sedimentation	and	

mechanical	separation	without	coagulation	flocculation	are	the	most	popular	methods	used	by	

animal	farms.	Similar	to	sand–liquid	manure	separation,	GHG	emissions	from	the	operation	are	

minimal;	however,	separation	has	an	impact	on	nutrient	distribution	in	separated	solid	and	liquid	

manure,	which	will	influence	GHG	emissions	from	the	next	stage	of	manure	storage	and	treatment	

for	solid	and	liquid	manure.	The	separated	liquid	manure	is	treated	as	the	influent	for	the	next	step	

of	storage	and	treatment	operations.	The	possible	storage	and	treatment	options	are	delineated	in	

Figure	5‐7.	

The	parameters	of	total	solids	(dry	manure),	total	nitrogen,	organic	nitrogen,	and	manure	

temperature	of	the	separated	liquid	and	solid	manure	should	be	measured,	and	used	as	the	inputs	

to	estimate	GHGs	emission	in	the	subsequent	storage	and	treatment	operations.	The	distribution	of	

total	solids	after	solid–liquid	separation	for	typical	mechanical	separators	are	listed	in	Table	5‐317	

(Ford	and	Fleming,	2002).		

Table 5‐31: Efficiency of Different Mechanical Solid‐Liquid Separation

Separation

Technique

Manure

Type

Screen

Size (mm)

Influent

(% DM)

Total Solid

Removal

Efficiency (%)

Source

Screen	

Stationary	

inclined	

screen	

Swine	 1.0	 0.0‐0.7 35.2 Shutt	et	al.	(1975)	

Beef 0.5	 0.97‐4.41 1‐13 Hegg	et	al.	(1981)	

Dairy	 1.5	 3.83 60.9 Chastain	et	al.	(2001)	

Vibrating	

screen	

Swine	 0.39	 0.2‐0.7 22.2 Shutt	et	al.	(1975)	

Beef	 0.52‐1.91	 5.5‐7.4 4‐44 Gilbertson	and	Nienaber	(1978)

Method for Estimating Emissions from Liquid Manure Storage and Treatment –

Solid– Liquid Separation

 No	method	is	provided	as	GHG	emissions	are	negligible.	However,	resulting	volatile	

solids,	total	nitrogen,	organic	nitrogen,	and	manure	temperature	of	the	separated	

liquid	and	solid	manure	should	be	measured	and	used	as	the	inputs	to	estimate	

emissions	of	GHGs	and	NH3	for	subsequent	storage	and	treatment	operations.	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-96 

Separation

Technique

Manure

Type

Screen

Size (mm)

Influent

(% DM)

Total Solid

Removal

Efficiency (%)

Source

Beef 0.64‐1.57	 1.55‐3.19 6‐16 Hegg	et	al.	(1981)	

Dairy		 0.64‐1.57	 0.95‐1.9 8‐16 Hegg	et	al.	(1981)

Swine	 0.64‐1.57	 1.55‐2.88 3‐27 Hegg	et	al.	(1981)

Swine	 0.10‐2.45	 1.5‐5.4 11‐67 Holmberg	et	al.	(1983)	

Rotating	

screen	

Beef 0.75	 1.56‐3.68 4‐6 Hegg	et	al.	(1981)

Dairy	 0.75	 0.52‐2.95 0‐14 Hegg	et	al.	(1981)

Swine	 0.75	 2.54‐4.12 4‐8 Hegg	et	al.	(1981)

In‐channel	

flighted	

conveyor	

screen	

Dairy	 3	 7.1	 4.22	 Møller	et	al.	(2000)	

Swine	 3	 5.66 25.8 Møller	et	al.	(2000)	

Centrifugal	

Centrifuge	 Beef 7.5 25 Glerum	et	al.	(1971)	

Centrisieve	 Swine	 5‐8 30‐40 Glerum	et	al.	(1971)	

Decanter	

centrifuge	

Beef 6.9 64 Chiumenti	et	al.	(1987)

Beef 6.0 45 Chiumenti	et	al.	(1987)

Swine	 7.58 66 Glerum	et	al.	(1971)	

Swine	 1.9‐8.0 47.4‐56.2 Sneath	et	al.	(1988)	

Liquid	

cyclone	 Swine	 26.5	 Shutt	et	al.	(1975)	

Filtration/pressing	

Roller	press	

Swine	 5.2 17.3 Pos	et	al.	(1984)	

Dairy 4.8 25 Pos	et	al.	(1984)

Beef 4.5 13.3 Pos	et	al.	(1984)

Belt	press	 Dairy	 1‐2	 7.1 32.4 Møller	et	al.	(2000)

Swine	 1‐2	 5.7 22.3 Møller	et	al.	(2000)

Screw	press	

Swine	 5 16 Chastain	et	al.	(1998)	

Swine	 1‐5 15‐30 Converse	et	al.	(1999)	

Dairy 1‐10 15.8‐47 Converse	et	al.	(1999)	

Dairy 2.6 23.8 Converse	et	al.	(1999)	

Dairy 4.9 33.4 Converse	et	al.	(1999)	

Fournier	

rotary	pressa Swine	 85	 Ford	and	Fleming	(2002)

Fournier	(2010)	

Rotary	

vacuum	filter	 Swine	 7.5	 51	 Glerum	et	al.	(1971)	

Pressure	filter	 Beef 7 76 Chiumenti	et	al.	(1987)

Continuous	Belt	Microscreening	Unit	

Swine	 2‐8 40‐60 Fernandes	et	al.	(1988) a	With	polymer	addition.
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-97 

5.4.10 Constructed Wetland		

Globally,	constructed	wetlands	are	used	for	the	treatment	of	wastewaters,	capture	of	sediments,	

and	drainage	water	abatement	(Hammer,	1989;	Kadlec	and	Knight,	1996;	Tanner	et	al.,	1997;	Hunt	

et	al.,	2002;	Hunt	et	al.,	2003;	Picek	et	al.,	2007;	Harrington	and	McInnes,	2009;	Mustafa	et	al.,	2009;	

Soosaar	et	al.,	2009;	Elgood	et	al.,	2010;	Harrington	and	Scholz,	2010;	VanderZaag	et	al.,	2010;	Chen	

et	al.,	2011;	Locke	et	al.,	2011;	Tanner	and	Headley,	2011;	Tanner	and	Sukias,	2011;	Vymazal,	

2011).	Constructed	wetlands	are	generally	classified	as	sub‐surface	or	surface	flow	wetlands	

(Kadlec	and	Knight,	1996).	The	sub‐surface	wetlands	typically	consist	of	wetland	plants	growing	in	

a	bed	of	highly	porous	media,	such	as	gravel	or	wood	chips.	They	are	commonly	used	to	improve	

drainage	water	quality.	These	wetlands	are	generally	rectangular	in	shape	and	one	to	two	meters	in	

depth.	There	is	lack	of	agreement	about	the	relative	impact	of	microbial	and	plant	processes	in	the	

function	of	subsurface	wetlands,	including	GHG	production	and	emissions.	However,	it	is	accurate	

to	say	that	plants	and	microbes	are	typically	interdependently	involved	(Picek	et	al.,	2007;	Zhu	et	

al.,	2007;	Wang	et	al.,	2008;	Faubert	et	al.,	2010;	Lu	et	al.,	2010;	Tanner	and	Headley,	2011).	The	

microbial	community	advances	biogeochemical	processes	(Tanner	et	al.,	1997;	Hunt	et	al.,	2003;	

Zhu	et	al.,	2007;	Dodla	et	al.,	2008;	Faulwetter	et	al.,	2009),	while	the	plant	community	advances	

transported	oxygen	into	the	depth	of	the	wetlands,	provides	root	surfaces	for	rhizosphere	

reactions,	and	vents	gases	to	the	atmosphere.	The	plant	processes	are	significantly	affected	by	plant	

community	composition	and	weather	conditions	(Towler	et	al.,	2004;	Stein	and	Hook,	2005;	Stein	et	

al.,	2006;	Zhu	et	al.,	2007;	Wang	et	al.,	2008;	Taylor	et	al.,	2010).		

Surface	flow	wetlands	have	a	much	more	direct	interchange	with	the	atmosphere	for	the	supply	of	

oxygen	and	nitrogen,	as	well	as	the	emissions	of	GHGs.	They	can	be	variable	in	shape	and	are	

generally	less	than	0.5	meters	deep.	Surface	wetlands	minimize	clogging	problems,	but	they	can	

have	significant	loss	of	treatment	as	a	result	of	channel	flow.	There	are	reasonably	functional	

models	for	wetland	design	optimized	for	either	carbon	or	nitrogen	removal	(Stone	et	al.,	2002;	

Stone	et	al.,	2004;	Stein	et	al.,	2006;	Stein	et	al.,	2007a).	The	management	of	GHGs	(principally	CH4

and	N2O)	from	treatment	wetlands	is	somewhat	similar	to	managing	GHGs	in	rice	(Freeman	et	al.,	

1997;	Tanner	et	al.,	1997;	Fey	et	al.,	1999;	Johansson	et	al.,	2003;	Mander	et	al.,	2005a;	Mander	et	

al.,	2005b;	Teiter	and	Mander,	2005;	Picek	et	al.,	2007;	Maltais‐Landry	et	al.,	2009;	Wu	et	al.,	2009).	

Of	particular	importance	is	the	maintenance	of	wetland	oxidative/reductive	potential	conditions	

sufficiently	positive	to	avoid	CH4	production	(Tanner	et	al.,	1997;	Insam	and	Wett,	2008;	Seo	and

DeLaune,	2010).	This	requires	higher	levels	of	oxygen	and	lower	levels	of	available	carbon.	It	has	

been	reported	that	the	fluxes	of	N2O	and	CH4	from	treatment	wetlands	are	generally	below	10	mg	

N2O‐N	m‐2	d‐1	and	300	mg	CH4‐C	m‐2	d‐1	(Mander	et	al.,	2005a;	Søvik	et	al.,	2006).	The	management	

of	N2O	emissions	is	complicated	by	the	fact	that	nitrates	are	often	present	in	the	wastewaters	or	

drainage	waters.	This	nitrate	will	be	denitrified	under	the	prevailing	anaerobic	condition	of	the	

treatment	wetlands—it	is	one	of	treatment	wetland’s	critical	functions.	However,	it	is	important	

that	the	preponderance	of	denitrification	proceeds	to	completion,	with	the	ultimate	production	of	

inert	di‐nitrogen	gas.	Complete	denitrification	requires	higher	carbon/nitrogen	ratios	

Method for Estimating Emissions from Liquid Manure Storage and Treatment –

Constructed Wetland

 Currently	no	method	is	provided	to	estimate	gas	emission	from	constructed	wetland

of	animal	manure,	although	GHG	sinks	are	noted	to	likely	be	greater	than	CH4	and	N2O

emissions,	which	are	considered	negligible.
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-98 

(Klemedtsson	et	al.,	2005;	Hwang	et	al.,	2006;	Hunt	et	al.,	2007).	Thus,	there	is	an	important	balance	

between	sufficient	carbon	for	complete	denitrification	and	copious	carbon	that	can	drive	wetlands	

into	the	low	reduction/oxidation	conditions	associated	with	CH4	production.		

Estimation	methods	are	very	complicated	and	case‐based.	In	an	approximate	estimation	manner	

that	considers	wetlands	very	similar	to	cropland,	treatment	wetlands	of	animal	manure	are	GHG	

sinks	more	than	sources.	The	CH4	and	N2O	emission	from	wetland	treatment	of	animal	manure	

could	be	negligible.	The	critical	activity	data	include	hydraulic	load;	inflow	water	composition,	

especially	carbon	and	nitrogen;	pretreatments	such	as	solids	removal	or	nitrification;	amendments;	

and	drying	cycles.	Critical	ancillary	data	include	rainfall,	temperature,	wind	speed,	storm	events,	

changes	in	livestock	stocking	rates,	cropping/tillage	systems,	and	fertilization	timing/rates.		

5.4.11 Thermo‐Chemical Conversion

Combustion,	the	most	primitive	and	exothermic	form	of	thermochemical	treatment	of	livestock	

waste,	has	been	in	use	since	antiquity;	however,	its	use	for	large‐scale	livestock	waste	treatment	

has	generally	been	hampered	by	economic,	health,	and	environmental	quality	issues	(Florin	et	al.,	

2009).	Principal	among	these	issues	has	been	components	that	degrade	air	quality,	including	GHGs	

(mainly	CO2).	Nonetheless,	thermochemical	treatment	of	livestock	manure	has	attributes	that	

continue	to	attract	efforts	to	make	it	economically	and	environmentally	effective	(Raman	et	al.,	

1980;	He	et	al.,	2000;	He	et	al.,	2001;	Ocfemia	et	al.,	2006;	Ro	et	al.,	2007;	Cantrell	et	al.,	2008a;	

Cantrell	et	al.,	2008b;	Powlson	et	al.,	2008;	Cantrell	et	al.,	2009;	Dong	et	al.,	2009;	Jin	et	al.,	2009;	Ro	

et	al.,	2009;	Xiu	et	al.,	2009;	Cantrell	et	al.,	2010a;	Cantrell	et	al.,	2010b;	Stone	et	al.,	2010;	Wang	et	

al.,	2011;	Xiu	et	al.,	2011).		

Recently,	pyrolysis/gasification	has	received	much	interest	for	its	treatment	of	livestock	waste.	

There	have	also	been	advances	in	the	cleaning	of	exhaust	gases	(He	et	al.,	2001;	Ro	et	al.,	2007;	

Cantrell	et	al.,	2008a;	Dong	et	al.,	2009;	Xiu	et	al.,	2009;	Xiu	et	al.,	2011).	Pyrolysis/gasification	

offers	three	principal	end	products:	syngas,	bio‐oil,	and	biochar	(Cantrell	et	al.,	2008a;	Xiu	et	al.,	

2011).	The	quality	and	quantity	of	end	products	will	vary	with	feedstock,	exposure	time,	and	

pyrolysis/gasification	temperature.	The	syngas	can	be	used	for	direct	combustion	or	to	run	an	

electrical	generator	(Ro	et	al.,	2010).	It	can	also	be	used	via	Fischer‐Tropsch	conversion	for	

production	of	liquid	fuel	(Cantrell	et	al.,	2008a).	Pyrolysis/gasification	for	syngas	and	eventual	

liquid	fuel	production	is	a	very	attractive	potential	business	model	for	specific	agricultural	fuels.		

In	terms	of	GHG	emission,	treatment	of	flue	gas	from	combustion	and	utilization	of	syngas	from	

pyrolysis/gasification	are	critical.	The	thermal	processes	with	a	flue	gas	clean‐up	unit	and	syngas	

utilization	unit	should	minimize	the	GHG	emission	from	the	thermal	conversion	processes.		

In	order	to	estimate	the	daily	emissions	of	CH4	and	N2O	the	following	information	is	needed:	type	of	

thermal	conversion	processes;	detailed	information	on	the	process,	such	as	with/without	flue	gas	

clean‐up	unit	or	syngas	utilization	unit;	inflow	composition,	such	as	moisture,	carbon,	and	nitrogen;	

and	mass	flow	through	the	process,	including	mass	in,	flue	gas/syngas,	and	ash/biochar.	The	

measurements	can	be	based	on	dietary	changes	or	seasonal	timeframe,	which	is	decided	by	

individual	farm	entity.	However,	due	to	the	dynamic	nature	of	manure	piles	and	the	rapid	changes	

that	can	occur	in	chemical	and	physical	composition,	frequent	measurements	are	recommended	to	

ensure	accuracy	of	the	estimation.	The	total	energy	balance	of	the	system	should	also	be	known.	For	

Method for Estimating Emissions from Solid Manure Storage and Treatment –

Thermochemical Conversion

 No	method	is	provided	as	CH4	and	N2O	emissions	are	considered	negligible.
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-99 

instance,	the	carbon	credits	of	biochar	cannot	be	claimed	while	ignoring	the	energy	required	to	

create	the	biochar.	The	effectiveness	of	the	exhaust	gas	cleaning	process	in	removing	air	quality	

degrading	components	must	be	certified.		

Due	to	the	nature	of	thermal	conversion,	much	lower	emissions	(CH4	and	N2O,)	are	generated	from	

the	thermal	conversion	compared	with	other	storage	or	treatment	methods.	The	CH4	and	N2O	

emissions	from	complete	thermal	conversion	processes	are	relatively	small	and	negligible.		

5.4.12 Limitations and Uncertainty in Manure Management Emissions Estimates

For	temporary	and	long‐term	storage,	composting,	and	aerobic	lagoons,	the	IPCC	Tier	2	

methodology	is	used	to	estimate	CH4	emissions.	The	maximum	CH4	production	capabilities	(B0)	for	

ruminant	animals	are	U.S.	specific	values	from	the	U.S.	EPA	Inventory	of	U.S.	GHG	Emissions	and	

Sinks.	IPCC	estimates	that	the	uncertainty	associated	with	these	country‐specific	factors	is	±20	

percent.	B0	values	for	other	animal	values	are	IPCC	defaults	and	have	an	associated	uncertainty	of	

±30	percent.	The	MCFs	provided	in	the	Guidelines	for	solid,	slurry,	and	solid/slurry	manure	are	

from	the	IPCC	Guidance	and	have	an	estimated	uncertainty	of	±30	percent.	The	B0	and	MCF	values	

provided	are	intended	for	use	at	the	national	level,	thus	application	of	these	factors	at	the	entity	

level	may	result	in	higher	uncertainty.	

A	modified	Tier	2	approach	is	provided	for	estimating	CH4	emissions	from	anaerobic	digesters.	The	

leak	rates	for	different	digester	types	is	taken	from	the	Clean	Development	Mechanism’s	

methodological	tool	for	project	and	leakage	emissions	from	anaerobic	digesters	(CDM,	2012).	The	

Clean	Development	Mechanism’s	leak	rates	are	based	on	IPCC	(2006),	Flesch	et	al.	(2011),	and	

Kurup	(2003).	The	leakage	rate	taken	from	Flesch	et	al.	(2011)	is	based	on	measurements	taken	

from	an	Integrated	Manure	Utilization	System	installed	in	Alberta,	Canada.	The	system	processes	

100	metric	tons	of	manure	daily	and	was	the	most	technologically	advanced	system	available	at	the	

time	of	the	study.	The	studies	performed	by	Kurup	(2003)	were	based	on	a	system	located	in	

Kerala,	India.	No	uncertainty	estimates	are	provided	for	these	leak	rates;	however,	the	actual	leak	

rate	of	an	entity	may	differ	due	to	differences	in	technology,	maintenance,	or	other	factors.		

The	Sommer	model	(Sommer	et	al.,	2004)	is	recommended	for	estimating	CH4	emissions	from	

anaerobic	lagoons,	runoff	holding	ponds,	and	storage	tanks.	Similar	to	the	IPCC	Tier	2	methods	used	

for	stockpiles,	composting,	and	aerobic	lagoons,	the	Sommer	model	requires	B0	values	from	IPCC.	

The	degradable	and	nondegradable	volatile	solids	can	be	calculated	using	the	B0	and	potential	CH4

yield	or	a	default	value	from	Møller	at	al.	(2004).	The	default	values	presented	are	based	on	typical	

concentrations	on	Danish	cattle	and	pig	slurries;	values	do	not	differentiate	between	type	of	cattle	

or	diet	of	the	animal	and	thus	there	is	higher	relative	uncertainty	associated	with	using	the	default	

values.		

Sommer	et	al.	(2004)	performed	an	analysis	to	determine	the	sensitivity	of	emission	estimates	

towards	different	factors.	One	factor	considered	is	the	effect	of	slurry	storage	temperature	on	CH4

emissions.	Sommer	et	al.	(2004)	applied	average	monthly	temperatures	for	seven	different	

locations	(all	Nordic	countries)	at	constant	volatile	solids	and	management.	When	compared	to	the	

model	results	for	Denmark	(which	are	calibrated	to	correspond	with	IPCC	methodology),	the	

emissions	estimates	varied	from	‐1	to	+36	percent	for	pig	slurry	and	‐23	to	+1	percent	for	cattle	

slurry.	Given	that	the	climatic	conditions	of	the	United	States	differs	from	Nordic	countries,	the	

variation	as	a	result	of	slurry	storage	temperature	is	expected	to	be	greater.	

IPCC	methodology	or	modified	methodology	is	used	to	estimate	the	N2O	emissions	from	temporary	

stack	and	long‐term	storage,	composting,	and	aerobic	lagoons.	IPCC	reports	large	uncertainties	with	

the	default	emission	factors	applied	(‐50	percent	to	+100	percent).	These	emission	factors	were	

intended	for	use	at	the	national	level	and	do	not	take	into	account	varying	temperature,	moisture	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-100 

content,	aeration,	manure	nitrogen	content,	metabolizable	carbon,	duration	of	storage,	and	other	

aspects	of	treatment	for	different	entities,	thus	the	uncertainty	is	expected	to	be	higher	than	

reported	by	IPCC.	

The	methods	recommend	that	the	user	send	manure	samples	to	a	laboratory	to	obtain	an	estimate	

of	the	volatile	solids,	NH3,	and	nitrogen	content	of	manure.	A	measurement	of	manure	

characteristics	can	help	minimize	uncertainty	by	providing	an	entity‐specific	value	that	takes	into	

account	animal	and	diet	characteristics.	If	laboratory‐tested	volatile	solids	values	are	not	available,	

default	values	from	the	American	Society	of	Agricultural	and	Biological	Engineers	(ASABE)	can	be	

applied.	ASABE	provides	default	manure	characteristics	based	on	data	from	published	and	

unpublished	information.	These	values	are	arithmetic	averages	and	may	not	represent	the	

differences	in	animal	age,	diet,	usage,	productivity,	and	management.	There	is	a	higher	amount	of	

uncertainty	associated	with	the	use	of	ASABE	values	but	there	is	no	quantified	uncertainty	provided	

for	these	values. Note	that	within	the	standard	cited	below	there	are	equations	provided	that	allow	

for	farm‐specific	values	to	be	determined	based	on	animal	characteristics	and	diet	composition.	The	

table	below	is	intended	to	provide	‘average’	values,	but	where	farm	data	are	available,	equations	

should	be	used	in	order	to	provide	more	estimates	that	better	reflect	farm	conditions	and	practices.	

Available	default	values	and	uncertainty	information	is	included	in	Table	5‐32.	

Table 5‐32: Available Uncertainty Data for Emissions from Manure Management		

Parameter

Abbreviation/Symbol

Data Input Unit

Estimated Value

Relative uncertainty Low

(%)

Relative uncertainty High

(%)

Effective Lower Limit

Effective Upper Limit

Data Source

Total	Dry	Manure	–	Beef	Finishing	

Cattle	 	 kg	dry	manure/animal/	day 2.4	 ‐20	 20	 	 	 ASABE	(2005)	

Total	Dry	Manure	–	Beef		Cow	

(confinement)	 kg	dry	manure/animal/	day 6.6	 ‐20	 20	 ASABE	(2005)	

Total	Dry	Manure	–	Beef		Growing	calf	

(confinement)	 	 kg	dry	manure/animal/	day 2.7	 ‐20	 20	 	 	 ASABE	(2005)	

Total	Dry	Manure	–	Dairy		Lactating	

cow	 kg	dry	manure/animal/	day 8.9	 ‐20	 20	 8.7	 11.3	 ASABE	(2005)	

Total	Dry	Manure	–	Dairy		Dry	cow	 	 kg	dry	manure/animal/	day 4.9	 ‐20	 20	 8.8	 11.2	 ASABE	(2005)	

Total	Dry	Manure	–	Dairy		Heifer		 kg	dry	manure/animal/	day 3.7	 ‐20	 20	 ASABE	(2005)	

Total	Dry	Manure	–	Dairy		Veal	118	kg	 	 kg	dry	manure/animal/	day 0.12	 ‐20	 20	 	 	 ASABE	(2005)	

Total	Dry	Manure	–	Horse		Sedentary	

500	kg	 kg	dry	manure/animal/	day 3.8	 ‐20	 20	 ASABE	(2005)	

Total	Dry	Manure	–	Horse		Intense	

exercise	500	kg	 	 kg	dry	manure/animal/	day 3.9	 ‐20	 20	 	 	 ASABE	(2005)	

Total	Dry	Manure	–	Poultry		Broiler	 kg	dry	manure/animal/	day 0.03	 ‐20	 20	 ASABE	(2005)	

Total	Dry	Manure	–	Poultry		Turkey	

(male)	 	 kg	dry	manure/animal/	day 0.07	 ‐20	 20	 	 	 ASABE	(2005)	

Total	Dry	Manure	–	Poultry		Turkey	

(females)	 kg	dry	manure/animal/	day 0.04	 ‐20	 20	 ASABE	(2005)	

Total	Dry	Manure	–	Poultry		Duck		 	 kg	dry	manure/animal/	day 0.04	 ‐20	 20	 	 	 ASABE	(2005)	

Total	Dry	Manure	–	Layer	 kg	dry	manure/animal/	day 0.02	 ‐20	 20	 ASABE	(2005)	

Total	Dry	Manure	–	Swine		Nursery	

pig	(12.5	kg)	 	 kg	dry	manure/animal/	day 0.13	 ‐20	 20	 	 	 ASABE	(2005)	
 Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

 5-101 

Parameter

Abbreviation/Symbol

Data Input Unit

Estimated Value

Relative uncertainty Low

(%)

Relative uncertainty High

(%)

Effective Lower Limit

Effective Upper Limit

Data Source

Total	Dry	Manure	–	Swine		Grow	

finish	(70	kg)	 kg	dry	manure/animal/	day 0.47	 ‐20	 20	 ASABE	(2005)	

Total	Dry	Manure	–	Swine		gestating	

sow	200	kg	 	 kg	dry	manure/animal/	day 0.5	 ‐20	 20	 	 	 ASABE	(2005)	

Total	Dry	Manure	–	Swine		Lactating	

sow	192	kg	 kg	dry	manure/animal/		day 1.2	 ‐20	 20	 ASABE	(2005)	

Total	Dry	Manure	–	Swine		Boar	200	

kg	 	 kg	dry	manure/animal/	day 0.38	 ‐20	 20	 	 	 ASABE	(2005)	

Volatile	solids	–	Beef		Finishing	cattle	 VS	 kg	VS/kg	dry	manure	 0.81	 ‐25	 25	 ASABE	(2005)	

Volatile	solids	–	Beef		Cow	

(confinement)	 VS	 kg	VS/kg	dry	manure	 0.89	 ‐25	 25	 	 	 ASABE	(2005)	

Volatile	solids	–	Beef		Growing	calf	

(confinement)	 VS	 kg	VS/kg	dry	manure	 0.85	 ‐25	 25	 ASABE	(2005)	

Volatile	solids	–	Dairy		Lactating	cow	 VS	 kg	VS/kg	dry	manure	 0.84	 ‐25	 25	 	 	 ASABE	(2005)	

Volatile	solids	–	Dairy		Dry	cow	 VS	 kg	VS/kg	dry	manure	 0.85	 ‐25	 25	 ASABE	(2005)	

Volatile	solids	–	Dairy		Heifer		 VS	 kg	VS/kg	dry	manure	 0.86	 ‐25	 25	 	 	 ASABE	(2005)	

Volatile	solids	–	Dairy		Veal	118	kg	 VS	 kg	VS/kg	dry	manure	 ‐25	 25	 ASABE	(2005)	

Volatile	solids	–	Horse		Sedentary	500	

kg	 VS	 kg	VS/kg	dry	manure	 0.79	 ‐25	 25	 	 	 ASABE	(2005)	

Volatile	solids	–	Horse		Intense	

exercise	500	kg	 VS	 kg	VS/kg	dry	manure	 0.79	 ‐25	 25	 ASABE	(2005)	

Volatile	solids	–	Poultry		Broiler	 VS	 kg	VS/kg	dry	manure	 0.73	 ‐25	 25	 	 	 ASABE	(2005)	

Volatile	solids	–	Poultry		Turkey	

(male)	 VS	 kg	VS/kg	dry	manure	 0.8	 ‐25	 25	 ASABE	(2005)	

Volatile	solids	–	Poultry		Turkey	

(females)	 VS	 kg	VS/kg	dry	manure	 0.79	 ‐25	 25	 	 	 ASABE	(2005)	

Volatile	solids	–	Poultry		Duck		 VS	 kg	VS/kg	dry	manure	 0.58	 ‐25	 25	 ASABE	(2005)	

Volatile	solids	–	Layer	 VS	 kg	VS/kg	dry	manure	 0.73	 ‐25	 25	 	 	 ASABE	(2005)	

Volatile	solids	–	Swine		Nursery	pig	

(12.5	kg)	 VS	 kg	VS/kg	dry	manure	 0.83	 ‐25	 25	 ASABE	(2005)	

Volatile	solids	–	Swine		Grow	finish	

(70	kg)	 VS	 kg	VS/kg	dry	manure	 0.8	 ‐25	 25	 	 	 ASABE	(2005)	

Volatile	solids	–	Swine		gestating	sow	

200	kg	 VS	 kg	VS/kg	dry	manure	 0.9	 ‐25	 25	 ASABE	(2005)	

Volatile	solids	–	Swine		Lactating	sow	

192	kg	 VS	 kg	VS/kg	dry	manure	 0.83	 ‐25	 25	 	 	 ASABE	(2005)	

Volatile	solids	–	Swine		Boar	200	kg	 VS	 kg	VS/kg	dry	manure	 0.89	 ‐25	 25	 ASABE	(2005)	

Total	nitrogen	at	a	given	day	–	beef		

finishing	cattle	 	 kg	N/kg	dry	manure	 0.07	 	 	 	 	 ASABE	(2005)	

Total	nitrogen	at	a	given	day	–	beef		

cow	(confinement)	 kg	N/kg	dry	manure	 0.03	 ASABE	(2005)	

Total	nitrogen	at	a	given	day	–	beef		

growing	calf	(confinement)	 	 kg	N/kg	dry	manure	 0.05	 	 	 	 	 ASABE	(2005)	

Total	nitrogen	at	a	given	day	–	dairy		

lactating	cow	 kg	N/kg	dry	manure	 0.05	 ASABE	(2005)	

Total	nitrogen	at	a	given	day	–	dairy		

dry	cow	 	 kg	N/kg	dry	manure	 0.05	 	 	 	 	 ASABE	(2005)	

Total	nitrogen	at	a	given	day	–	dairy		

heifer		 kg	N/kg	dry	manure	 0.03	 ASABE	(2005)	

Total	nitrogen	at	a	given	day	–	dairy		

veal	118	kg	 	 kg	N/kg	dry	manure	 0.13	 	 	 	 	 ASABE	(2005)	

Total	nitrogen	at	a	given	day	–	Horse	

Sedentary	500	kg	 kg	N/kg	dry	manure	 0.02	 ASABE	(2005)	

Total	nitrogen	at	a	given	day	–	Horse	

Intense	Exercise	 	 kg	N/kg	dry	manure	 0.04	 	 	 	 	 ASABE	(2005)	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-102 

Parameter

Abbreviation/Symbol

Data Input Unit

Estimated Value

Relative uncertainty Low

(%)

Relative uncertainty High

(%)

Effective Lower Limit

Effective Upper Limit

Data Source

Total	nitrogen	at	a	given	day	–	

poultry,		broiler	 kg	N/kg	dry	manure	 0.04	 ASABE	(2005)	

Total	nitrogen	at	a	given	day	–	

poultry,	turkey	(male)	 	 kg	N/kg	dry	manure	 0.06	 	 	 	 	 ASABE	(2005)	

Total	nitrogen	at	a	given	day	–	

poultry,	turkey	(females)	 kg	N/kg	dry	manure	 0.06	 ASABE	(2005)	

Total	nitrogen	at	a	given	day	–	

poultry,		duck		 	 kg	N/kg	dry	manure	 0.04	 	 	 	 	 ASABE	(2005)	

Total	nitrogen	at	a	given	day	–	layer	 kg	N/kg	dry	manure	 0.07	 ASABE	(2005)	

Total	nitrogen	at	a	given	day	–	swine		

nursery	pig	(12.5	kg)	 	 kg	N/kg	dry	manure	 0.09	 	 	 	 	 ASABE	(2005)	

Total	nitrogen	at	a	given	day	–	swine		

grow	finish	(70	kg)	 kg	N/kg	dry	manure	 0.08	 ASABE	(2005)	

Total	nitrogen	at	a	given	day	–	swine		

gestating	sow	200	kg	 	 kg	N/kg	dry	manure	 0.06	 	 	 	 	 ASABE	(2005)	

Total	nitrogen	at	a	given	day	–	swine		

lactating	sow	192	kg	 kg	N/kg	dry	manure	 0.07	 ASABE	(2005)	

Total	nitrogen	at	a	given	day	–	swine		

boar	200	kg	 	 kg	N/kg	dry	manure	 0.07	 	 	 	 	 ASABE	(2005)	

Methane	Conversion	Factor	(MCF)	a–	

Dairy	Cow	 MCF	 %	 ‐30	 30	 IPCC	(2006)	

Methane	Conversion	Factora	–	Cattle	 MCF	 %	 	 ‐30	 30	 	 	 IPCC	(2006)	

Methane	Conversion	Factora	–	Buffalo	 MCF	 %	 ‐30	 30	 IPCC	(2006)	

Methane	Conversion	Factora	–	Market	

Swine	 MCF	 %	 	 ‐30	 30	 	 	 IPCC	(2006)	

Methane	Conversion	Factora	–	

Breeding	Swine	 MCF	 %	 ‐30	 30	 IPCC	(2006)	

Methane	Conversion	Factora	–	Layer	

(Dry)	 MCF	 %	 	 ‐30	 30	 	 	 IPCC	(2006)	

Methane	Conversion	Factora	–	Broiler	 MCF	 %	 ‐30	 30	 IPCC	(2006)	

Methane	Conversion	Factora	–	Turkey	 MCF	 %	 	 ‐30	 30	 	 	 IPCC	(2006)	

Methane	Conversion	Factora	–	Duck	 MCF	 %	 ‐30	 30	 IPCC	(2006)	

Methane	Conversion	Factora	–	Sheep	 MCF	 %	 	 ‐30	 30	 	 	 IPCC	(2006)	

Methane	Conversion	Factora	–	Goat	 MCF	 %	 ‐30	 30	 IPCC	(2006)	

Methane	Conversion	Factora	–	Horse	 MCF	 %	 	 ‐30	 30	 	 	 IPCC	(2006)	

Methane	Conversion	Factora	–	

Mule/Ass	 MCF	 %	 ‐30	 30	 IPCC	(2006)	

Methane	Conversion	Factora	–	Buffalo	 MCF	 %	 	 ‐30	 30	 	 	 IPCC	(2006)	

Methane	Conversion	Factora	–	In	

vessel	manure	composting	 MCF	 %	 ‐30	 30	 IPCC	(2006)	

Methane	Conversion	Factora	–	Static	

pile	manure	composting	 MCF	 %	 	 ‐30	 30	 	 	 IPCC	(2006)	

Methane	Conversion	Factora	–	

Intensive	windrow	 MCF	 %	 ‐30	 30	 IPCC	(2006)	

Methane	Conversion	Factora	–	Passive	

windrow	 MCF	 %	 	 ‐30	 30	 	 	 IPCC	(2006)	

Maximum	Methane	Producing	

Capacities	–	Beef	Replacement	

Heifers	

Bo m3		CH4/kg	VS	 0.33	 ‐20	 20	 U.S.	EPA	(2011)	

Maximum	Methane	Producing	

Capacities	–	Dairy	Replacement	 Bo	 m3		CH4/kg	VS	 0.17	 ‐20	 20	 	 	 U.S.	EPA	(2011)	

Maximum	Methane	Producing	

Capacities	–	Mature	Beef	Cows	 Bo	 m3		CH4/kg	VS	 0.33	 ‐20	 20	 U.S.	EPA	(2011)	

Maximum	Methane	Producing	

Capacities	–	Steers	(>500	lbs)	 Bo	 m3		CH4/kg	VS	 0.33	 ‐20	 20	 	 	 U.S.	EPA	(2011)	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-103 

Parameter

Abbreviation/Symbol

Data Input Unit

Estimated Value

Relative uncertainty Low

(%)

Relative uncertainty High

(%)

Effective Lower Limit

Effective Upper Limit

Data Source

Maximum	Methane	Producing	

Capacities	–	Stockers	(All)	 Bo m3		CH4/kg	VS	 0.17	 ‐20	 20	 U.S.	EPA	(2011)	

Maximum	Methane	Producing	

Capacities	–	Cattle	on	Feed	 Bo m3		CH4/kg	VS	 0.33	 ‐20	 20	 U.S.	EPA	(2011)	

Maximum	Methane	Producing	

Capacities	–	Dairy	Cow	 Bo m3		CH4/kg	VS	 0.24	 ‐20	 20	 U.S.	EPA	(2011)	

Maximum	Methane	Producing	

Capacities	–	Cattle	 Bo m3		CH4/kg	VS	 0.19	 ‐20	 20	 U.S.	EPA	(2011)	

Maximum	Methane	Producing	

Capacities	–	Buffalob Bo m3		CH4/kg	VS	 0.1	 IPCC	(2006)	

Maximum	Methane	Producing	

Capacities	–	Market	Swine	 Bo m3		CH4/kg	VS	 0.48	 ‐30	 30	 IPCC	(2006)	

Maximum	Methane	Producing	

Capacities	–	Breeding	Swine	 Bo m3		CH4/kg	VS	 0.48	 ‐30	 30	 IPCC	(2006)	

Maximum	Methane	Producing	

Capacities	–	Layer	(dry)	 Bo m3		CH4/kg	VS	 0.39	 ‐30	 30	 IPCC	(2006)	

Maximum	Methane	Producing	

Capacities	–	Layer	(wet)	 Bo m3		CH4/kg	VS	 0.39	 ‐30	 30	 IPCC	(2006)	

Maximum	Methane	Producing	

Capacities	–	Broiler	 Bo m3		CH4/kg	VS	 0.36	 ‐30	 30	 IPCC	(2006)	

Maximum	Methane	Producing	

Capacities	–	Turkey	 Bo m3		CH4/kg	VS	 0.36	 ‐30	 30	 IPCC	(2006)	

Maximum	Methane	Producing	

Capacities	–	Duck	 Bo m3		CH4/kg	VS	 0.36	 ‐30	 30	 IPCC	(2006)	

Maximum	Methane	Producing	

Capacities	–	Sheep	 Bo m3		CH4/kg	VS	 0.19	 ‐20	 20	 IPCC	(2006)	

Maximum	Methane	Producing	

Capacities	–	Feedlot	sheep	 Bo m3		CH4/kg	VS	 0.36	 ‐20	 20	 IPCC	(2006)	

Maximum	Methane	Producing	

Capacities	–	Goat	 Bo m3		CH4/kg	VS	 0.17	 ‐30	 30	 IPCC	(2006)	

Maximum	Methane	Producing	

Capacities	–	Horse	 Bo m3		CH4/kg	VS	 0.3	 ‐30	 30	 IPCC	(2006)	

Maximum	Methane	Producing	

Capacities	–	Mule/Ass	 Bo m3		CH4/kg	VS	 0.33	 ‐30	 30	 IPCC	(2006)	

Emission	factor	for	the	fraction	of	CH4

produced	that	leaks	from	the	

anaerobic	digester	–	Digesters	with	

steel	or	lined	concrete	or	fiberglass	

digesters	with	a	gas	holding	system	

(egg	shaped	digesters)	and	

monolithic	construction	

EFCH4,	

leakage

%	 2.8	 CDM	(2012)	

Emission	factor	for	the	fraction	of	CH4

produced	that	leaks	from	the	

anaerobic	digester	–	UASB	type	

digesters	with	floating	gas	holders	

and	no	external	water	seal	

EFCH4,	

leakage

%	 5	 CDM	(2012)	

Emission	factor	for	the	fraction	of	CH4

produced	that	leaks	from	the	

anaerobic	digester	–	Digesters	with	

unlined	concrete/ferrocement/brick	

masonry	arched	type	gas	holding	

section;	monolithic	fixed	dome	

digesters	

EFCH4,	

leakage

%	 10	 CDM	(2012)	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-104 

Parameter

Abbreviation/Symbol

Data Input Unit

Estimated Value

Relative uncertainty Low

(%)

Relative uncertainty High

(%)

Effective Lower Limit

Effective Upper Limit

Data Source

Emission	factor	for	the	fraction	of	CH4

produced	that	leaks	from	the	

anaerobic	digester	–	Other	digester	

configurations	

EFCH4,	

leakage

%	 10	 CDM	(2012)	

Temporary	storage	of	liquid/slurry	

manure	–N2O	emission	factorc EFN20	 kg	N2O‐N/kg	N	 0.005	 ‐50	 100	 U.S.	EPA	(2011)	

Long‐term	storage	of	solid	manure	–

N2O	emission	factorc EFN20 kg	N2O‐N/kg	N	 0.002	 ‐50	 100	 U.S.	EPA	(2011)	

Long‐term	storage	of	slurry	manure	–	

N2O	emission	factorc EFN20	 kg	N2O‐N/kg	N	 0.005	 ‐50	 100	 U.S.	EPA	(2011)	

Cattle	and	Swine	Deep	Bedding	

(Active	Mix)‐	N2O	emission	factorc EFN20 kg	N2O‐N/kg	N	 0.07	 IPCC	(2006)	

Cattle	and	Swine	Deep	Bedding	(No	

Mix)‐	N2O	emission	factorc EFN20	 kg	N2O‐N/kg	N	 0.01	 IPCC	(2006)	

Pit	Storage	Below	Animal	

Confinements‐	N2O	emission	factorc EFN20 kg	N2O‐N/kg	N	 0.002	 IPCC	(2006)	

Natural	aeration	aerobic	lagoons	–	

N2O	conversion	factorc EFN20	 kg	N2O‐N/kg	N	 0.01	 ‐50	 100	 IPCC	(2006)	

Forced	aeration	aerobic	lagoons	–	

N2O	conversion	factorc EFN20 kg	N2O‐N/kg	N	 0.005	 ‐50	 100	 IPCC	(2006)	

N2O	emission	factor	for	liquid	storage	

–	uncovered	liquid	manure	with	a

crustc

EFN20	 kg	N2O‐N/kg	N	 0.8	 ‐50	 100	 IPCC	(2006)	

N2O	emission	factor	for	liquid	storage	

–	uncovered	liquid	manure	without	a	

crustc

EFN20 kg	N2O‐N/kg	N	 0	 ‐50	 100	 IPCC	(2006)	

N2O	emission	factor	for	liquid	storage	

–	covered	liquid	manurec EFN20	 kg	N2O‐N/kg	N	 0	 ‐50	 100	 IPCC	(2006)	

Manure	Management	–	Multiple	

Sources	–	collection	efficiency,	

covered	storage	(with	or	without	

crust)	

η	 Percentage	 1	 Sommer	et	al.	(2004)

Manure	Management	–	Multiple	

Sources	–	collection	efficiency,	

uncovered	storage	with	crust	

formation	

η Percentage	 0	

Sommer	et	al.	(2004)

Manure	Management	–	Multiple	

Sources	–	collection	efficiency,	

uncovered	storage	without	crust	

formation	

η Percentage	 ‐0.40	

Sommer	et	al.	(2004)

Manure	Management	–	Multiple	

Sources	–	Rate	correcting	factors	(b1)	 b1	 Dimensionless	 1	 Sommer	et	al.	(2004)

Manure	Management	–	Multiple	

Sources	–	Rate	correcting	factors	(b2)	 b2 Dimensionless	 0.01	 Sommer	et	al.	(2004)

Manure	Management	–	Multiple	

Sources	–	Arrhenius	parameter,	cattle	 A	 g	CH4	/kg	VS/hr	 43.33	 Sommer	et	al.	(2004)

Manure	Management	–	Multiple	

Sources	–	Arrhenius	parameter,	

swine	

A	 g	CH4	/kg	VS/hr	 43.21	

Sommer	et	al.	(2004)

Potential	methane	yield	of	the	

manure	cattle	

ECH4,	

pot	‐

kg	CH4/kg	VS	 0.48	 	 	 Sommer	et	al.	(2004)

Potential	methane	yield	of	the	

manure		‐	swine	

ECH4,	

pot

kg	CH4/kg	VS	 0.5	 Sommer	et	al.	(2004)
 Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

 5-105 

Parameter

Abbreviation/Symbol

Data Input Unit

Estimated Value

Relative uncertainty Low

(%)

Relative uncertainty High

(%)

Effective Lower Limit

Effective Upper Limit

Data Source

Temporary	stack	and	long‐term	

stockpile	–	Ratio	degradable	volatile	

solids	to	total	volatile	solids	‐	cattle		

liquid	manure	

VSd/	

VST

Unitless	 0.46	 	 	 	 	 Møller	et	al.	(2004)	

Temporary	stack	and	long‐term	

stockpile	–	Ratio	degradable	volatile	

solids	to	total	volatile	solids	‐	swine		

liquid	manure	

VSd/	

VST

Unitless	 0.89	 Møller	et	al.	(2004)	

Temporary	stack	and	long‐term	

stockpile	–	Ratio	Non‐degradable	

volatile	solids	to	total	volatile	solids	‐	

cattle		liquid	manure	

VSnd/

VST

Unitless	 0.54	 	 	 	 	 Møller	et	al.	(2004)	

Temporary	stack	and	long‐term	

stockpile	–	Ratio	non‐degradable	

volatile	solids	to	total	volatile	solids	–	

swine	liquid	manure	

VSnd/

VST

Unitless	 0.11	 Møller	et	al.	(2004)	

a	The	values	for	methane	conversion	factor	(MCF)	vary	depending	on	the	temperature	and	the	manure	management	

system.	IPCC	(2006)	provides	estimated	uncertainty	ranges	for	these	MCFs.	 b	There	are	no	data	for	North	America	region;	the	data	from	Western	Europe	are	used	to	calculate	the	estimation.	There	is	

no	reported	uncertainty	for	this	adapted	value.

c	IPCC	(2006)	reports	large	uncertainties	with	default	N2O	emission	factors.	The	N2O	EF	values	vary	depending	on	the	

animal	species	and	temperature	of	the	manure	management	system.

5.5 Research Gaps

Research	gaps	have	been	identified	for	animal	production	systems,	covering	activity	data,	as	well	as	

key	areas	that	would	facilitate	more	accurate	estimation	of	emissions	from	enteric	fermentation	

and	manure	management	systems.	Recommendations	are	discussed	below.	

5.5.1 Enteric Fermentation

Cattle

Future	research	related	to	improving	emissions	estimates	should	be	aimed	at	expanding	the	

options	within	existing	models	to	better	describe	an	individual	farm	system	and	incorporate	more	

options	for	mitigation	strategies	to	see	how	emissions	might	change	with	implementation	of	these	

strategies	as	well	as	consider	the	interactive	effects	of	multiple	strategies.		

Beef Cow‐Calf, Bulls, Stocker, and Sheep

Key	data	needs	include	measurement/prediction	of	feed	intake	on	pasture,	

measurement/prediction	of	CH4	from	grazing	animals	(larger	numbers	of	animals),	and	methods	by	

which	to	characterize	range	forage	and	intake	under	production	conditions.	

Feedlot

There	is	a	need	for	equations	and	models	to	accurately	predict	enteric	CH4	emissions	from	cattle	

and	sheep	fed	high‐concentrate	finishing	diets.		
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

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Dairy

One	of	the	largest	research	gaps	is	the	lack	of	basic	data	related	to	emissions	from	calves,	heifers,	

and	dry	cow	housing	systems.	In	addition,	there	is	a	need	for	equally	consistent	and	reliable	

methods	for	measuring	relative	differences	in	emissions	associated	with	the	implementation	of	a	

variety	of	management	practices.	Further	model	development	for	estimating	emissions	should	

include	an	expansion	of	options	to	describe	the	production	facility	and	inclusion	of	management	

practices	that	can	be	adopted	to	mitigate	emissions.		

Swine

Future	research	related	to	improving	emissions	estimates	should	be	aimed	at	expanding	the	

options	within	these	models	to	better	describe	an	individual	farm	system	and	incorporate	more	

options	for	management	and	mitigation	strategies	to	see	how	emissions	might	change	with	

implementation	of	different	practices.	Minimally,	the	diet	considerations	in	Holos	need	to	be	

incorporated	into	the	MANURE	model	and	expanded	to	reflect	production	phase.

Poultry

Future	research	related	to	improving	emissions	estimates	should	be	aimed	at	expanding	the	

options	within	these	models	to	better	describe	an	individual	farm	system	and	incorporate	more	

options	for	management	and	mitigation	strategies	to	see	how	emissions	might	change	with	

implementation	of	different	practices.	

5.5.2 Manure Management

Greenhouse	gas	emissions	from	a	variety	of	manure	management	systems	have	been	developed	

from	a	limited	number	of	studies	and	a	limited	number	of	potential	variations	in	management	and	

the	environmental	conditions	around	a	particular	manure	management	system.	The	largest	

deficiency	in	the	current	GHG	studies	is	the	lack	of	characterization	of	the	temporal	variation	in	the	

GHG	emissions	from	different	systems	and	the	spatial	variation	in	GHG	emissions	induced	by	

meteorological	conditions	among	specific	locations.	In	general,	the	research	needed	to	develop	a	

more	complete	understanding	of	the	GHG	emissions	can	be	summarized	as:	

 Develop	data	bases	from	research	observations	of	commercial	facilities	that	characterize

the	storage	system,	time	in	storage,	environmental	conditions	and	location,	and	the

attributes	of	the	manure	source,	e.g.,	type	of	animal,	diet,	loading	rate.

 Utilize	the	databases	to	derive	simulation	models	to	quantify	the	GHG	emissions	from

different	manure	management	systems.

 Validate	the	models	using	independent	observations	from	manure	management	systems

distributed	around	the	United	States.

 Develop	operational	models	capable	of	being	applied	to	production	scale	systems	which

utilize	simple	parameters	as	input	variables	and	produce	results	in	agreement	with	the

more	complex	simulation	models.

 Utilize	these	models	to	develop	potential	strategies	which	could	be	employed	to	mitigate

GHG	emissions	from	manure	management	systems.

Temporary Stack and Long‐Term Stockpile

Methane	emission	data	from	solid	storages	in	different	regions	under	different	climates	are	limited.	

In	order	to	develop	a	more	accurate	model	to	estimate	the	CH4	emission	from	solid	manure	

storages,	in‐depth	studies	are	needed	to	integrate	temperature,	storage	time,	storage	method,	and	

mass	flow	with	CH4	emission	in	different	regions.	As	for	N2O	emission,	systematically	collecting	

more	intense	data	(a	variety	of	spatial	and	temporal	scales)	from	different	regions	will	be	a	good	

first	step	toward	accurate	N2O	emission	models.	Once	these	data	are	collected	and	used	to	

develop/validate	models,	work	will	likely	be	needed	to	develop	farmer‐friendly	models	using	
 Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

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simple	farm	parameters	as	input	variables,	resulting	in	emissions	estimates	that	are	correlated	with	

those	of	more	complex	models.	For	example,	these	models,	if	synchronized,	could	form	part	of	a	

comprehensive	manure	stewardship	toolkit.		

There	is	a	paucity	of	data	on	CH4	and	N2O	emissions	from	open	lot	(beef	feedlots	and	dairies)	pen	

surfaces	and	runoff	control	structures	and	on	the	chemical	and	physical	factors	controlling	those	

emissions.		

Composting

Greenhouse	gas	emissions	data	from	composting	in	different	regions	under	different	operational	

conditions	are	limited.	A	good	first	step	toward	an	accurate	GHG	emissions	model	would	be	to	

collect	more	data	from	different	regions	and	different	operational	conditions.	Consequently,	in‐

depth	studies	integrating	compost	pile	size/surface	area,	pile	shape,	aeration	rate,	storage	time,	

composting	temperature,	etc.,	with	GHG	emissions	need	to	be	conducted	to	develop	complex	

models	describing	GHG	emissions	from	composting.	Furthermore,	work	will	likely	be	needed	to	

develop	farmer‐friendly	models	using	simple	farm	parameters	as	input	variables,	resulting	in	

emission	estimations	that	are	correlated	with	those	of	more	complex	models.	

There	have	been	some	studies	performed	to	estimate	the	emission	factors	for	N2O	from	composting	

manure	in	different	systems	and	for	different	livestock	categories.	(Fukummoto	et	al.,	2003;	Szanto	

et	al.,	2006)	have	conducted	studies	on	composting	swine	manure	at	specific	ambient	

temperatures.	Factors	have	been	presented	in	the	studies	but	there	is	significant	uncertainty	due	to	

the	limited	data	available.	Further	research	is	needed	to	refine	these	emission	factors	as	well	as	

develop	factors	for	other	animals.		

Aerobic Lagoon

In‐depth	studies	are	needed	to	integrate	lagoon	depths,	aeration	rate,	pH,	temperature,	and	

nutrient	conditions	of	manure	with	GHG	emissions,	which	will	facilitate	the	development	of	

comprehensive	models	to	predict	GHG	emissions	under	different	operational	and	climate	

conditions.	Simplified	and	farm‐friendly	models	using	farm	operational	parameters	as	inputs	

should	be	developed	to	help	farms	estimate	the	GHG	emissions	at	the	entity	level.	

Anaerobic Lagoon, Runoff Holding Pond, and Storage Tanks

All	models	to	estimate	GHG	emissions	from	liquid	manure	storage	are	relatively	inaccurate,	due	to	

the	complexity	and	variety	of	livestock	manure	operations.	In	order	to	develop	a	more	accurate	

model	to	estimate	emissions	from	liquid	manure	storages,	in‐depth	studies	are	needed	to	integrate	

manure	storage	configuration,	temperature,	storage	time,	storage	method,	mass	flow,	and	surface	

turbulence	with	emissions	in	different	regions.	In	addition,	systematically	collecting	more	data	from	

different	regions	will	be	very	helpful	to	develop	more	statistically	accurate	models	to	estimate	GHG	

emissions.		

Anaerobic Digestion

Changes	in	chemical	oxygen	demand,	volatile	solids,	total	solids,	and	nitrogen	in	the	anaerobic	

digestion	process	are	indirectly	linked	to	GHG	emissions	from	post‐treatment	of	anaerobic	

digestion	effluent.	The	effectiveness	of	anaerobic	digestion	at	mitigating	GHG	emissions	has	been	

studied	intensively.	However,	anaerobic	digestion	effluent	can	lead	to	GHG	emissions.	More	in‐

depth	studies	are	needed	to	develop	integrated	models	that	can	accurately	predict	the	overall	GHG	

emission	from	the	combination	of	anaerobic	digestion	and	post‐treatment	approaches.	

Combined Aerobic Treatment Systems

Methods	and	techniques	to	reduce	the	capital	and	operating	costs	are	needed.	There	is	also	a	need	

to	develop	better	ways	to	conserve	and	derive	energy	from	the	waste	material.	There	is	a	paucity	of	
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data	on	GHG	emissions	from	these	systems	and	development	of	emission	models	will	require	

integration	of	data	characterizing	these	systems	and	the	climatic	conditions	in	order	to	develop	

these	models.	These	models	will	need	to	be	validated	against	observed	data.		

Nutrient Removal

Various	methods	of	nitrogen	removal,	such	as	biological	nitrogen	removal,	Anamox,	NH3	stripping,	

ion	exchange,	and	struvite	crystallization,	should	be	investigated	at	commercial‐scale	animal	

operations	under	different	climate	conditions.	Characteristics	of	manure,	mass	flow,	and	gas	

emissions	should	be	closely	monitored	in	order	to	provide	the	data	needed	to	construct	relatively	

precise	estimation	models.	In	addition,	further	research	is	needed	to	pilot	innovative	beef	and	dairy	

GHG	emission	reduction	strategies	in	feedlots	and	dairies.		

Constructed Wetland		

Although	there	are	numerous	papers	published	about	various	aspects	of	treatment	wetland	

effectiveness	and	emissions,	there	currently	is	not	an	established	method	for	calculation	of	GHG	

emissions	from	any	of	the	treatment	wetland	types.	Moreover,	there	are	not	sufficient	unifying	

publications	to	suggest	that	a	reliable	method	could	be	established	within	the	scope	of	this	report.	A	

more	robust	and	extensive	database	on	GHG	emissions	from	treatment	wetlands	is	needed.	

Concomitantly,	there	is	a	need	for	better	predictive	equation	and	models.		

Thermo‐Chemical Conversion

More	studies	are	needed	on	the	effects	of	thermal	conversion	of	animal	manure	on	GHG	emission	in	

order	to	conclude	detailed	emission	profiles	corresponding	to	different	type	of	manure.	These	

studies	would	entail	detailed	observations	of	the	manure	conversion	system	along	with	GHG	

emissions	and	information	on	the	environmental	conditions.		
 Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

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Appendix 5‐A: Enteric CH4 from Feedlot Cattle – Methane Conversion

Factor (Ym)

As	noted	in	the	Beef	Production	Systems	section	(Section	5.3.2.2),	a	modified	IPCC	(2006)	method	is	

proposed	to	estimate	enteric	CH4	emissions	from	finishing	beef	cattle.	For	this	report,	a	baseline	

scenario	based	on	typical	U.S.	beef	cattle	feeding	conditions	was	established	and	baseline	values	

were	set	based	on	published	research.	To	estimate	methane	emissions,	emission	values	are	

modified	using	adjustment	factors	that	are	based	on	changes	in	animal	management	and	feeding	

conditions	from	the	baseline	scenario.	This	appendix	presents	background	information	on	the	

baseline	scenario	and	adjustment	factors.		

The	following	baseline	scenarios	are	established	for	beef	cattle	in	U.S.	feedlots:		

1. Medium	to	large	frame	steer	(or	heifer)	yearlings	are	fed	a	high	concentrate	finishing	diet	

containing	<=10	percent	forage	in	diet	dry	matter	(=	to	8	to	18	percent	NDF)	in	dry‐lot,	soil‐

surfaced	pens.	

2. The	grain	portion	of	the	diet	is	at	least	70	percent	of	diet	dry	matter.	

3. The	grain	source	is	steam	flaked	(SFC)	or	high	moisture	corn	(HMC).	

4. The	dietary	crude	protein	concentration	is	12.5	to	13.5	percent	of	diet	dry	matter	

(Vasconcelos	and	Galyean,	2007).	

5. The	dietary	ruminally	degradable	protein	(DIP	or	RDP)	concentration	is	7.5	to	9	percent	of	

diet	dry	matter	(Vasconcelos	and	Galyean,	2007).	

6. The	diet	contains	monensin	(Rumensin,	Elanco	Animal	Health)	at	recommended	

concentrations	(Vasconcelos	and	Galyean,	2007).	

7. Diets	for	heifers	contain	melengestrol	acetate	(MGA)	at	the	recommended	concentrations	

(Vasconcelos	and	Galyean,	2007).	

8. Cattle	are	implanted	with	an	estrogenic	implant	throughout	the	feeding	period	(Vasconcelos	

and	Galyean,	2007).	

9. No	beta‐agonist	is	fed.	

10. The	diet	contains	no	supplemental	fat	(vegetable	oil,	yellow	grease,	etc.)	and	has	a	total	fat	

concentration	of	less	than	4.5	percent	of	diet	dry	matter.	

11. Enteric	CH4	emission	is	three	percent	of	gross	energy	intake	(GEI:	(IPCC,	2006).	

12. The	dietary	forage	is	chopped	alfalfa,	sorghum,	or	grass	hay	at	seven	to	10	percent	of	diet	

dry	matter.	

13. The	diet	contains	minerals	and	vitamins	at	the	recommended	level	(NRC,	2000).	

14. Temperatures	are	mild/moderate	during	the	feeding	period.	

15. Cattle	are	slaughtered	at	an	average	body	weight	of	approximately	582	kg	(1,280	lb.)	(KSU,	

2012).	

16. Average	dressing	percent	is	61	percent.	

17. Cattle	are	fed	150	days.	

The	Ym	adjustment	factors	for	feedlot	cattle	fed	high‐concentrate	diets	in	Table	5‐11	were	

determined	based	on	the	following	literature	reviews	and	analyses.	

Ionophores:	On	average,	the	feeding	of	ionophores	decreases	DMI	by	about	five	percent	(Delfino	et	

al.,	1988;	Vogel,	1995;	Robinson	and	Okine,	2001;	Tedeschi	et	al.,	2003)	and	decreases	ADG	by	

about	two	percent	(Delfino	et	al.,	1988;	Tedeschi	et	al.,	2003).	Feeding	ionophores	decreases	enteric	

methane	emissions	approximately	20	percent	for	the	first	two	to	four	weeks	on	feed	(Tedeschi	et	

al.,	2003;	Guan	et	al.,	2006).	Therefore,	over	a	150‐day	feeding	period,	overall	enteric	methane	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-110 

emissions	are	decreased	approximately	4	percent.	Because	of	an	increase	in	the	gain:feed	ratio,	

enteric	methane	emissions	per	unit	of	production	are	decreased	when	ionophores	are	fed.	

Supplemental	Fat:	For	each	one	percent	increase	in	supplemental	fat	(up	to	a	maximum	of	four	

percent	added	fat),	enteric	methane	emissions	(as	a	percentage	of	gross	energy	intake)	decrease	

approximately	3.8	to	5.6	percent	(Zinn	and	Shen,	1996;	Beauchemin	et	al.,	2008;	Martin	et	al.,	

2010).	A	conservative	value	of	four	percent	per	one	percent	increase	in	supplemental	fat	is	

recommended	because	many	fat	sources	used	in	the	industry	are	partially	saturated	and	may	have	

less	effect	on	enteric	CH4	production	than	the	highly	unsaturated	fats	used	in	most	studies.	For	

example	if	three	percent	supplemental	fat	is	added	to	the	diet,	then	CH4	production	is	decreased	12	

percent	(three	percent	added	fat	times	four	percent	is	equivalent	to	a	12	percent	decrease).	The	

revised	enteric	CH4	emission	is	2.64	percent	of	GEI	(three	percent	baseline	*	0.88	=	2.64	percent	of	

GEI).	Many	distiller’s	grains	contain	approximately	8	to	12	percent	fat.	Addition	of	distiller’s	grain	

may	serve	as	a	source	of	supplemental	fat,	and	thus	decrease	enteric	CH4	(McGinn	et	al.,	2009).	

However	Hales	et	al.	(2013)	noted	that	feeding	increasing	concentrations	of	Wet	Distillers	Grains	

with	Solubles	(WDGS)	in	equal‐fat	diets	increased	enteric	CH4,	likely	due	to	the	increased	NDF	

intake.12

Grain	processing	&	Grain	source:	Grain	processing	directly	affects	enteric	CH4	production	via	its	

effects	on	ruminal	fermentation.	Enteric	CH4	emissions,	as	a	percent	of	GEI,	are	20	percent	greater	

with	diets	based	on	DRC	than	in	diets	based	on	steam‐flaked	corn	(SFC)	or	high	moisture	corn	

(HMC)	(Archibeque	et	al.,	2006;	Hales	et	al.,	2012).	More	extensive	grain	processing	may	also	

improve	the	gain:feed	ratio	about	10	percent	(Owens	et	al.,	1997;	Zinn	and	Barajas,	1997)	and	may,	

decrease	manure	CH4	emissions	via	decreased	fecal	starch	excretion	(Zinn	and	Barajas,	1997;	Hales	

et	al.,	2012).	Enteric	CH4	emissions	are	20	to	40	percent	greater	with	finishing	diets	based	on	barley	

than	diets	based	on	corn;	presumably	because	of	the	lower	starch	and	higher	fiber	content	of	barley	

(Benchaar	et	al.,	2001;	Beauchemin	and	McGinn,	2005).	A	mean	(30%)	for	these	studies	is	

recommended	for	a	barley	adjustment	factor.	

Dietary	Forage	and	Grain	Concentration	effects:	Limited	data	exists	to	evaluate	effects	of	dietary	

forage	and	grain	concentration	on	enteric	methane	production	from	beef	cattle	that	are	fed	typical	

U.S‐based,	high	concentrate	finishing	diets.	Equations	from	Ellis	et	al.	(2007;	2009)	illustrate	the	

effects	of	dietary	forage,	NDF,	and	starch	on	enteric	CH4	production.	In	particular,	the	following	10	

equations	illustrate	the	relationships:	

 CH4	(MJ/day)	=	3.96	+	0.561	×	DMI	(kg/day)	

 CH4	(MJ/day)	=	4.79	+	0.0492	×	Forage	(%)	

 CH4	(MJ/day)	=	5.58	+	0.848	×	NDF	(kg/day)	

 CH4	(MJ/day)	=	5.70	+	1.41	×	ADF	(kg/day)		

 CH4	(MJ/day)	=	2.29	+	0.670	×	DMI	(kg/day)	

 CH4	(MJ/day)	=	4.72	+	1.13	×	Starch	(kg/day)	

 CH4	(MJ/day)	=	‐1.01	+	2.76	×	NDF	(kg/day)	+0.722	×	Starch	(kg/day)	

 CH4	(MJ/day)	=	2.68	–	1.14	(Starch:NDF)	+	0.786	×	DMI	(kg/day)	

 CH4	(MJ/day)	=	2.50	=	0.367	×	Starch	(kg/day)	+	0.766	×	DMI	(kg/day)	

 CH4	(MJ/day)	=	2.70	+	(1.16	×	DMI	(kg/day))	–	(15.8	×	ether	extract	(kg/day))	

																																																													

12	Ym	is	adjusted	for	distiller	grains	by	changes	in	fat	content	and	grain	concentration.	For	example,	a	30	

percent	concentration	of	distiller	grains	in	the	finishing	diet	will	typically	increase	the	dietary	fat	level	by	2	to	

3	percent	and	decrease	the	grain	content	by	25	to	30	percent.	The	resulting	change	in	Ym	is	a	decrease	by	8	

percent	to	account	for	increase	in	fat	content	and	an	increase	of	10	percent	to	account	for	a	decrease	in	grain	

content	(i.e.,	Ym	=	3%	x	0.92	x	1.10	=	3.036	%).		
 Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

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To	develop	adjustment	factors	for	grain	concentrations	in	diets,	artificial	data	sets	were	created	

that	varied	in	forage	(range	of	5	to	25	percent),	NDF	(range	10	to	20	percent),	fat	(range	of	3	to	6	

percent),	and	starch	(range	of	30	to	60	percent	of	diet	dry	matter)	content.	Using	these	data	sets,	

enteric	CH4	emissions	were	estimated	using	the	appropriate	equation(s)	of	Ellis	et	al.	(2007;	2009).	

Effects	of	dietary	changes	on	enteric	CH4	were	then	determined	by	linear	regression	analysis.	On	

average,	enteric	CH4	production	(MJ/day)	increased	five	percent	for	each	one	percent	increase	in	

dietary	forage	concentration;	increased	13	percent	for	each	one	kg	increase	in	dietary	NDF	intake,	

increased	five	percent	for	each	one	kg	increase	in	starch	intake	and	decreased	five	percent	for	each	

one	unit	increase	in	the	dietary	starch:NDF	ratio.	Small	increases	in	forage	concentration	from	the	

baseline	value	had	small	effects	on	Ym;	whereas,	greater	increases	had	a	larger	effect	(Hales	et	al.,	

2012;	Hales	et	al.,	2014).	An	evaluation	of	these	factors	indicated	an	enteric	CH4	Ym	adjustment	

factor	of	10%	for	small	increases	in	forage	(and	decreases	in	grain	concentration)	and	a	larger	

correction	factor	of	40	percent	for	greater	changes	(diet	concentrate	less	than	45	percent).	These	

factors	are	recommended	for	accounting	for	the	grain	concentration	in	finishing	diets.	

No	Ym	adjustment	factor	was	explicitly	modeled	to	account	for	the	following	dietary	management	

factors:13

 Beta‐agonists:	Beta‐agonists	do	not	directly	affect	the	Ym	(i.e.,	enteric	CH4	emissions	per	

unit	of	gross	energy	intake),	therefore	no	adjustment	factor	is	recommended.	However,	

because	of	a	4	percent	increase	in	feed	efficiency,	a	2.5	to	3.5%	increase	in	hot	carcass	

weight	(HCW),	and	an	increase	in	live	body	weight	(Vasconcelos	et	al.,	2008;	Elam	et	al.,	

2009;	Montgomery	et	al.,	2009;	Delmore	et	al.,	2010;	Radunz,	2011),	enteric	CH4	emissions	

per	unit	of	production	are	decreased	when	beta‐agonists	are	fed.	

 Melengestrol	acetate	(MGA:	heifers	only):	Feeding	MGA	to	heifers	does	not	directly	affect	

enteric	CH4	emissions.	However,	because	of	a	nine	percent	increase	in	the	gain:feed	ratio	

(Hill	et	al.,	1988;	Kreikemeier	and	Mader,	2004)	enteric	CH4	emissions	per	unit	of	

production	are	decreased	when	MGA	is	fed.	

 Direct	Fed	Microbials:	Most	direct	fed	microbials	do	not	appear	to	directly	affect	enteric	CH4

emissions	and	effects	on	animal	performance	are	somewhat	variable	(Krehbiel	et	al.,	2003).	

No	adjustment	factor	is	recommended	for	the	feeding	of	direct	fed	microbials.		

 Dietary	Crude	Protein	and	Ruminal	Degradable	Protein	(RDP):	Dietary	protein	may	

potentially	affect	animal	performance	and	enteric	CH4	emissions	via	effects	on	ruminal	

fermentation.	However,	there	is	no	readily	available	data	with	modern	feedlot	diets	with	

which	to	compare	(Berger	and	Merchen,	1995;	Robinson	and	Okine,	2001;	Gleghorn	et	al.,	

2004;	Cole	et	al.,	2006;	Wagner	et	al.,	2010).	There	is	no	recommended	Ym	adjustment	

factor	for	dietary	protein.	Dietary	protein	may	affect	emissions	of	manure	greenhouse	gases	

(N2O)	and	definitely	affects	NH3	emissions	(Todd	et	al.,	2013).	

 Implanting	regimens:	Implants	do	not	directly	affect	enteric	CH4	emissions.	However	

because	of	an	increase	in	feed	efficiency,	live	body	weight,	and	HCW	(Herschler	et	al.,	1995;	

Robinson	and	Okine,	2001;	Wileman	et	al.,	2009),	enteric	CH4	emissions	per	unit	of	

production	are	decreased	when	implants	are	used.	

 Ambient	temperature:	Cold	and	hot	temperatures	may	potentially	affect	enteric	CH4

emission	due	to	effects	on	feed	intake,	ruminal	digestion	and	rate	of	passage	(Young,	1981);	

however,	the	actual	effects	are	not	clear.	Therefore	no	adjustment	factor	for	environmental	

temperature	is	used.	Cold	temperatures	may	decrease	CH4,	N2O	and	NH3	losses	from	pen	

																																																													

13	Although	these	management	factors	are	not	modeled	to	impact	Ym,	some	of	them	do	impact	enteric	CH4	per	

unit	of	production.	Hence,	in	evaluating	methane	intensity	per	unit	of	production,	these	factors	would	have	an	

impact.	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

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surfaces	via	effects	on	microbial	activity	in	the	manure.	Conversely,	warm	temperatures	

may	increase	emissions	from	manure	via	increased	microbial	activity.	

The	IPCC	Tier	2	model	is	currently	the	most	useful	for	predicting	emissions	from	cow‐calf	and	

stocker	production,	as	well,	as	noted	in	the	earlier	cow‐calf	and	stocker	Sections	(5.3.2.2).	Enteric	

emissions	from	all	cattle	other	than	dairy	cows	and	dairy	heifers	are	estimated	using	the	IPCC	Tier	

2	equation	or	the	modified	IPCC	Tier	2	previously	discussed	for	feedlot	cattle.	To	use	these	

equations,	it	is	necessary	to	make	sure	the	inputs	to	the	equations	are	as	accurate	as	possible.	For	

DE	(as	a	percentage	of	GE),	we	recommend	using	the	feedstuffs	composition	table	provided	in	NRC	

(1989)	and	Ewan	(1989).	Several	feedstuffs	from	the	table	are	included	in	Table	5‐C‐1.	After	review	

of	the	models,	their	strengths	and	limitations,	models	based	on	the	Mills	equations	(e.g.,	DairyGEM,	

COWPOLL,	IFSM)	appear	to	be	the	most	useful	for	predicting	emissions	from	dairy	cattle.	The	Mits3	

equation	recommended	for	calculating	enteric	CH4	emissions	from	dairy	cows	and	dairy	heifers	

(used	in	DairyGEM/IFSM)	requires	different	dietary	input	information	than	that	required	for	the	

IPCC	Tier	II	equation.	Specifically,	DairyGEM/IFSM	requires	the	starch	and	ADF	content	of	feeds.	

Because	starch	is	nearly	equivalent	to	NFC	(which	is	starch	+	sugar	+	pectin)	in	high	forage	diets	

(dairy	diets),	we	use	NFC	in	the	Mits3	equation	(NFC	=	100	–	(NDF	+	CP	+	EE	+	Ash)).	These	values	

can	be	found	in	Appendix	5‐B.

	 	
 Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-113

Appendix 5‐B: Feedstuffs Composition Table

This	table	provided	data	inputs	for	enteric	fermentation	emissions	calculations	for	cattle	and	sheep.	

Table 5‐B‐1: Feedstuffs Composition Table (Preston 2013, except where noted for digestible

energy)

Feedstuff DM 

%

Energy Protein Fiber 

EE

%

ASH

%

Ca 

% 

P 

%

K 

%

Cl

%

S 

%

Zn 

ppm TDN 

%

NEm NEg NEl 

(Mcal/cwt.)

DE 

(% 

of 

GE)a

CP

% 

UIP

%

CF

%

ADF

%

NDF

%

eNDF

%

Alfalfa Cubes x91 57 57 25 57 18 30 29 36 46 40 2.0 11 1.30 0.23 1.9 0.37 0.33 20 

Alfalfa 

Dehydrated 

17% CP

92 61 62 31 61 65.16 19 60 26 34 45 6 3.0 11 1.42 0.25 2.5 0.45 0.28 21 

Alfalfa Fresh 24 61 62 31 61 62.54 b 19 18 27 34 46 41 3.0 9 1.35 0.27 2.6 0.40 0.29 18 

Alfalfa Hay 

Early Bloom 90 59 59 28 59 63.72 19 20 28 35 45 92 2.5 8 1.41 0.26 2.5 0.38 0.28 22 

Alfalfa Hay 

Midbloom 89 58 58 26 58 61.79 17 23 30 36 47 92 2.3 9 1.40 0.24 2.0 0.38 0.27 24 

Alfalfa Hay Full 

Bloom 88 54 54 20 54 55.71 16 25 34 40 52 92 2.0 8 1.20 0.23 1.7 0.37 0.25 23 

Alfalfa Hay 

Mature 88 50 50 12 49 54.18 13 30 38 45 59 92 1.3 8 1.18 0.19 1.5 0.35 0.21 23 

Alfalfa Seed 

Screenings 91 84 92 61 87 34 13 15 10.7 6 0.30 0.67 

Alfalfa Silage 30 55 55 21 55 60.71 c 18 19 28 37 49 82 3.0 9 1.40 0.29 2.6 0.41 0.29 26 

Alfalfa Silage 

Wilted 39 58 58 26 58 60.71 d 18 22 28 37 49 82 3.0 9 1.40 0.29 2.6 0.41 0.29 26 

Alfalfa Leaf 

Meal 89 60 60 30 60 26 15 16 24 34 35 3.0 10 2.88 0.34 2.2 0.32 39 

Alfalfa Stems 89 47 47 7 46 11 44 44 51 68 100 1.3 6 0.90 0.18 2.5 

Almond Hulls 89 56 56 23 56 59.90 3 60 16 29 36 100 3.1 7 0.24 0.10 2.0 0.03 0.07 20 

Ammonium 

Chloride 99 0 0 0 0 163 0 0 0 0 0 0.0 0.00 0.00 0.0 66.00 0.00 0 

Ammonium 

Sulfate 99 0 0 0 0 132 0 0 0 0 0 0.0 24.15

Apples 17 70 73 44 71 3 10 7 9 25 10 2.2 2 0.06 0.60 0.8 

Apple Pomace 

Wet 20 68 70 41 69 5 10 18 27 36 27 5.2 3 0.13 0.12 0.5 0.04 11 

Apple Pomace 

Dried 89 67 69 40 68 56.69 5 15 18 28 38 29 5.2 3 0.13 0.12 0.5 0.04 11 

Artichoke Tops 

(Jerusalem) 27 61 62 31 61 6 18 30 41 40 1.1 10 1.62 0.11 1.4 

Avocado Seed 

Meal 91 52 52 16 51 20 19 24 1.2 16 

Bahiagrass Hay 90 53 53 18 53 54.85 6 37 32 41 72 98 1.8 7 0.47 0.20 1.4 0.21 

Bakery Product 

Dried 90 90 100 68 94 81.31 11 30 3 9 30 0 11.5 4 0.16 0.27 0.4 2.25 0.15 33 

Bananas 24 84 92 61 87 4 4 5 0.8 3 0.03 0.11 1.5 8 

Barley Hay 90 57 57 25 57 60.89 9 28 37 65 98 2.1 8 0.30 0.28 1.6 0.19 25 

Barley Silage 35 59 58 26 58 12 22 34 37 58 61 3.0 9 0.46 0.30 2.4 0.22 28 

Barley Silage 

Mature 35 58 58 26 58 12 25 30 34 50 61 3.5 9 0.30 0.20 1.5 0.15 25 

Barley Straw 90 44 44 1 43 43.98 4 70 42 55 78 100 1.9 7 0.32 0.08 2.2 0.67 0.16 7 

Barley Grain 89 84 92 61 87 12 28 5 7 20 34 2.1 3 0.06 0.38 0.6 0.18 0.16 23 

Barley Grain, 

Steam Flaked 85 90 100 70 100 12 39 5 7 20 30 2.1 3 0.06 0.35 0.6 0.18 0.16 23 
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-114 

Feedstuff DM 

%

Energy Protein Fiber 

EE

%

ASH

%

Ca 

% 

P 

%

K 

%

Cl

%

S 

%

Zn 

TDN ppm

%

NEm NEg NEl 

(Mcal/cwt.)

DE 

(% 

of 

GE)a

CP

% 

UIP

%

CF

%

ADF

%

NDF

%

eNDF

%

Barley Grain 

Steam Rolled 86 84 92 61 87 12 38 5 7 20 27 2.1 3 0.06 0.41 0.6 0.18 0.17 30 

Barley Grain 2-

row 87 84 92 61 87 12 6 8 24 34 2.3 2 0.05 0.31 0.6 0.18 0.17 

Barley Grain 6-

row 87 84 92 61 87 11 6 8 24 34 2.2 3 0.05 0.36 0.6 0.18 0.15 

Barley Grain Lt. 

Wt. (42-44 

lb/bu)

88 78 83 54 80 13 30 9 12 30 34 2.3 4 

Barley Feed 

Pearl Byproduct 90 74 78 49 76 15 25 12 15 3.9 5 0.05 0.45 0.7 0.06 

Barley Bran 91 59 59 28 59 12 28 21 27 36 6 4.3 7 

Barley Grain 

Screenings 89 71 74 46 73 12 9 11 2.6 4 0.35 0.33 0.9 0.15 

Beans Navy 

Cull 90 84 92 61 87 84.52 24 25 5 8 20 0 1.4 5 0.15 0.60 1.4 0.06 0.26 45 

Beet Pulp Wet 17 77 82 53 79 75.09 9 35 20 25 45 30 0.7 5 0.65 0.08 0.9 0.40 0.22 21 

Beet Pulp Dried 91 76 81 52 78 79.81 9 44 21 26 46 33 0.7 5 0.65 0.08 0.9 0.40 0.22 21 

Beet Pulp Wet 

with Molasses 24 77 82 53 79 11 25 16 21 39 33 0.6 6 0.60 0.10 1.8 0.42 11 

Beet Pulp Dried 

with Molasses 92 77 82 53 79 82.52 11 34 17 23 40 33 0.6 6 0.60 0.10 1.8 0.42 11 

Beet Root 

(Sugar) 23 80 86 56 83 4 5 7 16 0.4 3 

Beet Tops 

(Sugar) 19 58 58 26 58 14 11 14 25 41 1.3 24 1.10 0.22 5.2 0.20 0.45 20 

Beet Top Silage 25 52 52 16 51 12 12 2.0 32 1.38 0.22 5.7 0.57 20 

Bermudagrass 

Coastal 

Dehydrated

90 62 63 33 63 16 40 26 29 40 10 3.8 7 0.40 0.25 1.8 0.72 0.23 18 

Bermudagrass 

Coastal Hay 89 56 56 23 56 53.05 10 20 30 36 73 98 2.1 6 0.47 0.21 1.5 0.70 0.22 16 

Bermudagrass 

Hay 89 53 53 18 53 50.79 10 18 29 37 72 98 1.9 8 0.46 0.20 1.5 0.70 0.25 31 

Bermudagrass 

Silage 26 50 50 12 49 10 15 28 35 71 48 1.9 8 0.46 0.20 1.5 0.72 0.25 31 

Birdsfoot Trefoil 

Fresh 22 66 68 38 67 21 20 21 31 47 41 4.4 9 1.78 0.25 2.6 0.25 31 

Birdsfoot Trefoil 

Hay 89 57 57 25 57 16 22 31 38 50 92 2.2 8 1.73 0.24 1.8 0.25 28 

Biuret 99 0 0 0 0 248 0 0 0 0 0 0.0 0 0.00 0.00 0.0 0.00 0.00 0 

Blood Meal, 

Swine/Poultry 91 66 68 38 67 92 82 1 2 10 0 1.4 3 0.32 0.28 0.2 0.30 0.70 22 

Bluegrass KY 

Fresh Early 

Bloom

36 69 71 43 70 75.62 15 20 27 32 60 41 3.9 7 0.37 0.30 1.9 0.42 0.19 25 

Bluegrass Straw 93 45 45 3 44 6 40 50 78 90 1.1 6 0.20 0.10 

Bluestem Fresh 

Mature 61 50 50 12 49 56.82 6 34 2.5 5 0.40 0.12 0.8 0.05 28 

Bread 

Byproduct 68 90 100 68 94 14 24 1 2 3 0 3.0 3 0.10 0.18 0.2 0.76 0.15 40 

Brewers Grains 

Wet 23 85 93 62 88 62.66 26 52 13 21 45 18 7.5 4 0.30 0.58 0.1 0.15 0.32 78 

Brewers Grains 

Dried 92 84 92 61 87 60.43 25 54 14 24 49 18 7.5 4 0.30 0.58 0.1 0.15 0.32 78 

Brewers Yeast 

Dried 94 79 85 55 81 48 3 1.0 7 0.10 1.56 1.8 0.41 41 

Bromegrass 

Fresh Immature 30 64 65 36 65 78.57 15 22 28 33 54 40 4.1 10 0.45 0.34 2.3 0.21 20 
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-115

Feedstuff DM 

%

Energy Protein Fiber 

EE

%

ASH

%

Ca 

% 

P 

%

K 

%

Cl

%

S 

%

Zn 

TDN ppm

%

NEm NEg NEl 

(Mcal/cwt.)

DE 

(% 

of 

GE)a

CP

% 

UIP

%

CF

%

ADF

%

NDF

%

eNDF

%

Bromegrass 

Hay 89 55 55 21 55 62.19 e 10 33 35 41 66 98 2.3 9 0.40 0.23 1.9 0.40 0.19 19 

Bromegrass 

Haylage 35 57 57 25 57 11 26 36 44 69 61 2.5 8 0.38 0.30 2.0 0.20 19 

Buckwheat 

Grain 88 75 79 50 77 72.27 12 13 17 2.8 2 0.11 0.36 0.5 0.05 0.16 10 

Buttermilk Dried 92 88 98 65 91 34 0 5 0 0 0 5.0 10 1.44 1.00 0.9 0.09 44 

Cactus, Prickly 

Pear 23 61 62 31 62 5 16 20 28 2.1 18 4.00 0.10 1.5 0.20 

Calcium 

Carbonate 99 0 0 0 0 0 0 0 0 0 0.0 99 38.50 0.04 0.1 0.00 0 

Canarygrass 

Hay 91 53 53 18 53 9 26 32 34 67 98 2.7 8 0.38 0.25 2.7 0.14 18 

Canola Meal, 

Solv. Ext. 90 72 75 47 74 41 30 11 19 29 23 2.0 8 0.74 1.14 1.1 0.07 0.78 68 

Carrot Pulp 14 62 63 33 63 6 19 23 40 0 7.8 9 

Carrot Root 

Fresh 12 83 90 60 86 92.29 10 9 11 20 0 1.4 10 0.55 0.32 2.5 0.50 0.17 

Carrot Tops 16 73 77 48 75 13 18 23 45 41 3.8 15 1.94 0.19 1.9 

Cattle Manure 

Dried 92 38 40 0 36 30.58 15 35 42 55 0 2.5 14 1.15 1.20 0.6 1.78 240 

Cheatgrass 

Fresh Immature 21 68 70 41 69 16 23 2.7 10 0.60 0.28 

Citrus Pulp 

Dried 90 78 83 54 80 7 38 13 20 21 33 2.9 7 1.81 0.12 0.8 0.04 0.08 14 

Clover Ladino 

Fresh 19 69 71 43 70 73.22 25 20 14 33 35 41 4.8 11 1.27 0.38 2.4 0.20 20 

Clover Ladino 

Hay 90 61 62 31 61 63.40 21 25 22 32 36 92 2.0 9 1.35 0.32 2.4 0.30 0.20 17 

Clover Red 

Fresh 24 64 65 36 65 18 21 24 33 44 41 4.0 9 1.70 0.30 2.0 0.60 0.17 23 

Clover Red Hay 88 55 55 21 55 58.33 15 28 30 39 51 92 2.5 8 1.50 0.25 1.7 0.32 0.17 17 

Clover Sweet 

Hay 91 53 53 18 53 16 30 30 38 50 92 2.4 9 1.27 0.25 1.8 0.37 0.46 

Coconut Meal, 

Mech. Ext. 92 76 81 52 78 79.66 21 56 13 21 56 23 6.8 7 0.40 0.30 1.0 0.33 0.04 

Coffee Grounds 88 20 36 0 16 13 41 68 77 10 15.0 2 0.10 0.08 

Corn Whole 

Plant Pelleted 91 63 64 34 64 9 45 21 24 40 6 2.4 6 0.50 0.24 0.9 0.14 

Corn Fodder 80 65 66 37 66 9 45 25 29 48 100 2.4 7 0.50 0.25 0.9 0.20 0.14 

Corn Stover 

Mature (Stalks) 80 54 54 20 54 5 30 35 43 70 100 1.3 7 0.45 0.15 1.2 0.30 0.14 22 

Corn Silage, 

Milk Stage 26 65 66 37 66 8 18 26 32 54 60 2.8 6 0.40 0.27 1.6 0.11 20 

Corn Silage, 

Mature Well 

Eared

34 72 75 47 74 72.88 8 28 21 27 46 70 3.1 5 0.28 0.23 1.1 0.20 0.13 22 

Corn Silage, 

Sweet Corn 24 65 66 37 66 11 20 32 57 60 5.0 5 0.24 0.26 1.2 0.17 0.16 39 

Corn Grain, 

Whole 88 88 98 65 91 88.85 9 58 2 3 9 60 4.3 2 0.02 0.30 0.4 0.05 0.14 18 

Corn Grain, 

Rolled 88 88 98 65 91 9 54 2 3 9 34 4.3 2 0.02 0.30 0.4 0.05 0.14 18 

Corn Grain, 

Steam Flaked 85 93 104 71 97 95.44 9 59 2 3 9 40 4.1 2 0.02 0.27 0.4 0.05 0.14 18 

Corn Grain, 

High Moisture 74 93 104 71 97 91.64 10 42 2 3 9 0 4.0 2 0.02 0.30 0.4 0.06 0.14 20 

Corn Grain, 

High Oil 88 91 102 69 95 8 54 2 3 8 60 6.9 2 0.01 0.30 0.3 0.05 0.13 18 
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-116 

Feedstuff DM 

%

Energy Protein Fiber 

EE

%

ASH

%

Ca 

% 

P 

%

K 

%

Cl

%

S 

%

Zn 

TDN ppm

%

NEm NEg NEl 

(Mcal/cwt.)

DE 

(% 

of 

GE)a

CP

% 

UIP

%

CF

%

ADF

%

NDF

%

eNDF

%

Corn Grain, HiLysine 92 87 96 64 90 12 58 4 4 11 60 4.4 2 0.03 0.24 0.4 0.05 0.11 18 

Corn and Cob 

Meal 87 82 89 59 85 83.15 9 52 9 11 26 56 3.7 2 0.06 0.27 0.5 0.05 0.13 16 

Corn Cobs 90 48 48 9 47 53.18 3 70 36 39 88 56 0.6 2 0.12 0.04 0.8 0.27 5 

Corn 

Screenings 86 91 102 69 95 10 52 3 4 9 20 4.3 2 0.04 0.27 0.4 0.05 0.12 16 

Corn Bran 91 76 81 52 78 11 10 17 51 0 6.3 3 0.04 0.15 0.1 0.13 0.08 18 

Corn Germ, 

Full-fat 97 135 198 160 198 12 55 6 11 36 20 44.9 2 0.02 0.28 0.1 0.02 0.17 60 

Corn Gluten 

Feed 90 80 86 56 83 78.47 22 25 9 12 38 36 3.2 7 0.11 0.84 1.3 0.25 0.47 84 

Corn Gluten 

Meal 41% CP 91 85 93 62 88 46 63 5 9 32 23 3.2 3 0.13 0.55 0.2 0.07 0.62 35 

Corn Gluten 

Meal 60% CP 91 89 99 67 93 75.29 67 65 3 6 11 23 2.5 2 0.06 0.54 0.2 0.10 0.90 40 

Corn Cannery 

Waste 29 68 70 41 69 8 15 28 36 59 0 3.0 5 0.10 0.29 1.0 0.13 25 

Cottonseed, 

Whole 91 95 107 73 99 23 38 27 37 47 100 19.4 5 0.16 0.64 1.0 0.06 0.24 34 

Cottonseed, 

Whole, Delinted 90 95 107 73 99 24 39 19 28 40 100 22.9 5 0.12 0.54 1.2 0.24 36 

Cottonseed, 

Whole, 

Extruded

92 87 98 67 91 26 50 32 44 53 33 9.5 5 0.17 0.68 1.3 0.24 38 

Cotton Gin 

Trash (Burrs) 91 42 43 0 40 9 35 50 70 100 2.0 14 1.40 0.18 1.9 0.14 25 

Cottonseed 

Hulls 90 45 45 3 44 44.30 5 45 48 70 87 100 1.8 3 0.15 0.08 1.0 0.02 0.05 10 

Cottonseed 

Meal, Solv. Ext. 

41% CP

90 77 82 53 79 72.85 47 42 13 18 25 23 1.5 7 0.22 1.23 1.6 0.05 0.44 66 

Cottonseed 

Meal, Mech. 

Ext. 41% CP

92 79 85 55 81 71.71 46 50 13 19 31 23 5.0 7 0.21 1.18 1.6 0.05 0.42 64 

Crab Waste 

Meal 91 29 37 0 30 32 65 11 13 3.0 43 15.00 1.88 0.5 1.63 0.27 107 

Crambe Meal, 

Solv. Ext. 91 81 88 58 84 31 45 25 35 47 23 1.4 8 1.27 0.86 1.1 0.70 1.26 44 

Crambe Meal, 

Mech. Ext. 92 88 98 65 91 28 50 24 33 42 25 17.0 7 1.22 0.78 1.0 0.65 1.18 41 

Cranberry Pulp 

Meal 88 49 49 11 48 7 26 47 54 33 15.7 2 

Crawfish Waste 

Meal 94 25 36 0 29 35 74 12 15 42 13.10 0.85 

Curacao 

Phosphate 99 0 0 0 0 0 0 0 0 0 0.0 95 34.00 15.00 

Defluorinated 

Phosphate 99 0 0 0 0 0 0 0 0 0 0.0 95 32.60 18.07 1.0 100 

Diammonium 

Phosphate 98 0 0 0 0 115 0 0 0 0 0 0.0 35 0.52 20.41 0.0 2.16 

Dicalcium 

Phosphate 96 0 0 0 0 0 0 0 0 0 0.0 94 22.00 18.65 0.1 1.00 70 

Distillers Grains, 

Wet 25 91 102 69 95 28 52 8 18 40 4 9.6 5 0.10 0.70 1.0 0.20 0.60 95 

Distillers Grain, 

Barley 90 75 79 50 77 30 56 16 20 44 4 8.5 4 0.15 0.67 1.0 0.18 0.43 50 

Distillers Grain, 

Corn, Dry 91 95 106 72 99 76.86 30 58 8 16 44 4 9.5 4 0.09 0.75 0.9 0.14 0.70 65 
 Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-117

Feedstuff DM 

%

Energy Protein Fiber 

EE

%

ASH

%

Ca 

% 

P 

%

K 

%

Cl

%

S 

%

Zn 

TDN ppm

%

NEm NEg NEl 

(Mcal/cwt.)

DE 

(% 

of 

GE)a

CP

% 

UIP

%

CF

%

ADF

%

NDF

%

eNDF

%

Distillers Grain, 

Corn, Wet 36 97 109 74 102 30 47 8 16 44 4 9.5 4 0.09 0.75 0.9 0.14 0.70 65 

Distillers Grain, 

Corn with 

Solubles

89 98 111 76 103 81.50 30 54 8 16 38 4 11.9 6 0.20 0.75 0.9 0.18 0.80 85 

Distillers Dried 

Solubles 93 87 96 64 91 79.45 31 47 4 7 22 4 13.0 8 0.35 1.20 1.8 0.28 1.10 91 

Distillers Corn 

Stillage 7 92 103 70 96 22 55 8 10 21 0 8.1 5 0.14 0.72 0.2 0.60 60 

Distillers Grain, 

Sorghum, Dry 91 84 92 61 87 72.85 33 62 13 20 44 4 10.0 4 0.20 0.68 0.3 0.50 50 

Distillers Grain, 

Sorghum, Wet 35 86 95 63 89 33 55 13 19 43 4 10.0 4 0.20 0.68 0.3 0.50 50 

Distillers Grain, 

Sorghum with 

Solubles

92 85 93 62 88 33 53 12 18 42 4 10.0 4 0.23 0.70 0.5 0.70 55 

Elephant 

(Napier) Grass 

Hay, Chopped

92 55 55 21 54 9 24 46 63 85 2.0 10 0.35 0.30 1.3 0.10 

Fat, Animal, 

Poultry, 

Vegetable

99 195 285 230 285 80.08 f 0 0 0 0 0 99.0 0 0.00 0.00 0.0 

Feather Meal 

Hydrolyzed 93 67 69 40 68 87 68 1 14 42 23 7.0 3 0.48 0.45 0.1 0.20 1.82 90 

Fescue KY 31 

Fresh 29 64 65 36 65 15 20 25 32 64 40 5.5 9 0.48 0.37 2.5 0.18 22 

Fescue KY 31 

Hay Early 

Bloom

88 60 60 30 60 53.57 18 22 25 31 64 98 6.6 8 0.48 0.36 2.6 0.27 24 

Fescue KY 31 

Hay Mature 88 52 52 16 51 11 30 30 42 73 98 5.0 6 0.45 0.26 1.7 0.14 22 

Fescue (Red) 

Straw 94 43 44 0 41 4 41 1.1 6 0.00 0.06 

Fish Meal 90 74 78 49 76 66 60 1 2 12 10 9.0 20 5.55 3.15 0.7 0.76 0.80 130 

Flax Seed Hulls 91 38 40 0 36 9 32 39 50 98 1.5 10 

Garbage 

Municipal 

Cooked

23 80 86 56 83 16 9 50 59 30 20.0 10 1.20 0.43 0.6 0.67 

Glycerol 

(Glycerin) 88 90 100 68 94 0 0 0 0 0 0 0.0 6 4.00 

Grain 

Screenings 90 65 66 37 66 14 14 5.5 9 0.25 0.34 30 

Grain Dust 92 73 77 48 75 10 11 2.2 10 0.30 0.18 42 

Grape Pomace 

Stemless 91 40 42 0 38 27.50 12 45 32 46 54 34 7.6 9 0.55 0.07 0.6 0.01 24 

Grass Hay 88 58 58 26 58 10 30 33 41 63 98 3.0 6 0.60 0.21 2.0 0.20 28 

Grass Silage 30 61 62 31 61 11 24 32 39 60 61 3.4 8 0.70 0.24 2.1 0.22 29 

Guar Meal 90 72 75 47 74 39 34 16 3.9 5 

Hominy Feed 90 89 99 67 93 11 48 5 8 21 9 6.5 3 0.04 0.55 0.6 0.06 0.10 32 

Hop Leaves 37 49 49 11 48 15 15 3.6 35 2.80 0.64 

Hop Vine Silage 30 53 53 18 53 15 21 24 3.1 20 3.30 0.37 1.8 0.22 44 

Hops Spent 89 35 39 0 33 23 26 30 4.6 7 1.60 0.60 

Kelp Dried 91 32 38 0 29 54.67 7 7 10 0.5 39 2.72 0.31 

Kenaf Hay 92 48 48 9 47 10 31 44 56 98 2.9 12 

Kochia Fresh 29 55 55 21 55 65.11 16 23 1.2 18 1.10 0.30 

Kochia Hay 90 53 53 18 53 14 27 1.7 14 1.00 0.20 

Kudzu Hay 90 54 54 20 54 16 33 2.6 7 3.00 0.23 
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-118 

Feedstuff DM 

%

Energy Protein Fiber 

EE

%

ASH

%

Ca 

% 

P 

%

K 

%

Cl

%

S 

%

Zn 

TDN ppm

%

NEm NEg NEl 

(Mcal/cwt.)

DE 

(% 

of 

GE)a

CP

% 

UIP

%

CF

%

ADF

%

NDF

%

eNDF

%

Lespedeza 

Fresh Early 

Bloom

25 60 60 30 60 16 50 32 2.0 10 1.20 0.24 1.1 0.21 

Lespedeza Hay 92 54 54 20 54 14 60 30 3.0 7 1.10 0.22 1.0 0.19 29 

Limestone 

Ground 98 0 0 0 0 0 0 0 0 0 0 0.0 98 34.00 0.02 0.03 

Limestone 

Dolomitic 

Ground

99 0 0 0 0 0 0 0 0 0 0 0.0 98 22.30 0.04 0.4 

Linseed Meal, 

Solv. Ext. 91 77 82 53 79 38 36 10 18 25 23 1.7 6 0.43 0.91 1.5 0.04 0.47 60 

Linseed Meal, 

Mech. Ext. 91 82 89 59 85 37 40 10 17 24 23 6.0 6 0.42 0.90 1.4 0.04 0.46 59 

Meadow Hay 90 50 50 12 49 63.37 7 23 33 44 70 98 2.5 9 0.61 0.18 1.6 0.17 24 

Meat Meal, 

Swine/Poultry 93 71 74 46 73 56 64 2 7 48 0 10.5 24 9.00 4.42 0.5 1.27 0.48 190 

Meat and Bone 

Meal, 

Swine/Poultry

93 72 75 47 74 56 24 1 5 34 0 10.0 29 13.50 6.50 

Milk, Dry, Skim 94 87 96 64 90 36 0 0 0 0 0 0.9 8 1.36 1.09 1.7 0.96 0.34 41 

Mint Slug Silage 27 55 55 21 55 14 24 1.8 16 1.10 0.57 

Molasses Beet 77 75 79 50 77 91.95 8 0 0 0 0 0 0.2 12 0.14 0.03 6.0 1.64 0.60 18 

Molasses Cane 77 74 78 49 76 86.63 6 0 0 0 0 0 0.5 14 0.95 0.09 4.2 2.30 0.68 15 

Molasses Cane 

Dried 94 74 78 49 76 82.12 9 0 2 3 7 0 0.3 14 1.10 0.15 3.6 3.00 30 

Molasses, 

Cond. 

Fermentation 

Solubles

43 69 71 43 70 16 0 0 0 0 0 1.0 26 2.12 0.14 7.5 2.73 0.93 30 

Molasses Citrus 65 75 79 50 77 84.11 9 0 0 0 0 0 0.3 8 1.84 0.15 0.2 0.11 0.23 137 

Molasses 

Wood, 

Hemicellulose

61 70 73 44 71 1 0 1 2 4 0 0.6 7 1.10 0.10 0.1 0.05 

Monoammoniu

m Phosphate 98 0 0 0 0 70 0 0 0 0 0 0.0 24 0.30 24.70 0.0 1.42 81 

Mono-Dicalcium 

Phosphate 97 0 0 0 0 0 0 0 0 0 0.0 94 16.70 21.10 0.1 1.20 70 

Oat Hay 90 54 54 20 54 59.36 10 25 31 39 63 98 2.3 8 0.40 0.27 1.6 0.42 0.21 28 

Oat Silage 35 60 60 30 60 64.00 g 12 21 31 39 59 61 3.2 10 0.34 0.30 2.4 0.50 0.25 27 

Oat Straw 91 48 48 9 47 49.64 4 40 41 48 73 98 2.3 8 0.24 0.07 2.5 0.78 0.22 6 

Oat Grain 89 76 81 52 78 75.63 13 18 11 15 28 34 5.0 4 0.05 0.41 0.5 0.11 0.20 40 

Oat Grain, 

Steam Flaked 84 88 98 65 91 13 26 11 15 30 32 4.9 4 0.05 0.37 0.5 0.11 0.20 40 

Oat Groats 91 91 102 69 95 88.29 18 15 3 6.6 2 0.08 0.47 0.4 0.10 0.20 

Oat Middlings 90 91 102 69 95 16 20 4 6 6.0 3 0.07 0.48 0.5 0.23 

Oat Mill 

Byproduct 89 33 38 0 30 7 27 37 2.4 6 0.13 0.22 0.6 0.24 

Oat Hulls 93 38 40 0 36 38.39 4 25 33 41 75 90 1.6 7 0.16 0.15 0.6 0.08 0.14 31 

Orange Pulp 

Dried 89 79 85 55 81 9 9 16 20 33 1.8 4 0.71 0.11 0.6 0.05 

Orchardgrass 

Fresh Early 

Bloom

24 65 66 37 66 60.13 14 23 30 32 54 41 4.0 9 0.33 0.39 2.7 0.08 0.20 21 

Orchardgrass 

Hay 88 59 59 28 59 64.29 h 10 27 34 40 67 98 3.3 8 0.32 0.30 2.6 0.41 0.20 26 

Pea Vine Hay 89 59 59 28 59 11 32 50 62 92 2.0 7 1.25 0.24 1.3 0.20 20 
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-119

Feedstuff DM 

%

Energy Protein Fiber 

EE

%

ASH

%

Ca 

% 

P 

%

K 

%

Cl

%

S 

%

Zn 

TDN ppm

%

NEm NEg NEl 

(Mcal/cwt.)

DE 

(% 

of 

GE)a

CP

% 

UIP

%

CF

%

ADF

%

NDF

%

eNDF

%

Pea Vine Silage 25 58 58 26 58 16 29 44 55 61 3.3 8 1.25 0.28 1.6 0.29 32 

Pea Vine Straw 89 51 51 14 50 49.62 7 41 49 72 98 1.4 7 0.75 0.13 1.1 0.15 

Peas Cull 88 85 93 62 88 23 22 7 9 12 0 1.4 4 0.14 0.46 1.1 0.06 0.26 30 

Peanut Hulls 91 22 36 0 18 23.17 7 63 65 74 98 1.5 5 0.20 0.07 0.9 

Peanut Meal, 

Solv. Ext. 91 77 82 53 79 71.90 51 27 9 16 27 23 2.5 6 0.26 0.62 1.1 0.03 0.30 38 

Peanut Skins 92 0 0 0 0 17 13 20 28 0 22.0 3 0.19 0.20 

Pearl Millet 

Grain 87 82 89 59 85 68.04 13 2 6 18 34 4.5 3 0.03 0.36 0.5 

Pineapple 

Greenchop 17 47 47 7 46 8 24 35 64 41 2.4 7 0.28 0.08 

Pineapple Bran 89 71 74 46 73 72.43 5 20 33 66 20 1.5 3 0.26 0.12 

Pineapple 

Presscake 21 71 74 46 73 5 24 35 69 20 0.8 3 0.25 0.09 

Potato Vine 

Silage 15 59 59 28 59 15 26 3.7 19 2.10 0.29 4.0 0.37 

Potatoes Cull 21 80 86 56 83 10 0 2 3 4 0 0.4 5 0.03 0.24 2.2 0.30 0.09 

Potato Waste 

Wet 14 82 89 59 85 7 0 9 11 18 0 1.5 3 0.16 0.25 1.2 0.36 0.11 12 

Potato Waste 

Dried 89 85 93 62 88 95.85 8 0 7 9 15 0 0.5 5 0.16 0.25 1.2 0.39 0.11 12 

Potato Waste 

Wet with Lime 17 80 86 56 83 5 0 10 12 16 0 0.3 9 4.20 0.18 

Potato Waste 

Filter Cake 14 77 82 53 79 5 0 2 7.7 3 0.10 0.19 0.2 

Poultry 

Byproduct Meal 93 79 85 55 81 62 49 2 14.5 17 4.00 2.25 0.5 0.58 0.56 129 

Poultry Manure 

Dried 89 38 40 0 36 67.83 28 22 13 15 35 0 2.1 33 10.20 2.80 2.3 1.05 0.20 520 

Prairie Hay 91 50 50 12 49 55.53 7 37 34 47 67 98 2.0 8 0.40 0.15 1.1 0.06 0.06 34 

Pumpkins, Cull 11 80 86 56 83 15 14 21 30 0 8.9 9 0.24 0.43 3.3 

Rice Straw 91 40 42 0 38 51.16 4 38 47 72 100 1.4 13 0.23 0.08 1.2 0.11 

Rice Straw 

Ammoniated 87 45 45 3 44 9 39 53 68 100 1.3 12 0.25 0.08 1.1 0.11 

Rice Grain 89 79 85 55 81 83.86 8 30 10 12 16 34 1.9 5 0.07 0.32 0.4 0.09 0.05 17 

Rice Polishings 90 90 100 68 94 14 4 5 14.0 9 0.05 1.34 1.2 0.12 0.19 28 

Rice Bran 91 71 74 46 73 66.64 14 30 13 18 24 0 16.0 11 0.07 1.70 1.8 0.09 0.19 40 

Rice Hulls 92 13 35 0 8 15.91 3 45 44 70 81 90 0.9 20 0.12 0.07 0.5 0.08 0.08 24 

Rice Mill 

Byproduct 91 39 41 0 37 7 32 50 60 0 5.7 19 0.25 0.48 2.2 0.30 31 

Rye Grass Hay 90 58 58 26 58 66.07 i 10 30 33 38 65 98 3.3 8 0.45 0.30 2.2 0.18 27 

Rye Grass 

Silage 32 59 59 28 59 14 25 22 37 59 61 3.3 8 0.43 0.38 2.9 0.73 0.23 29 

Rye Straw 89 44 44 1 43 33.72 4 44 55 71 100 1.5 6 0.24 0.09 1.0 0.24 0.11 

Rye Grain 89 80 86 56 83 84.83 14 20 3 9 19 34 2.5 3 0.07 0.55 0.5 0.03 0.17 33 

Safflower Meal, 

Solv. Ext. 91 56 56 23 56 57.72 24 33 41 57 36 1.3 6 0.35 0.79 0.9 0.21 0.23 65 

Safflower Meal 

Dehulled, Solv. 

Ext.

91 75 79 50 77 70.55 47 11 20 27 30 0.8 7 0.38 1.50 1.2 0.18 0.22 36 

Safflower Hulls 91 14 35 0 34 4 58 73 90 100 3.7 2 

Sagebrush 

Fresh 50 50 50 12 49 59.04 j 13 25 30 38 9.2 10 1.00 0.25 0.22 

Sanfoin Hay 88 61 62 31 62 14 60 24 3.1 9 
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-120 

Feedstuff DM 

%

Energy Protein Fiber 

EE

%

ASH

%

Ca 

% 

P 

%

K 

%

Cl

%

S 

%

Zn 

TDN ppm

%

NEm NEg NEl 

(Mcal/cwt.)

DE 

(% 

of 

GE)a

CP

% 

UIP

%

CF

%

ADF

%

NDF

%

eNDF

%

Shrimp Waste 

Meal 90 48 48 9 47 50 60 11 5.5 25 8.50 1.75 1.15 

Sodium 

Tripolyphosphat

e

96 0 0 0 0 0 0 0 0 0 0.0 96 0.00 25.98 0.0 0.00 

Sorghum Stover 87 54 54 20 54 5 33 41 65 100 1.8 10 0.50 0.12 1.2 

Sorghum Silage 32 59 59 28 59 65.58 9 25 27 38 59 70 2.7 6 0.48 0.21 1.7 0.45 0.11 30 

Sorghum Grain 

(Milo), Ground 89 82 89 59 85 11 55 3 6 15 5 3.1 2 0.04 0.32 0.4 0.10 0.14 18 

Sorghum Grain 

(Milo), Flaked 82 90 100 68 94 11 62 3 6 15 38 3.1 2 0.04 0.28 0.4 0.10 0.14 18 

Soybean Hay 89 52 52 16 51 54.10 16 33 40 55 92 3.5 8 1.28 0.29 1.0 0.15 0.24 24 

Soybean Straw 88 42 43 0 40 45.98 5 44 54 70 100 1.4 6 1.59 0.06 0.6 0.26 

Soybeans 

Whole 88 92 103 70 96 41 28 8 11 15 100 18.8 5 0.27 0.64 1.9 0.03 0.34 56 

Soybeans 

Whole, 

Extruded

88 93 104 71 97 40 35 9 11 15 100 18.8 5 0.27 0.64 2.0 0.03 0.34 56 

Soybeans 

Whole, Roasted 88 93 104 71 97 40 48 9 11 15 100 18.8 5 0.27 0.64 2.0 0.03 0.34 56 

Soybean Hulls 90 77 82 52 79 66.86 13 28 39 48 62 28 2.3 5 0.60 0.19 1.3 0.02 0.12 38 

Soybean Meal, 

Solv. Ext. 44% 

CP

89 84 92 61 87 79.50 49 35 7 10 15 23 1.5 7 0.36 0.70 2.2 0.07 0.41 62 

Soybean Meal, 

Solv. Ext. 49% 

CP

89 87 96 64 90 54 36 4 6 8 23 1.3 7 0.28 0.71 2.2 0.08 0.45 61 

Soybean Mill 

Feed 90 50 50 12 49 15 36 46 1.9 6 0.46 0.19 1.7 0.07 

Spelt Grain 88 75 79 50 77 77.18 13 27 10 17 21 34 2.1 4 0.04 0.40 0.4 0.15 47 

Sudangrass 

Fresh Immature 18 70 73 44 71 73.27 17 23 29 55 41 3.9 9 0.46 0.36 2.0 0.11 24 

Sudangrass 

Hay 88 57 57 25 57 62.67 9 30 36 43 67 98 1.8 10 0.50 0.22 2.2 0.80 0.12 26 

Sudangrass 

Silage 31 58 58 26 58 60.29 10 28 30 42 64 61 3.1 10 0.58 0.27 2.4 0.52 0.14 29 

Sunflower Meal, 

Solv. Ext. 92 65 66 37 66 44.89 40 27 18 22 36 23 2.8 8 0.44 0.97 1.1 0.15 0.33 55 

Sunflower Meal 

with Hulls 91 57 57 25 57 31 35 27 32 44 37 2.4 7 0.40 1.03 1.0 0.30 85 

Sunflower Seed 

Hulls 90 40 42 0 38 4 65 52 63 73 90 2.2 3 0.00 0.11 0.2 0.19 200 

Sugar Cane 

Bagasse 91 39 41 0 37 52.15 1 49 60 86 100 0.6 4 0.90 0.29 0.5 0.10 

Tapioca Meal, 

Cassava 

Byproduct

89 82 89 59 85 1 5 8 34 0.8 3 0.03 0.05 

Timothy Fresh 

Pre-Bloom 26 64 65 36 65 11 20 31 36 59 41 3.8 7 0.40 0.28 1.9 0.57 0.15 28 

Timothy Hay 

Early Bloom 88 59 59 28 59 60.75 11 22 32 39 63 98 2.7 6 0.58 0.26 1.9 0.51 0.21 30 

Timothy Hay 

Full Bloom 88 57 57 25 57 58.68 8 30 34 40 65 98 2.6 5 0.43 0.20 1.8 0.62 0.13 25 

Timothy Silage 34 59 59 28 59 59.32 10 25 34 45 70 61 3.4 7 0.50 0.27 1.7 0.15 

Tomatoes 6 69 71 43 70 16 9 11 4.0 6 0.14 0.35 4.2 

Tomato 

Pomace Dried 92 64 65 36 65 53.98 23 26 50 55 34 10.6 6 0.43 0.59 3.6 

Triticale Hay 90 56 56 23 56 10 34 41 69 98 0.30 0.26 2.3 25 
 Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-121

Feedstuff DM 

%

Energy Protein Fiber 

EE

%

ASH

%

Ca 

% 

P 

%

K 

%

Cl

%

S 

%

Zn 

TDN ppm

%

NEm NEg NEl 

(Mcal/cwt.)

DE 

(% 

of 

GE)a

CP

% 

UIP

%

CF

%

ADF

%

NDF

%

eNDF

%

Triticale Silage 34 58 58 26 58 14 30 39 56 61 3.6 0.58 0.34 2.7 0.28 36 

Triticale Grain 89 85 93 62 88 83.82 14 25 4 5 22 34 2.4 2 0.07 0.39 0.5 0.17 37 

Turnip Tops 

(Purple) 18 68 70 41 69 18 10 13 2.6 14 3.10 0.40 3.0 1.80 0.27 

Turnip Roots 9 86 95 63 89 92.94 12 0 11 34 44 40 1.6 9 0.65 0.31 3.1 0.65 0.43 40 

Urea 46% N 99 0 0 0 0 288 0 0 0 0 0 0.0 0 0.00 0.00 0.0 0.00 0.00 0 

Vetch Hay 89 58 58 26 58 59.44 18 14 30 33 48 92 1.8 8 1.25 0.34 2.4 0.13 

Wheat Fresh, 

Pasture 21 71 74 46 73 76.07 20 16 18 30 50 41 4.0 13 0.35 0.36 3.1 0.67 0.22 

Wheat Hay 90 57 57 25 57 62.73 9 25 29 38 66 98 2.0 8 0.21 0.22 1.4 0.50 0.19 23 

Wheat Silage 33 59 59 28 59 63.99 12 21 28 37 62 61 3.2 8 0.40 0.28 2.1 0.50 0.21 27 

Wheat Straw 91 43 44 0 41 45.77 3 60 43 57 81 98 1.8 8 0.17 0.06 1.3 0.32 0.17 6 

Wheat Straw 

Ammoniated 85 50 50 12 49 9 25 40 55 76 98 1.5 9 0.15 0.05 1.3 0.30 0.16 6 

Wheat Grain 89 88 98 65 91 86.45 k 14 23 3 4 12 0 2.3 2 0.05 0.43 0.4 0.09 0.15 40 

Wheat Grain 

Hard 89 88 98 65 91 88.54 l 14 28 3 6 14 0 2.0 2 0.05 0.43 0.5 0.16 45 

Wheat Grain 

Soft 89 88 98 65 91 89.96

m 12 23 3 4 12 0 2.0 2 0.06 0.40 0.4 0.15 30 

Wheat Grain, 

Steam Flaked 85 91 102 69 95 14 29 3 4 12 0 2.3 2 0.05 0.39 0.4 0.15 40 

Wheat Grain 

Sprouted 86 88 98 65 91 12 18 3 4 13 0 2.0 2 0.04 0.36 0.4 0.17 45 

Wheat Bran 89 70 73 44 71 71.16 17 28 11 14 46 4 4.4 7 0.13 1.32 1.4 0.05 0.24 96 

Wheat 

Middlings 89 80 86 56 83 18 22 8 11 36 2 4.7 5 0.14 1.00 1.3 0.05 0.20 98 

Wheat Mill Run 90 76 81 52 78 79.11 17 28 9 12 37 0 4.5 6 0.11 1.10 1.2 0.07 0.22 90 

Wheat Shorts 89 78 83 54 80 19 25 8 10 30 0 5.3 5 0.10 0.93 1.1 0.08 0.20 118 

Wheatgrass 

Crested Fresh 

Early Bloom

37 60 60 30 60 79.78 11 25 26 28 50 41 1.6 7 0.46 0.32 2.4 

Wheatgrass 

Crested Fresh 

Full Bloom

50 55 55 21 55 65.89 10 33 33 36 65 41 1.6 7 0.39 0.28 2.1 

Wheatgrass 

Crested Hay 92 54 54 20 54 56.51 10 33 33 36 65 98 2.4 7 0.33 0.20 2.0 32 

Whey Dried 94 82 89 59 85 91.47 n 14 15 0 0 0 0 0.9 10 0.98 0.88 1.3 1.20 0.92 10 

Yeast, Brewer's 92 79 85 55 81 73.76 47 30 3 4 0 0.9 7 0.13 1.49 1.8 

DM	 =	Dry	matter	 ADF = Acid	detergent	fiber

TDN	 =	Total	digestible	nutrients	 NDF = Neutral	detergent	fiber

NEm	 =	Net	energy	for	maintenance	 eNDF = effective	neutral	detergent	fiber

NEg	 =	Net	energy	for	growth	 EE = Ether	extract

NEl	 =	Net	energy	for	lactation	 ASH =	Ash

Mcal	 = Megacalories	 Ca	 =	Calcium	

cwt	 =	Centum	weight	(hundredweight)	 P =	Phosphorous

DE	 =Digestible	energy	 K = Potassium

GE	 =	Gross	energy	 Cl =	Chlorine

CP	 =Crude	protein	 S =	Sulfur

UIP	 =	Undegradable	intake	protein	 Zn =	Zinc

CF		 =	Crude	fiber	 ppm =	parts	per	million

a	DE	(%	of	GE)	values	from	Ewan	(1989)	

b	Average	of	fresh,	late	vegetative;	fresh,	early	bloom;	fresh,	midbloom;	fresh,	full	bloom	 c	Average	of	silage	wilted	–	early	bloom;	silage	wilted	–	midbloom;	silage	wilted	–	full	bloom	 d	Average	of	silage	wilted	–	early	bloom;	silage	wilted	–	midbloom;	silage	wilted	–	full	bloom	
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e	Average	of	hay	–	sun‐cured,	late	vegetative;	hay	–	sun‐cured,	late	bloom	 f

	Average	of	fat,	animal	poultry;	oil,	vegetable	

g	Average	of	silage,	late	vegetative;	silage,	dough	stage	 h	Average	of	hay,	sun‐cured,	early	bloom;	hay,	sun‐cured,	late	bloom	 i

	Average	of	ryegrass,	Italian	Lolium multiflorum:	hay,	sun‐cured,	late	vegetative;	hay,	sun‐cured,	early	bloom;	average	of	

ryegrass,	perennial	Lolium perenne:	hay,	sun‐cured	

j

	Average	of	sagebrush,	big	Artemisia tridentate:	browse,	fresh,	stem‐cured;	sagebrush,	bud	Artemisia spinescens:	browse,	

fresh,	early	vegetative;	browse,	fresh,	late	vegetative;	and	sagebrush,	fringed	Artemisia frigida:	browse,	fresh,	midbloom;	

browse,	fresh,	mature	

k	Average	of	wheat,	Durum Triticum	durum	and	wheat	Triticum aestivum grain	 l

	Average	of	grain,	hard	red	spring;	grain,	hard	winter	

m	Average	of	grain,	soft	red	winter;	grain,	soft	white	winter;	grain,	soft	white	winter,	pacific	coast	 n	Average	of	dehydrated	(cattle)	and	low	lactose,	low	lactose,	dehydrated	(dried	whey	product)(cattle)	

	 	
 Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-123

Appendix 5‐C: Estimation Methods for Ammonia Emissions from Manure

Management Systems

This	appendix	presents	methods	for	estimating	NH3	from	manure	management	systems.	NH3,	

although	not	a	GHG,	is	emitted	in	large	quantities	from	animal	housing	and	manure	management	

systems	and	is	an	indirect	precursor	to	nitrous	oxide	(N2O)	emissions	as	well	as	an	environmental	

concern.		

5‐C.1 Method for Estimating Ammonia Emissions Using Equations from

Integrated Farm System Model		

Ammonia	emissions	from	manure	storage	are	mainly	from	total	ammoniacal	nitrogen	(TAN).	For	

many	animal	confinement	systems,	it	has	been	reported	that	most	of	the	urea	in	manure	has	been	

converted	to	TAN	and	lost	as	NH3	by	the	time	manure	is	transferred	to	storage	(Rotz	et	al.,	2011b);	

therefore,	only	organic	nitrogen	in	the	manure	at	the	storage	stage,	which	is	mineralized	to	TAN,	is	

used	to	estimate	NH3	release.	There	are	four	main	steps	related	to	NH3	release	to	the	atmosphere:	

diffusion,	dissociation,	aqueous	to	gas	partitioning,	and	mass	transport	away	from	the	manure	

surface	(Rotz	et	al.,	2011b).	For	solid	manure,	diffusion	through	the	manure	is	a	main	constraint	to	

the	emission	rate.	For	liquid	manure,	NH3	emissions	are	a	function	of	the	overall	mass	transfer	rate	

and	the	difference	in	the	NH3	concentration	between	the	lagoon	and	the	surrounding	atmosphere.		

5‐C.1.1 Rationale for Selected Method

Ammonia	emissions	from	temporary	stack	and	long	term	stockpiles,	aerobic	lagoons,	anaerobic	

lagoons,	runoff	holding	ponds,	and	storage	tanks	can	be	calculated	using	equations	from	the	

DairyGEM	Model	(a	subset	of	the	Integrated	Farm	System	Model)	(Rotz	et	al.,	2011b).	The	

equations	from	Rotz	et	al.	are	the	only	available	methods	for	estimating	NH3	emissions	from	these	

systems	and	best	describes	the	quantitative	relationship	amongst	activity	data	at	the	entity	level.	

5‐C.1.2 Activity Data

In	order	to	estimate	the	daily	NH3	emission	from	temporary	stack,	long‐term	stockpiles,	anaerobic

lagoons,	runoff	holding	ponds,	and	storage	tanks,	the	following	information	is	needed:	

 Total	nitrogen	content	of	manure		

 Manure	total	NH3‐N	content		

 Surface	area	of	manure	pile	

 Temperatures	(local	ambient	temperature	and	manure	temperature)

 Local	ambient	air	velocity	

 For	aerobic	lagoons,	the	pH	of	the	lagoon	is	also	needed.	

The	timing	of	measurements	can	be	based	on	dietary	changes	or	seasonal	timeframe,	which	is	

decided	by	individual	farm	entity.	However,	due	to	the	dynamic	nature	of	manure	piles	causing	the	

Ammonia

 Method	is	a	function	of	the	surface	area	of	the	storage	unit,	resistance	to	mass	transfer,	

ambient	air	velocity,	total	NH3	and	organic	nitrogen	content,	rate	of	organic	nitrogen	

transformation	to	total	ammoniacal	nitrogen,	and	manure	temperature	as	defined	by	

Rotz	et	al.	(2011b).	

 Ammonia	and	organic	nitrogen	content	can	be	obtained	from	sampling	and	lab	testing.		
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

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changes	of	the	variables,	frequent	measurements	of	manure	characteristics	are	recommended	to	

ensure	accuracy	of	the	estimation.		

5‐C.1.3 Ancillary Data

The	ancillary	data	used	to	estimate	NH3	emission	for	temporary	storage	are	kinematic	viscosity	of	

air,	mass	diffusivity	of	NH3,	and	resistance	to	mass	transfer.	The	kinematic	viscosity	of	air	at	

standard	atmospheric	pressure	is	listed	in	Table	5‐C‐1.	The	mass	diffusivity	of	NH3	is	obtained	from	

references	(Paul	and	Watson,	1966;	Baker,	1969)	and	listed	in	Table	5‐C‐2.	The	resistance	to	mass	

transfer	for	different	solid	manure	storages	are	obtained	from	the	DairyGEM	model	(Rotz	et	al.,	

2011a).		

5‐C.2 Method for Ammonia Emissions from Temporary Stack, Long‐Term

Stockpile, Anaerobic Lagoons/Runoff Holding Ponds/Storage Tanks, and

Aerobic Lagoons

Temporary	Stack,	Long‐Term	Stockpile,	and	Anaerobic	Lagoons/Runoff	Holding	Ponds/Storage	

Tanks	

As	indicated	in	Equation	5‐C‐1,	NH3	emissions	are	a	function	of	the	overall	mass	transfer	rate	and

the	difference	in	NH3	concentration	between	the	manure	and	surrounding	atmosphere.	The	mean	

ambient	air	NH3	concentration	is	1.3	μg/m3	based	on	passive	measurements	from	35	locations	

across	24	States	in	the	U.S.	with	one	year	or	more	of	measurements	(Ammonia	Monitoring	

Network,	National	Atmospheric	Deposition	Program).	The	Henry’s	Law	constant	is	used	to	define	

the	ratio	of	NH3	concentration	in	a	solution	in	equilibrium	with	gaseous	NH3	concentration	in	air	

and	is	exponentially	related	to	temperature.		

a	Ammonia	concentration	in	ambient	air	can	be	obtained	from	National	Atmospheric	Deposition	Program	

(nadp.sws.uiuc.edu/amon/).	

b	Shape	factors	( )	are	listed	in	Appendix	5‐D.	

	Equation	5‐C‐2	describes	the	calculation	for	Henry’s	Law	Constant.	The	manure	temperature	is	

calculated	as	the	average	ambient	temperature	over	the	previous	10	days.	

Equation 5‐C‐1: Ammonia Emissions from Temporary Stack, Long Term Stockpiles, and

Anaerobic Lagoons/Runoff Holding Ponds/Storage Tanks

۳ۼ۶ ૜ൌ ૛૝	 ൈ ૜૟૙૙ ൈ ܍܋܉܎ܚܝܛۯ ൈ ۹ ൈ ሺܕۼۯ܂ െ ۶ ൈ ܉ۼۯ܂ሻ	

Where:	

ENH3	 =	NH3	emissions	per	day	(kg	NH3	day‐1)	

24		 =	Hours	per	day	(hr	day‐1)	

3,600		 =	Seconds	per	hour	(s	hr‐1)	

Asurface	 =	Footprint	of	manure	storage	(m2)	×	shape	factorb

K		 =	Overall	mass	transfer	coefficient	(m	s‐1)	as	defined	in	Equation	5‐C‐3	

TANm	 =	Total	ammoniacal	nitrogen	in	the	manure	(kg	m‐3)	

TANa	 =	NH3	concentration	in	ambient	aira	(kg	m‐3)	

H		 =	Henry’s	Law	constant	as	defined	in	Equation	5‐C‐2	
 Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-125

The	overall	mass	transfer	coefficient	is	expressed	as	the	reciprocal	of	the	overall	effective	

resistance	of	the	manure.	The	mass	transfer	coefficient	through	gaseous	phase	on	the	top	of	manure	

is	calculated	using	Equation	5‐C‐3.	The	resistance	to	mass	transfer	is	calculated	in	Equation	5‐C‐5.	It	

has	been	reported	that	the	mass	transfer	coefficient	through	manure	has	relatively	little	effect	on	

the	mass	transfer	of	NH3	(Ni,	1999)	and	thus	the	1/Kl	is	considered	negligible	in	the	following	

equation.		

The	mass	transfer	coefficient	through	gaseous	phase	(Equation	5‐C‐4)	is	estimated	from	the	air	

friction	velocity	and	Schmidt	number	of	air.	The	Turbulent	Schmidt	number	is	dependent	on	the	

characteristics	of	the	gas	and	the	scales	of	atmospheric	turbulence.	Since	turbulence	is	highly	

dependent	on	many	complex	interactions,	the	Turbulent	Schmidt	number	was	approximated	by	

only	accounting	for	the	gas	characteristics.	These	characteristics	are	expressed	in	the	molecular	

Schmidt	number,	defined	as	SC	=ν/D,	where	ν	is	the	kinematic	viscosity	of	air	(m2	s‐1),	and	D	is	the	

mass	diffusivity	of	NH3	(m2	s‐1).	In	order	to	calculate	Schmidt	number,	the	dynamic	viscosity	of	air,	

the	density	of	the	air,	and	the	mass	diffusivity	of	NH3	are	given	based	on	air	temperature	in	Table	5‐

C‐1	and	Table	5‐C‐2.		

Equation 5‐C‐2: Calculation Henry’s Law Constant

۶ ൌ

܂

૙. ૛૚૜ૡ ൈ ૚૙ሺ

૚ૡ૛૞

ሻ૚૛૜.૟ି ܂

Where:	

H	=	Henry’s	Law	constant	for	NH3	(aqueous	to	gas)	

T	=	Manure	temperature	(Kelvin	degree)

Equation 5‐C‐3: Overall Mass Transfer Coefficient

۹ ൌ

૚

ሺ ۶

۹܏

൅ ૚

ܔ۹

ሻܕ܀ ൅

Where:	

K		 =	Overall	mass	transfer	coefficient	(m	s‐1)	

H		 =	Henry’s	Law	constant	for	NH3	(aqueous	to	gas)	

Rm	 =	Resistance	to	mass	transfer	(s	m‐1)	

Kg		 =	Mass	transfer	coefficient	through	gaseous	phase	on	the	top	of	manure	(m	s‐1)	

Kl		 =	Mass	transfer	coefficient	through	manure	(m	s‐1)	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-126 

The	mass	transfer	coefficient	through	manure	has	little	effect	on	the	mass	transfer	of	NH3,	so	it	is	

negligible.	The	resistance	to	mass	transfer	is	the	sum	of	the	resistance	through	the	manure	and	the	

resistance	of	cover	materials	over	the	manure	(Equation	5‐C‐5).	The	values	for	resistance	to	mass	

transfer	through	the	manure	and	resistance	to	mass	transfer	through	the	cover	are	listed	in	Table	

5‐C‐3	for	temporary	stack	and	long‐term	stockpile	and	in	Table	5‐C‐4	for	anaerobic	lagoons,	runoff	

holding	ponds,	and	storage	tanks.	

		

Table 5‐C‐3: Resistance to Mass Transfer for Solid Manure Storage		

Type of Manure Storage Rs (s m‐1) Rc (s m‐1)

Uncovered	solid	manure	(dry	matter	>15%) 3×105 0	

Covered	solid	manure	(dry	matter	>15%) 3×105 2×105

Uncovered	slurry	manure	(dry	mater,	10‐15%) 2×105 0	

Table 5‐C‐1: Kinematic Viscosity of Air at

Different Temperature at Standard

Atmospheric Pressure

Temperature (°C) Kinematic Viscosity

(m2/s) x 10‐5

‐40	 1.04

‐20	 1.17

0	 1.32

5	 1.36

10	 1.41

15	 1.47

20	 1.51

25	 1.56

30	 1.60

40	 1.66

50	 1.76

Source:	White	(1999).	

Table 5‐C‐2: Mass Diffusivity of Ammonia

at Standard Atmospheric Pressure

Temperature (°C) Diffusivity of Ammonia

(m2/s) x 10‐4

‐40 0.106

0 0.110

30 0.200

40 0.209

50 0.233

Source:	Paul	and	Watson (1966)	and	Baker	(1969).

Equation 5‐C‐4: Calculating Mass Transfer Coefficient through Gaseous Phase

۹܏ ൌ ૙. ૙૙૚ ൅ ૙. ૙૝૟૛ ൈ ሺ૙. ૙૛ ൈ ܉܄

૚.૞ሻ ൈ ሺ܁۱ሻି૙.૟ૠ

Where:	

Kg		=	Mass	transfer	coefficient	through	gaseous	phase	on	the	top	of	manure	(m	s‐1)	

Va	=	Ambient	air	velocity	(m	s‐1)	that	can	be	obtained	from	National	Weather	Service	by	

searching	the	target	location	

SC	=	Turbulent	Schmidt	number	of	NH3	in	the	air	above	manure	surface	(dimensionless)	

Equation 5‐C‐5: Calculation of Resistance to Mass Transfer

۱܀ ൅ ܁܀ ൌ ܕ܀

Where:	

Rm	=	Resistance	to	mass	transfer	(s	m‐1)	

Rs	=	Resistance	to	mass	transfer	through	the	manure	(s	m‐1)	

Rc	=	Resistance	to	mass	transfer	through	the	cover	(s	m‐1)	
 Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-127

Type of Manure Storage Rs (s m‐1) Rc (s m‐1)

Covered	slurry	manure	(dry	mater,	10‐15%) 2×105 2×105

Source:	Rotz	et	al.	(2011b).	

Table 5‐C‐4: Resistance to Mass Transfer For Anaerobic Lagoons, Runoff Holding Ponds, and

Storage Tanks

Type of Cover Rs (s m‐1) Rc (s m‐1)

Uncovered	liquid	manure	 0 0	

Covered	liquid	manure	 0 2×105

Source:	Rotz	et	al.	(2011a).	

Aerobic	Lagoons	

The	method	for	estimating	NH3	emissions	from	aerobic	lagoons	(Equation	5‐C‐6)	is	similar	to	that	

for	stockpiles	and	anaerobic	lagoons	but	accounts	for	the	concentration	of	NH3	in	the	liquid.		

The	overall	mass	transfer	coefficient	is	calculated	using	Equation	5‐C‐3	with	resistance	to	mass	

transfer	assumed	to	be	zero.	Henry’s	Law	Constant	is	calculated	using	Equation	5‐C‐2	and	the	mass	

transfer	coefficient	through	a	gaseous	phase	is	calculated	using	Equation 5‐C‐4.	The	mass	transfer	

through	the	liquid	film	layer	is	calculated	using	Equation	5‐C‐7.	

Equation	5‐C‐8	describes	the	estimation	method	for	NH3	concentration	in	the	liquid.	The	NH3

fraction	of	TAN	in	the	lagoon	liquid	is	a	function	of	pH	and	a dissociation	constant	according	to	

Equation	5‐C‐9.	

Equation 5‐C‐6: Resistance to Mass Transfer for Solid Manure Storage (Rotz et al.,

2011b)

۳ۼ۶ ૜ൌ ૛૝	 ൈ ૜૟૙૙ ൈ ۹ ൈ ܍܋܉܎ܚܝܛۯ ൈ ۼ۶૜

Where:	

ENH3		 =	NH3	emissions	per	day	(kg	day‐1)	

24		 =	Hours	per	day	(hr	day‐1)	

3,600		=	Seconds	per	hour	(s	h‐1)	

K		 =	Overall	mass	transfer	coefficient	(m	s‐1)	

Asurface	=	Surface	area	of	lagoon	(m2)	

NH3		 =	Concentration	in	the	liquid	(kg	m‐3)	

Equation 5‐C‐7: Calculating the Mass Transfer Coefficient through the Liquid Film Layer

Klൌ	1.417	ൈ	10‐12	ൈT4

Where:	

Kl	=	Mass	transfer	coefficient	through	the	liquid	film	layer	(m	s‐1)	

T		=	Manure	temperature	(Kelvin)	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-128 

5‐C.3 Method for Estimating Ammonia Emissions from Composting Using

IPCC Tier 2 Equations

Composting	is	the	controlled	aerobic	decomposition	of	organic	material	into	a	stable,	humus‐like	

product	(USDA	NRCS,	2007).	Eghball	et	al.	(1997)	reported	that	19	to	45	percent	of	the	nitrogen	

present	in	manure	was	lost	during	composting,	with	the	majority	of	this	presumably	as	NH3.		

5‐C.3.1 Rationale for Selected Method

The	IPCC	method	is	adapted	for	estimating	NH3	emissions	and	incorporates	NH3	emission	factors	

from	a	study	of	composting	cattle	and	swine	manure	(Hellebrand	and	Kalk,	2000).	The	IPCC	

equation	is	the	only	available	method	for	estimating	NH3	emissions	from	composting.	This	

methodology	best	describes	the	quantitative	relationship	amongst	activity	data	at	the	entity	level.	

Equation 5‐C‐8: Calculating the Ammonia Concentration in the Liquid

ۼۯ܂ ൈ ۴ ൌ૜ ۶ۼ

Where:	

NH3	 =	Concentration	in	the	liquid	(kg	m‐3)		

F		 =	NH3	of	TAN	in	the	lagoon	liquid	

TAN	 =	Total	ammonia	nitrogen	in	the	manure	liquid	(kg	m‐3)	

Equation 5‐C‐9: Calculating the Ammonia Fraction of TAN in the Lagoon Liquid

۴ ൌ

૚

۶ܘି૙૚ ൅ ૚

܉۹

Where:	

F		 =	NH3	of	TAN	in	the	lagoon	liquid	

pH	 =	Hydrogen	ion	concentration	

Ka	 =	Dissociation	constant,	where	Kୟ ൌ 10ሺ଴.଴ହିమళఴఴ

౐ ሻ

T	 =	Temperature	(Kelvin)	

Ammonia

 IPCC	Tier	2	approach	adjusted	to	estimate	NH3	emissions	utilizing	data	on	an	NH3

emission	factor,	total	initial	nitrogen,	and	dry	manure.	

 The	NH3	emission	factor	is	obtained	from	a	study	of	composting	mixture	of	cattle	and	

swine	manure	by	Hellebrand	and	Kalk	(2000).		

 Nitrogen	content	can	be	obtained	from	sampling	and	lab	testing.

 Method	is	the	only	readily	available	method.	
 Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-129

5‐C.3.2 Activity Data

In	order	to	estimate	the	daily	NH3	emissions,	the	following	information	is	needed:		

 Total	dry	manure	in	the	storage	

 Total	nitrogen	in	manure	

The	timing	of	measurements	can	be	based	on	dietary	changes	or	on	a	seasonal	timeframe,	which	is	

decided	by	individual	farm	entity.	However,	due	to	the	dynamic	nature	of	manure	storage	causing	

changes	in	the	variables,	frequent	measurements	of	manure	characteristics	(e.g.,	volatile	solids,	

temperature,	total	dry	manure)	are	recommended	to	improve	accuracy	of	the	estimation.		

5‐C.3.3 Ancillary Data

The	ancillary	data	used	to	estimate	NH3	emission	for	manure	composting	is	NH3	emission	factor	

(Hellebrand	and	Kalk,	2000).		

5‐C.4 Method for Ammonia Emissions from Composting

Ammonia	emissions	from	composting	are	dependent	on	volatilization	and	mineralization	after	

nitrification,	decomposition	of	organic	nitrogen	compounds,	or	urea	hydrolysis.	An	IPCC	Tier	2	

approach	for	estimating	N2O	emissions	is	adapted	to	estimate	NH3	emissions	from	composting	of	

solid	manure.	The	NH3	emission	factor	of	0.05	is	obtained	from	a	study	of	composting	mixture	of	

cattle	and	swine	manure	(Hellebrand	and	Kalk,	2000).	Equation	5‐C‐10	provides	the	equations	for	

estimating	NH3	emissions.	

5‐C.5 Uncertainty in Ammonia Emissions Estimates

Estimation	methods	from	Rotz	et	al.	(2011b)	are	used	to	estimate	NH3	emissions	from	temporary	

stack	and	long‐term	stockpiles	and	aerobic	lagoons.	Rotz	et	al.	takes	into	account	the	amount	of	

emissive	surface	area	of	the	pile	or	lagoon.	Given	the	difficulty	of	measuring	the	surface	area	of	a	

manure	pile,	shape	factors	have	been	developed	to	approximate	surface	area	based	on	general	

shape	and	footprint.	These	shape	factors	provide	an	estimate	total	surface	area	only;	there	is	

associated	uncertainty	based	on	the	accurracy	of	the	footprint	measurements	and	how	well	the	

shape	of	the	pile	matches	the	shape	factors	defined.	

The	Rotz	et	al.	equations	require	the	NH3	concentration	in	the	ambient	air	on	site.	National	data	on	

ambient	NH3	concentrations	are	available	from	the	National	Atmospheric	Deposition	Program.	The	

Equation 5‐C‐10: IPCC Tier 2 Approach for Calculating NH3 Emissions from Composting

of Solid Manure

ൈ ۼ܂ ൈ૜ ۶ۼ۳۴ ൈ ܕ ൌ૜ ۶ۼ۳

૚ૠ

૚૝

Where:	

ENH3	 =	NH3	emissions	per	day	(kg	NH3	day‐1)	

m		 =	Total	dry	manure	(kg	day‐1)	

EFNH3	 =	NH3	emission	(loss)	relative	to	total	nitrogen	in	manure	(kg	NH3‐N	(kg	TN)‐1;	=0.05)	

TN		 =	Total	nitrogen	in	the	initial	(fresh)	manure	(kg	TN	(kg	dry	manure)‐1)	

ଵ଻

ଵସ

	 =	Conversion	of	NH3	to	nitrogen	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

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Program	provides	ambient	NH3	concentrations	from	approximately	60	active	monitoring	sites	

across	the	country.	Given	the	dearth	of	monitoring	sites	and	the	potentially	long	distances	between	

the	entity	and	the	nearest	measurement,	there	can	be	a	large	amount	of	uncertainty	associated	with	

the	ambient	air	NH3	concentrations	used	for	estimating	NH3	emissions.		

Table 5‐C‐5: Available Uncertainty Data for Ammonia Emissions Estimates

Parameter

Abbreviation/Symbol

Data Input Unit

Estimated Value

Relative uncertainty Low

(%)

Relative uncertainty High

(%)

Effective Lower Limit

Effective Upper Limit

Data Source

pH	 pH	 ‐	 7.5	 	 	 6.5	 8.5	 Expert	Assessment	

Total	ammonia	nitrogen	in	the	

manure	–	beef		earthen	lot	 TAN	 kg	NH3/m3 0.1	 0	 0.02	 ASABE	(2005)	

Total	ammonia	nitrogen	in	the	

manure	–	poultry,		leghorn	pullets	 TAN	 kg	NH3/m3	 0.85	 	 	 0.66	 1.04	 ASABE	(2005)	

Total	ammonia	nitrogen	in	the	

manure	–	poultry,		leghorn	hen	 TAN	 kg	NH3/m3 0.88	 0.54	 1.22	 ASABE	(2005)	

Total	ammonia	nitrogen	in	the	

manure	–	poultry,		broiler	 TAN	 kg	NH3/m3	 0.75	 	 	 	 	 ASABE	(2005)	

Ammonia	concentration	in	the	

liquid	–	dairy	lagoon	effluent	 NH3 kg	NH3/m3 0.08	 ASABE	(2005)	

Ammonia	concentration	in	the	

liquid	–	dairy	slurry	(liquid)	 NH3	 kg	NH3/m3	 0.14	 	 	 	 	 ASABE	(2005)	

Ammonia	concentration	in	the	

liquid	–	Swine	Finisher‐Slurry	

wet‐dry	feeders	

NH3 kg	NH3/m3 0.5	 ASABE	(2005)	

Ammonia	concentration	in	the	

liquid	–	Swine	Slurry	storage‐dry	

feeders	

NH3	 kg	NH3/m3	 0.34	 	 	 0.19	 0.49	 ASABE	(2005)	

Ammonia	concentration	in	the	

liquid	–	Swine	flush	building	 NH3 kg	NH3/m3 0.14	 ASABE	(2005)	

Ammonia	concentration	in	the	

liquid	–	Swine	agitated	solids	and	

water	

NH3	 kg	NH3/m3	 0.05	 	 	 	 	 ASABE	(2005)	

Ammonia	concentration	in	the	

liquid	–	Swine	Lagoon	surface	

water	

NH3 kg	NH3/m3 0.04	 ASABE	(2005)	

Ammonia	concentration	in	the	

liquid	–	Swine	Lagoon	sludge	 NH3	 kg	NH3/m3	 0.07	 	 	 	 	 ASABE	(2005)	

Composting	–	Ammonia	emission	

(loss)	relative	to	total	nitrogen	in	

manure	

EFNH3 kg	NH3‐N/kg	N	 0.05	 Hellebrand	and	

Kalk	(2000)	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

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Appendix 5‐D: Manure Management Systems Shape Factors ( )

Factors	can	be	applied	to	account	for	the	differences	in	emissive	surface	areas	for	different	shapes	

of	manure	piles.	The	equations	provided	below	provide	estimates	for	the	surface	area	for	common	

pile	shapes;	these	estimates	are	applied	for	calculating	NH3	emissions	from	temporary	stacks.	

Figure 5‐D‐1: Equations for Calculating the Shape Factor for a 2‐Sided Storage Bin with

Quarter‐Cone Pile
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

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Figure 5‐D‐2: Equations for Calculating the Shape Factor for a 3‐Sided Storage Bin

Figure 5‐D‐3: Equations for Calculating the Shape Factor for a Conical Manure Pile
 Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

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Figure 5‐D‐4: Equations for Calculating the Shape Factor for a Free‐Standing, Truncated

Conical Stack

Figure 5‐D‐5: Equations for Calculating the Shape Factor for a Windrow with Triangular

Cross Section
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

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Appendix 5‐E: Model Review: Review of Enteric Fermentation Models

A	number	of	empirical	and	mechanistic	models	have	been	developed	to	estimate	enteric	CH4	

production	(Table	5‐E‐1).	Two	of	the	factors	that	affect	enteric	CH4	production	to	the	greatest	

extent	are	diet	composition	and	level	of	intake.	Prediction	equations	and	models	constructed	to	

predict	enteric	CH4	are	generally	based	on	these	factors.	Most	statistical	equations	developed	to	

estimate	enteric	CH4	emissions	have	been	developed	using	data	sets	of	animals	fed	high‐forage	

diets	or	mixed	diets;	few	studies	have	fed	high‐concentrate	diets	typical	of	today’s	U.S.	feedlots.		

Table 5‐E‐1: Models Potentially Useful in Estimating Enteric CH4 Emissions from Typical U.S.

Ruminant Animals

Reference Variable modeled Inputs/Comments

Empirical Models

IPCC	(2006)	 Enteric	CH4

No.	of	animals,	animal	species,	animal	type,	emission	

factor	for	each	animal	type	(Tier	2	CH4	conversion	

factor;	Ym)	

Kriss	(1930)		 Enteric	CH4	 Dry	matter	intake	(DMI)

Axelsson	(1949)	 Enteric	CH4 DMI

Bratzler	&	Forbes	

(1940)	 Enteric	CH4	 Digested	carbohydrate	

Mills	et	al.	(2003)	 Enteric	CH4 Metabolizable	energy	(ME)	intake,	starch	and	acid	

detergent	fiber	(ADF)	intake	

Blaxter	&	Clapperton	

(1965)	 Enteric	CH4

Digestible	energy	(DE)	(%)	at	maintenance	intake,	gross	

energy	intake	(GEI),	feeding	level	(multiple	of	

maintenance)	

Moe	&	Tyrrell	(1979)	 Enteric	CH4 Digestible	soluble	carbohydrates,	digestible	

hemicellulose,	digestible	cellulose	

Holter	&	Young	(1992)	 Enteric	CH4 Digestible	soluble	carbohydrates,	cellulose,	

hemicellulose,	fat	intake		

Yan	et	al.	(2009)	 Enteric	CH4 Digestible	energy,	silage,	and	total	DMI,	silage,	and	diet	

ADF	

Ellis	et	al.	(2007)	 Enteric	CH4	 Metabolizable	energy	intake,	ADF,	lignin	intake	

Ellis	et	al.	(2009)	 Enteric	CH4

Metabolizable	energy	intake,	cellulose,	hemicellulose,	

and	fat	intake;	non‐fiber	carbohydrate,	neutral	

detergent	fiber	(NDF),	and	DMI		

Mills	et	al.	(2001)	 Enteric	CH4	 DMI

Holos	(Little	et	al.,	

2008)	

Enteric	CH4,	manure	

CH4 Based	on	IPCC	(2006)	

CNCPS	(2010)	

Enteric	CH4,	DMI,	

nutrient	excretion,	

urine	nitrogen	

excretion;		

Uses	equation	of	Mills	et	al.	(2003)	for	dairy	and	Ellis	et	

al.	(2007)	for	beef.	Animal	characteristics,	diet	nutrient	

composition,	feed	protein	fractions,	animal	performance,	

animal	management,	in	situ	degradability	of	feeds	

Integrated	Farm	

System	Model	(Rotz	et	

al.,	2011b)	

Enteric	CH4,	nutrient	

excretion,	urine	

nitrogen,	DMI,	

manure	NH3,	CH4,	

and	N2O	

Uses	the	Mits3	equation	of	Mills	et	al.	(2003)	for	enteric	

CH4,	IPCC	(2006)	for	manure	CH4,	and	either	DAYCENT	

(Chianese	et	al.,	2009d)	or	IPCC	(2006)	for	manure	N2O		

Phetteplace	et	al.	

(2001)	

Enteric	CH4,	manure	

CH4

Animal	class,	animal	age	and	body	weight,	quantity	of	

meat/mile	produced,	feed	type,	feed	intake,	manure	

management	

Process‐based Models
 Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

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Reference Variable modeled Inputs/Comments

Kebreab	et	al.,	(2004;	

2009)	

Enteric	CH4,	nutrient	

excretion	

DMI,	NDF,	degradable	NDF,	total	starch,	degradable	

starch,	soluble	sugars	in	diet,	diet	nitrogen,	NHx‐N	in	

diet,	indigestible	protein,	rate	of	degradation	of	starch,	

and	protein	

COWPOLL	(Dijkstra	et	

al.,	1992;	Mills	et	al.,	

2003;	Bannink	et	al.,	

2006;	Kebreab	et	al.,	

2008)	

Enteric	CH4

DMI,	NDF,	degradable	NDF,	total	starch,	degradable	

starch,	soluble	sugars	in	diet,	diet	nitrogen,	NHx‐N	in	

diet,	indigestible	protein,	rate	of	degradation	of	starch,	

and	protein	

MOLLY	(Baldwin,	

1995)	 Enteric	CH4 Similar	to	COWPOLL	

Prediction models for enteric emissions.	The	following	is	a	brief	summary	of	the	models	evaluated	

and	their	strengths	and	limitations.		

Simple Regression Model Based on Digestible Energy.	Blaxter	and	Clapperton	(1965)developed	

a	simple	regression	equation	to	estimate	enteric	CH4	based	on	digestible	energy,	feed	intake	as	a	

percentage	of	maintenance	and	GEI.	The	data	set	used	to	create	this	empirical	model	was	composed	

mostly	of	data	from	sheep	fed	low‐concentrate	diets	in	respiration	chambers,	which	may	account	

for	its	limited	accuracy	in	predicting	CH4	emissions	across	ruminant	diets	(Johnson	et	al.,	1991).		

Empirical Model.	Moe	and	Tyrrell	(1979)	developed	an	empirical	model	to	estimate	enteric	CH4

emission	from	dairy	cows	based	on	diet	composition.	This	empirical	model	was	developed	with	

high‐forage	diets	in	dairy	cows	fed	in	respiration	chambers;	its	use	for	estimating	beef	cattle	enteric	

emissions	is	therefore	limited.		

Regression Model.	Yan	et	al.	(2000)	developed	regression	equations	to	predict	enteric	CH4

emissions	from	beef	and	dairy	cattle	fed	diets	based	on	grass	silage.	Concentrates	represented	from	

0	to	81.5	percent	of	the	DMI,	with	a	mean	of	46.7	percent	of	diet	DMI.	When	corrected	to	equal	feed	

intakes,	animal	body	weight	had	no	effect	on	enteric	CH4	emissions.	(Yan	et	al.,	2000)	validated	

their	equations	using	data	from	the	literature,	mostly	dairy	studies	with	all	diets	based	on	grass	

silage.		

Regression Equations.	Ellis	et	al.	(2009)	developed	regression	equations	to	estimate	enteric	CH4

production	from	beef	cattle	based	on	studies	in	which	cattle	were	fed	high‐concentrate	or	

moderate‐concentrate	(50	percent)	diets.	These	equations	were	compared	with	14	equations	

developed	earlier	by	Ellis	et	al.	(2007),	seven	developed	by	Mills	et	al.	(2003),	the	Blaxter	and	

Clapperton	(1965)	equation,	and	the	Moe	and	Tyrrell	(1979)	equation.	The	mean	enteric	CH4

production	(MJ	day‐1	and	percent	of	GEI)	in	all	12	of	the	studies	was	greater	than	values	noted	more	

recently	(Hales	et	al.,	2012),	possibly	because	of	differences	in	dietary	grain	content	and	fat	

supplementation.	However,	some	of	the	Ellis	(2007;	2009)	equations	estimated	CH4	emissions	

similar	to	those	reported	by	Todd	et	al.	(2014a;	2014b)	in	open	lot	feedlots.	

The	linear	model	with	the	lowest	residual	mean	square	prediction	error	(RMSPE)	was	Equation	5‐

E‐1	as	follows:	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-136 

A	possible	advantage	to	using	this	equation,	compared	with	other	empirical	equations,	is	that	the	

variables	required	for	the	calculations	can	be	readily	obtained	with	some	training	in	nutrition.	

Another	is	that	the	independent	variables	in	the	model	(energy,	fiber,	and	fat	intake)	are	the	

primary	differences	that	would	occur	in	various	beef	and	dairy	cattle	diets.	However,	a	major	

concern	with	their	use	for	finishing	cattle	is	that	a	number	of	the	studies	used	to	develop	the	

equations	were	high‐forage	diets	and/or	did	not	use	either	supplemental	fat	or	monensin	in	the	

diet.	As	previously	noted,	when	compared	with	emissions	from	cattle	fed	typical	finishing	diets	

based	on	steam‐flaked	corn	(SFC)	or	dry‐rolled	corn	(DRC),	this	equation	greatly	overestimated	CH4

emissions	(Hales	et	al.,	2012).	Linear	equations	using	nutrient	ratios	(starch:NDF,	etc.)	were	also	

developed,	but	all	had	greater	RMSPE	than	the	previous	equation	(Ellis	et	al.,	2009).	Nonlinear	

equations	were	also	developed.	Despite	being	more	biologically	defendable,	the	nonlinear	

equations	all	had	greater	RMSPE	than	the	linear	equation.	

In	a	later	study,	Yan	et	al.	(2009)	developed	additional	equations	using	a	database	of	108	

measurements	for	beef	steers	of	varied	breeding	in	respiration	chambers	and	fed	diets	that	ranged	

from	100	to	30	percent	roughage.	They	also	compared	a	number	of	equations	developed	elsewhere.	

Equations	were	“validated”	using	one‐third	of	the	original	data	set.	Emissions	were	highly	

correlated	to	live	body	weight,	DMI,	and	GEI,	but	live	body	weight	was	a	poor	predictor	of	enteric	

CH4	emissions.	The	ability	of	a	number	of	equations	to	predict	enteric	CH4	measured	in	the	study	

was	varied	(eight	percent	overpredicted,	to	33	percent	underpredicted).	The	poorest	results	were	

with	four	linear	equations	developed	by	Ellis	et	al.	(2007)	that	used	DMI,	MEI,	and/or	forage	intake	

as	independent	variables.	They	attributed	the	poor	response	to	the	fact	that	a	good	portion	of	the	

data	for	Ellis	et	al.	(2007)	was	from	grazing	animals	using	the	SF6	technique,	which	would	not	

include	CH4	from	the	lower	gut.	The	Blaxter	and	Clapperton	(1965)	equation	did	a	respectable	job	

(93	percent	of	actual	with	R2	=	0.69;	mean	prediction	error	=	0.12;	and	63	percent	of	means	square	

prediction	error	due	to	random	effects,	and	29	percent	due	to	a	mean	bias).		

Empirical and Mechanistic Model.	The	IFSM	Model	(and	its	subset	DairyGEM)	(Rotz	et	al.,	2005;	

Chianese	et	al.,	2009b;	2009c;	2009a;	2009d)	is	a	combination	empirical	and	mechanistic	model	of	

whole	farm	nutrient	management.	The	submodel	to	estimate	enteric	CH4	emissions	from	beef	or	

dairy	cattle	uses	the	Mits3	equation	of	Mills	et	al.	(2003).	Ellis	et	al.	(2007)	reported	that	the	Mills	et	

al.	(2003)	equations	were	poor	at	predicting	CH4	from	beef	cattle,	probably	because	they	were	

developed	from	dairy	data.	In	fact,	one	equation	that	worked	well	with	dairy	cows	actually	

predicted	negative	CH4	emissions	from	beef	cattle	fed	high‐concentrate,	low‐forage	diets.	Thus,	the	

current	IFSM	may	not	be	appropriate	to	estimate	enteric	CH4	emissions	from	beef	cattle,	especially	

feedlot	cattle.	

Mechanistic Models.	MOLLY	(Baldwin	et	al.,	1987;	Baldwin,	1995)	is	a	mechanistic	model	that	

estimates	ruminal	CH4	production	based	on	a	hydrogen	balance	within	the	rumen.	Input

Equation 5‐E‐1: Linear Model with the Lowest RMSPE

۱۶૝ ൌ ૛. ૠ૛ ൅ ሺ૙. ૙ૢ૜ૠ ൈ ۻ۳	ܑ܍ܓ܉ܜܖሻ ൅ ሺ૝. ૜૚ ൈ ۱۳ۺۺሻ െ ሺ૟. ૝ૢ ൈ ۶۱ሻ െ ሺૠ. ૝૝ ൈ ۴ܜ܉ሻ	

Where:	

CH4		 =	Methane	per	day	(MJ	day‐1)	

	ME	intake		=	ME	intake	in	(MJ	day‐1)	

CELL		 =	Cellulose	intake	(kg	day‐1)	

HC		 =	Hemicellulose	intake	(kg	day‐1)	

Fat		 =	Fat	intake	(kg	day‐1)	
 Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-137

parameters	to	the	model	are	daily	DMI,	chemical	composition	of	the	diet,	solubility	of	protein	and	

starch,	degradability,	ruminal	passage	rates,	ruminal	volume,	and	ruminal	pH.	COWPOLL	(Dijkstra	

et	al.,	1992;	Mills	et	al.,	2001)	is	another	mechanistic	model.	Input	parameters	to	the	model	are	

similar	to	MOLLY.	MOLLY	and	COWPOLL	both	use	an	H‐balance	to	estimate	enteric	CH4	production.	

However,	they	use	different	VFA	stoichiometry	submodels.	Both	models	require	significant	inputs	

that	are	probably	beyond	the	scope	of	typical	producers.	However,	they	are	excellent	research	

tools.	

The	Cornell	Net	Carbohydrate	and	Protein	System	model	(CNCPS,	2010)	calculates	nutrient	

requirements,	nutrient	inputs,	animal	production	(weight	gain	and/or	milk	production),	and	

nutrient	excretion	in	beef	and	dairy	cattle.	It	recently	added	a	submodel	(VanAmburgh	et	al.,	2010)	

to	calculate	enteric	CH4	emissions.	The	submodel	uses	an	equation	of	Mills	et	al.	(2003)	to	estimate	

enteric	emissions	from	dairy	cows	and	an	equation	of	Ellis	et	al.	(2007)	to	estimate	enteric	

emissions	from	beef	cattle.	At	present,	to	our	knowledge	there	are	no	comparisons	or	independent	

validations	of	the	new	submodels	that	have	been	published,	and	the	extent	to	which	the	model	is	

responsive	to	mitigation	strategies	is	unclear.	

Comparative Analyses using Independent Data Sets.	Several	studies	have	attempted	to	evaluate	

the	predictive	ability	of	enteric	CH4	models	by	using	an	independent	data	set.	Benchaar	et	al.	(1998)

compared	two	mechanistic	(Baldwin	et	al.,	1987;	Dijkstra	et	al.,	1992;	Baldwin,	1995);	and	two	

linear	(Blaxter	and	Clapperton,	1965;	Moe	and	Tyrrell,	1979)	models	with	a	data	set	of	32	diets	

from	13	publications	in	the	literature.	They	noted	that	the	mechanistic	models	were	better	

predictors	than	the	regression	equations.	The	linear	regression	models	could	only	explain	42	to	57	

percent	of	the	variation	in	predicted	values,	whereas	the	mechanistic	models	explained	more	than	

70	percent	of	the	variation.	The	model	of	Dijkstra	et	al.	(1992)	tended	to	underestimate	actual	CH4	

production	(mean	error	=	0.30	Mcal	day‐1),	with	the	error	being	greater	at	higher	CH4	productions.	

The	model	of	Baldwin	(Baldwin	et	al.,	1987;	Baldwin,	1995)	overestimated	CH4	production	by	about	

0.93	Mcal	day‐1,	primarily	due	to	a	high	intercept.	The	equations	of	Moe	and	Tyrrell	(1979)	and	

Blaxter	and	Clapperton	(1965)	tended	to	overestimate	CH4	production,	especially	at	low	production	

rates.	

Comparative Analysis/Lactating and Nonlactating Cows.	Wilkerson	et	al.	(1995)	compared	

several	published	equations	(Kriss,	1930;	Bratzler	and	Forbes,	1940;	Axelsson,	1949;	Blaxter	and	

Clapperton,	1965;	Moe	and	Tyrrell,	1979;	Holter	and	Young,	1992)	for	their	ability	to	predict	

enteric	CH4	production	from	lactating	and	nonlactating	Holstein	cows.	In	general,	equations	that	

were	based	on	total	DMI	or	on	intake	of	digested	cellulose,	hemicellulose,	and	nonfiber	

carbohydrates,	provided	the	highest	correlation	and	lowest	errors	of	prediction.	Prediction	

equations	that	used	a	quadratic	function	of	DMI	were	poor	at	predicting	enteric	CH4.	In	general,	the	

equations	predicted	emissions	from	nonlactating	cows	more	accurately	than	from	lactating	cows.	

Comparative Analysis Linear Models.	Kebreab	et	al.	(2006)	compared	two	linear	models	(Moe	

and	Tyrrell,	1979;	Mills	et	al.,	2003),	a	nonlinear	model	(Mills	et	al.,	2003),	the	IPCC	Tier	1	and	Tier	

2	models	(IPCC,	1997),	and	a	dynamic	mechanistic	model	(Kebreab	et	al.,	2004)	using	data	from	

studies	conducted	in	North	America.	They	recommended	that	the	linear	models	be	used	when	there	

is	limited	information	on	nutrient	intake	and	when	the	expected	emissions	are	within	the	range	of	

data	from	which	the	model	was	developed.	The	nonlinear	model	of	Mills	et	al.	(2003)	could	be	used	

for	extrapolating	beyond	the	range	of	data	used	to	develop	the	equation,	but	the	mechanistic	model	

was	recommended	for	evaluation	of	mitigation	options.	The	IPCC	Tier	1	model	was	found	to	be	

adequate	for	general	inventory	purposes.	The	predictive	ability	of	the	Tier	2	model,	while	most	

useful,	was	limited.	
Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-138 

Comparative Analysis Mechanistic Models.	Kebreab	et	al.	(2008)	also	compared	two	mechanistic	

models,	MOLLY	(Baldwin	et	al.,	1987;	Baldwin,	1995)	and	COWPOLL	(Dijkstra	et	al.,	1992;	Mills	et	

al.,	2001;	Bannink	et	al.,	2006),	to	the	IPCC	Tier	2	(2006)	and	linear	equation	of	Moe	and	Tyrrell	

(1979).	Using	a	beef	cattle	data	set,	MOLLY	and	IPCC	tended	to	be	more	accurate	than	the	other	

models,	although	MOLLY	was	more	precise.	MOLLY	and	IPCC	Tier	2	had	minimal	mean	bias,	

whereas	COWPOLL	and	the	Moe	and	Tyrrell	(1979)	equation	greatly	overpredicted	average	

emissions.	COWPOLL,	which	is	based	on	the	enteric	CH4	prediction	equations	of	Mills	et	al.	(2001)	

and	the	updated	rumen	stoichiometry	for	lactating	cows	(Bannink	et	al.,	2006),	had	the	poorest	

ability	to	predict	enteric	CH4	emission	from	feedlot	cattle	and	tended	to	overpredict	CH4	emissions	

(MJ	day‐1)	by	as	much	as	50	percent.	Although	on	average	MOLLY	and	IPCC	Tier	2	(2006)	gave	

predicted	values	similar	to	measured	values,	there	was	a	large	variability	in	individual	animals,	

with	errors	of	75	percent	or	greater.	The	large	variability	in	predicted	values	indicates	that	there	

can	be	large	animal‐to‐animal	variation	in	enteric	CH4	production,	even	when	animals	are	fed	the	

same	diets	at	similar	feed	intakes.		

Comparative Analysis/Feedlots.	McGinn	et	al.	(2008)	compared	measured	(using	bLS	model)	CH4

emissions	(enteric	plus	pen	surface)	from	feedlots	in	Australia	and	Canada	with	estimates	using	the	

IPCC	Tier	1,	IPCC	Tier	2,	Blaxter	and	Clapperton	(1965),	and	Moe	and	Tyrrell	(1979)	equations.	The	

Tier	2	method	underestimated	CH4	at	both	locations.	Estimates	using	the	IPCC	Tier	1	methods	were	

close	to	measured	values	in	Australia;	however,	Tier	1	underestimated	values	for	the	Canada	

feedlot.	Estimates	made	using	the	Blaxter	and	Clapperton	(1965)	and	Moe	and	Tyrrell	(1979)	

equations	were	close	to	measured	values	in	Canada,	but	overestimated	values	in	Australia.	Methane	

emissions	had	a	significant	diel	pattern	indicating	that	short‐term	measurement	of	CH4	emissions	at	

feedlots	may	overestimate	or	underestimate	daily	emissions.		

Comparative Analysis of Stoichiometric Models. Alemu	et	al.	(2011)	compared	enteric	CH4

emissions	from	dairy	cows	using	a	variety	of	stoichiometric	models	of	ruminal	fermentation	

(Murphy	et	al.,	1982;	Bannink	et	al.,	2006;	Sveinbjornsson	et	al.,	2006;	Nozière	et	al.,	2010),	and	

noted	that	mechanistic	models	such	as	Bannink	et	al.	(2006)	are	more	accurate	for	predicting	

enteric	CH4	from	dairy	cows	than	the	IPCC	Tier	2	(2006)	method.	However,	these	models	required	a	

considerable	quantity	of	data	regarding	the	animals	and	their	diet.	

Comparative Analysis Measurement Data and Models.	Tomkins	et	al.	(2011)	measured	enteric	

CH4	emissions	of	steers	on	pasture	using	a	micrometeorological	method	and	respiration	chambers.	

Emissions	estimated	using	an	Ellis	(2009)	equation	(CH4,	MJ	day‐1	=	3.272	+0.736	(DMI,	kg	day‐1))	

were	similar	(112.7	g	day‐1)	to	measured	emissions.	Estimates	using	the	equations	of	Kurihara	et	al.	

(1999)	as	modified	by	Hunter	(2007)	(109.1	g	day‐1),	Yan	et	al.	(2009)	(105.6	g	day‐1),	and	Charmley	

et	al.	(2008)	(2008:NABCEMS;	100.2	g	day‐1)	were	slightly	lower,	but	not	as	low	as	the	IPCC	(2006)	

model	(82.7	g	day‐1).	

Comparative Analyses/Models.	Legesse	et	al.	(2011)	compared	enteric	CH4	emission	estimates	

using	MOLLY,	COWPOLL,	IPCC	Tier	2,	and	one	equation	of	Ellis	et	al.	(2007)	under	various	Canadian	

beef	cow‐calf	management	systems.	Differences	among	the	models	(26	to	35	percent)	were	much	

greater	than	differences	among	management	systems	(three	to	five	percent).	The	authors	suggested	

that	these	differences	limited	the	model’s	utility	in	predicting	CH4	emission	from	beef	cow	systems.	

Evaluation of Models.	Yan	et	al.	(2000;	2009)	noted	that	CH4	production	(percent	of	GEI	or	

digestible	energy)	decreased	with	increasing	DMI	(as	multiples	of	maintenance)	and	with	

increasing	forage	in	the	diet.	Thus,	they	suggested	that	models	that	do	not	consider	feeding	level	

will	underpredict	CH4	at	low	planes	of	nutrition	and	overpredict	enteric	CH4	at	high	levels	of	

feeding.	Similarly,	Kebreab	et	al.	(2006)	noted	that	linear	models	tend	to	give	unrealistically	high	
 Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems 

5-139

emission	values	when	DMI	increases,	whereas	nonlinear	models	gave	values	approaching	the	

theoretical	maximum	emission,	which	is	biologically	reasonable.

Although	several	equations	of	Ellis	et	al.	(2009)	appeared	to	be	good	predictors	of	enteric	CH4

losses	from	feedlot	cattle	based	on	Canadian	studies,	when	compared	with	data	from	cattle	fed	a	

typical	corn‐based	finishing	diet	(Hales	et	al.,	2012)	most	tended	to	greatly	overestimate	enteric	

losses.	At	the	present	time,	the	IPCC	Tier	2	model	with	some	modifications	may	be	the	most	useful	

for	prediction	of	enteric	emissions	from	feedlot	beef	cattle.	

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This	page	is	intentionally	left	blank.	
Authors:

Coeli	Hoover,	USDA	Forest	Service	(Lead	Author)	

Richard	Birdsey,	USDA	Forest	Service	(Co‐Lead	Author)	

Bruce	Goines,	USDA	Forest	Service	

Peter	Lahm,	USDA	Forest	Service	

Gregg	Marland,	Appalachian	State	University	

David	Nowak,	USDA	Forest	Service	

Stephen	Prisley,	Virginia	Polytechnic	Institute	and	State	University	

Elizabeth	Reinhardt,	USDA	Forest	Service	

Ken	Skog,	USDA	Forest	Service	

David	Skole,	Michigan	State	University	

James	Smith,	USDA	Forest	Service	

Carl	Trettin,	USDA	Forest	Service	

Christopher	Woodall,	USDA	Forest	Service	

Contents:

6 Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems ....................6‐4

6.1	 Overview	.........................................................................................................................................................	6‐5	

6.1.1	 Overview	of	Management	Practices	and	Resulting	GHG	Emissions	.........................	6‐6	

6.1.2	 System	Boundaries	and	Temporal	Scale	..............................................................................	6‐9	

6.1.3	 Summary	of	Selected	Methods/Models	..............................................................................	6‐10	

6.1.4	 Sources	of	Data	..............................................................................................................................	6‐11	

6.1.5	 Organization	of	Chapter/Roadmap	......................................................................................	6‐12	

6.2	 Forest	Carbon	Accounting	......................................................................................................................	6‐15	

6.2.1	 Description	of	Forest	Carbon	Accounting	..........................................................................	6‐15	

6.2.2	 Data	Collection	for	Forest	Carbon	Accounting	.................................................................	6‐23	

6.2.3	 Estimation	Methods	....................................................................................................................	6‐25	

6.2.4	 Limitations,	Uncertainty,	and	Research	Gaps	...................................................................	6‐28	

6.3	 Establishing,	Re‐establishing,	and	Clearing	Forests	....................................................................	6‐29	

6.3.1	 Description	.....................................................................................................................................	6‐29	

6.3.2	 Activity	Data	Collection	.............................................................................................................	6‐33	

6.3.3	 Estimation	Methods	....................................................................................................................	6‐34	

6.3.4	 Specific	Protocol	for	Computation	........................................................................................	6‐37	

6.3.5	 Actual	GHG	Removals	and	Emissions	by	Sources	and	Sinks	from	Forest	Clearing	...		

	.............................................................................................................................................................	6‐43	

6.3.6	 Limitations	and	Uncertainty	....................................................................................................	6‐44	

6.4	 Forest	Management	..................................................................................................................................	6‐45	

Chapter 6

Quantifying Greenhouse Gas 

Sources and Sinks in Managed 

Forest Systems 
Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

6-2

6.4.1	 Description	.....................................................................................................................................	6‐45	

6.4.2	 Activity	Data	...................................................................................................................................	6‐53	

6.4.3	 Management	Intensity	Categories	........................................................................................	6‐57	

6.4.4	 Estimation	Methods	....................................................................................................................	6‐64	

6.4.5	 Limitations	and	Uncertainty	....................................................................................................	6‐66	

6.5	 Harvested	Wood	Products	.....................................................................................................................	6‐66	

6.5.1	 General	Accounting	Issues	.......................................................................................................	6‐66	

6.5.2	 Estimation	Methods	....................................................................................................................	6‐68	

6.5.3	 Activity	Data	Collection	.............................................................................................................	6‐69	

6.5.4	 Limitations,	Uncertainty,	and	Research	Gaps	...................................................................	6‐70	

6.6	 Urban	Forests	..............................................................................................................................................	6‐71	

6.6.1	 Description	.....................................................................................................................................	6‐71	

6.6.2	 Activity	Data	Collection	.............................................................................................................	6‐73	

6.6.3	 Estimation	Methods	....................................................................................................................	6‐74	

6.6.4	 Limitations	and	Uncertainty	....................................................................................................	6‐80	

6.7	 Natural	Disturbance	–	Wildfire	and	Prescribed	Fire	...................................................................	6‐82	

6.7.1	 Description	.....................................................................................................................................	6‐82	

6.7.2	 Activity	Data	Collection	.............................................................................................................	6‐82	

6.7.3	 Estimation	Methods	....................................................................................................................	6‐82	

6.7.4	 Limitations	and	Uncertainty	....................................................................................................	6‐87	

Appendix	6‐A:	Harvested	Wood	Products	Lookup	Tables	.....................................................................	6‐88

Chapter	6	References	..........................................................................................................................................	6‐107	

Suggested	Chapter	Citation:		Hoover,	C.,	R.	Birdsey,	B.	Goines,	P.	Lahm,	G	Marland,	D.	Nowak,	S.	

Prisley,	E.	Reinhardt,	K.	Skog,	D.	Skole,	J.	Smith,	C.	Trettin,	C.	Woodall,	2014.	Chapter	6:		Quantifying	

Greenhouse	Gas	Sources	and	Sinks	in	Managed	Forest	Systems.		In Quantifying Greenhouse Gas

Fluxes in Agriculture and Forestry:		Methods for Entity‐Scale Inventory.	Technical	Bulletin	Number	

1939.		Office	of	the	Chief	Economist,	U.S.	Department	of	Agriculture,	Washington,	DC.	606	pages.		

July	2014.		Eve,	M.,	D.	Pape,	M.	Flugge,	R.	Steele,	D.	Man,	M.	Riley‐Gilbert,	and	S.	Biggar,	Eds.	

USDA	is	an	equal	opportunity	provider	and	employer.	
 Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

6-3 

Acronyms, Chemical Formulae, and Units

BA	 Basal	area	

C	 Carbon	

CH4	 Methane	

cm	 Centimeters	

CO2	 Carbon	dioxide	

CO2‐eq	 Carbon	dioxide	equivalents

COLE	 CarbonOnLineEstimator

CRM	 Component	ratio	method

DBH	 Diameter	at	breast	height

DDW	 Down	dead	wood	

DOE	 Department	of	Energy	

EPA	 Environmental	Protection	Agency

FFE	 Fire	and	Fuels	Extension

FIA	 Forest	Inventory	and	Analysis

FIADB	 Forest	Inventory	and	Analysis	Database

FIDO	 Forest	Inventory	Data	Online

FOFEM	 First	Order	Fire	Effects	Model

FVS	 Forest	Vegetation	Simulator model

ft	 Feet

g	 Gram	

GHG	 Greenhouse	gas	

H	 Height

ha	 Hectare	

hp	 Horse	power	

hr	 Hour	

HW	 Hardwood	

HWP	 Harvested	wood	products

in	 Inches	

lbs	 Pounds	

IPCC	 Intergovernmental	Panel	on	Climate	Change

m	 Meters	

mm	 Millimeters	

Mcf	 Thousand	cubic	feet

N2O	 Nitrous	oxide	

NOx	 Mono‐nitrous	oxides	

O2	 Oxygen	

PW	 Pulpwood	

SL	 Sawlogs	

SOC	 Soil	organic	carbon	

SSURGO	 Soil	Survey	Geographic	database

STATSGO	 State	Soil	Geographic	database

SW	 Softwood	

Tg	 Teragrams	

UFORE	 Urban	Forest	Effects	model

UNFCCC	 United	Nations	Framework	Convention	on	Climate	Change

USDA	 U.S.	Department	of	Agriculture
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Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

6-5

6 Quantifying Greenhouse Gas Sources and Sinks in Managed Forest

Systems

This	chapter	provides	guidance	for	reporting	greenhouse	gas	(GHG)	emissions	associated	with	

entity‐level	fluxes	from	the	forestry	sector.	In	particular,	it	focuses	on	methods	for	estimating	

carbon	stocks	and	stock	change	from	managed	forest	systems.	Section	6.1	provides	an	overview	of	

the	sector.	Section	6.2	describes	the	methods	for	forest	carbon	stock	accounting.	Section	6.3	

describes	the	methods	for	estimating	carbon	stocks	and	stock	change	from	establishing	and	

clearing	forest.	Section	6.4	describes	methods	for	estimating	carbon	stocks	and	stock	change	from	

forest	management.	Section	6.5	describes	methods	for	estimating	carbon	stocks	and	stock	change	

from	harvested	wood	products.	Section	6.6	describes	methods	for	estimating	carbon	stocks	and	

stock	change	from	urban	forests	(i.e.,	trees	outside	of	forests).	Finally,	Section	6.7	describes	

methods	for	estimating	emissions	from	natural	disturbances	including	forest	fires.	

6.1 Overview

A	summary	of	proposed	methods	and	models	for	estimating	GHG	emissions	from	managed	forest	

systems	is	provided	in	Table	6‐1.	

Table 6‐1: Overview of Managed Forest Systems Sources, Method and Section

Section Source Method

6.2.3	 Forest	Carbon	

Accounting	

Range	of	options	dependent	on	the	size	of	the	entities’	forest	land	including:

Forest	Vegetation	Simulator	model	with	Fire	and	Fuels	Extension	(FVS‐FFE)	

(entities	that	fit	the	large	landowner	definition);	and	default	lookup	tables	

(entities	fitting	the	small	landowner	definition).	

6.3.3	

Establishing,	Re‐

establishing,	and	

Clearing	Forests	

Intergovernmental	Panel	on	Climate	Change (IPCC) algorithms	developed	

by	Aalde	et	al.	(2006).	These	options	use:	allometric	equations	from	Jenkins	

et	al.	(2003a),	or	FVS	with	the	Jenkins	et	al.	equations	where	applicable;	and	

default	lookup	tables	from	Smith	et	al.	(2006;	GTR	NE‐343)—default	

regional	values	based	on	forest	type	and	age	class	developed	from	FIA	data.	

6.4.4	 Forest	

Management	

Range	of	options	dependent	on	the	size/management	intensity/data	

availability	of	the	entity’s	forest	land	including:	FVS‐FFE	with	Jenkins	

(2003a) allometric	equations;	Default	lookup	tables	of	management	practice	

scenarios;	and	FVS	may	be	used	to	develop	a	supporting	product	providing	

default	lookup	tables	of	carbon	stocks	over	time	by	region;	forest	type	

categories,	including	species	group	(e.g.,	hardwood,	softwood,	mixed);	

regeneration	(e.g.,	planted,	naturally	regenerated);	management	intensity	

(e.g.,	low,	moderate,	high,	very	high);	and	site	productivity	(e.g.,	low,	high).	

6.5.2	 Harvested	Wood	

Products	

Method	uses	U.S.‐specific	harvested	wood	products	(HWPs)	tables.	The	

HWPs	tables	are	based	on	WOODCARB	II	model	used	to	estimate	annual	

change	in	carbon	stored	in	products	and	landfills	(Skog,	2008).	The	entity	

uses	these	tables	to	estimate	the	average	amount	of	HWP	carbon	from	the	

current	year’s	harvest	that	remains	stored	in	end	uses	and	landfills	over	the	

next	100	years.	

6.6.3	 Urban	Forests	

Range	of	options	depends	on	data	availability	of	the	entity’s	urban	forest	

land.	These	options	use:	i‐Tree	Eco	model	(http://www.itreetools.org)	to	

assess	carbon	from	field	data	on	tree	populations;	and	i‐Tree	Canopy	model	

(http://www.itreetools.org/canopy/index.php)	to	assess	tree	cover	from	

aerial	images	and	lookup	tables	to	assess	carbon.	Quantitative	methods	are	

also	described	for	maintenance	emissions	and	altered	building	energy	use	

and	included	for	information	purposes	only.	
Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

6-6

Section Source Method

6.7.3	

Natural	

Disturbance—

Wildfire	and	

Prescribed	Fire	

Range	of	options	depends on	the	data	availability	of	the	entity’s	forest	land	

including:	First	Order	Fire	Effects	Model	(FOFEM)	entering	measured	

biomass;	and	FOFEM	model	using	default	values	generated	by	vegetation	

type.	These	options	use	Reinhardt	et	al.	(1997).	

6.1.1 Overview of Management Practices and Resulting GHG Emissions

6.1.1.1 Description of Sector

Forestry	activities	represent	significant	opportunities	to	manage	GHGs	(Caldeira	et	al.,	2004;	Pacala	

and	Socolow,	2004).	There	are	many	kinds	of	forestry	activities	that	may	be	considered	by	entities	

as	a	means	to	reduce	GHGs,	such	as	establishing	new	forests,	agroforestry,	improved	forest	

management,	and	avoided	forest	clearing.	Cost	is	a	major	factor	guiding	decisions	about	which	

activities	in	forestry	to	pursue	(Lewandrowski	et	al.,	2004;	Stavins	and	Richards,	2005;	U.S.	EPA,	

2005).	In	the	annual	GHG	inventory	reported	by	the	U.S.	Department	of	Agriculture	(USDA)	and	the	

U.S.	Environmental	Protection	Agency	(EPA),	forests	and	forest	products	sequester	an	average	of	

790	million	metric	tons	carbon	dioxide	(CO2)	per	year	on	253	million	hectares	(ha)	of	forest	land,	

making	it	the	main	land	category	sequestering	carbon	(U.S.	EPA,	2012b;	USDA,	2011).	Most	of	the	

carbon	sequestered	(89	percent)	is	in	the	forest	ecosystem,	with	the	remainder	added	to	the	pool	of	

carbon	in	wood	products.	

6.1.1.2 Resulting GHG Emissions

Forests	remove	carbon	from	the	atmosphere	and	store	it	in	vegetative	tissue	such	as	stems,	roots,	

barks,	and	leaves.	Through	photosynthesis,	all	green	vegetation	removes	CO2	and	releases	oxygen	

(O2)	to	the	atmosphere.	The	remaining	carbon	is	used	to	create	plant	tissues	and	store	energy.	

During	respiration,	carbon‐containing	compounds	are	broken	down	to	produce	energy,	releasing	

CO2	in	the	process.	Any	remaining	carbon	is	sequestered	until	the	natural	decomposition	of	dead	

vegetative	matter	or	combustion	releases	it	as	CO2	to	the	atmosphere.	The	net	carbon	stock	in	

forests	increases	when	the	amount	of	carbon	withdrawal	from	the	atmosphere	during	

photosynthesis	exceeds	the	release	of	carbon	to	the	atmosphere	during	respiration.	The	net	carbon	

stock	decreases	when	biomass	is	burned.	

Other	GHGs,	such	as	nitrous	oxide	(N2O)	and	methane	(CH4),	are	also	exchanged	by	forest	

ecosystems.	N2O	may	be	emitted	from	soils	under	wet	conditions	or	after	nitrogen	fertilization;	it	is	

also	released	when	biomass	is	burned.	CH4	is	often	absorbed	by	the	microbial	community	in	forest	

soils	but	may	also	be	emitted	by	wetland	forest	soils.	When	biomass	is	burned	in	either	a	

prescribed	fire/control	burn	or	in	a	wildfire,	precursor	pollutants	that	can	contribute	to	ozone	and	

other	short‐lived	climate	forcers	as	well	as	CH4	are	emitted.	A	wildfire	is	an	unplanned	ignition	

caused	by	lightning,	volcanoes,	unauthorized	activity,	accidental	human‐caused	actions,	and	

escaped	prescribed	fires.	A	prescribed	fire/control	burn	is	any	fire	intentionally	ignited	by	

management	under	an	approved	plan	to	meet	specific	objectives.	

Some	of	the	carbon	in	forests	is	released	to	the	atmosphere	after	the	harvest	of	timber.	However,	

the	amount	of	the	carbon	released,	and	when,	depends	on	the	fate	of	the	harvested	timber.	If	the	

timber	is	used	to	make	wood	products,	a	portion	of	the	sequestered	carbon	will	remain	stored	for	

up	to	several	decades	or	longer.	If	the	harvested	trees	are	burned	and	used	to	produce	energy,	

carbon	will	be	released	through	combustion	but	may	also	prevent	carbon	emissions	that	would	

have	been	released	through	the	burning	of	fossil	fuels.	Such	emissions	from	biomass	energy	use	are	
 Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

 6-7

typically	combusted	with	higher	efficiency	as	compared	to	open	biomass	burning	as	would	occur	in	

a	wildfire	situation	netting	lower	carbon	emissions.	

6.1.1.3 Forest Sector Schematic

Figure	6‐1	is	a	simplified	representation	of	the	key	forest	carbon	pools,	carbon	transfers,	and	GHG	

fluxes	for	the	forest	system.	At	this	time,	CO2	is	the	main	GHG	represented	comprehensively.	

Emissions	of	non‐CO2	GHGs	interact	with	other	sectors;	at	this	time,	potential	fluxes	of	non‐CO2

GHGs	are	represented	in	a	general	manner	on	the	schematic.	The	proportion	of	total	system	carbon	

in	each	pool	can	vary	over	time	depending	on	a	variety	of	factors;	rates	of	carbon	transfer	are	also	

variable.	

6.1.1.4 Management Interactions

Forestry	practices	typically	trigger	ecosystem	responses	that	change	over	time.	For	example,	a	

newly	established	forest	will	take	up	carbon	at	a	low	rate	initially,	and	then	pass	into	a	period	of	

relatively	rapid	carbon	accumulation.	The	carbon	uptake	rate	will	then	typically	decline	as	

heterotrophic	and	autotrophic	respiration	increase	and	growth	is	balanced	against	mortality	in	the	

older	forest.	From	this	point	in	time,	standing	live	tree	biomass	may	not	increase,	but	evidence	

suggests	that	carbon	may	continue	to	flow	into	other	forest	carbon	pools	until	the	forest	is	removed	

by	harvest	or	a	natural	disturbance	event.	

The	net	effects	of	management	activities	on	carbon	flows	in	forest	ecosystems	include	changes	in	

many	different	pools	of	carbon	(such	as	aboveground	biomass,	belowground	biomass,	litter,	soil,	

etc.).	Carbon	accounting	should	be	comprehensive,	addressing	the	net	effects	of	activities	on	all	

carbon	flows.	Forestry	activities	cause	carbon	to	move	between	the	various	pools	and	to/from	the	

atmosphere.	For	example,	forest	management	may	be	very	effective	at	increasing	the	accumulation	

of	biomass	in	commercially	valuable	forms—that	is,	in	the	trunks	of	commercial	tree	species.	This	

increased	growth	may	simply	result	from	reducing	competition	from	other	types	of	trees,	causing	a	

transfer	of	carbon	uptake	from	one	group	of	trees	to	another.	Forestry	activities	can	also	have	

effects	on	forest	soils,	woody	debris,	and	the	amount	of	carbon	in	wood	products.	The	net	carbon	

flow	effects	of	any	activity	will	be	the	sum	of	all	the	individual	effects	on	the	different	carbon	pools.	

In	addition,	there	may	be	interactions	between	biological	and	physical	processes	that	are	affected	

by	forest	management	treatments	or	natural	disturbances	(e.g.,	changes	in	albedo	during	forest	

regeneration,	after	wildfires).	While	these	interactions	occur,	research	in	this	field	is	in	the	early	

stages	and	such	interactions	are	beyond	the	scope	of	this	guidance.	

6.1.1.5 Risk of Reversals

Carbon	that	is	sequestered	in	soils,	vegetation,	or	wood	products	is	not	necessarily	permanently	

removed	from	the	atmosphere.	Forestry	activities	intended	for	one	purpose	may	be	changed	by	a	

different	landowner	or	a	change	in	management	objectives.	Landowners	may	change	their	

practices,	causing	the	release	of	stored	carbon,	or	natural	disturbances	may	cause	the	loss	of	stored	

carbon	to	the	atmosphere.	Insect	epidemics,	drought,	or	wildfire	may	happen	at	any	time	and	may	

affect	all	or	only	a	portion	of	the	land	area	within	activity	or	entity	boundaries.	Natural	

disturbances	may	be	rare	events,	in	which	case	the	effects	on	estimated	carbon	flows	may	be	small	

when	averaged	over	large	forested	areas	or	long	periods	of	time.	Catastrophic	disturbances	such	as	

wind	storms	may	cause	obvious	and	easily	estimated	changes	in	carbon	stocks,	while	in	other	cases,	

such	as	a	one‐year	period	of	insect	defoliation,	it	may	be	difficult	after	a	few	years	to	separate	the	

effects	of	the	natural	disturbance	from	other	factors.	It	should	be	noted	that	GHG	registries	

generally	require	entities	to	calculate	carbon	stocks	and	fluxes	and	generally	require	entities	to	

conduct	an	assessment	of	risk	of	reversal	of	projected	carbon	values.	Such	assessments	generally
Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

Figure6-8

6‐1: Schematic of Forest Carbon Pools, Carbon Transfers, and Greenhouse Gas Flux
 Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

 6-9 

include	risk	of	natural	disturbances	such	as	fire,	drought,	insect	and	disease	mortality,	wind	throw	

(hurricane,	tornado,	high	wind	events),	as	well	as	financial	risks,	management	risks,	and	social	

political	risks.	These	risk	assessments	are	commonly	used	to	generate	a	value	that	discounts	the	

projected	carbon	value	of	management	activities	and	to	provide	an	“insurance	policy”	against	

reversals	that	may	be	used	to	ensure	that	a	program’s	climate	benefits	are	realized.	Many	forest	

management	practices	can	reduce	these	natural	hazard	risks	(such	as	fuel	hazard	reduction,	forest	

thinning	for	growth	or	resilience	to	droughts,	climate	change,	insect	or	disease	agents,	and	use	of	

prescribed	fire	to	reduce	risk	of	fires).	Reducing	the	risk	of	reversal	through	management	may	lead	

to	reduced	emissions,	long‐term	net	increase	in	carbon	stocks,	and	improved	results	in	a	risk	

assessment.	

6.1.2 System Boundaries and Temporal Scale

For	this	report,	the	nominal	system	boundaries	are	the	extent	of	the	landowner’s	property.	

Estimation	methods	presented	in	this	section	are	for	the	forest	sector;	however,	where	the	forest	

sector	may	interact	with	the	animal	agriculture	or	croplands	and	grazing	lands	sectors,	these	

instances	are	noted	and	landowners	should	refer	to	the	relevant	sector	guidance.	A	landowner	may	

need	to	use	estimation	methods	for	several	sectors	to	achieve	a	comprehensive	report	of	GHG	

sources	and	sinks	for	their	property,	ensuring	that	double	counting	does	not	occur.	In	addition,	if	

land‐use	transitions	occur	within	the	property,	these	must	be	accounted	for	so	that	apparent	

changes	in	carbon	stocks	or	fluxes	are	“real”	and	not	the	result	of	an	unrecorded	transfer	from	one	

sector	to	another.	While	GHG	fluxes	will	occur	across	the	system	boundary,	these	are	generally	not	

estimated	except	in	the	instance	of	harvested	wood	products	(HWPs).	

The	forest	sector	presents	an	accounting	challenge	related	to	temporal	scale	that	may	not	occur	in	

other	sectors.	While	many	farms	operate	on	an	annual	cycle,	forestry	operations,	by	their	nature,	

occur	over	multiple	years	and	decades.	While	annual	estimation	and	reporting	are	required,	annual	

measurements	of	forest	carbon	pools	are	not	economically	feasible,	nor	are	changes	in	carbon	

stocks	generally	detectable	within	acceptable	error	levels	on	an	annual	basis.	This	necessitates	the	

use	of	models	and	projections	to	assess	the	carbon	consequences	of	management	practices	and	

evaluate	the	possible	GHG	benefits	of	a	change	in	management	practices.	Throughout	the	forest	

guidance,	references	will	be	made	to	several	types	of	estimates	that	may	be	generated.	A	Type	I	

estimate	is	the	estimate	of	the	carbon	stock	in	the	current	year	(or	a	recent	past	year)	based	on	field	

measurements	and	other	data.	To	assess	the	carbon	impacts	of	a	practice	over	time,	a	necessary	

step	to	generate	an	annual	estimate,	projections	of	future	carbon	stocks	must	be	made.	This	will	be	

referred	to	as	a	Type	II	estimate	and	will	require	the	use	of	lookup	tables,	simulation	models,	or	

other	tools.	A	Type	III	estimate	is	used	to	assess	the	change	in	the	GHG	footprint	as	a	result	of	a	

change	in	management	practice.	To	generate	a	Type	III	estimate,	a	landowner	will	need	to	produce	

Type	II	estimates	for	the	current	practice	and	the	practice	under	consideration	and	compare	the	

two.	While	some	landowners	may	require	only	an	estimate	of	current	carbon	stocks	(Type	I	

estimate),	many	will	be	interested	in	generating	estimates	of	the	rate	of	carbon	storage	over	time	

(Type	II	estimate),	which	necessitates	the	use	of	models	to	project	forest	growth.	The	overall	goal	of	

this	guidance	is	to	enable	a	landowner	to	develop	an	estimate	of	their	GHG	footprint	and	to	assess	

the	potential	effects	of	changes	in	management	practices	or	land	use	on	this	footprint	(for	forest	

systems,	this	will	be	dominated	by	carbon).	Type	II	estimates	can	be	generated	and	compared	for	

the	current	management	scheme	and	multiple	alternatives	(which	may	include	a	“no	action”	

scenario).	Comparing	the	estimates	permits	landowners	to	evaluate	the	potential	impacts	of	a	wide	

range	of	possible	factors,	including	foregone	growth,	land‐use	change,	and	changes	in	management	

practices.	

Generally,	entities	report	annually	for	the	life	of	a	project.	Since	forests	may	last	indefinitely,	there	

is	no	biological	ending,	although	events	such	as	land‐use	change,	a	natural	disturbance,	or	biome	
Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

6-10

shift	from	climate	change	may	effectively	end	the	life	of	a	specific	forest	or	forest	type.	Various	

programs	may	impose	time	limits	for	reporting,	or	the	entity	may	choose	a	project	length	that	is	

consistent	with	management	objectives.	The	accounting	methods	are	not	affected	by	project	or	

reporting	period	length;	therefore	no	specific	recommendations	are	made	in	this	guidance.	

6.1.3 Summary of Selected Methods/Models

6.1.3.1 Field Measurements of Carbon Pools and Fluxes

Methods	for	estimating	the	key	forest	carbon	pools	are	well	developed	and	fairly	standard.	Pools	

are	defined	in	Section	6.2,	although	detailed	methods	are	not	given.	Methods	for	measuring	forest	

carbon	stocks	are	described	in	a	variety	of	publications,	including	the	IPCC	Good	Practice	Guidance	

for	Land	Use,	Land	Use	Change,	and	Forestry	(IPCC,	2003),	Pearson	et	al.	(2007),	and	Hoover	

(2008),	among	others.	As	the	Forest	Inventory	and	Analysis	(FIA)	program	of	the	USDA	Forest	

Service	is	the	Federal	program	tasked	with	providing	national‐scale	estimates	of	the	U.S.	forest	

carbon	stocks/flux	(Heath	et	al.,	2011),	documented	inventory	procedures	from	this	program	

(USDA	Forest	Service,	2010a;	2010b)	serve	as	a	basis	for	many	facets	of	entity	level	carbon	

reporting	prescribed	in	this	document.	

6.1.3.2 Lookup Tables and Regional Estimates

The	most	comprehensive	collection	of	tables	of	carbon	stock	estimates	is	Smith	et	al.	(2006).	

Estimation	methods	are	described,	and	estimates	for	each	carbon	pool	are	provided	by	forest	type	

for	each	region	of	the	conterminous	United	States.	The	volume	includes	methods	and	tables	to	

estimate	carbon	in	HWPs.	

6.1.3.3 Models

A	variety	of	models	may	be	used	to	assist	in	the	estimation	of	forest	carbon	stocks	and	stock	

changes.	Models	will	be	described	in	more	detail	in	the	sections	that	follow,	but	for	reference	

purposes,	brief	summaries	of	the	most	commonly	used	models	are	provided	below.	Some	of	these	

models	are	complex	and	may	require	a	substantial	time	investment.	Interacting	with	some	of	these	

models	often	requires	specialist	knowledge	or	training	or	both.	For	such	models,	an	online	

estimation	tool	could	be	developed	so	that	landowners	would	not	need	to	learn	each	individual	

model,	but	would	interact	with	them	through	the	interface	of	an	estimation	tool,	while	the	

components	operate	in	the	background.	While	all	models	have	strengths	and	limitations,	the	

models	recommended	for	use	in	each	section	of	this	report	were	selected	because	of	their	

nationwide	coverage,	history	of	performance,	and	suitability	for	this	task.	

Forest Vegetation Simulator and Fire and Fuels Extension Carbon Reports.	The	Forest	Vegetation	

Simulator	(FVS)	is	a	national	system	of	growth	and	yield	models,	with	multiple	regional	variants,	

that	can	be	used	to	simulate	growth	and	yield	for	U.S.	forests.	FVS	is	a	stand‐level	model	and	can	

simulate	nearly	any	type	of	forest	management	practice.	The	Fire	and	Fuels	Extension	(FFE)	to	FVS	

can	be	used	to	generate	reports	of	all	carbon	pools	except	soil	but	including	HWPs;	non	CO2	GHGs	

are	not	included.1	A	number	of	geographic	variants	are	available,	each	with	regionally	specific	

equations	and	default	values.2

i‐Tree.	Two	of	the	tools	in	i‐Tree	estimate	carbon	storage	within	urban	trees,	annual	carbon	

sequestration,	and	carbon	emissions	avoided	through	energy	conservation	due	to	urban	trees.	One	

tool,	the	Urban	Forest	Effects	(UFORE)	model,	focuses	on	an	entire	urban	forest.	The	other	tool,	

																																																													

1	See	http://www.fs.fed.us/fmsc/fvs/index.shtml 2	Suggested	variants	may	be	found	here	:	http://www.fs.fed.us/fmsc/fvs/whatis/index.shtml
 Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

 6-11 

STRATUM,	focuses	on	street	tree	populations.	Tree	sample	(e.g.,	from	random	field	plots)	or	

inventory	data	are	required	to	run	the	model.	Models	to	estimate	future	carbon	effects	based	on	

local	field	data	and	user‐defined	mortality	and	planting	rates	have	also	been	developed.3

First Order Fire Effect Model.	The	First	Order	Fire	Effects	Model	(FOFEM)	is	a	national	level	model	

with	geographic	variants,	designed	to	predict	tree	mortality,	fuel	consumption,	smoke	production,	

and	soil	heating	caused	by	prescribed	fire	or	wildfire.4

COMSUME.	CONSUME	is	a	decision‐making	tool	designed	to	assist	resource	managers	in	planning	

for	prescribed	fire	and	impacts	of	wildfire.	CONSUME	predicts	fuel	consumption,	pollutant	

emissions,	and	heat	release	based	on	fuel	loadings,	fuel	moisture,	and	other	environmental	factors.5

It	allows	estimation	of	GHG	emissions	and	consumption	from	post‐harvest	and	thinning	activities.	

6.1.4 Sources of Data

Sources	of	available	data	that	may	be	appropriate	for	use	in	developing	estimates	of	GHG	emissions	

and	carbon	sequestration	vary	by	carbon	pool	(or	flux).	In	all	cases,	field	collection	of	data	is	

possible,	and	may	be	the	only	available	approach	for	those	instances	where	credible	default	values	

have	not	been	developed	and/or	lookup	tables	are	not	available;	this	may	be	particularly	relevant	

for	agroforestry	and	urban	forestry	applications.	In	the	case	of	many	of	the	non‐living	forest	carbon	

pools,	regional	default	values	are	available	for	down	dead	wood	(DDW),	forest	floor,	and	standing	

dead	wood	through	the	FIA	program,	as	well	as	a	number	of	documents	developed	in	support	of	

official	U.S.	government	estimates.	All	FIA	data	are	available	through	a	number	of	portals,	including	

the	FIA	database	tools—Forest	Inventory	Date	Online	(FIDO)	and	EVALIDator—and	the	

CarbonOnLineEstimator	(COLE),6	which	interacts	directly	with	the	FIA	database.	See	Table	6‐2	for	a	

partial	list	of	potential	data	sources.	

Currently,	values	for	soil	organic	carbon	(SOC)	stocks	are	drawn	from	the	State	Soil	Geographic	

(STATSGO)	database,	and	are	of	coarse	spatial	resolution.	A	limited	amount	of	field‐sampled	SOC	

data	are	also	available	through	the	FIA	database	as	part	of	the	Forest	Health	Monitoring	portion	of	

the	inventory	process.	Carbon	in	live	tree	biomass	is	also	available	from	FIA	and	like	other	variables	

can	be	retrieved	at	the	county	level.	The	FIA	sampling	design	is	intended	to	meet	a	specified	error	

target	at	large	areas	of	forest	land;	so	FIA	data	may	not	be	appropriate	for	use	at	smaller	spatial	

scales.	Estimates	based	on	a	small	number	of	plots	may	present	an	unacceptable	error	level.	COLE	

and	EVALIDator	provide	error	estimates	for	all	variables;	these	values	should	be	carefully	

considered	before	the	data	are	used	to	develop	estimates	for	a	particular	site.	

Data	for	emissions	of	other	GHGs	from	forests	are	not	widely	available,	although	estimates	and	

calculation	methods	are	better	developed	for	N2O	than	CH4.	The	U.S.	EPA	and	IPCC	provide	

estimation	methods	and	emissions	factors	for	both	gases	from	wildfires,	and	for	N2O	from	forest	

fertilization	(IPCC,	2006;	U.S.	EPA,	2011).	The	U.S.	EPA	publishes	a	National	Emissions	Inventory	

every	three	years,	which	provides	estimates	for	wildfire	as	well	as	prescribed	fire	for	criteria	

pollutants	as	well	as	hazardous	air	pollutants,	including	some	GHG	species	(U.S.	EPA,	2012a).	

																																																													

3	See	http://www.itreetools.org/ 4	See	http://www.firelab.org/science‐applications/fire‐fuel/111‐fofem

5	See	http://www.fs.fed.us/pnw/fera/research/smoke/consume/index.shtml 6	See	http://www.ncasi2.org/COLE/index.html.	COLE	was	developed	through	USDA	Forest	Service	financial	

support,	but	is	currently	hosted	by	NCASI.	
Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

6-12

6.1.5 Organization of Chapter/Roadmap

This	chapter	provides	guidance	on	estimating	carbon	sequestration	and	GHG	emissions	for	the	

forest	sector.	In	cases	where	a	landowner’s	holdings	involve	multiple	land	uses,	guidance	for	the	

other	sectors	should	be	consulted.	In	this	chapter,	attempts	to	note	areas	where	cross‐sector	

interactions	are	likely	to	occur	have	been	made.	Wetlands	and	hydrologically	managed	soils	are	

important	in	several	sectors,	and	for	this	reason	guidance	for	estimating	GHG	emissions	and	

sequestration	from	wetland	systems	is	covered	in	a	separate	section,	outside	of	the	

croplands/grazing	lands	and	forest	sectors.	

The	chapter	is	organized	to	provide	an	overview	of	the	elements	of	forest	carbon	accounting,	

including	definitions	of	the	key	carbon	pools	and	basic	methods	for	their	estimation.	Next	is	a	

section	relating	to	estimation	methods	in	cases	where	forests	have	been	established,	re‐established,	

and/or	cleared.	The	forest	management	section	considers	the	GHG	implications	of	a	variety	of	

commonly	employed	management	practices,	and	is	followed	by	guidance	on	the	estimation	of	

carbon	in	HWPs.	While	agroforestry	systems	and	urban	forests	may	not	be	considered	as	

traditional	forest	landscapes,	the	working	group	recognizes	the	importance	of	trees	located	outside	

of	forests.	Since	the	most	important	component	in	these	systems	is	often	the	live	biomass,	urban	

systems	have	been	included	in	the	forest	sector.	Agroforestry	is	a	complex	topic,	combining	aspects	

of	forestry,	cropland	agriculture,	and	animal	agriculture.	Since	agroforestry	is	most	likely	to	be	

practiced	on	lands	primarily	used	for	agriculture,	the	estimation	guidance	is	provided	in	the	

croplands	and	grazing	lands	section	of	the	document.	It	is	important	to	note	that	agroforestry	has	

many	cross‐sector	linkages,	and	a	complete	estimate	of	the	GHG	implications	of	agroforestry	

practices	may	necessitate	consultation	of	the	forest	methods	provided	here.	As	noted	above,	natural	

disturbance	is	one	of	the	important	risks	of	reversal	in	the	forest	sector,	and	the	final	section	

provides	guidance	on	estimating	the	impacts	from	natural	disturbance	in	forested	systems.	

The	remainder	of	this	chapter	is	organized	as	follows:	

 Section	6.2:	Forest	Carbon	Accounting	

 Section	6.3:	Establishing,	Re‐establishing,	and	Clearing	Forest

 Section	6.4:	Forest	Management	

 Section	6.5:	Harvested	Wood	Products	

 Section	6.6:	Urban	Forests	

 Section	6.7:	Natural	Disturbances	

Table	6‐2	shows	internet	sites	available	for	information	on	carbon	estimation.	Figure	6‐2	shows	a	

decision	tree	for	the	forest	sector	showing	which	forest	chapter	sections	(i.e.,	source	categories)	are	

relevant	depending	on	which	forest	activities	are	taking	place	for	an	entity.	
 Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

 6-13 

Table 6‐2: Internet Sites Available for Information on C Estimation

Internet site Organization Relevant Content

http://fia.fs.fed.us/ USDA	Forest	Service,	Forest	

Inventory	and	Analysis	

 Forest	statistics	by	state,	including	carbon

estimates	

 Sample	plot	and	tree	data	

 Forest	inventory	methods	and	basic	definitions	

http://www.fhm.fs.fed.

us/

USDA	Forest	Service,	Forest	

Health	Monitoring	

 Forest	health	status

 Regional	data	on	soils	and	dead	wood	stocks	

 Forest	health	monitoring	methods	

http://www.usda.gov/o

ce/climate_change/gree

nhouse.htm		

USDA	GHG	Inventory	  State‐by‐State	forest	carbon	estimates	

http://unfccc.int/

http://www.ipcc.ch/ UNFCCC	and	IPCC	  International	guidance	on	carbon	accounting	and	estimation	

http://soildatamart.nrc

s.usda.gov/

USDA	Natural	Resources	

Conservation	Service	  Soil	Data	Mart:	access	to	a	variety	of	soil	data	

http://www.nrs.fs.fed.u

s/carbon/tools/

USDA	Forest	Service,	Northern	

Research	Station	

 Accounting	and	reporting	procedures

 Software	tools	for	carbon	estimation	

http://www.eia.gov/oia

f/1605/gdlins.html

U.S.	Energy	Information	

Administration,	Voluntary	

GHG	Reporting	

 Methods	and	information	for	calculating	

sequestration	and	emissions	from	forestry;	see	

Part	I,	Appendix	

http://www.epa.gov/cli

matechange/emissions

/usinventoryreport.htm

l

U.S.	Environmental	Protection	

Agency	

 Methods	and	estimates	for	GHG	emissions	and	

sequestration	

http://www.comet2.col

ostate.edu/

USDA	Natural	Resources	

Conservation	Service	and	

Colorado	State	University	

Natural	Resources	Ecology	

Lab	

 Web‐based	tool	for	estimating	carbon	

sequestration	and	net	GHG	emissions	from	soils	

and	biomass	for	U.S.	farms	and	ranches	
Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

6-14

Figure 6‐2: Decision Tree for Forest Sector Showing Relevant Chapter Sections Depending on

Applicable Source Categories

NO

NO

YES

YES

NO

See Section 6.7:

Natural Disturbances

YES

YES

NO

Start

Chapter 6 is not

applicable for your

entity.

Did you have any

natural disturbances

(e.g., fires, pests, storms)

in your forest

stands?

Did you

establish new,

re‐establish, or clear

forest stands on your

land?

Did you initiate

any improved forest

management practices on

your forest stands?

Did you

harvest any wood for

products from your forest

stands?

Do you have forest

stands that are located

in an urban area?

See Section 6.3:

Establishing,

Re‐establishing, and

Clearing Forest

See Section 6.4:

Forest Management

See Section 6.2:

Forest Carbon

Accounting

See Section 6.6:

Urban Forestry

See Section 6.5:

Harvested Wood

Products

YES

NO

NO

YES

NO

YES

YES

Do you have forest

stands on your

land?

Do you have

additional forest

stands ?

End

NO

Did you use

agroforestry: e.g.,

windbreaks, riparian forest

buffers, alley cropping,

silvopastures?

See Section 3.4:

Agroforestry
 Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

 6-15 

6.2 Forest Carbon Accounting

6.2.1 Description of Forest Carbon Accounting

The	basic	question	inherent	within	the	broader	context	of	forest	carbon	estimation	is:	“How	much	

carbon	is	in	this	forest?”	Any	discussion	of	forests	or	forestry	activities	in	the	context	of	GHGs	

depends	on	quantifying	forest	carbon.	Forest	ecosystems	are	generally	recognized	as	significant	

stocks	of	carbon,	and	aggrading,	or	growing,	forests	can	be	strong	carbon	sinks.	Disturbances	and	

forest	management	influence	the	size	and	rates	of	change	of	these	stocks.	It	is	important	to	note	

that	forest	carbon	generally	is	not	measured	directly	(e.g.,	collecting	forest	biomass	samples	for	

laboratory	determination	of	carbon	content).	It	is	usually	quantified	indirectly	from	standard	forest	

inventories	and	associated	carbon	models	(e.g.,	litter	carbon	dependent	on	forest	type	and	stand	

age).	For	live	tree	pools,	forest	inventories	often	only	measure	limited	dimensional	attributes	(e.g.,	

diameter	and	height)	of	individual	trees	and	use	biomass	component	models	(e.g.,	bole	and	crowns)	

and	wood	density	values	to	convert	these	values	into	an	estimate	of	total	tree	biomass.	Once	an	

estimate	of	biomass	is	attained,	a	standard	carbon	conversion	constant	is	applied	to	produce	a	

carbon	stock	estimate.	Carbon	conversions	vary	slightly,	but	50	percent	of	dry	weight	is	a	useful	

round	value	applicable	to	all	vegetation	and	sound	wood	(IPCC,	2006).	For	other	pools,	such	as	

litter	layers	and	soil	organic	matter,	specific	carbon	content	per	unit	volume	depends	on	decay	and	

composition	of	the	material	and	is	generally	less	than	50	percent	carbon.	Given	the	diversity	of	

estimation	procedures	and	carbon	pool	definitions,	a	reasonable	selection	of	methodologies	should	

be	available	for	entities	wishing	to	assess	their	forest	carbon.	

A	major	attribute	of	carbon	“accounting”	is	to	explicitly	document	and	define	accounting	

procedures	such	that	forest	carbon	reports	are	comparable	across	ownerships	and	forest	

ecosystems.	Absolute	quantities	of	carbon,	or	carbon	mass,	are	not	only	a	function	of	a	specific	

forest	but	also	dependent	on	how	pools	are	defined	and	how	the	mass	of	carbon	within	the	pool	is	

estimated.	For	example,	both	remotely	sensed	images	and	ground‐based	tree	measurements	can	

provide	separate	estimates	of	the	same	forest.	These	two	techniques	are	unlikely	to	provide	

identical	estimates	due	to	methodological	differences,	including	the	fact	that	each	approach	may	

define	different	populations	of	interest	and	thus	account	for	different	sets	of	trees.	Identifying	and	

resolving	such	issues	is	an	objective	of	forest	carbon	research.	Not	all	forest	carbon	assessments	or	

management	plans	need	to	encompass	all	carbon	(or	GHGs)	pools	if	the	carbon	is	properly	

identified.	Measuring	the	current	state	of	a	forest’s	carbon	stocks	and	recent	changes	is	a	part	of	

Methods for Forest Carbon Accounting Utilized in this Guidance

 Range	of	options	dependent	on	the	size	of	the	entities’	forest	land	including:	

− FVS‐FFE	module	(entities	that	fit	the	large	landowner	definition),	and	

− Default	lookup	tables	(entities	fitting	the	small	landowner	definition).	

 These	options	use:	

− Allometric	equations	from	Jenkins	et	al.	(2003a),	and	

− Default	lookup	tables	from	Smith	et	al.	(2006;	GTR	NE‐343)—default	regional	values	

based	on	forest	type	and	age	class	developed	from	FIA	data.	

 These	methods	were	selected	because	they	provide	a	range	of	options	dependent	on	the	

size	of	the	entities'	forest	land.	
Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

6-16

developing	a	baseline,	which	can	then	be	used	for	additional	analysis.	A	baseline	of	past	carbon	

stocks	and	change	can	be	constructed	and	used	with	modeling	to	determine	projections	of	likely	

future	carbon.	Similarly,	a	baseline	is	necessary	for	analysis	of	alternate	management	options	to	

evaluate	potential	for	sequestration/emission.	The	technical	specifications	of	baselines	(e.g.,	

starting	year	and	included	stock	categories)	are	often	a	social/political	decision,	and	are	beyond	the	

purview	of	this	document.	However,	to	standardize	forest	carbon	accounting	options	for	the	

purpose	of	entity	reporting	(e.g.,	woodland	owners),	this	document	will	propose	a	single	set	of	

forest	carbon	pool	definitions.	The	specific	recommendations	included	here	are	intended	to	direct	

landowners	to	tools	and	data	sources	specially	developed	for	quantifying	forest	carbon.	Note	that	

these	listed	processes	are	not	intended	to	exclude	alternative	data	summaries	that	may	be	available	

to	entities.	Details	are	discussed	below	in	the	discussion	of	the	respective	forest	carbon	pools,	but	

the	general	options	listed	in	decreasing	accuracy	(and	cost)	include	the	following:	

(1) Measure/sample	your	forest	and	estimate	carbon	from	these	data	(reduce	sample	data	so	as	

to	then	apply	available	biomass	equations	or	other	carbon	conversion	factors);	

(2) Characterize	your	forest	according	to	classifications	(i.e.,	lookup	tables)	based	on	stand	or	

site	attributes	derived	from	records	in	the	nation’s	forest	inventory	database	(FIADB)	

(Woodall	et	al.,	2010;	Woudenberg	et	al.,	2010);	or	

(3) Use	associated	models	(FIDO,	COLE,	etc.),	which	base	your	forest’s	carbon	estimates	on	

representative	data	sampled	by	others	with	critical	dependent	user	variable	input	(e.g.,	

stand	age).	

Note	that	the	above	three	options	are	not	necessarily	mutually	exclusive.	For	example,	FIADB	data	

or	similar	models	(Option	2)	are	based	on	permanent	inventory	plot	sampling	and	carbon	

conversion	(Option	1),	and	lookup	tables	(Option	3)	are	based	on	the	FIADB	(Option	2).	The	

recommended	forest	carbon	inventory	options	involve	tradeoffs	in	costs	and	level	of	information	

unique	to	the	entities’	forest	land.	

The	process	of	obtaining	forest	carbon	estimates	depends	on	circumstances	unique	to	each	entity,	

but	mostly	depends	on	the	intended	audience	and	the	resources	available	for	forest	inventory.	For	

this	guidance,	a	two‐tier	system	is	in	place.	The	goal	is	to	be	as	inclusive	as	possible	while	not	

creating	a	measurement	burden.	Smaller	holdings	that	are	not	actively	managed	are	unlikely	to	be	

inventoried;	a	two‐tier	approach	permits	owners	of	such	holdings	to	estimate	their	footprint	and	

the	potential	changes	from	changes	in	practices	applied	without	incurring	the	costs	of	

measurement.	Smaller	landowners	who	have	inventory	data	or	who	wish	to	acquire	it	should	use	

the	tools	and	protocols	described	for	large	landowners.	

Landowner	size	classes	are	defined	as	follows:	

Landowners	who	hold	200	or	more	acres	(80.9	hectares	[ha])	of	forest	land	should	follow	the	

methods	for	large	landowners.	Also,	landowners	who	hold	less	than	200	acres	(80.9	ha)	of	forest	

land	should	follow	the	methods	for	large	landowners	if	three	or	more	of	the	following	are	true:	

 Landowner	owns	or	manages	more	than	50	forested	acres	(20.2	ha)

 Landowner’s	forest	is	certified	

 Landowner	has	developed	a	forest	management	plan	

 Landowner’s	forested	property	has	a	history	of	timber	harvesting	

 Landowner	participates	in	State	forest	tax	abatement	programs	

Landowners	not	meeting	the	definition	of	large	landowner	should	follow	the	methods	for	small	

landowners.	
 Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

 6-17 

Recommended	methods	depend	on	forest	landowner	size.	Small	landowners	may	use	generalized	

lookup	tables	based	on	region,	forest	type,	and	age	class	to	estimate	carbon	stocks.	Large	

landowners	should	collect	standard	forest	inventory	data	and	use	the	FVS‐FFE	module	with	Jenkins	

et	al.	(2003a)	allometric	equations.	It	should	be	noted	that	FVS	and	the	FFE	are	large	and	

complicated	models;	any	tool	that	implements	these	methods	will	require	development	of	a	

simplified	user	interface	that	interacts	with	FVS	and	FFE.	

At	this	time,	the	Jenkins	et	al.	(2003a)	equations	are	specified	since	they	are	nationally	consistent.	

Future	development	is	likely	to	include	the	implementation	of	a	more	recent	FIA	biomass	

estimation	method	in	FVS,	enabling	the	production	of	estimates	that	match	the	official	U.S.	forest	

carbon	estimates.	While	local	volume	or	biomass	equations	may	be	more	accurate	for	a	given	

location,	use	of	such	equations	will	result	in	additional	inconsistencies	in	results,	so	no	other	

equations	are	approved	for	use	at	this	time	under	this	methodology.	

Although	carbon	reporting	beyond	that	of	the	entity	level	(e.g.,	major	timberland	owner	or	national	

forest)	may	use	refined	measurement	protocols,	expanded	carbon	pool	definitions,	and/or	ancillary	

data	(e.g.,	remotely	sensed	imagery),	the	proposed	pools	and	inventory	methodologies	in	this	

document	serve	as	a	starting	point.	Classification	of	carbon	estimates	within	multi‐tiered	systems,	

and	links	to	models	to	project	future	change	under	alternate	scenarios	are	addressed	at	the	end	of	

Section	6.2.

To	facilitate	accounting,	forest	carbon	is	typically	classified	into	a	few	discrete	pools,	which	should	

be	comprehensive	(all	organic	carbon)	with	no	gaps	and	no	overlap.	The	purpose	of	establishing	

these	separate	pools,	or	bins,	of	forest	carbon	is	twofold:	(1)	to	align	appropriate	data	with	

ecosystem/product	components	(e.g.,	tree	inventories	and	live	tree	carbon	pool),	or	alternatively	to	

identify	gaps;	and	(2)	as	a	part	of	the	accounting	process,	not	all	reported	stock	or	change	

necessarily	needs	to	include	all	of	the	carbon	pools,	but	what	is	included	must	be	unambiguously	

identified.	Note	that	the	carbon	pools	(or	bins	or	classifications)	focus	on	carbon	from	phytomass.	

Strictly	speaking,	total	carbon	stocks	within	a	forest	include	a	non‐plant	(not	originating	from	the	

plant	kingdom)	percentage,	but	such	pools	are	not	defined	because	this	is	generally	an	insignificant	

proportion.	Exceptions	are	the	forest	floor	and	soil	pools,	which	include	decomposers	and	soil	

fauna.	A	sometimes	significant	amount	of	carbon	is	removed	from	forests	as	wood	is	harvested	and	

used	in	wood	products.	Some	of	that	carbon	remains	sequestered	for	long	periods	of	time,	

depending	on	the	products.	Thus,	harvested	wood	should	be	included	in	forest	carbon	estimates.	

Figure	6‐3	is	a	decision	tree	for	the	forest	carbon	accounting	source	category	showing	which	carbon	

accounting	assumptions	(e.g.,	simulation	models,	allometric	equations,	biomass	expansion	factors,	

lookup	tables)	are	recommended	for	an	entity	depending	on	the	type	of	activity	data	available.	

However,	it	should	be	noted	that	for	national	reporting—i.e.,	the	annual	GHG	inventory	reported	by	

USDA	and	U.S.	EPA—where	individual	tree	measurements	from	FIA’s	inventory	plots	are	available,	

the	component	ratio	method	(CRM)	for	estimating	biomass	(Woodall	et	al.,	2011)	is	currently	used.	

Again,	future	development	will	likely	bring	these	methods	into	alignment.	
Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

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Figure 6‐3: Decision Tree for Forest Carbon Accounting Showing Methods Appropriate for

Estimating Forest Carbon Stocks

1	Small	landowners	(as	defined	in	Section	6.2.1)	may	use	generalized	lookup	tables	based	on	region,	forest	type,	and	age	

class	to	estimate	carbon	stocks.	Large	landowners	should	collect	standard	forest	inventory	data	and	use	allometric	

equations	to	estimate	live	tree	biomass	carbon	(other	carbon	pools	may	be	obtained	from	lookup	tables).	

2	Jenkins	et	al.	(2003a).	

3	Note	that	volume	equations	used	by	landowners	should	align	with	“mean	volume”	specifications	(e.g.,	rotten/cull	

deductions)	of	Smith	et	al.	(2006).	Different	volume	equations	and	deductions	will	produce	volume	estimates	that	differ	

from	those	used	in	the	tables.

4	Smith	et	al.	(2006).	
 Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

 6-19 

Another	aspect	of	a	carbon	accounting	framework	is	consistent	or	comparable	representation	of	

change,	which	goes	beyond	the	identification	of	carbon	pools.	Change	is	affected	by	processes	of	

recruitment	and	growth	as	well	as	disturbance,	mortality,	and	harvest.	In	the	most	basic	sense,	

change	can	be	the	difference	between	two	successive	stock	estimates.	This	is	common	for	GHG	

reporting	based	on	standard	forest	inventories.	Some	components	of	change	can	be	measured	with	

intensive	sampling	at	small	scales,	but	in	general	change	is	estimated	from	measurements	at	two	

successive	inventory	times	(e.g.,	total	stock	change,	or	growth/removals/mortality	estimates,	or	

remotely	sensed	data),	or	based	on	models	of	ecosystem	or	biogeochemical	change.	A	basic	

approach	to	quantifying	change	in	forest	carbon	is	based	on	the	quantities	defined	for	forest	carbon	

stocks.	Net	annual	carbon	stock	changes	are	calculated	by	taking	the	difference	between	the	

inventories	and	dividing	by	the	number	of	years	between	the	inventories	for	a	selected	forest	or	

forest	area	(e.g.,	Δ	stock	=	(stock2	–	stock1)/time).	This	stock‐change	approach	(IPCC,	2006)	is	the	

change	method	applied	to	FIA	strategic‐scale	inventories	for	the	stock‐change	values	reported	in	

the	U.S.	National	GHG	Inventories	(e.g.,	U.S.	EPA,	2011).	

Six Steps to Forest Entity Carbon Estimation

The	approach	to	estimation	of	carbon	stocks	and	fluxes	in	the	forest	sector	is	as	follows:	

Step	1:	Determine landowner size class based on forest area.	Based	on	the	acreage	under	

consideration,	landowners	are	divided	into	two	groups:	“small”	landowners	and	“large”	

landowners	as	defined	in	Section	6.2.1.	

Step	2:	Collect forest data. For	both	size	classes	of	landowners,	some	level	of	forest	inventory	

(i.e.,	field	survey)	data	is	required.	However,	there	are	differing	data	requirements	for	small	

landowners	and	large	landowners.	

Small landowners	should	collect	basic	data	on	species	mix	(i.e.,	type	of	forest)	and	stand	age	(or	

time	since	last	major	disturbance)	within	their	forest.	Greater	inventory	detail	can	lead	to	more	

precise	estimates	of	carbon,	but	even	broad	generalizations	about	the	region,	age	(and/or	mean	

volume),	and	type	of	forest	can	lead	to	a	carbon	estimate.	The	objective	is	to	obtain	reasonable	

and	consistent	estimates	over	time	at	the	lowest	cost.	If	a	small	landowner	wishes	to	conduct	an	

inventory	and	follow	the	recommended	guidance	for	large	landowners,	they	are	free	to	choose	

this	option.	The	principal	tradeoff	is	between	cost	and	accuracy;	collecting	inventory	data	

increases	the	cost	of	developing	estimates	but	increases	accuracy.	

Large landowners	should	gather	more	extensive	data	about	forest	and	stand	characteristics.	A	

thorough	forest	inventory	is	created	using	industry	standards	and	practices	of	the	type	

described	in	GTR	NRS‐18:	Measurement Guidelines for the Sequestration of Forest Carbon.

Variables	considered	must	include	dominant	species,	dominant	age	class,	stand	density,	and	site	

class.	Inclusion	of	additional	variables,	while	not	required,	will	improve	accuracy	of	carbon	

estimates.	

Step	3:	Estimate initial forest carbon stock and annual fluxes.	Quantities	of	carbon	change	

over	time.	Forest	carbon	estimates	are	divided	into	six	discrete,	mutually	exclusive	pools,	

including	live	trees,	standing	dead	trees,	understory	vegetation,	down	dead	wood,	forest	floor,	

and	soil	organic	carbon.	A	number	of	pool‐specific	carbon	conversion	methods	are	available;	

these	methods	use	the	inventory	data	gathered	in	Step	2	to	quantify	carbon	for	each	pool.	

However,	the	specific	methods	to	be	used	differ	depending	on	the	landowner	size	class.	

(Continued)
Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

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(Continued)

Small landowners, after	collecting	observational	data,	can	use	lookup	tables	from	Smith	et	al.	

(2006)	(also	known	as	GTR‐NE‐343:	Methods for calculating forest ecosystem and harvested

carbon with standard estimates for forest types of the United States)	to	estimate	carbon	stocks	

and	carbon	stock	changes.	The	lookup	tables	are	categorized	by	region,	forest	type,	previous	

land	use,	and	in	some	cases,	management	activity.	Users	must	identify	the	categories	for	their	

forests	and	estimate	the	area	of	forestland.	To	facilitate	use	of	the	data	from	GTR‐NE‐343,	a	tool	

could	incorporate	the	data	such	that,	in	most	cases,	landowners	would	be	able	to	select	their	

stand	characteristics	from	a	drop‐down	menu	of	defaults.	Based	on	the	landowner’s	selections	

from	the	default	menus,	the	tool	would	produce	estimates	of	carbon	stocks	in	each	of	the	six	

carbon	pools.	

Large landowners	should	use	the	data	collected	in	their	forest	surveys	to	perform	model	runs	

using	the	FVS	model.	FVS	will	use	the	site‐	and	stand‐specific	data	to	provide	more	accurate	

estimates	of	carbon	stocks	in	each	of	the	carbon	pools	(excluding	soil	carbon,	which	FVS	does	

not	estimate).	Soil	carbon	estimates	can	be	determined	from	a	range	of	methods	including	

sampling	or	existing	forest	soil	carbon	estimate	datasets	depending	on	a	specific	entity’s	

circumstances.	

Though	the	methods	differ	for	small	landowners	and	large	landowners,	both	calculate	initial	

carbon	stocks	and	expected	annual	rates	of	accumulation	under	average	conditions	(repeating	

the	field	survey	at	prescribed	intervals	will	help	calibrate	or	validate	the	stock	change	

estimates).	

The	methods	also	allow	for	adjustments	due	to	HWPs	(Step	4),	forest	management	practices	

(Step	5),	and	natural	disturbances	(Step	6).	

Step	4:	Adjust carbon estimates due to HWPs. Harvesting	activities	can	have	considerable	

impact	on	carbon	quantity	across	the	six	forest	carbon	pools.	In	terms	of	emissions,	the	fate	of	

the	harvested	material	must	be	considered	as	well,	including	whether	the	material	is	used	in	

HWPs	or	for	energy.	As	above,	the	methods	for	estimating	these	impacts	differ	depending	on	the	

landowner	size	class.	

For	HWPs,	small landowners should	rely	on	data	provided	in	lookup	tables	in	GTR‐NE‐343,	

which	provides	factors	for	calculation	of	carbon	in	HWPs	based	on	region,	timber	type,	and	

industrial	roundwood	category.	The	lookup	tables	divide	the	harvested	forest	materials	pool	

into	four	distinct	fates:	products	in	use,	landfill,	emitted	with	energy	capture,	and	emitted	

without	energy	capture.	Carbon	emissions	differ	depending	on	the	fate,	which	in	turn	depends	

on	the	region	and	harvest	material	characteristics.	By	using	the	lookup	tables,	landowners	can	

adjust	carbon	estimates	accordingly.	

Large landowners should	rely	on	FVS	to	model	forest	management	practices,	resulting	in	

estimates	of	the	carbon	impact	of	these	practices	(e.g.,	harvesting).	For	example,	FVS	can	

consider	the	type	of	harvest	(e.g.,	clear	cut	versus	strategic	thinning)	and	project	the	results	of	

this	harvest	on	carbon	stocks,	thus	allowing	users	to	quantify	the	carbon	impact	of	various	

harvesting	activities,	as	well	as	adjusting	for	the	ultimate	fate	of	harvested	materials.	The	

harvested	forest	material	pool	is	divided	by	FVS	into	the	same	four	distinct	fates	as	for	GTR‐NE‐

343:	products	in	use,	landfill,	emitted	with	energy	capture,	and	emitted	without	energy	capture.	

Harvests	also	impact	forest	growth	over	time,	which	is	modeled	by	FVS.	

(Continued)
 Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

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6.2.1.1 Forest Carbon Pools

Carbon	reporting—such	as	for	the	U.S.	reporting	commitment	to	the	United	Nations	Framework	

Convention	on	Climate	Change	(UNFCCC),	which	is	met	by	the	U.S.	EPA’s	official	GHG	inventory	(e.g.,	

(Continued)

Step	5:	Adjust carbon estimates due to improved forest management. Forest	management	

practices,	such	as	thinning	or	fertilization,	may	impact	carbon	fluxes	as	well.	As	above,	the	

methods	for	estimating	these	impacts	differ	depending	on	the	landowner	size	class.	

FVS	allows	large landowners to	quantify	the	impact	of	various	forest	management	practices.	For	

example,	using	keywords	(or	combinations	of	keywords)	provided	by	FVS,	users	can	generate	

estimates	for	the	impact	of	stand	density	management,	site	preparation	methods,	vegetation	

controls,	various	densities	of	planting	stock,	fertilization,	rotation	length	management,	

prescribed	fire/control	burns	and	fuel	load	management,	and	pest	and	disease	control.	With	

given	stand	and	tree‐list	data,	users	can	develop	a	baseline,	which	can	then	be	compared	to	

alternative	management	strategies.	This	allows	for	assessment	of	carbon	impact	of	

implementing	those	management	practices.	It	should	be	noted	that	FVS	is	the	recommended	

method,	even	if	a	large	landowner	has	its	own	custom	inventory	and	modeling	system,	which	

might	be	considered	superior	to	regional	models	such	as	FVS.	The	adoption	of	a	single,	

recommended	method	for	landowners	allows	for	transparent,	consistent,	comparable,	and	

complete	estimates	across	landowners	appreciating	that	there	will	be	a	likely	trade	off	in	the	

accuracy,	cost	effectiveness,	and	ease	of	use	of	the	method	for	those	landowners	with	custom	

systems.	Future	development	may	include	a	means	for	large	landowners	to	use	custom	models	

in	this	framework,	but	this	option	is	not	available	at	this	time.	

Unfortunately,	the	lookup	tables	do	not	allow	for	estimates	associated	with	improved	forest	

management.	If	prescribed	fire/control	burning	is	used	by	either	landowner	type,	it	is	

recommended	that	the	emissions	for	the	activity	be	calculated	as	guided	in	Step	6.	

Step	6:	Adjust carbon estimates due to forest fires and other natural disturbances. Natural	

disturbances,	such	as	forest	fires,	storms,	wind,	drought,	or	pest/insect	infestation,	can	also	have	

considerable	impact	on	carbon	quantities	across	the	six	forest	carbon	pools.	Landowners	should	

estimate	the	carbon	impact	of	natural	disturbances.	

For	forest	fires,	wildfires,	and	prescribed/controlled	burns,	both	small	and	large	landowners	

should	rely	on	FOFEM	to	generate	carbon	estimates.	FOFEM	input	requirements	include	basic	

forest	type,	site	location,	and	dominant	species	data,	but	also	allows	users	to	input	additional	

information,	depending	on	a	specific	entity’s	circumstances,	on	amount	of	duff,	moisture	

content,	and	other	variables	associated	with	fire.	The	severity	of	the	fire	can	be	categorized	by	

percent	of	the	land	affected.	The	resulting	output	includes	estimates	of	carbon	emissions.	

The	methods	assume	small landowners	can	provide	observational	estimates	for	the	impacts	of	

natural	disturbances	such	as	pests,	based	on	the	percentage	of	forestland	affected	by	the	

disturbance.	Large landowners may	model	impacts	of	pests	through	available	keywords	and	

extensions	provided	by	FVS.	

The	philosophy	behind	these	six	steps	is	that	they	allow	the	entity	to	assess	what	carbon	stocks	

they	have	under	any	present	conditions	and	what	stocks	they	might	expect	given	

implementation	of	a	particular	harvesting	regime,	change	in	forest	management	practices,	

and/or	a	variety	of	natural	disturbances.	
Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

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U.S.	EPA,	2011)—provides	a	framework	for	the	pools	described	here.	However,	the	pools	are	

modified	to	more	closely	correspond	to	types	of	forest	inventory	data.	For	example,	forest	carbon	

can	be	easily	categorized	according	to	aboveground	versus	belowground,	or	living	versus	dead	

plant	material.	In	practice,	classifications	of	carbon	pools	depend	on	the	forest	data	and	how	they	

are	used.	As	such,	the	pools	described	below	are	jointly	defined	by	UNFCCC	reporting	requirements	

and	the	use	of	FIA	forest	inventory	as	the	primary	data	source.	In	other	words,	the	pools	defined	

below	are	a	convenient	set,	but	definitions	and	boundaries	around	pools	can	vary	according	to	

specific	carbon	estimation	procedures/capabilities	and	reporting	needs	(see	Figure	6‐4).	

Figure 6‐4: Forest Carbon Pool Hierarchy Showing How Forest Carbon Pools Can Be

Delineated into Even Smaller Pools Dependent on the Entity Needs and Inventory

Capabilities

Live trees:	A	large	woody	perennial	plant	(capable	of	reaching	at	least	15	feet	(4.6	m)	in	height)	

with	a	diameter	at	breast	height	(DBH)	or	at	root	collar	(if	multistemmed	woodland	species)	

greater	than	1	inch	(2.5	centimeters	[cm]).	Includes	the	carbon	mass	in	roots	(i.e.,	live	belowground	

biomass)	with	diameters	greater	than	0.08	in	(2	millimeters	[mm],	stems,	branches,	and	foliage.	

Understory:	Roots,	stems,	branches,	and	foliage	of	tree	seedlings,	shrubs,	herbs,	forbs,	and	grasses.	

Standing dead trees:	Dead	trees	of	at	least	1	inch	(2.5	cm)	DBH	that	have	not	yet	fallen,	including	

carbon	mass	of	coarse	roots,	stems,	and	branches,	but	that	do	not	lean	more	than	45	degrees	from	

vertical	(Woudenberg	et	al.,	2010),	including	coarse	nonliving	roots	more	than	0.08	in	(2	mm)	in	

diameter.	

Down dead wood	(also	known	as	coarse	woody	debris):	All	nonliving	woody	biomass	with	a	

diameter	of	at	least	3	inches	(7.6	cm)	at	transect	intersection,	lying	on	the	ground.	This	pool	also	
 Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

 6-23 

includes	some	less‐than‐obvious	components	of	DDW:	(1)	debris	piles,	usually	from	past	logging;	

and	(2)	previously	standing	dead	trees	that	have	lost	enough	height	or	volume,	or	lean	greater	than	

45	degrees	from	vertical,	so	they	do	not	qualify	as	standing	dead	trees.	

Forest floor:	The	litter,	fulvic,	and	humic	layers,	and	all	fine	woody	debris	with	a	diameter	less	than	

3	inches	(7.6	cm)	at	transect	intersection,	lying	on	the	ground	above	the	mineral	soil.	

Soil organic C:	All	organic	material	in	soil	to	a	depth	of	generally	3.3	feet	(1	meter	[m]),	including	

the	fine	roots	(e.g.,	less	than	0.08	in	(2	mm)	in	diameter)	of	the	live	and	standing	dead	tree	pools,	

but	excluding	the	coarse	roots	of	the	pools	mentioned	earlier.	

Harvested wood:	Wood	removed	from	the	forest	ecosystem	for	processing	into	products,	not	

including	logging	debris	(slash)	left	in	the	forest	after	harvesting.	

These	pool	definitions	are	developed	around	a	common	set	in	use	by	a	number	of	publications	(e.g.,	

Smith	et	al.,	2006)	and	at	the	forest	stand	level,	which	in	turn	differ	from	stock	definitions	used	by	

the	United	States	to	meet	UNFCCC	national	reporting	requirements.	

Also	notable	(in	the	reporting	list)	is	the	inclusion	of	HWP	(covered	in	detail	in	Section	6.5),	which	

assumes	that	a	measurable	portion	of	wood	removed	at	harvest	remains	sequestered	from	

reemission	to	the	atmosphere	for	a	period	of	time	that	can	be	estimated.	Pools	and	estimation	of	

stocks	are	organized	primarily	according	to	data	collection	and	estimation	with	FIA’s	permanent	

inventory	plots	(phase	two	(P2),	the	standard	inventory	measurements;	and	phase	three	(P3),	the	

forest	health	measurements).	Note	that	pool	definitions	are	not	independent	of	related	estimators;	

details	related	to	estimation	are	not	addressed	until	subsequent	sections	of	this	guidance.	

6.2.2 Data Collection for Forest Carbon Accounting

Forest	carbon	is	typically	estimated	indirectly,	through	applying	conversion	constants	to	a	standard	

forest	inventory,	using	a	localized	biogeochemical	model,	or	simply	looking	up	specific	forest	

attributes	(e.g.,	stand	age,	forest	type)	in	a	lookup	table	(e.g.,	Smith	et	al.,	2006).	For	the	purposes	of	

this	documentation,	a	standard	set	of	carbon	pool	definitions	that	are	part	of	FIA’s	national	

inventory	are	delineated	that	correspond	to	available	lookup	tables	(Smith	et	al.,	2006).	

6.2.2.1 Live Trees

The	tree	carbon	pools	include	aboveground	and	belowground	(coarse	root)	carbon	mass	of	live	

trees.	Separate	estimates	are	made	for	full‐tree	and	aboveground‐only	biomass	to	estimate	the	

belowground	component.	Tree	carbon	estimates	within	the	FIADB	(USDA	Forest	Service,	2012;	

Woudenberg	et	al.,	2010)	are	based	on	Woodall	et	al.	(2011)	and	Jenkins	et	al.	(2003a).	The	per‐

tree	carbon	estimates	are	a	function	of	tree	species,	diameter,	height,	and	volume	of	wood.	

Belowground	biomass	is	calculated	as	a	varying	proportion	of	aboveground	biomass.	Again,	this	is	

dependent	on	species	and	size	of	individual	trees.	The	pool	of	live	trees	within	the	FIADB	is	defined	

as	trees,	or	woody	biomass	with	greater	or	equal	to	1	inch	(2.5	cm)	DBH.	However,	trees	less	than	5	

inches	(12.7	cm)	DBH	are	sampled	differently	than	those	that	are	5	inches	(12.7	cm)	or	more.	These	

differences	should	not	affect	precision	in	the	overall	amount	of	tree	carbon	or	stand	level	density.	

Saplings	are	trees	at	least	1	inch	(2.5	cm)	but	less	than	5	inches	(12.7	cm)	DBH.	The	“sapling”	versus	

larger	tree	distinction	is	based	on	sampling	differences	on	the	FIA	plots.	This	illustrates	that	pool	

classification	is	dependent	on	both	the	obvious	physical	and	spatial	separation	in	a	stand	as	well	as	

data	sources.	

6.2.2.2 Understory

Understory	vegetation	is	a	minor	component	of	biomass	or	the	live	plant	component.	Understory	

vegetation	is	defined	as	all	biomass	of	undergrowth	plants	in	a	forest,	including	woody	shrubs	and	
Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

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trees	less	than	1	inch	(2.5	cm)	DBH.	In	FIADB‐based	carbon	inventory,	it	is	assumed	that	10	percent	

of	understory	carbon	mass	is	belowground.	This	general	root‐to‐shoot	ratio	(0.11)	is	near	the	lower	

range	of	temperate	forest	values	provided	in	IPCC	(2006)	and	was	selected	based	on	two	general	

assumptions:	ratios	are	likely	to	be	lower	for	light‐limited	understory	vegetation	compared	with	

larger	trees,	and	a	greater	proportion	of	all	root	mass	will	be	less	than	0.08	in	(2	mm)	in	diameter.	

Estimates	of	carbon	density	are	based	on	information	in	Birdsey	(1996),	which	was	applied	to	FIA	

permanent	plots.	

6.2.2.3 Standing Dead

The	standing	dead	tree	carbon	pools	include	aboveground	and	belowground	(coarse	root)	mass.	

Estimates	and	allometry	are	essentially	similar	to	those	for	live	trees,	with	some	additional	

considerations	for	decay	and	mechanical/structural	damage	(Domke	et	al.,	2011;	Harmon	et	al.,	

2011).	Carbon	conversions	vary	slightly,	but	50	percent	is	a	useful	round	value	for	dead	wood.	

However,	specific	carbon	content	is	less	for	the	litter	and	organic	layers	of	the	forest	floor.	There	is	

not	a	dead	plant	material	pool	corresponding	to	understory;	it	is	assumed	these	very	quickly	

become	litter	or	small	woody	debris.	Pairing	pool	definitions	(boundaries)	with	data	sources	is	also	

very	important	with	the	pools	of	dead	plant	material,	because	measurements	specific	to	estimates	

are	much	less	likely	for	DDW,	forest	floor,	etc.	In	the	FIADB	the	distinction	between	“standing”	and	

“down”	dead	wood	is	based	on	angle	of	lean	and	is	applied	to	P2	(phase	two,	“standard”	forest	

inventory	plot)	and	P3	(phase	three,	a	smaller	number	of	plots	that	include	additional	

measurements	such	as	soils	and	forest	floor)	data;	other	definitions	may	vary.	For	small	diameter	

standing	dead	trees,	estimates	exist	but	are	problematic:	FIA	data	only	provide	samples	of	standing	

dead	trees	at	5	inches	(12.7	cm)	DBH	or	larger.	Estimates	of	saplings	(1–5	inch	(2.5—12.7	cm)	DBH	

trees)	necessarily	will	be	modeled	(Woodall	et	al.,	2012).	

6.2.2.4 Down Dead Wood

DDW	is	defined	as	pieces	of	dead	wood	no	longer	a	part	of	standing	dead	or	snags,	yet	distinct	from	

smaller	or	advanced	decayed	wood	of	the	forest	floor.	The	definition	largely	corresponds	to	the	P3	

down	woody	material	pool,	and	represents	a	slight	change	from	the	past	definition.	This	pool	also	

includes	some	less‐than‐obvious	components	of	DDW:	(1)	debris	piles,	usually	from	past	logging;	

(2)	previously	standing	dead	trees	that	have	lost	enough	height	or	volume	or	lean	greater	than	45	

degrees	from	vertical	so	they	do	not	qualify	as	standing	dead;	(3)	stumps	with	coarse	roots	(as	

previously	defined);	and	(4)	nonliving	vegetation	that	otherwise	would	fall	under	the	definition	of	

understory.	

6.2.2.5 Forest Floor or Litter

The	forest	floor	is	the	layers	of	litter,	often	classified	as	the	fibric	(Oi),	hemic	(Oe),	and	sapric	(Oa)	

organic	layers	above	the	mineral	soil	and	smaller	than	DDW.	This	classification	represents	a	change	

from	the	past	definition,	which	also	included	the	small	woody	debris	from	the	DDW	pool.	Organic	

soils	present	additional	challenges	when	delimiting	this	pool.	

6.2.2.6 Forest Soil Organic Carbon (SOC)

This	pool	is	organic	carbon	within	the	soil	but	excluding	coarse	roots	as	defined	for	live	trees,	

understory,	standing	dead	trees,	and	stumps—all	as	defined	above.	By	convention,	large	pieces	of	

woody	material	that	are	separately	and	independently	estimated	through	sampling	and	allometry	

are	excluded.	Depth	is	arbitrary	and	so	far	has	been	defined	by	the	dataset	in	use.	The	dataset	

should	represent	samples	of	as	much	of	the	organic	carbon	as	possible,	although	peatlands	present	

a	unique	problem.	A	common	sampling	depth	is	1	m,	although	this	is	not	an	IPCC	standard.	

Adequate	sampling	depth	may	be	ascertained	through	local	knowledge;	3.9	to	7.9	inches	(10	to	20	
 Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

 6-25 

cm)	may	be	adequate	for	some	forest	ecosystems,	while	others	require	greater	depths.	Datasets	of	

soil	maps	from	surveys	are	another	source	of	data	(in	addition	to	P3	plots).	SOC	variability	extends	

to	relatively	large‐scale	maps	such	as	locations	surrounding	P2/P3	plots.	That	is,	soils	maps	are	

based	on	data	with	the	same	variability	as	seen	in	the	P3	subplot‐to‐subplot	precision.	

Note	that	the	pool	definitions	used	by	FVS	do	not	match	definitions	used	by	FIA	in	all	cases.	While	

the	main	categories	of	live	and	dead	biomass	will	include	the	same	elements,	the	FIA	definition	of	

forest	floor	includes	fine	woody	debris,	while	the	FVS‐FFE	definition	places	fine	woody	debris	in	the	

DDW	category.	FIA	considers	trees	under	1	inch	(2.5	cm)	DBH	to	be	part	of	the	understory	pool,	

while	FVS	tracks	these	as	trees	regardless	of	size.	Future	work	is	likely	to	include	the	capability	of	

FVS‐FFE	to	generate	a	carbon	report	with	pools	corresponding	to	the	definitions	used	by	FIA	in	

national	accounting.	

6.2.3 Estimation Methods

The	flexibility	in	using	the	best	obtainable	data	balanced	with	the	needs	and	resources	of	each	

individual	forest	owner	can	provide	good/valid	forest	carbon	estimates	if	some	basic	guidelines	are	

followed:	

 Carbon	pools	should	be	explicitly	identified	to	make	it	possible	to	identify	possible	gaps	or	

overlaps	between	pools.	Identifying	and	recognizing	that	a	gap	exists	(for	example,	there	are	no	

seedling	data,	or	standing	dead	trees	were	not	measured)	is	more	useful	than	fuzzy	boundaries	

between	pools.	

 Consistent	pool	definitions	and	methods	for	carbon	estimation	within	those	pools	are	required	

for	valid	estimates	of	change.	That	is,	change	should	be	based	on	the	same	pools	and	methods	at	

both	time	1	and	time	2.	

6.2.3.1 Live Trees

Various	approaches	are	used	for	estimates	of	tree	biomass	or	carbon	content;	ultimately,	each	relies	

on	allometric	relationships	developed	from	a	characteristic	subset	of	trees.	Here,	live	trees	include	

stems	with	DBH	of	at	least	1	inch	(2.5	cm).	Allometry	can	incorporate	whole	trees	or	components	

such	as	coarse	roots	(greater	than	0.08	to	0.20	inches	(0.2	to	0.5	cm);	published	distinctions	

between	fine	and	coarse	roots	are	not	always	clear),	stems,	branches,	and	foliage.	Live	tree	

belowground	carbon	estimates	can	be	troublesome,	but	overall	accuracy	is	best	if	the	boundary	is	

set	to	conform	to	available	data	rather	than	a	predefined	threshold.	

Recommended	options	for	obtaining	estimates	of	carbon	stock	of	live	trees	are:	

 Small	landowners	(as	defined	in	Section	6.2.1):	Values	obtained	from	lookup	tables	(e.g.,	either	

those	in	Smith	et	al.,	2006,	or	as	otherwise	provided)	categorized	by	geographic	region,	forest	

type,	and	age	class.	

 Large	landowners	(as	defined	in	Section	6.2.1):	Standard	forest	inventory,	estimates	calculated	

using	individual	tree	measurement	(diameter)	and	the	FVS‐FFE	module	with	the	Jenkins	

biomass	equations	(Jenkins	et	al.,	2003a).	

Biomass	equations	must	be	applied	appropriately;	using	equations	outside	the	diameter	or	

geographic	ranges	for	which	they	were	developed	will	introduce	additional	error	to	the	estimates.	

Given	the	hundreds	of	different	tree	species	growing	in	diverse	habitats	across	the	United	States,	it	

is	beyond	the	scope	of	this	document	to	suggest	the	magnitude	of	the	effect	of	alternative	tree	

volume	models	beyond	the	national‐scale	models	suggested	herein.	Regardless	of	the	estimation	

approach	selected,	it	is	critical	to	use	that	method	consistently	over	time.	Estimates	produced	from	

different	methods	will	vary;	changing	estimation	methods	over	time	will	introduce	additional	error.	
Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

6-26

Although	we	are	currently	specifying	only	the	use	of	biomass	equations	by	Jenkins	et	al.	(2003a),	it	

is	understood	that	these	equations	may	not	be	the	most	appropriate	in	all	circumstances.	For	

example,	using	equations	outside	the	diameter	or	geographic	ranges	for	which	they	were	developed	

will	introduce	additional	error	to	the	estimates.	Some	Jenkins	equations	have	limits	to	the	allowable	

diameters.	Specific	guidance	will	be	developed	in	the	future	to	facilitate	the	use	of	different	biomass	

equations	such	as	those	used	by	FIA	based	on	the	CRM	and	locally‐specific	equations.	Refer	to	

Figure	6‐3	for	a	decision	tree	for	the	forest	carbon	accounting	source	category	showing	which	

carbon	accounting	assumptions	(e.g.,	simulation	model,	allometric	equations,	and	lookup	tables)	

are	recommended	for	an	entity	depending	on	the	size	class	and	type	of	activity	data	available.

Sampling and Allometry.	Recommended	approaches	are	based	on	the	application	of	allometric	

relationship	to	sampled	inventory	data.	The	FIADB‐based	estimates	of	live	tree	carbon	are	based	on	

the	plot	data–P2	data	and	CRM	biomass	estimation	(Woodall	et	al.,	2011).	In	addition,	a	large	

number	of	other	allometric	relationships	have	been	developed	for	tree	biomass	(biomass	

regression	equations).	Many	biomass	equations	are	available	for	a	variety	of	forest	types;	for	

example,	possible	older	citations	are	Ter‐Mikaelin	and	Korzukhin	(1997);	see	also	citations	in	

Jenkins	et	al.	(2003b).	The	equations	recommended	in	this	report	are	the	Jenkins	et	al.	(2003a)	

equations,	which	are	nationally	consistent	and	straightforward	to	apply.	Future	development	or	

integration	of	this	method	into	a	software	tool	should	consider	implementation	of	the	CRM	biomass	

estimation	method	in	order	to	better	align	with	the	methods	used	for	U.S.	GHG	inventory	reporting.	

The	CRM	approach	is	computationally	complex,	and	is	not	included	at	this	time.	

Inventory	designs	and	protocols	are	well	documented	by	a	variety	of	authors	and	will	not	be	

discussed	further	here.	A	good	example	is	Pearson	et	al.	(2007),	which	is	written	specifically	for	

carbon	inventories.	

Lookup Tables.	Published	summary	values	of	similar	or	representative	forests	provide	quick	and	

inexpensive	means	of	roughly	assessing	likely	forest	carbon.	A	good	example	of	such	lookup	values	

are	the	past	revised	1605(b)	guidelines,	with	the	forest	tables	published	as	Smith	et	al.	(2006).	

Alternative	versions	of	representative	values	include	FIA	online	applications	such	as	FIDO	or	

EVALIDator,	FIA‐related	applications	such	as	COLE,	or	models	from	spatial	data	such	as	the	FIA	

biomass	map	or	the	National	Land	Cover	Dataset	layers.	

Simulations/Modeling.	Not	only	do	forest	biometrical	models	provide	a	platform	for	estimating	

future	scenarios	of	forest	carbon	stocks,	but	they	can	also	be	a	rapid	methodology	for	entity‐level	

calculation	of	current	forest	carbon	stocks.	The	FVS	is	one	such	simulation	tool	that	can	provide	

estimates	of	current	forest	carbon	stocks	given	an	elementary	forest	inventory	was	conducted	(e.g.,	

number	of	trees,	size,	and	species).	In	addition,	and	perhaps	a	more	powerful	aspect	of	such	a	tool,	

is	that	projections	of	future	stand	attributes	can	be	acquired	(e.g.,	forest	carbon	stocks	50	years	

from	present)	as	described	in	Dixon	(2002)	and	Hoover	and	Rebain	(2008;	2011).	

6.2.3.2 Understory

Estimation	procedures	and	data	sources	are	limited	for	this	pool.	Unless	an	entity	has	the	capability	

to	develop	localized	understory	models	and	allometric	relationships,	the	development	of	carbon	

estimates	for	these	pools	will	be	limited	to	lookup	tables	and	simulations/modeling.	Values	are	

provided	in	the	Smith	et	al.	(2006)	lookup	tables,	which	are	based	on	Birdsey	(1996)	and	modified	

to	apply	to	FIA	data;	see	U.S.	EPA	Annex	3.12	(2010)	for	additional	details.	The	FIADB	condition	

table	includes	estimates	based	on	this	model,	so	estimates	based	on	similar	stands	can	be	obtained	

from	the	FIADB.	Understory	values	are	provided	in	the	carbon	reports	in	FVS	and	are	regional	

default	values	set	within	the	model.	
 Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

 6-27 

6.2.3.3 Standing Dead

The	prevailing	difference	in	volume/biomass/carbon	estimation	of	standing	dead	trees	from	live	

trees	is	the	incorporation	of	decay	reduction	factors	and	rotting/missing/cull	components	(Domke	

et	al.,	2011;	Harmon	et	al.,	2011).	

Sampling and Allometry.	FIA	inventory‐based	estimation	for	standing	dead	trees	is	from	P2	plot,	

condition,	and	tree	records.	Tree	mass	in	the	FIADB	is	calculated	according	to	CRM	methods	

(Woodall	et	al.,	2011)	with	refinements	to	the	CRM	approach	specific	to	standing	dead	trees	

proposed	by	Domke	et	al.	(2011).	During	a	standard	forest	inventory,	standing	dead	trees	are	

measured	and	tallied,	and	large	landowners	can	use	this	information	with	FVS	to	produce	estimates	

of	the	biomass	and	carbon	in	this	pool.	

Lookup Tables.	Published	summary	values	of	similar	or	representative	forests	provide	quick	and	

inexpensive	means	of	roughly	assessing	likely	forest	carbon.	A	good	example	of	such	lookup	values	

are	the	past	revised	1605(b)	guidelines,	with	the	forest	tables	published	as	Smith	et	al.	(2006).	

Alternative	versions	of	representative	values	include	FIA	online	applications	such	as	FIDO	or	

EVALIDATOR,	and	FIA‐related	applications	such	as	COLE.	Note	that	some	differences	may	appear	

among	pool	estimates	compared	to	the	sample	estimates,	because	some	or	all	are	based	on	

empirical	models	(regressions)	and	not	the	direct	plot‐level	measurements	that	are	now	available	

within	the	FIADB.	Small	landowners	can	obtain	estimates	of	the	standing	dead	pool	using	the	Smith	

et	al.	(2006)	lookup	tables.	

6.2.3.4 Down Dead Wood

The	recommended	method	for	obtaining	estimates	of	carbon	stock	of	DDW	for	large	landowners	is	

estimation	from	transect	data	collected	during	the	inventory.	Care	should	be	taken	to	adhere	to	the	

bounds	between	the	DDW	and	forest	floor	pools	(noting	that	fine	woody	debris	is	considered	part	

of	the	forest	floor	pool	in	this	guidance).	Small	landowners	may	refer	to	the	lookup	tables	for	pool	

estimates.	

Sampling and Allometry.	A	variety	of	sampling	and	estimation	protocols	is	available	for	the	DDW	

pool;	a	straightforward	and	commonly	used	approach	can	be	found	in	Pearson	et	al.	(2007).	

Lookup Tables.	Regional	averages	by	forest	type	are	as	described	in	Smith	et	al.	(2006),	or	

estimates	can	be	summarized	and	extracted	from	the	FIADB	condition	table	to	correspond	to	the	

entity’s	forest.	However,	note	that	the	current	FIADB’s	DDW	from	the	condition	table	is	a	model	

independent	of	P3	sampling.	See	Smith	et	al.	(2006),	U.S.	EPA	Annex	3.12	(2010),	Woodall	et	al.	

(2013),	and	Domke	et	al.	(2013)	for	details.	

Simulations/Modeling.	DDW	carbon	values	are	provided	in	the	carbon	reports	in	FVS.	Values	may	

be	supplied	by	the	landowner;	if	these	data	are	not	available,	regional	default	values	based	on	P3	

data	or	available	data	for	the	region	and	forest	type	are	automatically	input	by	the	model.	

6.2.3.5 Forest Floor or Litter

Recommended	options	for	obtaining	estimates	of	carbon	stock	of	forest	floor	for	all	landowners	is	

the	use	of	lookup	tables	based	on	forest	type,	region,	and	stand	age.	Large	landowners	who	are	

changing	land	uses	from	non‐forest	to	forest	may	wish	to	collect	data	for	this	pool.	

Sampling and Allometry.	Landowners	wishing	to	estimate	these	pools	from	field	data	can	use	fine	

woody	debris	sampling	and	carbon	conversion	according	to	Woodall	and	Monleon	(2008),	and	

forest	floor	using	the	approach	described	by	Pearson	et	al.	(2007).	Note	that	while	Pearson	et	al.	

(2007)	apply	a	mass	to	carbon	conversion	factor	of	0.5	(Smith	et	al.,	2006)),	others	use	a	conversion	
Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

6-28

factor	of	0.37.	Landowners	who	are	estimating	the	forest	floor	pool	using	field	data	should	apply	

the	0.37	conversion	factor.	

Lookup Tables.	Regional	averages	by	forest	type	are	as	described	in	Smith	et	al.	(2006);	estimates	

can	also	be	summarized	and	extracted	from	the	FIADB	condition	table	to	correspond	to	the	entity’s	

forest.	These	estimates	are	based	on	simulations	described	in	Smith	and	Heath	(2002).	Note	that	

the	current	FIADB	condition	table	estimates	of	forest	floor	are	these	modeled	values	independent	of	

the	P3	sampling.	

Simulations/Modeling.	Forest	floor	carbon	values	are	provided	in	the	carbon	reports	in	FVS.	

Values	may	be	supplied	by	the	landowner;	if	these	data	are	not	available,	regional	default	values	

based	on	P3	data	or	available	data	for	the	region	and	forest	type	are	automatically	input	by	the	

model	(FVS	employs	the	0.37	mass	to	carbon	conversion	factor	when	estimating	this	pool).	

6.2.3.6 Soil Organic Carbon

Possible	options	for	obtaining	estimates	of	SOC	stocks	are:	

 Sampling,	following	standard	field	methods;	

 Datasets	such	as	the	Soil	Survey	Geographic	(SSURGO)	Database,	State	Soil	Geographic	

(STATSGO)	Database,	or	the	Digital	General	Soil	Map	of	the	United	States	(STATSGO2);	and	

 Stand/forest	classification:	extract	range	of	modeled	estimates	from	FIADB	condition	table.	

Sampling and Allometry.	Soil	sampling	and	carbon	estimation	according	to	FIA	P3	plot	protocols	

can	be	found	at	the	USDA	Forest	Service	FIA	Library:	Field	Guides	for	Standards	(Phase	3)	

Measurements;7	methods	are	also	available	in	Pearson	et	al.	(2007),	Hoover	(2008),	and	others.	

Soils	data	are	generally	considered	difficult	to	measure	and	spatially	quite	variable.	The	

consequence	is	that	the	costs	are	high	and	the	payoff	is	likely	low.	Our	recommendation	is	that	

sampling	is	only	useful	if	there	is	an	important	reason	to	do	so,	such	as	a	change	from	non‐forest	to	

forest	or	vice	versa.	If	a	wildfire	occurs	and	there	is	significant	consumption	of	peatlands,	sampling	

should	be	conducted	and	emissions	calculated	using	FOFEM	and/or	CONSUME	models.	This	

situation	is	most	likely	to	be	found	in	the	Southeast	or	North	Central	States.	

Lookup Tables.	Forest	soil	organic	carbon	estimates—representative	values	or	lookup	tables.	Data	

sets	such	as	STATSGO	or	SSURGO	are	possible	sources.	Estimates	can	be	summarized	and	extracted	

from	the	FIADB	condition	table	to	correspond	to	the	entity’s	forest;	these	are	based	on	a	

STATSGO/P2	overlay	(Smith	et	al.,	2006;	U.S.	EPA,	2010).	

6.2.4 Limitations, Uncertainty, and Research Gaps

There	is	often	tremendous	uncertainty	associated	with	estimates	of	forest	carbon	baselines,	such	

that	even	at	large	scales	(e.g.,	state‐level)	the	power	to	detect	statistically	significant	changes	in	

forest	carbon	stocks	is	limited	to	major	disturbances	(Westfall	et	al.,	2013).	Compounding	the	

sampling	error	often	associated	with	forest	inventories,	there	is	measurement	and	model	error	that	

may	not	be	acknowledged.	Users	of	any	inventories,	lookup	tables,	or	models	should	remain	aware	

of	these	potential	errors	during	their	application	of	information.	

There	is	a	level	of	uncertainty	associated	with	not	only	tree	volume/biomass	equations,	but	also	

with	the	various	forest	carbon	pools	(e.g.,	belowground	to	forest	floor)	found	across	a	diversity	of	

forest	ecosystems	(e.g.,	tropical	to	boreal)	in	the	United	States.	Research	to	refine	approaches	to	

forest	carbon	accounting	and	refinements	of	associated	models	is	currently	in	progress.	Perhaps	

																																																													

7 http://fia.fs.fed.us/library/field‐guides‐methods‐proc/
 Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

 6-29 

some	of	the	most	needed	improvements	are	for	individual	tree	volume/biomass	equations,	

especially	for	traditionally	non‐commercial	species.	Another	forest	carbon	pool	that	is	being	

investigated	is	soil	organic	carbon.	Although	the	soil	carbon	pool	is	not	expected	to	change	quickly	

in	comparison	to	live	tree	pools,	in	many	areas	of	the	United	States	it	is	the	largest	carbon	stock	

(e.g.,	northern	Minnesota).	Beyond	reducing	the	uncertainty	associated	with	estimates	of	carbon	

pools,	research	is	being	conducted	to	refine	understanding	of	the	effects	of	disturbance	and	climate	

change	on	carbon	pools.	

6.3 Establishing, Re‐establishing, and Clearing Forests

6.3.1 Description

Conventional	parlance	attributes	changes	of	carbon	on	a	site	undergoing	land‐use	change	into	three	

directional	processes:	establishing	(i.e.,	afforestation),	re‐establishing	(i.e.,	reforestation),	and	

clearing	forest	(i.e.,	deforestation).	In	recent	years,	the	term	forest	degradation	has	been	used	to	

acknowledge	that	an	existing	forest	can	be	significantly	reduced	in	carbon	stocks	and	can	be	

considered	a	source	of	emissions,	as	long	as	the	reduction	in	carbon	stocks	is	not	an	aspect	of	

normal	forest	management.	However,	this	is	not	a	form	of	land‐use	change	because	the	land	

remains	in	forests.	This	is	an	important	consideration	under	forest	management,	but	may	also	be	

important	when	human	use	and	removals	of	forest	stocks	take	place	even	when	not	prescribed	by	a	

management	regime.	The	most	important	source	of	GHG	emissions	from	forests	is	associated	with	

forest	clearing	(IPCC,	2007).	The	conversion	of	forests	to	other	land	uses	immediately	reduces	the	

stock	of	carbon	in	aboveground	biomass	and	soil	organic	matter,	and	is	likely	to	reduce	the	long‐

term	carbon	storage	potential	of	the	land.	The	carbon	that	was	once	stored	in	forest	biomass	and	

soil	is	reduced	through	rapid	oxidation	by	fire	or	slowly	over	time	by	microbial	decomposition.	

Some	of	the	biomass	can	also	be	removed	from	the	site	and	converted	to	forest	products	such	as	

lumber,	paper,	pulp,	and	other	products	that	have	longer	term	but	variable	decomposition	rates—

and	hence	longer	term	and	variable	emissions	over	time.	All	of	these	components	of	land‐use	

change	need	to	be	accounted	for	when	determining	the	changes	in	site	carbon	stocks	due	to	land‐

use	change.	

A	parcel	of	land	can	be	converted	to	forest,	plantation,	or	other	treed	landscape	either	through	

intentional	planting	or	the	natural	process	of	secondary	succession.	Land	that	had	once	been	in	

forest	is	returned	to	forest	through	re‐establishment.	Note	that	this	applies	to	land	that	is	not	

currently	in	forest,	not	to	forest	land	that	is	regenerated	as	part	of	forest	management.	Land	that	

had	not	been	in	forest,	such	as	grasslands,	can	be	converted	to	forests	through	establishment.	In	

Methods for Establishing, Re‐establishing, and Clearing Forest

 IPCC	algorithms	developed	by	Aalde	et	al.	(2006).	

 These	options	use:	

− Allometric	equations	from	Jenkins	et	al.	(2003a),	or	FVS	with	the	Jenkins	et	al.	

equations	where	applicable;	and	

− Default	lookup	tables	from	Smith	et	al.	(2006;	GTR	NE‐343)—default	regional	values	

based	on	forest	type	and	age	class	developed	from	FIA	data.	

 These	methods	were	selected	because	they	provide	a	range	of	options	dependent	on	the	

size	of	an	entity's	forest	land.	
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either	case,	generally	speaking,	the	stock	of	carbon	in	biomass	and	soil	organic	matter	will	increase	

over	time	as	a	result	of	this	type	of	land‐use	change.	Biomass	increases	predictably	as	trees	and	

other	vegetation	are	established	on	the	site.	Soil	organic	matter	also	changes,	but	in	less	predictable	

ways.	For	instance,	the	establishment	of	a	forest	plantation	on	grassland	in	cool	temperate	regions	

may	result	in	a	temporary	loss	of	carbon	in	soil	organic	matter	before	it	builds	up	again	after	the	

plantation	is	fully	established.	For	both	accounting	and	planning	purposes,	these	changes	in	stocks	

of	carbon	must	be	estimated	and	accounted	for	when	assessing	the	effects	of	land‐use	change.	

Current	international	definitions	are	presented	below	and	draw	a	distinction	between	lands	that	

have	never	been	under	forest	cover	and	those	which	were	in	forest	cover	in	the	past	but	have	not	

been	forested	recently	(e.g.,	for	the	last	50	years).	These	definitions	are	presented	here	because	

they	are	commonly	used	in	the	literature;	however,	in	terms	of	carbon	accounting	for	live	biomass,	

there	is	no	practical	difference	between	the	two	categories.	The	greatest	impact	is	on	the	soil	

carbon	pool.	Where	the	aim	is	to	estimate	entity‐level	GHG	fluxes,	these	two	categories	will	be	

treated	together	and	termed	“establishing	forest”	in	this	guidance.	

6.3.1.1 Establishing Forest

Establishment	is	the	conversion	of	a	non‐forest	site	that	is	not	naturally	a	forested	or	treed	

ecosystem	or	had	never	been	in	forest	to	a	forest	or	similar	tree‐dominated	land	cover.	Examples	of	

establishment	include	the	conversion	of	bare	land	to	a	forest	and	conversion	of	grasslands	to	

forests	or	plantation.	In	practical	terms,	and	for	the	sake	of	this	guidance,	land	that	had	been	in	

agriculture	or	other	non‐forest	land	cover	for	a	long	time	(e.g.,	more	than	50	years)	that	is	

converted	to	tree	cover	can	also	be	viewed	as	establishment.	Hence,	established	forest	land	is	that	

which	has	not	been	dominated	by	trees	for	more	than	50	years.	

6.3.1.2 Re‐establishing Forest

Re‐establishment	is	the	reversion	of	forests	or	tree	cover	on	sites	that	had	formerly	and	recently	

been	(e.g.,	less	than	50	years)	in	forest	or	dominated	by	tree	cover.	Examples	of	re‐establishment	

include	natural	regeneration	of	a	disturbed	or	cleared	parcel	of	forest	to	a	secondary	forest,	

conversion	of	agricultural	land	to	a	forest,	and	establishment	of	a	plantation	on	a	site	that	had	once	

been	forest	but	is	now	in	another	land	use	(such	as	cropland).	It	is	important	to	distinguish	

between	re‐establishment	as	a	land‐use	change	and	forest	regrowth	as	part	of	forest	management	

or	the	result	of	a	natural	disturbance.	For	example,	a	land‐use	change	from	agriculture	to	forest	is	

considered	here	as	re‐establishment,	where	forest	regeneration	following	a	wind	throw	or	clear‐

cutting	is	not	considered	a	land‐use	change	resulting	in	re‐establishment.	

In	the	international	conventions,	the	IPCC	Special	Report	on	Land	Use,	Land‐Use	Change,	and	

Forestry	(IPCC,	2000),	which	was	developed	explicitly	for	carbon	inventory,	defines	re‐

establishment	as	"the	establishment	of	trees	on	land	that	has	been	cleared	of	forest	within	the	

relatively	recent	past;	the	planting	of	forests	on	lands	which	have,	historically,	previously	contained	

forests	but	which	have	been	converted	to	some	other	use." Establishment	and	re‐establishment	

both	refer	to	establishment	of	trees	on	non‐treed	land.	Re‐establishment	refers	to	creation	of	forest	

on	land	that	had	recent	tree	cover,	whereas	establishment	refers	to	land	that	has	been	without	

forest	for	much	longer.	A	variety	of	definitions	differentiate	between	these	two	processes.	Some	

definitions	of	establishment	are	based	on	phrases	such	as	"has	not	supported	forest	in	historical	

time;"	others	refer	to	a	specific	period	of	years,	and	some	make	reference	to	other	processes,	such	

as	"under	current	climate	conditions."	The	IPCC	Guidelines	define	establishment	as	the	"planting	of	

new	forests	on	lands	which,	historically,	have	not	contained	forests"	(IPCC,	2000).	

As	noted	above,	for	the	practical	purposes	of	reporting	under	these	methods,	a	change	from	non‐

forest	to	forest	cover	will	be	termed	establishing	forest,	and	the	50	year	time	horizon	will	not	apply.	
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6.3.1.3 Clearing Forest

Clearing	is	the	conversion	of	a	forest	or	tree‐dominated	site	to	another	land	use	other	than	forest	or	

a	tree‐dominated	site.	Often	clearing	results	in	the	complete	removal	of	aboveground	live	biomass.	

Examples	of	clearing	include	the	conversion	of	a	forest	woodlot	to	cropland	or	pasture,	conversion	

of	a	forest	woodlot	to	commercial	or	residential	use,	and	conversion	of	a	natural	forest	to	

agriculture.	

6.3.1.4 Other Important Considerations

Distinction between Land‐Use Change and Land‐Cover Change.	It	is	very	important	to	

understand	and	delineate	the	difference	between	land‐cover change	and	land‐use	change.	Because	

the	terms	“land	use”	and	“land	management”	are	often	confused	or	used	interchangeably	the	

distinction	is	defined	here.	A	basic	definition	of	land	cover	is	“the	observed	physical	and	biological	

cover	of	the	Earth’s	land	as	vegetation	or	human‐made	features.”	A	basic	definition	of	land	use	is	

“the	total	of	arrangements,	activities,	and	inputs	undertaken	in	a	certain	land‐cover	type	(a	set	of	

human	actions).	The	social	and	economic	purposes	for	which	land	is	managed	(e.g.,	grazing,	timber	

extraction,	conservation).”	The	conventions	found	in	the	literature—Turner	et	al.	(1994),	Skole	

(1994),	and	Lambin	et	al.	(2006)—are	followed	and	were	adopted	by	the	IPCC	in	2000.	It	is	

recognized	that	in	adoption	of	the	terminology	of	land	use,	land‐use	change,	and	forestry,	the	IPCC	

Good	Practice	Guidance	document	(IPCC,	2006)	generalized	the	use	of	terms	to	include	the	six	

broad	land‐use	categories	defined	in	IPCC	(2003)	Chapter	2		and	recognized	that	these	land‐use	

categories	are	a	mixture	of	land	cover	(e.g.,	forest,	grassland,	wetlands)	and	land	use	(e.g.,	cropland,	

settlements)	classes.	For	convenience,	they	are	here	referred	to	as	land‐use	categories.	

We	recognize	here	that	the	term	land‐use	change	can	be	adopted	to	include	land‐cover	changes,	as	

well	as	land‐use	changes.	Thus,	for	this	guidance,	as	with	IPCC,	land‐use	change	will	be	the	

conversion	of	the	“type	of	vegetation”	from	one	cover	type,	such	as	a	forest	dominated	by	trees,	to	a	

completely	different	cover	type,	such	as	cropland	dominated	by	non‐woody	food	crops.	The	

direction	of	cover	change	determines	the	nature	of	the	change	in	carbon	stocks	(e.g.,	forest	clearing	

versus	establishment).	Generally	speaking,	land‐use	change	is	the	most	important	consideration	for	

a	landowner,	since	this	process	usually	results	in	the	largest	change	in	onsite	carbon.	

However,	we	also	recognize	that	landowners	will	have	important	changes	to	their	lands	through	

the	management	activities	that	they	deploy,	and	these	activities	can	have	important	implications	for	

carbon	stocks	and	GHG	emissions	and	removals.	Thus,	we	also	recognize	the	concept	and	

terminology	of	land‐management change,	which	is	a	change	in	the	type	of	activity	being	carried	out	

on	a	unit	of	land,	and	thus	how	it	is	managed	or	used,	such	as	changing	the	management	practices	

within	a	forest	from	selective	harvest	to	protection.	Land‐management	change	may	or	may	not	have	

a	significant	impact	on	carbon	and	other	GHGs.	

Land	management	explicitly	refers	to	how	the	land	is	being	managed	or	used,	while	land	use	refers	

to	what	is	on	the	land.	An	example	of	land	management	is	a	tree‐dominated	site	that	is	used	as	a	

working	forest	or	woodlot.	As	such,	a	landowner	can	change	the	management	plan	for	the	site—for	

instance,	changing	its	use	to	a	forest	reserve	—without	radically	changing	its	cover.	Nonetheless,	

even	such	change	in	use	can	affect	the	amount	of	carbon	stored	on	the	site	and	in	the	soils.	

Typically,	when	a	forest	stand	land	management	is	changed	without	affecting	its	cover	type	it	is	

considered	a	managed	forest,	and	its	accounting	protocols	follow	those	for	forest	management	

rather	than	for	establishing	forests.	Thus	it	is	important	to	determine	and	document	both	the	land‐

use	and	land‐management	changes	that	occur	on	the	site,	and	explicitly	associate	the	carbon	

estimation	approach	to	either	establishing/clearing	forests	(Section	6.3)	or	forest	management	

(Section	6.4),	but	not	both.	
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Establishing and Clearing Forest versus Forest Management.	For	reasons	of	order	and	

consistency,	establishing	and	clearing	forest	is	distinguished	from	management,	which	is	addressed	

in	Section	6.4.	Forestry	operations	such	as	thinning,	artificial	regeneration,	and	harvesting	are	

associated	with	managed	forest	systems.	Unless	forestry	activities	lead	to	a	change	from	one	land	

use	to	another	land‐use,	these	activities	are	not	treated	using	establishing	and	clearing	forest	

accounting	principles.	The	initial	conversion	from	forest	to	agriculture,	for	example,	would	use	the	

establishing	and	clearing	forest	rules,	followed	by	the	application	of	rules	for	agriculture.	Similarly,	

when	a	non‐forest	land	cover	is	converted	to	a	managed	forest	the	initial	conversion	would	be	

treated	as	establishing	forest	and	use	these	methods,	but	subsequent	management	of	the	stand	

would	follow	forest	management	(e.g.,	forest	carbon	accounting	and	forest	management)	methods.	

Types of Forest.	From	a	strict	carbon	accounting	point	of	view,	the	land‐cover	designation	does	not	

matter,	nor	does	its	change	in	cover	type	as	long	as	one	has	good	estimates	of	carbon	stocks,	and	

can	measure	or	estimate	their	changes.	However,	data	used	to	estimate	changes	in	carbon	are	often	

reported	and	organized	by	forest	type,	so	the	composition	and	structure	of	the	forest	often	comes	

into	the	computation	methods.	Moreover,	to	avoid	double	counting,	it	is	important	to	define	what	

type	of	landscapes	can	be	considered	as	a	forest	for	establishing	and	clearing	forest.	There	are	two	

elements	of	a	definition	of	forests	that	are	warranted.	The	first	is	a	basic	definition	of	a	forest.	There	

are	a	range	of	conditions	of	treed	landscapes	where	establishing	and	clearing	forest	activities	can	

take	place,	from	preserved	forests	to	woodlots	to	open	and	widely	spaced	tree	landscapes	and	

urban	treed	landscapes.	There	are	hundreds	of	variations	of	definitions	of	forest	(Lund,	1999)	and	

for	each	of	these	there	are	subtypes.	Examining	the	implications	of	each	variant	would	not	be	

fruitful;	the	result	would	be	greater	confusion,	rather	than	the	clarity	sought.	In	a	strict	sense,	a	

forest	is	defined	here	using	the	U.S.‐specific	definition	of	forest	land	(Smith	et	al.,	2009).	These	are	

lands	with	tree	crown	cover	(or	equivalent	stocking	level)	of	more	than	10	percent,	width	of	at	least	

120	feet	(36.6	m),	and	area	of	1	acre	(0.4	ha).	Trees	should	be	able	to	reach	a	minimum	height	of	

6.6–16.4	feet	(2–5	m)	at	maturity	in situ.	A	forest‐land	unit	may	consist	of	closed	forest	formations	

where	trees	of	various	stories	and	undergrowth	cover	a	high	proportion	of	ground,	or	open	forest	

formations	with	a	continuous	vegetation	cover	in	which	tree	crown	cover	exceeds	10	percent.	

Second,	landowners	may	have	a	diverse	land	base	that	is	affected	by	different	forestry	activities,	

managed	at	different	intensities,	or	that	has	a	variety	of	existing	data.	One	of	the	first	steps	in	

preparing	entity‐wide	or	sub‐entity	estimates	of	carbon	fluxes	from	forests	is	to	organize	the	

underlying	data	on	land	conditions	into	manageable	units,	referred	to	here	as	forest	strata.	Land	

should	be	grouped	into	forest	strata	using	a	logical	framework	that	aggregates	similar	land	units.	

For	example,	land	could	be	partitioned	by	average	tree	age,	forest	type,	productivity	class,	and	

management	intensity.	In	many	cases	forest	strata	will	be	contiguous,	although	this	is	not	a	

necessary	condition.	The	landowner	can	select	the	type	of	stratification	scheme	to	employ;	and	

there	are	several	guides	available	to	do	this.	The	better	the	stratification,	the	more	accurate	and	

precise	are	the	carbon	estimations	with	the	minimal	amount	of	data	collection.	

The	definition	of	a	forest	is	useful	for	consistency	in	reporting	and	covers	a	wide	range	of	

conditions.	However,	note	that	the	technical	methods	can	apply	to	any	treed	landscape.	The	

adoption	of	the	international	nomenclature	for	forests	allows	the	consideration	of	a	range	of	site	

conditions	and	situations.	Forests	in	the	United	States	are	varied,	from	scrub	woodlands	in	semi‐

arid	zones	to	mature	deciduous	and	coniferous	complexes	in	the	humid	zones.	In	addition,	human	

managed	systems,	such	as	woodlots	and	plantations,	are	considered	as	forests.	

Similar Modalities and Variants of Establishing, Re‐establishing, and Clearing Forest.	This	

section	recognizes	that	establishing	and	clearing	forest	are	similar	to	and	indeed	conceptually	

related	to	several	other	land‐cover	change	modalities,	which	are	treated	in	other	protocols.	These	

include	but	are	not	limited	to	agro‐forestry,	which	involves	the	use	of	trees	on	farms;	urban	forests	
 Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

 6-33 

and	widely	spaced	tree	complexes;	trees	on	landscapes	outside	of	forests;	woodlands	and	savanna	

systems;	orchards;	and	palm	and	horticulture	complexes.	Although	the	measurement	and	

estimation	methods	described	here	may	be	easily	adapted	to	these	land	covers	and	land	uses,	they	

are	not	treated	in	this	section.	

6.3.2 Activity Data Collection

Activity	data	are	measurements	or	estimations	of	magnitude	of	human	activity	resulting	in	

emissions	or	removals	taking	place	during	a	given	period	of	time.	Most	often	the	area	of	land	that	is	

converted	from	one	land	use	to	another	is	the	most	important	type	of	activity	data.	Data	on	area	

burned,	management	practices,	and	lime	and	fertilizer	use	are	other	examples	of	activity	data.	For	

establishing	and	clearing	forest,	activity	data	consists	mostly	of	information,	preferably	in	map	

form	with	delineated	boundaries.	For	small	landowners,	it	is	possible	to	delineate	an	area	of	land‐

cover	change	by	foot	using	simple	distance	measurements	or	with	the	aid	of	a	GPS.	A	landowner	

may	have	different	activities	occurring	on	a	single	property,	and	thus	each	of	the	forest	strata	

should	be	mapped	and	have	separately	delineated	activities.	Remote	sensing	or	aerial	photography	

can	be	useful	for	any	landowner	with	access	to	these	data,	but	are	especially	useful	for	larger	land	

units.	Historical	information	on	changes	in	the	areas	of	land	uses	on	a	property	is	also	important,	

and	these	data	are	frequently	found	in	air	photo	archives	or	other	map	records.	In	addition	to	the	

areas	and	rates	of	clearing	and/or	establishment,	it	is	necessary	to	collect	data	on	specific	aspects	

and	details	of	these	activities.	This	may	include	data	on	tree	types,	biomass,	clearing	intensity,	wood	

removals,	tree	planting	densities,	and	other	factors	that	described	the	modality	of	the	establishing	

and	clearing	forest	activities.	

6.3.2.1 Establishing Forest

For	an	establishment	activity,	it	is	important	to	gather	basic	information	on	the	area	and	location	of	

each	stratum	of	land	use	that	is	being	established.	For	the	most	part	an	establishment	activity	will	

be	a	plantation	or	similar	type	of	establishment/forestation	activity.	Thus,	basic	information	on	site	

preparation,	species	selection,	and	densities	of	plantings	can	be	used	with	a	projection	of	the	long‐

term	plan	for	the	site	to	make	a	reasonable	ex‐ante calculation.	If	natural	regeneration	is	the	

primary	means	of	establishment,	estimates	of	seedling	counts	can	be	used	to	develop	a	growth	

projection.	Alternatively,	regional	yield	tables	may	be	used	to	estimate	projected	stocks.	The	prior	

use	and	management	of	the	stratum	or	land	use	should	also	be	documented,	since	the	historical	use	

of	the	land	influences	carbon	stock	and	stock	change	estimates.	For	instance	establishment	of	a	

forest	stand	on	grassland	will	have	a	different	result	in	terms	of	carbon	than	establishment	on	a	

row	crop	agricultural	field.	Once	a	forest	is	well	established,	for	all	practical	purposes	it	becomes	a	

managed	forest	and	should	be	treated	using	the	methods	in	the	next	section	on	forest	management.	

We	consider	the	land‐use	stratum	to	be	a	forest	when	the	characteristics	of	the	stand	meet	the	

definition	of	a	forest.	Most	often	this	will	be	when	the	site	is	well	stocked	to	the	definitional	crown	

cover	and	height	of	trees.	

6.3.2.2 Clearing Forest

The	most	important	activity	data	to	collect	are	the	area	and	rates	of	forest	clearing	for	each	stratum	

or	parcel	in	the	project	area.	It	is	also	important	to	know	the	intensity	of	clearing	and	if	there	are	

remaining	trees	or	other	vegetation	left	on	site	after	clearing.	To	estimate	emissions,	it	is	necessary	

to	know	also	the	characteristics	of	the	stratum	that	is	to	be	cleared,	including	the	biomass	and	soil	

organic	matter	of	the	site.	The	process	of	clearing	a	site	is	an	activity	that	can	also	be	characterized.	

Information	needed	includes	the	fraction	of	the	aboveground	biomass	that	would	be	burned,	the	

fraction	that	is	left	behind	onsite	as	slash	and	debris,	the	fraction	that	would	be	removed	in	the	

form	of	wood	products,	and	the	fraction	that	is	removed	in	the	form	of	other	products.	
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6.3.3 Estimation Methods

This	section	lays	out	the	minimum	necessary	parts	of	a	computation	scheme	for	estimating	carbon	

stocks	and	carbon	emissions	in	biomass	and	soil	associated	with	establishing	and	clearing	forest.	

The	descriptions	laid	out	here	are	generalized.	The	basic	concept	behind	them	is	simple:	the	stock,	

or	mass,	of	carbon	on	a	site	changes,	and	the	task	of	estimation	is	to	compute	the	difference	in	

stocks	between	the	land	use	before	and	after	the	intervention	or	disturbance.	When	a	site	is	

cleared,	stocks	go	down	and	this	results	in	emissions	to	the	atmosphere.	When	a	site	is	established,	

stocks	go	up	and	this	results	in	removals	from	the	atmosphere.	

6.3.3.1 Units of Measurement

All	stock	computations	are	performed	in	terms	of	mass	of	carbon	in	kilograms	or	metric	tons	per	

unit	area	in	metric	system	units	(carbon	per	hectare	or	C	ha−1).	Rate	data	are	reported	in	terms	of	

change	in	carbon	per	ha	over	time,	as	in	carbon	per	hectare	per	year	(C	ha−1	year−1).	All	carbon	

biomass	is	referenced	to	its	dry	weight	basis	and	the	fraction	of	biomass	in	carbon.	For	the	purpose	

of	this	guidance,	the	fraction	of	dry	biomass	that	is	carbon	is	0.5.	An	example	stock	is	100	metric	

tons	C	ha−1,	and	an	example	stock	change	is	100	metric	tons	C	ha−1	year−1.	It	is	important	to	

differentiate	between	units	of	carbon	and	CO2	equivalents	(CO2‐eq)	and	report	the	appropriate	

units	to	the	reporting	entity.	For	example,	some	reporting	programs	(e.g.,	carbon	markets)	require	

the	conversion	of	metric	tons	of	carbon	to	metric	tons	CO2‐eq.	This	convention	places	all	carbon	

mass	estimates	into	units	of	CO2,	which	can	be	derived	by	multiplying	the	carbon	mass	by	44/12.

6.3.3.2 Stocks and Fluxes

The	stock	of	carbon	is	the	amount	of	carbon	in	biomass	and	soil	on	a	site.	The	stock	change	is	the	

difference	in	the	stocks	from	one	time	period	to	the	next.	This	change	can	be	positive	or	negative,	

depending	on	whether	the	site	is	experiencing	clearing,	degradation,	restoration,	or	establishment.	

Declining	stocks	over	time	from	clearing	or	degradation	result	in	emissions,	while	accumulating	

stocks	over	time	from	establishment	or	restoration	are	referred	to	as	sequestration.	

6.3.3.3 Delineating and Characterizing the Site Used in Computation

To	estimate	carbon	stocks	and	fluxes,	it	is	necessary	to	define	the	mapped	extent	and	the	features	of	

the	site.	For	small	areas,	such	as	a	farm	woodlot	or	forest	stand,	the	boundaries	are	defined	

geographically	using	a	GPS	device.	If	surveyors’	reports	or	other	forms	of	maps	and	photos	such	as	

aerial	imagery	are	available,	they	can	be	used.	There	are	a	growing	number	of	online	tools	that	are	

available	(e.g.,	Google	Maps)	that	provide	detailed	imagery	of	land	that	can	be	used	to	draw	

boundaries	of	the	proposed	sites.	After	defining	the	precise	boundaries,	a	land‐cover	classification	

should	be	performed	to	define	the	various	vegetation,	cover,	or	soil	strata	within	the	site.	For	

instance,	a	re‐establishment	project	with	two	zones	within	the	boundaries,	one	for	a	commercial	

plantation	and	the	other	for	natural	regeneration,	would	be	stratified	into	two	stands.	If	the	project	

or	property	is	to	be	a	single	cover,	such	as	a	natural	regeneration	forest	or	a	plantation	forest,	the	

project	site	can	be	a	single	stratum;	but	other	factors	may	be	important,	such	as	land	slope	or	soil	

conditions.	If	there	will	be	a	future	management	activity	associated	with	the	project,	this	stratum	

should	also	be	delineated.	In	short,	any	area	within	the	project	boundary	that	would	have	different	

cover	or	carbon	characteristics	should	be	separately	delineated.	Standard	mapping	coordinates,	

projections,	and	geodetic	datums	should	be	used.	

6.3.3.4 Carbon Pools under Consideration

Generally,	IPCC	and	other	sources	reference	five	pools	of	carbon	to	measure—aboveground	live	

biomass,	belowground	live	biomass,	standing	dead	and	downed	debris,	litter,	and	soil	organic	
 Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

 6-35 

carbon.	The	landowner	or	project	developer	should	identify	from	the	beginning	the	pools	that	will	

be	accounted.	All	pools	should	be	included,	unless	one	can	show	that	a	pool’s	stock	changes	are	

small	and	unimportant—the	de minimis	assumption	(less	than	10	percent	of	the	total	baseline	

stock,	see	more	below)—or	can	show	that	a	pool	would	not	have	stock	losses	or	emissions	(e.g.,	

forest	clearing).	In	these	cases,	the	landowner	is	choosing	to	be	conservative	in	estimation	of	the	

impact	of	the	establishing	and	clearing	forest	on	the	atmosphere	for	that	pool.	For	instance,	in	an	

establishment	project	where	the	estimation	of	soil	carbon	change	may	be	difficult,	time	consuming,	

or	costly,	and	the	soil	carbon	change	is	assumed	to	be	de minimis	in	magnitude,	it	may	be	

eliminated.	Or,	if	it	can	be	demonstrated	that	the	soil	pool	will	be	accumulating	carbon,	the	

landowner	may	select	to	not	count	that	pool	and	thus	be	conservative	in	the	sequestration	potential	

of	the	project.	Wood	products	that	are	removed	from	the	site	through	harvest	are	not	by	

themselves	considered	a	separate	pool,	but	the	landowner	is	advised	to	document	this	amount	and	

its	fate,	whereby	fate	can	be,	for	example	hardwood	products,	paper	products,	or	firewood	(see	

Section	6.5).	

6.3.3.5 Initial Carbon Stock Measurement

The	carbon	stocks	in	the	measured	pools	that	are	to	be	reported	need	to	be	determined	at	the	

beginning	of	the	project	in	order	to	define	a	reference	carbon	amount	to	which	future	changes	will	

be	compared.	Whether	the	site	is	a	forest	before	its	conversion	or	agricultural	land	before	re‐

establishment	of	tree	cover,	the	initial	conditions	in	terms	of	carbon	must	be	reported.	The	initial	

carbon	stocks	in	all	strata	are	individually	determined	from	lookup	tables,	satellite	imagery,	or	FIA	

database,	or	are	measured	and	reported	according	to	the	detailed	measurement	methods	given	

below.	The	reporting	of	the	baseline	can	get	complicated	in	some	cases.	Typically	the	baseline	is	the	

current	carbon	stocks.	However,	in	situations	where	the	carbon	stocks	are	changing,	the	baseline	is	

computed	over	time	as	the	forward	looking	carbon	stocks	that	would	occur	in	the	absence	of	the	

project	or	intervention.	

6.3.3.6 The Ex‐Ante Computation

Once	initial	carbon	stocks	are	determined	(the	Type	I	estimate),	the	project	developer	needs	to	

make	a	forward	projection	of	the	expected	carbon	stock	changes,	and	its	deviation	from	what	would	

have	occurred	on	the	site	without	the	intervention	of	a	project	or	land‐cover	change	(Type	II	and	III	

estimates).	This	is	somewhat	problematic	since	it	is	not	possible	to	predict	the	future	with	

certainty.	However,	a	number	of	tools	and	methods	are	available	to	make	these	projections	with	

reasonable	certainty	(see	Table	6‐3).	An	important	reason	for	making	this	computation	is	that	the	

carbon	stock	would	change	over	time	in	the	absence	of	the	project’s	intervention.	For	example,	an	

abandoned	farm	field	could	be	expected	to	naturally	go	through	old‐field	succession	even	without	a	

reestablishment	project.	Hence,	the	project‐related	carbon	changes	need	to	be	compared	with	the	

no	intervention/no	action	estimate	over	time,	not	just	from	the	start	of	the	project,	to	get	a	true	

accounting	of	net	carbon	benefits.	Landowners	would	want	to	make	the	ex‐ante	computation	so	

that	they	can	evaluate	a	range	of	future	establishment,	clearing,	or	management	options	to	select	

the	one	that	best	suits	their	carbon	and	other	outcome	needs.	

6.3.3.7 Measurement and Monitoring

After	the	initiation	of	the	project	intervention	(e.g.,	tree	planting),	ongoing	measurements	of	actual	

carbon	stock	changes	need	to	occur.	This	is	often	referred	to	as	the	monitoring	phase	of	the	project.	

Methods	for	ongoing	measurement	are	described	below.	The	project	developer	should	keep	

organized	records	of	the	measurements	made	over	a	routine	and	standard	time	frame.	Annual	

measurements	are	usually	either	not	logistically	possible	or	too	time‐consuming	and	expensive.	
Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

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Thus,	it	is	recommended	that	after	the	initial	measurement,	these	measurements	are	repeated	

every	5	years.	

6.3.3.8 Permanent Sample Plots

For	small	projects	such	as	farm	woodlots,	or	tree	and	forest	stands,	a	complete	inventory	of	carbon	

in	the	reporting	pools,	strata,	and	project	land	can	be	performed.	However,	for	large	areas,	

installing	and	delineating	a	number	of	sample	plots	is	required.	These	sample	plots	are	established	

in	the	project	area	on	a	stratified	basis,	laid	out	randomly	or	systematically—i.e.,	each	land	cover	

stratum	has	an	established	number	of	systematically	or	randomly	placed	plots.	Methods	for	forest	

inventory	are	well	described	and	available	from	a	variety	of	sources	and	will	not	be	further	

described	here	(e.g.,	Pearson	et	al.,	2007).	Both	the	number	and	location	of	the	plots	need	to	be	

considered.	It	is	important	to	remember	that	the	plots	are	established	for	the	purpose	of	sampling	a	

forest	stand	or	project	stratum.	The	sample	estimate	will	be	as	accurate	as	the	number	and	location	

of	the	sample	plots	permit.	The	number	of	plots	will	relate	to	the	accuracy	of	the	estimates;	in	

simple	strata	such	as	plantations,	the	number	of	sample	plots	can	be	extremely	low,	but	in	complex	

natural	stands	the	number	will	have	to	be	greater.	A	good	stratification	will	reduce	the	necessary	

number	of	plots.	The	location	of	the	plots	is	important	to	capture	the	spatial	heterogeneity	of	the	

stand.	The	plots	are	to	be	well	marked	and	made	permanent	for	repeat	measurements	over	many	

years.	For	forest	clearing	computations,	it	is	not	necessary	to	make	permanent	plots	unless	the	

process	of	clearing	is	selective	degradation	over	a	long	period	of	time.	For	forest	clearing,	lots	only	

need	to	be	measured	once	before	the	intervention	and	once	after	the	intervention	has	been	

completed.	

6.3.3.9 Measurement versus Estimation

In	some	cases,	it	will	not	be	possible	to	measure	the	initial	carbon	stocks	or	post‐intervention	

carbon	directly.	For	instance,	a	forest	clearing	event	may	occur	without	the	opportunity	to	establish	

plots	in	the	forest,	or	it	may	not	be	possible	to	measure	a	large‐area	establishment	event.	In	these	

cases,	regional	summary	values	of	the	forest	carbon	stocks	may	be	of	use	(Smith	et	al.,	2006).	

6.3.3.10 Allometry, Biomass Expansion Factors, and Standard Values

The	conventional	approach	to	biomass	estimation	is	to	use	allometric	equations	based	on	species‐

specific	information	(Jenkins	et	al.,	2003b;	2003a).	An	allometric	approach	can	be	based	on	DBH	or	

a	combination	of	DBH,	canopy	height	(H),	and	wood	density	on	an	individual	tree	basis	for	the	

entire	stand	or	for	trees	in	the	permanent	plots.	The	allometric	equation	predicts	either	volume	of	

wood	in	the	main	stem	or	whole	tree	biomass	or	carbon.	In	the	former	case,	it	is	then	necessary	to	

estimate	a	whole	tree	biomass	expansion	factor	(Smith	et	al.,	2003).	Alternatively,	the	entity	can	use	

standard	values	for	stocks	and	growth	rates	based	on	lookup	tables	(DOE,	1992;	Smith	et	al.,	2006).	

For	large	areas	of	forests	converted	through	clearing,	it	may	be	acceptable	to	use	standard	values	

for	stocks	per	unit	area,	such	as	those	published	by	IPCC	(2003;	2006).	

6.3.3.11 Stocks versus Change in Stocks over Time

For	estimation	of	forest	establishment	it	is	necessary	to	compute	the	change	in	stocks	over	time,	

which	will	be	a	measurement	of	net	sinks	of	carbon	through	sequestration.	Forest	clearing	

computation	is	essentially	the	same	but	with	the	opposite	sign	to	indicate	emissions.	The	subtle	

difference	is	that	establishment	requires	some	means	to	estimate	the	accumulation	of	carbon	on	the	

project	site	over	time.	This	is	accomplished	using	either	direct	measures	or	yield	models.	For	forest	

clearing,	it	is	necessary	to	know	the	initial	stock	of	carbon	in	the	forest	stand,	and	how	it	then	

changes	with	disturbance.	The	latter	requires	data	on	the	partitioning	of	post‐disturbance	carbon	

components,	as	removals,	and	slash	and	debris	left	on	site.	
 Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

 6-37 

6.3.3.12 Forest Clearing Removals and Dead Material on Site

The	difference	of	carbon	stocks	before	and	after	forest	clearing	is	the	carbon	that	has	been	removed	

by	harvest	as	wood	products	or	other	products	(e.g.,	energy	feedstocks),	and	that	left	behind	on	the	

site	as	slash	and	debris	(Skog,	2008).	If	these	mass	amounts	are	known,	they	can	be	included	

directly	into	the	computations.	If	they	are	not	known,	they	can	be	estimated	and	represented	as	

fractions	of	the	original	standing	stocks	prior	to	disturbance.	All	removals	such	as	these	constitute	

immediate	and	future	emission	sources,	as	they	decay	over	different	time	scales.	Therefore,	it	is	

necessary	to	assign	mass	amounts	to	four	long‐term	decay	pools	with	turnover	times	of	1,	10,	100,	

and	1,000	years.	The	emissions	are	computed	along	an	exponential	decay	function	related	to	the	

turnover	time	of	the	pool.	For	example,	carbon	lost	due	to	immediate	oxidation	by	fire	is	placed	into	

the	1‐year	pool,	and	the	charcoal	component	is	placed	into	the	1,000‐year	pool.	Other	removals	are	

placed	into	the	10‐	and	100‐year	pools.	

6.3.4 Specific Protocol for Computation

6.3.4.1 Actual Carbon Removals by Sinks in Establishing Forest

The	basic	approach	to	estimation	of	emissions	to,	or	removals	from,	the	atmosphere	is	to	multiply	

the	activity	data	by	emission	factors	or,	in	this	case,	multiply	the	land‐use	change	area	by	site	

biomass	carbon	and	soil	organic	matter	carbon.	These	procedures	describe	the	recommended	

method	of	estimating	carbon—using	allometric	equations	to	estimate	biomass	directly	from	DBH	

using	the	equations	of	Jenkins	et	al.	(2003a).	

Stratification	of	the	project	area	may	be	carried	out	to	improve	the	accuracy	and	the	precision	of	

the	carbon	estimates.	Where	required,	stratification	could	be	made	according	to	tree	species,	age	

classes,	or	forest	management	practices.	Figure	6‐5	shows	a	decision	tree	indicating	which	method	

is	more	applicable	for	a	particular	landowner.	

This	protocol	will	follow	the	two‐tier	approach	described	earlier	in	the	document.	Small	

landowners	can	use	default	tables	(i.e.,	Smith	et	al.,	2006)	and	equations	for	the	appropriate	region	

and	forest	type	group	to	estimate	biomass	of	their	forest	systems.	Large	landowners	should	use	

basic	forest	data	collected	in	the	field	on	sample	plots	with	allometric	equations	(Jenkins	et	al.,	

2003a)	to	estimate	the	biomass	of	individual	trees	and	entire	stands.	If	small	landowners	want	to	

use	sample	plots	and	allometric	equations,	they	are	free	to	do	so.	Small	landowners	should	contact	

a	consulting	forester	or	perhaps	a	university	extension	person	to	best	understand	requirements	for	

field	sampling.	

While	most	of	the	fluxes	from	an	establishment	project	are	removals	from	the	atmosphere,	there	

may	be	some	emissions	associated	with	some	aspects	of	the	project.	The	actual	net	CO2	removals	by	

sinks	can	be	estimated	using	the	equations	in	this	section.	When	applying	these	equations	for	ex‐

ante calculations	of	net	anthropogenic	CO2	removals	by	sinks,	landowners	will	provide	estimates	of	

the	values	of	those	parameters	that	are	not	available	before	the	start	of	the	project	period	and	

commencement	of	the	monitoring	activities.	Participants	should	retain	a	conservative	approach	in	

applying	these	estimates.	
Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

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Figure 6‐5: Decision Tree for Establishing, Re‐establishing, and Clearing Forests Showing

Methods Appropriate for Estimating Forest Carbon Stocks

1	Small	landowners	(see	Section	6.2	for	definition)	may	use	generalized	lookup	tables	based	on	region,	forest	type,	and	

age	class	to	estimate	carbon	stocks.	Large	landowners	(see	Section	6.2	for	definition)	should	collect	standard	forest	

inventory	data	and	use	allometric	equations	to	estimate	live	tree	biomass	carbon	(other	carbon	pools	may	be	obtained	

from	lookup	tables).	However,	large	landowners	who	do	not	engage	in	any	management	activities	or	plan	to	manage	their	

holdings	may	use	lookup	tables	for	all	pools;	but	if	active	management	occurs,	the	inventory	approach	should	be	used.	

2	Jenkins	et	al.	(2003a).	

3	Smith	et	al.	(2006).	
 Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

 6-39 

The	actual	net	CO2	removals	by	sinks in	year	t are	equal	to:	

Estimation of Carbon Stock in Living Biomass of Trees at the Stratum Level.	The	carbon	stock	

in	living	biomass	of	trees	for	stratum	i (Ctrees,i,t)	is	estimated	using	the	following	approach:	The	mean	

carbon	stock	in	aboveground	biomass	per	unit	area	is	estimated	based	on	field	measurements	in	

permanent	sample	plots.	

Step 1: Determine	based	on	measurements	(ex post),	the	DBH	at	typically	4.3	feet	(1.3	m)	above	

ground	level,	and	also	preferably	height	(H),	of	all	the	trees	above	some	minimum	DBH	in	the	

permanent	sample	plots.	

Step 2: Calculate	the	aboveground	biomass	for	each	individual	tree	of	a	species,	using	allometric	

equations	appropriate	to	the	tree	species	(or	groups	of	them	if	several	tree	species	have	similar	

growth	habits)	in	the	stratum.	

Step 3: Estimate	carbon	stock	in	aboveground	biomass	for	each	individual	tree	l of	species	j in	the	

sample	plot	located	in	stratum	i using	the	selected	or	developed	allometric	equation	applied	to	the	

Equation 6‐1: The Actual Net CO2 Removals by Sinks in Year t

Δ	C ACTUAL,t =	Δ	C PJ, t

Where:	

Δ	C	ACTUAL,t			 =	Actual	net	CO2	removals	by	sinks	in	year	t	(metric	tons	CO2	eq	year−1)	

Δ	C	PJ,	t		 =	Project	CO2	removals	by	sinks	in	year	t	(metric	tons	CO2	eq	year−1)	

Equation 6‐2: Project CO2 Removals by Sinks are Calculated as Follows (between two

dates for a time period of t)

t

Δ	C PJ, t =	Σ	Δ	C project, i, t × 44/12		 i=1

Δ	C project, i, t = [(C trees, i, t2 – C trees, i, t1) / T] +	Δ	C soil, i, t

Where:	

Δ	C	PJ,t		 =	Project	CO2	removals	by	sinks	in	year	t	(metric	tons	CO2	eq	year−1)	

Δ	C	project,i,t		 =	Average	CO2	removals	by	living	biomass	of	trees	and	soil	for	stratum	i,	for	year	

t	(metric	tons	carbon	year−1)	

C	trees,	i,	t		 =	Carbon	stock	in	living	biomass	of	trees	for	stratum	i,	in	year	t	(metric	tons	

carbon)	

Δ	C	soil,	t		 =	Average	annual	change	in	carbon	stock	in	soil	organic	matter	for	stratum	i,	for	

year	t	(metric	tons	carbon	year−1)	

T		 =	Number	of	years	between	years	t2	and	t1	(years)	
Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

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tree	dimensions	resulting	from	Step 1,	or	multiply	the	result	of	Step 2	by	0.5	(i.e.,	the	fraction	of	

dry	biomass	to	carbon	conversion	factor),	and	sum	the	carbon	stocks	in	the	sample	plot.	

Step 4: Convert	the	carbon	stock	in	aboveground	biomass	to	the	carbon	stock	in	belowground	

biomass	using	the	equations	provided	in	Jenkins	et	al.	(2003a)	or	by	multiplying	the	result	of	Step 3

by	0.26	(i.e.,	the	root‐to‐shoot	ratio).	Sum	the	aboveground	carbon	stock	and	belowground	carbon	

stocks.	

Step 5: Calculate	total	carbon	stock	in	the	living	biomass	of	all	trees	present	in	the	sample	plot	sp in	

stratum	i at	time	t.	

Step 6:	Calculate	the	mean	carbon	stock	in	living	biomass	of	trees	for	each	stratum,	as	per	Equation	

6‐6.	

Equation 6‐3: Estimate Carbon Stock in Aboveground Biomass for Each Individual Tree

N j, sp

CAB, i, sp, j, t =	Σ	CFj× ƒj (DBH, H)

t=1

Where:	

C	AB,i,	sp,	j,	t		 =	Carbon	stock	in	aboveground	biomass	of	trees	of	species	j,	on	sample	plot	sp,	

for	stratum	i (metric	tons	carbon)	

CFj	 =	Carbon	fraction	of	dry	matter	(dm)	for	species	or	group	of	species	type	j

(metric	tons	carbon	(metric	ton	dm)‐1)	

f	j	(DBH,H)		=	An	allometric	equation	linking	aboveground	biomass	of	a	living	tree	(metric	

tons	dm)	to	DBH	and	possibly	tree	height	(H)	for	species	j,	in	year	t	(metric	

tons	dm)	

Note:	For	ex‐ante estimations,	mean	DBH	and	H values	should	be	estimated	for	stratum	i, in	

year	t using	a	growth	model	or	yield	table	that	gives	the	expected	tree	dimensions	as	a	

function	of	tree	age. The	allometric	relationship	between	aboveground	biomass	and	DBH

and	possibly	H is	a	function	of	the	species	considered.	Alternatively	there	are	estimators	and	

tools	that	project	carbon	growth	rates	directly	without	input	of	DBH.	

i	=	1,	2,	3,	...	M	PS	strata	in	the	project	scenario	

j	=	1,	2,	3,	...	S	PS	tree	species	in	the	project	scenario	

l	=	1,	2,	3,	...	N	j,sp	sequence	number	of	individual	trees	of	species	j,	in	sample	plot	sp

t	=	1,	2,	3,	...	t*	years	elapsed	since	the	start	of	the	project	activity	
 Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

 6-41 

Equation 6‐4: Convert the Carbon Stock in Aboveground Biomass to the Carbon Stock in

Belowground Biomass

C BB, i, sp, j, t = C AB, i, sp, j, t × R j

Where:	

C	BB,	i,	sp,	j,	t	 =	Carbon	stock	in	belowground	biomass	(BB)	of	trees	of	species j,	in	plot	sp,	in	

stratum	i,	for	year	t (metric	tons	carbon)	

C	AB,	i,	sp,	j,	t	 =	Carbon	stock	in	aboveground	biomass	(AB)	of	trees	of	species j,	in	plot	sp,	in	

stratum	i,	for	year	t	(metric	tons	carbon)	

R	j	 =	Root:shoot	ratio	appropriate	for	biomass	stock,	for	species	j	(dimensionless)	

Equation 6‐5: Calculate Total Carbon Stock in the Living Biomass of All Trees Present in

the Sample Plot

Sps

C tree, i, sp, t =	Σ	(C AB,i,sp,j,t + C BB,i,sp,j,t)

j=1	 	

Where:	

C	tree,	i,	sp,	t	 =	Carbon	stock	in	living	biomass	of	trees	on	plot	sp	of	stratum	i,	for	year	t	(metric	

tons	carbon)	

C	AB,	i,	sp,	j,	t	 =	Carbon	stock	in	aboveground	biomass	(AB)	of	trees	of	species j,	in	plot	sp,	in	

stratum	i,	for	year	t (metric	tons	carbon	tree−1)	

C	BB,	i,	sp,	j,	t	 =	Carbon	stock	in	belowground	biomass	(BB)	of	trees	of	species j,	in	plot	sp,	in	

stratum	i,	for	year	t	(metric	tons	carbon	tree−1)	

i	 =	1,	2,	3,	...	MPS	strata	in	the	project	scenario	(PS)	

j	 =	1,	2,	3,	...	SPS	tree	species	in	the	project	scenario	(PS)	

t	 =	1,	2,	3,	...	t*	years	elapsed	since	the	start	of	the	project	activity	
Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

6-42

Soil Organic Carbon.	For	strata	that	contain	only	mineral	soils,	ex‐ante and	ex‐post ΔC	soil, i, t change	

is	estimated	from	Equation	6‐7.	

The	default	value	of	Δ C forest i =	0.5	metric	tons	C	ha−1	year−1,	and	a	t equilibrium	of	20	years,i shall	be	

used.	

Changes	in	carbon	stock	in	soil	organic	matter	are	not	monitored	ex‐post	(i.e.,	measured	before	and	

after	the	equilibrium	period),	but	are	instead	estimated	ex‐ante	(i.e.,	predicted	based	on	the	

specified	default	value	and	equilibrium	period).	

Other Pools.	Sample	plots	need	to	be	set	up	in	such	a	ways	that	the	small	herbs	and	bushes,	as	well	

as	forest	floor	litter	is	also	measured.	To	do	this,	establish	several	small	collection	plots	measuring	

3.3	feet	by	3.3	feet	(1	m	by	1	m)	on	the	forest	floor.	Collect	all	liter,	herbs,	and	small	debris	in	the	

subplot	and	weigh	it	using	a	field	scale,	and	dry	small	sample	to	get	the	dry	weight	fraction.	

Equation 6‐6: Calculate Mean Carbon Stock in Tree Biomass for Each Stratum

Pi

C tree, i, t = (Ai / Aspi)	Σ	C tree, i, sp, t	 	 sp = 1

Where:	

C	tree,i,t		 =	Carbon	stock	in	living	biomass	of	trees	in	stratum	i,	for	year	t	(metric	tons	

carbon)	

C	tree,	i,	sp,	t		 =	Carbon	stock	in	living	biomass	of	trees	on	plot	sp,	of	stratum i,	for	year	t

(metric	tons	carbon)	

Asp	i		 =	Total	area	of	all	sample	plots	in	stratum	i	(ha)	

Ai		 =	Area	of	stratum	i	(ha)	

sp	=	1,	2,	3,	...	 =	Pi	sample	plots	in	stratum	i	in	the	project	scenario	

i	=	1,	2,	3,	...		 =	MPS	strata	in	the	project	scenario	(PS)	

t	=	1,	2,	3,	...		 =	t*	years	elapsed	since	the	start	of	the	project	activity	

Equation 6‐7: Estimating Change in Carbon Stocks for Strata That Contain Only Mineral

Soils

Δ	C soil, i, t = Ai *	ΔC forest, i for t	≤	t equilibrium, i

Δ	C soil, i, t = 0 for t > t equilibrium, i

Where:	

Δ	C	soil,	i,	t	 =	Average	annual	change	in	carbon	stock	in	soil	organic	matter	for	stratum i,	

for	year t	(metric	tons	C	year−1)	

A	i	 =	Area	of	stratum	i;	hectare	(ha)	

ΔC	forest,	i	 =	Average	annual	increase	in	carbon	stock	in	soil	organic	carbon	pool	for	forest	

system	in	stratum	i	(metric	tons	C	ha−1	year−1)	

tequilibrium,i	=	Time	from	start	of	the	project	activity	until	a	new	equilibrium	in	carbon	stock	in	

soil	organic	matter	is	reached	for	forest	system	in	stratum	i	(years)	
 Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

 6-43 

Multiply	the	average	dry	weight	of	litter	by	0.37	to	compute	the	plot	litter	carbon,	and	by	0.5	to	

compute	the	plot	herbs	and	seedling	carbon.	For	small	trees	and	bushes	establish	a	few	small	plots	

measuring	16.4	feet	by	16.4	feet	(5	m	by	5	m)	in	the	sample	plot.	Cut	and	weigh	all	small	trees	and	

bushes.	Establish	a	dry	weight	basis	and	multiply	the	dry	weight	by	0.5	to	compute	a	subsample	

carbon	value.	Standing	dead	wood	also	needs	to	be	estimated.	Most	published	studies	suggest	this	

pool	is	small	and	can	be	ignored.	

Non‐CO2 GHGs.	Non‐CO2	GHGs,	including	CH4	and	N2O	are	calculated	based	on	emission	factors	

applied	to	the	parcel	biomass.	Thus,	the	parcel	biomass	is	multiplied	by	a	factor	from	default	values	

for	that	time	of	stand	or	planting	activity.	These	emissions	and	removals	will	vary	depending	on	the	

management	practice,	e.g.,	natural	succession,	plantations,	fertilization.	

6.3.5 Actual GHG Removals and Emissions by Sources and Sinks from Forest Clearing

The	above	suite	of	equations	can	be	used	to	estimate	the	sources	and	sinks	of	carbon	from	forest	

clearing,	with	the	results	having	a	different	sign	than	establishment	and	re‐establishment.	The	

fundamental	computation	is	in	Equation	6‐8.	

The	precise	computation	in	Equation	6‐9	requires	the	measurement	or	estimation	of	the	differences	

in	carbon	stocks	in	the	forest	system	and	the	land‐cover	system	that	it	is	converted	to.	It	also	

requires	an	understanding	a	computation	of	the	partitioning	of	the	products	that	were	removed	

from	the	site	or	left	as	slash	and	debris.	For	material	left	onsite	and	burned,	GHG	emissions	should	

be	calculated	using	the	CONSUME	model.	Hence,	Cf	is	estimated	from	standard	per‐area	forest	type	

carbon	stocks	or	from	plot	data.	The	fractions	fy	and	dy	are	estimated	or	directly	measured	(for	

simplicity	it	is	possible	to	assume	that	dy	is	the	fraction	of	the	turnover	time,	as	in	1/1,	1/10,	1/100	

or	1/1,000).	Es	is	the	soil	flux	that	is	represented	in	lookup	tables,	and	based	on	the	time‐varying	

rate	of	carbon	loss	as	a	percentage	of	the	original	forest	soil	carbon.	

Equation 6‐8: Computing Emissions of Carbon from a Forest Clearing

Ed = f(D × C/ha)

Where:	

Ed		 =	Emissions	of	carbon	from	forest	clearing,	D	(metric	tons	carbon	year‐1)		

D		 =	The	rate	of	forest	clearing	(ha	year‐1)	

C/ha		 =	The	stock	of	carbon	in	the	forest	system	prior	to	clearing (metric	tons	carbon	ha‐1)
Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

6-44

6.3.6 Limitations and Uncertainty

There	are	published	methods	for	formally	estimating	uncertainty	of	the	estimation,	generally	based	

on	the	number	and	distribution	of	the	permanent	plots,	and	how	they	are	applied	to	the	whole	

stratum.	These	uncertainty	estimates	can	be	used	a priori	to	establish	the	number	of	plots	needed	

to	achieve	a	level	of	accuracy.	They	can	also	be	used	to	attach	an	uncertainty	value	to	the	final	

estimate.	But	perhaps	the	most	challenging	component	of	uncertainty	lies	in	the	use	of	various	

expansion	factors	where	precise	field	estimates	are	not	known.	In	particular,	the	estimation	of	non‐

CO2	GHG	fluxes	is	very	uncertain,	and	must	be	used	with	some	degree	of	caution.	This	is	especially	

true	for	N2O	in	all	activities	and	CH4	in	cases	of	forest	establishment.	Considerably	more	research	is	

necessary	to	make	these	estimates.	

Another	uncertainty	in	most	estimates	is	the	fraction	of	standing	dead	biomass.	Based	on	some	

work	(Woodall	and	Monleon,	2008),	it	is	believed	to	be	small,	but	the	variation	with	forest	types,	

stand	age,	conditions,	and	activities	is	large.	When	using	default	values	this	may	be	a	challenge	to	

the	final	estimation.	In	the	case	where	direct	measurements	are	to	be	made	onsite,	the	standing	

dead	can	be	measured	along	with	standing	live	biomass.	This	may	be	an	approach	that	has	special	

benefit	if	the	site	being	cleared	has	been	intensely	damaged	by	pests	or	disease.	

Perhaps	the	most	problematic	area	is	the	computation	of	whole	tree	biomass	from	allometry.	There	

is	a	very	good	North	American	literature	on	allometry	for	stem	volumes	and	biomass	but	less	on	

whole	tree	volume	and	biomass.	Most	allometry	is	based	on	volumes	rather	than	whole	tree	

biomass	or	carbon.	Frequently	a	limited	number	of	simple	expansion	factors	are	deployed	to	

expand	the	volume	of	the	main	stem	to	the	biomass	of	the	whole	tree	including	its	branches.	These	

models	need	to	be	refined	to	better	make	the	estimation.	This	may	be	important	since	most	

landowners	will	not	have	the	ability	or	interest	to	conduct	their	own	destructive	tree	sampling	to	

extract	local	whole	tree	biomass	allometry	(i.e.,	a	Tier	3	approach).	

Equation 6‐9: Computing the Partitioning of the Products That Were Removed from the

Site or Left as Slash or Debris in 1 Year

Ed = D [ (Cf – Cc) × ∑ ሺ	࢟ࢌ ࢟

࢏ୀ૚ ൈ ࢟ࢊሻ ] + Es

Where:	

Ed	=	Emissions	of	carbon	from	forest	clearing,	D	(metric	tons	carbon	year‐1)		

D		 =	The	rate	of	forest	clearing	(ha	year‐1)	

Cf		=	The	carbon	stock	prior	to	forest	clearing	(metric	tons	carbon	ha‐1)	

Cc		=	The	carbon	stock	after	forest	clearing	(metric	tons	carbon	ha‐1)	

fy		=	The	fraction	of	original	carbon	stock	in	long‐term	decay	pool	y

dy	=	The	decay	function	for	the	mass	quantities	in	decay	pool	y		

	 (long‐term	decay	pools	are	1‐,	10‐,	100‐	and	1,000‐year	turnover	times)	

Es	=	Emissions	from	soil	(metric	tons	carbon	year‐1)	
 Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

 6-45 

Table 6‐3: Examples of Forest Carbon Calculators

Developer Website

USDA	Forest	Service	tools	for	carbon	

inventory,	management,	and	reporting	

http://www.nrs.fs.fed.us/carbon/tools/

FAO	ExACT	 http://www.fao.org/tc/exact/en/

TARAM	(BioCF	and	CATIE)	 http://wbcarbonfinance.org/Router.cfm?Page=DocLib&Catalog

ID=31252

CO2Fix	 http://www.efi.int/projects/casfor/models.htm

GORCAM	 http://www.joanneum.at/gorcam.htm

CASS	 http://www.steverox.info/software_downloads.htm

FullCam	 http://www.ieabioenergy‐

task38.org/workshops/canberra01/cansession1.pdf

COLE	 http://www.ncasi2.org/COLE/

Reforestation/Afforestation	Project	

Carbon	Online	Estimator	

http://ecoserver.env.duke.edu/RAPCOEv1/

Winrock	AFOLU	Calculator	 http://winrock.stage.datarg.net/CarbonReporting/Welcome

6.4 Forest Management

6.4.1 Description

Forest	management	is	concerned	with	meeting	landowner	objectives	for	a	forest	while	satisfying	

biological,	economic,	and	social	constraints.	Forest	managers	use	a	wide	variety	of	silvicultural	

techniques	to	achieve	management	objectives,	most	of	which	will	have	impacts	on	the	carbon	

dynamics	(see	Table	6‐4).	The	primary	impacts	of	silvicultural	practices	on	forest	carbon	include	

enhancement	of	forest	growth	(which	increases	the	rate	of	carbon	sequestration)	and	forest	

harvesting	practices	(which	transfers	carbon	from	standing	trees	into	wood	products	and	residues,	

which	eventually	decay).	Some	forest	management	activities	will	result	in	accelerated	loss	of	forest	

carbon,	such	as	when	soil	disturbance	increases	the	oxidation	of	soil	organic	matter,	or	when	

prescribed	burning	releases	CO2.	Furthermore,	some	forest	management	activities	result	in	fossil	

fuel	emissions	(e.g.,	from	the	utilization	of	mechanized	equipment,	transportation).	However,	

recent	evidence	suggests	these	emissions	are	fairly	minor.	Markewitz	(2006)	estimated	that	fossil	

emissions	from	silvicultural	activities	in	intensively	managed	pine	plantations	were	about	3	Mg	C	

ha−1	over	a	25‐year	rotation.	These	emissions	were	very	low	relative	to	the	subsequent	

Methods for Forest Management

 Range	of	options	dependent	on	the	size/management	intensity/data	availability	of	the	

entity’s	forest	land	including:	

− FVS‐FFE	with	Jenkins	(2003a)	allometric	equations;	

− Default	lookup	tables	of	management	practice	scenarios;	and	

− FVS	may	be	used	to	develop	a	supporting	product	providing	default	lookup	tables	of	

carbon	stocks	over	time	by	region;	forest	type	categories,	including	species	group	

(e.g.,	hardwood,	softwood,	mixed);	regeneration	(e.g.,	planted,	naturally	regenerated);	

management	intensity	(e.g.,	low,	moderate,	high,	very	high);	and	site	productivity	

(e.g.,	low,	high).	

 The	methods	were	selected	because	they	provide	a	consistent	and	comparable	set	of	

carbon	stocks	over	time	under	management	scenarios	common	to	the	forest	types	and	

management	intensities.	
Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

6-46

sequestration	of	carbon	in	the	forest	and	in	wood	products.	Côté	et	al.	(2002)	report	emissions	from	

silvicultural	activities	totaled	about	9	percent	of	total	emissions	from	a	pulp	and	paper	operation	

and	about	4	percent	of	gross	forest	sequestration.	In	a	life‐cycle	analysis	from	the	Pacific	

Northwest,	Johnson	et	al.	(2005)	reported	fossil	emissions	of	CO2	from	forestry	operations	

amounted	to	8.02	to	8.12	kg	CO2‐eq	m−3	of	harvested	logs,	or	less	than	1	percent	of	the	935	kg	CO2‐

eq	contained	in	a	cubic	meter	of	a	Douglas‐fir	log.	In	the	dry	Ponderosa	pine	forests	of	Arizona,	a	

thinning	treatment	resulted	in	CO2	emissions	from	fossil	fuels	of	334	kg	CO2‐eq	ha−1,	about	1.1	

percent	of	the	30,213	kg	CO2‐eq	ha−1	of	firewood	removed	in	the	thinning	operation	(Finkral	and	

Evans,	2008).	

This	section	describes	general	categories	of	forest	management	activities	and	their	impacts	on	

carbon	storage.	The	details	vary	widely	across	the	United	States	with	different	forest	types,	

ownership	objectives,	and	forest	stand	conditions.	It	is	important	to	engage	professional	foresters	

when	considering	harvests	or	other	silvicultural	practices.	An	important	distinction	to	be	made	at	

the	outset	is	between	planted	forests,	or	plantations,	and	forests	that	have	been	naturally	

regenerated.	Productivity	rates,	silvicultural	practices,	and	management	objectives	may	be	

markedly	different	for	planted	versus	natural	forests.	In	planted	forests,	conditions	are	typically	

optimized	for	increased	growth,	which	increases	carbon	sequestration	over	slower	growing,	

naturally	regenerated	forests.	However,	methods	for	inventorying,	monitoring,	and	assessing	

carbon	storage	in	both	planted	and	natural	forests	are	the	same;	variability	may	be	less	in	single‐

species	plantations,	but	approaches	are	identical.	Small	landowners	will	use	the	regional	default	

tables	to	estimate	the	potential	changes	in	GHG	fluxes	from	changes	in	forest	management,	while	

large	landowners	will	use	standard	forest	inventory	data	in	combination	with	the	simulation	

feature	of	the	FVS‐FFE	to	assess	changes	in	sequestration	and	emissions	from	changes	in	practice.	

Table 6‐4: Common Forest Management Practices

Practice Description Benefits

Stand	density	

management	

Controlling	the	numbers	of	trees	per	unit	

area	in	a	stand	through	a	variety	of	

techniques,	such	as	underplanting,	

precommercial	thinning,	and	commercial	

thinning	

 Maintains	stand	at	a	tree	density	that	

provides	optimal	growing	space	per	tree	

for	best	utilization	of	site	resources	

 Allows	concentration	of	site	resources	on	

“crop”	trees	

Site	preparation	 Preparing	an	area	of	land	for	forest	

establishment	by	removing	debris,	removing	

competing	vegetation,	and/or	scarifying	soil	

when	needed	

 Improves	survival	and	initial	growth	of	

planted	or	naturally	regenerated	

seedlings	or	sprouts	

 Enhances	regeneration	of	desired	species	

 Provides	conditions	favorable	for	planting	

of	seedlings	

Vegetation	

control	

Removing,	through	chemical	or	mechanical	

means,	undesirable	vegetation	that	would	

compete	with	the	desired	species	being	

regenerated	

 Improves	survival	and	growth	of	desired	

trees/species	

Planting	 Planting	of	seedlings	by	hand	or	machine	to	

establish	a	new	forest	stand	

 Controls	species	composition	and	

genetics	of	newly	established	stand	

 Controls	stocking	(density)	of	trees	per	

unit	area	for	optimal	growth/survival	
 Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

 6-47 

Practice Description Benefits

Natural	

regeneration	

Establishing	a	new	forest	stand	by	

allowing/enhancing	natural	seeding	or	

sprouting	

 Results	in	mix	of	species	

 Species	that	sprout	from	stumps	and	

roots	will	rapidly	recapture	the	site	

 Low	cost	relative	to	planting	

 May	involve	less	soil	disturbance	thereby	

reducing	erosion	

Fertilization	 Augmenting	site	nutrients	through	the	

application	of	nitrogen,	phosphorous,	or	

other	elements	essential	to	tree	growth	

 Enhances	growth	of	trees	

 Reduces	the	time	for	trees	to	reach	

merchantable	size		

 Eliminates	or	reduces	nutrient	

deficiencies	that	would	impair	forest	

growth/survival	

Selection	of	

rotation	length	

Choosing	the	timing	of	final	harvest	so	as	to	

optimize	the	mix	of	forest	products	that	can	

be	obtained	from	the	stand	

 Controls	the	relative	amounts	of	

pulpwood	and	sawtimber	products	

 Allows	landowner	to	respond	to	wood	

products	markets	by	optimizing	product	

mix	

Harvesting	and	

utilization	

Removal	of	trees	from	the	forest,	and	cutting	

and	separating	logs	for	forest	products	

markets	

 Selection	of	appropriate	harvesting	

systems	can	provide	logs	for	markets	

while	minimizing	damage	to	residual	

trees	or	disturbance	of	soil	

 Choice	of	harvesting	and	silvicultural	

cutting	system	will	impact	subsequent	

regeneration	of	the	stand;	systems	can	be	

chosen	to	influence	the	species	

composition	of	the	regenerated	stand	

Fire	and	fuel	

load	

management	

Reducing	the	risk	of	loss	to	wildfire	by	

controlling	the	quantity	of	fuels	in	a	forest	

stand	by	controlled	fire	or	mechanical	

treatments	

 Reduces	the	damage	caused	by	severe	

wildfires	by	eliminating	excessively	high	

fuel	loads	

 May	influence	the	species	composition	of	

the	understory	

Reducing	risk	of	

emissions	from	

pests	and	

disease	

Recovering	value	of	timber	after	damaging	

events	and/or	preventing	further	damage	by	

interrupting	spread	of	pests/diseases	

 Salvage	harvests	recovers	value	in	

damaged	timber	by	removing	it	before	it	

is	unusable	

 Sanitation	harvests	prevent	spread	of	

pests/diseases	

Short‐rotation	

woody	crops	

Producing	merchantable	trees	in	very	short	

time	periods	through	intensive	management	

(genetics,	herbicide,	fertilization)	

 Reduces	the	time	for	trees	to	reach	

merchantable	size	

The	remainder	of	this	section	describes	these	forest	management	practices	and	their	impact	on	

carbon	stocks.	

6.4.1.1 Stand Density Management

Management	of	forest	stand	density	(number	of	trees	per	unit	area)	is	important	to	achieve	optimal	

growth.	Overstocked	stands	(too	many	trees)	or	understocked	stands	(too	few	trees)	will	grow	less	

fiber,	and	therefore	store	less	carbon,	than	might	be	desirable.	In	overstocked	stands,	trees	compete	

with	each	other	for	scarce	resources	(nutrients,	water,	and	light),	and	such	stands	may	have	high	

numbers	of	trees	of	poor	size	and	quality	and	are	highly	susceptible	to	wildfire	or	other	reversal	

disturbances.	Reducing	the	stocking	in	overstocked	stands	will	concentrate	growth	in	trees	of	more	
Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

6-48

desirable	species	and	quality.	Understocked	stands	do	not	fully	utilize	the	resources	of	the	site	and	

therefore	do	not	achieve	the	growth	potential	of	a	fully	stocked	stand.	Stand	density	management	

seeks	to	maintain	a	fully	stocked	stand.	

Density	of	an	existing	forest	stand	may	be	increased	by	underplanting,	which	involves	planting	

additional	trees	(possibly	of	different	species)	beneath	an	existing	tree	canopy.	This	treatment	may	

be	desirable	for	stands	in	which	adequate	advanced	regeneration	of	desired	species	is	lacking.	

Underplanting	is	designed	to	increase	the	likelihood	of	successful	regeneration	following	the	

eventual	harvest	of	the	overstory.	Thus,	while	the	immediate	carbon	impact	of	this	treatment	is	low,	

there	may	be	substantial	eventual	improvement	in	carbon	stocks	compared	with	a	stand	without	

underplanting.	

Decreasing	the	density	of	a	forest	stand	is	accomplished	through	thinning,	or	cutting	some	

proportion	of	the	trees	in	a	stand.	This	may	be	done	as	precommercial	thinning,	in	which	case	most	

of	the	trees	to	be	cut	are	too	small	to	economically	justify	their	removal	from	the	forest,	and	they	

are	left	in	the	stand	to	decay	naturally.	While	precommercial	thinning	provides	no	immediate	

economic	benefits,	it	may	be	used	to	improve	the	stocking	level,	species	composition,	and	overall	

health	of	a	stand;	it	represents	an	investment	in	creating	a	more	valuable,	productive	forest.	

Precommercial	thinning	and	stand	density	management	also	can	reduce	the	risk	of	reversal	from	

drought,	insects,	disease,	and	possibly	fire.	From	a	carbon	standpoint,	precommercial	thinning	will	

remove	carbon	from	the	live	tree	pool	and	increase	the	carbon	in	the	dead	wood	pool.	If	the	slash	is	

burned,	the	GHG	emissions	should	be	accounted	for	using	the	CONSUME	model	when	the	burn	

occurred.	

If	trees	to	be	thinned	are	of	proper	species,	size,	and	quality,	commercial	thinning	may	be	

performed.	In	commercial	thinning,	trees	are	targeted	for	removal	based	on	their	species,	size,	and	

the	management	objectives.	Thinned	trees	are	removed	from	the	stand	and	sold	to	appropriate	

forest	products	markets.	Thus,	commercial	thinning	will	shift	carbon	from	the	live	tree	pool	and	

into	dead	wood	and	litter	(branches,	foliage,	and	stumps	remaining	in	the	stand	after	harvest),	and	

HWP	pools.	

6.4.1.2 Site Preparation Techniques

Regenerating	a	forest	stand	after	harvest	may	require	treatments	to	create	the	most	desirable	

conditions	for	development	of	the	new	stand.	This	may	involve	removing	debris	from	the	prior	

stand,	removing	undesirable	competing	vegetation,	scarifying	or	disturbing	the	soil	for	enhanced	

regeneration	of	species	that	require	such	conditions,	and	creating	space	or	proper	conditions	for	

planting	trees.	

A	wide	variety	of	techniques	are	available	to	meet	the	specific	regeneration	objectives;	they	vary	

considerably	across	geographic	regions,	topography,	site	conditions,	and	forest	species	under	

management.	General	categories	of	site	preparation	techniques	include	mechanical	methods,	

chemical	applications,	and	prescribed	fire.	

Mechanical	methods	displace	unwanted	vegetation,	move	or	break	down	logging	residues,	and/or	

cultivate	the	soil	(Nyland,	2002).	Mechanical	site	preparation	uses	a	variety	of	machines	and	

equipment,	and	may	be	limited	by	site	factors	such	as	terrain	and	soil	conditions.	Because	

mechanical	site	preparation	involves	soil	disturbance,	there	is	increased	oxidation	and	emission	of	

CO2	from	the	soil	organic	matter	for	a	period	of	time	after	site	preparation.	

Chemical	applications	involve	the	use	of	herbicides	targeted	at	controlling	undesirable	vegetation	

so	that	the	preferred	species	of	trees	have	improved	survival.	Chemicals	may	be	applied	through	

ground	or	air	spraying	or	injection	into	individual	trees.	Chemical	site	preparation	involves	little	to	

no	soil	disturbance	and	has	minimal	effect	on	soil	carbon	emissions.	
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Prescribed	burning may	be	used	to	reduce	the	amount	of	debris	(limbs,	tops,	and	foliage)	from	

prior	harvests,	kill	advanced	regeneration	of	trees	of	undesirable	species,	and	control	pests	that	

inhabit	decaying	wood	left	from	the	prior	stand.	Some	fire‐adapted	species	require	burning	to	open	

cones	and	disperse	seed	for	the	new	stand.	Clearly,	prescribed	fire	for	site	preparation	will	result	in	

combustion	and	emission	of	CO2	from	woody	materials	left	on	the	site,	but	will	avoid	the	soil

disturbance	of	mechanical	techniques.	The	FOFEM	model	for	natural	fuels	and	the	COMSUME	

model	for	activity	generated	fuels	can	be	used	to	address	this	type	of	burning	and	allows	estimation	

of	GHG	emissions	and	consumption.	

6.4.1.3 Vegetation Control

Control	of	competing	vegetation	is	one	means	of	enhancing	the	growth	of	desirable	trees	in	a	forest.	

For	example,	in	a	pine	plantation,	where	pine	trees	are	the	species	of	primary	interest,	growth	of	

pines	is	increased	when	hardwood	competition	is	removed.	Vegetation	control	may	be	

accomplished	mechanically	(such	as	girdling	undesirable	trees)	or	chemically.	Vegetation	control	is	

especially	important	at	two	stages	in	the	life	of	a	stand:	at	establishment	(planting	or	regeneration)	

and	later	in	the	rotation	but	before	trees	are	past	the	sapling	stage.	

At	establishment	(e.g.,	of	a	plantation),	the	primary	competition	may	come	from	herbaceous	

vegetation	that	can	quickly	outgrow	the	planted	trees	and	suppress	their	growth	or	increase	

mortality.	Herbicides	may	be	effective	at	controlling	herbaceous	competition	and	providing	the	

newly	planted	trees	a	chance	to	grow	sufficiently	to	capture	the	site.	Mid‐rotation	release	of	trees	

may	require	an	additional	application	of	chemical	control	to	reduce	competition	and	focus	growth	

on	desirable	trees.	

Vegetation	control	has	been	estimated	to	have	contributed	35	percent	of	the	substantial	gain	in	

plantation	productivity	relative	to	unimproved	plantations	(Stanturf	et	al.,	2003).	The	primary	

carbon	stock	impact	of	vegetation	control	is	a	transfer	of	carbon	stock	from	the	live	tree	to	standing	

dead	biomass	pool.	Trees	released	from	competition	will	usually	exhibit	a	growth	response	to	

balance	the	loss	of	growth	on	the	vegetation	removed	(i.e.,	overall	forest	productivity	and	

sequestration	will	remain	unchanged).	

6.4.1.4 Planting

One	popular	form	of	regenerating	a	forest	stand	following	clearcutting	is	to	establish	a	plantation	

by	planting	trees	of	a	desirable,	fast‐growing	species,	potentially	utilizing	an	improved	genetic	

source,	at	a	consistent	spacing	selected	to	optimize	growth.	Plantation	management	practices	

include	combinations	of	treatments	to	control	competing	vegetation	and	manage	tree	nutrition	

through	fertilization,	thinning,	and	use	of	genetically	improved	stock	(Vance	et	al.,	2010).	Because	

of	these	efforts,	plantations	may	be	up	to	six	times	more	productive	than	naturally	regenerated	

stands	of	the	same	species	(Carter	and	Foster,	2006).	Successful	plantation	establishment	entails	

careful	selection	of	species,	genetics,	and	spacing	(planting	density).	

Species	used	in	planted	stands	typically	are	selected	for	high	growth	rates,	low	susceptibility	to	

damage	from	insects	and	disease,	and	quality	and	value.	For	example,	in	the	U.S.	South,	loblolly	pine	

is	the	most	widely	planted	tree	species	because	it	is	native	to	the	area,	fast‐growing	relative	to	

other	pines,	and	resistant	to	disease	(Schultz,	1997).	Longstanding	genetic	improvement	programs	

have	led	to	the	production	of	improved	genetic	sources	for	forest	plantation	species.	Genetically	

improved	seedlings	are	available	from	commercial	and	state	tree	nurseries;	essentially	all	of	the	1.2	

billion	loblolly	pine	seedlings	planted	annually	in	the	U.S.	South	are	the	result	of	tree	improvement	

programs	(McKeand	et	al.,	2003).	In	the	Pacific	Northwest,	genetic	improvement	in	Douglas	fir	trees	

has	led	to	increases	in	productivity	(volume	production)	in	excess	of	25	percent	(St.	Clair	et	al.,	

2004).	Finally,	selection	of	planting	density	(trees	per	unit	area)	can	affect	overall	stand	
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productivity,	necessity	for	thinning,	ability	to	access	the	stand	with	equipment	to	conduct	

silvicultural	operations,	and	time	required	until	trees	reach	merchantable	diameters.	All	of	these	

factors	combine	to	determine	the	likely	survival	and	growth	rates	of	a	forest	plantation.	Plantation	

productivity	is	directly	related	to	rate	of	forest	sequestration.	Any	activity	increasing	productivity	

will	improve	sequestration	rates.	

6.4.1.5 Natural Regeneration

Certain	forest	types	are	regenerated	most	efficiently	using	natural	regeneration,	in	which	seedlings	

and	sprouts	from	a	recently	harvested	or	disturbed	forest	will	grow	quickly	after	removal	of	a	

portion	or	all	of	the	forest	overstory.	In	this	case,	the	species	will	be	predictable	based	on	the	

species	composition	of	advanced	regeneration	from	the	previous	stand,	or	if	species	present	in	the	

previous	stand	are	prolific	in	sprouting.	The	species	can	also	be	predicted	based	on	post‐harvest	

regeneration	of	seedlings	from	residual	overstory	trees	or	from	surrounding	stands.	Density	will	

not	be	controlled	during	the	regeneration	process;	frequently	natural	regeneration	results	in	very	

dense	vegetation	that	then	goes	through	a	natural	process	of	competition.	

Because	neither	the	genetic	source	nor	density	are	controlled	during	natural	regeneration,	these	

stands	are	frequently	less	productive	than	plantations	but	may	be	more	desirable	based	on	the	

objectives	of	the	landowner	(e.g.,	for	recreation,	wildlife,	or	different	products	than	plantations	

would	provide).	The	process	of	natural	regeneration	may	entail	minimal	(if	any)	site	preparation	

and	less	soil	disturbance	and	cost	than	would	plantations.	Depending	on	the	level	of	soil	

disturbance	from	the	harvest	of	the	previous	stand,	early	soil	CO2	emissions	may	be	lower	than	in	

planted	stands.	

6.4.1.6 Fertilization

Fertilization	has	been	shown	to	dramatically	improve	the	productivity	of	forest	stands	in	which	

nutrients	are	limiting	plant	growth.	For	example,	in	the	U.S.	South,	nitrogen	and	phosphorus	are	

commonly	deficient	in	pine	plantations	(Fox	et	al.,	2007).	In	these	areas,	phosphorus	fertilization	

may	increase	volume	production	by	more	than	100	percent	(Jokela	et	al.,	1991).	Nitrogen	and	

phosphorus	fertilization	has	been	shown	to	increase	growth	by	1.6	tons	acre−1	year−1	(Fox	et	al.,	

2007).	

The	two	primary	types	of	forest	fertilization	currently	practiced	in	the	South	are	phosphorus‐

fertilization	on	deficient	sites	(usually	at	or	near	time	of	planting),	and	nitrogen	and	phosphorus	

fertilization	in	mid‐rotation	stands	(e.g.,	ages	8	to	12).	Volume	gains	vary,	with	highest	gains	where	

stands	are	most	nutrient‐limited.	

The	direct	carbon	impact	of	fertilization	of	forests	is	the	observable	increase	in	growth	and	

therefore	sequestration.	Other	impacts	have	been	noted	in	agricultural	settings,	including	increased	

emissions	of	other	GHGs	such	as	NOx	and	N2O.	Results	from	agricultural	fertilizer	applications	may	

not	be	directly	applicable	to	forestry	operations.	Recent	research	in	western	Canadian	forests	

showed	soil	GHG	fluxes	were	neutral	following	fertilization	(Basiliko	et	al.,	2009).	In	an	analysis	of	

fertilization	of	pine	plantations	in	the	southeastern	United	States,	Albaugh	et	al.	(2012)	found	that	

carbon	sequestration	in	forest	growth	far	exceeded	the	emissions	associated	with	fertilizer	

production,	transport,	and	application	(8.70	Tg	year−1	CO2	sequestration	versus	0.36	Tg	year−1

emissions).	Thus,	forest	fertilization	when	applied	appropriately	can	dramatically	increase	carbon	

sequestration	when	compared	to	unfertilized	stands.	

6.4.1.7 Selection of Rotation Length

One	significant	decision	that	forest	managers	make	is	the	selection	of	the	rotation	length,	or	target	

age	at	which	a	regeneration	harvest	(final	harvest;	often	but	not	necessarily	a	clearcut)	will	occur.	
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The	decision	affects	the	timing	of	other	stand	treatments.	For	example,	thinnings	and	some	

fertilization	treatments	are	targeted	for	a	certain	time	before	final	harvest.	It	also	affects	the	mix	of	

forest	products	that	might	be	expected	from	the	harvested	stand.	Stands	harvested	at	relatively	

young	ages	will	yield	primarily	trees	suitable	for	pulpwood	markets,	while	longer	rotations	may	

involve	more	thinnings	and	will	increase	the	proportion	of	sawtimber‐sized	trees	in	the	stand.	

Because	these	different	products	have	different	longevities	(see	Section	6.5),	the	rotation	length	

will	have	a	significant	impact	on	the	overall	carbon	dynamics	of	a	forest	(and	its	subsequent	pool	of	

carbon	in	HWPs).	Furthermore,	longer	rotations	result	in	greater	average	carbon	storage	in	the	

forest,	with	resulting	higher	levels	of	sequestration	(Stainback	and	Alavalapati,	2002).	It	is	widely	

recognized	that	increasing	rotations	from	harvesting	at	financial	maturity	to	harvesting	closer	to	

ages	at	which	stands	reach	a	steady	state	between	growth	and	mortality	can	be	beneficial	for	

carbon	storage	(van	Kooten	et	al.,	1995).	

A	variety	of	decision	criteria	are	available	for	identifying	the	optimal	rotation	length	for	different	

sets	of	objectives.	If	carbon	storage	is	one	of	the	important	objectives,	longer	rotations	will	be	

beneficial	(Liski	et	al.,	2001).	

6.4.1.8 Harvesting and Utilization Techniques

Regeneration	harvests	(also	called	rotation	harvests	or	final	harvests)	are	conducted	to	harvest	

trees	for	forest	products	markets	and	to	promote	the	regeneration	of	desirable	species	for	the	next	

stand.	To	meet	the	twin	objectives	of	regeneration	and	production	of	merchantable	timber,	forest	

managers	may	choose	from	a	wide	array	of	techniques	and	operational	approaches.	The	

silvicultural	system	will	be	chosen	to	determine	which	trees	are	to	be	removed	from	the	stand,	and	

a	harvesting	system	will	be	chosen	to	determine	the	best	logging	approach	to	do	so.	

The	silvicultural	system	determines	what	proportion	of	the	forest	stand	is	to	be	removed	in	the	

harvest,	and	will	dictate	whether	the	resulting	stand	will	be	even‐aged	(a	stand	of	trees	of	a	single	

age	class)	or	uneven‐aged	(a	stand	of	trees	with	three	or	more	age	classes)	(Helms,	1998).	Harvests	

range	from	clearcuts,	in	which	most	or	all	of	the	overstory	is	removed,	to	a	variety	of	partial	

harvests.	Partial	harvests	include	systems	such	as	seed‐tree,	shelterwood,	group	selection,	

individual	tree	selection,	diameter‐limit,	and	others.	Harvest	techniques	that	open	most	or	all	of	the	

canopy	(such	as	clearcutting	or	seed‐tree	harvests)	will	promote	the	regeneration	of	species	that	

thrive	in	sunlight	and	do	not	tolerate	shade.	Clearcutting	is	also	the	preferred	technique	when	the	

next	stand	is	to	be	established	by	planting	rather	than	natural	regeneration.	

After	selection	of	a	silvicultural	system	for	regeneration,	forest	managers	will	select	a	harvesting	

system	for	the	felling	and	extraction	of	trees	from	the	site.	Again	a	wide	variety	of	systems	are	

available,	from	individual	tree‐felling	by	chain	saw	with	extraction	by	horse	teams,	to	highly	

mechanized	systems	involving	skidders,	feller‐bunchers,	forwarders,	and	other	types	of	equipment.	

When	terrain	conditions	prevent	ground‐based	vehicular	extraction	of	felled	trees,	it	may	be	done	

using	cable	yarding	systems	or	helicopters.	Logging	systems	that	minimize	soil	disturbance	and	

impacts	on	unharvested	trees	and	understory	may	reduce	these	harvest‐associated	emissions.	

When	trees	are	harvested	from	a	forest,	they	may	produce	a	variety	of	products	for	specific	

markets.	For	example,	large‐diameter	trees	of	certain	species	are	preferred	for	sawtimber	markets,	

while	pulpwood	markets	accept	roundwood	with	smaller	diameters	or	even	chips.	Thus,	a	

harvesting	operation	will	often	involve	merchandising—the	sorting,	cutting,	and	separating	of	logs	

for	delivery	to	different	markets.	Depending	on	the	silvicultural	system	chosen,	trees	without	

market	value	(e.g.,	too	small,	poor	form,	or	undesirable	species)	may	be	cut	and	left	onsite	to	decay.	

In	addition,	a	great	deal	of	logging	“slash”	may	be	produced;	this	material	may	consist	of	branches,	

portions	of	trees	beyond	merchantability	limits	(tops),	roots,	and	foliage.	Where	biomass	energy	

markets	exist,	some	of	this	material	may	be	removed	and	used	to	replace	fossil	energy	GHG	sources;	
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otherwise	it	may	be	left	onsite	to	decay	or	be	burned	during	site	preparation	with	associated	GHG	

emissions.	The	proportion	of	woody	material	removed	from	a	harvesting	operation	is	termed	

utilization;	high	levels	of	utilization	mean	more	woody	biomass	is	removed	and	less	remains	on	

site.	

There	are	many	carbon	consequences	to	the	selection	of	a	silvicultural	and	harvest	system.	Partial	

harvests	will	leave	substantial	carbon	in	live	trees	on	the	site,	whereas	clearcut	harvest	will	leave	

very	little.	On	certain	soils,	mechanized	systems	for	felling	and	extracting	trees	will	result	in	more	

soil	disturbance	and	subsequent	CO2	emissions	than	low‐impact	systems	(Nave	et	al.,	2010).	The	

harvesting	impact	on	soil	carbon	is	greater	for	the	forest	floor	than	for	carbon	in	the	mineral	soil,	

but	these	effects	are	shorter	lived	and	may	be	modest	over	longer	time	intervals	(Nave	et	al.,	2010).	

The	availability	of	markets	for	smaller‐diameter	material	or	trees	of	nonmerchantable	species	will	

affect	how	much	residue	(slash)	is	left	on	the	site.	Availability	of	strong	markets	will	generally	lead	

to	higher	utilization	and	less	residue.	It	is	important	to	keep	accounting	boundaries	in	mind	to	

ensure	that	there	is	no	omission	or	double	counting	of	emissions	or	removals.	The	IPCC	

methodologies	have	adopted	the	convention	that	emissions	from	burning	biomass	for	energy	

should	not	be	accounted	in	the	energy	sector,	but	should	be	accounted	in	the	land‐use	sector.	We	

conform	to	this	convention.	If,	for	example,	forest	residues	are	burned	for	energy,	the	CO2	emissions	

are	not	counted	in	the	energy	sector,	and	there	should	be	a	reduction	in	the	amount	of	fossil	fuel	

burned.	But	the	CO2	emissions	from	the	burned	residue	will	be	accounted	as	a	decrease	in	carbon	

stocks	in	the	land‐use	sector,	and	emissions	will	be	no	different	than	if	the	residues	had	been	piled	

and	burned	in	the	forest.	That	is,	a	complete	accounting	of	emissions	when	residues	are	burned	for	

energy	will	show	emissions	saved	in	the	energy	sector	but	no	change	in	the	land‐use	sector.	

6.4.1.9 Fire and Fuel Load Management

Many	forest	types	have	a	natural	dependence	on	disturbance	from	fire.	As	mentioned	previously,	it	

may	play	a	role	in	natural	regeneration,	but	it	has	many	other	functions	including	nutrient	release,	

natural	thinning	and	pruning,	as	well	as	modifying	fuel	structure	and	loading.	Without	prescribed	

fire,	many	forest	types	may	be	at	a	much	higher	risk	of	reversal	of	growing	carbon	stock.	In	regions	

of	the	country	where	wildfire	is	a	concern,	forest	managers	may	take	a	more	active	role	in	

managing	the	levels	of	potential	fuels	in	a	forest.	Fuel	management	cannot	prevent	ignitions	of	

wildfires,	but	can	decrease	levels	of	intensity,	severity,	and	spread.	Two	common	approaches	to	fuel	

load	management	are	prescribed	burning	and	mechanical	fuel	treatments.	

Prescribed	fire	is	any	fire	intentionally	ignited	by	management	under	an	approved	plan	to	meet	

specific	objectives.	When	forest	fuels	are	burned	under	carefully	selected	conditions	(weather,	fuel,	

moisture,	etc.),	fuels	can	be	reduced	to	levels	that	decrease	the	risk	of	damaging	wildfires.	Other	

objectives	for	use	of	fire	and	controlled	burn	may	be	to	reduce	threat	from	non‐native	invasive	

species	and	maintenance	of	many	endangered	species	throughout	the	United	States.	

Mechanical	fuel	treatments	are	similar	to	harvesting	operations,	in	that	specific	classes	of	trees	are	

cut	and	removed.	For	example,	all	trees	below	a	threshold	diameter	may	be	removed	in	a	thinning	

(Johnson	et	al.,	2007).	The	result	should	be	decreased	availability	of	fuels	that	would	increase	

wildfire	severity.	

The	carbon	impact	of	fuel	treatments	is	two‐fold.	First,	it	inevitably	results	in	emissions	of	CO2	from	

the	material	removed	or	burned.	However,	second,	its	goal	is	to	reduce	the	potential	for	much	

larger	future	emissions	(and	increased	environmental	damage)	from	wildfires	in	areas	where	they	

are	a	threat.	A	wildfire	could	result	in	a	reversal	of	the	previous	gains	in	carbon	on	the	site.	Wildfire	

intensity	and	resultant	loss	of	carbon	is	highly	variable	and	depends	upon	site	specific	conditions	

and	effects.	Wildfire	can	occur	at	low	to	moderate	intensity,	which	like	a	prescribed	fire	may	result	

in	a	more	resilient	and	productive	site	over	the	long	term.	The	challenge	is	that	the	immediate	CO2
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emissions	from	a	wildfire	or	prescribed	fire/control	burn	are	readily	quantifiable,	whereas	the	

avoided	emissions	from	potential	wildfires	are	not	and,	because	treatments	may	not	take	place	in	

the	areas	where	wildfire	occurs,	they	could	create	extra	emissions	that	would	not	otherwise	have	

happened.	Recent	research	indicates	that	prescribed	burning	has	a	minimal	impact	on	forest	carbon	

budgets,	especially	in	the	eastern	United	States.	Impacts	observed	from	mechanical	and	fire	

treatments	were	also	fairly	short‐lived	(Boerner	et	al.,	2008).	Disposition	of	removed	materials	is	a	

key	factor	to	consider	when	assessing	the	GHG	implications	of	fuel	management	treatments.	

Prescribed	fire	can	have	significant	effects	on	reducing	the	risk	of	reversal	that	could	result	from	a	

wildfire.	

6.4.1.10 Reducing Risk of Emissions from Pests and Disease

Silvicultural	intervention	may	also	be	called	for	when	forests	are	damaged	by	weather,	insects,	or	

disease.	For	example,	when	insect	outbreaks	such	as	pine	beetle	infestations	kill	patches	of	trees,	

removal	of	trees	at	or	near	the	infestation	site	may	prevent	populations	of	harmful	insects	from	

spreading	further.	When	harvests	are	designed	to	respond	to	pest	and	disease	problems,	they	may	

be	called	sanitation	harvests.	

When	weather	events	such	as	ice	storms,	hurricanes,	or	severe	winds	(or	a	wildfire)	cause	

extensive	damage	to	forest	stands,	quick	removal	of	the	downed	timber	may	provide	an	

opportunity	to	recover	some	of	the	financial	value	of	the	timber	and	may	prevent	the	buildup	of	

very	large	fuel	loads.	When	economic	value	is	captured	from	a	harvest	of	damaged	timber,	it	is	

termed	a	salvage	harvest.	

Both	salvage	and	sanitation	harvests	remove	trees,	sometimes	with	market	value	and	sometimes	

without.	The	carbon	impacts	are	reflected	in	the	amount	of	woody	material	removed	from	the	

forest	and	whether	the	material	removed	enters	markets	for	wood	products	or	for	energy.	Similar	

to	wildfire	treatments,	in	both	sanitation	and	salvage	harvests,	however,	the	removal	of	biomass	

may	be	compared	with	the	alternative	of	leaving	the	material	in	the	forest	to	decay	or	burn,	

resulting	in	CO2	emissions.	For	some	carbon	accounting	systems,	this	difference	is	crucial;	the	

assumption	that	emissions	would	have	occurred	without	the	activity	affects	baseline	assumptions	

against	which	carbon	sequestration	is	measured.	

6.4.1.11 Short‐Rotation Woody Crops

Short‐rotation	woody	crops,	also	called	biomass	plantations	or	biomass	energy	plantations,	are	tree	

plantations	managed	with	a	very	high	intensity	to	produce	fiber	crops	in	a	relatively	short	time	

frame	(e.g.,	5–10	years).	These	plantations	are	more	like	agricultural	crops	in	the	level	of	intensity	

of	treatments	(e.g.,	fertilization,	weed	control,	and	sometimes	irrigation).	Wood	grown	in	this	

manner	is	usually	suitable	for	use	by	biomass	energy	facilities	or	possibly	pulp	mills,	but	the	cost	to	

produce	this	wood	is	very	high	compared	with	traditional	plantations.	For	some	species,	it	is	

possible	to	regenerate	these	stands	by	coppicing,	or	cutting	to	promote	sprouting	from	intact	root	

systems,	which	avoids	the	cost	of	planting	new	trees.	Regeneration	by	sprouts	can	result	in	dense	

stands	exhibiting	very	fast	growth.	

The	carbon	dynamics	in	a	short‐rotation	woody	crop	system	are	similar	to	conventional	

plantations,	except	for	the	accelerated	growth	and	reduced	rotation	length.	In	some	short‐rotation	

woody	crop	systems,	cover	crops	may	be	grown	to	prevent	erosion	and	maintain	soil	fertility.	Cover	

crops	would	also	serve	to	increase	carbon	storage	on	site.	

6.4.2 Activity Data

Carbon	storage	from	forest	management	activities	is	estimated	applying	three	different	types	of	

estimates.	Estimate	Type	I	focuses	on	the	effects	of	management	activities	on	carbon	stocks	for	a	
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given	year.	Estimate	Type	II	focuses	on	the	effects	of	management	activities	on	carbon	stocks	over	a	

period	of	years	in	the	future	and	must	be	based	on	projections.	Estimate	Type	III	examines	the	

difference	in	projected	carbon	stocks	between	sets	of	alternative	scenarios	of	potential	

management.	This	section	will	discuss	the	activity	data	needs	for	each	of	the	types	of	estimates	for	

the	various	forest	management	activities.	In	general,	however,	the	estimation	approaches	and	data	

needs	will	be	of	two	types:	(1)	forest	inventory	data;	and	(2)	stand	projection	models.	

For	Type	I,	in	cases	where	a	management	activity	has	altered	the	carbon	stock	in	specific	pools,	the	

best	estimates	may	be	obtained	by	having	forest	inventory	data	before	and	after	the	treatment,	

such	that	the	difference	can	be	attributed	to	the	management	activity.	Forest	inventory	data	should	

include	measurements	obtained	in	the	forest	at	a	series	of	plots,	with	lists	of	the	trees	in	each	plot.	

Usually	for	each	tree	it	is	necessary	to	know	the	species,	diameter,	and	sometimes	height.	From	

these	measurements,	stand‐level	estimates	of	tree	density	(trees	per	unit	area),	basal	area	(cross‐

sectional	bole	area	at	4.5	feet	(1.4	m)	from	the	ground),	species	composition,	and	tree	volume	and	

biomass	can	be	computed.	

Another	approach,	used	for	Type	II	and	Type	III	estimates,	requires	the	use	of	stand	projection	

models	to	estimate	the	responses	of	the	forest	to	management	activities.	Such	models	have	been	

created	for	a	wide	variety	of	forest	types	and	treatments;	an	example	is	the	FVS	family	of	models	

discussed	earlier.	Projection	models	for	forecasting	forest	conditions	(and	carbon	stocks)	typically	

require	measures	or	indices	of	forest	productivity.	A	commonly	used	measure	of	forest	productivity	

is	site	index,	which	represents	the	height	that	trees	on	a	site	will	reach	by	a	certain	base	age.	For	

example,	on	land	with	a	site	index	of	65	(base	age	25),	the	average	height	of	dominant	and	co‐

dominant	trees	in	a	stand	will	be	65	feet	(19.8	m)	when	the	trees	reach	age	25.	

The	most	accurate	Type	II	and	Type	III	estimates	are	from	models	developed	specifically	for	a	given	

plantation	species	or	narrowly	defined	forest	type.	For	example,	there	are	many	models	available	to	

estimate	effects	of	management	on	commonly	planted	and	highly	researched	species	such	as	

Douglas	fir	or	loblolly	pine	(e.g.,	Amateis	and	Burkhart,	2005;	Burkhart,	2008;	Carlson	et	al.,	2008;	

Li	et	al.,	2007;	Sucre	et	al.,	2008).	At	this	time,	the	FVS	family	of	models	is	the	recommended	

method	for	estimating	forest	carbon	stocks.	In	incorporating	this	method	into	any	software	tool,	a	

data	portal	that	allows	the	user	to	load	their	existing	stand	data	and	management	activity	data	for	

translation	into	the	FVS	format	is	recommended	and	would	prove	useful.	Future	development	may	

also	permit	custom	models	to	interface	with	an	estimation	tool.	At	this	time,	however,	such	

capability	is	not	available.	In	cases	where	such	models	are	not	available,	it	may	be	necessary	to	

generalize	by	aggregating	forest	types	and	management	activities	and	perform	projections	based	

on	categories	of	management	intensity	for	general	forest	types.	Management	intensity	categories	

are	defined	in	Section	6.4.3.	

The	remainder	of	this	section	is	organized	as	follows:	

 Stand	Density	Management	

 Site	Preparation	Techniques	

 Vegetation	Control	

 Planting	

 Natural	Regeneration	

 Fertilization	

 Selection	of	Rotation	Length	

 Harvesting	and	Utilization	Techniques	

 Fire	and	Fuel	Load	Management	
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 Reducing	Risk	of	Emissions	from	Pests	and	Disease	

 Short‐Rotation	Woody	Crops	

6.4.2.1 Stand Density Management

Stand	density	management	activities	include	underplanting,	precommercial	thinning,	and	

commercial	thinning.	In	each	case,	the	primary	data	requirements	for	Type	I	estimates	are	tree	

inventories	before	and	after	the	treatment,	which	can	indicate	the	change	in	stocking	levels	and	the	

quantity	of	biomass	removed	during	thinnings.	In	the	case	of	thinnings,	it	is	important	to	know	the	

volume	or	biomass	directed	to	different	wood	products	markets	(e.g.,	pulpwood,	sawtimber,	or	

energy)	to	properly	account	for	the	carbon	in	HWPs.	

For	Type	II	and	III	estimates	of	the	future	carbon	dynamics	of	the	stand	after	these	treatments,	

stand	projection	models	will	require	a	measure	of	site	index	in	addition	to	the	inventory	

information	collected	for	Type	I	estimates.	

6.4.2.2 Site Preparation		

The	primary	information	requirement	for	estimates	of	stock	changes	due	to	site	preparation	is	

whether	soil	disturbance	has	occurred	during	site	preparation.	Mechanical	site	preparation	

techniques	that	involve	soil	disturbance	will	be	assumed	to	lead	to	a	short‐term	loss	of	soil	carbon	

storage	followed	by	a	recovery.	Chemical	or	other	treatments	that	don’t	involve	soil	disturbance	

will	not	result	in	soil	CO2	emissions	beyond	what	may	have	occurred	during	harvesting.	For	Type	II	

and	III	estimates,	the	site	preparation	technique	should	be	recorded	in	the	event	that	models	may	

differentiate	between	growth	rates	corresponding	to	various	site	preparation	techniques.	

6.4.2.3 Vegetation Control

For	Type	I	estimates,	it	is	necessary	to	have	inventory	information	before	and	after	vegetation	

control	treatments	if	the	vegetation	control	involves	woody	material.	(Carbon	stocks	are	not	

expected	to	be	substantially	different	for	herbaceous	control	treatments	near	time	of	planting.)	

When	vegetation	is	killed	but	not	removed,	the	carbon	stock	impacts	involve	primarily	the	

redirection	of	stock	from	one	pool	(live	trees)	to	another	(standing	dead	trees).	

For	Type	II	and	III	estimates,	some	models	may	project	stand	growth	differently	if	competing	

vegetation	is	removed.	In	such	cases,	similar	inventory	information	before	and	after	treatment	will	

be	necessary.	

6.4.2.4 Planting

The	act	of	planting	itself	involves	a	negligible	carbon	stock	change	for	the	year	of	planting.	Thus,	a	

Type	I	estimate	would	show	no	carbon	stock	change	following	a	planting.	

For	all	subsequent	years,	however,	critical	parameters	are	the	species	planted,	the	original	planting	

density	(trees	per	acre),	and	the	survival	rate	(in	percent)	after	one	growing	season.	Because	most	

early	mortality	occurs	within	one	year	of	planting,	the	percentage	of	trees	surviving	at	year	one	

provides	a	robust	estimate	of	stand	density	for	growth	projections.	It	will	also	be	important	for	

Type	II	and	III	estimates	to	record	the	genetic	stock	used	(e.g.,	first	generation,	open‐pollinated,	

mass‐controlled	pollinated,	clonal)	in	the	event	that	projection	models	are	developed	for	specific	

genetic	sources.	Some	measure	of	site	productivity	(e.g.,	site	index)	will	be	needed	as	well.	

6.4.2.5 Natural Regeneration

As	in	the	case	of	plantation	establishment,	carbon	stock	changes	at	the	time	of	natural	regeneration	

are	negligible.	
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Type	II	and	III	estimates	will	require	information	on	species	mix,	stand	density,	and	some	

information	on	stand	productivity.	In	cases	in	which	stand	productivity	cannot	be	measured	

directly	(by	measuring	existing	trees	for	site	index),	some	estimates	can	be	derived	from	soils	

databases	such	as	SSURGO,	or	from	field	characterization	of	soil	series	and	reference	to	soil	maps	

and	manuals.	

6.4.2.6 Fertilization

Type	I	estimates	will	show	no	immediate	carbon	stock	changes	relative	to	fertilization	for	the	year	

in	which	the	activity	occurred.	N2O	emissions	will	occur	at	time	of	fertilization;	activity	data	should	

include	number	of	acres	fertilized,	application	rate,	and	type	of	nitrogen	applied.	

Type	II	and	III	estimates	involving	stand	projection	may	make	use	of	models	which	incorporate	

information	about	the	fertilization	treatment.	Application	rates	(pounds	per	acre)	and	elemental	

composition	(nitrogen,	phosphorus,	potassium)	should	be	recorded.	

6.4.2.7 Selection of Rotation Length

Type	I	estimates	are	not	applicable	to	selection	of	rotation	length.	Type	II	and	III	estimates	may	

entail	experimentation	with	rotation	lengths	in	modeling	exercises	to	test	the	carbon	stock	

implications	of	different	rotation	length	strategies.	Such	experimentation	will	simply	involve	the	

comparison	of	models	run	with	all	parameters	held	constant	except	for	rotation	length.	

6.4.2.8 Harvesting and Utilization Techniques

Harvesting	has	the	largest	immediate	impact	on	forest	carbon	stocks.	Consequently,	for	Type	I	

estimates,	the	landowner	needs	to	collect	accurate	and	sufficiently	detailed	forest	inventory	

information	before	harvest	and	after	harvest	in	the	case	of	partial	cutting.	Because	ongoing	

sequestration	of	carbon	stocks	follows	different	pathways	for	different	forest	products,	the	

disposition	of	the	harvested	material	into	different	product	pools	(e.g.,	pulpwood,	sawtimber)	

needs	to	be	recorded.	This	information	should	be	readily	available	as	part	of	sales	records.	Default	

factors	are	available	to	estimate	carbon	in	harvesting	residues	(slash).	

In	the	case	of	partial	harvests	(where	there	is	a	residual	stand	to	project),	or	projections	of	impacts	

of	different	harvesting	or	silvicultural	systems,	complete	inventory	data	and	productivity	estimates	

(e.g.,	site	index)	for	the	stand	are	needed.	

6.4.2.9 Fire and Fuel Load Management

For	Type	I	estimates,	pre‐treatment	data	on	fuel	loading	with	focus	on	the	material	to	be	removed	

in	the	treatment	needs	to	be	collected.	An	example	of	data	collection	protocols	for	fuel	data	can	be	

found	in	Brown	(1974).	Post‐treatment	assessment	of	residual	material	will	indicate	the	amount	

removed	in	the	treatment.	The	type	of	treatment	(burn	or	mechanical)	and	the	disposition	of	fuel	

(consumed,	left	onsite,	removed)	should	be	recorded.	If	consumed,	FOFEM	or	CONSUME	can	be	

used	to	calculate	the	GHG	emissions	from	a	prescribed	burn.	

Type	II	and	III	estimates	of	the	carbon	stock	impacts	of	fuel	treatments	will	require	specialized	fire	

models	that	could	indicate	likely	outcomes	of	the	fuel	treatment	relative	to	no	treatment	and	a	

subsequent	wildfire;	available	tools	include	models	such	as	CONSUME	(Joint	Fire	Science	Program,	

2009)	and	the	FVS‐FFE	module	(Reinhardt	and	Crookston,	2003).	See	Table	6‐13	where	a	low‐

severity	fire	could	be	compared	to	the	crown	fire	effect	based	on	FOFEM	outputs.	

6.4.2.10 Reducing Risk of Emissions from Pests and Disease

For	estimates	of	carbon	stock	impacts	of	sanitation	and	salvage	harvests,	pretreatment	and	post‐
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treatment	inventories	are	required.	In	the	pretreatment	inventory,	the	extent	and	nature	of	damage	

are	needed	to	estimate	the	carbon	stock	that	has	shifted	from	live	to	dead	biomass	prior	to	

treatment.	

Modeling	for	Type	II	and	III	estimates	may	entail	simply	projecting	the	residual	(post‐treatment)	

stand.	To	fully	evaluate	the	carbon	stock	impacts	of	the	treatment,	models	or	assumptions	are	

needed	for	estimating	the	spread	of	the	insect	or	disease	absent	the	treatment.	Tools	for	such	

modeling	or	assumptions	may	be	hard	to	obtain.	

6.4.2.11 Short‐Rotation Woody Crops

Estimation	of	carbon	stock	impacts	from	plantations	of	short‐rotation	woody	crops	would	follow	

the	same	general	procedure	as	other	plantation	estimates.	No	stock	changes	would	be	expected	at	

time	of	planting	(carbon	in	seedlings	or	planting	stock	is	negligible).	Projections	for	Type	II	and	III	

estimates	require	the	availability	of	models	to	project	growth	and	yield	of	the	species	planted	under	

the	management	scenarios	envisioned.	

6.4.3 Management Intensity Categories

In	the	previous	section,	the	use	of	models	to	predict	forest	responses	to	management	activities	was	

discussed.	Many	such	models	are	available	for	specific	management	practices	in	plantations	of	

certain	species	or	in	specific	forest	types.	These	models	are	varied	in	their	input	requirements	and	

their	applications.	To	develop	a	nationally	consistent	approach,	the	infinite	combinations	of	

sequences	of	specific	management	activities	and	forest	types	need	to	be	generalized.	Using	a	single	

modeling	framework,	such	as	FVS	(Dixon,	2002)	and	categories	of	management	intensities,	allows	

for	the	simulation	of	suites	of	management	activities	in	a	wide	variety	of	forest	types	and	conditions	

with	a	single	set	of	inputs.	This	approach	to	defining	management	intensity	categories	is	similar	to	

that	used	by	Siry	(2002).	

Therefore,	in	this	section	categories	of	forest	types	and	management	intensities	that	represent	

broad	combinations	of	commonly	applied	activities	in	the	forest	types	of	the	United	States	are	

defined.	Default	tables	of	carbon	stocks	for	these	categories	could	then	be	developed	to	provide	

consistent	and	useful	information	about	likely	carbon	stock	implications	of	forest	management	

activities	across	the	country.	

6.4.3.1 Defining Forest Type Categories

The	first	distinction	in	defining	management	intensity	categories	is	the	identification	of	the	broad	

species	grouping:	hardwood,	softwood,	or	mixed.	Hardwood	forest	types	are	dominated	by	

hardwood	tree	species	such	as	oak,	maple,	cottonwood,	birch.	Softwood	types	are	dominated	by	

softwood	tree	species	such	as	pine,	spruce,	or	Douglas	fir.	Mixed	types	exhibit	no	clear	dominance	

of	one	species	group.	The	second	major	distinction	is	whether	the	stand	was	planted	or	naturally	

regenerated.	Certain	management	activities	are	far	more	likely	to	be	applied	to	plantations	than	

natural	stands.	Most	plantations	are	softwoods,	with	the	exception	of	some	short‐rotation	woody	

crops	of	hardwood	types	such	as	cottonwood,	willow,	hybrid	poplar,	or	aspen.	

6.4.3.2 Defining Categories of Management Intensity

Four	categories	of	management	intensity	are	defined	based	on	commonly	encountered	practices.	

For	example,	almost	all	forest	fertilization	is	applied	to	plantations	rather	than	naturally	

regenerated	stands,	so	fertilization	will	be	considered	part	of	management	intensities	related	only	

to	plantations.	Similarly,	stands	that	are	fertilized	are	usually	also	treated	with	herbicide	to	control	

competing	vegetation	so	that	the	fertilization	benefit	accrues	to	the	desired	crop	species.	
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The	four	categories	of	management	intensity	are	low,	moderate,	high,	and	very	high.	Low	intensity	

generally	refers	to	minimal	management	intervention	(e.g.,	natural	regeneration	or	older	softwood	

plantations	without	genetically	improved	stock).	Moderate	intensity	incorporates	some	level	of	

active	management	such	as	intermediate	harvests	(e.g.,	thinnings).	High	intensity	applies	only	to	

plantations	and	incorporates	the	use	of	superior	genetic	stock	and	vegetation	control.	Very	high	

intensity	management	applies	to	aggressively	managed	softwood	or	hardwood	plantations	in	which	

substantial	effort	is	made	to	maximize	growth	using	genetics,	vegetation	control,	and	fertilization.	

The	resulting	combinations	of	forest	types,	intensities,	and	management	practices	are	summarized	

in	Table	6‐5.	

Table 6‐5: Management Intensity Categories

Forest Typea/Management

Intensityb

Stand

Density

Mgmt

Planting Superior

Genetics

Vegetation

Control Fertilization

Hardwood/low

Hardwood/moderate X

Mixed/low

Mixed/moderate X

Softwood	(Nat)/low

Softwood	(Nat)/moderate X

Softwood	(Plt)/low X 	

Softwood	(Plt)/moderate X X X

Softwood	(Plt)/high X X X X	

Softwood	(Plt)/very	high X X X X	 X

Hardwood	(Plt)/very	highc X X X X	 X a	Forest	type	refers	to	the	combination	of	species	group	and	regeneration	(Nat	=	naturally	regenerated;	Plt	=	Planted).	 b	An	X	indicates	that	the	practice	indicated	is	applied	for	the	management	intensity	category.	 c	Very	high	intensity	hardwood	plantations	are	usually	encountered	in	the	context	of	short‐rotation	wood	crops	or	

biomass	plantations.	

Figure	6‐6	shows	the	specific	regions	(e.g.,	Pacific	Northwest,	West;	Pacific	Northwest,	East;	Pacific	

Southwest;	Rocky	Mountain	North;	Rocky	Mountain	South;	Great	Plains;	Northern	Lake	States;	

Central;	South	Central;	Northeast;	and	Southeast)	for	which	silvicultural	options	by	the	most	

commonly	managed	forest	type	were	developed.	
 Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

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Figure 6‐6: Map of Specific Regions of Forest Management

For	the	management	intensity	categories	illustrated	in	Table	6‐5,	common	silvicultural	options	by	

the	most	commonly	managed	forest	types	for	specific	regions	of	forest	management	(see	Table	6‐6)	

are	described.	This	list	is	not	exhaustive,	since	silvicultural	prescriptions	may	often	be	tailored	to	

site	specific	conditions;	however,	the	list	provides	the	practices	frequently	applied	in	commonly	

managed	forest	types.	The	management	objective	may	not	necessarily	be	timber	production;	in	

some	regions	and	types	habitat	restoration,	rangelands,	or	forest	health	may	be	the	primary	

management	objectives.	Table	6‐6	provides	a	list	of	commonly	used	silvicultural	prescriptions	for	

common	forest	types	in	each	region.	

Table 6‐6: Common Silvicultural Options by Most Commonly Managed Forest Type

Region Forest Type Generalized Practice

Northeasta	

Northern	hardwoods:	

beech,	sugar	maple,	

yellow	birch,	and	

associates	

Single	tree	selection:	harvest	40–50	ft2	per	acre	every	20	

years	across	a	range	of	size	classes	in	stands	with	120–130	

ft2	basal	area	(BA)	

Clearcut:	when	120–130	ft2,	then	commercial	thinning

Commercial	thin:	At	age	90–100	(120ft2)	thin	to	70–80	ft2

Standard	shelterwood:	Harvest	40–50	ft2	from	below,	

leaving	80	ft2	in	overstory;	remove	overstory	in	10–15	

years	

Spruce–fir:	red/white	

spruce,	balsam	fir	

Shelterwood:	Harvest	60	ft2 from	below	(leave	100	ft2);	

harvest	remainder	in	10–15	years	

Single	tree	selection:	At	160	ft2,	remove	50	ft2	in	all	sizes,	

every	20	years	
Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

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Region Forest Type Generalized Practice

Commercial	thinning:	At	age	50–60,	thin	from	150	down	to	

100	ft2

Centralb

Oak–hickory	

Clearcut

Shelterwood: following	local	guidelines	

Group	selection	with	commercial	thinning	to	B‐level	

stocking	on	Gingrich	Guide	(Gingrich,	1967)	

Precommercial/commercial	thinning	to	B‐level	stocking	on	

Gingrich	Guide	

Diameter	limit	cut:	To	12	inches	DBH	

Prescribed	fire:	to	promote	oak	regeneration	or	woodland	

restoration	

Elm–ash–cottonwood	

Clearcut

Individual	tree	selection: following	local	guidance

Diameter	limit	cut:	To	12	inches	DBH	

Maple–beech–birch	

Clearcut

Shelterwood: following	local	guidance	

Group	selection	with	commercial	thinning	to	B‐level	

stocking	on	Gingrich	Guide	

Individual	tree	selection:

Commercial	thinning	to	B‐level	stocking	on	Gingrich	Guide

Diameter	limit	cut:	To	12	inches	DBH	

Oak–pine	

Clearcut:

Shelterwood:

Group	selection	with	commercial	thinning	to	B‐level	

stocking	on	Gingrich	Guide	

Diameter	limit	cut:	To	12	inches	DBH	

Prescribed	fire:	To	promote woodland	restoration

Rocky	Mountain	

Southc	

Dry	montane:	ponderosa	

pine,	Douglas	fir	

Selection	cutting:	Harvest	20–30	ft2	per	acre	every	20–30	

years	across	size	classes	in	stands	to	40–80	ft2	BA	

Commercial	thinning:	At	age	60–80	thin	to	50–60	ft2 BA

Shelterwood:	Harvest	60–80	ft2 BA	from	below;	leave	30	

ft2	in	overstory;	remove	overstory	in	5–10	years	

Aspen	 Coppice: At	age	100

Lodgepole	pine	 Clearcut: At	age	120–150

Spruce–fir	 Single	tree	selection:	Harvest	20–30ft2	per	acre	every	20–

30	years	across	size	classes	in	stands	to	80–120	ft2	BA	

Woodland	types:	pinyon–

juniper,	Gambrel	oak	 Selection	cutting:	Harvest		to	40–60	ft2	BA	

Southeastd

Upland	hardwood	

Clearcut:	At	age	35–50

Single	tree	selection:	Harvest	40–60	ft2	per	acre	in	stands	

with	100–140	ft2	per	acre	

Bottomland	hardwood	 Single	tree	selection:	Harvest	40–60	ft2	per	acre	in	stands	

with	100–140	ft2	per	acre	

Pine	plantation	–	low	

intensity	

Plant	with	non‐improved	seedlings	600–700	per	acre,	thin	

to	60–70	ft2	per	acre	at	age	18–24,	clearcut	at	age	25–35	

Pine	plantation	–	medium	

intensity	

Plant	with	improved	seedlings	600–700	per	acre,	thin	to	

60–70	ft2	per	acre	at	age	18–22,	fertilize	after	thinning	

with	nitrogen	and	phosphorus	(if	needed),	clearcut	5–7	

years	after	thinning	
 Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

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Region Forest Type Generalized Practice

Pine	plantation	–	high	

intensity	

Plant	with	improved	seedlings	600–700	per	acre,	

herbaceous	weed	control	age	2–4,	thin	to	60–70	ft2	per	

acre	at	age	16–20,	fertilize	after	thinning	with	nitrogen	and	

phosphorus	(if	needed),	clearcut	5–7	years	after	thinning	

South	Centrald

Upland	hardwood	

Clearcut:	At	age	35–50

Single	tree	selection:	Harvest	40–60	ft2	per	acre	in	stands	

with	100–140	ft2	per	acre	

Bottomland	hardwood	 Single	tree	selection:	Harvest	40–60	ft2	per	acre	in	stands	

with	100–140	ft2	per	acre	

Pine	plantation	–	low	

intensity	

Plant	with	non‐improved	seedlings	450–700	per	acre, on	

lower	quality	sites,	thin	to	60–70	ft2	per	acre	at	age	18–20;	

on	higher	quality	sites,	thin	to	60–70	ft2	per	acre	at	age	12–

16,	on	higher	quality	sites,	thin	again	at	age	20–24,	clearcut	

5–7	years	after	thinning	

Pine	plantation	–	medium	

intensity	

Plant	with	improved	seedlings	600–700	per	acre,	on	lower	

quality	sites,	thin	to	60–70	ft2	per	acre	at	age	18–20;	on	

higher	quality	sites,	thin	to	60–70	ft2	per	acre	at	age	12–16,	

fertilize	after	thinning	with	nitrogen	and	phosphorus	(if	

needed),	on	higher	quality	sites,	thin	again	age	20–24,	

clearcut	5–7	years	after	thinning	

Pine	plantation	–	high	

intensity	

Plant	with	improved	seedlings	600–700	per	acre,	

herbaceous	weed	control	age	2–4,	on	lower	quality	sites,	

thin	to	60–70	ft2	per	acre	at	age	18–20;	on	higher	quality	

sites,	thin	to	60–70	ft2	per	acre	at	age	12–16,	fertilize	after	

thinning	with	nitrogen	and	phosphorus	(if	needed),	on	

higher	quality	sites,	thin	again	at	age	20–24,	clearcut	5–7	

years	after	thinning	

Northern	Lake	

Statese

Aspen–birch	

Clearcut:	50–60	year	rotation

Shelterwood:	When	birch	is	main	component:	two	cut	

system,	commercial	thinning	at	age	40–50	on	high	quality	

sites	

Northern	hardwoods	

Shelterwood:	two	stage;	first	cut	20	years	prior	to	rotation	

age;	commercial	thinning	as	required	

Single	tree/group	selection	with	10–20	year	cutting	cycle

Oak	

Clearcut:	On	lower	quality	sites,	and	on	high	quality	sites	

where	adequate	advanced	regeneration	is	present;	

commercial	thinning	as	required	

Shelterwood:	On	high	quality	sites	when	adequate	

advanced	regeneration	is	not	present;	commercial	thinning	

as	required	

Jack	pine	

Clearcut:	50–60	year	rotation (note	that	jack	pine	managed	

for	Kirtland’s	warbler	habitat	will	have	additional	

management	requirements)	

Red	pine	

Clearcut:	Commonly	followed	by	site	preparation	and	

planting	900	per	acre,	commercial	thinning	beginning	at	

age	25–40	

Shelterwood:	Where	disease	risk	is	low;	often	used	with	

prescribed	fire;	commercial	thinning	beginning	at	age	25–

40	

White	pine	 Shelterwood:	Two	stage	system;	commercial	thinning	

beginning	at	age	40	
Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

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Region Forest Type Generalized Practice

White	spruce/balsam	fir	

Clearcut:	When	adequate	regeneration	is	present	

Shelterwood:	Two	stage	system,	when	adequate	

regeneration	is	not	present	

Lowland	conifer	

Clearcut:	When	adequate	regeneration	is	present; patch	

and	strip	clearcuts	may	be	used	in	some	cases	

Shelterwood:	Two	stage	system,	when	adequate	

regeneration	is	not	present	

Great	Plainsf	 Ponderosa	pine	

Two‐cut	Shelterwood:	reduce	basal	area	to	below	60	ft2

per	acre,	then	remove	remaining	overstory	after	adequate	

regeneration	is	present	

Precommercial	thinning	as	necessary	to	maintain	desired	

densities	

Artificial	regeneration	may	be	required	after	catastrophic	

disturbances	or	to	establish	forests	on	previously	

unforested	land;	this	may	be	done	through	broadcast	

seeding	or	planting	

Rocky	Mountain	

Northg

Ponderosa	pine	

Plant	400–500	trees per	acre,	precommercial	thin	to	200–

300	trees	per	acre,	commercial	thin	to	150–200	trees	per	

acre	at	age	30–40;	clearcut	harvest	at	age	60–80	

Lodgepole	pine	

Site	prepare	to	expose	mineral	soil	seedbed,	natural	

regeneration	by	seeding,	precommercial	thin	to	200–400	

trees	per	acre,	patch	clearcut	harvest	at	age	80–100	

Pacific	

Southwesth

Mixed	conifer:	ponderosa	

pine,	sugar	pine,	Douglas	

fir,	incense	cedar,	white	

fir,	Jeffrey	pine,	and	

California	black	oak	

Commercial	thin:	Starting	at	ages	near	40	and	continuing	

at	various	periodic	cycles	until	regeneration;	post‐thinning	

stocking	generally	ranges	between	150–250	ft2;	variable	

rotation	length,	depending	on	objectives	

Commercial	thinning	with	both	patch	regeneration	and	

reserved	areas:	Similar	to	above,	but	with	higher	levels	of	

variation	in	post‐thinning	stocking	levels,	small	patches	of	

regeneration,	primarily	to	increase	pine	species,	and	small	

areas	reserved	from	harvest,	maintaining	larger/older	

trees	providing	relatively	unique	wildlife	habitats;	variable	

rotation	length,	depending	on	objectives	

Pacific	

Northwest,	Easti

Douglas	fir/Ponderosa	

pine	–	low	intensity	

Site	preparation	by	site	scarification	in	small	spots,	natural	

regeneration,	precommercial	thin	at	age	20–25	years	to	

100–250	trees	per	acre,	patch	clearcut	or	seed‐tree	harvest	

at	age	50–70	

Douglas	fir/Ponderosa	

pine	–	medium	intensity	

(on	more	productive	

sites)	

Mechanical	site	preparation	to	scarify	soil	and	remove	

competing	vegetation,	plant	with	improved	seedlings	at	

approx.	400–500	per	acre,	precommercial	thin	at	age	15–

20,	commercial	thin	at	age	30–40,	patch	clearcut	or	seed‐

tree	harvest	at	age	50–70	

Pacific	

Northwest,	

Westj

Douglas	fir	

Site	prepare	stand	with	pre‐emergent	herbicides,	plant	

with	improved	seedlings	at	approx.	450	per	acre,	

commercial	thinning	as	needed	at	age	20–30,	fertilize	as	

needed	at	age	30–40,	clearcut	harvest	at	age	40–50	

DBH	=	Diameter	at	breast	height	

a	Personal	communication:	Bill	Leak.	

b	Personal	communication:	Steve	Shifley.	 c	Personal	communication:	James	Youtz,	Jim	Thinnes.	

d	Personal	communication:	Steve	Prisley.	

e	Planning	documents	and	silviculture	guides,	and	personal	communication	with	staff	on	the	Huron‐Manistee,	Ottawa,	and	
 Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

 6-63 

Hiawatha	National	Forests.	

f

	See	Shepperd	and	Battaglia	(2002).	

g	See	Youngblood	(2005).	

h	Personal	communication:	Joe	Sherlock.	

i

	See	Briggs	(2007).	

j

	See	Hanley	and	Baumgartner	(2005).	

6.4.3.3 Applying Default Tables of Management Practice Scenarios

Once	the	general	categories	of	forest	types	and	management	intensities	are	defined,	a	modeling	

framework	such	as	FVS	could	be	used	to	develop	sets	of	default	tables	of	carbon	stocks	in	various	

pools	over	time	under	management	scenarios	common	to	the	forest	types	and	management	

intensities.	Note	that	at	this	time,	these	lookup	tables	are	not	available;	developing	default	carbon	

stock	values	for	forest	management	practices	is	a	task	requiring	a	significant	level	of	time	and	

effort.	In	the	absence	of	such	tables,	small	landowners	wishing	to	estimate	the	effects	of	changing	

management	practices	(a	Type	III	estimate)	will	need	to	use	the	methods	described	for	large	

landowners.	

Table	6‐7	shows	an	unpopulated	example	for	the	default	lookup	tables	of	management	practice	

scenarios.	The	default	tables	would	provide	regional	estimates	of	timber	volume	and	carbon	stocks	

for	a	specific	forest	type	group	(e.g.,	loblolly‐shortleaf	pine	stands)	under	a	specific	(e.g.,	Softwood	

(planted)/very	high)	management	intensity	on	forest	land	after	clearcut	harvest	in	a	specific	region	

(e.g.,	the	Southeast)	for	low	productivity	and	high	productivity	sites.	

Table 6‐7: Regional Estimates of Timber Volume and Carbon Stocks for a Specific Forest

Type Group (e.g., Loblolly‐Shortleaf Pine Stands) Under a Specific (e.g., Softwood

(Planted)/Very High) Management Intensity on Forest Land after Clearcut Harvest in a

Specific Region (e.g., the Southeast) for Low Productivity and High Productivity Sites

Note:	At	this	time,	populated	tables	are	not	available;	development	of	such	tables	is	not	certain.	

Age Mean

Volume

Mean Carbon Density

Live Tree Standing

Dead Tree

Down Dead

Wood

Forest

Floor or

Litter

Total

Nonsoil

Years m3 ha−1 ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐Metric Tons C ha−1 (Low Productivity)‐‐‐‐‐‐‐‐‐‐‐‐‐	

0	 ‐	 ‐	 ‐ ‐ ‐	 ‐

5	 ‐	 ‐	 ‐ ‐ ‐	 ‐

10	 ‐	 ‐	 ‐ ‐ ‐	 ‐

15	 ‐	 ‐	 ‐ ‐ ‐	 ‐

20	 ‐	 ‐	 ‐ ‐ ‐	 ‐

25	 ‐	 ‐	 ‐ ‐ ‐	 ‐

30	 ‐	 ‐	 ‐ ‐ ‐	 ‐

35	 ‐	 ‐	 ‐ ‐ ‐	 ‐

40	 ‐	 ‐	 ‐ ‐ ‐	 ‐

45	 ‐	 ‐	 ‐ ‐ ‐	 ‐

50	 ‐	 ‐	 ‐ ‐ ‐	 ‐

55	 ‐	 ‐	 ‐ ‐ ‐	 ‐

60	 ‐	 ‐	 ‐ ‐ ‐	 ‐

65	 ‐	 ‐	 ‐ ‐ ‐	 ‐

70	 ‐	 ‐	 ‐ ‐ ‐	 ‐

75	 ‐	 ‐	 ‐ ‐ ‐	 ‐

80	 ‐	 ‐	 ‐ ‐ ‐	 ‐

85	 ‐	 ‐	 ‐ ‐ ‐	 ‐

90	 ‐	 ‐	 ‐ ‐ ‐	 ‐
Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

6-64

Age Mean

Volume

Mean Carbon Density

Live Tree Standing

Dead Tree

Down Dead

Wood

Forest

Floor or

Litter

Total

Nonsoil

Years m3 ha−1 ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐Metric tons C ha−1 (high productivity)‐‐‐‐‐‐‐‐‐‐‐‐

0	 ‐	 ‐	 ‐ ‐ ‐	 ‐

5	 ‐	 ‐	 ‐ ‐ ‐	 ‐

10	 ‐	 ‐	 ‐ ‐ ‐	 ‐

15	 ‐	 ‐	 ‐ ‐ ‐	 ‐

20	 ‐	 ‐	 ‐ ‐ ‐	 ‐

25	 ‐	 ‐	 ‐ ‐ ‐	 ‐

30	 ‐	 ‐	 ‐ ‐ ‐	 ‐

35	 ‐	 ‐	 ‐ ‐ ‐	 ‐

40	 ‐	 ‐	 ‐ ‐ ‐	 ‐

45	 ‐	 ‐	 ‐ ‐ ‐	 ‐

50	 ‐	 ‐	 ‐ ‐ ‐	 ‐

55	 ‐	 ‐	 ‐ ‐ ‐	 ‐

60	 ‐	 ‐	 ‐ ‐ ‐	 ‐

65	 ‐	 ‐	 ‐ ‐ ‐	 ‐

70	 ‐	 ‐	 ‐ ‐ ‐	 ‐

75	 ‐	 ‐	 ‐ ‐ ‐	 ‐

80	 ‐	 ‐	 ‐ ‐ ‐	 ‐

85	 ‐	 ‐	 ‐ ‐ ‐	 ‐

90	 ‐	 ‐	 ‐ ‐ ‐	 ‐

6.4.4 Estimation Methods

6.4.4.1 Stand Density Management

Type	I	estimates	may	be	developed	for	stand	density	management.	For	underplanting,	carbon	

stocks	are	essentially	unchanged	immediately	after	the	treatment.	For	precommercial	thinnings,	

carbon	is	moved	from	the	live	tree	pool	to	the	standing	dead	pool	and/or	forest	floor	pool;	

quantities	will	be	low	and	essentially	just	accelerate	the	natural	mortality	of	these	smaller	trees,	

thus	accounting	for	this	activity	may	be	unnecessary.	For	commercial	thinning,	the	live	tree	carbon	

stock	is	reduced	and	carbon	is	moved	into	HWPs,	so	these	pools	need	to	be	estimated	using	

procedures	outlined	in	Section	6.2	and	Section	6.5.	

Type	II	and	III	estimates	may	be	developed	using	forest	growth	models	(i.e.,	FVS)	specific	to	the	

forest	type	and	practices	used.	

6.4.4.2 Site Preparation Techniques

Carbon	stock	changes	that	are	due	to	mechanical	site	preparation	techniques	will	consist	of	some	

oxidation	of	soil	organic	carbon	that	will	be	replaced	over	time	by	forest	growth.	For	long‐term	

monitoring,	it	may	be	assumed	that	soil	carbon	stocks	will	be	stable	under	sustainable	forest	

management	(Smith	et	al.,	2006).	Thus,	Type	I	estimates	could	reflect	short‐term	losses	of	soil	

carbon	stocks	based	on	assumptions	appropriate	to	the	forest	type	and	region.	

6.4.4.3 Vegetation Control

Control	of	woody	vegetation	will	exhibit	patterns	similar	to	precommercial	thinning:	transfer	of	

carbon	stocks	from	live	tree	to	dead	tree	pools.	Quantities	will	likely	be	small	and	the	effect	of	short	

duration;	hence	accounting	for	these	impacts	using	Type	I	estimates	may	be	unnecessary.	

For	Type	II	and	III	estimates,	vegetation	control	may	be	expected	to	have	a	beneficial	impact	on	the	
 Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

 6-65 

growth	of	the	residual	stand	that	should	be	modeled	accordingly.	

6.4.4.4 Planting

Negligible	carbon	stock	changes	are	expected	at	the	time	of	establishment	of	a	new	plantation,	so	

Type	I	estimates	will	show	no	stock	changes.	For	Type	II	and	III	estimates,	the	plantation	activity	

establishes	a	new	stand	that	can	then	be	modeled	based	on	species,	site	index,	and	initial	stocking	

(planting	density	times	year	1	survival	percent).	

6.4.4.5 Natural Regeneration

As	in	the	case	of	plantation	establishment,	carbon	stock	changes	at	the	time	of	natural	regeneration	

are	negligible.	For	Type	II	and	III	estimates	involving	projections	of	stand	growth	over	time,	initial	

stocking,	species	mix,	and	site	productivity	will	define	the	stand	parameters	for	growth	projections.	

6.4.4.6 Harvesting and Utilization		

Depending	on	the	harvesting	and	silvicultural	system	used,	multiple	stock	changes	occur	with	a	

rotation	harvest.	Live	tree	biomass	stocks	are	reduced	by	the	amount	of	harvested	wood	(up	to	100	

percent	of	the	live	tree	biomass	pool).	These	removals	should	be	balanced	by	additions	to	HWP	

pools	and	slash/residue	in	the	forest	floor	and	dead	wood	pools.	Because	losses	to	soil	organic	

carbon	pools	from	disturbance	by	mechanized	harvesting	systems	are	of	relatively	short	duration,	

it	is	common	to	consider	the	loss	and	recapture	as	a	steady	state	(e.g.,	Smith	et	al.,	2006),	though	

this	may	differ	depending	on	soil	characteristics.	

In	the	case	of	partial	harvests,	there	is	a	residual	stand	for	which	carbon	stocks	remain	to	be	

projected	over	time.	Post‐harvest	inventory	information	provides	the	critical	stand	parameters	to	

be	input	into	growth	models.	In	the	absence	of	a	post‐harvest	inventory,	pre‐harvest	inventory	data	

can	be	adjusted	to	reflect	the	loss	of	trees	removed	by	the	harvest	(e.g.,	by	decreasing	the	numbers	

of	trees	by	species	and	diameter	class	based	on	harvest	records).	

6.4.4.7 Fire and Fuel Load Management

Type	I	estimates	of	carbon	stock	changes	due	to	fuel	treatments	or	prescribed	fire	should	reflect	

losses	to	live	tree	biomass	according	to	the	material	burned,	killed,	or	removed	(from	pre	and	post‐

treatment	inventory	data).	For	a	prescribed	fire,	emissions	can	be	calculated	using	FOFEM.	If	slash	

is	left	from	the	fuel	treatment,	CONSUME	may	also	be	used.	

Type	II	and	III	estimates	simply	involve	projecting	the	stand	based	on	information	from	the	post‐

treatment	inventory.	

6.4.4.8 Reducing Risk of Emissions from Pests and Disease

Type	I	carbon	stock	estimates	will	involve	computation	of	losses	to	live	tree	biomass	from	the	

sanitation	or	salvage	harvest,	with	additions	to	HWP	pools	as	appropriate.	

Type	II	and	III	estimates	simply	involve	projecting	the	stand	based	on	information	from	the	post‐

treatment	inventory.	

6.4.4.9 Short‐Rotation Woody Crops

Negligible	carbon	stock	changes	are	expected	at	the	time	of	establishment	of	a	new	plantation,	so	

Type	I	estimates	will	show	no	stock	changes.	For	Type	II	and	III	estimates,	the	plantation	activity	

establishes	a	new	stand	that	can	then	be	modeled	based	on	species,	site	index,	and	initial	stocking	

(planting	density	times	year	1	survival	percent).	
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6.4.5 Limitations and Uncertainty

6.4.5.1 Measurement Uncertainties

Forest	inventory	data,	from	which	most	estimates	in	this	section	are	derived,	contain	uncertainty	as	

a	result	of	sampling	and	measurement	error.	Furthermore,	equations	are	used	to	estimate	biomass	

from	tree	measurements	(species,	diameters,	heights),	and	these	equations	introduce	additional	

errors.	These	uncertainties,	however,	are	well	documented	and	can	be	quantified.	

6.4.5.2 Model Uncertainties		

For	the	development	of	Type	II	and	Type	III	estimates,	models	are	used	to	project	current	

conditions	into	the	future.	These	types	of	estimates	are	based	initially	on	inventory	data	and	are	

subject	to	the	measurement	uncertainties	discussed	above,	but	are	also	subject	to	modeling	error.	

Modeling	error	can	be	documented	in	part	based	on	the	diagnostics	reported	(if	any)	from	the	

model	development	process.	Greater	uncertainties	are	introduced	when	models	are	applied	beyond	

the	conditions	for	which	they	were	developed	(e.g.,	biomass	equations	applied	to	species	for	which	

no	biomass	data	were	collected,	forest	growth	models	applied	to	stands	receiving	different	

management	than	the	stands	used	for	model	development,	etc.).	Model	uncertainties	also	increase	

with	the	projection	period	(the	distance	into	the	future	for	which	estimates	are	obtained).	Some	of	

the	model	uncertainties	are	cancelled	out	when	results	from	two	similar	model	runs	are	compared	

(i.e.,	a	Type	III	estimate).	For	example,	if	a	model	slightly	overestimates	carbon	stock	in	a	forest	

with	and	without	some	treatment,	the	difference	between	the	two	model	estimates	may	be	accurate	

even	if	the	individual	estimates	are	not.	

6.4.5.3 Generalization Uncertainties

For	the	purpose	of	applying	nationally	consistent	estimation	methods	to	Type	II	and	III	estimates,	it	

is	necessary	to	generalize	situations	into	broad	forest	types	and	management	intensities.	Thus,	

some	precision	is	lost	in	applying	a	generalized,	aggregated	estimate	to	a	particular	set	of	

management	activities.	

6.5 Harvested Wood Products

6.5.1 General Accounting Issues

When	forest	landowners	harvest	wood	for	products,	a	portion	of	the	wood	carbon	ends	up	in	

solidwood	or	paper	products	in	end	uses,	and	eventually	in	landfills,	and	can	remain	stored	for	

years	or	decades.	This	report	suggests	a	specific	measure,	along	with	estimation	methods,	that	

Method for Harvested Wood Products

 Method	uses	U.S.‐specific	HWPs	tables.	

 The	HWPs	tables	are	based	on	WOODCARB	II	model	used	to	estimate	annual	change	in	

carbon	stored	in	products	and	landfills	(Skog,	2008).	

 The	entity	uses	these	tables	to	estimate	the	average	amount	of	HWP	carbon	from	the	

current	year’s	harvest	that	remains	stored	in	end	uses	and	landfills	over	the	next	100	

years.	

 This	method	was	selected	because	it	is	suitable	to	represent	the	amount	of	carbon	stored	

in	products	in	use	and	in	landfills.	
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forest	landowners	can	use	to	report	carbon	additions	to	the	stock	of	HWPs	from	wood	they	harvest.	

The	accounting	framework	used	to	track	HWP	carbon	is	similar	to	the	framework	that	the	United	

States	uses	to	report	national‐level	annual	changes	in	HWP	carbon	stocks	under	UNFCCC.	

The	national	accounting	framework	and	these	methods	adopt	the	production	approach,	which	

entails	the	following:	(1)	estimating	the	annual	carbon	additions	to	and	removals	from	the	stock	of	

carbon	held	in	wood	products	in	use	and	in	landfills,	(2)	tracking	only	carbon	in	wood	that	was	

harvested	in	the	United	States	(U.S.	EPA,	2011),	and	(3)	providing	estimates	that	track	wood	carbon	

held	in	products,	even	if	is	the	products	are	exported	to	other	countries.	

Estimates	of	the	annual	contribution	of	HWPs	to	carbon	stocks	may	be	made	for	Type	I,	Type	II,	and	

Type	III	estimates	of	forest	carbon	change	as	outlined	in	Section	6.2:	

 For	Type	I	estimates,	the	focus	is	on	estimating	the	annual	contribution	of	HWPs	to	carbon	

stocks	for	a	given	current	year	or	recent	past	years.	

 For	Type	II	estimates,	the	focus	is	on	estimating	the	annual	contribution	of	HWPs	to	carbon	

stocks	for	a	projected	period	of	years	in	the	future.	

 For	Type	III	estimates,	the	focus	is	on	estimating	the	change	in	the	annual	contribution	of	HWPs	

to	carbon	stocks	between:	(1)	a	base	case	with	one	scenario	for	forest	management	(and	

harvest);	and	(2)	a	second	scenario	for	forest	management	(and	harvest)	that	is	intended	to	

change	carbon	flux.	

For	each	of	the	Type	I,	II,	or	III	estimates,	these	methods	recommend	that	forest	landowners	report	

the	annual	contribution	of	HWPs	to	carbon	stocks	using	a	specific	measure	intended	to	

approximate	the	climate	mitigation	benefit	associated	with	storing	carbon	in	HWPs	over	time.	The	

recommended	measure	is	the	estimated	average	amount	of	HWP	carbon	from	the	current	year’s	

harvest	that	remains	stored	in	end	uses	and	landfills	over	the	subsequent	100	years.	

The	intent	of	this	measure	is	to	approximate	the	average	annual	climate	benefit	of	withholding	

carbon	from	the	atmosphere	by	a	certain	amount	each	year	for	100	years	as	described	by	a	“decay”	

curve.	This	average	benefit	is	one	that	can	be	credited	in	the	year	of	harvest.	This	estimate	of	

average	effect	is	conceptually	similar	to	the	measure	of	the	radiative	forcing	impact	of	a	current	

year	emission	of	CO2,	CH4,	or	other	GHG.	One	ton	of	CO2	emissions—in	GHG	accounting—is	equated	

to	the	radiative	forcing	it	causes	over	the	100	years	following	the	emission.	The	radiative	forcing	

caused	in	each	year	is	weighted	the	same	over	each	of	the	100	years.	We	are	suggesting	the	same	

convention	in	weighting	the	carbon	storage	in	wood	products	equally	for	each	of	100	years.	

An	estimate	of	average	fraction	of	HWP	carbon	stored	over	100	years	(average	amount	stored	over	

100	years	divided	by	the	original	product	carbon	produced)	is	not	exactly	the	same	as	the	fraction	

of	radiative	forcing	avoided	by	storing	wood	products	carbon	(and	emitting	carbon	slowly)	over	

100	years.	For	decay	curves	where	a	constant	fraction	of	remaining	HWP	carbon	is	emitted	each	

year	the	fraction	of	radiative	forcing	avoided	over	100	years	can	be	0	to14	percent	less	than	the	

average	fraction	of	HWP	carbon	stored	over	100	years	depending	on	the	decay	rate.8	Estimates	of	

the	fraction	of	radiative	forcing	avoided	over	100	years	could	be	used	in	place	of	the	average	carbon	

storage.	Given	the	uncertainty	in	decay	rates	as	an	influence	on	estimates	and	the	greater	

complexity	of	the	radiative	forcing	measure,	we	recommend	the	measure	of	average	carbon	stored	

as	an	adequate	proxy	for	the	effect	of	wood	products	produced	in	the	current	year	and	stored	over	

																																																													

8	The	fraction	of	radiative	forcing	avoided	over	100	years	was	estimated	(and	compared	to	average	carbon	

stored	over	100	years)	assuming	a	range	of	decay	rates	for	first	order	decay	curves	for	wood	products	and	

using	the	CO2	radiative	forcing	response	curve	from	the	IPCC	Working	I	4th	Assessment	Report	(footnote	a,	p.	

213)	(IPCC,	2007).	
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100	years.	

The	measure—average	carbon	stored	in	HWP	over	100	years	(with	variations	on	how	landfill	

carbon	is	included)—is	used	in	the	Climate	Action	Reserve	(2010)	Forest	Project	Protocols	adopted	

by	the	California	Air	Resources	Board.	The	protocols	indicate	how	to	calculate	the	level	of	annual	

carbon	credits	that	may	be	sold	by	forest	landowners	who	enter	carbon	contracts.	

Note	that	use	of	the	production	approach	to	accounting	is	not	a	life‐cycle	assessment	accounting	

approach	that	could	take	into	account	how	carbon	emissions	from	increased	wood	burning	or	

increased	use	of	wood	products	might	offset	fossil	fuel	emissions	or	emissions	from	making	non‐

wood	products	over	time.	The	estimates	of	annual	change	in	carbon	in	HWPs	are	not	intended	to	

indicate	the	total	impact	on	GHG	levels	in	the	atmosphere	of	using	HWPs	(including	use	of	wood	for	

energy),	nor	are	they	intended	to	indicate	that	the	emission	to	the	atmosphere	took	place	in	the	

United	States	versus	other	countries	where	products	were	exported.	Estimation	of	Type	III	

secondary	GHG	reduction	effects	of	substitution	of	wood	for	fossil	fuels	or	non‐wood	construction	

products	are	complex	and	would	require	specification	of	a	baseline	from	which	change	is	measured	

and	other	assumptions	that	are	beyond	the	scope	of	these	methods.	

The	production	approach	is	used	to	acknowledge	that	harvesting	of	forests	does	not	immediately	

release	all	of	the	contained	carbon	to	the	atmosphere;	the	accounting	counts	only	the	carbon	

change	in	HWPs	in	order	to	allow	annual	carbon	changes	in	HWPs	to	be	deducted	or	added	to	

annual	emissions	in	the	energy	and	manufacturing	sectors	and	carbon	changes	in	forests,	so	there	

will	be	no	omission	or	double	counting	of	sequestration	or	emissions	to	the	atmosphere.	In	the	

national	accounting	framework,	the	annual	emissions	from	wood	energy	are	accounted	for	as	part	

of	the	aggregated	annual	change	in	forest	plus	HWP	carbon.	

6.5.2 Estimation Methods

6.5.2.1 Wood Products Fate/Longevity

To	allow	forest	landowners	to	estimate	carbon	additions	to	HWP	stocks—using	average	carbon	

stored	in	HWP	over	100	years—lookup	tables	are	provided	that	give	estimates	of	carbon	remaining	

stored	after	harvest	out	to	100	years.	

There	are	two	types	of	lookup	tables:	a	“roundwood”	type	and	a	“primary	product”	type.	

For	the	roundwood	type,	the	landowner	needs	estimates	of	the	carbon	in	harvested	amounts	of	

industrial	roundwood:	hardwood	(HW)	or	softwood	(SW),	sawlogs	(SL),	or	pulpwood	(PW).	

Industrial	roundwood	is	wood	used	for	solidwood	or	paper	products	and	excludes	bark	and	

fuelwood.	The	landowner	can	begin	with	estimates	in	cubic	units	and	convert	them	to	carbon	

weight	or	wood	weight	units	then	convert	them	to	carbon	weight	(assuming	0.5	metric	tons	carbon	

per	metric	ton	dry	wood).	Separate	lookup	“decay”	tables	are	provided	by	major	U.S.	region	and	

roundwood	type	(HW	or	SW,	SL,	or	PW)	that	show	the	fraction	of	carbon	in	wood	typically	stored	in	

wood	products	in	use	and	in	landfills,	out	to	100	years	after	the	year	of	harvest,	and	the	average	

fraction	stored	over	100	years.	

For	the	primary	product	type	of	lookup	tables,	the	landowner	needs	estimates	of	the	primary	wood	

products	made	from	the	wood	harvested;	i.e.,	SW	or	HW	lumber,	SW	or	HW	plywood,	oriented	

strandboard,	or	paper	(in	conventional	product	units).	The	landowner	then	converts	these	amounts	

to	carbon	weight.	For	each	primary	product,	the	lookup	“decay”	tables	show	the	fraction	of	wood	

carbon	that	is	typically	stored	in	wood	products	in	use	and	in	landfills,	from	the	year	of	harvest	out	

to	100	years,	and	the	average	fraction	of	carbon	stored	over	100	years.	
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6.5.3 Activity Data Collection

6.5.3.1 Primary Product Decay Tables

In	order	to	construct	the	primary	product	type	decay	tables,	data	are	used	for	each	U.S.	region	on:	

 The	disposition	of	each	primary	product	(e.g.,	lumber,	structural	panels)	to	major	end	uses	(e.g.,	

percentage	of	product	going	to	residential	housing,	non‐residential	housing,	manufacturing	

(furniture)),	and	percentage	going	to	exports;	

 The	decay	functions	indicating	how	quickly	products	go	out	of	use	for	each	end	use;	

 The	fraction	of	material	going	out	of	use	that	goes	to	landfills;	and	

 The	fraction	of	material	in	landfills	that	does	not	decay,	and	the	decay	rate	for	material	in	

landfills	that	does	decay.	

It	is	assumed	that	there	is	a	national	market	for	primary	products	and	the	percentage	of	primary	

products	going	to	each	end	use	will	be	the	same	for	each	U.S.	region.	It	is	also	assumed	that	primary	

products	exported	from	the	United	States	are	used	in	the	same	way	as	domestic	products.	That	is,	

there	is	a	national	market	for	each	of	the	primary	wood	and	paper	products.	Data	for	items	(1)	

through	(4)	come	from	the	WOODCARB	II	model	used	to	estimate	annual	change	in	carbon	stored	

in	products	and	landfills	for	the	U.S.	Inventory	of	GHG	Emissions	and	Sinks	report	(Skog,	2008;	U.S.	

EPA,	2010).	

If	a	landowner	knows	the	traditional	number	of	units	of	primary	products	(e.g.,	thousand	board	feet	

of	lumber)	that	were	made	from	the	timber	harvested	from	their	land	in	a	given	year,	they	can	use	

Tables	6‐A‐1,	6‐A‐2,	and	6‐A‐3	to	estimate	the	carbon	contents	in	these	products	(Table	6‐A‐1)	and	

estimate	the	amount	of	carbon	stored	in	these	products	(in	use	and	in	landfills)	out	to	100	years	

and	the	average	amount	of	carbon	stored	over	100	years	(Table	6‐A‐2	[in	use]	and	Table	6‐A‐3	[in	

landfills]).	

The	average	amount	of	carbon	stored	over	100	years	for	a	particular	primary	product	is	the	total	of	

the	averages	for	products	in	use	and	products	in	landfills	shown	in	Tables	6‐A‐2	(in	use)	and	Table	

6‐A‐3	(in	landfills).	

6.5.3.2 Roundwood Decay Tables

In	order	to	construct	the	roundwood	type	of	decay	tables,	data	are	needed	for	each	region	on	the	

percentage	of	HW	or	SW,	SL,	or	PW	that	goes	to	various	primary	wood	products;	for	example,	the	

fraction	of	SW	SLs	in	the	South	that	goes	to	lumber,	panels,	and	paper.	After	the	amounts	of	primary	

wood	products	are	estimated,	the	primary	products	type	decay	tables	can	be	used	to	construct	

roundwood	decay	tables.	Data	needed	to	divide	roundwood	into	primary	products	for	each	region	

include	Forest	Service	FIA	timber	product	output	data	and	national	data	on	primary	wood	products	

production	(Howard,	2012;	Smith	et	al.,	2007).	

If	a	landowner	knows	the	cubic	feet	of	roundwood,	in	the	form	of	HW	or	SW	SLs	or	PW	that	is	

harvested from	their	land	in	a	given	year,	they	can	use	Table	6‐A‐4	and	6‐A‐5	to	(1)	estimate	the	

weight	of	wood	harvested;	(2)	convert	weight	of	wood	to	carbon	by	multiplying	by	0.5	(i.e.,	the	

fraction	of	dry	biomass	to	carbon	conversion	factor);	and	(3)	estimate	the	total	amount	of	carbon	

stored	in	the	products	(the	sum	of	amounts	in	use	and	in	landfills)	each	year	out	to	100	years,	and	

the	average	stored	over	100	years.	

If	the	landowner	knows	the	weight	of	roundwood	harvested	rather	than	cubic	feet,	it	would	use	

steps	2	and	3	above.	

Annual	HWP	carbon	(average	stored	over	100	years)	is	given	for	each	region	and	roundwood	type	
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in	Table	6‐A‐5.	

If	the	landowner	is	making	forest	growth	and	harvest	projections	(Type	II	and	Type	III	estimates)	

and	only	knows	the	cubic	feet	(or	weight)	of	growing	stock	of	HW	and	SW	SLs	and	PW	that	will	be	

harvested	in	given	future	years,	then	Table	6‐A‐6	can	be	used	to	estimate	the	total amount	of	

roundwood	that	can	expected	to	be	harvested	(growing	stock	and	non‐growing	stock).	These	total	

amounts	of	roundwood	(HW	and	SW	SLs	and	PW	may	then	converted	to	carbon	and	to	carbon	

stored	(and	average	carbon	stored	over	100	years)	using	Table	6‐A‐4	and	Table	6‐A‐5,	as	discussed	

above.	To	convert	1	cubic	foot	of	dry	wood	to	pounds	multiply	density	by	62.4	lbs	ft−3.	To	convert	1	

cubic	foot	to	kilograms	multiply	density	by	28.3	kg	ft−3.	

A	spreadsheet	is	available	showing	all	the	parameters	and	calculations	that	produce	the	carbon	

storage	tables	that	start	with	primary	products	or	roundwood	harvest	(Skog,	2013).	

6.5.4 Limitations, Uncertainty, and Research Gaps

6.5.4.1 Uncertainty in C change estimate

General	estimates	of	uncertainty,	given	as	the	95	percent	confidence	intervals,	can	be	made	for	

HWP	measure	used	in	Type	I	carbon	change	estimates	(current	year	or	recent	past	years).	These	

estimates	of	uncertainty	could	be	provided	with	each	of	the	two	types	of	lookup	tables,	and	can	be	

made	using	Monte	Carlo	simulations	and	assumptions	about	HWP	uncertainty	that	are	used	for	the	

Inventory	of	U.S.	Greenhouse	Gas	Emissions	and	Sinks	report	(U.S.	EPA,	2011).	Uncertainty	could	be	

specified	for	key	variables	including:	(1)	fractions	of	SLs	PW	going	to	various	primary	products;	(2)	

fractions	of	primary	products	going	to	various	end	uses;	(3)	rate	at	which	products	are	discarded	

from	each	end	use;	(4)	fraction	of	discarded	wood	or	paper	that	goes	to	landfills;	(5)	fraction	of	

wood	or	paper	set	to	landfills	that	is	subject	to	decay;	and	(6)	rate	of	decay	in	landfills	of	degradable	

wood/paper	carbon.	

A	spreadsheet	is	available	the	could	be	used	as	a	basis	for	Monte	Carlo	simulations	to	estimate	

overall	uncertainty	for	estimates	of	average	carbon	stored	over	100	years	(Skog,	2013).	

It	would	be	possible	but	more	complex	to	make	uncertainty	estimates	for	Type	II	and	Type	III	

carbon	change	estimates	by	adding	estimates	of	uncertainty	in	parameters	used	to	make	

projections	of	harvest.	

Additional	research	is	needed	to	improve	differentiation	of	the	various	rates	at	which	solidwood	

products	are	discarded	from	uses	such	as	pallets,	railroad,	railcars,	and	furniture	that	are	currently	

grouped	into	one	category.	This	further	differentiation	would	refine	estimates	of	average	carbon	

stored	when	the	landowner	knows	which	primary	wood	products	are	made	from	the	wood	that	is	

harvested	on	their	land.	Alternate	curves	for	discard	rates	from	end	uses,	particularly	discards	from	

housing,	if	empirically	verified,	could	improve	estimates	of	average	carbon	stored.	Estimates	of	

uncertainty	in	parameters	over	100	year	projections	are	needed	to	give	a	sound	estimate	of	the	

uncertainty	in	average	carbon	stored	over	100	years.	
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6.6 Urban Forests

6.6.1 Description

6.6.1.1 Defining Urban Areas and Forests

Urban	forests	are	composed	of	

a	population	of	all	trees	within	

an	urban	area.	To	delimit	the	

extent	of	an	urban	forest,	the	

boundaries	of	the	urban	area	

must	be	drawn.	This	boundary	

issue	can	be	problematic,	as	

people	may	conceive	or	define	

“urban”	differently.	To	delimit	

urban	areas	in	the	United	

States,	U.S.	Census	bureau	

definitions	are	used.	These	

definitions	differ	from	those	

used	in	the	National	Resources	

Inventory,	which	aims	to	

identify	areas	that	are	removed	

from	the	rural	land	base	and	

includes	land	uses	such	as	

transportation	corridors.	

The	U.S.	Census	Bureau	(2007)	

defines	urban	as	all	territory,	

population,	and	housing	units	

located	within	urbanized	areas	

or	urban	clusters.	Urbanized	

area	and	urban	cluster	

boundaries	encompass	densely	settled	territories,	which	are	described	by	one	of	the	following:	(1)	

one	or	more	block	groups	or	census	blocks	with	a	population	density	of	at	least	386.1	people	km−2

Figure 6‐7: Urban and Community Land in Connecticut

Source:	U.S.	Census	Bureau	(2007).	

Methods for Urban Forests

 Range	of	options	depends	on	data	availability	of	the	entity’s	urban	forest	land.	

 These	options	use:	

− i‐Tree	Eco	model	(http://www.itreetools.org)	to	assess	carbon	from	field	data	on	tree	

populations;	and	

− 	i‐Tree	Canopy	model	(http://www.itreetools.org/canopy/index.php)	to	assess	tree	

cover	from	aerial	images	and	lookup	tables	to	assess	carbon.	

 Quantitative	methods	are	also	described	for	maintenance	emissions	and	altered	building	

energy	use	and	included	for	information	purposes	only.	

 The	methods	were	selected	because	they	provide	a	range	of	options	dependent	on	the	

data	availability	for	the	entity's	urban	forest	land.	
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(1,000	people	mile−2);	(2)	surrounding	block	groups	and	census	blocks	with	a	population	density	of	

193.1	people	km−2	(500	people	mile−2);	and	(3)	less	densely	settled	blocks	that	form	enclaves	or	

indentations	or	are	used	to	connect	discontinuous	areas.	More	specifically,	urbanized	areas	consist	

of	territories	of	50,000	or	more	people.	Urban	clusters,	a	concept	new	to	the	2000	Census,	consist	of	

territories	with	at	least	2,500	people	but	fewer	than	50,000	people.	

In	addition	to	urban	land,	the	Census	Bureau	designates	places	that	delimit	population	

concentrations	based	on	incorporated	or	unincorporated	places,	such	as	a	city,	town,	village,	and	

census‐designated	place.	These	places,	or	“communities,”	also	define	areas	where	people	reside,	but	

often	with	a	lower	population	density.	The	geographic	areas	of	urban	and	communities	overlap	

(see	Figure	6‐7),	and	either	or	both	could	be	used	to	define	urban	forests.	The	urban	land	

designation	delimits	higher	population	densities,	but	does	not	follow	the	boundaries	of	cities	or	

towns	that	most	people	can	relate	to.	The	place	or	community	boundaries	follow	these	political	

boundaries,	but	often	include	both	rural	and	urban	land.	

Urban	land	is	defined	based	on	population	density,	and	community	land	is	often	based	on	political	

boundaries.	Thus,	urban	forest	land	overlaps	with	forest	lands.	That	is,	forested	stands	that	are	

measured	as	part	of	other	programs	can	exist	within	urban	or	community	boundaries.	Assessments	

of	urban	forest	effects	thus	have	the	potential	to	double‐count	effects	found	in	forests	within	

regional	or	national	scale	assessments.	The	amount	of	this	overlap	is	estimated	as	13.8	percent	of	

urban	area	or	1.5	percent	of	forest	area	in	the	conterminous	United	States	(Nowak	et	al.,	2013)	and	

is	an	important	consideration	for	larger	scale	assessments.	This	section	focuses	on	assessing	the	

carbon	effects	of	urban	or	community	trees	and	forests	in	the	United	States.	

Urban	or	community	forests	(hereafter	referred	to	as	urban	forests)	affect	the	carbon	cycle	by	

directly	storing	atmospheric	carbon	within	the	woody	vegetation,	but	also	by	affecting	the	local	

climate	and	thereby	altering	carbon	emissions	affected	by	local	climatic	conditions.	Urban	tree	

maintenance	activities	also	affect	carbon	emissions	in	urban	areas.	For	a	true	accounting	of	carbon	

effects,	all	of	these	factors	need	to	be	considered.	This	report	focuses	on	trees	(defined	as	woody	

vegetation	with	a	diameter	of	at	least	1	inch	(2.5	cm)	DBH),	but	similar	accounting	could	be	

conducted	for	all	urban	vegetation.	

6.6.1.2 Accounting for Primary Urban Forest Carbon Effects

Trees	sequester	and	store	carbon	in	their	tissue	at	differing	rates	and	amounts,	based	on	such	

factors	as	tree	size,	life	span,	and	growth	rate.	After	a	tree	is	removed,	the	tree	can	decompose	with	

the	carbon	stored	in	that	tree	emitted	back	to	the	atmosphere,	or	the	carbon	may	be	stored	in	wood	

products	or	the	soil.	Thus,	in	order	to	account	for	the	total	carbon	in	the	system	at	one	time,	one	

needs	to	understand	how	many	trees	there	are	in	the	system	along	with	information	such	as	species	

and	size	(e.g.,	Nowak	and	Crane,	2002).	To	account	for	how	the	carbon	stock	will	change	through	

time,	one	must	also	account	for	growth	rates,	tree	mortality	and	removals,	and	the	disposition	of	

the	wood	after	removal	(e.g.,	chipping,	burning,	products),	which	affect	decomposition	rates	and	

carbon	emissions.	In	addition,	the	number	of	new	trees	entering	the	system	through	tree	planting	

and	natural	regeneration	must	be	considered.	

6.6.1.3 Accounting for Secondary Effects

In	addition	to	the	carbon	stored	in	trees,	the	urban	forest	has	secondary	impacts	on	atmospheric	

carbon	by	affecting	carbon	emissions	from	urban	areas.	Tree	care	and	maintenance	practices	often	

release	carbon	back	to	the	atmosphere	via	fossil‐fuel	emissions	from	maintenance	equipment	(e.g.,	

chain	saws,	trucks,	chippers).	Thus,	some	of	the	carbon	gains	from	tree	growth	are	offset	by	carbon	

losses	to	the	atmosphere	via	fossil	fuels	used	in	maintenance	activities	(Nowak	et	al.,	2002).	Trees	

strategically	located	around	buildings	can	reduce	building	energy	use	(e.g.,	Heisler,	1986),	and	
 Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

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consequently	reduce	carbon	emissions	from	fossil‐fuel‐burning	power	plants.	These	energy	effects	

are	caused	primarily	by	tree	transpiration	(lowering	of	air	temperatures),	blocking	of	winds,	and	

shading	of	buildings	and	other	surfaces.	Trees	typically	lower	building	energy	use	in	summer,	but	

can	either	lower	or	increase	building	energy	use	in	the	winter	depending	upon	the	tree’s	location	

relative	to	a	building.	

“Altered	building	energy	use”	and	“maintenance	emissions”	for	urban	trees	are	described	in	Section	

6.6.3.1.	However,	while	quantitative	methods	are	described	for	estimating	altered	building	energy	

use	and	maintenance	emissions	for	urban	forestry,	they	are	included	for	information	purposes	only,	

since	they	have	already	been	developed	as	part	of	the	i‐Tree	software	suite.	However,	as	previously	

mentioned	in	Chapter	1,	the	scope	of	this	guidance	does	not	include	other	energy‐related	source	

categories	that	are	associated	with	management	activities	related	to	certain	agriculture	and	

forestry	activities	(e.g.,	transportation,	fuel	use,	heating	fuel	use).	

6.6.2 Activity Data Collection

To	estimate	carbon	storage,	annual	sequestration,	and	long‐term	carbon	changes,	two	general	

approaches	could	be	used.	The	first	method	is	based	on	collecting	data	on	trees	in	the	urban	area	of	

interest;	the	second	method	involves	collecting	aerial	data	on	tree	cover	in	the	area,	and	using	

tables	to	estimate	effects	based	on	field	data	from	other	areas.	The	first	method,	using	local	field	

data,	will	produce	the	most	accurate	estimates	for	the	local	area,	but	at	increased	costs	and	time	

spent	by	the	landowner.	The	second	method	is	more	cost‐effective	and	more	straightforward,	but	

its	accuracy	is	more	limited	(see	Table	6‐8).	

Table 6‐8: Comparison of the “Field Data” and “Aerial” Methods for Estimating the Changes in

Carbon Stocks for Urban Forests

Field Data Method Aerial Method

Requires	significant	time	commitment	to	take	field	

measurements	

Requires	less	time	to	extract	necessary	aerial	data	

from	an	existing	database	

Requires	access	to	several	sample	plots	across	an	

area	

Does	not	require	field	measurements,	only	a	

computer	with	internet	access	

Increases	specificity	and	accuracy	 Returns	a	more	approximate	estimate	

Provides	a	variety	of	output	data	including	current	

carbon	stock,	annual	carbon	sequestration,	and	long	

term	effects	

Provides	only	information	on	total	carbon	stored	

and	annual	carbon	sequestration	

The	output	data	from	the	field	data	method	includes	current	carbon	stock	(existing	carbon	storage),	

annual	carbon	sequestration	by	trees,	and	long	term	effects	of	the	forest	(accounting	for	changes	in	

tree	population	and	disposition	of	carbon	from	trees).	For	the	field	data	method	(or	for	producing	

the	default	tables	that	are	used	in	the	aerial	approach)	the	following	items	need	to	be	measured	and	

input	by	the	landowner:	

 Current	Stock:	

− Number	of	trees	by	species	and	size	class	(species,	DBH,	height,	condition,	competition	

factor)	

 Annual	Sequestration:	

− Number	of	trees	by	species	and	size	class	(species,	DBH,	height,	condition,	competition	

factor)	

− Annual	growth	rates	for	each	tree	based	on	tree	and	site	conditions	(inches	per	year)	

 Long	Term	Effects:	
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− Number	of	trees	by	species	and	size	class	(species,	DBH,	height,	condition,	competition	

factor)	

− Annual	growth	rates	for	each	tree	based	on	tree	and	site	conditions	(inches	per	year)	

− Changes	in	tree	population	due	to	tree	death	and	removals,	and	new	trees	planted	or	

naturally	regenerated	(numbers	of	trees	dying	by	species	and	size	class,	number	of	new	

trees	by	species	and	size	class)	(number	per	year)	

 Proportion	of	removed	tree	biomass	that	is:	

− Chipped/mulched	

− Burned	

− Burned	to	produce	energy	(e.g.,	heat	buildings)	

− Below	the	ground	in	roots	

− Used	for	long‐term	wood	products	

− Left	on	the	ground	to	decompose	naturally	

− Put	in	landfills	

 Decomposed;	decomposition	rates	for	wood	from	removed	trees:	

− Percentage	of	biomass	per	year	decomposed	per	removal	class	above	

 Maintenance	Emissions:	

− Amount	(number	and	hours	per	year)	of	maintenance	equipment	used	(e.g.,	vehicles,	

chippers,	chain	saws)	for	vegetation	maintenance	(e.g.,	planting,	maintenance,	tree	

removal)	

− Emission	factors	(g	C	hr−1)	for	each	maintenance	equipment	used	

 Altered	Building	Energy	Use:	

− Number	of	trees	by	species	and	size	class	within	60	feet	(18.3	m)	of	residential	building	

by	cardinal	and	ordinal	direction	

For	estimating	tree	cover	using	the	aerial	approach,	one	would	need	to	know	the	extent	(ha)	of	the	

urban	area	and	the	percentage	of	tree	cover	within	the	area,	and	use	a	default	table	of	values	to	

convert	ha	of	tree	cover	to	primary	and	secondary	tree	effects	in	a	city.	To	estimate	change	in	the	

population,	the	tree	cover	would	need	to	be	re‐measured	through	time.	

As	previously	mentioned,	altered	building	energy	use	and	maintenance	emissions	for	urban	trees	

are	described	in	Section	6.6.3.1.	However,	while	quantitative	methods	are	described	for	estimating	

altered	building	energy	use	and	maintenance	emissions	for	urban	forestry,	they	are	included	for	

information	purposes	only,	as	they	are	part	of	the	i‐Tree	software	suite	or	can	be	calculated	from	i‐

Tree	data.	

6.6.3 Estimation Methods

The	methods	for	estimating	carbon	effects	in	an	urban	forest	will	be	detailed	for	the	field	data	and	

aerial	approaches	separately.	The	field	data	method	and	aerial	method	for	urban	forests	are	

described	in	Section	6.6.3.1	and	Section	6.6.3.2.	Figure	6‐8	shows	a	decision	tree	indicating	which	

method	is	more	applicable	for	each	type	of	activity	data.	
 Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

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Figure 6‐8: Decision Tree for Urban Forests Showing Methods Appropriate for Estimating

Urban Forest Carbon Stocks

1	The	U.S.	Census	Bureau	(2007)	defines	urban	as	all	territory,	population,	and	housing	units	located	within	urbanized	

areas	or	urban	clusters.	Urbanized	area	and	urban	cluster	boundaries	encompass	densely	settled	territories,	which	are	

described	by	one	of	the	following:	(1)	one	or	more	block	groups	or	census	blocks	with	a	population	density	of	at	least	

386.1	people	km−1	(1,000	people	mile−2);	(2)	surrounding	block	groups	and	census	blocks	with	a	population	density	of	

193.1	people	km−2	(500	people	mile−2);	and	(3)	less	densely	settled	blocks	that	form	enclaves	or	indentations,	or	are	used	

to	connect	discontinuous	areas.	More	specifically,	urbanized	areas	consist	of	territories	of	50,000	or	more	people.	Urban	

clusters,	a	concept	new	to	the	2000	Census,	consist	of	territory	with	at	least	2,500	people	but	fewer	than	50,000	people.	

6.6.3.1 Field Data Method for Estimating Carbon Storage and Annual Sequestration

The	field	data	method	involves	using	field	measurements	of	urban	trees	(i.e.,	a	“tree	list”)	to	build	a	

tailored,	accurate	estimate	of	carbon	storage	and	sequestration	in	an	urban	forest.	The	various	

steps	for	estimating	carbon	(and	altered	building	energy	use)	effects	from	an	urban	forest	are:	

(1) Delimit boundary of urban area to be analyzed.	This	information	is	essential	to	set	the	boundary	

of	the	analysis.	U.S.	Census	boundary	files	of	urban	areas	or	places	can	be	used	to	delimit	the	

boundaries	(U.S.	Census	Bureau,	2011).	Information	on	these	boundaries	can	be	used	to	determine	

areas	of	potential	overlap	with	other	carbon	estimates	(e.g.,	non‐urban	forests),	and	to	help	set	up	a	
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sampling	design	to	collect	necessary	field	data	as	desired	by	the	landowner.	

(2)	Measure all trees within the urban area or sample the tree population.	Within	the	defined	

geography,	all	trees	can	be	measured,	or	a	random	distribution	of	field	plots	can	be	measured	to	

quantify	the	urban	tree	population	as	desired	by	the	landowner.	To	conduct	this	field	sampling	and	

analysis,	the	i‐Tree	Eco	model	(formerly	UFORE	model)	is	available	free	of	charge	at	

www.itreetools.org.	Field	manuals	exist	on	how	to	randomly	select	plots	locations	and	collect	the	

needed	field	data	(http://www.itreetools.org/resources/manuals.php).	Details	on	model	methods	

also	exist	(e.g.,	Nowak	and	Crane,	2002;	Nowak	et	al.,	2008).	

The	basic	field	data	procedure	is	to	record	information	on	all	trees	within	the	field	plots.	This	

information	includes:	

 Tree	species	

 DBH	

 Tree	height	

 Dieback	

 Crown	light	exposure	

 Distance	and	direction	to	buildings	

These	variables	are	needed	to	assess	carbon	effects,	but	other	tree	variables	(e.g.,	crown	width,	

percentage	of	crown	missing)	can	also	be	collected	to	assess	other	ecosystem	services	(e.g.,	air	

pollution	removal,	volatile	organic	compound	emissions,	effects	on	building	energy	use,	rainfall	

interception,	and	runoff).	

(3)	Enter data into i‐Tree and run analyses.	After	field	data	are	collected	(via	paper	forms	or	on	a	

mobile	device),	data	are	entered	into	i‐Tree,	and	the	program	produces	standard	tables,	graphs,	and	

reports	that	detail	carbon	and	other	ecosystem	service	information.	In	relation	to	carbon,	results	

along	with	sampling	standard	errors	are	specifically	produced	by	species	and	land	use	regarding:	

 Carbon	storage:	amount	of	carbon	currently	in	the	existing	tree	stock;	

 Gross	annual	carbon	sequestration:	one‐year	estimate	as	sequestration	based	on	estimated	

annual	tree	growth,	which	varies	by	location,	tree	condition,	and	crown	competition;	and	

 Net	annual	carbon	sequestration:	gross	sequestration	minus	estimated	carbon	lost	from	dead	or	

removed	trees	due	to	decomposition.	

Altered Building Energy Use.	In	addition	to	the	carbon	effects	estimated	by	the	field	data	method,	

the	i‐Tree	program	can	estimate	tree	effects	on	residential	building	energy	use	and	consequent	

carbon	emissions	using	methods	detailed	in	McPherson	and	Simpson	(1999).	

Maintenance Emissions.	For	estimating	maintenance	emission	effects,	the	following	steps	are	

suggested:	

	 	
 Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

 6-77 

(1) Determine vehicle use related to tree maintenance.	Determine	the	number	of	miles	driven	by	

various	vehicle	types.	

	(2) Calculate carbon emissions from vehicles.	To	estimate	carbon	emissions	from	vehicles,	the	latest	

fuel	efficiency	information	(mpg)	will	be	needed	for	each	vehicle	class.	Divide	the	miles	driven	by	

the	vehicle	class	mpg	to	determine	the	total	gallons	of	gasoline	(or	other	fuel)	used.	Multiply	total	

gallons	(or	other	units)	used	by	the	emissions	factor	in	Table	6‐9	to	estimate	carbon	emissions	from	

vehicle	use	(Nowak	et	al.,	2002).	

Table 6‐9: Emission Factors for Common Transportation Fuels

Fuel Emissions (lbs CO2 per unit volume)

B20	biodiesel	 17.71 per	gallon

B10	biodiesel	 19.93 per	gallon

Diesel	fuel	(No.1	and	No.	2)	 22.15 per	gallon

E85	ethanol	 2.9 per	gallon

E10	ethanol	 17.41 per	gallon

Gasoline	 19.36 per	gallon

Natural	gas	 119.90 per	Mcf

Propane	 5.74 per	gallon

Source:	Table	1.D.1,	U.S.	DOE	(2007).

	(3) Determine maintenance equipment use.	Estimate	the	number	of	run	hours	used	for	all	fossil‐

fuel‐based	maintenance	equipment	used	on	trees	(e.g.,	chainsaws,	chippers,	aerial	lifts,	backhoes,	

and	stump	grinders).	Estimates	of	run	time	for	various	pruning	and	removal	equipment	are	given	in	

Table	6‐10.	

Table 6‐10: Total Hours of Equipment Run‐Time by DBH Class for Tree Pruning and Removal

DBH

Pruning Removal

2.3

hp

3.7

hp Bucket

Chipperb

2.3

hp

3.7

hp

7.5

hp Bucket

Chipperb Stump

Saw Saw Trucka Saw Saw Saw Trucka Grinderb

1–6	 0.05	 NA	 NA	 0.05 0.3 NA NA 0.2 0.1	 0.25

7–12	 0.1	 NA	 0.2	 0.1 0.3 0.2 NA 0.4 0.25	 0.33

13–18	 0.2	 NA	 0.5	 0.2 0.5 0.5 0.1 0.75 0.4	 0.5

19–24	 0.5	 NA	 1.0	 0.3 1.5 1.0 0.5 2.2 0.75	 0.7

25–30	 1.0	 NA	 2.0	 0.35 1.8 1.5 0.8 3.0 1.0	 1.0

31–36	 1.5	 0.2	 3.0	 0.4 2.2 1.8 1.0 5.5 2.0	 1.5

+36+	 1.5	 0.2	 4.0	 0.4 2.2 2.3 1.5 7.5 2.5	 2.0

Note:	Table	is	based	on	ACRT	data	(Wade	and	Dubish,	1995)	and	assumes	that	crews	work	efficiently	and	equipment	is	

not	run	idle	(Nowak	et	al.,	2002).	

hp = Horsepower 

DBH = Diameter at breast height 

a

 Mean hp = 43 (U.S. EPA, 1991) 

b

 Mean hp = 99 (U.S. EPA, 1991) 

(4) Calculate carbon emissions from maintenance equipment.	Calculations	for	emissions	from	

equipment	are	based	on	the	formula:	
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Typical	load	factors	and	average	carbon	emissions	for	equipment	are	given	in	Table	6‐11.	

Table 6‐11: Typical Load Factors (U.S. EPA, 1991), Average Carbon Emissions, and Total

Carbon Emissions for Various Maintenance Equipment (from Nowak et al., 2002)

Equipment Typical Load

Factora

Average Carbon

Emission

(g hp−1 hr−1)b

Total Carbon

Emission

(kg hr−1)c

Aerial	lift	 0.505 147.2 3.2d

Backhoe	 0.465 147.3 5.3e

Chain	saw	<4	hp	 0.500 1,264.4 1.5f

Chain	saw	>4	hp	 0.500 847.5 3.2g

Chipper/stump	grinder	 0.370 146.4 5.4h

a	Average	value	from	two	inventories	(conservative	load	factor	of	0.5	from	inventory	B	was	used	for	chain	saws	>4	hp	due	

to	disparate	inventory	estimates;	inventory	average	for	this	chain	saw	type	was	0.71).	 b	Calculated	from	estimates	of	carbon	monoxide	(U.S.	EPA,	1991),	hydrocarbon	crankcase	and	exhaust	(U.S.	EPA,	1991),	

and	carbon	dioxide	emissions	(Charmley,	1995),	adjusted	for	in‐use	effects.	Total	carbon	emissions	were	calculated	based	

on	the	proportion	of	carbon	of	the	total	atomic	weight	of	the	chemical	emission.	Multiply	by	0.0022	to	convert	to	

lbs	hp−1	hr−1.	 c	Multiply	by	2.2	to	convert	to	lbs	hr−1.	 d	Mean	hp	=	43	(U.S.	EPA,	1991).	

e	Mean	hp	=	77	(U.S.	EPA,	1991).

f

	hp	=	2.3.

g	hp	=	7.5.

h	Mean	hp	=	99	(U.S.	EPA,	1991).	

(5) Calculate total maintenance carbon emissions.	Add	results	of	carbon	emissions	from	vehicles	and	

maintenance	equipment.	

Combined Carbon Sequestration, Altered Building Energy Use, and Maintenance Emissions.	

To	determine	current	net	annual	urban	forest	effect	on	carbon,	the	carbon	emissions	from	tree	

maintenance	should	be	contrasted	to	net	carbon	sequestration	from	trees	and	altered	carbon	

emissions	from	altered	building	energy	use	effects.	

Changes in Carbon Sequestration, Altered Building Energy Use, and Maintenance Emissions.	

To	determine	how	tree	and	maintenance	effects	on	carbon	change	through	time,	the	field	plots	or	

trees	inventoried	can	be	re‐measured,	and	results	between	the	years	contrasted	to	estimate	

changes	in	carbon	stock,	net	annual	carbon	effects,	and	altered	building	energy	use	effects.	In	

Equation 6‐10: Calculate Carbon Emissions from Maintenance Equipment

C = N × HRS × HP × LF × E

Where:	

C		 	 =	Carbon	emissions	(g)	

N		 	 =	Number	of	units	(dimensionless)	

HRS		 =	Hours	used	(hr)	

HP			 =	Average	rated	horsepower	(hp)	

LF		 	 =	Typical	load	factor	(dimensionless)	

E		 	 =	Average	carbon	emissions	per	unit	of	use	(g	hp−1	hr−1)	(U.S.	EPA	1991)	
 Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

 6-79 

addition,	maintenance	activity	estimates	should	be	updated	when	the	re‐measurement	occurs.	

6.6.3.2 Aerial Data Method

The	aerial	data	method	uses	aerial	tree	cover	estimates	and	lookup	tables	to	provide	a	more	

approximate	(i.e.,	higher	degree	of	uncertainty),	but	less	resource	intensive	estimate	of	annual	

carbon	sequestration	in	an	urban	forest	compared	to	the	field	data	method.	The	various	steps	for	

estimating	carbon	effects	from	an	urban	forest	are:	

(1) Delimit boundary of urban area to be analyzed.	This	information	is	essential	to	set	the	boundary	

of	the	analysis.	U.S.	Census	boundary	files	of	urban	or	places	can	be	used	to	delimit	the	boundaries	

(U.S.	Census	Bureau,	2011).	Information	on	these	boundaries	can	be	used	to	determine	areas	of	

potential	overlap	with	other	carbon	estimates	(e.g.,	non‐urban	forests).	

(2) Conduct photo interpretation of tree cover in urban area.	To	determine	percentage	of	tree	cover,	

the	urban	area	can	be	photo	interpreted	using	i‐Tree	Canopy	

(http://www.itreetools.org/canopy/index.php).	This	web	tool	allows	users	to	import	a	shape	file	

of,	or	manually	delimit	their	area,	and	then	randomly	locate	points	within	the	area	on	Google®

aerial	imagery.	The	user	then	classifies	each	point	according	to	its	cover	class	(e.g.,	tree	or	non‐

tree).	The	program	produces	estimates	of	percentage	cover	and	associated	standard	error	for	the	

cover	classes.	This	same	type	of	analysis	could	also	be	performed	with	digital	aerial	images	using	a	

Geographic	Information	System.	

(3) Estimate total tree cover in urban area.	Multiply	the	percentage	of	tree	cover	and	standard	error	

by	urban	area	(ha)	to	produce	an	estimate	of	total	tree	cover	and	standard	error	(ha).	Note	that	i‐

Tree	Canopy	will	make	these	calculations.	

(4)	Estimate carbon effects.	Multiply	total	tree	cover	(ha)	by	average	carbon	storage	or	annual	

sequestration	per	ha	of	tree	cover	in	places	or	urban	areas	(Table	6‐12).	i‐Tree	Canopy	will	make	

these	calculations	based	on	average	state	or	national	data.	

Note	that	to	estimate	effects	for	maintenance	emissions	and	altered	building	energy	use	based	on	

total	tree	cover,	a	table	similar	to	Table	6‐12	would	need	to	be	developed	containing	emission	rates	

for	these	source	categories.	

Table 6‐12: Metric Tons Carbon Storage and Annual Sequestration per Hectare of Tree Cover

in Selected Cities and Urban Areas of Selected States (from Nowak et al., 2013)

City, State

Storage Sequestration

Metric tons C

ha−2 Standard Error Metric tons Cha−2 year−1 Standard Error

Arlington,	TXa 63.7 7.3 2.9 0.28

Atlanta,	GAa 66.3 5.4 2.3 0.17

Baltimore,	MDa 87.6 10.9 2.8 0.36

Boston,	MAa 70.2 9.6 2.3 0.25

Casper,	WYb 69.7 15.0 2.2 0.39

Chicago,	ILc 60.3 6.4 2.1 0.21

Freehold,	NJa 115.0 17.8 3.1 0.45

Gainesville,	FLd 63.3 9.9 2.2 0.32

Golden,	COa 58.8 13.3 2.3 0.45

Hartford,	CTa 108.9 16.2 3.3 0.46

Jersey	City,	NJa 43.7 8.8 1.8 0.34

Lincoln,	NEa 106.4 17.4 4.1 0.63

Los	Angeles,	CAe 45.9 5.1 1.8 0.17

Milwaukee,	WIa 72.6 11.8 2.6 0.33
Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

6-80

City, State

Storage Sequestration

Metric tons C

ha−2 Standard Error Metric tons Cha−2 year−1 Standard Error

Minneapolis,	MNf 44.1 7.4 1.6 0.23

Moorestown,	NJa 99.5 9.3 3.2 0.30

Morgantown,	WVg 95.2 11.6 3.0 0.37

New	York,	NYh 73.3 10.1 2.3 0.29

Oakland,	CAi 52.4 1.9 na na

Omaha,	NEa 141.4 22.9 5.1 0.81

Philadelphia,	PAj 67.7 9.0 2.1 0.27

Roanoke,	VIa 92.0 13.3 4.0 0.58

Sacramento,	CAk 78.2 15.7 3.8 0.64

San	Francisco,	CAl 91.8 22.5 2.4 0.50

Scranton,	PAm 92.4 12.8 4.0 0.52

Syracuse,	NYa 85.9 10.4 2.9 0.30

Washington,	DCn 85.2 10.4 2.6 0.30

Woodbridge,	NJa 81.9 8.2 2.9 0.28

Indianao 88.0 26.8 2.9 0.77

Kansasp 74.2 13.0 2.8 0.48

Nebraskap 66.7 18.6 2.7 0.74

North	Dakotap 77.8 24.7 2.8 0.79

South	Dakotap 30.6 6.6 1.3 0.26

Tennesseeq 64.7 5.0 3.4 0.21

Average 76.9 13.6 2.8 0.45 a	Unpublished	data	analyzed	using	the	UFORE	model.

b	Nowak	et	al.	(2006a).	

c	Nowak	et	al.	(2011).	

d Escobedo	et	al.	(2009).	

e	Nowak	et	al.	(2011).	

f

	Nowak	et	al.	(2006c).	

g	Nowak	et	al.	(2012c).	

h	Nowak	et	al.	(2007d).	

i

	Nowak	(1991).	

j Nowak	et	al.	(2007c).

k	Nowak	et	al.	(In	review).		

l

	Nowak	et	al.	(2007b).	

m	Nowak	et	al.	(2010).	

n	Nowak	et	al.	(2006b).	

o	Nowak	et	al.	(2007a).	

p	Nowak	et	al.	(2012b).	

q	Nowak	et	al	(2012a).	

Combined Carbon Sequestration, Altered Building Energy Use, and Maintenance Emissions.	

To	determine	current	net	annual	urban	forest	effect	on	carbon,	the	carbon	emissions	from	tree	

maintenance,	if	available,	should	be	contrasted	to	the	net	carbon	sequestration	from	trees	and	

altered	carbon	emissions	from	altered	building	energy	use	effects.	

Changes in Carbon Sequestration, Altered Building Energy Use, and Maintenance Emissions.	

To	determine	tree	effects	on	carbon	change	through	time,	the	photo‐interpretation	points	can	be	

re‐measured	when	newer	photos	become	available	to	assess	change	in	tree	cover	(e.g.,	Nowak	and	

Greenfield,	2012).	The	i‐Tree	Canopy	program	saves	the	geographic	coordinates	of	each	point	so	

the	points	can	be	re‐measured	in	the	future.	Changes	in	tree	cover	and	associated	carbon	effects	

between	the	years	can	be	contrasted	to	estimate	changes	in	carbon	stock	and	net	annual	carbon	

effects.	Changes	in	altered	building	energy	use	effects	and	maintenance	effects	could	also	be	

estimated	if	the	appropriate	tables	are	developed.	

6.6.4 Limitations and Uncertainty

Field	data	collection	estimates	have	fewer	limitations	than	the	aerial	approach,	but	some	

limitations	exist	(Nowak	et	al.,	2008).	The	main	advantage	of	carbon	estimation	using	the	field	data	
 Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

 6-81 

approach	and	i‐Tree	is	having	accurate	estimates	of	the	tree	population	(e.g.,	species,	size,	

distribution)	with	a	calculated	level	of	precision.	The	modeled	carbon	values	are	estimates	based	on	

forest‐derived	allometric	equations		(Nowak,	1994;	Nowak	and	Crane,	2002).	The	carbon	estimates	

yield	a	standard	error	of	the	estimate	based	on	sampling	error,	rather	than	error	of	estimation.	

Estimation	error	is	unknown,	and	likely	larger	than	the	reported	sampling	error.	Estimation	error	

includes	the	uncertainty	of	using	biomass	equations	and	conversion	factors,	which	may	be	large,	as	

well	as	measurement	error,	which	is	typically	small.	The	standardized	carbon	values	(e.g.,	kg	C	ha−1

or	lbs	C	(acre	of	tree	cover)−1)	fall	in	line	with	values	for	forests	(Birdsey	and	Heath,	1995),	but	

values	for	cities	(places)	can	be	higher	(Table	6‐12),	likely	due	to	a	larger	proportion	of	large	trees	

in	city	environments	and	relatively	fast	growth	rates	due	to	a	more	open	urban	forest	structure	

(Nowak	and	Crane,	2002).	

There	are	various	means	to	help	improve	the	carbon	storage	and	sequestration	estimates	for	urban	

trees.	Carbon	estimates	for	open‐grown	urban	trees	are	adjusted	downward	based	on	field	

measurements	of	trees	in	the	Chicago	area	(Nowak,	1994).	This	adjustment	may	lead	to	

conservative	estimates	of	carbon.	More	research	is	needed	on	the	applicability	of	forest‐derived	

equations	to	urban	trees.	In	addition,	more	urban	tree	growth	data	are	needed	to	better	understand	

regional	variability	of	urban	tree	growth	under	differing	site	conditions	(e.g.,	tree	competition)	for	

better	annual	sequestration	estimates.	Average	regional	growth	estimates	are	used	based	on	

limited	measured	urban	tree	growth	data	standardized	to	length	of	growing	season	and	crown	

competition.	

There	are	currently	a	very	limited	number	of	biomass	equations	for	tropical	trees	in	i‐Tree.	The	

model	needs	to	be	updated	with	tropical	tree	biomass	equations	for	more	accurate	estimates	in	

tropical	cities.	Future	research	is	needed	to	obtain	biomass	equations	for	urban	or	ornamental	tree	

species.	Estimates	of	tree	decay	and	net	annual	sequestration	in	i‐Tree	are	quite	rudimentary	

(Nowak	et	al.,	2010),	and	can	be	improved	with	future	research.	The	degree	of	uncertainty	of	the	

net	carbon	sequestration	estimates	is	unknown.	

Estimates	of	maintenance	emissions	and	altered	building	energy	use	effects	are	also	rather	crude.	

Accurate	maintenance	emissions	estimates	require	good	estimates	of	vehicle	and	maintenance	

equipment	use;	then	they	rely	on	an	average	multiplier	for	emissions	from	the	literature.	Energy	

effects	estimates	are	based	on	sampling	proximity	of	trees	near	buildings	within	various	tree	size,	

distance,	and	direction	classes	from	a	building.	Energy	factors,	converted	to	carbon	emission	factors	

based	on	state	average	energy	distribution	(e.g.,	electricity,	oil)	are	applied	to	trees	in	each	building	

location	class	based	on	U.S.	climate	zone	and	average	building	types	in	a	state	to	estimate	energy	

effects	(see	McPherson	and	Simpson,	1999).	Though	these	estimates	are	crude,	with	an	unknown	

certainty,	they	are	based	on	reasonable	approaches	that	provide	first‐order	estimates	of	effects.	It	

should	be	noted	that	emission	reductions	from	altered	building	energy	use	effects	might	also	be	

implicitly	included	in	any	emission	estimation	an	entity	might	perform	based	on	actual	energy	use	

data	(e.g.,	meter	readings)	for	the	building	in	question.	

Estimates	based	on	aerial	tree	canopy	effects	have	the	same	limitations	as	field	data	approaches,	

plus	some	additional	limitations	and	advantages.	The	advantages	include	a	simple,	quick,	and	

accurate	means	to	assess	the	amount	of	canopy	cover	in	an	area,	with	measures	that	are	repeatable	

through	time.	The	disadvantages	are	that	the	user	must	use	a	lookup	value	from	a	table	(e.g.,	mean	

value	per	unit	of	canopy	cover)	to	estimate	carbon	effects.	Though	the	tree	cover	estimate	will	be	

accurate	with	known	uncertainty	(i.e.,	standard	error),	the	carbon	multipliers	may	be	off	depending	

upon	the	urban	forest	characteristics.	If	average	multipliers	are	used,	the	accuracy	of	those	

estimates	will	decline	as	the	difference	increases	between	the	local	urban	characteristics	and	the	

values	of	the	average	multipliers.	If	local	field	data	are	not	collected,	then	the	discrepancy	between	

the	urban	forest’s	characteristics	and	those	of	average	values	is	unknown.	If	the	average	values	in	
Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

6-82

Table	6‐12	truly	represent	averages,	the	estimates	over	a	large	population	of	urban	areas	should	be	

reliable.	However,	local	estimates	may	be	inaccurate	depending	upon	the	extent	to	which	

characteristics	of	the	local	urban	forest	diverge	from	the	average	values.	

Both	approaches	can	provide	carbon	estimates	for	urban	areas,	with	differing	degrees	of	

uncertainty	and	work	required.	Both	approaches	can	also	be	improved	with	more	field	data	

collection	in	urban	areas,	and	with	model	and	method	improvements	related	to	carbon	estimation.	

6.7 Natural Disturbance – Wildfire and Prescribed Fire

6.7.1 Description

Fire	produces	GHG	emissions	directly	through	fuel	consumption.	Emissions	produced	are	directly	

proportional	to	fuel	consumed.	Fuel	consumption	is	in	turn	influenced	by	fuel	quantity	and	fuel	

characteristics	such	as	size,	moisture	content,	fire	weather,	and	fire	severity.	Algorithms	exist	for	

estimating	fuel	consumption	for	a	variety	of	fuel	types	and	conditions.	Fire	and	other	disturbances	

also	convert	live	vegetation	to	dead,	altering	subsequent	carbon	dynamics	on	the	site	by	reducing	

carbon	captured	by	photosynthesis	in	the	short	run	due	to	reduced	vegetative	cover,	and	increasing	

emissions	from	decomposition	of	dead	vegetation.	Fire	severity,	which	is	driven	by	the	onsite	

factors	that	drive	consumption	as	well	as	other	physical	factors,	will	drive	the	subsequent	carbon	

dynamics	and	area	where	reversal	of	carbon	retention	may	occur.

6.7.2 Activity Data Collection

For	all	disturbances,	key	activity	data	is	the	area	affected.	A	simple	descriptor	is	used	to	

characterize	the	severity	of	the	event.	For	both	wildfire	and	prescribed	fire/control	burns,	

descriptors	of	severity	include	crown	fire,	stand‐replacement	underburn,	mixed‐severity	

underburn	(some	tree	mortality),	and	low‐severity	underburn.	Typically	wildfire	will	be	more	

weighted	towards	crown	fire	and	higher	severity	versus	lower	severity	from	prescribed	fire.	For

other	disturbances,	the	percentage	of	live	trees	killed	(or	percentage	basal	area	mortality)	and	the	

percentage	of	killed	trees	that	are	still	standing	as	was	covered	previously	in	Section	6.4.2.10	and	

Section	6.4.4.8	are	used.	

6.7.3 Estimation Methods

FOFEM9	(Reinhardt	et	al.,	1997)	is	recommended	for		estimating	GHG	emissions,	because	it	is	

applicable	nationally,	computer	code	is	available	that	can	be	linked	to	or	incorporated	into	other	

																																																													

9 http://www.firelab.org/science‐applications/fire‐fuel/111‐fofem		

Methods for Emissions from Natural Disturbances

 Range	of	options	depends	on	the	data	availability	of	the	entity’s	forest	land	including:	

− FOFEM	model	entering	measured	biomass;	and	

− FOFEM	model	using	default	values	generated	by	vegetation	type.	

 These	options	use	Reinhardt	et	al.	(1997).	

 The	methods	were	selected	because	they	provide	a	range	of	options	dependent	on	the	

data	availability	of	the	entity's	disturbed	forest	land.	
Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

6-83 

code,	and	inputs	are	defined	so	that	measured	biomass	can	be	entered	or	default	values	generated	

by	vegetation	type.	FOFEM	produces	direct	estimates	of	total	CO2,	CO,	CH4,	and	NOx	emitted,	as	well	

as	estimates	of	fuel	consumption	by	component,	which	can	be	used	to	determine	residual	fuel	

quantities	for	estimating	subsequent	decomposition.	FOFEM	and/or	CONSUME	(Joint	Fire	Science	

Program,	2009)	can	also	be	used	directly	to	compute	emissions	and	consumption	from	fire.	FOFEM	

algorithms	can	also	be	used	to	compute	tree	mortality	in	order	to	update	estimates	of	live	and	dead	

biomass.	Although	another	option	is	to	use	FVS‐FFE10	(Rebain,	2010;	Reinhardt	and	Crookston,	

2003),	it	is	not	the	recommended	approach	for	wildfire	GHG	calculation.	FVS‐FFE	uses	many	of	the	

same	internal	algorithms	for	estimating	tree	mortality,	fuel	consumption,	and	emissions	as	FOFEM,	

but	also	simulates	stand,	fuel,	and	carbon	dynamics	over	time.	It	is	a	more	powerful	predictive	tool,	

but	substantially	more	work	is	involved	in	understanding	the	modeling	framework,	setting	up	

model	runs	and	data	preparation.	Alternatively,	lookup	tables	can	be	built	using	these	tools	for	a	

range	of	vegetation	types,	fuel	loadings	from	natural	and/or	management	processes,	and	fire	

severities,	or	a	simplified	algorithm	can	be	developed	as	in	the 2006	IPCC	Guidelines	for	National	

GHG	Inventories (IPCC,	2006).	

10 http://www.fs.fed.us/fmsc/fvs/whatis/index.shtml
Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

6-84

Figure 6‐9: Decision Tree for Natural Disturbances Showing Methods Appropriate for

Estimating Emissions from Forest Fires Depending on the Data Available

6.7.3.1 Estimation of Greenhouse Gas Emissions from Fire

The	calculation	of	GHG	emissions	from	fires	can	be	seen	in	Equation	6‐11	below.	
Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

6-85 

In	order	to	use	this	algorithm,	an	estimate	of	A	by	fire	severity	is	used.	For	MB,	the	understory,	

DDW,	and	forest	floor	are	assumed	to	be	available	for	combustion.	In	addition,	an	estimate	of	what	

portion	of	the	live	tree	biomass	is	available	for	combustion	(typically	only	foliage	and	fine	

branchwood)	is	used.	For	Cf,	IPCC	(2006)	protocols	use	0.45	for	temperate	forests.	Separate	values	

for	Cf	for	biomass	pools	for	crown	fire,	stand‐replacement	underburn,	mixed‐severity	underburn,	

and	low‐severity	underburn,	by	forest	type,	using	FOFEM	are	provided	(see	Table	6‐13).	For	Gef

emission	factors	from	Urbanski	et	al.	(2009)	are	recommended:	1619	g	(kg	dry	matter	burnt	for	

CO2)−1,	89.6	g	(kg	dry	matter	burnt	for	CO)−1,	3.4	g	(kg	dry	matter	burnt	for	CH4)−1,	and	from	Akagi	

et	al.	(2011),	2.5	g	(kg	dry	matter	burntfor	NOx)−1.	Note	that	not	all	biomass	is	available	for	

combustion;	in	particular,	standing	live	tree	boles	are	not	available.	

For	subsequent	effects,	the	GHG	estimation	methods	adopted	should	match	as	closely	as	possible	

those	used	in	other	sections	(e.g.,	HWPs).	Decomposition	of	dead	material	over	time	will	be	

projected	using	a	fixed	annual	loss	rate.	The	conversion	of	standing	dead	to	dead‐and‐down	should	

also	be	projected	using	a	fixed	rate	and	approximating	the	methods	in	FVS‐FFE.	

GHG	emissions	from	natural	disturbance	wildfires	and	prescribed	fires	used	for	site	maintenance	

and	restoration	should	be	reported	separately	from	emissions	resulting	from	management	(sites	

with	thinning	slash,	machine	or	hand	piles,	or	logging	slash)	to	facilitate	the	use	of	the	estimates	in	

decision	making	regarding	management	practices.		

Table	6‐13	shows	an example	for	the	default lookup	tables	for consumption	fraction	(Cf).	Regions	

are	those	shown	in	Table 6‐13,	with	the	exception	of	the	West	region,	which	represents	an	average	

of	all	western	regions.	

Table 6‐13: Cf Consumption Fraction

Region Forest Type

Cf Crown

Fire

Cf Stand

Replacement

Underburn

Cf Mixed

Severity

Cf Low

Severity

Underburn

%

Northeast	

Aspen–birch	 84 69 59 45	

Elm–ash–cottonwood	 74 47 35 20	

Maple–beech–birch	 77 60 44 35	

Oak–hickory 63 49 41 32	

Oak–pine	 80 61 50 38	

Spruce–fir	 73 73 69 62	

White–red–jack	pine	 55 45 37 26	

Equation 6‐11: Calculate GHG Emissions from Fire

Lfire = A × MB × Cf × Gef × 10−3

Where:	

Lfire		 =	Amount	of	greenhouse	gas	emissions	from	fire	(metric	tons	of	each	GHG,	i.e.,	CH4,	

N2O,	etc.)	

A		 =	Area	burned	(ha)	

MB		 =	Mass	of	fuel	available	for	combustion	(metric	tons	ha−1)	

Cf		 =	Combustion	factor	(dimensionless)	

Gef		 =	Emission	factor	(g	(kg	dry	matter	burnt)−1)
Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

6-86

Region Forest Type

Cf Crown

Fire

Cf Stand

Replacement

Underburn

Cf Mixed

Severity

Cf Low

Severity

Underburn

%

Northern	Lake	

States	

Aspen–birch	 84 69 59 45	

Elm–ash–cottonwood	 74 47 35 20	

Maple–beech–birch	 77 60 44 35	

Oak–hickory 80 61 50 38

Spruce–fir	 73 73 69 62	

White–red–jack	pine	 55 45 37 26	

Northern	Prairie	

States	

Elm–ash–cottonwood	 74 47 35 20	

Maple–beech–birch	 77 60 44 35	

Oak–hickory 80 61 50 38	

Ponderosa	pine	 60 53 47 37	

Pacific	

Northwest,	East	

Douglas	fir	 85 79 72 60	

Fir–spruce–m.hemlock 67 64 58 44	

Lodgepole	pine	 77 72 64 52	

Ponderosa	pine	 78 53 41 27	

Pacific	

Northwest,	West	

Alder–maple	 82 67 48 42	

Douglas	fir	 71 62 55 43	

Fir–spruce–m.hemlock 67 64 58 44	

Hemlock–Sitka	spruce 85 77 69 55	

Pacific	

Southwest	

Mixed	conifer	 79 69 50 46	

Douglas	fir	 66 42 30 17	

Fir–spruce–m.hemlock 67 64 58 44	

Ponderosa	Pine	 78 53 41 27	

Redwood	 82 76 69 56	

Rocky	Mountain,	

North	and	South	

Aspen–birch	 80 61 50 35	

Douglas	fir	 85 79 72 60	

Fir–spruce–m.hemlock 67 64 58 44	

Lodgepole	pine	 77 72 64 52	

Ponderosa	pine	 78 53 41 27	

Mixed	conifer	 79 69 50 46	

Southeast 

Elm–ash–cottonwood	 76 45 29 19	

Loblolly–shortleaf	pine 66 52 44 35	

Oak–hickory 61 50 44 36

Oak–pine	 62 55 51 45	

South	Central	

Elm–ash–cottonwood	 76 45 29 19	

Loblolly–shortleaf	pine 66 52 44 35	

Longleaf–slash	pine	 69 63 57 47	

Oak–hickory 61 50 44 36	

Oak–pine	 62 55 51 45	

Westa

Pinyon–juniper	 64 55 49 41	

Tanoak–laurel	 70 52 43 32	

Western	larch	 76 68 60 47	

Western	oak	 65 62 56 48	

Western	white	pine	 68 56 47 33	

a	Represents	an	average	over	all	western	regions	for	the	specified	forest	types	(PNW‐W,	PNW‐E,	PSW,	RMN,	RMS).	
Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

6-87 

6.7.3.2 Estimation of Greenhouse Gas Emissions from Other Disturbances

For	other	disturbances,	the	primary	effects	are	indirect:	by	converting	live	biomass	to	dead—and	in	

some	cases	standing	trees	to	dead,	down	trees—decomposition	is	accelerated.	Currently	grouping	

non‐fire	disturbance	into	two	categories	is	suggested:	disturbances	that	leave	dead	trees	standing	

(insect	and	disease‐caused	mortality)	and	disturbance	that	leaves	the	trees	on	the	ground	(wind	or	

ice	storms).The	landowner	will	have	to	estimate	mortality	(Section	6.7.2);	then	as	in	decomposition	

of	fire‐killed	trees,	a	fixed	decomposition	rate	(default	value	0.015)	will	be	used	to	simulate	

subsequent	decomposition.	

For	insect	or	pathogen‐caused	mortality,	the	trees	are	assumed	to	be	initially	standing	after	death.	

Conversion	of	standing	dead	to	dead‐and‐down	will	be	projected	using	a	fixed	rate	and	

approximating	the	methods	in	FVS‐FEE.	Once	down,	the	default	decomposition	rate	from	FVS‐FFE	

of	0.015	for	dead	and	down	wood	will	be	used	to	simulate	decomposition.	For	blowdowns	or	ice	

storms,	the	impacted	trees	are	assumed	to	be	dead	and	down.	In	this	case	decomposition	begins	

immediately.	

6.7.4 Limitations and Uncertainty

A	major	source	of	uncertainty	in	predicting	fire	emissions	is	the	preburn	fuel	quantities.	If	

landowners	are	doing	some	kind	of	inventory	of	live	and	dead	biomass	(see	Section	6.7.2)	they	will	

have	relatively	robust	estimates	of	available	fuel.	If	they	are	using	lookup	table	values	by	forest	

type,	there	will	be	more	uncertainty	associated	with	the	estimates	since	fuel	quantities	vary	greatly	

within	forest	type.	

A	related	challenge	is	determining	the	appropriate	degree	of	specificity	for	tracking	biomass	by	

pools	(e.g.,	live,	dead).	Any	kind	of	management	or	disturbance	changes	biomass	at	the	time	of	

occurrence,	and	also	the	subsequent	trajectory.	Subsequent	management	or	disturbance	should	be	

applied	to	the	changed	and	changing	values,	not	the	original	values.	This	can	result	in	a	complicated	

simulation	model	like	FVS,	rather	than	a	calculator.	Since	prefire	fuel	quantity	is	the	strongest	

predictor	of	fuel	consumption,	determining	the	appropriate	degree	of	specificity	for	tracking	

biomass	by	pools	is	not	a	completely	academic	question.	
Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

6-88

Appendix 6‐A: Harvested Wood Products Lookup Tables

Table 6‐A‐1: Factors to Convert Primary Wood Products to Carbon Mass from the Units

Characteristic of Each Product

Solidwood Product or Paper Unit

Factor to Convert

Units to Tons

(2,000 lbs) C

Factor to

Convert Units to

Metric Tons C

Softwood	lumber/laminated	veneer	

lumber/glulam	lumber/I‐joists	 Thousand	board	feet	 0.488	 0.443

Hardwood	lumber	 Thousand	board	feet 0.844	 0.765

Softwood	plywood	 Thousand	square	feet,	

3/8‐inch	basis	 0.260	 0.236

Oriented	strandboard	 Thousand	square	feet,	

3/8‐inch	basis	 0.303	 0.275

Non‐structural	panels	(average)	 Thousand	square	feet,	

3/8‐inch	basis	 0.319	 0.289

Hardwood	veneer/plywood	 Thousand	square	feet,	

3/8‐inch	basis	 0.315	 0.286

Particleboard/medium		density	

fiberboard	

Thousand	square	feet,	

3/4‐inch	basis	 0.647	 0.587

Hardboard	 Thousand	square	

feet,1/8‐inch	basis	 0.152	 0.138

Insulation	board	 Thousand	square	feet,	

1/2‐inch	basis	 0.242	 0.220

Other	industrial	products	 Thousand	cubic	feet 8.250	 7.484

Paper	 Tons,	air	dry 0.450	 0.496
 Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

 6-89 

Table 6‐A‐2: Fraction of Carbon in Primary Wood Products Remaining in End Uses up to 100

Years After Production (year 0 indicates fraction at time of production)

Year after

Production

Softwood

Lumber

Hardwood

Lumber

Softwood

Plywood

Oriented

Strandboard

Non‐

Structural

Panels

Misc.

Products Paper

0	 1.000	 1.000 1.000 1.000 1.000	 1.000 1.000

1	 0.908	 0.909 0.908 0.908 0.908	 0.903 0.880

2	 0.892	 0.893 0.893 0.896 0.892	 0.887 0.775

3	 0.877	 0.877 0.878 0.884 0.876	 0.871 0.682

4	 0.863	 0.861 0.863 0.872 0.861	 0.855 0.600

5	 0.848	 0.845 0.848 0.860 0.845	 0.840 0.528

6	 0.834	 0.830 0.834 0.848 0.830	 0.825 0.465

7	 0.820	 0.815 0.820 0.837 0.816	 0.810 0.354

8	 0.806	 0.801 0.807 0.826 0.801	 0.795 0.269

9	 0.793	 0.786 0.794 0.815 0.787	 0.781 0.205

10	 0.780	 0.772 0.781 0.804 0.774	 0.767 0.156

15	 0.718	 0.705 0.719 0.753 0.708	 0.700 0.040

20	 0.662	 0.644 0.663 0.706 0.649	 0.639 0.010

25	 0.611	 0.589 0.613 0.662 0.595	 0.583 0.003

30	 0.565	 0.538 0.567 0.622 0.546	 0.532 0.001

35	 0.523	 0.492 0.525 0.585 0.501	 0.486 0.000

40	 0.485	 0.450 0.487 0.551 0.460	 0.444 0.000

45	 0.450	 0.411 0.452 0.519 0.423	 0.405 0.000

50	 0.418	 0.376 0.420 0.490 0.389	 0.370 0.000

55	 0.389	 0.344 0.391 0.462 0.358	 0.338 0.000

60	 0.362	 0.315 0.364 0.437 0.329	 0.308 0.000

65	 0.338	 0.288 0.340 0.413 0.303	 0.281 0.000

70	 0.315	 0.264 0.317 0.391 0.280	 0.257 0.000

75	 0.294	 0.242 0.296 0.370 0.258	 0.234 0.000

80	 0.276	 0.221 0.277 0.351 0.238	 0.214 0.000

85	 0.258	 0.203 0.260 0.333 0.220	 0.195 0.000

90	 0.242	 0.186 0.244 0.316 0.203	 0.178 0.000

95	 0.227	 0.170 0.229 0.300 0.188	 0.163 0.000

100	 0.213	 0.156 0.215 0.285 0.174	 0.149 0.000

Average 0.466 0.430 0.468 0.526 0.441 0.424 0.059
Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

6-90

Table 6‐A‐3: Fraction of Carbon in Primary Wood Products Remaining in Landfills up to 100

Years after Production (year 0 indicates fraction at time of production)

Year after

Productio

n

Softwoo

d

Lumber

Hardwood

Lumber

Softwood

Plywood

Oriented

Strandboar

d

Non‐

Structura

l Panels

Misc.

Products Paper

0	 0.000	 0.000	 0.000 0.000 0.000 0.000	 0.000

1	 0.061	 0.060	 0.061 0.061 0.061 0.064	 0.040

2	 0.071	 0.070	 0.071 0.068 0.071 0.074	 0.073

3	 0.080	 0.080	 0.080 0.076 0.081 0.084	 0.102

4	 0.089	 0.090	 0.089 0.083 0.090 0.094	 0.127

5	 0.098	 0.099	 0.097 0.090 0.099 0.103	 0.147

6	 0.106	 0.109	 0.106 0.097 0.108 0.112	 0.164

7	 0.114	 0.117	 0.114 0.103 0.117 0.121	 0.197

8	 0.122	 0.126	 0.122 0.110 0.125 0.129	 0.220

9	 0.130	 0.134	 0.130 0.116 0.134 0.138	 0.236

10	 0.138	 0.143	 0.137 0.122 0.142 0.146	 0.247

15	 0.173	 0.181	 0.172 0.151 0.179 0.184	 0.256

20	 0.203	 0.214	 0.202 0.176 0.211 0.217	 0.241

25	 0.230	 0.243	 0.229 0.199 0.239 0.246	 0.223

30	 0.253	 0.269	 0.252 0.220 0.265 0.272	 0.207

35	 0.274	 0.292	 0.273 0.238 0.287 0.296	 0.195

40	 0.293	 0.313	 0.292 0.255 0.307 0.316	 0.185

45	 0.310	 0.332	 0.308 0.271 0.325 0.335	 0.177

50	 0.325	 0.348	 0.324 0.285 0.341 0.352	 0.171

55	 0.338	 0.363	 0.337 0.298 0.356 0.367	 0.166

60	 0.351	 0.377	 0.349 0.310 0.369 0.380	 0.163

65	 0.362	 0.389	 0.361 0.321 0.381 0.393	 0.160

70	 0.372	 0.400	 0.371 0.331 0.391 0.404	 0.158

75	 0.381	 0.410	 0.380 0.341 0.401 0.414	 0.156

80	 0.390	 0.419	 0.389 0.350 0.410 0.423	 0.154

85	 0.398	 0.427	 0.397 0.359 0.418 0.431	 0.153

90	 0.405	 0.435	 0.404 0.366 0.426 0.439	 0.153

95	 0.412	 0.442	 0.411 0.374 0.432 0.446	 0.152

100	 0.418	 0.448	 0.417 0.381 0.438 0.452	 0.151

Average 0.297 0.317 0.296 0.264 0.311 0.321 0.178
 Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

 6-91 

Table 6‐A‐4: Density of Softwood and Hardwood Sawlogs/Veneer Logs and Pulpwood by

Region and Forest Type Groupa

Region Forest type Specific Gravityd of

Softwoods

Specific Gravityd of

Hardwoods

Northeast	

Aspen–birch	 0.353 0.428	

Elm–ash–cottonwood 0.358 0.470	

Maple–beech–birch	 0.369 0.518	

Oak–hickory 0.388 0.534	

Oak–pine	 0.371 0.516	

Spruce–fir	 0.353 0.481	

White–red–jack	pine 0.361 0.510	

Northern	Lake	

States	

Aspen–birch	 0.351 0.397	

Elm–ash–cottonwood 0.335 0.460	

Maple–beech–birch	 0.356 0.496	

Oak–hickory	 0.369 0.534	

Spruce–fir	 0.344 0.444	

White–red–jack	pine 0.389 0.473	

Northern	Prairie	

States	

Elm–ash–cottonwood 0.424 0.453	

Loblolly–shortleaf	pine 0.468 0.544	

Maple–beech–birch	 0.437 0.508	

Oak–hickory	 0.448 0.565	

Oak–pine	 0.451 0.566	

Ponderosa	pine	 0.381 0.473	

Pacific	Northwest,	

East	

Douglas	fir	 0.429 0.391	

Fir–spruce–m.hemlock 0.370 0.361	

Lodgepole	pine	 0.380 0.345	

Ponderosa	pine	 0.385 0.513	

Pacific	Northwest,	

West	

Alder–maple	 0.402 0.385	

Douglas	fir	 0.440 0.426	

Fir–spruce–m.hemlock 0.399 0.417	

Hemlock–Sitka	spruce 0.405 0.380	

Pacific	Southwest	

Mixed	conifer	 0.394 0.521	

Douglas	fir	 0.429 0.483	

Fir–spruce–m.hemlock 0.372 0.510	

Ponderosa	Pine	 0.380 0.510	

Redwood	 0.376 0.449	

Rocky	Mountain,	

North	

Douglas	fir	 0.428 0.370	

Fir–spruce–m.hemlock 0.355 0.457	

Hemlock–sitka	spruce 0.375 0.441	

Lodgepole	pine	 0.383 0.391	

Ponderosa	pine	 0.391 0.374	

Rocky	Mountain,	

South	

Aspen–birch	 0.355 0.350	

Douglas	fir	 0.431 0.350	

Fir–spruce–m.hemlock 0.342 0.350	

Lodgepole	pine	 0.377 0.350	

Ponderosa	pine	 0.383 0.386	
Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

6-92

Region Forest type Specific Gravityd of

Softwoods

Specific Gravityd of

Hardwoods

Southeast	

Elm–ash–cottonwood 0.433 0.499	

Loblolly–shortleaf	pine 0.469 0.494	

Longleaf–slash	pine 0.536 0.503	

Oak–gum–cypress	 0.441 0.484	

Oak–hickory	 0.438 0.524	

Oak–pine	 0.462 0.516	

South	Central		

Elm–ash–cottonwood 0.427 0.494	

Loblolly–shortleaf	pine 0.470 0.516	

Longleaf–slash	pine 0.531 0.504	

Oak–gum–cypress	 0.440 0.513	

Oak–hickory	 0.451 0.544	

Oak–pine	 0.467 0.537	

Weste

Pinyon–juniper	 0.422 0.620	

Tanoak–laurel	 0.430 0.459	

Western	larch	 0.433 0.430	

Western	oak	 0.416 0.590	

Western	white	pine 0.376 ‐‐	

‐‐	=	No	hardwood	trees	in	this	type	in	this	region.	

a	Estimates	based	on	survey	data	for	the	conterminous	United	States	from	USDA	Forest	Service,	FIA	Program’s	database	of	

forest	surveys	(FIADB)	(USDA	Forest	Service,	2005)	and	include	growing	stock	on	timberland	stands	classified	as	

medium‐	or	large‐diameter	stands.	Proportions	are	based	on	volume	of	growing	stock	trees.	 d	Average	wood	specific	gravity	is	the	density	of	wood	divided	by	the	density	of	water	based	on	wood	dry	mass	associated	

with	green	tree	volume.	

e	West	represents	an	average	over	all	western	regions	for	these	forest	types.
 Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems

 6-93 

Table 6‐A‐5: Average Disposition Patterns of Carbon as Fractions in Roundwood by Region

and Roundwood Category; Factors Assume No Bark on Roundwood and Exclude Fuelwood

Year after

Production

Northeast, Softwood

In Use

Sawlog

Total

Emissions In Use

Pulpwood

Total

Emissions In

Landfills

Total

Stored

In

Landfills

Total

Stored

0	 0.569	 0.000	 0.569 0.431 0.513 0.000	 0.513	 0.487

1	 0.521	 0.029	 0.550 0.450 0.452 0.021	 0.473	 0.527

2	 0.505	 0.037	 0.542 0.458 0.400 0.038	 0.438	 0.562

3	 0.491	 0.044	 0.535 0.465 0.355 0.052	 0.407	 0.593

4	 0.478	 0.050	 0.528 0.472 0.315 0.064	 0.379	 0.621

5	 0.465	 0.056	 0.522 0.478 0.279 0.074	 0.354	 0.646

6	 0.453	 0.062	 0.516 0.484 0.248 0.083	 0.331	 0.669

7	 0.438	 0.069	 0.507 0.493 0.193 0.099	 0.293	 0.707

8	 0.425	 0.075	 0.500 0.500 0.152 0.111	 0.263	 0.737

9	 0.414	 0.080	 0.494 0.506 0.120 0.119	 0.239	 0.761

10	 0.403	 0.085	 0.489 0.511 0.096 0.124	 0.220	 0.780

15	 0.363	 0.105	 0.468 0.532 0.038 0.130	 0.168	 0.832

20	 0.332	 0.121	 0.453 0.547 0.022 0.124	 0.146	 0.854

25	 0.306	 0.134	 0.440 0.560 0.017 0.116	 0.133	 0.867

30	 0.282	 0.146	 0.428 0.572 0.015 0.109	 0.124	 0.876

35	 0.260	 0.156	 0.417 0.583 0.014 0.103	 0.117	 0.883

40	 0.240	 0.166	 0.406 0.594 0.013 0.099	 0.111	 0.889

45	 0.222	 0.174	 0.397 0.603 0.012 0.095	 0.107	 0.893

50	 0.206	 0.182	 0.388 0.612 0.011 0.093	 0.104	 0.896

55	 0.191	 0.189	 0.380 0.620 0.010 0.091	 0.101	 0.899

60	 0.177	 0.195	 0.372 0.628 0.009 0.089	 0.099	 0.901

65	 0.165	 0.201	 0.365 0.635 0.009 0.088	 0.097	 0.903

70	 0.153	 0.206	 0.359 0.641 0.008 0.087	 0.095	 0.905

75	 0.143	 0.210	 0.353 0.647 0.008 0.086	 0.094	 0.906

80	 0.133	 0.214	 0.347 0.653 0.007 0.086	 0.093	 0.907

85	 0.124	 0.218	 0.342 0.658 0.007 0.085	 0.092	 0.908

90	 0.116	 0.221	 0.337 0.663 0.006 0.085	 0.091	 0.909

95	 0.108	 0.224	 0.332 0.668 0.006 0.085	 0.091	 0.909

100	 0.101	 0.227	 0.328 0.672 0.006 0.085	 0.090	 0.910

Average 0.235 0.166 0.402 0.041 0.095 0.136

	 	
Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

6-94

Table 6‐A‐5—continued

Year after

Production

Northeast, Hardwood

In Use

Sawlog Total

Emissions

In

Use

Pulpwood Total

Emissions In

Landfills

Total		

Stored

In

Landfills

Total		

Stored

0	 0.614 0.000	 0.614 0.386 0.650 0.000	 0.650	 0.350

1	 0.559 0.034	 0.594 0.406 0.580 0.032	 0.613	 0.387

2	 0.544 0.042	 0.586 0.414 0.540 0.046	 0.586	 0.414

3	 0.530 0.049	 0.579 0.421 0.503 0.059	 0.562	 0.438

4	 0.516 0.056	 0.573 0.427 0.471 0.070	 0.541	 0.459

5	 0.504 0.063	 0.567 0.433 0.443 0.079	 0.522	 0.478

6	 0.491 0.069	 0.561 0.439 0.417 0.087	 0.504	 0.496

7	 0.477 0.076	 0.553 0.447 0.374 0.101	 0.475	 0.525

8	 0.463 0.083	 0.546 0.454 0.341 0.111	 0.453	 0.547

9	 0.452 0.089	 0.540 0.460 0.316 0.119	 0.434	 0.566

10	 0.441 0.094	 0.535 0.465 0.295 0.125	 0.420	 0.580

15	 0.397 0.117	 0.514 0.486 0.239 0.137	 0.376	 0.624

20	 0.361 0.136	 0.497 0.503 0.215 0.140	 0.355	 0.645

25	 0.330 0.152	 0.482 0.518 0.199 0.141	 0.340	 0.660

30	 0.301 0.167	 0.468 0.532 0.186 0.142	 0.328	 0.672

35	 0.275 0.180	 0.455 0.545 0.175 0.144	 0.319	 0.681

40	 0.252 0.192	 0.444 0.556 0.164 0.146	 0.310	 0.690

45	 0.230 0.202	 0.432 0.568 0.155 0.148	 0.302	 0.698

50	 0.211 0.211	 0.422 0.578 0.146 0.150	 0.296	 0.704

55	 0.193 0.220	 0.412 0.588 0.138 0.152	 0.290	 0.710

60	 0.176 0.227	 0.403 0.597 0.130 0.154	 0.285	 0.715

65	 0.162 0.234	 0.395 0.605 0.123 0.157	 0.280	 0.720

70	 0.148 0.240	 0.388 0.612 0.116 0.159	 0.275	 0.725

75	 0.136 0.245	 0.380 0.620 0.110 0.161	 0.271	 0.729

80	 0.124 0.250	 0.374 0.626 0.104 0.163	 0.268	 0.732

85	 0.114 0.254	 0.368 0.632 0.099 0.165	 0.264	 0.736

90	 0.104 0.258	 0.362 0.638 0.094 0.167	 0.261	 0.739

95	 0.096 0.261	 0.357 0.643 0.089 0.169	 0.258	 0.742

100	 0.088 0.264	 0.352 0.648 0.085 0.171	 0.255	 0.745

Average 0.244 0.192 0.437 0.178 0.145 0.323

	 	
 Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems

 6-95 

Table 6‐A‐5—continued

Year after

Production

North Central, Softwood

In

Use

Sawlog

Total

Emissions

In

Use

Pulpwood

Total

Emissions In

Landfills

Total		

Stored

In

Landfills

Total		

Stored

0	 0.630	 0.000	 0.630 0.370 0.514 0.000	 0.514	 0.486

1	 0.579	 0.031	 0.610 0.390 0.454 0.021	 0.475	 0.525

2	 0.561	 0.039	 0.601 0.399 0.402 0.038	 0.440	 0.560

3	 0.545	 0.047	 0.592 0.408 0.357 0.052	 0.409	 0.591

4	 0.530	 0.055	 0.585 0.415 0.317 0.064	 0.381	 0.619

5	 0.516	 0.062	 0.577 0.423 0.281 0.074	 0.356	 0.644

6	 0.502	 0.068	 0.570 0.430 0.250 0.083	 0.333	 0.667

7	 0.485	 0.076	 0.561 0.439 0.196 0.099	 0.295	 0.705

8	 0.470	 0.083	 0.553 0.447 0.154 0.111	 0.265	 0.735

9	 0.457	 0.089	 0.546 0.454 0.123 0.119	 0.241	 0.759

10	 0.446	 0.094	 0.540 0.460 0.098 0.124	 0.223	 0.777

15	 0.401	 0.116	 0.517 0.483 0.041 0.130	 0.171	 0.829

20	 0.366	 0.133	 0.500 0.500 0.025 0.124	 0.148	 0.852

25	 0.336	 0.148	 0.485 0.515 0.020 0.116	 0.135	 0.865

30	 0.310	 0.162	 0.471 0.529 0.018 0.109	 0.126	 0.874

35	 0.286	 0.173	 0.459 0.541 0.016 0.103	 0.120	 0.880

40	 0.264	 0.184	 0.447 0.553 0.015 0.099	 0.114	 0.886

45	 0.243	 0.193	 0.437 0.563 0.014 0.096	 0.110	 0.890

50	 0.225	 0.202	 0.427 0.573 0.013 0.093	 0.106	 0.894

55	 0.208	 0.209	 0.418 0.582 0.012 0.091	 0.103	 0.897

60	 0.193	 0.216	 0.409 0.591 0.012 0.089	 0.101	 0.899

65	 0.179	 0.222	 0.401 0.599 0.011 0.088	 0.099	 0.901

70	 0.166	 0.228	 0.394 0.606 0.010 0.087	 0.098	 0.902

75	 0.154	 0.233	 0.387 0.613 0.010 0.087	 0.097	 0.903

80	 0.144	 0.237	 0.381 0.619 0.009 0.086	 0.095	 0.905

85	 0.134	 0.242	 0.375 0.625 0.009 0.086	 0.095	 0.905

90	 0.125	 0.245	 0.370 0.630 0.008 0.086	 0.094	 0.906

95	 0.116	 0.249	 0.365 0.635 0.008 0.086	 0.093	 0.907

100	 0.108	 0.252	 0.360 0.640 0.007 0.086	 0.093	 0.907

Average 0.258 0.184 0.442 0.043 0.095 0.138

	 	
Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

6-96

Table 6‐A‐5—continued

Year after

Production

North Central, Hardwood

In Use

Sawlog Total

Emissions

In

Use

Pulpwood Total

Emissions In

Landfills

Total		

Stored

In

Landfills

Total		

Stored

0	 0.585 0.000	 0.585 0.415 0.685 0.000	 0.685	 0.315

1	 0.533 0.032	 0.565 0.435 0.613 0.035	 0.648	 0.352

2	 0.518 0.040	 0.558 0.442 0.575 0.049	 0.624	 0.376

3	 0.504 0.047	 0.550 0.450 0.541 0.061	 0.602	 0.398

4	 0.490 0.054	 0.544 0.456 0.511 0.071	 0.582	 0.418

5	 0.477 0.060	 0.537 0.463 0.484 0.080	 0.565	 0.435

6	 0.465 0.066	 0.531 0.469 0.460 0.089	 0.548	 0.452

7	 0.450 0.073	 0.523 0.477 0.421 0.101	 0.522	 0.478

8	 0.437 0.080	 0.517 0.483 0.390 0.111	 0.501	 0.499

9	 0.425 0.085	 0.511 0.489 0.365 0.119	 0.484	 0.516

10	 0.415 0.090	 0.505 0.495 0.346 0.125	 0.471	 0.529

15	 0.372 0.112	 0.484 0.516 0.290 0.139	 0.429	 0.571

20	 0.339 0.130	 0.468 0.532 0.263 0.144	 0.408	 0.592

25	 0.309 0.145	 0.454 0.546 0.245 0.148	 0.393	 0.607

30	 0.282 0.158	 0.441 0.559 0.229 0.151	 0.380	 0.620

35	 0.258 0.170	 0.428 0.572 0.216 0.154	 0.370	 0.630

40	 0.236 0.181	 0.417 0.583 0.203 0.158	 0.360	 0.640

45	 0.216 0.191	 0.407 0.593 0.191 0.161	 0.352	 0.648

50	 0.197 0.199	 0.397 0.603 0.180 0.165	 0.345	 0.655

55	 0.181 0.207	 0.388 0.612 0.170 0.168	 0.338	 0.662

60	 0.165 0.214	 0.379 0.621 0.160 0.171	 0.332	 0.668

65	 0.151 0.220	 0.372 0.628 0.152 0.174	 0.326	 0.674

70	 0.138 0.226	 0.364 0.636 0.143 0.178	 0.321	 0.679

75	 0.127 0.231	 0.358 0.642 0.136 0.180	 0.316	 0.684

80	 0.116 0.235	 0.351 0.649 0.129 0.183	 0.312	 0.688

85	 0.106 0.239	 0.346 0.654 0.122 0.186	 0.308	 0.692

90	 0.098 0.243	 0.340 0.660 0.116 0.188	 0.304	 0.696

95	 0.089 0.246	 0.336 0.664 0.110 0.191	 0.300	 0.700

100	 0.082 0.249	 0.331 0.669 0.104 0.193	 0.297	 0.703

Average 0.229 0.182 0.411 0.212 0.158 0.370

	 	
 Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems

 6-97 

Table 6‐A‐5—continued 		 	 	 	

Year after

Production

Pacific Northwest, East, Softwood 		 	 	 	

In Use

All

Total

Emissions

		 	 	 	

In

Landfills

Total		

Stored 		 	 	 	

0	 0.637	 0.000	 0.637 0.363

1	 0.574	 0.036	 0.610 0.390 		 	 	 	

2	 0.551	 0.046	 0.597 0.403

3	 0.530	 0.055	 0.585 0.415 		 	 	 	

4	 0.511	 0.063	 0.574 0.426

5	 0.494	 0.070	 0.564 0.436 		 	 	 	

6	 0.478	 0.077	 0.555 0.445

7	 0.455	 0.086	 0.541 0.459 		 	 	 	

8	 0.436	 0.093	 0.529 0.471

9	 0.420	 0.100	 0.520 0.480 		 	 	 	

10	 0.406	 0.105	 0.512 0.488

15	 0.359	 0.125	 0.484 0.516 		 	 	 	

20	 0.327	 0.139	 0.466 0.534

25	 0.301	 0.150	 0.451 0.549 		 	 	 	

30	 0.278	 0.160	 0.438 0.562

35	 0.258	 0.169	 0.427 0.573 		 	 	 	

40	 0.239	 0.177	 0.416 0.584

45	 0.222	 0.185	 0.406 0.594 		 	 	 	

50	 0.206	 0.191	 0.397 0.603

55	 0.191	 0.198	 0.389 0.611 		 	 	 	

60	 0.178	 0.203	 0.381 0.619

65	 0.166	 0.208	 0.374 0.626 		 	 	 	

70	 0.155	 0.213	 0.368 0.632

75	 0.145	 0.217	 0.362 0.638 		 	 	 	

80	 0.136	 0.221	 0.356 0.644

85	 0.127	 0.224	 0.351 0.649 		 	 	 	

90	 0.119	 0.227	 0.347 0.653

95	 0.112	 0.230	 0.342 0.658 		 	 	 	

100	 0.105	 0.233	 0.338 0.662

Average 0.238 0.177 0.415 		 	 	 	

	 	
Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

6-98

Table 6‐A‐5—continued

Year after

Production

Pacific Northwest, West, Softwoods

In

Use

Sawlog Total

Emissions

In

Use

Pulpwood Total

Emissions In

Landfills

Total		

Stored

In

Landfills

Total		

Stored

0	 0.740	 0.000	 0.740 0.260 0.500 0.000	 0.500	 0.500

1	 0.674	 0.039	 0.713 0.287 0.440 0.020	 0.460	 0.540

2	 0.652	 0.049	 0.702 0.298 0.387 0.037	 0.424	 0.576

3	 0.632	 0.059	 0.691 0.309 0.341 0.051	 0.392	 0.608

4	 0.613	 0.068	 0.681 0.319 0.300 0.063	 0.364	 0.636

5	 0.596	 0.076	 0.672 0.328 0.264 0.074	 0.338	 0.662

6	 0.579	 0.083	 0.663 0.337 0.233 0.082	 0.315	 0.685

7	 0.558	 0.093	 0.651 0.349 0.177 0.099	 0.276	 0.724

8	 0.539	 0.101	 0.640 0.360 0.134 0.111	 0.245	 0.755

9	 0.524	 0.108	 0.631 0.369 0.102 0.119	 0.221	 0.779

10	 0.510	 0.114	 0.624 0.376 0.078 0.124	 0.202	 0.798

15	 0.457	 0.139	 0.596 0.404 0.020 0.129	 0.149	 0.851

20	 0.418	 0.158	 0.576 0.424 0.005 0.122	 0.127	 0.873

25	 0.384	 0.174	 0.558 0.442 0.001 0.113	 0.114	 0.886

30	 0.355	 0.188	 0.543 0.457 0 0.105	 0.105	 0.895

35	 0.328	 0.201	 0.529 0.471 0 0.098	 0.099	 0.901

40	 0.303	 0.213	 0.516 0.484 0 0.093	 0.093	 0.907

45	 0.281	 0.223	 0.504 0.496 0 0.090	 0.090	 0.910

50	 0.260	 0.232	 0.493 0.507 0 0.086	 0.086	 0.914

55	 0.242	 0.241	 0.482 0.518 0 0.084	 0.084	 0.916

60	 0.224	 0.248	 0.473 0.527 0 0.082	 0.082	 0.918

65	 0.209	 0.255	 0.464 0.536 0 0.080	 0.080	 0.920

70	 0.194	 0.261	 0.456 0.544 0 0.079	 0.079	 0.921

75	 0.181	 0.267	 0.448 0.552 0 0.078	 0.078	 0.922

80	 0.169	 0.272	 0.441 0.559 0 0.078	 0.078	 0.922

85	 0.158	 0.276	 0.434 0.566 0 0.077	 0.077	 0.923

90	 0.148	 0.281	 0.428 0.572 0 0.077	 0.077	 0.923

95	 0.138	 0.285	 0.423 0.577 0 0.076	 0.076	 0.924

100	 0.129	 0.288	 0.417 0.583 0 0.076	 0.076	 0.924

Average 0.298 0.213 0.511 0.030 0.090 0.119

	 	
 Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems

 6-99 

Table 6‐A‐5—continued

Year after

Production

Pacific Northwest, West, Hardwood Pacific Southwest, Softwood

In Use

All Total

Emissions

In

Use

All Total

Emissions In

Landfills

Total		

Stored

In

Landfills

Total		

Stored

0	 0.531 0.000	 0.531 0.469 0.675 0.000	 0.675	 0.325

1	 0.476 0.027	 0.503 0.497 0.611 0.036	 0.647	 0.353

2	 0.447 0.038	 0.485 0.515 0.587 0.047	 0.634	 0.366

3	 0.421 0.048	 0.469 0.531 0.566 0.056	 0.622	 0.378

4	 0.397 0.057	 0.454 0.546 0.546 0.065	 0.611	 0.389

5	 0.376 0.064	 0.440 0.560 0.528 0.072	 0.600	 0.400

6	 0.357 0.071	 0.428 0.572 0.511 0.080	 0.591	 0.409

7	 0.327 0.081	 0.408 0.592 0.488 0.089	 0.577	 0.423

8	 0.303 0.089	 0.393 0.607 0.468 0.097	 0.565	 0.435

9	 0.284 0.096	 0.380 0.620 0.451 0.104	 0.555	 0.445

10	 0.269 0.101	 0.369 0.631 0.437 0.110	 0.547	 0.453

15	 0.222 0.115	 0.337 0.663 0.387 0.131	 0.518	 0.482

20	 0.197 0.122	 0.319 0.681 0.353 0.146	 0.499	 0.501

25	 0.179 0.127	 0.306 0.694 0.324 0.159	 0.483	 0.517

30	 0.164 0.132	 0.295 0.705 0.299 0.170	 0.469	 0.531

35	 0.150 0.136	 0.286 0.714 0.276 0.180	 0.457	 0.543

40	 0.137 0.140	 0.278 0.722 0.256 0.190	 0.445	 0.555

45	 0.126 0.144	 0.270 0.730 0.237 0.198	 0.435	 0.565

50	 0.115 0.148	 0.263 0.737 0.220 0.205	 0.425	 0.575

55	 0.106 0.151	 0.257 0.743 0.204 0.212	 0.416	 0.584

60	 0.097 0.155	 0.252 0.748 0.189 0.218	 0.408	 0.592

65	 0.089 0.157	 0.247 0.753 0.176 0.224	 0.400	 0.600

70	 0.082 0.160	 0.242 0.758 0.164 0.229	 0.393	 0.607

75	 0.075 0.163	 0.238 0.762 0.153 0.233	 0.387	 0.613

80	 0.069 0.165	 0.234 0.766 0.143 0.238	 0.381	 0.619

85	 0.064 0.167	 0.231 0.769 0.133 0.241	 0.375	 0.625

90	 0.059 0.169	 0.227 0.773 0.125 0.245	 0.370	 0.630

95	 0.054 0.171	 0.224 0.776 0.117 0.248	 0.365	 0.635

100	 0.050 0.172	 0.222 0.778 0.109 0.251	 0.361	 0.639

Average 0.145 0.139 0.284 0.254 0.190 0.444

	 	
Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

6-100

Table 6‐A‐5—continued 		 	 	 	

Year after

Production

Rocky Mountain, Softwood 		 	 	 	

In Use

All

Total

Emissions

		 	 	 	

In

Landfills

Total		

Stored 		 	 	 	

0	 0.704	 0.000	 0.704 0.296

1	 0.640	 0.037	 0.677 0.323 		 	 	 	

2	 0.615	 0.048	 0.663 0.337

3	 0.592	 0.057	 0.650 0.350 		 	 	 	

4	 0.572	 0.066	 0.638 0.362

5	 0.552	 0.075	 0.627 0.373 		 	 	 	

6	 0.535	 0.082	 0.617 0.383

7	 0.510	 0.092	 0.602 0.398 		 	 	 	

8	 0.489	 0.101	 0.590 0.410

9	 0.472	 0.108	 0.579 0.421 		 	 	 	

10	 0.457	 0.114	 0.571 0.429

15	 0.404	 0.136	 0.540 0.460 		 	 	 	

20	 0.368	 0.152	 0.520 0.480

25	 0.338	 0.166	 0.504 0.496 		 	 	 	

30	 0.312	 0.177	 0.489 0.511

35	 0.288	 0.188	 0.476 0.524 		 	 	 	

40	 0.266	 0.198	 0.464 0.536

45	 0.247	 0.206	 0.453 0.547 		 	 	 	

50	 0.229	 0.214	 0.443 0.557

55	 0.212	 0.221	 0.433 0.567 		 	 	 	

60	 0.197	 0.228	 0.425 0.575

65	 0.183	 0.234	 0.417 0.583 		 	 	 	

70	 0.170	 0.239	 0.409 0.591

75	 0.159	 0.244	 0.403 0.597 		 	 	 	

80	 0.148	 0.248	 0.396 0.604

85	 0.138	 0.252	 0.390 0.610 		 	 	 	

90	 0.129	 0.256	 0.385 0.615

95	 0.121	 0.259	 0.380 0.620 		 	 	 	

100	 0.113	 0.262	 0.375 0.625

Average 0.265 0.198 0.463 		 	 	 	

	 	
 Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems

 6-101 

Table 6‐A‐5—continued

Year after

Production

Southeast, Softwood

In Use

Sawlog Total

Emissions

In

Use

Pulpwood Total

Emissions In

Landfills

Total		

Stored

In

Landfills

Total		

Stored

0	 0.636 0.000	 0.636 0.364 0.553 0.000	 0.553	 0.447

1	 0.578 0.034	 0.612 0.388 0.490 0.024	 0.514	 0.486

2	 0.557 0.043	 0.600 0.400 0.442 0.040	 0.482	 0.518

3	 0.537 0.052	 0.589 0.411 0.399 0.054	 0.453	 0.547

4	 0.519 0.060	 0.578 0.422 0.361 0.066	 0.427	 0.573

5	 0.502 0.067	 0.569 0.431 0.328 0.076	 0.403	 0.597

6	 0.486 0.074	 0.560 0.440 0.298 0.084	 0.382	 0.618

7	 0.465 0.083	 0.547 0.453 0.247 0.100	 0.347	 0.653

8	 0.447 0.090	 0.537 0.463 0.208 0.111	 0.319	 0.681

9	 0.432 0.096	 0.528 0.472 0.178 0.119	 0.297	 0.703

10	 0.418 0.102	 0.520 0.480 0.155 0.124	 0.279	 0.721

15	 0.371 0.122	 0.494 0.506 0.098 0.132	 0.230	 0.770

20	 0.339 0.137	 0.476 0.524 0.079 0.128	 0.208	 0.792

25	 0.311 0.150	 0.461 0.539 0.071 0.123	 0.194	 0.806

30	 0.287 0.161	 0.448 0.552 0.066 0.118	 0.184	 0.816

35	 0.265 0.171	 0.436 0.564 0.062 0.115	 0.177	 0.823

40	 0.245 0.180	 0.425 0.575 0.058 0.112	 0.170	 0.830

45	 0.227 0.188	 0.415 0.585 0.055 0.110	 0.165	 0.835

50	 0.210 0.195	 0.405 0.595 0.052 0.109	 0.161	 0.839

55	 0.195 0.202	 0.397 0.603 0.049 0.108	 0.157	 0.843

60	 0.181 0.208	 0.389 0.611 0.046 0.108	 0.154	 0.846

65	 0.169 0.213	 0.382 0.618 0.044 0.108	 0.151	 0.849

70	 0.157 0.218	 0.375 0.625 0.041 0.108	 0.149	 0.851

75	 0.146 0.222	 0.369 0.631 0.039 0.108	 0.147	 0.853

80	 0.137 0.226	 0.363 0.637 0.037 0.108	 0.145	 0.855

85	 0.127 0.230	 0.358 0.642 0.035 0.108	 0.143	 0.857

90	 0.119 0.233	 0.353 0.647 0.033 0.109	 0.142	 0.858

95	 0.111 0.236	 0.348 0.652 0.031 0.109	 0.141	 0.859

100	 0.104 0.239	 0.344 0.656 0.030 0.110	 0.140	 0.860

Average 0.243 0.180 0.423 0.082 0.109 0.191

	 	
Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

6-102

Table 6‐A‐5—continued

Year after

Production

Southeast, Hardwood

In

Use

Sawlog Total

Emissions

In

Use

Pulpwood Total

Emissions In

Landfills

Total		

Stored

In

Landfills

Total		

Stored

0	 0.609	 0.000	 0.609 0.391 0.591 0.000	 0.591	 0.409

1	 0.552	 0.035	 0.587 0.413 0.525 0.027	 0.552	 0.448

2	 0.534	 0.043	 0.577 0.423 0.480 0.043	 0.522	 0.478

3	 0.518	 0.051	 0.569 0.431 0.439 0.056	 0.495	 0.505

4	 0.503	 0.058	 0.561 0.439 0.404 0.067	 0.471	 0.529

5	 0.488	 0.065	 0.553 0.447 0.372 0.077	 0.449	 0.551

6	 0.475	 0.071	 0.546 0.454 0.344 0.085	 0.430	 0.570

7	 0.457	 0.079	 0.537 0.463 0.296 0.100	 0.397	 0.603

8	 0.442	 0.086	 0.528 0.472 0.260 0.111	 0.371	 0.629

9	 0.429	 0.092	 0.521 0.479 0.231 0.119	 0.350	 0.650

10	 0.418	 0.097	 0.515 0.485 0.209 0.124	 0.334	 0.666

15	 0.373	 0.119	 0.492 0.508 0.153 0.134	 0.287	 0.713

20	 0.338	 0.136	 0.475 0.525 0.132 0.133	 0.265	 0.735

25	 0.309	 0.151	 0.460 0.540 0.121 0.130	 0.251	 0.749

30	 0.282	 0.164	 0.446 0.554 0.113 0.127	 0.240	 0.760

35	 0.258	 0.176	 0.434 0.566 0.106 0.126	 0.232	 0.768

40	 0.236	 0.186	 0.422 0.578 0.100 0.125	 0.225	 0.775

45	 0.216	 0.196	 0.412 0.588 0.094 0.125	 0.218	 0.782

50	 0.198	 0.204	 0.402 0.598 0.089 0.125	 0.213	 0.787

55	 0.181	 0.212	 0.393 0.607 0.084 0.125	 0.209	 0.791

60	 0.166	 0.218	 0.384 0.616 0.079 0.126	 0.205	 0.795

65	 0.152	 0.224	 0.376 0.624 0.075 0.126	 0.201	 0.799

70	 0.139	 0.230	 0.369 0.631 0.071 0.127	 0.198	 0.802

75	 0.127	 0.235	 0.362 0.638 0.067 0.128	 0.195	 0.805

80	 0.117	 0.239	 0.356 0.644 0.063 0.129	 0.193	 0.807

85	 0.107	 0.243	 0.350 0.650 0.060 0.130	 0.190	 0.810

90	 0.098	 0.247	 0.345 0.655 0.057 0.131	 0.188	 0.812

95	 0.090	 0.250	 0.340 0.660 0.054 0.132	 0.186	 0.814

100	 0.083	 0.253	 0.336 0.664 0.051 0.133	 0.185	 0.815

Average 0.231 0.187 0.417 0.119 0.123 0.242

	 	
 Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems

 6-103 

Table 6‐A‐5—continued

Year after

Production

South Central, Softwood

In Use

Sawlog Total

Emissions

In

Use

Pulpwood Total

Emissions In

Landfills

Total		

Stored

In

Landfills

Total		

Stored

0	 0.629 0.000	 0.629 0.371 0.570 0.000	 0.570	 0.430

1	 0.569 0.035	 0.603 0.397 0.506 0.026	 0.532	 0.468

2	 0.547 0.044	 0.591 0.409 0.459 0.041	 0.500	 0.500

3	 0.527 0.053	 0.580 0.420 0.417 0.055	 0.472	 0.528

4	 0.509 0.061	 0.569 0.431 0.380 0.066	 0.447	 0.553

5	 0.492 0.068	 0.560 0.440 0.348 0.076	 0.424	 0.576

6	 0.477 0.075	 0.551 0.449 0.319 0.085	 0.404	 0.596

7	 0.455 0.083	 0.538 0.462 0.270 0.100	 0.370	 0.630

8	 0.437 0.091	 0.527 0.473 0.232 0.111	 0.343	 0.657

9	 0.421 0.097	 0.518 0.482 0.202 0.119	 0.321	 0.679

10	 0.408 0.102	 0.510 0.490 0.180 0.124	 0.304	 0.696

15	 0.362 0.122	 0.484 0.516 0.123 0.133	 0.256	 0.744

20	 0.330 0.136	 0.466 0.534 0.103 0.130	 0.234	 0.766

25	 0.303 0.148	 0.451 0.549 0.094 0.126	 0.220	 0.780

30	 0.280 0.158	 0.439 0.561 0.087 0.122	 0.210	 0.790

35	 0.259 0.168	 0.427 0.573 0.082 0.120	 0.202	 0.798

40	 0.240 0.176	 0.416 0.584 0.077 0.118	 0.195	 0.805

45	 0.222 0.184	 0.406 0.594 0.072 0.117	 0.189	 0.811

50	 0.206 0.191	 0.397 0.603 0.068 0.116	 0.185	 0.815

55	 0.192 0.197	 0.389 0.611 0.064 0.116	 0.181	 0.819

60	 0.178 0.203	 0.381 0.619 0.061 0.116	 0.177	 0.823

65	 0.166 0.208	 0.374 0.626 0.058 0.116	 0.174	 0.826

70	 0.155 0.213	 0.368 0.632 0.054 0.117	 0.171	 0.829

75	 0.145 0.217	 0.362 0.638 0.051 0.117	 0.169	 0.831

80	 0.135 0.221	 0.356 0.644 0.049 0.118	 0.167	 0.833

85	 0.126 0.225	 0.351 0.649 0.046 0.119	 0.165	 0.835

90	 0.118 0.228	 0.346 0.654 0.044 0.119	 0.163	 0.837

95	 0.111 0.231	 0.342 0.658 0.042 0.120	 0.161	 0.839

100	 0.104 0.234	 0.338 0.662 0.039 0.121	 0.160	 0.840

Average 0.239 0.176 0.415 0.099 0.116 0.215

	 	
Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

6-104

Table 6‐A‐5—continued

Year after

Production

South Central, Hardwood

In Use

Sawlog Total

Emissions

In

Use

Pulpwood Total

Emissions In

Landfills

Total		

Stored

In

Landfills

Total		

Stored

0	 0.587	 0.000	 0.587	 0.413	 	 0.581	 0.000	 0.581	 0.419	

1	 0.531	 0.033	 0.564	 0.436	 0.516	 0.027	 0.542	 0.458	

2	 0.512	 0.042	 0.554	 0.446	 	 0.470	 0.042	 0.512	 0.488	

3	 0.495	 0.050	 0.545	 0.455	 0.429	 0.055	 0.484	 0.516	

4	 0.479	 0.057	 0.536	 0.464	 	 0.392	 0.067	 0.459	 0.541	

5	 0.464	 0.064	 0.528	 0.472	 0.360	 0.077	 0.437	 0.563	

6	 0.450	 0.070	 0.521	 0.479	 	 0.332	 0.085	 0.417	 0.583	

7	 0.432	 0.078	 0.510	 0.490	 0.283	 0.100	 0.383	 0.617	

8	 0.416	 0.085	 0.501	 0.499	 	 0.246	 0.111	 0.357	 0.643	

9	 0.403	 0.091	 0.493	 0.507	 0.217	 0.119	 0.336	 0.664	

10	 0.391	 0.096	 0.487	 0.513	 	 0.195	 0.124	 0.319	 0.681	

15	 0.347	 0.116	 0.463	 0.537	 0.138	 0.133	 0.272	 0.728	

20	 0.314	 0.132	 0.446	 0.554	 	 0.118	 0.131	 0.250	 0.750	

25	 0.286	 0.145	 0.432	 0.568	 0.108	 0.128	 0.236	 0.764	

30	 0.262	 0.157	 0.419	 0.581	 	 0.101	 0.125	 0.226	 0.774	

35	 0.239	 0.168	 0.407	 0.593	 0.095	 0.123	 0.217	 0.783	

40	 0.219	 0.177	 0.396	 0.604	 	 0.089	 0.121	 0.210	 0.790	

45	 0.200	 0.186	 0.386	 0.614	 0.084	 0.121	 0.204	 0.796	

50	 0.183	 0.193	 0.377	 0.623	 	 0.079	 0.120	 0.199	 0.801	

55	 0.168	 0.200	 0.368	 0.632	 0.075	 0.121	 0.195	 0.805	

60	 0.154	 0.206	 0.360	 0.640	 	 0.070	 0.121	 0.191	 0.809	

65	 0.141	 0.212	 0.353	 0.647	 0.067	 0.121	 0.188	 0.812	

70	 0.129	 0.217	 0.346	 0.654	 	 0.063	 0.122	 0.185	 0.815	

75	 0.118	 0.222	 0.340	 0.660	 0.060	 0.123	 0.182	 0.818	

80	 0.108	 0.226	 0.334	 0.666	 	 0.057	 0.124	 0.180	 0.820	

85	 0.099	 0.229	 0.329	 0.671	 0.054	 0.124	 0.178	 0.822	

90	 0.091	 0.233	 0.324	 0.676	 	 0.051	 0.125	 0.176	 0.824	

95	 0.084	 0.236	 0.319	 0.681	 0.048	 0.126	 0.174	 0.826	

100	 0.077	 0.238	 0.315	 0.685	 	 0.046	 0.127	 0.173	 0.827	

Average 0.215 0.177 0.393 0.110 0.119 0.229

	 	
 Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems

 6-105 

Table 6‐A‐5—continued 		 	 	 	

Year after

Production

Other West, Hardwood 		 	 	 	

In Use

All Total

Emissions

		 	 	 	

In

Landfills

Total		

Stored 		 	 	 	

0	 0.568	 0.000	 0.568 0.432

1	 0.516	 0.028	 0.544 0.456 		 	 	 	

2	 0.494	 0.038	 0.532 0.468

3	 0.473	 0.046	 0.520 0.480 		 	 	 	

4	 0.455	 0.054	 0.509 0.491

5	 0.438	 0.061	 0.499 0.501 		 	 	 	

6	 0.422	 0.068	 0.490 0.510

7	 0.399	 0.077	 0.476 0.524 		 	 	 	

8	 0.381	 0.084	 0.465 0.535

9	 0.365	 0.090	 0.455 0.545 		 	 	 	

10	 0.352	 0.095	 0.447 0.553

15	 0.307	 0.113	 0.421 0.579 		 	 	 	

20	 0.277	 0.126	 0.403 0.597

25	 0.253	 0.136	 0.389 0.611 		 	 	 	

30	 0.232	 0.146	 0.377 0.623

35	 0.212	 0.154	 0.366 0.634 		 	 	 	

40	 0.195	 0.162	 0.356 0.644

45	 0.179	 0.169	 0.347 0.653 		 	 	 	

50	 0.164	 0.175	 0.339 0.661

55	 0.151	 0.181	 0.331 0.669 		 	 	 	

60	 0.138	 0.186	 0.324 0.676

65	 0.127	 0.190	 0.318 0.682 		 	 	 	

70	 0.117	 0.195	 0.312 0.688

75	 0.108	 0.198	 0.306 0.694 		 	 	 	

80	 0.099	 0.202	 0.301 0.699

85	 0.091	 0.205	 0.296 0.704 		 	 	 	

90	 0.084	 0.208	 0.292 0.708

95	 0.078	 0.210	 0.288 0.712 		 	 	 	

100	 0.072	 0.213	 0.284 0.716

Average 0.195 0.161 0.357 		 	 	 	

	 	
Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems 

6-106

Table 6‐A‐6: Regional Factors to Estimate Carbon in Roundwood Logs, Bark on Logs, and

Fuelwood

Regiona Timber

Type

Roundwood

Category

Ratio of

Roundwood to

Growing‐Stock

Volume that is

Roundwoodb

Ratio of

Carbon in

Bark to

Carbon in

Woodc

Fraction of

Growing‐Stock

Volume that is

Roundwoodd

Ratio of

Fuelwood to

Growing‐Stock

Volume that is

Roundwoodb

Northeast	

SW	 Sawlog	 0.991 0.182 0.948	 0.136 Pulpwood	 3.079 0.185

HW	 Sawlog	 0.927 0.199 0.879	 0.547 Pulpwood	 2.177 0.218

North	

Central	

SW	 Sawlog	 0.985 0.182 0.931	 0.066 Pulpwood	 1.285 0.185

HW	 Sawlog	 0.960 0.199 0.831	 0.348 Pulpwood	 1.387 0.218

Pacific	

Coast	

SW	 Sawlog	 0.965 0.181 0.929	 0.096 Pulpwood	 1.099 0.185

HW	 Sawlog	 0.721 0.197 0.947	 0.957 Pulpwood	 0.324 0.219

Rocky	

Mountain	

SW	 Sawlog	 0.994 0.181 0.907	 0.217 Pulpwood	 2.413 0.185

HW	 Sawlog	 0.832 0.201 0.755	 3.165 Pulpwood	 1.336 0.219

South	

SW	 Sawlog	 0.990 0.182 0.891	 0.019 Pulpwood	 1.246 0.185

HW	 Sawlog	 0.832 0.198 0.752	 0.301 Pulpwood	 1.191 0.218

SW=Softwood,	HW=Hardwood.	

a	North	Central	includes	the	Northern	Prairie	States	and	the	Northern	Lake	States;	Pacific	Coast	includes	the	Pacific	

Northwest	(West	and	East)	and	the	Pacific	Southwest;	Rocky	Mountain	includes	Rocky	Mountain,	North	and	South;	and	

South	includes	the	Southeast	and	South	Central.	

b	Values	and	classifications	are	based	on	data	in	Tables	2.2,	3.2,	4.2,	5.2,	and	6.2	of	Johnson	(2001).	 c	Ratios	are	calculated	from	carbon	mass	based	on	biomass	component	equations	in	Jenkins	et	al.	(2003a),	applied	to	all	

live	trees	identified	as	growing	stock	on	timberland	stands	classified	as	medium‐	or	large‐diameter	stands	in	the	survey	

data	for	the	conterminous	United	States	from	USDA	Forest	Service,	FIA	Program’s	database	of	forest	surveys	

(FIADB)(Alerich	et	al.,	2005;	USDA	Forest	Service,	2005).	Carbon	mass	is	calculated	for	boles	from	stump	to	4‐inch	

(10.2	cm)	top,	outside	diameter.	

d	Values	and	classifications	are	based	on	data	in	Tables	2.9,	3.9,	4.9,	5.9,	and	6.9	of	Johnson	(2001).	
 Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems

 6-107 

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This	page	is	intentionally	left	blank.	
Author:		

Stephen	M.	Ogle,	Colorado	State	University	

Contents:

7 Quantifying Greenhouse Gas Sources and Sinks from Land‐Use Change .............................7‐3

7.1	 Overview	...........................................................................................................................................................	7‐3	

7.2	 Definitions	of	Land	Use	...............................................................................................................................	7‐4	

7.3	 Caveats	...............................................................................................................................................................	7‐6	

7.4	 Estimating	GHG	Flux	from	Land‐Use	Change	.....................................................................................	7‐6	

7.4.1	 Carbon	Pools	in	Live	Biomass,	Dead	Biomass,	and	Soil	Organic	Carbon	..	7‐8	

7.4.2	 Changes	in	Soil	Carbon	..................................................................................................	7‐8	

7.4.3	 Changes	in	other	GHG	emissions	.............................................................................	7‐13	

Chapter	7	References	.............................................................................................................................................	7‐14	

Suggested	Chapter	Citation:		Ogle,	S.M.,	2014.	Chapter	7:		Quantifying	Greenhouse	Gas	Sources	and	

Sinks	from	Land	Use	Change.		In	Quantifying Greenhouse Gas Fluxes in Agriculture and Forestry:		

Methods for Entity‐Scale Inventory.	Technical	Bulletin	Number	1939.		Office	of	the	Chief	Economist,	

U.S.	Department	of	Agriculture,	Washington,	DC.	606	pages.		July	2014.		Eve,	M.,	D.	Pape,	M.	Flugge,	

R.	Steele,	D.	Man,	M.	Riley‐Gilbert,	and	S.	Biggar,	Eds.	

USDA	is	an	equal	opportunity	provider	and	employer.	

Chapter 7

Quantifying Greenhouse Gas 

Sources and Sinks from Land-Use 

Change 
Chapter 7: Quantifying Greenhouse Gas Sources and Sinks from Land-Use Change 

7-2 

Acronyms, Chemical Formulae, and Units

C	 Carbon	

CH4	 Methane	

CO2	 Carbon	dioxide	

CO2‐eq	 Carbon	dioxide	equivalents

DOM	 Dead	organic	matter	

EPA	 Environmental	Protection	Agency

FIA	 Forest	Inventory	and	Analysis

GHG	 Greenhouse	gas	

ha	 Hectare	

IPCC	 Intergovernmental	Panel	on	Climate	Change

N2O	 Nitrous	Oxide	

NRI	 Natural	Resources	Inventory

PRISM	 Parameter‐Elevation	Regressions	on	Independent	Slopes	Model	

SOC	 Soil	organic	carbon	

SSURGO	 Soil	Survey	Geographic	Database

USDA		 U.S.	Department	of	Agriculture

	 	
 Chapter 7: Quantifying Greenhouse Gas Sources and Sinks from Land-Use Change 

 7-3 

7 Quantifying Greenhouse Gas Sources and Sinks from Land‐Use

Change

This	chapter	provides	guidance	on	estimating	the	net	greenhouse	gas	(GHG)	flux	resulting	from	

changes	between	land	use	types—i.e.,	conversions	into	and	out	of	cropland,	wetland,	grazing	land,	

or	forestland—at	the	entity	scale.	In	some	cases,	it	is	sufficient	to	estimate	the	net	GHG	flux	

associated	with	the	new	land	use.	If	changing	from	one	land	use	to	another	has	a	significant	effect	

on	carbon	stocks	(e.g.,	changes	in	forest	carbon	stocks,	changes	in	soil	carbon),	it	will	be	necessary	

to	represent	that	influence	associated	with	a	specific	land‐use	change	(e.g.,	wetland	to	cropland,	

grazing	land	to	cropland,	forestland	to	cropland).	Table	7‐1	provides	a	summary	and	description	of	

the	sources	covered	in	this	chapter.	

Table 7‐1: Overview of Land‐Use Change Sources and Associated GHGs

Source

Method for GHG

Estimation Description

CO2 N2O CH4

Annual	change	

in	carbon	

stocks	in	dead	

wood	and	litter	

due	to	land	

conversion	

 	 	

Live	and	dead	biomass	carbon	stocks	and	soil	organic	carbon	

constitutes	a	significant	carbon	sink	in	many	forest	and	agricultural	

lands.	Following	land‐use	conversion,	the	estimation	of	dead	

biomass	carbon	stock	changes	during	transition	periods	requires

that	the	area	subject	to	land‐use	change	on	the	entity’s	operation	

be	tracked	for	the	duration	of	the	20‐year	transition	period.		

Change	in	soil	

organic	carbon	

stocks	for	

mineral	soils	



Soil	organic	carbon	stocks	are	influenced	by	land‐use	change	

(Aalde	et	al.,	2006)	due	to	changes	in	productivity	that	influence	

carbon	inputs,	and	to	changes	in	soil	management	that	influence

carbon	outputs	(Davidson	and	Ackerman,	1993;	Ogle	et	al.,	2005;

Post	and	Kwon,	2000).	The	most	significant	changes	in	soil	organic	

carbon	occur	with	land‐use	change,	particularly	conversions	to	

croplands,	due	to	changes	in	the	disturbance	regimes	and	

associated	effects	on	soil	aggregate	dynamics	(Six	et	al.,	2000).	

7.1 Overview

In	many	cases,	the	methods	proposed	to	estimate	contributions	to	the	GHG	flux	resulting	from	land‐

use	change	are	the	same	as	those	used	to	estimate	carbon	stock	changes	in	the	individual	chapters	

on	Cropland	and	Grazing	Land,	Forestry,	and	Wetlands;	although,	in	specific	cases	guidance	is	also	

provided	on	reconciling	carbon‐stock	estimates	between	discrete	data	sets	and	estimation	methods	

(e.g.,	reconciling	forest	soil	carbon	estimates	and	cropland	soil	carbon	estimates	for	land‐use	

change	from	forest	land	to	cropland).	Table	7‐2	presents	the	methodologies	for	each	source	and	

indicates	their	section.		
Chapter 7: Quantifying Greenhouse Gas Sources and Sinks from Land-Use Change 

7-4 

Table 7‐2: Overview of Cropland and Grazing Land Systems Sources, Method and Section

Section Source Proposed Method

7.4.1	 Annual	change	in	

carbon	stocks	in	dead	

wood	and	litter	due	

to	land	conversion	

The	change	in	carbon	stocks	in	dead	wood	and	litter	due	to	land	conversion	

is	estimated	as	the	difference	in	carbon	stocks	in	the	old	and	new	land‐use	

categories		applied	in	the	year	of	the	conversion	(carbon	losses),	or	

distributed	uniformly	over	the	length	of	the	transition	period	(carbon	gains)	

(Aalde	et	al.,	2006).	

7.4.2	 Change	in	soil	organic	

carbon	stocks	for	

mineral	soils	

The	methodologies	to	estimate	soil	carbon	stock	changes	for	organic	soils	

and	mineral	soils	are	adopted	from	IPCC	(Aalde	et	al.,	2006).	

The	remainder	of	this	chapter	is	organized	as	follows:	

 Definitions	

 Caveats	

 Steps	for	estimating	GHG	flux	from	land‐use	change	

 Overlaps,	issues,	and	assembly	instructions	for	GHG	flux	estimation	from	land‐use	change	

7.2 Definitions of Land Use

A	land‐use	categorization	system	that	is	consistent	and	complete	(both	temporally	and	spatially)	is	

needed	to	assess	land	use	and	land‐use	change	status	within	an	entity’s	boundaries.	Each	entity	

should	ensure	that	it	characterizes	all	of	the	land	within	its	boundary	according	to	the	following	

land‐use	types:	cropland,	grazing	land,	forest	land,	wetland,	settlements	(e.g.,	residential,	farm,	and	

commercial	buildings),	and	other	land	(e.g.,	bare	soil,	rock).	The	land‐use	definitions	provided	

below	are	expected	to	be	adopted	by	entities	using	this	report.	It	is	critical	that	individual	parcel	

areas	are	estimated	accurately	and	when	combined	add	up	to	the	total	land	area	reported	by	the	

entity	before	and	after	the	land‐use	change.	

Current	definitions	for	land	use	that	are	consistent	with	other	policy	programs	related	to	GHG	

estimation	(e.g.,	Intergovernmental	Panel	on	Climate	Change	(IPCC),	Natural	Resources	Inventory	

(NRI))	are	provided	below.	These	definitions	are	specific	to	the	United	States	and	are	based	

predominantly	on	criteria	used	in	the	land‐use	surveys	for	the	United	States.	Specifically,	the	

definition	of	forest	land	is	based	on	the	Forest	Inventory	and	Analysis	(FIA)	definition	of	forest,1

while	definitions	of	cropland,	grazing	land,	and	settlements	are	based	on	the	NRI.2	The	definitions	

for	other	land	and	wetlands	are	based	on	the	IPCC	(2006)	definitions	for	these	categories.	

 Forest Land:	A	land‐use	category	that	includes	areas	at	least	36.6	meters	wide	and	0.4	

hectares	in	size	with	at	least	10	percent	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.	Forest	land	includes	transition	zones,	such	as	areas	between	forest	

and	non‐forest	lands	that	have	at	least	10	percent	cover	(or	equivalent	stocking)	with	live	

trees	and	forest	areas	adjacent	to	urban	and	built‐up	lands.	Roadside,	streamside,	and	

shelterbelt	strips	of	trees	must	have	a	crown	width	of	at	least	36.6	meters	and	continuous	

length	of	at	least	110.6	meters	to	qualify	as	forest	land.	Unimproved	roads	and	trails,	

																																																													

1	See	FIA	Glossary	http://socrates.lv‐hrc.nevada.edu/fia/ab/issues/pending/glossary/Glossary_5_30_06.pdf 2	See	National	Resource	Inventory	Glossary	of	Selected	Terms	(p.	9)	

http://www.nrcs.usda.gov/Internet/FSE_DOCUMENTS/stelprdb1041379.pdf
 Chapter 7: Quantifying Greenhouse Gas Sources and Sinks from Land-Use Change 

 7-5 

streams,	and	clearings	in	forest	areas	are	classified	as	forests	if	they	are	less	than	36.6	

meters	wide	or	0.4	hectares	in	size;	otherwise	they	are	excluded	from	forest	land	and	

classified	as	settlements.	Tree‐covered	areas	in	agricultural	production	settings,	such	as	

fruit	orchards,	or	tree‐covered	areas	in	urban	settings,	such	as	city	parks,	are	not	

considered	forest	land	(Smith	et	al.,	2009).	

 Cropland:	A	land‐use	category	that	includes	areas	used	for	the	production	of	adapted	crops	

for	harvest;	this	category	includes	both	cultivated	and	non‐cultivated	lands.	Cultivated	

crops	include	row	crops	or	close	grown	crops	and	also	hay	or	pasture	in	rotation	with	

cultivated	crops.	Non‐cultivated	cropland	includes	continuous	hay,	perennial	crops	(e.g.,	

orchards),	and	horticultural	cropland.	Cropland	also	includes	land	with	alley	cropping	and	

windbreaks,	as	well	as	lands	in	temporary	fallow	or	enrolled	in	conservation	reserve	

programs	(i.e.,	set‐asides3).	Roads	through	cropland,	including	interstate	highways,	state

highways,	other	paved	roads,	gravel	roads,	dirt	roads,	and	railroads	are	excluded	from	

cropland	area	estimates	and	are,	instead,	classified	as	settlements.	

 Grazing Land:4	A	land‐use	category	under	which	the	plant	cover	is	composed	principally	of	

grasses,	grass‐like	plants,	forbs,	or	shrubs	suitable	for	grazing	and	browsing.	This	category	

includes	both	pastures	and	native	rangelands	and	areas	where	practices	such	as	clearing,	

burning,	chaining,	and/or	chemicals	are	applied	to	maintain	the	grass	vegetation.	Savannas,	

some	wetlands	and	deserts,	and	tundra	are	considered	grazing	land.	Woody	plant	

communities	of	low	forbs	and	shrubs,	such	as	mesquite,	chaparral,	mountain	shrub,	and	

pinyon‐juniper,	are	also	classified	as	grazing	land	if	they	do	not	meet	the	criteria	for	forest	

land.	Grazing	land	includes	land	managed	with	agroforestry	practices	such	as	silvopasture	

and	windbreaks,	assuming	the	stand	or	woodlot	does	not	meet	the	criteria	for	forest	land.	

Roads	through	grazing	land,	including	interstate	highways,	state	highways,	other	paved	

roads,	gravel	roads,	dirt	roads,	and	railroads	are	excluded	from	grazing	land	area	estimates	

and	are,	instead,	classified	as	Settlements.	

 Wetlands:5	A	land‐use	category	that	includes	land	with	hydric	soils,	native	or	adapted	

hydrophytic	vegetation,	and	a	hydrologic	regime	were	the	soil	is	saturated	during	the	

growing	season	in	most	years.	Wetland	vegetation	types	may	include	marshes,	grasslands,	

or	forests.	Wetlands	may	have	water	levels	that	are	artificially	changed,	or	where	the	

vegetation	composition	or	productivity	is	manipulated.	These	lands	include		undrained	

forested	wetlands,	grazed	woodlands	and	grasslands,	impoundments	managed	for	wildlife,	

and	lands	that	are	being	restored	following	conversion	to	a	non‐wetland	condition	

(typically	as	a	result	of	agricultural	drainage).	Provisions	for	engineered	wetlands	including	

																																																													

3	A	set‐aside	is	cropland	that	has	been	taken	out	of	active	cropping	and	converted	to	some	type	of	vegetative	

cover,	including,	for	example,	native	grasses	or	trees.	

4	Note	that	this	definition	is	the	“grassland”	definition	from	the	NIR	with	“grassland”	replaced	with	“grazing	

land.”	

5 The	jurisdictional	definition	of	a	wetland	is	“those	areas	that	are	inundated	or	saturated	by	surface	or	

groundwater	at	a	frequency	and	duration	sufficient	to	support,	and	that	under	normal	circumstances	do	

support,	a	prevalence	of	vegetation	typically	adapted	for	life	in	saturated	soil	conditions.	Wetlands	generally	

include	swamps,	marshes,	bogs,	and	similar	areas”	(EPA,	1980).	The	1987	Corps	of	Engineers	Wetland	

Delimitation	Manual	&	Regional	Supplements	(U.S.	Army	Corps	of	Engineers,	1987)	is	used	to	identify	

wetlands	in	the	field.		
Chapter 7: Quantifying Greenhouse Gas Sources and Sinks from Land-Use Change 

7-6 

storm	water	detention	ponds,	constructed	wetlands	for	water	treatment,	and	farm	ponds	or	

reservoirs	are	not	included.	Natural	lakes	and	streams	are	also	not	included.	

 Settlements:	A	land‐use	category	representing	developed	areas	consisting	of	units	of	0.25	

acres	(0.1	ha)	or	more	that	includes	residential,	industrial,	commercial,	and	institutional	

land;	construction	sites;	public	administrative	sites;	railroad	yards;	cemeteries;	airports;	

golf	courses;	sanitary	landfills;	sewage	treatment	plants;	water	control	structures	and	

spillways;	parks	within	urban	and	built‐up	areas;	and	highways,	railroads,	and	other	

transportation	facilities.	Also	included	are	tracts	of	less	than	10	acres	(4.05	ha)	that	may	

meet	the	definitions	for	Forest	Land,	Cropland,	Grassland,	or	Other	Land	but	are	completely	

surrounded	by	urban	or	built‐up	land,	and	so	are	included	in	the	settlement	category.		Rural	

transportation	corridors	located	within	other	land	uses	(e.g.,	Forest	Land,	Cropland,	and	

Grassland)	are	also	included	in	Settlements.	

 Other Land:	A	land‐use	category	that	includes	bare	soil,	rock,	ice,	and	all	land	areas	that	do	

not	fall	into	any	of	the	other	five	land‐use	categories,	which	allows	the	total	of	identified	

land	areas	to	match	the	managed	land	base.	

7.3 Caveats

The	methods	presented	here	for	quantifying	GHG	flux	from	land‐use	change	are	intended	for	use	at	

the	entity	scale	on	lands	managed	to	enhance	the	production	of	food,	feed,	fiber,	and	renewable	

energy.	Methods	are	currently	not	provided	for	estimating	emissions	from	energy	used	when	

converting	land	use	from	one	category	to	another.	Methods	are	also	not	provided	for	land‐use	

change	from	settlements	or	the	“other	land”	category	to	forest	land,	cropland,	grazing	land,	or	

wetlands.	These	methods	have	been	developed	for	U.S.	conditions	and	are	considered	applicable	to	

agricultural	and	forestry	production	systems	in	the	United	States.		

7.4 Estimating GHG Flux from Land‐Use Change

Rationale	for	Selected	Method	

This	method	is	based	on	the	IPCC	2006	Guidelines	(IPCC,	2006)and	represents	the	most		consistent	

method	for	estimating	emissions	from	land‐use	change.		Other	methods	are	provided	for	land	

parcels	that	are	not	undergoing	land	use	change,	and	arguably	those	methods	are	more	

comprehensive	for	estimating	emissions	for	the	specific	land	use.		However,	it	is	critical	that	an	

individual	land	parcel	has	a	consistent,	seamless	method	for	estimating	carbon	stock	changes	

throughout	the	time	series.		Otherwise	artificial	changes	in	stocks	can	be	estimated	due	to	a	change	

Method for Estimating GHG Flux from Land‐Use Change

 The	GHG	flux	associated	with	land‐use	change	is	estimated	based	on	the	balance	of	carbon	

losses	from	the	previous	land	use	following	conversion	and	the	carbon	gains	with	the	

current	land	use.	

 This	section	only	covers	methodologies	for	dead	organic	matter	carbon	and	soil	organic	

matter	carbon.	Guidance	on	biomass	carbon	methods	are	provided	in	the	land‐use‐

specific	sections	(Cropland,	Grazing	land,	Forest	Land,	and	Wetlands).	

 The	change	in	carbon	stocks	in	dead	wood	and	litter	due	to	land	conversion	is	estimated	

as	the	difference	in	carbon	stocks	in	the	old	and	new	land‐use	categories		applied	in	the	

year	of	the	conversion	(carbon	losses),	or	distributed	uniformly	over	the	length	of	the	

transition	period	(carbon	gains).	

 The	methodologies	to	estimate	soil	carbon	stock	changes	for	organic	soils	and	mineral	

soils	are	adopted	from	IPCC	(Aalde	et	al.,	2006).		
 Chapter 7: Quantifying Greenhouse Gas Sources and Sinks from Land-Use Change 

 7-7 

in	the	method.		The	methods	based	on	the	IPCC	2006	Guidelines	(IPCC,	2006)	provide	this	

consistency	and	seamless	integration.		Further	testing	and	development	will	be	needed	before	the	

more	comprehensive	methods	provided	in	each	land	use	section	can	be	integrated	into	a	seamless	

approach	for	estimating	the	carbon	stock	changes.			

Description	of	Method	

For	inventory	purposes,	changes	in	carbon	stock	in	biomass	should	be	estimated	for:	(1)	land	

remaining	in	the	same	land‐use	category;	and	(2)	land	converted	to	a	new	land‐use	category.	The	

methods	provided	in	this	section	are	strictly	for	portions	of	an	entity’s	operation	that	have	

undergone	a	land	use	change.	The	soil	carbon	changes	must	be	addressed	over	a	20‐year	period.	

Aboveground	and	below	ground	biomass	are	estimated	on	an	annual	basis.	Note	that	this	section	

only	addresses	dead	organic	matter	carbon	and	soil	organic	matter	carbon.	Biomass	carbon	

methods	should	follow	the	guidance	provided	in	the	land‐use‐specific	sections	(Cropland,	Grazing	

land,	Forest	Land,	and	Wetlands).		

The	reporting	convention	is	that	all	carbon	stock	changes	and	non‐CO2	GHG	emissions	associated	

with	a	land‐use	change	are	reported	in	the	new	land‐use	category.	For	example,	in	the	case	of	

conversion	of	forest	land	to	cropland,	both	the	carbon	stock	changes	associated	with	the	clearing	of	

the	forest	as	well	as	any	subsequent	carbon	stock	changes	that	result	from	the	conversion,	are	

reported	under	cropland		(IPCC,	2006).	

The	GHG	flux	associated	with	land‐use	change	is	essentially	the	sum	of	the	GHG	fluxes	associated	

with	previous	(i.e.,	old)	land‐use	categories	plus	the	sum	of	the	GHG	fluxes	associated	with	the	

current	(i.e.,	new)	land‐use	categories	for	a	specified	area	undergoing	conversion	from	the	old	to	

new	land‐use	category.	GHG	emissions	and	stock	changes	not	resulting	from	a	land‐use	change	are	

estimated	with	methods	in	the	land‐use‐specific	sections.	

For	each	land‐use	category	undergoing	a	land‐use	change,	it	is	important	to	estimate	the	annual	

carbon	stock	change	occurring	within	each	stratum	or	subdivision	(e.g.,	carbon	pool,	management	

regime)	for	that	land‐use	category.	

Equation 7‐1: Annual Carbon Stock Changes for a Land‐Use Change Estimated as the

Sum of Changes in All Land‐Use Categories

ΔCLUC=	ΔCLUC o +	ΔCLUC n

and	

ΔCLUC =	ΔCLUC FL +	ΔCLUC CL +	ΔCLUC GL +	ΔCLUC WL		

Where:	

ΔC	=	carbon	stock	change	(metric	tons	CO2‐eq	ha‐1	year‐1)	

Indices	denote	the	following	land‐use	categories:	

LUC		 =	land‐use	change	

o		 =	old	land‐use	category	

n		 =	new	land‐use	category	

FL		 =	forest	land	

CL		 =	cropland	

GL		 =	grazing	land	

WL		 =	wetlands	
Chapter 7: Quantifying Greenhouse Gas Sources and Sinks from Land-Use Change 

7-8 

For	example,	in	the	case	of	conversion	of	forest	land	to	cropland,	the	carbon	stock	changes	

associated	with	each	of	the	forest	carbon	pools	plus	harvested	wood	products	should	be	assessed,	

as	well	as	any	subsequent	carbon	stock	changes	that	result	from	the	conversion	(specific	

annualized	changes	in	dead	organic	matter,	soil	carbon,	etc.).	

7.4.1 Carbon Pools in Live Biomass, Dead Biomass, and Soil Organic Carbon

Live	and	dead	biomass	carbon	stocks	and	soil	organic	carbon	constitute	a	significant	carbon	sink	in	

many	forest	and	agricultural	lands.	Sector‐specific	methods	for	estimating	changes	in	biomass	

carbon	stocks	are	detailed	in	the	individual	sector	chapters	and	should	be	used	when	estimating	the	

effect	of	land‐use	change.	In	addition	to	estimating	the	changes	in	biomass	carbon	stocks	before	and	

after	the	land‐use	change	using	the	sector‐specific	methods,	it	is	also	important	to	estimate	any	

increase	in	the	harvested	wood	pool	resulting	from	clearing/harvest	of	the	forest	following	the	

methods	outlined	in	Chapter	6,	Forestry.	Any	biomass	that	is	retained	on	the	land	during	the	land‐

use	conversion	will	need	to	be	included	in	the	estimation,	such	as	conversion	of	forest	to	

grasslands,	where	some	trees	are	left	to	provide	shade	for	grazing	livestock.	

Following	land‐use	conversion,	the	estimation	of	dead	biomass	carbon	stock	changes	during	

transition	periods	requires	that	the	area	subject	to	land‐use	change	on	the	entity’s	operation	be	

tracked	for	the	duration	of	the	20‐year	transition	period.	For	example,	dead	organic	matter	(DOM)	

stocks	are	assumed	to	increase	for	20	years	after	conversion	to	forest	land.	After	20	years,	the	area	

converted	becomes	forest	and	remaining	forest	land,	and	no	further	DOM	changes	are	assumed.	

The	conceptual	approach	to	estimating	changes	in	carbon	stocks	in	dead	wood	and	litter	pools	is	to	

estimate	the	difference	in	carbon	stocks	in	the	old	and	new	land‐use	categories	and	to	apply	this	

change	in	the	year	of	the	conversion	(carbon	losses),	or	to	distribute	it	uniformly	over	the	length	of	

the	transition	period	(carbon	gains).	

7.4.2 Changes in Soil Carbon

Soil	organic	carbon	stocks	are	influenced	by	land‐use	change	(Aalde	et	al.,	2006)	due	to	changes	in	

productivity	that	influence	carbon	inputs,	and	to	changes	in	soil	management	that	influence	carbon	

outputs	(Davidson	and	Ackerman,	1993;	Ogle	et	al.,	2005;	Post	and	Kwon,	2000).	The	most	

significant	changes	in	soil	organic	carbon	occur	with	land‐use	change,	particularly	conversions	to	

croplands,	due	to	changes	in	the	disturbance	regimes	and	associated	effects	on	soil	aggregate	

dynamics	(Six	et	al.,	2000).	While	there	is	considerable	evidence	and	mechanistic	understanding	

about	the	influence	of	land‐use	change	on	soil	organic	carbon,	there	is	less	known	about	the	effect	

on	soil	inorganic	carbon.	Consequently,	current	methods	do	not	include	impacts	on	inorganic	

carbon	uncertainty	associated	with	estimates	of	land	use	and	management	impacts	on	soil	carbon	

stocks.	

Equation 7‐2: Annual Carbon Stock Changes for a Land‐Use Change as a Sum of Changes

in Each Stratum Within a Land‐Use Change

ΔCLUC = ∑i∆CLUC I

Where:	

ΔCLUC		 =	carbon	stock	changes	for	a	land‐use	change	as	defined	in	Equation	7.1	(metric	tons	

CO2‐eq	ha‐1	year‐1)	

i		 =	denotes	a	specific	stratum	or	subdivision	within	the	land	uses	undergoing	land‐use	

conversion	(by	any	combination	of	species,	climatic	zone,	ecotype,	management	

regime,	etc.),	i	=	1	to	n	
 Chapter 7: Quantifying Greenhouse Gas Sources and Sinks from Land-Use Change 

 7-9 

Estimating	changes	in	GHG	emissions,	including	carbon	stocks,	require	consistency	in	the	methods	

that	are	applied	across	a	time	series.	Applying	different	methods	to	account	for	changes	in	carbon	

stocks	as	the	land	shifts	from	one	land	use	to	another	will	lead	to	artificial	changes	in	the	stocks	

beyond	the	actual	change	occurring	on	the	land.	Thus,	in	order	to	ensure	consistency,	changes	in	

soil	organic	carbon	stocks	will	be	estimated	for	the	entire	time	series	being	reported,	using	the	

method	described	in	this	section.	As	noted	earlier,	estimates	should	be	made	separately	for	each	

parcel	of	land	that	undergoes	a	change	in	land	use.	However,	the	stock	changes	will	only	be	

reported	as	a	land‐use	change	effect	for	a	20‐year	transition	period.	Applying	the	same	method	

across	the	entire	time	series	will	limit	errors	in	the	estimation	of	mineral	soil	organic	carbon	stock	

changes	that	would	result	from	changing	methods	after	the	20‐year	transition	period.	

7.4.2.1 Description of Method

Models	have	been	adopted	from	the	IPCC	methods	to	estimate	soil	organic	carbon	stock	change	

(Aalde	et	al.,	2006).	For	mineral	soils,	the	method	will	require	estimates	of	carbon	stocks	at	the	

beginning	and	end	of	the	year	in	order	to	estimate	the	annual	change	using	the	equation	below.	

Emissions	occur	in	organic	soils	following	drainage	due	to	the	conversion	of	an	anaerobic	

environment	with	a	high	water	table	to	aerobic	conditions	(Armentano	and	Menges,	1986),	

resulting	in	a	significant	loss	of	carbon	to	the	atmosphere	(Ogle	et	al.,	2003).	Emission	estimation	

methods	from	organic	soils	should	be	consistent	with	their	appropriate	sector	methodologies	(i.e.,	

forestry,	croplands,	grazing	lands,	or	wetlands).	

Mineral Soils:	The	model	to	estimate	changes	in	soil	organic	carbon	stocks	for	mineral	soils	has	been	

adopted	from	the	method	developed	by	IPCC	(Aalde	et	al.,	2006).	The	change	would	need	to	be	

estimated	separately	for	each	area	in	the	entity’s	operation	that	is	converted	from	one	land	use	to	

another.	The	change	in	stocks	for	each	area	is	estimated	over	five	year	intervals	for	the	entire	

reporting	time	series,	using	the	following	equation:	

Equation 7‐3: Annual Change in Carbon Stocks in Dead Wood and Litter Due to Land

Conversion

ΔCDOM = (Cn	‐	Co) × Aon ÷ Ton

Where:	

ΔCDOM		 =	annual	change	in	carbon	stocks	in	dead	wood	or	litter	(metric	tons	C	year‐1)	

Co		 =	dead	wood/litter	stock,	under	the	old	land‐use	category	(metric	tons	C	ha‐1)	

Cn		 =	dead	wood/litter	stock,	under	the	new	land‐use	category	metric	tons	C	ha‐1)	

Aon		 =	area	undergoing	conversion	from	old	to	new	land‐use	category	(ha)	

Ton		 =	time	period	of	the	transition	from	old	to	new	land‐use	category	(year)	(The	default	

is	20	years	for	carbon	stock	increases	and	1	year	for	carbon	losses.)	
Chapter 7: Quantifying Greenhouse Gas Sources and Sinks from Land-Use Change 

7-10 

Carbon	stocks	are	estimated	using	the	following	equation	adapted	from	the	IPCC	(Aalde	et	al.,	

2006):	

The	stock	change	factors	(FLU,	FMG,	FI)	and	reference	carbon	stocks	(SOCREF)	are	country‐specific	

values	developed	for	the	United	States	(EPA,	2011;	Ogle	et	al.,	2003;	Ogle	et	al.,	2006).	The	

reference	stocks	are	based	on	the	SOC	stocks	in	croplands	(Table	7‐3),	while	the	land‐use	factors	

represent	the	relative	change	in	SOC	between	cropland	and	grazing	lands,	forest	land,	and	set‐aside	

cropland	(Table	7‐4).	The	management	factors	represent	the	influence	of	tillage	in	croplands	and	

grassland	condition	in	grazing	lands.	The	input	factors	represent	influences	of	changing	plant	

productivity	on	carbon	input	to	soils.	Management	and	input	factors	are	not	needed	for	forest	lands	

(Factors	are	set	to	a	value	of	1).	

Organic Soils:	The	methodology	for	estimating	soil	carbon	stock	changes	in	organic	soils	has	been	

adopted	from	IPCC	(Aalde	et	al.,	2006),	and	is	described	accordingly	in	Chapter	4,	Wetlands,	and	

Chapter	3,	Croplands,	and	Grazing	Lands. 	Chapter	6,	Forestry,	recommends	soil	sampling	in	cases	

where	there	have	been	significant	changes	in	soil	carbon	(e.g.,	land	conversion).		

Equation 7‐4: Change in Soil Organic Carbon Stocks for Mineral Soils

ΔCMineral = [(SOCf − SOCi) × CO2MW] ÷ D

Where:	

ΔCMineral		 =	annual	change	in	mineral	soil	organic	carbon	stock	(metric	tons	CO2‐eq	year‐1)	

SOCf		 =	soil	organic	carbon	stock	at	the	end	of	year	5	(metric	tons	C)	

SOCi		 =	soil	organic	carbon	stock	at	the	beginning	of	year	1	(metric	tons	C)	

CO2MW		 =	ratio	of	molecular	weight	of	CO2	to	C	=	44/12	(dimensionless)	

D		 =	time	dependence	of	stock	change	factors	(20	years)	

Equation 7‐5: Soil Organic Carbon Stock for Mineral Soils

SOC = SOCREF × FLU × FMG × FI × A

Where:	

SOC		 =	soil	organic	carbon	stock	at	the	beginning	(SOCi)	and	end	of	the	five	years	(SOCf)	

(metric	tons	C)	

SOCREF		 =	reference	soil	organic	carbon	stock	(metric	tons	C	ha‐1)	

FLU		 =	stock	change	factor	for	land	use	(dimensionless)	

FMG		 =	stock	change	factor	for	management	(dimensionless)		

FI		 =	stock	change	factor	for	input	(dimensionless)	

A		 =	area	of	land‐use	change	(ha)	
 Chapter 7: Quantifying Greenhouse Gas Sources and Sinks from Land-Use Change 

 7-11 

Table 7‐3:		Reference Carbon Stocks (Mg C ha‐1) (±1 SE) To Estimate Soil Organic Carbon

Stock Changes for Mineral Soils

Soil Type USDA

Taxonomy

Cool

Temperate

Dry

Cool

Temperate

Moist

Warm

Temperate

Dry

Warm

Temperate

Moist

Sub‐

Tropical

Dry

Sub‐

Tropical

Moist

High	

activity	

clay	soils	

Vertisols,	

Mollisols,	

Inceptisols,	

Aridisols,	and	

high	base	

status	Alfisols	

42±1.4	 65±1.1	 37±1.1	 51±1.0	 42±2.6	 57±13.0	

Low	

activity	

clay	soils	

Ultisols,	

Oxisols,	acidic	

Alfisols,	and	

many	entisols	

45±3.0	 52±2.3	 25±1.4	 40±1.2	 39±4.8	 47±13.9	

Sandy	

soils	

Any	soils	with	

greater	than	

70%	sand	and	

less	than	8%	

clay	(often	

Entisols)	

24±4.8	 40±3.7	 16±2.4	 30±2.0	 33±1.9	 50±7.9	

Volcanic	

soils	 Andisols	 124±11.4	 114±16.7	 124±11.4	 124±11.4	 124±11.4	 128±15.0	

Spodic	

soils	 Spodosols	 86±6.5	 74±6.8	 86±6.5	 107±8.3	 86±6.5	 86±6.5	

Wetland	

soils	

Soils	with	

Aquic	

suborder	

86±	 89±	 48±	 51±	 63±	 48±	

Source:	EPA	(2011)	and	Ogle	et	al.	(2003)	Ogle	et	al.	(2006).	

Table 7‐4: Carbon Stock Change Factors (±1 SE) to Estimate Soil Organic Carbon Stock

Changes for Mineral Soils

Factor

Warm Temperate

Moist/Subtropical

Moist

Warm

Temperate

Dry/Subtropical

Dry

Cool

Temperate

Moist

Cool Temperate

Dry

Land‐Use	Factor

Long‐term	cultivated	 1	 1 1 1	

Forest/grassland	 1.42±0.06	 1.37±0.05 1.24±0.06	 1.20±0.06

Set‐aside	 1.31±0.06	 1.26±0.04 1.14±0.06	 1.10±0.05

Cropland	Management	

Full	till	 1	 1 1 1	

Reduced	till	 1.08±0.03	 1.01±0.03 1.08±0.03	 1.01±0.03

No‐till		 1.13±0.02	 1.05±0.03 1.13±0.02	 1.05±0.03

Grassland	Managementa
Chapter 7: Quantifying Greenhouse Gas Sources and Sinks from Land-Use Change 

7-12 

Factor

Warm Temperate

Moist/Subtropical

Moist

Warm

Temperate

Dry/Subtropical

Dry

Cool

Temperate

Moist

Cool Temperate

Dry

Non‐degraded	 1	 1 1 1	

Moderately	degraded	 0.95±0.06	 0.95±0.06 0.95±0.06	 0.95±0.06

Severely	degraded	 0.7±0.14	 0.7±0.14 0.7±0.14 0.7±0.14

Improved	 1.14±0.06	 1.14±0.06 1.14±0.06	 1.14±0.06

Cropland	input	

Low	 0.94±0.01	 0.94±0.01 0.94±0.01	 0.94±0.01

Medium	 1	 1 1 1	

High	 1.07±0.02	 1.07±0.02 1.07±0.02	 1.07±0.02

High	with	amendmenta 1.38±0.06	 1.34±0.08 1.38±0.06	 1.34±0.08

Grassland	inputa

Medium	 1	 1 1 1	

High	 1.11±0.04	 1.11±0.04 1.11±0.04	 1.11±0.04

a	Grassland	management	and	input	factors	are	from	the	2006	IPCC	Guidelines	(Verchot	et	al.,	2006)	as	well	as	the	high	

input	systems	with	manure	in	croplands	(Lasco	et	al.,	2006).	

7.4.2.2 Activity Data

Mineral	soils	require	the	following	activity	for	croplands:	

 Crop	selection	and	rotation	sequence;	

 Residue	management,	including	harvested,	burned,	grazed,	or	left	in	the	field;	

 Irrigation,	yes	or	no;	

 Mineral	fertilization,	yes	or	no;	

 Lime	amendments,	yes	or	no;	

 Organic	amendments,	yes	or	no;	

 Tillage	implements,	which	can	be	used	to	determine	tillage	classification	(i.e.,	full	tillage,	

reduced	tillage,	and	no‐till);	and	

 Cover	crops,	yes	or	no.	

The	method	for	grazing	land	requires	the	following	management	activity	data:	

 Degradation	status,	non‐degraded,	moderately	degraded,	severely	degraded;	

 Irrigation,	yes	or	no;	

 Mineral	fertilization,	yes	or	no;	

 Seeding	legumes,	yes	or	no;	

 Lime	amendments,	yes	or	no;	and	

 Organic	amendments,	yes	or	no.		

The	method	for	forest	land	does	not	require		any	management	activity	data	because	the	method	

provided	here	assumes	limited	influence	on	soil	organic	carbon	stock	changes	associated	with	

forest	management	after	a	land‐use	change	(i.e.,	the	land‐use	change	has	the	largest	impact).	

The	activity	data	are	used	to	classify	land‐use,	management,	and	input	classes.	The	classifications	

can	be	found	in	Lasco	et	al.	(2006)	for	cropland	(Figure	5.1),	and	Verchot	et	al.	(2006)	for	grassland	

(Figure	6.1).	
 Chapter 7: Quantifying Greenhouse Gas Sources and Sinks from Land-Use Change 

 7-13 

7.4.2.3 Ancillary Data

Ancillary	data	include	climate	regions	and	soil	types,	consistent	with	the	method	developed	by	the	

IPCC	(Bickel	et	al.,	2006).	Weather	data	may	be	based	on	national	datasets	such	as	the	Parameter‐

Elevation	Regressions	on	Independent	Slopes	Model	(PRISM)	data	(Daly	et	al.,	2008)	and	are	

classified	according	to	the	IPCC	classification	as	refined	for	the	United	States	(Table	7‐5).	Soils	data	

may	also	be	based	on	national	datasets	such	as	the	Soil	Survey	Geographic	Database	(SSURGO)	(Soil	

Survey	Staff,	2011),	and	are	classified	according	to	the	IPCC	classification	(Bickel	et	al.,	2006;	Figure	

3A.5.3).	However,	entities	may	also	substitute	field‐specific	soils	data,	as	long	as	entities	

characterize	the	soil	pedons	necessary	for	use	of	the	IPCC	classifications.	These	characteristics	

include	sand	and	clay	content,	soil	order,	and	suborder	(See	Table	7‐3).	

Table 7‐5:		Climate Classification for the Soil Organic Carbon Methods Associated with Land‐

Use Change

Climate Type Mean Annual Temperature (°C) Mean		Annual Precipitation (mm)

Cool	temperate	dry	 <10 <Potential	evapotranspiration

Cool	temperate	moist	 <10 ≥Potential	evapotranspiration

Warm	temperate	dry	 10‐20 <Potential	evapotranspiration

Warm	temperate	moist	 10‐20 ≥Potential	evapotranspiration

Subtropical	dry	 >20 <1000	

Subtropical	moist	 >20 1000‐2000	

Source:	Bickel	et	al.	(2006).	

7.4.2.4 Model Output

Model	output	is	generated	as	an	absolute	quantity	of	emissions.	The	change	in	mineral	soil	organic	

carbon	stocks	is	estimated	based	on	stock	changes	over	five‐year	time	periods	(Equation	7.4).	In	

addition,	trends	in	soil	organic	carbon	will	be	estimated	for	the	entire	time	series	associated	with	

the	parcel	of	land,	including	20	previous	years	of	history,	in	order	to	present	the	longer	term	trends	

and	provide	an	adequate	baseline	of	data	and	consistency	in	the	time	series	for	reporting	purposes.	

7.4.2.5 Limitations and Uncertainty

The	limitations	of	the	mineral	soil	organic	carbon	method	include	no	assessment	of	the	effect	of	

land‐use	change	at	deeper	depths	in	the	profile	(IPCC	method	only	addresses	changes	in	top	30	cm	

of	soil	profile;	Aalde	et	al.,	2006),	and	no	assessment	of	erosion,	transport,	and	deposition	of	carbon.	

Uncertainties	in	the	mineral	soil	methods	include	imprecision	in	the	emission	factors,	in	addition	to	

uncertainties	in	the	activity	and	ancillary	data.	Uncertainty	in	the	emission	factors	is	provided	in	

this	guidance	(Ogle	et	al.,	2003;	Ogle	et	al.,	2006).	Uncertainty	in	the	activity	data	is	based	on	the	

entity	input,	as	well	as	the	ancillary	data	to	the	extent	that	this	information	is	provided	by	the	

entity.	Uncertainties	can	be	combined	using	a	Monte	Carlo	simulation	approach.	

7.4.3 Changes in other GHG emissions

As	previously	mentioned,	changes	in	other	GHG	emissions—i.e.,	non‐CO2	emissions—	associated	

with	a	land‐use	change	should	be	included	in	any	estimation	of	the	GHG	flux	strata	associated	with	

the	outgoing	or	incoming	land‐use	change.	While	changes	in	biomass	and	soil	carbon	stocks	are	

likely	to	dominate	the	GHG	flux,	there	are	a	number	of	activities	that	may	occur	during	land‐use	

conversion	that	might	result	in	non‐CO2	emission.	For	example,	if	forest	harvest	residues	(and	other	

dead	organic	matter)	are	piled	and	burnt	as	part	of	the	conversion	of	forest	land	to	another	land	

use,	in	addition	to	the	change	in	carbon	stock	the	residue	burning	will	result	in	emissions	of	N2O	

and	CH4;	and	if	wetlands	are	cleared	and	drained	prior	to	conversion	to	another	land	use	(e.g.,	

grazing	lands,	peat	extraction),	in	addition	to	the	change	in	carbon	stock	from	clearing,	the	draining	
Chapter 7: Quantifying Greenhouse Gas Sources and Sinks from Land-Use Change 

7-14 

will	result	in	a	reduction	in	emissions	of	CH4,	and	a	possible	increase	in	emissions	of	N2O,	

depending	on	the	nitrogen	content	of	the	wetland	soil	(i.e.,	peat).	

Sector‐specific	methods	for	estimating	changes	in	biomass	burning	non‐CO2	emissions	(e.g.,	for	

cropland	and	grazing	land	systems)	and	soil	non‐CO2	emissions	(e.g.,	for	wetland	systems)	are	

detailed	in	the	individual	sector	chapters.	

Chapter 7 References

Aalde,	H.,	P.	Gonzalez,	M.	Gytarski,	T.	Krug,	et	al.	2006.	Chapter	2:	Generic	methodologies	applicable	

to	multiple	land‐use	categories.	In	2006 IPCC Guidelines for National Greenhouse Gas

Inventories,	S.	Eggleston,	L.	Buendia,	K.	Miwa,	T.	Ngara	and	K.	Tanabe	(eds.).	Japan:	IGES.	

Armentano,	T.V.,	and	E.S.	Menges.	1986.	Patterns	of	change	in	the	carbon	balance	of	organic	soil.	

Bickel,	K.,	G.	Richards,	M.	Kohl,	R.L.V.	Rodrigues,	et	al.	2006.	Chapter	3:	Consistent	representation	of	

land.	In	2006 IPCC Guidelines for National Greenhouse Gas Inventories,	S.	Eggleston,	L.	

Buendia,	K.	Miwa,	T.	Ngara	and	K.	Tanabe	(eds.).	Japan:	IGES.	

Daly,	C.,	M.	Halbleib,	J.I.	Smith,	W.P.	Gibson,	et	al.	2008.	Physiographically	sensitive	mapping	of	

climatological	temperature	and	precipitation	across	the	conterminous	United	States.	

International Journal of Climatology,	28:2031‐2064.	

Davidson,	E.,	and	I.	Ackerman.	1993.	Changes	in	soil	carbon	inventories	following	cultivation	of	

previously	untilled	soils.	Biogeochemistry,	20(3):161‐193.	

EPA.	1980.	US EPA Regulation 40 CFR 230.3(t):	U.S.	Environmental	Protection	Agency.		

EPA.	2011.	Inventory of U.S. greenhouse gas emissions and sinks: 1990‐2009.	Washington,	D.C.:	

Environmental	Protection	Agency.		

IPCC.	2006.	IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas

Inventories Programme.	Hayama,	Kanagawa,	Japan:	The	Intergovernmental	Panel	on	Climate	

Change.		

Lasco,	R.D.,	S.	Ogle,	J.	Raison,	L.	Verchot,	et	al.	2006.	Chapter	5:	Cropland.	In	2006 IPCC Guidelines for

National Greenhouse Gas Inventories,	S.	Eggleston,	L.	Buendia,	K.	Miwa,	T.	Ngara	and	K.	

Tanabe	(eds.).	Japan:	IGES,	IPCC	National	Greenhouse	Gas	Inventories	Program.	

Ogle,	S.M.,	F.J.	Breidt,	M.D.	Eve,	and	K.	Paustian.	2003.	Uncertainty	in	estimating	land	use	and	

management	impacts	on	soil	organic	carbon	storage	for	US	agricultural	lands	between	1982	

and	1997.	Global Change Biology,	9(11):1521‐1542.	

Ogle,	S.M.,	F.J.	Breidt,	and	K.	Paustian.	2005.	Agricultural	management	impacts	on	soil	organic	

carbon	storage	under	moist	and	dry	climatic	conditions	of	temperate	and	tropical	regions.	

Biogeochemistry,	72(1):87–121.	

Ogle,	S.M.,	F.J.	Breidt,	and	K.	Paustian.	2006.	Bias	and	variance	in	model	results	associated	with	

spatial	scaling	of	measurements	for	parameterization	in	regional	assessments.	Global

Change Biology,	12:516‐523.	

Post,	W.M.,	and	K.C.	Kwon.	2000.	Soil	Carbon	Sequestration	and	Land‐Use	Change:	Processes	and	

Potential.	Global Change Biology,	6:317‐327.	

Six,	J.,	E.T.	Elliot,	and	K.	Paustian.	2000.	Soil	macroaggregate	turnover	and	microaggregate	

formation:	a	mechanism	for	C	sequestration	under	no‐tillage	agriculture.	Soil Biol. Biochem.,	

32:2099‐2103.	

Smith,	W.B.,	P.D.	Miles,	C.H.	Perry,	and	S.A.	Pugh.	2009.	Forest Resources of the United States, 2007.	

Washington,	DC:	U.S.	Department	of	Agriculture	Forest	Service.	

Soil	Survey	Staff.	2011.	Soil	Survey	Geographic	(SSURGO)	Database:	Natural	Resource	Conservation	

Service	and	United	States	Department	of	Agriculture.	

	 	
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U.S.	Army	Corps	of	Engineers.	1987.	Wetland Delimitation Manual & Regional Supplements Wetlands	

Research	Program	Technical	Report	Y‐87‐1:	U.S.	Army	Corps	of	Engineers,	Environmental	

Laboratory,.	

http://www.usace.army.mil/Missions/CivilWorks/RegulatoryProgramandPermits/reg_sup

p.aspx.	

Verchot,	L.,	T.	Krug,	R.D.	Lasco,	S.	Ogle,	et	al.	2006.	Chapter	5:	Grassland.	In	2006 IPCC Guidelines for

National Greenhouse Gas Inventories,	S.	Eggleston,	L.	Buendia,	K.	Miwa,	T.	Ngara	and	D.	L.	

Tanaka	(eds.).	Japan:	IGES.	

	 	
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This	page	is	intentionally	left	blank.	
		

E	

Authors:1		

Jay	Breidt,	Colorado	State	University	

Stephen	M.	Ogle,	Colorado	State	University	

Wendy	Powers,	Michigan	State	University	

Coeli	Hoover,	USDA	Forest	Service	

Contents:

8 Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks ...................8‐3

8.1	 Components	and	Inputs	to	an	Entity‐Scale	Monte	Carlo	Uncertainty	Assessment	............	8‐4	

8.1.1	 Parameter	Uncertainty	..................................................................................................	8‐5	

8.1.2	 Sampling	Method	Uncertainty	....................................................................................	8‐6	

8.1.3	 Large	Dataset	Uncertainty	............................................................................................	8‐9	

8.1.4	 Model	Uncertainty	.........................................................................................................	8‐16	

8.2	 Research	Gaps	...............................................................................................................................................	8‐20	

Appendix	8‐A:	Example	Output	File	from	FVS	Sampling	Uncertainty	Bootstrapping	Application	

FVSBoot	(as	provided	in	Gregg	and	Hummel,	2002)	................................................................................	8‐21	

Appendix	8‐B:	Uncertainty	Tables	....................................................................................................................	8‐22	

Chapter	8	References	.............................................................................................................................................	8‐55	

Suggested	Chapter	Citation:	Breidt,	F.J.,	Ogle,	S.M.,	Powers,	W.,	Hover,	C.,	2014.	Chapter	8:	

Uncertainty	Assessment	for	Quantifying	Greenhouse	Gas	Sources	and	Sinks.		In	Quantifying

Greenhouse Gas Fluxes in Agriculture and Forestry:		Methods for Entity‐Scale Inventory. Technical	

Bulletin	Number	1939.		Office	of	the	Chief	Economist,	U.S.	Department	of	Agriculture,	Washington,	

DC.	606	pages.		July	2014.		Eve,	M.,	D.	Pape,	M.	Flugge,	R.	Steele,	D.	Man,	M.	Riley‐Gilbert,	and	S.	

Biggar,	Eds.	

USDA	is	an	equal	opportunity	provider	and	employer.	

																																																													

1	All	authors	of	Chapters	3,	4,	5,	and	6	provided	strategic	input	in	the	parameters	in	the	uncertainty	chapter.	

Chapter 8

Uncertainty Assessment for 

Quantifying Greenhouse Gas 

Sources and Sinks
Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

8-2

Acronyms, Chemical Formulae, and Units

CONUS	STATSGO	 Continental	United	States	Soil	Geographic	Database

DBH	 Diameter	at	breast	height

DNDC	 DeNitrification‐DeComposition

EPA	 U.S.	Environmental	Protection	Agency

ERS	 USDA	Economic	Research	Service

FOFEM	 First	Order	Fire	Effects	Model

FVS	 Forest	Vegetation	Simulator

GHG	 Greenhouse	gas	

IPCC	 Intergovernmental	Panel	on	Climate	Change

NADP	 National	Atmospheric	Deposition	Program

NARR	 North	American	Regional	Reanalysis

NASS	 USDA	National	Agricultural	Statistics	Service

NCEP	 National	Centers	for	Environmental	Prediction

NLCD	 National	Land	Cover	Database

NOAA	 National	Oceanic	and	Atmospheric	Administration

NRCS	 USDA	Natural	Resources	Conservation	Service

NRI	 National	Resource	Inventory

PDF	 Probability	density	function

PRISM	 Parameter‐elevation	Regressions	on	Independent	Slopes	Model	

SOC	 Soil	organic	carbon

SSURGO	 Soil	Survey	Geographic

USDA		 U.S.	Department	of	Agriculture
 Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

 8-3 

8 Uncertainty Assessment for Quantifying Greenhouse Gas Sources and

Sinks

Quantifying	the	uncertainty	of	greenhouse	gas	(GHG)	emissions	and	reductions	from	agriculture	

and	forestry	practices	is	an	important	aspect	of	decision‐making	for	farmers,	ranchers	and	forest	

landowners	as	the	uncertainty	range	for	each	GHG	estimate	communicates	our	level	of	confidence	

that	the	estimate	reflects	the	actual	balance	of	GHG	exchange	between	the	biosphere	and	the	

atmosphere.	In	particular,	a	farm,	ranch,	or	forest	landowner	may	be	more	inclined	to	invest	in	

management	practices	that	reduce	net	GHG	emissions	if	the	uncertainty	range	for	an	estimate	is	

low,	meaning	that	higher	confidence	in	the	estimates	exists.	This	chapter	presents	the	approach	for	

accounting	for	the	uncertainty	in	the	estimated	net	emissions	based	on	the	methods	presented	in	

this	report.2		A	Monte	Carlo	approach	was	selected	as	the	method	for	estimating	the	uncertainty	

around	the	outputs	from	the	methodologies	in	this	report	as	it	is	currently	the	most	comprehensive,	

sound	method	available	to	assess	the	uncertainty	at	the	entity	scale.	Limitations	and	data	gaps	

exist;	however,	as	new	data	become	available	the	method	can	be	improved	over	time.	

Implementation	of	a	Monte	Carlo	analysis	is	complicated	and	requires	the	use	of	a	statistical	tool	to	

produce	a	probability	density	function	(PDF)3	around	the	GHG	emissions	estimate.4	From	the	PDF,	

the	uncertainty	estimate	can	be	derived	and	reported.	

																																																													

2	The	IPCC	Good	Practice	Guidance	(IPCC,	2000)	recommends	two	approaches—Tier	1	and	Tier	2—for	

developing	quantitative	estimates	of	uncertainty	for	emissions	estimates	for	source	categories.	The	Tier	1	

method	uses	error	propagation	equations.	These	equations	combine	the	uncertainty	associated	with	the	

activity	data	and	the	uncertainty	associated	with	the	emission	(or	other)	factors.	This	approach	is	appropriate	

where	emissions	(or	removals)	are	estimated	as	the	product	of	activity	data	and	an	emission	factor	or	as	the	

sum	of	individual	sub‐source	category	values.	The	Tier	2	method	utilizes	the	Monte	Carlo	Stochastic	

Simulation	technique.	Using	this	technique,	an	estimate	of	emission	(or	removal)	for	a	particular	source	

category	is	generated	many	times	via	an	uncertainty	model,	resulting	in	an	approximate	PDF	for	the	estimate.	

Where	sufficient	and	reliable	uncertainty	data	for	the	input	variables	are	available,	the	Tier	2	method	is	the	

preferred	option.	

3	The	integral	of	a	PDF	over	a	given	interval	of	values	is	the	probability	for	a	random	variable	to	take	on	some	

value	in	the	interval.	That	is,	the	PDF	is	a	function	giving	probability	“densities”	and	its	integral	gives	

probabilities.		A	narrower	PDF	for	an	estimate	indicates	smaller	variance	around	the	central/most	likely	

value,	i.e.,	a	higher	probability	of	the	value	to	be	closer	to	the	central/most	likely	value.	The	uncertainty	for	

such	an	estimate	is	lower.	

4	Given	the	complexity	of	Monte	Carlo	analysis	and	the	necessity	for	a	tool,	the	approach	presented	here	is	not	

intended	for	development	by	a	landowner,	rather	it	is	intended	for	use	in	developing	a	tool	that	a	landowner	

would	use	to	assess	uncertainty	estimates.	
Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

8-4

Uncertainty	in	GHG	emissions	estimation	arises	because	of	unknown	or	incompletely	known	factors	

associated	with:	

 Parameters	–	Due	to	limitations	associated	with	available	input	data	(e.g.,	activity	data	and	

emission	factors).	

 Sampling	methods	–	Due	to	either	measurement	errors	during	sample	collection	or	

potential	variations	in	values	obtained	from	sampling	(i.e.,	when	the	chosen	sample	is	not	

fully	representative	of	the	entire	population).	

 Large	datasets	–	Due	to	measurements	errors	during	data	collection,	and	variations	in	

dataset	values	for	a	given	set	of	conditions.	

 Models	–	Due	to	approximation	errors	and	estimation	errors.	Approximation	error	arises	

because	the	model	is	a	simplification	of	the	real	system,	while	estimation	error	arises	

because	the	theoretical	model	is	fitted	using	limited	data.	

 Concepts	–	This	is	closely	related	to	model	approximation	error	and	occurs	because	the	

conceptual	scope	does	not	capture	the	actual/real	scope	thus	creating	a	bias.	For	an	entity,	

this	conceptualization	uncertainty	may	be	relatively	small.	

The	approach	to	address	uncertainty	does	not	address	conceptual	uncertainty	because	it	is	

expected	to	be	small	and	difficult	to	quantify.	This	chapter	addresses	parameter	uncertainty,	

sampling	uncertainty,	large	dataset	uncertainty,	and	model	approximation	uncertainty.	Where	data	

are	currently	unavailable	or	incomplete	for	establishing	PDF’s	and	estimating	uncertainty,	the	

authors	provide	expert	judgment	and/or	a	qualitative	description	of	uncertainty	in	the	interests	of	

making	the	GHG	management	methods	as	transparent	and	complete	as	possible.	In	the	future,	new	

data	can	be	used	to	refine	and	improve	the	estimation	of	uncertainty.		

In	this	chapter,	Section	8.1	includes	the	components	and	inputs	to	an	entity‐scale	Monte	Carlo	

uncertainty	assessment,	and	Section	8.2	highlights	research	gaps.	

8.1 Components and Inputs to an Entity‐Scale Monte Carlo Uncertainty

Assessment		

To	conduct	a	Monte	Carlo	uncertainty	analysis	for	each	of	the	GHG	quantification	methods	and	

resulting	net	GHG	emissions,	information	is	required	about	the	uncertainty	associated	with:	(1)	the	

input	variables	(i.e.,	parameters);	(2)	sampling	methods	used	to	obtain	data;	(3)	existing	large	

datasets	used	as	data	sources;	and	(4)	external	models	used.	Ideally,	this	information	would	consist	

of	specific	PDFs	(e.g.,	normal,	triangular,	uniform,	beta).	Alternatively,	the	uncertainty	might	be	

Monte Carlo Analysis for Assessing Uncertainty

 In	the	Monte	Carlo	method,	uncertain	inputs	(parameters	and	other	data)	and	uncertain	model	

structure	are	described	via	PDFs.	By	randomly	selecting	from	each	of	these	PDFs,	and	running	

the	selected	inputs	through	the	selected	model,	an	uncertainty	model	output	is	obtained.	

Combining	these	model	outputs	across	many	random	selections	leads	to	an	approximate	PDF	

describing	uncertainty	in	the	model	output,	reflecting	known	sources	of	uncertainty	in	the	

inputs	and	model	structure.		

 A	tool	is	needed	to	run	a	Monte	Carlo	analysis	to	assess	the	uncertainty	for	model	outputs.	

Farmers	and	landowners	are	not	expected	to	perform	a	Monte	Carlo	analysis	on	their	own.	

 A	centralized	database	is	needed	to	store	information	on	the	known	uncertainties	associated	

with	the	activity	and	emission	factor	data	for	each	emissions	source.		This	report	presents	

readily	available	data	that	can	form	the	initial	foundation	for	such	a	database.	
 Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

 8-5 

described	with	summary	statistics,	such	as	lower	and	upper	bounds	for	intervals	with	specified	

confidence,	minimum,	maximum,	mean,	and	standard	deviation.	This	summary	information	forms	

the	basis	for	constructing	approximate	PDFs	for	the	Monte	Carlo	method.	Repeated	selections	are	

made	from	these	PDFs.	These	selections	represent	the	range	of	possible	outcomes	from	each	PDF.	

Random	sampling	from	the	PDFs	will	ensure	such	representativeness.5	By	randomly	selecting	from	

each	of	these	PDFs	and	running	the	selected	inputs	through	the	model,	a	range	of	outputs	is	

obtained.	Combining	these	model	outputs	across	many	random	selections	leads	to	an	output	PDF	

that	can	be	used	to	describe	uncertainty	in	the	estimate,	accounting	for	known	sources	of	

uncertainty	in	the	inputs	and	model	structure.	

This	section	presents	readily	available	information	on	each	of	the	key	components	of	uncertainty.		

In	summary,	although	information	on	all	the	components	are	described	here,	the	Monte	Carlo	

method	for	assessing	net	GHG	emissions	uncertainty	relies	most	heavily	on	parameter	uncertainty,	

for	which	the	best	PDF	data	and	information	are	available.	Other	components	of	uncertainty	are	

discussed,	including	limitations	such	as	characterizing	the	uncertainty	associated	with	other	

components.	These	components	can	be	readily	improved	or	refined	in	the	uncertainty	analysis	as	

additional	information	becomes	available.	Overall	uncertainty	is	typically	greater	than	any	

particular	uncertainty	component	(e.g.,	sampling,	large	data	sets,	models)	and	can	be	readily	

improved	or	refined	as	additional	information	becomes	available.	As	the	uncertainty	associated	

with	the	other	components	is	addressed,	the	uncertainty	will	increase	(i.e.,	addressing	only	

parameter	uncertainty	sets	a	lower	bound	for	overall	uncertainty).	Therefore,	the	quantification	of	

parameter	uncertainty	sets	a	lower	bound	for	overall	uncertainty.			

8.1.1 Parameter Uncertainty

Parameter	uncertainty	is	the	primary	source	of	uncertainty	in	the	net	GHG	estimates.	This	section	

presents	readily	available	information	on	parameters	used	to	estimate	net	GHG	emissions	from	

animal	production	systems,	croplands	and	grazing	lands,	and	forestry	GHG	estimation	methods.	For	

each	input	variable,	readily	available	information	was	collected	on	the	probability	distribution;	

variance;	standard	deviation;	expected	mean,	median,	and	mode;	most	likely	value;	minimum;	

maximum;	relative	uncertainty	absolute	values;	confidence	interval;	and	data	sources.	The	

information	was	collected	primarily		from	published	literature,	such	as	the	Intergovernmental	

Panel	on	Climate	Change	(IPCC)	Guidelines	(2006),	the	U.S.	National	GHG	Inventory	Report	(U.S.	

EPA,	2012),	and	peer‐reviewed	journals.	In	the	absence	of	published	data,	default	factors	are	

indicated	based	on	expert	judgment	obtained	from	the	Working	Groups.	The	information	obtained	

to	date	is	presented	in	Appendix	8‐B.6,	7,	8

																																																													

5	An	alternative	approach	to	selecting	from	the	PDFs	is	Latin hypercube sampling (McKay	et	al.,	1979;	Helton	

and	Davis,	2003).		

6	Uncertainty	for	the	forestry	sector	is	mainly	driven	by	modeling	and	sampling	uncertainty;	consequently,	

only	a	few	parameters	have	been	listed	in	Appendix	8‐B.	 7The	Wetlands	Chapter	methods	suggest	use	of	the	FVS	and	DNDC	models	in	combination	with	the	lookup	

tables	for	dominant	shrub	and	grassland	vegetation	types	found	in	Chapter	3,	for	estimating	biomass	carbon,	

soil	carbon,	N2O,	and	CH4	emissions	and	removals	in	wetlands.		Descriptions	of	these	models	and	the	

uncertainty	associated	with	the	look‐up	tables	are	included	in	the	Uncertainty	Assessment	(Chapter	8).		 8	An	uncertainty	assessment	was	not	completed	for	the	Land‐use	Change	Chapter	methods	(i.e.,	annual	

change	in	carbon	stocks	in	dead	wood	and	litter	due	to	land	conversion,	change	in	soil	organic	carbon	stocks	

for	mineral	soils)	as	they	are	based	upon	IPCC	2006	Guidance	and	no	U.S.	specific	customizations	were	made	

to	these	methods.	Uncertainty	assessments	for	each	land‐use	and	transition	into	or	out	of	a	land‐use	category	
Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

8-6

In	order	to	make	the	uncertainty	estimation	process	feasible	and	consistent	at	the	entity	scale	for	

use	by	an	entity	or	landowner,	a	tool	will	be	needed	that	provides	the	following	uncertainty	

information	for	input	variables:	

 PDFs	or	distributions	–	Default	

 Emission	factors	–	Default	

 Activity	data	–	Default,	but	customizable	

With	default	uncertainty	information	available,	it	is	feasible	to	quantify	parameter	uncertainty	via	

PDFs	and	to	combine	the	uncertainty	via	Monte	Carlo	methods.	These	PDFs	are	often	relatively	

crude,	relying	on	default	values	and	conservative	expert	judgment.	Options	to	improve	the	PDFs	

(i.e.,	improve	parameter	uncertainty	quantification)	are	to:	(1)	develop	a	method	to	help	elicit	and	

refine	these	uncertainty	distributions	at	an	entity	scale;	and	(2)	conduct	new	research	to	better	

understand	the	key	parameters	identified	in	this	report	and	to	quantify	their	uncertainties.9		

The	uncertainty	associated	with	the	various	inputs	to	the	GHG	estimation	equation	or	models	are	

combined	to	estimate	overall	uncertainty	at	the	entity	level	for:	(1)	each	source	category	emission	

estimate;	and	(2)	total	emission	estimate	arrived	at	by	aggregating	each	source	category’s	estimate.	

Although	most	inputs	within	a	category	and	across	categories	are	independent,	certain	variables	

might	be	the	same,	similar,	or	highly	co‐related,	and	will	need	to	be	accounted	for	appropriately	in	

the	uncertainty	analysis.	

8.1.2 Sampling Method Uncertainty

Some	sampling	methods	(i.e.,	field	measurements)	will	be	conducted	to	support	the	estimation	of	

emissions	using	the	GHG	quantification	methods.	For	example,	for	the	forestry	sector,	conducting	

field	measurements	on	sampling	plots	for	large	forest	and	on	urban	forests	is	used	to	determine	

aggregate	forest	characteristics	(e.g.,	tree	cover).	Additionally,	some	large	datasets	and	external	

models	that	the	methods	use	also	utilize	data	that	were	obtained	from	a	variety	of	outside	sampling	

methods.	For	example,	forest	inventory	data	used	in	the	forest	vegetation	simulator	(FVS)	model	

and	the average	carbon	sequestration	rates	used	in	the	i‐Tree	model	use	data	obtained	through	

sampling	methods.	In	addition,	there	are	instances	in	these	external	models	and	large	datasets	

where	the	variation	in	measurements	obtained	from	the	sampling	methods	is	not	taken	into	

account,	but	they	can	impact	uncertainty.		

If	forest	stand	sampling	is	conducted	at	an	entity	level	using	a	formal	probability	sampling	design,	

then	unbiased	estimates	of	sampling	error	variance	can	be	computed	via	standard	techniques	from	

the	field	of	survey	statistics.	The	exact	form	of	the	variance	estimate	depends	on	the	particular	

design	used	for	the	stand	sampling.	Though	additional	uncertainties	arise	from	the	actual	

measurement	protocols	used	in	the	field,	the	sampling	error	variance	is	a	major	part	of	the	

sampling	uncertainty.	A	currently	feasible	approach	to	incorporating	information	on	sampling	error	

variances	into	the	uncertainty	analysis	is	to	model	the	sampling	error	PDF	as	a	normal	distribution	

with	zero	as	its	mean	and	the	estimated	sampling	error	variance	as	its	variance.10	This	section	

																																																																																																																																																																																																				

are	contained	in	the	associated	land‐use	category	chapter	of	the	2006	IPCC	Guidelines.	http://www.ipcc‐

nggip.iges.or.jp/public/2006gl/vol4.html

9	A	tool	could	provide	an	option	to	use	pre‐defined	values	such	as	those	provided	in	Appendix	8‐B	or	user	(i.e.,	

landowner)	supplied	values	to	define	PDFs.	 10	Similarly,	estimates	derived	from	existing	surveys	and	tools	such	as	Forest	Inventory	Data	Online	in	the	

Forest	Inventory	and	Analysis	National	Program	or	the	USDA	Natural	Resources	Inventory	(NRI)	Summary	

Reports	have	associated	sampling	error	variances	from	well‐established	statistical	procedures.	In	these	cases,	
 Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

 8-7 

provides	the	sampling	methods	and	their	potential	sources	of	uncertainty.		However,	given	the	

complexity	in	incorporating	information	on	uncertainty	for	sampling	data	in	the	Monte	Carlo	

uncertainty	assessment,	a	tool	will	be	needed	to	quantify	the	impact	of	sampling	uncertainty	on	the	

estimate	of	net	GHG	emissions.11

8.1.2.1 Forest Stand Sampling with Plots for Use with the Forest Vegetation

Simulator Model

The	FVS	model	is	a	family	of	forest	growth	simulation	model	variants.	FVS	estimates	forest	carbon	

stocks	based	on	sample	data	parameters	(e.g.,	the	diameter,	height,	species,	and	canopy	density	of	

trees	from	representative	sample	plots	established	across	the	forest).	For	sampling	purposes,	a	

number	of	plots	are	established	within	a	forest	that	can	serve	as	a	representative	sample	of	the	

entire	forest.	As	the	variance	in	forest	types	increases,	the	number	of	plots	will	increase.	The	size	

and	number	of	plots	should	be	determined	based	on	the	variance	in	carbon	stocks	between	plots.	

Complete	forest	estimates	have		sampling	uncertainty,	but	larger	and	more	numerous	plots	help	to	

create	a	more	representative	sample	and	lower	the	uncertainty	associated	with	carbon	stock	

estimates	produced	by	FVS.	Both	permanent	and	temporary	plots	can	be	used	in	sampling;	

however,	a	larger	uncertainty	is	associated	with	temporary	plots.	Note	that	the	use	of	permanent	

plots	is	recommended	in	this	report.	This	type	of	sampling	methodology	is	commonly	referred	to	as	

a	forest	inventory.	

Once	plots	have	been	defined,	all	trees	above	a	certain	diameter	at	breast	height	(DBH)	(commonly	

2.5	cm.	or	1	in.)	are	measured	and	recorded.12	DBH,	height,	and	a	variety	of	other	measurements	are	

recorded,	but	DBH	alone	is	sufficient	for	use	with	FVS.	FVS	uses	DBH	and	available	information	to	

develop	carbon	density	estimates	for	the	entire	forest.	If	provided,	FVS	models	growth	estimates	for	

future	years	based	on	average	growth	rates	and	variables	such	as	thinning.13	Selecting	plots	to	

represent	entire	forests	means	FVS	outputs	are	subject	to	the	sampling	method	uncertainty.	

However,	uncertainty	in	the	FVS	outputs	can	be	lowered	(i.e.,	more	representative	carbon	stock	

estimates	can	be	obtained)	by	collecting	more	detailed	tree	data	beyond	DBH	as	well	as	ensuring	

that	sample	plots	are	large	and	numerous	enough	to	cover	the	variety	of	tree	growth	settings	in	a	

forest.	

The	forest	inventory	data	recommended	for	use	with	FVS	in	this	report	is	based	on	the	sampling	

methods	described	in	the	U.S.	Department	of	Agriculture	(USDA)	Measurement	Guidelines	for	the	

Sequestration	of	Carbon	(Pearson	et	al.,	2007).	These	guidelines	also	describe	the	potential	

uncertainty	associated	with	such	sampling	methods.	According	to	these	measurement	guidelines,	a	

reasonable	estimate	of	the	net	change	in	carbon	stocks	would	be	within	10	percent	of	the	true	value	

of	the	mean	at	the	95	percent	confidence	level	that	can	be	achieved	by	having	a	sufficiently	large	

sample	size	(Pearson	et	al.,	2007).	Different	carbon	pools	in	a	forest	can	have	different	variances;	

however,	focusing	on	the	standing	live	tree	component	for	forestry	activities	can	capture	most	of	

the	total	variance.	

																																																																																																																																																																																																				

it	may	be	feasible	to	use	these	estimated	variances	fairly	directly.	In	other	cases,	it	might	be	necessary	to	

consider	small	area	estimation	techniques	to	describe	uncertainty	as	large‐scale	survey	data	are	downscaled	

to	entity	levels.	Describing	this	type	of	uncertainty	would	require	building	statistical	models	for	complex	

survey	data	and,	hence,	is	not	addressed	in	this	report	as	additional	research	is	required.	 11	For	example,	in	addressing	uncertainty	in	growth	of	forest	biomass,	algorithms	to	account	for	nonlinear	

growth	patterns	will	be	needed.	

12	Under	common	stand	exams,	even	trees	less	than	2.5	cm	or	1	in	can	be	measured,	but	these	trees	are	often	

considered	to	be	part	of	the	understory	(e.g.,	by	FIA).	

13	Other	variables,	such	as	fertilization,	only	apply	to	a	few	FVS	variants.	
Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

8-8

The	forest	inventory	data	that	is	used	for	modeling	the	changes	in	forest	stands	is	likely	the	largest	

source	of	parameter	uncertainty	for	the	inputs	and	assumptions	used	in	the	FVS	model;	in	addition,	

there	are	many	components	in	the	FVS	model	where	variation	in	measurements	for	this	data	is	not	

taken	into	account.	For	example,	the	potential	error	distribution	from	month	to	month	in	carbon	

storage	associated	with	leaves	and	foliage	is	not	accounted	for	in	the	FVS	model.	

In	a	research	paper	on	obtaining	sampling	uncertainty	in	FVS,	experts	note	the	challenge	of	

reporting	distributions	of	model	inputs	that	include	sampling	uncertainty	in	FVS	projections	(Gregg	

and	Hummel,	2002).	They	state	in	their	introduction	that,	“it	hasn’t	been	possible	to	compute	the	

effects	of	sampling	uncertainty	because	classical	statistical	methods	are	not	available	to	make	

inferences	about	FVS	projections.	A	variance	estimator	is	not	available	for	the	results	of	

simulation.”	

As	provided	in	Appendix	8‐A,	the	FVS	model	provides	quantitative	information	on	the	range	and	

variability	of	sampling	data.	This	FVS	application,	called	FVSBoot,	uses	“bootstraps”	to	determine	

fluctuation	in	estimate	outcomes	(Gregg	and	Hummel,	2002)	(i.e.,	allows	modeler	to	empirically	

approximate	the	sampling	distribution	of	any	statistic/FVS	attribute	for	which	the	modeler	wants	

to	make	inferences).	

Bootstrap	sampling14	using	the	FVSBoot	program	can	be	used	to	empirically	approximate	the	

sampling	distribution	of	statistics	for	which	inferences	are	to	be	drawn.	New	samples	of	stand	

conditions	can	be	generated	by	sampling	the	original	plots	with	replacement	to	create	a	bootstrap	

sample.	A	bootstrap	mean	can	be	generated	from	the	bootstrap	sample.	Repeating	this	process	

multiple	times	will	generate	a	Monte	Carlo	approximation	of	the	distribution	of	bootstrap	means.	

The	standard	deviation	of	this	approximation	will	be	an	estimation	of	the	true	standard	deviation	

for	the	entire	population.	FVSBoot	does	not	cover	all	potential	sources	of	variation	but	it	can	give	a	

measure	of	important	components	of	uncertainty	in	FVS	model	projections.	While	the	FVSBoot	

program	can	be	used	to	determine	the	sampling	uncertainty	in	FVS,	it	was	not	developed	originally	

to	produce	an	overall	uncertainty	estimate	for	FVS	outputs.	However,	FVSBoot	has	been	used	for	

sensitivity	analysis	of	some	FVS	outputs	(Hummel	et	al.,	2013).		A	tool	would	be	needed	to	facilitate	

developing	an	estimate	of	the	uncertainty	based	on	a	combination	of	the	results	from	the	FVSBoot	

program	and	underlying	equations	used	in	the	FVS	model.		

8.1.2.2 Urban Tree Population Sampling for Use with the Field Data Method

Using the i‐Tree Eco Model

The	i‐Tree	Eco	model	estimates	urban	forest	carbon	stocks	and	gross	and	net	annual	carbon	

sequestration	based	on	sample	data	parameters	(e.g.,	the	tree	species,	diameter,	height,	dieback,	

and	crown	light	exposure)	with	a	calculated	level	of	precision.	As	desired	by	the	landowner,	all	

trees	can	be	measured	or	a	random	distribution	of	field	plots	can	be	measured	to	quantify	the	

urban	tree	population.	Larger	and	more	numerous	plots	help	to	create	a	more	representative	

sample	and	lower	the	uncertainty	associated	with	the	sampling.	The	i‐Tree	Eco	model	uses	the	

sample	data	parameters	and	forest‐derived	allometric	equations	to	estimate	carbon	values.	The	

model	also	estimates	the	standard	error	of	the	estimated	carbon	value,	which	is	based	on	the	

sampling	uncertainty,	rather	than	the	error	of	estimation	from	applying	the	allometric	equations.	

Estimation	error	is	unknown	and	likely	larger	than	the	reported	sampling	error.	A	Monte	Carlo	

																																																													

14	Bootstrapping	is	the	process	of	estimating	variance	by	repeated	random	sampling	with	replacement	of	an	

existing	data	set.	For	example,	to	determine	the	probability	distribution	of	average	DBH	for	a	sample	of	100	

trees,	resamples	of	the	data	set	of	100	trees	can	be	taken	to	approximate	the	variance.		
 Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

 8-9 

analysis	tool	could	use	the	standard	error	of	the	estimated	carbon	value	to	evaluate	the	uncertainty	

associated	with	an	entity’s	total	net	GHG	emissions.	

8.1.2.3 Sampling and DAYCENT for Estimating Biomass Carbon in Grazing Land

and Agroforestry Systems

Sampling	uncertainty	will	exist	when	estimating	biomass	carbon	using	the	method	provided	in	

Chapter	3.	For	example,	peak	forage	estimates	for	grazing	lands	can	be	sampled	using	the	biomass	

clipping	method.15	This	method	is	destructive	with	the	removal	of	forage	samples	from	the	field.	

This	method	has	been	shown	to	produce	estimates	with	low	uncertainty	(Lauenroth	et	al.,	2006;	

Byrne	et	al.,	2011).	Non‐destructive	methods	can	also	be	used	including	the	comparative	yield	

method	for	rangelands,16	or	the	robel	pole	method	on	rangelands	or	pastures	(Harmoney	et	al.,	

1997;	Vermeire	et	al.,	2002).	The	biomass	clipping	method	and	comparative	yield	methods	have	

less	uncertainty	than	the	robel	pole	or	visual	obstruction	method.	Destructive	sampling	methods,	

however,	are	more	time	and	labor	intensive.	Uncertainty	associated	with	the	robel	pole	method	was	

assessed	in	the	Black	Hills	of	South	Dakota	in	a	study	by	Uresk	and	Benzon	(2007).	The	authors	

compared	destructive	(clipping)	methods	for	estimating	biomass	and	the	robel	pole	method.	They	

found	there	was	a	linear	relationship	between	the	two	methods	and	the	standard	error	of	the	robel	

pole	estimate	for	a	single	mean	was	373	kg	ha‐1.	The	study	further	recommends	that	a	minimum	of	

three	transects	be	sampled	for	monitoring	areas	less	than	259	ha	to	be	within	20	percent	of	the	

mean	and	80	percent	confident	(Uresk	and	Benzon,	2007).		In	a	similar	study	by	Vermeire	et	al.,	

(2002),	a	single	visual	obstruction	model	(i.e.,	robel	pole	method)	effectively	estimated	herbage	

standing	crop	across	range	types	and	produced	a	coefficient	of	determination	of	0.93.	Any	sampling	

that	is	done,	whether	destructive	or	non‐destructive,	should	occur	at	locations	that	are	

representative	of	the	land	parcel.		If	sampling	the	forage	is	not	feasible,	default	forage	production	

values	are	provided	by	the	USDA	Natural	Resources	Conservation	Service	(USDA‐NRCS)	in	

Ecological	Site	Descriptions.17	The	sampling	uncertainty	will	depend	on	the	method	used	to	collect	

the	sample	and	should	be	provided	by	the	farmer	or	landowner.		

8.1.3 Large Dataset Uncertainty

Information	from	several	large	datasets	will	be	used	with	the	GHG	quantification	methods	to	

estimate	emissions	from	the	animal	agriculture,	cropland	and	grazing	land,	and	forestry	sectors.	

Large	datasets	can	be	considered	any	grouping	of	data	points	that	cover	a	wide	time‐series	and/or	

level	of	reported	variables.	These	data	sets	include	multiple	data	layers,	GIS	data,	databases,	and	

other	such	reporting	catalogues.	

The	large	datasets	to	be	used	include	the	Smith	et	al.	(2006,	also	known	as	GTR‐NE‐343),	Forest	

Inventory	and	Analysis,	FVS,	Daymet,	and	the	dataset	from	the	i‐Tree	model.	These	datasets	provide	

values	for	estimation	equation	and	model	inputs.	These	inputs	include	region‐	and	species‐specific	

tree	growth	rates,	land	and	tree	cover,	inferred	and	observed	meteorological	data,	soil	type	and	

distribution,	ammonia	content,	and	historical	climate	data	for	the	North	American	continent.	These	

data	will	be	used	to	inform	the	carbon	densities	of	small	forest	holdings,	coverage	of	urban	trees,	

direct	and	indirect	N2O	emissions,	soil	pH,	organic	matter	values,	ambient	air	ammonia	

concentrations,	and	daily	air	temperature	and	velocity.	

																																																													

15	See	Section	15,	“Standing	Biomass”		(USDA	NRCS,	2011b).	 16	See	Section	13,	“Dry	Weight	Rank”	(USDA	NRCS,	2011b).

17	See	USDA	NRCS	(2011a).
Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

8-10

This	section	includes	a	description	of	the	large	datasets	used	for	estimating	forestry	and	

agroforestry	sector	carbon	stocks	and	stock	changes,	GHG	emissions	and	removals	from	wetlands,	

soil	carbon	stocks,	and	ammonia	emissions.	The	section	also	provides	uncertainty	information	

obtained	from	the	dataset	developers	and	an	approach	to	incorporating	uncertainty	associated	with	

these	datasets	into	the	overall	uncertainty	analysis.	

Many	of	the	large	datasets	are	complex	and	cover	multiple	parameters.	In	some	instances	

uncertainty	information	is	available	for	some	variables	but	not	for	others,	making	it	difficult	to	

assess	the	uncertainty	of	the	entire	dataset.	Table	8‐1	below	summarizes	the	uncertainty	

documentation	available	for	each	large	dataset	used.	The	majority	of	datasets	did	not	have	

publically	available	documentation	characterizing	the	associated	uncertainty.	

Table 8‐1: Availability of Uncertainty Information for Large Datasets

Dataset Name Dataset

Abbreviation Availability of Uncertainty Documentation

Methods	for	Calculating	Forest	

Ecosystem	and	Harvested	Carbon	with	

Standard	Estimates	for	Forest	Types	

of	the	United	States	(Smith	et	al.,	

2006)	

GTR‐NE‐343	

No	published	quantification	of	uncertainty.	

Standard	errors	available	for	carbon	density	for	

live	and	standing	dead	trees	at	the	50th	and	99th	

percentile	of	volume.	

National	Land	Cover	Database	 NLCD	

No	published	quantitative	uncertainty	

information	found.	Authors	only	provide	

information	on	contributing	factors.	

Daily	Surface	Weather	and	

Climatological	Summaries	 Daymet	 No	published	quantitative	uncertainty	

information	found.	

Contiguous	United	States	Soil	

Geographic	Database	

CONUS	

STATSGO	

No	published	quantitative	uncertainty	

information	found.	

Soil	Survey	Geographic	Database	 SSURGO	 No	published	quantitative	uncertainty	

information	found.	

Ammonia	Monitoring	Network	 AMoN	 No	published	quantitative	uncertainty	

information	found.	

Parameter‐Elevation	Regressions	on	

Independent	Slopes	Model	 PRISM	 No	published	quantitative	uncertainty	

information	found.	

North	American	Regional	Reanalysis	 NARR	 Regional‐scale	accuracy	and	bias	reported	by	

Mesinger	(2006).	

Natural	Resources	Inventory	 NRI	

Data	are	collected	using	a	two‐stage	sampling	

process.	Statistically	valid	uncertainties	in	

management	practices	are	computable	at	Major	

Land	Resource	Areas	or	State	level.	

National	Agricultural	Statistics	Service	

Agricultural	Census	

NASS‐

agricultural	

census	

No	published	quantitative	uncertainty	

information	found.	

National	Agricultural	Statistics	Service	

–	Cropland	Data	Layer	

NASS	–	

Cropland	Data	

Layer	

NASS	provides accuracy	information	and	error	

matrices	(total	accuracy,	errors	of	omission	and	

co‐mission),	but	not	on	an	annual	basis	for	crops	

and	States.	

Economic	Research	Service	Cropping	

Practices	Survey	 ERS‐CPS	 No	published	quantitative	uncertainty	information	found.	

Economic	Resource	Service	

Agricultural	Resource	Management	

Survey	

ERS‐ARMS	 No	published	quantitative	uncertainty	

information	found.	
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Dataset Name Dataset

Abbreviation Availability of Uncertainty Documentation

National	Climatic	Data	Center	of	the	

National	Oceanic	and	Atmospheric	

Administration	

NCDC	(NOAA)	

NCDC	provides	values	that	describe	the	range	of	

the	uncertainty,	or	simply	"range,"	of	each	

month’s,	season's,	or	year's	global	temperature	

anomaly.	These	values	are	provided	as	

plus/minus	values.	

Modern‐Era	Retrospective	Analysis	

for	Research	and	Applications	 MERRA	(NASA)	 No	published	quantitative	uncertainty	

information	found.	

National	Centers	for	Environmental	

Prediction		 NCEP	(NOAA)	 No	published	quantitative	uncertainty	information	found.	

National	Atmospheric	Deposition	

Program	 NADP	 Regional‐scale	uncertainty	was	assessed	in	

Dennis	et	al.	(2011).	

8.1.3.1 GTR‐NE‐343 Carbon Density Values

Estimates	of	carbon	stocks	and	stock	changes	from	the	report,	“Methods	for	Calculating	Forest	

Ecosystem	and	Harvested	Carbon	with	Standard	Estimates	for	Forest	Types	of	the	United	States”	

(Smith	et	al.,	2006)	(USDA	Forest	Service,	General	Technical	Report	NE‐343),	are	based	on	regional	

averages	and	reflect	the	current	best	available	data.	However,	according	to	GTR‐NE‐343,	

“quantitative	expressions	of	uncertainty	are	not	available	for	most	data	summaries,	coefficients,	or	

model	results	presented	in	the	[GTR‐NE‐343]	tables.”	GTR‐NE‐343	lookup	tables	include	some	

information	about	the	confidence	intervals	for	live	and	standing	dead	tree	carbon	densities	at	two	

different	average	volumes	(see	Table	20	of	GTR‐NE‐343),	but	it	does	not	prescribe	a	method	for	

applying	these	summary	uncertainty	statistics	to	stand	level	carbon	stock	estimates.	

The	uncertainty	associated	with	these	reported	regional	average	carbon	stock	values	is	likely	

higher	as	these	values	are	applied	to	smaller‐scale	projects	rather	than	regions.	Sampling	

uncertainty	associated	with	the	regional	averages,	that	are	based	on	data	summaries	or	models,	can	

influence	estimates	for	specific	projects.	These	projects	are	generally	small	subsets	of	a	region.	Yet,	

variability	within	a	region	for	values	in	a	dataset	will	likely	have	a	much	greater	influence	on	

uncertainty	than	the	actual	sampling	uncertainty	associated	with	collecting	regional	values	(Smith	

et	al.,	2006).	

Once	the	user	finds	the	table	in	GTR‐NE‐343	that	describes	the	forest’s	species	mix	and	region,	the	

user	can	use	the	age	(or	volume)	of	the	forest	stand	(which	is	also	collected	with	a	high	level	of	

uncertainty)	to	find	out	the	metric	tons	of	carbon	per	acre	density	value	for	live	tree	carbon,	down	

deadwood,	organic	soil	carbon,	and	other	categories.	The	uncertainty	information	is	given	as	95	

percent	confidence	intervals	for	the	carbon	density	of	live	and	standing	dead	trees,	at	two	different	

growing	stock	volumes—the	50th	percentile	and	the	99th	percentile.	These	confidence	intervals	are	

given	for	each	forest	type	and	region.	To	use	this	information	in	an	uncertainty	analysis	requires	

extrapolation	to	other	growing	stock	volumes,	which	requires	modeling	the	relationship	between	

growing	stock	volume	and	variation	in	carbon	density.	While	these	tables	are	simple	and	easy	to	

use,	the	uncertainty	of	results	obtained	by	using	representative	average	values	may	be	high	relative	

to	other	techniques	that	use	site‐	or	project‐specific	data.	Additional	research	is	needed	to	include	

this	uncertainty	into	a	Monte	Carlo	analysis	framework.				

8.1.3.2 National Land Cover Database Map

The	National	Land	Cover	Database	(NLCD)	Map	is	the	product	of	the	Multi‐Resolution	Land	

Characterization	partnership,	a	consortium	of	Federal	agencies	including	the	U.S.	Geological	Survey,	

Environmental	Protection	Agency	(EPA),	National	Oceanic	and	Atmospheric	Administration	

(NOAA),	and	the	USDA	Forest	Service	that	are	continuously	developing	digital	land	cover	data.	This	
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8-12

association	has	successfully	provided	land	cover	data	for	the	lower	48	States,	Hawaii,	Alaska,	and	

Puerto	Rico	from	decadal	Landsat	satellite	imagery	and	other	associated	imaging	datasets.	The	

database	provides	Landsat‐based,	30‐meter	resolution,	land	coverage	characteristics	including	

thematic	class	(e.g.,	urban,	agriculture,	and	forest),	percent	impervious	surface,	and	percent	tree	

canopy	cover.	

Regarding	uncertainty,	the	NLCD	map	documentation	indicates,	“Unfortunately,	there	is	no	readily	

available	reference	dataset	with	which	to	compare	the	inventory	to	generate	accuracy	statistics.	

Reference	data	have	to	be	specifically	generated	through	manual	interpretation	of	remote	sensing	

data	for	a	sample	of	locations,	as	has	been	done	for	accuracy	assessment	of	land	cover	maps.	In	lieu	

of	such	an	approach,	which	is	outside	the	scope	of	this	study,	the	best	that	can	be	done	currently	to	

describe	the	uncertainty	of	the	inventory	data	is	to	identify	the	known	conditions	that	contribute	to	

it”	(National	Land	Cover	Database,	2008).	

8.1.3.3 Continental United States Soil Geographic Database

The	Continental	United	States	Soil	Geographic	Database	(CONUS	STATSGO)	is	a	digital	general	soil	

association	map	that	has	been	developed	by	the	National	Cooperative	Soil	Survey	and	distributed	

by	the	USDA	NRCS.	It	consists	of	broad	based	inventory	of	soils	and	non‐soil	areas	that	occur	in	a	

repeatable	pattern	on	the	landscape	and	that	can	be	cartographically	shown	at	scale	and	mapped.	

No	information	is	readily	available	on	the	uncertainty	associated	with	this	dataset.	

8.1.3.4 Soil Survey Geographic Database

The	Soil	Survey	Geographic	(SSURGO)	database	has	been	developed	by	the	National	Geospatial	

Management	Center,	formerly	the	National	Cartography	and	Geospatial	Center.	The	SSURGO	

database	depicts	information	about	the	kinds	of	soils	and	distribution	of	soils	on	the	landscape.	This	

dataset	is	a	digital	soil	survey	and	generally	is	the	most	detailed	level	of	soil	geographic	data	

available.	Uncertainty	information	was	not	readily	available	for	this	database	beyond	the	disclaimer	

that	the	accuracy	of	data	points	‘met	national	map	accuracy	standards.’	

8.1.3.5 Ammonia Monitoring Network

The	Ammonia	Monitoring	Network	is	part	of	the	National	Atmospheric	Deposition	Program	

(NADP),	and	was	originally	initiated	by	the	U.S.	State	Agricultural	Experiment	Stations.	The	dataset	

provides	consistent,	long	term	record	of	ammonia	gas	concentrations	in	the	United	States,	drawing	

from	50	monitoring	sites	across	37	states	in	total.	Uncertainty	was	not	directly	addressed	in	the	

dataset	materials,	aside	from	the	disclaimer	that	the	NADP’s	Central	Analytical	Laboratory	(CAL)	

analyzes,	quality	assures,	and	provides	the	analytical	data	to	the	NADP	(2011).	

8.1.3.6 Parameter‐elevation Regressions on Independent Slopes Model

The	Parameter‐elevation	Regressions	on	Independent	Slopes	Model	(PRISM)	is	a	climate	mapping	

system	developed	by	the	PRISM	Climate	Group.	PRISM	is	a	knowledge‐based	system	that	uses	point	

measurements	of	precipitation,	temperature,	and	other	climatic	factors	to	produce	continuous,	

digital	grid	estimates	of	monthly,	yearly,	and	event‐based	climatic	parameters.	No	information	is	

readily	available	on	the	uncertainty	associated	with	this	dataset.	

8.1.3.7 Daymet Weather Dataset

Daymet	is	a	weather	model	developed	by	Oak	Ridge	National	Laboratory	that	provides	

interpolations	extracted	from	daily	meteorological	observations	onto	a	gridded	dataset	where	no	

such	observations	are	present.	Daymet	provides	output	parameters	including	temperature,	

precipitation,	humidity,	solar	radiation,	and	snow	water	equivalent.	The	Daymet	dataset	is	based	on	
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the	spatial	convolution	of	a	truncated	Gaussian	weighting	filter	with	the	set	of	station	locations.	

Sensitivity	to	the	typical	heterogeneous	distribution	of	stations	in	complex	terrain	is	accomplished	

with	an	iterative	station	density	algorithm.	The	weather	datasets	are	produced	as	outputs	from	the	

Daymet	model	run.	This	dataset	is	used	as	an	input	for	estimating	GHG	emissions	from	croplands	

and	grazing	lands,	and	ammonia	emissions	from	manure	management.	No	information	is	readily	

available	on	uncertainty	associated	with	this	dataset.	

8.1.3.8 North American Regional Reanalysis Weather Dataset

The	DAYCENT	model	simulations	use	the	North	American	Regional	Reanalysis	(NARR)	data	product	

for	daily	temperature	and	precipitation.	The	NARR	dataset	was	chosen	because	it	provides	full,	gap‐

filled	coverage	for	the	conterminous	U.S.	and	is	maintained	and	updated	regularly.	As	described	by	

Mesinger	(2006),	“The	National	Centers	for	Environmental	Prediction	(NCEP)	North	American	

Regional	Reanalysis	(NARR)	is	a	long‐term,	dynamically	consistent,	high‐resolution,	high‐frequency,	

atmospheric	and	land	surface	hydrology	dataset	for	the	North	American	domain.	It	covers	the	25‐

year	period	1979–2003,	and	is	being	continued	in	near‐real	time	as	the	Regional	Climate	Data	

Assimilation	System,	R‐CDAS.	Essential	components	of	the	system	used	to	generate	NARR	are	the	

lateral	boundaries	from	and	the	data	used	for	the	NCEP/DOE	Global	Reanalysis,	the	NCEP	Eta	

Model	and	its	Data	Assimilation	System,	a	recent	version	of	the	NOAA	land	surface	model,	and	the	

use	of	numerous	data	sets	additional	to	or	improved	compared	to	those	of	the	Global	Reanalyses.	In	

particular,	NARR	has	successfully	assimilated	high	quality	and	detailed	precipitation	observations	

into	the	atmospheric	analysis.	Consequently,	the	forcing	to	the	land	surface	model	component	of	

the	system	is	more	accurate	than	in	previous	reanalyses,	so	that	NARR	provides	a	much	improved	

analysis	of	land	hydrology	and	land‐atmosphere	interaction.”	No	quantitative	information	is	readily	

available	on	uncertainty	associated	with	this	dataset.	

8.1.3.9 DAYCENT Land Management Data Sets

Data	on	past	land	use	and	management	(prior	to	the	year	2000)	are	the	basis	for	representative	

cropland	management	systems,	selected	by	the	entity	landowner,	that	are	used	to	initialize	

(“spinup”)	the	DAYCENT	model	for	computing	soil	organic	carbon	stock	changes.		The	attributes	of	

the	management	systems	are	based	primarily	on	three	large	datasets	for	the	US:		the	National	

Resources	Inventory	(NRI),	the	National	Agricultural	Statistics	Service	(NASS)	cropland	surveys,	

and	USDA	Economic	Research	Service	Cropping	Practices	Survey.	The	use	of	representative	crop	

management	systems	for	the	DAYCENT	initialization	process	introduces	some	uncertainty	when	

applied	to	a	specific	farm	or	ranch	entity	(which	has	a	unique	management	history	that	may	be	

different	from	the	regionally‐based	representative	management	histories	specified	by	Major	Land	

Resource	Areas.	However,	the	major	uncertainty	for	the	model	initialization	is	driven	by	the	timing	

of	major	land‐cover	change	(e.g.,	conversion	of	grassland	to	cropland)	which	can	be	user‐specified	

for	the	particular	entity	and	land	parcel.	

National Resources Inventory.	The	NRI	is	an	inventory	of	land	cover	and	use,	soil	erosion,	prime	

farmland,	wetlands,	and	other	natural	resource	characteristics.	NRI	was	designed	as	a	tool	to	assess	

conditions	and	trends	for	soil,	water,	and	related	natural	resources	primarily	on	non‐Federal	lands	

of	the	United	States	(Nusser	and	Goebel,	1997).	The	NRI	is	a	stratified	two‐stage	area	sample	of	

over	several	hundred	thousand	points	distributed	across	the	United	States	and	Caribbean.	Each	

point	in	the	survey	is	assigned	an	area	weight	(i.e.,	expansion	factor)	based	on	other	known	areas	

and	land‐use	information	so	that	each	point	has	a	statistically	assigned	area	that	it	represents	

(Nusser	and	Goebel,	1997).	It	should	be	noted	that	there	is	some	uncertainty	associated	with	

scaling	the	point	data	to	a	region	or	the	country	using	the	expansion	factors.		In	general,	those	

uncertainties	decline	at	larger	scales,	such	as	States	compared	to	smaller	county	units,	because	of	a	

larger	sample	size.		
Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

8-14

National Agricultural Statistics Service Crop Surveys. Data	from	the	NASS	county	agricultural	

production	surveys	were	used	to	construct	representative	crop	rotations	for	the	period	prior	to	

(i.e.,	before	1979)	the	data	record	in	the	NRI.	NASS	conducts	thousands	of	surveys	each	year	

covering	many	facets	of	U.S.	agriculture.	Estimates	include	crop	acreage,	yield,	production,	

irrigation,	and	livestock	numbers.	State‐level	crop	estimates	are	available	from	as	early	as	1866	

depending	on	the	State	and	variable	of	interest.	Some	county‐level	crop	data	is	available	from	as	

early	as	1915,	with	most	crops	available	for	most	States	by	about	1960.	Data	aggregated	to	the	

county	level	are	subject	to	a	high	level	of	quality	control,	including	data	screening	for	outliers,	

double	checking	with	primary	data	collectors	and	comparisons	with	other	aggregate	data	sets	such	

as	from	the	USDA	Farm	Services	Agency.		

USDA Economic Research Service (ERS) Cropping Practices Survey. Ancillary	data	on	historical	

management	practices	used	in	the	DAYCENT	model	initialization	include	nitrogen	fertilizer	rates	

(USDA	ERS,	1997;	2011).	Mean	fertilizer	rates	since	1990	were	estimated	for	all	major	crops,	

summarized	by	ERS	at	the	State‐level.	If	a	State	was	not	surveyed	for	a	particular	crop	or	if	there	

were	not	enough	data	to	produce	a	State	level	estimate,	then	data	were	aggregated	to	USDA	Farm	

Production	Regions	in	order	to	estimate	a	mean	and	standard	deviation	for	fertilization	rates	(Farm	

Production	Regions	are	groups	of	States	with	similar	agricultural	commodities).	Crop‐specific	

regional	fertilizer	rates	prior	to	1990	were	based	largely	on	extrapolation	or	interpolation	of	

fertilizer	rates	from	the	years	with	available	data.	For	crops	in	some	agricultural	regions,	little	or	no	

data	were	available,	and,	therefore,	a	geographic	regional	mean	was	used	to	simulate	nitrogen	

fertilization	rates	(e.g.,	no	data	are	available	for	the	State	of	Alabama	during	the	1970s	and	1980s	

for	corn	fertilization	rates;	therefore,	mean	values	from	the	southeastern	United	States	were	used	

to	simulate	fertilization	to	corn	fields).	No	uncertainty	data	are	available	for	this	dataset.	

8.1.3.10 DNDC Input Datasets

The	DeNitrification‐DeComposition	(DNDC)	model	is	proposed	to	estimate	GHG	emissions	and	

removals	from	wetlands	systems.	DNDC	is	a	soil	biochemistry	model	that	simulates	thermodynamic	

and	reaction	kinetic	processes	of	carbon,	nitrogen,	and	water	driven	by	the	plant	and	microbial	

activities	in	ecosystems	(Olander	and	Haugen‐Kozyra,	2011).		The	DNDC	model	relies	on	specific	

input	datasets	that	can	be	categorized	into	five	sources:	(1)	cropland/land‐use	data;	(2)	crop	

management	data;	(3)	soils	data;	(4)	weather	data;	and	(5)	atmospheric	deposition	data	(Salas	et	

al.,	2012).	These	primary	sources	of	data	and	uncertainty	associated	with	the	dataset	are	provided	

below.		

National Agricultural Statistics Service Cropland Data Layer dataset.	The	DNDC	model	uses	the	

NASS	Cropland	Data	Layer	as	a	source	of	cropland/land‐use	data.	The	NASS	Cropland	Data	Layer	is	

an	online	geospatial	exploring	tool	generated	from	satellite	image	observations	at	a	30	meter	

resolution.	NASS	provides	accuracy	information	and	error	matrices	(total	accuracy,	errors	of	

omission	and	co‐mission),	but	not	on	an	annual	basis	for	crops	and	States.		

NASS Agricultural Census.	The	census	is	available	every	five	years,	and	used	at	the	county	scale.	It	

provides	information	on	U.S.	farms	and	ranches	and	is	the	only	source	of	uniform,	comprehensive	

agricultural	data	at	the	county	level.	Farmers	and	ranchers	are	asked	to	respond	to	the	census	by	

mail	or	online.	Information	including	production	expenses,	market	value	of	products,	and	operation	

characteristics	are	a	few	of	the	categories	of	data.	Uncertainty	is	not	assessed	for	these	data.	

Remote Sensing.	DNDC	uses	remote	sensing	to	build	regional	databases	on	cropland	on	a	project	

and	as	needed	basis.	The	range	of	sensors	used	includes	RapidEye,	Landsat,	MODIS,	and	SAR	

(PALSAR,	Radarsat,	ENVISAT,	etc.).	Remote	sensing	is	used	for	estimating	hydroperiods	(i.e.,	where	

the	water	table	is	at	any	given	time).	As	DNDC	does	not	have	a	groundwater	modeling	component,	
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remote	sensing	is	used	to	identify	when	wetlands	are	flooded.	Uncertainty	is	not	assessed	for	these	

data.	

USDA, ERS Agricultural Resource Management Survey (ARMS).	ARMS	data	are	used	to	populate	

the	crop	management	component	of	the	DNDC	module.	USDA	ERS	ARMS	provide	data	on	the	

financial	condition,	production	practices,	and	resource	use	of	farmers	at	the	field	level	within	the	

United	States.	ARMS	data	are	released	and/or	revised	twice	a	year.	Uncertainty	is	not	assessed	for	

these	data.	

CONUS STATSGO	(See	description	above).	These	data	are	used	to	associate	soil	types	and	

uncertainty	of	soils	data	within	the	model.	

SSURGO	(See	description	above).	SSURGO	data	are	retrieved	by	DNDC	via	an	automated	retrieval	

script	and	extract	four	key	soil	attributes:	clay	content	(texture),	bulk	density,	organic	matter	(soil	

organic	carbon),	and	pH.	

NOAA National Climatic Data Center.	DNDC	uses	station	data	from	the	NOAA	National	Climatic	

Data	Center	(NCDC)	to	input	temperature,	dew	point,	relative	humidity,	precipitation,	wind	speed	

and	direction,	visibility,	and	atmospheric	pressure.	Data	are	provided	at	the	subhourly,	hourly,	

daily,	monthly,	annual,	and	multiyear	timescale.	NCDC	provides	values	that	describe	the	range	of	

the	uncertainty,	or	simply	"range,"	of	each	month,	season,	or	year	global	temperature	anomaly.	

These	values	can	be	used	as	plus/minus	values	within	an	overall	Monte	Carlo	framework;	however,	

a	tool	is	needed	to	utilize	this	information.

Daymet (See	description	above).	These	weather	data	are	used	by	DNDC	and	have	been	available	for	

much	of	North	America	from	1980	to	2012.	Uncertainty	information	is	not	available	for	this	dataset.	

National Aeronautics and Space Administration Modern‐Era Retrospective Analysis for

Research and Applications (MERRA).	The	DNDC	model	relies	on	MERRA	satellite	data	as	input	

for	the	hydrological	cycle.	MERRA	provides	global	data	on	various	aspects	of	moisture	distribution	

and	variability.	Nearly	30	years	of	data	are	available	and	has	undergone	an	online	bias	correction	

for	satellite	radiance	observations.	This	was	done	to	calibrate	observations	from	different	satellites.	

Uncertainty	data	are	not	available	for	MERRA	output.		

National Oceanic and Atmospheric Administration National Center for Environmental

Prediction (NCEP). DNDC	inputs	NCEP	national	weather,	water,	and	climate	data	into	the	NCEP	

model.	NCEP	creates	climate,	water,	ocean,	space,	and	environmental	hazard	outputs.	Uncertainty	

data	are	not	available	for	NCEP	output.

National Atmospheric Deposition Program National Trends Network (NTN) Stations.		DNDC	

requires	total	nitrogen	deposition	and	estimates	of	average	concentration.	DNDC	relies	on	the	

NADP	NTN	stations	to	input	total	nitrogen	deposition	(NO3	and	NH4)	into	the	model.	NADP	NTN	

stations	collect	precipitation	and	chemistry	samples	away	from	urban	area	and	point	sources	of	

pollution.	The	station’s	Central	Analytical	Laboratory	reviews	data	for	completeness	and	accuracy	

and	flags	samples	that	were	mishandled	or	compromised.	Sample	data	are	further	reviewed	by	the	

NADP	program	office	to	do	a	final	check	to	resolve	discrepancies.	Once	data	are	made	available	

online,	DNDC	calculates	mean	nitrogen	deposition	for	the	simulation	time	period	and	incorporates	

the	data	into	the	project	database.	NADP	NTN	station	data	do	not	have	associated	uncertainty	data	

available,	however	regional	uncertainty	was	analyzed	in	a	presentation	by	Dennis	et	al.	(2011).	
Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

8-16

8.1.3.11 Approach for Incorporating Large Dataset Uncertainty

Among	the	large	datasets	to	be	used	for	the	GHG	quantification	methods,	only	GTR‐NE‐343	has	

some	quantified	uncertainty	information	for	use	in	a	Monte	Carlo	assessment	of	net	GHG	emissions.	

Because	confidence	intervals	for	only	two	stock	volumes	are	available,	only	a	linear	relationship	

can	be	modeled	with	GTR‐NE‐343	information,	and	no	departures	from	linearity	can	be	assessed.	

Further	analysis	of	carbon	density	at	other	growing	stock	volumes	requires	computation	of	

additional	confidence	intervals.	

Given	the	lack	of	uncertainty	information	for	most	of	the	relevant	large	datasets,	estimating	this	

source	of	uncertainty	is	not	feasible.	Instead,	reliance	of	the	methods	on	the	large	datasets	is	

explicitly	acknowledged	and	readily	available	information	on	uncertainty	is	summarized	as	

provided	above.	

Some	large	“wall‐to‐wall”	datasets	are	formed	via	interpolation	of	existing	data	from	a	fixed	set	of	

measurement	locations.	For	such	datasets,	a	potential	near‐term	next	step	might	be	to	incorporate	

uncertainty	by	imputing	measurements	from	randomly‐selected	measurement	locations.	This	

random	selection	could	use	probabilities	inversely	proportional	to	the	distance	between	the	

measurement	locations	and	the	entity.	If	most	locations	are	far	from	the	entity,	then	the	

imputations	are	increasingly	uncertain.	

In	the	longer	term,	both	new	research	and	synthesis	of	existing	research	will	be	required	to	

quantify	large	dataset	uncertainty.	Methods	from	geostatistics,	for	example,	might	be	used	to	

describe	an	uncertain	large	dataset	obtained	by	interpolation.	

8.1.4 Model Uncertainty

In	the	case	of	the	external	models,	it	is	hard	to	appropriately	account	for	approximation	error	and	

often	only	one	model	exists	to	represent	or	estimate	emissions	(or	removals)	from	a	specific	

activity	or	process.	Since	comparable	models	do	not	exist,	it	is	almost	impossible	to	estimate	the	

uncertainty	associated	with	using	one	particular	model	versus	another.	While	this	report	specifies	

the	use	of	several	external	models—DAYCENT,	DNDC,	FVS,	i‐Tree	Canopy,	i‐Tree	Eco,	First	Order	

Fire	Effects	Model	(FOFEM)—given	the	above	considerations,	limited	published	data	was	found	on	

external	model	uncertainty	inherent	with	these	models.	

This	section	includes	a	description	of	the	external	models	used	for	estimating	carbon	stocks	and	

stock	changes	from	the	croplands	and	grazing	lands,	wetlands,	and	forestry	sectors,	uncertainty	

information	obtained	from	the	model	developers.	These	models	help	provide	a	quantitative	and	

geographical	view	into	the	emissions	associated	with	a	variety	of	factors	from	agricultural	and	

forestry	systems.	For	example,	given	inputs	such	as	area,	tree	diameter,	tree	height,	species,	soil	

type,	and	geography,	the	suite	of	forestry	models	can	provide	emission	estimates	from	fire	

disturbances,	approximate	changes	in	forest	carbon	stocks,	or	provide	urban	forest	carbon	stock	

data.	Table	8‐2	below	summarizes	the	uncertainty	information	obtained	from	the	model	developers	

for	each	of	the	models	used	to	estimate	net	GHG	emissions.	Given	the	lack	of	quantitative	

information	on	model	uncertainty,	this	component	of	uncertainty	will	not	be	part	of	the	Monte	

Carlo	uncertainty	assessment.	

Table 8‐2: Uncertainty Information for Process‐based Models

Model Availability of Uncertainty

Documentation Occurrence of Uncertainty Biases

DAYCENT	 Ogle	et	al.,	2010	 Biases	by	practice	are	quantified	in	Ogle	et	al.	

(2010).	

DNDCa Input	uncertainty:	Li	et	al.	(2002) and	

Zhang	et	al.	(2009).	There	have	been	

A	Monte	Carlo	approach	or	Most	Sensitive	Factor	

analysis	can	be	run	on	certain	input	parameters	
 Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

 8-17 

Model Availability of Uncertainty

Documentation Occurrence of Uncertainty Biases

no	papers	focused	on	quantification	of	

DNDC	model	structural	uncertainty.	

(i.e.,	soil	measurements)	to	assess	the	variability	of	

the	parameters		(includes	excerpts	from	C_AGG	

whitepaper	by	Salas	et	al.,	2012).	

Forest	

Vegetation	

Simulator		

No	published	quantification	of	model	

uncertainty	was	found.		 Exists	but	not	quantifiable	according	to	experts.	

i‐Tree	Canopy	

(Aerial	Data	

Method)	

No	published	quantification	of	model	

uncertainty	found.	

Model	bias	is	likely	low,	according	to	model	

developer.	

i‐Tree	Eco	

(Field	Data	

Method)	

No	published	quantification	of	model	

uncertainty	found.	

Values	are	standardized,	bias	is	minimized.	

Unknown	bias	for	national	density	estimates.	

First	Order	

Fire	Effects	

Model		

No	published	quantification	of	model	

uncertainty	found.	

Regional	biases	(North	Rocky	Mountains,	Pacific	

Northwest	regions).	

a	DNDC	does	not	provide	uncertainty	parameterization	of	outputs	at	the	site	level,	however,	the	regional	model	provides	

an	option	for	assessing	uncertainty	due	to	input	uncertainty.	

8.1.4.1 DAYCENT Model

The	DAYCENT	model	has	inherent	uncertainty	associated	with	predicting	soil	organic	carbon	(SOC)	

stock	changes	(Ogle	et	al.,	2010;	U.S.	EPA,	2013).	The	uncertainty	is	associated	with	imperfect	

simulation	of	the	plant	and	soil	processes	associated	with	the	algorithms	and	parameters.	To	

address	this	uncertainty,	the	simulated	model	predictions	of	SOC	stocks	need	to	be	compared	to	

measurements.	The	comparison	leverages	the	scalability	of	the	process‐based	model	to	the	wide	

range	of	conditions	that	exist	in	agricultural	lands,	while	having	an	underlying	measurement	basis	

to	support	the	reporting	(Conant	et	al.,	2011).	

The	differences	between	measurements	and	simulated	SOC	stocks	and	stock	changes	have	been		

analyzed	using	an	empirically	based	approach	in	which	a	statistical	model	was	developed	that	

quantifies	the	accuracy	and	precision	in	the	simulated	predictions	(Ogle	et	al.,	2007).	The	linear	

mixed‐effect	modeling	approach	was	used	for	this	analysis,	and	various	environmental	conditions	

(e.g.,	climate	and	soil	characteristics)	and	management	practices	were	evaluated	to	determine	if	the	

model	is	more	accurate	or	precise	for	particular	conditions	or	management	systems.	The	approach	

relied	on	measurements	of	SOC	stocks	from	a	network	of	sites	across	the	U.S.	agricultural	lands.	A	

network	is	currently	being	expanded	by	the	USDA	NRCS	that	is	expected	to	provide	additional	

measurements	supporting	the	entity‐scale	methods	for	estimating	SOC	stock	changes.	This	

uncertainty	analysis	will	be	updated	as	new	measurements	become	available	from	the	network	and	

will	be	incorporated	into	a	Monte	Carlo	assessment.	

8.1.4.2 DNDC Model

Structural	uncertainty	is	related	to	the	inherent	uncertainty	of	a	model	that	remains	even	if	none	of	

the	input	data	had	any	variability.	Estimating	model	structural	uncertainty	requires	the	use	of	

independent	validation	data	(i.e.,	field	measurement	data	that	were	not	used	to	develop	the	model	

algorithms).	This	approach	requires	not	only	access	to	sufficient	independent	field	data,	but	also	

that	the	data	include	all	the	input	data	that	DNDC	requires.	A	number	of	validation	tests	with	

independent	field	data	have	been	published	although	summary	studies	are	currently	not	available	

to	quantify	DNDC	structural	uncertainty.	
Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

8-18

8.1.4.3 Forest Vegetation Simulator

As	previously	described,	a	source	of	uncertainty	for	the	FVS	model	is	sampling	uncertainty	

associated	with	the	tree	list	(the	main	user	input).	The	additional	uncertainty	associated	with	the	

model	uncertainty	is	difficult	to	quantify.	

In	the	FVS	model,	diameter	growth	is	the	only	variable	that	is	considered	stochastic.	For	the	

diameter	growth	module,	a	random	seed	is	used	for	projections	of	changes	in	forest	stands	rather	

than	using	the	mean	diameter	value	to	avoid	underestimating	growth.	This	process	increases	error	

propagation	because	the	results	of	the	diameter	growth	module	are	used	to	make	further	estimates	

in	the	model,	e.g.,	using	growth	and	yield	equations	(i.e.,	Jenkins	equations).	However,	the	

stochasticity	of	diameter	growth	is	not	the	main	driver	of	model	uncertainty.	Uncertainty	

associated	with	the	FVS	model	is	complex	because	it	is	derived	from	20	different	regionally	specific	

model	variants	that	were	developed	independently.	Each	model	run	or	analysis	has	to	be	calibrated	

to	account	for	local	tree	variety	and	growth	rates,	introducing	another	level	of	complexity	(Van	

Dyck,	2012).	Additionally,	errors	may	propagate	from	the	bias	in	regional	factors,	adjusting	to	local	

geographies,	climates,	the	use	of	field	data,	and	sampling	uncertainty.	Given	the	overall	complexity	

inherent	in	the	model,	FVS	does	not	incorporate	uncertainty	in	the	output	or	post‐analysis	of	model	

runs	and	additional	research	is	required	to	quantify	model	uncertainty.	

8.1.4.4 i‐Tree Model

i‐Tree	(formerly	the	Urban	Forest	Effects	model)	is	an	urban	forestry	analysis	model	developed	by	

David	J.	Nowak	(USDA	FS),	Daniel	E.	Crane	(NRS),	and	Patrick	McHale	(SUNY	College	of	

Environmental	Science	and	Forestry).	The	i‐Tree	model	helps	quantify	the	structure	of	community	

trees	and	the	environmental	services	that	they	provide.	It	provides	six	analytical	tools	including:	

 i‐Tree Eco:	Provides	a	full	picture	of	the	entire	urban	forest	(used	in	the	Field	Data	

Method)	

 i‐Tree Streets:	Quantifies	benefits	from	a	municipalities	street	level	trees	

 i‐Tree Hydro:	Models	the	effects	of	trees	on	watershed	stream	flow	and	water	quality	

 i‐Tree Vue:	Uses	NLCD	satellite	imagery	to	assess	tree	canopy	

 i‐Tree Design:	Assesses	multiple	trees	at	parcel	level	

 i‐Tree Canopy:	Provides	a	quantifiable	estimate	of	tree	cover	and	other	land	cover	types	

(using	in	the	Aerial	Data	Method)	

i‐Tree	Eco	and	i‐Tree	Canopy	are	recommended	in	this	report	for	use	by	an	entity	to	estimate	the	

change	in	carbon	stocks	in	their	urban	forests.	

i‐Tree Eco Uncertainty Information:	The	i‐Tree	Eco	model	produces	uncertainty	estimates	based	

on	sampling	error,	but	it	does	not	calculate	a	model	estimation	error.	According	to	i‐Tree	

developers,	estimation	error	is	based	on	the	uncertainty	inherent	in	the	biomass	conversion	

equations	and	emission	factors.	The	developers	also	note	that	model	bias	is	likely	low	given	that	the	

input	assumes	a	given	random	sample	of	trees,	and	tree	species	equations	are	selected	based	on	

stand	height.	If	a	particular	species	equation	is	not	available	the	model	uses	the	average	of	available	

equations	from	the	closest	genera	(Nowak,	2012).	A	Monte	Carlo	analysis	tool	could	use	the	

standard	error	of	the	estimated	carbon	value	to	evaluate	the	uncertainty	associated	with	an	entity’s	

total	net	GHG	emissions.	

i‐Tree Canopy Uncertainty Information:	The i‐Tree	Canopy	model	produces	a	statistical	estimate	

of	the	standard	error	of	the	percent	tree	cover	estimate	based	on	the	ratio	of	sample	points	
 Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

 8-19 

classified	as	trees	to	total	sample	points.	In	i‐Tree	Canopy	the	user	imports	a	shape	file,	samples	

points,	and	classifies	them	as	either	trees	or	non‐trees.	An	analysis	of	the	tree	point	to	total	point	

ratio	is	used	to	estimate	the	standard	error	associated	with	the	percent	tree	cover	estimate,	as	

described	in	the	i‐Tree	Canopy	technical	notes,18	and	shown	in	Equation	8‐1	below.	

Table	8‐3	shows	estimates	of	the	standard	error	as	related	to	

the	ratio	of	tree	points	to	total	sample	points	(p	value),	where	

the	total	number	of	sampled	points	(N)	equals	1,000.

Based	on	the	standard	error	formula,	standard	error	is	greatest

when	p	equals	0.5,	and	is	least	when	p	is	very	small	or	very	

large	(see	Table	8‐3).	A	Monte	Carlo	analysis	tool	could	use	the	

standard	error	of	the	estimated	percent	tree	cover	value	to	

evaluate	the	uncertainty	associated	with	an	entity’s	total	net	

GHG	emissions.

8.1.4.5 First Order Fire Effects Model

FOFEM	is	a	computational	model	for	predicting	tree	mortality,	

fuel	consumption,	smoke	production,	and	soil	heating	caused	by	

either	prescribed	fire	or	wildfire.	FOFEM	was	developed	by	the	

Intermountain	Fire	Sciences	Laboratory	in	Missoula,	MT,	of	the	USDA	Forest	Service.	First	order	fire	

effects	are	those	characterized	with	the	direct	immediate	consequences	of	a	fire	including	GHG	

emission	estimates.	FOFEM	is	divided	into	four	national	regions:	Pacific	West,	Interior	West,	North	

East,	and	South	East.	The	model	includes	several	forest	cover	types	to	provide	an	additional	level	of	

detail	resolution.	The	quantitative	output	can	be	used	in	assessments	after	fire	damage,	in	analyzing	

prescribed	fire	impacts,	and	modeling	vulnerabilities	in	regional	forest	groups.	

FOFEM	has	a	regional	bias	given	that	the	empirical	relationships	and	assumptions	are	based	on	

forested	systems	in	the	North	Rocky	Mountains	and	the	Pacific	Northwest.	However,	these	

uncertainties	are	not	quantified	or	adjusted	for	use	in	different	regions.	For	instance,	Southeast	

fires	burn	well	at	humidity	levels	that	would	not	support	them	in	the	West.	This	phenomenon	is	not	

accounted	for	in	the	model	and	there	is	no	uncertainty	quantification	around	the	output.	There	are	

also	material	differences	such	as	litter	bulk	density	that	influences	consumption	and	emission	

which	can	vary	considerably	region	to	region	(Lutes,	2012).	

																																																													

18	I‐Tree	Canopy	Technical	Notes:	

http://www.itreetools.org/canopy/resources/iTree_Canopy_Methodology.pdf

Table 8‐3: Estimates of

Standard Error (SE) (N =

1,000) of Percent Tree Cover

from i‐Tree Canopy with

Varying p Values

p SE

0.01	 0.0031

0.1	 0.0095

0.3	 0.0145

0.5	 0.0158

0.7	 0.0145

0.9	 0.0095

0.99	 0.0031

Equation 8‐1: Estimating Standard Error of Percent Tree Cover from i‐Tree Canopy

ܵܧ ൌ ඥݍ݌/ܰ)	e.g.,	ඥ0.33 ൈ 0.67/1000	=	0.0149)	

Where:	

N=	Total	number	of	sampled	points	(e.g.,	1,000)	

n	=	Total	number	of	points	classified	as	a	tree	(e.g.,	330)	

p	=	n/N	(e.g.,	330/1,000	=	0.33)	

q	=	1	− p	(e.g.,	1	− 0.33	=	0.67)	
Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

8-20

8.1.4.6 Approach for Incorporating Model Uncertainty

Given	the	lack	of	uncertainty	information	for	most	of	the	relevant	external	models,	it	is	not	

currently	feasible	for	the	GHG	quantification	methods	to	quantify	this	source	of	uncertainty.	

Instead,	reliance	of	the	methods	on	the	models	will	be	explicitly	acknowledged.	The	potential	

impacts	of	uncertain	models	on	the	accuracy	and	precision	of	the	resulting	estimates	is	described	

qualitatively	in	the	previous	sections.	

It	may	be	possible	in	the	near	term	to	elicit	expert	judgments	on	the	level	of	model	uncertainty	at	

the	entity	level.	Models	used	in	the	GHG	quantification	methods	are	typically	constructed	at	scales	

no	smaller	than	the	entity	level.	It	is	expected	that	the	model	uncertainty	at	the	entity	level	would	

be	no	smaller	than	the	model	uncertainty	at	the	model’s	scale,	and	possibly	larger	due	to	additional	

error	from	downscaling	to	the	entity	level.	

In	the	longer	term,	more	research	is	needed	to	evaluate	model	predictions	with	independent	data,	

not	used	in	the	development	of	the	model.	The	differences	between	model	predictions	and	

independent	data	are	the	best	possible	source	of	information	regarding	model	uncertainty.	

8.2 Research Gaps

The	readily	available	information	on	parameter	uncertainty	is	provided	in	the	tables	in	Appendix	8‐

B.	As	indicated,	much	of	the	information	to	characterize	the	uncertainty	is	not	available	and	the	

data	that	are	provided	are	mostly	default	values	from	the	literature	and	assumed	probability	

density	functions.	To	conduct	a	Monte	Carlo	analysis	for	uncertainty	estimation,	it	is	important	to	

obtain	probability	density	functions	or	summary	statistics	for	all	uncertain	variables.	Significant	

research	is	needed	to	obtain	new	data	and	to	synthesize	existing	and	new	data	in	order	to	truly	

assess	uncertainty	associated	with	a	range	of	factors	causing	uncertainty	in	the	GHG	estimates	

developed	using	the	recommended	methods	described	in	this	report.	In	particular,	more	research	is	

needed	to	assess	parameter,	sampling,	large	data	sets,	and	model	uncertainties.	

	 	
 Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

 8-21 

Appendix 8‐A: Example Output File from FVS Sampling Uncertainty

Bootstrapping Application FVSBoot (as provided in Gregg and Hummel, 2002)

The	following	table	illustrates	standard	deviation	surrounding	the	sampling	error	of	the	Basal	Area	

outputs.	FVSBoot	can	be	configured	to	determine	standard	deviation	of	the	sampling	error	for	any	

FVS	output.	

Table 8‐A‐1: Example Output File from FVS Sampling Uncertainty Bootstrapping Application

FVSBoot (as provided in Gregg and Hummel, 2002)
Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

Appendix8-22

8‐B: Uncertainty Tables

This	section	presents	readily	available	data	on	the	uncertainty	associated	with	activity	and	emission	factor	data.	Table	8‐B‐1	lists	the	data	

elements	that	are	provided	in	the	subsequent	tables	for	each	agriculture	system.	In	particular,	readily	available	uncertainty	information	is	

provided	in	the	following	tables:	

 Table	8‐B‐2:	Cropland	Uncertainty	Template	 Table	8‐B‐3:	Animal	Population	Uncertainty	Template	 Table	8‐B‐4:	Enteric	Fermentation	and	Housing	Uncertainty	Template	 Table	8‐B‐5:	Manure	Management	Uncertainty	Table	 Table	8‐B‐6:	Forestry	Uncertainty	Table	

Table 8‐B‐1: Data Elements Provided

Column Label Description

Data	Element	Name	 The	name	of	the	variableAbbreviation	/	Symbol	 The	shorthand	representation	used	in	the	reportEmission	Type	 Emissions estimates	that	depend	on	the	data	element	(CH4,	N2O,	NH3,	CO2)Data	Input	Unit	 Unit	associated	with	the	data	elementInput	Source	 Entity	entry,	default	entry,	model	output,	or	from	a	databaseStatistic	 Available	statistic	for	the	parameterType	of	Statistic	 Mean,	median,	or	modeProbability	Distribution	Type	 The	probability	distribution	function	of	the	data	element	(normal,	lognormal,	uniform,	triangular,	beta)Relative	Uncertainty	 Range	of	values	around	the	most	likely	value,	expressed	as	a	percent	of	the	most	likely	valueConfidence	Level		 The	probability	that	the	confidence	range	captures	the	true	value	of	the	data	element	given	a	distribution	of	 samples.	Effective	Lower	Limit	 Minimum	value	for	data	element	(excluding	outliers)	Effective	Upper	Limit	 Maximum	value	for	data	element	(excluding	outliers)	Data	Source	 Reference	for	information	related	to	the	data	element	and	associated	uncertainty	
Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

8-23

8-23 

Table 8‐B‐2: Cropland Uncertainty Template

Cropland Sub‐Source

Category

Name Element Data

Abbreviation/Symbol

Type Emission

Unit Input Data

Source Input

Statistic

Statistic of Type

Type Distribution Probability

(%) Low uncertainty Relative

(%) High uncertainty Relative

(%) Level Confidence

Limit Lower Effective

Limit Upper Effective

Source Data

Croplands	–	Multiple	Sub‐sources	

Area	 A	 CH4,	N2O,	 CO2 Hectares	 Entity	 Entry	

Croplands	–	Multiple	Sub‐sources	 Crop	Yield	 Y	 CH4,	N2O,	 CO2 Metric	tons	dry	 matter	crop	 yield/	year	 Entity	 Entry	Croplands	–	Multiple	Sub‐sources	

Meat	yield	per	

parcel	of	land	 Meat	 CO2 kg	carcass	 yield	 Entity	 Entry	

Croplands	–	Multiple	Sub‐sources	

Milk	production	

per	parcel	of	land	 Milk	Prod.	 CO2 kg	fluid	milk	 yield	 Entity	 Entry	

Biomass	Carbon	Stock	Changes	

Mean	annual	

woody	biomass	

(t=current	year's	

stocks)	

Wt	 CO2 Metric	tons	 CO2‐eq	year‐1 Model	 Output	

DAYCENT	

model	

simulations	

and	growth	

functions	

for	agro‐

forestry	

Biomass	Carbon	Stock	Changes	

Mean	annual	

woody	biomass	(t‐

1=Previous	year's	

stocks)	

Wt‐1 CO2 Metric	tons	 CO2‐eq	year‐1 Model	 Output	

DAYCENT	

model	

simulations	

and	growth	

functions	

for	agro‐

forestry	

Biomass	Carbon	Stock	Changes	

Mean	annual	

herbaceous	

biomass	

(t=current	year's	

stocks)	

Ht	 CO2 Metric	tons	 CO2‐eq	year‐1 Entity	 Entry	 	 	 	 	 	 	

Biomass	Carbon	Stock	Changes	

Mean	annual	

herbaceous	

biomass	(t‐

1=Previous	year's	

stocks)	

Ht‐1 CO2 Metric	tons	 CO2‐eq	year‐1 Entity	 Entry	
Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

8-24

Cropland Sub‐Source

Category

Name Element Data

Abbreviation/Symbol

Type Emission

Unit Input Data

Source Input

Statistic

Statistic of Type

Type Distribution Probability

(%) Low uncertainty Relative

(%) High uncertainty Relative

(%) Level Confidence

Limit Lower Effective

Limit Upper Effective

Source Data

Biomass	Carbon	Stock	Changes	 Forage	Yield	

Forage	yield	

for	grazing	

lands	

CO2

Metric	tons	dry	

matter	per	

hectare	

Entity	

Entry	 	 	 	 	 	 	

Biomass	Carbon	Stock	Changes	

Number	of	trees	

by	age	of	diameter	

class	for	each	

agroforestry	

practice	

Number	of	

Trees	 CO2 Number	 Entity	 Entry	

Biomass	Carbon	Stock	Changes	

Diameter	at	breast	

height	for	a	

subsample	of	trees	

DBH	 CO2	 Meters	 Entity	 Entry	 	 	 	 	 	 	

Biomass	Carbon	Stock	Changes	

Root	to	Shoot	

Ratio	 R:S	 CO2 Ratio	 Default	 Entry	 West	et	al.	 (2010)	

Biomass	Carbon	Stock	Changes	

Dry	matter	

content	of	

harvested	crop	

biomass	or	forage	

DM	 CO2	 Dimensionless Entity	 Entry	 	 	 	 	 	 	

Biomass	Carbon	Stock	Changes	 Harvest	Index	 HI	 CO2 Fraction	 Default	 Entry	 West	et	al.	 (2010)	Biomass	Carbon	Stock	Changes	

Crop	harvest	or	

forage	yield,	

corrected	for	

moisture	content	

Ydm		 CO2 Metric	tons	 biomass	ha‐1 Entity	 Entry	 	 	 	 	 	 	

Biomass	Carbon	Stock	Changes	

Approximate	

fraction	of	

calendar	year	

representing	the	

growing	season	

Yf CO2 Fraction	 Entity	 Entry	

Biomass	Carbon	Stock	Changes	

Carbon	fraction	of	

aboveground	

biomass	

C	 CO2	 Fraction	 Default	 Entry	 0.45	 Normal	 11.0	 11.0	 IPCC	(1997)

CO2	from	Liming	 Annual	application	 of	lime	 M	 CO2 Metric	tons/	 year	 Entity	 Entry	
Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

8-25

8-25 

Cropland Sub‐Source

Category

Name Element Data

Abbreviation/Symbol

Type Emission

Unit Input Data

Source Input

Statistic

Statistic of Type

Type Distribution Probability

(%) Low uncertainty Relative

(%) High uncertainty Relative

(%) Level Confidence

Limit Lower Effective

Limit Upper Effective

Source Data

CO2	from	Liming	

Metric	tons	CO2

emissions	per	

metric	tons	of	lime	

EF	 CO2

Metric	tons	

C/metric	tons	

lime	

Default	

Entry	 ‐0.04	 46.0	 46.0	

West	and	

McBride	

(2005)	

CO2	from	Urea	Fertilizer	Application	

Annual	amount	of	

urea	fertilization	 M	 CO2 Metric	tons	 urea/year	 Entity	 Entry	

CO2	from	Urea	Fertilizer	

Application	

Proportion	of	C	in	

urea	

EF	 CO2

Metric	tons	

C/metric	tons	

urea	

Default	

Entry	 0.2	 de	Klein	et	 al.	(2006)	

Direct	N2O	Emissions	

Area	of	organic	

soils	(histosols)	

drained	on	a	

parcel	of	land	(ha)	

Aos N2O	 ha	 Entity	 Entry	

Direct	N2O	Emissions	 Prior‐year	crop	 type	 N2O	 Metric	tons	N	 year‐1	ha‐1 Entity	 Entry	Direct	N2O	Emissions	

Emission	rate	

modeled	at	0	level	

of	N	input	(Nt	=0)	

ER0 N2O	

Metric	tons	

N2O‐N	ha‐1year‐

1

Model	

Output	

Direct	N2O	Emissions	

Emission	factor	

for	the	typical	

fertilization	rate	

EFtypical	 N2O	

Metric	tons	

N2O‐N	metric	

tons‐1	N	

Model	

Output	 	 	 	 	 	 	 	 	 	

Direct	N2O	Emissions	 Typical	N	fertilizer	 rate	 Ntf N2O	 Metric	tons	N	 ha‐1	year‐1 Database	Direct	N2O	Emissions	

Emission	rate	for	

the	typical	case	

modeled	

ERtypical	 N2O	 Metric	tons	N	 ha‐1	year‐1 Model	 Output	 	 	 	 	 	 	 	 	 	

Direct	N2O	Emissions	

Actual	N	fertilizer	

rate,	including	

synthetic	and	

organic	

Nf N2O	 Metric	tons	N	

year‐1	ha‐1

Entity	

Entry	
Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

8-26

Cropland Sub‐Source

Category

Name Element Data

Abbreviation/Symbol

Type Emission

Unit Input Data

Source Input

Statistic

Statistic of Type

Type Distribution Probability

(%) Low uncertainty Relative

(%) High uncertainty Relative

(%) Level Confidence

Limit Lower Effective

Limit Upper Effective

Source Data

Direct	N2O	Emissions	

Base	EF	scalar	for	

ΔNf	>	zero	and	

non‐grassland	

crops	

SEF N2O	

(metric	tons	

N2O‐N	(metric	

tons	N)‐2)	ha	

year

Default	

Entry	 0.0274	 Appendix	3‐A

Direct	N2O	Emissions	

Base	EF	scalar	for	

ΔN=	>	zero	and	

grassland	

SEF N2O	

(metric	tons	

N2O‐N	(metric	

tons	N)‐2)	ha	

year

Default	

Entry	 0.117	 Appendix	3‐A

Direct	N2O	Emissions	 Base	EF	scalar	for	 ΔNf		<	zero	 SEF	 N2O	

(metric	tons	

N2O‐N	(metric	

tons	N)‐2)	ha	

year

Default	

Entry	 0	 	 	 	 	 	 	 	 Appendix	3‐A

Direct	N2O	Emissions	

Dry	matter	

content	of	

harvested	biomass	

DM	 N2O	 Entity	 Entry	

Direct	N2O	Emissions	 Residue:yield	 ratios	 	 N2O	 Ratio	 Entity	 Entry	Direct	N2O	Emissions	

Amount	of	residue	

harvested,	burned	

or	grazed	

Rr N2O	 Entity	 Entry	

Direct	N2O	Emissions	

Fraction	of	live	

biomass	removed	

by	grazing	

Fr	 N2O	 	 Entity	 Entry	

Direct	N2O	Emissions	 N	mineralization	 from	manure	 Nman N2O	

Entity	

Entry	and	

Model	

Output	

Direct	N2O	Emissions	 N	mineralization	 from	compost	 Ncomp	 N2O	 	

Entity	

Entry	and	

Model	

Output	

Direct	N2O	Emissions	 Total	dry	matter	 yield	of	crop	 N2O	 Metric	tons	dry	 matter	year‐1 Entity	 Entry	Direct	N2O	Emissions	 Stocking	rates	and	 methods	 	 N2O	 Head/acre	 Entity	 Entry	 	 	 	 	 	 	 	 	 	
Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

8-27

8-27 

Cropland Sub‐Source

Category

Name Element Data

Abbreviation/Symbol

Type Emission

Unit Input Data

Source Input

Statistic

Statistic of Type

Type Distribution Probability

(%) Low uncertainty Relative

(%) High uncertainty Relative

(%) Level Confidence

Limit Lower Effective

Limit Upper Effective

Source Data

Direct	N2O	Emissions	

Scaling	factor	for	

slow‐release	

fertilizers,	0	where	

no	effect	

Ssr N2O	 Dimensionless Default	 Entry	 ‐0.21	 Normal	 ‐0.3	 ‐0.12 Meta‐analysis

Direct	N2O	Emissions	

Scaling	factor	

nitrification	

inhibitors	–	semi‐

arid/arid	climate	

Sinh		 N2O	 Dimensionless Default	 Entry	 ‐0.38	 	 Normal	 	 	 	 ‐0.51	 ‐0.21 Meta‐analysis

Direct	N2O	Emissions	

Scaling	factor	

nitrification	

inhibitors	–	mesic	

climate	

Sinh N2O	 Dimensionless Default	 Entry	 ‐0.4	 Normal	 ‐0.52	 ‐0.24 Meta‐analysis

Direct	N2O	Emissions	

Scaling	factor	for	

no	till,	semi‐

arid/arid	climate,	

<10	years	

following	no‐till	

adoption	

Still		 N2O	 Dimensionless Default	 Entry	 0.38	 	 	 	 	 	 0.04	 0.72 van	Kessel	et	 al.	(2012);	Six	 et	al.	(2004)

Direct	N2O	Emissions	

Scaling	factor	for	

no	till,	semi‐

arid/arid	climate,	

≥10	years	

following	no‐till	

adoption	

Still N2O	 Dimensionless Default	 Entry	 ‐0.33	 ‐0.5	 ‐0.16 van	Kessel	et	 al.	(2012);	Six	 et	al.	(2004)

Direct	N2O	Emissions	

Scaling	factor	for	

no	till,	mesic/wet	

climate,	<10	years	

following	no‐till	

adoption	

Still		 N2O	 Dimensionless Default	 Entry	 ‐0.015	 	 	 	 	 	 ‐0.16	 0.16 van	Kessel	et	 al.	(2012);	Six	 et	al.	(2004)

Direct	N2O	Emissions	

Scaling	factor	for	

no	till,	mesic/wet	

climate,	≥10	years	

following	no‐till	

adoption	

Still N2O	 Dimensionless Default	 Entry	 ‐0.09	 ‐0.19	 0.01 van	Kessel	et	 al.	(2012);	Six	 et	al.	(2004)
Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

8-28

Cropland Sub‐Source

Category

Name Element Data

Abbreviation/Symbol

Type Emission

Unit Input Data

Source Input

Statistic

Statistic of Type

Type Distribution Probability

(%) Low uncertainty Relative

(%) High uncertainty Relative

(%) Level Confidence

Limit Lower Effective

Limit Upper Effective

Source Data

Direct	N2O	Emissions	

N	in	slow‐release	

N	fertilizer	applied	

to	the	parcel	of	

land	

Nsr	 N2O	 Metric	tons	N	

year‐1	ha‐1

Entity	

Entry	

Direct	N2O	Emissions	

N	in	manure	

amendments	(and	

sewage	sludge)	

added	to	the	

parcel	

Nman

N2O	 Metric	tons	N	

year‐1	ha‐1

Entity	

Entry	

Direct	N2O	Emissions	

N	excreted	by	

cattle,	poultry	and	

swine	directly	on	

the	parcel	of	land	

(metric	tons	N	

year‐1	ha‐1)	

Nprp	 N2O	 Metric	tons	N	 year‐1	ha‐1 Entity	 Entry	

Direct	and	Indirect	N2O	Emissions	

N	in	synthetic	

fertilizer	applied	

to	a	parcel	of	land	

Nsfert	

N2O	 Metric	tons	N	

year‐1	ha‐1

Entity	

Entry	

Direct	N2O	Emissions	

N	from	a	change	in	

soil	organic	matter	

mineralization	due	

to	LUC	or	tillage	

change	applied	to	

a	parcel	of	land	

Nmin	 N2O	 Metric	tons	N	

year‐1

Entity	

Entry	

DAYCENT	

model	

derived	

Direct	N2O	Emissions	

N	fraction	of	

aboveground	

biomass	for	the	

crop	or	forage		

Na N2O	 Dimensionless Entity	 Entry	

Direct	N2O	Emissions	

N	fraction	of	

belowground	

biomass	for	the	

crop	or	forage		

Nb	 N2O	 Dimensionless Entity	 Entry	
Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

8-29

8-29 

Cropland Sub‐Source

Category

Name Element Data

Abbreviation/Symbol

Type Emission

Unit Input Data

Source Input

Statistic

Statistic of Type

Type Distribution Probability

(%) Low uncertainty Relative

(%) High uncertainty Relative

(%) Level Confidence

Limit Lower Effective

Limit Upper Effective

Source Data

Indirect	N2O	Emissions	 Emission	rate	for	 cropped	histosols,	 EROS N2O	 Metric	tons	 N2O‐N	ha‐1 year‐1 Default	 Entry	 0.008	 Uniform	 0.002	 0.024	 IPCC	(2006)Indirect	N2O	Emissions	

N	fertilizer	applied	

of	organic	origin	

including	manure,	

sewage	sludge,	

compost	and	other	

organic	

amendments	

FON	 N2O	 Metric	tons	N	

year‐1

Entity	

Entry	 	 	 	 	 	 	 	 	 	

Indirect	N2O	Emissions	

Fraction	of	NSN	

that	volatizes	as	

NH3	and	NOx	

FRSN N2O	 kg	N	kg‐1	Nsfert Default	 Entry	 0.1	 Uniform	 0.03	 0.3	 IPCC	(2006)

Indirect	N2O	Emissions	

Fraction	or	

proportion	of	FON

that	volatizes	as	

NH3	and	NOx

FRON	 N2O	 kg	N	kg‐1	NON Default	 Entry	 0.2	 Uniform	 0.05	 0.5	 IPCC	(2006)

Indirect	N2O	Emissions	

Emission	factor	

for	volatilized	N	or	

proportion	of	N	

volatilized	as	NH3

and	NOx	that	is	

transformed	to	

N2O	in	receiving	

ecosystem	

EFvol N2O	 kg	N2O‐N	 kg‐1	N	 Default	 Entry	 0.01	 Uniform	 0.002	 0.05 IPCC	(2006)

Indirect	N2O	Emissions	

Fraction	or	

proportion	of	Nt	

that	leaches	or	

runs	off	

FRLeach	 N2O	 kg	N	kg‐1	N	 Default	 Entry	 0.3	 Uniform	 0.1	 0.8	 IPCC	(2006)
Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

8-30

Cropland Sub‐Source

Category

Name Element Data

Abbreviation/Symbol

Type Emission

Unit Input Data

Source Input

Statistic

Statistic of Type

Type Distribution Probability

(%) Low uncertainty Relative

(%) High uncertainty Relative

(%) Level Confidence

Limit Lower Effective

Limit Upper Effective

Source Data

Indirect	N2O	Emissions	

Emission	factor	

for	leached	and	

runoff	N	or	

proportion	of	

leached	and	runoff	

N	that	is	

transformed	to	

N2O	in	receiving	

ecosystem	

EFLeach N2O	 kg	N2O‐N	 kg‐1	N	 Default	 Entry	 0.0075	 Uniform	 0.0005 0.025	 IPCC	(2006)

Methane	from	Wetland	Rice	Cultivation	

Cultivation	period	

for	rice	under	i,	j,	

and	k	conditions	

tijk	 CH4	 Days	 Entity	 Entry	 	 	 	 	 	 	 	 	 	

Methane	from	Wetland	Rice	Cultivation	

Annual	harvested	

area	of	rice	for	i,	j,	

and	k	conditions	

Aijk CH4 Hectares/	year Entity	 Entry	

Methane	from	Wetland	Rice	Cultivation	

Application	rate	of	

organic	

amendment(s)	

ROAi	 CH4 Metric	tons/	 hectare	 Entity	 Entry	

Methane	from	Wetland	Rice	Cultivation	

Baseline	emission	

factor	for	

continuously	

flooded	fields	

without	organic	

amendments	

EFc CH4 kg	CH4/ha/	 day	 Default	 Entry	 1.3	 Uniform	 0.8	 2.2	 IPCC	(2006)

Methane	from	Wetland	Rice	Cultivation	

Water	regime	

during	the	

cultivation	period	

–	Scaling	Factor	

SFw	for	

continuously	

flooded	

CH4 Scaling	Factor	 from	IPCC	 Default	 Entry	 1	 	 Uniform	 	 	 	 0.79	 1.26 IPCC	(2006)

Methane	from	Wetland	Rice	Cultivation	

Water	regime	

during	the	

cultivation	period	

–	Scaling	Factor	

SFw	for	single	

aeration	 CH4 Scaling	Factor	 from	IPCC	 Default	 Entry	 0.6	 Uniform	 0.46	 0.8	 IPCC	(2006)
Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

8-31

8-31 

Cropland Sub‐Source

Category

Name Element Data

Abbreviation/Symbol

Type Emission

Unit Input Data

Source Input

Statistic

Statistic of Type

Type Distribution Probability

(%) Low uncertainty Relative

(%) High uncertainty Relative

(%) Level Confidence

Limit Lower Effective

Limit Upper Effective

Source Data

Methane	from	Wetland	Rice	Cultivation	

Water	regime	

during	the	

cultivation	period	

–	Scaling	Factor	

SFw	for	

multiple	

aerations	

CH4 Scaling	Factor	 from	IPCC	 Default	 Entry	 0.52	 	 Uniform	 	 	 	 0.41	 0.66 IPCC	(2006)

Methane	from	Wetland	Rice	Cultivation	

Water	regime	

before	the	

cultivation	period	

–	Scaling	factor	

SFp	for	non‐

flooded	pre‐

season	<180	

days	

CH4 Scaling	Factor	 from	IPCC	 Default	 Entry	 1	 Uniform	 0.88	 1.14 IPCC	(2006)

Methane	from	Wetland	Rice	Cultivation	

Water	regime	

before	the	

cultivation	period	

–	Scaling	factor	

SFp	for	non‐

flooded	pre‐

season	>	180	

days	

CH4 Scaling	Factor	 from	IPCC	 Default	 Entry	 0.68	 	 Uniform	 	 	 	 0.58	 0.8	 IPCC	(2006)

Methane	from	Wetland	Rice	Cultivation	

Water	regime	

before	the	

cultivation	period	

–	Scaling	factor	

SFp	for	flooded	

pre‐season	>	

30	days	

CH4 Scaling	Factor	 from	IPCC	 Default	 Entry	 1.9	 Uniform	 1.65	 2.18 IPCC	(2006)

Methane	from	Wetland	Rice	Cultivation	

Organic	

amendment	used	

–	scaling	factor	

SFo	 CH4 Scaling	Factor	 from	IPCC	 Default	 Entry	

Methane	from	Wetland	Rice	Cultivation	

Organic	

amendment	

conversion	factor	

CFOAi	for	

straw	

incorporation	

less	than	30	

days	before	

cultivation	

CH4

Conversion	

factor	from	

IPCC	

Default	

Entry	 1	 Uniform	 0.97	 1.04 IPCC	(2006)

Methane	from	Wetland	Rice	Cultivation	

Organic	

amendment	

conversion	factor	

CFOAi	for	

straw	

incorporation	

more	than	30	

days	before	

cultivation	

CH4

Conversion	

factor	from	

IPCC	

Default	

Entry	 0.29	 	 Uniform	 	 	 	 0.2	 0.4	 IPCC	(2006)
Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

8-32

Cropland Sub‐Source

Category

Name Element Data

Abbreviation/Symbol

Type Emission

Unit Input Data

Source Input

Statistic

Statistic of Type

Type Distribution Probability

(%) Low uncertainty Relative

(%) High uncertainty Relative

(%) Level Confidence

Limit Lower Effective

Limit Upper Effective

Source Data

Methane	from	Wetland	Rice	Cultivation	

Organic	

amendment	

conversion	factor	

CFOAi	for	

compost	 CH4

Conversion	

factor	from	

IPCC	

Default	

Entry	 0.05	 Uniform	 0.01	 0.08 IPCC	(2006)

Methane	from	Wetland	Rice	Cultivation	

Organic	

amendment	

conversion	factor	

CFOAi	for	farm	

yard	manure	 CH4

Conversion	

factor	from	

IPCC	

Default	

Entry	 0.14	 	 Uniform	 	 	 	 0.07	 0.2	 IPCC	(2006)

Methane	from	Wetland	Rice	Cultivation	

Organic	

amendment	

conversion	factor	

CFOAi	for	

green	manure

CH4

Conversion	

factor	from	

IPCC	

Default	

Entry	 0.5	 Uniform	 0.3	 0.6	 IPCC	(2006)

Methane	Uptake	by	Soils	 Potential	CH4 Oxidation	in	soils	 PCH4	for	 grassland	 CH4 kg	CH4	ha‐1 year‐1 Default	 Entry	 3.2	 	 Normal	 	 	 	 0	 6.9	 Del	Grosso	et	 al.	(2000)	Methane	Uptake	by	Soils	 Potential	CH4	 Oxidation	in	soils	 PCH4	for	 coniferous	 forest	 CH4 kg	CH4	ha‐1 year‐1 Default	 Entry	 2.8	 Normal	 0.1	 5.5	 Del	Grosso	et	 al.	(2000)	Methane	Uptake	by	Soils	 Potential	CH4 Oxidation	in	soils	 PCH4	for	 deciduous	 forest	 CH4 kg	CH4	ha‐1 year‐1 Default	 Entry	 11.8	 	 Normal	 	 	 	 1.9	 21.6 Del	Grosso	et	 al.	(2000)	Methane	Uptake	by	Soils	

CH4	oxidation	

attenuation	factor:	

cropland	including	

set‐aside	(CRP)	

grassland,	grazing	

land,	and	fertilized	

or	recently	

harvested	forests	

AF	 CH4 N/A	 Default	 Entry	 0.30	 Normal	 0.07	 1	 Smith	et	al.	 (2000)	

Methane	Uptake	by	Soils	

CH4	oxidation	

attenuation	factor:	

natural	vegetation,	

0‐100	years	after	

abandonment	of	

agricultural	

production	or	

timber	harvest	

AF	 CH4	 N/A	 Default	 Entry	

0.3	+	(0.007	

×	years	

since	

abandonme

nt)	

	 Normal	 	 	 	

0.07	+	

(0.007	

×	years	

since	

abando

nment)

1	 Smith	et	al.	 (2000)	
Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

8-33

8-33 

Cropland Sub‐Source

Category

Name Element Data

Abbreviation/Symbol

Type Emission

Unit Input Data

Source Input

Statistic

Statistic of Type

Type Distribution Probability

(%) Low uncertainty Relative

(%) High uncertainty Relative

(%) Level Confidence

Limit Lower Effective

Limit Upper Effective

Source Data

Methane	Uptake	by	Soils	

CH4	oxidation	

attenuation	

factor:>100	years	

post‐management	

or	never	used	for

agricultural	

management	or	

timber	harvest	

AF	 CH4 N/A	 Default	 Entry	 1	 Normal	 0.07	 1	 Smith	et	al.	 (2000)	

N2O	from	Wetland	Rice	

Total	N	inputs	

from	all	

agronomic	

sources:	mineral	

fertilizer,	organic	

amendments,	

residues,	and	

additional	

mineralization	

from	LUC	or	tillage	

change	(metric	

tons	N	year‐1)	

Nt	 N2O	 Metric	tons	N	

year‐1

Entity	

Entry	

N2O	from	Wetland	Rice	 Fertilizer	N	 management	 N2O	 Rate	 Entity	 Entry	N2O	from	Wetland	Rice	 Organic	fertilizer	 N	 	 N2O	 %	N	 Entity	 Entry	N2O	from	Wetland	Rice	 Crop	residue	N	 N2O	 %	N	 Entity	 Entry	N2O	from	Wetland	Rice	

Emission	factor	or	

proportion	of	Nt

transformed	to	

N2O	

EF	 N2O	 kg	N2O‐N		 (kg	N)‐1 Default	 Entry	 0.0022	 	 Normal	 0.2%	 0.2%	 	 	 	 Akiyama	et	 al.	(2005)	

N2O	from	Wetland	Rice	

Scaling	factor	to	

account	for	

drainage	effects	

SFD	for	

continuously	

flooded	

systems	

N2O	 Dimensionless Default	 Entry	 0	 Akiyama	et	 al.	(2005)	
Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

8-34

Cropland Sub‐Source

Category

Name Element Data

Abbreviation/Symbol

Type Emission

Unit Input Data

Source Input

Statistic

Statistic of Type

Type Distribution Probability

(%) Low uncertainty Relative

(%) High uncertainty Relative

(%) Level Confidence

Limit Lower Effective

Limit Upper Effective

Source Data

N2O	from	Wetland	Rice	

Scaling	factor	to	

account	for	

drainage	effects	

SFD	for	aerated	

systems	 N2O	 Dimensionless Default	 Entry	 0.59	 	 Normal	 0.4%	 0.4%	 	 	 	 Akiyama	et	 al.	(2005)	

Non	CO2	Emissions	Biomass	Burn	 Boreal	Forest	(all)	 Combustion	 Efficiency	(C)	 CH4/N2O	 Default	 Entry	 0.34	 Normal	 102% 102%	 IPCC	(2006)Non	CO2	Emissions	

Biomass	Burn	 Wildfire	 Combustion	 Efficiency	(C)	 CH4/N2O	 	 Default	 Entry	 0.4	 	 Normal	 340% 340%	 	 	 	 IPCC	(2006)

Non	CO2	Emissions	Biomass	Burn	 Crown	fire	 Combustion	 Efficiency	(C)	 CH4/N2O	 Default	 Entry	 0.43	 Normal	 104% 104%	 IPCC	(2006)Non	CO2	Emissions	

Biomass	Burn	 Surface	fire	 Combustion	 Efficiency	(C)	 CH4/N2O	 	 Default	 Entry	 0.15	 	 Normal	 96%	 96%	 	 	 	 IPCC	(2006)

Non	CO2	Emissions	Biomass	Burn	

Post	logging	slash	

burn	

Combustion	

Efficiency	(C)	 CH4/N2O	 Default	 Entry	 0.33	 Normal	 130% 130%	 IPCC	(2006)

Non	CO2	Emissions	

Biomass	Burn	

Temperate	Forest	

(all)	

Combustion	

Efficiency	(C)	 CH4/N2O	 	 Default	 Entry	 0.45	 	 Normal	 51%	 51%	 	 	 	 IPCC	(2006)

Non	CO2	Emissions	Biomass	Burn	

Post	logging	slash	

burn	

Combustion	

Efficiency	(C)	 CH4/N2O	 Default	 Entry	 0.62	 Normal	 264% 264%	 IPCC	(2006)

Non	CO2	Emissions	

Biomass	Burn	 Shrublands	(all)	 Combustion	 Efficiency	(C)	 CH4/N2O	 	 Default	 Entry	 0.72	 	 Normal	 147% 147%	 	 	 	 IPCC	(2006)

Non	CO2	Emissions	Biomass	Burn	 Calluna	health Combustion	 Efficiency	(C)	 CH4/N2O	 Default	 Entry	 0.71	 Normal	 121% 121%	 IPCC	(2006)Non	CO2	Emissions	

Biomass	Burn	 Fynbos	 Combustion	 Efficiency	(C)	 CH4/N2O	 	 Default	 Entry	 0.61	 	 Normal	 195% 195%	 	 	 	 IPCC	(2006)

Non	CO2	Emissions	Biomass	Burn	

Savanna	

woodlands	(early	

dry	season	burns)	

(all)	

Combustion	

Efficiency	(C)	 CH4/N2O	 Default	 Entry	 0.4	 Normal	 93%	 93%	 IPCC	(2006)

Non	CO2	Emissions	

Biomass	Burn	

Savanna	

woodlands	

(mid/late	dry	

season	burns)	(all)	

Combustion	

Efficiency	(C)	 CH4/N2O	 	 Default	 Entry	 0.74	 	 Normal	 99%	 99%	 	 	 	 IPCC	(2006)
Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

8-35

8-35 

Cropland Sub‐Source

Category

Name Element Data

Abbreviation/Symbol

Type Emission

Unit Input Data

Source Input

Statistic

Statistic of Type

Type Distribution Probability

(%) Low uncertainty Relative

(%) High uncertainty Relative

(%) Level Confidence

Limit Lower Effective

Limit Upper Effective

Source Data

Non	CO2	Emissions	Biomass	Burn	

Savanna	woodland	

(mid/late)	

Combustion	

Efficiency	(C)	 CH4/N2O	 Default	 Entry	 0.72	 Normal	 270% 270%	 IPCC	(2006)

Non	CO2	Emissions	

Biomass	Burn	 Tropical	savanna	 Combustion	 Efficiency	(C)	 CH4/N2O	 	 Default	 Entry	 0.73	 	 Normal	 598% 598%	 	 	 	 IPCC	(2006)

Non	CO2	Emissions	Biomass	Burn	

Other	savanna	

woodlands	

Combustion	

Efficiency	(C)	 CH4/N2O	 Default	 Entry	 0.68	 Normal	 931% 931%	 IPCC	(2006)

Non	CO2	Emissions	

Biomass	Burn	

Savanna	

grasslands	(early	

dry	season	burns)	

(all)	

Combustion	

Efficiency	(C)	 CH4/N2O	 	 Default	 Entry	 0.74	 	 Normal	 183% 183%	 	 	 	 IPCC	(2006)

Non	CO2	Emissions	Biomass	Burn	

Tropical/sub‐

tropical	grassland	

Combustion	

Efficiency	(C)	 CH4/N2O	 Default	 Entry	 0.74	 Normal	 270% 270%	 IPCC	(2006)

Non	CO2	Emissions	

Biomass	Burn	

Tropical/sub‐

tropical	grassland	

Combustion	

Efficiency	(C)	 CH4/N2O	 	 Default	 Entry	 0.92	 	 Normal	 151% 151%	 	 	 	 IPCC	(2006)

Non	CO2	Emissions	Biomass	Burn	 Tropical	pasture	 Combustion	 Efficiency	(C)	 CH4/N2O	 Default	 Entry	 0.35	 Normal	 427% 427%	 IPCC	(2006)Non	CO2	Emissions	

Biomass	Burn	 Savanna	 Combustion	 Efficiency	(C)	 CH4/N2O	 	 Default	 Entry	 0.86	 	 Normal	 85%	 85%	 	 	 	 IPCC	(2006)

Non	CO2	Emissions	Biomass	Burn	 Emission	factor	 EF	for	 grassland	 CH4 g	GHG/kg	 burned	 biomass	 Default	 Entry	 2.3	 Mean Normal	 8.0%	 8.0%	 IPCC	(2006)Non	CO2	Emissions	

Biomass	Burn	 Emission	factor	 EF	for	crop	 residue	 CH4 g	GHG/kg	 burned	 biomass	 Default	 Entry	 2.7	 Mean Normal	 50.0% 50.0% 	 	 	 IPCC	(2006)

Non	CO2	Emissions	Biomass	Burn	 Emission	factor	 EF	for	 grassland	 N2O	 g	GHG/kg	 burned	 biomass	 Default	 Entry	 0.21	 Mean Normal	 93.0% 93.0% IPCC	(2006)Non	CO2	Emissions	

Biomass	Burn	 Emission	factor	 EF	for	crop	 residue	 N2O	 g	GHG/kg	 burned	 biomass	 Default	 Entry	 0.07	 Mean Normal	 50.0% 50.0% 	 	 	 IPCC	(2006)
Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

8-36

Cropland Sub‐Source

Category

Name Element Data

Abbreviation/Symbol

Type Emission

Unit Input Data

Source Input

Statistic

Statistic of Type

Type Distribution Probability

(%) Low uncertainty Relative

(%) High uncertainty Relative

(%) Level Confidence

Limit Lower Effective

Limit Upper Effective

Source Data

Non	CO2	Emissions	Biomass	Burn	

Combustion	

efficiency	

C	for	

shrublands	 CH4/N2O	 %	Burned	 Default	 Entry	 0.72	 Mean Normal	 68.0% 68.0% IPCC	(2006)

Non	CO2	Emissions	

Biomass	Burn	

Combustion	

efficiency	

C	for	

grasslands	

with	early	

season	burns	

CH4/N2O	 %	Burned	 Default	 Entry	 0.74	 Mean Normal	 50.0% 50.0% 	 	 	 IPCC	(2006)

Non	CO2	Emissions	Biomass	Burn	

Combustion	

efficiency	

C	for	

grasslands	

with	mid	to	

late	season	

burns	

CH4/N2O	 %	Burned	 Default	 Entry	 0.77	 Mean Normal	 66.0% 66.0% IPCC	(2006)

Non	CO2	Emissions	

Biomass	Burn	

Combustion	

efficiency	

C	for	small	

grains	 CH4/N2O	 %	Burned	 Default	 Entry	 0.9	 Mean Normal	 50.0% 50.0% 	 	 	 Expert	 Assessment	

Non	CO2	Emissions	Biomass	Burn	

Combustion	

efficiency	

C	for	large	

grain	and	other	

crop	residues	

CH4/N2O	 %	Burned	 Default	 Entry	 0.8	 Mean Normal	 50.0% 50.0% Expert	 Assessment	

Non	CO2	Emissions	

Biomass	Burn	

Moisture	content	

of	residues	and	

forage	

	 CH4/N2O	 %	moisture	 Default	 Entry	

Non	CO2	Emissions	Biomass	Burn	

Residue	to	yield	

ratio	of	crop	 R:Y	 CH4/N2O	

Metric	tons	

residue	/	

metric	tons	dry	

matter	yield	

Default	

Entry	

SOC	Change	Mineral	Soils	

Soil	organic	C	

stock	at	the	end	of	

the	year	

SOCt	 CO2 Metric	tons	C	 ha‐1 Model	 Output	 DAYCENT	 Model	 derived	 Ogle	et	al.	 (2007);	EPA	 (2013)	
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8-37 

Cropland Sub‐Source

Category

Name Element Data

Abbreviation/Symbol

Type Emission

Unit Input Data

Source Input

Statistic

Statistic of Type

Type Distribution Probability

(%) Low uncertainty Relative

(%) High uncertainty Relative

(%) Level Confidence

Limit Lower Effective

Limit Upper Effective

Source Data

SOC	Change	Mineral	Soils	

Soil	organic	C	

stock	at	the	

beginning	of	the	

year	

SOCt‐1 CO2 Metric	tons	C	 ha‐1 Model	 Output	 DAYCENT	 Model	 derived	 Ogle	et	al.	 (2007);	EPA	 (2013)	

SOC	Change	Mineral	Soils	 Crop	selection	and	 Rotation	Sequence	 	 CO2 Management	 List	Developed	 by	Experts	 Entity	 Entry	SOC	Change	Mineral	Soils	 Irrigation	 application	rate	 CO2 Gallons	per	 minute	 Entity	 Entry	SOC	Change	Mineral	Soils	 Mineral	Fertilizer	 application	Rate	 	 CO2	 lbs/square	foot Entity	 Entry	SOC	Change	Mineral	Soils	 Lime	Amendment	 application	Rate	 CO2 lbs/square	foot Entity	 Entry	SOC	Change	Mineral	Soils	

Organic	

Amendment	

application	Rate	

	 CO2	 lbs/square	foot Entity	 Entry	

SOC	Change	Mineral	Soils	 Number	of	passes	 in	each	operation	 CO2 Number	 Entity	 Entry	SOC	Change	Mineral	Soils	 Depth	of	drainage	 	 CO2	 Meters	 Entity	 Entry	SOC	Change	Mineral	Soils	 Length	of	field	 CO2 Meters	 Entity	 Entry	SOC	Change	Mineral	Soils	 Historical	Weather	 Patterns	 	 CO2 PRISM	 Weather	Data Model	 Output	SOC	Change	Mineral	Soils	

Physical	and	

Chemical	

Properties	of	Soil	

CO2 NRCS	SURRGO	 database	 Model	 Output	

SOC	Change	Mineral	Soils	–	Grazing	Land	

Animal	Size	used	

for	grazing	 	 CO2	 lbs	 Entity	 Entry	
Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

8-38

Cropland Sub‐Source

Category

Name Element Data

Abbreviation/Symbol

Type Emission

Unit Input Data

Source Input

Statistic

Statistic of Type

Type Distribution Probability

(%) Low uncertainty Relative

(%) High uncertainty Relative

(%) Level Confidence

Limit Lower Effective

Limit Upper Effective

Source Data

SOC	Change	Mineral	Soils	–	Grazing	Land	 Stocking	Rate	 CO2 Head/acre	 Entity	 Entry	SOC	Change	Mineral	Soils	–	Grazing	Land	

Irrigation	

application	rate	 	 CO2 Gallons	per	 minute	 Entity	 Entry	

SOC	Change	Mineral	Soils	–	Grazing	Land	

Mineral	Fertilizer	

application	Rate	 CO2 lbs/square	foot Entity	 Entry	

SOC	Change	Mineral	Soils	–	Grazing	Land	 Depth	of	drainage	 	 CO2	 Meters	 Entity	 Entry	SOC	Change	Organic	Soils	 Emission	factor	

EF	for	cropland	

in	cool	

temperate	

regions	

CO2 Metric	tons	C	 ha‐1	year‐1 Default	 Entry	 11	 Mean Normal	 45.0	 45.0	 Ogle	et	al.	 (2003)	

SOC	Change	Organic	Soils	 Emission	factor	

EF	for	cropland	

in	warm	

temperate	

regions	

CO2 Metric	tons	C	 ha‐1	year‐1 Default	 Entry	 14	 Mean Normal	 35.0	 35.0	 	 	 	 Ogle	et	al.	 (2003)	

SOC	Change	Organic	Soils	 Emission	factor	

EF	for	cropland	

in	subtropical	

regions	

CO2 Metric	tons	C	 ha‐1	year‐1 Default	 Entry	 14	 Mean Normal	 46.0	 46.0	 Ogle	et	al.	 (2003)	

SOC	Change	Organic	Soils	 Emission	factor	

EF	for	grazing	

land	in	cool	

temperate	

regions	

CO2 Metric	tons	C	 ha‐1	year‐1 Default	 Entry	 2.8	 Mean Normal	 45.0	 45.0	 	 Ogle	et	al.	 (2003)	

SOC	Change	Organic	Soils	 Emission	factor	

EF	for	grazing	

land	in	warm	

temperate	

regions	

CO2 Metric	tons	C	 ha‐1	year‐1 Default	 Entry	 3.5	 Mean Normal	 35.0	 35.0	 Ogle	et	al.	 (2003)	

SOC	Change	Organic	Soils	 Emission	factor	

EF	for	grazing	

land	in	

subtropical	

regions	

CO2 Metric	tons	C	 ha‐1	year‐1	 Default	 Entry	 3.5	 Mean Normal	 46.0	 46.0	 	 Ogle	et	al.	 (2003)	
Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

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8-39 

Table 8‐B‐3: Animal Population Uncertainty Template

Animal Population Data

Element Name

Abbreviation/

Symbol

Type Emission

Unit Input Data

Source Input

Statistic

Statistic of Type

Probability

Type Distribution

Uncertainty Relative

(%) Low

Uncertainty Relative

(%) High

Level Confidence

(%)

Limit Lower Effective

Limit Upper Effective

Source Data

Number of AnimalsBeef	replacement	heifers	 N	 CH4,	N2O	 Entity	Entry	 ‐1.0%	 1.0%	 Expert	Assessment	Dairy	replacement	heifers	 N	 CH4,	N2O	 	 Entity	Entry	 	 	 		 ‐1.0%	 1.0%	 Expert	Assessment	Mature	beef	cows	 N	 CH4,	N2O	 Entity	Entry	 ‐1.0%	 1.0%	 Expert	Assessment	Steers	(>500	lbs)	 N	 CH4,	N2O	 	 Entity	Entry	 	 	 		 ‐1.0%	 1.0%	 Expert	Assessment	Bulls	 N	 CH4,	N2O	 Entity	Entry	 ‐1.0%	 1.0%	 Expert	Assessment	Stockers	(All)	 N	 CH4,	N2O	 	 Entity	Entry	 	 	 		 ‐1.0%	 1.0%	 Expert	Assessment	Cattle	on	feed	 N	 CH4,	N2O	 Entity	Entry	 ‐1.0%	 1.0%	 Expert	Assessment	Dairy	cow	 N	 CH4,	N2O	 	 Entity	Entry	 	 	 		 ‐1.0%	 1.0%	 Expert	Assessment	Cattle	 N	 CH4,	N2O	 Entity	Entry	 ‐1.0%	 1.0%	 Expert	Assessment	American	bison	 N	 CH4,	N2O	 	 Entity	Entry	 	 	 		 ‐1.0%	 1.0%	 Expert	Assessment	Sheep	NOF	 N	 CH4,	N2O	 Entity	Entry	 ‐1.0%	 1.0%	 Expert	Assessment	Feedlot	sheep	 N	 CH4,	N2O	 	 Entity	Entry	 	 	 		 ‐1.0%	 1.0%	 Expert	Assessment	Goats	 N	 CH4,	N2O	 Entity	Entry	 ‐1.0%	 1.0%	 Expert	Assessment	Horses	 N	 CH4,	N2O	 	 Entity	Entry	 	 	 		 ‐1.0%	 1.0%	 Expert	Assessment	Mules/burros/asses	 N	 CH4,	N2O	 Entity	Entry	 ‐1.0%	 1.0%	 Expert	Assessment	
Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

Table8-40

8‐B‐4: Enteric Fermentation and Housing Uncertainty Template

Data Element Name

Abbreviation/Symbol

Type Emission

Unit Input Data

Source Input

Statistic

		 Statistic of 		 Type

Probability

Type Distribution

uncertainty Relative

(%) Low

uncertainty Relative

(%) High

(%) Level Confidence

Limit Lower Effective

Limit Upper Effective

Source Data

Daily	Milk	Production	 Milk	 CH4	 kg	milk/animal/day	 Entity	Entry	 3%	 5%	 	 Expert	 AssessmentDays	in	milk	 DIM	 CH4 Days	 Entity	Entry	Dry	matter	intake	 DMI	 CH4	 kg/animal/day	 Entity	Entry	 	 	 	 	 	 	 	 	 	Daily	Work	Done	by	Animal	 Work	 CH4 Hours/day	 Entity	Entry	Average	live	body	weight	–	lactating	beef	cows	 BW	 CH4	 kg	 Entity	Entry	 	 	 	 	 	 	 	 	 	Beef	Cow	Mature	Weight	 MW	 CH4 lbs	 Entity	Entry	Steer	Daily	Weight	Gain	to	24	months		 WG	 CH4	 lbs/day	 Entity	Entry	 	 	 	 	 	 	 	 	 	Beef	Steer	Mature	Weight	 MW	 CH4 lbs	 Entity	Entry	Beef	Heifer	Mature	Weight		 MW	 CH4	 lbs	 Entity	Entry	 	 	 	 	 	 	 	Net	energy	required	by	the	animal	for	maintenance		 NEm	 CH4	 MJ	day‐1	 Entity	Entry	Milk	Fat	Content	 Fat	 CH4	 Percent	 Entity	Entry	 	 	 	 	 	 	 	 	 	Starch	Content	of	Diet	(Dairy	Cows)	 Starch	 CH5 kg/animal/day	 Entity	Entry	Acid	Detergent	Fiber	Content	of	Diet		 ADF	 CH4	 kg/head/day	 Entity	Entry	 	DE	–	Each	Feed	Type		 DE	 CH4 Percent	of	gross	energy	 Entity	Entry	Neutral	Detergent	Fiber	in	Diet	(Dairy	Cows)	 NDF	 CH4	 Percent	 Entity	Entry	 	 	 	 	 	 	 	 	 	Crude	Protein	in	Diet		 CP	 CH4 Percent	 Entity	Entry	Acid	Detergent	Fiber	Content	of	Diet	(Dairy	Cows)	 ADF	 CH4	 Percent	 Entity	Entry	 	 	 	 	 	 	 	 	 	Neutral	Detergent	Fiber	in	Diet		 NDF	 CH4	 Percent	 Entity	Entry	 	 	 	 	 	 	 	 	 	Supplemental	Fat	(feedlot)	 S.Fat	 CH4 Percent	 Entity	Entry	 3%	 Mean	 2	 4	 Expert	 AssessmentDietary	Forage	%	 	 CH4	 Percent	 Entity	Entry	 	 	 	 	 	 	 	 	 	Total	Digestible	Nutrients	(Dairy	Cows)	 TDN	 CH4 kg	 Entity	Entry	Ym		Feedlot	–	All	Regions	 Ym	 CH4	 %	GE	converted	to	CH4	 Default	Entry 	 	 	 	 	 	 	 	 	Ym	Beef	Cattle	Not	on	Feed	(stocker)	 Ym	 CH4 %	GE	converted	to	CH4 Default	EntryYm	Beef	Cattle	Not	on	Feed	(all	foraging	animals	except	dairy)	 Ym	 CH4	 %	GE	converted	to	CH4	 Default	Entry 	 	 	 	 	 	 	 	 	
Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

8-41

8-41 

Data Element Name

Abbreviation/Symbol

Type Emission

Unit Input Data

Source Input

Statistic

		 Statistic of 		 Type

Probability

Type Distribution

uncertainty Relative

(%) Low

uncertainty Relative

(%) High

(%) Level Confidence

Limit Lower Effective

Limit Upper Effective

Source Data

Ym	Dairy	Repl.	Heif.	–	California		 Ym	 CH4 %	GE	converted	to	CH4 Default	EntryYm	Dairy	Repl.	Heif.	–	West	 Ym	 CH4	 %	GE	converted	to	CH4	 Default	Entry 	 	 	 	 	 	 	 	 	Ym	Dairy	Repl.	Heif.	–	Northern	Great	Plains	 Ym	 CH4 %	GE	converted	to	CH4 Default	EntryYm	Dairy	Repl.	Heif.–	Southcentral	 Ym	 CH4	 %	GE	converted	to	CH4	 Default	Entry 	 	 	 	 	 	 	 	 	Ym	Dairy	Repl.	Heif.	–	Northeast	 Ym	 CH4 %	GE	converted	to	CH4 Default	EntryYm	Dairy	Repl.	Heif.	–	Midwest	 Ym	 CH4	 %	GE	converted	to	CH4	 Default	Entry 	 	 	 	 	 	 	 	 	Ym	Dairy	Repl.	Heif.	–	Southeast	 Ym	 CH4 %	GE	converted	to	CH4 Default	EntryMaximum	daily	emissions	for	dairy	cows	

Emax	 CH4	 MJ/head	 Default	Entry 45.98	 	 	 	 	 	 	 	 Mills	et	al.	 (2003)	

Average	live	body	weight	for	lactating	cows	

BW	 N2O/NH3 kg	 Entity	Entry	

Typical	Ammonia	Losses	from	Dairy	Housing	Facilities	–Open	dirt	lots	(cool,	humid	region)		

NH3	loss N2O	3	 Percent	of	Nex	 Default	Entry 	 	 	 	 	 	 15%	 30%	

Koelsh	and	

Stowell	

(2005)	

Typical	Ammonia	Losses	from	Dairy	Housing	Facilities	–Open	dirt	lots	(hot,	arid	region)	 	

NH3	loss N2O	 Percent	of	Nex Default	Entry 30%	 45%	

Koelsh	and	

Stowell	

(2005)	

Typical	Ammonia	Losses	from	Dairy	Housing	Facilities	–Roofed	facility	(flushed	or	scraped)		Roofed	facility	(daily	scrape	and	haul)	

NH3	loss N2O	 Percent	of	Nex	 Default	Entry 	 	 	 	 	 	 5%	 15%	

Koelsh	and	

Stowell	

(2005)	

Typical	Ammonia	Losses	from	Dairy	Housing	Facilities	–Roofed	facility	(shallow	pit	under	floor)		

NH3	loss N2O	 Percent	of	Nex Default	Entry 10%	 20%	

Koelsh	and	

Stowell	

(2005)	

Typical	Ammonia	Losses	from	Dairy	Housing	Facilities	–Roofed	facility	(bedded	pack)	

NH3	loss N2O	 Percent	of	Nex	 Default	Entry 	 	 	 	 	 	 20%	 40%	

Koelsh	and	

Stowell	

(2005)	

Typical	Ammonia	Losses	from	Dairy	Housing	Facilities	–Roofed	facility	(deep	pit	under	floor,	includes	storage	loss)	

NH3	loss N2O	 Percent	of	Nex Default	Entry 30%	 40%	

Koelsh	and	

Stowell	

(2005)	

Typical	Ammonia	Losses	from	Beef	Housing	Facilities	–	Open	dirt	lots	(cool,	humid	region)	

NH3	loss N2O	 Percent	of	Nex	 Default	Entry 	 	 	 	 	 	 30%	 45%	

Koelsh	and	

Stowell	

(2005)	
Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

8-42

Data Element Name

Abbreviation/Symbol

Type Emission

Unit Input Data

Source Input

Statistic

		 Statistic of 		 Type

Probability

Type Distribution

uncertainty Relative

(%) Low

uncertainty Relative

(%) High

(%) Level Confidence

Limit Lower Effective

Limit Upper Effective

Source Data

Typical	Ammonia	Losses	from	Beef	Housing	Facilities	–	Open	dirt	lots	(hot,	arid	region)	

NH3	loss N2O	 Percent	of	Nex Default	Entry 40%	 60%	

Koelsh	and	

Stowell	

(2005)	

Typical	Ammonia	Losses	from	Beef	Housing	Facilities	–Roofed	facility	(bedded	pack)	

NH3	loss N2O	 Percent	of	Nex	 Default	Entry 	 	 	 	 	 	 20%	 40%	

Koelsh	and	

Stowell	

(2005)	

Typical	Ammonia	Losses	from	Beef	Housing	Facilities	–Roofed	facility	(deep	pit	under	floor,	includes	storage	loss)	

NH3	loss N2O	 Percent	of	Nex Default	Entry 30%	 40%	

Koelsh	and	

Stowell	

(2005)	

N2O	Emission	Factor	for	manure	in	

housing	(dry	lots	and	pit	storage)	 EFN2O	 N2O	 kg	N2O‐N/kg	N	 Default	Entry 	 	 	 	 	 	 	 	 IPCC	(2006)

Nitrogen	Excretion	from	Beef	Cattle—	Days	on	feed	for	an	individual	ration		 DOFx N2O,	NH3 Days	 Entity	Entry	Nitrogen	Excretion	from	Beef	Cattle—Live	body	weight	at	finish	of	feeding	period		

BWF	 	N2O,	NH3 kg	 Entity	Entry	 	 	 	 	 	 	 	 	 	

Nitrogen	Excretion	from	Beef	Cattle—Live	body	weight	at	the	start	of	feeding	period		

BWI	 	N2O,	NH3 kg	 Entity	Entry	

Nitrogen	Excretion	from	Beef	Cattle—Standard	reference	weight	for	expected	final	body	fat		

SRW	 	N2O,	NH3 kg	 Entity	Entry	 	 	 	 	 	 	 	 	 	

Nitrogen	Excretion	from	Beef	Cattle—Concentration	of	crude	protein	of	total	ration		

CCP‐x 	N2O,	NH3 g	crude	protein/g	dry	 feed	 Entity	Entry	

Monthly	Beef	Feedlot	NH3	Emissions—	

Dietary	crude	protein	 CP	 NH3	 Percent	of	dry	matter	 Entity	Entry	 	 	 	 	 	 	 	 	 	

Average	monthly	temperature	 T	 N2O,	NH3 Degrees	Kelvin	 Entity	Entry	Nitrogen	Excretion	from	Grow‐Finish	Pigs	–Average	daily	feed	intake	over	finishing	period		

ADFIG	 N2O,	NH3 g/	day	 Entity	Entry	 	 	 	 	 	 	 	 	 	

Nitrogen	Excretion	from	Grow‐Finish	Pigs	–Concentration	of	crude	protein	of	total	(wet)	ration		

CCP N2O,	NH3 Percent	 Entity	Entry	
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8-43 

Data Element Name

Abbreviation/Symbol

Type Emission

Unit Input Data

Source Input

Statistic

		 Statistic of 		 Type

Probability

Type Distribution

uncertainty Relative

(%) Low

uncertainty Relative

(%) High

(%) Level Confidence

Limit Lower Effective

Limit Upper Effective

Source Data

Nitrogen	Excretion	from	Grow‐Finish	Pigs	–Days	on	feed	to	finish	animal	(grow‐finish	phase)		

DOFG	 N2O,	NH3 Days	 Entity	Entry	 	 	 	 	 	 	 	 	 	

Nitrogen	Excretion	from	Grow‐Finish	Pigs	–Final	(market)	body	weight	 BWF N2O,	NH3 kg	 Entity	Entry	Nitrogen	Excretion	from	Grow‐Finish	Pigs	–Average	dressing	percent	(yield)	at	final	weight	

DPF	 N2O,	NH3 Percent	 Entity	Entry	 	 	 	 	 	 	 	 	 	

Nitrogen	Excretion	from	Grow‐Finish	Pigs	–Initial	body	weight		 BWI N2O,	NH3 kg	 Entity	Entry	Nitrogen	Excretion	from	Grow‐Finish	Pigs	–Average	fat‐free	lean	percentage	at	final	weight		

FFLPF	 N2O,	NH3 Percent	 Entity	Entry	 	 	 	 	 	 	 	 	 	

Nitrogen	Excretion	from	Weaning	Pigs	–Average	daily	feed	intake	over	finishing	period		

ADFIG N2O,	NH3 g/	day	 Entity	Entry	

Nitrogen	Excretion	from	Weaning	Pigs	–Concentration	of	crude	protein	of	total	(wet)	ration		

CCP	 N2O,	NH3 Percent	 Entity	Entry	 	 	 	 	 	 	 	 	 	

Nitrogen	Excretion	from	Weaning	Pigs	–Days	on	feed	to	finish	animal	(nursery	phase)		

DOFN N2O,	NH3 Days	 Entity	Entry	

Nitrogen	Excretion	from	Weaning	Pigs	–Average	fat‐free	lean	gain	from	20	to	120kg		

FFLPG	 N2O,	NH3 g/	day	 Entity	Entry	 	 	 	 	 	 	 	 	 	

Nitrogen	Excretion	from	Weaning	Pigs	–Final	body	weight	in	nursery	phase	 BWF‐N N2O,	NH3 kg	 Entity	Entry	Nitrogen	Excretion	from	Weaning	Pigs	–Initial	body	weight	in	nursery	phase	 BWI‐N	 N2O,	NH3 kg	 Entity	Entry	 	 	 	 	 	 	 	 	 	Nitrogen	Excretion	from	Gestating	Sows	–Average	daily	feed	intake	during	gestation		

ADFIS N2O,	NH3 g/	day	 Entity	Entry	

Nitrogen	Excretion	from	Gestating	Sows	–Concentration	of	crude	protein	 CCP	 N2O,	NH3 Percent	 Entity	Entry	 	 	 	 	 	 	 	 	 	Nitrogen	Excretion	from	Gestating	Sows	–Gestation	period	length	 GL	 N2O,	NH3 Days	 Entity	Entry	Nitrogen	Excretion	from	Gestating	Sows	–Gestation	lean	tissue	gain	 GLTG	 N2O,	NH3 Kg	 Entity	Entry	 	 	 	 	 	 	 	 	 	
Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

8-44

Data Element Name

Abbreviation/Symbol

Type Emission

Unit Input Data

Source Input

Statistic

		 Statistic of 		 Type

Probability

Type Distribution

uncertainty Relative

(%) Low

uncertainty Relative

(%) High

(%) Level Confidence

Limit Lower Effective

Limit Upper Effective

Source Data

Nitrogen	Excretion	from	Gestating	Sows	–Number	of	pigs	in	litter		 LITTER	 N2O,	NH3 Head	 Entity	Entry	Nitrogen	Excretion	from	Lactating	Sows—Average	daily	feed	intake	during	lactation		

ADFILACT N2O,	NH3 g/	day	 Entity	Entry	 	 	 	 	 	 	 	 	 	

Nitrogen	Excretion	from	Lactating	Sows—Concentration	of	crude	protein		 CCP N2O,	NH3 Percent	 Entity	Entry	Nitrogen	Excretion	from	Lactating	Sows—Lactation	length	(days	to	weaning)		

LL	 N2O,	NH3 Days	 Entity	Entry	 	 	 	 	 	 	 	 	 	

	Nitrogen	Excretion	from	Lactating	Sows—Lactation	lean	tissue	gain	 LLTG	 N2O,	NH3 Kg	 Entity	Entry	Nitrogen	Excretion	from	Lactating	Sows—	Litter	weight	at	weaning		 LWEAN	 N2O,	NH3 Kg	 Entity	Entry	 	 	 	 	 	 	 	 	 	Nitrogen	Excretion	from	Lactating	Sows—Litter	weight	at	birth		 LWBIRTH N2O,	NH3 kg	 Entity	Entry	Typical	Ammonia	Losses	from	Swine	Housing	Facilities	–Roofed	facility	(flushed	or	scraped)	Roofed	facility	(daily	scrape	and	haul)		

%NH3

loss	 NH3	 Percent	of	Nex	 Default	Entry 	 	 	 	 	 	 5%	 15%	

Koelsh	and	

Stowell	

(2005)	

Typical	Ammonia	Losses	from	Swine	Housing	Facilities	–Roofed	facility	(shallow	pit	under	floor)	

%NH3

loss	 NH3 Percent	of	Nex Default	Entry 10%	 20%	

Koelsh	and	

Stowell	

(2005)	

Typical	Ammonia	Losses	from	Swine	Housing	Facilities	–Roofed	facility	(bedded	pack)		

%NH3

loss	 NH3	 Percent	of	Nex	 Default	Entry 	 	 	 	 	 	 20%	 40%	

Koelsh	and	

Stowell	

(2005)	

Typical	Ammonia	Losses	from	Swine	Housing	Facilities	–Roofed	facility	(deep	pit	under	floor,	includes	storage	loss)		

%NH3

loss	 NH3 Percent	of	Nex Default	Entry 30%	 40%	

Koelsh	and	

Stowell	

(2005)	

Nitrogen	Excretion	from	Broilers,	Turkeys,	and	Ducks—Feed	intake	per	phase		

FIx	 N2O,	NH3 g	feed/	finished	animal	 Entity	Entry	 	 	 	 	 	 	 	 	 	

Nitrogen	Excretion	from	Broilers,	Turkeys,	and	Ducks—Concentration	of	crude	protein	of	total	ration	in	each	phase		

CCP‐X N2O,	NH3 g	crude	protein/	g	(wet)	 feed	 Entity	Entry	
Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

8-45

8-45 

Data Element Name

Abbreviation/Symbol

Type Emission

Unit Input Data

Source Input

Statistic

		 Statistic of 		 Type

Probability

Type Distribution

uncertainty Relative

(%) Low

uncertainty Relative

(%) High

(%) Level Confidence

Limit Lower Effective

Limit Upper Effective

Source Data

Nitrogen	Excretion	from	Broilers,	Turkeys,	and	Ducks—Retention	factor	for	nitrogen		

NRF	 N2O,	NH3 Fraction	 Entity	Entry	 	 	 	 	 	 	 	 	 	

Nitrogen	Excretion	from	Laying	Hens—Feed	intake		 FI	 N2O,	NH3 g	feed/	finished	animal	 Entity	Entry	Nitrogen	Excretion	from	Laying	Hens—Concentration	of	crude	protein	of	total	ration		

CCP	 N2O,	NH3 g	crude	protein/	g	(wet)	 feed	 Entity	Entry	 	 	 	 	 	 	 	 	 	

Nitrogen	Excretion	from	Laying	Hens—Egg	weight		 Eggwt N2O,	NH3 g	 Entity	Entry	Nitrogen	Excretion	from	Laying	Hens—	Fraction	of	eggs	produced	each	day		 Eggpro	 N2O,	NH3 Eggs/	hen/	day	 Entity	Entry	 	 	 	 	 	 	 	 	 	Typical	Ammonia	Losses	from	Poultry	Housing	–Roofed	facility	(litter)	(Meat	Producing	birds)	

%NH3

loss	 NH3 Percent	of	Nex Default	Entry 25%	 50%	

Koelsh	and	

Stowell	

(2005)	

Typical	Ammonia	Losses	from	Poultry	Housing	–Roofed	facility	(stacked	manure	under	floor	‐	,	includes	storage	loss)	(Egg‐producing	birds)	

%NH3

loss	 NH3	 Percent	of	Nex	 Default	Entry 	 	 	 	 	 	 25%	 50%	

Koelsh	and	

Stowell	

(2005)	

Methane	Emissions	from	Goats	–	Emission	factor	for	goats	 EFG CH4 kg	CH4/head/day	 Default	Entry 0.0137	 Mean	 IPCC	(2006)Methane	Emissions	from	Bison	–	Emission	factor	for	bison	 EFAB	 CH4	 kg	CH4/head/day	 Default	Entry 	 	 	 	 	 	 	 	 	
Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

Table8-46

8‐B‐5: Manure Management Uncertainty Template

Data Element Name

Element Data

Abbreviation/Symbol

Type Emission

Unit Input Data

Type Input

Value Estimated

Estimate of Type

uncertainty Relative

(%) Low

uncertainty Relative

(%) High

Limit Lower Effective

Limit Upper Effective Data Source

Total	Dry	Manure	–	Beef	Finishing	Cattle	 	 CH4,	N2O,	 NH3 kg	dry	 manure/animal/day	 Entity	Entry	 2.4	 Mean	 ‐20	 20	 ASABE	(2005)Total	Dry	Manure	–	Beef		Cow	(confinement)	

CH4,	N2O,	

NH3

kg	dry	

manure/animal/day	 Entity	Entry	 6.6	 Mean	 ‐20	 20	 ASABE	(2005)

Total	Dry	Manure	–	Beef		Growing	calf	(confinement)	

CH4,	N2O,	

NH3

kg	dry	

manure/animal/day	 Entity	Entry	 2.7	 Mean	 ‐20	 20	 ASABE	(2005)

Total	Dry	Manure	–	Dairy		Lactating	cow	 CH4,	N2O,	 NH3 kg	dry	 manure/animal/day	 Entity	Entry	 8.9	 Mean	 ‐20	 20	 8.7	 11.3 ASABE	(2005)Total	Dry	Manure	–	Dairy		Dry	cow	 	 CH4,	N2O,	 NH3 kg	dry	 manure/animal/day	 Entity	Entry	 4.9	 Mean	 ‐20	 20	 8.8	 11.2 ASABE	(2005)Total	Dry	Manure	–	Dairy		Heifer		 CH4,	N2O,	 NH3 kg	dry	 manure/animal/day	 Entity	Entry	 3.7	 Mean	 ‐20	 20	 ASABE	(2005)Total	Dry	Manure	–	Dairy		Veal	118	kg	 	 CH4,	N2O,	 NH3 kg	dry	 manure/animal/day	 Entity	Entry	 0.12	 Mean	 ‐20	 20	 ASABE	(2005)Total	Dry	Manure	–	Horse		Sedentary	500	kg	

CH4,	N2O,	

NH3

kg	dry	

manure/animal/day	 Entity	Entry	 3.8	 Mean	 ‐20	 20	 ASABE	(2005)

Total	Dry	Manure	–	Horse		Intense	exercise	500	kg	

CH4,	N2O,	

NH3

kg	dry	

manure/animal/day	 Entity	Entry	 3.9	 Mean	 ‐20	 20	 ASABE	(2005)

Total	Dry	Manure	–	Poultry		Broiler	 CH4,	N2O,	 NH3 kg	dry	 manure/animal/day	 Entity	Entry	 0.03	 Mean	 ‐20	 20	 ASABE	(2005)Total	Dry	Manure	–	Poultry		Turkey	(male)	 	 CH4,	N2O,	 NH3 kg	dry	 manure/animal/day	 Entity	Entry	 0.07	 Mean	 ‐20	 20	 ASABE	(2005)Total	Dry	Manure	–	Poultry		Turkey	(females)	

CH4,	N2O,	

NH3

kg	dry	

manure/animal/day	 Entity	Entry	 0.04	 Mean	 ‐20	 20	 ASABE	(2005)

Total	Dry	Manure	–	Poultry		Duck		 	 CH4,	N2O,	 NH3 kg	dry	 manure/animal/day	 Entity	Entry	 0.04	 Mean	 ‐20	 20	 ASABE	(2005)Total	Dry	Manure	–	Layer	 CH4,	N2O,	 NH3 kg	dry	 manure/animal/day	 Entity	Entry	 0.02	 Mean	 ‐20	 20	 ASABE	(2005)Total	Dry	Manure	–	Swine		Nursery	pig	(12.5	kg)	

CH4,	N2O,	

NH3

kg	dry	

manure/animal/day	 Entity	Entry	 0.13	 Mean	 ‐20	 20	 ASABE	(2005)

Total	Dry	Manure	–	Swine		Grow	finish	(70	kg)	

CH4,	N2O,	

NH3

kg	dry	

manure/animal/day	 Entity	Entry	 0.47	 Mean	 ‐20	 20	 ASABE	(2005)

Total	Dry	Manure	–	Swine		gestating	sow	200	kg	

CH4,	N2O,	

NH3

kg	dry	

manure/animal/day	 Entity	Entry	 0.5	 Mean	 ‐20	 20	 ASABE	(2005)
Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

8-47

8-47 

Data Element Name

Element Data

Abbreviation/Symbol

Type Emission

Unit Input Data

Type Input

Value Estimated

Estimate of Type

uncertainty Relative

(%) Low

uncertainty Relative

(%) High

Limit Lower Effective

Limit Upper Effective Data Source

Total	Dry	Manure	–	Swine		Lactating	sow	192	kg	

CH4,	N2O,	

NH3

kg	dry	

manure/animal/day	 Entity	Entry	 1.2	 Mean	 ‐20	 20	 ASABE	(2005)

Total	Dry	Manure	–	Swine		Boar	200	kg	 CH4,	N2O,	 NH3 kg	dry	 manure/animal/day	 Entity	Entry	 0.38	 Mean	 ‐20	 20	 ASABE	(2005)Volatile	solids	–	Beef		Finishing	cattle	 VS	 CH4,	N2O	 kg	VS/kg	dry	manure	 Entity	Entry	 0.81	 Mean	 ‐25	 25	 ASABE	(2005)Volatile	solids	–	Beef		Cow	(confinement)	 VS	 CH4,	N2O	 kg	VS/kg	dry	manure	 Entity	Entry	 0.89	 Mean	 ‐25	 25	 	 	 ASABE	(2005)Volatile	solids	–	Beef		Growing	calf	(confinement)	 VS	 CH4,	N2O	 kg	VS/kg	dry	manure	 Entity	Entry	 0.85	 Mean	 ‐25	 25	 ASABE	(2005)Volatile	solids	–	Dairy		Lactating	cow	 VS	 CH4,	N2O	 kg	VS/kg	dry	manure	 Entity	Entry	 0.84	 Mean	 ‐25	 25	 	 	 ASABE	(2005)Volatile	solids	–	Dairy		Dry	cow	 VS	 CH4,	N2O	 kg	VS/kg	dry	manure	 Entity	Entry		 0.85	 Mean	 ‐25	 25	 ASABE	(2005)Volatile	solids	–	Dairy		Heifer		 VS	 CH4,	N2O	 kg	VS/kg	dry	manure	 Entity	Entry	 0.86	 Mean	 ‐25	 25	 	 	 ASABE	(2005)Volatile	solids	–	Dairy		Veal	118	kg	 VS	 CH4,	N2O	 kg	VS/kg	dry	manure	 Entity	Entry	 Mean	 ‐25	 25	 ASABE	(2005)Volatile	solids	–	Horse		Sedentary	500	kg	 VS	 CH4,	N2O	 kg	VS/kg	dry	manure	 Entity	Entry	 0.79	 Mean	 ‐25	 25	 	 	 ASABE	(2005)Volatile	solids	–	Horse		Intense	exercise	500	kg	 VS	 CH4,	N2O	 kg	VS/kg	dry	manure	 Entity	Entry	 0.79	 Mean	 ‐25	 25	 ASABE	(2005)Volatile	solids	–	Poultry		Broiler	 VS	 CH4,	N2O	 kg	VS/kg	dry	manure	 Entity	Entry	 0.73	 Mean	 ‐25	 25	 	 	 ASABE	(2005)Volatile	solids	–	Poultry		Turkey	(male)	 VS	 CH4,	N2O	 kg	VS/kg	dry	manure	 Entity	Entry	 0.8	 Mean	 ‐25	 25	 ASABE	(2005)Volatile	solids	–	Poultry		Turkey	(females)	 VS	 CH4,	N2O	 kg	VS/kg	dry	manure	 Entity	Entry	 0.79	 Mean	 ‐25	 25	 	 	 ASABE	(2005)Volatile	solids	–	Poultry		Duck		 VS	 CH4,	N2O	 kg	VS/kg	dry	manure	 Entity	Entry	 0.58	 Mean	 ‐25	 25	 ASABE	(2005)Volatile	solids	–	Layer	 VS	 CH4,	N2O	 kg	VS/kg	dry	manure	 Entity	Entry	 0.73	 Mean	 ‐25	 25	 	 	 ASABE	(2005)Volatile	solids	–	Swine		Nursery	pig	(12.5	kg)	 VS	 CH4,	N2O	 kg	VS/kg	dry	manure	 Entity	Entry	 0.83	 Mean	 ‐25	 25	 ASABE	(2005)Volatile	solids	–	Swine		Grow	finish	(70	kg)	 VS	 CH4,	N2O	 kg	VS/kg	dry	manure	 Entity	Entry	 0.8	 Mean	 ‐25	 25	 	 	 ASABE	(2005)Volatile	solids	–	Swine		gestating	sow	200	kg	 VS	 CH4,	N2O	 kg	VS/kg	dry	manure	 Entity	Entry	 0.9	 Mean	 ‐25	 25	 ASABE	(2005)Volatile	solids	–	Swine		Lactating	sow	192	kg	 VS	 CH4,	N2O	 kg	VS/kg	dry	manure	 Entity	Entry	 0.83	 Mean	 ‐25	 25	 	 	 ASABE	(2005)Volatile	solids	–	Swine		Boar	200	kg	 VS	 CH4,	N2O	 kg	VS/kg	dry	manure	 Entity	Entry	 0.89	 Mean	 ‐25	 25	 ASABE	(2005)Storage	temperature	 T	 CH4	 Kelvin	 Entity	Entry	 	 	 	 	 	 	 	Manure	temperature	 Tmanure NH3 Kelvin	 Entity	Entry		Ambient	air	velocity	 Va	 NH3	 m/s	 Default	Entry 	 	 	 	Height	 h	 N2O	 m	 Entity	Entry	Width	 W	 NH3	 m	 Entity	Entry	Radius	 r	 NH3 m	 Entity	Entry	pH	 pH	 NH3	 ‐	 Entity	Entry	 7.5	 6.5	 8.5	 Expert	 Assessment	
Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

8-48

Data Element Name

Element Data

Abbreviation/Symbol

Type Emission

Unit Input Data

Type Input

Value Estimated

Estimate of Type

uncertainty Relative

(%) Low

uncertainty Relative

(%) High

Limit Lower Effective

Limit Upper Effective Data Source

Total	nitrogen	at	a	given	day	–	beef		finishing	cattle	 N2O	 kg	N/kg	dry	manure	 Entity	Entry	 0.07	 Mean	 ASABE	(2005)Total	nitrogen	at	a	given	day	–	beef		cow	(confinement)	 	 N2O	 kg	N/kg	dry	manure	 Entity	Entry	 0.03	 Mean	 	 	 	 	 ASABE	(2005)Total	nitrogen	at	a	given	day	–	beef		growing	calf	(confinement)	 N2O	 kg	N/kg	dry	manure	 Entity	Entry	 0.05	 Mean	 ASABE	(2005)Total	nitrogen	at	a	given	day	–	dairy		lactating	cow	 	 N2O	 kg	N/kg	dry	manure	 Entity	Entry	 0.05	 Mean	 	 	 	 	 ASABE	(2005)Total	nitrogen	at	a	given	day	–	dairy		dry	cow	

N2O	 kg	N/kg	dry	manure	 Entity	Entry	 0.05	 Mean	 ASABE	(2005)

Total	nitrogen	at	a	given	day	–	dairy		heifer		 	 N2O	 kg	N/kg	dry	manure	 Entity	Entry	 0.03	 Mean	 	 	 	 	 ASABE	(2005)Total	nitrogen	at	a	given	day	–	dairy		veal	118	kg	 N2O	 kg	N/kg	dry	manure	 Entity	Entry	 0.13	 Mean	 ASABE	(2005)Total	nitrogen	at	a	given	day	–	Horse	Sedentary	500	kg	 	 N2O	 kg	N/kg	dry	manure	 Entity	Entry	 0.02	 Mean	 	 	 	 	 	ASABE	(2005)Total	nitrogen	at	a	given	day	–	Horse	Intense	Exercise	 N2O	 kg	N/kg	dry	manure	 Entity	Entry	 0.04	 Mean	 	ASABE	(2005)Total	nitrogen	at	a	given	day	–	poultry,		broiler	 	 N2O	 kg	N/kg	dry	manure	 Entity	Entry	 0.04	 Mean	 	 	 	 	 ASABE	(2005)Total	nitrogen	at	a	given	day	–	poultry,	turkey	(male)	 N2O	 kg	N/kg	dry	manure	 Entity	Entry	 0.06	 Mean	 ASABE	(2005)Total	nitrogen	at	a	given	day	–	poultry,	turkey	(females)	 	 N2O	 kg	N/kg	dry	manure	 Entity	Entry	 0.06	 Mean	 	 	 	 	 ASABE	(2005)Total	nitrogen	at	a	given	day	–	poultry,		duck		 N2O	 kg	N/kg	dry	manure	 Entity	Entry	 0.04	 Mean	 ASABE	(2005)Total	nitrogen	at	a	given	day	–	layer	 	 N2O	 kg	N/kg	dry	manure	 Entity	Entry	 0.07	 Mean	 	 	 	 	 ASABE	(2005)Total	nitrogen	at	a	given	day	–	swine		nursery	pig	(12.5	kg)	 N2O	 kg	N/kg	dry	manure	 Entity	Entry	 0.09	 Mean	 ASABE	(2005)Total	nitrogen	at	a	given	day	–	swine		grow	finish	(70	kg)	 	 N2O	 kg	N/kg	dry	manure	 Entity	Entry	 0.08	 Mean	 	 	 	 	 ASABE	(2005)Total	nitrogen	at	a	given	day	–	swine		gestating	sow	200	kg	 N2O	 kg	N/kg	dry	manure	 Entity	Entry	 0.06	 Mean	 ASABE	(2005)Total	nitrogen	at	a	given	day	–	swine		lactating	sow	192	kg	 	 N2O	 kg	N/kg	dry	manure	 Entity	Entry	 0.07	 Mean	 	 	 	 	 ASABE	(2005)Total	nitrogen	at	a	given	day	–	swine		boar	200	kg	 N2O	 kg	N/kg	dry	manure	 Entity	Entry	 0.07	 Mean	 ASABE	(2005)Total	ammonia	nitrogen	in	the	manure	–	beef		earthen	lot	 TAN	 NH3	 kg	NH3/m3	 Entity	Entry	 0.1	 Mean	 	 	 0	 0.02 ASABE	(2005)
Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

8-49

8-49 

Data Element Name

Element Data

Abbreviation/Symbol

Type Emission

Unit Input Data

Type Input

Value Estimated

Estimate of Type

uncertainty Relative

(%) Low

uncertainty Relative

(%) High

Limit Lower Effective

Limit Upper Effective Data Source

Total	ammonia	nitrogen	in	the	manure	–	poultry,		leghorn	pullets	 TAN	 NH3 kg	NH3/m3 Entity	Entry	 0.85	 Mean	 0.66	 1.04 ASABE	(2005)Total	ammonia	nitrogen	in	the	manure	–	poultry,		leghorn	hen	 TAN	 NH3	 kg	NH3/m3	 Entity	Entry	 0.88	 Mean	 	 	 0.54	 1.22 ASABE	(2005)Total	ammonia	nitrogen	in	the	manure	–	poultry,		broiler	 TAN	 NH3 kg	NH3/m3 Entity	Entry	 0.75	 Mean	 	ASABE	(2005)Ammonia	concentration	in	the	liquid	–	dairy	lagoon	effluent	 NH3	 NH3	 kg	NH3/m3	 Calculated	 0.08	 Mean	 	 	 	 	 ASABE	(2005)Ammonia	concentration	in	the	liquid	–	dairy	slurry	(liquid)	 NH3 NH3 kg	NH3/m3 Calculated	 0.14	 Mean	 ASABE	(2005)Ammonia	concentration	in	the	liquid	–	Swine	Finisher‐Slurry	wet‐dry	feeders	 NH3	 NH3	 kg	NH3/m3	 Calculated	 0.5	 Mean	 	 	 	 	 ASABE	(2005)Ammonia	concentration	in	the	liquid	–	Swine	Slurry	storage‐dry	feeders	 NH3 NH3 kg	NH3/m3 Calculated	 0.34	 Mean	 0.19	 0.49 ASABE	(2005)Ammonia	concentration	in	the	liquid	–	Swine	flush	building	 NH3	 NH3	 kg	NH3/m3	 Calculated	 0.14	 Mean	 	 	 	 	 ASABE	(2005)Ammonia	concentration	in	the	liquid	–	Swine	agitated	solids	and	water	 NH3 NH3 kg	NH3/m3 Calculated	 0.05	 Mean	 ASABE	(2005)Ammonia	concentration	in	the	liquid	–	Swine	Lagoon	surface	water	 NH3	 NH3	 kg	NH3/m3	 Calculated	 0.04	 Mean	 	 	 	 	 ASABE	(2005)Ammonia	concentration	in	the	liquid	–	Swine	Lagoon	sludge	 NH3 NH3 kg	NH3/m3 Calculated	 0.07	 Mean	 ASABE	(2005)Methane	Conversion	Factor	(MCF)	a–	Dairy	

Cow	 MCF	 CH4	 %	 Default	Entry 	 	 ‐30	 30	 	 	 IPCC	(2006)	

Methane	Conversion	Factora	–	Cattle	 MCF	 CH4 %	 Default	Entry ‐30	 30	 IPCC	(2006)	Methane	Conversion	Factora	–	Buffalo	 MCF	 CH4	 %	 Default	Entry 	 	 ‐30	 30	 	 	 IPCC	(2006)	Methane	Conversion	Factora	–	Market	Swine	 MCF	 CH4 %	 Default	Entry ‐30	 30	 IPCC	(2006)	Methane	Conversion	Factor	a	–	Breeding	

Swine	 MCF	 CH4	 %	 Default	Entry 	 	 ‐30	 30	 	 	 IPCC	(2006)	

Methane	Conversion	Factor		a	–	Layer	(Dry)	 MCF	 CH4 %	 Default	Entry ‐30	 30	 IPCC	(2006)	Methane	Conversion	Factor	a	–	Broiler	 MCF	 CH4	 %	 Default	Entry 	 	 ‐30	 30	 	 	 IPCC	(2006)	Methane	Conversion	Factor	a	–	Turkey	 MCF	 CH4 %	 Default	Entry ‐30	 30	 IPCC	(2006)	Methane	Conversion	Factora	–	Duck	 MCF	 CH4	 %	 Default	Entry 	 	 ‐30	 30	 	 	 IPCC	(2006)	Methane	Conversion	Factora	–	Sheep	 MCF	 CH4 %	 Default	Entry ‐30	 30	 IPCC	(2006)	Methane	Conversion	Factora	–	Goat	 MCF	 CH4	 %	 Default	Entry 	 	 ‐30	 30	 	 	 IPCC	(2006)	Methane	Conversion	Factora	–	Horse	 MCF	 CH4 %	 Default	Entry ‐30	 30	 IPCC	(2006)	Methane	Conversion	Factora	–	Mule/Ass	 MCF	 CH4	 %	 Default	Entry 	 	 ‐30	 30	 	 	 IPCC	(2006)	
Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

8-50

Data Element Name

Element Data

Abbreviation/Symbol

Type Emission

Unit Input Data

Type Input

Value Estimated

Estimate of Type

uncertainty Relative

(%) Low

uncertainty Relative

(%) High

Limit Lower Effective

Limit Upper Effective Data Source

Methane	Conversion	Factora	–	Buffalo	 MCF	 CH4 %	 Default	Entry ‐30	 30	 IPCC	(2006)	Methane	Conversion	Factora	–	In	vessel	

manure	composting	 MCF	 CH4	 %	 Default	Entry 	 	 ‐30	 30	 	 	 IPCC	(2006)	

Methane	Conversion	Factora	–	Static	pile	manure	composting	 MCF	 CH4 %	 Default	Entry ‐30	 30	 IPCC	(2006)	Methane	Conversion	Factora	–	Intensive	

windrow	 MCF	 CH4	 %	 Default	Entry 	 	 ‐30	 30	 	 	 IPCC	(2006)	

Methane	Conversion	Factora	–	Passive	windrow	 MCF	 CH4 %	 Default	Entry ‐30	 30	 IPCC	(2006)	Maximum	Methane	Producing	Capacities	–	Beef	Replacement	Heifers	 Bo	 CH4	 m3		CH4/kg	VS	 Default	Entry 0.33	 	 ‐20	 20	 	 	 U.S.	EPA	 (2011)	Maximum	Methane	Producing	Capacities	–	Dairy	Replacement	Heifers	 Bo	 CH4 m3		CH4/kg	VS	 Default	Entry 0.17	 ‐20	 20	 U.S.	EPA	 (2011)	Maximum	Methane	Producing	Capacities	–	Mature	Beef	Cows	 Bo	 CH4	 m3		CH4/kg	VS	 Default	Entry 0.33	 	 ‐20	 20	 	 	 U.S.	EPA	 (2011)	Maximum	Methane	Producing	Capacities	–	Steers	(>500	lbs)	 Bo CH4 m3		CH4/kg	VS	 Default	Entry 0.33	 ‐20	 20	 U.S.	EPA	 (2011)	Maximum	Methane	Producing	Capacities	–	Stockers	(All)	 Bo	 CH4	 m3		CH4/kg	VS	 Default	Entry 0.17	 	 ‐20	 20	 	 	 U.S.	EPA	 (2011)	Maximum	Methane	Producing	Capacities	–	Cattle	on	Feed	 Bo CH4 m3		CH4/kg	VS	 Default	Entry 0.33	 ‐20	 20	 U.S.	EPA	 (2011)	Maximum	Methane	Producing	Capacities	–	Dairy	Cow	 Bo	 CH4	 m3		CH4/kg	VS	 Default	Entry 0.24	 	 ‐20	 20	 	 	 U.S.	EPA	 (2011)	Maximum	Methane	Producing	Capacities	–	Cattle	 Bo CH4 m3		CH4/kg	VS	 Default	Entry 0.19	 ‐20	 20	 U.S.	EPA	 (2011)	Maximum	Methane	Producing	Capacities	–	Buffalob Bo	 CH4	 m3		CH4/kg	VS	 Default	Entry 0.1	 	 	 	 	 	 IPCC	(2006)	Maximum	Methane	Producing	Capacities	–	Market	Swine	 Bo CH4 m3		CH4/kg	VS	 Default	Entry 0.48	 ‐30	 30	 IPCC	(2006)	Maximum	Methane	Producing	Capacities	–	Breeding	Swine	 Bo	 CH4	 m3		CH4/kg	VS	 Default	Entry 0.48	 	 ‐30	 30	 	 	 IPCC	(2006)	Maximum	Methane	Producing	Capacities	–	Layer	(dry)	 Bo CH4 m3		CH4/kg	VS	 Default	Entry 0.39	 ‐30	 30	 IPCC	(2006)	Maximum	Methane	Producing	Capacities	–	Layer	(wet)	 Bo	 CH4	 m3		CH4/kg	VS	 Default	Entry 0.39	 	 ‐30	 30	 	 	 IPCC	(2006)	Maximum	Methane	Producing	Capacities	–	Broiler	 Bo CH4 m3		CH4/kg	VS	 Default	Entry 0.36	 ‐30	 30	 IPCC	(2006)	
Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

8-51

8-51 

Data Element Name

Element Data

Abbreviation/Symbol

Type Emission

Unit Input Data

Type Input

Value Estimated

Estimate of Type

uncertainty Relative

(%) Low

uncertainty Relative

(%) High

Limit Lower Effective

Limit Upper Effective Data Source

Maximum	Methane	Producing	Capacities	–	Turkey	 Bo	 CH4	 m3		CH4/kg	VS	 Default	Entry 0.36	 	 ‐30	 30	 	 	 IPCC	(2006)	Maximum	Methane	Producing	Capacities	–	Duck	 Bo CH4 m3		CH4/kg	VS	 Default	Entry 0.36	 ‐30	 30	 IPCC	(2006)	Maximum	Methane	Producing	Capacities	–	Sheep	 Bo	 CH4	 m3		CH4/kg	VS	 Default	Entry 0.19	 	 ‐20	 20	 	 	 IPCC	(2006)	Maximum	Methane	Producing	Capacities	–	Feedlot	sheep	 Bo CH4 m3		CH4/kg	VS	 Default	Entry 0.36	 ‐20	 20	 IPCC	(2006)	Maximum	Methane	Producing	Capacities	–	Goat	 Bo	 CH4	 m3		CH4/kg	VS	 Default	Entry 0.17	 	 ‐30	 30	 	 	 IPCC	(2006)	Maximum	Methane	Producing	Capacities	–	Horse	 Bo CH4 m3		CH4/kg	VS	 Default	Entry 0.3	 ‐30	 30	 IPCC	(2006)	Maximum	Methane	Producing	Capacities	–	Mule/Ass	 Bo	 CH4	 m3		CH4/kg	VS	 Default	Entry 0.33	 	 ‐30	 30	 	 	 IPCC	(2006)	Emission	factor	for	the	fraction	of	CH4produced	that	leaks	from	the	anaerobic	digester	–	Digesters	with	steel	or	lined	concrete	or	fiberglass	digesters	with	a	gas	holding	system	(egg	shaped	digesters)	and	monolithic	construction	

EFCH4,	

leakage

CH4 %	 Default	Entry 2.8	 CDM	(2012)	

Emission	factor	for	the	fraction	of	CH4

produced	that	leaks	from	the	anaerobic	digester	–	UASB	type	digesters	with	floating	gas	holders	and	no	external	water	seal	

EFCH4,	

leakage

CH4	 %	 Default	Entry 5	 	 	 	 	 	 CDM	(2012)	

Emission	factor	for	the	fraction	of	CH4produced	that	leaks	from	the	anaerobic	digester	–	Digesters	with	unlined	concrete/ferrocement/brick	masonry	arched	type	gas	holding	section;	monolithic	fixed	dome	digesters	

EFCH4,	

leakage

CH4 %	 Default	Entry 10	 CDM	(2012)	

Emission	factor	for	the	fraction	of	CH4

produced	that	leaks	from	the	anaerobic	digester	–	Other	digester	configurations	

EFCH4,	

leakage

CH4	 %	 Default	Entry 10	 	 	 	 	 	 CDM	(2012)	

Temporary	storage	of	liquid/slurry	manure	–N2O	emission	factorc EFN20 N2O	 kg	N2O‐N/kg	N	 Default	Entry 0.005	 ‐50	 100	 U.S.	EPA	 (2011)	Long‐term	storage	of	solid	manure	–N2O	

emission	factorc EFN20	 N2O	 kg	N2O‐N/kg	N	 Default	Entry 0.002	 	 ‐50	 100	 	 	 U.S.	EPA	 (2011)	
Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

8-52

Data Element Name

Element Data

Abbreviation/Symbol

Type Emission

Unit Input Data

Type Input

Value Estimated

Estimate of Type

uncertainty Relative

(%) Low

uncertainty Relative

(%) High

Limit Lower Effective

Limit Upper Effective Data Source

Long‐term	storage	of	slurry	manure	–	N2O	emission	factorc EFN20 N2O	 kg	N2O‐N/kg	N	 Default	Entry 0.005	 ‐50	 100	 U.S.	EPA	 (2011)	Cattle	and	Swine	Deep	Bedding	(Active	Mix)‐	N2O	emission	factorc EFN20	 N2O	 kg	N2O‐N/kg	N	 Default	Entry 0.07	 	 	 	 	 	 IPCC	(2006)	Cattle	and	Swine	Deep	Bedding	(No	Mix)‐	N2O	emission	factorc EFN20 N2O	 kg	N2O‐N/kg	N	 Default	Entry 0.01	 IPCC	(2006)	Pit	Storage	Below	Animal	Confinements‐	N2O	emission	factorc EFN20	 N2O	 kg	N2O‐N/kg	N	 Default	Entry 0.002	 	 	 	 	 	 IPCC	(2006)	Natural	aeration	aerobic	lagoons	–	N2O	conversion	factorc EFN20 N2O	 kg	N2O‐N/kg	N	 Default	Entry 0.01	 ‐50	 100	 IPCC	(2006)	Forced	aeration	aerobic	lagoons	–	N2O	conversion	factorc EFN20 N2O	 kg	N2O‐N/kg	N	 Default	Entry 0.005	 ‐50	 100	 IPCC	(2006)	N2O	emission	factor	for	liquid	storage	–	

uncovered	liquid	manure	with	a	crustc EFN20	 N2O	 kg	N2O‐N/kg	N	 Default	Entry 0.8	 	 ‐50	 100	 	 	 IPCC	(2006)	

N2O	emission	factor	for	liquid	storage	–	uncovered	liquid	manure	without	a	crustc EFN20 N2O	 kg	N2O‐N/kg	N	 Default	Entry 0	 ‐50	 100	 IPCC	(2006)	N2O	emission	factor	for	liquid	storage	–	

covered	liquid	manurec EFN20	 N2O	 kg	N2O‐N/kg	N	 Default	Entry 0	 	 ‐50	 100	 	 	 IPCC	(2006)	

Composting	–	Ammonia	emission	(loss)	relative	to	total	nitrogen	in	manure	 EFNH3 NH3 kg	NH3‐N/kg	N	 Default	Entry 0.05	

Hellebrand	

and	Kalk	

(2000)	

Manure	Management	–	Multiple	Sources	–	collection	efficiency,	covered	storage	(with	or	without	crust)	

η CH4	 percentage	 Default	Entry 1	 	 	 	 	 	 Sommer	et	al.	 (2004)	

Manure	Management	–	Multiple	Sources	–	collection	efficiency,	uncovered	storage	with	crust	formation	

η CH4 percentage	 Default	Entry 0	 Sommer	et	al.	 (2004)	

Manure	Management	–	Multiple	Sources	–	collection	efficiency,	uncovered	storage	without	crust	formation	

η CH4	 percentage	 Default	Entry ‐0.40	 	 	 	 	 	 Sommer	et	al.	 (2004)	

Manure	Management	–	Multiple	Sources	–	Rate	correcting	factors	(b1)	 b1 CH4 dimensionless	 Default	Entry 1	 Sommer	et	al.	 (2004)	Manure	Management	–	Multiple	Sources	–	Rate	correcting	factors	(b2)	 b2	 CH4	 dimensionless	 Default	Entry 0.01	 	 	 	 	 	 Sommer	et	al.	 (2004)	Manure	Management	–	Multiple	Sources	–	Arrhenius	parameter,	cattle	 A	 CH4 g	CH4	/kg	VS/hr	 Default	Entry 43.33	 Sommer	et	al.	 (2004)	Manure	Management	–	Multiple	Sources	–	Arrhenius	parameter,	swine	 A	 CH4	 g	CH4	/kg	VS/hr	 Default	Entry 43.21	 	 	 	 	 	 Sommer	et	al.	 (2004)	
Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

8-53

8-53 

Data Element Name

Element Data

Abbreviation/Symbol

Type Emission

Unit Input Data

Type Input

Value Estimated

Estimate of Type

uncertainty Relative

(%) Low

uncertainty Relative

(%) High

Limit Lower Effective

Limit Upper Effective Data Source

Potential	methane	yield	of	the	manure	cattle	 ECH4,	pot	‐ CH4 kg	CH4/kg	VS	 Default	Entry 0.48	 Sommer	et	al.	 (2004)	Potential	methane	yield	of	the	manure		‐	swine	 ECH4,	pot CH4	 kg	CH4/kg	VS	 Default	Entry 0.5	 	 	 	 	 	 Sommer	et	al.	 (2004)	Manure	Management	–	Multiple	Sources	–	Kinematic	viscosity	of	aire v NH3 m2/s	 Default	Entry White	(1999)Manure	Management	–	Multiple	Sources	–	Mass	diffusivity	of	NH3e D NH3	 m2/s	 Default	Entry 	 	 	 	 	 	

Watson	

(1966)	and	

Baker	(1969)

Temporary	stack	and	long‐term	stockpile	–	Resistance	to	mass	transfer	through	the	manuree

Rs NH3 s/m	 Default	Entry Rotz	et	al.	 (2011)	

Temporary	stack	and	long‐term	stockpile	–	Resistance	to	mass	transfer	through	the	covere

Rc	 NH3	 s/m	 Default	Entry 	 	 	 	 	 	 Rotz	et	al.	 (2011)	

Temporary	stack	and	long‐term	stockpile	–	Ratio	degradable	volatile	solids	to	total	volatile	solids	‐	cattle		liquid	manure	

VSd/VST CH4 Unitless	 Default	Entry 0.46	 Møller	et	al.	 (2004)	

Temporary	stack	and	long‐term	stockpile	–	Ratio	degradable	volatile	solids	to	total	volatile	solids	‐	swine		liquid	manure	

VSd/VST CH4	 Unitless	 Default	Entry 0.89	 	 	 	 	 	 Møller	et	al.	 (2004)	

Temporary	stack	and	long‐term	stockpile	–	Ratio	Non‐degradable	volatile	solids	to	total	volatile	solids	‐	cattle		liquid	manure	

VSnd/VS

T

CH4 Unitless	 Default	Entry 0.54	 Møller	et	al.	 (2004)	

Temporary	stack	and	long‐term	stockpile	–	Ratio	non‐degradable	volatile	solids	to	total	volatile	solids	–	swine	liquid	manure	

VSnd/VS

T

CH4	 Unitless	 Default	Entry 0.11	 	 	 	 	 	 Møller	et	al.	 (2004)	

Solid‐liquid	separation	–	Efficiency	of	mechanical	solid‐liquid	separatione

CH4,	N2O,	

NH3 Percent	 Entity	Entry	

Ford	and	

Fleming	

(2002)	

a	The	values	for	methane	conversion	factor	(MCF)	vary	depending	on	the	temperature	and	the	manure	management	system.	IPCC	(2006)	provides	estimated	uncertainty	

ranges	for	these	MCFs.	

b	There	are	no	data	for	North	America	region;	the	data	from	Western	Europe	are	used	to	calculate	the	estimation.	There	is	no	reported	uncertainty	for	this	adapted	value.	c	IPCC	(2006)	reports	large	uncertainties	with	default	N2O	emission	factors.	The	N2O	EF	values	vary	depending	on	the	animal	species	and	temperature	of	the	manure	

management	system.	

d	Values	for	N2O	conversion	factors	are	available	for	dairy	cow,	cattle,	swine,	and	other	animals	and	can	be	found	in	the	chapter.		e	Default	values	are	available	in	the	chapter.	
Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

Table8-54

8‐B‐6: Forestry Uncertainty Table

Forestry Sub‐

Source Category

Name Element Data

Abbreviation/Symbol

Type Emission

Unit Input Data

Source Input

Statistic

Statistic of Type

Distribution Probability

Type

uncertainty Relative

(%) Low

uncertainty Relative

(%) High

(%) Level Confidence

Limit Lower Effective

Limit Upper Effective Data Source

Urban	Forestry	–	Aerial	Data	Method	

Tree	Cover	Percent	 CO2 %	 Model	 Output	 	i‐Tree	Canopy	

Urban	Forestry	–	Aerial	Data	Method	 Urban	Area	 CO2 m2 Entity	 Entry	Urban	Forestry	–	Aerial	Data	Method	

Average	Annual	Carbon	Sequestration	

	 CO2 kg	 C/m2/year	 Default	 Entry	 2.8	 	mean	 14.8	 14.8	 		 		 Nowak	et	al.	(2013)	

Urban	Forestry	–	Aerial	Data	Method	

Average	Carbon	Storage	

CO2 kg	C/m2 Default	 Entry	 7.69	 	mean	 Nowak	et	al.	(2013)	
 Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 

8-55

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