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MONITORING FOREST SOIL
PROPERTIES TO MAINTAIN
PRODUCTIVITY
J. M. Geist
R. T. Meurisse
T.A. Max
assessment methodology could be developed as a part of
monitoring, as an independent research effort, or a combination of both. In this paper, we will only discuss monitoring to detect changes in soil characteristics.
A sound monitoring system should contain several key
elements to ensure the sampling system will accurately
reflect the properties of the area(s) assessed. These key
elements include:
ABSTRACT
This paper addresses considerations necessary to construct a scientifically based sampling system for monitoring
soils. A statistically sound method, which uses line transects, is described, and several applications for various
sampling objectives are discussed. Results from sampling
forest harvest units illustrate how data variability and
choice of statistical precision levels affect sample size requirements. The transect system has been found to be an
objective and easy-to-apply approach to monitoring forest
soils in a variety of situations.
1. Ability to objectively assess soil characteristics regardless of their spatial distribution.
2. Unbiased choices of sampling locations.
3. Well-distributed coverage of the monitored area.
4. Flexibility in the kind of information gathered and
the tools that might be used.
5. Statistically valid sampling design to provide objective estimates of sampling error; statistical tests if needed'
and methods for controlling precision of estimaU:s.
'
INTRODUCTION
Various Federal legislation directs natural resource
management agencies to steward the public land in a
manner that maintains or enhances productivity. The
Multiple-Use Sustained Yield Act of 1960 called for management of renewable resources of the National Forests
without impairment ofland productivity, and more recently, the National Forest Management Act of 1976 emphasized protection and improvement of soil resources. The
latter act also included instructions to monitor the effectiveness of management in meeting planned goals. Such
monitoring includes soil resource monitoring.
Monitoring needs may vary relative to management
objectives and the soil characteristics measured. To date,
however, most forest soil monitoring has focused on alteration of physical properties. mtimately, there is a need
to translate soil status to site productivity-commonly
measured as tree growth. To do this, research is needed
to establish relations between specific changes in soil condition and plant growth. Managers, then, have two major
jobs: detection of changes in soil characteristics through
moni toring, and interpretation of the effects of these changes
on site productivity based on growth or other measures
provided by research. It is imperative that research scientists and land managers work together to improve detection of changes in soil characteristics and to translate these
changes into effects on forest soil produCtivity. Improved
STANDARDS AND EXAMPLE SYSTEM
A transect system that includes these key elements and
utilizes existing statistical theory for monitoring forest soils
in the northwestern United States was developed and described by Hazard and Geist (1984). The rationale and
steps used were later related to National Forest planning
by Miller and Hazard (1988). The field and office procedures for conducting the monitoring were published as
a "how-to" guide for soils specialists and other monitoring
personnel of the Forest Service (Howes and others 1983).
The transect system was subsequently adopted as the
"standard" for monitoring soil physical conditions of
National Forests in the Pacific Northwest Region, and
formed the basis for nationwide soil monitoring methods
adopted by the Forest Service in the National Soil Management Handbook (FSH 2509.18).
Various standards or guideposts are a necessary part
of monitoring so the effectiveness of management prescriptions can be judged relative to provisions of forest plans.
These standards relate to limits of overall operation or
individual operational effects, which on the basis of biological or productivity knowledge reflect the tolerance of
soil-vegetation systems to manipulations of various kinds.
The Pacific Northwest Region, and the Forest Service
nationally, have adopted several standards for maintaining
both long- and short-term soil productivity. The Pacific
Northwest regional standards are based on the premise
that productivity is a function of several state variables
including available soil moisture, available soil nutrients
and soil aeration as discussed by Meurisse (1988). Furth~r
Paper presented at the Symposium on Management and Productivity
of Western-Montane Forest Soils, Boise, rD, April 10-12, 1990.
J. M. Geist is Principal Research Soil Scientist, Pacific Northwest Research Station, Forestry and Range Sciences Laboratory, Forest Service,
U.S. Department of Agriculture, 1401 Gekeler Lane La Grande OR 97850
R. T .. Meurisse is Regional Soil Scientist, Pacific Northwest Region, Forest·
~ervlce, U.S. Department of Agriculture, Portland, OR 97208. T. A. Max
IS Station Statistician, Pacific Northwest Research Station Portland OR
97208.
'
,
90
discussion about the soil capacity factors is included in
these proceedings (Meurisse and others). The national
standard for determining significant changes in productivity of the land is from a consensus of experts based on available research and current technology. National Forests
are encouraged to establish standards of their own, if justifiable. The general standard for National Forests in the
Pacific Northwest Region is: "a minimum of 80 percent of
an activity area, including transportation system, should
be left in a condition of acceptable (nondetrimental) productivity potential for trees and other managed vegetation
following land management activities." Defined detrimental conditions are:
1. Compacted-an increase in soil bulk density of 20
percent or more in volcanic ash and pumice soils, or an
increase in soil bulk density of 15 percent or more or macropore space reduction of 50 percent or more in other soils.
2. Puddled-depth of rutting is 6 inches or more.
3. Displaced-removal of 50 percent of the topsoil
or humus-enriched Al and/or AC horizons from an area
100 square feet or more which is at least 5 feet in width.
4. Severely burned soils-those where the top layer of
mineral soil has been changed in color, usually to red, and
the next half-inch blackened from organic matter charring.
Figure 1-An example of the randomly positioned, systematic grid with randomly oriented
transects.
More detailed descriptions of these standards are included
in Meurisse (1988).
This monitoring system and standards have been widely
used in National Forests of the Pacific Northwest, primarily in monitoring soil compaction and displacement associated with timber harvesting. Sullivan (1988) monitored
other effects as well. Briefly, the system uses a systematic
grid of points that is randomly positioned over the area
to be sampled. Randomly oriented transects radiate from
each grid point (fig. 1). Surface and subsurface monitoring
information is gathered by categorizing segments of each
transect line according to soil condition and by collecting
soil samples at intervals along the line. A 100-foot transect length is used. As a result, summed segment lengths,
expressed in feet, are percentages of transect lines in a
given soil condition. The average, by condition, over all
transects represents the percentage of area in that condition for the monitored unit. Other line lengths can be used,
so long as measurements are converted to percentages, but
we have found that 100 feet is an adequate length. Soil
condition percentages by line are used to calculate statistical attributes of the sampled area, such as means, standard deviations, and standard errors. Interval samples
along the transects are used for assessments like soil compaction, for which intact cores or penetration readings may
be taken. The system can also accommodate fertility samples. In the case of compaction, core samples are extracted
from a specific depth or soil horizon at 10-foot intervals
beginning at the 5-foot mark. Bulk density values are
determined and then compared to the average bulk density
of additional random transects (at least three) located on
an untreated area (one which provides an appropriate predisturbance background value), probably adjacent to the
sampled area. The average value of bulk density from
transects in undisturbed areas plus 15 or 20 percent (that
is, undisturbed average multiplied by 1.15 or 1.20) represents the lower limit of unacceptable or detrimental degree
of compaction. Each core sample represents 10 percent of
each transect length. Thus, if three core samples of a line
transect exceed the bulk density limit, then 30 percent of
the line, or area the transect represents, has been detrimentally compacted.
The soil condition categories monitored by practitioners
in the Pacific Northwest vary somewhat, but they include
slash piles, landings, skid trails, spur roade:, compacted,
displaced, puddled, eroded, miscellaneous, deposited, and
undisturbed. Categories can readily be changed, so they
apply to a wide variety of situations. The latter two categories are not considered to be detrimental.
Two computer programs were developed to save computation time. One calculated bulk densities from core measurements (Starr and Geist 1988), and the second summarized the data and computed statistical estimates (Hazard
and others 1985). The calculated variance for overall detrimentally affected area provides users a statistical test for
whether general limits of total damage were exceeded. A
statistical test may not be needed or desired in some cases,
but this option is available. The same prl)gram can compute estimates of numbers of samples (number of transects)
needed to satisfy specified precision requirements. We
present example data from using this option later in the
paper.
APPLICATIONS
There are a number of ways this sampling scheme can
be applied in meeting monitoring needs, and this flexibility
increases its usability for land managers. We present some
examples here.
91
Single-Activity Area Evaluations
Stratified Sampling
To date, this system has primarily been used to estimate
the final status of soil characteristics for an individual area,
like one unit of a timber sale (fig. 1). Repeated application
of the sampling design on the same unit can be used to
assess the effects of a series of activities either individually
or collectively (that is, cumulative effects). Repeated measures optimize sensitivity for detecting change by reusing
the same gridpoints and transects at each sampling time.
When performing repeated measurements, slight offsets
are required where destructive samples are obtained; the
procedural decisions for selecting the offset positions are
part of the rules established before fieldwork begins.
Stratification can increase the efficiency of sampling.
In general, improved efficiency occurs when a population
can be divided into strata so that the mean values (of the
variable of interest) differ among the strata, yet strata are
relatively homogeneous. The transect system can be used
here too, but the user may need to apply procedures for
shifting transect positions because they cannot cross strata
boundaries.
Sampling soil compaction is an example where stratification might work well. Much of the compaction occurring
on a logged area may be concentrated in rather easily discernible skid trails. In such a case, two strata would be
defined as skid trails and nonskid trails. The expectation
is that the percentage of area compacted on skid trails is
much higher than for the rest of the unit. Variation within
each strata would be less than variation across the unit as
a whole. Hence, we expect stratified sampling to be more
efficient in this case, ifit is not too expensive to determine
strata boundaries.
Part of the increased efficiency of stratified sampling
results from more information about the population being
included in the estimators. In particular, strata sizes (or
at least their relative sizes) must be known, which requires
area determinations. Areas must be measured easily and
cheaply for stratified sampling to be efficient.
A remedy for transects crossing boundaries would be
to reorient them by choosing a new random direction for
each. Other remedies might include a different grid size,
a shorter transect length, or a different transect configuration for the skid trail stratum. Such solutions may require
additional research to verify statistical adequacy.
Comparisons Among Areas
Sometimes the question arises: Are the average values
for soil conditions significantly different among units?
Statistics can help handle the variability that often confuses our ability to otherwise make objective comparisons.
Moni toring is carried out on each of the units using the
same sampling approach as for a single unit. The percentages by line are the observations used to statistically test
differences among units. An unpaired t-test would be an
appropriate statistical test to contrast two units. Comparisons among more than two units would require a different
statistical approach (for example, a multiple comparison
procedure) to properly account for multiple nonindependent
statistical tests.
A word of caution is noteworthy here. Comparisons among
single-unit means would not suffice as a comparison of
different management activities or treatments that happened to be associated with each mean. This is because no
measure of variability exists for the activities. We address
activity comparisons next.
Double Sampling
Core sampling is an accurate method of evaluating soil
compaction, but it is too time consuming to meet the rapid
survey needs that commonly arise. In such cases we would
encourage exploratory tests of rapid surveys using double
sampling. For double sampling, a large and a small sample are taken. The small sample is taken using a standard,
more time-consuming method. The large sample is taken
using a faster, less precise method. The small sample is
a subset of the large sample, hence the term double sampling. For example, we will use core sampling and probing
with a sharpshooter shovel as the two methods to assess
compacted percentages of transects (still the basic sampling unit). The sampler must first obtain a "feel" for excessively compacted conditions in a controlled area where
conditions are known. The sharpshooter measurements
would be categorical, that is, classified above or below the
specified detrimental compaction limit, rather than continuous values. Once an individual develops the needed
feel for a given soil, rechecking calibration will require
less core sampling. In this application, the large sample
is taken by probing sample points along all transects on
the area to detennine the compacted percentage of each
transect. The small sample, of transects selected systematically or randomly from all transects on the area, is
taken by core sampling at the same sample points, offset
to avoid disturbance from the shovel. Compaction percentages of the subsample transects are then computed from
Sampling to Compare Differences
Among Activities
Where a comparison of the effects of two (or more) kinds
of activities on soil properties is desired (for example, random skidding vs. designated skid trails), sets of management units are chosen for each practice. Activities are
assumed to be applied to the whole unit. Each set of units
represents the range of conditions over which one of the
activities occur (the sample of units represents a well-defined
population of interest). The procedures for transect-sampling
each area are the same as described for an individual unit.
The unit average of transect percentages represents a single
observation, a replicate, in the array of unit averages for a
soil condition associated with the activity. The two arrays
of averages are used for statistically comparing activities.
A one-way analysis of variance would be an appropriate statistical test. The number of units used to assess the effects
of each activity should be similar but not necessarily equal.
Sensitivity of the test to detect differences is increased as
the number of units (n) increases, but also depends on the
magnitudes of the true differences among activities. AuxilIary
information to identify differences among units, independent of the activity, may be used in covariance analysis
or blocking to increase the sensitivity of the statistical
comparison.
92
Sullivan (1988) used this monitoring system to assess soil
physical conditions following tractor logging and machine
piling in 24 timber sale units in the Malheur National Forest in Oregon. He determined the "Average Percent Detrimental Impact" (APDI) and found that 15 of the units exceeded the general regional standard of 20 percent area.
He summarized results in various ways: by general soil
type, by timber type, by silvicultural prescription, and by
previous activity. The latter involved repeated measures
to determine cumulative effect. Some 95 percent of the
APDI detected was from compaction. The results of his
sampling have been used to take preventive or remedial
measures including reducing the degree of slash disposal,
using designated skid trails approved prior to tree felling,
and using a winged subsoiler to treat portions of units that
exceeded the general standard.
In a spinoff application of the sampling system, Cochran
and Brock (1985) used the point grid and transect approach
to study soil compaction and growth of young trees planted
in two clearcuts. Growth measurements of trees within
1.2 m of the transects were compared to soil bulk density
near the tree. Tree growth and bulk density data were
summarized on a transect basis, and regression analysis
was used to test relations between variables.
the core sampling data. The correlation between transect
proportions, as determined by the two methods for individuallines in the subsample, as well as the relative costs of
the two measurement techniques, determine the efficiency
of double sampling and the optimal size of the small
sample.
Details about the theory of double sampling are covered
by Cochran (1977). Some helpful discussions on the use
of double sampling are given by Geist and Hazard (1975),
even though the application is different. Another potential
application of double sampling was reported by Clayton
and others (1987), who used a penetrometer and sharpshooter shovel to classify compaction.
RESULTS FROM SINGLE-AREA
SAMPLING
The transect sampling system was developed in cooperation with the Pacific Northwest Region of the Forest Service, and the Umatilla, Malheur, and Wallowa-Whitman
National Forests. One objective was to assess the amount
of detrimental conditions (excluding transportation system
in this case) for an array of 11 harvest units (Geist and
others 1989). These efforts tested the system, gave useful
information on harvest effects, and initiated a data base
for the variability of soil characteristics. This information
provides a basis for estimating numbers of samples at prescribed levels of precision at specified statistical probabilities. For example, the number of transects (grid points)
needed to determine the average detrimentally impacted
area within 20 percent error at two levels of probability
are shown in table 1. More transects are necessary when
variability is higher, when a higher probability is desired
(90 percent vs. 80 percent) or when a smaller percentage
of error is desired. An 80 percent probability translates
to a 1 in 5 chance of error; a 90 percent probability means
a 1 in 10 chance.
CONCLUSIONS
The sampling system discussed here should not be considered the last word in strategies for soil monitoring; however, it is flexible, objective, and easy to apply. It is hoped
these pioneering efforts will evolve into progressively better
sampling methods. If other applications of this system are
explored, statistical consultation should be sought to help
meet needs and retain statistical validity.
Those specialists who become involved in monitoring
natural resources often find knowledge gaps that limit
their ability to measure and interpret changes in productivity or other environmental attributes. Some knowledge
gaps will not be filled soon, and those who seek to fill them
will find a challenging arena for investigation because of
the complex interactions and frequently confounded effects
of management on ecosystems. Studies relating adverse
soil conditions to plant growth have commonly been afterthe-fact observational studies rather than designed experiments. Some future research should be designed to quantitatively define cause-effect relationships. The results would
help guide monitoring to maintain forest soil productivity.
Table 1-Number of transects required to estimate average
detrimental soil conditions within 20 percent of the
true mean percentage, at two probability levels for
11 harvest units (adapted from Geist and others
1989)
Harvest
unit
1
2
3
4
5
6
7
8
9
10
11
Summary statistics 1
i
s
Probability level
Percent of area
No. of transects
25
21
44
37
27
23
21
19
34
41
24
20
25
25
17
14
18
15
15
22
15
16
0.80
27
58
13
8
12
27
21
24
17
5
20
0.90
REFERENCES
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21
13
20
43
35
40
28
9
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lMean and standard deviation, respectively, of percent area in detrimental condition.
93
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