United States

Department of

Agriculture

Forest

Service

North Central

Research Station

General Technical

Report NC-258

Soils as an Indicator 

of Forest Health:

A Guide to the Collection,

Analysis, and Interpretation of

Soil Indicator Data in the Forest

Inventory and Analysis Program

Katherine P. O’Neill, Michael C. Amacher, and Charles H. Perry
Photographs courtesy of

USDA Agricultural Research Service and USDA Natural Resources Conservation Service.
Abstract

The Montreal Process was formed in 1994 to develop an internationally agreed upon set of criteria

and indicators for the conservation and sustainable management of temperate and boreal forests. In

response to this effort, the Forest Inventory and Analysis (FIA) and Forest Health Monitoring (FHM)

programs of the USDA Forest Service have implemented a national soil monitoring program to address

specific questions related to: (1) the current and future status of soil resources and (2) the contribution of forest soils to the global carbon cycle. As the first and only nationally consistent effort to monitor forest soil quality in the United States, this program provides critical baseline information on the

current status of the soil resource and the potential effects of natural and human disturbance on forest

health and productivity.

This report provides documentation on the types of data collected as part of the FIA soil indicator, the

field and laboratory methods employed, and the rationale behind these data collection procedures.

Particular emphasis is placed upon describing generalized approaches for analyzing and interpreting

soil indicator variables and discussing the strengths and limitations of individual soil variables. The

analytical techniques detailed in this report are not intended to be exhaustive. Details of specific analytical approaches will be provided in a series of subsequent publications. Rather, the purpose of this

report is to provide guidance to analysts and researchers on ways to incorporate soil indicator data

into reports and research studies.

O’Neill, Katherine P.; Amacher, Michael C.; Perry, Charles H. 

2005. Soils as an indicator of forest health: a guide to the collection, analysis, and interpretation of

soil indicator data in the Forest Inventory and Analysis program. Gen. Tech. Rep. NC-258. St. Paul,

MN: U.S. Department of Agriculture, Forest Service, North Central Research Station. 53 p.

Documents the types of data collected as part of the Forest Inventory and Analysis soil indicator, the

field and laboratory methods used, and the rationale behind these data collection procedures. Guides

analysts and researchers on incorporating soil indicator data into reports and research studies.

KEY WORDS:

Forest inventory, FIA, soils, forest health monitoring, sampling, estimation, analysis, reporting.

The U.S. Department of Agriculture (USDA) prohibits discrimination

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Published by:

North Central Research Station

Forest Service U.S. Department of Agriculture

1992 Folwell Avenue

St. Paul, MN 55108

2005

Web site: www.ncrs.fs.fed.us
Acknowledgments

The soil indicator program began in the early 1990s and has benefited from the efforts and direction

of numerous investigators since then. The original pilot studies were led by Mike Papp and Rick Van

Remortel at the U.S. Environmental Protection Agency. Berman Hudson (USDA Natural Resources

Conservation Service) continued the development of Forest Health Monitoring soil protocols from

1994 to 1996. Craig Palmer (University of Nevada-Las Vegas) served as the Forest Health Monitoring

lead for this indicator from 1995 until 2000 and was responsible for developing and implementing

many of the protocols described in this document.

Before 2000, laboratory analysis of soil samples was directed by Russ Dresbach and R. David Hammer

at the University of Missouri Soil Characterization Lab. In 2001, three laboratories were added to the

program: (1) the North Central Forestry Sciences Laboratory in Grand Rapids, Minnesota, previously

managed by Don Nagel and William Pettit; (2) the Rocky Mountain Forestry Sciences Laboratory in

Logan, Utah, directed by Mike Amacher; and (3) the International Institute of Tropical Forestry directed

by Mary Jeanne Sanchez.

In addition, of vital importance to the soil monitoring program are the State forest health coordinators,

regional trainers, field crews, quality assurance staff, and laboratory technicians who collect, verify,

and analyze the soil data. Without their efforts and commitment to data quality, the soil monitoring

program would not be possible.
Table of Contents

Page

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

1.1 Indicators of Conservation and Sustainable Management . . . . . . . . . . . . . . . . . . . . . . . . . . .1

1.2 History of the Soil Indicator Program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2

2. SAMPLING DESIGN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5

3. COMPACTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .9

3.1 Rationale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .9

3.2 Variables Used to Assess Compaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .9

3.2.1 Percent compacted area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .9

3.2.2 Type of compaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .10

3.2.3 Bulk density (see also section 5.3.3) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .10

3.3 Analysis and Interpretation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .11

3.4 Examples of Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .11

3.5 Limitations to Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .14

4. EROSION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .15

4.1 Rationale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .15

4.2 Variables Used to Assess Soil Erosion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .15

4.2.1 Percent bare soil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .15

4.2.2 Soil texture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .16

4.2.3 Slope angle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .16

4.2.4 Vegetation structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .16

4.2.5 Forest floor thickness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .16

4.3 Models Used to Assess Erosion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .17

4.3.1 Universal Soil Loss Equation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .17

4.3.2 Water Erosion Prediction Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .18

4.4 Analysis and Interpretation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .18

4.5 Examples of Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .18

4.6 Data and Model Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .21

4.6.1 USLE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .21

4.6.2 WEPP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .22

5. SOIL PHYSICAL AND CHEMICAL PROPERTIES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .23

5.1 Rationale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .23

5.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .24

5.2.1 Forest floor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .24

5.2.2 Upper 20 cm of soils . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .24

5.2.3 Organic soils . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .24

5.3 Variables Used to Assess Soil Physical Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .24

5.3.1 Forest floor thickness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .24

5.3.2 Moisture content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .25

5.3.3 Bulk density (see also section 3.2.3) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .25

5.3.4 Coarse fragment content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .25

5.3.5 Depth to restrictive horizon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .26

5.4 Variables Used to Assess Chemical Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .26

5.4.1 Soil acidity (pH) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .27

5.4.2 Exchangeable cations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .28

5.4.2.1 Exchangeable potassium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .29

5.4.2.2 Exchangeable calcium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .29

5.4.2.3 Exchangeable magnesium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .29

5.4.2.4 Exchangeable sodium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .29

5.4.2.5 Exchangeable aluminum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .29

5.4.2.6 Effective cation exchange capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .30
Page

5.4.3 Extractable phosphorus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .30

5.4.4 Total nitrogen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .30

5.4.5 Total, organic, and inorganic carbon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .30

5.4.6 C:N ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .31

5.4.7 Extractable sulfur . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .31

5.4.8 Micronutrients and trace metals (Mn, Fe, Ni, Cu, Zn, Cd, Pb) . . . . . . . . . . . . . . . . .31

5.5 Analysis and Interpretation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .32

5.5.1 Unit conversions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .32

5.6 Examples of Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .34

5.7 Limitations to Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .35

6. POTENTIAL PROBLEMS WITH COMBINING DATA FROM MULTIPLE YEARS (1998-2002) . . . .37

7. QUALITY ASSURANCE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .39

7.1 Field . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .39

7.2 Laboratory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .39

8. REGULATIONS PERTAINING TO THE SOIL INDICATOR . . . . . . . . . . . . . . . . . . . . . . . . . . .41

8.1 Plot Confidentiality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .41

8.2 Sample Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .41

8.3 Shipping, Storage, and Disposal of Pest-regulated Soils . . . . . . . . . . . . . . . . . . . . . . . . . . .41

9. SUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .43

10.GLOSSARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .45

11.REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .47

12.LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .51

13.LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .53

Table of Contents continued
1. INTRODUCTION

Because of the long association between soil science and agriculture, the term “soil health” has

traditionally been associated solely with the

capacity of the soil to promote plant growth.

Renewed interest in principles of sustainable

management has expanded this definition to recognize the broader role that soils play in regulating key ecosystem functions such as protecting

watersheds through regulation of infiltration and

runoff, preventing and mitigating pollution

inputs, and providing physical support as a

foundation material for roads and other development (Lal et al. 1997). In response to the need

for more detailed information about changes in

the status of forest soils, the USDA Forest Service

has implemented a national monitoring program

as part of its Forest Inventory and Analysis (FIA)

plot network to address specific questions related to the long-term sustainability of our Nation’s

soil and water resources.

The purpose of this report is to present an

overview of the current stage of development of

this soil monitoring program and to provide

guidance to analysts, researchers, and managers

interested in using FIA soil data to address questions related to conservation and sustainable forest management. The discussion is divided into

sections addressing the three primary foci of the

soil indicator: soil compaction, soil erosion, and

soil physical and chemical properties. For each

topic, detailed information is provided regarding

the types of data collected by FIA, the monitoring questions that these measurements were

designed to address, and the importance of these

measurements to the assessment of forest health.

Field and laboratory methods are described for

each measured variable along with a discussion

of the strengths and potential limitations of these

approaches. Finally, this report provides examples of analytical techniques that may be used to

interpret these variables and to link soil measurements to ancillary data sets and models.

1.1 Indicators of Conservation

and Sustainable Management

Regional and national monitoring of ecosystem

function is a new and developing field of inquiry.

Implementing and analyzing data from these

large-scale monitoring programs requires field

and analytical techniques that may differ from

those typically applied in basic research or other

technical fields. One of the underlying concepts

in Forest Health Monitoring (FHM) is the use of

“indicator” variables to assess change over time.

Indicators are qualitative or quantitative variables that function as signals to relay complex

ecological information in a simple and useful

manner (Kurtz et al. 2001). To be effective, indicator variables must be indicative of a larger ecological process, easily measured, cost-effective,

and repeatable (Burger and Kelting 1999). The

large number of operational requirements necessary to make an indicator variable a useful monitoring tool limits the level of the detail that can

be obtained. Taken in and of themselves, indicators cannot fully explain the causes for the

observed changes or predict the future effects on

ecosystem function. Once a trend has been identified during monitoring, indicator data must be

evaluated in association with other monitoring

data and targeted research to assess the potential

impact on ecosystem function.

Because indicator variables are functionally

defined, comparison of data over time and

between different collection agencies requires

1

Soils as an Indicator of Forest Health:

A Guide to the Collection, Analysis, and 

Interpretation of Soil Indicator Data in 

the Forest Inventory and Analysis Program

ABOUT THE AUTHORS

Katherine O’Neill1 was formerly a

Research Soil Scientist with the Forest

Inventory and Analysis unit at the North

Central Research Station in St. Paul,

MN. She holds a B.S. in geology from

the College of William and Mary and a

Ph.D. in biogeochemistry and ecosystem ecology from Duke University. She

joined the North Central Research

Station in 2001 where she focused on

the effects of disturbance on soil carbon and nutrient cycling and on the

development of techniques for monitoring changes in forest soil properties.

She also served as the soil indicator

advisor for the eastern U.S. within the

Forest Inventory and Analysis program

from 2001 to 2003.

Michael Amacher is a Research Soil

Scientist with the Reclamation of

Disturbed Lands unit at the Forestry

Sciences Laboratory in Logan, UT. He

holds a B.S. and an M.S. in chemistry

and a Ph.D. in soil chemistry, all from

The Pennsylvania State University. He

joined the Rocky Mountain Research

Station in 1989. He studies the effects

of natural and human-caused disturbances on soil properties and develops

methods for restoring disturbed

ecosystems. He is the western soil

indicator advisor for FIA.

Charles H. (Hobie) Perry is a Research

Soil Scientist with the Forest Inventory

and Analysis unit at the North Central

Research Station in St. Paul, MN. He

holds a B.A. in philosophy from the

University of Michigan and an M.S. and

a Ph.D. in forestry from the University

of Minnesota. He was on the faculty of

Humboldt State University for 5 years

and joined the NCRS in 2004. His current research focuses on estimation of

the soils component of carbon stocks

and methods of generalizing and summarizing FIA data across varying spatial

scales. He is the eastern soil indicator

advisor for FIA.

1 Current affiliation: USDA Agricultural

Research Service, Appalachian Farming

Systems Research Center, Beaver, WV 25813.
consistency in applying and interpreting these

definitions. The Montreal Process was formed in

1994 to develop an internationally agreed upon

set of criteria and indicators for the conservation

and sustainable management of temperate and

boreal forests (Anonymous 1995). At a meeting

of this working group in 1995, the participants

reached consensus on seven criteria (table 1).

For each of these criteria, the working group

established specific indicator variables that

would be monitored to quantify the criteria. In

the United States, a number of the indicator

variables were already measured as part of

national or regional monitoring efforts (e.g., FIA,

National Water Quality Assessment Program,

National Atmospheric Deposition Program).

However, quantification of other indicator variables, such as those related to the conservation

of soil resources (table 2), required the development of new monitoring initiatives. Together, the

FIA, FHM, and Forest Health Protection (FHP)

programs currently collect data that can be used

to address more than half of the 67 Montreal

Process indicators.

1.2 History of the Soil Indicator

Program

FIA has served as the Nation’s primary source of

information on forest resources for more than 70

years. Over the past two decades, the FIA program

has responded to evolving customer concerns and

research needs by expanding its core inventory

program to address a broader perspective on 

integrated resource management and sustainable

management and to monitor all forest lands

regardless of ownership (Miles 2002, Stolte et al.

2002). During this same period, increasing concerns about forest health led State and Federal

agencies to establish FHM, a cooperative effort to

monitor and report on the long-term status,

changes, and trends in U.S. forests. Beginning in

the early 1990s, FHM developed, tested, and

implemented a suite of monitoring variables that

were closely related to indicator variables proposed by the Montreal Process: crown condition,

ozone bioindicator species, lichen abundance

and diversity, vegetation structure and diversity,

down woody materials, and soil properties.

Forest health indicators were developed with

support from the scientific research community

and represent a cooperative effort between university, State, and Federal investigators.

The Agricultural Research, Extension, and

Education Reform Act (PL 105-185) of 1998

authorized transferring the Detection Monitoring

portion of FHM plots to the Forest Inventory

and Analysis program beginning with the 2001

field season. This integration provides a mechanism for repeated, systematic sampling of forest

health indicators using nationally standardized

collection, preparation, and data distribution formats that are compatible with forest inventory

(McRoberts et al. 2004). Additional references

documenting the merger of these two plot networks and the implications for statistical analyses may be found at the FIA library (available

online at http://fia.fs.fed.us/library).

2

Table 1.—Montreal Process criteria

Criterion Description

1 Conservation of biological diversity

2 Maintenance of productive capacity of forest ecosystems

3 Maintenance of forest ecosystem health and vitality

4 Conservation of soil and water resources

5 Maintenance of forest contributions to global carbon cycles

6 Maintenance and enhancement of long-term multiple socio-economic benefits to meet the needs of societies

7 Legal, institutional, and economic framework for forest conservation and sustainable management
3

Table 2.—Indicators relating to the conservation of soil and water resources (Criterion 4) of the Montreal Process

Indicator Description

18 Area and percent of forest land with significant soil erosion

19 Area and percent of forest land managed primarily for protective functions (e.g., watersheds, flood protection, avalanche 

protection, riparian zones)

20 Percent of stream kilometers in forested catchments in which stream flow and timing has significantly deviated from the 

historic range of variation

21 Area and percent of forest land with significantly diminished soil organic matter and/or changes in other soil chemical properties

22 Area and percent of forest land with significant compaction or change in soil physical properties resulting from human activities

23 Percent of water bodies in forest areas with significant variance of biological diversity from the historic range of variability

24 Percent of water bodies in forest areas with significant variation from the historic range of variability in pH, dissolved oxygen, 

levels of chemicals (electrical conductivity), sedimentation, or temperature change

25 Area and percent of forest land experiencing an accumulation of persistent toxic substances
4
2. SAMPLING DESIGN

The joint FIA and FHM sampling design is

derived from a systematic array of hexagons

superimposed across the United States.

Locations of permanent plots were randomly

selected within each hexagon, and the entire plot

array is considered an equal probability sample

of the total surface area of the Nation. Each

hexagon represents approximately 2,428 ha and

is systematically assigned to one of five interpenetrating, non-overlapping panels (Brand et al.

2000). Panels are measured on a rotating basis

with targets of one panel per year in the Eastern

U.S. and one 50-percent subpanel per year in

the Western U.S. (McRoberts et al. 2004). This

design is derived from a global framework developed by the U.S. Environmental Protection

Agency (USEPA) as part of the Environmental

Monitoring and Assessment Program (EMAP)

(Overton et al. 1990, USEPA 1997, White et al.

1992) and later implemented by FHM.

The FIA program consists of three phases (figs. 1

and 2). In Phase 1, land area is stratified using

aerial photography or classified satellite imagery

to increase the precision of estimates using stratified estimation. This process entails assigning

each plot to a single stratum and determining the

proportion of the land area represented by each

stratum. In Phase 1, remotely sensed data may

also be used to determine if plot locations have

accessible forest land cover. In Phase 2, field

crews visit plot locations that have accessible 

forest land cover, and they collect data on more

than 300 variables, including land ownership,

forest type, tree species, tree size, tree condition,

and other site attributes (e.g., slope, aspect, 

disturbance, land use) (Smith 2002). Plot intensity for Phase 2 measurements is approximately

one plot for every 2,428 ha of forested land

(125,000 plots nationally).

5

Ecosystem

Index Sites

21 permanent intensive-site

ecosystem monitoring

locations across US

8,000 permanent forest plots

13 mile (22 km) grid

Permanent–125,000 forest plots

3 mile (5 km) grid

Millions of 1m–1km pixels

Temporary or permanent

100,000+ plots-various scales

INTEGRATED FIA

Phase 3

Forest Health

Phase 2

Forest Inventory

Local

management

inventories

Phase 1

Remote Sensing

Ground

Figure 1.—A schematic outline of the

three-phase FIA sampling design. 

[Diagram from the FIA fact sheet series

(available online: http://fia.fs.fed.us/library).]
Phase 3 plots represent a 1:16 subset of Phase 2

sample plots (1 plot per 38,450 ha of forest

land) that are measured for a broader suite of

forest health attributes including tree crown conditions, lichen community composition, vegetation diversity and structure, down woody materials, and soil attributes. Phase 3 plots are

assigned to one of five interpenetrating panels

and are sampled once every 5 years. Data collection and analysis on Phase 3 plots is administered cooperatively by FIA, FHM, the FHP, State

natural resource agencies, and universities.

Because each Phase 3 plot is also a Phase 2 plot,

all forest mensuration and ownership survey

data are collected in concert with forest health

data. As of the 2003 field season, soil monitoring

had been implemented in 45 States and Puerto

Rico. When the program is fully implemented,

soil variables will be collected on approximately

7,800 plots measured over a 5-year cycle.

Comprehensive reviews of the FIA sampling

strategy may be found in Brand et al. (2000) and

McRoberts et al. (2004).

Each FIA plot represents a circular sampling area

of 1 ha and consists of a cluster of four 7.32-m-

(1/60-ha-) radius subplots arranged in a triangular pattern around a central subplot (fig. 3).

Subplot centers are located 36.6 m (120 ft) apart

with the centers of subplots 2, 3, and 4 oriented

at 120o angles around the plot center. The plot

configuration includes smaller components for

sampling other forest attributes such as small

trees, nonwoody vegetation, down woody debris,

and vegetation diversity and structure. Each subplot is surrounded by a 17.95-m- (58.9-ft-)

radius annular plot that is used for destructive

sampling, including the collection of soil samples for chemical analysis. Plots are arranged in a

fixed pattern regardless of the conditions (e.g.,

forest, grassland, road) on the plot.

Because of the interrelationship between the FIA

and FHM programs, the soil monitoring program

should also be considered as one part of a multitiered approach in FHM for assessing the ecological significance of changes in soil properties

across the landscape. FHM consists of five interrelated and complementary activities: Detection

Monitoring, Evaluation Monitoring, Intensive

Site Monitoring, Research on Monitoring

Techniques, and Analysis and Reporting (Tkacz

2003). Detection monitoring consists of nationally standardized aerial and ground surveys

designed to collect baseline information on the

current condition of forest ecosystems and to

detect changes from those baselines over time.

The ground survey portion of the FHM

Detection Monitoring program is also now Phase

3 in FIA. Once a potential forest health concern

has been identified on Detection

Monitoring/Phase 3 plots, Evaluation Monitoring

studies are used to examine the extent, severity,

and probable causes of these changes. Intensive

6

Figure 2.—An example of 

the three-phase FIA sampling

design as implemented in

Minnesota.
Site Monitoring projects are conducted to

increase understanding of cause and effect relationships and assess specific issues at multiple

spatial scales. Finally, Research on Monitoring

Techniques focuses on developing and refining

measurements to improve the efficiency and reliability of data collection and analysis.

7

Figure 3.—Diagram of an FIA Phase 3

plot showing soil measurement locations. [Erosion and compaction are measured on the 7.32-m- (24-ft-) radius subplot. Soil samples are collected along

soil sampling lines that run at a tangent

to subplots 2, 3, and 4. During the first

visit to a plot, field crews collect soils at

point 1. Subsequent samples are spaced

at 3-m (10-ft) intervals alternating on

opposite sides of starting point 1.]
8
9

3. COMPACTION

The FIA Phase 3 soil compaction measurements

were developed to address Criterion 4, Indicator

22 from the Montreal Process: area and percent

of forest land with significant compaction or

change in soil physical properties resulting from

human activities.

3.1 Rationale

Although soils are typically considered in terms

of their mineral fractions, the term “soil” actually refers to a multiphase matrix of minerals, organic

matter, water, and air. The ratio between these

components largely determines the ability of the

soil to support plant life. For optimal plant

growth, about 50 percent of the total soil volume

should consist of pore space that is equally filled

with air and water (Fisher and Binkley 2000).

Soil compaction occurs when the mineral portion of the soil becomes compressed by heavy

equipment or by repeated passes of light equipment, people, or animals, thus reducing pore

space and decreasing the volume of air in the

soil. Under severe compaction, soil aggregates

may break down entirely, resulting in “puddling”

or the loss of soil structure by particles being

dispersed in water and then settling to form a

dense crust.

Compaction can have a variety of negative effects

on soil fertility resulting from changes in both

physical and chemical properties. Reduction in

pore space makes the soil more dense and difficult to penetrate and can constrain the size, reach, and extent of root systems. In severe

cases, this reduced rooting volume can lead to

structural failure of the plant support system and

destabilization of the entire tree. Reduction in

soil aeration and water movement can reduce

the ability of roots to absorb water, nutrients,

and oxygen, resulting in shallow rooting and

stunted tree forms. At the landscape scale,

destruction of soil structure can limit water infiltration sufficiently to increase rates of runoff and

soil loss from erosion. In addition to changes in

soil physical properties, compaction can also significantly impact biological and chemical

processes occurring in the soil. By reducing the

oxygen content of the soil below that required

for adequate respiration, severe compaction can

disrupt root metabolism and move the soil

towards an anaerobic condition.

3.2 Variables Used to 

Assess Compaction

Variables used to assess soil compaction are

measured on all four of the FIA subplots (fig. 3).

These measurements are based primarily on

visual assessments of compacted areas and crew

assessments of the type of compaction associated

with compacted areas.

3.2.1 Percent compacted area

Field crews record a two-digit code indicating

the percentage of the subplot that exhibits evidence of compaction (table 3). Soil compaction

is assessed relative to the conditions of adjacent

undisturbed soil. Improved roads are not included in this evaluation.

To quantify compacted areas, crews are trained

to identify five different “evidences” of compaction: (1) increased soil density; (2) platy soil

structure; (3) impressions or ruts in the soil (at

least 5 cm into the mineral soil); (4) loss of soil

structure (e.g., puddling); and, (5) mottling

(specks of orange and/or green indicating a recent change in soil aeration). For evidences

that require excavation, compacted soils are

assessed relative to undisturbed soils off of the

subplot to minimize plot disturbance. Before

2002, crews recorded the presence or absence of

these evidences as part of the compaction data.

In 2002, these variables were dropped from the

indicator because they were not quantitative and

data from the subplots could not be aggregated

to the plot level.

Compaction is evaluated only for forested portions

of the subplot. If the subplot includes nonforested

areas, crews multiply the percentage of compaction

on the forested part of the subplot by the fraction

of the subplot that is in forested area. For example,

if 50 percent of the subplot is forested and the

compaction on the forested part is 30 percent, then

compaction for the entire plot is 15 percent.
3.2.2 Type of compaction

If crews report any compacted area on a subplot,

they also record the types of compaction

encountered. For each of the compaction types,

a value of “1” is recorded if the type is present; a

value of “0” is recorded if it is not (table 4).

3.2.3 Bulk density (see also section 5.3.3)

Bulk density is the weight of a unit volume of dry

soil, typically expressed in units of grams per cubic

centimeter (g cm-3). Higher bulk density values

indicate a lower volume of pore space available for

air and water exchange. High bulk densities also

impede root growth and penetration. Root growth

is typically impaired at bulk densities greater than

1.6 g cm-3 (Brady and Weil 1996). Because of their

tendency to form aggregates, fine-textured soils

such as clays and loams will tend to have lower

bulk densities than coarse-textured soils (table

5). Mineral soils with a large fraction of rocks

and coarse fragments (> 2 mm in diameter) will

have higher bulk densities than soils without significant coarse fragment content. In contrast,

organic matter tends to reduce bulk density. As a

result of its high organic matter content, the forest floor, which is comprised of the decomposing

litter layer and the decomposed humus layer,

will nearly always have a lower bulk density

than mineral soils. Details on procedures and

calculations for bulk density may be found in

section 5.3.3.

10

Table 3.—Codes and cover classes used in assessing evidence of surface compaction

Code Surface compaction Code Surface compaction Code Surface compaction

percent percent percent

00 Absent 35 31-35 75 71-75

01 Trace 40 36-40 80 76-80

05 1-5 45 41-45 85 81-85

10 6-10 50 46-50 90 86-90

15 11-15 55 51-55 95 91-95

20 16-20 60 56-60 99 96-100

25 21-25 65 61-65

30 26-30 70 66-70

Table 4.—Types of compaction recorded

Code Description

TyRutTr Rutted trail. Ruts must be at least 5 cm (2 in.) deep into mineral soil or 15 cm (6 in.) deep from the undisturbed forest litter surface.

TyComTr Compacted trail (usually the result of many passes of heavy machinery or vehicles).

TyComAr Compacted area. Examples include the junction areas of skid trails, landing areas, heavily grazed soils, and work areas.

TyOther Other. An explanation must be entered in the plot notes.
3.3 Analysis and Interpretation

Data from the compaction indicator can be analyzed at the regional or national level. The compacted area reported for each subplot may be

averaged to determine the mean compacted area

per plot.

(1)

where is the mean compaction on the plot, Cs

is the area of the subplot compacted, and n is the

number of subplots. Because of the highly localized nature of soil compaction, attention should

also be paid to plots on which two or more subplots show “significant” levels of compaction.

Percent compacted area is recorded by cover class,

and crews record a value of “01” to indicate trace

levels of compacted soils. In practice, trace levels

of surface soil compaction may occur during the

course of plot measurements and are not usually

indicative of a management effect.

Despite the importance of aeration and soil

strength in regulating soil fertility, there are no

widely accepted standards of what constitutes

significant compaction. Compaction varies greatly in nature and depends on physical properties

and the moisture content of the soil. As a result,

the extent of soil compaction that should be

considered ecologically significant will differ

regionally depending on soil texture, forest

cover, and landscape position.

Wherever possible, data from the compaction

variables should be interpreted with respect to

the texture and bulk density of samples collected

from the same plot. Under comparable conditions, soils with a range of soil particle sizes (i.e.,

fine sandy loam) are generally more susceptible

to compaction than soils with a more uniform

particle size distribution. In these soils, disturbance causes the finer soil particles to fill the

larger macropores formed by the coarse particle

fraction, producing a denser soil. Although finely

textured soils have more pore space and can

hold more water per unit volume, the effects of

compaction may be less permanent in these soils

because of shrinking and swelling in response to

wetting and drying. In general, the risk of compaction damage also tends to increase with

increasing moisture content; the greatest sensitivity to compaction occurs at moisture contents

that are near but below field capacity (the percentage of water remaining in a soil 2 or 3 days

after it has been saturated and free drainage has

ceased). This occurs because water reduces soil

strength and acts as a lubricant between soil particles, increasing the degree of compaction following application of a load.

3.4 Examples of Analyses

To illustrate how soil erosion data may be used

in FIA, FHM, or other reporting efforts, evidence

of surface compaction and bulk density was

evaluated for 227 plots in Minnesota, Wisconsin,

and Michigan. During 1999 to 2000, the majority of plots (166 plots or 73.1 percent) reported

no evidence of surface compaction (fig. 4). Only

25 of the 227 plots (11.0 percent) reported compaction on more than 10 percent of the plot area

(fig. 4). The 10 plots reporting evidence of compaction on more than half of the plot were distributed across the three-State region and did not

appear to correspond to a particular forest or soil

type (fig. 5).

11

n

C

C ∑ s =

Table 5.—Approximate bulk densities for a variety of soils and soil materials1

Material Approximate bulk density (g cm-3)

Organic soils 0.1-0.7

Pine wood 0.7

Forest loamy A horizons 0.7-1.2

Water 1.0

Cultivated clay and silt loams 0.9-1.6

Cultivated sandy loams and sands 1.2-1.8

Concrete 2.1

Compacted glacial till 1.9-2.2

Quartz mineral 2.65

1Adapted from Brady and Weil (1996).

C
12

Figure 4.—Frequency distribution of

soil compaction (surface disturbance)

reported for plots measured in

Minnesota, Michigan, and Wisconsin

(1999-2000).

Figure 5.—Soil compaction reported for

plots measured in Minnesota, Michigan,

and Wisconsin (1999-2000). [Mean plot

values were determined as the mean

value from measurements made on the

three subplots.]
13

From 2000 to 2002, the mean bulk density ± 1

standard deviation for the 0- to 10-cm soil layer

was 1.01 ± 0.32 g cm-3 (n = 290) (fig. 6). Mean

bulk densities measured for the 10- to 20-cm

layer were higher at 1.36 ± 0.26 g cm-3 (n =

293; two-tailed t-test, P < 0.0001). Bulk density

values were highly correlated with organic carbon (C) content for the 0- to 10-cm cores, suggesting that samples toward the lower end of the

bulk density range may be a result of the incorporation of some O horizon materials due to

crew error in identifying the break between the

forest floor and mineral soil horizons (fig. 7).

Only 2.8 percent of the 0- to 10-cm samples (8

out of 290 samples) had a measured bulk density greater than 1.5 g cm-3, supporting the assessment that surface compaction was not widespread on the FIA plots measured.

Figure 6.—Frequency distribution of bulk

density values collected from 0- to 10-cm

and 10- to 20-cm cores collected in

Minnesota, Michigan, and Wisconsin

(2000-2002).
3.5 Limitations to Data

Soil physical properties are not conventionally

monitored in a way that facilitates national reporting. Changes in soil bulk density in response to

harvesting or other disturbances are usually measured on a site-specific basis. More quantitative

measurements of compaction severity using techniques such as cone penetrometer or shear

strength estimates are sensitive to variations in

soil moisture content, complicating comparison

of data collected on different sampling dates or

in different regions. Current measurements of

compaction on FIA plots are based primarily on

visual estimates of compacted area. Subsurface

compaction more than a few years old may not

be readily visible to field crews and may be

under-reported. In addition, measurements do

not reflect the degree or intensity of compaction.

Pilot studies are underway to develop additional,

more quantitative, measures of soil compaction

based on the use of pocket penetrometers

(Amacher and O’Neill 2004).

14

Figure 7.—Organic carbon content as a

function of bulk density (0- to 10-cm cores)

(O’Neill et al., in press).
15

4. EROSION

The FIA Phase 3 soil erosion measurements were

developed to address Criterion 4, Indicator 18

from the Montreal Process: area and percent of

forest land with significant soil erosion.

4.1 Rationale

Soil erosion is a natural geologic process in the

building up and wearing down of the land surface. However, for resource managers concerned

with optimizing production on a specific parcel

of land, erosion can threaten soil, water, and

related forest and plant resources. Extensive

areas of soil erosion can have a major effect on

aquatic ecosystems associated with forests, recreational opportunities, potable water supplies,

and the lifespan of river infrastructure such as

dams. By removing stored nutrients and organic

matter from the soil surface, accelerated erosion

also diminishes the capacity of the soil to support vegetation. On a global basis, the amount of

nitrogen (N), phosphorus (P), and potassium (K)

removed by erosion is estimated at more than 38

million Mg yr-1 (Brady and Weil 1996).

Although rates of erosion in undisturbed forest

ecosystems tend to be low due to thick surface

organic layers and tree roots that hold the soil in

place, accelerated losses of surface soils can

result from the removal of plant cover and the

breakdown in root system integrity following site

disturbance, harvest, and site preparation. High

rates of localized soil erosion can also occur in

response to the construction of roads on steep

hillsides or to the harvesting of trees on sites

with fragile or erodible soils. In addition,

hydrophobic (water repellent) soils that result

from wildfires are susceptible to erosion because

water will tend to flow across the soil surface as

runoff instead of infiltrating into the soil profile.

4.2 Variables Used to Assess 

Soil Erosion

Variables used to assess soil erosion are measured on all four of the FIA subplots (fig. 3).

These measurements are based primarily on

measurements of exposed bare soil, plant cover,

soil texture, and slope. Plot data are then combined with ancillary information on climate and

landscape position to parameterize soil erosion

models.

4.2.1 Percent bare soil

On each subplot, field crews visually assess

ground cover and record a two-digit code to indicate the percentage of the 7.31-m-radius subplot

that is covered by bare soil (mineral or organic)

(table 6). Fine gravel (2-5 mm) is considered part

of the bare soil. However, large rocks protruding

through the soil (e.g., bedrock outcrops) are not

included in this category since these are not erodible surfaces. For the soil indicator, cryptobiotic

crusts are not considered bare soil.

Percent bare soil is evaluated only for forested

portions of the subplot. If the subplot includes

nonforested areas, crews multiply the percentage

of bare soil on the forested part of the subplot by

the fraction of the subplot that is in forested

Table 6.—Codes and cover classes used in evaluating percent bare soil

Code Bare soil Code Bare soil Code Bare soil

percent percent percent

00 Absent 35 31-35 75 71-75

01 Trace 40 36-40 80 76-80

05 1-5 45 41-45 85 81-85

10 6-10 50 46-50 90 86-90

15 11-15 55 51-55 95 91-95

20 16-20 60 56-60 99 96-100

25 21-25 65 61-65

30 26-30 70 66-70
area. For example, if 50 percent of the subplot is

forested and the coverage of bare soil on the

forested part is 30 percent, then bare soil for the

entire plot is 15 percent.

4.2.2 Soil texture

After collecting a sample core for chemical analysis (described in section 5.2), field crews assess

the texture of soils in the 0- to 10-cm and 10- to

20-cm layers (table 7). Texture estimation is

done in the field by hand texturing a moistened

sample (approximately the consistency of modeling clay or wet newspaper) between the thumb

and forefinger. The moistened sample is wet

enough to saturate all of the particles but not so

moist that excess water flows freely from the

sample when squeezed. Soils that cannot form a

ball and have a grainy texture are coded either as

sandy or coarse sand. Soils that will form a ball

and a self-supporting ribbon are coded as clayey.

Soils that will form a ball but not a ribbon are

coded as loamy.

4.2.4 Vegetation structure

Before 2002, percent vegetation cover (< 6 ft tall)

was estimated for each 24-ft-radius subplot

based on visual assessments. Detailed measurements of understory plant canopy height were

then collected on three 4-ft-diameter erosion

microplots established on each subplot. Details

of this sampling design are provided in table 8.

This sampling design was developed for use with

specific tables in the Universal Soil Loss

Equation (USLE) model (Renard et al. 1997, Wischmeier and Smith 1978). With the addition of the vegetation diversity and

down woody materials indicators to the FIA

Phase 3 program, analysis of soil erosion models

indicated that measurements on the soil erosion

plots could be replaced with data collected by

other indicators. On plots where all three indicators have been implemented, this restructuring

has significantly reduced time for the field crews

without a loss of analytical power. In particular,

more detailed information on the cover and

structure of understory vegetation may now be

obtained from measurements collected as part of

the vegetation diversity indicator, allowing for

the use of a number of different erosion models.

In cases where the vegetation diversity data are

not collected on a plot, other variables within

the soil indicator (e.g., bare soil cover, forest

floor thickness) are sufficient to model erosion

losses using the Water Erosion Prediction Project

(WEPP) model.

4.2.5 Forest floor thickness

When collecting soil samples (section 5.2), field

crews record the thickness of the forest floor

measured from the top of the litter layer to the

boundary between the forest floor and mineral

soil. Measurements are made at the north, east,

west, and south edges of a 12-inch-diameter circular sampling frame (for a total of four measurements). Where bare soil or bedrock material

is exposed, a depth of “0” is recorded.

Forest floor thicknesses are collected from soil

sampling sites adjacent to subplots 2, 3, and 4.

Samples are collected if, and only if, the soil

sampling sites are forested. Additional information on forest floor thickness is collected as part

of the down woody materials indicator and can

be combined with these soil data to improve

estimates of mean values.

16

Table 7.—Codes used for recording soil texture

Code Description

0 Organic 1 Loamy

2 Clayey 3 Sandy 4 Coarse sand

4.2.3 Slope angle

Slope angle is measured as part of the Phase 2

data collection by sighting a clinometer along a

line parallel to the average incline (or decline) of

each subplot. This angle is measured along the

shortest pathway downslope before the drainage

direction changes. Data are recorded to the nearest 1 percent. In cases where the slope changes

gradually across the subplot, the average slope is recorded. However, if the slope change is predominantly of one direction, crews code the predominant slope percentage rather than the average. Should the subplot fall directly on or straddle a canyon bottom or narrow ridgetop, crews

code the average slope of the side hills (if subplot center falls directly in the center) or the

slope of the side hill on which the majority of

the plot falls.
4.3 Models Used to Assess Erosion

Because of the expense and logistical difficulty of

directly measuring erosion losses in the field,

potential erosion losses are frequently estimated

by using of empirical or functional models based

on field data. FIA is currently evaluating two

models for estimating soil erosion risk: USLE

and WEPP. A complete description of these 

models is beyond the scope of this report.

Parameterization of both of these erosion models

will be documented in detail in a future set of

publications (e.g., Amacher et al., in review).

4.3.1 Universal Soil Loss Equation

USLE is an empirical model designed to predict

the longtime average soil loss from runoff from

specific field areas under specified cropping and

management systems (Dissmeyer and Foster

1981, 1984; Renard et al. 1991; Wischmeier and

Smith 1978). The USLE models sediment

detachment as a function of five parameters, 

following:

A = RKLSCP

The definitions for these variables are provided

in table 9. Some of these factors may be directly

measured on FIA plots; others are determined

from modeling or from ancillary data sets.

Each of these five parameters represents the

numerical estimate of a specific condition that

affects the severity of soil erosion at a particular

location. However, the erosion values reflected

by these factors can vary considerably under different weather conditions. Therefore, the values

obtained from USLE more accurately represent

long-term averages and cannot be applied to

model losses from a specific year or storm. In

addition, USLE does not account for soil losses

from gully, wind, or tillage erosion.

USLE was designed for use in agricultural systems, and as such, applying the model to a

forested landscape requires interpretation and

additional research to develop consistent criteria

for assigning these parameters. USDA Forest

Service scientists are currently working with the

Natural Resources Conservation Service to develop

17

Table 8.—Changes in field protocols, 1998-2001

Year(s) of Method

implementation

Vegetation height 

1998–2000 Three 4-ft-diameter erosion miniplots established on each subplot centered 12 ft north, south, and west of the subplot

center. If duff thickness < 5 cm, mean height to lowest overhanging vegetation estimated from miniplot.

2001 If duff < 5 cm in thickness, height measured to lowest overhanging vegetation at the center and N, S, E, and W edges 

of the erosion miniplot. Median value recorded.

2002–present Variable dropped because it overlaps with measurements made as part of the vegetation diversity indicator.

Slope length

1998–2001 Estimated from the center of each subplot to the break in slope.

2002–present Variable dropped.

Depth to restrictive horizon

1998–2000 Depth to restrictive layer measured within forest floor sampling area. Mean value recorded.

2001–present Depth to restrictive horizon measured at center and N, S, E, and W edges of forest floor sampling area. Median value recorded.

Forest floor thickness

1998–2000 On each erosion plot, crew recorded the median of five measurements of forest floor thickness.

2001–present Variable dropped from the erosion plots because it overlaps with measurements made as part of the down woody materials and

vegetation diversity indicators.

Ground cover

1998–2001 Visual percent cover estimates of bare soil, litter, and vegetation cover (< 6 ft tall) on all subplots. 

2002 - present Visual percent cover estimates of bare soil on all subplots.
methods for assigning variables to field plot conditions. Detailed information about the USLE

and its application to forested soils can be found

in Wischmeier and Smith (1978) and Renard et

al. (1991).

4.3.2 Water Erosion Prediction Project

WEPP is a process-oriented, continuous simulation, erosion prediction model developed by an

interagency team of researchers from the U.S.

Departments of Agriculture (Forest Service,

Agricultural Research Service, and Natural

Resources Conservation Service) and Interior

(Bureau of Land Management and U.S.

Geological Survey). The model is applicable to

small watersheds and mimics the natural

processes that are important in soil erosion.

Processes addressed in the model structure

include infiltration and runoff; soil detachment,

transport, and deposition; and plant growth,

senescence, and residue decomposition.

Researchers at the USDA Forest Service Forestry

Sciences Laboratory in Moscow, ID, have developed an interface to the WEPP model that

specifically addresses erosion prediction on forest lands. This interface provides results in a

summary form as well as probability tables that

estimate the likelihood of a given level of erosion

occurring following a particular disturbance. The

technical documentation for Disturbed WEPP

(Elliot et al. 2000) provides additional information about the disturbed forest interface and the

WEPP model.

An example of a WEPP input table and guidelines for inputting model parameters is shown in

table 10. Because WEPP uses slightly different

texture designations from those collected by FIA

field crews, a texture conversion table that shows

the WEPP soil textures that correspond to the

textures used by FIA is provided in table 11.

4.4 Analysis and Interpretation

Although the field methods for the erosion variables are relatively straightforward, analysis of

soil erosion data is perhaps the most computationally difficult type of analysis within the soil

indicator. Because of inherent differences in soil

types, landscape positions, and climatic conditions, data on soil erosion are challenging to

summarize in a statistically meaningful way. For

both the USLE and WEPP modeling approaches,

mean erosion rates may be calculated for each

FIA plot (table 12). Plot-level data may then be

aggregated to the regional level for population

estimation.

4.5 Examples of Analyses

To illustrate how soil erosion data may be used

in FIA, FHM, or other reporting efforts, erosion

rates of 177 plots sampled in Washington,

Oregon, and California in 1999 were modeled

using WEPP. For this analysis, all plots were

modeled as if they were in an undisturbed forest.

Under average precipitation events, the WEPP

model predicted no erosion on 49 percent of the

plots (87 out of 177). Only 7.9 percent of plots

18

Table 9.—Erosion factors used in the USLE model

Symbol Factor Description Data source for

modeling FIA plots

R Climatic erosivity Calculated from the annual summation of rainfall energy in every storm Estimated

multiplied by its maximum 30-minute intensity.

K Soil erodibility The average soil loss in tons acre-1 per unit area for a particular soil in cultivated, continuous Estimated

fallow with an arbitrarily selected slope length of 72.6 ft and slope steepness of 9%.

L Slope length A ratio of soil loss under given conditions to that at a site with the “standard” slope steepness Estimated

of 9% and slope length of 72.6 ft.

S Slope gradient See above Measured

C Cover and management Reflects the effects of practices that reduce the quantity and rate of water runoff and thus Measured 

reduce the amount of erosion.

P Erosion control practice The ratio of soil loss from land cropped under specified conditions to corresponding loss Estimated

under tilled, continuous fallow conditions. Used to determine the relative effectiveness of 

soil and crop management systems in preventing soil loss.
had a predicted erosion rate of greater than 

1.0 Mg ha-1 (fig. 8). Under a more severe precipitation event (100-year storm), the amount of

modeled soil erosion increased, and 46.3 percent

of plots had a modeled erosion rate greater than

1 Mg ha-1 (median 0.74 Mg ha-1). Although the

potential for erosion is greater under more

extreme climate events, these mean erosion losses are still lower than the estimated 6.9 Mg ha-1

annually lost from U.S. croplands (U.S.

Department of Agriculture 2000). (Note: for

State or regional reports, analysts may wish to

19

Table 10.—Example of a WEPP input table for an FIA Phase 3 plot1

Location2 Paradise Dam

Soil texture3 Loam

Element4 Treatment5 Gradient6 Length7 Area8 Cover9

(%) (feet) (acres) (%)

35

Upper 20-year-old forest 112 99 

35

1.33

35

Lower 20-year-old forest 112 99

35

1 Modified from Amacher et al., in review.

2 Select from climate station database.

3 Choose sandy loam, silt loam, clay loam, and loam.

4 WEPP divides a hillslope into upper and lower areas or elements, but it can also be applied to portions of a hillslope (e.g., plot area).

5 Choose a forest or disturbance type. Choices for forest type are 20-year-old forest (mature forest) or 5-year-old forest 

(young forest). Choices for disturbance type are low-severity fires, high-severity fires, or skid trails.

6 Use the mean of the slopes for all four subplots in the WEPP input table. 

7 Divide the whole plot area into upper and lower elements of equal slope length.

8 Corresponds to a rectangular area surrounding all four subplots.

9 Soil cover = 100 - percent bare soil. Use mean for all four subplots.

Table 11.—Soil texture conversion table for converting soil texture data collected by FIA field

crews into soil texture classes required by WEPP

Soil textures in FIA field guide Soil textures in WEPP

Coarse sand Sandy loam

Sand Sandy loam

Loam Loam

Clay Clay loam

Table 12.—Soil erosion data collected from Phase 3 plots and input data needed for the USLE and WEPP models1

FIA field data USLE inputs Disturbed WEPP inputs

— Rainfall erosivity factor (R) Climate station data

Soil texture Soil erodibility factor (K) Soil texture

Percent bare soil Cover factor (C) Total soil cover

Slope angle and length Slope factor (LS) Slope angle and length

— Cultural practice factor (P) —

Forest condition class & ancillary data — Forest or disturbance type

1 Adapted from Amacher et al., in review.
reference National Resources Inventory or

Natural Resources Conservation Service data for

that specific region; c.f. U.S. Department of

Agriculture 2000.) Under severe weather

events, the highest potential erosion rates were

associated with plots on steep slopes located

within the Coast Range in California, the

foothills of the Sierra Nevada Mountains in

California, and the Cascade Mountains in

Washington and Oregon (fig. 9).

The majority of the factors used to model erosion rates (e.g., climate, soil texture, slope) are

relatively static with regards to monitoring objectives. The primary management factor controlling changes in erosion losses from forested systems is the amount of bare soil exposed at the

ground surface following disturbance (Conkling

et al., in press; O’Neill and Amacher 2004). Data

collected in 1999 indicate that, throughout the

20

Figure 8.—Frequency distribution of

WEPP-modeled erosion rates for plots in

California, Oregon, and Washington (1999)

under an average storm event 

(2-year return interval) and a 100-year

storm event. [As an initial analysis, model

runs assume an undisturbed forest.]

Figure 9.—WEPP modeled erosion on 

FIA plots in California, Oregon, and

Washington (1999) for two different

storm intensities.
Pacific Northwest, the number of plots reporting

levels of bare soil large enough to increase erosion estimates is relatively small. The majority of

plots (57 percent) reported cover on more than

95 percent of the plot area (fig. 10). Less than 2

percent of plots (3 plots) measured in 1999

recorded areas of bare soil that covered more

than half of the plot area.

Analysts should keep in mind that erosion estimates represent only modeled potentials under

different rainfall intensities and do not reflect

actual erosion losses. However, change in erosion rates, or a change in the distribution of

plots exhibiting a high potential for erosion, may

reflect trends in exposed ground cover, harvesting, or surface disturbance. 

4.6 Data and Model Limitations

Because erosion estimates are made on the basis

of modeled results, analysis of this indicator is

necessarily limited by the assumptions of these

models. It is also important to recognize that

soils vary naturally in terms of their potential for

soil erosion and their ability to tolerate these soil

losses. For this reason, aggregate estimates of soil

erosion have little meaning in and of themselves.

Agricultural erosion monitoring programs typically measure soil erosion losses relative to the

tolerable loss (T factor) for a given soil type. T

factors are available from Natural Resources

Conservation Service soil survey maps and digital products such as the NRCS STATSGO

(1:250,000) and SSURGO (1:12,000 to

1:63,360) databases. However, these tolerance

values may not be directly applicable to forested

soils, and additional research is needed to develop similar erosion loss terms for forested systems. Finally, even in regions where rates of erosion can be reliably estimated, the links between

soil erosion and forest productivity are not

always well understood.

4.6.1 USLE 

USLE is an empirical model developed for use as

a management tool for small tracts of land under

agricultural management and was not designed

for use in forested landscapes. As a result, USLE

requires a number of assumptions that limit its

usefulness in modeling soil erosion on forested

lands. First, USLE calculates only an average soil

erosion rate for a given precipitation and runoff

factor based on long-term average climatic conditions. Second, the land practice factors in

USLE were designed to represent agricultural

management practices and, as such, parameterization of the model for common forest management practices is difficult and requires refinements to the published model parameters.

Finally, USLE represents the first generation in a

series of empirical models developed by the

Natural Resources Conservation Service. Current

21

Figure 10.—Frequency distribution of

mean soil cover on plots measured in

California, Oregon, and Washington in

1999. [The value for each plot represents

the percent of the plot covered by forest

floor or other materials. Mean plot 

values were determined as the mean

value from measurements made on the

three subplots.]
estimates of erosion on agricultural lands are

made using the Revised Universal Soil Loss

Equation (RUSLE), which incorporates results

from additional field experiments and other

refinements to the original model (Foster et al.

1996). However, these refinements were not

made to the woodland portion of the model, and

estimates from forest lands must still be done

using the earlier version. Additional research is

needed to adapt the USLE model for use in FIA,

and at present, modeled data should be reported

as relative rather than as absolute values.

In addition to limitations of the model itself,

there are analytical constraints associated with

our current ability to apply USLE to FIA plots.

Although soil erodibility (K) factors may be

determined from soil texture measurements

made in the field, these texture evaluations are

only broken down into four general categories.

In addition, the methods for slope length estimation (L) are currently being revised, and the

availability of these data will be limited until the

revised methods are reviewed and implemented.

An alternate approach for determining erodibility

factors is to apply georeferenced data from digital

soil surveys (e.g., the NRCS SSURGO databases).

At present, digital county-level soil surveys are not

available for all regions of the country. Use of digital products at coarser scales (NRCS STATSGO,

1:250,000) introduces a greater level of uncertainty and complexity into estimates because

map units represent a composite of multiple soil

components. As a result, there is no way to be

certain that the mapped soil type reflects the

actual conditions on the plot. Additional research

is needed to develop standardized approaches for

linking FIA plot data with soil survey data for use

in automating erosion modeling.

4.6.2 WEPP

The WEPP model overcomes many of the limitations of USLE and provides a more realistic simulation of erosion processes operating on forested sites. First, instead of a single climate term

based on long-term average values, WEPP uses

climate data from a database of more than 2,600

weather stations to calculate precipitation,

runoff, and soil erosion rates for episodic climate

events of different return intervals (e.g., 100, 50,

20, 10, and 5 years). This direct linkage to longterm climate records makes it easy both to tabulate erosion rates for high, average, and low

water years and quantify the potential effects of

climatic extremes on soil erosion. Second, the

databases contained within WEPP allow for soil

erosion predictions under a variety of disturbance regimes such as fire and specific management practices. Third, fewer parameters are

needed to initialize WEPP, and those parameters

that are needed are easier to collect in the field.

Forest Service scientists are currently working to

develop techniques for modeling erosion from

FIA plots using the WEPP model (Amacher et

al., in review).

22
23

The FIA Phase 3 soil chemistry measurements

were developed in response to several indicators

of the Montreal Process (table 13).

5.1 Rationale

The health and productivity of forest ecosystems

may be adversely affected by changes in soil

chemical or physical properties following disturbance or certain management practices. The goal

of the soil chemical measurements is to quantify

changes in soil properties relative to long-term

average values that are sufficient to impact soil

fertility and site productivity.

Soil organic matter (SOM) was selected as a key

index of soil quality because of its importance

as a regulator of soil chemical, biological, and

physical properties. Forest ecosystems obtain

most of their nutrients from the decomposition

of litter, branches, and other organic materials

near the soil surface. SOM contains a large

number of exchange sites that increase the

capacity of the soil to adsorb these nutrients

and prevent them from leaching below the rooting zone. SOM can also adsorb potentially

toxic levels of some elements (e.g., copper

(Cu), cadium (Cd), aluminum (Al), lead (Pb)),

thereby decreasing their bioavailability to

microorganisms, plants, and animals. In addition to nutrient retention, SOM facilitates the

transport of air and water through the soil by

increasing moisture holding capacity and promoting the development of soil aggregates.

Finally, SOM serves as a major reservoir for ter- restrial C. On a global scale, soils are estimated

to contain more C than the atmosphere itself

(Schlesinger 1991). As concern grows about

possible climatic responses to increased CO

2

emissions, an improved understanding of the

capacity of forested systems to sequester C in

soils is critical for developing national policy

initiatives. Because SOM is concentrated at the

soil surface, both its quantity and quality may

change following certain forest operations and

management practices.

Although SOM is a useful index variable for

looking at trends across broad areas of the

landscape, a more detailed evaluation of soil

chemical and physical properties is necessary to

address specific forest health and productivity

concerns. For example, soil pH is a primary

factor in determining the productivity of the

soil through its regulation of soil nutrient availability, aggregate stability, and microbial activity.

Maps of soil pH in relation to texture and forest

type provide a baseline index of the weathering

status and potential nutrient holding capacity

of soils. Together with the concentrations of

exchangeable sulfur (S), Al, and base cations

such as sodium (Na), potassium (K), magnesium (Mg), and calcium (Ca), changes in soil

pH over time provide a mechanism for monitoring the potential effects of soil acidification

in response to industry and fossil fuel emissions. Concentrations of plant nutrients (N, P,

K) may provide additional insight into questions about stand productivity, growth, and

mortality. Finally, changes in concentrations of

trace metals in the upper mineral soil may be

used to monitor the effects of pollution and the

accumulation of plant toxic substances in the

soil.

5. SOIL PHYSICAL AND CHEMICAL PROPERTIES Table 13.—Montreal Process indicators addressed by soil chemistry measurements

Indicator Description

4.21 Area and percent of forest land with significantly diminished soil organic matter and/or

changes in other soil chemical properties

4.25 Area and percent of forest land experiencing an accumulation of persistent toxic substances

5.27 Contribution of forest ecosystems to the total global carbon budget, including 

absorption and release of carbon
5.2 Methods

Soil chemical and physical properties are

assessed through the collection of soil samples,

which are then submitted to a regional laboratory for analysis. Samples of the forest floor and

mineral soil are collected within the annular plot

at pre-assigned locations along soil sampling

lines adjacent to subplots 2, 3, and 4 (fig. 3).

Initial sampling points are located 27.4 m (90 ft)

from the plot center along the azimuth lines to

the centers of subplots 2, 3, and 4. Subsequent remeasurement locations are spaced at 3-m (10-

ft) intervals alternating on opposite sides of the

initial sampling point. Soils are collected if, and

only if, the pre-assigned soil sampling location is

forested, regardless of the condition of the subplot. On plots where an obstruction (e.g., boulder, standing water) prevents collection at the

designated sampling point, the sampling point

may be relocated within a radius of 1.5 m (5 ft).

5.2.1 Forest floor

The forest floor is comprised of two layers. The

upper layer of fresh or decomposing plant parts

is referred to as the litter layer. The primary distinguishing characteristic of this layer is that

plant parts are either undecomposed or are only

partially decomposed but still recognizable. Twigs, deciduous tree leaves, and conifer needles

are examples of plant parts in the litter layer.

Underneath the litter layer is the humus or duff

layer. This layer is a dark, light-textured organic

material that has decomposed to such an extent

that plant parts can no longer be recognized.

The thickness of the litter and duff layers are

measured at the north, south, east, and west

edges of the sampling frame. The forest floor

(subplots 2, 3, and 4) is then sampled by collecting all organic materials less than 0.64 cm (1/4

inch) in diameter within a 30-cm- (12-inch-)

diameter sampling frame. Only organic material

within this size range is collected; rocks and

larger woody materials are discarded.

5.2.2 Upper 20 cm of soils

Once the forest floor has been removed, mineral

and organic soils are sampled volumetrically on

subplot 2 using an impact-driven bulk density

corer (AMS Core Sampler Model #910) with an

internal diameter of 4.8 cm. Two 10-cm-long

plastic or stainless steel liners are inserted inside

of the core head before sampling and used to

divide core samples into two sections: 0-10 cm

and 10-20 cm. Soils that cannot be sampled

with the impact-driven corer (e.g., rocky soils,

wetland soils) are sampled using a nonvolumetric excavation method. Soils are collected only if

the soil sampling location is forested. The texture of each layer is estimated in the field and

characterized as organic, loamy, clayey, sandy, or

coarse sandy (see section 4.2.2).

5.2.3 Organic soils

In the FIA program, the term “organic” is used to

designate a soil with more than 20 cm of organic

material overlying mineral soil, bedrock, or

water. These soils are primarily associated with

wetlands and are prevalent in certain regions of

the country. The term “organic” as used in FIA

should not be confused with an organic horizon

(or O horizon) as commonly designated in soil

terminology. On organic soils, only the litter, and

not the entire volume of the forest floor, is collected from the sampling frame. Crews attempt

to collect a soil sample using the impact-driven

corer. However, in many cases, this will not be

possible without severely compacting the sample. If compaction occurs, or if crews have difficulty in obtaining a complete core, samples may

be collected at the 0- to 10-cm and 10- to 20-cm

depth increments using a Dutch auger or shovel.

5.3 Variables Used to Assess 

Soil Physical Properties

5.3.1 Forest floor thickness

The thickness of the forest floor and litter layers

are used in calculating bulk density values for forest floor samples and in expanding C concentrations to a volumetric basis. When collecting soil

samples, field crews record the thickness of the

forest floor measured from the top of the litter

layer to the boundary between the forest floor and

mineral soil. Measurements are made at the north,

east, west, and south edges of a 30-cm- (12-in-)

diameter circular sampling frame (for a total of

four measurements). Where bare soil or bedrock

material is exposed, a depth of “0” is recorded.

Forest floor thicknesses are collected from soil

sampling sites adjacent to subplots 2, 3, and 4.

Additional information on forest floor thickness is

collected as part of the FIA down woody materials

indicator (Woodall and Williams 2005).

24
5.3.2 Moisture content

Gravimetric soil moisture content is determined

for each soil sample. Samples are shipped to the

lab immediately after field collection and sealed

in plastic bags to prevent the loss of soil moisture. When they arrive in the lab, the field-moist

samples are weighed and dried at ambient temperature to a constant weight. Drying time varies

depending upon the moisture content and the

physical properties of the sample. After being reweighed, a subsample split of the air-dried

sample is placed into an oven where it is dried at

a temperature of 105

oC for 24 hours.

Air ( ) and residual ( ) moisture contents

are then calculated as:

(3)

(4)

where FM is the field-moist sample weight (g),

AD is the air-dry sample weight (g), AD

s is the

air-dry weight of the moisture subsplit (g), and

OD

s is the oven-dry weight of the subsplit (g).

is used to express the weight of the entire

sample on an oven-dry basis (SWOD), following:

(5)

Total moisture content for the entire sample

is then calculated as:

(6)

5.3.3 Bulk density (see also section 3.2.3)

Bulk density is the weight of a unit volume of dry

soil, typically expressed in units of grams per

cubic centimeter (g cm-3). In addition to providing important information about soil aeration and rooting strength, bulk density is the primary

parameter used to scale soil chemical data

(expressed in g element g-1 soil) to a volumetric

(e.g., g cm-3) or areal (e.g., Mg ha-1) basis that is

more meaningful for analysis of plant nutrition. Typical bulk density values for a range of soil

types and soil materials are provided in table 5.

Bulk density is a calculated variable that requires

volumetric sampling in the field and quantitative

measurement of soil moisture content. In FIA,

bulk density is determined for all mineral and

organic soil samples by using an impact-driven

corer to collect a sample of known volume from

the soil sampling site adjacent to subplot 2.

Samples are sent to one of three regional labs

for determining moisture content (see discussion of moisture content above). Bulk density

(BD

M; g cm-3) is then calculated as:

(7)

where V is the volume of the soil core (181 cm

3).

For forest floor and litter samples, volumetric

samples are collected by measuring the thickness

of the forest floor at four points along the outer

edge of a 30-cm- (12-in-) diameter sampling

frame and then collecting all of the organic material less than 0.64 cm (1/4 inch) in diameter

within the sampling frame. The sample is submitted to the lab where it is weighed and a residual

moisture content is determined. Bulk density for

forest floor samples (BD

F) may be calculated as:

(8)

where T is the mean thickness of the forest floor

(cm) and A is the area of the sampling frame (cm).

5.3.4 Coarse fragment content

By convention, soil chemical analyses are conducted only on particles < 2 mm in diameter. To

accurately assess the soil chemical concentrations, it is necessary to know what fraction of the

sample was composed of particles > 2 mm in

diameter. The presence of coarse fragments in the

soil also influences aeration and drainage properties, and this variable may be useful in assessing

soils that may have lower moisture holding

capacities or excessive internal drainage. For a

given texture, soils with high coarse fragment

content may have a higher bulk density than soils

with low coarse fragment content.

Coarse fragment content is measured by sieving

air-dried mineral soil samples through a 2 mm

mesh and determining the weight of the > 2 mm

fraction. This weight is then expressed as a percentage of the total air-dry sample weight.

(9)

where Cw is the weight of the > 2 mm fraction (g).

25

θAD

θOD

=−1 AD

FM

θAD

=−1

ss OD OD

AD

θ

(+1)

= OD

OD

AD SW θ = ( −1)

*100 OD

T

SW

FM

θ

θOD

θ

T

V

SW BD OD M = BD SWOD F (T × A

)

==

× 100 ADC CF w
5.3.5 Depth to restrictive horizon

The depth to a restrictive horizon is used to indicate the potential for physical barriers in the soil

that might impede plant rooting or drainage. In

this context, a restrictive horizon is defined as any

soil condition that increases soil density to the

extent that it might limit root growth. This limitation may be caused by physical properties (hard

rock), chemical properties (acid layer), or both.

Measurements of depth to a restrictive horizon are

collected on subplots 2, 3, and 4 within the 12-

inch diameter area sampled for forest floor chemistry. Crews insert a tile probe into five locations

within the soil sampling area (center, north, east,

south, and west edges) and push it into the soil to

identify whether a restrictive horizon exists. The

maximum depth for testing is 50 cm (20 in). If a

restrictive layer is encountered within the upper

50 cm of the soil, the median depth (cm) measured at these five points is recorded. If a restrictive

horizon is not encountered, the variable is

assigned a value of “50.” Crews assign a code of

“00” if surficial bedrock is present and “99” if too

many rock fragments or cobbles prevent them

from inserting the soil probe.

5.4 Variables Used to Assess

Chemical Properties

Mineral soil samples collected on FIA plots are

analyzed for a suite of physical and chemical

properties (table 14). Each of these variables, the

methods used in analysis, and guidance for

interpretation of results are described in the following section. However, a complete discussion

of lab procedures is beyond the scope of this

report. Documentation of laboratory procedures

(Amacher et al. 2003) may be obtained by contacting the authors of this report or your regional

FIA program.

26

Table 14.—Physical and chemical analyses conducted on mineral soils1

Chemical soil property Method Units Reference

Soil pH Measurement with a combination pH electrode in a 1:1 soil-water pH units Thomas (1996)

suspension (1:2 soil-water suspension for high organic matter samples)

Salt pH Measurement with a combination pH electrode in a 1:1 soil-0.01 M CaCl2 pH units Thomas (1996)

suspension

Exchangeable cations 1 M NH4Cl extraction with ICP-OES analysis mg kg-1 Sumner and Miller (1996);

(Na, K, Mg, Ca, Al) and S Amacher et al. (1990)

Trace metals (Mn, Fe, 1 M NH4Cl extraction with ICP-OES analysis mg kg-1 Sumner and Miller (1996); 

Ni, Cu, Zn, Cd, and Pb) Amacher et al. (1990)

Extractable P for soils Bray-1 (0.03 M NH4F + 0.025 M HCl) extraction with colorimetric mg kg-1 Kuo (1996); Soil and Plant

with pH < 6 analysis by ascorbic acid method Analysis Council (1999)

Extractable P for soils Olsen (pH 8.5, 0.5 M NaHCO3) extraction with colorimetric analysis mg kg-1 Kuo (1996); Soil and Plant

with pH > 6 by ascorbic acid method Analysis Council (1999)

Total C Combustion analyzer for total C (preferred for forest floor and % Soil Survey Laboratory (1996)

noncalcareous mineral soil samples)

Organic and inorganic C Combustion analyzer for multiple forms of C (preferred for % Amacher et al. (2003)

calcareous mineral soil samples)

Organic C only Dichromate oxidation with heating % Nelson and Sommers (1996)

Inorganic C (carbonates) only Pressure calcimeter % Sherrod et al. (2002)

Total N Combustion analyzer % Soil Survey Laboratory (1996)

1 Additional detail is available in Amacher et al. (2003).
5.4.1 Soil acidity (pH)

Soil pH is often referred to as a “master variable”

because it influences nearly all physical, chemical,

and biological processes in the soil. Soil pH is a

measure of hydrogen ion activity (H+) equal to:

pH = -log (H+) (10)

where (H+) refers to the activity of the hydrogen

ion. The pH scale ranges from 0 to 14, with pH

7.0 as the neutral point. The more acidic a soil

is, the lower its pH value. The scale for pH is

logarithmic such that each 1-unit change in pH

correlates to a tenfold change in soil acidity.

Examples of pH ranges for common substances

are found in figure 11.

Soil pH is a primary factor in determining the

fertility of the soil through its regulation of soil

nutrient availability, aggregate stability, and

microbial activity. Both natural and human activities can influence soil pH. For example, chemical fertilizers and organic wastes added to the

soil can react with water in the soil solution to

form strong acids that increase soil acidity.

Similarly, acid deposition associated with the

combustion of fossil fuels forms when gases containing N and S are introduced into the atmosphere where they combine with water to form

nitric and sulfuric acid. In soils that are poorly

buffered against acidic inputs, toxic quantities of

Al may be mobilized and important plant nutrients such as Ca may become depleted. Over

time, forest species may become weakened or

even killed.

Soil acidity may be divided into three 

general categories (Brady and Weil 1996):

Active acidity (water pH) A measure of the H+

activity in the soil at a given time.

Salt-replaceable acidity (salt pH) Associated

with the exchangeable Al3+ and H+ on soil colloids. These ions can be released into the soil

solution by unbuffered salt solutions.

27

Figure 11.—pH ranges for common 

materials and soil types. [Figure adapted

from Brady and Weil (1996).]
Residual acidity The acidity that remains in

the soil after the active and salt-replaceable

acidity has been neutralized. Generally associated with H+ and Al3+ ions bound in nonexchangeable forms.

FIA analyzes soils for both active and salt- replaceable pH. Air-dried and prepared mineral

soil samples are mixed with deionized water in a

1:1 ratio (1 mL water per 1 g air dried soil) to

form a slurry and allowed to sit for 30 minutes;

organic soil samples are mixed at a 2:1 ratio. The

water pH of the slurry is then measured using a

pH meter and electrode. Samples are then spiked

with 200 μL of 1 M CaCl

2, allowed to sit for

another 30 minutes, and remeasured to determine salt pH. Measurements are made to the

nearest 0.1 pH unit.

Acidification of the soil in response to pollution

and acid deposition is an issue of great concern

in some regions of the country. Although the FIA

soils data provide critical monitoring data to

assess the impacts of acidification, the effects of

acid deposition cannot be determined by looking

solely at the current pH of the soil. Soil pH

varies as a function of factors such as clay mineralogy, soil age, weathering status, climate, vegetation, and organic matter. For example, highly

weathered soils in the Southeast may have very

low pH values that do not indicate a forest

health problem. However, if these same low pH

values were found in naturally alkaline soils of

the Upper Midwest, it might well indicate cause

for concern. The ability of a particular soil to

withstand changes in pH is primarily a function

of the acid neutralizing capacity of the soil and

the dominant clay mineralogy. For this reason,

acidification needs to be evaluated within the

context of soil type and must incorporate other

soil chemical parameters such as exchangeable

Al and S.

Many of the available soil maps and surveys are

based largely upon analysis of agricultural soils,

and care must be taken when comparing pH values from forested soils to survey data. In general,

forest soils tend to be naturally more acidic than

agricultural soils due to the higher levels of

organic acids produced by decomposition and

leaching through the forest floor.

5.4.2 Exchangeable cations 

Clay minerals and organic matter in the soil

function as potential reservoirs for plant nutrients because they have a net negative charge.

Negatively charged exchange sites on the surface

of soil particles bind with positively charged ions

(cations) in the soil solution (e.g., Na

+, Mg

2

+

,

Ca

2

+). These cations are not permanently bound

to the particle and can be replaced by other

cations as the chemistry of the soil solution

changes. The cation exchange capacity (CEC) refers to the total number of exchangeable

cations that a soil can hold, and is equivalent to

the amount of its negative charge. The higher the

CEC, the more cations a soil can retain.

The nutrient holding capacity of a particular soil

depends upon a number of factors, including

texture, mineralogy, and pH. Only clays and

organic matter carry a negative charge and are

capable of contributing to the CEC. As a result,

fine-textured soils will generally have higher

CECs than sandier soils. Different clay minerals

also have different CECs. In general, the more

highly weathered the clay mineral, the lower the

CEC. Because of this strong association with texture and mineralogy, the spatial distribution of

nutrient holding capacity closely follows the

underlying geology of the parent material.

Finally, the CEC of a soil varies as a function of

pH. Soil exchange sites preferentially adsorb H

+

ions, and as the number of H

+ ions in solution

increases (pH decreases), the number of

exchange sites available for nutrient retention

declines. As a result, these nutrients may leach

through the soil where they become unavailable

for plant uptake.

FIA determines exchangeable base cation concentrations based on an extraction with an

unbuffered solution of ammonium chloride

(NH

4Cl). The extract solution is then analyzed

by ICP-OES (inductively coupled plasma optical

emission spectroscopy). Results are expressed in

units of milligram of cation per kilogram of soil.

Reporting of cation data from Phase 3 plots

depends highly on the method used, and any reporting should specify both the method and

extractant used. One of the more commonly

used methods for determining exchangeable

cations uses an ammonium acetate (NH

4OAc)

28
extraction buffered at a pH of 7.0 or 8.2,

depending on which version of the method is

used. By buffering the extract at such a high pH,

there is little effect of pH-dependent charge.

However, few forest soils have a pH of 8.2 or

even 7.0 in the field, and this cation concentration represents a maximum value. In the FIA

method, the pH of the solution varies depending

upon the pH of the sample, and extraction provides a more accurate representation of the CEC

under field conditions. An additional advantage

of the NH

4Cl method is that Al, S, and trace

metals may also be determined from the same

extraction, greatly reducing analysis time and lab

expenses. However, it also means that the

exchangeable cations measured at a given site

may have a higher degree of variability from year

to year than when measured by a buffered

extraction. As a result of differences in methods,

cation concentrations reported in the literature

may be higher than those reported by FIA.

5.4.2.1 Exchangeable potassium

Potassium (K

+) is one of the three major plant

nutrients (along with N and P). In addition to

functioning as a primary component in the gas

exchange mechanism of plants by facilitating the

operations of the stomata, K

+ plays a role in the

synthesis of starch and the translocation of carbohydrates in plants. Potassium is also essential

to a plant’s capacity to resist disease, survive cold

temperatures, and provide drought protection

(Potash and Phosphate Institute 1995). Although

most soils contain large amounts of K

+ in clay

minerals and rocks, the majority of this is

unavailable for plant uptake (Brady and Weil

1996). FIA measures only the exchangeable, or

plant-available, forms of K

+ that are found in the

soil solution or held in an exchangeable form by

soil organic matter and clay.

5.4.2.2 Exchangeable calcium

Calcium (Ca

2

+) is used by plants for a variety of

functions, including stimulation of root and leaf

development, formation of cell walls, enzyme

activation, and neutralization of organic acids.

Deficiencies can result in poor root growth and

gelatinous leaf tips and growing points (Taiz and

Zeiger 1991). However, deficiencies are rarely

observed in the field because secondary effects

such as low pH usually limit growth first (Potash

and Phosphate Institute 1995). Calcium is the

dominant cation in most neutral to alkaline forest floor solutions and typically occupies 70-90

percent of soil exchange sites (Fisher and

Binkley 2000). Total amounts of Ca

2

+ in the soil

range from less than 0.1 percent in newly

drained organic soils to as much as 25 percent in

calcareous soils of arid regions (Potash and

Phosphate Institute 1995).

5.4.2.3 Exchangeable magnesium

Magnesium (Mg

2

+) is a key element required for

plant photosynthesis because of its role as the

central atom in the chlorophyll molecule (Potash

and Phosphate Institute 1995, Taiz and Zeiger

1991). Magnesium is also involved in phosphate

metabolism, plant respiration, and the activation

of many enzyme systems. Deficiencies are often

associated with coarse-textured, acidic soils in regions of high precipitation. In soils with a low

CEC, Mg

2

+ uptake may be reduced by high Ca

2

+

concentrations, resulting in plant deficiencies

(Potash and Phosphate Institute 1995).

5.4.2.4 Exchangeable sodium

High concentrations of Na

+ in the soil may be

detrimental both physically and chemically. At

high levels, Na

+ tends to break apart soil aggregates. These dispersed clay particles clog the soil

pores as they move through the profile, reducing

water and air infiltration. In addition, plant

growth on salt-affected soils tends to be limited

by high levels of Na

+, OH-

, and HCO

3

- ions

(Brady and Weil 1996). Because Na

+ may easily

be leached from the upper part of the soil profile

in areas with high rates of precipitation,

exchangeable Na

+ tends to be greatest in arid

regions such as the Western U.S.

5.4.2.5 Exchangeable aluminum

At very low pH values (pH < 5.0), Al in clay

minerals becomes soluble and exists as positively

charged aluminum (Al

3

+) or aluminum hydroxy

(e.g., Al(OH)

2

+, Al(OH)

2

+) cations. These cations

can become adsorbed on negatively charged clay

particles in the same way that base cations do

(Brady and Weil 1996). Aluminum ions can react with water molecules to release H+ ions

into solution and increase soil acidity (lower

pH). For example:

Al

3

+ + H

2O 

⇔ AlOH

2

+ + H

+ (11)

Each Al

3

+ ion can produce three H

+ ions, greatly

decreasing soil pH (Sparks 1995). In addition to

29
its effects on soil acidity, Al is toxic to some

plants and can cause roots to become short and

stunted (Taiz and Zeiger 1991). Susceptibility to

Al toxicity varies by species.

5.4.2.6 Effective cation exchange capacity

Effective cation exchange capacity (ECEC) reflects the CEC of soils after the effects of saltextractable acidity have been removed. In practice, ECEC is determined as the sum of

exchangeable base cations plus exchangeable Al:

ECEC = ∑ [Ca

2

+], [Na

+], [Mg

2

+], [K

+], [Al

3

+

] (12)

ECEC is expressed in terms of milligram equivalents per 100 g of soil (meq/100 g) or centimoles

of charge per kilogram of soil (cmol

c kg

-

1); the

two units are equivalent. ECEC is a useful index

of total nutrient holding capacity in a particular

soil and will vary as a function of clay mineralogy, weathering status, and soil pH.

5.4.3 Extractable phosphorus

Phosphorus (P) is an essential component of

nearly all metabolic processes in both plants and

animals. Phosphorus forms a high-energy bond

in the organic compound ATP (adenosine

triphosphate), which drives most energydependent biochemical reactions (Brady and Weil 1996). In addition, P is a key component of

DNA and cellular membranes (Taiz and Zeiger

1991). Despite its great importance in plant

metabolism, soil P is frequently bound in forms

unavailable for plant uptake and may become a

limiting factor for site productivity (Brady and Weil 1996).

Determination of P availability depends highly

on the method used. Extractants used in soil P

determination estimate the capacity of the soil to

provide P by dissolving and/or desorbing a particular fraction of the labile P. Unlike other soil

nutrients measured in FIA that are found as positively charged ions (e.g., Ca2+, Mg+, Na+), P typically occurs as part of the negatively charged

phosphate complex (PO

4

-

). Since clay particles

and organic matter are also negatively charged,

the bonding mechanisms for P are more complex

(Brady and Weil 1996, Sparks 1995).

FIA measures extractable P on all mineral soil

samples. Soils are extracted with a solution 

tailored to the type of dominant P mineralogy

in the soils, filtered, and then analyzed 

colorimetrically. The extractant used in this

analysis varies depending upon the pH. A Bray-1

extractant (0.03 M NH

4F + 0.025 M HCl) is

used for soils with a pH < 6.0. Near-neutral and

alkaline soils (pH > 6.0) are extracted following

the Olsen method (pH 8.5, 0.5 M NaHCO

3).

Results are reported in units of milligrams of P

per kilogram of soil.

5.4.4 Total nitrogen 

Nitrogen (N), a key factor in the development of

leaf area, regulates the amount of photosynthate

available for plant growth and reproduction, and

increases the protein content of plants. Yet,

despite its great importance in plant nutrition,

the amount of available N in the soil is small.

The majority of soil N is contained in soil organic matter (organic N) in a form that is unavailable to plants. Plant-available soil N (inorganic

N) is produced when microorganisms mineralize

organic forms of N. The two primary resulting

ions are ammonium (NH

4

+) and nitrate (NO

3

-

);

some plant species prefer NH

4

+ nutrition while

others utilize NO

3

- (Marschner 1986). This

plant-available N represents 2-3 percent of the

total amount of N in the soil (Potash and

Phosphate Institute 1995).

In FIA, total soil N is measured by dry combustion. A small sample of air-dried soil is combusted in a pure oxygen (O) atmosphere. The combustion products are separated and quantified by

passing them through a packed column. Results

are expressed as a percentage of air-dry sample

weight. When interpreting total-N data, it is

important to keep in mind that the majority of

this N is not available for plant uptake. Total-N

data should be used only as an index of soil N

availability.

5.4.5 Total, organic, and inorganic carbon

Soil organic matter is important for water retention, C sequestration, and soil organisms and is an

indication of soil nutrient status (Brady and Weil

1996). Changes in soil organic matter or nutrients

can affect the vitality of forest ecosystems through

diminished regeneration capacity of trees, lower

growth rates, and changes in species composition.

The accumulation of biomass as living vegetation,

debris, peat, and soil C is an important forest

function in regulating atmospheric C concentrations. The production rate of biomass is also a

measure of forest health and vitality.

30
FIA analyzes soils for total C concentration by

dry combustion following the method outlined

above for total N. Results are expressed as a percent of air-dry sample weight. In high pH soils,

some fraction of total C may be derived from

inorganic carbonates in the soil parent material.

Mineral soils with a pH > 7.5 are analyzed for

either inorganic (TIC) or organic carbon (TOC),

depending on the instrumentation at the lab,

and the remaining pool is determined by subtraction from total carbon (TC):

TC = TIC + TOC (13)

One of the objectives of the soil indicator is to

provide a mechanism for monitoring changes in

soil organic matter and C sequestration in response to management practices and disturbances. Inorganic C does not represent C

sequestered in the system as a result of plant

activity, and only organic C concentrations

should be used for developing C budgets. Details

on converting concentrations to volumetric and

areal estimates are provided in section 5.5.

5.4.6 C:N ratio

The ratio of organic C to N is frequently used by

ecologists as an index of litter quality (Brady and Weil 1996, Fisher and Binkley 2000, Waring and

Schlesinger 1985). The majority of soil microorganisms obtain their energy by metabolizing soil

C (decomposition). During this process, they

withdraw nutrients from the soil. On average, soil

microorganisms must incorporate 1 part of N for

every 8 parts of C metabolized (C:N ratio of 8:1).

As a result, the C:N ratio of organic matter tends

to decline as organic material becomes more highly decomposed (Brady and Weil 1996, Schlesinger

1991). For reference, C:N ratios from common

organic materials are provided in table 15.

5.4.7 Extractable sulfur

Sulfur (S) is a constituent of many amino acids

and is closely associated with N in protein and

enzyme formation. Both inadequate and excess

amounts of S in the soil can cause forest health

problems (Potash and Phosphate Institute 1995, Taiz and Zeiger 1991). Plant-available S (inorganic S) in soil occurs as the SO4

2- anion.

Because of this negative charge, SO

4

2- is not usually adsorbed onto soil particles and remains in

the soil solution where it can be removed by

leaching (Brady and Weil 1996). In the United

States, deficiencies of S are most commonly

observed in the Southeast, the Pacific Northwest,

and the Great Plains. Soils in regions with a high

concentration of heavy industry, such as the

Northeast, are less likely to be deficient in S

(Brady and Weil 1996).

At an ecosystem level, excess S is associated with

several types of air and water pollution that can result in forest decline and other health problems. High levels of atmospheric S are associated

with industry and the combustion of fossil fuels.

Some of these materials can become oxidized in

the atmosphere to form sulfuric acid and sulfate

salts, which are then deposited in forests

through wet and dry deposition (Waring and

Schlesinger 1985). Elevated levels of atmospheric S, and the acidification that these compounds

can cause in the soil, may result in serious damage to forest ecosystems (Schlesinger 1991). In

combination with information about pH and

exchangeable Al, analysis of temporal trends in

soil S concentration relative to background levels

will provide needed information for monitoring

the effects of acid deposition on forest soils.

5.4.8 Micronutrients and trace metals 

(Mn, Fe, Ni, Cu, Zn, Cd, Pb)

Although required in smaller quantities,

micronutrients may be just as important to plant

nutrition as major nutrients. Micronutrients

measured in FIA include manganese (Mn), iron

(Fe), copper (Cu), and zinc (Zn). Some of the

additional metals selected for analysis can be

used to address Criterion 4, Indicator 25: Area

and percent of forest land experiencing an accumulation of persistent toxic substances. These

include nickel (Ni), cadmium (Cd), and lead (Pb).

31

Table 15.—C:N ratios from common organic materials

1

Organic material C:N ratio

Spruce sawdust 600:1

Hardwood sawdust 400:1

Wheat straw 80:1

Household compost 16:1 Average B horizon (mineral soil) 9:1

Soil bacteria 5:1

1 Adapted from Brady and Weil (1996).
Table 16.—Example table for reporting physical properties of forest floor and mineral soils by forest type group

Number of Depth to % coarse Forest floor Bulk density 

Soil layer and forest type group samples subsoil fragments thickness (cm) (g/cm3

)

Forest floor

Elm/ash/cottonwood group 3 — — 2.02 —

Loblolly/shortleaf pine group 2 — — 5.80 —

Maple/beech/birch group 2 — — 4.52 —

Oak/hickory group 83 — — 4.76 —

Oak/pine group 5 — — 3.58 —

Pinyon/juniper group 3 — — 5.78 —

All forest type groups 98 — — 4.66 —

Mineral (0-10 cm)

Elm/ash/cottonwood group 2 38.96 1.26 — 1.26

Loblolly/shortleaf pine group 1 19.40 48.69 — 1.39

Oak/hickory group 63 17.33 23.94 — 1.20

Oak/pine group 4 13.53 31.57 — 1.13

All forest type groups 70 18.24 23.73 — 1.20

Mineral (10-20 cm)

Elm/ash/cottonwood group 2 — 1.04 — 1.55

Loblolly/shortleaf pine group 1 — 47.56 — 1.43

Oak/hickory group 61 — 27.28 — 1.56

Oak/pine group 4 — 49.89 — 1.57

All forest type groups 68 — 27.61 — 1.56

All table cells without observations in the inventory sample are indicated by —. Table value of 0.0 indicates the volume rounds to less than 0.01.

Columns and rows may not add to their totals due to rounding.

Micronutrients and trace metals are determined

by extraction of air-dried mineral soils with a 1

M NH4Cl solution and analysis on ICP. Analysis

is concurrent with exchangeable cations (see section 5.4.2). At present, the suite of metals analyzed varies depending on lab instrumentation.

5.5 Analysis and Interpretation

FIA chemical data are available in online databases and will be included in FIA State and regional

reports. Table 16 and table 17 provide an example

of how soils data may be reported at the State

level. Although data can be reported in tables,

information about changes in soil chemical and

physical properties is likely to be more meaningful to managers and researchers when reported

within maps. Soil chemical and physical data

needs to be analyzed in conjunction with other

physical and chemical characteristics, soil type,

and ecosystem. Mapped products allow for the

integration of monitoring data with soil survey

data and/or other base mapping products such as

ecoregion section or subsection and provide the

landscape perspective that is critical to interpreting soils data. There is no reason to expect that

soil type is directly related to forest cover type,

and aggregation of soils data into cover classes for

reporting purposes may mask important trends.

Regardless of how the data are presented, interpretation of a single chemical variable in isolation

may result in misleading findings.

5.5.1 Unit conversions

Elemental data from soil analyses are typically

expressed in units of concentration (e.g., %, mg

kg-1, cmolc kg-1). However, because plants exist

within a volume of soil, units of volume may be

more ecologically meaningful for addressing forest

health questions. Concentration data are converted to volumetric units by using the soil bulk density (weight soil per unit volume). Mathematically,

this conversion can be represented as:

(14)

where EVOL is the element of interest expressed

volumetrically (g cm-3), [E] is the elemental

32

EVOL = [E]× BD
33

Table 17.—Example table for reporting chemical properties of forest floor and mineral soils by forest type groupSoil layer and Number Organic Inorganic Total Extractable Extractable cations Extractableforest type group of pH carbon carbon nitrogen phosphorus Na K Mg Ca Al ECEC sulfur 

samples H2O CaCl2 (%) (%) (%) (mg/kg) (cmolc/kg) (mg/kg)

Forest floorElm/ash/cottonwood group 3 — — 28.50 — 1.09 — — — — — — — —Loblolly/shortleaf pine group 2 — — 31.78 — 0.88 — — — — — — — —Maple/beech/birch group 2 — — 30.03 — 1.26 — — — — — — — —Oak/hickory group 81 — — 33.20 — 1.15 — — — — — — — —Oak/pine group 5 — — 34.10 — 1.16 — — — — — — — —Pinyon/juniper group 3 — — 33.42 — 1.26 — — — — — — — —All forest type groups 94 — — 33.04 — 1.15 — — — — — — — —Mineral (0-10 cm)Elm/ash/cottonwood group 2 6.76 6.42 2.00 0.01 0.14 2.26 15.00 190.50 302.50 2,264.50 — 14.34 6.65Loblolly/shortleaf pine group 2 5.38 4.92 3.32 — 0.21 3.31 8.60 81.00 246.20 1,254.80 4.60 8.59 10.52Oak/hickory group 66 5.44 4.88 3.06 — 0.19 5.24 14.10 103.60 169.99 1,088.23 69.69 7.93 8.92Oak/pine group 4 5.75 5.30 3.98 — 0.26 4.11 17.46 111.77 371.62 1,846.23 5.77 12.70 9.00Pinyon/juniper group 1 6.97 6.57 4.66 — 0.32 — 11.00 240.00 970.00 3,615.00 — 26.68 9.00All forest type groups 75 5.51 4.97 3.10 — 0.19 4.99 14.14 108.11 196.57 1,198.75 62.40 8.63 8.89Mineral (10-20 cm)Elm/ash/cottonwood group 2 6.24 5.69 1.09 0.02 0.10 0.72 11.00 98.50 206.50 1,397.50 30.00 9.31 4.05Loblolly/shortleaf pine group 2 5.20 4.67 1.54 — 0.12 2.00 8.20 49.80 147.40 575.60 33.00 4.62 6.76Oak/hickory group 63 5.24 4.57 1.20 — 0.09 4.96 14.68 70.71 135.81 676.47 105.10 5.90 7.15Oak/pine group 4 5.79 5.19 2.24 — 0.17 3.04 23.62 79.38 377.77 1,199.46 5.38 9.46 6.69Pinyon/juniper group 1 7.11 6.69 2.96 — 0.23 — 11.00 215.00 1,030.00 3,535.00 — 26.71 7.00All forest type groups 72 5.33 4.67 1.28 — 0.10 4.60 14.83 73.79 163.82 766.06 94.89 6.48 7.02All table cells without observations in the inventory sample are indicated by —. Table value of 0 indicates the value rounds to less than 0.01. Columns and rows may not add to their totals due to rounding.

ECEC = Effective cation exchange capacity.Extractable phosphorous is reported for the Bray-1 method. Results for the Olsen method are also available.
concentration determined from laboratory analysis, and BD is the sample bulk density (g cm-3).

Additional conversion factors may also be necessary depending on the reporting units for a particular element. Conversion factors for soil

chemical data are summarized in table 18.

In some applications, elemental data are

expressed on an areal basis. For example, C and

N budgets are typically expressed in units of

weight area-1 (e.g., kg m-2, Mg ha-1). Conversion

of volumetric data to an areal basis requires multiplication by the thickness of soil represented by

the sample core. For example:

(15)

where Es = elemental storage, %E is the weight

percent of the element, BD is the bulk density 

(g cm-3), and T is the thickness of the sample

(cm). In FIA, mineral soil samples are collected

using a 10-cm core, such that T = 10.

Depending on the analysis, it may be desirable

to adjust elemental data for the coarse fragment

content. This may be done by modifying

Equation 15, following:

(16)

where S is the volume of the coarse fragment

fraction (> 2 mm diameter). For forest floors,

samples are collected from a known area rather

than a known volume, so concentrations are

converted to an areal basis by multiplying by the

ratio of the sample weight to the area of the sampling frame, following:

(17)

where Es is elemental storage, %E is the weight

percent of the element, SWOD is the oven-dry sample weight, and A is the area of the sampling frame.

5.6 Examples of Analyses

To illustrate how soil chemical data may be used

in FIA, FHM, or other reporting efforts, 383

plots sampled in the Southeastern U.S. in 1998

and 1999 were evaluated for organic matter storage and pH. For upper mineral soils (defined as

“A Horizons” in 1998 and 0-10 cm in 1999),

60.3 percent of the plots (231 out of 383) had

organic C values of less than or equal to 3 percent (fig. 12). Highest C concentrations were

associated with the mountainous regions of

Virginia and West Virginia and central Alabama

(fig. 12). Carbon data may also be aggregated by

soil order and masked using a forest-nonforest

GIS to produce an integrated estimate of C storage within the forest floor and upper 20 cm of

mineral soils at the regional scale (O’Neill et al.

2004; O’Neill et al., in progress1).

34

Table 18.—Factors for converting soil chemical concentrations in areal units

Element Units reported Conversion factor1 (kg m-2)

pH pH units None

Carbon (total, inorganic, organic) Weight % (g C per g soil) Bulk density (g cm-3) * 100

Total nitrogen (N) Weight % (g N per g soil) Bulk density (g cm-3) * 100

Extractable phosphorus (P) mg P per kg soil Bulk density (g cm-3) * 0.0001

Calcium (Ca2+) mg Ca2+ per kg soil Bulk density (g cm-3) * 0.0001

Magnesium (Mg2+) mg Mg2+ per kg soil Bulk density (g cm-3) * 0.0001

Potassium (K+

) mg K+ per kg soil Bulk density (g cm-3) * 0.0001

Sodium (Na+

) mg Na2+ per kg soil Bulk density (g cm-3) * 0.0001

Sulfur (S) mg S per kg soil Bulk density (g cm-3) * 0.0001

Aluminum (Al) mg Al per kg soil Bulk density (g cm-3) * 0.0001

Other base cations mg cation per kg soil Bulk density (g cm-3) * 0.0001

1 Assumes a 10-cm core thickness.

Es = %E × BD ×T

Es = %E × BD ×T × (1− S)

A

SW E E OD

S = % ×
Samples collected throughout the region indicate

that soils were strongly acidic, with only 3 plots

(0.8%) having a pH value above 6.0. Nearly onefifth (17.8% or 68 plots) had a pH value of less

than 4.0 (fig. 13). Under these acidic conditions,

Al in the soil becomes soluble and may cause

toxicity to plants. Soils with the lowest pH values tended to be clustered within the mountains

of West Virginia and the coastal plain of North

Carolina, South Carolina, and Georgia (fig. 13).

After describing the general trends in soil chemistry, analysts may then compare soil properties

with other forest health components, such as

growth/mortality ratios, dieback, or indices of air

quality from the lichen and ozone indicators to

determine whether there is any relationship

between soil nutrient content and forest productivity. As remeasurement data become available,

analysts may examine regional trends in C, pH,

and soil nutrients in conjunction with changes in

forest growth and productivity.

5.7 Limitations to Data

Soil chemical and physical properties can be

highly variable in the field and are expensive to

analyze. As a result, interpretation of soil chemical data is confounded by spatial variability

within the plot. In addition, depending upon the

soil type, both the number of samples and the

methods used in collecting these samples may

vary between plots, complicating compilation

and estimation procedures. Finally, soil samples

reflect conditions only in the forest floor and

upper 20 cm of the soil. In many systems, the

upper portion of the soil profile is likely to be

more responsive to disturbance, providing a useful index for monitoring changes in soil properties over time. However, conclusive answers to

forest health questions may require additional

analyses of samples taken from within the entire

rooting zone.

35

Figure 12.—Organic carbon values for

0- to 10-cm soils sampled in the

Southeastern U.S. from 1998 to 1999.

Figure 13.—pH values for 0- to 10-cm

soils sampled in the Southeastern U.S.

from 1998 to 1999.
36
37

As with any new research program, modifications

to the sampling and measurement protocols are a

necessary part of indicator development and

implementation. During the first 4 years of the

soil indicator (1998-2001), evaluation of quality

assurance data along with feedback from the field

crews, regional trainers, and lab personnel resulted in a series of improvements to the measurement protocol that have enhanced both the efficiency of the program and the data quality.

However, these changes also seriously complicate

the analysis of data collected across multiple field

seasons. Analysts and researchers interested in

incorporating data collected before the 2001 field

season need to be aware of the ways in which the

protocols have changed and account for these

interannual differences in their analyses.

Table 8, table 19, and table 20 outline those

changes in the field methods, soil sampling protocols, and laboratory analyses that could create

difficulties when analyzing and interpreting

results. Detailed information about field methods

for a given year may be found in the FIA field

manuals (contact your regional FIA office for

additional information; http://www.fia.fs.fed.us).

Some of the most difficult changes to address

analytically occur in the soil chemistry portion of

the indicator. Since 1998, the number of samples collected, the depths of these samples, the

method used to collect the samples (core vs.

shovel), and the laboratory methods used to analyze the samples have all been modified. The net

result of these changes is that soil chemical values from samples collected in 1998 and 1999

are not directly comparable with those obtained

from samples collected since 2000. In general,

combined data from these first 5 years of implementation should not be used to develop baseline statistics for State or regional reporting.

6.POTENTIAL PROBLEMS WITH COMBINING

DATA FROM MULTIPLE YEARS (1998-2002)

Table 19.—Changes in soil sampling protocols, 1998-2001

Year(s) Method

of implementation

Number of samples

1998–1999 Maximum of 7 samples per plot: 1 forest floor sample (subplot 2); 2 mineral samples each on subplots 2, 3, and 4.

2000 Maximum of 3 samples per plot: 1 forest floor sample and 2 mineral samples from subplot 2 only.

2001–present Maximum of 5 samples per plot: 3 forest floor samples and 2 mineral samples from subplot 2 only.

Bulk density

1998–1999 Samples collected using a shovel; no bulk density measurement.

2000 Samples collected using a bulk density sampler in some States.

2001–present Samples collected using a bulk density sampler in all States.

Mineral soils

1998 Samples collected by genetic soil horizon.

1999–present Samples collected by two depth increments (0-10 cm and 10-20 cm).

Forest floor

1998–1999 If forest floor > 5 cm, then litter and duff sampled separately.

2000–present Forest floor sampled as single unit.

Composite samples

1998–2000 Samples from multiple subplots could be composited if in same condition class and soil texture.

2001–present No compositing. 
38

Table 20.—Changes in lab protocols, 1998-2001

Year(s) Method

of implementation

Percent moisture

1998–1999 Samples frozen prior to stabilization for pest control.

2000–present Samples air-dried immediately upon arrival at lab.

Extractable phosphorus (P)

1998–1999 Bray-1 method for all soils.

2000–present Bray-1 method for soils with pH < 6.0; Olsen method for soils with pH > 6.0.

Base cations

1998–1999 Ammonium acetate extraction with analysis by atomic absorption spectroscopy (AAS).

2000–present Ammonium chloride extraction with analysis by inductively coupled plasma optical emissions spectroscopy (ICP-OES).

Aluminum, sulfur, and trace metals

1998–1999 Not done.

2000–present Ammonium chloride extraction with analysis by inductively coupled plasma optical emissions spectroscopy (ICP-OES).

pH

1998–1999 Solid state electrode.

2000–present Glass bulb electrode.
39

7. QUALITY ASSURANCE

7.1 Field

Field personnel receive thorough training in field

procedures and soil sampling techniques at the

beginning of each field season and must pass a

method-specific certification test in which they

demonstrate the ability to conduct soil measurements within established measurement quality

objectives and tolerances. Regional trainers for

the soil indictor are certified each year at a

national pre-training session conducted by the

soil indicator advisors. The purpose of the pretraining session is to ensure national consistency

in the dissemination of information and training

materials and to minimize regional variations in

measurements.

Quality control checks on field data collection

are conducted through audits and plot remeasurements throughout the field season. Feedback

from the crews and regional coordinators is a

key part of the quality assurance program and is

critical to identifying and correcting problems

with training, data quality, consistency, and

methodology. Quality assurance procedures for

the field portion of the soil indicator consist of

three activities:

Hot Checks (audits)

Auditors observe field crew members during

data collection on a field plot, review the methods, identify problems, and suggest any needed

corrective actions. Auditors may also conduct

independent soil measurements.

Cold Checks

Auditors conduct spot checks on field measurements and compare these to data collected by

the crews. Field crews are not present for the

audit. Auditors may correct any error that they

find in the data.

Blind Checks

The complete remeasurement of a plot by the

auditors. Auditors do not have access to the data

from the field crew and may not correct any

errors. These are used to provide an unbiased

estimate of data variance.

Remeasurements of field observations by regional trainer crews occur on routine plots recently

visited by a standard field crew (cold checks or

hot checks) or on reference plots. A national

standard of 5 percent remeasurement has been

established for all FIA plots. During a remeasurement, all erosion and soil compaction remeasurements are taken on the subplots as described

in the soil measurement methods. However,

because of the cost of analysis, soil samples are

collected only during blind checks. A national

standard for blind checks has been set at a minimum of 10 plots per region.

7.2 Laboratory

Quality control protocols used by the FIA

regional labs are documented in Amacher et al.

(2003) and include the use of blanks, calibration

standards, and independent check standards.

Interlaboratory compatibility is ensured through

participation in the North American Proficiency

Testing program (NAPT) administered by the

American Society of Agronomy. The NAPT is a

sample exchange program designed to monitor

interlaboratory differences and assess overall lab

performance. Samples used in the program are

contributed from throughout the continental

U.S. and represent a wide range of soil types.

Because of the large number of labs participating

in the program (> 100) and the wide range of

soil types analyzed, this program is a robust

measure of lab performance.
40
41

8.1 Plot Confidentiality

To protect landowner privacy and prevent sampling bias, the locations of FIA plots are kept

confidential. These data are exempted from the

Freedom of Information Act. Researchers interested in spatial analyses of soil indicator data

may contact their regional FIA program office for

more information.

8.2 Sample Collection

The National Historic Preservation Act of 1966

(as amended) provides for the protection of historical and cultural artifacts. Because of the random placement of the Phase 3 monitoring

design, Phase 3 plots may possibly be located on

sites of prehistoric or historical significance.

Crews do not collect soil samples on plots for

which there is any evidence of cultural or historical artifacts. These sites are coded as not sampled, and plot notes are recorded to explain why

soil samples were not taken. A memorandum of

understanding is currently in place to allow collection of samples of National Forest System

lands. To obtain permission to sample on national parks and other Federal lands, FIA must present a research plan and apply for a permit.

8.3 Shipping, Storage, and 

Disposal of Pest-regulated Soils

To limit the movement of agricultural pests (e.g.,

fire ant, corn cyst nematode, golden nematode,

karnal bunt, witchweed, and Mexican fruit fly),

the shipment of soil samples across State boundaries is strictly regulated by the Plant Protection

and Quarantine program of the USDA Animal

and Plant Health Inspection Service (APHIS).

States with regulated pests are primarily located

in the South, but other locations are restricted as

well (fig. 14). Detailed information on APHIS

quarantine regulations are available online (see

http://www.aphis.usda.gov/ppq/permits/soil/).

To receive a permit to accept soil samples from

these areas, FIA soil labs have signed compliance

agreements with APHIS. These compliance

agreements require that all soil samples be stored

or disposed of in a prescribed manner. Labs

must also pass an inspection before receiving

their permit and are subject to unannounced

inspections by regional APHIS officers. APHIS

samples are clearly labeled as regulated soils

throughout the analysis process and are sterilized

before disposal. All packing materials that come

into contact with regulated soils are incinerated.

Before sending samples to the lab, crews are

required to double bag or enclose all samples

inside of a larger plastic bag or other leak proof

container. All shipments must be clearly identified as “Regulated Material,” and a label containing the appropriate APHIS permit number must

be attached to the outside of the box.

8. REGULATIONS PERTAINING TO THE SOIL INDICATOR
42

Consult your State or Federal plant protection inspector or your county agent for assistanceregarding exact areas under regulation and requirements for moving regulated articles.

Regulated Area

Restrictions are imposed on the movement of the regulated articles from the quarantined (shaded) areas into or through the unshaded areas. Movement within shaded areas may also be regulated.

Updated January 9, 2002

Puerto Rico

United States Department of Agriculture - Animal and Plant Health Inspection ServicePlant Protection and Quarantine - Invasive Species and Pest Management http://www.aphis.usda.gov/ppq/maps/soil2002.pdf

NY

Soil Movement Regulations

Federal soil movement regulations are designatedto stop the human-assisted spread of agriculturalpests such as imported fire ant, golden nematode,karnal bunt, witchweed, and Mexican fruit fly.

There are other soil borne organisms of statequarantine significance (not listed on this map)which may require certification for movement atorigin. Prior to movement, consult with destinationstates concerning their entry requirement for soil.

FL

TX

MS

AL

GA

SC

OK AR TN NC

NM

AZ

CA

AL

LA

FL

Figure 14.—States and counties with soil movement restrictions. [Adapted from APHIS.]
43

9. SUMMARY

The FIA soil indicator program represents the

only repeated, systematic sampling of key indicators of soil quality in all forested regions of the

U.S. regardless of ownership. Integration of soil

assessments within the larger framework of the

national FIA program provides a mechanism for

systematic monitoring of soil properties using

nationally standardized collection, preparation,

and data distribution formats that are fully compatible with existing forest inventory and forest

health data (McRoberts et al. 2004). When fully

implemented, the soil indicator program will

provide critical information on the status of forest soils that can be used to meet regional,

national, and international reporting requirements. Analysis of soil indicator data requires

interpreting soil variables in the context of the

larger landscape. Soil properties naturally vary as

a function of factors such as climate, vegetation,

topography, parent material (geology), and the

age of the soil. As a result, it is often not possible

to establish a threshold value for “good” and

“bad” levels of a particular soil nutrient, erosion,

or compaction level that will be valid across the

entire country. The strength of the soil indicator

lies in its ability to detect change over time.

However, in the early years of implementation,

the focus of the program must be on establishing

baseline levels for soil variables. Additional

research is still needed to determine appropriate

baselines and thresholds for assessing forest

health and linking plot measurements to the

underlying processes regulating forest productivity and ecological function.

For more information on the soil indicator or for

assistance with specific questions or analyses,

please contact your regional FIA office

(http://www.fia.fs.fed.us) or the authors of this

report.
44
45

10 GLOSSARY

Unless stated otherwise, all definitions are from the

Soil Science Society of America glossary, available

online at: http://www.soils.org/sssagloss/.

Acidity, active

The activity of hydrogen ion in the aqueous

phase of a soil expressed as a pH value.

Acidity, residual

Soil acidity that is neutralized by lime or a

buffered salt solution to raise the pH to a specified value (usually 7.0 or 8.0) but that cannot be

replaced by an unbuffered salt solution. It can be

calculated by subtracting salt replaceable acidity

from total acidity.

Acidity, exchangeable

The aluminum and hydrogen that can be

replaced from an acid soil by an unbuffered salt

solution such as KCl or NaCl.

Adsorption

The attraction of ions or compounds to the surface of a solid.

Anion

A negatively charged ion (e.g., PO4

3-

, NO3

-

).

Bulk density

The weight of dry soil per unit bulk volume. The

value is expressed as grams per cubic centimeter

(g cm-3).

C:N ratio

The ratio of the weight of organic carbon to the

weight of organic nitrogen in soil, organic material, plants, or microbial cells.

Cation

A positively charged ion (e.g., Na+, Ca2+).

Cation exchange capacity (CEC)

The sum of exchangeable bases plus total soil

acidity at a specific pH value, usually 7.0 or 8.0.

Expressed in centimoles of charge per kilogram

of exchanger (cmolc kg-1).

Clay

(i) A soil separate consisting of particles <0.002

mm in equivalent diameter. (ii) A textural class.

(iii) A naturally occurring material composed primarily of fine-grained minerals, which is generally

plastic at appropriate water contents and will

harden when dried or fired. 

Coarse fragment content

In FIA, the gravimetric fraction of the bulk soil

derived from particles larger than 2 mm in

diameter.

Effective cation exchange capacity (ECEC)

When acidity is expressed as salt extractable

acidity, the cation exchange capacity is called the

effective cation exchange capacity (ECEC)

because this is considered to be the CEC of the

exchanger at the native pH value. Expressed in

centimoles of charge per kilogram of exchanger

(cmolc kg-1).

Erosion

The wearing away of the land surface by rain or

irrigation water, wind, ice, or other natural or

anthropogenic agents that abrade, detach, and

remove geologic parent material or soil from one

point on the earth’s surface and deposit it elsewhere.

Exchangeable nutrient

A plant nutrient held by the adsorption complex

of the soil and easily exchanged with the anion

or cation of neutral salt solutions.

Litter

The surface layer of the forest floor that is not in

an advanced stage of decomposition, usually

consisting of freshly fallen leaves, needles, twigs,

stems, bark, and fruits.

Mineral soil

A soil consisting predominantly of products

derived from the weathering of rocks (e.g.,

sands, silts, and clays).

Nutrient

Elements or compounds essential as raw materials for organism growth and development.

O horizon

A layer of organic material that has undergone

little or no decomposition (fibric material). On

the forest floor this layer consists of freshly fallen

leaves, needles, twigs, stems, bark, and fruits.
Organic soil

In FIA, any soil in which the organic horizon is

greater than (20 cm) 8 inches in thickness.

These soils are prevalent in wetland areas such

as bogs and marshes and may be frequently

encountered in certain regions (e.g., Maine,

northern Minnesota, coastal regions).

pH

The acidity of a solution in equilibrium with

soil. It is determined by means of an electrode or

other indicator at a specified soil-solution ratio

in a specified solution, usually distilled water,

0.01 M CaCl2, or 1 M KCl.

Porosity

The volume of pores in a soil sample (nonsolid 

volume) divided by the bulk volume of the sample.

Restrictive layer

In FIA, any soil condition that increases soil 

density to the extent that it may limit root

growth. This limitation may be physical (hard

rock) or chemical (acid layer) or both.

Sampling frame

In FIA, a frame used to collect forest floor samples

from a known area. A bicycle tire 12 inches in

diameter has been selected as the national standard.

Soil profile

A vertical section of the soil through all its 

horizons and extending into the C horizon.

46
47

11. REFERENCES

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Amacher, M.C.; O’Neill, K.P. 2004.

Assessing soil compaction on Forest Inventory &

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Collins, CO: U.S. Department of Agriculture, Forest

Service, Rocky Mountain Research Station. 7 p.

Amacher, M.C.; O’Neill, K.P.; 

Conkling, B.L. [In review]. Watershed Erosion Prediction Project (WEPP)

soil erosion rates for Forest Inventory & Analysis

(FIA)/Forest Health Monitoring (FHM) Phase 3

plots in Idaho. Gen. Tech. Rep. RMRS-XXX. Fort

Collins, CO: U.S. Department of Agriculture,

Forest Service, Rocky Mountain Research

Station.

Amacher, M.C.; O’Neill, K.P.; 

Dresbach, R.; Palmer, C.J. 2003. 

Laboratory methods in the Forest Inventory and

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MN: U.S. Department of Agriculture, Forest

Service, North Central Research Station.

[Available online: www.ncrs.fs.fed.us/4801/

national-programs/indicators/soils/analysis/]

(accessed 5 November 2004).

Amacher, M.C.; Henderson, 

R.E.; Breithaupt, M.D.; et al. 1990. 

Unbuffered and buffered salt methods for

exchangeable cations and effective cationexchange capacity. Soil Science Society of

America Journal. 54: 1036-1042.

Brady, N.C.; Weil, R.R. 1996.

The nature and properties of soil. 11th ed.

Upper Saddle River, NJ: Prentice-Hall. 740 p.

Brand, G.J.; Nelson, M.D.; Wendt, D.G.; Nimerfro, K.K. 2000.

The hexagon/panel system for selecting FIA plots

under an annual inventory. In: McRoberts, R.E.;

et al. eds. Proceedings, First annual Forest

Inventory and Analysis symposium; 1999

November 2-3; San Antonio, TX. Gen. Tech.

Rep. NC-213. St. Paul, MN: U.S. Department of

Agriculture, Forest Service, North Central

Research Station: 8-13.

Burger, J.A.; Kelting, D.L. 1999.

Using soil quality indicators to assess forest

stand management. Forest Ecology and

Management. 122: 155-166. 

Conkling, B.L.; Coulston, J.W.; 

Ambrose, M.J., eds. [In press]. 

2001 Forest Health Monitoring national technical report. Gen Tech. Rep. SRS-XXX. Asheville,

NC: U.S. Department of Agriculture, Forest

Service, Southern Research Station.

Dissmeyer, G.E.; Foster, G.R. 1981.

Estimating the cover management factor (C) in

the universal soil loss equation for forest conditions. Journal of Soil and Water Conservation.

36(4): 235-240.

Dissmeyer, G.E.; Foster, G.R. 1984.

A guide for predicting sheet and rill erosion on

forest land. Tech. Pub. R8-TP 6. Atlanta, GA:

U.S. Department of Agriculture, Forest Service,

Southern Region. 40 p.

Elliot, W.J.; Hall, D.E.; Scheele, D.L. 2000.

Disturbed WEPP: WEPP interface for disturbed forest and range runoff, erosion, and sediment delivery. Technical Documentation. Moscow, ID: U.S.

Department of Agriculture, Forest Service, Rocky

Mountain Research Station. [Available online: 

forest.moscowfsl.wsu.edu/fswepp/docs/

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Fisher, R.F.; Binkley, D. 2000.

Ecology and management of forest soils. 3d ed.

New York: John Wiley. 489 p.

Foster, G.R.; Renard, K.G.; Yoder, D.C.; et al. 1996.

RUSLE user’s guide. Ankeny, IA: Soil and Water

Conservation Society. 173 p.

Kuo, S. 1996. 

Phosphorus. In: Sparks, D.L.; et al., eds.

Methods of soil analysis. Part 3. Chemical methods. Madison, WI: Soil Science Society of

America: 869-919. 
Kurtz, J.C.; Jackson, L.E.; Fisher, W.S. 2001.

Strategies for evaluating indicators based on

guidelines from the Environmental Protection

Agency’s Office of Research and Development.

Ecological Indicators. 1: 49-60.

Lal, R.; Blum, W.H.; Valentinin, C.; Stewart, B.A. 1997.

Methods for assessment of soil degradation.

Advances in soil science. Chelsea, MI: Lewis

Publishers. 558 p.

Marschner, H. 1986.

Mineral nutrition of higher plants. London:

Academic Press. 674 p.

McRoberts, R.E.; McWilliams, 

W.H.; Reams, G.A.; et al. 2004.

Assessing sustainability using data from the

Forest Inventory and Analysis program of the

United States Forest Service. Journal of

Sustainable Forestry. 18 (1): 23-46.

Miles, P.D. 2002.

Using biological criteria and indicators to

address forest inventory data at the State level.

Forest Ecology and Management. 155: 171-185.

Nelson, D.W.; Sommers, L.E. 1996. Total carbon, organic carbon, and organic matter.

In: Sparks, D.L.; et al., eds. Methods of soil

analysis. Part 3. Chemical methods. Madison,

WI: Soil Science Society of America: 961-1010. 

O’Neill, K.P.; Amacher, M.C. 2004.

Criterion 4. Conservation of soil and water resources. Indicator 18: area and percent of

forestland with significant soil erosion. In:

National report on sustainable forests, 2003.

Rep. FS-766. Washington, DC: U.S. Department

of Agriculture, Forest Service: 32.

O’Neill, K.P.; Woodall, C.; 

Amacher, M.C. 2004.

Cross-Indicator analysis: combining soil and

down woody material inventories to monitor

changes in carbon storage at the regional scale.

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Society of American Foresters 2003 national

convention; 2003 October 25-29; Buffalo, NY.

Bethesda, MD: Society of American Foresters:

102-110.

O’Neill, K.P.; Amacher, 

M.C.; Palmer, C.J. [In press].

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chemical indicators of soil quality on U.S. forestlands as part of the Forest Inventory and

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and Assessment. 

O’Neill, K.P.; Woodall, C.; 

Amacher, M.C. [In progress].

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wood in forests of the North Central U.S. using

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Ecology and Management.

Overton, W.S.; White, D.; 

Stevens, D.L., Jr. 1990.

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116 p.

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.

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Campbell, S.; Gillespie, A. 2002.

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Madison, WI: Soil Science Society of America:

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methods. Madison, WI: Soil Science Society of

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and Private Forestry, Forest Health Protection.

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index.htm] (accessed 5 November 2004).

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White D.; Kimerling, A.J.; 

Overton, W.S. 1992.

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global sampling design for environmental monitoring. Cartography and Geographic Information

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Woodall, C.W.; Williams, M.S. 2005.

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49
50
51

12. LIST OF FIGURES

Figure 1.—A schematic outline of the threephase FIA sampling design. [Diagram from the

FIA fact sheet series (available online:

http://fia.fs.fed.us/library).] (see page 5)

Figure 2.—An example of the three-phase FIA

sampling design as implemented in Minnesota.

(see page 6)

Figure 3.—Diagram of an FIA Phase 3 plot showing soil measurement locations. [Erosion and

compaction are measured on the 7.32-m- (24-ft-)

radius subplot. Soil samples are collected along

soil sampling lines that run at a tangent to subplots 2, 3, and 4. During the first visit to a plot,

field crews collect soils at point 1. Subsequent

samples are spaced at 3-m (10-ft) intervals alternating on opposite sides of starting point 1.] 

(see page 7)

Figure 4.—Frequency distribution of soil compaction (surface disturbance) reported for plots

measured in Minnesota, Michigan, and

Wisconsin (1999-2000). (see page 12)

Figure 5.—Soil compaction reported for plots

measured in Minnesota, Michigan, and

Wisconsin (1999-2000). [Mean plot values were

determined as the mean value from measurements made on the three subplots.] (see page 12)

Figure 6.—Frequency distribution of bulk density values collected from 0- to 10-cm and 10- to

20-cm cores collected in Minnesota, Michigan,

and Wisconsin (2000-2002). (see page 13)

Figure 7.—Organic carbon content as a function

of bulk density (0- to 10-cm cores) (O’Neill et

al., in press). (see page 14)

Figure 8.—Frequency distribution of WEPPmodeled erosion rates for plots in California,

Oregon, and Washington (1999) under an average storm event (2-year return interval) and a

100-year storm event. [As an initial analysis,

model runs assume an undisturbed forest.] 

(see page 20)

Figure 9.—WEPP modeled erosion on FIA plots

in California, Oregon, and Washington (1999)

for two different storm intensities. (see page 20)

Figure 10.—Frequency distribution of mean soil

cover on plots measured in California, Oregon,

and Washington in 1999. [The value for each

plot represents the percent of the plot covered

by forest floor or other materials. Mean plot values were determined as the mean value from

measurements made on the three subplots.] 

(see page 21)

Figure 11.—pH ranges for common materials

and soil types. [Figure adapted from Brady and

Weil (1996).] (see page 27)

Figure 12.—Organic carbon values for 0- to 10-

cm soils sampled in the Southeastern U.S. from

1998 to 1999. (see page 35)

Figure 13.—pH values for 0- to 10-cm soils sampled in the Southeastern U.S. from 1998 to 1999.

(see page 35)

Figure 14.—States and counties with soil movement restrictions. [Adapted from APHIS.] 

(see page 42)
52
53

13. LIST OF TABLES

Table 1.—Montreal Process Criteria 

(see page 2)

Table 2.—Indicators relating to the conservation

of soil and water resources (Criterion 4) of the

Montreal Process (see page 3)

Table 3.—Codes and cover classes used in

assessing evidence of surface compaction 

(see page 10)

Table 4.—Types of compaction recorded 

(see page 10)

Table 5.—Approximate bulk densities for a variety of soils and soil materials (see page 11)

Table 6.—Codes and cover classes used in evaluating percent bare soil (see page 15)

Table 7.—Codes used for recording soil texture

(see page 16)

Table 8.—Changes in field protocols, 1998–2001

(see page 17)

Table 9.—Erosion factors used in the USLE model

(see page 18)

Table 10.—Example of a WEPP input table for

an FIA Phase 3 plot (see page 19)

Table 11.—Soil texture conversion table for converting soil texture data collected by FIA field

crews into soil texture classes required by WEPP

(see page19)

Table 12.—Soil erosion data collected from

Phase 3 plots and input data needed for the

USLE and WEPP models (see page 19)

Table 13.—Montreal Process indicators

addressed by soil chemistry measurements 

(see page 23)

Table 14.—Physical and chemical analysis conducted on mineral soils (see page 26)

Table 15.—C:N ratios from common organic

materials (see page 31)

Table 16.—Example table for reporting soil

physical properties in mineral soils (see page 32)

Table 17.—Example table for reporting soil

chemical properties in mineral soils 

(see page 33)

Table 18.—Factors for converting soil chemical

concentrations in areal units (see page 34)

Table 19.—Changes in soil sampling protocols,

1998–2001 (see page 37)

Table 20.—Changes in lab protocols, 1998–2001

(see page 38)
Mission Statement

We believe the good life has its roots in clean air,

sparkling water, rich soil, healthy economies and

a diverse living landscape. Maintaining the good

life for generations to come begins with everyday

choices about natural resources. The North

Central Research Station provides the knowledge

and the tools to help people make informed

choices. That’s how the science we do enhances

the quality of people’s lives.

For further information contact:

North Central

Research Station

USDA Forest Service

1992 Folwell Ave., St. Paul, MN 55108

Or visit our web site:

www.ncrs.fs.fed.us

54