United States Department of Agriculture / Forest Service

Rocky Mountain Research Station

Proceedings RMRS-P-59

April 2010 

Scientific Background for Soil Monitoring 

on National Forests and Rangelands

Workshop Proceedings, April 29-30, 2008, Denver, Colorado
Page-Dumroese, Deborah; Neary, Daniel; Trettin, Carl, tech. eds. 2010. 

Scientific background for soil monitoring on National Forests and 

Rangelands: workshop proceedings; April 29-30, 2008; Denver, CO. 

Proc. RMRS-P-59. Fort Collins, CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station. 126 p.

Abstract

This workshop was developed to determine the state-of-the-science for soil 

monitoring on National Forests and Rangelands. We asked international experts in 

the field of soil monitoring, soil monitoring indicators, and basic forest soil properties 

to describe the limits of our knowledge and the ongoing studies that are providing 

new information. This workshop and the proceedings are particularly important as 

National Forests wrestle with determining how (or if) to modify their existing soil 

quality standards and guidelines.

Compilers Deborah Page-Dumroese, Research Soil Scientist, USDA Forest Service, Rocky 

Mountain Research Station, Moscow, ID.

Daniel Neary, Research Soil Scientist, USDA Forest Service, Rocky Mountain 

Research Station, Flagstaff, AZ.

Carl Trettin, Research Soil Scientist, USDA Forest Service, Southern Research Station, 

Cordesville, SC.

Front cover schematic: In: Napper, C.; Howes, S.; Page-Dumroese, D. (In process). 

Soil disturbance field guide. 0820 1815-SDTDC. San Dimas, CA: U.S. Department 

of Agriculture, Forest Service, San Dimas Technology Center.

You may order additional copies of this publication by sending your 

mailing information in label form through one of the following media. 

Please specify the publication title and series number.

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Papers were edited to a uniform style; however, authors are responsible for content 

and accuracy of their papers.
Scientific Background for Soil Monitoring on 

National Forests and Rangelands

Workshop Proceedings, April 29-30, 2008, Denver, Colorado
Preface

These proceedings are the result of interactions with Regional Soil 

Program Managers, Forest Soil Scientists, Regional Timber Sale 

Administrators, Research Soil Scientists and Silviculturists, University 

Professors, and the BC Ministry of Forest and Range Soil Scientists 

through the National Soil Quality Standards working group established by the Region 1 Regional Forester (Abigail Kimbell) in 2002. 

This group helped guide the development of a national Forest Soil 

Disturbance Monitoring Protocol, developed the idea for a picture 

guide to forest soil disturbance, and brought together leaders in soil 

quality and soil quality monitoring to establish the state-of-the-science 

documented in these proceedings. This documentation is meant to 

provide the information needed for revision of Regional Soil Quality 

Standards and Guidelines.

ii
Contents

Soil Quality: Some Basic Considerations and Case Studies...............................................1

Dale W. Johnson, Natural Resources and Environmental Science, 

University of Nevada, Reno, Reno, NV

Using Soil Quality Indicators for Monitoring Sustainable Forest Management.............13

James A. Burger, Professor Emeritus of Forestry and Soil Science, 

Department of Forestry, Virginia Polytechnic Institute and State 

University, Blacksburg, VA

Garland Gray, Professor of Forestry and Soil Science, Department of 

Forestry, Virginia Polytechnic Institute and State University, Blacksburg, VA

D. Andrew Scott, Research Soil Scientist, Southern Research Station, 

USDA Forest Service,Pineville, LA

The North-American Long-Term Soil Productivity Study: Concepts and Literature .....43

Deborah S. Page-Dumroese, USDA Forest Service, Rocky Mountain 

Research Station, Moscow, ID

Soil Quality Monitoring: Examples of Existing Protocols .................................................61

Daniel G. Neary, Research Soil Scientist, Rocky Mountain Research 

Station, USDA Forest Service, Flagstaff, AZ

Carl C. Trettin, Research Soil Scientist, Southern Research Station, 

USDA Forest Service, Charleston, SC

Deborah Page-Dumroese, Research Soil Scientist, Mountain Research 

Station, USDA Forest Service, Moscow, ID

The Soil Indicator of Forest Health in the Forest Inventory and Analysis Program .......83

Michael C. Amacher, Research Soil Scientist, Forest Inventory and 

Analysis Program, Rocky Mountain Research Station, Logan, UT

Charles H. Perry, Research Soil Scientist, Forest Inventory and Analysis 

Program, Northern Research Station, St. Paul, MN

Statistical Sampling Methods for Soils Monitoring.........................................................109

Ann M. Abbott, Moscow Forestry Sciences Laboratory, Rocky 

Mountain Research Station, Moscow, ID

Soil Quality Standards Monitoring Program Administration and Implementation.....121

Randy L. Davis, USDA Forest Service, Washington, DC

Felipe Sanchez, USDA Forest Service, Southern Research Station, 

Research Triangle Park, NC

Sharon DeHart, USDA Forest Service, Job Corp, Fort Collins, CO

iii
USDA Forest Service Proceedings RMRS-P-59 2010. 1

Soil Quality: Some Basic Considerations and 

Case Studies

Dale W. Johnson, Natural Resources and Environmental Science, University of Nevada, Reno,

Reno, NV

Abstract—Some fundamental properties of soils that pertain to the concept of soil quality 

are discussed including a discussion of what can and cannot be changed with management. 

Case studies showing the effects of N-fixing vegetation and N-enrichment effects on 

invasive species are provided to illustrate the complications that may arise from applying 

one soil quality standard to all cases. Finally, the “nitrogen problem is discussed: nitrogen 

is the most frequently growth-limiting nutrient and yet it is also the nutrient that is often the 

most problematic to manage without causing deleterious effects on the availability of other 

nutrients and water quality.

Introduction

Soil quality is a concept that, in theory, all soil scientists should embrace. Concerns 

have been expressed, however, that the concept is too broad and encompassing to be 

meaningful, and that if soil quality indicators are applied, this must be done on a sitespecific basis with specific management objectives in mind (Karlen and others 1997; 

Page-Dumroese and others 2000; Schoenholtz and others 2000). The first questions 

that come to mind for this author are who will define soil quality? Soil Scientists? 

Farmers? Water quality experts? Conservationists? Lawyers? What criteria or outcome 

will be used to set the criteria for soil quality? Plant growth? Water quality? The soils 

themselves? Will one definition fit all? Not likely. Will the various definitions conflict? 

Almost certainly.

Several excellent reviews of soil quality have already been published and the reader 

is referred to them for details on potential criteria as well as more philosophical aspects 

of the issue (e.g., Karlen and others1997; Page-Dumroese and others 2000; Schoenholtz 

and others 2000). In this paper, I will only briefly review some basic soil properties with 

an eye to what we might be able to change by human intervention and what cannot be, 

and how (if at all) these changes can be translated into the concept of soil quality.

Some Basic Soil Concepts

Factors of Soil Formation

Jenny (1941) defined factors of soil formation as parent material, climate, topography, and biota, all of which are integrated over time:

Soil = ∫f(parent material, climate, biota, topography)

There have been elaborations of this model over the years since its inception, but for our 

purposes it will suffice. The factors of soil formation that we can and often do modify 

include biota, most usually by modifying vegetation, and with heavy equipment and 

great effort we can also modify topography. Vegetation effects on soils are very well 

documented, including nutrient depletion by uptake (species variation being a major 

factor here; Binkley and Menyailo 2005). Introducing nitrogen fixing species can greatly enhance soil C and N status, but also may cause soil acidification by producing excess 

nitrification (Van Miegroet and Cole 1984). Further aspects of the effects of too much 

N fixation and other inputs of N will be discussed later. We can also modify soil biota, 
2 USDA Forest Service Proceedings RMRS-P-59 2010.

although with far less precision, by introducing mycorrhizae (e.g., Hererra and others 

1993; Trappe 1997), modifying the C:N ratio either by adding N or organic C, or adding 

nutrients via fertilization (Miller 1981). On the other hand, we cannot modify parent 

material (except perhaps with heavy equipment and great difficulty), climate, or time.

The factors of soil formation listed above lead to the development of soils as we see 

them, and many soil classification systems have been proposed (Buol and others 2003). 

In the U.S. 12th approximation, (USDA 1999), the highest level classification is the soil 

order (table 1). The U.S. system becomes very complex and specific from that point on, 

and a full description of it is well beyond the scope of this paper. The 12 soil orders have 

some inherent properties that affect properties commonly associated with their “quality.” Some, such as Entisols and Inceptisols, can be so poorly developed because of their 

young age (the time factor) or parent material (for example, weathering-resistant and 

inherently nutrient poor quartz sand). Others, such as Mollisols and many Alfisols, can, 

for the same reasons, be quite rich in nutrients commonly associated with high “quality.” Many of these soil orders (for example, Ultisols, Mollisols, Oxisols, Spodosols, 

and especially Aridisols and Gelisols) have a rather specific geographic distribution that 

is influenced mainly by climate whereas others (Andisols and to some degree Vertisols) 

are more strongly influenced by parent material. So arises the first question regarding 

soil quality: do we judge soil quality from a single standard, by lumping all 12 orders 

and the myriad subdivisions within them into one bucket and assessing their quality 

from an overall standard, (which some, because of their very nature, will of course 

never be able to achieve), or do we restrict our assessments of soil quality to within soil 

order, at a minimum, and perhaps even at a lower order of classification such as great 

group or even lower? If the latter, we may soon find, for example, that a high “quality” 

Mollisol is quite a different thing from a high “quality” Oxisol.

Soil Physical Properties

The soil physical properties commonly listed in basic soil texts (Brady and Weil 

2008; Gardiner and Miller 2008; Singer and Munns 2006) include texture, structure, 

coarse (rock) fragments (which is that particle size >2 mm by convention), bulk density, 

and porosity. These basic physical properties lend the soil its properties associated with 

water, namely, field moisture capacity (FMC) (the maximum amount of water held in 

the soil after drainage, typically at tensions of –10 m to –33 kPa), permanent wilting 

percentage (PWP) (soil water content at which plants can no longer remove water from 

soil, usually defined at –1,500 kPa, but for desert plants can be as high at –6,000 kPa), 

available water capacity (FMC-PWP), and hydraulic conductivity. Water available to 

plants at any given time (plant available water, PAW) is in theory equal to the difference between soil moisture content at the time in question minus soil moisture content 

at PWP; thus, after gravitational drainage has occurred, PAW ≤ AWC (available water 

Table 1. An abbreviated description of the 12 soil orders according to the U.S. 12th Approximation.

Soil order Description

Alfisols	 Clay migration, moderately high % BS

Andisols	 Volcanic parent material, high P fixation

Aridisols	 Arid soils, high in salts and pH

Entisols	 Not well-developed even after long periods (can occur anywhere)

Gelisols	 Permafrost

Histosols	 Soils.formed from organic matter (peats and mucks)

Inceptisols	 Still forming, water is available for soil formation

Mollisols	 Organic-rich A horizons, % BS usually > 50%

Oxisols	 Highly-weathered (e.g., tropical rainforest)

Spodosols	 Fe, Al, and organic matter transport, whitish E Horizon (e.g., boreal forest)

Ultisols	 Clay transport like Alfisols, but much more acidic; higher temperature; often highly

weathered (e.g., Southeastern United States)

Vertisols	 Mixed soils; swelling clays, frost, etc. cause lower horizons to mix with upper

horizons; often characterized by cracks

Johnson Soil Quality: Some Basic Considerations and Case Studies
USDA Forest Service Proceedings RMRS-P-59 2010. 3

content) (fig. 1). These soil water properties are a function of texture and structure as 

well as organic matter content, but this relationship is complex. The idealized (round) 

maximum pore size that will hold water at any given tension can be calculated from the 

capillary rise equation, and thus soil water properties are a function not only of total soil 

porosity (often calculated as 1 – bulk density/2.65 for mineral soils, assuming the density of the soil mineral fraction to be 2.65 g cm-2) but also of pore size distribution, which 

in turn is a function of soil texture and soil structure (e.g., aggregation). Thus, soils with 

high clay content and poor structure may have very high FMC, but will also likely have 

high PWP so that AWC is actually lower than soils with a loamy structure (fig. 2).

Soil structure and bulk density can both be changed by management and so can soil 

water properties. Soil texture cannot be changed. Adding organic matter to clay soils 

can improve aggregation, which in turn can lessen the problem with too many fine 

pores and high PWP. On the other hand, repeated disturbance leading to organic matter loss can have the opposite effect. Bulk density, total porosity, pore size distribution 

and, therefore, soil water properties can all be modified by management. Compaction 

reduces total porosity and usually creates more fine pores, perhaps increasing both FMC 

and PWP with variable effects on AWC. Compaction is usually seen as an undesirable 

effect, but, as shown by Gomez and others (1999), compaction can cause AWC and tree 

growth to go in either a positive or a negative direction. Specifically, Gomez and others 

(1999) found that compaction in clay textured soils caused the expected effect of reducing AWC, but in a sandy loam soil, compaction caused greater increases in FMC than 

in PWP, thus increasing AWC and tree growth. Thus, compaction can actually improve 

soil “quality.”

Figure 1. Schematic representation 

of soil water fractions.

Figure 2. Schematic representation 

of available soil water with 

changing soil texture.

Soil Quality: Some Basic Considerations and Case Studies Johnson
4 USDA Forest Service Proceedings RMRS-P-59 2010.

Soil Chemical Properties

Soil chemical properties commonly measured include total carbon, organic carbon, 

total nitrogen, C:N ratio (including organic carbon only), cation exchange capacity, 

exchangeable Ca2+, K+, Mg2+, Na+, Aln+, base saturation, extractable NH4

+

 and NO3

-

, 

extractable ortho-P, extractable SO4

2-, and extractable micronutrients. All of these parameters can be modified by management (e.g., fertilization) and this is commonly done 

in agricultural and intensively managed forest soils. There are, however, problems with 

measurement of many of these soil chemical parameters. Total C and total N are easily 

measured with modern combustion equipment, but it must be borne in mind that these 

analyses include both organic and inorganic forms. In the case of C, this can be significant if carbonates are present, and typically they must be removed by acid pretreatment 

if proper C:N ratio calculations are to be made. Ammonium and NO3

-

 (mineral N) analyses provide data on readily available N, analogous to “cash in the wallet”; but, like “cash 

in the wallet,” they do not necessarily provide a good index of soil N status as they are 

ephemeral quantities and turn over rapidly in soils. (To carry the analogy a step further, 

it could well be that the millionaire carries little cash in his wallet.) It is the flux from 

organic to mineral N, not the standing pool of mineral N, that gives the best estimates of 

N availability (as cash flow gives a better estimate of wealth than cash in the wallet), and 

thus there are many methods proposed to measure “N mineralization”(Robertson and 

others 1999). Unfortunately, all of these methods have artifacts associated with them. 

All N mineralization methods not involving isotopes require that plant N uptake be 

prevented; thus, roots must either be removed or killed. In the former case, the process 

of root removal may create the “assart” effect, whereby soil disturbance increases N 

mineralization and in the latter case, N mineralization may be artificially augmented by 

inputs of mineral N from decaying roots. Or, alternatively, methods that eliminate roots 

also eliminate the possibility of rhizosphere-enhanced soil organic matter mineralization, thus underestimating real, in-situ N mineralization (e.g., Hamilton and Day 2001). 

Isotope methods seek to avoid these problems (see reference for a full discussion), but 

are expensive and have artifacts of their own to deal with. In short, there is no reliable, 

standard, and relatively inexpensive method for measuring N availability in soils. In this 

author’s opinion, organic C, organic N, and organic C:N ratio are the best compromises 

in terms of information provided compared to cost.

There are far fewer problems with exchangeable cations and cation exchange capacity (CEC) than there are for N, but care must be taken to be consistent with methods. 

Most agricultural labs in the United States use ammonium acetate for cation exchange 

capacity, which in theory buffers pH to 7. As noted by Sumner and Miller (1996), the 

continued use of this method is unfortunate because it grossly overestimates the CEC 

of acidic soils. The Ba-TEA method that raises pH to 8.3 causes an even greater inflation of CEC values in acid soils. While the pH in most agricultural soils is manipulated 

routinely, it often has a target value of 7. Seldom are forest soils at pH 7, and even 

more seldom do we attempt to bring them to pH 7; thus, many forest soils laboratories 

use the neutral salt method, (1 M NH4

Cl; Skinner and others 2001), which measures 

CEC at normal soil pH, or close to it, perhaps with some depression in pH due to the 

salt effect, where NH4

+

 displaces some exchangeable H+ and Al3+ by mass action. In 

theory, either ammonium acetate or ammonium chloride will extract approximately the 

same amount of base cations, but ammonium acetate will measure a greater proportion 

of pH-dependent CEC, which will be an important factor in organic-rich surface horizons. Thus, the two methods could yield two different measures of base saturation for 

the same soil at the same time, leading the unsuspecting to believe that some change 

has occurred. This is illustrated schematically in figure 3, where a hypothetical soil is 

extracted by these two methods.

Extractable ortho-P methods yield results that are nearly as ephemeral as is the case 

for mineral N. We have found substantial seasonal variation in Bray-extractable P (weak 

solutions of HCl + NH4

F) (Johnson and Todd 1984; Johnson and others 1988) and also 

some inconsistent differences between Bray- and bicarbonate-extractable P (Johnson 

and others 1997; Susfalk 2000). Specifically, Bray extractions consistently yield greater 

values for P in soils derived from decomposed granite in our sites in the eastern Sierra 

Johnson Soil Quality: Some Basic Considerations and Case Studies
USDA Forest Service Proceedings RMRS-P-59 2010. 5

Nevada mountains (Johnson and others 2005; Susfalk 2000) whereas bicarbonate extractions often yield greater values for P in soils derived from andesite (Susfalk 2000). 

Susfalk (2000) studied P in soils from this region in great depth and concluded that 

the andesite-derived soils had far greater buffering power for ortho-P than the soils 

derived from decomposed granite, causing the andesitic soils to both quickly adsorb 

added ortho-P and also to release greater quantities of ortho-P with repeated extractions. 

He concluded that “a one‐point P extraction index (bicarbonate‐P or Bray‐P) was a poor 

indicator of extractable P in [the andesitic soils] because it was unable to account for 

buffering effects, and [also a poor indicator of extractable P in granitic soils] because 

they extracted non-labile forms of P that may not have been plant-available.”

Far less work has been done on extractable sulfate, but it is well known that pH has a 

very strong influence on it (Singh 1984) and that soils enriched in sesquioxides and high 

amounts of pollutant sulfur inputs have the highest levels of soil sulfate (Johnson 1984).

Many of the micronutrients are especially sensitive to pH. Copper, zinc, and iron, in 

particular, become less available at higher pH as they begin to precipitate as hydroxides. 

Molybdenum and, to a lesser extent, boron (being in anionic form in soil solution) become more available at higher pH as they desorb from sesquioxides. Copper is strongly 

absorbed by organic matter and, therefore, can be deficient in high-OM soils.

Soil Biological Properties

We can, with relative ease, cause changes in vegetation, which will in turn cause 

changes in soil biological and chemical properties. For example, planting with N-fixers 

can greatly increase soil C and N status, but also can result in greater soil acidification 

(Johnson and Curtis 2001; Van Miegroet and Cole 1984). We can also, to a more limited 

degree, change soil microbiota by introducing mycorrhizal innoculum, fertilizers, or 

raising C:N ratio by adding woody materials or even sugar to tie up available N. One 

Figure 3. Example of how different methods 

for measuring cation exchange capacity 

can lead to misleading conclusions 

about base saturation.

Soil Quality: Some Basic Considerations and Case Studies Johnson
6 USDA Forest Service Proceedings RMRS-P-59 2010.

of the easiest soil biological factors to change is that of nitrifying organisms: add available N, and they will increase their activity in most cases, probably quite quickly, and 

not entirely without negative consequences. We can also, with somewhat more effort, 

stimulate denitrifiers by creating anaerobic conditions and supplying organic substrates.

In the context of soil biology, it is perhaps germane to consider the results of longterm decomposition studies conducted by Berg and others (2003). Textbook knowledge 

says that adding material with high C:N ratios such as woody tissues will cause slower 

decomposition; also, available N immobilization as microbes, with their own C:N ratios 

of between 6 and 12 to 1, try to digest materials with C:N ratios an order of magnitude 

or more greater. In order to adjust the C:N ratios of the material they consume, they 

(1) release organic C as CO2, and (2) import available N from soil pools. As a consequence, the C:N ratio of decomposing materials decreases during decomposition. As 

the material reaches a value of approximately 20:1, microbes begin releasing, instead of 

taking up, available N and, therefore, shift from the immobilization to the mineralization phase (fig. 4).

A significant complication to this textbook scenario was introduced by Berg and 

others (2003) with their concept of limit values. Their long-term (two-decade) decomposition studies showed that, indeed, materials with higher C:N ratios initially 

decomposed more slowly than those with lower C:N ratios. Over time, however, they 

found that materials with initially higher C:N ratios leveled off sooner, reaching a quasisteady-state condition referred to as the limit value for remaining mass that was lower 

than that reached by materials with an initially lower C:N ratio. Thus, over the long 

term, organic C and N contents of materials with initially lower C:N ratios remained 

greater than those with initially higher C:N ratios—presumably this material is very 

humic and stable in nature, perhaps entering the stable soil organic matter pool. Thus, 

litter with initially greater “quality,” which is often indexed as litter with lower C:N 

ratio, certainly leads to short-term increases in decomposition and, therefore, initially 

lower initial C and N pools in the O horizon. Over the long run this higher quality litter 

also leads to greater soil organic C and N contents (fig. 5). This example illustrates how 

a soil quality parameter, such as available N, can change with time—even without any 

intervention by humans or disturbance—simply by the nature of decomposition and its 

interaction with C and N in decomposing material, as manifested in both its short- and 

long-term effects. This reversal of N availability and C sequestration over time would 

seem to make an assignment of a soil quality value a very elusive thing indeed.

Figure 4. Schematic 

representation of the 

effects of soil C:N ratio on 

net N mineralization and 

immobilization.

Johnson Soil Quality: Some Basic Considerations and Case Studies
USDA Forest Service Proceedings RMRS-P-59 2010. 7

Case Studies

So what constitutes a good quality soil? The Soil Science Society of America defined soil quality as “the capacity of a specific kind of soil to function, within natural or 

managed ecosystem boundaries, to sustain plant and animal productivity, maintain or 

enhance water and air quality, and support human health and habitation” (Karlen and 

others 1997). The questions being asked in the following case studies are (1) are these 

goals mutually compatible? (2) are these goals always desirable? and finally, and most 

importantly, (3) does the very concept of soil quality add to or detract from the concept 

of soil fertility? It seems to this author that the problem with the concept of soil quality is that it implies a blanket value judgment on soils that one fears will be applied 

(perhaps legally) to all soils in all situations. We are more familiar with the concept of 

soil fertility, and while it also implies a value judgment it is more specific (is the soil 

good for plant growth?) and can more readily be quantified with standard soil analyses. 

When we speak of water quality, we generally refer to conditions of low fertility (oligotrophic) as being desirable and waters of high fertility (eutrophic) being “polluted” and 

undesirable. To some degree, the same judgments may well apply to soil quality in some 

circumstances. For example, an endangered species that is thrifty with nitrogen is being 

threatened by increasing competition from a nitrogen-loving invasive species as a result 

of increased nitrogen fertility due to pollutant inputs. And, of course, more fertile soils 

are in fact more likely to produce more fertile surface waters, thus creating an automatic 

conflict between the concepts of soil and water quality.

Case Study 1: The Effects of Red Alder on Soil Quality

Red alder (Alnus rubra) is a native N-fixing tree in the Pacific Northwest that occupies sites after disturbances such as fire, logging, erosion, etc. It has long been 

known that red alder improves soil N status and that subsequent stands of Douglas-fir 

(Pseudotsuga menziesii) greatly benefit from this (Binkley 2003). A little over two decades ago, however, Van Miegroet and Cole (1984) found that red alder simply cannot 

stop itself from fixing N even when no more N is needed by any biological entity in 

the ecosystem except nitrifying bacteria. Nitrification results in the creation of nitrate 

and acid, and when the nitrate is not taken up, the acid exchanges for base cations on 

the soil exchange complex, nitrate leaches and soil acidifies. There is also the suggestion that soil available P is tied up because of the excess N, but this is somewhat more 

controversial (Compton and others 1998; Giardina and others 1998). Furthermore, red 

alder apparently makes soil conditions less suitable for itself, and does not do well on 

sites formerly occupied by red alder (Van Miegroet and others 1992). Douglas-fir, on the 

other hand, is not bothered by the acidity or lower soil P status and thrives in the N-rich 

Figure 5. Schematic representation of the 

decomposition of litter with initially 

high vs. initially low C:N ratio (after 

Berg and others 2003).

Soil Quality: Some Basic Considerations and Case Studies Johnson
8 USDA Forest Service Proceedings RMRS-P-59 2010.

environment. How, then, does one define soil quality in this instance? The quality for 

red alder is poor, that for Douglas fir is good, and, while red alder occupies the site, 

water quality is degraded by high nitrate concentrations (which cease quickly after the 

red alder is cut down, apparently causing cessation of N fixation). Compton and others 

(2003) find that the degree of occupancy of red alder in watersheds was directly related 

to the concentrations of nitrate in streamwaters.

Case Study 2: The Effects of Snowbrush on Soil Fertility

Snowbrush (Ceanothus velutinus) is a major fire-adapted N-fixing species occurring 

on disturbed sites in the Sierra Nevada and southern Cascade mountains. The heat generated from fire is thought to be the major mechanism for seed germination (Gratkowski 

1962). Nitrogen (N) fixation in snowbrush is associated with Frankia spp. actinomycetes, and reported fixation rates of up to 142 kg N ha-1 yr-1 (Binkley and others 1982; 

Youngberg and Wollum 1976). Snowbrush is generally intolerant to shade, but it has 

been observed to dominate a site for over 50 years following a wildfire by suppressing 

forest regeneration (Conrad and Radosevich 1982). Zavitkovski and Newton (1968) 

describe four stages to snowbrush growth following fire or disturbance: (1) growth with 

little or no accumulation of organic matter between the ages of 1 to 5 years; (2) rapid 

growth with increasing biomass, increase of N in biomass, and accumulation of organic 

matter between the ages of 5 to 10 years; (3) an equilibrium stage, which lasts between 

the ages of 10 to 15 or as long as 50 years; and (4) the final stage of decline and decomposition, usually due to the growth of a forest canopy shading the snowbrush out.

We have made several comparisons of soils beneath snowbrush and adjacent Jeffrey 

pine (Pinus jeffreyi) stands in Little Valley, Nevada, a site just east of Lake Tahoe 

(Johnson 1995; Johnson and others 2005). We have found that soils beneath snowbrush 

consistently have lower bulk density and higher total C and total N concentrations than 

in adjacent pine stands, as would be expected. Unlike the case with red alder, however, 

we have also found that there are no differences in extractable P concentrations in soils 

beneath snowbrush as compared to soils beneath pine, but snowbrush soils have consistently higher concentrations of exchangeable Ca2+, K+

, and Mg2+ than in adjacent 

pine stands. The higher base cation status beneath snowbrush is consistent with the fact 

that we have found no evidence of elevated levels of NO3

-

 leaching beneath snowbrush 

stands (Johnson 1995; Stein 2006).

Thus, it appears that snowbrush improves soil bulk density and nutrition—soil “quality”—in most measurable ways. Is this a desirable outcome? Purely from the perspective 

of soil “quality” it certainly is; however, because snowbrush does not “poison itself out” 

of a site like red alder apparently does, it can persist for many decades after fire and prevent reforestation. Thus, we have high quality soils but an undesirable vegetative cover 

to go with these high quality soils.

Case Study 3: Cheatgrass in the Great Basin

Cheatgrass (Bromus tectorum), an exotic annual grass, is rapidly expanding throughout the Great Basin. This highly competitive invader is resulting in the widespread 

deterioration of mid- to low-elevation sagebrush ecosystems and, more recently, salt 

desert ecosystems (Brooks and Pyke 1991). Cheatgrass has altered fire regimes in native 

ecosystems because it increases fine fuels, is highly flammable, and increases the rate 

of fire spread (Link and others 2006). In many parts of the region an annual grass-fire 

cycle now exists in which fire return intervals have decreased from about 60 to 110 

years to as little as 3 to 5 years (Whisenant 1990). Recent field studies have shown the 

importance of available inorganic nitrogen in controlling cheatgrass establishment and 

growth (McLendon and Redente 1991; Young and others 1999). Experiments with sugaring soils to stimulate microbial competition for N, thus reducing mineral N supplies 

in soils have proven to severely limit cheatgrass growth and to favor native species by 

reducing competition (Young and others 1999).

Johnson Soil Quality: Some Basic Considerations and Case Studies
USDA Forest Service Proceedings RMRS-P-59 2010. 9

An alternative approach to sugaring to tie up mineral N might be to reduce total N 

supplies and, therefore, mineral N supplies by repeated burning. It is well documented 

that nearly all N contained in organic material that is burned is volatilized and lost from 

the system, potentially causing long-term declines in ecosystem N capital unless the N 

is replaced by atmospheric deposition, N-fixation, or fertilization (Blair 1997; Neary 

and others 1999; Raison and others 1985). On the other hand, burning commonly causes 

short-term increases in soil ammonium levels because of the heat-induced denaturing of 

soil organic N (Neary and others 1999). The pulse of ammonium is often followed by a 

pulse of nitrate and nitrate leaching once nitrifying bacteria occupy the site again. The 

short-term pulse of ammonium after fire is thought to be one factor favoring nitrophilic 

cheatgrass after rangeland fire (Monaco and others 2003). Over the long-term, however, 

one would expect that repeated burning without replacement of lost N could cause reductions in soil mineral N levels, at least after the initial post-fire pulse has passed (Blair 

1997; Johnson and Matchett 2001; Ojima and others 1994). This is attributed to both 

volatile losses of N and also to a form of progressive N deficiency, where N concentrations in vegetation decline over time in response to reduced soil N availability, causing 

inputs of detritus with lowering C:N ratios. The N deficiency is further exacerbated by 

increasing microbial competition for N in much the same manner as the short-term sugaring experiments described above (Blair 1997; Johnson and Matchett 2001; Ojima and 

others 1994). Although it has been shown that cheatgrass invasion can rapidly alter N 

cycling (Evans and others 2001), little is known about the effects of repeated fire on N 

availability in cheatgrass dominated rangelands. Our objective in this study is to explore 

the prospects for “burning out” cheatgrass with repeated fires designed to reduce total 

and available soil N and, consequently, cheatgrass growth and reproduction.

The preceding discussion clearly shows that greater soil “quality”—specifically, better soil N status—favors one of the most destructive invasive species in the Great Basin. 

Reducing soil N availability (quality?) seems to be the best, albeit somewhat faint, hope 

of controlling this species.

The Nitrogen Problem

Nitrogen is unique among nutrients in many ways. Unlike P, K, Ca, and Mg, for 

example, N is rarely present in parent rock, and in the vast majority of cases is naturally 

introduced to the soil from the atmosphere by symbiotic and non-symbiotic fixation, 

atmospheric deposition, and lightning. The major inorganic forms of N in soils include 

both a cation (NH4

+

) that is strongly absorbed to soils and an anion ( NO3

-

) that is very 

weakly absorbed to soils. Unlike most other macronutrients, however, inorganic forms 

of nitrogen do not persist in non-aridic soils for long. Nitrogen is the most frequently 

limiting nutrient in terrestrial ecosystems, and inorganic forms of N are rapidly taken 

up and depleted under N deficient conditions that are common in terrestrial ecosystems. 

When N is supplied in excess of biological demand, it does not accumulate to any 

significant degree on soil exchange sites or as precipitation. While the NH4

+

 form is 

strongly held on soil exchange sites, it does not persist for long in most soils, even when 

N supplies greatly exceed biological demand, before it is converted to NO3

-

 during the 

nitrification process (which also produces H+ - nitric acid). Nitrate so produced, if not 

taken up, is poorly adsorbed and will leach from soils, taking with it base cations and 

thus acidifying the soil. In contrast, additions of P, K, Ca, or Mg in excess of biological 

demands can result in large and prolonged accumulations of the ionic forms of these 

nutrients on exchange and adsorption sites.

Thus, N is a difficult nutrient to manage, which is unfortunate because it is the most 

often limiting nutrient. All nutrients (in fact all substances, including water) have regions of deficiency, sufficiency, and toxicity with increasing supplies (fig. 6). We can 

think of this classical curve not only in terms of plant response, but also environmental 

response. So, for example, while the sufficiency plateau for K, Ca, and Mg can be quite 

broad, as supplies of these nutrients exceed biological demands and accumulate on soil 

exchange or adsorption sites, the sufficiency plateau for N is very narrow, with little 

space between deficiency on the left and undesirable consequences such as excessive 

Soil Quality: Some Basic Considerations and Case Studies Johnson
10 USDA Forest Service Proceedings RMRS-P-59 2010.

nitrate leaching on the right. In the cases of K, Ca, and Mg, increases in leaching rates 

may occur without undue harm to water quality; in the case of P, leaching is nearly always minimized by various adsorption and precipitation processes in the soil and most 

P transport from the ecosystem is by erosion. For N, on the other hand, there is a very 

narrow sufficiency plateau for N between the deficiency region and the point quickly 

thereafter when excess N leaches from the system and degrades water quality. Thus, trying to manage for a high quality soil will necessarily require that it have good N status, 

but it is very difficult to achieve good N status without tipping over the edge into the 

toxicity region.

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The content of this paper reflects the views of the authors, who are responsible for the facts 

and accuracy of the information presented herein.

Johnson Soil Quality: Some Basic Considerations and Case Studies
USDA Forest Service Proceedings RMRS-P-59 2010. 13

Using Soil Quality Indicators for Monitoring 

Sustainable Forest Management

James A. Burger, Professor Emeritus of Forestry and Soil Science, Department of Forestry, 

Virginia Polytechnic Institute and State University, Blacksburg, VA

Garland Gray, Professor of Forestry and Soil Science, Department of Forestry, Virginia 

Polytechnic Institute and State University, Blacksburg, VA

D. Andrew Scott, Research Soil Scientist, Southern Research Station, USDA Forest Service, 

Pineville, LA

Abstract—Most private and public forest land owners and managers are compelled to 

manage their forests sustainably, which means management that is economically viable, 

environmentally sound, and socially acceptable. To meet this mandate, the USDA Forest 

Service protects the productivity of our nation’s forest soils by monitoring and evaluating 

management activities to ensure they are both scientifically wise and socially responsive. 

The purpose of this paper is to review soil quality indicators and models for their possible 

use in soil management and evaluation programs. The Forest Service has taken a progressive 

stance on adapting their long-used soil quality monitoring program to take advantage of 

new science and technology. How forest soils function in terms of their stability, hydrology, 

and nutrient cycling is better understood, and indicators of these functions have been 

identified and tested for cause and effect relationships with tree growth and ecosystem 

health. Soil quality models are computer-based evaluation tools that quantify soil change 

and potential change in forest productivity due to management inputs or unintended 

detrimental disturbances. Soil quality models, when properly conceptualized, developed, 

and implemented, can provide a legally defensible monitoring and evaluation program 

based on firm scientific principles that produce unequivocal, credible results at minimum 

cost.

Introduction

Most private and public forest land owners are compelled to manage their forests 

sustainably. Sustainable forest management (SFM) is a 21st century management approach that has been branded by the forestry community in the United States and other 

parts of the world as a concept that provides the basis for site-specific management 

practices and guidelines. Sustainable forestry is economically viable, environmentally 

sound, and socially acceptable (Sample and others 2006).

Based on these SFM principles, groups of countries sharing similar forest resources developed criteria and indicators (C&Is) that measure and monitor sustainability 

(Montreal Process 1995). The C&Is serve as policy and management tools; they are 

neither management standards nor regulations. They provide a framework for determining the status of ecological, economic, and social conditions of forests, landowners and 

communities, and they provide the basis for SFM programs on private and public land 

(Roundtable on Sustainable Forests 2008). For example, Criterion 4, conservation and 

maintenance of soil and water resources, has two indicators pertaining to soil resources: 

(1) proportion of forest management activities that meet best management practices or 

other relevant legislation to protect soil resources; and (2) area and percent of forest land 

with significant soil degradation.

It remains the task of landowners or their representatives to develop and apply appropriate best management practices as called for by indicator #1, and to monitor the 

level of “significant soil degradation” referred to in indicator #2. Many private landowners have their forest operations certified by third-party entities against a set of 

standards (Rametsteiner and Simula 2002). Examples of certification programs include 
14 USDA Forest Service Proceedings RMRS-P-59 2010.

the Sustainable Forestry Initiative (SFI 2004), Forest Stewardship Council (FSC 1996), 

and the Canadian Standards Association (CSA 2003).

The U.S. National Forest System applies the Montreal Process C&Is through ecosystem management policies guided by federal law (the Multiple Use and Sustained Yield 

Act of 1960, The National Environmental Policy Act of 1969, the Forest and Rangeland 

Renewable Resources Planning Act of 1974, and the National Forest Management Act 

of 1976 [NFMA]). The NFMA requires that national forests be managed in a way that 

protects and maintains soil productivity (USDA Forest Service 1983). Section 2550.5 

of the Forest Service Manual under soil management program (FSM 2009) defines 

soil productivity as “...the inherent capacity of the soil resource to support appropriate site-specific biological resource management objectives, which includes the growth 

of specified plants, plant communities, or a sequence of plant communities to support 

multiple land uses.” The objective of the soil management program is to “maintain or 

improve soil quality on National Forest System lands to sustain ecological processes 

and function so that desired ecosystem services are provided in perpetuity.” Soil quality management (FSM section 2551) is used to accomplish this objective by (1) using 

adaptive management (FSM 1905) to design and implement land management activities in a manner that achieves desired soil conditions to ensure that soil and water 

conservation practices are implemented and effective; (2) assessing the current condition of soil resources; and (3) monitoring resource management activities and soil 

conditions to ensure that soil and water conservation practices are implemented and 

effective (italics added for emphasis). Regional foresters, forest supervisors, district 

rangers, and soil scientists within each of the 10 Forest Service regions all play a role 

in achieving this objective. Soil quality monitoring programs are standardized in objectives and principles, but are region-specific to account for varying soils and ecosystems. 

The environmental and technical soundness of the soil quality monitoring program is 

important because it must withstand both scientific scrutiny and legal challenges. The 

Air, Water, and Soil Division and the research wing of the Forest Service periodically 

review the soil quality monitoring protocol to ensure that the standards and procedures 

are scientifically and technically up to date, and to ensure that the monitoring process is 

systematically achieved.

To help that review process, this paper provides an overview of soil quality principles and monitoring approaches that can be incorporated in an adaptive management 

process for achieving sustainable forest management.

Some Background

Adaptive Management

Various forest land management agencies and industries have developed processes 

for achieving SFM using logic models, reliable processes, and adaptive management. 

Several models are shown in figure 1. Each is conceptualized a little differently, but 

all contain the same basic elements: (1) an explicit or implied definition of SFM; (2) a 

knowledge database from which to develop management guidelines; (3) the guidelines 

or regulations from which best management practices are prescribed; (4) a process for 

monitoring compliance, effectiveness, and long-term efficacy; and (5) a research program that creates new knowledge for adaptive management.

As an example, we adapted and expanded the Heninger and others (1998) model 

with an SFM goal of maintaining forest and soil productivity after stand replacement 

harvesting (fig. 2), one of the key provisions of the “environmentally sound” component 

of SFM. The first step in the process after establishing or assuming a cause-and-effect 

relationship between harvesting disturbance and soil quality is to use existing data and 

knowledge (everything we know) from a “strategic database” to develop management 

“guidelines” that would prevent detrimental effects. All involved in applying the guidelines are trained. The guidelines, as applied in the forest, are the “best management 

practices” (BMPs), which are written policy guidelines that describe the manner in 

which specific forest operations or management activities will be conducted. They are 

Burger, Gray, Scott Using Soil Quality Indicators for Monitoring Sustainable Forest Management
USDA Forest Service Proceedings RMRS-P-59 2010. 15

based on accomplishing the management objective in a cost-effective manner while 

maintaining or improving soil and forest productivity, and are subject to change as science and practice show ways for improvement.

Monitoring BMPs Used for Sustainable Forest Management

The next step is to determine if the BMPs are working as intended. Forest practices 

should be monitored for BMP compliance, a short-term indication of effectiveness of 

the BMPs, and long-term validation of SFM (Avers 1990) as defined by policy (e.g., 

same growth potential and forest composition). Compliance monitoring simply ensures 

implementation of the BMPs. Effectiveness monitoring uses visual and measured soil 

disturbance indicators (DIs) and measured soil quality indicators (SQIs) to make a judgment of the efficacy of the BMPs, and whether they are likely to maintain soil and 

hydrologic function based on our cumulative research and knowledge. Because maintaining forest productivity and other services through time is the sustainability goal, 

long-term monitoring to determine if the forest is functioning the way it did before disturbance is validation that the BMPs are working as intended. When DIs and SQIs are 

properly chosen and calibrated, judgments on effectiveness of the BMPs can be made 

Figure 1. Examples of adaptive management models used for achieving sustainable forest management.

Figure 2. Components of an adaptive management model.

Raison et al., 2001 Rametsteiner, 2001 Heninger et al., 1998

Australia Europe USA

Using Soil Quality Indicators for Monitoring Sustainable Forest Management Burger, Gray, Scott
16 USDA Forest Service Proceedings RMRS-P-59 2010.

within weeks or months and guidelines can be modified as needed to improve forest 

practices. Because forests are long-lived, it may take years or decades to finally validate 

SFM. If monitoring shows that we need better guidelines, BMPs, or SQIs, targeted research should be conducted to expand our knowledge in the strategic database to further 

adapt our management to meet SFM goals. This adaptive management model, or some 

variant, can be applied to all managed forests, regardless of ownership, to achieve SFM 

required by law or compelled by forest certification processes.

For the purpose of this paper, we will assume that a primary SFM goal is maintaining 

soil and hydrologic function (Montreal Process Criterion #4) so that forest productivity 

(rate of biomass production per unit time and area) is not impaired. To accomplish this 

goal, BMPs are used by most public and private forest land owners, and BMP compliance (i.e., were the prescribed practices implemented?) is easily monitored. However, 

monitoring and demonstrating BMP effectiveness is challenging because forest managers must establish with certainty in a short period (e.g., within 1 yr after completion 

of the operation) that forest operations in an activity area have not impaired soil and 

hydrologic function. The assumption is that pre- and post-disturbance soil and hydrologic function can be determined and compared. If they are the same, the BMPs were 

effective, and post-operation forest productivity and other forest services should be the 

same. This is the basis of the SFI and FSC standards and the USDA Forest Service soil 

management program (FSM 2009). However, the relationship between the measures of 

soil and hydrologic function and forest productivity must eventually be validated with 

long-term trials so that the standards and BMPs can be modified if needed (adaptive 

management process) (fig. 2).

The assumption that soil productivity, and by extension forest productivity, can 

be monitored, measured, and judged based on its combined attributes (properties and 

processes) is important because it provides a tool for land managers to meet forest sustainability standards established by law or policy (e.g., U.S. National Environmental 

Policy Act of 1969). Because trees are long-lived, management impacts on productivity—positive or negative—may take decades to discern. Therefore, changes in soil and 

hydrologic properties and processes that can be measured immediately after a disturbance can serve as surrogates or proxies for change in soil and forest productivity as 

long as they are based on science and legally defensible. The change in soil properties 

and processes that results in an improved or degraded soil condition is a measure of soil 

quality.

Soil Quality Concepts and Principles

Soil Productivity Versus Soil Quality

Soil productivity is usually defined as a soil’s ability to produce biomass or some 

harvestable crop. If not modified, soil has a natural or inherent productive potential 

based on its genesis and setting in the landscape. Some soils are naturally more productive than others, but not necessarily more valuable in terms of the role they play 

in their natural setting. For example, an Aridisol supporting a pinion-juniper forest in 

New Mexico is less productive than an Andisol supporting a mixed conifer forest in 

California, but each soil is providing ecosystem services commensurate with its development and setting. Within a given forest ecosystem, some soils are naturally more 

productive than others. This difference in soil productivity is reflected in a measure 

of forest site index or volume production after a given amount of time. Soil quality 

has been defined as its ability to provide services important to people. It is useful as a 

measure of the extent to which a managed soil is improved or degraded from its natural 

state or some other selected reference condition. Soil is complex; it has many physical, 

chemical, and biological properties that define its natural state and determine its productivity. Disturbances or management inputs usually change multiple properties at once. 

To evaluate soil change or soil quality, all or most of the important properties that were 

affected by the disturbance must be measured.

Burger, Gray, Scott Using Soil Quality Indicators for Monitoring Sustainable Forest Management
USDA Forest Service Proceedings RMRS-P-59 2010. 17

Agriculture scientists define soil quality as its ability to function (Larson and Pierce 

1994) in a way that sustains biological productivity, environmental quality, and plant, 

animal, and human health and habitation (Doran and Parkin 1994; SSSA 1995). It 

is not a new concept. It was used by Storie (1933) 75 years ago to rate agricultural 

value of California soils. More recently, Warkentin and Fletcher (1977) recommended 

its use for monitoring the effects of intensive agriculture on soils. Karlen, and others 

(2003) reviewed its development and use in agriculture, and Burger and Kelting (1999) 

showed how one might use soil quality models to assess the impacts of intensive forest 

management.

Soil quality is analogous to the concepts of air and water quality where judgments 

are made concerning their fitness to breathe and drink based on selected, measurable 

standards. However, extending the air and water quality concepts to soil is less intuitive 

and more complex because we do not ingest soil directly. Its “fitness” is judged based on 

habitation and growth of plants and animals that are in turn ingested by humans; therefore, it is once removed from our personal experience. Soil also has multiple functions 

beyond food production: carbon sequestration, waste processing, and water regulation, 

among others. Furthermore, soil quality can change at different rates. Change can be 

slow and cumulative over time, and it can change in both negative and positive directions due to management. Finally, there is no “pure” (as in pure air or pure water) soil 

baseline against which to make judgments; there are many different soil types in nature 

each of which has its own natural condition. Nonetheless, the analogy with air and water 

holds in the sense that soil quality can be used to make judgments about the impacts of 

management, both negative and positive, against predetermined conditions or standards.

Soil Services, Functions, and Indicators

In order to use soil quality as a uniformly applied monitoring tool, there must be 

some agreement on its definition and use as a concept and monitoring tool. Similar 

to the concept of sustainable forestry, it is a work in progress. As a starting point, it 

is helpful to conceptualize soil in terms of “what it does for us” (services), “how it 

does it” (functions), “its character or attributes” (properties and processes), and “how 

we monitor and measure its performance or change in the level of services provided” 

(indicators).

Forest productivity, carbon sequestration, and a regulated hydrologic cycle are examples of soil services, sometimes called management goals (Andrews and others 2004) 

(table 1). Some soil services are more important than others in a given forest ecosystem. 

Therefore, forest managers should judge soil quality in terms of how management affects the most important services that soils provide. Soil services may not be completely 

complementary with respect to soil quality; one soil service may, in fact, reduce soil 

quality for another service. For example, longleaf pine ecosystems are managed primarily for biodiversity, not productivity. Longleaf pine as a species can be used effectively 

in production-based silvicultural systems, but generally speaking the interest in longleaf 

pine as opposed to other southern pines is the biodiversity value the entire ecosystem 

provides. However, the longleaf pine ecosystem thrives on disturbance, and in fact, 

the ecosystem loses much of its biodiversity value without disturbance. These disturbances clearly have the potential to alter soil quality, but the alterations may be positive 

or negative depending on the soil service. If the service managed for is biodiversity, 

repeated burning or other disturbances required for the main soil service increase the 

potential risk for surface erosion (reduction of soil quality for water quality protection), 

and nutrient loss (reduction of soil quality for soil productivity), but increase soil quality 

for a multitude of herbaceous plants that require not only the open conditions that burning provides, but also the specific soil conditions that allow them to compete with more 

nutrient-demanding plants. In other words, the best soils for the highest biodiversity in 

the longleaf pine ecosystem may not be the best soils for tree growth, and they may not 

be as capable of protecting water quality or sequestering carbon.

Using forest productivity as an example of a desired service, the soil functions to 

provide this service in several ways: (1) it remains stable and intact as a medium for 

root growth and habitat for soil animals; (2) it accepts, holds, and supplies water; (3) it 

Using Soil Quality Indicators for Monitoring Sustainable Forest Management Burger, Gray, Scott
18 USDA Forest Service Proceedings RMRS-P-59 2010.

promotes optimum gas exchange; (4) it sequesters, holds, and cycles organic matter and 

nutrients; and (5) it promotes biological activity (Doran and Parkin 1994; Burger and 

Kelting 1999; Andrews and others 2004). In the context of forest soils and forestry operations, these functions might be consolidated to soil stability, soil hydrology, and nutrient 

cycling (table 1). If a soil is protected from erosion, mass wasting, and displacement, 

it is stable and can provide a medium for plant growth. If it is protected from compaction, rutting, and puddling, it can function hydrologically, that is, water can infiltrate 

the soil, be stored, and be released for uptake by plants, and the soil will have the right 

proportion of macro- and micropore space so that it can drain properly. In forest soils, 

nutrient supply and biological activity are intimately tied to organic matter and nutrient 

cycling processes, including rates of input, decomposition and mineralization, storage, 

and release or uptake. Protection of these processes from soil surface disturbances, displacement of soil organic matter layers, and severe burns should maintain function in 

a given soil of a certain ecosystem. Of course, soil function is ecosystem-specific and 

must be assessed in the context of desired ecological condition. For example, soils in tupelo-cypress, longleaf pine, pinion-juniper, and black spruce ecosystems have the same 

functional elements, but each ecosystem will have different levels of soil properties and 

processes considered “normal.”

Examples of the soil properties and processes, sometimes called soil attributes 

(Nortcliff 2002), associated with the first function (soil stability) are horizonation, 

strength, depth, and water content (table 1). Some soil properties and processes cannot be measured directly or efficiently; therefore, DIs, SQIs, measurable surrogates, 

or proxies of soil function must be used. Indicators may be a soil condition, property, 

or process such as soil compaction, soil strength, or water infiltration, or a combination of several soil properties such as soil tilth (soil tilth combines a measure of bulk 

density, strength, aggregate uniformity, soil organic matter, and plasticity index [Singh 

and others 1990]). Soil DIs or SQIs may be determined visually, or via measurement by 

laboratory or field testing (table 1).

Regardless of their simplicity or complexity, ideal indicators should (1) have a baseline against which to compare change; (2) provide a sensitive and timely measure of a 

soil’s ability to function within a given ecosystem; (3) be applicable over large areas; 

(4) be capable of providing a continuous assessment; (5) be inexpensive and easy to 

Table 1—Examples of soil services, functions, properties, processes, and indicators useful for monitoring sustainable 

forest management.

Soil indicators

Soil services Soil function

Soil properties 

and processes Disturbance Soil quality

Soil stability:

Intact medium to 

promote root growth 

and provide habitat for 

soil animals

Horizonation

Depth

Strength

Water content

Mass movement

Erosion

Ground cover

Soil horizon depth

Strength

Soil loss (t/ac)

Aggregate uniformity

SOM

Forest 

productivity

Soil hydrology:

(accept, hold, and 

supply water, and drain 

properly for optimum 

gas exchange)

Texture

Structure

Porosity

Infiltration

Conductivity

Water storage

Gas exchange

Soil compaction

Rutting

Puddling

Impeded drainage

Surface runoff

θ vol. between 1/3 bar 

and 15 bar

Soil structure

Soil consistence

Macroporosity

Redox potential

O2 level

Regulated 

hydrologic cycle

Regulated 

carbon balance

Waste 

bioremediation

Nutrient cycling:

(sequester, hold, and 

cycle organic matter 

and nutrients and 

promote biological 

activity)

SOM content

Nutrient content

pH

CEC

Decomposition

Mineralization

N fixation

Acidification

Leaching

CWD amount and

distribution

Litter displacement

Severe burn

Organic matter loss

Acid deposition

Accelerated nutrient 

leaching

C content

Active organic matter

Effective CEC

Extractable nutrients

N mineralization

Microbial biomass

Biopores

Fecal deposits

Soil respiration

Burger, Gray, Scott Using Soil Quality Indicators for Monitoring Sustainable Forest Management
USDA Forest Service Proceedings RMRS-P-59 2010. 19

use, collect, and calculate; (6) discriminate between natural changes and those induced 

by management; (7) have a cause-and-effect connection with forest productivity; and 

(8) be responsive to corrective measures (Burger and Kelting 1999).

These indicator characteristics are mostly obvious and intuitive, but two common 

monitoring pitfalls are using indicators too broadly, and not having a cause-and-effect 

relationship with the soil service or management goal. The ideal indicator would be applicable over large areas, but in reality indicators and their relative importance are quite 

soil- and site-specific.

Perhaps the most serious monitoring pitfall is using indicators with no cause-andeffect relationship with the soil service (e.g., soil productivity) (Powers and others 

1998; Miller and others, in preparation). Many forest disturbances, both natural and 

human-induced, are totally benign. In fact, the health and productivity of some forest 

ecosystems require disturbance (e.g., ground fire, litter layer disturbance by animals). 

A detrimental disturbance in one forest ecosystem may be a beneficial process in another. Furthermore, disturbances are often soil- and species-specific (Page-Dumroese 

and others 2000; Powers and others 2005; Kranabetter and others 2006). Indicators of 

detrimental disturbance must be applied carefully, and they should have known correlations with forest productivity or some other service or management goal. All indicators 

will not have all eight features listed above, which is why several may be needed to 

adequately measure BMP effectiveness.

Different Indicators Needed for Different Soils

Soil services (what soils do for us) and soil functions (how they do it) are fairly universal. However, soil types and their properties and processes (attributes) vary greatly, 

which requires site-specific selection of indicators for monitoring the most important 

soil functions for a given soil type and disturbance activity. Furthermore, some soils 

are more resistant to impact than others; a given impact may be detrimental to one soil 

and have no effect on another. This is illustrated in the example in figure 3: Soil quality 

is shown as a function of a soil’s ability to hold, supply, and cycle organic matter and 

nutrients (nutrient cycling) on the y axis, and the ability to accept, hold, and supply 

water, air and heat (air/water balance) on the x axis (Burger 1997); both are important 

forest soil functions identified by several researchers (Powers and others 1998; Burger 

and Kelting 1998). Soil quality generally increases as organic matter and nutrients are 

conserved, and soil quality increases as the air/water ratio is balanced. Soil specificity is 

shown in several general ways:

• Alfisols (e.g., Soil A) are more likely to be detrimentally impacted by changes in air/

water balance than changes in fertility, while the opposite is true for Entisols (e.g., 

Soil B). Alfisols are usually better buffered than Entisols against nutrient removals, 

while Entisols usually have a coarser texture and resist compaction and loss of 

macropore space. Ultisols and Inceptisols are likely to be more equally impacted by 

changes in both soil functions, but are better buffered against extreme changes in air/

water balance and nutrient cycling, respectively, for the Alfisols and Entisols.

• The risk of a detrimental impact varies within a soil order. For example, a lowquality Entisol (well-drained marine sand, Soil C) is more likely to be detrimentally 

impacted by organic matter and nutrient removal (Brendemuehl 1967) than a highquality Entisol (alluvial flood plain soil, Soil B) (Aust and others 1997), which is 

illustrated in figure 3 by convergence of a possible response surface toward higher 

soil quality.

• Soil compaction and organic matter removal may be good indicators for air/water 

balance and nutrient cycling, respectively, for most soils, but their relative importance 

(weight) would be different for different soils. Soil compaction would be more 

detrimental to most Alfisols than organic matter removal, and organic matter removal 

would be more detrimental to most Entisols than compaction. Therefore, a uniform, 

one-size-fits-all soil quality monitoring program would not be applicable across all 

soils and forest sites. This was illustrated in a study by Page-Dumroese and others 

(2000) who evaluated the effectiveness of applying uniform soil quality standards 

Using Soil Quality Indicators for Monitoring Sustainable Forest Management Burger, Gray, Scott
20 USDA Forest Service Proceedings RMRS-P-59 2010.

to disturbances caused by forest operations over diverse forest landscapes in the 

Pacific Northwest. They concluded that application of selected USDA Forest Service 

standards (USDA Forest Service 1991) did not provide a comparative accounting of 

detrimental change in soil quality for the sites measured, and that some level of soil 

and site specificity needs to be incorporated in monitoring protocols.

USDA Forest Service Soil Monitoring and Research Programs

Soil Quality Monitoring

The USDA Forest Service has a well-established soil quality monitoring program 

that has been in place for several decades (USDA Forest Service 1991; Powers and 

others 1998). The program is a process by which data are collected to determine if soil 

management objectives have been achieved. It is meant to assist land managers in making better decisions on how to maintain or improve long-term soil productivity. The 

program and its evolution were described by Powers and others (1998) and by PageDumroese and others (2000). A fundamental assumption is that forest operations cause 

soil disturbances at some critical level that interfere with soil function (soil stability, soil 

hydrology, and nutrient cycling), which in turn have a detrimental effect on soil and forest productivity. A second assumption is that measures of one or more soil disturbances 

can be used to judge whether an operation had a detrimental impact on productivity, 

provided the disturbance, or a combination of disturbances, exceeded a predetermined 

threshold (usually 15 percent of the pre-disturbance condition) on more than 15 percent 

of the activity area. Disturbance and SQIs used by Forest Service Regions as reported in 

supplements to FSH 2509.18 are shown in table 2. Regions 1, 2, 4, 6, 8, and 9 use DIs 

for monitoring sustainable management, while Regions 3 and 5 use SQIs representing 

soil functions (table 2). The use of different sets of indicators and different approaches 

suggest a degree of region-specific application of the soil quality monitoring process; 

however, standardization of approach to the extent feasible would be advantageous for 

withstanding public and legal scrutiny.

Figure 3. Soil quality response 

surface defined by soil nutrient 

cycling and hydrology (after 

Burger 1997).

Burger, Gray, Scott Using Soil Quality Indicators for Monitoring Sustainable Forest Management
USDA Forest Service Proceedings RMRS-P-59 2010. 21

According to Powers and others (1998), the soil quality standards are meant as early 

warning thresholds of impaired soil conditions. When threshold standards for detrimental disturbance are exceeded, a 15 percent decline in productivity is assumed. Threshold 

standards are based on scientific findings or best professional judgment, but there is 

little or no documented evidence of any connection between disturbance thresholds 

and productivity. When critical data are lacking, it is prudent to err on the conservative 

side to ensure that productivity is not impaired; on the other hand, unreasonably strict 

standards having no basis in fact can limit forest use opportunities and tie up human 

resources in unnecessary litigation.

Following an assessment of soil disturbance in forests of the Interior Columbia 

Basin, Miller and others (in preparation) suggest that current soil quality methodology 

is inadequate, and they make a case for a more rigorous approach underpinned by research findings and sound scientific interpretations. Their finding was based on 15 soil 

monitoring projects after logging in which they visually classified disturbance and took 

bulk density samples along transects. They concluded that (1) different applications 

of a visual assessment protocol by different people led to different conclusions as to 

whether a logging operation is judged detrimental; (2) visual versus measured estimates 

of bulk density showed that visual estimates are unreliable; (3) the effect of equipment 

tracks and surface soil displacement is often over estimated, which overstates detrimental impacts of logging operations; (4) because current interpretations of detrimental 

disturbance are seldom justified by scientific investigations (e.g., the assumption that 

a 15 percent increase in bulk density reduces tree growth on all soils is not supported 

by research), classification of soil disturbance should be for descriptive purposes only; 

(5) given broad variation in soils and climate among national forests, using the same 

standards for defining detrimental disturbance as it affects tree growth is not reasonable; 

and (6) current soil disturbance interpretations are based on experience and opinions of 

local specialists that are seldom documented or peer-reviewed. To overcome these limitations, they recommend a formal process for selecting activity areas for monitoring, 

Table 2—Detrimental soil disturbances or soil functions monitored by Forest Service Region (R1 through R10) and 

those listed in the Soil Management Handbook (USDA Forest Service 1991).

Region and effective date

R1 

1999

R2 

1992

R3 

1999

R4 

2003

R5 

1995

R6 

1998

R8 

2003

R9 

2005

R10 

1992

HB 

1991

Disturbance:

Compaction X X X X X X X X

Rutting X X X X X

Displacement X X

Severely burned X X X X X X X

Surface erosion X X X X X X

Organic matter loss X X X X X

Mass movement X X X X

Puddling X X X X X

Ground cover X X X

Altered wetness X

Functions:

Stability X

Hydrology X X

Nutrient cycling X

Soil productivity X

Buffering capacity X

Using Soil Quality Indicators for Monitoring Sustainable Forest Management Burger, Gray, Scott
22 USDA Forest Service Proceedings RMRS-P-59 2010.

and a revised set of descriptive disturbance and SQIs that account for both severity and 

extent of disturbance. For making judgments on impaired productivity, they recommend 

using risk-rating models based on research findings and collective expert opinion that 

account for specific site factors, potential vegetation, and forestry activity. Risk rating 

can then be used for site-specific prescriptions allocated to high-risk sites.

Synthesis of LTSP Research Findings

If the critique of the Forest Service’s soil quality monitoring program by Miller and 

his co-workers has merit, the adaptive management model (fig. 2) suggests that the way 

to improve effectiveness monitoring is to adjust DIs and SQIs using current research 

findings. The North American long-term soil productivity study (LTSP) (Powers and 

others 1990) was installed, in part, to validate or improve SQIs used for short-term 

judgments of sustainable forest management. The study addressed organic matter removal and compaction DIs each at three levels: stem-only harvest, whole-tree harvest, 

and whole-tree harvest plus litter layer removal; and none, moderate, and high levels of 

compaction, respectively. Although still a relatively young project after only 15 years, 

preliminary results have been reported that suggest several ways in which the selection 

and interpretation of USFS DIs and SQIs might be reconsidered or adjusted.

Powers and others (2005) reported findings from the first 10 years of study for a 

range of LTSP study sites in CA, ID, LA, MI, MS, and NC. Several other key papers 

reported site-specific responses to the LTSP treatments at different locations. Key findings include the following:

• Soil organic matter across all sites was generally unaffected by complete removal 

of surface organic matter (stem-only versus whole-tree plus litter removal). Based 

on composite results, it appears that carbon inputs to mineral soil horizons are due 

primarily to root decomposition, while carbon mineralized in the surface Oi and Oe 

layers efflux as CO2

.

• For four contrasting CA sites, whole-tree plus litter removal caused substantial 

declines in soil C and N concentrations and mineralizable N. In a later report for the 

NC and LA loblolly pine LTSP plots (age 10 data), Sanchez and others (2006) reported 

no organic matter removal effects on tree growth. Heavy compaction resulted in a 

slight increase in stand volume on LA plots and a slight decrease in growth on NC 

plots. Organic matter removal had little effect on soil N but significantly reduced 

extractable P. This effect on P was also reported by Scott and others (2004) for LA 

plots at age 5.

• Composite data for all sites indicated no general decline in productivity with organic 

matter removal, which is consistent with the observation by Blake and Ruark (1992) 

that effects of organic matter removal is confounded by an array of influences both 

positive and negative. One exception was that aspen biomass on the MI plots was 

significantly less on plots where trees and litter were removed due to vigorous 

sprouting and dieback of root suckers. Another was on some inherently P-deficient 

soils in LA and MS, which showed substantial declines due to whole-tree harvesting 

at age 10 (Scott and Dean 2006).

• Severe soil compaction increased Db

 an average of 18 percent in the 10- to 20-cm soil 

layer, but little compaction occurred if initial Db was >1.4 Mg m-3. Composite data for 

all sites showed that severe compaction had little or no effect on standing biomass; 

however, biomass on sandy sites increased by 40 percent while that on clayey sites 

decreased by half. This textural influence was clearly demonstrated across three 

CA LTSP sites (Gomez and others 2002). The authors reported growth responses 

to compaction by mixed conifers that decreased, remained the same, and increased 

for a clay, loam, and sandy loam, respectively. The soil series, in the same order, 

were Challenge (Typic Palexerults), Cohasset (Ultic Haploxeralfs), and Chaix (Typic 

Dystroxerepts). The different impacts of compaction among soils (negative, benign, 

positive) were attributed to changes in strength, pore space distribution (which 

changed available water holding capacity), and an interaction between these factors. 

Burger, Gray, Scott Using Soil Quality Indicators for Monitoring Sustainable Forest Management
USDA Forest Service Proceedings RMRS-P-59 2010. 23

This finding corroborates the Greacen and Sands (1980) model showing that strength 

and porosity are the static physical properties most directly affecting the tree (fig. 4). 

The clay soil suffered the greatest increase in soil strength and the greatest loss in 

porosity with no increase in available water holding capacity (AWHC) resulting in 

decreased tree growth on compacted plots. Although the loam soil had a strength 

exceeding 3 MPa below 10 cm, its AWHC increased significantly, which resulted in a 

negative/positive tradeoff and a net result of no change in tree response. Compaction 

increased strength of the sandy loam soil, but AWHC increased at all depths of the 

measured profile, resulting in a net positive change in growth.

Implications of LTSP Research Findings for Soil Quality Monitoring

Collectively, the LTSP research results have the following implications for the Forest 

Service’s soil quality monitoring protocol:

• The age-10 LTSP data clearly demonstrate site- and soil-specific responses to 

disturbance, which further explains the inconsistent conclusions provided by soil 

disturbance monitoring when applied across different sites (Page-Dumroese and 

others 2000) or when applied by different people (Miller and others, in preparation). 

Currently used detrimental DIs are all good in principle, but they need to be selectively 

applied and weighted by importance in different regions and within regions.

• The effect of organic matter removal (e.g., whole-tree plus litter) from the surface 

of a forest site is clearly site-specific (sucker sprouting in aspen; P depletion in Gulf 

Coast loblolly pine; N depletion in CA mixed conifers). The LTSP data show that 

much higher levels of removal are needed to affect a detrimental response than are 

currently set as regional standards on most sites, yet some highly sensitive sites may 

be impaired by removals currently allowed. Organic matter is a master variable in the 

sense that it plays multiple roles in forest ecosystems. In addition to N and P cycling 

and natural regeneration demonstrated in the LTSP trials, it is habitat for myriad 

animals, protects mineral soil from erosion, buffers temperature and water extremes 

in the surface mineral soil, and is an energy source for plants and animals. Some of 

these functions are more important than others on a given site, but, in any case, those 

that play a clear role in productivity should be monitored. In addition to the DI (area 

and degree of organic matter displacement), one or more soil/site quality indicators 

(N mineralization, sucker sprouting, etc.) should be used to make judgments about 

SFM.

• Soil compaction is an important and useful DI, but it is clear from the LTSP data 

that it is not always detrimental; in fact, it clearly enhances soil productivity in some 

cases. In other cases, forest productivity may be improved while soil productivity 

is unchanged. Stagg and Scott (2006) found that planted loblolly pine growth was 

increased by compaction through reducing understory competition. Planted tree growth 

on plots with herbicide applications to control competition showed little response to 

Figure 4. Root and tree growth as a 

function of soil compaction effects on 

bulk density, soil strength, porosity, 

and water content (after Greacen and 

Sands 1980).

Using Soil Quality Indicators for Monitoring Sustainable Forest Management Burger, Gray, Scott
24 USDA Forest Service Proceedings RMRS-P-59 2010.

compaction. This finding reinforces the principle that many types of disturbance in 

ecosystems are beneficial and sometimes necessary for normal ecosystem function 

(for example, fire, windthrow, and deposition of sediment by natural processes); 

human influences often enforce these positive processes. Therefore, simple visual 

indicators of compaction are inadequate for judging detrimental disturbance (Aust 

and others 1998; Steber and others 2007). A measure of bulk density, the one 

commonly measured SQI in Forest Service monitoring protocols, will often lead to 

erroneous conclusions because detrimental effects of compaction can occur in clayey 

soils with less than a 15 percent change, and beneficial effects can occur in sandy 

soils with an even greater change. Better indicators of compaction are soil strength 

and the ratio between macro- and micro-porosity as shown by the conceptual model 

by Greacen and Sands (1980) (fig. 4). Compaction increases Db, but the impact of the 

Db change on strength and pore space distribution are the real drivers of root growth 

and productivity (fig. 4), and Db change is not always a reliable surrogate for these 

soil properties. Attempts have been made to determine root-growth limiting Db for 

forests (Daddow and Warrington 1983), but rules of thumb from these attempts have 

not been successfully applied to forests.

More Known About Soil Response to Disturbance Than Reflected in Current 

Monitoring Protocols

The old cliché “more research is needed” certainly applies to our quest for a better understanding of site-specific forest response to disturbances for achieving SFM. 

However, we maintain that more is known about soil disturbance processes and effects 

than is currently reflected in Forest Service SQM protocols. For example, a 15 percent 

increase in Db is used by most Forest Service regions as an indication of detrimental 

disturbance. The empirical findings by Gomez and others (2002) clearly show that this 

indicator will lead to erroneous conclusions on many sites and strongly suggests that 

we need to move beyond a blanket approach of using visually estimated or measured 

Db. Gomez and others (2002) showed that soil strength and pore space distribution 

were better SQIs than Db, as conceptualized by Greacan and Sands (1980) decades 

ago. Furthermore, we understand the basis for this model given decades of research on 

the interactions among factors in the model. Recent work by Siegel-Issem and others 

(2005) contrasting data from California and Missouri LTSP sites demonstrates our understanding of compaction effects that can be extrapolated to many soils across regions. 

A brief summary of selected bits of their results are presented to make the point that a 

synthesis of knowledge can be used to improve SQM.

The California soil was a Cohasset coarse sandy loam (Haploxeralf) (fig. 5A) from 

the Tahoe National Forest similar to the one Gomez and others (2002) studied, but with 

a sandy loam texture. Its parent material is an andesitic mudflow and the dominant 

vegetation is mixed conifers. The Missouri soil was a Clarksville silt loam (Paleudult) 

(fig. 5B) from the Carr Creek State Forest. Its parent material is a sandstone residuum and the dominant vegetation is oak-hickory with a component of shortleaf pine. 

Given the contrasting particle size distributions and different levels of organic matter, 

the soils reacted very differently to compaction. The MO soil reached proctor level Db

(maximum possible under controlled conditions) at 1.53 Mg kg-3 compared to 1.25 Mg 

kg-3 for the CA soil. As Db increased and volumetric water content (Ө) decreased, soil 

strength increased. For the CA coarse sandy loam, above Db 1.00 Mg kg-1 and below 

35 percent Ө, soil strength approached or exceeded 2MPa, the strength that becomes 

root-limiting. Below 1.00 Mg kg-1, Db had virtually no effect on soil strength at any Ө

(fig. 5C). By contrast, soil strength of the MO silt loam did not reach the 2MPa threshold 

until Db

 exceeded 1.5 Mg kg-1, which was nearly the proctor limit (fig. 5D).

The total and available water holding capacity (AWHC) of the CA soil increased 

significantly with increasing Db (fig. 6A), but there was little change in the AWHC of the 

MO soil (fig. 6B). Increasing Db dramatically reduces the non-capillary or macropore 

space in most soils. When macropore space drops below 10 percent, roots of upland species become hypoxic due to inadequate gas exchange rates (Grable and Siemer 1968). 

Burger, Gray, Scott Using Soil Quality Indicators for Monitoring Sustainable Forest Management
USDA Forest Service Proceedings RMRS-P-59 2010. 25

This is illustrated in figure 6D for shortleaf pine in the MO soil. Root length density 

followed a classic bell-shaped response for upland species in loam soils, decreasing 

from optimum water content as the soil became both drier and wetter due to inadequate 

available water on the dry end and inadequate aeration on the wet end of the soil water 

gradient (da Silva and others 1994). As Db increases, the range in soil water content 

within which roots can grow narrows, which in turn causes a decrease in root length 

density. The trees growing in the CA soil suffered from increased strength on the dry 

end of the Ө gradient, but not at all on the wet end of the Ө gradient, despite reduced 

aeration porosity (fig. 6C).

These soil and tree responses to compaction under controlled lab conditions corroborate the field results reported by Gomez and others (2002). Soil texture and organic 

matter content influence the extent to which a soil can be compacted and the relative 

influence of strength versus pore size distribution. The degree and influence of compaction are predictable based on texture and organic matter content and thus could be 

used to adjust the importance of Db

 change relative to other DIs. Furthermore, soil 

strength and pore space distribution could be used as soil texture-specific SQIs in lieu 

of estimated or measured Db. Clearly, we know enough about soil physical processes to 

create a combined basic/empirical mathematical model to estimate and make definitive 

judgments of detrimental compaction, rutting, and puddling impacts on productivity. 

The same could probably be said for organic matter displacement and loss, and good 

models already exist for soil erosion prediction and risk assessment (Laflen and others 

1997). A similar argument was made by Miller and others (in preparation) based on their 

firsthand experience with the limitations of current SQM protocols. Modeled soil disturbance processes that address the stability, hydrology, and nutrient cycling functions 

Figure 5. Particle size distribution of a Clarksville and Cohasset soil series from MO and CA LTSP study sites, respectively (from 

Siegel-Issem and others 2005).

Using Soil Quality Indicators for Monitoring Sustainable Forest Management Burger, Gray, Scott
26 USDA Forest Service Proceedings RMRS-P-59 2010.

of soils need to be combined in a single, workable, cost-effective protocol that can be 

continuously updated as new findings warrant.

Figure 6. Pore space distribution and root length density of shortleaf pine seedlings and ponderosa pine seedlings grown 

on Clarksville and Cohasset soils, respectively, as a function of soil bulk density and volumetric water content (SiegelIssem and others 2005).

Burger, Gray, Scott Using Soil Quality Indicators for Monitoring Sustainable Forest Management
USDA Forest Service Proceedings RMRS-P-59 2010. 27

Modeling Soil Quality

An Approach for Modeling Soil Quality

A number of efforts have been made to model soil quality (Doran and Parkin 1994; 

Carter and others 1997), quantitatively score soil quality for use as a performance standard (Larson and Pierce 1994; Andrews and others 2004), and extrapolate soil quality 

classes or risk assessments to an activity area (Halvorson and others 1996; Wendroth 

and others 1997; Kelting and others 1999). Most of these efforts have been made on 

agricultural landscapes, and extensive reviews of these topics are covered in several 

publications (Doran and Parkin 1994; Doran and Jones 1996; Gregorich and Carter 

1997; Lal 1999). Several compilations have also been made for forest landscapes 

(Ramakrishna and Davidson 1998; Raison and others 2001).

This approach is conceptualized in figure 7. Forest practices can degrade or improve 

soil quality compared to a pre-disturbance or reference condition (solid circle in diagram). Often, positive and negative effects occur simultaneously. Degrading processes 

include soil displacement or erosion, water logging, compaction, organic matter loss, 

nutrient depletion, and acidification, among others. Soil improvement can include enhanced fertility, better tilth, increased available water holding capacity, better drainage 

of excess water, organic matter addition, and liming. Intensive industrial forest operations may impose a combination of these effects with a net result of better, same, or 

worse soil quality. Extensive forest operations that only include harvesting during wet 

weather could have a net negative effect on soil quality due to soil compaction and 

water logging. Soil quality is the ability of the soil to function by storing and releasing 

water to plants, cycling nutrient elements, buffering organisms from temperature extremes, decomposing organic debris, etc. As mentioned above, they can be categorized 

as soil stability, hydrology, and nutrient cycling functions (table 1). These soil functions 

can be monitored and measured using soil properties or processes (depicted by letters 

A through G in fig. 7), or by using DIs or SQIs that serve as surrogates for properties 

and processes (table 1). Forest operations may improve some properties (arc of wedges 

exceeding the pre-disturbance or reference condition), and they may degrade others (arc 

of wedges less than the reference condition) (fig. 7). The net effect of the disturbance on 

soil quality may be the same (sum of the area of the wedges equal to the area of the reference condition), or the net effect may be better or worse than the reference condition. 

Some soil properties may be more important to forest productivity than others (greater 

angle, thus area, of some wedges compared to others), but seldom is one “all” important or even dominantly important. However, if Liebig’s principle of “most limiting” 

factor applied, one could select and monitor the function most affected (e.g., function 

A) as it is degraded most from the reference condition and is below the standard or allowable limit (dashed circle). In most cases, all properties (A through G) contribute to 

soil quality in interactive ways, and those interactions are often complex and unknown. 

A better judgment of soil quality change would entail a composite, weighted score of 

all soil functions (sum of the area of the wedges compared to the area of the allowable 

condition).

Forest Service Regions 3 and 5 use this general approach as reported in supplements 

to 2509.18 (USDA Forest Service 1991). Region 3 (R3) defines soil function in terms 

of stability, hydrology, and nutrient cycling and uses a combination of DIs and SQIs 

as indicators of those functions to classify soil condition as satisfactory, impaired, or 

unsatisfactory. Given our previous discussion of the limitations of arbitrarily (meaning 

no evidence of cause and effect) applying visual DIs, we suggest that the R3 approach 

is the most comprehensive and sophisticated. Lacking are justifications for indicator 

selection, site-specific weighting, and relationships with vegetative productivity, and a 

scoring mechanism to show that combined indicators will result in a specified amount of 

productivity decline over a specified areal extent. Nonetheless, the approach is conceptually based with logical linkages among soil function, properties, and indicators, and it 

includes a risk assessment within three categories.

Using Soil Quality Indicators for Monitoring Sustainable Forest Management Burger, Gray, Scott
28 USDA Forest Service Proceedings RMRS-P-59 2010.

Steps for Building a Soil Quality Model

A common approach to soil quality monitoring is to (1) select key disturbance or 

soil quality indicators representing soil function, (2) develop sufficiency relationships 

between soil services and the indicators, and (3) weight and combine sufficiency levels 

for all indicators in additive or multiplicative models based on their importance and 

vertical and spatial extent in an activity area.

Step 1: Select Key Soil Quality Indicators—Two good review papers on indicator 

selection for forest soils are by Schoenholtz and others (2000) and Moffat (2003). Both 

reviews provide lists of physical, chemical, and biological indicators with a rationale 

for their potential use. Ultimately, selection of indicators for a given forest type and land 

region must be done by scientists and practitioners with expert knowledge of specific 

forest ecosystems, forestry operations, and forest response to disturbances. However, in 

addition to local expertise, there is a large body of research literature on soil/site effects 

on growth and yield for forest ecosystems for every region of the country. This research 

has been ongoing for nearly a century as foresters have striven to understand fundamental relationships underpinning productivity.

Carmean (1975) did an early review of this literature, and Pritchett and Fisher (1987) 

did a follow-up review listing the number of reports in which a given soil property was 

found to be a determinant of growth and yield. For example, for western conifers the key 

soil properties and the number of times reported were effective soil depth (20), available 

water (8), surface soil texture (8), soil fertility (4), subsoil texture (3), and stone content 

(4). For southern pines the key soil properties and number of times reported were subsoil depth and consistency (23), surface soil depth (21), surface and internal drainage 

(19), depth to least permeable horizon (14), depth to mottling (13), subsoil imbibitional 

water value (8), N, P, or K content, and surface organic content (3). Moffat (2003) also 

has a short literature synthesis on soil/site growth and yield relationships in his review. 

These reviews demonstrate that there is a huge knowledge base on which to draw for 

first approximation soil quality models.

Step 2: Developing Soil Quality Sufficiency Curves—Central to soil quality 

models are sufficiency curves, which are cause-and-effect relationships between a soil 

service such as forest productivity and a soil indicator. For forest productivity, sufficiency of a given soil indicator is often based on its ability to support root growth. 

The assumption is that if a soil indicator is sufficient for root growth, it will be sufficient for tree growth. Sufficiency for each soil indicator is scaled from 0 to 1, where 

a value of 0 is totally root-growth limiting and a value of 1 has no limitations for root 

growth. Sufficiency relationships can be developed based on the literature, designed 

Figure 7. Conceptualization 

of the effects of forest 

management practices 

on soil quality.

Burger, Gray, Scott Using Soil Quality Indicators for Monitoring Sustainable Forest Management
USDA Forest Service Proceedings RMRS-P-59 2010. 29

experiments, or professional experience and judgment. For example, Kelting and others 

(1999) developed sufficiency relationships for loblolly pine response to soil conditions 

on poorly drained soils. The curves were based on a combination of compiled literature 

and research. Lister and others (2004) used these relationships to judge the effect of 

different levels of ground cover vegetation on soil quality recovery after wet-weather 

logging (fig. 8).

Furthermore, most of this work was regression-based, so sufficiency curves are often 

reported or can be constructed from reported data. Lacking past research of this type, 

soil scientists can develop their own soil/site growth and yield relationships for specific 

forests or land types. The results accumulating from LTSP studies that have been targeted for this purpose are even better.

Step 3: Combining and Weighting Indicators in a Soil Quality Model—After 

indicators are selected and their sufficiency curves established, they can be incorporated 

in a model for an overall index of soil quality (Gale and others 1991). Eq. (1) is a soilquality model developed by Kelting and others (1999) and Lister and others (2004) for 

loblolly pine on an affiliate LTSP site on Mead-Westvaco property in the lower coastal 

plain of SC. The soils were predominantly poorly drained Argent loam (Ochraqualf) 

and Santee loam (Argiaquoll) subject to compaction, rutting, and puddling when tree 

stands are harvested under wet conditions. The model provides an index of the net effect 

of harvesting disturbance using key soil quality indicators that are disturbed by wetweather logging and influence tree growth predictably:

SQ = ∑ [(Db × wt) + (Pa × wt) + (AD × wt) + (Θ / Pt × wt)] × WFarea

area

i = 1 (1)

where SQ is the overall soil quality index (0 to 1), Db the sufficiency for bulk density, 

Pa

 the sufficiency for aeration porosity, AD the sufficiency for aeration depth, Θ/Pt the 

Figure 8. Sufficiency curves for vegetation treatment effect on (A) the soil rooting environment, (B and C) 

aeration, and (D) soil biological activity.

Using Soil Quality Indicators for Monitoring Sustainable Forest Management Burger, Gray, Scott
30 USDA Forest Service Proceedings RMRS-P-59 2010.

sufficiency for biological activity, wt the relative weight or standardized coefficient for 

each indicator, WFarea the weighting factor for the extent of the overall activity area impacted, and area is each subsection of the overall activity area surveyed.

Jones and others (2005) developed a soil quality model to judge suitability of land 

reclaimed to forest after mining disturbance. Their work demonstrates all steps in the 

development of a soil quality modeling approach and might be used as a template for 

similar efforts. Previous soil/site regression studies suggested that the major mine soil 

growth limiting factors were soil density, P deficiency, toxic levels of soluble salts, 

extremes in pH, soil texture, coarse fragment content (Torbert and others 1988a, b; 

Torbert and others 1990; Andrews and others 1998; Rodrigue and Burger 2004). Using 

these reported relationships between tree growth and mine soil properties, Jones and 

co-workers developed sufficiency curves for mine soil properties that were consistently 

related to growth in these regression studies, and then used the following general soil 

quality model as a first approximation:

SQI = (pH × texture × density × CF)1/4 × depth (2)

where SQI = site quality index; pH = sufficiency of pH; texture = sufficiency of texture; 

density = sufficiency of soil density; CF = sufficiency of coarse fragments; and depth 

= sufficiency of rooting depth (equivalent to WF in Eq. 1). To test the performance of 

the model, a SQI was calculated for each of 52 reclaimed sites planted with white pine. 

Tree height and age were used to determine site index (SI), and soils were sampled for 

pH, texture, density, CF, and depth. SQI values were calculated using Equation 2 and 

regressed with white pine SI. SI was significantly linearly related to SQI (calculated 

from Eq. 2) with an R2

 value of 0.63 (fig. 9), showing that this general SQI model could 

be used with acceptable accuracy to predict forest productivity based on mine soil properties; that is, it could be used as a performance standard to determine if post-mining 

productivity equaled pre-mining productivity as required by law.

The SQI model (Eq. 2) assumes that all soil variables are equally important, which 

is unlikely. Jones and co-workers refined the model to make it locally specific. They regressed measured SI with measured soil properties from the 52 study sites. Standardized 

coefficients were calculated and used to develop relative importance factors for weighting the soil variables in the final site-specific model:

SQIss = (pH × IF) + (texture × IF) + (density × IF) + (depth × IF) (3)

where SQIss = site-specific SQI; pH = sufficiency of pH; texture = sufficiency of texture; density = sufficiency of soil density; depth = sufficiency of rooting depth; and IF 

= importance factor for each soil property (table 3). This weighted, additive, site-specific model improved the fit with measured SI somewhat with an R2

 of 0.68 (fig. 10). 

This model can and should be further validated with additional field testing. It, along 

Figure 9. Relationship between 

site index (tree height at age 50) 

of white pine and a productivity 

index (soil quality) calculated 

from literature-based sufficiency 

curves for pH, soluble salts, soil 

density, slope, coarse fragment 

content, and aspect. Site index 

and soil measurements were for 

52 reclaimed mined sites in the 

Appalachian region of Virginia 

and West Virginia.

Burger, Gray, Scott Using Soil Quality Indicators for Monitoring Sustainable Forest Management
USDA Forest Service Proceedings RMRS-P-59 2010. 31

with similar earlier work (Torbert and others 1994; Burger and others 1994, 2002), is 

currently being advocated for use as a mechanism to judge post-mining forest productivity in the Appalachian region.

Site quality models as outlined above can easily be applied to different sections 

of an activity area by calculating SQIs by section (e.g., percent of area compacted) 

and weighting indices by areal extent. The model, sufficiency calculations, weighting 

by importance, and weighting by areal extent can all be part of a SQI algorithm programmed in field computers. Immediately after field and laboratory sampling data are 

entered, an area based SQI can be generated.

This work by Jones and others (2005) shows that a first approximation general SQ 

model can be developed based on a compilation and synthesis of research results for 

a given area, and that further refinement can improve its specificity. Using this model within current operational and regulatory frameworks is entirely feasible. General 

models that incorporate the known productivity determinants could be made for general forest types across Forest Service regions and made more region- and site-specific 

with local data on sufficiency curves for specific forest types and plant species.

Table 3—Standardized coefficients, importance factors, and significance 

values for the independent variables used in the final model (Equation 4). 

Variable

Standardized 

coefficient

Importance 

factor p-value

Density –0.54789 0.44 <0.0001 

Rooting depth 0.34989 0.28 0.0004 

Texture –0.25135 0.20 0.0039 

pH –0.10393 0.08 0.2167 

Figure 10. Relationship between site index (tree height at age 50) of white pine and a productivity 

index (soil quality) calculated from literature-based sufficiency curves for pH, soil density, soil 

depth, and soil texture. Sufficiency values for the four soil properties were weighted based on their 

relative contribution to white pine site index. Soil measurements were for 52 reclaimed mined 

sites in the Appalachian region of Virginia and West Virginia.

Using Soil Quality Indicators for Monitoring Sustainable Forest Management Burger, Gray, Scott
32 USDA Forest Service Proceedings RMRS-P-59 2010.

Classifying and Mapping Risk of Soil Impairment Across 

Landscapes

Once armed with a good soil quality monitoring protocol, another consideration is 

applying monitoring effort proportional to risk of soil impairment due to natural or 

human-caused disturbances. Some soils and sites are relatively more resistant than others to the same disturbance impacts, and some soils and sites rebound to pre-disturbance 

conditions faster than others. GIS-based risk assessments at a landscape, watershed, or 

national forest scale would be helpful for allocated monitoring resources and prescribing appropriate management practices.

Elias and Burger (in preparation) recently developed acid deposition (AD) resistance 

maps for the Monongahela National Forest in West Virginia to help target monitoring 

efforts cost effectively. Increasing soil acidification, base leaching, and soil Al toxicity 

may adversely impact forest productivity. Stand volume in about one-third of 91 Forest 

Inventory and Analysis (FIA) plots recently (10-yr period between 1989-2000) declined 

periodic annual increment (PAI) of by up to 9.5 m3ha-1yr-1, while another one-third was 

less than 3 m3ha-1yr-1 growth (Elias and others 2009), which is less than expected growth. 

Incremental growth was not correlated with site index, but was strongly correlated with 

Ca/Al molar ratio, effective base saturation, and other indicators of acidification. Given 

the broad range in periodic annual increment (PAI) and the diverse terrain and soil parent materials that range from acid sandstones to limestone, a GIS-based acid deposition 

resistance index was modeled to help direct monitoring efforts.

Elias and Burger (in preparation) created AD resistance relationships for parent 

material, slope, aspect, elevation, soil mineralogy, depth, texture, and rock fragments 

based on published relationships and expert knowledge to encompass the range of each 

factor found on the Monongahela National Forest (MNF) (table 4). All soil and site factors were tied to existing MNF GIS layers. At each FIA plot location, values for each 

site factor were determined using 30 by 30 m U.S. Geologic Survey Digital Elevation 

Models (USGS DEM), SSURGO, and MNF maps (table 4). A resistance index (RIgeneral) 

was then calculated for each FIA plot using the following model:

RIgeneral = [.2 (parent material score) + .2(aspect score) + (4)

.2(elevation score) + .2(soil depth score) + .2(texture score)]2

PAI was significantly correlated with RIgeneral indicating that the combined soil/site 

factors were associated with forest productivity and that the modeling approach had 

merit. A site-specific AD resistance model (RIMNF) was then developed by weighting 

Table 4—Range of site factors used to create a Resistance Index for the Monongahela National Forest 

in West Virginia.

Range of characteristics and resistance:

Factor 0 1

Parent material‡ Acidic Calcareous

Slope Resistance = –0.00005x2 + 0.0055x*2.7

Aspect 235 – 286 197 – 234/

285 – 325

145 – 196/

326 – 15

107 – 144/

16 – 55

56 – 106

Elevation Resistance = –0.0005* e0.005x + 1

Mineralogy Siliceous Mixed

Depth Resistance = 1.3* e-55/(x + 0.0001)

Rock fragments Resistance = –0.0175* e0.045x + 1.015

Texture Resistance = –0.001x2

 + 0.06x

Burger, Gray, Scott Using Soil Quality Indicators for Monitoring Sustainable Forest Management
USDA Forest Service Proceedings RMRS-P-59 2010. 33

the influence of each site factor to reflect current forest conditions as measured on MNF 

FIA plots.

The relationship between RIMNF and significant indicators (pH, EBS, Ca/Al ratio, Al 

content) were used to create RI classes (slightly, moderately, and highly resistant). Class 

breaks were made at indicator levels associated with forest response in similar ecosystems (Cronan and Grigal 1995; Fenn and others 1998). A resistance index based on the 

classes of weighted site and soil factors (RIMNF) was mapped across the Monongahela 

National Forest (fig. 11). Across the MNF, 14 percent of the land area was mapped as 

highly resistance to acidification (RIMNF ≥ 0.7), 57 percent was mapped as moderately 

resistant (0.7 > RIMNF > 0.45), and 29 percent was mapped as slightly resistant (RIMNF

≤ 0.45).

This work by Elias (2008) demonstrates the use of soil quality monitoring principles for assessing risk of soil quality change across a forest. Correlation between forest 

growth and disturbance (PAI and AD) was established; criteria and indicators were selected based on a synthesis of previous research; the indicators were tested and those 

correlated with growth were selected; and a gradient of sensitivity (RI) to AD was developed and mapped based on available GIS layers. A systematic monitoring protocol 

using these soil quality indicators can now be directed to the least resistant sites, but 

soil-specific soil quality standards still need to be established for triggering mitigative 

and preventive management practices.

Figure 11. Map of resistance 

to acidification on the 

Monongahela National 

Forest.

Using Soil Quality Indicators for Monitoring Sustainable Forest Management Burger, Gray, Scott
34 USDA Forest Service Proceedings RMRS-P-59 2010.

Incorporating Adaptive Management and Soil Quality Models 

Into the Forest Service Soil Management Program

Stewards of the public’s forests are compelled to manage in a way that is economically viable, environmentally sound, and socially acceptable; this is called sustainable 

forest management (SFM). The Montreal Process is a multi-national initiative providing policy and management tools for achieving SFM. The United States is a Montreal 

Process signatory and the U.S. Forest Service represents the United States on its various committees. The organization establishes criteria and indicators for monitoring the 

status and health of temperate forests (Montreal Process 1995). Criterion #4 calls for 

monitoring the level of significant soil degradation. Various monitoring methods have 

been proposed and tried throughout the world with varying degrees of success, but the 

general approach of using indicators to measure change in soil function due to forest 

management disturbances is central to all.

The USDA Forest Service has a long-established soil quality monitoring program 

(USDA Forest Service 1991) with a goal of “developing a legally defensible monitoring 

and evaluation program based on firm scientific principles that produces unequivocal, 

credible results at minimum cost.” Attaining this goal is a work in progress, as it is for 

all land management agencies, private landowners, and third-party certification entities. Due to recent legal challenges associated with management activities within the 

National Forest System, the Forest Service is especially compelled to review and update 

its soil management program.

The current objectives of the Forest Service Soil Management program as recently 

amended in the Forest Service Manual (FSM 2500-2009-1) are good and should meet 

the spirit and letter of the authorities that govern Forest Service management, but the 

policies and program approach for achieving the objectives fall short of getting the job 

done. The current approach is essentially one of inventorying the soil resource, classifying and describing its current condition, and monitoring its condition after management 

activities using disturbance indicators with threshold levels that, if exceeded, indicate 

that the soil has been impaired. This approach has limitations: (1) it is a passive and 

reactive approach; (2) it requires the use of disturbance indicators that have little or 

no science-based cause-and-effect relationship with ecological processes and function; 

(3) it uses the same disturbance indicators (one size fits all) across a gradient of highly 

variable soils and forest ecosystem, which is not workable; and (4) experience shows 

that different people applying current methods on the same site produce different results 

and assessments. Increasingly, elements of the public are challenging this approach as 

being inadequate for protecting soil quality and forest productivity.

We believe a broader, proactive, adaptive management approach that would 

(1) explicitly define best management practices for use on NFS lands, (2) monitor 

their implementation and effectiveness using science-based soil quality models, and 

(3) continually incorporate research results into the adaptive management process via 

established mechanisms would better serve the soil management program and achieve 

the overall goal of SFM. The use of adaptive management is now policy according to 

the recently revised Forest Service Manual (Section 2551.02). The overall approach, 

objective, policy, and even the general ecological processes and functions being sustained could be common across the NFS. However, the soil and ecosystem services, 

the indicators of change, and soil quality models, and the interpretations of the models 

regarding risk and judgments of impairment and mitigation need to be region-, forest-, 

and soil-specific as needed, although much overlap is possible and desirable.

Using similar adaptive management approaches across Forest Service Regions, to 

the extent possible, would provide better credibility with the public, and it would be 

more efficient to share techniques, models, and protocols. Choices for the hierarchical 

components of adaptive management would best follow biological, not jurisdictional 

boundaries. In order to develop guidelines for BMPs and evaluate soil quality, the soil 

services in question must first be selected. These would most likely be selected at large 

biological and jurisdictional scales. For example, the NFS would likely choose soil productivity, protection of water quality, biodiversity, and ability to sequester or buffer C 

Burger, Gray, Scott Using Soil Quality Indicators for Monitoring Sustainable Forest Management
USDA Forest Service Proceedings RMRS-P-59 2010. 35

and pollutants as major soil services that differ in relative importance at smaller scales. 

Within each soil service, soil functions can generally be set at broad biological spatial scales, because the fundamental functions that allow soils to provide services are 

not specific to biological systems. To protect soil and ecosystem function, management 

guidelines applied as BMPs could be developed inter-regionally in many cases. Some 

management practices are site- and forest-specific, while others can be broadly applied 

across Forest Service regions.

The attributes and indicators that provide the details of soil quality modeling, however, cannot cross biological boundaries as well as they can cross jurisdictional boundaries. 

Sufficiency curves for a given indicator are generally forest-type specific. For example, 

sufficiency curves for soil productivity of upland oak-dominated forests are likely to be 

similar in Tennessee or Wisconsin, even though these forests are located in two separate 

Forest Service regions. Similarly, ponderosa pine likely has more in common with loblolly pine than with redwood. In some cases, different forest types might have more in 

common with respect to soil indicator sufficiency responses than site types within a forest type. Coastal Douglas-fir may respond to soil indicators more similarly to redwood 

than to Douglas-fir in the Rocky Mountains. The best first approximation would likely 

be to adapt Bailey’s (1995) ecoregions for development of SQMs.

In many cases, SQMs might be developed at the province or section level, while in 

other cases land type association might be more appropriate. While this would require 

increased regional cooperation, and in some cases more local involvement, it would 

reduce duplicative efforts where provinces or land type associations crossed regional 

boundaries, and it could increase the reliability and appropriateness of an SQM. The 

relative importance of specific land type associations or the relative management intensity within land types would help to prioritize the scale at which SQMs would need to be 

developed. SQMs might be able to be developed at the province level for provinces that 

have few management activities or for which certain services are of less importance, 

while heavily managed or critical areas might require SQMs at land type association 

levels to ensure their effectiveness.

Compared to current use of disturbance indicators with ill-defined “impairment” 

thresholds, soil quality models have the potential to improve monitoring and evaluation 

protocols when based on the following: (1) a clear management goal is defined (e.g., 

maintain soil and function for long-term forest productivity); (2) soil function (stability, 

hydrology, nutrient cycling) is monitored and evaluated using site-specific indicators 

based on a synthesis of research and expert opinion; (3) indicators, both disturbance and 

soil quality, are correlated with productivity; (4) disturbance and soil quality indicators 

can be uniformly used and applied by trained technicians; (5) measures of disturbance 

and soil quality can be weighted based on importance and areal extent and combined 

into a single index that is correlated with tree growth or some other measure of productivity; (6) performance standards (some score or level of the combined indicators) can 

be established based on pre-disturbance conditions.

Powers and others (1998) stress that SQM protocols must be operationally feasible 

and cost effective, and they and others (Fox 2000) have criticized soil quality models as 

too complicated and too costly for routine monitoring. We believe this criticism is based 

on a misunderstanding of effort and cost of developing the models and protocol versus 

applying them. The models and protocols are developed by soil scientists as relatively 

simple and straightforward decision-support computer programs. Soil technicians apply 

the field protocols and enter data for computation. We believe the extent and quality of 

our current research database and our ability to select good, cost-effective indicators 

has been underestimated. The general literature, combined with up-to-date results from 

LTSP trials, could serve as a source for a refined soil quality monitoring protocol. For 

example, several soil properties recently shown to be correlated with both disturbance 

and tree growth are pore size distribution, strength, extractable P, and mineralizable N. 

Sampling for all these properties, except strength, is no more complicated than taking a 

soil core sample for bulk density, and strength is measured directly in the field using a 

penetrometer. Testing for density, pore size distribution, N, and P are routine tests that 

can be done locally or via contract.

Using Soil Quality Indicators for Monitoring Sustainable Forest Management Burger, Gray, Scott
36 USDA Forest Service Proceedings RMRS-P-59 2010.

In any case, implementation protocols for Soil Quality Management policy (FSM 

Section 2551.03) need to be reviewed and revised to be legally defensible. For years, 

soil quality managers have used disturbance and soil productivity indicators in the same 

way that air and water quality indicators are used, yet soil quality indicators do not 

perform properly alone or apart from a more comprehensive soil quality assessment. 

Similarly, reporting monitoring results without putting them in proper context within 

an adaptive management program (FSM 2009: 2551.03) will likely be inefficient or 

counterproductive.

Soil quality cannot be defined by individual indicator threshold values the way indicators for air and water quality can be. Water quality, for example, can be defined based 

on whether values for temperature, oxygenation, sediment load, and various chemicals 

are within some defined tolerance level. Tolerance levels are easily set because the effects have been directly observed in either humans or other animals. In soils, indicators 

work indirectly in concert with other indicators. Soil quality indicators show the sufficiency of a combination of soil properties and processes to function toward providing 

a service. Sufficiency is based on a reference level (e.g., pre-harvest soil condition) 

specific for a given soil in a given forest ecosystem.

Critics of the soil quality modeling approach for assessing soils worry about a lack of 

threshold values for soil quality indicators beyond which a soil is “impaired”; however, 

currently used threshold values for individual indicators are usually not appropriate for 

judging impairment because they do not have actual cause-effect relationships with 

soil functions. There is little or no science for establishing threshold levels for soils. By 

contrast, the basic science needed to create and develop first-approximation sufficiency 

curves for most soil functions is widely available. Sufficiency curves can be improved 

with additional research and monitoring over time, but the basic structure of each curve 

can be developed today with our current understanding of soil functions.

Soil quality models created with a set of well-selected indicators and associated sufficiency curves do not provide threshold levels. SQMs provide a scaled “score” that 

indicates the direction and magnitude of change in the ability of a soil to function to 

provide a particular service. For example, Kelting and others (1999) developed a soil 

quality model that used bulk density, aeration porosity, and nitrogen mineralization (indicators) to evaluate sufficiency for root growth and biological activity (soil functions). 

They used the SQM to evaluate the impact of wet-weather harvesting (management 

action) on intensively managed loblolly pine growth (soil service) in the lower coastal 

plain of South Carolina. The SQM was scaled to actual loblolly pine growth on these 

sites. The SQM could be generally adapted to most southern pine forests with imperfect 

drainage, but the score would need to be scaled to be site- and species-specific (e.g., 

naturally managed longleaf pine on the flatwoods of central Louisiana).

Soil quality models also have the ability to provide much more information about soil 

services other than soil productivity. Because of forest management’s agronomic-based 

background and focus on producing timber, soil scientists and forest managers have focused on soil productivity (measured as wood production: m3

 ha-1 yr-1). However, across 

the National Forest System, other soil services such as water quality protection, wildlife 

habitat, and carbon, nutrient and pollutant sequestration and processing are vitally important. These services are even more difficult to measure directly, and threshold values 

for individual indicators are probably even less useful. However, sufficiency curves and 

SQMs can be created for the soil functions that provide these services (Scott and others 

2006), and they can be continually improved through targeted research and monitoring.

The final key to developing soil quality models is to recognize their proper place 

within an adaptive management program. As mentioned above, soil quality models do 

not provide threshold standards for individual indicators that can be applied across sites, 

forests and regions; they provide relative values for overall sufficiency or ability to 

provide a soil service that changes in response to management. Threshold values can 

be set for the overall change in soil quality, but not individual indicators. Because of 

this, soil quality models (and their indicators) do not function well as broad spatial scale 

monitoring tools. Rather, they work best as tools to help evaluate management impacts 

at the site level. They provide the ability to evaluate BMP effectiveness within adaptive 

management frameworks.

Burger, Gray, Scott Using Soil Quality Indicators for Monitoring Sustainable Forest Management
USDA Forest Service Proceedings RMRS-P-59 2010. 37

In summary, we believe there is ample opportunity given our current knowledge and 

technical skills to improve soil management in the context of adaptive management 

programs. Action and change are needed in order to meet the goal of legally defensible, science-based soil management that produces “unequivocal and credible results.” 

Required is a commitment by regional foresters and soil specialists to accept the challenge of developing sophisticated, computer-based soil quality models as part of the 

monitoring process. Also required is a commitment by Forest Service soil scientists to 

be part of the adaptive management process by providing input for the selection of soil 

quality indicators, development of sufficiency curves, and construction of the actual 

SQMs. The process of discovering “how the forest works” (creating knowledge) may be 

more enticing to soil scientists than applying knowledge for protecting it; but we would 

argue that the outcome of applying existing knowledge for a good adaptive management 

for the NFS is equally important and rewarding.

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The content of this paper reflects the views of the authors, who are responsible for the facts 

and accuracy of the information presented herein.

Using Soil Quality Indicators for Monitoring Sustainable Forest Management Burger, Gray, Scott
42 USDA Forest Service Proceedings RMRS-P-59 2010.
USDA Forest Service Proceedings RMRS-P-59 2010. 43

The North-American Long-Term Soil 

Productivity Study: Concepts and Literature

Deborah S. Page-Dumroese, USDA Forest Service, Rocky Mountain Research Station, 

Moscow, ID

Abstract—The resiliency of forest sites after a pulse disturbance is one of the key questions 

mandated by the National Forest Management Act (NFMA) of 1976. This Act mandated 

that we maintain the productive capacity of federally managed stands. The original USDA 

Forest Service soil quality standards were based largely on professional judgment. The 

North American Long-Term Soil Productivity (LTSP) study was founded to provide a 

scientific basis for validating or proposing changes to the current standards. Research on the 

100 field installations centers around how two key properties, site organic matter and soil 

porosity, affect a forest’s long-term productivity capacity. Results from these installations 

are listed in a bibliography.

Introduction

The Long-Term Soil Productivity (LTSP) program began in 1989 as a “grass roots” 

effort that quickly grew to a national program within the USDA Forest Service (Powers 

2006). LTSP was founded to examine the long-term consequences of soil disturbance on 

fundamental forest productivity. Today more than 100 installations and affiliated sites 

comprise the world’s largest coordinated research network.

Background

The National Forest Management Act (NFMA) of 1976 specified that the “Secretary 

of Agriculture shall limit the sale of timber from each National Forest to a quantity 

equal to or less than the quantity which can be removed from such forest annually in 

perpetuity on a sustained-yield basis.” This landmark land-ethics statement mandated 

that the USDA Forest Service conduct research, monitoring, and assessments to evaluate management effects and to manage for sustained-yield in perpetuity in a manner that 

protects all resources and values. NFMA led to the development, by National Forest 

Systems, of their soil quality standards. The standards that evolved were based on professional judgment and were meant to act as an early warning rather than absolute limits 

(Cline and others 2006). Forest managers expressed a desire for simplicity and nationally consistent metrics, although many authorities have since pointed out that single 

parameters, values, or measurement methods are not appropriate in all cases (PageDumroese and others 2006). Therefore, the LTSP study is critical to development of 

more site-specific soil quality standards, guidelines, or prescriptions.

Development of useful indicators of soil quality will continue to be tied to the intensive investigations by the LTSP program and its many collaborators. Results from this 

research can lead to the development of indicators of best management practices. These 

indicators of sustainable forestry should be (1) scientifically sound, (2) operationally 

practical, (3) socially responsible and credible, (4) standard methodology for measurement, (5) easily interpretable, (6) integrated, (7) linked to silvicultural prescriptions, 

(8) easily measured and cost effective.

There are five key findings from the LTSP installations that have direct impact on 

forest management and soil quality: (1) soil organic matter is the link between most 

management systems and sustainable site productivity (e.g., maintain the forest floor 

during management activities), (2) nutrient deficiencies can be corrected, (3) soil texture 
44 USDA Forest Service Proceedings RMRS-P-59 2010.

is the key variable affecting soil organic matter and site productivity, (4) return of crop 

residues enhances soil organic matter and site productivity, and (5) productive cropping 

systems have environmental benefits (Cline and others 2006).

All the collaborators with the LTSP study share a commitment to practice ethical 

stewardship and sustainable forest management. All collaborators agree that achieving 

sustainable forest operations is an iterative process and that altering existing soil quality 

standards is one step in developing best management practices and the indicators for 

monitoring.

LTSP References

Alban, D. H.; Host, G. E.; Elioff, J. D.; Shadis, D. 1994. Soil and vegetation response to soil 

compaction and forest floor removal after aspen harvesting. Res. Pap. NC-315. USDA Forest 

Service, St. Paul, MN. 8 p.

Alban, D. H.; Tiarks, A. E.; Powers, R. F.; Page-Dumroese, D. S.; Ponder, F., Jr.; Buford, M. 

A. 1993. Forest management impacts on long-term productivity-early results form a U.S. 

research program. IEA New Brunswick meeting, May 17, 1993. Abstract and paper published 

as symposium proceedings.

Amaranthus, M. P.; Page-Dumroese, D. S.; Harvey, A.; Cazares, E.; Bednar, L. F. 1996. Soil 

compaction and organic matter affect conifer seedling nonmycorrhizal and ectomycorrhizal 

root tip abundance and diversity. PNW-RP-494, USDA Forest Service Pacific Northwest 

Research Station, Portland, OR. 12 p.

Archibald, D. J.; Wiltshire, W. B.; Morris, D. M.; Batchelor, B. 1997. Forest management 

guidelines for the protection of the physical environment. Version 1.0. Ontario Government, 

Ministry of Natural Resources. MNR #51032. Queen’s Printer, Toronto. 47 p. http://www.mnr.

gov.on.ca/MNR_E000531.pdf.

Arencena, J. M. 2000. Cations in solutions form forest soil subjected to forest floor removal and 

compaction treatments. For. Ecol. Manage. 133: 71-80.

Ares, A.; Terry, T.; Harrison, R. B.; Piatek, K.; Flaming, F.; Licata, C.; Strahm, B.; Meade, R.; 

Miller, R.; Devine, W.; Peter, P.; Petersen, K.; Anderson, H.; Harrington, T. 2006. Achieving 

long-term site productivity in the Pacific Northwest: Research-driven strategic databases to 

guide best management practices. In: Proceedings: IUFRO 4.04.02 Forest Plantation Meeting: 

Sustainable Forest Management with Fast Growing Plantations. October 10-13, 2006, 

Charleston, SC.

Ares, A.; Terry, T. A.; Harrington, C. A.; Devine, W. D.; Peter, D.; Bailey, J. D. 2007. Biomass 

removal, soil compaction, and vegetation control effects on five-year growth of Douglas-fir in 

Coastal Washington. For. Sci. 53(5): 600-610.

Ares, A.; Terry, T. A.; Piatek, K. B.; Harrison, R. B.; Miller, R. E.; Flaming, B.; Licata, C.; 

Strahm, B.; Harrington, C. A.; Meade, R.; Anderson, H. W.; Brodie, L. C.; Kraft, J. M. 2007. 

The Fall River Long-term Site Productivity study in coastal Washington: site characteristics, 

experimental design, and biomass, carbon and nitrogen stores before and after harvest. Gen. 

Tech. Rep. PNW-GTR-691. USDA Forest Service, Pacific Northwest Research Station, 

Portland, OR. 85 p.

Ares, A.; Terry, T. A.; Piatek, K. B.; Harrison, R. B.; Harrington, C. A.; Meade, R.; Leon, R.; 

Miller, R. E.; Flaming, B. L.; Licata, C. W.; Petersen, K.; Strahm, B. D.; Anderson, H. W.; 

Brodie, L. C.; Kraft, J. M. 2007. Pre- and post-harvest stores of carbon and nitrogen in a highly 

reproductive forest site subjected to increasing biomass removals in coastal Washington. 

Weyerhaeuser Company Technical Note. 15 p.

Ares, A.; Terry, T. A.; Miller, R. E.; Anderson, H. W.; Flaming, B. L. 2005. Forest harvesting 

effects on soil physical properties and Douglas-fir growth on a Coastal Washington site. Soil 

Sci. Soc. Am. J. 69: 1822-1832.

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for forest management in central and northeastern British Columbia. Can. J. Soil Sci. 79: 

571-592.

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in black spruce ecosystems. Thesis. Lakehead University, Thunder Bay, ON.

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D. A.; Meurisse, R.; Thomas, B.; Miller, R.; Boyle, J.; Means, J.; Perry, C. R.; Powers, R. F., 

(eds.). Maintaining the long-term productivity of Pacific Northwest forest ecosystems. Timber 

Press, Inc. Portland, OR.

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site preparation in the eastern United States—an overview of water quality and productivity 

research during the past 20 years. Water, Air, and Soil Pollution 4: 5-36.

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soil disturbance classes used as indices of forest harvesting disturbance. South. J. Appl. For. 

22: 245-250.

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fertilization ameliorate effects of designated wet-weather skid trails after four years for 

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soils subjected to disturbance. Can. J. Microbiol. 48: 643-654.

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characterization of bacterial diversity from British Columbia forest soils subjected to 

disturbance. Can. J. Microbiol. 48: 655-674.

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intensity and soil disturbance on early tree growth and earthworm populations in a Missouri 

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Powers, R. F. 2006. Long-term soil productivity: genesis of the concept and principles behind the 

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Stone, D. M. 2005. The North American long-term soil productivity experiment: findings from 

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national silviculture workshop; 2003 September 8-11; Granby, CO. Proceedings RMRS-P-34. 

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managed forests. P. 63-82. In: Verner, J., (comp.). The Kings River Sustainable Ecosystem 

Project. USDA Forest Service. Gen. Tech. Rep. PSW-GTR-183. Pacific Southwest Research 

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Powers, R. F. 2001. Assessing potential sustainable wood yield. Chapter 5. P. 105-128. In: Evans, 

Julian (ed.) The forests handbook. Blackwell Science, Oxford, UK.

Powers, R. F.; Hauxwell, D. L; Nakamura, G. O., (eds.). 2000. Forest soils biology and forest 

management. USDA Forest Service Gen. Tech. Rep. PSW-GTR-178. Pacific Southwest 

Research Station, Albany, CA. 113 p.

Powers, R. F. 1999. If you build it, will they come? Survival skills for silvicultural studies. 

Forestry Chron. 75: 367-373.

Powers, R. F. 1999. On the sustainability productivity of planted forests. New Forests 17: 253-306.

Powers, R. F.; Alves, T. M.; Spear, T. H. 1999. Soil compaction: can it be mitigated? Reporting 

a work in progress. P. 47-56. In: Proceedings, 20th annual forest vegetation management 

conference, Redding, CA.

Powers, R. F.; Aune, P. S. 1998. Communicating silviculture: values and benefits for the new 

millennium. P. 3-9. In: Communication the role of silviculture in managing the national 

forests. Gen. Tech. Rep. NE-238. USDA Forest Service Northeastern Forest Experiment 

Station. Randor, PA.

Powers, R. F.; Tiarks, A. E.; Boyle, J. R. 1998. Assessing soil quality: practicable standards for 

sustainable forest productivity in the United States. P. 53-80. In: Adams, M. B.; Ramakrishna, 

K.; Davidson, E. A., (eds.). Criteria and indicators of soil quality for sustainable forest 

productivity. Spec. Publ. 53, Soil Sci. Soc. Am., Madison, WI.

Powers, R. F.; Fiddle, G. O. 1997. The North American long-term soil productivity study: 

progress through the first 5 years. P. 88-102. In: Proceedings, 18th Annual Forest Vegetation 

Conference, Redding, CA.

Powers, R. F.; Morrison, I. K. 1996. Soil and sustainable forest productivity: a preamble. Soil Sci. 

Soc. Am. J. 60: 1613.

Powers, R. F.; Tiarks, A. E.; Burger, J. A.; Carter, M. C. 1996. Sustaining the productivity of 

planted forests. P. 97-134. In: Carter, M. C. (Ed.). Proceedings, 35th forestry symposium: 

Growing trees in a greener world: Industrial forestry in the 21st century. School of Forestry, 

Wildlife and Fisheries, Louisiana State University, Baton Rouge, LA.

Powers, R. F.; Avers, P. E. 1995. Sustaining forest productivity through soil quality standards: a 

coordinated U.S. effort. P. 147-190. In: Abboud, S. A.; McGill, W. B., (eds.). Environmental 

soil science: anthropogenic chemicals and soil quality criteria. Can. Soc. Soil Sci. Brandon, 

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Powers, R. F.; Tiarks, A. E.; Boyle, J. R. 1995. Soil properties as indicators of soil quality and 

forest health: a critique. Agron. Abstr. P. 312. In: International meeting, American Society of 

Agronomy, Oct. 29-Nov. 3, 1995, St. Louis, MO. American Society of Agronomy, Madison, 

WI.

Powers, R. F.; Tiarks, A. E.; Buford, M. A. 1995. Influence of site disturbance on fundamental 

productivity and soil processes in pine plantations of the U.S.: the “LTSP experiment.” P. 68-

70. In: Gaskin, R. E.; Zabkiewicz, J. A., (comp.) Proceedings, 2nd International Conference on 

Forest Vegetation Management; 20-24 March, 1995: Rotorua, New Zealand. FRI Bull. 192. 

Forest Res. Inst., Rotorua, New Zealand.

Powers, R. F.; Mead, D. J.; Burger, M. W.; Ritchie, M. W. 1994. Designing long-term site 

productivity experiments. P. 247-286. In: Dyck, W. J.; Cole, D. W.; Comerford, N. B., (eds.). 

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through a network of long-term studies. P. 240-248. In: Pacific Rim forestry—bridging the 

world. Proceedings, 1991 annual convention, Society of American Foresters. August 1991, 

San Francisco, CA. Society of American Foresters, Bethesda, MD.

Powers, R. F. 1992. Soil as a controlling factor in forest health and sustainability. P. 6-8. In: 

Proceedings, 40th anniversary meeting, California Forest Pest Council. Sacramento, CA. 

November 1991. Forest Pest Council, San Francisco, CA.

Powers, R. F. 1991. Are we maintaining productivity of forest lands? Establishing guidelines 

through a network of long-term studies. P. 70-81. In: Harvey, A. E.; Neuenschwander, L. F. 

(comps.). Proceedings, symposium on management and productivity of western-montane forest 

soils. Boise, ID. April 1990. Gen. Tech. Rep. INT-280. USDA Forest Service Intermountain 

Research Station, Ogden, UT.

Powers, R. F. 1991. Do timber management operations degrade long-term productivity? A 

research and national forest systems cooperative study. P. 101-115. In: Proceedings, national 

silviculture workshop. Petersburg, AK. July 1989. USDA Forest Service, Timber Management, 

Washington, DC.

Powers, R. F. 1990. Are we maintaining productivity of forest lands? Establishing guidelines 

through a cooperative national program. P. 98-112. In: Schwitzer, D. L.; MacNaughton, M. 

J., (comps.). Proceedings, national workshop on monitoring Forest Plan implementation. 

Minneapolis, MN. May 1990. USDA Forest Service Land Management Planning, Washington, 

DC.

Powers, R. F. 1990. Designed stress experiments to evaluate the impacts of management practices 

on long-term site productivity. P. 39-40. In: Proceedings, forestry and the world around us. 

1989 Western forestry conference. December 1989, Sacramento, CA. Western Forestry & 

Conservation Assoc. Portland, OR.

Powers, R. F. 1990. Forest soils research to meet changing future needs. P. 91. In: Abstracts, 1990 

annual meeting of the American Association for the Advancement of Science, New Orleans, 

LA.

Powers, R. F. 1990. The soil as a fundamental resource. P. 4-5. In: Powers, R. F., (ed.). Sustaining 

site productivity of forest lands. A user’s guide to good soil management. Publ. 21481. 

Division of Agriculture and Natural Recourses, University of California, Oakland, CA.

Powers, R. F. 1990. Sustaining site productivity on forestlands: A user’s guide to good soil 

management. Publ. 21481. Division of Agriculture and Natural Resources. University of 

California, Oakland, CA. 30 p.

Powers, R. F.; Alban, D. H.; Miller, R. E.; Tiarks, A. E.; Wells, C. G.; Avers, P. E.; Cline, R. 

G.; Fitzgerald, R. O.; Loftus, N. S., Jr. 1990. Sustaining site productivity in North America: 

problems and prospects. P. 49-79. In: Gressel, S. P.; Lacate, D. S.; Weetman, G. F.; Powers, R. 

F., (eds.). Sustained productivity of forest soils. Proceedings, 7th North American forest soils 

conference. July 1988, Vancouver, BC. Faculty of Forestry, University of British Columbia, 

Vancouver, BC.

Powers, R. F.; Alban, D. H.; Ruark, G. A.; Tiarks, A. E. 1990. A soils research approach to 

evaluating management impacts on long-term productivity. P. 127-145. In: Dyck, W. J.; Mees, 

C. J., (eds.). Impacts of intensive harvesting on forest site productivity. IEA/BE T6/A6 Rep. 

No. 2. FRI Bull. No. 159. Forest Research Institute, Rotorua, New Zealand.

Powers, R. F. 1989. Maintaining long-term forest productivity in the Pacific Northwest: defining 

the issues. P. 3-16. In: Perry, D. A.; Meurisse, R.; Thomas, B.; Miller, R.; Boyle, J.; Means, J.; 

Perry, C. R.; Powers, R. F., (eds.) Maintaining the long-term productivity of Pacific Northwest 

forest ecosystems. Timber Press, Inc. Portland, OR.

Powers, R. F.; Goudey, C.; Ragus, J.; Tiarks, A.; Alban, D.; Ruark, G. 1989. A proposed national 

study: Evaluating timber management impact on long-term soil productivity: a Research and 

National Forests Systems cooperative study. P. 121-138. In: Proceedings, 1989 National soil 

and water monitoring workshop. March 1989, Sacramento, CA. WO-WSA-1. USDA Forest 

Service, Washington, DC.

Preston, D. P. 1996. Harvesting effects on the hydrology of wet pine flats. Thesis. 126 p. Virginia 

Polytechnic and State University, Blacksburg, VA.

Ratcliff, A. W.; Busse, M. D.; Shestak, C. J. 2006. Changes in microbial community structure 

following herbicide (glyphosate) additions to forest soils. Applied Soil Ecology 34: 114-124.

Risch, A.; Jurgensen, M. F.; Page-Dumroese, D. S.; Schutz, M. 2004. Above- and below- ground 

carbon and nutrient pools during succession following land use change in subalpine ecosystems 

of the Central European Alps. In: Risch, A. C., (ed.). Above- and below-ground patterns and 

processes following land use change in subalpine conifer forests of the Central European Alps. 

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moisture, soil temperature, N availability, and Douglas-fir growth. Forest Ecology Manage. 

205: 333-350.

Ross, J. 2007. The effects of harvest intensity on regeneration potential and black spruce 

seedling performance on black spruce-dominated ecosites: 10th year results. Thesis. Lakehead 

University, Thunder Bay, Ontario.

Sampson, M. T. 2007. Saving the soil. Lessons from the long-term soil productivity experiment. 

Science Perspectives (summer 2007). Pacific Southwest Research Station, USDA Forest 

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Sanchez, F. G.; Scott, D. A.; Ludovici, K. H. 2006. Negligible effects of severe organic matter 

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mineral soil carbon and nitrogen contents for sites across the United States and Canada. Can. 

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Scheerer, G. A. 1994. Site preparation effectiveness in the amelioration of compaction and rutting 

damage on wetland sites of the South Carolina lower coastal plain. M.S. Thesis. 132 p. Virginia 

Polytechnic and State University, Blacksburg, VA.

Scott, D. A.; Novosad, J.; Goldsmith, G. (In press). Long-term soil productivity of western Gulf 

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forests. Agron. Abstr. 288-3 (CD). International meeting of the American Society of Agronomy, 

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The content of this paper reflects the views of the authors, who are responsible for the facts 

and accuracy of the information presented herein.

Page-Dumroese The North-American Long-Term Soil Productivity Study: Concepts and Literature
USDA Forest Service Proceedings RMRS-P-59 2010. 61

Soil Quality Monitoring: Examples of Existing 

Protocols

Daniel G. Neary, Research Soil Scientist, Rocky Mountain Research Station, USDA Forest 

Service, Flagstaff, AZ

Carl C. Trettin, Research Soil Scientist, Southern Research Station, USDA Forest Service, 

Charleston, SC

Deborah Page-Dumroese, Research Soil Scientist, Mountain Research Station, USDA Forest 

Service, Moscow, ID

Abstract—Many forestry and agricultural agencies and organizations worldwide have 

developed soil monitoring and quality standards and guidelines to ensure future sustainability 

of land management. These soil monitoring standards are typically developed in response to 

international initiatives such as the Montreal Process, the Helsinki Ministerial Conference, 

or in support of Best Management Practices program development and Code of Forest 

Practices regulations. This paper describes international (Australia, New Zealand, Canada, 

and the European Union) and U.S. efforts and perspectives on soil quality monitoring, and 

offers suggestions on how to use the existing USDA Forest Service standards and modify 

them for future relevance.

Introduction

International Approaches

The 1990 Helsinki Ministerial Conference began the process for developing management guidelines and criteria to ensure conservation and sustainable management of 

forests in Europe and elsewhere (Helsinki 1994). In 1993, the United Nations convened 

an international seminar in Montreal, Canada, on the sustainable development of temperate and boreal forests. This conference led Canada and nine other nations to form 

the Working Group on Criteria and Indicators for the Conservation and Sustainable 

Management of Temperate and Boreal Forest. This working group soon became known 

as the “Montreal Process.” The Montreal Process was a parallel, but independent, initiative to the Helsinki Process that developed similar criteria (Anon. 1995). Criterion 5 of 

the six Helsinki Process criteria is to maintain and develop the role of forests in water 

supply and protection against erosion. Criterion 4 of the Montreal Process is to conserve 

and maintain soil and water resources. The latter Criterion includes the conservation of 

soil and water resources and the protective and productive functions of forests. Since 

the chemical, physical and biological characteristics of aquatic systems are excellent 

indicators of the condition and sustainability of the lands around them (Breckenridge 

and others 1995), key conditions of soil and water resources were selected as indicators 

of sustainability.

The original Montreal Process countries met in Santiago, Chile, in 1995 to endorse 

a statement of commitment, known as the “Santiago Declaration,” along with a comprehensive set of seven criteria and 67 indicators for the conservation and sustainable 

management of temperate and boreal forests. This new set of criteria and indicators 

added to the growing body of type-specific measurement and assessment systems already underway through the Helsinki Process in Europe and elsewhere. Eight out of 67 

indicators selected in the Montreal Process and endorsed by the nations that drafted the 
62 USDA Forest Service Proceedings RMRS-P-59 2010.

Santiago Declaration in 1995 pertain to Criterion 4. Following are those indicators that 

specifically concern soil impacts:

(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;

(24) Percent of water bodies in forest areas (e.g., stream kilometers, lake 

hectares) with significant variation from the historic range of variability in 

pH, dissolved oxygen, levels of chemicals (electrical conductivity), sedimentation or temperature change.

The Montreal Process criteria are distinguished from those developed by other sustainability efforts in that they recognize a fundamental connection between forests and 

people. The criteria function on the assumption that a nation cannot achieve forest 

sustainability without the support and understanding of its public. The criteria and indicators provide a common understanding and implicit definition of sustainable forest 

management. They are to be considered tools for assessing trends in forest conditions, 

and they provide a framework for describing, monitoring, and evaluating progress toward sustainability. An important consideration is that the Criteria and Indicators should 

not be confused as performance standards for certifying management or products.

Criteria are envisioned as a national-scale consensus of public values. They are meant 

to communicate an overview of what participating countries want to see in the conditions of their forests. Indicators provide the means for assessing forest conditions and 

for tracking trends. The Indicators are intended to be flexible components of resource 

monitoring protocols that can be adjusted to provide the most accurate assessment of 

environmental, economic, and social trends.

Sustainability is the stewardship goal of forestry, but a more specific definition of its 

goals and attributes is often complex and open to considerable interpretation (Moir and 

Mowrer 1995). Many ecologists have attempted to answer the “what,” “what level,” “for 

whom,” “biological or economic,” and “how long” questions of sustainability. Allen 

and Hoekstra (1994) discussed the emergence of the concept of sustainability and the 

difficulty in defining it. They clearly pointed out that there is no absolute definition of 

sustainability, and that it must be viewed within the context of human conceptual frameworks and societal decisions on the type of ecosystem to be sustained and the spatial and 

temporal scales over which attainment of sustainability is to be judged. Sustainability 

is also defined in terms of society’s needs, the experiential frame of reference of ecosystem managers, and the ecological models that are used to predict future conditions 

for natural resources. However, our ability to predict future ecosystem conditions is 

confounded by the uncertainties of increasing encounters with extreme events, poorly 

understood ecological processes and linkages, surprises by the law of unintended consequences, the development of critical thresholds, and chaotic system behavior. Another 

approach to the definition of sustainability is to define the conditions that warn of or 

mark ecosystem deterioration into unsustainability (Moir and Mowrer 1995). Although 

the goals of the Montreal Process and Santiago Declaration are to ensure management 

of forest lands for sustainability, the Criteria and Indicators are in essence warning flags 

to obtain the attention of land managers before ecosystems decline into unsustainability.

Neary, Trettin, Page-Dumroese Soil Quality Monitoring: Examples of Existing Protocol
USDA Forest Service Proceedings RMRS-P-59 2010. 63

Soil compaction, erosion, and organic matter losses are the chief factors that affect 

decline of ecosystem productivity (Burger 2002; Powers and others 1990). These factors can alter ecosystem carbon allocation and storage, nutrient content and availability, 

water storage and flux, rhizosphere processes, and insect and disease dynamics. The 

chief disturbances that affect these three factors are wildfire, insect and disease outbreaks, climate extremes, vegetation management (wood harvesting and stand tending 

activities, grazing, prescribed fire, chemical weed control, and manual removal of plant 

species), and recreation (foot traffic and vehicles) (Hart and Hart 1993). Management 

activities that eliminate natural disturbances (e.g., fire suppression, insect control) or 

alter ecosystem properties can also affect ecosystem sustainability.

Why Soil Monitoring?

Soil quality monitoring was developed as a means of evaluating the effects of management or harvesting practices on soil functions that affect site productivity (Doran 

and Jones 1996). Specific reasons might include elevating general awareness of soil 

condition, education, evaluating specific practices, problem solving, and comparing the 

effects of alternative management practices and techniques. A number of soil physical, biological, and chemical parameters, which have linkages to soil productivity, have 

been proposed as forming the minimum data set for screening the condition, quality, 

and health of soils (Doran and others 1998). Evaluation of soil conditions develops a 

time-trend analysis that can then be used to assess the sustainability of land management practices.

Soil monitoring developed as a natural outcome from the Helsinki and Montreal 

Process efforts on sustainability. Codes of Forest Practice, which then were developed, 

sought to incorporate Best Management Practices and soil monitoring into up-front operations planning rather than post-operation environmental assessment. The approach 

to soil monitoring varies by country and consists of combinations of self-assessment, 

independent agency monitoring or combinations of the two approaches. Since soils are 

vital resources for both natural ecosystems and human endeavors, and they are not easily restored, monitoring of soil conditions and trends is viewed as a necessary activity to 

maintain their functions and quality (Morvan and others 2007).

In Ireland, the Code of Best Forest Practices has a focus on achieving sustainable 

forest production by implementing safe and environmentally sound forest harvesting 

practices. A component of that effort involves routine soil monitoring to verify that acceptable practices are followed and that they do not adversely affect the soil resource 

(Ireland Forest Service 2000).

The U.S. Forest Service direction on protecting the soil resource is detailed in its 

Forest Service Manual 2554, Soil Quality Monitoring. The Agency’s stated purpose 

in soil monitoring is to (1) meet direction in the National Forest Management Act of 

1976 and other legal mandates, (2) ensure management of National Forest lands under 

ecosystem management principles without permanently degrading land productivity, 

and (3) maintain or improve soil quality (O’Neill and others 2005; U.S. Forest Service 

2009).

In Australia, State Forestry Practices Codes have been established to provide legally enforceable guidelines and standards to ensure reasonable protection of the 

natural resources such as soils (Grove 2007). Soil monitoring takes the form of selfmonitoring by forestry agencies and companies as well as selected audits by the State 

Forest Practices Authorities. The belief in soil and other monitoring by the Forestry 

Consultative Committee is that it will improve forestry operations as well as ensure 

long-term sustainability.

Curran and others (2005) discussed requirements for sustainable management of forests in Canada and elsewhere. They noted that maintenance of the biological, chemical, 

and physical properties and processes of soils was crucial for long-term sustainability. A 

key component for improving the understanding of site productivity and predicting the 

consequences of forest disturbances and practices was a reliable soil monitoring system.

Soil Quality Monitoring: Examples of Existing Protocol Neary, Trettin, Page-Dumroese
64 USDA Forest Service Proceedings RMRS-P-59 2010.

Concepts and Basis for Monitoring

Characteristics

Soil monitoring must be both logistically effective and scientifically sound in order to 

achieve the objectives of land management agencies and regulatory authorities. Lovett 

and others (2007) discussed the important characteristics of monitoring programs in 

their treatise “The Seven Habits of Highly Effective Monitoring.” They recommended 

that effective monitoring programs should

• be designed around clear and compelling questions;

• include review, feedback, and adaptation components;

• choose measurements carefully and consider future uses;

• have systems to maintain data quality and consistency;

• incorporate plans for long-term data accessibility;

• have internal checks and controls to ensure careful examination, interpretation, and 

delivery of the monitoring data; and

• incorporate an integrated research and development program or strong linkages to 

other existing research programs.

Another important characteristic of an effective soil monitoring program is a statistically sound protocol for location selection and sampling procedure design. Soil 

monitoring can be conducted separate from other monitoring programs or within existing programs such as the U.S. Forest Service’s FIA Program (O’Neill and others 2005).

Location and Design

The location and design of soil quality monitoring projects are discussed in more detail by Doran and Jones (1996). Sampling locations and designs vary widely depending 

on the country, state, or province conducting the monitoring. Basic designs fall into the 

categories of simple random sampling, stratified random sampling, and systematic sampling (Elzinga and others 2001). Examples include random sampling on line transects, 

random sampling on Cartesian coordinate grids (fig. 1), stratified sampling of stand 

components (e.g., old-growth, pole stands, sapling clusters, clearings, coarse woody 

debris piles). Systematic sampling would include evenly spaced sample points on grids 

established on the monitoring area. This analysis does not compare and contrast soil 

monitoring location and design techniques. The purpose of this effort is to examine the 

basic approaches used in a selected number of locations in the world.

Figure 1. Cartesian coordinate 

sampling system (adapted from 

Johnson and Curtis 2001).

Neary, Trettin, Page-Dumroese Soil Quality Monitoring: Examples of Existing Protocol
USDA Forest Service Proceedings RMRS-P-59 2010. 65

Existing Approaches

A number of soil monitoring approaches and systems have been implemented worldwide with mostly similar objectives but sometimes different perspectives. Specifically, 

the approaches of New Zealand, Australia, Canada, the European Union, and the new 

Forest Soil Disturbance Monitoring Protocol developed for use within the U.S. Forest 

Service will be examined.

New Zealand

Forest Code of Practice—New Zealand’s 27 million ha of land consists of pasture 

and arable land (52 percent), native forests (23 percent), and plantation forests (5 percent). The remaining 20 percent is mountains, water, and urban areas. Planting of exotic 

species plantation forests began in the 1920s. These forests now account for 19 percent 

of New Zealand’s forests but they produce 99 percent of the country’s wood requirements. A Forest Code of Practice was established in 1990 (Vaughan and others 1993). 

The New Zealand Government passed the Resource Management Act of 1991 (RMA) 

to promote sustainable management of natural resources. The RMA is an effects-based 

resource law that focuses on land management activities that cause adverse environmental effects. The Forest Code of Practice sets out guidelines to maintain and protect 

forest values such as soils, water, scenery, recreation, cultural sites, site productivity, 

and off-site impacts. The Code focuses on both planning and operations to achieve sustainable forest management.

The key components of the planning process in the Code, before any operations 

are conducted, are (1) identifying important site values, (2) identifying operations that 

could have significant impact, (3) selecting low impact techniques and methods, (4) establishing protocols to check on compliance to the Code and obtaining approvals, and 

(5) monitoring actual performance during and after operations. Inputs to the planning 

come from both external and internal sources (fig. 2). Monitoring then uses an operations database, a rating system, checklists, an operations self appraisal, and finally a 

compliance check with District and Regional rules.

Figure 2. New Zealand 

Forestry Code of Practice 

environmental planning 

flow chart (adapted from 

Vaughan and others 1993).

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66 USDA Forest Service Proceedings RMRS-P-59 2010.

The rating system utilizes a four-level risk rating system involving both short- and 

long-term impacts, minor or major risks, and categories of minimal, low, intermediate, or high (table 1). Symbols that correspond to each are then used on the planning 

forms. An example checklist from a Wairarapa woodlot near Wellington is shown in 

table 2 (Vaughan and others 1993). Forest managers are then required to develop mitigation plans based on the pre-harvest assessment. The Forest Code of Practice database 

provides detailed information on identifying risks and planning mitigation measures. 

Activities that can potentially have a significant impact on the environment require 

planning review and consent by District or Regional Councils. Post-operational selfmonitoring and regular, periodic monitoring and maintenance are required to achieve 

the desired outcome of maintaining sustainable management of forest lands.

National Soil Quality Survey—New Zealand conducted a national-scale soil quality monitoring program between 1995 and 2001 at 222 sites in five regions of New 

Zealand (12 soil orders and 9 land-use categories) (Sparling and Schipper 1998, 2002). 

Land uses in the survey included arable cropping, mixed cropping, pasture, grassland, 

Table 2—Example planning checklist for a Wairarapa, New Zealand, woodlot (adapted from Vaughan and others 1993).

Identified environmental values

Operation Water quality Wetland areas Slope stability Erosion Water supply

Access

Roading - • - - - - - - -

Land preparation

Herbicides	 - - • • • - -

Oversowing + + • + + •

Tracking - - • - - - - - - -

Grazing - - - - - - - - - - - - -

Establishment

Planting + • + + + + + + + +

Releasing • • • • • - -

Grazing - - - - - - - - - - - - - -

Fertilizing - - • + + - -

Tending

Pruning + • • + + •

Waste thin	 + + + + + + + + + •

Protection

Animal control • • • • - -

Roads - - • - - - - -

Weed control- - - - • • •

Harvesting

Roading - - - • • - - - - - - - - -

Landings - - - • •

Felling • + + + + • •

Processing + + • • • •

Extraction - - - - - - - - - - - -

Stream cross - - - - - - •

Transportation • • - - •

Table 1—New Zealand Forest Code of Practice monitoring rating system and

symbols (adapted from Vaughan and others 1993).

New Zealand Forestry Code of Practice monitoring rating system

Time Risk Impact Symbols

Short-term Minor Minimal

Long-term Minor Low +/-

Short-term Major Intermediate ++/--

	 Long-term	 Major	 High	 +++/---

Neary, Trettin, Page-Dumroese Soil Quality Monitoring: Examples of Existing Protocol
USDA Forest Service Proceedings RMRS-P-59 2010. 67

plantation forests, and native forest. Sampling of the topsoil (0–10 cm) was done and the 

propertiesmeasured were total carbon (C) and nitrogen (N), potentially mineralizableN, 

pH, Olsen phosphorus (P), cation exchange capacity, bulk density, totalporosity, macroporosity, and total available and readily available water. Seven of these soil parameters 

(total C, total N, mineralizable N, pH, Olsen P, bulk density, and macroporosity) explained 87 percent of the total variability. Some of the issues that arose during the soil 

quality sampling were minimum data set, how to stratify, level of precision, cost, centralized data and sample management, re-sampling for trends, and sampling strategy. 

Important recommendations that came out of the survey were that (1) a precision of 

10 percent was impractical due to cost, (2) a precision of 25 percent was more realistic, 

(3) central storage of data and samples was essential to success of this type of survey, 

(4) re-sampling needs to be over a 3- to 10-year time period with some being done every 

year, and (5) current financial constraints prohibit random sampling.

Following are the key findings from the New Zealand Soil Quality Survey:

• Soil Order had a strong effect on the results.

• Land use accounted for only 21 percent of total C variability.

• There was no evidence of acidification under exotic tree species.

• Changes in soil quality between land uses can be detected.

• Biochemical and total C indices are more sensitive to land management differences 

than physical parameters.

• Soil quality of mature pine plantations before and after logging were similar to native 

forests or low-productivity pasture.

• Many research needs were identified to make a national-scale soil quality survey a 

viable management tool.

• Changes in soil quality characteristics can be detected, but there is a general lack of 

a scientific framework to define acceptable and unacceptable ranges of soil quality 

parameters.

Australia

Australian Forestry—The total area of native forest reported in the latest Australia’s 

State of the Forests Report (National Forest Inventory 2008) is estimated at 162.7 million ha, which is about 21 percent of Australia’s land area. Some 75 percent of native 

forest estate was on public land, and the remainder was private land or unresolved tenure. About 70 percent of Australia’s native forests were privately managed. Australia’s 

plantation estate continues to expand, reaching 1.8 million ha in December 2006, an 

increase of 78,000 ha (4.5 percent) over the prior year 2005. The proportion of hardwood species has increased to 44 percent of the total, with softwood species making up 

the remainder. About 95 percent of the softwood plantations are Pinus radiata and other 

introduced pines. P. radiata is grown on a 30 to 40 year rotation and supplies about 50 

percent of the domestic wood demand. Nearly all of the hardwood plantations are native eucalypts, including Tasmanian blue gum (Eucalyptus globulus), shining gum (E. 

nitens) and flooded gum (E. grandis).

A diverse range of ownership arrangements exists in the Australian plantation industry, including a variety of joint venture and annuity schemes between public and 

private parties. For several years, most investments in new plantations have been by the 

private sector. The proportion of public and private plantations was equal (46 percent) in 

1999; however, privately owned plantations now account for 59 percent, far exceeding 

public plantations at 36 percent. This difference is especially pronounced for hardwood 

plantations, about 86 percent of which are privately owned compared with 36 percent 

of softwood plantations.

Australian Codes of Forest Practice—In Australia, Codes of Forest Practice are 

State-based and tied to sustainable yield. Except in Tasmania and Victoria, the Codes 

are applicable to only public lands. There are 14 State and territory Codes that began 

development in 1978. They all put an emphasis on quantitative performance standards 

Soil Quality Monitoring: Examples of Existing Protocol Neary, Trettin, Page-Dumroese
68 USDA Forest Service Proceedings RMRS-P-59 2010.

that are keyed into sustainable timber yield and timber harvest planning (McCormack 

1996). Old growth and rain forests were the critical issues that lead to these Codes. 

While soils and soil quality are not directly mentioned as major concerns (table 3), they 

are inherent in a number of the topics of concern to Australian Codes of Forest Practice.

Tasmanian Code of Forest Practice—The Tasmania Forest Practices Act 1985 was 

the first Australian Code of Forest Practice (McCormack 1996; Tasmanian Forestry 

Commission 1993). It deals with a number of issues that relate to soils and soil quality 

(table 3). First and foremost, the Tasmania Code focuses on proper designing and planning prior to tree harvesting. The Code is administered by the Forest Practices Authority 

(FPA) but is a co-regulatory adaptive management process in nature (fig. 3). The first 

level of monitoring is provided by each forest owner, with random independent monitoring conducted by the FPA through Forest Practices Officers (FPO). The FPOs have 

regulatory powers and can insist on remedial work being done through court actions and 

fines. However, the main emphasis of FPOs is placed on education and demonstration 

of Best Management Practices rather than regulatory enforcement.

The Tasmanian FPA employs specialists in forestry, soil science, botany, zoology, 

geology, hydrology, and archeology whose research and monitoring supports the Code 

of Forest Practice. The FPA trains and provides advice to forest industry personnel and 

also conducts the independent audits of forest industry operations (fig. 3).

Canada

Canada has 404 million ha of forested land, accounting for 10 percent of the world’s 

forests and 30 percent of the boreal forests (Natural Resources Canada 2009). Less than 

1 percent of Canada’s forests are harvested each year, and all Public forests must be successfully regenerated by natural (50 percent) or planting and direct-seeding techniques. 

Table 3—Topics addressed in four Australian Codes of Forest Practice (adapted from McCormack 1996).

Soils-related ones are in italics.

Tasmania New South Wales Victoria Western Australia

Design & Planning	 Design & Planning	 Design & Planning	 Design & Planning

Tree Marking	 Tree Marking

Tree Felling Tree Felling

Log Skidding/Tracks Log Skidding/Tracks Log Skidding/Tracks Log Skidding/Tracks

Log Landings Log Landings Log Landings Log Landings

Wet Weather Wet Weather

Water Quality Water Quality Water Quality

Slope Limitations Slope Limitations

Landscape Values Landscape Values Landscape Values

Wildlife habitat	 Wildlife habitat	 Wildlife habitat

Fire Fire

Plant Diversity	 Plant Diversity

Site Rehabilitation

Fuel Dumps

Licensing

Cultural Resources

Geomorphology

Crop Trees

Figure 3. Tasmania 

adaptive management 

framework (Code of 

Forest Practice 1985).

Neary, Trettin, Page-Dumroese Soil Quality Monitoring: Examples of Existing Protocol
USDA Forest Service Proceedings RMRS-P-59 2010. 69

About 36 percent of the country’s forests have been certified as being sustainably managed by globally recognized certification standards. Codes of Forest Practice are in 

place in Nova Scotia, Ontario, and British Columbia. Canada’s forest laws and regulations are considered to be among the strictest in the world.

British Columbia has led Canada in developing procedures to ensure forest sustainability. The “Forest Practices Code of British Columbia” of 1996 established the legal 

framework for monitoring soil disturbances caused by forest operations. It has since 

been augmented by the “Forest and Range Practices Act of 2002.” The Province has an 

iterative adaptive-management process that provides constant feedback to forest operations and research to improve Best Management Practices and operations planning and 

execution (fig. 4).

The Soil Conservation Guidebook (British Columbia Ministry of Forests 2001) 

provides an overview of post-harvest monitoring inspection procedures, the current requirements, and definitions for Soil Conservation Surveys; it also provides instructions 

on how to conduct surveys. Silvicultural prescriptions define the maximum percent of 

the net area to be reforested that may be occupied by disturbed soils and the extent to 

which that area of disturbance can be temporarily exceeded. The operations site plans 

identify sensitive soils and spell out the maximum percentage of the total harvest area 

that can be permanent access roads, temporary roads and skid trails, and roadside work 

areas. Visual Soil Conservation Survey reports are required to verify that prescription 

limits were not exceeded. If they are, then a formal survey is required. The Surveys focus on disturbance to soil caused by roads and skid trails and the amount of forest floor 

displacement or damage. In order to “standardize” what can be recognized as soil disturbance by equipment operators, contractors, inspectors, the public, or research scientists, 

a set of representative visual examples is provided (figs. 5a,b).

A transect survey is installed if a formal Soil Conservation Survey is warranted. 

Methods are specified in the Soil Conservation Guidebook (British Columbia Ministry 

of Forests 2001). This type of survey is usually completed as soon as possible after the 

operations disturbance and it requires site familiarity. The survey transects are documented in case follow-up measurements are needed (fig. 6)

European Union

Forests cover 160 million ha within the European Union, or about 42 percent of the 

27-member Union. Six countries account for two-thirds of the forest area with Sweden 

and Finland alone accounting for 30 percent of the total forest area (Eurostat 2009). 

Official protocols exist in most member States of the European Union (EU) for soil 

monitoring (Morvan and others 2007); however, there is a lot of variation in the methodologies used and the intensity of sampling. The EU Monitoring Network has been active 

for 20 years using a 50 by 50 km grid with variable re-measurement periods. Parts of the 

Figure 4. British Columbia 

soil monitoring adaptive 

management process 

(adapted from Curran 

2007).

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70 USDA Forest Service Proceedings RMRS-P-59 2010.

EU Network contain dense established sampling grids (e.g., United Kingdom, Ireland, 

Austria, Denmark) while in other areas the network is still sparse (e.g., Spain, Italy, 

Greece) (fig. 7). About 90 percent of the EU soils and the land cover classes have at least 

one monitoring site. However, the density of soil monitoring sites within the European 

Soil Database units is highly variable. Some units (7 percent) do not have any monitoring sites. Pasture lands have the highest density of soil monitoring sites, but arable land 

and forests, while slightly less, are comparable in density. A grid of 16 by 16 km has 

been established for forest soils (ICP 2004).

The key soil parameters being monitored in the EU include erosion risk, compaction risk, the presence of peat, heavy metals, desertification, and presence of livestock. 

Other indicators being measured are texture, pH, organic matter, bulk density, cations, 

Figure 5. British Columbia Forest Practices Branch, Ministry of Forests visual soil disturbance indicator cards 

for (a) wheel ruts, machine traffic, and bladed trails; and (b) gouges and scalps (from Curran and others 

2005 and British Columbia Ministry of Forests 2001).

Neary, Trettin, Page-Dumroese Soil Quality Monitoring: Examples of Existing Protocol
USDA Forest Service Proceedings RMRS-P-59 2010. 71

and earthworm activity (Morvan and others 2007). The EU Soil Monitoring Network 

is simply an inventory system, does not have any interaction with land management 

entities, and does not have any regulatory power. Soil Network needs include adding 

4,100 sites in the lower density part of the network and standardizing sampling and 

analytical methods. Of the countries with mandated soil monitoring (table 4), Sweden 

requires measurements of soil physical conditions, coarse woody debris, and soil chemistry. Ireland requires measurements of soil condition, soil fertility, erosion, and other 

Figure 6. Example of a British Columbia formal soil conservation survey site documentation.

Figure 7. European Union soil monitoring network, GIS repartition (right) and actual density (left) in km2

 for 

one monitoring site in the 50 by 50 km Cooperative Program for Monitoring and Evaluation of the Longrange Transmission of Air Pollutants in Europe (EMEP) grid of the soil monitoring sites in Europe (Moran and 

others 2007).

Soil Quality Monitoring: Examples of Existing Protocol Neary, Trettin, Page-Dumroese

+

+

+

+

+

+

+

+
72 USDA Forest Service Proceedings RMRS-P-59 2010.

parameters as needed. Although the United Kingdom does not require soil monitoring 

at the present time, changes of Codes of Forest Practice will mandate this activity in the 

future (Hall 2008, personal communication).

Ireland—Over 70 percent of Ireland’s 636,164 ha of forests are owned by the Irish 

Forestry Board (Coillte Teoranta). Soil monitoring in Ireland is contained within the 

country’s Code of Best Forest Practice and is based on EU and National laws (Ireland 

Forest Service 2000). Like a number of other countries, the Irish Code is focused on 

planning, monitoring, and adaptive management rather than regulatory punitive actions. 

Monitoring is performed to evaluate the performance of the Ireland Code of Best Forest 

Practice as well as the skills of individual forest harvesting operators. It consists of a 

self-evaluation impact appraisal by the individual operators and an external assessment 

by the Irish Forestry Board.

The Ireland impact appraisal evaluates environmental, economic, and social impacts of forestry operations. The focus is on assessing potential impacts in terms of 

their level, likely consequence, importance, and length of time that the impacts will 

occur. Potential impacts are evaluated descriptively or on a “points” system on the 

basis of four subjective severity levels (very high, high, moderate, and low), and 

follow-up mitigation actions are then planned (table 5). Soil fertility was evaluated at 

being at high risk because of the soil type and the whole-tree harvesting planned for 

the cut block. So the mitigation technique prescribed for this stand was the addition 

of a nitrogen-phosphorus-potassium fertilizer. The other potential soil impacts were 

evaluated as being low so no mitigations were planned.

United States

Natural Resources Conservation Service and the Agricultural Research 

Service—Both the Natural Resources Conservation Service (NRCS) and the Agricultural 

Research Service (ARS) conduct research and development activities related to soil 

Table 4—European Union countries with conventional forestry and forest bioenergy monitoring standards and

requirements.

Harvest code Monitoring

of forest Bioenergy

Country practices guidelines Required Type Soil

Denmark	 Yes	 No	 No	 None	 No

Netherlands	 Yes	 No	 Yes	 Operations	 No

Finland	 Yes	 Yes	 Yes	 Operations	 No

Sweden	 Yes	 Yes	 Yes	 Multiple	 Yes

Germany	 Yes	 Yes	 Yes	 Inventory	 No

Ireland	 Yes	 No	 Yes	 Operations	 Yes

United Kingdom	 Yes	 Yes	 No	 None	 No

Table 5—Ireland forest soil monitoring impacts assessment, example from County Roscommon

(adapted from Forest Service 2000).

Ireland Code of Best Forest Practice Soil Impact Assessment

County Roscommon

Site Coillte 529

Operation	 Whole-tree harvesting

Timeframe Long-term

Value	 Impact factor	 Severity	 Mitigation action

VH	 H	 M	 L

Soil	 Fertility	 	 X	 	 	 NPK fertilizer

Condition	 	 	 	 X	 None

Erosion	 	 	 	 X	 None

Other

Neary, Trettin, Page-Dumroese Soil Quality Monitoring: Examples of Existing Protocol
USDA Forest Service Proceedings RMRS-P-59 2010. 73

quality and soil monitoring (Doran and Jones 1996; Doran and Parkin 1994; Doran and 

others 1998; Karlen and others 1997; USDA NRCS 2001). Additional information can 

be found at http://www.ars.usda.gov/main/site_main.htm?modecode=36-25-15-10 and 

http://www.usda.gov/sqi/. The ARS has also developed and standardized methods for 

monitoring grassland, shrubland, and savanna ecosystems (Herrick 2005a, b). These 

manuals deal with vegetation, soil, hydrologic, and geomorphic monitoring methods

U.S. Forest Service: Forest Inventory and Analysis—The U.S. Forest Service 

conducts soil monitoring as part of its Forest Inventory and Analysis (FIA) Program. 

Soil monitoring conducted by the FIA is discussed in detail in the following chapter by 

Amacher and Perry (Amacher and Perry 2010) and by O’Neill and others (2005a,b).

Weyerhaeuser—The Weyerhaeuser Company is committed to soil productivity by 

using a two-step strategy (Heninger and others 1997, 2002). First, Company operations 

use equipment and operations practices that are appropriate to the soil, topography, 

and weather to minimize erosion and harmful soil disturbance. Secondly, Weyerhaeuser 

employs forestry practices and technology to retain organic matter and soil nutrients 

on site. The components of the process to achieve sustainability are shown in figure 8 

(Heninger 1997) and include (1) a research database; (2) common goals and standards 

leading to management guidelines; (3) education, training and teaming; (4) selection 

and use of Best Management Practices (BMPs); (5) independent monitoring of performance and compliance with BMPs; and (6) continuous feedback to the operations side 

of the organization, and implementation of adaptive experimentation where warranted. 

Guidelines and BMPs have been developed to minimize detrimental soil disturbance 

as indicated in figure 9. The key components of this system are the strategic database 

on soil disturbance impacts, the classification system described in figure 9, a soil operability risk rating system, and a close working relationship between the Research and 

Development and Operations units to develop BMPs. A key component of this process 

is monitoring soil impacts of operational practices by independent contractors to assess performance against specified standards. The monitoring provides feedback and 

information to the corporate soils database, Research and Development, and Operations 

training programs to continuously improve BMPs to meet Weyerhaeuser’s sustainable 

site productivity strategy (fig. 8).

United States: Forest Soil Disturbance Monitoring Protocol

Background—At the end of the 20th Century, about 33 percent of the U.S. land area 

or 302 million ha was forest land, 71 percent of the area that was forested in the latter 

part of the 17th Century (Smith and others 2001). The U.S. Forest Service (USFS) manages around 59 million ha in the National Forest System (NFS) including 39 million 

Figure 8. Weyerhaeuser 

sustainable site 

productivity process 

components (adapted 

from Heninger and 

others 1998).

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74 USDA Forest Service Proceedings RMRS-P-59 2010.

ha that are classified as capable of producing 1.4 m3 ha–1 yr-1 of industrial wood and 

not legally reserved from timber harvest. Four Acts of Congress important to the issue 

of resource sustainability, and the soil resource in particular, provide enabling legislation for NFS lands (U.S. Forest Service 1993): (1) The Organic Administration Act of 

1897, (2) The Multiple-Use Sustained-Yield Act of 1960, (3) The Forest and Rangeland 

Renewable Resources Planning Act of 1974 and amendments, and (4) The National 

Forest Management Act of 1976 (Cline and others 2006; U.S. Forest Service 1993). This 

legislation sets forth three points that support the need for a long-term soil monitoring 

program. First, land management should not produce substantial and permanent impairment of site productivity. Second, trees should be harvested only where soil, slope, or 

other watershed conditions will not be irreversibly damaged. Lastly, tree cutting should 

occur in a manner that ensures protection of soil, watershed, fish, wildlife, recreation 

and esthetic resources, and the regeneration of the tree resource. The essence of these 

key statements of land ethics is a legislative mandate that the USFS conduct research, 

monitoring, and other assessments to evaluate management effects and to manage for 

sustained site productivity in a manner that assures protection of all resources and values. The monitoring provisions caused considerable concern among field soil scientists 

in the NFS with regard to determining baseline soil productivity and what parameters 

might be used to measure management effectiveness in maintaining soil productivity 

(Cline and others 2006).

USFS Regions were directed to develop soil quality standards based on Agency 

guidelines in Forest Service Handbook (FSH) 2509.18: Soil Quality Monitoring. In 

Chapter 2 of FSH 2509.18, the soil quality monitoring program is spelled out as a 

Figure 9. Weyerhaeuser soil disturbance classification system for Western Timberlands (adapted from Heninger and 

others 1997 and Curran and others 2007).

Neary, Trettin, Page-Dumroese Soil Quality Monitoring: Examples of Existing Protocol
USDA Forest Service Proceedings RMRS-P-59 2010. 75

systematic process in which data are collected to determine if soil management objectives of maintaining long-term soil productivity and development of operational 

standards are achieved. It was clearly the policy of the USFS to

• design and implement Best Management Practices,

• maintain or improve long-term site productivity,

• plan and conduct soil quality monitoring,

• evaluate the results of management actions, and

• recommend mitigation measures for measured soil changes.

Responsibilities were delegated to Regional Foresters, Forest Supervisors, District 

Rangers, and Soil Scientists to develop the soil quality monitoring program. Soil 

Scientists were specifically given the charge to conduct and supervise effectiveness and 

validation monitoring, to report management results and recommend changes in actions, and to coordinate validation monitoring with research units. However resources 

and time were not provided to adequately achieve these directions.

Chapter 2 of FSH 2509.18 went on further to list some “example” soil quality 

standards. These included increase in bulk density >15 percent, reduction in porosity 

>10 percent, forest floor removal along with 25 mm (1 inch) of mineral soil, macropore 

space reduction >50 percent, and erosion losses exceeding 2.2 to 4.4 Mg ha-1 (1-2 tons 

ac-1 yr-1). A footnote on a table that listed these “standards” indicated that these were examples only and not intended to be actual soil quality standards; regional soil scientists 

were charged with that task. Chapter 2 in FSH 2509.18 also discussed topics such as 

establish threshold values causing significant changes, allowable area extent of disturbance, monitoring projects and plans, sample size and variability, sample design, data 

collection, and data analysis

The net result for USFS was that Washington Office guidance in FSH 2509.18 was 

carried forward and detrimental soil disturbance on greater than 15 percent of an activity area was selected as the soil quality standard for many of the Forest Service Regions.

Detrimental soil disturbance was defined as compaction >15 percent, rutting, soil 

displacement, severely burned areas, surface erosion, and soil mass movement. In essence, an “example” in FSH 2509.18 became the Region 1 “standard” and every other 

Region went its own way on setting standards. Region 1 issued a Manual supplement 

to describe its soil monitoring program (U.S. Forest Service 1999). However, it did not 

take long for problems to develop. There was inconsistent use of the standard with regard to soil type, soil properties, and across jurisdictional (Regional) boundaries. None 

of the Regional standards were really validated in cooperation with USFS Research 

and Development, except for the Long-Term Site Productivity Study (Powers and others 2005). Eventually, the original soil monitoring program was challenged in Federal 

District Court in Montana. This situation led to the development of the new Region1 

Soil Monitoring Protocol prototype, and it soon became a National Forest soil monitoring protocol, because it describes a consistent approach and common language for soil 

monitoring within forested ecosystems.

New Soil Monitoring Protocol—A reliable monitoring protocol has been identified 

as a critical component of any adaptive management process for forest and rangeland 

soil conservation programs (Curran and others 2005). Uniform and unambiguous definitions of soil disturbance categories must also relate forest productivity and hydrologic 

function (Curran and others 2007). A soil monitoring protocol must incorporate a statistically rigorous sampling procedure and firm definitions of visually observable soil 

disturbance categories

The Protocol, first developed by USFS Region 1 and the Rocky Mountain Research 

Station, incorporates soil quality monitoring efforts pioneered in the Pacific Northwest 

(Region 6) (Howes and others 1983). The Protocol is a multi-faceted approach to the 

soil disturbance and forest sustainability issue (fig.10). The Protocol uses visual soil 

disturbance classes (Howes and others 1983; Page-Dumroese and others 2006), and a 

standard inventory, monitoring, and assessment tool. It employs common terminology 

and has an accessible database. The visual disturbance considerations are soil resilience, 

degree of disturbance, duration, distribution, and location in relation to other resources. 

Soil Quality Monitoring: Examples of Existing Protocol Neary, Trettin, Page-Dumroese
76 USDA Forest Service Proceedings RMRS-P-59 2010.

Descriptions of the disturbance classes pre- and post-harvest are listed in tables 6 and 7. 

Full details of the Forest Soil Disturbance Monitoring Protocol can be found in Volume I 

and Volume II of the technical guides (Page-Dumroese and others 2009a, b).

In order to reduce monitoring variability, a visual guide of soil disturbance is being 

developed by the U.S. Forest Service’s San Dimas Technology and Development Center 

with a draft title of Soil Disturbance Field Guide (Napper and others, 2009). The guide 

Figure 10. USFS Region 1 Soil Monitoring 

Protocol multi-faceted approach to forest 

site productivity.

Table 6—Forest Soil Disturbance Monitoring Protocol pre-harvest soil disturbance class definitions (Page-Dumroese and others 2009a, b).

Class 0—Undisturbed natural state

Soil surface:

• No evidence of past equipment operation.

• No depressions or wheel tracks evident.

• Forest floor layers present and intact.

• No soil displacement evident.

Class 1—Low soil disturbance

Soil surface:

• Faint wheel tracks or slight depressions evident and are <15 cm deep.

• Forest floor layers present and intact.

• Soil surface has not been displaced and shows minimal mixing with subsoil.

• Some evidence of burning impacts include a mosaic of charred and intact forest floor layers to partially consumed surface OM

with blackened surface soil. Root crowns and surface roots of grasses are not consumed.

Class 2—Moderate disturbance

Soil surface:

• Wheel tracks or depressions are >15 cm deep.

• Forest floor layers partially intact or missing.

• Surface soil partially intact and may be mixed with subsoil.

• Burning consumed forest floor, root crowns, and surface roots of grasses. Surface soil is blackened.

Class 3—High disturbance

Soil surface:

• Wheel tracks and depressions highly evident with depth being >30 cm deep.

• Forest floor layers are missing.

• Evidence of topsoil removal, gouging, and piling.

• Soil displacement has removed the majority of the surface soil. Surface soil may be mixed with subsoil. Subsoil partially or totally

exposed.

• Burning consumed the forest floor, root crowns and surface roots of grasses. Evidence of severely burned soils (mineral soil red

in color).

Neary, Trettin, Page-Dumroese Soil Quality Monitoring: Examples of Existing Protocol
USDA Forest Service Proceedings RMRS-P-59 2010. 77

displays the same four classes of disturbance (none, low, moderate, and high) described 

in the Forest Soil Disturbance Monitoring Protocol across a range of forest ecosystems 

in the United States. It is meant for use as a guide to train field crews, a means to provide 

a high level of consistency, and a focal point for discussions to improve communication 

among professionals interested in assessing soil disturbance. Two examples for a class 

2, low soil disturbance, are shown in figure 11 for lodgepole pine and ponderosa pine.

A standardized data sheet is part of the protocol to ensure that the same data are 

collected on each site (SoLo 2008). The form header contains basic site data, location, 

and general sampling details (table 8). The remainder of the table contains the specific 

soil descriptive and disturbance information (table 9). This protocol takes the first steps 

in describing how forest management alters soil surface conditions from a pre-harvest 

condition. Local specialists are charged with defining how those alterations might affect 

Table 7—Forest Soil Disturbance Monitoring Protocol post-harvest/burn disturbance class definitions (Page-Dumroese and others 2009a, b).

Class 0—Undisturbed natural state

Soil surface:

• No evidence of past equipment operation.

• No depressions or wheel tracks evident.

• Forest floor layers present and intact.

• No soil displacement evident.

Class 1—Low soil disturbance

Soil surface:

• Faint wheel tracks or slight depressions evident and are <15 cm deep.

• Forest floor layers present and intact.

• Soil surface has not been displaced and shows minimal mixing with subsoil.

• Some evidence of burning impacts include a mosaic of charred and intact forest floor layers to partially consumed surface OM

with blackened surface soil. Root crowns and surface roots of grasses are not consumed.

Soil resistance to penetration with tile spade or probe:

• Resistance of surface soils may be slightly greater than observed under natural conditions. Concentrated in the top 0-10 cm.

Observations of soil physical conditions:

• Change in soil structure from crumb or granular structure to massive or platy structure, restricted to the surface 0-10 cm.

Class 2—Moderate disturbance

Soil surface:

• Wheel tracks or depressions are >15 cm deep.

• Forest floor layers partially intact or missing.

• Surface soil partially intact and may be mixed with subsoil.

• Burning consumed forest floor, root crowns, and surface roots of grasses. Surface soil is blackened.

Soil resistance to penetration with tile spade or probe:

• Increased resistance is present throughout top 10-30 cm of soil.

Observation of soil physical condition:

• Change in soil structure from crumb or granular structure to massive or platy structure, restricted to the surface 10-30 cm.

• Platy structure is generally continuous

• Large roots may penetrate the platy structure, but fine and medium roots may not.

Class 3—High disturbance

Soil surface:

• Wheel tracks and depressions highly evident with depth being >30 cm deep.

• Forest floor layers are missing.

• Evidence of topsoil removal, gouging, and piling.

• Soil displacement has removed the majority of the surface soil. Surface soil may be mixed with subsoil. Subsoil partially or totally

exposed.

• Burning consumed the forest floor, root crowns and surface roots of grasses. Evidence of severely burned soils (mineral soil red

in color).

Soil resistance to penetration with tile spade or probe:

• Increased resistance is deep into the soil profile (> 30 cm)

Observations of soil physical conditions:

• Change in soil structure from granular structure to massive or platy structure extends beyond the top 30 cm.

• Platy structure is continuous.

• Roots do not penetrate the platy structure.

Soil Quality Monitoring: Examples of Existing Protocol Neary, Trettin, Page-Dumroese
78 USDA Forest Service Proceedings RMRS-P-59 2010.

long-term soil productivity and forest sustainability. As with the British Columbia 

Ministry of Forests project, these disturbance classes need to be locally validated to 

ensure visual classes and forest growth are appropriately defined.

An integral component of the Protocol (figure 10) is the soil risk rating system (Curran 

and others 2005; Reynolds and others, in preparation). Its function is to predict the degree of risk of environmental degradation from detrimental soil disturbance. It accounts 

for variations in soil texture, rock content, organic matter, and vegetation. Like a lot of 

other soil monitoring systems in the world, the risk rating system is meant to provide 

Figure 11. Soil disturbance class 

2, low, from the San Dimas 

Technology Development Center 

Soil Disturbance visual Guide 

for (a) lodgepole pine, and (b) 

ponderosa pine (Napper and 

others, 2009).

Table 8—SoLo soil disturbance monitoring form basic site data (adapted from SoLo 2008).

SoLo soil disturbance monitoring form header data

General details Location information Sampling details

Project	 GPS start point	 Date

Unit identification	 Latitude/Longitude	 Monitoring type

Observer	 UTM coordinates E & W	 Point spacing

UTM zone	 Confidence level

Minimum required Samples

Interval width

Neary, Trettin, Page-Dumroese Soil Quality Monitoring: Examples of Existing Protocol
USDA Forest Service Proceedings RMRS-P-59 2010. 79

Table 9—SoLo soil disturbance monitoring form detailed soil data (adapted from SoLo 2008).

Direction:

Sample point 1 2 3 4 5 6 7

f. floor depth (cm):

Forest floor impacted?

Live plant?

Invasive plant?

Fine woody? <7 cm

Coarse woody? >7cm

Bare soil?

Rock?

Topsoil displacement?

Erosion?, comment!

Rutting? <5cm

Rutting? 5-10cm

Rutting? >10cm

Burning light

Burning moderate

Burning severe

Compaction? 0-10 cm

Compaction? 10-30 cm

Compaction? >30cm

Platy/massive/puddled

structure 0-10 cm

Platy/massive/puddled

structure 10-30 cm

Platy/massive/puddled

structure >30 cm

N Needed (round UP)

#DIV/0!

Estimated soil 

disturbance class

Detrimental? Enter 1 if

Yes, 0 if No

Comments

input to Project planning to ensure that adequate Best Management Practices are employed during the operations phase of projects. One attempt at “soil Best Management 

Practices” has been described by Page-Dumroese and others (2010).

Another important component of the soil monitoring Protocol is training to ensure 

uniform evaluations of soil conditions by different field crews across the country. As 

part of this effort, work is in progress to develop a “standardized” training curriculum 

and materials as well as preparation of a task book similar to those used for Incident 

Team positions to ensure mastery of key elements. Future web site development will 

involve additional training modules.

Soil Quality Monitoring: Examples of Existing Protocol Neary, Trettin, Page-Dumroese
80 USDA Forest Service Proceedings RMRS-P-59 2010.

Summary and Conclusions

This paper has reviewed a number of approaches to soil monitoring in Australia, 

New Zealand, the European Union, Canada, and the United States. Specific cases were 

evaluated in Tasmania, New Zealand, Ireland, British Columbia, Weyerhaeuser, and the 

U.S. Forest Service. These States, Companies, and Agencies all have guidance directives from Codes of Forest Practice, Company policy, or National management that 

focus on soil disturbance. They rely on adaptive management, co-regulation between 

forest operations and government regulatory authorities, operations planning, and Best 

Management Practices. The scientific basis for soils monitoring comes from the involvement of Research and Development organizations. Constant feedback from monitoring 

results and Research and Development efforts results in the type of soil management 

that will maintain future forest site productivity.

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Neary, Trettin, Page-Dumroese Soil Quality Monitoring: Examples of Existing Protocol
USDA Forest Service Proceedings RMRS-P-59 2010. 83

The Soil Indicator of Forest Health in the 

Forest Inventory and Analysis Program

Michael C. Amacher, Research Soil Scientist, Forest Inventory and Analysis Program, Rocky 

Mountain Research Station, Logan, UT

Charles H. Perry, Research Soil Scientist, Forest Inventory and Analysis Program, Northern 

Research Station, St. Paul, MN

Abstract—Montreal Process Criteria and Indicators (MPCI) were established to monitor 

forest conditions and trends to promote sustainable forest management. The Soil Indicator 

of forest health was developed and implemented within the USFS Forest Inventory and 

Analysis (FIA) program to assess condition and trends in forest soil quality in U.S. forests 

regardless of ownership. The Soil Indicator differs from intensive site monitoring programs 

in that it is a nationally applied, landscape-scale, grid-based design across all ecoregions, 

forest types, and land ownership categories. To date, the Soil Indicator has provided 

the only national assessment of soil erosion potential, areal extent of soil compaction, 

measured organic C stocks inventory, and soil physical and chemical properties of forest 

soils in the United States.

Introduction

Forested lands comprise approximately 750 million acres in the United States, about 

33 percent of total land area (Smith and others, in press). Forest soils have unique properties, in part because of the types of vegetation, microbial activity, and soil organisms 

that influence forest soil development. But organisms are not the only factor influencing 

soil development. Soils on the landscape are the result of five interactive soil forming 

factors (Jenny 1994): parent material, climate, landscape position (topography), organisms (vegetation and soil organisms), and time.

Many external forces can have a profound influence on forest soil condition and 

hence forest health. These include agents of change or disturbances to apparent steadystate conditions such as shifts in climate, fire, insect and disease activities, land use 

activities, and land management actions. Yet, until recently, a systematic monitoring or 

assessment program that tracks changes in indicators of environmental condition for 

many resource bases was lacking.

The Montreal Process Criteria and Indicators (MPCI) program was developed to assess the condition and trend of forest resources of member countries (Montreal Process 

Working Group 2005). This information is used for sustainable forest management and 

includes indicators of forest health. The condition and trend of forest soils is part of 

those indicators of forest health that are inventoried by the USFS Forest Inventory and 

Analysis (FIA) program. This paper will review the development of the Soil Indicator 

of forest health and present a review and summary of recent Soil Indicator condition 

assessments. Topics to be covered include:

• Overview of FIA and forest health indicators program

• History of Soil Indicator development

• Forest health monitoring and the USFS integrated monitoring framework

• Broad-scale (landscape-scale) versus intensive site monitoring

• Attributes and strengths of forest health indicators including the Soil Indicator

• Soil Indicator monitoring questions and objectives

• Sampling design

• Field and laboratory analysis methods
84 USDA Forest Service Proceedings RMRS-P-59 2010.

• Quality control/quality assurance

• Data analysis and reporting framework including post-analysis stratification 

approaches

• Status of U.S. forest soils—review and summary of recent findings

• Sampling variability

• Soil Indicator weaknesses

• Soil Indicator and Soil Quality Standards monitoring

Forest Health Indicators and the FIA Program

The Soil Indicator and all other forest health indicators are part of the FIA program. 

FIA is the nation’s forest census. It began some 80 years ago as a periodic inventory of 

timber resources and has evolved into a continuous, annualized inventory of U.S. forest 

resources across all public and private ownership categories (Smith 2008; USDA Forest 

Service 2009a).

FIA collects and reports data on the status and trend of

• Forest area and locations,

• Species, size, and health of trees,

• Total tree growth, mortality, and removals,

• Wood production and utilization,

• Forest land ownership, and

• Forest health.

Various indicators of forest health are included as part of the FIA program:

• Crown condition (Schomaker and others 2007)

• Ozone injury to vegetation (Smith and others 2007)

• Tree growth, damage, and mortality (Bechtold 2003a,b)

• Lichen communities (McCune 2000)

• Understory vegetation structure and diversity (Schulz and others 2009)

• Down woody material (Woodall and Monleon 2008)

• Soil quality (O’Neill and others 2005c)

Soil Indicator Development

Although a comprehensive history of the development of the Montreal Criteria and 

Indicators process and the development of the FIA indicators of forest health is beyond the scope of this paper, some historical background will be presented to indicate 

how the Soil Indicator evolved. The Soil Indicator actually began as part of the U.S. 

Environmental Protection Agency (EPA) Environmental Monitoring and Assessment 

Program (EMAP) (USEPA 2009a). The purpose of EMAP is to develop the tools needed 

to monitor and assess the status and trend of national ecological resources at multiple 

spatial and temporal scales (USEPA 2009a). But beyond that, forest status and trend 

assessment programs are driven by MPCI concepts and framework of sustainable ecosystems (Montreal Process Working Group 2005).

Following its beginnings within EMAP, the Soil Indicator was pilot tested throughout the 1990s within the Forest Health Monitoring (FHM) program. At the time, FIA 

conducted forest inventories whereas FHM conducted forest health assessments. In 

2000, the FHM forest health indicators transitioned from FHM to FIA. By then, many 

changes, improvements, and add-ons had been made to the Soil Indicator. From 2001 

onward, the Soil Indicator of forest health was fully implemented as part of the FIA forest health indicators program with little change to its core measurements and protocols.

Amacher, Perry The Soil Indicator of Forest Health in the Forest Inventory and Analysis Program
USDA Forest Service Proceedings RMRS-P-59 2010. 85

Because the forest health indicators were developed within the FHM program, a brief 

review of the four core areas of FHM is needed to better understand overall indicator 

development and implementation. Forest Health Monitoring previously consisted of 

four programmatic areas:

• Detection monitoring—Uses the FIA P3 plot grid consisting of one plot for every 

96,000 acres. Detection monitoring is used to uncover forest health threats as they 

develop.

• Evaluation monitoring—This is a more spatially intensive monitoring of forest health 

problems uncovered by Detection Monitoring. Examples would include intensified 

grid special project monitoring on National Forests.

• Intensive site monitoring (ISM)—Generally, more detailed, process-oriented research 

at specific sites. An example includes the joint USGS-USFS Delaware River Basin 

project (USDA Forest Service 2009b).

• Research on monitoring techniques (ROMT)—Basically, a monitoring tool development program.

The relationship among the various monitoring programs and scales can best be described with the USFS Integrated Monitoring Framework (fig. 1). In phase 1, remote 

sensing is used to delineate forest from nonforest lands. Next in scale are various local 

management inventories. These may be done using temporary or permanent plots at 

various spatial scales, for example, National Forest Systems (NFS) inventory projects 

on individual forests.

Figure 1. USFS integrated monitoring framework showing the relationship of FIA P1, P2, and P3 plot network to the 

forested landscape.

The Soil Indicator of Forest Health in the Forest Inventory and Analysis Program Amacher, Perry
86 USDA Forest Service Proceedings RMRS-P-59 2010.

Field measurements in the integrated FIA program are done in two phases. Phase 2 

(P2) is the annualized forest inventory and consists of a permanent plot network with 

approximately 125,000 forest plots on a 3-mile (5-km) grid. Phase 3 (P3) is the forest 

health monitoring part of the program and is a 1/16 subset of the P2 grid (about 7,800 

permanent plots on a 13-mile (22-km) grid. Ecosystem index site monitoring consists of 

21 permanent intensive monitoring sites across the United States.

Landscape-Scale Versus Intensive Site Monitoring

Figure 1 clearly shows the relationship among multiple spatial monitoring scales. 

At this point, it is worthwhile to contrast broad or landscape-scale monitoring with 

intensive site monitoring because the purpose and objectives of these two monitoring 

program are vastly different.

There are three main landscape-scale monitoring programs in the United States:

• USEPA EMAP—Develop the tools needed to monitor and assess the status and trend 

of national ecological resources at multiple spatial and temporal scales.

• National Resource Conservation Service (NRCS) programs:

• National Cooperative Soil Survey (USDA NRCS 2007, 2008a)—Basically a 

soil mapping program.

• National Resources Inventory (NRI) (Nusser and Goebel 1997; Nusser and 

others 1998; USDA NRCS 2008b)—Statistical survey of land-use and natural 

resource condition and trend on U.S. non-federal lands. The NRCS NRI is 

somewhat analogous to the Forest Service FIA program.

• U.S. Forest Service FIA (Smith 2002; USDA Forest Service 2009a)—Continuous, 

annualized inventory of U.S. forest resources across all public and private ownership 

categories. FIA collects and reports data on the status and trend of forest resources 

and forest health.

The principal similarity among all three programs is that they all collect data to 

assess condition and trend of various U.S. resources at the landscape scale using a gridbased monitoring system. Or to describe this in a simplified way, if you want to know 

what is going on ‘out there,’ you have to measure it. And ‘it’ needs to be measured at a 

sufficient spatial scale to provide a reasonably accurate snapshot of current conditions 

and to provide a suitable baseline to track future trends, if any.

A very different approach is used by intensive site monitoring projects. These tend 

to be focused on gaining a better understanding of ecosystems processes operating at a 

fixed number of sites representing key ecosystems or areas. They often rely on spatially 

and temporally intensive measurements to quantify key ecosystem processes. Findings 

from intensive site projects are often extrapolated elsewhere on the landscape. This 

works for sites with similar characteristics, but is unreliable for different areas.

The following are examples of intensive site monitoring networks:

• Experimental forests, rangelands, and watersheds (fig. 2) (Adams and others 2008; 

USDA Forest Service 2008);

• Long-term ecological research (LTER) sites (fig. 3) (Hobie and others 2003; U.S. 

Long Term Ecological Research 2007);

• Long-term soil productivity (LTSP) sites (fig. 4) (Powers and others 2005);

• Fire and fire-surrogate plots (fig. 5) (Fire Research and Management Exchange 

Systems 2008);

• National Ecological Observatory Network (NEON)—Continental-scale research 

platform for discovering and understanding impacts of climate change, land-use 

change, and invasive species on ecology (fig. 6) (National Ecological Observatory 

Network 2008); and

• Critical-Zone Exploration Network (CZEN)—Established to investigate the coupling 

between physical, chemical, geological, and biological processes in the critical (lifesupporting) zone (Critical Zone Exploration Network 2008).

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Figure 2. Map of experimental 

forests, rangelands, and 

watersheds in North America.

Figure 3. Map of long-term ecological research (LTER) sites in North 

America. The site names corresponding to the site abbreviations 

are AND = Andrews , ARC = Arctic, BES = Baltimore Ecosystem 

Study, BNZ = Bonanza Creek, CAP = Central Arizona – Phoenix, 

CCE = California Current Ecosystem, CDR = Cedar Creek, CWT = 

Coweeta, FCE = Florida Coastal Everglades, GCE = Georgia Coastal 

Ecosystems, HFR = Harvard Forest, HBR = Hubbard Brook, JRN 

= Jornada Basin, KBS = Kellogg Biological Station, KNZ = Konza, 

LUQ = Luquillo, MCM = McMurdo Dry Valleys, MCR = Moorea 

Coral Reef, NWT = Niwot Ridge, NTL = North Temperate Lakes, 

PAL = Palmer Station, PIE = Plum Island Ecosystem, SBC = Santa 

Barbara Coastal, SEV = Sevilleta, SGS = Shortgrass Steppe, VCR = 

Virginia Coastal Reserve.

Figure 4. Map of long-term soil 

productivity (LTSP) sites in 

North America.

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88 USDA Forest Service Proceedings RMRS-P-59 2010.

A main difference between NEON and CZEN is that NEON is more ecology oriented 

whereas CZEN is more geosciences oriented.

The landscape-scale and intensive site monitoring programs listed above tend to be 

land-based although they often include water and air measurements. There are monitoring programs run by the USEPA and U.S. Geological Survey (USGS) that are focused 

primarily on air and water quality assessments.

• Air quality monitoring

◦ National Atmospheric Deposition Program (NADP) (National Atmospheric 

Deposition Program 2008)

◦ Clean Air Status and Trends Network (CASTNET) (USEPA 2009b)

• Water quality monitoring

◦ USEPA National Assessment Database (USEPA 2009c)

◦ USGS Hydrologic Benchmark Network (HBN) (USGS 2002)

◦ USGS National Stream Quality Accounting Network (NASQAN) (USGS 

2009a)

◦ USGS National Water Quality Assessment (NAWQA) (USGS 2009b)

One way to link landscape-scale and intensive site monitoring is to co-locate landscape-scale monitoring plots, such as FIA P3 plots, on intensive site monitoring areas. 

This provides a direct linkage between what would otherwise be disparate databases and 

allows for more reliable quantitative estimates of ecosystem states and rates of change. 

This approach was used for the joint USGS-USFS Delaware River Basin project.

Figure 5. Map of U.S. fire and 

fire surrogate plots.

Figure 6. Map of National Ecological 

Observatory Network (NEON) 

monitoring areas. The core site areas 

corresponding to the numbered 

regions are 1 = Northeast, 2 = MidAtlantic, 3 = Southeast, 4 = Atlantic 

Neo-Tropical, 5 = Great Lakes, 6 = 

Prairie Peninsula, 7 = Appalachian 

Cumberland, 8 = Ozarks Complex, 

9 = Northern Plains, 10 = Central 

Plains, 11 = Southern Plains, 12 = 

Northern Rockies, 13 = Southern 

Rockies, 14 = Desert Southwest, 

15 = Great Basin, 16 = Pacific 

Northwest, 17 = Pacific Southwest, 

18 = Tundra, 19 = Taiga, 20 = Pacific 

Tropical.

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Since this paper is an overview of the FIA Soil Indicator, it is instructive to list the 

key attributes of the Soil Indicator program:

• Condition and trend assessments at multiple spatial and temporal scales—detection 

and monitoring of soils-related forest health problems and threats;

• Integration with other forest data and with other forest health indicators;

• Standardized, unbiased, grid-based measurement and sampling design;

• A national and comprehensive scope: all U.S. forest lands are measured regardless of 

ownership; all ecoregions, forest types, and forest soil types are included;

• Standardized, reproducible, nationally consistent protocol;

• Standardized nationally consistent training;

• Quality Control and Quality Assurance (QC/QA) programs; and

• Standardized estimation and reporting of forest resources inventory data.

These key attributes are major strengths in that they directly overcome major weaknesses in intensive site monitoring programs. The Soil Indicator shares these attributes 

with other FIA program indicators.

Before turning to a detailed description of the Soil Indicator, we must also indicate 

that the Soil Indicator does not replace or overlap existing USDA NRCS soils programs. 

Specifically, the Soil Indicator is not a soil survey, is not a soil mapping program, and 

is not a soil characterization program, although it does characterize (measure properties 

of) some aspects of forest soils.

Soil Indicator and Monitoring Questions

The Soil Indicator was developed to specifically address monitoring questions posed 

by the Montreal Process Criteria and Indicators (MPCI): What is the current status and 

projected trend in the area and percent of forest land with

• Accelerated soil erosion?

• Compaction or change in soil physical properties resulting from human activities?

• Changes in the amount of moisture holding capacity, internal drainage, and rooting 

depth?

• Diminished soil organic matter and/or changes in other soil chemical properties?

• Contributions to the global carbon budget including absorption and release of carbon?

• Accumulations of persistent toxic substances?

Thus, in summary the FIA Soil Indicator provides data to assess (1) productivity 

and sustainability of forest ecosystems, (2) conservation of soil and water resources, 

(3) contributions of forest soils to the global carbon cycle, and (4) accumulation of 

persistent toxic substances.

Sampling Design

The USFS integrated monitoring framework was presented in figure 1. The statistical 

design of the integrated FIA program is based on a hexagonal grid or network of plots 

(Brand and others 2000; Bechtold and Patterson 2005). Grid density is illustrated in figure 7 using the state of Minnesota as an example. In phase 1 (P1), forest land is mapped 

via remote sensing using 3,000,000 national 1-m pixels. The forest map of Minnesota 

produced by phase I mapping is shown on the left side of figure 7. In phase 2 (P2), forest inventory data are collected on a national network of approximately 125,000 plots 

(3-mile grid) with each one representing 6,000 acres. P2 plot density for Minnesota is 

represented in the middle of figure 7. A 1/16 subset of P2 plots is used to collect forest health data. This phase 3 (P3) plot network consists of approximately 7,800 plots 

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(13-mile grid) with each plot representing 96,000 acres. P3 plot density for Minnesota 

is shown on the right side of figure 7.

Figure 7 clearly shows that a hexagonal sampling design can be used at any spatial 

scale. Thus, sample ‘hexes’ can be virtually any size. The various FIA plots are assigned 

to the hexes. One of the requirements of the legislative authorization of the annualized 

FIA inventory is that plot locations are not released as public information. This is to 

protect landowner confidentiality. EMAP hexagons are often used to represent P3 data 

since they are approximately the same size. A national network of EMAP hexes containing a plot already visited for Soil Indicator measurement and sampling is shown on the 

U.S. map in fig. 8, but plot locations within the hexes are not disclosed.

The FIA program is a continuous annualized inventory of U.S. forest resources, but 

resources do not permit every plot to be assessed each year. Thus, hexes and plots within 

hexes are assigned to one of five panels and only one panel of plots (20% of plots) is 

sampled each year. In a 5-year cycle, all five panels would be visited and measured.

Each P3 plot is measured and sampled once every 10 years for Soil Indicator variables. In the eastern United States (Northern and Southern FIA regions), the Soil 

Indicator alternates with the Lichen Indicator over a 10-year cycle of the 5 panels of 

plots. In one cycle of 5 panels, each plot is sampled for soils over the 5-year cycle (one 

panel of plots per year). In the next 5-year cycle, each plot is sampled for lichens. Thus, 

after a 10-year cycle, the plots in the first panel are again sampled for Soil Indicator 

variables and so on. In the western United States (Interior West and Pacific West FIA 

regions), plots within each panel are assigned to sub-panels. In year 1, plots in sub-panel 

A of panel 1 are sampled. In year 2, plots in sub-panel B of panel 1 are sampled. In year 

3, plots in sub-panel A of panel 2 are sampled, and so forth. Thus, it takes 10-years to 

visit each plot and then the process begins again. Thus, Soil Indicator data are collected 

on each plot in the East and West every 10 years, but the panel schedules differ among 

the FIA regions.

The standard FIA plot design consists of four circular subplots (24-ft radius) arranged in a triangle design with 120 ft between subplot centers (fig. 9). Forest inventory 

and forest health indicator measurements are made within each subplot. Surrounding 

each subplot is an annular plot (59-ft radius) reserved for sampling.

Figure 7. Map of hexagonal grid system scales using Minnesota as an example. The phase 1 (P1) grid 

consists of 3,000,000 points across the United States. A map of forested areas within Minnesota 

defined using this scale is shown on the left. The phase 2 (P2) grid consists of 125,000 plots (1 plot per 

6,000 acres on a 3-mile (5-km) grid). P2 plot density for Minnesota is shown on the middle map. The 

phase 3 (P3) grid consists of 7,800 plots (1 plot per 96,000 acres on a 13-mile (22-km) grid). P3 plot 

density for Minnesota is shown on the right side map.

Amacher, Perry The Soil Indicator of Forest Health in the Forest Inventory and Analysis Program
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Figure 9. Standard FIA plot design.

Figure 8. U.S. map of P3 plot hexagons that have been sampled for the Soil Indicator from 2000 through 2005.

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Measurement and Sampling Protocols

The FIA Soil Indicator consists of three main assessments:

1. Erosion assessment

a. Percent of each subplot area with bare soil

2. Soil compaction assessment

a. Percent of each subplot area with evidences of compaction

b. Compaction type

3. Soil sampling and associated measurements

a. Forest floor and litter thickness

b. Forest floor sample collection

c. Depth to restrictive layer

d. Soil core collection for mineral or organic soils

i. 0–10 cm

ii. 10–20 cm

e. Soil texture

Protocols for these measurements and soil sampling have been established and are 

outlined in detail in the FIA P3 field manual (USDA Forest Service 2007). A general 

description of the measurements and sampling is given below.

Visual estimation of the area of bare soil within each of the four subplots is expressed 

as percent of subplot area in 5 percent classes (table 1). Field crews are trained to identify bare soil and then to estimate the percent of plot area consisting of bare soil. Bare soil 

is the single most important variable in assessing erosion potential. Bare soil along with 

additional soil data (soil texture) and ancillary data (precipitation history from nearby 

weather stations, slope, and plot area) can be used to estimate soil erosion potential with 

the Watershed Erosion Prediction Program (WEPP) (Elliot and others 2000). Because 

the areal extent of bare soil on FIA plots is estimated to assess soil erosion potential, 

bare soil is defined in terms of particle sizes most likely to move via raindrop impact and 

runoff. For the FIA Soil Indicator, bare soil is defined as follows:

• Bare mineral soil consisting of fine gravel (2–5 mm), sand, silt, and clay sized 

particles.

• Bare organic soil; although interlocking organic fibers usually guard against organic 

soil erosion.

• Bedrock outcrops, rocks, and talus are excluded; rock cover often provides some 

measure of erosion protection in all but the most extreme storm events.

Table 1—Bare soil as a percent of subplot area data attributes for soil erosion potential

assessment in the FIA P3 Soil Indicator (FIA 2008).

•	 Where collected: subplots 1, 2, 3, and 4.

•	 When collected: any portion of a subplot containing at least one

accessible forested condition class.

•	 Field width: 2 digits

•	 Tolerance: ± 10 percent

•	 Measurement quality objective (MQO): within tolerance 75 percent of the

time.

PDR code: bare soil range

00: none	 25: 21-25	 55: 51-55	 85: 81-85

01: trace	 30: 26-30	 60: 56-60	 90: 86-90

05: 01-05	 35: 31-35	 65: 61-65	 95: 91-95

10: 06-10	 40: 36-40	 70: 66-70	 99: 96-100

15: 11-15	 45: 41-45	 75: 71-75	

20: 16-20	 50: 46-50	 80: 76-80	

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• Cryptobiotic crusts are excluded; these are mats of living organisms (e.g., 

cyanobacteria and algae) covering bare soil and are usually present in arid ecosystems. 

They provide some measure of erosion protection against raindrop impacts.

• Basal tree area and stumps are excluded; these usually occupy a very small total area 

of a plot and protect against raindrop impact and runoff.

After assessing areal extent of bare soil on each subplot, field crews next look for 

evidences of compaction. Field crews are trained to identify several disturbances as 

evidences of soil compaction (table 2). They then estimate the area of compaction within each of the four subplots (percent of subplot area) in 5 percent classes (table 3). 

Following this, field crews identify the type of compaction (table 4). All the bare soil 

and compacted area and type data are entered into data recorders or are recorded on 

standardized data recording forms for later computer data entry.

Following bare soil and compaction estimations, forest floor and soil core samples 

are collected and forest floor and litter thicknesses, depth to restrictive layer (if any), 

and soil texture measurements associated with soil sampling are made. Soil samples are 

collected in the annular plots surrounding subplots 2, 3, and 4 (fig. 10). Soil sampling 

Table 2—Evidence of soil disturbance related to compaction.

Visual disturbance	 Evidence of compaction

Change in density	 A noticeable change in density compared to nearby undisturbed soil.

Most easily recognized by a difference in resistance to penetration

with a soil probe assuming similar soil moisture content.

Platy structure	 Coarse platy structure not evident in nearby undisturbed soil.

Loss of structure	 Loss of normal soil structure found in nearby undisturbed soil (e.g., soil

puddling, pulverized dust).

Ruts	 Ruts at least 2 inches (5 cm) deep in mineral soil or 6 inches (15 cm) deep

from undisturbed forest litter surface.

Mottling	 Formation of mottles in disturbed area. Not present in nearby undisturbed soil.

Table 3—Compacted soil area (percent of subplot area) data attributes for areal extent

of soil compaction assessment in the FIA P3 Soil Indicator (FIA 2008).

•	 Where collected: subplots 1, 2, 3, and 4.

•	 When collected: any portion of a subplot containing at least one accessible

forested condition class.

•	 Field width: 2 digits

•	 Tolerance: ± 15 percent

•	 MQO: within tolerance 75 percent of the time.

PDR code: compacted area range

00: none	 25: 21-25	 55: 51-55	 85: 81-85

01: trace	 30: 26-30	 60: 56-60	 90: 86-90

05: 01-05	 35: 31-35	 65: 61-65	 95: 91-95

10: 06-10	 40: 36-40	 70: 66-70	 99: 96-100

15: 11-15	 45: 41-45	 75: 71-75

20: 16-20	 50: 46-50	 80: 76-80

Table 4—Types of soil compaction in the FIA P3 Soil Indicator.

Type of compaction	 Definition

Rutted trail	 Ruts at least 2 inches deep in mineral soil or 6 inches deep from top of

undisturbed forest litter surface.

Compacted trail	 Linear compacted feature resulting from multiple passes by people,

animals, or vehicles.

Compacted area	 Examples include junctions of skid trails, landing areas, work areas,

campsites, animal bedding areas.

Other Explanation entered into plot notes.

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transects with sampling points for each visit are located 30-ft from the centers of subplots 2, 3, and 4 as shown in figure 10. On the initial Soil Indicator visit, samples are 

collected at point 1 on each transect. Ten years later, on the second visit, samples are 

collected at point 2, which is located 10 ft from point 1. Subsequent visits at 10-year 

intervals are at points 3 through 9. On the next cycle, sampling begins again at point 1.

At each sample point associated with subplots 2, 3, and 4, forest floor samples are 

collected. Soil cores (0-10 and 10-20 cm) are collected at the sampling point associated 

with subplot 2 only. There are certain sampling rules governing if and where samples 

get collected:

• Soil samples are only collected if the soil sampling location in the annular plot is in a 

forested condition class regardless of the forested condition of the subplot.

• If cultural artifacts are found, soil samples are not collected.

• Certain other conditions may prevent soil sample collection (table 5).

• Field crews may collect a soil sample set within a 5-ft radius circle around the 

soil sampling point (fig. 10). A 5-ft radius circle does not impinge on the next soil 

sampling point that would be visited in 10 years, but allows for sample collection if 

there is an obstruction (e.g., large log or rock) directly over the sample point.

Figure 10. Location of soil sampling points 

along transects within annular plots 

surrounding subplots 2, 3, and 4. The 

sampling line associated with subplot 2 is 

located 30 ft due south (azimuth 180 deg) 

from the center of subplot 2. The sampling 

line associated with subplot 3 is located 

30 ft northwest (azimuth 300 deg) from 

the center of subplot 3. The sampling line 

associated with subplot 4 is located 30 ft 

northeast (azimuth 60 deg) from the center 

of subplot 4.

Table 5—Soil sampling status codes for FIA P3 Soil Indicator.

•	 Where collected:

•	 Forest floor: Soil sampling points associated with subplots 2, 3, and 4.

•	 0-10 and 10-20 cm soil cores: Soil sampling points associated with subplot 2 only.

•	 When collected: Soil sampling point is in a forested condition.

•	 Field width: 1 digit

•	 Tolerance: no errors

•	 MQO: at least 99 percent of the time.

PDR code Soil sample status

1 Sampled

2 Not sampled: non-forest

Not sampled codes for forested condition

3	 Not sampled: too rocky

4	 Not sampled: water or too boggy

5	 Not sampled: access denied

6	 Not sampled: too dangerous

7 Not sampled: obstruction in sampling area

8	 Not sampled: broken or lost equipment

9	 Not sampled: other (explanation entered in plot notes)

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The cross-section diagram (fig. 11) shows the forest floor and litter thickness and 

depth to restrictive layer measurements associated with forest floor and soil core sampling. A general description of the measurement and sampling protocol is as follows:

• The entire forest floor (litter + humus) within a 30-cm diameter plot frame is collected 

down to the surface of the mineral soil. Woody pieces larger than 0.25-cm diameter 

are discarded (coarse and fine down wood are assessed as part of the down woody 

material indicator).

• Forest floor and litter thicknesses are measured at the north, south, east, and west 

compass points along the inner edge of the sample frame (fig. 11).

• A probe is used to measure depth to any restrictive layer (soil physical condition 

limiting root growth) within 50 cm of the mineral soil surface. Five measurements 

are made (center, north, south, east, west compass points) and the median of the five 

measurements is recorded. The maximum depth of evaluation is 50 cm.

• Two 2-inch diameter soil cores (0-10 and 10-20 cm) are collected using a coring head 

with two 10-cm long soil core liners attached to a slide hammer attachment. The 

volume of the cores is known and the soil weights (oven-dry basis) within the cores 

are used to calculate soil bulk density. If excessive coarse fragment content prevents 

soil core collection, then a hand excavation method is used to collect soil samples. 

Bulk density calculations are not made for manually excavated soil samples.

• The soil texture of the 0-10 and 10-20 cm mineral soil layers is determined with 

small samples from the sides of the coring or excavation hole.

The entire forest floor thickness and the litter layer thickness are measured as part of 

the sampling protocol. Field crews are trained to recognize the boundary between litter 

layer and humus layers:

• Litter layer—Decomposing plant parts can still be identified (e.g., leaves, needles, 

twigs, bark, etc).

• Humus layer—Plant parts can no longer be identified because decomposition has 

proceeded to the point where stable humus has been formed (dark color—almost 

black—crumbly, organic layer).

Since the entire forest floor is sampled, field crews are taught to distinguish between 

the bottom of the forest floor (humus) layer and the top of the mineral soil. Sometimes 

the boundary is indistinct and the forest floor transitions into the underlying mineral 

soil. Field crews are taught to look for the following distinguishing characteristics:

Figure 11. Cross-section diagram 

of forest floor and litter 

thickness measurements, depth 

to restrictive layer (if any), 

and forest floor and soil core 

sampling.

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• Evidence of plant parts—If they can be seen still decomposing in place, then that is 

still part of the forest floor.

• Texture—Crumbly (humus), or gritty (sand), silty, or clayey. The latter three are 

evidence that the mineral soil has been reached.

• Shiny flecks of mica or quartz—Will only help in those soils with that type of mineral 

soil mineralogy clearly present.

• Change in color—Humus layer is nearly black to black. Mineral soil is more brown 

color.

• Change in density—Humus layer is light. Mineral soil feels more dense.

Soil texture is collected primarily as a variable needed in the WEPP program for 

soil erosion potential assessment. For the FIA Soil Indicator, five soil texture classes 

estimated by feel are used: organic, loam, clay, sand, coarse sand. Organic soils are 

also tentatively identified in the field using texture, color, landscape setting, and vegetation characteristics. If an organic soil is being sampled, the forest floor is only the 

litter layer, and soil cores are collected from the underlying organic layer as with 

mineral soils.

Following collection, forest floor samples and soil cores are placed in sealed plastic bags and are sent to one of three FIA regional soil analysis laboratories for the 

north, south, and western states. The complete list of physical and chemical properties 

measured on the forest floor and soil cores is listed in table 6. Confirmation of organic 

soils is made using the percent organic C content of the soil cores. Since the entire 

solum is not sampled, an organic soil within the FIA Soil Indicator has an organic C 

content of 20 percent or greater in both sampled cores (0-10 and 10-20 cm).

Along with standardized training, quality control and quality assurance (QC/QA) 

are important components of measurement and sampling protocols. Quality control is 

the set of processes used to establish measurement quality objectives (MQOs) and to 

ensure quality standards are met. Quality assurance is the documentation that quality 

control protocols were followed. Tolerance levels and MQOs have been established 

for forest health indicators. Some of these Soil Indicator MQOs are listed in tables 

1, 3, and 5. In addition to tolerance and MQOs, a series of interactive and non-interactive field plot checks has also been established (table 7). In addition, to provide an 

unbiased estimate of measurement and sampling variance, 5 percent of the plots are 

re-measured and re-sampled within the same field season.

Table 6—Soil physical and chemical properties measured in the FIA P3 Soil Indicator program.

Forest floor	 Soil cores

Physical properties: Physical properties:

	 Field-moist and air-dry weights	 	 Field-moist and air-dry weights

	 Subsample oven-dry weight	 	 Subsample oven-dry weight

	 Field-moist, residual, and total water	 	 Field-moist, residual, and total water

content content

Chemical properties: 	 Coarse fragments (>2 mm)

	 Total C (organic)	 	 Bulk density

Total N Chemical properties:

	 Total S (special project)	 	 Organic, inorganic (carbonates), and

	 Total Hg (special project)	 	 Total N

	 	 	 Soil pH (water and 0.01 M CaCl2

)

	 	 	 1 M NH4Cl extraction:

 • Exchangeable cations (Na, K, Mg, Ca, Al)

 • Extractable metals (Mn, Fe, Ni, Cu, Zn, Cd, Pb)

 • Extractable S (SO4-S)

	 	 	 Extractable P:

 • Bray 1 (0.03 M NH4F + 0.025 M HCl)

 • Olsen (pH 8.5, 0.5 M NaHCO3

)

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The FIA regional soil laboratories also have their own separate QC/QA programs for 

the lab portion of the Soil Indicator. These QC/QA programs include

• Reagent and method blanks—Reagent blanks are used to establish baseline 

instrument calibrations. Method blanks are carried through all procedural steps of a 

given analysis method and are used to monitor for contamination.

• Instrument calibration standards—Used to calibrate instrument operation.

• Instrument check standards—Independent standards used to verify correct instrument 

operation and quantify analysis precision, bias, and accuracy. Accuracy is the sum of 

precision and bias measurements.

• Method check samples—Samples with ‘known’ or established values and tolerances 

based on repeat measurements among multiple laboratories and if possible, using 

multiple methods. These are used to check overall method repeatability and reliability.

• North American Proficiency Testing (NAPT) program—Quarterly sample exchange 

program administered by the Soil Science Society of America and involving more 

than 100 soil analysis laboratories.

Status of Forest Soils in the United States: Example of Soil 

Indicator Results to Date

Soil Indicator data along with that of other P3 forest health indicators plus the P2 

forest inventory data are loaded into the FIA National Information Management System 

(NIMS). FIA also has an on-line datamart, which is the publicly accessible portion of 

the data known as FIADB (see http://fiatools.fs.fed.us). Various FIA analysts as well as 

the Forest Health Indicator Advisors and outside users access the database to analyze 

FIA data to assess various forest resource inventory questions.

Much of the data analysis uses post data collection stratification to derive population estimates (Scott and others 2005). For the Soil Indicator, data could be stratified 

by ecoregion, forest type, soil type, and major resource land area (USDA NRCS 2006). 

Results are often presented as shaded point maps, data distribution plots (box plots, histograms, cumulative frequency plots), and statistical summary tables (e.g., means and/

or medians and various measures of data variability (e.g., standard deviation, coefficient 

of variation, standard error). Stratified results may also be presented as pixilated maps 

or summary tables with values reported by strata.

Once data analysis is complete, various data reporting and results interpretation outlets are available to communicate findings to science users, clients, and various publics. 

Following are some examples of how FIA Soil Indicator results get reported:

• National and international reports

◦ MPCI Sustainable Forests reports: 2003 printed report with web-based 

background technical reports (O’Neill and others 2004)

◦ Resource Planning Act (RPA) report (Perry and Amacher, in press)

Table 7—Field data collection QA/QC in the FIA program.

Type of QC/QA QC/QA steps

Hot checks	 Interactive—crews are present.

Auditors review protocols with crew members, identify

problems, suggest corrective actions, and conduct

independent measurement checks.

Cold checks	 Non-interactive—crews not present.

Auditors conduct spot checks and do follow-up corrections.

Re-measurement and re-sampling Used to provide unbiased estimate of sampling variance.

(5 percent of plots)

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◦ FHM National Technical reports: The 2001 and 2005 reports contain Soil 

Indicator data and interpretations (O’Neill and others 2005a; Perry and 

Amacher 2007a,b,c)

◦ Scientific literature (book chapter, journal papers, Forest Service research 

papers, general technical reports (GTRs), proceedings)—Examples include 

Perry and others 2008; O’Neill and others 2005b,c; Amacher and others 2007.

• Regional reports—none devoted to Soil Indicator yet

• State reports (resource bulletins)—Examples of state reports with Soil Indicator data 

include those of Minnesota (Miles and others 2007) and Virginia (Rose 2007).

Examples from recent publications showing Soil Indicator results give a snapshot of 

current forest soil conditions in the United States. Western forests tended to have more 

bare soil than eastern forests (fig. 12) because of lower overall tree canopy coverage and 

lesser amounts of forest floor material.

Soil compaction is not a widespread problem on forest soils of the United States. 

Most FIA P3 plots showed no evidence of compaction (fig. 13). Observed evidences 

of soil compaction tended to be found more in eastern forests than in the west (fig. 13) 

perhaps reflecting higher density of forest usage.

The impact of soil compaction on soil bulk density and forest productivity is complex 

(Powers and others 2005). Soils with bulk densities greater than 1.4 g/cm3

 tend to resist 

compaction. Forest productivity response to soil compaction depends on soil texture 

and understory vegetation. Production declined on compacted clay soils, increased on 

sandy soils, and was unaffected if an understory was absent (Powers and others 2005).

Figure 12. Spatial distribution of maximum observed percent bare soil by EMAP hexagon for FIA plots visited in 2001-2007.

Amacher, Perry The Soil Indicator of Forest Health in the Forest Inventory and Analysis Program
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Figure 13. Spatial distribution of maximum observed percent compacted area by EMAP hexagon for FIA plots visited in 2001-

2007.

Low soil nutrient and high acidity conditions may be found in forest soils throughout 

the United States, but strongly acid soils with low Ca and high Al levels are concentrated in the Northeast and South, primarily in the Appalachian regions (fig. 14). The 

most serious soils-related landscape-scale forest health threat uncovered by the FIA 

detection monitoring network is increasing soil acidity and associated decreases in soil 

Ca reserves along with potentially toxic levels of exchangeable Al. Calcium depletion 

and associated increases in available Al is strongly linked to atmospheric deposition 

(Driscoll and others 2001). Cronan and Grigal (1995) used soil solution Ca/Al molar 

ratios as an indicator of forest stress and indicated a near 100 percent probability of 

adverse impacts to forest health at a soil solution Ca/Al molar ratio of 0.2. The Ca/Al 

ratios presented in fig. 14 are 1M NH4Cl exchangeable values rather than soil solution 

values, but exchangeable and soil solution concentrations are closely associated via 

exchange coefficients.

The FIA Soil Indicator has provided the first national inventory of measured C stocks 

in U.S. forest soils to a depth of 20 cm. Forest soils in colder wetter regions tend to 

have higher organic C levels (fig. 15). These latitudinal and elevational gradients in 

soil organic C levels are expected since organic matter decomposition rates tend to be 

higher under warmer and drier conditions (Schlesinger 1997). Regional organic C and 

total N amounts in the forest floor and 0-10 and 10-20 cm layers are summarized in 

fig. 16. More organic C is stored in the Northeast and Pacific States FIA regions than in 

the South or Interior West. The Northeast and North Central FIA regions store the most 

total N.

At the request of Soil Indicator analysts and users, a Soil Quality Index (SQI) was 

developed that integrates 19 separate measured physical and chemical properties into a 

The Soil Indicator of Forest Health in the Forest Inventory and Analysis Program Amacher, Perry
100 USDA Forest Service Proceedings RMRS-P-59 2010.

single index number that can be used to track soil quality condition and trend (Amacher 

and others 2007). Soils with lower SQI levels (< 50 %) are at increased risk of soilsrelated forest health decline. These soils tend to be concentrated in the Northeast and 

South where soils are more highly weathered and depleted of nutrients (fig. 17).

Sample Variability

Magnitude of variability for a given Soil Indicator source of variation generally increases in the order shown in figure 18. Repeat analysis usually has the least variation 

while variation among plots has the most. Since the FIA Soil Indicator is designed to 

Figure 14. Spatial distribution 

of minimum observed 

exchangeable Ca/Al molar 

ratios by EMAP hexagon 

and soil depth (top: 0-10 

cm; bottom: 10-20 cm) for 

FIA plots sampled in 2000-

2007. Source: USFS FIA Soil 

Indicator. Geographic base data 

provided by the National Atlas 

of the U.S.A. EMAP hexagons 

provided by the U.S. EPA.

Amacher, Perry The Soil Indicator of Forest Health in the Forest Inventory and Analysis Program
USDA Forest Service Proceedings RMRS-P-59 2010. 101

measure condition and trend at the landscape scale, the number of plots within a stratification layer (ecoregion, forest type, etc.) is an important factor influencing measured 

variance. The Soil Indicator is not designed to measure small-scale soil spatial variability. It is well recognized, based on decades of research, that soil properties are variable 

at multiple spatial scales (Gassner and Schnug 2006). It is also well established that 

closely spaced samples in time or space tend to be more closely correlated to each other. 

The central concept of spatial autocorrelation was first stated in Tobler’s first law of geography: Everything is related to everything else, but near things are more related than 

distant things (ESRI 2006). Thus, landscape-scale assessments rely on spacing plots at 

far enough distance apart to reduce spatial correlation among samples to achieve a truer 

assessment of changes across the entire landscape.

Figure 15. Spatial distribution of 

forest floor (top) and 0-20 cm 

soil (bottom) organic C by EMAP 

hexagon for FIA plots sampled 

in 2000-2004. Source: USFS FIA 

Soil Indicator. Geographic base 

data provided by the National 

Atlas of the U.S.A. EMAP 

hexagons provided by the U.S. 

EPA.

The Soil Indicator of Forest Health in the Forest Inventory and Analysis Program Amacher, Perry
102 USDA Forest Service Proceedings RMRS-P-59 2010.

Figure 16. Regional soil organic C and 

total N amounts in the forest floor and 

0-10 and 10-20 cm layers for FIA plots 

sampled in 2000-2005. Means for each 

layer (stacked bars) not indicated by the 

same letter across regions are significantly 

different.

Table 8—Sources of variance in the FHM soil C re-measurement study (Conkling and others

2000). Thirty plots in Georgia were measured.

Soil depth	 Source of variation	 Bulk density	 Percent C	 C stock

Percent variance

0–5 cm	 Plots (30)	 70.8 **	 72.2 **	 77.8 **

Subplots (3/plot)	 22.4 **	 21.8 **	 17.5 **

Within subplots	 6.8 ns	 6.0 ns	 4.7 ns

5–10 cm	 Plots (30)	 65.3 **	 62.7 **	 70.0 **

Subplots (3/plot)	 25.1 **	 35.0 **	 27.5 **

Within subplots	 9.5 ns	 2.3 ns	 2.6 ns

10–20 cm	 Plots (30)	 63.4 **	 69.4 **	 71.4 **

Subplots (3/plot)	 34.6 **	 25.1 **	 20.5 **

Within subplots	 2.0 ns	 5.4 ns	 8.1 ns

** = Significant at p < 0.0001, ns = not significant.

Amacher, Perry The Soil Indicator of Forest Health in the Forest Inventory and Analysis Program
USDA Forest Service Proceedings RMRS-P-59 2010. 103

Figure 17. Spatial distribution of soil 

quality index (SQI) relative to 

the mean by EMAP hexagon and 

soil depth (top: 0-10 cm; bottom: 

10-20 cm) for FIA plots sampled 

in 2000-2007. Source: USFS FIA 

Soil Indicator. Geographic base 

data provided by the National 

Atlas of the U.S.A. EMAP 

hexagons provided by the U.S. 

EPA.

Figure 18. Sources of variation in Soil 

Indicator measurements arranged 

in order of increasing magnitude of 

variability.

The Soil Indicator of Forest Health in the Forest Inventory and Analysis Program Amacher, Perry
104 USDA Forest Service Proceedings RMRS-P-59 2010.

The FHM C re-measurement study using 30 FIA plots in Georgia showed the magnitude of variability (percent variance) within subplots, among subplots, and among plots 

for three Soil Indicator variables (bulk density, percent organic C, and C stocks) at three 

soil depths (0-5, 5-10, and 10-20 cm) (Conkling and others 2000) (table 8). Within subplot 

variance was not significant compared to among subplot variance. As expected, the greatest variance was among plots. To fully capture spatial variability at the landscape scale, 

more plots across landscape scale strata are needed rather than more samples per plot, 

which only captures within-site variance.

To provide an unbiased estimate of measurement and sampling variance, 5 percent of 

plots are re-measured and re-sampled in the same sampling year (Hansen and others, in 

press). Re-sampling is done adjacent to the sample hole associated with the established 

sampling point for that plot visit. Thus re-sampling produces a paired set of soil samples that represents about 5 percent of the total plot population. For each pair of samples 

from re-sampled plots, mean, standard deviation and coefficient of variation statistics are 

computed. Because the magnitudes of the various soil properties display a wide range of 

values, expressing the standard deviation of each pair of soil samples as a percentage of 

the mean (coefficient of variation, cv) normalizes the standard deviations and makes for 

easier comparisons over the complete range of observed values and among different soil 

properties. The least and most variable soil properties within a sampling site can be assessed at a glance.

Median cv values for several important soil properties are shown in table 9. The numbers of pairs of soil samples are also shown. Coarse fragment content is the most spatially 

variable soil property within sample sites with a median cv of 37 percent. Water pH is the 

least spatially variable soil property with a median cv of only 2.5 percent. Table 9 provides 

valuable information about which soil properties tend to be the most or least spatially variable within the sampling area for a large population of re-sampled plots across multiple 

ecoregions, forest types, and soil types. As the database grows, we will be able to identify 

those areas with the most within-plot spatially variable soil properties. Within-plot sampling variability data can be used to design more efficient sampling intensification for 

follow-up evaluation monitoring studies.

Soil Indicator Weaknesses

Key strengths of the Soil Indicator were listed and discussed previously. However, 

there are some weaknesses within the program as it is presently constituted.

• This is a forest health detection monitoring program at the landscape scale. Small-scale 

spatial variability is not captured with the current strategic approach to sampling design. 

However, one of the valuable attributes of the hexagonal sampling grid is that the grid 

can be intensified to address specific monitoring questions: (1) spatially-intensified 

evaluation monitoring projects based on detection monitoring results; (2) National 

Forest intensified-grid measurement and sampling, (3) Intensive site monitoring (e.g., 

Delaware River Basin study).

• The Soil Indicator is currently confined to an inventory of soil properties within the 

upper 20-cm of mineral or organic soil beneath the forest floor (the entire forest floor 

Table 9—Median coefficients of variation (standard deviation as a percent of mean) for selected soil

properties calculated from the population of FIA P3 plot re-measurement pairs.

Number of pairs

Soil property of soil cores	 Median cv. (percent)

Bulk density	 119	 10.0

Coarse fragments	 145	 37.2

Organic C	 368	 15.3

Total N	 368	 14.3

Water pH	 144	 2.5

Effective cation exchange capacity (ECEC)	 146	 10.7

Extractable P (Bray 1 and Olsen)	 129	 22.7

42	 23.3

ECEC = sum of exchangeable cations (Na, K, Mg, Ca, Al).

Amacher, Perry The Soil Indicator of Forest Health in the Forest Inventory and Analysis Program
USDA Forest Service Proceedings RMRS-P-59 2010. 105

within a plot frame is sampled). Thus, in the case of C, the total organic C inventory 

for the entire solum is not measured. Soil bulk density and organic C levels change 

with depth. In general, bulk density will increase with depth while organic C levels 

will decrease in mineral soils, but will remain at high levels in organic soils such as 

forested peat bogs. Soil depth to parent material is highly variable on the landscape. 

Furthermore, mineral soil profiles often grade into parent material lithology without 

distinct boundaries. Without an unambiguous definition of what constitutes the 

entire solum for sampling and inventory purposes, any soil inventory defaults to an 

operationally defined program based on a fixed sampling depth. The Soil Indicator 

program allows for deeper sampling for special projects. Although the current protocol 

samples the upper 20-cm of soil, manually operated soil core samplers can sample to 

30-cm in all but the rockiest soils. Sampling deeper than 30 cm almost always requires 

a motor-driven soil core sampler. Hand augering can collect soil samples to depths of 

1 m or more if coarse fragment content is low. However, hand augering precludes soil 

bulk density measurements because augered samples are disturbed and don’t preserve 

the original weight/volume ratio of undisturbed soil cores.

• The Soil Indicator does not include several highly important soil property measurements 

as yet. For example, no measures of soil biological properties are included. Such 

properties as enzyme activity, microbial population activity (e.g., microbial 

community-level physiological profiling (Biolog)), and in situ soil respiration and C 

and N mineralization/utilization would provide valuable additional information.

Potential Use of FIA Soil Indicator for Soil Quality Standards Monitoring

The concept of soil quality standards to maintain soil productivity and hence forest productivity following timber harvest activities is undergoing increased scrutiny. Typically, 

soil quality standards monitoring occurs at the project scale within various National 

Forests. Soil quality standards monitoring is chiefly concerned with documenting severity 

of soil disturbance (Neary and others 2010), whereas the FIA Soil Indicator documents the 

areal extent of bare soil (whether disturbance-related or not) and evidences of compaction 

within FIA subplot areas. It is possible to link the two approaches.

Potentially, FIA plots could be established on delineated project areas. Furthermore, 

FIA forest productivity and other indicator data linked to Soil Indicator data can be used to 

establish current and historic conditions for forest and soil types similar to proposed project areas. In addition, the soil quality standards disturbance severity protocols could be 

added to Soil Indicator protocols as a regional add-on for more intensified soil monitoring.

One of the Soil Indicator’s greatest strengths is the collection of data across the broader landscape. The collection of Soil Indicator data in or around projects would facilitate 

comparisons with areas not included in the project or held by adjacent landowners. In 

this regard, Soil Indicator data could answer questions about the unique impact of Forest 

Service land management.

Summary

The Soil Indicator was developed to assess the condition and trend of forest soils 

throughout the United States regardless of ownership as part of a larger forest health indicators monitoring effort within FIA. It is the first comprehensive national inventory of 

forest soil properties using common protocols with a QC/QA program. The Soil Indicator 

was developed in response to Montreal Process Criteria and Indicators monitoring questions. Two key accomplishments of the Soil Indicator are the first comprehensive national 

inventory of organic C stocks in forest soils based on measured values and the first landscape-scale assessment of the severity of Ca depletion and associated high levels of soil 

Al in forest soils of the Northern and Southern Appalachians. Since current soil conditions 

are now well-quantified, the Soil Indicator provides the means to track changes in forest 

soil conditions going forward. This can lead to a refinement of the MPCI as well as refine 

the Soil Indicator assessment process to better measure soils-related forest health risks.

The Soil Indicator of Forest Health in the Forest Inventory and Analysis Program Amacher, Perry
106 USDA Forest Service Proceedings RMRS-P-59 2010.

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The content of this paper reflects the views of the authors, who are responsible for the facts 

and accuracy of the information presented herein.

Amacher, Perry The Soil Indicator of Forest Health in the Forest Inventory and Analysis Program
USDA Forest Service Proceedings RMRS-P-59 2010. 109

Statistical Sampling Methods for Soils 

Monitoring

Ann M. Abbott, Moscow Forestry Sciences Laboratory, Rocky Mountain Research Station, 

Moscow, ID.

Abstract—Development of the best sampling design to answer a research question should 

be an interactive venture between the land manager or researcher and statisticians, and 

is the result of answering various questions. A series of questions that can be asked to 

guide the researcher in making decisions that will arrive at an effective sampling plan are 

described, and a case study is used to explain how the sampling effort was designed for the 

Forest Soil Monitoring Protocol.

Introduction

In general, the goal of statistics is to be able to make inferences about a population 

based on information gathered from a sample of units from that population. For the 

inferences to have meaning, then, the sample must be representative of the entire population in question.

To appropriately draw a sample from a population, there are several considerations 

that must be met.

• The sampling must be done in such a way that it will meet the objectives of the 

research.

• The sample itself must be representative of the population.

• The sampling plan must be feasible, and the plan must be cost effective.

An appropriate sampling plan is the result of answering a series of questions, and 

it is the answers to the questions that lead to the best sampling design, data analysis 

methods, and subsequent interpretation of the analysis. Knowing the questions to ask, 

therefore, is the key to designing a good sampling plan. Some of questions that must be 

asked include:

• What are the objectives of the research?

• What is the population about which inferences will be made?

• What are the sampling units?

• What is the translation of the objectives into specific questions that can be answered 

with measurements from the sampling units?

• What preliminary information is available about the population?

• What choice of sampling design will be used?

• What sample size is necessary to answer the research questions with acceptable 

accuracy?

• Are there any auxiliary variables that can provide additional information?

• How will the randomization be performed?

• How will the results of the sampling effort be recorded?

• How will the data be analyzed?

This paper will explain how to design a sampling plan using the Forest Soil Monitoring 

Protocol as a Case Study.
110 USDA Forest Service Proceedings RMRS-P-59 2010.

Questions

What Are the Objectives of the Research?

It is imperative that clear objectives be stated prior to beginning any data collection. 

They must be clearly and explicitly stated, and the reasons for undertaking the research 

must also be documented. A subsequent step in defining the objectives of the research 

is to translate the objectives into precise questions that the sample measurements can 

answer. The translation of the objective into precise questions is the link between the 

initial research question and a question that may be answered with sampling and statistics. To answer a question about a population with statistics, the question must be asked 

in terms of measurements that may be taken on individuals within a population.

An example of defining and translating objectives is as follows:

• The amount of soils disturbed by management activities must be documented.

• The objective, then, is to quantify the disturbance. While this is a good objective, it is 

not, as stated, something that can immediately be answered with sampling.

• A precise question is “What proportion of points within a transect is compacted to 

10 cm?”

What Is the Population About Which Inferences Will Be Made?

Once the initial objectives or research questions have been formulated, the population about which information is desired must be defined, which can be a somewhat 

circular process. Often, the definition step will refine the overall population about which 

information is desired into a population from which a sample may be drawn. When 

considering the population, it is helpful to think of it as a collection of individuals or 

sampling units that can be listed. Such a list may also be used as the sampling frame, 

from which the sample will be drawn. Defining the population as a collection of individuals that may be listed will determine whether any constraints are present that will 

limit the overall population into a smaller segment that can be sampled. This will also 

help ensure that the sampling units are representative of the population. It is possible 

that the population from which the sample is drawn may be different from the population as a whole if the entire population cannot be sampled.

What Are the Sampling Units?

Sampling units are defined as ‘non-overlapping collections of elements from the 

population that cover the entire population.’ A successful sampling scheme includes the 

selection of an appropriate sampling unit. The sampling unit is the individual within the 

population on which measurements and inferences will be made, so it is critical that the 

unit be carefully defined and possible to measure, as well as meet the objectives of the 

study. The sampling unit is also the subject of the randomization scheme for the study. 

Some examples of sampling units include quadrats, leaves of a plant, individual organisms, belt transects, or points.

Further questions that should be asked when considering the choice of sampling unit 

include:

• Are the sampling units naturally defined?

• If not, how will they be defined?

• Is the number of sampling units finite?

• If it is finite, is the total number of units in the population large enough to ignore 

finite sampling considerations?

• Is the definition of the sampling units appropriate to the objectives?

Abbott Statistical Sampling Methods for Soils Monitoring
USDA Forest Service Proceedings RMRS-P-59 2010. 111

There are some important considerations that should be made when choosing the unit 

for sampling. The sampling unit must be the unit upon which you wish to make inferences and estimates. It is the subject of the randomization process used in the sampling 

design. Although it is common that the measurements taken in the study are performed 

on the sampling unit, it is not a requirement and usually occurs when the measurements 

cannot be performed on the randomization units. When the objects upon which the measurements are taken are not those which were randomly selected, however, the analysis 

is performed on the randomized units.

Sampling units for estimates of characteristics of a particular area can be either point 

samples or area samples. For either a point sample or an area sample, they should be 

sampled without replacement to ensure that any particular sampling unit is only sampled once. Point samples allow inferences to be made on the number of observations 

in the sample, and the inferences are often made on the means or percentages from the 

sample observations. Area samples are generally measured with densities of percent of 

area covered, and inferences are made by extrapolating the sample density to the entire 

area. Area samples can yield more detailed information but can also be more time consuming to carry out.

Translating the Objectives

The translation of the research objectives into specific questions that can be answered 

with measurements from the sampling units is often the most challenging step and a 

good time to consult with a statistician. The translation is the integration of the research 

question into the quantitative question “What exactly is to be estimated or tested?”

Part of the translation step will identify whether the required estimates are proportions, totals, means, totals or means over subpopulations, or some other quantitative 

estimate. Constructing the blank data sheet for recording observations will assist in the 

translation step as that step will clearly identify the measurements that will be taken. It 

is critical that once the data sheets have been constructed and the measurements to be 

taken are identified that one revisit the research question to ensure that the observations 

and resulting summaries will, in fact, answer the research question.

What Preliminary Information Is Available About the Population?

Information that can be gathered about the population of interest prior to sampling 

can help ensure that the sampling design will be successful in providing the necessary 

information to answer the research question. Such information includes whether estimates of the likely variability are available. If variability estimates are available, they 

can be used to determine the necessary sample size to provide estimates within specified 

confidence levels.

If there are no variability estimates, then one should determine whether a pilot study 

is desirable and/or feasible. A pilot study can be used to determine variability estimates 

as well as to test the sampling methods.

If there are factors within the population that affect the results of the observations, it 

is possible that such factors can be used to stratify the population into separate groups 

for randomization. This information, if available prior to sampling and when used to 

develop a stratified sampling design, can reduce the variability around the estimates, 

thus improving the statistical efficiency of the estimates.

Accounting for Variability

The variation that is inherent in soils data must be accounted for during the design 

phase of a soil sampling plan, including the sampling design, data collection procedures, and data analysis. Researchers have long been cautioned about failing to consider 

the variability in soil sampling when dealing with any study of the soils system (Cline 

1944).

Statistical Sampling Methods for Soils Monitoring Abbott
112 USDA Forest Service Proceedings RMRS-P-59 2010.

Variability can be accounted for by ensuring that the sample adequately covers the 

entire population, by reporting the variability estimates along with central tendency 

estimates and by reporting interval estimates.

A good sampling design will use an interactive approach to balance the data quality 

needs and resources with designs that will either control variation, stratify to reduce 

variation, or reduce the influence of variation on the decision process.

Precision, Bias, and Accuracy

Precision is a measure of the reproducibility of the measurements of a particular soil 

condition or constituent. Precision is increased as the variability around the estimates 

is decreased. If the variability in the observations is constant, precision can also be increased by taking a larger number of measurements (increasing the sample size). The 

statistical techniques seen in soil sampling are designed to measure precision and not 

accuracy.

Bias is a systematic error that contributes to the difference between the mean of a 

large number of test results and an accepted reference value. Bias is often the result of 

an imperfect measurement technique (the characteristic measured does not match the 

characteristic in question in a systematic way) or an imperfect measurement instrument 

(the measurement tool must be calibrated).

Accuracy is the correctness of the measurement and cannot be directly measured. It 

is the sum of precision and bias, and can be improved by taking care that the estimates 

are as precise as is required and that bias is as small as possible.

Sampling Designs

The choice of a sampling design often depends on what is available for a sampling 

frame, whether the population can be divided into a natural grouping in terms of the 

measurement variables, variability within the population, and the cost of sampling. 

There are three initial questions that can be posed when considering the four commonly 

used sampling designs:

• Does the population contain a natural grouping in terms of the variables that will be 

measured?

• Does the grouping variable affect the results of the measurement variable?

• Can the efficiency of the sampling effort be improved by separating the population 

into such groups?

If there are no natural groupings, then two possible sampling designs are Simple 

Random Sampling and Systematic Random Sampling. The choice between these two 

designs depends on the answer to the question “Is a comprehensive list of sampling 

units available?”

Simple Random Sampling—Simple Random Sampling is the basis for most other 

sampling designs. It is used when a comprehensive list of all population units is available and either no information is known about the population or a natural grouping 

does not exist. A randomization scheme is used to select individuals for measurement in 

which each element in the population has an equal probability of being selected. Simple 

random sampling is the basis for all probability sampling techniques and is the point 

of reference from which modifications to increase sampling efficiency may be made. 

Alone, simple random sampling may not give the desired precision.

A formal definition of simple random sampling is:

If a sample of size n is drawn from a population in such a way that every possible sample of size n has the same chance of being selected, the 

sampling procedure is called simple random sampling and the sample thus 

obtained is called a simple random sample.

Abbott Statistical Sampling Methods for Soils Monitoring
USDA Forest Service Proceedings RMRS-P-59 2010. 113

To draw a simple random sample, all of the possible elements in the population are 

listed to form a sampling frame. A randomization scheme, often from a random number 

table, is used to draw elements from the sampling frame without replacement.

Common estimators calculated for continuous variables are the estimator of the 

population mean, the variance of the population mean (to evaluate the goodness of the 

estimated mean), a confidence interval around the estimated mean, and a required sample size to estimate the population mean. For binomial variables (those with either a yes 

or no response), the estimator of the proportion of the population possessing the yes response is often of interest, along with it’s variance, confidence interval and sample size.

To calculate the estimators, we use the following equations:

Estimator of the population mean:

n

y

y

n

i

∑ i

= = 1 [1]

where i y is the observation from element i and n is the number of elements sampled.

Estimator of the population variance:





 



 − = N

N n

n

s V y

2

( ) ˆ [2]

where 2s is the sample standard deviation.





 



 −

N

N n

 is the finite population correction factor (fpc). When n is small relative the 

population size N, the fpc is close to unity.

1

( )

1

2

2

−

−

=

∑=

n

y y

s

n

i

i [3]

Confidence interval around the estimated mean:

( ) ˆ , 1 2y t V y n− ± α [4]

where , 1 2 n− tα is the coefficient from t distribution with n -1 degrees of freedom.

Estimator of the population proportion:

n

y

p y

n

i

∑ i

= = = 1 ˆ [5]

Estimated variance of the population proportion:





 



−  − = NN n

n

p p V p ˆ(1 ˆ) ( ˆ) ˆ [6]

Confidence interval around the population proportion:

( ˆ) ˆ ˆ 2p ± zα V p [7]

Statistical Sampling Methods for Soils Monitoring Abbott
114 USDA Forest Service Proceedings RMRS-P-59 2010.

Systematic Random Sampling—Systematic Random Sampling is an alternative to 

simple random sampling. It is used when a comprehensive list of sampling units is not 

available but an estimate of the total number of units within the population can be obtained. The randomization aspect occurs in the starting point. When systematic random 

sampling is performed, the sample size must be determined so that a sampling interval 

may be computed. When a random start is selected within the first sampling interval, 

then each subsequent element from the following intervals are also random by default.

The goal of systematic random sampling is to provide better coverage of the study 

area or population than that provided by a simple random sample or from a stratified 

random sample, and is a simple random sample based on spatial distribution over the 

population. To use systematic random sampling, some estimate of the total number of 

sampling units in the population must be estimated. The required sample size must also 

be known so that the interval for sampling can be calculated.

Systematic random sampling is a useful alternative to simple random sampling:

1. Systematic sampling is easier to perform in the field and hence is less subject to 

selection errors by field workers than either simple random sampling or stratified 

random sampling.

2. Systematic random sampling can provide greater information per unit cost than 

simple random sampling can provide.

Transect sampling is a version of systematic random sampling, and when using transects, they should be randomly oriented or the starting point should be randomly chosen.

A danger in systematic random sampling is that if the sampling interval is chosen in 

such a way that it matches any periodicities in the population, the resulting estimates 

could be biased. Knowledge of the population is useful to avoid this danger so that care 

can be taken to avoid sampling along any periodicities.

Estimators from systematic random sampling can be calculated using the same equations as those used for simple random sampling.

If there are natural groups within the population, then the question becomes “Are the 

groups likely to be similar to each other in terms of the measurement variables or are the 

groups different?” An alternative phrasing for this question is “Is the variability within 

groups larger than the variability between groups?”

Stratified Random Sampling—Stratified random sampling is used when the groups 

are different from each other, or when the variability is larger between the groups compared to variability within groups. Each group (stratum) is sampled individually, using 

either a simple random sample or a systematic random sample.

Prior knowledge of the sampling area and information obtained from background 

data are required for stratified random sampling. The goal is to increase precision and 

control sources of variability in the data, and a potential result is that the overall sample 

size may be reduced. For stratified random sampling to be efficient (the overall variability estimates from a stratified design are smaller than those from simple random 

sampling), the variability between strata must be larger than variability within strata.

The advantages of stratified random sampling include obtaining estimates for subgroups, potentially more precise estimates than those from simple random sampling, 

and can be more convenient to implement. Disadvantages are that prior information 

about the population is necessary and the computations are more complex.

Some additional notation is required for computational formulas for stratified random sampling.

L = number of strata

Ni

 = number of sampling units in stratum i

N = number of sampling units in the population = N1 + N2 + ... + NL

Estimator of the population mean from a stratified random sample:

∑=

= + + + =

L

i

st L L i i N y N

N y N y N y N y

1

1 1 2 2

1 [ ] 1  [8]

Abbott Statistical Sampling Methods for Soils Monitoring
USDA Forest Service Proceedings RMRS-P-59 2010. 115

Estimated variance of the mean from a stratified random sample:

( )] ˆ ( ) ˆ ( ) ˆ [ 1 ( ) ˆ 2

2

2

1 2

2

st 2 1 L L N V y N V y N V y N

V y = + ++ [9]

Confidence interval around the estimated mean:

( ) ˆ , 1 2 n y t V y − st ± α st [10]

The goal of the allocation scheme to divide the overall sample size into the different 

strata depends on three factors:

3. The total number of elements in each stratum.

4. The variability of observations in each stratum.

5. The cost of obtaining an observation from each stratum.

The number of elements in each stratum affects the quality of information in the 

sample. A sample size 20 from a population of 200 elements should contain more information than a sample of 20 from 20,000 elements. Thus, larger sample sizes should be 

assigned to strata containing larger numbers of elements. Variability must be considered 

because a larger sample is needed to obtain a good estimate of a population when the 

observations are less homogeneous. If the cost of obtaining a sample varies from stratum to stratum, smaller samples from strata with higher costs is advisable when the goal 

is to keep the cost of sampling at a minimum.

An approximate allocation that minimizes cost for a fixed value of ( ) ˆV yst or that 

minimizes ( ) ˆV yst for a fixed cost:

















+ + + =

L L L

i i i

i

N s c N s c N s c

N s c

n n

1 1 1 2 2 2  [11]

where Ni denotes the size of the ith stratum, 2

i s is the estimated variance from the ith 

stratum, and ci is the cost of obtaining a single observation from the ith stratum.

Estimator for the population proportion from stratified random sample:

[12]

Estimator of the variance of the estimated proportion from a stratified random sample:

( ˆ ) ˆ ( ˆ ) ˆ ( ˆ ) ˆ 1 ( ˆ ) ˆ 2

2

2

1 2

2

2 N1V p N V p NLV pL N

V pst = + ++ [13]

Confidence interval around a proportion from a stratified random sample:

st st ( ˆ ) ˆ ˆ 2

p ± zα V p [14]

Approximate allocation that minimizes cost for a fixed value of st ( ˆ ) ˆV p or minimizes

st ( ˆ ) ˆV p for a fixed cost:

L L L L

i i i i

i

N p p c N p p c N p p c

N p p c

n n ˆ (1 ˆ )/ ˆ (1 ˆ )/ ˆ (1 ˆ )/

ˆ (1 ˆ )/

1 1 − 1 1 + 2 2 − 2 2 + + −

− =  [15]

Post-stratification can be used when stratification is appropriate for some key variable, but cannot be done until after the sample is selected. This is often appropriate 

when a simple random sample is not properly balanced according to major groupings. 

While the mean from a post stratification scheme is calculated in the same way as for a 

Statistical Sampling Methods for Soils Monitoring Abbott
116 USDA Forest Service Proceedings RMRS-P-59 2010.

designed stratified random sample, the variance must be estimated differently since the 

stratification was not designed into the plan.

Estimated variance of the mean from post stratification:

[16]

where Wi is the weight proportion for each stratum.

Cluster Sampling—Cluster sampling is used when the groups are similar to each 

other (there is more variability within groups than among groups). Here, the clusters 

themselves are randomly sampled so that not every cluster within the population is 

sampled. In some cases, individuals within clusters are also randomly sampled for 

measurement (multistage sampling), and in others every element within the cluster is 

sampled.

Cluster sampling can be less costly than simple or stratified random sampling if the 

cost of obtaining a frame that lists all population elements is very high or if the cost of 

obtaining observations increases as the distance separating the elements increases.

To calculate the estimators obtained in a cluster sample, we use the following 

equations:

Estimator for the sample mean from a cluster sample:

∑

∑

=

= = m

i

i

n

i

i

m

y

y

1

1 [17]

where mi is the number of elements in cluster i.

Estimated variance of the sample mean from a cluster sample:

[18]

where

1

( )

1

2

2

−

−

=

∑=

n

y ym

s

n

i

i i

r

[19]

and

N

m

M

n

i

∑ i

= = 1 (the average cluster size for the population), and can be estimated

with ∑=

=

n

i

i mi

n

m

1

1

(the average cluster size for the sample).

Confidence interval around the estimated mean for a cluster sample:

( ) ˆ , 1 2y t V y n− ± α [20]

Estimate of the population proportion from a cluster sample:

∑

∑

=

= = n

i

i

n

i

i

m

y

p

1

1 ˆ [21]

Abbott Statistical Sampling Methods for Soils Monitoring
USDA Forest Service Proceedings RMRS-P-59 2010. 117

Estimated variance of the estimated population proportion from a cluster sample:

Nn

2

2 ( ˆ) ˆ p s

M

N n V p 



 



 − = [22]

where 

1

( ˆ )

1

2

2

−

−

=

∑=

n

y pm

s

n

i

i i

p

Confidence interval around a proportion from a cluster sample:

( ˆ) ˆ ˆ 2p z V p ± α [23]

To aid in the selection of the best sampling design, a series of questions can be asked 

that will help make the best choice:

• If there is no information on population groupings, will simple random sampling or 

systematic random sampling better meet the objectives?

• Is simple random sampling likely to be effective?

• If not, have the reasons for not using simple random sampling been clearly stated?

• If systematic random sampling is chosen, what interval will separate the sampling 

units?

• Is there a likelihood that the interval will coincide with periodicity in the data?

• If so, what steps will be taken to avoid the resulting bias in the estimates?

• If there is a grouping in the population, will stratification improve the precision of 

the estimates?

• Has the efficiency of the stratification been calculated?

• What is the basis of the stratification?

• How will the sampling units be allocated?

• If there is a grouping in the population, is there an advantage to cluster sampling?

• Has the efficiency of using clusters been calculated?

What Sample Size Is Necessary to Answer the Research Questions With 

Acceptable Accuracy?

Once the sampling design has been chosen, the number of observations (sample size) 

must be calculated. Sample calculations are based on the variability within the population and the desired precision of the estimate (the confidence level). In order to calculate 

sample size, one must obtain an estimate of the variability within the population to be 

sampled, either from prior data or from a pilot study. One must then decide what level of 

confidence is required for the estimates, realizing that as the confidence level increases, 

so does the number of observations required to make the estimate.

Sample sizes for the four sampling designs described here are as follows:

Sample size to estimate the population mean with an interval width w for a simple random sample:

D

t s

n n

2 2

, 1 2 −

= α [24]

Statistical Sampling Methods for Soils Monitoring Abbott
118 USDA Forest Service Proceedings RMRS-P-59 2010.

where

4

2

w D =

Sample size to estimate the population proportion with an interval width w for a simple 

random sample:

( 1) ˆ(1 ˆ)

ˆ(1 ˆ)

N D p p

Np p n − + −− = [25]

Sample size for estimating the population mean from a stratified random sample:

∑

∑

=

=

+

= L

i

i i

L

i

i i i

N D N s

N s w

n

1

2 2

1

2 2

[26]

where wi is the fraction of observations allocated to stratum i.

Sample size to estimate a population proportion from a stratified random sample with 

interval width w:

∑

∑

=

=

+ −

−

= L

i

i i i

L

i

i i i i

N D N p p

N p p w

n

1

2

1

2

ˆ (1 ˆ )

ˆ (1 ˆ )

[27]

Sample size to estimate the population mean from a cluster sample with interval width 

w:

[28]

where ( )/ 4 2 2 D = w M .

Sample size to estimate the proportion from a cluster sample:

[29]

where / 4 2 2 D = w M and 

1

( ˆ )

1

2

2

−

−

=

∑=

n

y pm

s

n

i

i i

p

How Will the Randomization be Carried Out?

It is critical that the randomization be carried out according to an objective mechanism. The sampling units must be chosen by an explicit randomization procedure that 

should be documented in the research. Any constraints in the sampling should be documented as well.

How Will the Results of the Sampling Effort be Recorded?

It can aid the sampling design process to create the data sheets for recording results 

of the sampling early on in the process. This will clarify the variables that will be measured and recorded, and will guide the analysis procedures.

Abbott Statistical Sampling Methods for Soils Monitoring
USDA Forest Service Proceedings RMRS-P-59 2010. 119

How Will the Data be Analyzed?

The analysis methods that will be used to answer the research questions should be 

determined prior to collecting the data. Once the data sheets are created, one can see 

the data structure, which will help with this step. It is critical to check again, to make 

sure that the variables collected and the analysis methods will meet the objectives of 

the research.

Case Study in Sampling Design: The Forest Soil Monitoring 

Protocol

The goal in developing a soil monitoring protocol for the Northern Region was to 

develop an easy-to-implement, cost effective and statistically defensible monitoring 

protocol for disturbance. The sampling design and analysis methods were arrived at 

by answering the questions illustrated in the previous section, and are described here.

Stating the objectives: The objective of the sampling effort was to characterize the 

activity area in terms of management related disturbance.

Defining the population: The population was defined to be all possible ‘points’ within 

the activity area.

What are the sampling units? The sampling units were defined as points along a transect where a point is a 6-inch radius. Since there are an infinite number of possible 

points in the population, finite sample correction factors do not need to be used.

What is the translation of the research objectives into specific questions that 

can be answered with measurements from the sampling units? It was decided 

to characterize the amount of disturbance related to management within a unit by 

measuring forest floor depth and observing a series of binomial (presence/absence) 

variables, such as presence of forest floor, displacement of topsoil, mixing of topsoil 

and subsoil, presence of erosion, presence of rutting, presence of burning, presence 

of compaction, and presence of five forest floor variables. By using the percents of 

observation that record ‘present’ for these variables, an estimate of managementrelated disturbance can be made.

What preliminary information about the population is available? The size and 

shape of the activity area is known, and in some cases soils information is available. 

In most cases, site specific estimates of variability are not known. Harvest history is 

generally available.

What sampling design will be used? Since there is not always information about 

groupings within activity areas, neither stratified nor cluster sampling were chosen as 

the first choice in sampling design. It is to be noted, however, that considerations are 

made for the use of both of these designs within the protocol when such information 

is available. For the ease of obtaining observations and to ensure that the entire activity is accounted for within the sampling, systematic random sampling was chosen as 

the optimal design, using a line transect to choose observation points.

What sample size is necessary to answer the research questions with acceptable 

accuracy? The proportions of the binomial indicator variables listed above were 

used to choose the sample size, all with an interval width of ±5 percent of the estimated proportion. The protocol allows for varying levels of confidence to be used 

with direction from the line officer. Once the sample size is computed for each of the 

individual variables, the largest sample size is chosen for sampling to be conservative. The first 30 observations made along the transect were used to calculate the site 

specific variability for each activity area.

How will the randomization be performed? The observations are randomized by 

choosing a random orientation for the beginning of the transect. Subsequent turns in 

the transects are made by choosing an angle in advance on which to turn when the 

transect reaches the activity area boundary.

Statistical Sampling Methods for Soils Monitoring Abbott
120 USDA Forest Service Proceedings RMRS-P-59 2010.

How will the results of the sampling effort be recorded? An Excel spreadsheet was 

developed to record the results of sampling. Observations can either be recorded on 

a paper sheet or directly into an electronic data recorder.

How will the data be analyzed? Confidence intervals are computed for each of the 

indicator variables, along with the estimated proportions. Summaries for multiple 

areas can be calculated using the methods for stratified random sampling.

References

Devore, Jay L. 1995. Probability and statistics for engineering and the sciences. 4th Edition. 

Duxbury Press. 743 p.

Raj, D. 1968. Sampling theory. New York: McGraw-Hill. 302 p.

Scheaffer, Richard L.; Mendenhall, William; Ott, Lyman. 1996. Elementary survey sampling. 5th

Edition. Duxbury Press. 501 p.

The content of this paper reflects the views of the authors, who are responsible for the facts 

and accuracy of the information presented herein.

Abbott Statistical Sampling Methods for Soils Monitoring
USDA Forest Service Proceedings RMRS-P-59 2010. 121

Soil Quality Standards Monitoring Program 

Administration and Implementation

Randy L. Davis, USDA Forest Service, Washington, DC

Felipe Sanchez, USDA Forest Service, Southern Research Station, Research Triangle Park, NC

Sharon DeHart, USDA Forest Service, Job Corp, Fort Collins, CO

Abstract—Forest managers and resource scientists and specialists are engaged in a 

partnership to sustain the natural resource value of our national forests. Managers are faced 

with deciding which activities provide the best resource benefits with the least resource 

damage. Many, but not all, aspects of the decision process must be based on the science 

supporting our current understanding of natural resources. Scientists are charged with 

continuing to build these understandings and interpreting their effects in an applications 

setting. The roles of land managers, subject matter experts (field soil scientists), and 

research soil scientists are distinctly different. Each brings unique skills to the resource 

management problem. Together they form a powerful team that can sustain forest and 

rangeland ecosystems and enhance resource values.

Organizational Structure

Organizationally, the USDA Forest Service (USDA FS) soil management program 

is divided between the National Forest System (NFS) division and the Research and 

Development (R&D) division. NFS is charged with managing lands and their respective 

resources while R&D provides the scientific foundations to improve land and resource 

management decisions on NFS lands.

NFS has three levels of administration: national, regional, and national forests or 

grasslands. There are nine regions and 175 national forests and grasslands. R&D has 

three levels of administration: national headquarters, experimental forests and grasslands, and research stations.

The Forest Service manages in excess of 193 million acres of federal forest and 

range land. The general requirements under which this land is managed are set forth 

in enabling legislation. Four legislative acts are of particular importance to the issue of 

resource sustainability and the soil resource in particular.

Laws

The Organic Administration Act of 1897 (USDA Forest Service 1993) created the 

National Forests and specified that “No national forest shall be established, except to 

improve and protect the forest within the boundaries, or for the purpose of securing 

favorable conditions of water flows, and to furnish a continuous supply of timber for 

the use and necessities of citizens of the United States...” The Multiple-Use SustainedYield Act of 1961 directs management to consider resource values but “not necessarily 

the combination of uses that will give the greatest dollar return or the greatest unit 

output...without impairment of the productivity of the land.” The Forest and Rangeland 

Renewable Resources Planning Act of 1974 and its amendment, the National Forest 

Management Act of 1976 (NFMA), set forth four important points that pertain to the 

need for continuous monitoring. The guidelines in land management plans are required 

to:

• “insure research on and (based on continuous monitoring and assessment in the 

field) evaluation of the effects of each management system to the end that it will 

Statistical Sampling Methods for Soils Monitoring Abbott
122 USDA Forest Service Proceedings RMRS-P-59 2010.

not produce substantial and permanent impairment of the productivity of the land” 

(Section 6(g)(3)(C));

• “insure that timber will be harvested from National Forest System lands only where 

soil, slope, or other watershed conditions will not be irreversibly damaged” (Section 

6(g)(3)(E)(i)); and

• “insure that clearcutting, seed tree cutting, and other cuts...are carried out in a 

manner consistent with the protection of soil, watershed, fish, wildlife, recreation 

and esthetic resources, and the regeneration of the timber resource.” (Section 6(g)

(3)(F)(v)).

In addition, Section 13 of the NFMA specifies that the “Secretary of Agriculture shall 

limit the sale of timber from each national forest to a quantity equal to or less than a 

quantity which can be removed from such forest annually in perpetuity on a sustainedyield basis.”

The essence of these legislative mandates is that the USDA FS is required to conduct 

research, monitoring, and assessments to evaluate management effects and to manage 

for sustained yield in perpetuity and in a manner that assures protection of all resources 

and values. This is a tall order; it demands from Forest Service managers a level of resource background and knowledge that does not reside in any single individual. In fact, 

the NFMA requires that NFS land management plans be developed by interdisciplinary 

teams and with public participation. This requirement makes research, monitoring, and 

assessment critical for both preparing plans and assuring that they can stand up under 

public review.

These laws are the foundation of the USDA FS soil management program. These 

and other laws have been used to develop the Agency’s soil management policy housed 

in the Forest Service Manual 2550. The manual outlines objectives and policy and assigns decisionmaker responsibility for its implementation. Technical aspects of soil 

management, including inventory and monitoring, are addressed in the Forest Service 

Handbook 2509.

The need to standardize field procedures for soil monitoring has been recognized 

for quite some time. The urgency to move forward with a standardization grew out of 

several recent court rulings that resulted in two major National Environmental Policy 

Act (NEPA) projects being overturned, i.e., Iron Honey (Lands Council v. Powell 2004) 

and Lolo Post-Burn (Ecology Center v. Austin 2005).

Lawsuits

In the Iron Honey lawsuit, the USDA FS used a “spreadsheet model” to estimate soil 

quality based on aerial photos and samples from throughout the Forest. The court ruled 

that soils analysis and effects should have been tested on the ground, not estimated from 

a spreadsheet model. The USDA FS did not walk, much less test, the land in the activity 

area. The Agency conceded that it did not test the activity area but argued that because 

it tested similar soils within the national forest, the methodology was sound. However, 

the court questioned whether the USDA FS internal conclusions of the reliability of the 

spreadsheet model should be trusted, since the model had not been independently validated. The court went on to say that in order to be reliable, the hypothesis and prediction 

of the model should be verified with observation. The predictions of the model, which 

may be reliable across the entire National Forest, were not verified with on-the-ground 

analysis. The USDA FS failed because it based the soils analysis entirely on the model, 

with no on-site inspection or verification, which violated NFMA (Smith 2007).

In the Lolo Post-Burn lawsuit, the USDA FS looked at maps of past activities on 

associated soils. Data was input from the National Forest Land Systems Inventory and 

Burned Area Emergency Response (BAER) report into models to generate estimates of 

the project’s possible effects. The BAER report was based on field reviews and helicopter flyovers as well as transects. Because the project was developed after the BAER 

transect surveys were conducted, the transect surveys did not cover the vast majority of 

the activity areas; only a few were crossed by coincidence. Soil sampling did not cover 

Davis, Sanchez, DeHart Soil Quality Standards Monitoring Program Administration and Implementation
USDA Forest Service Proceedings RMRS-P-59 2010. 123

the vast majority of 128 sale units in BAER transects. At the time, the Northern Regional 

Soil Scientist, John Nesser, questioned the project soil analysis because it had failed to 

assess soil conditions by field testing the actual activity areas. The Draft Environmental 

Impact Statement states that not all proposed harvest units were visited with line transects and that much of the soil quality determination was based on information from the 

USDA FS Northern Region transportation and timber units with respect to past activities and regeneration level of jammer roads. The court ruled that the project was similar 

to that in the Lands Council v. Powell case where much of activity area was not tested; 

therefore, the analysis was inadequate under NEPA and NFMA (Smith 2007).

NEPA requires federal agencies to consider the environmental impacts of their proposed actions and reasonable alternatives to those actions as a means of integrating 

environmental values into their decisionmaking plans. National Forest land management plans typically outline, in general terms, monitoring protocols. Because there 

are no nationally recognized soil quality monitoring protocols, field personnel typically use the best available methodologies. Unfortunately, some of these methodologies 

are incompletely documented and thus subject to scientific and legal challenges when 

follow-up re-sampling is requested or when trend monitoring is conducted. Comparing 

data sets is also difficult, if not impossible, when different methodologies are used. 

Finally, database design and population is also complicated when similar data is collected using both well-documented and poorly documented methods.

Monitoring Methodologies

In general, there are at least three intensity levels of monitoring and assessment 

projects: national, regional, and project. National monitoring is generally conducted 

using high elevation aerial photography with statewide field sampling procedures with 

a data resolution to the state datasets. The best example of this protocol is the Forest 

Information and Analysis (FIA) project. Regional monitoring and assessment are usually conducted using National Forest and Rangeland administrative units, and use a 

variety of methodologies depending on the subject matter being assessed or monitored. 

Confidence-level for these types of efforts is generally limited to National Forest, 

Rangeland, or Grassland datasets but can also include associated state data. Examples 

of these regional types of monitoring and/or assessments include the Interior Columbia 

Basin Ecosystem Management Project: Scientific Assessment (Quigley and others 

1999) and the Southern Forest Resource Assessment (Wear and Greis 2002).

Project level monitoring and assessments are conducted on areas within a national 

forest or grassland where some type of land management prescription is proposed or 

has been implemented (i.e., timber sales, fuel treatments, or rangeland improvements). 

There are three types of monitoring conducted at the project level: implementation, effectiveness, and validation. Implementation monitoring is intended to evaluate whether 

a particular land management prescription was conducted as directed by the NEPA 

decision memo and/or as directed by a project contract. Effectiveness monitoring evaluates the ability of project mitigation practices to prevent resource damage either within 

the project area or on adjacent resources. Validation monitoring is intended to address 

resource management assumptions commonly used to narrow the scope of the environmental assessment. Validation monitoring is often conducted by R&D scientists and 

requires a longer commitment of time to gather and analyze data. The overall protocol 

is intended to be used on project level monitoring efforts. As the reader might imagine, 

national and regional monitoring and assessments use more generalized datasets than 

those at the project level.

Standardization

Standardization of data collection protocols is essential at any of the levels of monitoring and assessment. Increasingly, datasets are needed and used beyond their initial 

purpose. All too often, inadequate documentation of how a particular dataset was 

Soil Quality Standards Monitoring Program Administration and Implementation Davis, Sanchez, DeHart
124 USDA Forest Service Proceedings RMRS-P-59 2010.

obtained results in duplicated efforts or incomplete analysis of existing data because the 

protocol was either not well documented or unacceptable. Monitoring without adequate 

documentation or the use of an inappropriate protocol has resulted in the loss of project data. This type of data often cannot be used with other datasets for other levels of 

monitoring or analysis and consequently results in increased costs for land management 

projects in both time and salary. Data collection using standardized peer reviewed protocols offers at least two advantages to land management: the collected data is repeatable 

and will hold up to scientific and legal scrutiny. Standardized data is essential to resource 

condition trend analysis and to the successful defense of the data to scientific and legal 

challenges. Incorporating a standard statistical design of the monitoring effort is essential to assuring that the results of the collected field data provide an accurate picture of 

what is occurring on the project area. Understanding the reliability and the confidence 

of the soil data being collected is important in helping land mangers decide their level 

of comfort in moving forward with a particular resource management prescription. An 

unbiased statistical analysis will also determine the number of observations needed to 

accomplish a defensible on-site evaluation of potential resource impacts.

Standardization is not intended to stifle or limit data collection but rather to maximize 

data utilization. There are several factors of standardization to consider: (1) identification of the question(s) to be answered, (2) size of the treatment area, (3) available staff 

resources, and (4) the amount of risk land managers are willing to tolerate in their decisionmaking on a proposed land management project. Depending on the outcome of 

the problem analysis using these factors, a monitoring project can be designed using 

this protocol as its foundation. One example of an experiment utilizing these important 

monitoring considerations is the Long-Term Soil Productivity (LTSP) experiment.

North American Long-Term Soil Productivity Experiment

To properly evaluate forest management impacts on soil quality, evidence of longterm impacts is needed. Considerable information can be found in the literature on the 

long-term impacts of soil disturbance. However, most of this information is from isolated experiments that often were not designed for long-term scrutiny and are too site 

specific to be broadly useful. An exception to this is the Long-term Soil Productivity 

(LTSP) experiment, which was specifically designed, among other things, to develop 

baseline indicators of soil quality and validate these indicators over multiple installations covering a broad range of soil classifications, ecosystems, and climates. The LTSP 

experiment began as a cooperative between scientists in USDA FS NFS and R&D to address legislative mandates, particularly the NFMA, which require that the management 

of federal lands be conducted in a manner that maintains site productivity. The cooperative grew with the inclusion of partners in the private and public sectors of the United 

States and Canada. Currently the cooperative includes well over 100 LTSP installations 

and affiliated sites, comprising the world’s largest coordinated network investigating 

the long-term impacts of forest management on sustainable site productivity. Excellent 

descriptions of the genesis and development of the LTSP cooperative are provided by 

Powers (2006) and Cline and others (2006).

The LTSP experiment specifically examines the impacts of changes in soil porosity 

and surface organic matter on sustainable soil productivity. These variables were selected after considerable examination of the literature elucidated that these variables are 

(1) directly affected by forest management, (2) can be readily monitored, and (3) regulate soil properties and processes that, in turn, directly impact site productivity. The 

LTSP experiment was designed to cover the entire range of soil compaction and site organic matter levels possible resulting from a harvest operation. Consequently, the LTSP 

experiment presents the unique opportunity to “tease out” the relative contribution of 

the different soil compaction and surface organic matter combinations on site productivity across a range of installations. The experiment is intended to be long-term in nature 

extending, at least, to the culmination of mean annual volume increment.

At a minimum, all LTSP installations collect eight core measurements, including 

five soil measures, at reoccurring intervals. The five soil measures are (1) moisture and 

Davis, Sanchez, DeHart Soil Quality Standards Monitoring Program Administration and Implementation
USDA Forest Service Proceedings RMRS-P-59 2010. 125

temperature (collected monthly), (2) bulk density (collected every 5 years), (3) soil 

strength (collected seasonally every 5 years), (4) organic matter content and chemical 

composition (collected every 5 years) and (5) water infiltration and saturated hydraulic 

conductivity (collected every 5 years). These soil measures provide a template for the 

current discussion on soil quality metrics.

Findings

The first LTSP installation was established in 1990 on the Palustris Experimental 

Forest on the Coastal Plain of Louisiana and was rapidly followed by installations in 

California, Minnesota, and North Carolina. Additional installations were established 

in subsequent years, providing the LTSP cooperative with data from installations 

approaching 20 years in age. To date, the LTSP cooperative has produced over 300 scientific publications on various subjects including soil properties and processes and site 

productivity. The majority of these publications describe information obtained from a 

single or a few installations; however, results that integrate the observations of several 

installations have also been published. Fifth-year results from a combination of several 

installations were presented in 2000 at the Conference on Long-Term Productivity of 

Forest Soils in Alexandria, Louisiana, near the site of the first LTSP installation. The 

papers presented were published in 2006 in the Canadian Journal of Forest Research 

(CJFR) as a special issue on long-term soil productivity (CJFR vol. 36). In 2003, tenthyear results from installations ≥10 years old were presented at the 10th North American 

Forest Soils Conference in Sault Ste. Marie, Ontario, and were published by Powers and 

others (2005). The general findings of the conference were that organic matter removal 

(1) decreased soil carbon (C) concentration but not soil C content, (2) decreased soil 

nitrogen (N) and phosphorus (P), (3) decreased foliar N and P, but (4) did not affect 

productivity. Other findings showed (1) small differences between moderate and severe 

soil compaction, (2) bulk density increases varied with initial bulk density, (3) most 

sites did not exhibit bulk density recovery, and (4) the affect on productivity varied with 

soil texture and presence of understory.

Since that time, several other installations have reached the 10-year benchmark and 

will be incorporated into a presentation in 2008 at the 11th North American Forest Soils 

Conference in Blacksburg, Virginia.

The early results from the LTSP installations speak volumes about the resiliency of 

the soil to disturbance. It might be tempting to conclude from this information that forest management does not impact soil productivity in the long-term. However, caution 

must be taken, since the information to date describes soil conditions early in the stand 

rotation and may not be indicative of conditions later in the rotation or into the next 

rotation. The general decrease in soil and foliar N and P with increasing organic matter 

removal may result in some sites becoming nutrient deficient, which might translate 

to lower productivity in subsequent rotations. Also, Ludovici (2008) found that soil 

compaction decreased loblolly pine (Pinus taeda L.) root production, thus resulting in 

C allocation patterns favoring aboveground productivity. Although soil compaction did 

not significantly affect aboveground productivity, the decrease in belowground C and 

nutrient stores from fewer roots may lead to lower productivity in future rotations.

Challenges

There are many challenges to maintaining a long-term study. In some cases, we have 

little or no control over these challenges, for example, natural disasters such as hurricanes and tornados. The LTSP installations in Mississippi and Louisiana faced a serious 

threat in the form of Hurricane Katrina. Although these sites survived the onslaught, 

they could have been wiped out. The beauty of the LTSP cooperative is that the number 

and variety of installations ensure that a loss of a few installations will not be fatal to the 

overall study. There are other situations, such as fires and insect infestations, where we 

have some control, but challenges can still occur despite our best planning. However, 

the most serious challenge facing long-term studies is the need to maintain consistent, 

Soil Quality Standards Monitoring Program Administration and Implementation Davis, Sanchez, DeHart
126 USDA Forest Service Proceedings RMRS-P-59 2010.

long-term commitment to the study. This is something that can be controlled. Over the 

course of a long-term study, there are invariably considerable changes in the people 

involved and their priorities. At a study’s initiation, the partners must have a long-term 

vision and commitment for the study, as was the situation for the LTSP cooperative. The 

LTSP partners recognized the need to keep the study relevant in the face of changing 

national priorities. This involves more than just political support but includes financial backing and, where appropriate, involvement of new partners. Recently, the NFS 

Southern Region and the R&D Southern Research Station renewed their Memorandum 

of Understanding (MOU) in support of the installations in the South. As part of this 

MOU, partners in the State and Private (S&P) branch of the USDA FS were included to 

assist in the dissemination of the LTSP findings. It is critical to periodically assess the 

political and scientific climate to maintain the relevancy of the LTSP experiment. Only 

by maintaining the long-term nature of the LTSP study will we truly be able to address 

the question of forest management effects on soil quality.

The Road Ahead

As previously discussed, laws and USDA FS policies are made to protect NFS lands 

and resources for future generations. The need to keep existing commitments to efforts 

like the LTSP experiment is critical. Changes in soil are complex, and it often requires 

several years—even decades—to fully observe the affects of a disturbance, especially 

when dealing with long-lived tree species. Small changes in soil properties as a result 

of soil disturbance that might seem scientifically insignificant at one point may become 

significant when the soil disturbance is repeated in multiple rotations as an acceptable 

management practice. As the LTSP experiment has demonstrated, measurable changes 

in soil conditions tend to be site and/or soil horizon specific, and the changes in the soil 

capability may not be observable within the current management period. With these 

casual generalizations in mind, we look ahead.

The USDA FS is pursuing the use of environmental management systems (EMS) to 

boost its resource management and monitoring endeavors. An EMS is a set of processes 

and practices that enable an organization to reduce its environmental impacts and increase its operating efficiency. An intriguing aspect to EMS implementation is in the 

arena of third party review and evaluation. The intent of the neutral third party review is 

to validate that the EMS parameters are truly being adhered to outside of the Agency’s 

influence. The 2008 Planning Rule (revision of the Forest Service’s land management 

planning policy) includes a provision to establish an EMS on national forests and grasslands. Although the areas the EMS would address have not been completely fleshed-out, 

the process offers an important opportunity to include monitoring parameters related to 

soil function and productivity. The hope is that with a future planning rule, land management plans will incorporate monitoring or an EMS that includes soil components 

such as, percentage of bare soil, soil porosity, and/or soil organic matter content, especially in light of ecosystem function and watershed condition.

On another policy front, NFS and R&D have been in continuing dialogues about the 

need to strengthen the collaboration in the natural resource area of soil science. These 

efforts were given further urgency after the USDA FS lost two lawsuits (Iron Honey 

and Lolo Post-Burn) in which the soil analysis was deemed to be inadequate. The 

National Soil Information Network (SoilNet) is the culmination of these collaborative 

discussions. The SoilNet charter establishes a formal process to raise soil-related land 

management questions to the R&D community. Once the questions have been identified, 

SoilNet provides a mechanism to identify and organize a network of R&D scientists 

and facilities across the United States to address them. SoilNet has three focus areas: 

(1) Science Integration and Delivery, (2) Resource Monitoring and Data Management, 

and (3) Research and Development. Organizationally, SoilNet is composed of a 

Technical Team and a Steering Team. Proposals are reviewed by the Technical Team 

who makes a recommendation to the Steering Team. The Steering Team then makes a 

recommendation to the directors of the Watershed, Fish, Wildlife, Air and Rare Plants 

and Environmental Sciences Research who approve the proposals for potential funding.

Davis, Sanchez, DeHart Soil Quality Standards Monitoring Program Administration and Implementation
USDA Forest Service Proceedings RMRS-P-59 2010. 127

Collaborations and Databases

The EMS and SoilNet are conduits we can use to strengthen our understanding of 

soil processes and functions on NFS lands. But, as often is the case, collaborations 

with other land managers (i.e., private land owners, other agencies) will be essential to 

getting the total picture of ecological processes and watershed condition. With the challenges posed by climate change, it will be more important than ever to have accurate 

information on associated lands and to be able to compile data across administrative 

boundaries. The development of electronic data warehouses with standardized protocols and well-documented monitoring records will be in greater demand as time passes. 

The soil quality protocol introduced in this paper is intended to advance that journey. 

It will only be successful if the data that is collected in the field is corporately stored 

in databases that are accessible by interagency resource specialists and land managers.

References

Cline, Richard G.; Ragus, Jerry; Hogan, Gary D.; Maynard, Douglas G.; Foster, Neil W.; Terry, 

Thomas A.; Heninger, Ronald L.; Campbell, Robert G.; Carter, Mason C. 2006. Policies 

and practices to sustain soil productivity: perspectives from the public and private sectors. 

Canadian Journal of Forest Research. 36:615-625.

Ecology Center, Inc. v. Austin 430 F.3d 1057 (9th Cir. 2005).

Lands Council v. Powell 395 F.3d 1019 (9th Cir. 2004).

Ludovici, Kimberly H. 2008. Compacting Coastal Plain soils changes mid-rotation loblolly pine 

allometry by reducing root biomass. Canadian Journal of Forest Research. 38:2169-2176.

Powers, Robert F. 2006. Long-term soil productivity: genesis of the concept and principles behind 

the program. Canadian Journal of Forest Research. 36:519-528.

Powers, Robert F.; Scott, D. Andrew; Sanchez, Felipe G.; Voldseth, Richard A.; Page-Dumroese, 

Deborah; Elioff, John D.; Stone, Douglas M. 2005. The North American long-term soil 

productivity experiment. Findings from the first decade of research. Forest Ecology and 

Management. 220(1-3):17-30.

Quigley, Thomas M.; Gravenmier, Rebecca A.; Arbelbide, S. J.; Cole, H. B.; Graham, Russell 

T.; Haynes, Richard W. 1999. The Interior Columbia Basin Ecosystem Management Project: 

Scientific assessment. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific 

Northwest Research Station. CD-ROM.

Smith, Carl. 2007. Court cases with soil issues in 2006-2007. Region-3 Rangeland Managers and 

Staff Officers Meeting and TES/TEUI Workshop.

USDA, Forest Service. 2000. USDA Forest Service resource management and soil sustainability. 

Washington, DC: U.S. Government Printing Office.

USDA, Forest Service. 1993. The principle laws relating to Forest Service activities. Washington, 

DC: U.S. Government Printing Office.

Wear, David N.; Gries, James G. 2002. Southern forest resource assessment. Gen. Tech. Rep. 

SRS-53. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research 

Station. 635 p.

The content of this paper reflects the views of the authors, who are responsible for the facts 

and accuracy of the information presented herein.

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