November 2014 H-1



# Appendix H: Illustrative Biogenic Landscape Attribu tes Using a Retrospective Reference Point Baseline

Table of Contents

1. Introduction ......................................

................................................... ................................................... ..................... H-2
2. Illustrative Method for Developing Regional Default Values for Biogenic Attributes: Southeast Roundwood .........................................

................................................... ................................................... ................... H-3
2.1. GROW

.................................................. ................................................... ................................................... ............. H-3
2.1.1. Data ..............................................

................................................... ................................................... .......... H-4
2.1.2. Forested Land Designation Used ....................

................................................... .............................. H-4
2.1.2.1. Defining the Working Forest .......................

................................................... .................................... H-5
2.1.3. Results ...........................................

................................................... ................................................... ........ H-7
2.2. AVOIDEMIT

.................................................. ................................................... ................................................... . H-8
2.3. SITETNC

.................................................. ................................................... ................................................... ........ H-8
2.3.1. Data ..............................................

................................................... ................................................... .......... H-9
2.3.2. Results ...........................................

................................................... ................................................... ........ H-9
2.3.3. Detecting Changes in Management Regimes in Detrital Pools through FIADB Sampling

Designs ...........................................

................................................... ................................................... ... H-10
2.3.4. Other Detrital Carbon Pools than Woody Debris in FI A ................................................. ..... H-10
2.3.5. Uncertainties and Areas of Future Research ........

................................................... ................. H-11
3. Illustrative Method for Developing Regional Default Values for Biogenic Attributes: Pacific Northwest Logging Residues ........................

................................................... .................................................. H-11
3.1. GROW

.................................................. ................................................... ................................................... .......... H-12
3.2. AVOIDEMIT

.................................................. ................................................... ................................................. H -12
3.2.1. AVOIDEMIT

for Logging Residues in Non-fire-prone Regions ... ....................................... H-12
3.2.2. AVOIDEMIT

for Logging Residues in Fire-prone Regions ....... ............................................ H-13
3.3. SITETNC

.................................................. ................................................... ................................................... ..... H-14
4. Illustrative Method for Developing Regional Default Values for Biogenic Attributes: Corn Belt Corn Stover .......................................

................................................... ................................................... ................... H-14
4.1. GROW

.................................................. ................................................... ................................................... .......... H-15
4.2. AVOIDEMIT

.................................................. ................................................... ................................................. H -15
4.3. SITETNC

.................................................. ................................................... ................................................... ..... H-16
4.3.1. Data and Methods ..................................

................................................... ........................................... H-16November 2014 H-2

4.3.2. Results ...........................................

................................................... ................................................... ..... H-18
5. Supplemental Information ..........................

................................................... ................................................... .. H-18
5.1. Details on the Meta-models Used to Derive Soil N 2 O and Soil Organic Carbon Stock Changes for SITETNC

.................................................. ................................................... .............................. H-18
5.1.1. DAYCENT Model......................................

................................................... .......................................... H-19
5.1.2. DAYCENT Simulations ...............................

................................................... ..................................... H-19
5.1.3. Meta-models .......................................

................................................... ................................................ H- 20
6. References ........................................

................................................... ................................................... .................... H-21


## List of Tables Table H-1. Proportions of Overall Forest Area and B iomass that Would Be Included as “Working Forest” for the Southeast and South Central RPA Reg ions .............................................. ................... H-6 Table H-2. GROW

Term for the Southeast Roundwood Example (2010 FIA Data, Based on Working Forests). .........................................

................................................... ................................................... .................... H-7 Table H-3. SITETNC

Term for the Southeast Roundwood Example (2010 FIA Data). ......................... H-10 Table H-4. Estimated AVOIDEMIT Values for Logging Residues.. .................... ............................................. H-14 Table H-5. Estimated SITETNC Values for Logging Residues. .....................

................................................... H-14 Table H-6. SITETNC

per Ton of Feedstock for Corn Stover in the Corn B elt. ........................................... H- 18 

## 1. Introduction
This appendix describes potential methods for calcu lating regional default values for the landscape biogenic attributes ( GROW , AVOIDEMIT

, and SITETNC

) associated with sample feedstocks and regions using a retrospective reference point basel ine. The calculations presented in this appendix are meant for illustration only, as proof-of-concep t exercises, to show how these values might be calculated. They are not meant as final values for use in a particular stationary source program for any particular feedstock.
This appendix uses the retrospective reference poin t baseline approach to produce illustrative equation term values for three of the landscape bio genic attribute terms ( GROW , AVOIDEMIT

, and SITETNC ) from the biogenic assessment factor equation in t he main report (Part 2):
 = ()(	
 + 

 +  + 
) (  ) () (EQ. H.1) Although leakage (represented by the LEAK term) is also a landscape biogenic attribute, this term is not calculated in the proof-of-concept term calcula tions presented here. Appendix E provides further discussion on leakage.
The framework defines the biogenic landscape attrib ute equation terms addressed in this appendix

as:

• GROW

: GROW represents the ratio of net feedstock growth on th e biogenic feedstock production landscape relative to landscape biogenic carbon removals. This term only includes biogenic carbon within the feedstock carbo n pool.November 2014 H-3

• AVOIDEMIT : AVOIDEMIT represents the ratio of avoided biogenic emissions that would have occurred on the feedstock production landscape with out biogenic feedstock removal (such as removal of corn stover and logging residues) to landscape biogenic carbon removals.

• SITETNC : SITETNC

represents the ratio of the estimated total net ch ange in non-feedstock carbon pools on the feedstock production site due t o land use management or land use management changes associated with feedstock produc tion to landscape biogenic carbon removals.
Illustrative equation term values are generated usi ng the retrospective reference point baseline approach in the context of three specific feedstock /region combinations:

• Roundwood in the Southeast (SE);

• Logging Residues in the Pacific Northwest (PNW); an d

• Corn Stover in the Corn Belt (CB).


## 2. Illustrative Method for Developing Regional Default Values for Biogenic Attributes: Southeast Roundwood
This section explains the method by which regional default values for GROW , AVOIDEMIT

, and SITETNC were developed for the illustrative application to Southeast Roundwood. In this context, roundwood is defined as the portion of tree biomass that would be defined as “merchantable” according to existing forest inventory definitions. This includes trees of commercial species, with good form (e.g., not hollow or “cull”), large enoug h to be harvested, and includes the main bole or stem but not branches or tops.



### 2.1. GROW
In general, the GROW

term represents net feedstock growth on the biogen ic feedstock production landscape. Estimating a value for GROW at a regional level using the retrospective refere nce point baseline could use an assessment of recent forest g rowth and harvest in the feedstock’s source region. Therefore, in this specific baseline contex t, GROW can be represented as the ratio of removals less growth over removals of roundwood in the source region over the most recent forest inventory cycle:

 Roundwood = 

			
 

 (EQ. H.2)
For this illustrative retrospective reference point baseline approach application, computation of the GROW term (( R

− G )/ R ) is based on forest inventory data collected by US DA Forest Service in the Forest Inventory and Analysis (FIA) program. The FI A data representing the most recently completed inventory for a region are used. The FIA program defines several types of growth and removals values. Growth is measured in FIA by compa ring tree measurements from specific plots in the current inventory cycle with measurements from previous cycles, enabling a tree-by-tree estimate within remeasured plots. In addition, as p lots that were previously not forested become forested, any tree volume on these newly forested p lots represents growth, or additions to the forest stocks. FIA defines “gross growth” as the su m of growth across all trees on all plots. FIANovember 2014 H-4

defines “net growth” as the gross growth minus the volume of trees that die during a remeasurement period, termed mortality:
Net Growth = Gross Growth – Mortality (EQ. H.3) Standard FIA reports include net growth and mortali ty, but not gross growth. As this framework application is meant to assess the feedstock carbon (C) stock on the landscape in the GROW term, and thus includes standing dead trees in the estima te of GROW . Thus the mortality is added back to the reported net growth to obtain an estimate of gr oss growth for use in the calculation of GROW

. Removals are estimated by FIA when remeasured plots reveal the absence of trees that were measured in a prior inventory cycle. Removals may i nclude wood biomass removed from forests during normal harvest cycles and during conversion of forest to some other land use. Both components are important in evaluating the balance of growth and removals in a region to assess the drivers of biomass removal and fluxes between t errestrial and atmospheric carbon pools.

2.1.1. Data The FIA program data is utilized as it represents f orest inventory data across all ownerships and regions across most of the United States. The FIA p rogram measures forest plots in different states annually, such that a portion of field plots (terme d a “panel”) are measured in each state each year. For example, in the east (easternmost five regions) , there are five panels so that it takes 5 years fo r all plots in a state to be remeasured. The length o f time it takes to remeasure all panels in a state is called the inventory cycle. Inventory cycles in the west are longer than in the east: up to 10 or 15 years may be required to measure all panels in a we stern state’s inventory. 1 All FIA data (except precise locations of field plots and specifics abou t ownership of each plot) are available to the public via the FIA web site: http://fia.fs.fed.us. For this example, data were downloaded in Access database format for each state in the Southeast. Th is analysis used data from the most recently completed inventory for each state in the region.

2.1.2. Forested Land Designation Used In some policy-specific framework applications, it may be appropriate to use a specific forested land designation, such as all forestland, all timbe rland, or working forest. In a situation where a “working forest” designation is appropriate, FIA da ta can be screened to develop estimates only for the forest resource in each region that is defined as the “working forest.” The approach and definitions of working forest used in this applicat ion, as well the implications of choosing alternate proof-of-concept, land-based designations—e.g., all forest lands, all timberlands, private forest

1 FIA data are now collected in a nationally consist ent manner, although the program is still in transi tion to this format and many western states do not yet have suff icient data collected under this program to enable computation of growth and removals. For example, remeasured dat a are currently not available for many states in th e west (the Intermountain, Pacific Northwest, and Pacific South west regions). Specifically, California, Oregon, an d Washington started collecting the nationally consistent data l ater than other states, and USDA Forest Service wil l only release data once a sufficient number of panels have been m easured to allow for reliable estimates.November 2014 H-5

lands, public forest lands, working timberlands, pr ivate working timberlands—are described below.

2.1.2.1. Defining the Working Forest The concept of a working forest recognizes that por tions of the forest resource within a region are unlikely to be used for feedstock production (Buchh olz et al., 2011). Examples of such forest resource areas include protected forest areas, area s not conducive to harvest due to physical conditions (e.g., inoperable soils or steep slopes) , areas subject to regulatory restrictions on harve st (e.g., elevation limits in the Northeast), and area s where harvest is not economically feasible (e.g., large distance to transportation networks).
Because harvest of forest resources for biogenic fe edstocks is unlikely for certain forest conditions, those areas are excluded from consideration in the GROW term in this specific illustrative application. If the growth/removals balance used to calculate values for the GROW term were applied to the entire forest landscape within a reg ion, and not to the working forest only, then growth (and related CO 2 capture) in areas protected from harvest or not vi able for harvest would be included along with removals (and related CO 2 emissions) from the working forest. This could mask the actual growth/removals dynamics related to the use of forest biogenic feedstocks, and the related carbon cycle effects on the landscape of su ch use. For application of this framework at the regional scale, the goal is to identify the biogeni c carbon cycle impacts related to stationary source s using biogenic feedstocks. Clearly defining the wor king forest, i.e., that portion of the landscape from which biogenic feedstocks are most likely prod uced, is an important first step.
There is an active debate about exactly what consti tutes the working forest land base (i.e., Alig et a l., 2002). Some fraction of the land base is “reserved” by legal limits on logging, and there is clearly a significant fraction of the remaining forest land t hat is not available for harvest because of a wide range of biological, physical, legal, economic, and social concerns (Buchholz et al., 2010; Butler, 2008). These limits on the availability of working forest land are difficult to quantify and may vary over time. For example, the increasing “parcelizati on” of forest land (i.e., subdivision into smaller ownerships) is generally assumed to reduce the land available for harvest because harvest operations are impractical on very small landholdin gs. To stratify by working forest for this analysis, th e first step is the selection of criteria (paramete rs) that can be used to define the working and non-work ing forest, and to set thresholds for those criteria. For example, if it is unlikely that harve st occurs on slopes exceeding 50%, then a slope threshold of less than 50% can be applied to a “slo pe” parameter to form a part of the working forest definition.

For the illustrative calculations for the GROW term below, working forest was defined using a set of physiographic, location, and other factors determin ed through the plot characteristics in the FIA dataset from the USDA Forest Service. For proof-of- concept, these national criteria nationwide areNovember 2014 H-6

applied, as working forest can be defined using cri teria that may differ by state and/or region, 2 using limits set by local expertise, or using analy sis of FIA plot characteristics that have had harvests recorded.

To obtain estimates of forest biomass in the workin g forest, the first step is to “screen” or filter F IA plots to determine which plots fall within definiti ons of the working forest. Then, FIA data are summarized for the screened subset of all forest pl ots. In this example, working forest is defined by five criteria, following Butler et al. (2010):
(1) Access: Areas greater than a mile from improved roa ds are considered too costly to harvest; (2) Physiographic condition: Hydric soils are indicativ e of wetland conditions and are often not suitable for operation of harvesting equipment, so sites classified as hydric physiographic conditions are excluded from the working forest; (3) Productivity: Sites with very low forest productivi ty are usually not suitable for biomass production and are thus not typically used for feed stock production; these are excluded from the working forest; (4) Harvest restrictions: Sites where timber harvest is legally restricted (for example, some national parks and wildlife refuges) are excluded f rom the working forest; and (5) Steep slopes: Timber harvest is typically constrain ed on steep slopes. Thus, sites with slope greater than 50% are excluded from the working fore st. The working forest was defined here by screening ou t FIA conditions (portions of plots) not meeting these specified criteria for working forest. Plots that do not match the definition of working forest were removed from the analysis dataset for each yea r. The remainder of the plots represents the working forest, and these were used to develop the carbon stock estimates for calculating values for the GROW

term).
Table H-1 shows the proportions of overall forest a rea and aboveground biomass that would be included as working forest for each region, based o n the five screening criteria described above.
Table H-1. Proportions of Overall Forest Area and Bi omass that Would Be Included as “Working Forest” for the Southeast and South Central RPA Regi ons (2010 FIA Data Using FIA Database (FIADB) (version 5; Woudenberg et al., 2010) Query Tools to Screen for Working Forest).

Region Percent of Forest Area Percent of Biomass Southeast 82.1% 80.4% South Central 62.4% 77.7%

FIA reports focus on specific variables with import ant meanings. First, they report on “forestland” and “timberland.” Timberland is a subset of forestl and that is not specifically reserved from timber production and meets a minimum productivity thresho ld. For maximum flexibility in application, the analysis herein is based on reports using “fore stland.” Second, volumes reported may be for “all

2 For example, some states have harvesting restricti ons that apply to certain elevations, slopes, or pr oximity to water that may not apply in other states.November 2014 H-7

live” trees or “growing stock” trees. Growing stock trees are limited to commercial species meeting specified standards of size, quality, and vigor. Be cause these default criteria are oriented towards traditional forest products and not biomass harvest , the analysis herein expanded the analysis to use “all live” tree reports. To include standing de ad trees in the estimates, mortality is added to ne t growth to obtain gross growth; if standard net grow th estimates had been used, standing dead trees would have been excluded from the analysis.
Standard FIA reports for growth, mortality, and rem ovals use units of cubic feet. Conversion to metric tons of CO 2 e involves multiplying by a constant conversion fac tor if metric tons CO 2 e are the units being used. However, if landscape attribute t erms are presented as unitless ratios in a specific application of the framework, it is not necessary t o convert to metric tons CO 2 e (and results can be left in the original units of cubic feet).

2.1.3. Results The methodology used for the analyses here began wi th identification of the time period for analysis, which was the inventory cycle ending in 2 010. Next, the extent of the working forest was identified. Then, using the FIA Database (FIADB) (v ersion 5; Woudenberg et al., 2010) query tools, the following queries were run for working forests in each state for each year:

• “Net growth of all live on forestland: cu. ft/year; ”

• “Mortality of all live on forestland: cu. ft/year;” and

• “Removals of all live on forestland: cu. ft/year.”
The annual net growth and mortality estimates were added to compute gross growth. 3 Estimates for each state in a region are summed. Then, GROW is computed as the ratio of removals less growth over removals.

Table H-2. GROW

Term for the Southeast Roundwood Example (2010 FIA Data, Based on Working Forests).

Region Gross Growth (million cu. ft/yr) Removals (million cu. ft/yr) GROW

Southeast 7,603.5 4,379.7 −0.74

3 Note that removals from plots that were converted from working forest at their previous measurement t o non-forest as of the most recent measurement were not included in this analysis. This occurred because the screen ing for working forest is based on the area of the working forest at the most recent remeasurement. For exampl e, consider a plot that is part of the working forest at the prio r inventory measurement. The plot is then harvested as part of a conversion to a non-forest type (e.g., development) . Because the plot is not part of the working fores t at the most recent remeasurement, these removals are not includ ed in the standard reports that are based on workin g forest plots. Thus, a more complex approach involving selection o f plots that have been converted from working fores t to something else, and then removals estimation for th ese plots, is needed. The removals from these conve rted plots should be added to the removals in the denominator of the GROW term. Because the number of plots in this category is so small, and the value of G/R is so comparative ly large, this omission is unlikely to change the GROW term substantially. It is possible that in cases where g ross growth barely exceeds removals, the inclusion of additional removals from land clearing could tip the balance s uch that removals exceed growth. However, at presen t, GROW in the sample region shows that current growth (includ ing accumulation of biomass in standing dead trees) is substantially higher than removals.November 2014 H-8 Region Gross Growth

(million cu. ft/yr) Removals (million cu. ft/yr) GROW

South Central 9,557.1 5,379.2 −0.78



### 2.2. AVOIDEMIT

AVOIDEMIT

is not applicable in this case study as there are no avoided emissions from removal of this roundwood feedstock, or emissions would have h appened regardless of the forest roundwood harvest that occurred (i.e., the trees would have b een kept in place); thus, AVOIDEMIT drops out of the equation for roundwood.



### 2.3. SITETNC
This section develops an illustrative regional appr oach to estimating default values for SITETNC for roundwood feedstock using a retrospective reference point approach. If changes in feedstock demand did not induce land use changes shifting non -forested lands into forests or vice versa, and if non-feedstock C pools were constant at the site of feedstock production during two consecutive measurements, then SITETNC would be 0. However, if emissions (or increased se questration) would occur from the non-feedstock carbon pools at the feedstock production site due to feedstock production and/or removal, SITETNC will be positive (or negative). In situations of r oundwood removal for wood products or combustion at a statio nary source, a certain percentage of logging residue is normally left on site. Thus the onset of increased roundwood removals may alter production site emissions because the corresponding increased input of those residues also occurs, causing higher C stock levels in the detrital pools such as the forest floor. Quantifying these change s is difficult, however, because site-level variabili ty is substantial and because any changes are typically small in comparison to the large C pools involved. Letting the variable “DETRITAL” represent the change in detrital pools, where a neg ative value represents sequestration (i.e., increases to the detrital pool) and a positive valu e represents emissions (i.e., decreases in the detrital pool), the calculation of SITETNC is given by Equation H.4:

 = 	
 

 (EQ. H.4)
In order to estimate the change in detrital and soi l pools on a regional basis associated with increased roundwood removals, this calculation uses empirical measurements to compare C stocks in these non-feedstock C pools at two points in tim e. While this baseline approach does allow for the detection of change in these pools, it cannot a ttribute the specific drivers of change. Therefore, where change in these pools occurred, the calculati on used data on estimated roundwood harvest to attribute the measured change to production site detrital pools. This approach, in which trends in C pools are monitored over a predetermined spati al scale, follows the logic of the GROW term under a reference point baseline.
When roundwood is harvested, there are changes to C stocks at the site of harvest in detrital forest C pools including coarse woody debris, forest floor C, and mineral soil C. While the forest floor C pool and the relationships between forest harvest p ractices and soil C responses are increasingly well understood (e.g., Lal, 2005), the impact of ha rvesting and utilization on mineral soil C is unknown (Buchholz et al., 2013).November 2014 H-9

Thus, while it is possible that increased roundwood removal and related detrital pool changes may affect the deep mineral soil C pool, quantification of these effects as they relate to roundwood removal are not be discussed here due to a lack of deep soil C data as well as scientific consensus on the issue. Forest floor and coarse woody debris C p ools are better understood, and data are available through the USDA Forest Service’s FIA pro gram (discussed further in the logging residues case study below).

2.3.1. Data Removal of roundwood from a forest site has the pot ential to affect all detrital carbon pools covered in the FIADB, namely above and below ground carbon in seedlings, shrubs and bushes, as well as carbon in stumps, coarse woody debris and litter. A s the standing dead C pool is already covered in the GROW

term, it is not further considered for SITETNC to avoid double counting. A major concern with the detrital C pools is that in the FIA datase t, all of them are modeled and based on other measurements and are therefore associated with unce rtainties (see Section 2.3.5).

2.3.2. Results As examined in this case study, the above and below -ground carbon in seedlings, shrubs and bushes, as well as carbon in stumps, coarse woody d ebris and litter are based on plot-level C models (Smith and Heath, 2008) in all of these esti mates. While model outputs for coarse woody debris are close to field-based estimates (Domke et al., 2013), these models were developed for national greenhouse gas inventories and not designe d to detect changes on a plot level itself based on a shift in management regimes. Moreover, uncerta inties accrue as some detrital C pool estimates rely on model outputs for other detrital pools. For instance, C in stumps and dead roots is currently calculated based on a ratio of down dead to live bi omass. If down dead biomass is removed (e.g., for bioenergy applications), this method would underrep resent C in stumps and roots (EPA, 2011). 4 To avoid attributing emissions or sequestration to the se C pools as land moves into and out of forest and timberland we base our estimate of change on th e change in per acre values of SITETNC between FIA inventories rather than on the aggregat e amounts. Results for the Southern regions are given in Table H-3. In the Southeast, 77.9 million tons of roundwood were removed annually and C stocks in det rital pools rose 1.9 million tons per year. These values leads to a SITETNC value of

− 0.024 tons of detrital carbon per ton of roundwood removals. Note that these calculations assume that a contribution to the atmospheric CO 2 stock would be a “positive” emissions flux, while an incr ease in terrestrial carbon uptake would have a negative sign. Thus, SITETNC impacts are negative as sequestration in Southeast ern detrital pools increases between the historical reference points u tilized.

4 Dead organic matter in FIA is initially calculated as three separate pools : (1) standing dead trees; (2) down dead wood; and (3) fore st floor carbon. Down dead wood includes stumps and roots of harvested trees. Ratios of down dead wood to live tree are used to estimate this qu antity.November 2014 H-10

Table H-3. SITETNC

Term for the Southeast Roundwood Example (2010 FIA Data).

Region Detrital Pool Change

(million tons/yr) Removals (million tons/yr) SITETNC

Southeast 1.9 77.9 −0.024 South Central 2.0 99.1 −0.020

2.3.3. Detecting Changes in Management Regimes in Detrital Pools through FIADB Sampling Designs
As described above, it is difficult to attribute ch ange in non-feedstock measured C pools directly to changes in management regime such as increased roun dwood production and resulting increased residue contributions to the detrital pool and impa cts of residue removal (the latter is discussed in the next section on logging residues). For example, a subset of the FIA plots (e.g., 1/6 th to 1/16 th ) are monitored for these residue pools across a meas urement cycle, and only a fraction of these plots might be affected by harvest activities, let alone residue removal. As such, the signal from changed forest management might be lost in the overall nois e (meaning that the change in residue C pool stats from the subsample of FIA plots may not be si gnificantly different from 0), particularly if large-scale stochastic disruptions occur such as fi res or insect outbreaks. While the latter issue is not of concern for the GROW term as long as the regions are held large enough, such stochastic events may affect the larger noise to signal ratio associated with a smaller plot size and smaller relative changes in the affected non-feedstock (det rital) C pools compared with the live biomass pool (e.g., Westfall et al., 2013). For instance, i f a region exhibits a 50-year harvest reentry inter val for harvest activities and FIA measurements occur i n 5-year cycles, around 10% of the C influx to the detrital pools would be removed if: (1) all har vest sites experience residue removal; and (2) all of the residues would be removed. As the reentry in terval is 50 years, this signal would occur only in 2% of the measured plots.
In addition to uncertainties derived from imprecisi on in residue C pool measurements or sampling sizes, another source of uncertainty lies in the co nversion factors applied to produce C pool estimates from direct measurements such as computin g the bulk density of rotting material (e.g., Russell et al., 2013).
Despite these uncertainties, the measurements of de trital pools developed as part of the SITETNC term do provide an estimated baseline value that ca n be used to monitor the extent to which these pools are changing due to increased roundwood produ ction, and thus the extent to which emissions are occurring from at the feedstock production site as a result of these changes.

2.3.4. Other Detrital Carbon Pools than Woody Debris in FI A
As described above, to allow for assessment of the impact of roundwood removal on all detrital forest C pools, it would be advisable to also inclu de the dynamics of the mineral soil pool. Other pools are expected to be covered by measurements in upcoming FIA P3 sampling designs, and pool dynamics are reasonably well understood with the ex ception of the mineral soil C pool (Harrison et al., 2010).November 2014 H-11

2.3.5. Uncertainties and Areas of Future Research There are several issues when monitoring detrital f orest in FIA’s assessments, which will require additional efforts to make the approach outlined ab ove a better representation of SITETNC for roundwood. First, merchantability of the roundwood resource is determined in large part by market conditions. In strong forest products market s pulp prices may be lower due to high mill residue availability, and more roundwood material i s left in the forest. Likewise, in weak forest products markets pulp prices might rise due to a co nstrained mill residue supply leading to greater recovery from forest harvesting operations. Thus, r oundwood utilization is a key component of quantifying the carbon impact of feedstock removal on detrital carbon pools. Second, the uncertainty derived from converting detrital field measurements (e.g., coarse woody debris diameter) to C pool estimates (e.g., coarse woody d ebris C) adds uncertainty to C pool estimates (Russell et al., 2013). Third, change in management regimes, i.e., the level of roundwood utilization and extraction, is difficult to detect using curren t FIA sampling designs (Westfall et al., 2013). Fourth, the specific impacts of forest management o n the large deep mineral soil C pool are not well understood, a fact which is unlikely to change anyt ime soon (Buchholz et al., 2013) adding additional uncertainty to any soil C estimates.
In summary, uncertainties in determining roundwood harvest impacts on detrital C pools are associated with the data for all detrital C pools c onsidered here, based in large part on conversion factors and extrapolation methods. Whether monitori ng detrital resources strictly through modelbased or field-based approaches, there is cumulativ e error (i.e., sampling error, measurement error, and model error) that should be acknowledged and in corporated into assessments.


## 3. Illustrative Method for Developing Regional Default Values for Biogenic Attributes: Pacific Northwest Logging Residues
This section develops illustrative regional default values for the use of logging residues, i.e., material that would otherwise have been left on the forest floor as harvest residue, as a feedstock for a stationary source that emits biogenic CO 2 , in the Pacific Northwest. It is important to note that this analysis includes data and results for avoided emissions from logging residue feedstock use that is not retrospective in nature: it is based on a literature-based alternative fate counterfactual assessment value which includes decay rates into th e future. In an application of the framework that necessitated only retrospective analysis, incl usion of decay rates and other future alternative fate counterfactual assessments could not be includ ed. This alternative fate analysis is included as an alternative method to the modeled detrital pool analysis presented in the Southeast Roundwood section above.

The discussion in this section is based on the assu mption that the only action involved is the extraction and utilization of logging residues from already occurring forest harvesting operations 5 ,

5 Forest products, in general under current market c onditions, are characterized by a joint production function, as many products/materials can be produced from the ha rvest of a single tree. Firms strive to optimize pr oduction to maximize the amount of high-value products (e.g., s aw lumber, paper) and minimize the amounts of lower valueNovember 2014 H-12

and thus the GROW

term is set to 0. Furthermore, this case study is not completely retrospective as it assumes some additional amount of logging residu e harvest that would not have occurred under business-as-usual conditions. In the previous round wood example, logging residues would ultimately contribute to the detrital carbon pools in SITETNC . As such, the methodology employed for logging residue use in this case study assumes that the biogenic feedstock is extracted from SITETNC pools, resulting in a positive emission that refle cts reduced sequestration in these detrital pools. This is a valid assumption as residues from logging operations would contribute to these pools in the absence of collection and utilization. Since logging residues are assumed to be additional, keeping this emissions impact of biomas s removal within the SITETNC pool (rather than GROW ) is justified. Finally, the resulting reduction in woody debris decay emissions in the forest is credited a negative AVOIDEMIT value.



### 3.1. GROW
For logging residues in this illustrative applicati on of the reference point baseline, the GROW term does not apply because the logging residue feedstoc k is assumed to taken place due to already occurring harvesting operations therefore not impac ting the GROW term’s growth or removals.



### 3.2. AVOIDEMIT

The AVOIDEMIT

term does apply for this feedstock because AVOIDEMIT represents the emissions that would have occurred at the field site had the feedstock (i.e., logging residues) not been removed for bioenergy. The carbon stocks considered in the context of AVOIDEMIT can be termed “detrital” stocks to denote that they are dead and “non-growing.” In forests, these detrital feedstocks could include tree tops, branches, and s tumps left after a roundwood harvest.
Deciding on an appropriate value of AVOIDEMIT requires an assessment of the ratio between the amount of C that is stored long-term on site via le aching into the soil C pool, and the amount of C that would have been emitted to the atmosphere via feedstock decay if the residue were left onsite or emitted to the atmosphere from open-burning onsi te (Miner et al., 2014). As some of the C in the residue feedstock would have leached into the fores t floor if the residue were not removed, the value of AVOIDEMIT

should include at least some level of long-term C storage in most cases (discussed below).

3.2.1. AVOIDEMIT

for Logging Residues in Non-fire-prone Regions

The literature was reviewed in order to assess the degree of long-term C storage on site associated with leaching of C into the soil C pool, in order t o estimate appropriate regional values for AVOIDEMIT

for logging residues. While there is a large scien tific literature on wood decomposition rates in U.S. forests (Jandl et al., 2007; Johnson and Curtis, 2001; Jones et al., 2011; Laiho et al.,

products (e.g., mill or logging residues). While th ere is some responsiveness to relative price moveme nts (e.g., higher demand and prices for wood pellets may lead to an increased proportion of mill or logging resid ues going to this use and a decreased proportion going to partic leboard or other uses), the elasticity of transform ation between outputs may be very inelastic, and even with a nega tive price some low-value products would still nece ssarily be produced as a byproduct of the production of higher value products (e.g., sawdust, black liquor).November 2014 H-13

2003; Smith and Heath, 2002), the fraction of detri tal C that will be mineralized and stored longterm in the forest soil remains largely unknown (Bu chholz et al., 2013; Nave et al., 2010), and is probably highly site- and management-specific. Deep mineral soil C measurements are not available for FIA plots (Harrison et al., 2010) and measuring deep soil C is difficult (e.g., Johnston et al., 2004): thus, an empirical dataset applicable for sm aller spatial scales that can link forest management activities with changes in deep soil C i s not currently available (Smith et al., 2012).

The values for AVOIDEMIT presented here utilize a non-combustion (not open burn) related AVOIDEMIT

term of −0.98, which means that 2% of the logging residue would not have been released into the atmosphere from on-site residue d ecomposition and would instead have entered long-term sequestration in deep soil pools (see Tab le H-4) (Zanchi et al., 2012). Soil type, microbiological activities, solar radiation reachin g the forest floor, land use, or climate can influe nce the rate of long-term sequestration in soils. While a value of -0.98 is used here, note that changes i n mineralization rates and long-term storage have als o been observed, but as of yet the drivers of these changes are not understood (e.g., Nave et al. , 2010; Pregitzer and Euskirchen, 2004; Zummo and Friedland, 2011).

3.2.2. AVOIDEMIT

for Logging Residues in Fire-prone Regions With the exception of the Northeast and North Centr al regions, all other regions in the United States practice slash burning to varying extents. 6 If current practice entails the burning of all log ging residues onsite in slash piles, such that combustio n of slash is the baseline, it can be argued that t he fraction of carbon remaining from the logging resid ues following the burning equals 0.06–0.08 for the affected regions. Finkral et al. (2012) found t hat “on average, burning released between 92% and 94% of the carbon in each slash pile to the atm osphere,” while DeLuca and Aplet (2008) discuss the longevity of charcoal C pools under typ ical U.S. forest floor conditions. Additional research will be required to: (1) produce evidence- based and proven numbers on the long-term fraction of carbon remaining from the logging resid ues for both combustion and non-combustion related soil C dynamics; (2) identify current slash handling practices across all regions to establish a defensible fraction of carbon remaining from the lo gging residues including ‘slash burning baselines;’ and (3) categorize intra-regional ecosy stem variations with potentially diverging logging residue baselines such as in the Pacific Northwest that combines forest types with both very high and very low fire frequencies.
Burning of logging residues is the common current p ractice, and assuming near-complete combustion, the content of C in wood ash is diminut ive (e.g., Demeyer et al., 2001) and AVOIDEMIT approaches a value of −1 (i.e., −0.98 as indicated in column one of Table H-4). In contrast, less complete combustion of residues leaves more ash beh ind, thereby changing the balance between emissions to the atmosphere and C stored long-term via leaching (i.e., the 7% difference from a value of −1 (i.e., −0.93) indicated in column two o f Table H-4).

6 Slash burning is the deliberate and controlled inc ineration of logging residues onsite to reduce the risk of uncontrolled ignition (Smith et al., 1997).November 2014 H-14

Table H-4. Estimated AVOIDEMIT Values for Logging Residues. Values Were Estimated for Two Management Practices: Without and With Onsite Combu stion of Logging Residues. Numbers are uncertain at this point and are presented as placeh olders until further research confirms long-term storage of combustion and non-combustion related so il C.
Management Practice AVOIDEMIT Current management does not combust logging residue on site −0.98 Current management combusts all logging residue on site −0.93


### 3.3. SITETNC
In logging residue collection systems, periodic ent ries to remove harvesting residuals alongside roundwood harvest operations leave the rest of the standing live C pool largely intact in comparison to a roundwood-only harvest. As long as the roundwood harvest is part of the normal operation for the working forests in the source reg ion (and does not represent a new management practice or a change in management intensity), then other impacts on SITETNC can be expected to be 0 (Masek et al., 2011). In traditional harvests tree boles are removed while tops and limbs are left on site (Stenzel et al., 1985). The addition o f these tops and limbs to the SITETNC C pool would constitute sequestration to the pool (negative emis sions flux to the atmosphere). However, with these tops and limbs being collected and utilized a s a biogenic feedstock that negative addition to SITETNC does not occur and so when compared to traditional practices declines in litter, dead wood, and soil C stocks will result. In this case, where a transition from a traditional practice of leaving tops and limbs on site to a logging residue collection system, the SITETNC impact is equal to 100% of the residue portion of the harvest removed. When SITETNC is considered together with AVOIDEMIT

, the combined assessment factor in the case where the logging residues would not have been burned is 0.02 (

− 0.98+1.00) representing the 2% of logging residue c arbon that would have remained long term in the soil.

Table H-5. Estimated SITETNC Values for Logging Residues.
Management Practice SITETNC Proportion of logging residues removed from the for est +1.00



## 4. Illustrative Method for Developing Regional Default Values for Biogenic Attributes: Corn Belt Corn Stover
Developing regional landscape attribute values for many non-traditional agriculture-derived biogenic feedstocks including corn stover as well a s switchgrass, hybrid poplar, and other dedicated energy crops using a retrospective reference point baseline is currently challenging. The main reason for this is that these feedstocks (unlike tr aditional crops used for liquid biofuel production) have either not been used traditionally for large s cale stationary source energy production (e.g., stover) or they have not been commercially cultivat ed to the extent that there is observed historical data at a regional or national scale (e.g., dedicat ed energy crops). Without such datasets, it is difficult to estimate the alterations in management practices or in land usage and the associated biogenic C impact profiles when the feedstock is co llected or produced for energy. In the future, it may be possible to calculate more concise SITETNC values with a retrospective reference pointNovember 2014 H-15

baseline using historical data providing that there is widespread use of and data collection pertaining to the feedstock of interest.
The discussion in this section focuses on possible ways to estimate AVOIDEMIT and SITETNC

values for corn stover residues if needed before such data sets are available (the GROW term is not relevant to this feedstock category). The following section delves further into the challenges of producing similar estimates for other agricultural and dedica ted energy crops using this baseline approach.



### 4.1. GROW
For crop residues created from annual crops such as corn stover, the carbon sequestered by the annual growth of the feedstock is counterbalanced b y the carbon in the feedstock at harvest, hence GROW is set to 0.



### 4.2. AVOIDEMIT

The AVOIDEMIT

term reflects the avoided emissions, those that wo uld have occurred anyway from the field site without the removal of the biogenic feedstock for bioenergy use (i.e., decay). The fate of agricultural residues is different from forest r esidues for a number of reasons including differing decay rates, no commonly measured long-term detrita l carbon pools, and site-specific management. That said, there are similarities between agricultu ral and forest residues. For example, when agricultural residues are burned, an unburned fract ion remains. The unburned fraction of agricultural feedstocks varies by feedstock (e.g., see EPA, 1994, 2013). However, agricultural residue burning did not contribute to long-term car bon storage in a 31-year study by Rumpel (2008).
For agricultural crop residues such as corn stover in this illustrative calculation, if the residues h ad not been removed from the field, they would have de composed, with all the carbon in the residues oxidizing before the start of the next production y ear. For example, leaving crop residues in the field was found not to contribute significantly to soil carbon in a study by Gale and Cambardella (2000). Tillage practices and fertilizer applicatio n had a larger effect on soil carbon change when compared to residue removal in a 30-year study by R eicosky et al. (2002) and in a study by Clapp et al. (2000). Dick et al. (1998) also found that till age and rotation played a larger role in soil carbo n change than did residues. Residues can, however, co ntribute to soil organic matter, provide a physical buffer, improve the chemical, physical and biological properties of the soil, reduce raindrop impact and wind shear, reduce erosion, and increase yield (under certain conditions) (Andrews, 2006; FAO, 2004). The magnitude of these effects is highly variable at a national scale. Variables such as crop type, growing conditions, an d agricultural practices all affect the potential quantities of residue converted to soil carbon (And rews, 2006). USDA recommendations for appropriate residue removal for biofuel production suggest that removal rates be based on regional yield, climatic conditions, and cultural practices with no specific national rates provided (Andrews, 2006).
Under a truly retrospective baseline approach, AVOIDEMIT could be assigned a value of −1 (i.e., all emissions would have occurred anyway). However, giv en data limitations on historic corn stover removals, this case study evaluates the net landsca pe emissions effect of a management switchNovember 2014 H-16

from conventional corn production to corn productio n with residue harvesting using biophysical processing modeling coupled with relevant informati on on regional crop-mix and management data. The approach compares the soil carbon profile s of two different management regimes, and implicitly includes the portion of emissions from f eedstock decomposition for each scenario. Thus, AVOIDEMIT

is given a value of 0 for this hypothetical case s tudy and all landscape emissions impacts are included in the SITETNC term (discussed in detail below). This approach al so acknowledges the variability and inherent uncertain ty in the contribution of crop residues to soil carbon at the national scale. It is noted that spec ific residue removal rates, if such data is gathere d and compiled nationally, might influence the estima ted contribution of crop residues to soil carbon (and other factors) under certain conditions.



### 4.3. SITETNC
This section discusses the data needs for deriving net landscape emission estimates for corn stover used in stationary sources for energy and then pres ents the methods used in this appendix to develop illustrative values for use in the illustra tive case study presented in Appendix I.

4.3.1. Data and Methods
In an evaluation of corn stover, information on man agement activities, whether land use and/or management activities have changed, and the associa ted biogenic CO 2 flux profile would be needed. For example, one would need measures of what corn s tover yield was and whether there were alterations in management that caused changes in so il carbon following corn stover harvest. Such information could be based on data collected by loc alized or national farm surveys such as the USDA ERS ARMS survey (if available), state-level Ag ricultural Extension Service reports (if available), the use of estimates from models with s oil and GHG modeling capabilities, some mixture of agronomic experimental data and field measuremen ts, or a combination of these methods.
Management activities such as increased stover remo val and related soil carbon impacts are currently not captured in national datasets and can vary significantly by soil type, environmental condition, and management profile. Therefore for th is illustrative framework application using the retrospective reference point baseline to evaluate corn stover impacts in the Corn Belt region, the SITETNC equation term uses a proxy that was generated by u sing results from the DAYCENT model in conjunction with data from USDA on the prevalenc e of particular tillage types by region and fertilizer use by crop and region (USDA ERS, 2013). These merged datasets were then applied to statistical meta-models (akin to response surface r egressions) developed by Dr. Stephen Ogle of the Natural Resource and Ecology Laboratory to estimate soil carbon and nitrous oxide emission changes between different scenarios (with and witho ut corn stover removals). This SITETNC calculation method differs from other methods used for agriculture- and forestry-derived feedstocks for which there is historical data that can be used to assess changes between two reference points in the past. For illustrative purp oses this method is included, despite not being truly retrospective. The rest of this section brief ly describes how the proxy values are generated and a detailed description of how the meta-models w ere derived and applied is provided in an Addendum to this section.November 2014 H-17

The SITETNC

estimate reflects the change in carbon when corn s tover collection occurs on land assumed to have previously been used to grow corn w ithout the corn stover being removed. This SITETNC term calculation accounts for the difference in th e amount of sequestered carbon on a CO 2 - equivalent basis per ton feedstock between these tw o scenarios. Also included in the discussion below is calculation of related nitrous oxide (N 2 O) emissions for use as a sensitivity in the case study appendix.
The first step includes estimation of sequestration and emissions from the land under corn production in both the without and with corn stover removal scenarios. These calculations required development of estimates of initial corn stover yie lds (estimated quantity of residues produced per acre) and initial soil carbon profile and N 2 O emissions under corn production without stover removal. Then, the soil carbon and N 2 O emissions profile or corn production was updated assuming management changes associated with corn stover remo val. Appendix D of the Beach and McCarl (2010) RFS2 report describes calculation of the qua ntity of residues produced per acre by crop and provides citations for what was used for the percen t of total residues that could be sustainably removed (Graham et al., 2007; Perlack et al., 2005) . As a general rule, USDA National Resources Conservation Service (NRCS) recommends that about 3 0% residue cover is adequate to control soil erosion (Maung, 2007). Removable residue values use d in the Forest and Agriculture Sector Optimization Model with Greenhouse Gases (FASOM-GHG ) are calculated by adjusting the residue production per acre based on the harvestable percen tages provided in Graham et al. (2007) and Perlack et al. (2005) which consider the effects of erosion and runoff. This approach uses a maximum percentage removal of residues, 7 which vary by crop and tillage.
The corn stover yield and related DAYCENT emissions estimates were developed in conjunction with regional dryland and irrigated crop budgets in the FASOM-GHG model. These budgets were developed over the years in FASOM-GHG based on farm budget data from regional extension services and publicly available USDA ERS datasets. In turn those data were disaggregated so that they contained different tillage and fertilization levels using runs from the DAYCENT model and budget data on costs from USDA NRCS. The budget dat a were updated to 2010 in terms of yields and nitrogen utilization based on USDA ERS ARMS dat a and USDA annual agricultural statistics at the state level.
With this information the two scenarios are develop ed:

• Corn without Stover Harvested ( CropEmissioncrop r,corn ). Emissions and sequestration estimates in the without stover removal scenario in cludes the carbon that resides in the soil per acre when stover is left on site. The land use associated with stover removal is assumed to not change. This calculation involved usage of D AYCENT results on carbon sequestration rates and N 2 O emissions in metric tons CO 2 equivalent per acre.

7 Many site specific factors associated with the sus tainable removal of residue (e.g., crop type, soil type, soil fertility, slope, and climate) affect which geographic regions are suitable for crop residue removal. Detailed mo deling of these factors was beyond the scope of this analysis

.November 2014 H-18

• Corn with Stover Harvested ( CropEmissioncrop r,stover ) . Consideration of emissions and sequestration estimates when stover is removed nece ssitated estimates of soil carbon stock changes and N 2 O emissions. The soil carbon sequestration and N 2 O estimates were derived from DAYCENT. The yield of harvestable corn stover was based on USDA NRCS estimates were deduced from the estimated amount of stover th at needed to remain on site to limit erosion. The change in N 2 O emissions also considered the need for additional nitrogen to replace the nutrients removed when the stover was r emoved.
The second step involves solving for the SITETNC estimate per ton of feedstock by subtracting the carbon sequestration and N 2 O emissions for the average acre with corn stover r emoved from the average acre without stover removed. With this calc ulation, a positive result indicates an increase in emissions or a decrease in sequestration. These terms and results are in terms of net emissions/sequestration per acre of feedstock grown in metric tons of CO 2 equivalent:
SITETNCperacre r,stover = CropEmissioncrop r,corn − CropEmissioncrop r,stover (EQ. H.5) The data were transformed to a per ton feedstock am ount by dividing by regional per acre yields of corn stover. These are in units metric tons of CO 2 equivalent emissions/sequestration per short ton (2000 lbs) feedstock. Note that this approach does not capture additional land use change or management emissions that are attributable to the i ncreased residue demand. That is, crop production area is held constant between the scenar ios, so no land use change or emissions associated with crop switching are captured.

4.3.2. Results The illustrative results presented in Table H-6 bel ow reflect the annual average SITETNC values with and without N 2 O emissions expressed in metric tons of CO 2 e per ton of feedstock for corn stover in the Corn Belt.

Table H-6. SITETNC

per Ton of Feedstock for Corn Stover in the Corn Bel t.

Feedstock Region SITE_TNC (carbon only) (metric tons CO 2 e per ton feedstock) SITE_TNC (carbon + nitrous oxide) (metric tons CO 2e per ton feedstock) Corn Stover Corn Belt +.0026 +0.0123



## 5. Supplemental Information
This section provides supplement information on the methods used to develop the illustrative SITETNC values for corn stover in the Corn Belt.



### 5.1. Details on the Meta-models Used to Derive Soil N 2 

### O and Soil Organic Carbon Stock Changes for SITETNC
Meta-models were developed to estimate soil organic carbon stock changes and soil nitrous oxide emissions associated with various management altern atives. The data were generated using theNovember 2014 H-19

DAYCENT ecosystem model, and were used to derive li near-mixed effect models that were incorporated into the FASOM-GHG model. This report provides information on how the meta-model were derived and applied.

5.1.1. DAYCENT Model The DAYCENT biogeochemical model is used to estimat e crop grain and straw yields (including corn stover), soil organic carbon stock changes, an d soil nitrous oxide emissions for different crops and management scenarios. DAYCENT (Del Grosso et al ., 2006; Parton et al., 1998) is a process based model of intermediate complexity, and simulat es the influence of management practices and other events, such as fire, grazing, cultivation, a nd fertilizer additions, on carbon and nutrient dynamics in plant-soil systems. The model requires several inputs, including soil texture; current and historical land use; and daily maximum/minimum temperature and precipitation data. Plant growth is a function of soil nutrient and water ava ilability, temperature, and plant specific parameters, such as maximum growth rate, minimum an d maximum biomass carbon to nutrient ratios, and above ground versus below ground carbon allocation. Soil organic carbon is represented as three pools that are kinetically-defined with sl ow, intermediate and long turnover times. Carbon is transferred from dead biomass into the soil orga nic carbon pools, and over time will decompose and return CO 2 to the atmosphere. Nitrogen gas emissions (nitrous oxide, nitrogen oxides, dinitrogen gas) from nitrification and denitrificat ion are controlled by soil mineral N levels (nitrat e and ammonium), water content, temperature, pH, plan t N demand, and labile carbon availability. Nitrate leaching losses are controlled by soil nitr ate availability, saturated hydraulic conductivity, and water inputs from rainfall, snowmelt, and irrig ation.
The ability of DAYCENT to simulate yields, soil org anic matter changes, nitrous oxide emissions, and nitrate leaching for conventional crops (e.g., corn, wheat, barley) has been validated by comparing model outputs with measurements from majo r commodity crops and grassland systems in North America (David et al., 2009; Del Grosso et al., 2005; Del Grosso et al., 2008). The model is also shown to simulate biomass yields reasonably we ll for switchgrass grown at different sites in Illinois (Davis et al., 2010) and the impact of nit rification inhibitors on nitrous oxide emissions (D el Grosso et al., 2008). DAYCENT has been applied for simulation of soil greenhouse gas fluxes at scales ranging from plots to regions and the globe (Del Grosso et al., 2010; Del Grosso et al., 2005). The model has been used since 2005 to estimate nitr ous oxide emissions from agricultural soils in the U.S. National Greenhouse Gas Inventory compiled by the EPA, and reported annually to the United Nations Framework Convention on Climate Chan ge (Del Grosso et al., 2010; Del Grosso et al., 2006; EPA, 2012).

5.1.2. DAYCENT Simulations DAYCENT is used to simulate cropping systems at the county scale across the contiguous United States. To compile model inputs, the centroid for t he largest cluster of cropland in each county is identified based on National Land-Cover Dataset (Ho mer et al., 2007). For the county-scale simulations, model inputs for daily weather are bas ed on the DAYMET dataset for the county centroid. DAYMET (Thornton and Running, 1999; Thorn ton et al., 1997) generates daily surface precipitation, temperature, and other meteorologica l data at 1 km 2 resolution using weather stationNovember 2014 H-20

observations and an elevation model. Soil propertie s were acquired from the dominant STATSGO (State Soil Geographic Database) (USDA NRCS, 1997) map unit at the centroid cropland in a county. Hydraulic properties are calculated from STATSGO su rface texture class and Saxton et al. (1986) hydraulic properties calculator.
Land management data for annual crops are compiled at the agricultural region level (McCarl et al., 1993). Most states correspond to one of the 63 regi ons, except a few states that were further divided into two or more regions. Data for average fertilization rates, timing of planting/harvest, and crop rotation schedules are obtained from vario us sources based on farmer surveys and fertilizer sales data (EPA, 2012).
Management alternatives included adoption of conser vation tillage practices; land use change to cropland management from grassland and forest land; reducing nitrogen fertilizer rates; applying organic fertilizers; varying timing of fertilizatio n events between fall and spring; and applying fertilizer with nitrification inhibitors. The simul ations are conducted by randomly combining the management options for these practices in a Monte C arlo analysis with 1000 simulation in each region. DAYCENT simulated direct N 2 O emissions, volatilization (nitrogen oxides, ammon ia) and nitrate leaching. Indirect soil nitrous emissions a re estimated based on converting 1% of volatilized nitrogen and 0.75% of leached/runoff nitrogen into N 2 O (IPCC, 2006).

5.1.3. Meta-models Meta-models for soil organic carbon stock changes, direct and indirect nitrous oxide emissions are derived for crops in each region based on the simul ation results. Meta-models are also derived for crop grain and straw yields. The meta-models are de veloped using a linear mixed-effect modeling approach (Pinheiro and Bates, 2000). The potential set of model predictors include nitrogen fertilization rates, timing of fertilizer applicati on, use of nitrification inhibitors, mean temperatu re, mean precipitation: potential evapotranspiration ra tio, soil texture, residue removal rate, tillage practice, and land use change. Only variables meeti ng an alpha level of 0.05 are included the model; additional variables is required to reduce the Akai ke Information Criteria by at least a value of 2 digits (Akaike, 1973; Burnham and Anderson, 2002). The variables included in each model vary by agricultural management region. The models are inco rporated directly into the FASOMGHG economic modeling framework.
Meta-models are developed to estimate the average c hange in soil organic carbon stocks over increments of five year time periods in the surface soil (20 cm). The resulting estimates are in metric tons C/ha, and can be annualized by dividing by 5. The annualized data can be converted into CO 2 equivalents using the conversion factor, 44/12. Fo llowing conversions, the value can be multiplied by the area of the crop or grass to obta in the total change in SOC stocks for the feedstock

.
N 2 O emissions directly emitted in the field are estim ated using a meta-model, and also N that is lost from a managed field through volatilization or leac hing/runoff, and later emitted as N 2 O in waterways or following atmospheric deposition in so ils. IPCC (2006) recommends that N leaching is not included in the estimate of indirect N 2 O emissions if annual precipitation minus potential evapotranspiration does not exceed field water hold ing capacity, with the possible exception ofNovember 2014 H-21

irrigated lands. The first step is to determine whi ch areas will have nitrate leaching according to th e IPCC guidelines. A logit model is developed to dete rmine if leaching will occur with irrigation in the regions that are too dry for leaching without irrig ation.

The meta-model results for the direct N 2 O models, nitrate leaching/runoff and volatilizatio n are in natural log transformed space and require a backtra nsform. Units are gNO 3 -N/m 2 /yr for the nitrate leaching/runoff, and gN-NH 3 +NO x /m 2 /yr for volatilization. To obtain the indirect N 2 O emissions, the leaching and volatilization estimates are multi plied by the indirect emission factors from IPCC, which are 0.0075 kgN 2 O-N/kgNO 3 -N/yr for nitrate leaching/runoff, and 0.010 kgN 2 O-N/kgNH 2 - N+NO x -N/yr for volatilization (IPCC, 2006).
The direct and indirect emission results are in kgN 2 O-N/m 2 /yr, and are converted into kgN 2 O/m 2 /yr using the conversion factor, 44/28. In turn, th e estimate can be converted into CO 2 equivalent using 310 or other alternative GWP conve rsion factors. Following conversions, the resulting value is multiplied by the area of crop o r grass to obtain the total direct N 2 O emissions on an annual basis in CO 2 equivalent units.


## 6. References
Akaike, H. 1973. Information theory and an extensio n of the maximum likelihood principle. Second International Symposium on Information Theory, Sept ember 2-8, 1971, Tsahkadsor, Armenian SSR, Hungary. Alig, R., J. Mills, and B. Butler. 2002. Private ti mberlands: Growing demands, shrinking land base. Journal of Forestry (100):32-35. Andrews, S. 2006. Crop Residue Removal for Biomass Energy Production: Effects on Soils and Recommendations . White Paper: U.S. Department of Agriculture, Natu ral Resource Conservation Service. Beach, R., and B. McCarl. 2010. U.S. Agricultural a nd Forestry Impacts of the Energy Independence and Security Act: FASOM Results and Model Descripti on. Research Triangle Park, NC: RTI International. Buchholz, T., C.D. Canham, and S.P. Hamburg. 2010. Forest Biomass and Bioenergy: Opportunities and Constraints in the Northeastern United States . Millbrook, NY: Cary Institute of Ecosystem Studie s. http://www.ecostudies.org/report_biomass_2011.pdf. Buchholz, T., C.D. Canham, and S.P. Hamburg. 2011. Forest Biomass and Bioenergy: Opportunities and Constraints in the Northeastern United States . Millbrook, NY: Cary Institute of Ecosystem Studie s. http://www.ecostudies.org/report_biomass_2011.pdf. Buchholz, T., A.J. Friedland, C.E. Horig, W.S. Keet on, et al. 2013. Mineral soil carbon fluxes in fore sts and implications for carbon balance assessments. Global Change Biology Bioenergy
, 6:305-311

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