United States

Department of

Agriculture

Forest Service

Northeastern

Research Station

Research Paper NE-722

James E. Smith

Linda S. Heath

A Model of Forest Floor Carbon Mass

for United States Forest Types
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The Authors

JAMES E. SMITH and LINDA S. HEATH are research plant physiologist and

research forester/project leader, respectively, with the Northeastern Research

Station at Durham, New Hampshire

Abstract

We summarize a large set of published values of forest floor mass and develop

large-scale estimates of carbon mass according to region and forest type.

Published values of forest floor carbon mass or information relevant to compiling

such summaries are scarce. We present a simulation model based on observations

obtained from literature surveys for use in the 2002 version of FORCARB, a carbon

budget model for U.S. forests. The forest floor is the distinct layer of dead and

decaying plant material that accumulates on the soil surface, which lies above the

mineral soil and includes small woody debris. Estimates of average forest floor

carbon mass per hectare of forest applied to a 1997 summary forest inventory, sum

to 4.5 Gt carbon stored in forests of the 48 contiguous United States.

Manuscript received for publication 22 April 2002
A Model of Forest Floor Carbon Mass

for United States Forest Types

James E. Smith and Linda S. Heath

Acknowledgments

We thank Coeli Hoover, Steve Prisley, Eric Vance, Ruth Yanai, and Sara Duke for helpful

comments on drafts of this manuscript. Eric Fiegenbaum provided the cover art. This work

was partly supported by the USDA Forest Service Northern Global Change Program.

Metric units and conversions

Mg = megagram, or metric tonne

= 1.102 U.S. ton, or 2,205 pounds

Mt = megatonne, 106

 tonne, or teragram

Gt = gigatonne, 109 tonne, or petagram

1 hectare = 2.471 acre
1

Introduction

The role of forests and land-use change in the global

carbon cycle interests scientists and policy makers because

of the contribution forests make to the large annual

carbon flux between terrestrial ecosystems and the

atmosphere (Tans and White 1998). Forests and forest

management projects have been a central component of

proposed actions to help mitigate greenhouse gas

emissions because forests can be an economical carbon

sink (Rotter and Danish 2000). Our objective is to

develop equations to estimate forest floor carbon for U.S.

forests based on common forest inventory variables. Little

comprehensive data exists for forest floor carbon pools of

the U.S.

The development of these equations is part of a larger

project to estimate forest carbon using a simulation model

called FORCARB (Plantinga and Birdsey 1993; Heath

and Birdsey 1993). The FORCARB model accounts for

carbon in U.S. forests (Birdsey and Heath 1995) and

forest products (Heath and others 1996; Skog and

Nicholson 1998), has been used to produce projections

for the 2001 U.S. Submission to the United Nations

Framework Convention on Climate Change on Land

Use, Land Use Change and Forestry (U.S. State

Department 2000), and has been used to examine

uncertainty in U.S. forest carbon budgets (Smith and

Heath 2000; Heath and Smith 2000). FORCARB

produces separate estimates for all forest carbon pools

such as live trees, down dead wood, and soil organic

carbon.

Predictions of forest floor carbon mass based on

comprehensive sampling are not currently available, but

such data are likely to accompany future forest

inventories. In this study, we attempt to meet current

needs for forest floor carbon information by developing

estimates of forest floor carbon based on deriving

relationships developed from published data. Our goal is

to develop and present a transparent and readily updated

simulation model to estimate forest floor carbon mass

based on forest type, regional characteristics, and forest

age since a major disturbance. “Transparent” means that it

is possible for users to understand how any part of the

estimate affects results, that is, all parts of the equations

are clearly outlined. These estimates are applicable to

developing a forest carbon budget of the entire U.S. or for

estimating regional averages, and can be used at the stand

level in the absence of local information.

Forest Floor

Sparse information and the immediate need for tractable

carbon estimates are the principal considerations in this

study. Our approach is (1) to define the forest floor, (2) to

develop a simple conceptual structure for making

quantitative estimates, (3) to compile useful data from the

literature, and (4) to sort and summarize data to develop

estimates.

Forest floor definitions vary and have evolved over time.

Establishing a working definition and conceptual

structure help to identify relevant information. We are

interested in broad classification variables to serve as

useful predictors of forest floor carbon. These variables

and their relative usefulness are discussed in detail below.

Values summarized from the directly measured

observations are assigned as representative of similarly

classified forest land. Our approach is conceptually

similar to other approaches that extracted data and made

broad generalizations from published measurements of

forest ecosystems (Vogt and others 1995; Vogt and others

1996; Johnson and others 2000). We identify important

predictors of forest floor mass from literature reviews, use

these relationships and published values to make modelbased estimates, and update estimates where possible.

The forest floor includes nonliving plant mass in various

states of mechanical and chemical decay. Large vertebrate

detritus may sometimes be included in forest floor

samples, but it is generally a small part of the forest floor.

Plant material includes leaves, twigs, bark, and woody

stems on the forest floor. Some forests also contain a

significant amount of fine roots in the forest floor.

Accumulation of plant material can form identifiable

layers above the mineral soil. Layer identities are based on

states of decomposition ranging from freshly fallen leaves

and twigs, which are easily identified, to lower layers of

humus that are at an advanced state of decomposition.

The lowest layer can be amorphous and sometimes almost

indistinguishable from upper layers of mineral soil

(Waring and Schlesinger 1985). Organic layers above the

mineral soil are usually identified as Oi, Oe, and Oa

(upper to lower) or L, F, and H according to a previous

classification system (Federer 1982).

Distinctions among pools of coarse woody debris, the

forest floor, and soil organic matter are necessary to avoid

over- or under-counting carbon when separate estimates

are made for each. Reconciling differences in forest floor

data collection was difficult as many studies featured

different protocols. For example, distinctions between

coarse woody debris and the small woody material that is

considered part of the forest floor are based on diameters

of recently fallen branches and stems. Diameters ranging

from less than a centimeter to more than 7 cm are

reported as the distinction between coarse woody debris

and forest floor (Rollinger and others 1998). Also, many

reports either do not specify how a distinction was made,

or they fail to identify the presence or absence of woody
2

material. The state of decomposition is also a basis for

distinguishing coarse woody debris from the forest floor

because rotting logs eventually become classified as forest

floor. The distinction between the lowest layers of forest

floor and upper (generally A horizon) layers of mineral

soil is sometimes clear. However, purely organic layers of

forest floor and organic-rich layers of the mineral soil are

sometimes difficult to discern. This can be even more

difficult when mechanical mixing of layers has occurred

(Ryan and others 1992). Field classifications are usually

based on morphology and are closely related to an

individual researcher’s experience (Federer 1982).

Accumulation of forest floor mass depends on rates of

detrital inputs and the rates of loss through processes such

as decomposition or physical damage or removal.

Accumulation and loss can be spatially (Simmons and

others 1996) and temporally (Nemeth 1973)

heterogeneous. Processes can be influenced by season,

climate, and edaphic factors, as well as stand

composition, age and disturbance history (Schlesinger

1977; Mattson and Smith 1993; Binkley 1995).

Forest floor carbon mass is related to total biomass

accumulation, but additional factors control specific

carbon content. Specific carbon content of forest floor

mass can vary by origin of material as well as history of

decay or residence time in the forest floor. For example,

freshly fallen wood usually contains more carbon per

gram dry weight than fresh leaf litter, approximately 50

percent versus 45 percent (Fassnacht and Gower 1999).

Loss of carbon through respiration and changes in

chemical composition with decay result in progressively

decreasing carbon content of the more decomposed

material of lower forest floor layers (Huntington and

others 1989; Schiffman and Johnson 1989).

Decomposition rates are principally affected by quality

and quantity of material as well as a favorable

microenvironment (Schlesinger 1977; Hendrickson and

others 1989; Berg and others 1996). Though quantity

and quality of inputs affect carbon mass stored in forest

floors, factors controlling carbon loss appear to be

somewhat more important in controlling the carbon level

in temperate forests (Vogt and others 1996). We assume

that factors affecting gain and loss co-vary with forest

type, location, and other general descriptors, and that

such generalizations are useful for developing simple

estimates at large scales.

Regional characteristics

Numerous reviews report forest location and composition

are associated with average amount of forest floor mass

(Schlesinger 1977; Vogt and others 1986; Vogt and others

1995; Vogt and others 1996; Rollinger and others 1998).

For example, Vogt and others (1995) reviewed a number

of broad influences on soil and forest floor carbon. The

goal was to identify influences on large-scale estimates.

Their results demonstrated a trend of greater forest floor

accumulation along a climatic gradient from tropical to

temperate to boreal forests. The result of adding evergreen

and deciduous classifications suggested their usefulness

for predicting forest floor mass. Evidence further suggests

that additional predictor variables such as soil type,

stocking, productivity, or management intensity can affect

forest floors, but these variables are not as consistently

useful as region or forest type for making large-scale

estimates (Vogt and others 1995; Vogt and others 1996).

Climate affects accumulation and loss of forest floor

mass. Longer growing seasons and warmer temperatures

are associated with greater productivity and litterfall, but

they also are associated with more rapid decomposition,

especially if litter retains sufficient moisture and aeration.

Within a specific forest type or region, latitude or

elevation gradients may be analogous to climate gradients.

Estimating forest floor mass with latitude may depend on

considering a wide range of either latitude or local

environments (Vogt and others 1986; Vogt and others

1995; Simmons and others 1996).

Forest Type by Region

Forests dominated by conifers usually accumulate more

forest floor mass than deciduous forests (Schlesinger

1977; McClaugherty and others 1985; Finzi and others

1998). However, a number of exceptions to this trend also

have been noted (Perala and Alban 1982a; Grigal and

Ohmann 1992; Vogt and others 1995). The accumulation

in softwoods is partly due to more decay-resistant litter,

conditions that favor fungal decomposers, and cooler

temperatures and microenvironmental conditions that

slow decomposition. Rates of litter decomposition are

usually lower for softwoods than for hardwoods. Vogt and

others (1995) found generally greater forest floor mass in

needle-leaved evergreen forests than in broadleaved

deciduous forests in warm temperate regions. However,

forest floor accumulation was greater in cold temperate

climates with the two forest types having similar forest

floor mass. Mixed forests of both needle-leaved evergreen

and broadleaf deciduous species had significantly more

forest floor mass than either type alone.

Forest type, as described by major component species, is

clearly an important factor in accumulation of forest floor

mass (Lang and Forman 1978; Cole and Rapp 1981;

Vitousek and others 1982; Vogt and others 1986; Vogt

and others 1996; Rollinger and others 1998). However,

there are enough exceptions to predictive ability within

forest types to suggest that classification by type alone
3

might not be sufficient to develop a simulation model.

For example, forest floor mass sampled from 40-year-old

stands of aspen, white spruce, jack pine, and red pine

were within a similar range. This included no substantial

differences between hardwood and conifer species (Perala

and Alban 1982b). Species composition alone also can

produce apparently contradictory results. For example,

sometimes the presence of alder leads to substantially

greater forest floor mass in Douglas-fir stands in the

Pacific Northwest, and sometimes inclusion of alder does

not affect accumulation (Binkley and others 1992).

Including bigleaf maple in Douglas-fir stands along the

coastal range in Oregon had no affect on forest floor

mass. Bigleaf maple did, however, increase measured

litterfall and rates of decomposition (Fried and others

1990). Despite the many documented exceptions,

location (region) and stand composition are probably the

most useful estimators for forest floor mass.

Development with Age

Forest age, in years since afforestation or a major

disturbance, is assumed to be the next most useful

predictor of forest floors after location-by-type influences.

A change in land use to forests, such as afforestation of

agricultural soils, often is associated with a rapid increase

in forest floor mass (Schlesinger 1977; Binkley 1995).

The surface organic layer, which becomes forest floor

with afforestation, is generally greater in forests than in

recently abandoned agricultural land or old-fields

(Schiffman and Johnson 1989; Homann and Grigal

1996). Net forest floor mass accumulates at a relatively

rapid rate during forest development. This rate slows

considerably as the forest floor reaches a more mature

state between 20 and 80 or more years, depending on the

forest (Switzer and others 1979; Covington 1981; Means

and others 1992; Edmonds and Chappell 1994; Smith

and Resh 1999). Relatively slower rates of change

characterize the much longer period from maturity

through old growth. Forest floors of older undisturbed

forests can sometimes be characterized as an

approximately steady state.

These estimates depend on assuming a threshold age for

mature forest floors based on the overstory age of a stand

and assigning an average carbon mass for an

approximately steady state. Covington (1981) and others

(Federer 1984; Hendrickson and others 1989; Mattson

and Smith 1993; Olsson and others 1996) identified

forest floor dynamics following harvest, and defined “fully

recovered” Northern hardwood forest floors between 40

and 60 years old. Development is much more rapid in

Southern pine forests with ages between 15 and 25 years

(Nemeth 1973; Switzer and others 1979; Gholz and

others 1985; Lockaby and Taylor-Boyd 1986). Evidence

suggests that western forests tend to develop forest floor

mass more slowly (Smith and Resh 1999). The exceptions

in the West are forests of the Pacific Northwest west of

the Cascades (Turner and Long 1975); Douglas-fir forests

develop forest floor mass relatively rapidly and continue

slow increases through old growth. Differences between

mature and old-growth forest floor mass vary by forest

and region. However, approximate steady-state averages

may be useful for long-term estimates for regions where

old-growth stands are an increasingly smaller proportion

of forest area.

Effects of Disturbance

Relationships between disturbance and forest floor mass

depend on severity of disturbance. Minor disturbances,

such as creation of small gaps or low-intensity fires, may

have little apparent measurable effect and can result in

rapid recovery. For example, windthrow — creating small

gaps — had no long-term effect on carbon storage in

forest floors of hemlock-hardwood and northernhardwood forest in the Great Lakes region (Liechty and

others 1997). Fire effects on forest floor mass, or carbon,

can vary considerably. Some regular or low-intensity fires

have little effect in reducing forest floor mass (Alban

1977; Little and Ohmann 1988; Clinton and others

1996), whereas most of the forest floor can be lost in

more severe fires (Dyrness and others 1989; Vose and

others 1999). Rapid recovery can occur when fire damage

to overstory is limited (Bell and Binkley 1989).

Reductions in forest floor carbon are possible following a

clearcut harvest. Harvests result in decreased litter input

and increased decomposition (Johnson and others 1991;

Alban and others 1994; Strong 1997; Rollinger and

others 1998). Changes in decomposition are influenced

by the altered microenvironment of the soil surface. In

addition to rapid decomposition, immediate losses of

forest floor with harvest may be due to mounding,

mixing, and burial of organic matter (Federer 1984; Ryan

and others 1992). Effects of harvesting on forest floor

mass and the dynamics accompanying forest regrowth can

have important consequences for estimates. If forest floor

mass is lost quickly after harvest, then patterns of carbon

storage will follow closely those of afforestation. Timeaveraged carbon content would be considerably greater,

however, if most forest floor mass remains after harvest,

followed by slow decay during forest regrowth and

accumulation of new forest floor mass.

Covington (1981) described the dynamics of forest floor

mass for northern hardwood forests following clearcut

harvesting. Forest floor organic matter decreased the first

15 years following clearcutting of northern hardwood

forests in New Hampshire. A 50 percent decrease was
4

followed by a slow recovery of forest floor for about 50

years before floor mass was within 5 percent of preharvest

levels. These results initiated numerous field

measurements and model simulations of this effect. Some

reports failed to demonstrate the same dynamics

(Hendrickson and others 1989; Schiffman and Johnson

1989), and others repeated the effect (Federer 1984;

Snyder and Harter 1987; Mattson and Smith 1993;

Johnson and others 1995), principally among northern

hardwoods. Differences among most results were usually

in the extent or timing of the minimum in forest floor

mass. Restoration of forest floor mass with regrowth was

due partly to rapid recovery of litterfall, often to

preharvest levels within a few years of harvest (Covington

and Aber 1980; Schiffman and Johnson 1989; Alban and

Perala 1990). Another explanation for the dynamics

described by Covington (1981) is that differences among

harvest methods over time have impacted sites differently

and forest floor mass partly reflects this record (Yanai and

others 2000).

Decomposition

Decomposition, a controlling process for net

accumulation of forest floor mass, is affected by quantity

and quality of material (Schlesinger 1977; Hyvonen and

others 1998). Quantity varies with forest productivity and

season. Quality varies with the input material and depth

in forest floor, with fresh litter subject to the most rapid

decomposition and humus decomposing slowly.

Additionally, favorable microenvironments are functions

of temperature and moisture. Controls on decomposer

activities — temperature, moisture, and tissue chemistry

— are reflected in large-scale attributes such as region,

annual temperature and precipitation, latitude, elevation,

and species composition (Vogt and others 1986). The

general observation that forests with lower mean annual

temperatures and dominated by evergreen species tended

to have greater accumulation of forest floor (Vogt and

others 1995; Vogt and others 1996) is consistent with

these broad controls. Thus, the same broad forest

characteristics that covary with forest floor mass — region

and type — also predict decomposition, as expected.

Literature Bases for Structuring Estimates

Based on literature and the goals of our work, we choose

to focus on region and latitude as climate analogs, and

forest type (also affected by climate) as the principal

predictors of forest floor mass. Forest age, in terms of

years since afforestation or a major disturbance, is

identified as the third general characteristic used in

forming these estimates. The basic assumption about age

effects is that greater changes occur in younger forests

developing toward maturity than in the years between a

mature state and old growth. “Mature” can be a difficult

state to define, but it serves to organize the format of

these basic estimates.

Forest Floor Data from Literature

Our goal is to develop a transparent simulation model to

estimate the forest floor carbon pool. The model is based

on data derived from literature searches and a series of

simplifying assumptions. We assume that relatively smallscale numerical relationships obtained from the literature

can be applied to determine carbon for large areas.

Estimates are based on averages of published values

grouped according to forest and location. Relationships

among average values are then applied to large areas

defined by roughly similar forest composition, history,

and regional characteristics.

Conceptual Organization and Assumptions

Based on Literature

Our base assumption is that mature forests, classified

according to type and region, achieve an approximately

steady state of forest floor carbon (Schlesinger 1977;

Switzer and others 1979; Hough 1982; Waring and

Schlesinger 1985; Lockaby and Taylor-Boyd 1986).

While true steady states are unlikely, this is a necessary

simplification. The changes in forest floor mass associated

with a progression from mature forests to old growth —

40 to 60 years to hundreds of years — are likely to be

much smaller than the accumulation during the first 20

to 60 years following regrowth after harvest. This

assumption is most appropriate for large aggregate values

(summed over thousands of hectares, for example),

whereas individual stands are likely to exhibit greater

temporal and spatial heterogeneity with respect to the

dynamics of forest floor accumulation.

Our second assertion is that disturbances alter forest floor

mass (Schlesinger 1977; Dyrness and others 1989;

Johnson and others 1991; Alban and others 1994; Strong

1997; Rollinger and others 1998; Vose and others 1999).

Any pattern of decrease or pulse increase in forest floor

mass following a disturbance depends on the type and

severity of the disturbance. Long-term effects depend on

the specifics of the disturbance as well as time since the

event. We also assume that over time the forest floor will

return to the approximately steady state level of a mature

forest.

Dynamics of forest regrowth following disturbance or

with afforestation are assumed to affect the forest floor

carbon pool. We assume that the early period of growth

ends at a threshold age for maturity and the onset of the

apparent steady state. This age may vary with forest type
5

and region. We also assume that the forest floor reaches

this approximately steady state sooner than soil carbon or

annual increment of tree biomass. The same time-torecovery is applied to afforestation, regrowth, and major

disturbances (such as widespread crown fires). We assume

afforestation is accompanied by a relatively rapid increase

from little or no surface organic layers to the mature level

of forest floor mass. Partial cutting may have the same

pattern with a more rapid recovery time.

Specific Value Needs

The structure of all estimates depends on assigning forest

area to generally homogeneous groups according to

region, forest type, and age. This is the minimum

classification scheme for all values extracted from the

literature. Classifications are applied to both inputs from

forest sector projections and from empirical relationships

identified in the literature. Published data are used to

determine an average age and carbon content for a mature

forest. Published discussions about forest floor maturity

and an informal examination of the data were the bases

for assigning mature ages.

The transition from age zero to the mature forest is a

relatively rapid growth phase for forest floor carbon.

Regrowth also includes decaying forest floor material that

starts at age zero with the precut mature level of carbon.

Decay rates are set according to forest floor mass mean

residence times by forest type and region. The initial

forest floor carbon in regenerated forests generally reaches

zero by the time the forest floor has returned to the

mature state. Though it was not our intent, the sum of

the two modeled estimates — rapid increase and

exponential decay — at the same age qualitatively

reproduces the time course described by Covington

(1981).

Identifying Published Values

The search for existing data on forest floor mass included

peer-reviewed journals, technical reports, and the

Internet. Information varied considerably among

publications. A few directly addressed mechanisms of

control on forest floor accumulation. However, the forest

floor mass data were peripheral to the main purpose of

most of these publications. The minimum data

requirements were some description of the forest and a

measure of forest floor mass.

We made an effort to avoid duplicate observations.

Research projects often use the same field site and much

of the site data can be reported in more than one

publication. Where a number of forest floor values were

reported for a single site within a publication, we pooled

the values to a single observation. Separate sites in the

same forest were counted as separate observations. Thus,

publications with a number of values were sometimes

used as a few points and sometimes included as a single

mean, depending on our interpretation of the data. We

made no effort to adjust for possible bias in location of

experimental versus actual forest distribution within a

forest-type by region classification. Similarly, seasonal

influences on forest floor mass can vary, but we did not

sort observations accordingly.

The values obtained from publications were as follows: a

description of forest type; mass per unit area; mass

identified as carbon, organic matter (ash free weight), or

dry weight; values for converting organic matter or dry

weight to carbon; stand age or time since significant

disturbance; site history in terms of management or

disturbances; location, including latitude, longitude, and

elevation; and a measure of decomposition or mean

residence time of forest floor material. Some discussion of

forest floor definition and measurement was noted.

Additional information, such as more precise species

composition, stand density, site productivity, other

carbon or biomass pools, or additional site history, was

occasionally included in publications.

Our definition of forest floor does not include down

coarse woody debris and mineral soil but these might

have been included in some observations. Published

values overestimate forest floor if larger woody material

was considered part of forest floor without being

mentioned in the method of collecting forest floor

material. Similarly, other estimates will understate forest

floor if they exclude all fine woody material from reported

values. The same uncertainties exist for the distinction

between forest floor and the upper layer of mineral soil.

Thus, precise definitions for forest floor were ostensibly

primary considerations in extracting data from the

literature survey. Most reports provided little information

on distinctions among coarse woody debris, forest floor,

and the mineral soil pools. For these, we simply relied on

the individual researcher’s experience for the vast majority

of observations. Where possible, we excluded woody

material greater than 7.5 cm in diameter, because this was

defined as coarse woody debris.

Forest-type and Region Classification

These estimates are based on region and forest-type

classifications shown in Figure 1 and Table 1, respectively.

General forest types were formed on the limited number

of observations available. For example, a single pine type

was formed for the South. Additional subdivision of types

is possible in the future. Forest types not explicitly

included were not represented in our dataset. Setting a

minimum age for a mature forest floor was a somewhat

subjective process. Ages were assigned after considering
6

reports in the literature and forest ages in growth and

yield models.

Northern forest floors were defined as mature when the

overstory age was 50 years. Conifers were divided into

two groups, pine and other conifer, which were mainly

spruce, fir, and hemlock. Aspen-birch and maple-beechbirch formed two distinct groups with a large number of

observations in each. Other hardwoods were lumped as a

single group that included mostly oaks and some oakhickory forests as well as miscellaneous hardwoods. A

sixth type was mixed conifer-hardwood.

Southern forest floors were defined as mature at overstory

age 30 years. Pine was the only conifer classification

possible from the dataset. All southern hardwoods were

pooled to a single group, which was mostly oak-hickory.

Very few bottomland hardwoods were included. A fourth

type was mixed conifer-hardwood. The large areas of pine

and the likely different effects of management make

southern pines a candidate for future subdivision of forest

types according to origin or productivity.

Pacific Northwest forest floors were defined as mature

when overstory age was 40 years. The west side of the

Pacific Northwest — the states of Oregon and

Washington west of the Cascade Mountains — has a very

different climate than the remainder of the Western

United States. It is the only distinct subregion we

established for the West. Conifers were divided into two

types according to elevation. Higher elevation species

were fir and hemlock. Lower elevation and coastal sites

were principally Douglas-fir, Western hemlock, and Sitka

spruce. All hardwoods were grouped as one type.

The remainder of the West was considered one large

region and included the Rocky Mountains, the

Intermountain/Great Basin, and California. The age for

mature forest floors was set at an overstory age of 80

years. The principal division in the West was between

most conifers as pine or mixed conifer groups. Large areas

of woodlands in the West including pinyon-juniper were

maintained as a separate group due to size of area,

however few values were identified. Hardwoods and

redwoods/giant sequoia were two additional

classifications.

We found some data for Alaska forests. However, too few

reports were available for usefully classifying forests by age

or type. Thus we do not develop equations for estimating

forest floor carbon, but we did summarize values for

interior Alaska (Table 1). We assume characteristics of

forest floors of southeastern Alaska are more similar to

those of the Pacific Northwest than of interior Alaska

(Figure 1).

Model-Based Estimates of Forest

Floor Carbon

Most published values for forest floor mass were reported

in dry weight. About 20 percent of the values were

reported as carbon mass per unit area, with another 20

percent reporting in terms of organic matter (that is, ashfree weight). We converted all values to megagram carbon

per hectare based on ratios identified in the dataset, which

were consistent with other reported values (Perala and

Alban 1982b; Vitousek and others 1982; Huntington and

others 1989; Cromack and others 1999; Vose and others

1999). Organic matter was converted to carbon by

South

West

Interior

Pacific

North

Northwest

Alaska

South

West

Interior

Pacific

North

Northwest

Alaska

Figure 1.—Regions used to classify forest types. Note that Alaska is not drawn to scale.
7

multiplying by 0.55. Mass of carbon per unit dry mass is

likely to vary by region, forest type and layer or state of

decomposition. Specific carbon contents reported in our

dataset did show distinct layer effects. However, there

were too few sites that included separate layers to

calculate averages on this basis. The layers were pooled for

average forest floor values of the subset of the data that

included specific carbon content. No general trends

emerged among regional averages or between conifers and

hardwoods (Table 2). We multiplied dry weight by 0.37,

a ratio obtained from our dataset, to estimate forest floor

carbon.

Mean forest floor carbon for mature forests is shown in

Table 1. Standard deviation, sample size, minimum and

maximum values also are presented to show availability

and variability in the data. The mature values represent

the averages where age was greater than 90 percent of the

minimum age set for mature forest floors or sites where

the researchers described the stand as mature. The ratio of

forest floor mass to mass of annual input to the forest

floor, or mean residence time as described above, is

provided in Table 3. The classification system and mean

values for forest floor carbon mass generally are consistent

with those reported by Birdsey (1992, 1996).

Latitude or Elevation Effects

Many published values included site-specific information

such as latitude and elevation. The expected association

with accumulation of forest floor made these variables

candidates for an additional level of classification. This

information was included with about one-third of the

observations identified as mature forest floors. Other

reports included enough information about location that

allowed us to estimate latitude. If the local topography

was relatively flat, we also estimated elevation.

Preliminary regression analyses found that neither latitude

nor elevation was a useful predictor of forest floor carbon

with the set of values we had obtained. Thus, latitude and

elevation were not included in our model. Latitude and

elevation are, however, included in the appendix of

Table 1.—Mean forest floor carbon by region and forest type for forests identified as mature.

Region and forest type Carbon Standard Sample Minimum Maximum

deviation size

(Mg/ha) (Mg/ha) (Mg/ha) (Mg/ha)

North

pine 13.8 4.6 13 5.4 19.8

spruce, fir, hemlock 33.7 23.2 6 4.6 68.1

mixed conifer-hardwood 29.7 26.6 5 5.4 75.0

aspen-birch 10.2 6.2 13 2.3 20.9

maple-beech-birch 27.7 17.8 28 2.8 89.5

mixed hardwood, oak 8.2 7.3 33 2.8 34.1

South

pine 12.2 4.6 28 1.4 21.9

mixed conifer-hardwood 10.3 3.6 6 6.7 16.8

mixed hardwood, oak-hickory 6.0 5.7 20 0.8 24.8

Pacific Northwest

Douglas-fir, western hemlock 27.5 31.3 69 2.6 165.6

fir-hemlock, higher elevation 29.5 16.3 7 16.8 55.3

hardwood 9.3 7.6 4 0.9 16.9

West

pine 24.1 12.2 29 3.3 46.0

redwood, sequoia 62.2 31.3 3 35.9 96.9

pinyon, juniper 21.1 1.6 2 20.0 22.2

mixed conifer 37.2 11.5 24 23.3 73.9

hardwood 31.7 14.1 11 4.7 51.1

Interior Alaskaa

all mature 32.2 19.1 13 14.2 73.3

a

Data for interior Alaska were few and showed no age effect; therefore, values represent all observations, not a

subset determined by age.
8

Table 2.—Summary of specific carbon content of entire forest floor, all layers grouped

together. Values are sorted according to general type or region and were taken from

observations extracted from literature (see Table A1).

Forest type or region Ratio of carbon mass to total dry mass

Mean Standard deviation Sample size

All values 0.370 0.0601 59

conifer 0.365 0.0525 27

hardwood 0.361 0.0603 22

mixed 0.402 0.0737 10

North 0.376 0.0490 26

South 0.368 0.0834 14

Pacific Northwest 0.355 0.0618 13

West 0.379 0.0427 6

Table 3.—Mean residence time in years (forest floor mass divided by annual biomass input) for

entire forest floor. Values reported as carbon, organic matter, or dry weight were pooled for

these averages. Values are sorted according to general type or region and were taken from

observations extracted from literature (see Table A1).

Region and forest type Mean residence Standard Sample Minimum Maximum

time deviation size

(years) (years)

North

conifer 8.4 6.02 18 3.2 29.1

hardwood 9.2 9.49 26 1.1 35.8

South

conifer 3.8 3.15 19 0.9 13.7

hardwood 3.2 1.92 16 0.4 6.3

Pacific Northwest

conifer 16.0 17.44 59 0.9 80.0

hardwood 3.4 0.55 4 2.9 4.2

West

conifer 24.1 16.04 22 8.7 60.0

hardwood 19.8 11.95 2 11.4 28.3

Alaska

conifer 433.0 201.0 3 220.0 620.0

hardwood 24.5 5.46 5 19.7 33.2
9

observations because of their expected usefulness in

simulation modeling.

Simulation Based on Stand Age

Our model is in two parts: net accumulation and decay.

Net accumulation of forest floor carbon mass with

increasing age is represented as a simple model construct.

Basic conditions for the model are that it passes through

the origin, represents continuous net accumulation with

age, and rate of accumulation decreases so that the line

approaches an asymptote. We model net accumulation of

forest floor carbon mass according to the following

relationship:

Age is stand age in years, and A and B are coefficients

describing the line relating age and forest floor carbon mass.

This relationship conforms to our conceptual model. The

coefficients are assigned according to the basic

assumptions discussed above. We assume minimum ages

represent mature forest floors and that means of

published values are representative of mature forests. We

set the upper limit for the model at the 95th percentile of

values for forest floor carbon mass. This is the value

assigned to coefficient A. Since average carbon mass for

mature forest floor represents all ages, we expect the line

to pass below the ordered pair for mature forests:

(minimum age, mean carbon mass). Preliminary

regression analysis for northern hardwoods and southern

pines (where many values were available) identified the

mean carbon mass at a point approximately 30 percent

greater than the assigned minimum age. Therefore, we

model the relationship as passing through the ordered

pair (minimum age × 1.33, mean carbon mass). Values

for coefficients A and B are provided in Table 4.

Table 4.—Coefficients to define modeled relationship between forest age and forest floor carbon mass

(Mg/ha) for each combination of forest type by region. Columns A and B define net accumulation

with age, and C and D describe the decay curve. Regrowth is the sum of accumulation and decay.

Forest type Accumulation: Decay:

AB Ca Da

North

pine 19.1 25.6 13.8 8.4

spruce, fir, hemlock 62.9 57.8 33.7 8.4

mixed conifer-hardwood 65.0 79.5 29.7 8.4

aspen-birch 18.4 53.7 10.2 9.2

maple-beech-birch 50.4 54.7 27.7 9.2

mixed hardwood, oak 24.9 134.2 8.2 9.2

South

pine 20.4 27.1 12.2 3.8

mixed conifer-hardwood 15.4 20.1 10.3 3.8

mixed hardwood, oak-hickory 15.3 61.8 6.0 3.2

Pacific Northwest

Douglas-fir, Western Hemlock 87.5 116.7 27.5 16.0

fir-hemlock, higher elevation 53.9 44.3 29.5 16.0

hardwood 16.5 41.1 9.3 3.4

West

pine 43.9 87.3 24.1 24.1

redwood, sequoia 92.6 52.1 62.2 24.1

pinyon, juniper 21.1

mixed conifer 53.6 47.0 37.2 24.1

hardwood 50.1 62.0 31.7 19.8

a

Values in Columns C and D are from Tables 1 and 3, respectively.

A age

B age

×

+ C e

age

D × −



 





Forest floor carbon (Mg/ha) = ×

+

A age

B age
10

The model for the decay line is based on the average

forest floor of mature forests and regional averages for

apparent decay rates. Forest floor carbon mass following

clearcutting is assumed to begin at the mature forest

floor level of carbon. Decay of forest floor mass existing

prior to the clearcut is described as an exponential

function of years and mean residence time:

Coefficient C is average mature forest floor carbon mass

from Table 1, coefficient D is the mean residence time

from Table 3, and e is the exponential function. Values

for coefficients C and D are provided in Table 4.

Total carbon mass during regrowth is the sum of

estimates from the two models — accumulation and

decay — defined in Table 4. Examples of simulated

accumulation, decay, and totals are shown in Figure 2.

Application of Estimators

Summaries in Tables 1 and 4 provide estimates of forest

floor carbon mass per unit area for region by forest type

definitions without and with the added influence of age,

respectively. Many, but not all, forest inventory

summaries provide age or disturbance history. However,

total area is common to all forest inventory data and is

an essential part of carbon budgets. Estimates presented

here complement area and age information. The total

carbon pool is the sum across all ages and areas.

Forest types defined for Tables 1 through 4 are based on

data available in the literature. Determining estimators

for all forest types entails establishing a reasonably close

match of types when necessary. For example, northern

maple-beech-birch values were combined with mean

residence times for northern hardwoods to estimate

forest floor response to regrowth. Similarly, the

summaries in Tables 1 and 4 must be linked to forest

inventory types to estimate total carbon pool sizes.

Forest Floor Carbon Pool

of the United States

Total carbon estimates were based on forest inventory

data (Smith and others 2001). The database is a

compilation of extensive forest inventory data for the

United States. We applied the model summarized in

Table 4 to the 1997 forest inventory database. The

resulting estimate of forest floor carbon mass density

(megagram per hectare), averaged over all forest types

Residual forest floor carbon (Mg/ha) = × −



 





C e

age

D

Figure 2.—Examples of forest floor carbon estimated as a

function of stand age for three forest types. Diamonds

represent observations with age, and x’s represent observations

not linked to a specific forest age but identified simply as

mature. The lower solid line represents estimated forest floor

carbon with afforestation. The dashed line is an estimated path

of decay following clearcut harvesting. The upper solid line

represents net accumulation during regrowth and is the sum

of afforestation and decay lines. The open circle represents the

average mature forest floor value from Table 1.

0

20

40

60

80

0 40 80 120

Stand age (years)

Mg 

C / ha

North, maple-beech-birch

0

10

20

30

0 25 50 75

Stand age (years)

Mg C / ha

South, pine

0

25

50

0 50 100 150 200 250

Stand age (years)

Mg C / ha

West, pine
11

0 10 20 30 40

North

South

West

Pacific Northwest

Alaska (entire state)

Forest floor carbon (Mg/ha)

0 500 1000 1500 2000 2500

North

South

West

Pacific Northwest

Alaska (entire state)

Forest floor carbon (Mt)

per region and totals, are shown in Figure 3.

Approximately 4.5 Gt carbon is stored in the forest floor

carbon pool in the 48 contiguous United States.

Forest floor carbon data should become increasingly

available from the annualized USDA Forest Service Forest

Inventory & Analysis Program. Assembling a database

that can be readily reclassified and resorted is a quick and

Figure 3.—Total (A) and average forest floor carbon per hectare (B) by region

of the U.S., 1997.

A

B

simple approach to developing empirical estimates of

forest floor carbon, which can be easily updated and

revised. This accomplished the goal of tractable and

transparent carbon estimates for U.S. forests, which

focuses on actual rather than potential vegetation.

Estimates are not restricted to any single form. The results

provide flexible, transparent forest floor carbon estimators

for U.S. forests.
12

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16

Appendix

Table A1.—Forest floor carbon mass values obtained from literature.

Column headings:

R Regions used for summarizing values (see Figure 1)

C Carbon mass used for each observation, Mg C per hectare

ff Form of forest floor mass for each observation: c, as carbon; o, as ash-free organic matter converted to

carbon by multiplying by 0.55; and d, as dry weight converted to carbon by multiplying by 0.37

T Forest type assigned for use in our analysis (see Tables A2, 1, and 4)

D Summary of forest composition from publication

L State, province, or region (LS=Lake States, IM=Intermountain)

A Years since major disturbance, or: m if information suggested mature; y, if information suggested not yet

mature; and blank indicates that information was not provided but these were assumed to be mature

La Latitude, degrees north

Lo Longitude, degrees west

E Elevation (km)

c:dw Ratio of forest floor carbon mass to dry weight, if given

mrt Mean residence time of forest floor, in years (forest floor biomass divided by annual biomass input)

rf Form of biomass in mrt, if reported: c, as carbon; o, as ash-free organic matter; and d, as dry weight

cite Citation

R C ff T D L Aa Laa Loa Ea c:dw mrt rf citeb

(Mg/ha) (years) (km) (years)

N p jack pine LS 0.35 99

N p red pine LS 0.35 99

N 4.5 d p jack pine MN 47 0.4 5.7 138

N 9.6 d p jack pine MN 47 0.4 12.5 138

N 8.9 d p jack pine MN 47 0.4 11.9 138

N 11.5 d p pine MN 47 0.4 13.6 138

N 7.7 d p white pine MN 47 0.4 10.4 138

N 15.4 d p pine MA 53 42 0.4 7.8 139

N 14.4 o p red pine plantation MN 90 2

N 16.5 o p red pine MN m 47 95 0.4 5

N 18.2 o p jack pine MN m 47 95 0.4 5

N 11.4 o p red pine plantation WI 37 44 90 0.4 18

N 10.1 o p red pine plantation WI 37 44 90 0.4 18

N 9.7 d p jack pine plantation LS 15-43 22

N 13.6 d p red pine plantation LS 21-180 22

N 13.0 d p jack pine MN m 48 92 0.5 45

N 18.6 d p jack pine MN m 48 92 0.5 45

N 16.0 c p jack pine LS 50

N 14.0 c p red pine LS 50

N 13.1 d p jack pine North 83 83, v

N 16.0 o p white pine MA 34-96 42 72 0.1 85

N 5.5 o p white pine WI m 43 40 0.3 3.2 o 90

N 16.9 c p jack pine MN 39 47 95 0.4 5.8 c 104

N 13.5 c p jack pine MN 41 47 95 0.4 5.5 c 104

N 15.8 c p red pine MN 39 47 95 0.4 4.7 c 104

N 14.9 c p red pine MN 41 47 95 0.4 5.2 c 104

N 22.9 c p red pine plantation LS 105, 112

N 12.1 c p pine plantation IL 111, 114

Continued
17

N 5.4 d p red pine MN m 47 93 0.4 4.1 d 126

N 19.8 c p red pine plantation MA 55 43 72 0.4 0.32 8.5 d 136

N 7.6 c p shortleaf pine plantation IN 33 39 86 0.2 0.44 3.5 d 136

N sf balsam fir LS 0.36 99

N 18.2 o sf spruce MN m 47 95 0.4 5

N 21.1 d sf hemlock MI, WI m 46 89 0.5 17

N 35.5 d sf spruce - fir North 8 28, v

N 35.5 d sf spruce - fir North 8 28, v

N 45.9 d sf spruce - fir North 8 28, v

N 42.2 d sf spruce - fir North 8 28, v

N 41.3 c sf hemlock CT 42 73 0.4 38

N 23.0 c sf balsam fir LS 50

N 68.1 o sf eastern hemlock MI m 46 89 0.5 57

N 43.0 c sf balsam fir NH m 44 72 1.3 0.38 76

N 4.6 o sf hemlock WI m 43 40 0.3 6.5 o 90

N 17.6 c sf spruce MN 39 47 95 0.4 7 c 104

N 14.1 c sf spruce MN 41 47 95 0.4 5.8 c 104

N 46.5 o sf fir NH 44 72 1.4 108

N 54.3 o sf fir NH 44 72 1.4 108

N 54.5 o sf fir NH 44 72 1.4 108

N 64.4 o sf fir NH 44 72 1.4 108

N 66.6 o sf fir NH 44 72 1.4 108

N 45.5 o sf spruce fir NH 44 72 1.0 108

N 47.0 c sf balsam fir NH 75 44 72 1.2 0.4 29.1 d 136

N 5.4 d mx mixed, oak-pine RI m 42 72 0.1 20

N 7.3 o mx mixed WI 35

N 18.4 o mx conifer - hardwood ON m 45 77 0.2 56

N 22.1 o mx conifer - hardwood ON 3 45 77 0.2 56

N 18.0 o mx conifer - hardwood ON 3 45 77 0.2 56

N 75.0 c mx hemlock - hardwood MI m 46 87 0.3 0.39 79

N 25.0 c mx aspen, jack pine North m 112

N 24.4 c mx oak - pine MA 65 42 70 0.0 0.42 13.8 d 136

N ab aspen LS 0.32 99

N 2.3 d ab paper birch MN 45 0.4 2.3 138

N 14.9 o ab aspen MN m 47 95 0.4 5

N 20.9 d ab aspen MI 47 47 89 0.5 4

N 7.0 d ab aspen MN 60 47 93 0.4 4

N 16.8 d ab aspen MN 66 47 95 0.4 4

N 16.6 c ab aspen MN 70 48 94 0.4 3

N 13.4 c ab aspen MN 70 48 94 0.4 3

N 10.7 d ab aspen - birch North 8, 20

N 15.0 c ab aspen LS 50

N 11.8 d ab aspen North 81 83, v

N 2.9 o ab aspen WI m 43 40 0.3 1.6 o 90

N 3.1 d ab aspen WI m 46 90 0.5 1.8 d 102

N 13.5 c ab aspen MN 40 47 95 0.4 6.7 c 104

N 11.6 c ab aspen MN 49 47 95 0.4 6.1 c 104

N 5.7 d ab trembling aspen WI 8 45 90 0.4 113

N 6.3 d ab trembling aspen WI 14 45 90 0.4 113

Table A1.—Continued

Continued

R C ff T D L Aa Laa Loa Ea c:dw mrt rf citeb

(Mg/ha) (years) (km) (years)
18

N 2.7 d ab trembling aspen WI 18 45 90 0.4 113

N 6.3 d ab trembling aspen WI 32 45 90 0.4 113

N 2.9 d ab trembling aspen WI 63 45 90 0.4 113

N 8.6 d ab birch-aspen MI m 48 89 0.2 122

N 2.3 d ab birch MN m 47 93 0.4 2 d 126

N mb sugar maple LS 0.29 99

N 1.9 d mb maple WI 35 43 0.3 1.8 139

N 9.8 d mb sugar maple MI, WI 46 89 6, v

N 10.8 d mb sugar maple MI, WI 50 46 89 6, v

N 18.9 d mb northern hardwood MI, WI m 46 89 0.5 17

N 26.4 o mb maple - beech - birch NH 60 44 0.6 8.2 o 25

N 35.8 o mb maple - beech - birch NH 1 44 71 0.7 36

N 28.6 o mb maple - beech - birch NH 4 44 71 0.7 36

N 22.6 o mb maple - beech - birch NH 10 44 71 0.7 36

N 27.5 o mb maple - beech - birch NH 13 44 71 0.7 36

N 27.0 o mb maple - beech - birch NH 24 44 71 0.7 36

N 38.5 o mb maple - beech - birch NH 31 44 71 0.7 36

N 25.9 o mb maple - beech - birch NH 34 44 71 0.7 36

N 36.3 o mb maple - beech - birch NH 40 44 71 0.7 36

N 28.6 o mb maple - beech - birch NH 44 44 71 0.7 36

N 46.2 o mb maple - beech - birch NH 70 44 71 0.7 36

N 38.5 o mb maple - beech - birch NH 100 44 71 0.7 36

N 41.3 o mb maple - beech - birch NH 100 44 71 0.7 36

N 52.3 o mb maple - beech - birch NH 100 44 71 0.7 36

N 23.1 c mb beech CT 42 73 0.4 38

N 21.2 c mb red maple CT 42 73 0.4 38

N 12.1 c mb sugar maple CT 42 73 0.4 0.38 38

N 25.7 o mb northern hardwood NH 60 44 72 0.7 8.2 o 43

N 18.0 c mb sugar maple, hardwood LS 50

N 47.0 o mb maple - beech - birch MI m 46 89 0.5 57

N 30.0 c mb maple - beech - birch NH 65 44 72 0.6 0.34 61

N 31.0 c mb northern hardwood NH m 44 72 0.6 62

N 39.0 c mb northern hardwood NH 3 44 72 0.6 62

N 22.0 c mb northern hardwood NH 8 44 72 0.6 62

N 22.0 c mb northern hardwood MI m 46 87 0.3 0.29 79

N 10.8 o mb northern hardwood LS y 86

N 12.6 o mb northern hardwood LS m 86

N 14.2 o mb northern hardwood LS m 86

N 2.8 o mb sugar maple WI m 43 40 0.3 1.3 o 90

N 14.9 c mb maple - birch ON 225 47 84 96

N 20.2 c mb maple - beech - birch NY 100 44 74 96

N 9.2 d mb northern hardwood MI 51 47 89 0.3 97

N 14.2 d mb northern hardwood MI 51 47 89 0.3 97

N 22.5 d mb northern hardwood MI 49 47 89 0.3 97

N 5.4 d mb northern hardwood MI 3 47 89 0.3 97

N 6.3 d mb northern hardwood MI 3 47 89 0.3 97

N 6.5 d mb northern hardwood MI 3 47 89 0.3 97

N 16.8 c mb northern hardwood LS 105, 112

N 118.3 o mb northern hardwood NH 44 72 0.7 108

Table A1.—Continued

Continued

R C ff T D L Aa Laa Loa Ea c:dw mrt rf citeb

(Mg/ha) (years) (km) (years)
19

N 42.3 c mb maple - beech - birch ME >80 47 69 0.3 0.37 35.8 c 117

N 32.7 c mb maple - beech - birch ME >80 45 68 0.1 0.44 28.7 c 117

N 32.8 c mb maple - beech - birch ME >80 44 70 0.2 0.41 26.5 c 117

N 27.1 c mb maple - beech - birch ME >80 44 69 0.2 0.34 19 c 117

N 89.5 c mb northern hardwood NH 93 44 71 0.7 0.47 120

N 72.1 c mb northern hardwood NH 3 44 71 0.7 0.4 120

N 37.6 c mb northern hardwood NH 10 44 71 0.7 0.42 120

N 39.4 c mb northern hardwood NH 30 44 71 0.7 0.33 120

N 3.6 c mb maple IN 95 39 86 0.2 0.37 1.9 d 136

N 25.1 c mb northern hardwood NH 66 44 72 0.7 0.43 12.3 d 136

N 21.6 c mb maple - beech - birch NH m 44 72 0.7 8.4 c 144

N 12.4 d oh oak - maple MA 80 42 0.4 7.6 139

N 5.4 d oh black oak MO 20 38 91 0.4 27

N 7.4 d oh black oak MO 40 38 91 0.4 27

N 1.7 d oh oak WI 43 0.3 1.1 29, 139

N 5.1 o oh hardwood WI 35

N 30.7 c oh red oak CT 42 73 0.4 38

N 12.1 c oh white ash CT 42 73 0.4 38

N 16.0 c oh broad leaf deciduous LS 50

N 9.2 c oh oak MN 67 45 93 0.2 58

N 6.5 c oh oak MN 67 45 93 0.2 58

N 4.7 c oh oak MN 67 45 93 0.2 58

N 7.3 c oh oak MN 67 45 93 0.2 58

N 7.0 c oh oak MN 67 45 93 0.2 58

N 7.5 c oh oak MN 67 45 93 0.2 58

N 4.4 c oh oak MN 67 45 93 0.2 58

N 8.5 c oh upland deciduous MN y 45 93 0.2 64

N 4.8 c oh upland deciduous MN y 45 93 0.2 64

N 8.4 o oh mixed oak NJ 250 41 0.0 6.2 o 77

N 5.8 d oh oak MO 40 38 91 0.4 82

N 4.3 d oh hardwood WV 1 40 80 0.6 88

N 4.8 d oh hardwood WV m 40 80 0.6 88

N 4.1 d oh hardwood WV 2 40 80 0.6 88

N 8.1 d oh hardwood WV m 40 80 0.6 88

N 2.3 d oh hardwood WV 2 40 80 0.6 88

N 5.2 d oh hardwood WV m 40 80 0.6 88

N 2.8 d oh hardwood WV 4 40 80 0.6 88

N 3.9 d oh hardwood WV m 40 80 0.6 88

N 4.6 d oh hardwood WV 4 40 80 0.6 88

N 5.9 d oh hardwood WV m 40 80 0.6 88

N 6.3 d oh hardwood WV 6 40 80 0.6 88

N 5.7 d oh hardwood WV m 40 80 0.6 88

N 2.8 d oh hardwood WV 7 40 80 0.6 88

N 4.3 d oh hardwood WV m 40 80 0.6 88

N 6.5 d oh hardwood WV 7 40 80 0.6 88

N 8.8 d oh hardwood WV m 40 80 0.6 88

N 2.4 d oh hardwood WV 8 40 80 0.6 88

N 6.3 d oh hardwood WV m 40 80 0.6 88

N 5.0 d oh hardwood WV 11 40 80 0.6 88

Table A1.—Continued

Continued

R C ff T D L Aa Laa Loa Ea c:dw mrt rf citeb

(Mg/ha) (years) (km) (years)
20

N 7.6 d oh hardwood WV m 40 80 0.6 88

N 2.9 d oh hardwood WV 20 40 80 0.6 88

N 8.3 d oh hardwood WV m 40 80 0.6 88

N 2.3 d oh hardwood WV 20 40 80 0.6 88

N 3.5 d oh hardwood WV m 40 80 0.6 88

N 5.6 d oh hardwood WV 23 40 80 0.6 88

N 4.0 d oh hardwood WV m 40 80 0.6 88

N 3.8 d oh hardwood WV 23 40 80 0.6 88

N 7.8 d oh hardwood WV m 40 80 0.6 88

N 5.3 d oh hardwood WV 23 40 80 0.6 88

N 9.1 d oh hardwood WV m 40 80 0.6 88

N 3.0 o oh white oak WI m 43 40 0.3 1.8 o 90

N 33.7 o oh oak MN 50 45 93 0.2 19 o 107

N 34.1 o oh oak MN 50 45 93 0.2 14.1 o 109, 77

N 2.8 o oh oak - hickory MO 35-92 39 1.6 o 110, 77

N 8.6 c oh oak IN 81 39 86 0.2 0.44 2.9 d 136

N 19.0 c oh oak - red maple MA 50 43 72 0.4 0.32 12.2 d 136

N 3.8 d oh oak North 115-120 148, v

N 3.6 d oh oak North 115-120 148, v

N 2.8 d oh oak North 115-120 148, v

N 6.9 o conifer WI 35

N 17.0 c needle leaf evergreen LS 50

N 10.9 c upland conifer MN y 45 93 0.2 64

N 100.0 c lowland conifer MN y 45 93 0.2 64

N 31.4 c black spruce wetland MI m 46 87 0.3 92

N 8.9 c black spruce wetland MI 5 46 87 0.3 92

N 12.7 c black spruce wetland MI 5 46 87 0.3 92

N 503.3 o cedar swamp MN 100 45 93 0.2 265 o 107

N 172.2 o krummholz spruce - fir NH 44 72 1.4 108

N 29.9 o krummholz spruce - fir NH 44 72 1.4 108

N 47.9 o krummholz spruce - fir NH 44 72 1.4 108

N 53.7 o krummholz spruce - fir NH 44 72 1.4 108

N 104.0 c lowland deciduous MN y 45 93 0.2 64

S 3.0 d p pine FL 7 3.1 139

S 12.2 d p pine FL 27 6.5 139

S 6.9 d p loblolly pine SC 34 0.2 3.7 138

S 5.4 d p loblolly pine SC 34 0.2 3.4 138

S 9.7 d p shortleaf pine SC 34 0.2 7.9 138

S 15.1 o p loblolly pine plantation SC 56 33 80 0.0 10

S 10.3 o p loblolly pine plantation SC 56 33 80 0.0 10

S 9.5 o p loblolly pine plantation SC 56 33 80 0.0 10

S 16.3 o p loblolly pine plantation SC 56 13

S 14.1 o p loblolly pine plantation SC 56 13

S 20.9 o p loblolly pine plantation SC 56 33 80 0.0 14

S 1.4 d p longleaf pine South m 31 87 0.1 1.9 d 21

S 14.9 o p shortleaf pine TN 30 36 0.3 6.5 o 25

S 17.2 d p pine TN 30 36 0.3 13.7 29, 139

S 5.3 d p white pine NC 35 0.8 3.9 29, 139

Table A1.—Continued

Continued

R C ff T D L Aa Laa Loa Ea c:dw mrt rf citeb

(Mg/ha) (years) (km) (years)
21

S 7.8 c p loblolly pine TN m 36 84 0.2 0.29 41

S 16.1 o p pitch - Virginia pine South 51, v

S 13.1 o p pitch - Virginia pine South 51, v

S 17.8 o p pitch - Virginia pine South 51, v

S 10.1 o p slash pine plantation FL 25 52

S 2.2 d p loblolly pine NC 35 0.1 1.2 53, 138

S 9.2 c p slash - loblolly pine South 37 60

S 9.7 o p slash pine plantation FL 16 31 82 0.0 65

S 16.8 c p loblolly pine NC 19 36 79 0.2 4.3 c 66

S 19.5 c p loblolly pine NC 40 36 79 0.2 5.1 c 66

S 9.0 c p loblolly pine plantation NC 14 36 0.1 3 69

S 13.4 d p loblolly pine plantation SC 14 74

S 7.3 d p loblolly pine South 7 75, v

S 5.8 d p loblolly pine South 7 75, v

S 4.1 d p loblolly pine plantation LA 19 32 92 0.1 1.7 d 81

S 14.4 o p loblolly pine SC 70 33 80 0.0 91

S 7.6 o p longleaf pine AL 65 31 87 0.1 91

S 21.9 o p longleaf pine LA 65 32 92 0.0 91

S 16.0 o p longleaf - slash pine FL 65 30 82 0.0 91

S 7.3 d p loblolly - shortleaf pine South 25 94, v

S 13.6 o p loblolly pine VA 16 37 79 0.2 95

S 8.3 o p shortleaf pine VA 16 37 79 0.2 95

S 9.1 o p Virginia pine VA 16 37 79 0.2 95

S 5.9 o p white pine VA 16 37 79 0.2 95

S 1.4 d p loblolly - slash pine NC 5 35 77 0.0 98

S 1.4 d p loblolly - slash pine NC 7 35 77 0.0 98

S 2.1 d p loblolly - slash pine NC 8 35 77 0.0 1.6 98

S 2.1 d p loblolly - slash pine NC 10 35 77 0.0 1.6 98

S 2.1 d p loblolly - slash pine NC 11 35 77 0.0 1.6 98

S 10.5 d p slash pine GA 22 32 84 0.2 103

S 1.6 c p loblolly & Virginia pine VA 1 38 78 0.1 114

S 1.7 c p loblolly & Virginia pine VA 2 38 78 0.1 114

S 5.1 c p loblolly & Virginia pine VA 3 38 78 0.1 114

S 3.5 c p loblolly & Virginia pine VA 5 38 78 0.1 114

S 6.1 c p loblolly & Virginia pine VA 8 38 78 0.1 114

S 4.8 c p loblolly & Virginia pine VA 11 38 78 0.1 114

S 9.1 c p loblolly & Virginia pine VA 14 38 78 0.1 114

S 8.6 c p loblolly & Virginia pine VA 17 38 78 0.1 114

S 7.3 c p loblolly & Virginia pine VA 20 38 78 0.1 114

S 10.4 c p loblolly & Virginia pine VA 23 38 78 0.1 114

S 10.1 c p loblolly & Virginia pine VA 26 38 78 0.1 114

S 16.2 c p loblolly & Virginia pine VA 25 38 78 0.1 114

S 7.4 c p loblolly & Virginia pine VA 32 38 78 0.1 114

S 7.6 c p loblolly & Virginia pine VA 34 38 78 0.1 114

S 8.8 c p loblolly & Virginia pine VA 35 38 78 0.1 114

S 11.8 c p loblolly & Virginia pine VA 36 38 78 0.1 114

S 11.8 c p loblolly & Virginia pine VA 40 38 78 0.1 114

S 14.5 c p loblolly & Virginia pine VA 46 38 78 0.1 114

S 9.3 c p loblolly & Virginia pine VA 47 38 78 0.1 0.38 114

Table A1.—Continued

Continued

R C ff T D L Aa Laa Loa Ea c:dw mrt rf citeb

(Mg/ha) (years) (km) (years)
22

S 10.4 o p loblolly pine NC 55 36 79 115

S 8.9 o p loblolly pine NC 55 36 79 115

S 11.7 o p loblolly pine NC 55 36 79 115

S 5.6 d p loblolly pine plantation MS 18 124, 81

S 9.7 c p pine MS 25 33 89 0.1 125

S 10.3 c p pine MS 40 33 89 0.1 125

S 6.9 d p loblolly pine plantation SC 17 132

S 7.7 d p loblolly pine plantation SC 18 134, 81

S 11.1 d p loblolly pine plantation NC 16 143, 81

S 6.1 d p loblolly pine South 12 146

S 9.6 c mx conifer - hardwood NC, TN m 36 84 0.9 0.24 41

S 16.8 c mx pine - hardwood NC, TN m 36 84 1.7 0.32 94, v

S 6.7 d mx hardwood - pine South 40 125

S 7.6 c mx mixed MS 200 33 89 0.1 125

S 9.6 c mx pine - hardwood MS 130 33 89 0.1 125

S 11.3 c mx pine - hardwood MS 65 33 89 0.1 140

S 14.3 c mx pine - hardwood NC 35 84 1.4 0.46 140

S 16.4 c mx pine - hardwood NC 35 84 1.4 0.41 140

S 12.6 c mx pine - hardwood NC 35 84 1.4 0.39 141

S 19.8 c mx pine - hardwood NC 35 84 1.4 0.46 141

S 10.6 c mx pine - hardwood NC 35 84 1.4 0.46 141

S 8.7 c mx hickory NC 35 84 1.4 0.46 138

S 3.3 d hw oak SC 34 0.2 1.9 138

S 5.4 d hw yellow poplar SC 34 0.2 3.7 138

S 5.0 d hw mixed hardwood SC 34 0.2 3.2 139

S 3.3 d hw Nyssa - Acer VA 78 1.4 139

S 2.4 d hw oak - hickory VA 52 1 11

S 4.2 d hw oak - hickory NC m 35 84 0.9 11

S 4.3 d hw oaks NC m 35 84 0.9 16, 77

S 2.3 o hw Liriodendron TN 75 36 25

S 3.3 o hw Liriodendron, hardwood TN 50 36 0.2 1.4 o 25

S 0.8 o hw oak - hickory TN 30-80 36 0.3 0.4 o 25

S 14.9 o hw chestnut oak TN 30-80 36 0.3 5.6 o 25

S 13.8 o hw oak - hickory TN 30-80 36 0.3 5.6 o 25

S 5.2 o hw Liriodendron NC 60-200 35 0.8 2.2 o 29, 139

S 8.7 d hw maple - oak TN 55 36 0.3 6.3 29, 139

S 3.5 d hw mixed hardwood NC 35 0.9 2.2 41

S 6.6 c hw mixed hardwood TN m 36 84 0.3 0.21 41

S 24.8 c hw chestnut oak NC, TN m 36 84 1.0 0.34 77

S 5.2 o hw oak - hickory TN 36 0.3 5.4 o 77

S 5.6 o hw oak - hickory TN 36 0.3 4.6 o 77

S 2.6 o hw oak - hickory GA 150 32 77

S 3.7 o hw oak FL 100 28 0.0 77

S 3.7 o hw oak - hickory NC 36 0.8 4.9 o 89

S 7.4 o hw oak - hickory NC 4 35 83 1.0 89

S 12.0 o hw oak - hickory NC 7 35 83 0.9 89

S 6.3 o hw hardwood NC 50 35 83 0.9 94, v

S 4.6 d hw oak - hickory South 12 94, 77

S 4.5 o hw scrub oak SC 150 34 1.9 o 103

Table A1.—Continued

Continued

R C ff T D L Aa Laa Loa Ea c:dw mrt rf citeb

(Mg/ha) (years) (km) (years)
23

S 4.3 d hw hardwood GA 22 32 84 0.2 116

S 3.0 d hw hardwood NC m 31 87 0.9 116

S 3.6 d hw hardwood NC 0 31 87 0.9 116

S 3.3 d hw pin oak NC m 31 87 0.8 118, 77

S 2.6 d hw upland hardwood NC m 35 127

S 7.0 c hw oak TN 36 84 0.3 0.36 133

S 3.4 d hw red spruce SC 35 83 41

S 53.8 c conifer - hardwood NC, TN m 36 84 1.7 0.36 41

S 2.4 d Chamaecyparis VA 57 0.9 139

S 2.5 d Taxodium VA 86 1 139

P 8.6 d df Douglas-fir WA 40 47 11.6 139

P 5.0 d df Douglas-fir OR 44 0.3 5.6 138

P 10.7 d df Douglas-fir OR 70 44 0.6 14.5 139

P 2.6 d df Douglas-fir OR 43 44 2.6 139

P 12.4 d df alder, Douglas-fir BC 23 50 124 0.5 12

P 8.5 d df alder, Douglas-fir WA 25 48 122 0.0 12

P 2.6 d df Douglas-fir BC 23 50 124 0.5 12

P 7.8 d df Douglas-fir WA 25 48 122 0.0 12

P 8.6 o df Douglas-fir WA 55 46 122 0.6 3.6 o 15

P 16.1 o df Douglas-fir, alder WA 55 46 122 0.6 2.4 o 15

P 14.3 o df Douglas-fir, conifer OR 55 45 124 0.2 3.8 o 15

P 11.6 o df Douglas-fir, conifer OR 55 45 124 0.2 0.9 o 15

P 5.3 d df Douglas-fir WA 26 0.3 19

P 9.0 d df Douglas-fir WA 30.5 0.3 19

P 9.2 d df Douglas-fir WA 33.3 0.3 19

P 8.0 d df Douglas-fir WA 38 0.3 19

P 120.2 o df Douglas-fir OR 450 44 0.6 35.6 o 25

P 165.6 o df western hemlock OR 121 45 0.2 49 o 25

P 43.1 c df Douglas-fir OR 150 44 124 0.1 0.47 26

P 9.2 c df Douglas-fir OR 150 44 124 0.1 0.42 26

P 3.7 c df Douglas-fir plantation OR 9 44 124 0.1 0.38 26

P 6.7 c df Douglas-fir WA 38 32

P 9.7 c df Douglas-fir WA 38 32

P 8.7 c df Douglas-fir WA 38 32

P 6.5 c df Douglas-fir OR 38 32

P 8.1 c df Douglas-fir OR 38 32

P 8.2 c df Douglas-fir OR 38 32

P 11.9 c df western hemlock WA 32 32

P 13.7 c df western hemlock WA 32 32

P 10.6 c df western hemlock OR 32 32

P 5.8 d df Douglas-fir OR 48 45 123 0.3 6.1 d 39

P 5.5 d df Douglas-fir OR 48 45 123 0.3 4.8 d 39

P 5.5 d df Douglas-fir OR 48 45 123 0.3 4.3 d 39

P 6.0 d df Douglas-fir OR 48 45 123 0.3 4 d 39

P 9.8 d df Douglas-fir OR 48 45 123 0.3 10.4 d 39

P 5.3 d df Douglas-fir, bigleaf maple OR 48 45 123 0.3 2.9 d 39

P 6.8 d df Douglas-fir, bigleaf maple OR 48 45 123 0.3 4.3 d 39

P 4.5 d df Douglas-fir, bigleaf maple OR 48 45 123 0.3 3.1 d 39

Table A1.—Continued

Continued

R C ff T D L Aa Laa Loa Ea c:dw mrt rf citeb

(Mg/ha) (years) (km) (years)
24

P 4.6 d df Douglas-fir, bigleaf maple OR 48 45 123 0.3 2.4 d 39

P 11.4 d df Douglas-fir, bigleaf maple OR 48 45 123 0.3 5.8 d 39

P 81.4 d df western hemlock - fir BC m 49 125 0.5 40

P 44.4 d df western hemlock - fir BC m 49 125 0.5 40

P 55.5 d df western hemlock - fir BC m 49 125 0.6 40

P 92.5 d df western hemlock - fir BC m 49 125 0.5 40

P 58.1 d df Douglas-fir OR, WA m 42, 33

P 11.3 d df Douglas-fir OR 44 0.3 8.6 138

P 14.5 d df Douglas-fir OR 44 0.9 9.9 138

P 12.6 d df hemlock - spruce OR 44 0.2 9.6 138

P 78.7 o df Douglas-fir OR, WA 40 48, v

P 7.9 o df Douglas-fir OR, WA 40 48, v

P 8.1 d df hemlock - spruce OR 26 45 0.2 3.1 46, 139

P 26.3 o df Douglas-fir OR, WA 23 49, v

P 82.2 o df Douglas-fir OR, WA 80 49, v

P 31.4 o df Douglas-fir OR 450 44 123 0.6 13.4 o 47

P 27.1 o df Douglas-fir OR 450 44 123 0.6 10 o 47

P 15.1 o df Douglas-fir OR 450 44 123 0.6 7.3 o 47

P 31.3 o df Douglas-fir OR 450 44 123 0.6 12.9 o 47

P 25.1 o df Douglas-fir OR 450 44 123 0.6 10.8 o 47

P 14.8 d df Douglas-fir WA 47 0.5 19.5 55, 138

P 8.3 d df Douglas-fir WA 47 0.2 18.3 55, 138

P 13.2 d df Douglas-fir WA 47 0.2 16 55, 138

P 8.8 d df Douglas-fir WA 47 0.1 12 55, 138

P 28.3 d df Douglas-fir WA 47 0.1 48.1 55, 138

P 2.9 c df Douglas-fir OR <30 46 123 0.38 59

P 8.2 c df Douglas-fir WA 63, 32

P 17.0 c df western hemlock OR 63, 32

P 33.0 d df Douglas-fir, w. hemlock OR m 80

P 25.7 d df Douglas-fir, w. hemlock OR m 80

P 31.2 d df Douglas-fir, w. hemlock OR m 80

P 36.2 d df Douglas-fir, w. hemlock OR m 80

P 44.3 d df Douglas-fir, w. hemlock OR m 80

P 45.4 d df Douglas-fir, w. hemlock OR m 80

P 45.3 d df Douglas-fir, w. hemlock OR m 80

P 59.8 d df Douglas-fir, w. hemlock WA m 80

P 71.3 d df Douglas-fir, w. hemlock WA m 80

P 55.2 d df Douglas-fir, w. hemlock WA m 80

P 91.1 d df Douglas-fir, w. hemlock WA m 80

P 31.4 c df Douglas-fir OR m 44 122 1.0 93

P 18.1 d df western hemlock WA 48 124 0.1 106

P 27.4 d df western hemlock WA 46 123 0.4 106

P 17.8 d df western hemlock WA 48 124 0.2 106

P 10.4 d df western hemlock WA 48 124 0.1 106

P 14.1 d df western hemlock WA 47 124 0.2 106

P 10.7 d df western hemlock WA 47 124 0.2 106

P 14.8 d df western hemlock WA 48 125 0.1 106

P 8.1 d df western hemlock WA 47 124 0.1 106

P 17.8 d df western hemlock WA 0.9 106

Table A1.—Continued

Continued

R C ff T D L Aa Laa Loa Ea c:dw mrt rf citeb

(Mg/ha) (years) (km) (years)
25

P 30.3 d df western hemlock WA 49 122 0.7 106

P 14.4 d df western hemlock WA 0.3 106

P 16.3 d df western hemlock WA 47 122 0.5 106

P 10.7 d df western hemlock WA 48 122 0.2 106

P 11.8 d df western hemlock WA 0.1 106

P 19.2 d df western hemlock WA 0.9 106

P 11.5 d df western hemlock WA 0.3 106

P 11.4 d df Douglas-fir OR, WA 46 129, v

P 1.4 d df Douglas-fir WA 9 47 0.2 1.6 128, 139

P 11.0 o df Douglas-fir WA 22 47 121 0.2 4.4 o 130

P 8.4 o df Douglas-fir WA 30 47 121 0.2 3.7 o 130

P 8.4 o df Douglas-fir WA 30 47 121 0.2 7.7 o 130

P 9.4 o df Douglas-fir WA 42 47 121 0.2 5.3 o 130

P 12.8 o df Douglas-fir WA 42 47 121 0.2 7.8 o 130

P 14.6 o df Douglas-fir WA 49 47 121 0.2 12.1 o 130

P 21.6 o df Douglas-fir WA 73 47 121 0.2 29.1 o 130

P 8.6 c df coastal hemlock OR 120 45 124 0.2 0.41 3.4 d 136

P 4.6 c df Douglas-fir WA 300 47 122 0.2 0.38 3.2 d 136

P 6.4 c df Douglas-fir WA 45 47 122 0.2 0.36 5.5 d 136

P 7.4 d df Douglas-fir WA 150 47 12.6 137, 139

P 10.4 d df Douglas-fir WA 70 47 31.1 137, 139

P 3.7 d df Douglas-fir WA 1 47 137, 139

P 5.9 d df Douglas-fir WA 10 47 80 137, 139

P 10.0 d df Douglas-fir WA 40 47 32.9 137, 139

P 8.5 d df Douglas-fir WA 40 47 20 137, 139

P 11.1 d df Douglas-fir WA 150 47 11.4 137, 139

P 12.2 d df Douglas-fir WA 1 47 137, 139

P 6.7 d df Douglas-fir WA 10 47 39.1 137, 139

P 8.5 d df Douglas-fir WA 70 47 22.8 137, 139

P 14.3 d df Douglas-fir OR, WA 100 149, v

P 17.7 d fh fir, hemlock WA 23 47 1.2 31.7 139

P 55.3 d fh fir, hemlock WA 180 47 1.2 68.6 139

P 31.6 d fh noble fir, Douglas-fir OR 44 1.2 17.4 138

P 35.1 d fh fir - hemlock OR 44 1.5 28.8 138

P 21.7 c fh mountain hemlock OR 215 44 122 1.8 0.28 87

P 17.7 c fh mountain hemlock OR 18 44 122 1.8 0.3 87

P 16.8 c fh mountain hemlock OR 35 44 122 1.8 0.25 87

P 18.3 c fh mountain hemlock OR 74 44 122 1.8 0.3 87

P 19.8 d fh fir, hemlock WA 175 47 121 1.2 17.7 d 131

P 50.8 c fh Pacific silver fir WA 200 47 121 1.2 0.35 66.5 d 136

P 23.5 d fh fir, hemlock OR, WA m 145

P 5.3 d hw red alder WA 5.2 0.3 19

P 7.6 d hw red alder WA 10 0.3 19

P 5.0 d hw red alder WA 11.5 0.3 19

P 4.8 d hw red alder WA 13.7 0.3 19

P 14.2 d hw red alder WA 24.4 0.3 19

P 13.1 d hw red alder WA 28.7 0.3 19

P 14.5 d hw red alder WA 40.6 0.3 19

P 36.5 o hw red alder WA 30 47 0.2 4.2 o 25

Table A1.—Continued

Continued

R C ff T D L Aa Laa Loa Ea c:dw mrt rf citeb

(Mg/ha) (years) (km) (years)
26

P 1.3 d hw red alder OR, WA 14 84, v

P 0.9 d hw red alder OR, WA 24 84, v

P 0.9 d hw red alder OR, WA 65 84, v

P 16.9 d hw elm WA 36 47 0.2 2.9 128, 139

P 5.0 c hw alder WA 40 47 122 0.3 0.34 3.1 d 136

P 7.0 d hw red alder OR 44 3.5 150, 138

W 9.0 c p lodgepole pine OR 100 44 121 1.7 23

W 19.5 o p lodgepole pine WY m 43 110 2.9 30

W 13.9 o p lodgepole pine WY 105 41 106 2.8 20.6 o 34

W 13.1 o p lodgepole pine WY 105 41 106 2.8 18.8 o 34

W 18.6 o p lodgepole pine WY 105 41 106 2.8 21.7 o 34

W 16.9 o p lodgepole pine WY 105 41 106 2.9 19.6 o 34

W 9.5 o p lodgepole pine WY 70 41 106 3.1 12 o 34

W 13.1 o p lodgepole pine WY 240 41 106 3.0 11.7 o 34

W 10.7 o p lodgepole pine WY 15 41 106 2.8 49.9 o 119

W 8.9 o p lodgepole pine WY 30 41 106 2.8 8.7 o 119

W 10.2 o p lodgepole pine WY 50 41 106 2.8 10.7 o 119

W 12.0 o p lodgepole pine WY 100 41 106 2.8 13.6 o 119

W 15.1 o p lodgepole pine WY 260 41 106 2.8 18.8 o 119

W 45.5 d p lodgepole pine CO m 40 106 2.8 123

W 34.0 d p lodgepole pine CO m 40 106 2.8 123

W 31.5 d p lodgepole pine CO m 40 106 2.8 123

W 27.8 d p lodgepole pine CO m 40 106 2.8 123

W 27.0 d p lodgepole pine CO m 40 106 2.9 123

W 36.3 d p lodgepole pine CO m 40 106 3.0 123

W 33.3 d p lodgepole pine CO m 41 107 3.0 123

W 40.0 d p lodgepole pine CO m 41 107 2.9 123

W 33.7 d p lodgepole pine CO m 41 106 3.2 123

W 41.4 d p lodgepole pine CO m 41 106 2.9 123

W 23.0 d p lodgepole pine CA m 135

W 15.6 o p lodgepole pine WY 85 41 106 2.8 20.3 o 147

W 69.7 d p ponderosa pine CA 38 1.5 60 138

W 12.1 d p ponderosa pine AZ 35 2.2 15.5 138

W 2.6 d p ponderosa pine, conifer CA 37 119 1.5 1

W 10.0 d p ponderosa pine IM 50 7, v

W 12.0 d p ponderosa pine IM 50 7, v

W 8.5 d p ponderosa pine IM 50 7, v

W 5.8 d p ponderosa pine IM v

W 7.7 d p ponderosa pine AZ y 37

W 3.3 d p ponderosa pine MT m 67

W 40.7 d p ponderosa pine, conifer CA 70 39 121 1.3 68

W 4.6 d p ponderosa pine IM 49 70, v

W 13.8 d p ponderosa pine IM 50 71, v

W 30.7 d p ponderosa pine IM 50 71, v

W 19.2 d p ponderosa pine West 73, 33

W 14.8 c p ponderosa pine AZ m 34 111 2.2 0.4 72

W 9.9 c p ponderosa pine AZ m 34 111 2.2 0.42 72

W 11.5 d p ponderosa pine AZ 35 112 2.1 78

Table A1.—Continued

Continued

R C ff T D L Aa Laa Loa Ea c:dw mrt rf citeb

(Mg/ha) (years) (km) (years)
27

W 24.4 d p ponderosa pine AZ 35 112 2.1 78

W 46.0 d p ponderosa pine CA m 135

W 35.8 c p ponderosa pine NM 200 36 106 2.7 0.32 48.7 d 136

W 18.1 d p ponderosa pine AZ 56 142, 139

W 25.0 d p Jeffrey pine, conifer CA m 39 121 1.4 68

W 34.6 d p Jeffrey pine CA m 135

W 9.9 d p western white pine CA m 135

W 3.3 d rs white fir, giant sequoia CA 37 119 1.8 1

W 35.9 o rs Sequoia CA 100, v

W 53.8 d rs giant sequoia CA m 135

W 96.9 c rs giant sequoia CA m 2.3 15.2 121

W 22.2 d pj singleleaf pinyon IM m 2.2 33

W 20.0 d pj pinyon - juniper CA m 135

W 23.2 o mc Douglas-fir, spruce, fir NM 50 35 107 2.9 13.4 o 44

W 23.3 d mc Douglas-fir MT m 67

W 47.0 d mc Douglas-fir, incense-cedar CA 110 39 121 1.0 68

W 23.6 d mc Douglas-fir CA m 135

W 25.2 d mc spruce - fir CO 350 40 106 3.6 40 d 9

W 26.3 d mc cedar - hemlock ID m 67

W 37.4 d mc cedar - hemlock MT m 67

W 26.6 d mc subalpine fir MT m 67

W 43.7 d mc spruce - fir CO m 40 106 3.1 123

W 37.0 d mc spruce - fir CO m 40 106 3.0 123

W 34.8 d mc spruce - fir CO m 40 106 3.2 123

W 43.3 d mc spruce - fir CO m 40 106 2.8 123

W 36.3 d mc spruce - fir CO m 40 106 3.0 123

W 38.5 d mc spruce - fir CO m 41 107 3.0 123

W 44.4 d mc spruce - fir CO m 40 106 3.4 123

W 54.8 d mc spruce - fir CO m 41 106 3.3 123

W 38.1 d mc spruce - fir CO m 41 106 3.0 123

W 45.5 d mc spruce - fir CO m 41 106 2.8 123

W 31.9 d mc incense-cedar CA m 135

W 41.9 d mc mountain hemlock CA m 135

W 31.6 d mc red and white fir CA m 135

W 24.1 c mc spruce - fir NM 300 36 106 3.4 0.37 58.9 d 136

W 19.3 d mc mixed conifer IM v

W 29.1 o mc fir, cedar, pine CA m 39 121 1.3 13.3 o 54

W 73.9 c mc fir - pine CA m 2.1 17 121

W 34.6 c mc mixed conifer NM 200 36 106 2.7 0.42 21 d 136

W 34.0 d hw aspen CO m 40 106 2.8 123

W 34.4 d hw aspen CO m 40 106 2.8 123

W 51.1 d hw aspen CO m 40 106 2.8 123

W 37.4 d hw aspen CO m 40 106 2.8 123

W 49.2 d hw aspen CO m 40 106 2.8 123

W 17.8 d hw aspen CO m 41 107 2.8 123

W 25.2 d hw aspen CO m 41 107 2.8 123

W 33.3 d hw aspen CO m 41 107 2.8 123

W 18.5 d hw aspen CO m 41 107 2.7 123

W 43.3 d hw aspen CO m 41 106 2.7 123

Table A1.—Continued

Continued

R C ff T D L Aa Laa Loa Ea c:dw mrt rf citeb

(Mg/ha) (years) (km) (years)
28

W 10.0 c hw aspen NM 60 36 106 3.1 0.35 11.4 d 136

W 16.2 d hw black oak CA 38 1.5 28.3 138

W 13.8 d hw gambel oak West 24, 33

W 20.7 d hw gambel oak AZ 35 112 2.1 78

W 4.7 d hw oak, chaparral AZ m 34 111 1.6 101

A 30.2 o sw black spruce AK 130 65 148 0.3 31

A 20.6 o sw white spruce AK 160 65 148 0.2 31

A 73.3 o sw black spruce AK 55 65 0.5 460 o 25

A 48.8 o sw black spruce (muskeg) AK 51 65 0.2 620 o 25

A 65.6 o sw black spruce AK 130 64 220 o 25

A 14.2 d hw aspen AK 64 0.1 20.5 138

A 21.9 d hw aspen AK 64 0.1 33.2 138

A 16.2 d hw birch AK 64 0.1 19.7 138

A 18.2 d hw birch AK 64 0.1 23 138

A 19.1 o hw aspen AK 70 65 148 0.3 31

A 27.2 o hw paper birch AK 110 65 148 0.3 31

A 25.5 d hw birch AK 50 64 26 139

A 37.8 o hw paper birch AK 50 64 26 o 25

a

Some papers did not explicitly provide age, latitude, longitude, or elevation. However, a number of these also provided enough

information to include an approximate value; these columns include these values as well as values directly from site descriptions. b

Observations with two citations are those where the primary reference was identified (the first citation) but the value was obtained

from another source (the second citation). Citations “v” are from an unpublished report by K. A. Vogt, 1996; on file at the USDA

Forest Service Northeastern Research Station, Durham, NH.

Table A1.—Continued

R C ff T D L Aa Laa Loa Ea c:dw mrt rf citeb

(Mg/ha) (years) (km) (years)
29

Table A2. —Forest types used for summarizing data.

Region Forest type Type description

North p pine

sf spruce, fir, hemlock

mx mixed conifer-hardwood

ab aspen-birch

mb maple-beech-birch, northern hardwood

oh mixed hardwood, oak, hickory

South p pine

mx mixed conifer-hardwood

hw mixed hardwood, oak, hickory

Pacific Northwest df Douglas-fir, Western hemlock, Sitka spruce

sfh fir-hemlock, higher elevation

hw hardwood

West p pine

rs redwood/sequoia

pj pinyon/juniper

mc mixed conifer

hw hardwood

Alaska sw softwood

hw hardwood
30

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Printed on Recycled Paper

Smith, James E.; Heath, Linda S. 2002. A model of forest floor carbon mass for

United States forest types. Res. Pap. NE-722. Newtown Square, PA: U.S.

Department of Agriculture, Forest Service, Northeastern Research Station. 37 p.

Includes a large set of published values of forest floor mass and develop largescale estimates of carbon mass according to region and forest type. Estimates of

average forest floor carbon mass per hectare of forest applied to a 1997 summary

forest inventory, sum to 4.5 Gt carbon stored in forests of the 48 contiguous United

States.

Keywords: forest carbon budget; carbon sequestration; afforestation
Headquarters of the Northeastern Research Station is in Newtown Square,

Pennsylvania. Field laboratories are maintained at:

Amherst, Massachusetts, in cooperation with the University of Massachusetts

Burlington, Vermont, in cooperation with the University of Vermont

Delaware, Ohio

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Hamden, Connecticut, in cooperation with Yale University

Morgantown, West Virginia, in cooperation with West Virginia University

Parsons, West Virginia

Princeton, West Virginia

Syracuse, New York, in cooperation with the State University of New York,

College of Environmental Sciences and Forestry at Syracuse University

Warren, Pennsylvania

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