November 2014 G-1 

Appendix G: Illustrative Biogenic Process Attributes 

Table of Contents 

1. Introduction ................................................................................................................................................................. G-2

2. Framework Representation of Feedstock Carbon Losses and Products for Forest- and 

Agriculture-derived Feedstocks .................................................................................................................... G-3

2.1. Equations Underlying P and L ..................................................................................................................... G-4

2.1.1. P ..................................................................................................................................................................... G-4

2.1.2. L ..................................................................................................................................................................... G-5

3. Evaluation of Post-Conversion Products ......................................................................................................... G-5

3.1. Co-firing in Electric Generating Units (EGUs) ...................................................................................... G-6

3.1.1. Estimated P Term Values .................................................................................................................... G-7

3.1.2. Discussion .................................................................................................................................................. G-8

3.2. Pyrolysis ............................................................................................................................................................... G-9

3.2.1. Estimated P Term Values ................................................................................................................. G-10

3.2.2. Discussion ............................................................................................................................................... G-11

3.3 Conversion of Forest-Derived Industrial Byproducts ............................................................................ G-11

3.3.1 Estimated P Term Values ................................................................................................................. G-12

3.3.2 Discussion ............................................................................................................................................... G-13

4. Evaluation of Pre-conversion Products and Losses During Transportation, Storage, and 

Processing ............................................................................................................................................................ G-13

4.1. Feedstock Losses during Transportation, Storage and Processing .......................................... G-14

4.2. Possible Methods for Developing Illustrative LOSS Values for Onsite Storage and 

Processing ........................................................................................................................................................ G-15

4.3. Representative Values for Storage Losses .......................................................................................... G-17

4.4. Discussion of Other Losses: Transportation and Harvest ............................................................ G-20

4.4.1. Transportation ...................................................................................................................................... G-20

4.4.2. Harvest ..................................................................................................................................................... G-21

4.5. Representative Factor Discussion .......................................................................................................... G-21

5. References .................................................................................................................................................................. G-21

6. Addendum I. Literature Reviewed for Storage and Processing at Stationary Sources .............. G-25

7. Addendum II. Literature Review of Dry Matter Losses from 

Woody Biogenic Material Storage .............................................................................................................. G-27
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7.1. Introduction .................................................................................................................................................... G-27

7.2. Storage Losses ................................................................................................................................................ G-27

7.2.1. Forest Biomass Storage Methods .................................................................................................. G-28

7.2.1. Storage Time and Challenges in Tracking Biomass Inventory ......................................... G-33

7.3. References ........................................................................................................................................................ G-34

List of Tables 

Table G-1. Carbon Content of Different Biogenic Feedstocks. .......................................................................... G-6

Table G-2. Sample P for Ash Information for Low Efficiency Boiler. .............................................................. G-8

Table G-3. Example Estimated Range of P Values for Co-Firing Biomass in 

Electrical Generating Units. ............................................................................................................................. G-8

Table G-4. Sample P Information for Pyrolysis of Corn Stover. ..................................................................... G-10

Table G-5. Example Representative Values for Storage Losses for Woodchips in the Northeast 

(based on Figure G-3). .................................................................................................................................... G-19

Table G-6. Dataset Sources and Cases by Source for Storage and 

Processing at Stationary Sources. .............................................................................................................. G-25

List of Figures 

Figure G-1. Diagram of Pyrolysis Process. ................................................................................................................. G-9

Figure G-2. Data Points for Herbaceous Crops Storage for Western (W), Southeastern (SE), 

Northeastern (NE) and Southwestern (SW) Regions. Storage losses are not clearly 

distinguishable by region or storage type (N = 69). ........................................................................... G-18

Figure G-3. Data Points and Trendlines for Storage Losses for Woodchips 

in the Northeast (N = 29). .............................................................................................................................. G-18

Figure G-4. Simplified Principle of Degradation Behavior (Wihersaari, 2005) ...................................... G-31

1. Introduction 

The purpose of this appendix is to describe possible methods for developing values for biogenic 

process attributes, which are attributes that reflect feedstock carbon procured by a stationary 

source that exits assessment through pathways other than emissions from the stack. These biogenic 

process attributes can include feedstock carbon that is deviated from the process prior to 

conversion1 to bioenergy (such as products produced from feedstock material or feedstock losses 

during transport, storing, and other processing) or feedstock carbon in products that exit the 

stationary source bioenergy conversion process (such as ethanol or ash). 

For demonstrative purposes in this appendix, illustrative values are presented by general 

stationary source technology and/or process type for specific feedstocks. Values for products can 

depend on the specific process used at a stationary source, as well as the type of feedstock used. 

 

1 Conversion refers to technologies or processes that convert biomass into energy directly, in the form of heat 

or electricity, or may convert it to another form, such as liquid biofuel or combustible biogas. Examples of 

biomass conversion processes include biomass-fired or co-fired boilers, biomass gasification or pyrolysis 

systems, and anaerobic digesters. 
November 2014 G-3 

Values for losses can depend on the type and duration of transport, storage, and processing. In 

cases where specific information is not available, or if the framework is being applied at a scale 

larger than stationary-source specific, estimated values for these terms may need to be generalized 

representative factors. The appendix also provides a detailed technical discussion of the data 

sources and analytical methods used to develop illustrative values. 

2. Framework Representation of Feedstock Carbon Losses and 

Products for Forest- and Agriculture-derived Feedstocks 

Feedstock carbon from forest- and agriculture-derived feedstocks can be transformed into products 

or exit the supply chain (pre- and post-conversion) through means other than the stack. The 

biogenic assessment factor (BAF) equation is designed to transform a measurable or estimated 

quantity (the carbon content of biomass feedstock used at the point of assessment, represented by 

potential gross emissions, PGE) into a quantity that cannot be directly measured (the net 

atmospheric contribution of biogenic carbon resulting from use of the quantity of biogenic 

feedstock that the entity is responsible for, represented by net biogenic emissions, NBE). The 

framework equation, as discussed in the main report as well as Appendix F, is:

 = 



= (
 +  +  + )()() (EQ. G.1)

The terms below each play a specific role in this equation: 

• PGE is carbon content of the biogenic feedstock used by a specific entity (or generally 

consumed). This is a quantity that could be measured or estimated at different points of 

assessment (e.g., at the boiler mouth, stationary source gate, feedstock production site, or at 

the stack: wherever the point of assessment needs to be. Thus, this term can have different 

values indicated by subscripts, representing different points along the supply chain). 

• L is a unitless adjustment factor greater than or equal to 1 that represents biogenic 

feedstock carbon that leaves the supply chain (e.g., via transit or decomposition, deviated 

for use as a product) between the feedstock production site and input into the conversion 

process at a stationary source. L scales PGE, as it was measured at the point of assessment, 

up to account for any losses during transportation or storage between the feedstock 

production site and the point of assessment. PGE times L is thus the carbon content of the 

biomass that was grown at the feedstock production site in order to deliver the quantity of 

feedstock measured at the point of assessment. 

• P is a unitless adjustment factor between zero and one, equal to the share of the carbon 

content of the feedstock at the point of assessment that is emitted to the atmosphere by a 

stationary source (versus that which is embedded in products). In effect, this term also 

reflects the share of carbon that remains in products, that is either not emitted to the 

atmosphere or is sold and eventually emitted to the atmosphere by a downstream user. 
November 2014 G-4 

2.1. Equations Underlying P and L 

2.1.1. P 

P accounts for the carbon content of the feedstock material that is emitted to the atmosphere and 

therefore also accounts for biogenic carbon in products that is not emitted from the stack and 

leaves the producing entity (pre- and post-conversion) for downstream use or disposal. Examples 

of products that may exit the supply chain prior to conversion include: 

• Wood material in products (lumber, wood pulp, panel products); 

• Mill residues sold/transferred to a separate stationary source for use as raw material or 

fuel; 

• Bark sold/transferred to a separate stationary source for fuel; 

• Bark sold for mulch; 

• Agricultural by-products (e.g., stover, stalks, straws, husks, hulls, etc.) sold/transferred to a 

separate stationary source for use as fuel; or 

• Pulping by-products (tall oil, turpentine). 

Examples of products that may exit the supply chain after conversion include: 

• Dried distillers grains (from ethanol production); 

• Ethanol; or 

• Bottom ash, flyash, or biochar (e.g., materials containing unburned carbon). 

Equation F.7 from Appendix F defines P as calculated in this example. 

 =  + ∑ 








(EQ. G.2)

PRODi represents the sum of all pre- and post-conversion products and PGEj is PGE evaluated at 

point of assessment (j). Note that for many stationary sources, biogenic feedstocks will be used to 

produce a number of final products, so the summation in Equation G.2 simply sums over all i

products that are produced prior to the bioenergy conversion process with biogenic CO2 stack 

emissions, indexed from 1 to S, where S is the last point on the carbon trail and represents the 

stack. For example, if PROD2 is the only product, this summation would simply be equal to PROD2. 

Since PRODi is always negative and the absolute value of the sum of all PRODi must be less than 

PGEj, the sum of all PRODi divided by PGEj will be a fraction that has a negative value, and P as 

calculated above will be a positive number between 0 and 1 (technically this expression is 1 plus a 

negative number). In effect, this calculation takes away the portion of carbon the products are 

responsible for, and attributing the remainder to the stationary source. Although this is a relatively

simple example, the mathematical representation of this term and more complicated scenarios are 

illustrated in Appendix F. 

The framework recognizes that some post-conversion materials—such as fly ash—can potentially 

be sold and used for a number of commercial purposes (i.e., cement manufacturing) or disposed of. 

Either way, such post-conversion materials are considered under the P term because they pass 
November 2014 G-5 

through the stationary source, exiting through means other than emissions out the stack. There is 

limited data on some post-conversion materials: limited and/or emerging scientific data in some 

cases (such as biochar) and publically available production and market data (e.g., end uses, sales of 

such materials). Also, whether or not these materials are sold, the carbon contained in this material 

can in some circumstances remain sequestered for some duration of time. 

2.1.2. L 

The L term reflects biogenic feedstock losses in transportation to, storage, and processing at a 

stationary source and any products that exit the supply chain prior to the conversion process. This 

value facilitates the link between the quantity of feedstock received at the stationary source, the 

quantity of feedstock that enters the stationary source process, and the quantity of feedstock grown 

on or removed from (e.g., in the case of crop residues) the land. Some stationary sources may 

measure both feedstock delivered at the gate and at the point of entry into the conversion process 

(e.g., boiler mouth) and thus have the data to calculate its onsite L term. Depending on the data that 

stationary sources have, PGE could be estimated, measured (i.e., the mass of the biogenic feedstock 

as it enters a stationary source process and converting that mass into its CO2 equivalents), or 

perhaps back calculated from the direct process emissions at the stationary source. An estimate of 

losses associated with storage at the stationary source and any pre-conversion products plays a 

role in the estimation of the amount of feedstock actually delivered to the site versus that entering 

the supply chain at the production landscape. 

The purpose of L here is twofold. First, L represents the transformation of PGE0 (PGE evaluated at 

the feedstock production/harvest site) to PGEj (PGE evaluated at point of assessment j), implicitly 

capturing any losses (LOSSi) and any pre-conversion products (PRODi) that occur between these 

two PGEs. Note that this fraction will result in a positive number. Second, if considered in 

combination with the landscape effects term, it serves to bring the landscape effects in relation to 

PGEj. 

 = 



	 (EQ. G.3)

Calculations presented in Section 4 of this document generate illustrative loss terms for when such 

site or supply chain-specific calculations are not possible or when representative factors are 

necessary within a specific program or policy application (e.g., regional application of the 

framework). 

3. Evaluation of Post-Conversion Products 

In addition to information on the carbon content of the input feedstock, the methodology applied in 

this appendix also requires knowledge of the relationship between input and output. This 

information is often necessary to properly express P as demonstrated in the equations below: 

P Equation 

 =  + ∑ (!""	#$%&#'()*×!&,#-	)#-*.-*	#$	%&#'()*

) 

(!""	$..'"*#)/	-%(*×!&,#-	)#-*.-*	#$	$..'"*#)/	-%(*) 
November 2014 G-6 

Thus: 

 =  + ∑ 




(EQ. G.4)

Where: 


 = (!""	#$	%&#'()* × !&,#-	)#-*.-*	#$	%&#'()*) (EQ. G.5)

 = (!""	$..'"*#)/	-%(* × !&,#-	)#-*.-*	#$	$..'"*#)/	-%(*)

 																			!*	*0.	%#-*	#$	!"".""1.-*	 

As explained in Appendix F, P is 1 minus the share of PGEj that remains in products or by-products 

and is either not emitted to the atmosphere or is sold and eventually emitted to the atmosphere by 

a different entity. (Note that because PROD is negative, technically this expression is 1 plus a 

negative number.) Because of the variability of different conversion technologies (e.g., boiler types), 

this appendix relies on mass balance data to establish the relationship between input and output 

and then uses the equations above to calculate example values for P. 

The following section explores methods that could be used to estimate PRODi parameters 

contributing to P. The example process and stationary source combinations below are addressed in 

this appendix for use in case study applications per different baselines: 

• Co-firing in electric generating units (EGUs) (using forest-derived feedstocks); and 

• Pyrolysis chambers in pyrolysis facilities (using corn stover). 

Carbon content values for agricultural feedstocks are taken from Spokas (2010), while values for 

forest-derived feedstocks are from Skog (2008). Table G-1 shows values for each, expressed as 

percent carbon on a dry weight basis.

Table G-1. Carbon Content of Different Biogenic Feedstocks. 

Input Feedstocks Percent C by Dry Weight 

Roundwood 50 

Logging Residue 50 

Corn Stover2 44 

3.1. Co-firing in Electric Generating Units (EGUs) 

Some EGUs are designed for 100% biomass combustion (Johansson et al., 2003). However, most 

EGUs are optimized for coal, natural gas, or oil. Yet, some EGUs can co-fire a certain percentage of 

biogenic fuel to be used in addition to the fossil-based feedstock (Demirbas, 2005), depending on 

the biogenic fuel and boiler design (e.g., solid-fuel boilers may be able to co-fire solid biomass). 

Generally, any type of biogenic feedstock can be used, but most often in the U.S., feedstocks are 

 

2

 Although presented more specifically in examples as 44% in this appendix, in Appendix L, the carbon content of 

corn stover is assumed to be 50% for consistency with other FABA feedstock case studies. 
November 2014 G-7 

forest-derived (e.g., roundwood, logging, or mill residues), often converted to pellets or chips 

(Demirbas, 2005). Regardless of feedstock blend (i.e., percentage of biomass in fuel input), an EGU 

combusts the feedstock in a boiler to heat water into steam, which is used to drive turbines and 

generate electricity. 

3.1.1. Estimated P Term Values 

Application of the framework could first reflect the relevant stationary source technologies and 

processes used at EGUs, then the applicable biogenic feedstock inputs and residual outputs. The 

process of co-firing biomass at an EGU does not produce non-energy/heat commercial products 

other than post-combustion materials like ash. As noted above, an EGU combusts the feedstock in a 

boiler to heat water and produce steam, which is used to drive turbines and generate electricity.3 

To calculate P in this illustrative scenario, the amount of carbon in the post-process materials 

produced can be expressed as a proportion of carbon in the input feedstock. This value differs 

depending on the boiler type and efficiency. Boilers vary from facility to facility, because there are a 

number of different boiler types to choose from, including fluidized bed boilers, stoker boilers, or 

cyclone boilers. Each functions at a different efficiency, and some can accept higher percentages of 

biogenic feedstock in the fuel blend. Differences among boilers in efficiency or temperature of 

combustion can result in different amounts of post-process materials, or different carbon content of 

the materials (Tarelho et al., 2011). Literature suggests boiler type is an important predictor of 

unburnt carbon remaining, but the feedstock blend could impact efficiency of combustion and thus 

result in more or less post-process materials (e.g., ash) or a higher or lower carbon content of the 

materials (Demirbas, 2005). Generally, agricultural biomass features higher ash content than 

woody biomass (Cassidy and Ashton, 2007). 

Regardless of process, the amount of unburnt carbon remaining in the ash is typically expected to 

be low compared to the amount of feedstock carbon entering the conversion process (Kaufmann et 

al., 2000), but, as it depends on the conversation process efficiency, rates can vary substantially. 

Compared with modern, high-efficiency boilers, traditional grate boilers often result in less efficient 

combustion—and therefore feature higher levels of unburnt carbon. Tollin (2000) suggests carbon 

content of ash can approach 50% in low efficiency grate boilers. Biomass stoker boilers may have 

30–40% carbon in the ash (Gustafson and Raffaeli, 2009). Pitman (2006) reports average carbon 

content in wood ash to range from 7 to 50% (average 26%) in commercial boilers in the eastern 

U.S. 

Those values pertain to carbon content of the ash itself, not the proportion of incoming feedstock 

carbon remaining. The amount of ash generated in the combustion process is typically ≈10% (by 

weight) of incoming feedstocks. Table G-2 shows an example calculation based on a feedstock with 

50% carbon content, as well as other hypothetical assumption values based on Table G-1. 

 

3Theoretically, if combustion in an EGU was 100% efficient in converting biogenic carbon to CO2, then P could be 

set to P = 1 to reflect that there is no carbon remaining in post-combustion material (ash) and that there are no 

products produced from the feedstock used in the EGU (i.e., PROD = 0). 
November 2014 G-8 

Table G-2. Sample P for Ash Information for Low Efficiency Boiler. 

Variable Value 

Incoming feedstock 1 ton 

Carbon content of feedstock 50% 

PGEj: Total incoming carbon 0.5 tons 

Ash content of feedstock 10% 

Ash remaining post-combustion 0.1 tons 

Carbon content of ash 26% 

Total remaining carbon post-combustion 0.026 tons 

PROD: Proportion of initial carbon remaining post-combustion to PGEj −0.0521

P 0.9482

Notes: 

1. Carbon in products is subtracted (i.e., has a negative value) because product carbon does not enter the 

atmosphere with the biogenic CO2 from the conversion process. PROD = -(0.026/0.5) 

2. P = 1 + (-0.052) = 0.948 

PROD (ash) values would be lower for modern, high-efficiency boilers, leading to higher P terms. 

Nussbaumer and Hasler (1999) and Johansson et al. (2003) report fly ash carbon concentrations of 

1–10% in modern boilers. Demirbas (2000) uses an average ash content of 0.5%. Kaufmann et al. 

(2000) analyzed ash samples and determined carbon content to be no more than 0.05%. Fluidizedbed boilers typically achieve nearly 100% combustion, meaning practically 0% carbon remains in 

ash (EPA, 2007; Gustafson and Raffaeli, 2009). As above, the mass of the ash itself is a small fraction 

of total incoming feedstock, meaning the remaining carbon is only a small percentage of the total 

incoming carbon. Tarelho et al. (2011) present the best data on this, showing total unburnt carbon 

left in post-combustion residuals to be only 0.7−2.8% of the original feedstock carbon (i.e., 

0.007−0.028 tons per ton of feedstock carbon input). 

The value varies depending on the efficiency of the boiler, but generally the majority of the 

incoming carbon will be released via combustion as CO2. Table G-3 shows example illustrative

values of the P term based on boiler efficiency. Actual values for P are expected to vary depending 

on boiler technology being used at a facility and feedstock type. 

Table G-3. Example Estimated Range of P Values for Co-Firing Biomass in Electrical Generating 

Units. 

Co-firing Boiler Type P 

High Efficiency 0.9721 to 0.9932

Low Efficiency (as shown in Table G-2 above) 0.948 

1 P = 1 + (-PROD/PGE) = 1 + (-0.028 tons C in ash/ton of C input in feedstock) = 0.972 

2 P = 1 + (-PROD/PGE) = 1 + (-0.007 tons C in ash/ton of C input in feedstock) = 0.993 

3.1.2. Discussion 

Serious data gaps exist in developing representative P values for co-firing boilers at EGUs. Overall, 

little information exists about carbon content of post-combustion materials. Because of this, 

determining representative P values is difficult, and considerable uncertainty remains. 
November 2014 G-9 

More detailed data from proximate and ultimate analysis of different biogenic feedstocks are 

required, as are engineering analyses related to combustion using different technologies. For 

example, ash content will impact boiler heat surface fouling, which will impact the completeness of 

combustion—and by extension the P value for different combustion technology—for different 

feedstock combinations. As noted above, the process of co-firing biomass at an EGU can be done 

with a number of different biogenic feedstocks and a number of different boilers. Furthermore, the 

fossil fuel/biomass blend can vary greatly depending on cost, supply, boiler specifics, and a number 

of other factors. 

However, despite the lack of data, it is clear that in most scenarios much of the carbon is emitted as

CO2 during combustion. Therefore, although the carbon content of post-combustion materials may 

vary, the average is expected to be low. 

3.2. Pyrolysis 

The pyrolysis process involves the thermal destruction of organic materials in an environment void 

of oxygen (Demirbas, 2005). When the biomass is heated in a pyrolysis chamber, it produces a 

hydrocarbon rich gas mixture (syngas), an oil-like liquid (bio-oil), and a carbon rich solid material 

(biochar). The relative amounts of each product depend on whether the facility employs high heat 

fast pyrolysis or low heat slow pyrolysis. Typical mass yield ranges for fast pyrolysis are bio-oil 50–

70%, syngas 10–30%, and biochar 10–25%. Typical mass yield ranges for slow pyrolysis are: bio-oil 

20–50%, syngas 20–50%, biochar 25–35% (UK Biochar Research Center (UK BRC), 2009). In most 

pyrolysis processes, the syngas is used to fuel the system (Mullen et al., 2010). Figure G-1 shows the

pyrolysis process in the context of the carbon cycle. 

Figure G-1. Diagram of Pyrolysis Process. 
November 2014 G-10 

3.2.1. Estimated P Term Values 

For purposes of this framework, biochar, bio-oil, and syngas can be considered under the P term. 

Values are estimated for both fast and slow processes, based on the sample mass balance 

information in Table G-4. Table G-4 uses Mullen et al. (2010) numbers for fast pyrolysis and Ringer 

et al. (2006) numbers for slow pyrolysis. Carbon content of biochar is derived from Roberts et al. 

(2010) and Brown et al. (2010). Corn stover is used as an example feedstock, assuming a carbon 

content of 44%. 

Note that carbon content of bio-oil and syngas is the same for both processes, the differences lie 

merely in the amount of each material produced (Mullen et al., 2010). 

Table G-4. Sample P Information for Pyrolysis of Corn Stover. 

Sample P Information 

Fast Pyrolysis 

(high heat, 600°C) 

Slow Pyrolysis 

(low heat, 300°C) 

% 

Input feedstock (Corn stover) 100 100 

Bio-oil 61.6 (includes water content) 30a

Syngas 21.9 35a

Biochar 17.0 351

Carbon Content of Bio-oil 53.97 53.972

Carbon Content of Syngas 16.7 16.73

Carbon Content of Biochar 57.29 67.68c

1Ringer et al. (2006). 

2 Mullen et al. (2010). 

3 Roberts et al. (2010). p. 9. 

This illustrative stover pyrolysis example assumes that the assessment boundary encapsulates the 

actual pyrolysis process chamber including the syngas boiler and represents a pyrolysis facility 

where all of the syngas is used onsite for process energy (instead of some of it being shipped 

offsite). In the syngas boiler stage, the syngas is fully combusted and therefore P (syngas) = 0. Thus, 

the P value in this example is only calculated for the bio-oil and biochar products. As explained in 

Appendix F, P is 1 minus the share of PGEj that remains in products or by-products and is either not 

emitted to the atmosphere or is sold and eventually emitted to the atmosphere by a different entity. 

(Note that because PROD is negative, technically this expression is 1 plus a negative number.) 

For fast pyrolysis, P values can be calculated as follows per ton of feedstock input: 

P Equation for Fast Pyrolysis 

 =  +	

∑ (!""	#$%&#'()* × !&,#-	)#-*.-*	#$	%&#'()*

) 

(!""	$..'"*#)/	-%(* × !&,#-	)#-*.-*	#$	$..'"*#)/	-%(*)

=																								

 + 2− 4(!""	,#-#5×	-	,#-#5)4	(!""	,#)0!&×	-	,#)0!&)

(!""	#$	)#&-	"*#6.&×!&,#-	)#-*.-*	#$	)#&-	"*#6.&) 7 (EQ. G.6)

											 =  +	8−[ + 	 (. ;; × . <=>?) + (. ? × . <?@>)] ÷ ( × . CC)D 
November 2014 G-11 

 =  + (−. >?;>) = 	. @=

For slow pyrolysis P values can be calculated as follows per ton of feedstock input: 

P Equation for Slow Pyrolysis 

 =  +	

∑ (!""	#$%&#'()* × !&,#-	)#-*.-*	#$	%&#'()*

) 

(!""	$..'"*#)/	-%(* × !&,#-	)#-*.-*	#$	$..'"*#)/	-%(*)

=																								

 + 2− 4(!""	,#-#5×	-	,#-#5)4	(!""	,#)0!&×	-	,#)0!&)

(!""	#$	)#&-	"*#6.&×!&,#-	)#-*.-*	#$	)#&-	"*#6.&) 7 (EQ. G.7)

 =  + 8−[ +	 (. = × . <=>?) + (. =< × . ;?;E)] ÷ ( × . CC)D 

 =  + (−. >;=) = 	. >=?

3.2.2. Discussion 

The values estimated in this appendix are meant to be illustrative examples only. Pyrolysis is not 

yet being undertaken on a commercial scale; as a result, it is difficult to derive robust values. 

Hammond (2011) notes that “pyrolysis has not yet been demonstrated at ... large scales,” and that 

“due to the immature state of [pyrolysis] technology ... there is a lack of good quality datasets and 

therefore greater uncertainty in the data than is desirable.” This is similarly true for stationary 

sources using dedicated energy crops (e.g., switchgrass) and short-rotation woody crops (e.g., 

poplar) as a feedstock for pyrolysis. 

For example, Ringer et al. (2006), Enders et al. (2012), and Wright et al. (2008) show the 

production percentage of biochar can range from 0–77%. Much of this range is the result of 

differences in process, namely the decision to employ fast pyrolysis or slow pyrolysis. Slow 

pyrolysis produces more biochar (35%); fast pyrolysis produces less (17%). Table G-4 combines 

values from Enders et al. (2012), Wright et al. (2008), and Ringer et al. (2006). 

Spokas (2010) notes that biochar “variability is based on the conditions of pyrolysis and the 

biomass parent material, with biochar spanning the range of various forms of black carbon. 

Thereby, this variability induces a broad spectrum in the observed rates of reactivity and, 

correspondingly, the overall chemical and microbial stability.” Furthermore, the literature is 

complicated by the lack of uniformity when it comes to the nomenclature for the products of 

biomass conversion. Char, charcoal, soot, graphitic carbon, ash, coal, and black carbon have all been 

used to describe the solid residual products.” As a result, the potential values of P for pyrolysis 

techniques could vary from facility to facility. Representative values could be calculated using 

ranges, averages or median values, as done in the example above. 

3.3 Conversion of Forest-Derived Industrial Byproducts 

Producers of industrial forest-derived products and by-products may use roundwood or wood 

chips as raw material to manufacture paper or wood products. Various wood product 

manufacturing processes can result in the production of mill residues including bark, saw dust, 

sander dust, and panel trim, which may be sold downstream to other users as products or used 

onsite as boiler fuel. The mass of wood converted to products is dependent on the specific products 
November 2014 G-12 

being produced and production processes used. The amount of mill residues used as onsite boiler 

fuel instead of being sold for use as a raw material or fuel at a separate entity is highly site-specific. 

This section provides an example of a hypothetical wood products facility that converts roundwood 

into veneer for onsite plywood manufacture, uses the cores remaining after peeling of veneer to 

produce lumber, and produces additional lumber from logs. This illustrative facility burns 60% of 

the mill residues it generates (bark, plywood trim, hog fuel, and sanderdust) in an onsite boiler and 

sells the remaining 40% of mill residues (sawdust and planar shavings) to other downstream users 

for fuel or use in other products (e.g., pellets, particleboard). 

3.3.1 Estimated P Term Values 

To calculate P for this example wood products facility, the amount of carbon in the products 

produced can be expressed as a proportion of carbon in the input feedstock. For each dry ton of 

carbon in the input feedstock, in this hypothetical example, a site-specific estimate of 0.2 dry tons of 

carbon in mill residues are generated, of which 0.6 x 0.2 = 0.12 dry tons of carbon are used onsite as

boiler fuel and 0.4 x 0.2 = 0.08 dry tons of carbon are sold as products. Mill residues are collected 

from various points in the production process and are either routed to onsite boiler fuel storage 

(0.12 dry tons of carbon per dry ton of carbon in the input feedstock) or to loadout bins for transfer

to other entities (0.08 dry tons of carbon per dry ton of carbon in the input feedstock). Mill residues 

collected for boiler fuel at this example mill are used within a few days so boiler fuel storage losses 

are negligible. In addition, this hypothetical example includes a site-specific estimate of 0.00012 

tons of carbon remaining in the boiler ash following combustion of mill residues. 

Because the boiler is supporting various different equipment for wood products manufacturing, it is 

separate from the plywood and lumber production lines. The mass of mill residues combusted in 

the boiler is not measured at every mill residue generation point in the facility, but instead is 

determined at the boiler. Similarly, the mass of mill residues (sawdust and planar shavings) sold as 

a product is determined at the loadout bins rather than being measured at every saw and planar 

within the facility. It may not be necessary to distinguish between pre- and post-conversion 

products because the facility can calculate the following dry mass amounts on a per-ton-of-carboninput basis: 

• Mass of carbon in the incoming logs (PGEj = 1 ton of C input); 

• Mass of carbon retained in the plywood and lumber products (PROD1 = 0.8 tons of C in 

plywood and lumber); 

• Mass of carbon in mill residues sold (PROD2 = 0.08 tons of C in residuals sold); 

• Mass of carbon in mill residues burned in the boiler (0.12 tons of C burned); and 

• Mass of carbon remaining in the boiler ash (PROD3 = 0.00012 tons of C remaining in ash). 

All products may be considered within the PROD and P terms. P can be calculated as follows: 

 =  + ∑ 








=  + F

G(
4
@4
=)



H (EQ. G.8)

 =  + I

G(.E4.E4.@)	*#-"		-	%&#'()*"

	*#-		-	*0.	$..'"*#)/	-%(* J = . @
November 2014 G-13 

Carbon in products is subtracted (i.e., has a negative value) because product carbon does not enter 

the atmosphere with the biogenic CO2 from the conversion process at this wood products facility. 

3.3.2 Discussion 

The hypothetical example above shows that, in the case of a wood products facility, the majority of 

the carbon entering the facility can remain in the wood products produced by the facility, including 

mill residues sold to other entities. The example also illustrates inclusion of all products (PROD) in 

the P term when it is not clear whether the products are pre- or post-conversion products because 

the material is collected from various points within the production process that are not necessarily 

upstream or downstream from the bioenergy conversion process (i.e., the boiler). In this case, use 

of the point of assessment (PGEj) as the feedstock carbon input to the facility (roundwood) allowed 

for the P calculation to be done without distinguishing between pre- and post-conversion products 

in the facility. 

As shown in Equation G.3 above, L in this example would be calculated as the ratio of PGEo (at the 

feedstock production site) and PGEj (roundwood entering the facility). Thus, for the calculation of P

in this example, it is assumed that there are no physical feedstock losses (LOSS) prior to the 

residues entering the conversion process and therefore no need to distinguish products generated 

within the facility as pre- or post-conversion for purposes of calculating L.

4

4. Evaluation of Pre-conversion Products and Losses During 

Transportation, Storage, and Processing 

There are few established methods for tracking and/or calculating feedstock losses along the 

supply chain, especially for raw herbaceous feedstock (Miranowski et al., 2010). However, when 

conducting a mass balance-based calculation, having some means to estimate the flows of biogenic 

feedstock carbon from feedstock production to either emissions out of the stationary source stack 

or post-conversion products, some assessment of these supply chain flows can relate biogenic CO2

emissions to feedstock production. This framework equation includes a term (L), which scales PGE

as it is estimated or measured at the point of assessment up to account for any pre-conversion 

products exiting the supply chain or feedstock losses during transportation, storage, or processing 

between the feedstock production site (e.g., forest, farm) and the point of assessment. 

As the feedstock moves through the supply chain, deviated or lost feedstock carbon may occur due 

to pre-conversion products or losses during transport, storage, and processing/handling between 

or within different stages of the supply chain. The L term actually refers to two separate loss 

components: one that reflects feedstock carbon taken from the supply chain for other purposes 

such as pre-conversion products (e.g., lumber, bark for mulch) and another that reflects physical 

losses between the feedstock production site and the point of assessment. The volume of physical 

losses can depend on feedstock type, technologies applied, storage length and type, and several 

other factors. Therefore, measuring direct stack CO2 emissions at the stationary source would likely 

 

4

 Losses and only the pre-conversion products occurring between PGEo and PGEj are included in L. 
November 2014 G-14 

be an underestimate of the total net biogenic CO2 emissions associated with procurement and use 

of the biogenic feedstock. The adjustment of PGE, the carbon content of the biomass feedstock used 

by a specific entity at different points of the assessment, by the term L can vary for each supply 

chain, unless generalizations are made to create representative values. 

Representative values for losses could depend on identification of different storage scenarios and 

transportation scenarios by feedstock type and could also take the region into consideration if 

appropriate (e.g., effects of regional climate on stored feedstock). Methods for calculating 

representative feedstock loss values related to transport, storage, and processing that could be 

developed for stationary sources to use, in lieu of case-specific, customized calculations, are shown 

below. This appendix does not develop values for possible products coming out of the supply chain 

between the feedstock production site and point of assessment because of the many different 

product possibilities and permutations that exist. More detailed discussion of the underlying 

mathematical computations related to how products can be accounted for in Appendix F. The 

reminder of this appendix focuses on the physical losses component of L. 

4.1. Feedstock Losses during Transportation, Storage and Processing 

Weighing the feedstock at the sale transaction point is considered common practice. In commercial 

operations, this is usually done at a stationary source’s delivery point scale. The delivered moisture

content can be used to calculate the dry weight in the load, and the payment is normally on a dry 

weight basis. The material is usually placed in storage prior to being introduced into the process. 

The conditions for storage, including feedstock type, moisture content, time of storage, climate 

conditions, and the amount of protection against added moisture offered by the stationary source, 

can affect feedstock storage losses. Feedstocks taken immediately from the scale to the process or 

into storage that is dry and cool are likely to have fewer losses than feedstocks stored in uncovered 

piles on the ground in a warm, moist climate for several months (see below for research findings 

relating to such losses). 

As described above, a portion of the L term represents the CO2 equivalent of feedstock losses (e.g., 

decomposition, too dirty or destroyed to use in boiler), or LOSSi

5, that occur between the weigh-in 

point of the feedstock at the stationary source and the entry point of the production process (boiler 

or other processor). If the feedstock is processed immediately by the stationary source, there may 

be minimal, or zero, losses in storage. In many situations, however, the feedstock must be stored 

prior to utilization, because harvest and delivery schedules are cyclical, whereas biogenic feedstock 

demand and utilization needs would be continuous. Those losses are due to the decomposition in a 

storage system, such as a chip pile and onsite storage, which is likely to last one year or less. 

Specific applications of the framework could include calculations of the emissions related to decay: 

however, the illustrative calculations in this appendix count those losses as immediate and do not 

include any long-term decay rates (see section below on onsite storage losses). 

 

5

 There can be various points of losses along the supply chain. These losses can be given individual loss terms (e.g., 

LOSS1, LOSS2, LOSS3). 
November 2014 G-15 

In contrast to stationary source onsite storage losses that can happen subsequent to the feedstock 

weighing, potential harvest and transport losses could also occur and thus could be considered for 

treatment in the framework, depending on available data. For some feedstocks, losses may occur 

during transportation between the feedstock production site and the stationary source. For 

example, some of the material harvested from a farm (e.g., switchgrass) may be lost from the truck 

while in transit, or decay might occur while the feedstock is in storage at the stationary source (Qin

et al., 2006). Such losses are not typically calculated or included in the delivered weight; thus, few

data are available on this aspect of losses. These losses are expected to be rather minor and likely 

depend on the biogenic feedstock material that is harvested or collected. 

4.2. Possible Methods for Developing Illustrative LOSS Values for Onsite 

Storage and Processing 

This section describes a methodology for calculating representative values for the relevant loss 

factors (LOSSi) included in the L term. Biomass types considered below include herbaceous 

(switchgrass, hay, etc.), woody biomass (logging residues, etc.), and agricultural residues (corn 

stover, etc.). 

In general, of all the biomass losses occurring across a given supply chain, the storage losses (e.g., 

decomposition, too dirty or destroyed to use in boiler) are potentially the most significant. Even for

long-distance supply chains including international shipping, Hamelinck et al. (2005) estimated a 

total loss throughout the international supply chain of 15% of dry matter; 13% of these losses were 

associated with storage, 2% with processing, and <1% with handling. 

One way to calculate onsite storage losses is to determine the feedstock delivered to the stationary 

source versus the amount that enters into the conversion process. The difference between the 

delivered weight and the pre-conversion input weight represents the onsite loss. This preconversion input weight can also be compared with the direct emissions (stack emissions) from the 

process to help determine accuracy of the calculation. However, such multi-point measurement 

onsite is currently not common practice. If the point of assessment, where PGE is calculated, is 

when the feedstock is entering the conversion process (e.g., after any storage losses have occurred), 

the L term (which includes losses represented by LOSSi) is needed in the equation that converts PGE

to NBE, to represent any losses (and pre-conversion products) that could have occurred prior to 

initial calculation of PGE. 

Biogenic feedstocks contain a significant amount of water which can change as the material dries in 

storage and/or absorbs water through precipitation. Dry matter refers to the weight of material 

without water (i.e., the weight of material when it is completely dried). Dry matter loss in storage 

(e.g., due to decay, physical losses) is a function of several factors that will differ between stationary 

sources. The factors include (1) the type and packaging (loose, bundles, etc.) of feedstock; (2) the 

moisture content of the stored feedstock; (3) the type of storage facility at the stationary source; (4) 

the length of time the material is stored; and (5) the climatic conditions (e.g., temperature, 

humidity) during the storage period. 
November 2014 G-16 

Several studies give estimates of dry matter loss for different types of feedstocks. For woody 

biomass, losses of 12% were found in green chips stored in a large pile for 7 months (Thornqvist 

and Jirjis, 1990). Although little to no dry matter losses can be expected when storing logs, bundles 

of logging residues exhibit higher dry matter losses (Jirjis, 1995). Afzal et al. (2010), for instance, 

observed higher dry matter losses in woodchip piles (8–27%) than in bundles (≈3%) over one year. 

Jirjis and Nordén (in Eriksson and Gustavsson, 2010; 2002) observed only 5.8% dry matter loss of 

green logging residue bundles after 51⁄2 months in Sweden. 

Moisture is the most important factor in dry matter loss, because it directly affects the microbial 

activity that produces the loss (Ashton, 2008). Studies of switchgrass losses show results ranging 

from zero for a year’s inside protected storage to 13% for unprotected bales stored outside for 6 

months (Sanderson et al., 1997). Buckmaster (1992) showed dry matter losses in baled hay under 

indoor storage ranging from near zero dry matter loss at 12% moisture to around 7% dry matter 

loss at 25% moisture at baling. Studies of indoor hay storage indicate that, to reduce the risk of 

severe heat production and fire in the storage stationary source, moisture content of incoming 

bales should be 25% or less (Buckmaster, 1992). 

The type of storage facility (indoor, outdoor unprotected, outdoor protected, etc.) and the 

underlying foundation (sod, concrete, gravel, etc.) affect storage losses, primarily through 

protection from increased moisture and differences in aeration (Sanderson et al., 1997). For 

instance, in a Swedish study on forest residue bales, the 10 month dry matter losses due to 

biological activity were highest in the outdoor-uncovered stack, which had an average total dry 

matter loss of 18.5%, while the indoor stacks had a dry matter loss of 14% (Eriksson and 

Gustavsson, 2010; Jirjis, 2003). For Atlantic Canada conditions, Afzal et al. (2010) estimate 6.6 to 

15.6% dry matter losses during 6 months for uncovered birch chip piles of (naturally) dried and 

fresh forest residue, respectively. 

Besides initial moisture content of feedstock, type of storage facility (indoor, outdoor unprotected, 

outdoor protected, etc.) and use of an appropriate foundation, storage losses also strongly depend 

on the “packaging” of the feedstock. For instance, losses are higher in chipped material than in loose

residue piles, because chipping increases the amount of exposed surfaces on which microbial action 

can occur and releases soluble contents of plant cells that provide nutrition to microbes 

(Richardson et al., 2002). Small chips also reduce air flow in piles and prevent heat dissipation and 

moisture release. 

The length of storage time affects losses in combination with moisture and facility type. The decay 

process starts as soon as 1 week, with the highest dry matter losses in the first weeks of storage 

(Wihersaari, 2005; for Finland). Indoor storage may result in moisture content going from 25% to 

around 12% after 2 months or more of storage (Buckmaster, 1992), at which point microbial 

activity will be minimized. 

These studies indicate that, if necessary, it may be possible to develop a representative estimate 

that could be applied to a given stationary source on the basis of the five variables (type of biomass

and particle size, initial moisture content, type of storage, average length of storage period, and 

regional climate conditions). Emissions will be composed almost entirely of CO2 except in storage 
November 2014 G-17 

facilities where aeration is limited. For example, one study of logging residues stored in laboratory 

vessels found maximum concentrations of CO2, CO, and CH4 to be 13.8%, 0.16%, and 0.15%, 

respectively, over a 35-day period (He et al., 2011). 

A literature search identified studies that investigate dry matter loss over time for various types of

biomass, including those with original research on dry matter loss. Based on 22 studies, a dataset 

containing 112 cases of reported dry matter loss over time was produced (see Addendum I in 

Section 6, with case categories including source, origin, region, biomass type, storage type, original

moisture content, storage length, and dry matter loss). If data were presented from abroad, the 

study location was matched by climatic zones with one of the U.S. regions described in Smith and 

Heath (2002). 

Addendum II (see Section 7) expands upon the literature review presented in this Section and in 

Addendum I, focusing on dry matter losses from storage of woody biogenic materials. 

4.3. Representative Values for Storage Losses 

Analysis of the dataset (see Addendum I in Section 6 below) revealed no clear trends in storage loss 

by biomass type. Even herbaceous crops that were assumed to produce comparable results across 

studies (usually baled and pre-dried) did not produce notable and distinguishable trend lines by 

region or storage type (see Figure G-2). Additional data and statistical analysis could provide more 

insights. Based on the available data, trend lines describing the storage losses for each of the three

storage types (outdoor, uncovered; outdoor, covered; indoor) were produced for one feedstock 

(woodchips) (see Figure G-3). Because of the limited research on indoor storage losses for 

woodchips, all indoor storage data were aggregated irrespective of region and biomass type and a 

“worst-case” scenario was used, assuming rapid storage losses. 
November 2014 G-18 

Figure G-2. Data Points for Herbaceous Crops Storage for Western (W), Southeastern (SE), 

Northeastern (NE) and Southwestern (SW) Regions. Storage losses are not clearly distinguishable 

by region or storage type (N = 69).

Figure G-3. Data Points and Trendlines for Storage Losses for Woodchips in the Northeast (N = 29). 
November 2014 G-19 

Table G-5 describes the trend lines shown in Figure G-3 and is a first approximation for a storage 

loss term lookup table for woodchips in the Northeast provided for illustrative purposes only. 

Table G-5. Example Representative Values for Storage Losses for Woodchips in the Northeast 

(based on Figure G-3). 

Storage Duration 

(months) Outdoor, Uncovered Outdoor, Covered Indoor 

1 11% 4% 2% 

2 14% 5% 3% 

3 16% 6% 3% 

4 17% 6% 4% 

5 18% 6% 4% 

6 19% 7% 4% 

7 20% 7% 4% 

8 21% 7% 4% 

9 21% 8% 4% 

10 22% 8% 4% 

11 22% 8% 4% 

12 23% 8% 5% 

Based on the data identified in the figures and tables above, four options for developing 

representative LOSS values are explored (though other options could be used in applications of the 

framework): 

1. Develop a “maximum” L term for each feedstock type by region. This could reflect a 

“highest possible case” system (see black line in Figure G-3) i.e., outside, uncovered, onground, long-term (>6 months) storage (assuming 50% initial moisture, which results 

in>20% loss (LOSS1 => 0.2). 

2. Provide “loss deductions” that could be used to reduce the maximum losses in situations in 

which stationary sources use improved management (e.g., a system that stores material at 

a moisture content of 20% indoors on concrete for <1 month could lead to an estimate of a 

zero loss). 

3. Provide for a stationary source-specific loss calculation method that could be used to 

estimate the LOSS term for a particular stationary source (Sale point dry weight (DW) − 

Process point DW) / Process point DW = L). 

4. Provide one single representative LOSS value (at a broader scale than an individual 

stationary source, e.g., region) that presents average losses for herbaceous and woody 

biomass for an average storage time. 

The following example discusses development of a regional representative LOSS value for 

illustrative purposes: 10% loss (LOSS = 0.1) for woody and herbaceous feedstocks, including stover, 

is discussed and calculated below using the Northeastern data. 

It is assumed that stationary sources consuming woody biomass usually store a 3-month supply 

uncovered on their premises. Averaging covered and uncovered outdoor storage data for 

woodchips in the Northeast (Figure G-3), this would equal a loss of 11%. 
November 2014 G-20 

There are limited data on storage-related data for stover and agricultural residues, but trends for 

these feedstocks were similar to herbaceous crops (Figure G-2), so both feedstock categories were 

combined. In terms of storage time for herbaceous feedstocks, this example assumes a 4-month 

harvest window. It is also assumed that during this harvest window, feedstocks are immediately 

converted to usable heat without storage losses. An average storage time of 2.7 months for 

feedstock was calculated based on an assumed 8 months of off-harvest season. This average storage 

time takes into account that some of the feedstock is burnt immediately after harvest (during the 

harvest months), while other sections of the feedstock are stored in decreasing quantities for up to 

8 months until the next harvest window. The data points for a 3-month outdoor storage (Figure G2) suggest a loss of 3 to 15% with an average of around 9 to 10% across all regions because no 

separate regional trends are discernible with current data. 

The dataset contained no reports on storage losses for roundwood, and only one case was recorded 

for wood pellets. Because of this lack of data, this appendix does not provide illustrative examples 

of storage losses for these feedstock categories. 

Potential feedstocks could also be oil seed crops and sugar-based feedstocks such as corn kernels or 

sweet sorghum. Sugar-based feedstocks can experience rapid storage losses in a matter of days 

(e.g., Bennett and Anex, 2009; Jasberg et al., 1984), which is usually not measured in dry matter 

losses and is therefore not compatible with the approach followed in the dataset described above. 

4.4. Discussion of Other Losses: Transportation and Harvest 

4.4.1. Transportation 

Dry matter lost in transportation is almost wholly due to mechanical losses. Chemical losses, such 

as the degradation of dry-matter, generally occur during storage and at a much larger timescale 

than that which occurs during transport (weeks to months of sitting in storage vs. hours to days in 

transit). Although mechanical losses occur in the loading and unloading of dry matter, these losses 

are small and are considered more or less as inevitable with few mitigation options available. 

Transport losses are often less that 1% (Sanderson et al., 1997) even in the case of long-distance 

supply chains for woody biomass (Hamelinck et al., 2005). One study found that bale weight 

changes and biomass loss of switchgrass during handling and transportation from the field resulted 

in a total dry matter loss of 0.4% (Sanderson et al., 1997). In the case of roundwood, transport 

losses are generally not expected to occur. As an example, developing a lookup table for transport 

losses could be done by using research specifying representative values by transport type (truck, 

vessel, train) and distance (e.g. <50 mi, 50–100 mi, >100 mi). If they are included, transport losses 

for one crop should be added together to provide an exhaustive view to systematically quantify 

total losses (Hamelinck, 2005). For instance, if biomass is transported by truck (e.g., 1% loss) 

followed by train (1% loss), total transport losses could equal 2%. However, due to lack of data, no 

such illustrative example is included in this appendix. 
November 2014 G-21 

4.4.2. Harvest 

Harvest losses are not included in the example calculations in this appendix, because such losses 

are not often measured or included in the delivered product weight and thus not typically 

calculated in yield computations. However, harvest losses could be significant in the case of specific

feedstocks or for vertically integrated stationary source operations that oversee farm/forest 

operations. Harvesting efficiencies for crop residues using conventional multipass harvesting 

systems are relatively low, with only one-third to two-thirds of the available crop residues actually 

collected (Hess et al., 2009). For instance, Monti et al. (2009) note that “Unlike storage, major 

biomass losses6 occurred during harvest [of hay], either due to the biomass not picked-up by the 

baler machine (up to 17%), and uncut biomass by the mower machine (up to 29%). Overall, of the 

potentially harvestable biomass, only 64% was actually baled.” However, Hess et al. (2009) noted 

that for most farmers, it is general agricultural practice to intentionally leave a certain amount of 

crop residue on the landscape to ensure soil health. The amount of crop residue left behind is 

somewhere between 40% and 70%, with an average amount of biomass left onsite of 60% (Hess et 

al., 2009). Nevertheless, these harvesting values are for specific crop yields and are highly variant in 

nature; more data would be required to estimate such losses. 

4.5. Representative Factor Discussion 

There may be cases where information on supply chain losses is unavailable between the feedstock 

production site (e.g., farm, forest) and the feedstock conversion facility. For example, if an 

intermediate supplier or primary processing facility supplies feedstock (e.g., woody mill residues) 

to the stationary source, there may be little information about the transportation and storage losses 

incurred between the landscape where the feedstock is grown and the stationary source. 

Representative values for losses may be determined based on a review of literature as described in 

Section 4.3 above. Alternatively, a representative factor derived from the literature may be assigned 

for a particular feedstock type in the absence of refined information. 

5. References 

Afzal, M., A. Bedane, S. Sokhansanj, and W. Mahmood. 2010. Storage of comminuted and 

uncomminuted forest biomass and its effect on fuel quality. BioResources 5(1):55-69. 

Ashton, S.F. Dry-Matter Loss. 2008. Forest Encyclopedia Network. Retrieved June 6, 2012 from 

http://www.forestencyclopedia.net/p/p1308. 

Bennett, A.S., and R.P. Anex. 2009. Production, transportation and milling costs of sweet sorghum as 

a feedstock for centralized bioethanol production in the upper Midwest. Bioresource Technology

100(4):1595-1607. 

Brown, T.R., M.M. Wright, and R.C. Brown. 2010. Estimating profitability of two biochar production 

scenarios: slow pyrolysis vs. fast pyrolysis. Bioguels, Bioproducts and Biorefining 5(1). 

 

6

 The word “losses” in this quote refers to biomass left behind as opposed to the definition being used in the rest of 

this appendix. 
November 2014 G-22 

Buckmaster, D.R. 1992. Indoor hay storage: Dry matter loss and quality changes, Fact Sheet PSU/92. 

State College, PA: Agricultural and Biological Engineering Department, Pennsylvania State 

University. 

Cassidy, P., and S. Ashton. 2007. Ash Content Fact Sheet: Sustainable Forestry for Biogenergy and 

Bio-Based Products. 

Demirbas, A. 2000. Recent advances in biomass conversion technologies. Energy Education Science 

and Technology 6:19-40. 

Demirbas, A. 2005. Potential applications of renewable energy sources, biomass combustion 

problems in boiler power systems and combustion related environmental issues. Progress in 

Energy and Combustion Science 31(2):171-192. 

Enders, A., K. Hanley, T. Whitman, S. Joseph, et al. 2012. Characterization of biochars to evaluate 

recalcitrance and agronomic performance. Bioresource Technology 114:644-653. 

EPA. 2007. Biomass Combined Heat and Power Catalog of Technologies: Combined Heat and Power 

Partnership. http://www.epa.gov/chp/documents/biomass_chp_catalog.pdf. 

Eriksson, L., and L. Gustavsson. 2010. Comparative analysis of wood chips and bundles – Costs, 

carbon dioxide emissions, dry-matter losses and allergic reactions. Biomass and Bioenergy, 

34(1):80-92. 

Gustafson, R., and N. Raffaeli. 2009. Washington State Pulp and Paper Mill Boilers: Current and 

Potential Renewable Energy Production. Seattle, WA: University of Washington, School of Forest 

Resources, Department of Ecology. 

https://fortress.wa.gov/ecy/publications/publications/0907048.pdf. 

Hamelinck, C.N., R.A.A. Suurs, and A.P.C. Faaij. 2005. International bioenergy transport costs and 

energy balance. Biomass and Bioenergy 29(2):114-134. 

Hammond, J., S. Shackley, S. Sohi, and P. Brownsort. 2011. Prospective life cycle carbon abatement 

for pyrolysis biochar systems in the UK. Energy Policy 39(5):2646-2655. 

He, X., A.K. Lau, S. Sokhansanj, C.J. Xiaotao, et al. 2011. Dry matter losses in combination with 

gaseous emissions during the storage of forest residues. Fuel 95:662-664. 

Hess J.R., et al. 2009. Uniform-format solid feedstock supply system: a commodity-scale design to 

produce an infrastructure-compatible bulk solid from lignocellulosic biomass. INL/EXT-08-

14752 

Jasberg, B.K., R.R. Montgomery, and A. R.A. 1984. Preservation of Sweet Sorghum Biomass. Peoria, IL: 

U. S. Department of Agriculture, Agricultural Research Service, Northern Regional Research 

Center. 

Jirjis, R. 1995. Storage and drying of wood fuel. Biomass and Bioenergy 9:181-190. 

Jirjis, R., and B. Nordén. 2002. Lagring av buntat skogsbränsle – små substansförluster, inget 

arbetsmiljöproblem [Stockpiling of composite residue logs (CRLs) – small biomass losses and 

no health problems]. Skogforsk Resultat 12:1-2. 

Jirjis, R. 2003. Storage of forest residues in bales. Uppsala, Sweeden: Swedish University of 

Agricultural Sciences, Department of Bioenergy. 
November 2014 G-23 

Johansson, L.S., C. Tullin, B. Leckner, and P. Sjövall. 2003. Particle emissions from biomass 

combustion in small combustors. Biomass and Bioenergy 25(4):435-446. 

Kaufmann, H., T. Nussbaumer, L. Baxter, and N. Yang. 2000. Deposit formation on a single cylinder 

during combustion of herbaceous biomass. Fuel 79:141-151. 

Miranowski, J., M. Khanna, J. Hess. Suntainable Feedstocks for Advanced Biofuels - Sustainable 

Alternative Fuel Feedstock Opportunities, Challenges and Roadmaps for Six U.S. Regions. Soil and 

Water Conservation Society, Proceedings of the Sustainable Feedstocks for Advance Biofuels 

Workshop - Atlanta, GA. September 28-30, 2010. Chapter 11 - Economics of Feedstock 

Production, Harvest, Storage, and Transport. 

Monti, A., S. Fazio, and G. Venturi. 2009. The discrepancy between plot and field yields: Harvest and 

storage losses of switchgrass. Biomass and Bioenergy 33(5):841-847. 

Mullen, C.A., A.A. Boateng, N.M. Goldberg, I.M. Lima, et al. 2010. Bio-oil and biochar production from

corn cobs and stover by fast pyrolysis. Biomass and Bioenergy 34(1):67-74. 

Nussbaumer, T., and P. Hasler. 1999. Formation and properties of aerosols from wood combustion. 

Raw Wood Materials 57:13-22. 

Pitman, R.M. 2006. Wood ash use in forestry – a review of the environmental impacts. Forestry

79(5). 

Richardson, J., R. Bjorheden, P. Hakkila, A.T. Lowe, et al., (eds.). 2002. Bioenergy from sustainable 

forestry: guiding principles and practice. Dordrecht, The Netherlands: Kluwer Academic 

Publishers. 

Ringer, M., V. Putsche, and J. Scahill. 2006. Large-scale pyrolysis oil production: A technology 

assessment and economic analysis. Golden, CO: National Renewable Energy Laboratory. 

Roberts , K.G., B.A. Gloy, S. Joseph, N.R. Scott, et al. 2010. Life cycle assessment of biochar systems: 

Estimating the energetic, economic and climate change potential. Environmental Science & 

Technology 12(2):107-115. 

Sanderson, M.A., R.P. Egg, and A.E. Wiselogel. 1997. Biomass losses during harvest and storage of 

switchgrass. Biomass and Bioenergy 12(2):107-115. 

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., and L.S. Heath. 2002. A model of forest floor carbon mass for United States forest types. 

Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northeastern Research 

Station. 

Spokas, K.A. 2010. Review of the stability of biochar in soils: predictability of O:C molar ratios. 

Carbon Management 1(2):289-303. 

Tarelho, L.A.C., D.S.F. Neves, and M.A.A. Matos. 2011. Forest biomass waste combustion in a pilot 

scale bubbling fluidized bed combustor. Biomass and Bioenergy 35(4):1511-1523. 

Thornqvist, T., and R. Jirjis. 1990. Changes in fuel chips during storage in large piles, Report No. 219. 

Uppsala, Sweden: Swedish University of Agricultural Sciences, Department of Forest Products. 

Tollin, J. 2000. Reburning biomass fly ash. CADDET Renewable Energy Newsletter, 13. 

UK Biochar Research Center (UK BRC). 2009. Working Paper. 
November 2014 G-24 

Wihersaari, M. 2005. Greenhouse gas emissions from final harvest fuel chip production in Finland. 

Biomass and Bioenergy 28(5):435-443. 

Wright, M.M., R.C. Brown, and A.A. Boateng. 2008. Distributed processing of biomass to bio-oil for 

subsequent production of Fischer-Tropsch liquids. Biofuels, Bioprocessing, and Biorefining

2:229-238. 
November 2014 G-25 

6. Addendum I. Literature Reviewed for Storage and Processing 

at Stationary Sources Facilities 

Table G-6. Dataset Sources and Cases by Source for Storage and Processing at Stationary Sources. 

Source No of 

cases 

Afzal, M., A. Bedane, S. Sokhansanj, and W. Mahmood. 2010. Storage of comminuted and 

uncomminuted forest biomass and its effect on fuel quality. BioResources, 5(1):55-69. 6 

Buckmaster, D.R. 1992. Indoor hay storage: Dry matter loss and quality changes, Fact Sheet 

PSU/92. State College, PA: Agricultural and Biological Engineering Department, Pennsylvania 

State University. 

2 

Collins, M., D. Dirtsch, J.C. Henning, L.W. Turner, S. Isaacs, and G.D. Lacefield 1997. Round Bale 

Hay Storage in Kentucky. AGR-171, Kentucky Cooperative Extension Service. 8 

Filbakk, T., HØibØ, O.A., Dibdiakova, J., Nurmi, J. 2011. Modelling moisture content and dry matter 

loss during storage of logging residues for energy. Scandinavian Journal of Forest Research, 26 

(3): 267-277 

2 

Fredriksson, H. and Rutegård, G. 1985. Storage of chunkwood and fuel chips in bins. Res. Notes 

no. 151, Dept of For. Prod. Swed. Univ. Agric. Sci., Uppsala, Sweden. (In Swedish, English 

Summary.) 

2 

Hamelinck C.N., Suurs, R.A.A., and Faaij, A.P.C. 2005. International bioenergy transport costs and 

energy balance. Biomass and Bioenergy 29(2): 114–134. 2 

He, X., Lau, A.K., Sokhansanj, S., Xiaotao, C.J., Bi, X.T., and Melin, S. 2011. Dry matter losses in 

combination with gaseous emissions during the storage of forest residues. Fuel 95:662–664. 1 

Hess, J.R., Kenney, K.L., Park Ovard, L., Searcy, E.M., Wright, C.T. 2009 Uniform-format solid 

feedstock supply system: a commodity-scale design to produce an infrastructure compatible bulk 

solid from lignocellulosic biomass. Idaho National Laboratory. 

22 

Hess, J.R., Kenney, K.L., Wright, C.T., Perlack, R., Turhollow, A. 2009. Corn stover availability for 

biomass conversion: Situation analysis. Cellulose 16 (4): 599-619 6 

Hudson, J. B., Mitchell, C. P., Gardner, D. and Storry, P. 1988. A comparative study on storage and 

drying of chips and chunkwood in the UK. In Proceedings of the lEA/BE Conference Task 

Ill/Activity 6&7: Production, Storage and Utilization of Wood Fuels, Vol. H, ed Danielsson, B.O., 

Dept of Operational Efficiency, Swed. University of Agricultural Science, Garpenberg, Sweden. 

4 

Huhnke, R.L. 2006. “Round Bale Hay Storage.” BAE-1716, Oklahoma Extension Service. 12 

Jirjis, R. 2003. Storage of forest residues in bales. Department of Bioenergy, Swedish University of 

Agricultural Sciences, Uppsala. p. 23. 2 

Jirjis, R., and Nordén, B. 2002. Lagring av buntat skogsbränsle – små substansförluster, inget 

arbetsmiljöproblem [Stockpiling of composite residue logs (CRLs) – small biomass losses and no 

health problems]. Skogforsk Resultat 12:1–2. 

1 

Jirjis, R., Pari, L., Sissot, F. 2008. Storage of poplar wood chips in Northern Italy. Proceedings of 

the 8th World Bioenergy Congress, 27-29 Maggio Jonkoping, Sweden: 85-89. 1 

Monti, A., Fazio, S., Venturi, G. 2009. The discrepancy between plot and field yields: Harvest and 

storage losses of switchgrass. Biomass and Bioenergy 33(5): 841-847. 1 

Mooney, D.F., Larson, J.A., English, B.C., Tyler, D.D. 2012. Effect of dry matter loss on profitability 

of outdoor storage of switchgrass. Biomass and Bioenergy 44: 33-41 20 

Nilsson, T. 1987. Comparison of storages of chunkwood and fuel chips. Report no. 192, Dept of 

For. Prod, Swed. Univ. Agric. Sci., Uppsala, Sweeden. (In Swedish, English summary.) 3 

Nurmi J. 1999. The storage of logging residue for fuel. Biomass and Bioenergy,17:41–7. 2 

Pettersson, M., and Nordfjell, T. 2007. Fuel quality changes during seasonal storage of compacted 

logging residues and young trees. Biomass Bioenergy 31: 782–792. 2 

Sanderson, M.A., Egg, R.P., Wiselogel, A.E. 1997. Biomass losses during harvest and storage of 

switchgrass. Biomass and Bioenergy 12(2): 107-114 4 
November 2014 G-26 

Source No of 

cases 

Shinners, K.J., B.N. Binversie, R.E. Muck, and P.J. Weimer. 2007. Comparison of Wet and Dry Corn 

Stover Harvest and Storage. Biomass & Bioenergy, 31:211-221. 7 

Thornqvist, T., (Storing of saplings of salix spp.) Report no. 152, Dept For. Prod., Swed. Univ. 

Agric. Sci., Uppsala, 1984. (In Swedish, English summary.) 2 

 
November 2014 G-27 

7. Addendum II. Literature Review of Dry Matter Losses from 

Woody Biogenic Material Storage 

7.1. Introduction 

This addendum expands upon the literature review presented above in Section 4 on storage dry 

matter losses for woody biogenic material. Woody biomass contains roughly 50% (by weight) 

water when harvested. The water content of biomass can fluctuate between harvest and processing 

or use for bioenergy as a result of drying or reabsorption of water from precipitation. Dry matter 

loss (DML) is of interest because dry matter excludes the weight of water in the biomass and water 

does not contain biogenic carbon. The biogenic carbon is part of the dry weight fraction of the 

biomass (e.g., woody biomass dry matter contains approximately 50% carbon). For mechanical 

losses due to feedstock handling, there is no chemical change in the feedstock so it is reasonable to 

conclude that the proportion of carbon in material lost due to mechanical processes is the same as 

the proportion of carbon in the bulk feedstock. For chemical losses due to degradation of dry matter 

in storage, the DML can be assumed to be reasonably proportional to the weight percentage of 

carbon loss from the feedstock (e.g., a 1% loss in dry matter can be assumed to represent a 1% loss 

in feedstock carbon).7,8 

7.2. Storage Losses 

Several factors, including storage method and time and related parameters such as material 

moisture content, temperature and particle size influence DML from woody biomass (He et al., 

2012). According to Forest Products Laboratory (FPL) (FPL, 2010), most decay can progress 

rapidly at temperatures that favor growth of plant life in general. For the most part, decay is 

relatively slow at temperatures below 10oC (50°F) and above 35oC (95°F). Decay essentially ceases 

when the temperature drops as low as 2oC (35°F) or rises as high as 38oC (100°F). 

Significant decay can occur only when the moisture content of wood is above the fiber saturation 

point (average 30% moisture). Only when previously dried wood is contacted by water in the form 

of rain or condensation or is in contact with wet ground will the fiber saturation point be reached. 

By itself, the water vapor in humid air will not wet wood sufficiently to support significant decay, 

but it will permit development of some mold fungi (FPL, 2010). Springer (1979) explained that 

 

7

 One Italian study (Barontini et al., 2014) of poplar chips from stem wood and crowns representative of 

logging residue found that the proportion of carbon in the feedstock before and after 6 months of storage 

was similar although the overall dry matter of wood decreased. For crowns, the fraction of carbon in the 

dry matter varied from 47.7 to 46.9% before and after storage, respectively. For stem wood, the fraction 

of carbon in the dry matter varied from 47.2 to 47.8% before and after storage, respectively. 

8

 In addition to observing a loss of dry matter over 1 year of storage, Nurmi (1999) found only a slight 

variation in the carbon fraction in the dry matter of comminuted Norway spruce logging residue feedstock 

(i.e., an increase of the carbon fraction in the dry matter from 50% to 51% before and after storage, 

respectively).
November 2014 G-28 

wood dried below its fiber saturation point (20% to 24% moisture) is not subject to bacterial and 

fungal attack. 

Anheller (2009) noted that DML becomes higher when woody biomass is stored for longer periods 

and in larger piles. The shape of the piles influences the heat development during storage of 

biomass. To minimize biomass losses, Anheller (2009) recommended keeping the biomass as dry as 

possible before storage, keeping as large a particle size as possible, preferably under roof, 

uncommuted and uncompacted, as long as possible before combustion. Anheller (2009) suggested 

that the most advantageous way to store wood fuels is as uncomminuted fuels, such as whole tree 

logs, reducing the size of fuel wood to chips as close as possible to the time of use, to shorten the 

time of storage as chips and decrease degradation losses associated with microbial activity. 

Similarly, Wilkerson et al. (2008) cited studies recommending that comminuted material be used as 

quickly as possible after grinding to minimize DML.

7.2.1. Forest Biomass Storage Methods 

There are a variety of forest biomass storage methods depending on the source and intended use 

for the material. Forest biomass may be stored as whole logs, in wood chip piles, as logging residue 

prior to chipping (piled or in bales), as bark, or as wood pellets. Whole logs may be processed into 

forest products such as lumber or veneer or chipped for use in pulp, wood product, or bioenergy 

production. Bark is often stored separately from wood chips, especially at mills with different uses 

for bark (e.g., as fuel) and chips (e.g., forest product raw material). 

Whole Logs 

Wood can be too wet for decay as well as too dry. If the wood is water-soaked, the supply of air to 

the interior of the wood may not be adequate to support development of typical decay fungi. For 

this reason, logs stored in a pond or under a suitable system of water sprays are not subject to 

decay by typical wood-decay fungi. For logs, rapid conversion into lumber or storage in water or 

under a water spray is used to avoid fungal damage. (FPL, 2010) 

FPL (2004) explained that, today, softwood logs decked in a log yard are typically protected by 

water sprinkling during warm weather. Sprinkling provides an effective method of reducing 

checking, sapwood stain, and decay. Sprinkling will not protect against insect attack, although it 

tends to be more effective than dry land storage. To be effective, the ends of logs and exposed wood 

must be kept wet continuously during the entire storage period. Sprinkling reduces available 

oxygen, thereby deterring sapwood staining and decay. Pond storage (e.g., in mill ponds, lakes, 

rivers, and saltwater estuaries), although once common practice, is seldom used in the continental 

U.S. today (FPL, 2004). 

Sprinkling of logs has been practiced for decades. In 1959, Wright et al. of the USDA Forest Service 

Station (Portland, Oregon) discussed sprinkling to prevent decay in decked Western hemlock logs. 

Wright et al. (1959) noted that wood containing less than 20% moisture (dry-weight basis) does 

not decay, and for decay to progress fairly rapidly, a moisture content of 30% or more is required. 

However, oxygen is also necessary for the growth of wood-rotting fungi, and decay will not develop 
November 2014 G-29 

when complete saturation with water eliminates air from the wood. Based on experimental 

research, Wright et al. (1959) concluded that: 

• Both green and old western hemlock logs can be stored in un-sprinkled decks for 1 year 

without appreciable additional decay; 

• Green logs can be decked for as long as 2 years without serious additional loss, but old logs 

containing decay when decked suffer considerable loss; and 

• Constant sprinkling with water during the warmer months of the year, to keep ends and 

faces of logs wet at all times, effectively inhibits the development of additional decay in old 

logs. 

Chip or Bark Piles 

Outside chip storage at pulp mills has been practiced since the 1950’s in order to maintain large 

inventories without the use of bins or silos. In Smook (2002), it is recognized that losses of 1% 

wood substance per month are typical of outside storage due to a combination of respiration, 

chemical reactions, and micro-organism activity. Smook (2002) reports that wood losses and 

degradation during outside chip storage can be minimized with effective chip pile management 

such as ground barriers (e.g., concrete, asphalt), fines screening, avoiding contamination of sound 

wood with decayed wood, and minimizing storage time. While bark may be a contaminant in the 

pulping process, bark is less problematic for chips destined for use as boiler fuel (Smook, 2002). 

Mill residues may be stored in piles (indoors or outdoors) or silos. Mills and other wood-using 

facilities keep chip or sawdust piles on-site or at nearby facilities when supply is low. Woody 

biomass is reduced in size in the forest and then transported for storage, or it is transported, 

reduced in size at the mill, and then stored. The resulting material, usually chips, is stored outside in 

large piles and under cover in large silos or bins. Chips stored in bins are typically used within 

several hours or days while silos are used for longer-term storage needs. Silos and bins protect 

against contamination while at the same time allowing for uniform feeding and metering of the 

material. While storing, comminuted biomass makes handling and transport relatively easy. If not 

managed carefully, the biomass will succumb to DML and in some cases self-ignition. High 

temperatures and acetic acid odor are signs that a chip pile is in danger of DML and self-igniting. 

Additionally, chip piles with excessive mold and fungi growth can lead to health risks for humans 

(Hubbard et al., 2007). 

Dry matter loss of forest biomass, which includes the degradation of lignin, cellulose, and 

hemicellulose, occurs when woody biomass, in any form, is not used immediately after harvesting 

and has to be stored within a facility. The degree to which DML occurs depends largely on the 

material’s moisture content. Woody biomass having high moisture content is more susceptible to 

colonization by fungi and mold and at a faster rate. These microorganisms, via metabolic activity, 

generate heat, which in turn accelerates oxidation, moisture adsorption, hydrolysis, pyrolysis, and 

other chemical processes resulting in DML (Hubbard et al., 2007). Fungal activity is also responsible 

for the fermentation of the fractionated holocellulose. However, when this happens in the presence 

of aerobic bacteria and oxygen, they can change the ethanol produced by natural degradation 

processes into acetic acid or vinegar. The degradation of biomass through hydrolysis, fermentation, 
November 2014 G-30 

and oxidation that results in the production of acetic acid lowers the average pH of the moisture in 

the wood chips. This decrease in pH can lead to rapid, premature decay of the piled material. For 

example, in a chip and bark pile during six months of storage, the initial pH dropped from nearneutral to neutral (5 to 7) to an average of 4 (Slaven et al., 2011). 

As noted above, biomass moisture can lead to an increase in temperature in piled biomass, which in 

turn leads to DML. As shown in Figure G-4, Wihersaari (2005) used temperature to illustrate the 

pattern of DML for dried and fresh (green) feedstock. Wihersaari (2005) and others explain how 

pile temperature relates to DML as follows: 

• Dry matter that is intentionally dried through a mechanical process and then stored 

undergoes an initial increase in temperature, which in turn increases microbial activity 

within the stored pile and acts as a catalyst to material degradation. However, this increase 

in temperature is short-lived since after approximately one week, microbial colonies begin 

to die out due to their residence in an inhospitable, low-moisture environment. The death of 

these microbes in turn reduces temperature within the pile and provides a decline in the 

rate of decay of the stored material (Wihersaari 2005). 

• Stored piles of fresh forest matter generally do not reach internal temperatures as high as 

piled dry forest materials, but they maintain a higher temperature (above 40oC [104oF]) 

much longer than dried forest materials. As Figure G-4 shows, fresh forest matter tends to 

maintain a temperature above 40oC (104oF) for longer than 27 weeks. While fresh forest 

materials stored outdoors will lose less material due to mechanical losses such as wind, due 

to their weight, storing forest materials without drying them first creates problems. Storing 

fresh forest materials in large piles increases microbial activity by providing a hospitable 

environment for growth, which leads to rapid material decay. These fresh forest material 

piles, with extended periods of high internal temperatures, are also more susceptible to 

spontaneous combustion (Wihersaari, 2005; Slaven et al., 2011; Hubbard et al., 2007). 
November 2014 G-31 

Figure G-4. Simplified Principle of Degradation Behavior (Wihersaari, 2005) 

In his study, Wihersaari (2005) found that when the average chip size in a pile of dried forest 

matter was 30 millimeters (mm), the temperature rose to 40 to 50oC (104 to 122oF); but when the 

average chip size was 70 mm, the temperature did not rise above 30oC (86oF). Wihersaari (2005) 

explained that the biological activity producing heat takes place on the surface of the chips; the 

smaller the chip size, the larger the surface area per volume and consequentially the higher the 

biological activity leading to higher temperatures. Hubbard et al. (2007) explained that the small 

particle size gained by chipping restricts air flow and prevents heat dissipation, while chipping 

releases the soluble contents of plant cells providing microbes with nutrients. 

Anheller (2009) also explained that heat development can occur in in chip piles if the moisture 

content is high enough for microbial growth, usually >20% for wood fuel. In fresh wood chips, 

respiration heat from the living cells contributes to initial heat development and the chipping 

process releases soluble sugar from the wood, which together with heat, moisture and oxygen can 

create a favorable environment for microbes. By chipping the material, the area where the microbes 

can attack increases. In a pile of chipped material, the air movement is also more limited because of 

the smaller material, which prevents heat dissipation and causes heat accumulation and thereby 

increases degradation losses. In large piles, the material can additionally become more compact 

because of weight, which further amplifies the abovementioned factors (Anheller 2009). 

Similarly, Janze (2011) concluded that particle size within a biomass pile effects moisture 

absorption, heat build-up, heat dissipation and DML. Piles containing a large amount of fines absorb 

greater amounts of water, generally heat up faster due to greater microbial action, and restrict air 

movement through the pile, thereby limiting heat dissipation; all of this leading to increased DML 

and possibly spontaneous combustion. Conversely, piles consisting of large wood chunks heat up 
November 2014 G-32 

more slowly, permit better air flow due to the large voids, dissipate the heat faster due to better air 

circulation, have lower rates of microbial action and lower DML. 

Janze (2011) recommended that for long storage periods that fuel be stored in fairly large particles 

and only re-processed to the final size shortly before usage in the boiler. Janze (2011) further 

recommended that radically different types of material such as clean wood chips, short rotation 

coppice and forest debris should not be stored in the same pile, as they decompose at different 

rates, which can lead to spontaneous combustion. Such materials should be stored separately and 

blended just prior to transport to the boiler (Janze, 2011). 

Logging Residue Prior to Chipping 

Logging residues may be left in small piles at the feedstock production site, chipped after some 

time, or compacted into bundles. Chipping is the method most commonly used in North America 

(Afzal et al., 2010). Hubbard et al. (2007) explained that there are several advantages to storing 

unconsolidated woody biomass immediately after harvest. When stored unconsolidated in 

mounded piles of moderate size, leaves and needles can fall, reducing the material’s ash content. 

Moreover, when woody biomass is stored in smaller piles, drying occurs (i.e., moisture escapes 

through leaves and other open wood surfaces). This process lowers the moisture content and 

increases the heating value. Drying occurs when biomass is stored in windrows as well, but it is not 

as efficient as small piles because foliage is not allowed to drop. Additionally, when stored on the 

harvesting site, vital nutrients are released back into the soil. The major disadvantages to storing 

woody biomass immediately after harvest on-site are (1) the need for detailed inventory tracking, 

(2) the cost of forgoing reforestation until the piles of biomass have been removed, and (3) the cost 

and time-sensitivity of having a contractor return to the site to collect, pre-process, and transport 

the material to the wood-using facility (Hubbard et al., 2007). 

Woody biomass can be bundled and stored under cover to gain advantages (ease of handling and 

transport) that come along with storing chipped material. At the same time this approach protects 

the material from the disadvantages that come along with chipped material: DML, moisture 

retention, heat generation, and health hazards (Hubbard et al., 2007). 

Logging Residue Chips 

Wilkerson et al. (2008) noted that whole tree chips containing a large proportion of leaves and bark 

have a propensity to self-heat and can possibly cause chip pile fires. Wet chip pile heating can be 

mitigated by keeping pile heights under 9 meters (m) (30 ft) and/or limiting the amount of time the 

chips are piled to less than 10 days. Wilkerson et al. (2008) suggested that whole tree chips for 

energy that must be stored for more than 10 days may need some form of treatment to avoid 

degradation or spontaneous combustion. Drying to less than 20% moisture is the only viable option 

for safe long term storage of whole wood chips, but drying results in added expense, and dried fuel 

would need to be protected from rain with shelter or covering. 

Earlier, Springer (1979) also explained that whole-tree chips deteriorate more rapidly than clean, 

debarked chips and present a greater hazard for spontaneous ignition when stored in outdoor piles. 

Springer (1979) noted that whole-tree chips can be stored for only a short period of time to prevent 

ignition, that frequently rotating storage piles increases handling costs, and drying prevents 
November 2014 G-33 

deterioration and heating if the dried chips are stored under cover. According to Springer (1979), 

the costs of drying can be recovered if the chips are burned for fuel. Springer also referenced 

studies conducted in Norway in the last 1970’s that concluded that bark and foliage increase the 

rate of chip deterioration, with chip weight loss during storage following the order: clean debarked 

chips < whole-tree chips < bark < foliage (Springer, 1979). 

Wood Pellets 

Wood used to form woody biomass pellets is dried prior to the pelletizing process. Drying 

temperatures from 100 to 400°C (212 to 750oF) are used to reduce moisture content to less than

10% (Yazdanpanah et al., 2014). The average moisture content of commercially produced pellets 

(in Sweden) is about 10% to 12% (Lehtikangas, 2001). The Pellet Fuels Institute (PFI) standards for 

pellets produced in the U.S. range from less than 8% to 10% moisture (PFI, 2011). 

Wood pellets have flow characteristics and are well suited for storage in silos (Janze, 2011). Pellets

must be kept dry during storage (Janze, 2011; NEBTWG, 2012). 

7.2.1. Storage Time and Challenges in Tracking Biomass Inventory 

Janze (2011) explained that the amount of woody biomass storage required depends upon: 

• Minimum fuel storage; how much storage the plant’s financiers, regulators, clients and/or 

insurers require as a minimum to ensure continuous operation; 

• Known fuel delivery interruptions; the length of periods when fuel delivery can be 

anticipated to be interrupted, say for long weekends, or when supplier’s mills are 

shutdown; 

• Reliability of fuel delivery; there must be enough fuel on hand to cover likely transportation 

delays; 

• Contingency supplies, to cover periods when forests are inaccessible for fire season, the wet 

season or during spring break-up; 

• Fiber supply contractual requirements; the ability to continue to stockpile fuel when the 

power plant is shut down for annual maintenance; and 

• Often the space available dictates the size and shape of the storage pile and the maximum 

amount of fuel that can be stored. 

According to Janze (2011), typically, biomass-fired power plants [in the European Union] will 

stockpile a minimum of 20 to 30 days of fuel, but many will store for 60 days or more (i.e., 1 to 2 

months storage). Wilkerson et al. (2008) noted that some areas of the U.S. can harvest woody 

biomass year around, and long term storage is not necessary. However, in other areas a 1 to 6 

month supply of biomass may be required. 

Hubbard et al. (2007) reported that chip-pile storage is most common type of storage in the 

southeastern U.S. and recommended that owners shorten the storage time of chipped material to 

minimize the risk of microbial decomposition which will in turn decrease DML. Hubbard et al. 

(2007) indicated that the ideal storage period typically varies from 2 to 6 weeks (0.5 to 1.5 months) 

as determined by each facilities wood supply situation. 
November 2014 G-34 

Smook (2002) indicated that, because chip deterioration is largely a function of storage time, the 

most effective way to minimize losses is to minimize storage time (e.g., use first-in, first-out 

practices). Referring to pulp wood chips, Smook (2002) noted that optimum chip handling depends 

on use for the chip. For example, 2-month storage of chips used for sulfite pulping reduces 

extractives problematic in the sulfite pulping process. However, if maximum recovery of 

extractives-based pulping byproducts (e.g., tall oil, turpentine) is desired then fresh chips should 

bypass storage in order to maximize byproduct yield. 

Janze (2011) explained some of the challenges with tracking biomass pile inventory, noting that 

problems stem from the variability in biomass physical properties, including different species, 

moisture contents, and bulk densities, and varying amounts of compaction and dry fiber loss. 

Additionally, using multiple inventory tracking measures, including “green” or “bone dry” mass, 

solid wood or bulk densities, and solid wood volumes or bulk volumes, lead to inventory errors 

when converting from one measure to another and back again (Janze 2011). 

7.3.References 

Afzal, M.T., A.H. Bedane, S. Sokhansanj and W. Mahmood. 2010. "Storage of Comminuted and 

Uncomminuted Forest Biomass and Its Effect on Fuel Quality." BioResources	5(1):55-69.	

Anheller,	M.	2009.	Biomass	Losses	During	Short-Term	Storage	of	Bark	and	Recovered	Wood.	Swedish	

University	of	Agricultural	Sciences.	

Barontini,	M.,	A.	Scarfone,	R.	Spinelli,	F.	Gallucci,	E.	Santangelo,	A.	Acampora,	R.	Jirjis,	V.	Civitarese	

and	L.	Pari.	2014.	Storage	Dynamics	and	Fuel	Quality	of	Poplar	Chips.	Biomass	and	Bioenergy

62:17-25.	

Forest	Products	Laboratory	(FPL).	2010.	Wood	handbook—Wood	as	an	engineering	material.	

General	Technical	Report	FPL-GTR-190.	Madison,	WI:	U.S.	Department	of	Agriculture,	Forest	

Service,	Forest	Products	Laboratory.	

Forest	Products	Laboratory	(FPL).	2004.	Storage	of	softwood	Logs.	Madison,	WI:	U.S.	Department	of	

Agriculture,	Forest	Service,	Forest	Products	Laboratory.	February	2004.	

He,	X.,	A.K.	Lau,	S.	Sokhansanj,	C.J.	Lim,	X.T.	Bi	and	S.	Melin.	2012.	Dry	Matter	Losses	in	Combination	

with	Gaseous	Emissions	During	the	Storage	of	Forest	Residues.	Fuel	95:662-664. 

Hubbard,	W.;	L.	Biles;	C.	Mayfield;	S.	Ashton	(Eds.).	2007.	Sustainable	Forestry	for	Bioenergy	and	Biobased	Products:	Trainers	Curriculum	Notebook.	Athens,	GA:	Southern	Forest	Research	

Partnership,	Inc.	

Janze,	P.	2011.	Biomass	Storage	Pile	Basics.	In	Biomass	Handling:	All	you	wanted	to	know	about	

biomass	handling	but	were	afraid	to	ask.	

Lehtikangas,	P.	2001.	Quality	Properties	of	Pelletised	Sawdust,	Logging	Residues	and	Bark.	Biomass	

and	Bioenergy	20(5):	351-360.	

NEBTWG	2012.	Wood	Pellets:	Quality	and	Safety	Assurance	for	Transportation	and	Delivery.		

Document	created	by	the	Northeast	Biomass	Thermal	Working	Group	(NEBTWG),	with	

assistance	from	the	Biomass	Thermal	Energy	Council	(BTEC)	and	the	Pellet	Fuels	Institute	

(PFI).	Available	at	http://nebioheat.org/pdf/woodPellets_safetyTransportDelivery_web.pdf
November	2014	 	 G-35	

Nurmi,	J.	1999.	The	Storage	of	Logging	Residue	for	Fuel.	Biomass	and	Bioenergy	17(1):41-47.	

Pellet	Fuels	Institute	(PFI).	2011.	Pellet	Fuels	Institute	Standard	Specification	for	

Residential/Commercial	Densified	Fuel.	Available	at	http://pelletheat.org/wpcontent/uploads/2011/11/PFI-Standard-Specification-November-2011.pdf

Slaven,	I.,	E.H.	and	D.Cassens.	2011.	Properties	of	Wood	Waste	Stored	for	Energy	Production.	

Department	of	Forestry	and	Natural	Resources	at	Purdue	University.	ID-421-W.	

Smook,	G.	2002.	Handbook	for	Pulp	&	Paper	Technologists.	3rd	Edition.	Angus	Wilde	Publications	

Inc.,	Bellingham	WA.	

Springer,	Edward	L.	1979.	Should	Whole-Tree	Chips	for	Fuel	Be	Dried	before	Storage?	FPL-0241.	

Wihersaari,	M.	2005.	Evaluation	of	Greenhouse	Gas	Emission	Risks	from	Storage	of	Wood	Residue.	

Biomass	and	Bioenergy	28(5):444-453.	

Wilkerson,	E.	D.B.	Blackwelder,	R.D.	Perlack,	D.J.	Muth,	and	J.R.	Hess.	2008.	A	Preliminary	Assessment	

of	the	State	of	Harvest	and	Collection	Technology	for	Forest	Residues.	Oak	Ridge	National	

Laboratory.	ORNL/TM-2007/195.	

Wright,	E.,	A.C.	Knauss,	R.M.	Lindgren.	1959.	Sprinkling	to	Prevent	decay	in	Decked	Western	

Hemlock	Logs.	U.S.	Department	of	Agriculture,	Forest	Service,	Pacific	Northwest	Forest	and	

Range	Experiment	Station.	Portland,	OR.	No.	177.	

Yazdanpanah,	F.,	S.	Sokhansanj,	H.	Rezaei,	C.	J.	Lim,	A.	K.	Lau,	X.T.	Bi,	S.	Melin,	J.S.	Tumuluru,	and	C.S.	

Kim.	2014.	Measurement	of	Off-Gases	in	Wood	Pellet	Storage.	Intech.	

http://cdn.intechopen.com/pdfs-wm/46210.pdf