PRELIMINARY REPORT FOR THE PILOT SOIL SURVEY
PROCEEDINGS OF AN INTERPRETIVE WORKSHOP
Corvallis, Oregon
January 21-25, 1985
Environmental Research Laboratory -- Corvallis
200 S.W. 35th Street
Corvallis, Oregon 97333
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PRELIMINARY REPORT FOR THE PILOT SOIL SURVEY
PROCEEDINGS OF AN INTERPRETIVE WORKSHOP
CorvaHis, Oregon
January 21-25, 1985
Environmental Research Laboratory --
200 S.W. 35th Street
Corvallis, Oregon 97333
Corvallis
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CONTENTS
1. Summary of Meeting and Status of Pilot Soil Survey
2. Survey Design Group
3. Modeling Group
4. Mapping and Sampling Group
5. Lab Analysis/Quality Assurance Group
fi. Data Analysis Group
7. Addendum: Interpretation of Results from Pilot Soil Survey
Appendices:
1. Physical Parameters Requested from Soil Survey by NSWS
2. Understanding Soil Maps
3. Development and Use of Graphical Solution of Binomial Confidence
Limits in Soil Survey
4. Summary of Planning Meeting, Soil/Water Pilot Survey, Orono, Maine,
July 23-24, 1984
5. Agenda Topics
6. Participants
7. Addresses and Telephone Numbers
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1.0 SIMMARY OF MEETING AND STATUS OF PILOT SOIL SURVEY
Jeffrey Lee
1.1 THE PILOT SOIL SURVEY: HISTORY AND OBJECTIVES
In the spring of 1985, EPA will initiate a Soil Survey as part of its
responsibilities under the National Acid Precipitation Assessment Program.
The purpose of the Soil Survey will be to provide regional data on soils and
other watershed characteristics that are hypothesized to be important factors
determining the response of surface waters to acidic deposition. This project
is a part of EPA's Direct/Delayed Response Program which will (1) estimate the
number of direct (rapid), delayed (slow), and non (very slow) response aquatic
systems in selected regions of the eastern U.S.; (2) predict the time courses
of changes in response to acidic deposition for key selected geographic
subregions.
To assist in designing the Soil Survey, EPA conducted a Pilot Soil Survey
in Maine, New York, and Virginia. The objectives of the Pilot Soil Survey
were:
1. determine the reliability of existing state soil association maps (i.e.,
generalized state soil maps) in predicting what soils occur at specific
National Surface Water Survey (NSWS) sites;
2. determine the homogeneity of soil associations on generalized state soil
maps with respect to soil characteristics that control the sensitivity of
surface water to acidic deposition;
3. determine whether the special soil characterization performed in the pilot
study (i.e., analyses not done in standard NCSS surveys) can be related to
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the standard NCSS characterization, so that existing data bases can be
used for extrapolation; and
4. develop and test the organizational structure, field procedures, and
laboratory capability required to carry out an expanded survey in 1985.
The Pilot was planned at a workshop in late July 1984 ("Summary of Planning
Meeting; Soil/Water Pilot Survey; Orono, Maine; July 23-24, 1984," attached as
Appendix 4). Vegetation mapping, soil mapping, and soil sampling were done by
National Cooperative Soil Survey (NCSS) personnel (USDA-SCS and cooperators at
land-grant universities) in Maine and New York and by the University of
Virginia in Virginia. Watersheds in New York (32) and Maine (25) were randomly
selected from NSWS watersheds with area less than 25 km?, after stratification
by the generalized state soil map "soil associations"; 3 "associations" were
sampled in each state. Watersheds in Virginia (6) were selected to represent 3
"soil associations" found in the Shenandoah National Park. Laboratory analyses
were done by Cornell University, University of Maine at Orono, and the National
Soil Survey Laboratory (USDA-SCS) in Lincoln, Nebraska.
An interpretive workshop was held in Corvallis, Oregon, January 21-25,
1985. The purpose of the workshop was to evaluate the objectives, approaches,
experiences, and available results of the Pilot as a basis for planning the
Soil Survey. This was accomplished through vigorous discussions at the Plenary
Sessions and in the Working Groups dedicated to specific topics.
1.2 CONCLUSIONS REGARDING PILOT OBJECTIVES
The overall conclusion from the Workshop was that the approach of the
Pilot was generally valid for the objectives of the Soil Survey, but the plan
for the Soil Survey must be substantially tighter and more specific than the
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plan for the Pilot. Preliminary conclusions regarding the objectives of the
Pilot Soil Survey were as follows:
Pilot Objective 1: Every watershed visited in the Pilot had soils consistent
with the generalized state soil maps. These maps, however, are at scales
of 1:500,000 to 1:750,000, and do not provide sufficient resolution to
characterize individual watersheds. This creates the need to map indi-
vidual watersheds. The generalized state soil maps might, however, be
useful for regionalization of results.
Pilot Objective 2: The delineations of the generalized state soil maps
encompass a variety of soils of differing characteristics. Chemical/
physical data are needed for the map units used to map watersheds, i.e.,
for soil series (Section 3).
Pilot Objective 3: Initial regression analyses indicate that neutral salt CEC
(a "special" variable) can be predicted from "standard" variables (Section
6). Analyses for relationships of other special variables (e.g., sulfate
adsorption isotherms) to standard variables are continuing. Based on the
success of the Pilot for neutral salt CEC, and of earlier, published,
studies for other variables, 1t seems likely that existing data bases can
be used for extrapolation.
Pilot Objective 4: Results from the Pilot Soil Survey have been used to show
(Section 7) that mean neutral salt base saturation 1s generally about
twice as high (0.22 to 0.27) for the soils collected from Maine as for
those from New York (0.09-0.12). This result demonstrates the ability of
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the approach used in the Pilot (and planned, with modifications, for the
Soil Survey) to make distinctions among soils that are highly relevant to
predicting watershed response to acidic deposition. Analyses are contin-
uing for other important variables.
The Pilot also demonstrated that the standard NCSS field procedures and
most, but not all, laboratory protocols are satisfactory; i.e., they can be
used to produce data of sufficiently high quality within the constraints on
time and resources. Watershed mapping and sampling approaches, laboratory
protocols, QA/QC, and data management procedures will incorporate modifications
based on the experiences of the Pilot Survey.
1.3 GENERAL POINTS OF CONSENSUS
Points of consensus at the meeting included the following:
1. The approach used in the Pilot Soil Survey, with some modifications, can
be used to make distinctions among soils that are highly relevant to the
objective of the Direct/Delayed Response Project (Sections 6, 7).
2. With one exception, outputs from the Soil Survey will provide the required
soil-based inputs of the models to be used by the Direct/Delayed Response
Program (Section 3). The Soil Survey can provide information on
mineralogy, but not on weathering rates; these must come from other
sources, including other Direct/Delayed Tasks.
3. As in the Pilot, watersheds should be selected in a stratified, random way
from the watersheds of the National Surface Water Survey (NSWS) (Section
2).
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4. The use of soil associations of the state generalized (schematic) soil
maps for selection of watersheds, as in the Pilot, is an unnecessary layer
of stratification, and is not recommended (Section 2).
5. Soil, vegetation, and depth-to-bedrock maps will be provided for each NSWS
watershed visited. These will be at a scale of 1:24,000. The basic
mapping unit for soils will be the consociation, rather than the 1:65,000
map units used in the Pilot (Sections 2 and 4).
6. Standard National Cooperative Soil Survey (NCSS) procedures will be used
for mapping, sampling, and quality control of field procedures. These
procedures are described in detail in the National Soil Handbook (with
Appendices) (Section 4).
7. Important soil units will be determined from the watershed maps. These
soil units (i.e., phases of soil series) will be sampled and characterized
in a statistically valid way. It is anticipated that 60 soil units will
each be sampled 5 times, with an average of 6 horizons (Sections 2, 4).
This data base will be used to characterize watersheds, including water-
sheds where the soil units have been identified but not sampled (Section
3).
8. Most laboratory protocols can be based on existing, published, methods; an
important exception is the determination of the sulfate adsorption
Isotherm (Section 5).
9. Quality control/quality assurance and data mangement will need to be much
more rigorous for the full Soil Survey than they were for the Pilot Soil
Survey (Sections 5, 6).
These points are discussed in more detail in the reports of the Individual
Workshop Groups. These reports also indicate issues which need to be resolved.
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1.4 STATUS OF UNRESOLVED ISSUES FROM WORKSHOP
From Section 2. Survey Design Issues
1. Stratify/select NSWS watersheds for mapping. Analysis proceeding. Needed
by April 15.
2. Define how the soil data are to be applied to the policy questions.
Planning for Direct/Delayed Project is continuing. John Reuss is using a
prototype aggregation procedure on soil and watershed data from the Pilot
Soil Survey, and linking the results to aquatic chemistry data from NSWS.
Final report due May 1.
3. Select soil series for sampling. This will be done at mini workshop
(mid-June) after maps are available.
From Section 5. Lab Analysis/Quality Assurance Issues
1. Sulfate adsorption isotherms. Development of procedure 1s being actively
pursued. Question of whether to use air-dried or field-moist samples is
being addressed through an experimental study.
2. Mineralogy. During a conference call with the modelers subsequent to the
workshop, it was decided that, for the most important soils (approximately
10% of the pedons), the major primary and clay minerals will be determined
for 4 horizons.
3. Development of QA/QC Protocols. The modelers agreed that, for their
purposes, knowledge of soil parameters to +_ 50% would be adequate. This
means that the routine procedures of soil laboratories (typically +_ 10%)
will be adequate. EMSL-LV will provide the following: a critical path
analysis for developing QA/QC by March 8; draft protocols for lab analysis
by March 22; draft protocols for field sampling by April 5. Mapping
protocols will be those specified 1n the National Soils Handbook.
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From Sections 6, 7. Analysis of Pilot Data
1. Completion of Pilot data bases. The data bases have been updated and are
in quasi-final form.
2. Completion of analysis of data from pilot. John Reuss has responsibility
for completing the analyses of Pilot Data (including linkage to NSWS data)
to the extent necessary for designing the Soil Survey. An interim report
from this effort is included as Section 7 (Addendum) of this workshop
report. Final report is due May 1.
NOTE: THIS IS A REPORT FROM AN INTERPRETIVE/PLANNING WORKSHOP. THUS, IT IS A
SNAPSHOT OF THE PLANNING PROCESS AS OF THE TIME OF THE WORKSHOP. ALL DECISIONS
AND RECOMMENDATIONS ARE PRELIMINARY, AND SURJECT TO CHANGE BASED ON FURTHER
DISCUSSION AND CONSIDERATION.
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2.0 SURVEY DESIGN GROUP
Compiled by Don Stevens and Dave Marmorek
2.1 SITE SELECTION
The NSWS sampled approximately 900 lakes in the Northeast (Figure 2.1),
and will sample approximately 60 stream reaches in the Southern Blue Ridge
Province. The sample sites for the Soil Survey will be selected in the
watersheds of some of these lakes and streams. The sites will be selected in a
multistage process:
1. For the Northeast, post-sample stratify the lakes in Region I of the NSWS
using a cluster analysis on the variables alkalinity, sum of base cations,
Si02» S04", A1, ratio of watershed area to lake area, and relative
flushing index. The clustering will be terminated at 3-5 clusters.
2. Select about 150 (exact number is budget dependent) watersheds from these
clusters. This will be done at random; however, some clusters may be
sampled less intensively, e.g., clusters corresponding to high alkalinity
lakes (Figure 2.2).
3. Randomly select 30-50 watersheds from those of the National Stream Survey.
4. Map the watersheds with respect to: (a) vegetation (b) soil series,
slope, and soil depth. As watershed maps are completed, they will be sent
to Corvallis to be compiled into a list of occurrence of mapping units.
Definitions of the mapping units to be field-sampled (e.g., which soil
series can be lumped together?) will be specified during a several day
miniworkshop. Participants will use their own professional judgment and
published information on soil physical characteristics, data from the
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NSWS LAKE LOCATIONS (ORNL 9/06/84)
VERIFIED REGION 1
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LAKES >6 ha IN NORTHEASTERN U-S-
Population
Stratified by
Subregion/Alk. Class
40
25
10
EJ
Lake Survey
Cluster
Analysi
Soil Survey
Watersheds
Classification by
Response Type
Direct
Delayed
Capacity
Protected
Proposed method of selection of Northeastern watersheds (Region
I) for the Direct/Delayed Response Project. The figure assumes
160 watersheds were sampled in the Project.
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pilot study, and existing soil chemistry data bases (available at Oak
Ridge National Laboratory).
5. Select sample sites within watersheds so that characteristics and vari-
ability of the mapping units are adequately defined. This is shown
schematically in the attached Figure 2.3.
The tasks for the Soil Survey are summarized in Figure 2.4. The uses of
the two data bases (soils, watersheds) developed by the Soil Survey are
indicated in Figure 2.5.
2.2 UNRESOLVED ISSUES
Several points still need to be resolved (see Section 1.4):
1. Should lakes with alkalinity > 200 yeq/1 be eliminated from the
sample? Some pros and cons:
Pro: 1. There is "no" chance that such a lake will acidifity within 50
years.
Con: 1. Dropping such lakes from the sample also drops them from the
population of inference; no inference can be drawn about the
soil properties that are associated with such lakes.
2. Dropping such lakes limits the range of response of watershed
models, such as ILWAS or MAGIC. It would be impossible to
determine what soil properties are associated with highly
alkaline lakes.
3. Dropping such lakes may be unnecessarily drastic. As an
alternative, instead of omitting highly alkaline lakes, they
could be sampled less intensively.
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Mapping
60-150 ws
Vegetati on
Overlays
for each ws
Identify 30-60
important map units (series)
from all ws based on
o soil series
o vegetation
o slope notation
I.
Sample
3-5 sites/map unit
4-8 horizons/site
Figure 2.3. Watershed sampling procedure.
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Select NSWS
Watersheds
Define Map Units
Prepare Pre-Visit Maps
Visit Watersheds to Confirm/
Correct or Develop Soil Maps
Develop Criteria
for Important Soils
Develop List of Important
Soils for Sampling
For Each Important Soil,
Develop List of
Watersheds of Occurrence
nIz
Randomly Select N
Watersheds of Occurrence
Within Watershed, Randomly
Select Map Unit Containing Soil
Map Vegetation
_5_
Verify/Revise Maps
(Field QA/QC)
Document Mapping
Watershed Data Rase
Sample and Describe Soil at
Representative Location
Perform Lab Analyses
_ ^ I
Verify/Validate Analyses (Lab QA/QC)
3Z
Acid Deposition
Soil Data Base
Figure 2.4. Task-flow for Soil Survey.
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Integration
Watershed
Data Base
Existing Soil
Data Bases
Acid Deposition
Soil Data Base
Input to Direct/
Delayed Analyses
Watershed Characteristics
Generalized Soil Maps
(States, Regions)
Augment Acid Deposition Soil
Data Base (Minor Soils)
Figure 2.5. Use of data bases from Soil Survey.
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It is not yet clear how the soil data are to be applied to the policy
question. The flow of data analysis needs to be defined. The
following should be specified:
1. the data and format needed by the modelers;
2. method for using sample data to arrive at a watershed description;
3. quantities to be calculated from the data and the levels of
aggregaation at which they are to be calculated. Are means and
variances needed? Or are statistical distributions needed? Should
these be calculated at the watershed level? Mapping unit?
Cluster? Subregion? Region?
The details of sample selection after definition of mapping units
need to be worked out. These details will be specified at the mini
workshop referenced in Section 2.1 (Site Selection). It is antici-
pated that 60 mapping units (series) will be sampled at 5 locations.
Possibilities:
1. Select (randomly) several watersheds from each cluster. Sample
these watersheds intensively. Use a less intensive sampling on
remaining watersheds.
2. Select same number of samples for each mapping unit, with sampling
sites on watersheds selected randomly. The sampling rate would be
sufficient to assess the variability within mapping units (series).
3. Select watersheds so that more frequently occurring mapping units
are sampled more frequently.
4. Can "access" be included as a criteria for selection of watersheds?
Should large watersheds be included? These decisions involve
similar problems as in unresolved issue 1. Dropping large or
inaccessible watersheds simply excludes them from the population
of inference.
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3.0 MODELING GROUP
Compiled by Kent Thornton
This group addressed the following issue: What approaches can be used to
assess direct, delayed, or no response systems based on: (a) existing data;
and (b) data collected during the Soil Survey?
3.1 LEVELS OF ANALYSIS
Three levels of analysis were identified. Level I analyses use existing
lake survey data while Level II and III analyses require supplemental informa-
tion provided by the Soil Survey. These analyses may provide regional esti-
mates.
3.1.1 Level I Analysis
1. The National Lake Survey (NLS, a component of NSWS) data can be used
to determine the number of presently acid lakes and to estimate the
number of direct response lakes. It can also be used to determine
the number of capacity protected lakes with alkalinities greater than
some value (perhaps 200 peq/1). Lakes with pH greater than some
value (such as 5.0) but with alkalinities less than some value (e.g.,
200 yeq/1) may be delayed systems. The number of delayed systems,
therefore, can be estimated using the NLS data base.
2. Level I analyses also include annual SO4 or S budgets computed based
on present deposition levels and lake SO4 concentrations. These
budgets can be computed for NLS lakes, ILWAS/RILWAS lakes, or other
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lakes with available S04 data (i.e., Norton, Schofield, etc.).
Correcting for runoff and ET (evapotranspiration) can estimate if
these lakes are in steady state with respect to SO4. This estimate
does not necessarily indicate a direct or delayed system, but is
useful in assessing whether or not watersheds are accumulating
sulfate through either S04" adsorption or other means.
3.1.2 Level II Analyses
Level II analyses represent order of magnitude estimates for determining
the potential number of delayed systems. These analyses can provide time-
varying estimates but require the type of data to be collected in the Soil
Survey.
1. SO4 adsorption. SO4 adsorption isotherms will be determined for
various soils throughout the Northeast using the Soil Survey data.
Assuming present SO4 deposition rates, those soils associated with
delayed lakes (identified in Level I) can be theoretically titrated
to determine the time until breakthrough. This can be done using
both wet SO4 deposition rate and an estimate of total SO4 deposition
rate as a multiple of wet [total S04 = (1 + x) * wet SO4]. If the
time to breakthrough is quite long (e.g., 500 years), these systems
may be considered to be capacity protected. However, if the time is
quite short, this does not necessarily imply that the system is
likely to acidify; other watershed properties might prevent this.
2. CEC and percent base saturation. CEC and percent base saturation
(BS) by neutral salts will be determined during the Soil Survey.
Assuming: (a) present deposition levels, and (b) no (zero) weather-
ing contributions to the base supply, the CEC and percent BS can be
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titrated theoretically to estimate the time until base cations are
depleted to some critical level. The endpoint will not be complete
depletion since A1 buffering will occur prior to complete depletion.
One rule of thumb may be to deplete half (0.5) of the existing CEC
(suggested by one group member but not universally endorsed).
Another option is to titrate to a base saturation such as 15% as an
estimate of the A1 buffering threshold. If this time is quite long
(e.g., 500 years), these systems may be considered to be capacity
protected. This approach can be expanded to other systems by
developing a regression approach to predict CEC and % BS by neutral
salts based on the CEC and % BS at buffered pH 7, organic carbon, and
other factors generally available in published information (see
Section 6).
3.1.3 Level III Analysis
Level III analyses use dynamic modeling approaches to provide approximate
time estimates for delayed systems. Three models exist that may be applied to
a selected number (i.e., 10-15) of intensively studied Northeast lakes: ILWAS
(Chen, Gherini et al_.), MAGIC (Cosby et jal_.), and Trickle-Down (Schnoor).
These models should be calibrated on several Northeast lakes and used to
predict the rate of change of representative chemical variables (e.g., pH)
important to aquatic sytsems. These models integrate mineral weathering, base
cation exchange, sulfate adsorption, and soil contact. Multiple simulations
can be conducted to look at the effect on lake chemistry response times of a
wide range of combinations of key parameters (soil depth, S0^~ adsorption
capacity, % base saturation, etc.). The results of these simulations could be
expressed as tables or nomograms that estimate a watershed's response time
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(plus or minus so many years) based on surveyed soil and hydrological para-
meters. An approximate estimate of the results for watersheds studied in the
soil survey can be developed with these approaches. These estimates would
still be "hypotheses," since the models that would create them have not been
tested on independent time series data.
3.2 REQUIRED MODELING PARAMETERS
The modeling parameters required for Level II and III analyses are listed
in the attached table. These have been delineated by hydrologic and chemical
categories. While not all these parameters are required for each model, this
list will permit the application of all three models to soil survey watersheds.
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Table 3.1. Modeling parameters.
Deposition:
NADP type data
Air temperature, other meteorological data
Hydrology:
Catchment slopes
Depth of permeable media
Permeabi1ity
Chemistry:
Cation Exchange
CEC
Base saturation
Sorbed bases
Lime potential
A1 potential
Soil Solution
pH
Alkalinity
Major cations and anions
Sulfate Adsorption
Isotherm parameters
Weathering
Carbonates
Weatherable minerals: heavies, plag
Vegetation
Percent coniferous
Percent deciduous
Percent open
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4.0 MAPPING AND SAMPLING GROUP
Compiled by Fred Gilbert and Oliver Rice
4.1 SAMPLING PROTOCOL
The selection of sample sites within the watershed is critical to the
ultimate use and value of the data for watershed and regional analysis. Soil
scientists have the capability of recognizing the typical landscape position of
the various taxa within a mapped area. Sample site selection will be formulated
after completion of soil mapping. A listing of frequency and extent of soil
series by state will be made from a compilation of the soil maps. At that time
a minimum number (tentatively, 5) of sampling sites for each soil series will
be determined to be in identified watersheds. Laboratory analysis of soil
samples from these sites will characterize the properties and variability of
the most important soil series (tentatively, 60) by horizon (average of 6
anticipated).
4.1.1 Soil Sampling and Description Procedure
Soil profiles and sampling sites will be described using SCS-232 according
to Chapter 4, Soil Survey Manual.
Typifying pedon(s) for that named map unit will be sampled as specified in
Chapter 8, Soil Survey Manual.
Pedon sampling will include the C horizon (if present); sampling horizons
as specified below.
Samples will be taken from all continuous horizons > 3 cm thick (1 gallon
of bulk sample from each horizon will be removed for lab analyses).
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In-place bulk density by Grossman method will be determined for all
horizons except Oi.
Each mineral horizon will be field tested for inorganic carbon (carbon-
ates), using method given in Section 5.2.
At each site a checklist will be completed.
4.1.2 Cost Estimate per Pedon
4 person sampling team requires:
Salary 2 - GS11 G> $25.63/hr x 8 hr = $205 x 2 = $410
? - GS04 0 $6.50/hr x 8 hr = $52 x 2 = 104
per diem $50 x 4 200
Supplies and Transportation 90
Subtotal 804
Overhead 26% 209
Total $1013
4.2 SOIL MAPPING — ASSUMPTIONS, PROCEDURES, COSTS
The relative value and costs of soil mapping at two different scales was
discussed by the field soil sampling group on Tuesday, 1/22/85. Specifically,
the group considered soil mapping at a scale of 1:24,000 and at a scale of
1:62,500. The question considered was whether the data transfer value of one
kind of map was better than the other. There was a consensus that we could
make similar regional generalizations of data for a soil taxa (series, family,
etc.) from maps made at both scales.
After we had this discussion, Rick Linthurst pointed out that those
involved in formulating policy statement materials will feel more secure if
they have soil maps available at a scale of 1:24,000. There will be many
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places where the relationships of the suite of soils and the water chemistry
will be unclear. Mapping at a scale of 1:24,000 may help clarify these
relationships for a moderate increase in cost (Tables 4.1, 4.2). Therefore, it
is recommended that watersheds be mapped at a scale of 1:24,000, and that the
mapping units be consociations (soil series), as practical.
4.2.1 Procedure Guidelines
The soil survey will be completed using the specified guidelines of the
National Cooperative Soil Survey (Soil Survey Manual, National Soils Handbook).
The soil mapping units will be designed according to the needs of this study.
They will be presented to EPA on USGS topographic sheets at a scale of
1:24,000. The soils will be classified according to Soil Taxonomy (Agriculture
Handbook 436).
The soil scientists will make a vegetative map for the watershed during
the soil mapping visit. The vegetation map will be made on a Mylar overlay of
the soil map. The vegetative units will be those described in "Forest Cover
Types of the United States and Canada" by F. H. Eyre, 1980, Society of American
Foresters.
There will be a second overlay made during the soil mapping visit showing
depth to bedrock classes. They will be shown as depth classes as follows:
Class Depth Confidence Level
I < 20" (< 0.5 m) High
II 20- 40" (0.5-1 m) High
III 40-100" (1-2 m) High
IV 100"-20' (2-5 m) Moderate
V 20-100' (5-30 m) Low
VI > 100' (> 30 m) Low
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Table 4.1. Soil mapping at 1:62,500 scale.
Acti vity
Field mapping
(soils and vegetation)
Travel (field mapping)
Map preparations
Photos
Statistical analysis
Coordi nation
acti viti es
Planning meetings
Travel (meetings)
Plane rental
(field mapping)
Subtotal
Overhead
Total
Average cost/watershed
Assumptions Costs
150 watersheds, 1000 acres/ws, 500
acres/day, $25.63/hr, 8 hrs/day $61,512
300 days, $50.00/day 15,000
1 day/ws, $20.35/hr, 8 hrs/day 24,420
3 photos/ws, $6/photo 2,700
1 day transect/ws, 1 day write up/ws,
1 day carto work/ws, $25.63/hr, 8 hrs/day 92,268
8 hrs/ws, $35.00/hr 42,000
4 meetings, 7 states, 1 person/state,
40 hr/meeting, $35.00/hr 39,200
SlOOO/meeting/person 28,000
20 hrs/$100/hr 2,000
307,100
79,846
$385,946
$2,580
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Table 2. Soil mapping at 1:24,000 scale.
Activity
Field mapping
(soils and vegetation)
Travel (field mapping)
Map preparations
Photos
Statistical analysis
Coordi nati on
activities
Planning meetings
Travel (meetings)
Plane rental
(field mapping)
Subtotal
Overhead
Total
Average cost/watershed
Assumptions Costs
150 watersheds, 1000 acres/ws, 200
acres/day, $25.63/hr, 8 hrs/day $153,780
750 days, $50.00/day 37,500
1 day/ws, $20.35/hr, 8 hrs/day 24,420
5 photos/ws, $6/photo 4,500
1 day transect/ws, 2 day write up/ws,
1 day carto work/ws, $25.63/hr, 8 hrs/day 123,000
8 hrs/ws, $35.00/hr 42,000
4 meetings, 7 states, 1 person/state,
40 hr/meeting, $35.00/hr 39,200
$1000/meeting/person 28,000
20 hrs/$100/hr 2,000
434,400
26% 118.144
$562,544
$3,750
25
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4.2.2 Costs
The estimated costs for making soil surveys, on maps with scales of
1:24,000 and 1:62,500, are attached as Tables 4.1 and 4.2. The assumptions
used in deriving these costs were that none of the watersheds have been mapped
in the past, and most will be difficult to access and difficult to traverse.
Calculations are based upon the assumption that there are 150 watersheds to be
mapped and that the average size of each watershed is 1000 acres. This allows
some manipulation of the cost estimate of any number of watersheds.
4.3 EVALUATE SOIL MAP BY TRANSECT
Transect 10% of watershed chosen randomly. Randomly assign transect line
across watershed and evaluate mapping by methods in Northeastern Soil Survey
Work Planning Conference Report 1982.
4.4 INTERSTATE TECHNICAL COORDINATION -- WATERSHED MAPPING
4.4.1 Purpose
The primary purpose of interstate technical coordination is to insure that
field mapping, map preparation, sampling, describing of soils, soil identifica-
tion, and interpreting laboratory data to classify soils is done in a technic-
ally sound, standard, uniform manner in all watersheds, and that there is close
adherence to mapping and sampling protocol by all involved field parties.
This becomes a large job because of the number of concurrent field
operations and time constraints. The general standards for quality evaluation
and control are those contained in the National Soils Handbook and Soil Survey
Manual. Within these are additional ones identified as special needs of the
Soil Survey for the NAPAP, covered in detail in the section on mapping and
sampling.
26
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4.4.2 Responsibilities
An outline of the responsibilities of interstate technical coordination
are:
1. For study-specified standards, obtain adherence to:
mapping and sampling protocol
mapping intensity
map preparation (cartographic standards)
identification of soil qualifiers
standards for locating pedons to sample
standards for sample taking, field processing, storage, etc.
standards for describing soils
2. To resolve unforeseen technical problems in a uniform manner.
3. To exercise National Cooperative Soil Survey standards or quality
control of soil mapping through:
coordination/correlation of series concepts
coordination/correlation of map unit concepts
application of identification legend
coordination of preparation of soil descriptions
4. To be involved as an advisor in all training, planning, and evalua-
tion meetings.
5. To evaluate proposed mapping protocol for practical and technical
soundness (can it be implemented and can results be satisfactory).
4.4.3 Operating Procedures
The proposed opperating procedure involves:
1. Coordinator/correlator will participate in planning, training, and
evaluation activities.
27
-------
2. With state staff counterparts, coordinator/coorelator will partici-
pate in field (watershed) reviews of work in progress. Each state
will be visited at least once and states containing several water-
sheds should be visited up to 3 times.
3. These visits will be to review all activities for technical adequacy
to meet standards of the NSH and the specific needs of the surveys as
contained in the mapping and sampling protocol.
4. A sample of maps and descriptions will be reviewed for completeness
and quality, including series identification and classification in
Soil Taxonomy before they are transmitted to EPA.
5. When laboratory data become available, these data and the field
descriptions will be reviewed for accuracy of classification of the
pedons sampled and the final correlation of the map units.
4.2.4 Cost for Interstate Technical Coordination
Cost estimate is salary at GM-14 plus overhead, plus travel for the full
period of preparation, training, mapping, and follow-up.
Salary + benefits = $24,000
Per diem = 5,000
Transportation to states = 5,000
Transportation to EPA meetings (2) = 1,800
Subtotal $35,800
Overhead at 26% 9,308
Total $45,108
Average cost per watershed $300
28
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4.5 REFERENCES
Arnold, R. W. 1980. Development and use of Graphical Solution of Binomial
Confidence Limits in Soil Survey. (Attached as Appendix 3.)
Eyre, E. J. Forest Cover Types of the United States and Canada. Society of
American Foresters.
Hanna, VI. E. 1982. Committee Report: Evaluating Soil Map Quality. _I_n
Proceedings of the Northeast Cooperative Soil Survey Conference.
United States Department of Agriculture, Soil Conservation Staff. 1980-1984.
Soil Survey Manual. Appendix to National Soils Handbook. Agric. Handbook
430.
United States Department of Agriculture, Soil Conservation Staff. 1975.
Taxonomy: A Basic System for Making and Interpreting Soil Surveys.
Agric. Handbook 436, 654 pp.
29
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5.0 LAB ANALYSIS/QUALITY ASSURANCE GROUP
Compiled by Paul Shaffer
5.1 LAB ANALYSES REQUIRED FOR SOIL SURVEY
1. pH -- distilled water, 0.01M CaCl2
2. Test for inorganic carbon
3. Total organic carbon
4. Total organic nitrogen
5. Total sulfur
6. Cation exchange capacity
a. 1.0N NH40Ac, pH = 7.0
b. 1.0N NH4CI, unbuffered
7. Exchangeable bases (Na, K, Mg, Ca)
a. extraction by 1.0N NH4OAC, pH = 7.0
b. extraction by 1.0N NH4CI, unbuffered
8. Exchangeable acidity
a. BaC12 -- TEA method @ pH 8.2
b. 1.0N KC1 -- total acidity, exchangeable A1
9. Extractable iron and aluminum
a. sodium pyrophosphate
b. ammonium oxalate
c. citrate-dithionite
10. Extractable sulfate
a. water soluble
b. phosphate extractable
30
-------
11. Sulfate adsorption isotherms
12. Mineralogy on selected pedons and horizons
a.x-ray diffraction
b.thin sections
13. Lime potential
14. Particle size distribution
5.2 LABORATORY METHODS FOR SOIL SURVEY
1. pH (reference: Kaisaki, pers. comm., 1985)
pH will be measured using both deionized water and 0.01M CaC^. For
mineral soil horizons, a 2:1 solution:soi1 ratio will be used; for organic
horizons, a 5:1 ratio soil. Soil-solution slurries will be mixed and allowed
to equilibrate for on hour, with occastional stirring. After one hour, the
sample will be stirred continuously for one minute, let stand for one minute,
then pH measured potentiometrically in the liquid phase.
2. Test for inorganic carbon (carbonate) (reference: NCASI, page A-25;
Kaisaki, pers. comm., 1985)
Place ground soil on a spot plate, moisten, and add several drops of 4N
HC1. Look for effervescence, which would indicate dissolution of carbonates
and release of CO2.
Comment: This test will be done in both field and laboratory; in the lab
the spot plate will be examined under a binocular microscope. It is assumed
that this test will show no carbonate in most samples; for those with a
positive test for carbonate, inorganic carbon must be quantified.
31
-------
3. Total organic carbon
4. Total organic nitrogen
5. Total sulfur
automated analysis, e.g.,
LECO, Perkin-Elker C-N
analyzer, etc.
6. Cation exchange capacity
a. NH40Ac, 1.ON, pH 7.0 (reference: SSIR method F5A8)
b. NH4CI, 1.0N, unbuffered (reference: SSIR method F5A9)
Comment: Automated extraction is recommended, if available, for
improved reproducibility.
7. Exchangeable bases (Mg, Ca, Na, K)
a. extraction by 1.0N NH4OAC, pH = 7.0
b. extraction by 1.0N NH4CI, unbuffered
Comment: Procedure is analysis of leachates (from 6a, 6b above)
for individual base cations. Analysis may be by atomic absorption/
flame emission/ICP.
8. Exchangeable acidity
a. BaCl2 -- TEA, pH 8.2 (reference: NCASI, page A-30)
Comment: Several variations of this method are possible. We
are not sure whether any version is better than the others, but we
need to specify one method.
b. KC1 (1.0N) extraction (reference: Thomas, 1982; also cited in
NCASI, page A-32)
Comment: This method will be used to quantify both total
acidity and aluminum acidity. A1 acidity can be measured by titra-
32
-------
tion as described in the method citation listed above, or by AT
analysis (AA/ICP) of the KC1 extract.
Note: Base saturation is not listed as an analytical measure-
ment. Base saturation is derived from analyses in #6-8, and the data
user can compute it in any of several ways.
9. Extractable iron and aluminum
a. sodium pyrophosphate extraction (organic Fe, A1) (reference:
Bascomb, 1968; McKeague et aj.., 1971; SSIR method GC8)
Comment: pH of pyrophosphate extract should be adjusted to
10.0.
b. ammonium oxalate (organic plus amorphous hydrous oxides)
(reference: McKeague and Day, 1966; modifed in NCASI, page A-38)
c. citrate-dithionite (non-silicate Fe and Al) (reference: NCASI,
page A-36)
Comment: Bicarbonate buffer may or may not be used in the
extraction; recommendations for and against it have been given, so it
will be used only if there are clear analytical advantages.
Iron and aluminum analyses for all fractions should be done be
AA and/or ICP.
Comment: Based on differences in Fe and Al content of the
procedures cited above, several Fe and Al fractions can be defined --
see Johnson and Todd, 1983. These extractions are generally very
specific for ion fractions, but less specific for Al.
10. Extractable sulfate
a. water soluble (reference: NCASI, page A-38)
33
-------
b. phosphate extractable (reference: modified from Ensminger, 1954)
Procedure: Sequential extractions of soil (4:1 solution:soi1
ratio), using 500 mg P/l (as NaH2P04), instead of 1000 mg P/l as in
Pilot; 4 extractions, with extracts pooled for analysis.
Comment: Sulfate by ion chromatography. Reduced concentration
of PO4 facilitates analysis by ion chromatography; use of multiple
extractions insures quantitative recovery of sulfate from soils.
11. Sulfate adsorption isotherms
Procedural details are still under discussion. There is a consensus
to do only B and C soil horizons, and to run only 5 point isotherms (0, 4,
8, 16, 32 mg S/l, or 0, 250, 500, 1000, 2000 meq SO4/I) using K2SO4.
Still to be determined is the question of which soil preparation to
use — air dried, or field moist.
Sulfate will be by ion chromatography.
12. Mineralogy on selected pedons and horizons
No consensus was reached on what types of mineralogy-soil analyses
should be performed, or for how many soils/depths per pedon (see Section
1.4).
The lack of a consensus stemmed largely from an uncertainty over end
uses of data.
13. Lime potential (reference: Reuss, pers. comm., 1985)
Equilibrate soil with 0.002 M CaC12• Measure pH, Al, Ca, Mg, Na, K.
34
-------
14. Particle size distribution (refeence: SSIR 3A1)
After removal of organic matter the percent sand, silt, and clay will
be determined by the standard pipette method.
Note: Bulk density will be measured in the field using the Grossman method.
5.3 REFERENCES FOR LAB METHODS
SSIR. Procedures for Collecting Soil Samples and Methods of Analysis for Soil
Survey. USDA, Soil Survey Investigations Report #1, revised July 1984.
NCASI, NCASI, 1983. Field Study Program Elements to Assess the Sensitivity of
Soils to Acidic Deposition Induced Alterations in Forest Productivity.
National Council of the Paper Industry for Air and Stream Improvement,
Inc., Technical Bulletin No. 404. 35 p. plus appendices.
Thomas, G. W. 1982. Exchangeable Cations. Chapter 9, pp. 159-166 in: Page,
A. L. (ed.). Methods of Soil Analysis -- Part 2. American Society of
Agronomy, Madison, Wisconsin.
Johnson, D. W., and D. E. Todd. 1983. Some Relationships Among Fe, Al, C, and
SO4 in a Variety of Forest Soils. Soil Sci. Soc. Am. J.
5.4 ISSUES RELATED TO SOIL SAMPLE COLLECTION, ANALYSIS, AND DATA REPORTING
5.4.1 Sample ID Numbers
What ID numbers are necessary? It was suggested that samples be assigned
two ID numbers -- a number compatible with NCSS ID system, since they will
collect and may process samples/perform some analyses. The second number would
be for survey use and would identify the sample by NSWS watershed ID. States
may also want special sample numbers for their own use. In any case, samples
will be assigned a unique ID number for the purposes of this survey. This will
be the controlling number, with any others carried as secondary identifiers.
35
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5.4.2 Data Values
It was strongly recommended that significant figures not be truncated from
the data set except by final data users. As an example., many of the base
cation analyses reported for Pilot Survey listed 0.0 as concentrations, because
of rounding to one decimal place. This was done even though analyses were
reliable to two decimal places.
This should not be a problem, as long as data reporting protocols are
specified in advance.
5.4.3 Choice of Analytical Lab
There was an assumption at the meeting that one lab would be selected,
with several contractors. It was suggested that one lab should perform one (or
a few) analysis for the entire sample set (e.g., CEC and extractable bases), in
order to avoid inter!aboratory variability. The lead laboratory would be
responsible for receiving and logging samples, making and sending splits to
subcontract labs, and tracking progress of subcontract labs in terms of
timelines and QA requirements.
5.4.4 OA/OC Protocols
QA/QC protocols will be developed by EPA Las Vegas (Lou Blume of LEMSCO
and Phil Arberg of EPA). They will work from procedures outlined above, and
will use estimates of detection limit/precision/etc. supplied by Kaisaki,
Shaffer, and others. Such information is needed ASAP, at the latest by the end
of February. In the absence of guidance on specific analyses, their thinking
will be a clone of lake survey methods and OA requirements (see Section 1.4).
36
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6.0 DATA ANALYSIS GROUP
Compiled by Ray Brant and William Waltman
6.1 STATUS OF LAB AND DATA ANALYSIS
At the Corvallis meeting, the lab data from Maine, Cornell, and the NSSL
were merged into one data base using R-base 4000. Included with the lab data
were SAF (Society of American Foresters) cover types, soil series and associa-
tions, taxonomic classification, watershed name, and the percent acreages of
the various associations. From this data base, R-base 4000 permits searches
and sorting of any attribute ( 107 variables) into SAS files or files for
Minitab. Basic summary statistics were computed on an IBM-PC-XT using Minitab
and SAS was used for cluster analysis and various regressions. The total data
base equaled 400K.
The pyrophosphate, dithionite-citrate, and Nfy-oxalate extractions for Fe
and A1 were not completed by Cornell at the time of the meeting. In addition,
the data base lacked particle size analysis for the Maine soils. Thus, the data
base was not complete at this time, but would be following the workshop (see
Section 1.4).
The sulfate adsorption isotherms, and water and sodium phosphate extract-
able sulfate analyses were also not completed by the NSSL, although some
regressions and clustering were run on a limited data set.
Soil differences have already been examined by preparation of tables of
means and variances of soil characteristics by soil series. These tables can
be summarized using a 1-way analysis of variance. This would identify series
effect, and would be an aid in grouping series. As a first cut, the analyses
37
-------
could be done separately for each parameter measured. Since some of the
parameters are highly correlated, multivariate analysis of variance should also
be considered. (Section 7, "Addendum," is an interim report from a continuing
analysis of Pilot data.)
It might also be worthwhile to make a preliminary examination of the
relation between lake chemistry and soil properties. One thing that could be
done fairly easily is to do an analysis of variance of alkalinity. A first
pass would look for differences in alkalinity that could be explained by the
dominant series in the watershed. A further refinment would be to carry out a
canonical regression between a multivariate lake chemistry parameter (e.g.,
Alk, SiO^, Al) and a multivariate average soil parameter (see Section 1.4).
6.? RESULTS FROM DATA ANALYSIS
The primary objectives of the laboratory analysis portion of the pilot
study were: (1) to determine whether existing state soil association maps, and
other soil groupings, reflect differences in chemical and physical properties
of soils that are of significance to acidification of a watershed; and (2) to
determine whether standard tests used for soil characterization can be used to
predict special analyses that are of importance as inputs to models of water-
shed acidification. Secondary objectives of the laboratory analysis portion of
the pilot study were: (1) to assess inter- and intra-lab variability; and (2)
to test laboratory methods and operations and recommend laboratory protocols
for the 1985 soil survey project.
6.2.1 Soil Differences
Chemical -- Summary statistics were generated for the New York data
stratified by major horizon designation (i.e., 0, A, E, B, C). Means and
38
-------
standard deviations were calculated for all horizons and were presented
graphically for CEC-pH 7, CEC-neutral salt, A1-KC1 extractable, and percent
base saturation by sum of extractable cations-pH 7 buffered (Figures 6.1, 6.2,
and 6.3). The results indicate that there are major differences among these
horizons that are of significance to the acidification process when flow path
is taken into consideration.
Summary statistics were generated for the aggregate data from New York
State and Maine, stratified by major horizon and grouped according to great
group classification. Means and standard deviations were calculated for all
horizons and were presented graphically for the same properties as before
(Figures 6.4, 6.5, and 6.6). The results indicate that there are differences
in some of the chemical characteristics between Haplorthods and Aquepts. These
two great groups are differentiated taxonomically primarily on the basis of
wetness characteristics; therefore the data show significant differences in
chemical properties associated with wetness. This is of particular signif-
icance to the question of acidification because of the implied differences in
flow paths and retention time in these two groups of soils.
Summary statistics were generated for the chemical data for both the
Chesuncook soil fromMaine (3 sites) and the Becket soil from New York (5
sites). Means and standard deviations were calculated for all horizons and
were presented graphicallyfor the same properties as before (Figures 7-11).
Both of these series are Haplorthods developed in glacial till deposits. The
data show differences in means between these series but the standard deviations
are large and the differences are not statistically significant based on such
low numbers of observations. The data also show a high degree of varibility
within series as reflected by high CV values. This is interpreted as inherent
variability in the natural soil system.
-------
CEC pH 7
CEC NS
(meq/100 gm)
0 10 20 30 40 50 60
0
37
A
"15
B
55
*
'35
FIGURE 1. NYS DATA
-------
Al KCl
(meq/100 gm)
0 2 4
0
A
X
B
X
FIGURE 2. NYS DATA
8
-------
BASE SATURATION
Sum of Cations (%)
10 20
FIGURE 3. NYS DATA
-------
0
A
B
C
CEC pH 7 ¦ H Haplorthods
—O— Aquepts
CEC NS -H Haplorthods
—o— Aquepts
0 10 20 30 40 50 60 70 80 90
3k
FIGURE 4. NYS AND MAINE
-------
_ ORTHODS
Al KCl
AQUEPTS
(meq/100 gm)
0 2
%
FIGURE 5. NYS AND MAINE
-------
BASE SATURATION -Jfc- Orthods
Sum of Cations Aquepts
10 20 30
FIGURE 6, NYS AND MAINE
-------
CEC pH 7
CEC NS
(meq/100 gm)
No.
Obs. 0 10 20 30 40 50 60
0 (3)
(3)
B (3)
(3)
(3)
o
FIGURE 7. MAINE CHESUNCOOK
-------
No,
Obs.
0 (3)
B (3)
C (3)
A l KCl
(meq/100 gm)
0 2 4 6 c.v,
(37)
(54)
(58)
FIGURE 8. MAINE CHESUNCOOK
-------
CEC NS
(meq/100 gm)
No.
Obs. 0 10 20 30 40 50 60 70 80 90 100 c.v.
(20)
( 9)
(28)
(58)
( 5)
( 5)
(58)
(104)
(24)
(12)
(47)
(40)
(18)
(11)
(63)
(56)
FIGURE 9. NYS BECKET
-------
No,
Obs.
0
0 (20)
E ( 5)
B m)
C (18)
Al KCl
(meq/100 gm)
c.v.
(152)
(54)
*
(79)
(49)
FIGURE 10. NVS BECKET
-------
BASE SATURATION
Sum of Cations (%)
0
10
20
30
40
c.v.
0 (20)
(62)
E ( 5)
X
(31)
B (23)
•X
(74)
C (18)
(178)
FIGURE 11. NYS BECKET
-------
Cluster analyses of the B horizons of all soils based on 10 common soil
chemical characteristics showed 3 major groupings of soils. With the exception
of a few outliers, the data clustered by state (i.e., Maine, New York, and
Virginia). This probably reflects regional differences in soils but may also
reflect lab differences. Within the major clusters for New York state, the
Becket, Adams, and Canaan series clustered separately.
Physical -- Physical properties of soils are of significance to hydrologic
flow paths and retention times in these soils. Series criteria (i.e.,
taxonomic definitions) carry information relative to depth of bedrock,
fragipans, dense basal till, or gravelly/sandy substratums where these charac-
teristics occur. Soil assocations in the pilot study vary with respect to
relative composition and spatial distribution of soils and these characteris-
tics. This data as it determines flow path coupled with measured differences
in chemical properties in these soils should relate to acidification processes
in the watersheds.
6.2.? Predicting Special Tests
Of primary concern were the special tests: netural salt CEC and sulfate
adsorption.
Neutral salt CEC was successfully predicted by standard characterization
tests. The regression equations are as follows:
For E, B, and C horizons (mineral horizons) from New York, Maine, and
Virginia where clay and spodic extraction analyses were not included, stepwise
regression showed correlations with R-squared values of up to 64.5.
CECNS = 0.48 (Organic Carbon) - 2.18 (pH water) + 0.73 (Sum Bases - NHOAC) +
0.175 (CEC-NHOAc) + 11.97 [R-squared = 64.15]
40
-------
and
CECns = -2.17 (pH-water) + 0.79 (Sum Bases-NHOAC) + 0.175 (CEC-NHOAc) + 11.027
[R-squared = 64.59]
For E, B, and C horizons (mineral horizons) from Maine where spodic extraction
analyses were included, stepwise regresion showed a correlation with an
R-squared value of 72.21.
CECns = 10.25 (CEC-NHOAc) - 7.0 (A1 extract pyrophosphate) + 1.424
[R-squared = 72.21]
For all 0 horizons (organic horizons) from New York, Maine, and Virginia,
stepwise regression showed a correlation with an R-squared value of 69.82.
CECns = 0.86 (Sum Bases-NHOAC) + 0.49 (Organic Carbon) + 0.150 (Exchangeable
Acidity BaCl) - 5.866
[R squared = 69.82]
For available total sulfate data (water extractable S plus phosphate extract-
able S), there were very low correlations with clay, organic matter, CEC,
extractable Fe and A1 by pyrophosphate and CBO. R-squares were less than 10
percent. The available data base was very small and further statistical
analysis is needed when lab analyses are completed.
6.2.3 Laboratory Variability in the Pilot Study
In the pilot survey, laboratory variability was compared between the
National Soil Survey Laboratory (NSSL, Lincoln, Nebraska; Nebraska), Cornell
Soil Characterization Laboratory (New York), and the University of Maine
(Maine). As part of the laboratory comparisons, the Maine and Cornell labora-
41
-------
tories exchanged standard samples of Ca, E, Bhsm, and Bs horizons. Each of
these four standards was replicated 10 times and randomly interspersed among
field samples for analysis. This interlaboratory comparison provides a good
estimate of the precision in soil characterization analyses and also indicates
the inherent variability for particular soil horizons (0, E, Bhs, and Bs).
Comparisons between the NSSL and the state laboratories are based upon
9-12 samples which were replicated twice. For example, from the samples sent
for special analyses, 10% of the samples were replicated two times for standard
characterization analyses. Thus, the statistical comparisons between the NSSL
and the state laboratories are rather weak.
Table 6.1 presents the summary statistics for previous analyses of labora-
tories standards used at the Cornell SCL. The standards selected closely
approximate the types of horizons encountered in the pilot survey. From these
standards, it is apparent that the lab variability is not uniform across the
different horizons. For 0a, Bhs, and Bs horizons, the coefficient of variation
is < 20% for most of the characterization analyses. However, in 01 and E
horizons (the extremes of % organic carbon), the CV values are considerably
greater and largely reflect the inherent variability of the soil material
rather than an analytical problem. Thus, the reproducibi1ity or precision of
these soil characterization analyses is largely a function of the types of soil
horizon and the heterogeneity of the soil mateiral (percent carbon and clay).
Table 6.2 provides the comparisons of Cornell and Maine for the E and Bhsm
laboratory standards. In this study, there is a significant difference in the
characterization data between the state laboratories. For example, the means
for CEC at pH 7.0 on the Bhsm are 18.1 meq/100 g (Cornell) and 8.9 meq/100 g
(Maine). Similarly, the exchangeable Ca, exchangeable Mg, KC1-A1, and organic
carbon analyses were found to be significantly different between the two state
42
-------
Table6.1 Sumnary statistics for laboratory standards used at Cornell Soil Characterization Laboratory.
Exch. CEC PCT PCT
Exchangeable Bases Acidity IN KC1 Sum of (Sum of Base Organic PCT
Standard Ca Mg K Na BaC^-TEA Extr. A1 Bases cations) Sat Carbon Nitrogen
meq/100 g
S84NY x± Std. 11.241.2 0.95±0.22 0.74±0.10 0.09±0.02 97.0±9.5 0.73±0.40 13.0±1.4 110.3±6.9 12.1+1.1 44.9+3.9 1.48+0.1
065-01-01 Oi Dev.
C.V.(*) 10.7 23.7 14.1 18.2 9.8 55.6 10.5 6.3 8.8 8.8 7.2
S89ME x± Std. 14.0+1.3 2.58+0.13 0.63+0.11 0.05+0.01 110.3+8.2 1.19±0.19 17.2±1.3 127.5+9.1 13.5±0.8 37.5+3.5 1.22+0.13
01-06-02 Oa Dev.
C.V.(%) 9.1 5.0 17.5 20.0 7.4 15.8 7.3 7.1 5.6 9.2 10.7
S84Pa x± Std. 0.05+0.01 0.02±0.01 0.014+0.01 0.01±0.00 2.6+0.7 0.20+0.03 0.1+0.00 2.7+0.7 3.9±1.1 0.24+0.10 0.01+0.01
053-07-01 E Dev.
C.V.(%) 13.9 40.8 36.9 0.00 27.0 16.7 0.0 24.4 26.7 41.6
S84NY x± Std. 0.98+0.03 0.06±0.01 0.02±0.00 0.0U0.00 21.0±1.6 0.69+0.04 1.1±0.0 22.1+1.6 5.0+0.5 1.5+0.1 0.05±0.01
019-01-04 Bhsm Dev.
C.V.(S) 3.1 16.7 0.0 0.00 7.8 5.9 - 7.4 9.2 6.7 20.0
S84ME x+ Std. 0.30*0.03 0.07±0.004 0.09+0.004 0.01+0.004 37.9±4.2 3.52+0.33 0.5+0.0 38.9±4.1 1.3+0.1 5.1+0.7 0.18±.01
01-06-04 Bs Dev.
C.V.(X) 9.3 5.7 4.4 25.8 11.2 9.4 - 10.5 10.3 15.5 6.3
-------
TABLE6.2 Comparison of Laboratory Standards used at Cornel} and Maine for the Pilot Survey.
Exch. Bases
(meq/100 g soil)
Exch. Acidity
BaCli-TEA
(meq/lOOg)
CEC pH 7.0
PCT
Organic
PCT
Total
IN KC1-A1
Laboratory
Standard
Ca
Mg
K
(meq/100 g)
Carbon
Nitrogen
(meq/100 g)
Cornell
S84
Pa 53-07 E
X
0.048
n.c.
n.c.
2.60*
1.52*
0.24*
0.012*
n.c.
cv(Z)
4.2Z
n.c.
n.c.
27.0%
25.7 Z
40.2 Z
35.2 Z
S84
MY 19-01-04
Bhsn x
0.99*
0.06*
0.02—
21.00^
18.1 *
1.51*
0.051—
0.69*
CVCE)
3.2
8.3
0.0 X
8.2 Z
4.4 Z
6.6 Z
12.5 Z
4.8 Z
Maine
S84
Pa 53-07 E
X
0.068
n.c.
n.c.
3.39*
1.27*
0.37*
0.03*
n.c.
cv(z)
13.5 Z
n. c.
n.c.
51.9 Z
57.6 Z
18.2 Z
106 Z
S84
NY 19-01-04
Bhsra x
0.88*
0.04*
0.04—
20.20^
8.87*
1.39*
0.051—
0.55*
CV(Z)
9.0
9.5
154 Z
5.6 Z
56.3 Z
2.3 Z
87.4 Z
15.0
n.c. » not calculated; values were exceeding low for comparison.
* - Indicates a significant difference between Cornell and Maine at a » 0.05 level.
-------
laboratories. In most of the wet chemical methods, the Cornell SCL had higher
values across all the extractions than Maine. Additionally, the coefficients
of variation are inflated in the E horizons where the low organic carbon and
clay contents result in very little exchange capacity. Therefore, very minor
differences between the laboratories appear significant in the E horizon
standards.
Comparisons of the state laboratories (New York, Maine) with NSSL
(Nebraska) are given in Table 6.3. All analyses performed by New York and
Nebraska were highly correlated (r j> 0.93). With the exception of Ca, results
from Maine and Nebraska were also highly correlated. The results of regression
analysis suggest that values from Nebraska tended to be higher than those from
Maine for Ca, CEC-pH 7, and Fe, and lower for organic C. Values for organic C
from Nebraska tended to be higher than those from new York. For many regres-
sions, the intercept was consistent with zero, suggesting that a simple
proportionality might be an adequate descriptor of inter-1aboratory relation-
ships. Statistical analyses of inter- and intra-1aboratory comparisons are
continuing.
fi.2.4 Summary and Recommendations from Laboratory Variability
1. The CV values for the characterization methods vary with the type of soil
horizon. E and 0 horizons present the greatest problems in terms of
reproducibility or precision.
2. For many of the wet chemical procedures, the Cornell and Maine laboratory
data are significantly different at the 0.05 probability level. However,
the differences may be significant for one type of horizon and nonsignif-
icant for another (e.g., E vs. Bhs).
43
-------
Table 6.3. Comparison of results from state laboratories (New York, Maine) and National Soil Survey
laboratory (Nebraska).
Correlation (r) Regression Analysis
Nebraska-Maine Nebraska-New York
Analysis
New York-
Maine
Nebraska-
New York
i
t
)
r^
a
b
r^
Exch. Ca
0.88
2.5
(2.8)
1.23
(0.25)
0.78
Exch. Mg
1.00
Exch. K
0.93
Exch. Acidity
0.97
0.93
0.95 (7.2)
1.00 (0.13)
0.86
OEC-pH 7
0.98
-9.9
(9.3)
1.15
(0.10)
0.95
CEC-Cation Sum
0.90
0.96
Sum of Rases
0.86
0.98
Base Saturation
0.96
3.4
(2.3)
1.08
(0.25)
0.73
pH-H20
0.96
pH-CaC^
0.98
0.98
A1-KC1
0.97
0.18
(0.30)
0.95
(0.08)
0.95
A1-Oxalate
-0.03
(0.04)
1.07
(0.11)
0.93
Fe-Dithionite-
Citrate
0.13
(0.03)
1.88
(0.07)
0.99
Fe-Oxalate
0.06
(0.02)
1.28
(0.05)
0.99
Organic-C
1.00
0.20
(0.62)
0.91
(0.02)
1.00
-0.47 (0.52)
1.15 (0.02)
1.00
Total-N
1.00
* Regressions are of the form XN£ = a + bX^g ny* Values in parentheses are standard deviations of
estimates of a and b. '
-------
3. A major source of laboratory variability is probably associated with the
heterogeneity of the soil materials rather than with the laboratory
method.
4. In establishing QA/QC levels of precision, a "sliding" window is needed
for the different types of soil horizons (e.g., < 20% precision window for
the Bhs horizon versus ~ 20-50% precision window for E horizons.)
5. In future projects, choose appropriate internal standards that resemble
the types of horizons to be analyzed (if sampling spodosols, use 0, E,
Bhs, C standards for comparison).
6. In the pilot project, Cornell used 5 standards replicated 10 times each
and randomly interspersed with the field samples. The QA/QC standards
represented 20% of the total number of samples analyzed. This provided a
reasonable estimate of within-laboratory and between-laboratory vari-
abi1ity.
46
-------
7.0 ADDENDUM: INTERPRETATION OF RESULTS FROM PILOT SOIL SURVEY
John Reuss
7.1 PRELIMINARY RESULTS
(Note: This section was prepared after the workshop, and represents an
interim report from a continuing analysis of Pilot data; see Section 1.4.)
Initial results from the Pilot Soil Survey are now available. These must
be interpreted with caution as data are still being entered and files edited.
Patterns discernible at this time appear to be highly relevant to water acid-
ification due to acid deposition. It seems highly likely that these patterns
will persist when all data have been entered and edited.
Parameters related to the ability of soils to buffer the system in such a
manner that input acidity will not be passed onto the surface waters can be
divided into three general classes, i.e.: (1) anion adsorption buffering
mechanisms (sulfate adsorption); (2) ion exchange buffering; and (3) replenish-
ment of bases through weathering of primary minerals. The discussion here
relates largely to parameters that determine the capability of the watershed to
buffer through ion exchange processes. A preliminary discussion of sulfate
adsorption measurements is also reported. No mineralogical analyses were
included in the Pilot effort.
Early work on "soil sensitivity" tended to focus on "soil pH," cation
exchange capacity (CEC), total exchangeable bases (SB), and base saturation
(BS). These parameters are interdependent, i.e., (BS) = (SB) / (CEC), and pH
has been proposed as a proxy for base saturation (McFee, 1980). Later workers
using mechanistic models (e.g.. Reuss and Johnson, 1985) have suggested
-------
modifications and/or additions to the list of parameters. In this modified
list base saturation is highly important. Total exchangeable bases and CEC are
important as factors that determine base saturation, and also in determining
the capacity to delay acidification. As used in these models, however, the CEC
used in calculating base saturation must represent the charge at soil pH
(neutral salt CEC) rather than that measured using solutions buffered at pH 7.0
or higher, as is common for agricultural or soil survey purposes. Additional
parameters that such models have shown to be important are the characteristic
relationships of pH to solution Al3+ levels (expressed as 3pH - pAl designated
here as K^), and relative affinity of the ion exchange for Ca^+ or Al^+
[this affinity may be expressed by the selectivity coefficient of Gaines and
Thomas (1953)], designated here as Ks. Some models (Chen et aK, 1984;
Christophersen and Wright, 1980; Reuss, 1978) use the lime potential (pH - 1/2
pCa) as an input rather than base saturation and the selectivity coefficient.
This is not an independent parameter, as it can be shown to be fucntionally
related to the base saturation, pAl, and K$. It is, however, relatively easy
to determine experimentally whereas most experimental methods for determining
K$ are very difficult. For this reason, in the Pilot Survey, lime potential,
and base saturation were determined experimentally, and the selectivity
coefficient (K$) has been calculated using the relationship of Reuss (1983).
The results reported here are from the "pedon analysis." These are
profiles sampled in or near the various NLS watersheds selected for the Pilot
Soil Survey. A sampled pedon is not necessarily intended to represent the
watershed from which is selected. Rather, it is intended to represent the soil
series as determined by the field surveyor. Soil series that are very common
on the watersheds are represented in the Pilot Survey by as many as 10 pedons,
whereas minor series are likely to be represented by only a single pedon.
47
-------
Samples from New York were all from NLS watersheds in the Adirondacks. Samples
from Maine were from watersheds sampled by the NLS and falling within their
specific mapping units as shown on the large scale state "soil association"
(schematic) mapping units. Watersheds > 25 km? or those including cropland or
housing developments were excluded.
The results reported here are from the mineral soil horizons. As
expected, we have found little indication of differences among organic horizons
attributed to regions or taxonomic groupings. Furthermore, it is likely that
these sensitivity parameters cannot be meaningfully applied to organic
hori zons.
Mean values for New York and Maine for the mineral horizons are shown in
Table 7.1. The CEC values are slightly higher in New York than in Maine, but
the differences are not significant nor are they likely to be important in
terms of acid deposition effects. The mean neutral salt base saturation,
however, is generally about twice as high (0.22 to 0.27) in Maine as in New
York (0.09-0.12). This effect appears to be real, although refinements in the
test of significance may be required due to an obvious non-homogeneity of
variance.
This result is important as the lower base saturation values should be
associated with drainage water acidity at lower values of input acidity. One
would expect that drainage waters from the watersheds in New York would become
acid at a lower deposition level than those in Maine. It is not possible to
discern whether this is due to an original lower base saturation level in the
New York soils or whether the original levels were similar and the differences
developed from greater cation losses due to acid deposition. Rough calcula-
tions suggest such differences could develop due to deposition over a couple of
decades if bases exported are not replaced.
48
-------
Table 7.1. Comparison of sensitivity parameter means from mineral soil
horizons sampled in New York and Maine. Data are preliminary and
means and tests of significance (t tests) must be regarded as
approximate.
Hori zon
E
B
C
NY
16
ME
13
NY
37
ME
22
NY
12
ME
9
CEC (Neutral Salt)
6.38
ns
7.89
11.2 ns
8.8
2.57
ns
2.20
Base Saturation
(Neutral Salt CEC)
0.11
•k
0.25
0.12 *
0.22
0.09
~
0.27
pH - 1/2 pCa
(Lime Potential)
1.83
ns
2.04
2.60 **
2.92
3.05
ns
3.24
3pH - pAl (Ka)
6.06
ns
6.71
8.45 **
9.30
9.23
10.17
Log Selectivity
coefficient (<5)
2.25
ns
1.79
1.70 ns
1.97
3.01
1.01
pH (Water)
3.82
3.49
3.43
4.20
5.01
4.60
* Significant at 5% level.
** Significant at 1% level,
ns = Not significant.
49
-------
Both the lime potential and the values tend to be lower in New York and
the differences appear highly significant in the B horizon, and in the case of
in the C horizon as well. Lower values of either of these parameters would
be associated with a greater sensitivity to acid deposition (i.e., alkalinity
loss would occur at a lower level of deposition). These sensitivity parameters
should not be regarded as independent, as intercorrelation may be expected.
For example, the lime potential should be related to both the base saturation
and Ka, and these parameters are both used in the calculation of K$. It would
probably be inappropriate to attempt any interpretation of the K$ values at
this time.
No tests were made to compare values of pH (determined in soil-water
suspensions) between the samples from different states. These analyses were
done in different laboratories and such comparisons probably are not justified
until interlaboratory comparisons of check samples are available.
Another tendency apparent from Table 7.1 is that and lime potential
tend to increase deeper in the profile. This would suggest that water passing
through C horizons might regain alkalinity, whereas those draining directly
from E or B horizons might remain acid. This tendency is also reflected in pH
of soil-water suspensions and is consistent with current thinking as well. The
low base saturation and higher pH and values in the C may reflect a hgher
content of unweathered primary minerals.
One point of considerable importance for the larger survey being planned
is the variability of sensitivity parameters within and among mapping units.
The basic mapping unit for the standard Order 2 National Cooperative Soil
Survey is the consociation (essentialy, soil series). In the Pilot Survey,
three series (Becket, Canaan, and Adams) were represented by five or more
pedons. The means and standard deviations for a selected set of parameters for
-------
all New York samples and for these three series (with the exception of one
Becket in Maine, these pedons were all from New York) are shown in Table
7.2. With the exception of CEC, the means and standard deviations among these
parameters for the Becket B horizon are about the same as those for all New
York pedons. Thus, knowing that the soil belongs to the Becket series seems to
give little or no more information concerning these particular parameters than
knowing that the pedon came from the New York population. The standard
deviation for the Adams and Canaan series, however, are generally well below
those for all New York pedons. Thus, knowing the soils are Canaan or Adams
series seems to place considerably tighter bounds than simply knowing that they
are from New York. These results suggest the variance of these parameters
within different series may be quite different. It is entirely possible that
as other parameters are considered (e.g., sulfate adsorption), we may find that
series that are highly variable in certain parameters may be quite homogeneous
in others.
Preliminary analysis of sulfate adsorption data is summarized in Tables
7.3 and 7.4. The first table describes distribution of total extractable
sulfate by horizon in soils from the three states, and indicates significant
differences among the states and between soil horizons. Adsorbed sulfate
concentrations are higher in Virginia soils than in Maine or, particularly, New
York soils -- concentrations are 1.5 to 3.5 fold higher in Virginia B horizons,
and 5 to 10 fold higher in the A/E and C horizons. Variability within states
has received only very preliminary analysis, but there appear to be significant
differences among soils from different series and associations in New York and
Maine. Note, however, that inasmuch as those analyses were based on unverified
data and small sample sizes, the finding of intra-state variabilty must be
regarded as tentative.
51
-------
Table 7.2
New York
Becket
Canaan
Adams
(n)
All (37)
(12)
(10)
(5)
CEC meq/100 g
Neutral Salt
~x
sd
11.22
7.44
11.08
4.65
11.67
5.02
2.32
2.. 06
Base Saturation
Neutral Salt CEC
"x
sd
0.12
0.07
0.14
0.07
0.12
0.10
0.13
0.04
pH - 1/2 pCa
Lime Potential
7
sd
2.60
0.48
2.54
0.60
2.64
0.18
3.19
0.09
3pH - pAl
«A
~x
sd
8.45
0.93
8.19
1.14
8.71
0.26
9.41
0.25
Log Selectivity
Coefficient K$
7
sd
1.70
0.63
1.60
0.50
1.55
0.23
3.00
0.19
52
-------
Table 7.3. Total extractable sulfate (water plus phosphate extractable) in
soils from Maine, New York, and Virginia soils sampled during EPA's
pilot survey in autumn 1984. Valuesa re expressed as mg S.kg-1
soil, the number in parentheses represents one standard deviation.
Maine
New York
Virginia
Hori zon
n
Total SO4
n
Total SO4
n
Total SO4
A
—
6
65.8 (+ 45.0)
E
8
6.5 (+ 1.3)
B
27
55.9 (+ 42.6)
38
24.7 (+ 24.8)
5
87.1 (+ 66.8)
C
19
18.5 (+ 12.7)
25
11.1 (+ 3.4)
5
62.8 (+ 39.3)
53
-------
Table 7.4. Sulfate adsorption capacity of soils from maine, New York, and
Virginia during EPA's pilot soil survey. Adsorption capacity was
determined using a Langmuir isotherm fit of adsorbed suflate and
experimetnal isotherm data for each horizon sampled. Values are
reported in mg S.kg"* soil; the value in parentheses is one
standard deviation.
Hori zon
Maine
New York
Vi rqinia
n
SAC
n
SAC
n
SAC
A
NA?
NA
6
83 (+ 43)
B3
22
130 (+ 45)
46
91 (+ 54)
5
121 (+ 64)
C3
18
36 (+ 22)
14
27 (+ 10)
5
98 (+ 39)
1 SAC = si 1 fate adsorption capacity.
2 NA = data not yet available.
3 Samples for Maine and Virginia used a pooled sample from all subhorizons of
the B and C horizon; New York samples had up to 5 samples from subhorizons of
the R horizon; the value reported is an ameliorated average of al1 B horizon
s amp1es.
54
-------
Data analyses are incomplete, but a somewhat different relationship exists
for sulfate adsorption capacity of soils from the EPA pilot Survey (Table
7.4). Maximum adsorption capacities (from a Langmuir isotherm fit of data)
are comparable for B horizon soils from the three states (Maine, New York,
Virginia), but the adsorption capacity of C horizon soils is substantially
higher for Virginia soils. This is consistent with findings of Johnson and
Todd (1983) who found relatively small differences in B horizon adsorption
capacity of Spodosols and Ultisols, but large differences for A and C horizon
soils, with markedly higher sorption capacity for southern Ultisols. The
implication of these findings is that although B horizon adsorption behavior
may be similar in the two regions, integrated adsorption capacity of the pedon
is higher for southern soils. Although this may be true on broad regional
scales, spatial heterogeneity within the regions is significant and localized
sulfate mobility may vary widely. There is a critical need for data on
variability within all the regions of interest as well as characterization of
sulfate adsorption by soils that have been poorly studied.
7.2 PRELIMINARY INTERPRETIVE SUMMARY -- PEDON ANALYSIS
Soil parameters in the watersheds sampled in Maine, New York, and Virginia
were generally within the expected range. One exception is the low neutral
salt base saturation in New York (mean for the mineral horizons in the range of
0.09-0.12). The indication that these New York base saturations are substan-
tially lower than those in the Maine watersheds (means 0.22-0.27) was
unexpected.
A similar pattern seems to be present for other sensitivity indicators,
that is, drainage waters from New York pedons might be expected to develop
55
-------
negative alkalinity (net acidity) at lower deposition levels than those in
Maine.
It is not possible to determine whether the difference in base status
reflects inherent differences in the soil or is due to greater cation export
due to (possible) heavier acid deposition loading in New York. Rough calcula-
tions suggest the differences are of a magnitude that could develop due to
differential loading over a few decades unless compensated by accelerated
weathering. This needs to be examined much more carefully, both in the pilot
data and in the larger survey.
On balance, there are few "surprises" in the adsorbed sulfate data from
the pilot survey. The current sulfate loading is higher in Virginia soils than
in the soils from New York and Maine, particularly in the A/E and C horizons.
Sulfate adsorption capacity is similar in B horizon soils from the three
states, but substantially higher in C horizon soils in Virginia. There also
appear to be significant differences in sulfate adsorption characteristics
among soil series in Maine and New York; future efforts in interpreting Pilot
data will be oriented toward correlating those differences with other soil
properti es.
The implications of current results for estimating sensitivity from
standard soil survey taxonomic groupings are mixed. For parameters examined to
date, variability within some series is substantially less than overall vari-
ation, whereas within other series this is not the case.
7.3 REFERENCES
Chen, C. W., S. A. Gherini, N. E. Peters, P. S. Murdoch, R. M. Newton, and R.
A. Goldstein. 1984. Hydrologic analyses of acidic and alkaline lakes.
Water Resources Res. 16:1975-1982.
56
-------
Christophersen, N., and R. G. Wright. 1980. Sulfate at Birkenes, a small
forested watershed in southernmost Norway. _I_n D. Drablos and A. Tollen
(ed.). Proceedings International Conference on Ecological Impact of Acid
Precipitation. Sandefjord, Norway. March 11-14, 1980.
Gaines, G. L., and H. C. Thomas. 1953. Adsorption studies on clay minerals.
II. A formulation of the thermodynamics of exchange adsorption. J. Chem.
Phys. 21:714-718.
McFee, W. W. 1980. Sensitivity of soil regions to long term acid precipita-
tion. pp. 495-505. Jjn D. S. Shriner et aj_. (ed.). Atmospheric sulfur
deposition: Environmetnal impact and health effects. Ann Arbor Science
Publishers, Inc., Ann Arbor, Michigan.
Reuss, J. 0. 1978. Simulation of nutrient losses resulting from rainfall
acidity. Ecol. Modeling 11:15-38.
Reuss, J. 0., and D. W. Johnson. 1985. Effects of soil processes on the
acidification of water by acid deposition. J. Environ. Qual. 14:26-31.
57
-------
Appendix 1
PHYSICAL PARAMETERS REQUESTED FROM SOIL SURVEY BY NSWS
[with comments from Pilot Soil Survey Workshop]
A. Geology
1. Type of bedrock [From bedrock map, unless exposed]
2. Percent of bedrock exposed [Primarily from air photos; classes to be
di fferenti ated?]
3. Degree of fractionation [Can't do with any accuracy; probably simple
yes/no designation]
4. Parent material {type of material, i.e., glacial till, outwash,
alluvium, collovium, residual, lacustsine, marine sediments, eolion
sands and beaches, etc.)
B. Site Description
1. Site position (i.e., upland, flood plain, stream terrace, moraine,
depression, kame terrace, etc.) [Need to use standard terminology]
2. Percent slope [Classes to be differentiated?]
3. Average slope length and configuration [Length classes? Whose
configuration convention?]
4. Stream type and density [Maps will indicate location; need geomorph-
ologist or hydrologist to define classes of type]
5. Vegetation type (major timber types and ground cover species)
[Society of American Foresters classification]
-------
C. Soil Morphology (by horizon)
1. Horizon designation
2. Horizon depth (upper and lower boundaries)
3. Type of boundary
4. Color
5. Structure
6. Consistence
7. Drainage class
8. Mottles (where, color, distinctness, frequency)
9. Root distribution
10. Presence of impermeable layers [estimation of permeable class by
horizon]
11. Field textures (including coarse fragments, types, and classes)
D. Physical Laboratory Parameters
1. Bulk density
2. Mineral soil texture
3. Percent coarse fragments
-------
Appendix 2
UNDERSTANDING SOIL MAPS
J. A. Ferverda
A soil map is a convenient way of showing the location and
extent of the kinds of soil of an area. Many kinds of soil maps
are being made throughout the world today. They fit into three
broad classes:
1. Soil Survey Maps
2. Generalized Soil Maps
3. Schematic Soil Maps
Soil survey maps are made by field methods and require field
investigations of the soils. Generalized soil maps are made by
combining the delineations of pre-existing soil survey maps to
form larger areas. Schematic soil maps are made by predicting the
geographic distribution of different kinds of soil from many
sources of information other than from existing soil survey maps.
Schematic maps are generally small scale maps—1:1,000,000 or
smaller—and often serve as preliminary maps for locating areas
that need further investigation.
Like other maps, soil maps have their merits and limita-
tions, generally governed by map scale and projected use. For
example, a traveler about to make an automobile trip from Bangor,
Maine, to San Francisco, California, to visit a friend does not
select a map scale of 1" • 1 mile to plan his trip. He generally
will sketch out his trip on a map of 1:7,500,000 scale or smaller
to determine which states to travel through. As he travels
through each state he will select a state map of about 1:500,000
scale that shows the major roads and highways. When he reaches
the city of San Francisco, he will need a still more detailed
city map at a scale of 1:7,000 or so that will show the indi-
vidual streets of San Francisco, and finally he will consult a
sketch map at 1:500 scale that will show the exact location of
the house he is looking for.
The same principle of credibility applies to soil maps.
What can be shown on a soil map depends primarily on its scale.
In designing soil surveys the projected use of the survey and the
complexity of the soil patterns on the ground largely determine
the scale of the soil map. One must always keep In mind that the
soil pattern on the ground is fixed—it does not change. What
one sketches on a piece of paper (air photo) does not change the
soil pattern on the ground. What one can show on the soil map is
determined by the scale of the map, the skills of the mapper, and
the complexity of the soil pattern on the ground.
-1-
-------
A soil map should be designed to provide the necessary
information and accuracy needed for a particular use. A 1:15,840
(4" - 1 mile) soil map does not provide enough information for
all uses but it does provide soil information for many planning
uses.
If soil information down to 0.5 acre is needed, the area
should be mapped at 1:7,920 (8" - 1 mile) or larger scale. If 40
acre differences in soil are needed, a soil map at 1:62,500 may
suffice.
When using soil maps, one must remember that scale, accuracy
and detail are not the same thing.
Map scale is the relationship between corresponding distance
on a map (a piece of paper) and the actual distance on the ground.
Map accuracy is the degree or precision with which map
information is obtained, measured, and recorded.
Map detail is the amount of information shown on a map. The
more information, the more detailed the map.
Obtaining map information that provides sufficient data for
the purpose of making the map is generally more important than
accuracy or scale. However, map scale, map accuracy, and map
detail are interrelated. Their degree of refinement depends on
the objective or purpose for making the soil survey map and the
complexity of the soils on the ground. A large-scale map is not
necessarily more accurate or more detailed than a small-scale
map. Generally, a large-scale map can and does show more detail
than a small-scale map. As stated earlier, soil survey maps are
made by field investigation methods. The accuracy of th$ maps is
determined largely by the complexity of the soils and the skills
of the mapper. In the United States, the National Cooperative
Soil Survey makes five kinds of soil survey maps designated
Orders 1 through 5. Order 1 maps provide the most detail (or
information) and Order 5 the least detail.
Order 1 soil survey maps are generally at a scale larger
than 1:12,000 (1* m 1,000'), At 1:12,000 scale the minimum size
delineation is about 1.5 acres. Order 1 soil maps are made for
purposes that require appraisal of the soil resources of areas as
small as a building site or an experimental plot. Mapping scale
can be as large as 1:1. The soils in each delineation in Order 1
soil survey maps are identified by transacting and traversing by
a soil scientist or mapper. Soil boundaries are observed
-------
throughout their length. Any soils that affect potential use but
are too small to be delineated are shown by defined spot symbols.
Mapping is generally done on a recent air photo base.
Order 2 soil survey maps are generally at a scale of 1:12,000
to 1:31,680 (1" ¦ 2,640' or 2" per mile). At these scales the
minimum size delineation is 1.5 to 10 acres. Order 2 soil survey
maps are made for purposes that require soil resource information
for planning use of fields, farms, and other land areas that
require intensive management. The soils in each delineation are
identified by transecting and traversing by a soil scientist.
Soil boundaries are plotted by observation and interpretation of
air photos; these boundaries are verified at closely spaced
intervals. Small areas of unlike soils are shown by defined spot
symbols.
Order 3 soil survey maps are generally at a scale of 1:24,000
to 1:250,000 (1" - 20,833' or 0.25 inches per mile). At these
scales the minimum size delineation ranges from 6 to 640 acres.
These soil maps are useful for planning: soil resources of large
forested tracts, watersheds, wildlife refuges, counties and other
land areas that have projected extensive land uses such as
rangeland, woodland, county, and multi-county planning. The
soils in each delineation are Identified by transecting, tra-
versing, and some field investigations. Boundaries are plotted
by observation and interpretation of remotely sensed data (air
photos mostly) and verified with some field observations.
Order 4 soil survey maps are made at scales of 1:100,000 to
1:300,000 (0.21" ¦ 1 mile). At these scales minimum soil deline-
ations range from 100 to 1,000 acres. These maps are generally
useful for planning the soil resource of large areas such as
counties. The soils in each delineation are identified and their
patterns and composition determined by transecting. Subsequent
delineations are mapped by some traversing, by some observation,
and by interpretation of remotely sensed data verified by occa-
sional ground truth observations. Soil boundaries are plotted by
air photo interpretations.
Order 5 soil survey maps are generally at a scale of 1:250,000
to 1:1,000,000 (0.063" per mile). Minimum size delineation
ranges from 640 to 10,000 acres. These maps are made when in-
formation about the kind and distribution of soils is needed
quickly for areas with limited soil data and for areas with
difficult access. They are also made to locate large areas of
soils for planning the soil resources of states and nations. The
soils, their patterns, and the composition of each map unit are
identified by mapping selected areas (15 to 25 sq. miles) with
-3-
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Order 1 or Order 2 surveys or, alternatively, by transecting.
Mapping is by widely spaced observations or by remotely sensed
data with occasional verification by observation or traversing.
Soil maps can be a very useful tool in planning the use or
development of a tract of land. Soil maps, however, must be used
within the purposes for which they were designed.
A generalized soil map of a state at 1:750,000 scale should
not be used to locate soils or soil suitability of individual
fields or house lots. It is useful for planning the broad use of
a state's soil resources. A 1:20,000 soil survey map is useful
for planning fields, farms, and communities. It is not useful
for planning 0.1 acre research plots.
In many places the pattern of soils is very complex, and in
some places soils grade Imperceptibly to other soils. Because of
this, the soil units, even on a large-scale soil survey map, may
not be absolutely homogenous or pure; thus on-site investigations
are needed for specific small land area uses. For example, on-
site investigations are needed to determine the suitability of a
0.1 acre plot for a septic tank installation for mapping units on
Order 2 soil survey maps.
A common misuse of soil maps is to "blow them up" to a
larger scale. This does not result in a more detailed or ac-
curate map. In fact, the "blown up" map is misleading because if
the mapping was made at the larger ("blown up") scale, more
detail could be shown. Soil survey maps at 1:20,000 scale "blown
up" to 1:12,000 are no more accurate or detailed than the original
1:20,000 map.
Many times the information on soil maps is transferred to
other base maps, often at different scales. This diminishes the
new map's accuracy, especially if the base map is not planimetrically
correct, and more so if the scale is larger than the soil survey
map.
An example is transferring a 1:20,000 soil survey map to a
larger scale, 1:12,000 town map. When the soil survey map in-
formation (1:20 scale) is transferred to a smaller scale map,
1.e., 1:50,000, accuracy is generally maintained but the detail
of the soil map is less.
-4-
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It is common practice to use soil maps as a basis for making
soil suitability or soil interpretation maps for a specific use.
Technically, these suitability maps are not "soil maps" but are
single purpose "soil use potential" maps. These maps are gen-
erally colored green, yellow and red to show good, fair and poor
suitability for a given use, or some other scheme is used to
combine like soils for a given use. These soil suitability maps
are very useful to planners when making decisions for land use.
These soil suitability maps have the same credibility and limita-
tions of the soil maps from which they are made. In addition, it
must be borne in mind that soil suitability maps are made only on
the basis of soil properties which affect that particular use.
Soil suitability maps do not take into account such things as
present land use, size of area, location, markets, roads, water
bodies, accessibility; and other economic, esthetic, and environ-
mental factors not tied directly to soil properties. These non-
soil factors also affect the use and potential of a parcel of
land. In some cases the non-soil factors may make it feasible
and desirable to develop a piece of land for a particular use
that has poor soil suitability for that use.
It is important to recogni2e the different kinds of soil
maps, to know their merits and limitations and to understand the
relationship of map scale, map accuracy, and map detail. With
these principles in mind, a soil map can be a useful tool for the
planner of small tracts of land, farms, communities, states,
nations, and even the world.
-5-
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GUIDE TO MAP SCALES AND MINIMUM SIZE DELINEATIONS
Map Scale
Inches Per Mile
Minimum Size Delineation1
Acres
Hectares
1:500
126.7
0.0025
0.001
1:2,000
31.7
0.040
0.016
1:5,000
12.7
0.25
0.10
1:7,920
8.00
0.62
0.25
1:10,000
6.34
1.00
0.41
1:12,000
5.28
1.43
0.57
1:15,840
4.00
2.5
1.0
1:20,000
3.17
4.0
1.6
1:24,000
2.64
5.7
2.3
1:31,680
2.00
10.0
4.1
1:62,500
1.01
39
15.8
1:63,360
1.00
40
16.2
1:100,000
0.63
100
40.5
1:125,000
0.51
156
63
1:250,000
0.25
623
252
1:300,000
0.21
897
363
1:500,000
0.127
2,500
1,000
1:750,000
0.084
5,600
2,270
1:1,000,000
0.063
10,000
4,000
1:5,000,000
0.013
249,000
101,000
1:7,500,000
0.0084
560,000
227,000
1:15,000,000
0.0042
2,240,000
907,000
1:30,000,000
0.0021
9,000,000
3,650,000
1:88,000,000
0.0007
77,000,000
31,200,000
*The "minimum size delineation" is taken as a 1/4 x 1/4 inch square area
(1/16 sq. in.)- Cartographically, this is about the smallest area in which
a symbol can be printed readily. Smaller areas can be delineated, and the
symbol lined in from outside, but such very small delineations drastically
reduce map legibility.
-7-
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A soil survey is a field investigation of the soils of a specific area,
supported by information from other sources. The kinds of soil in the sur-
vey area are identified and their extent shown on a map, and an accom-
panying report describes, defines, classifies, and interprets the soils.
Interpretations predict the behavior of the soils under different uses and
the soils' responses to management. Predictions are made for areas of soil
at specific places. Soils information collected in a soil survey is useful
in developing land-use plans and alternatives involving soil management
systems and in evaluating and predicting the effects of land use.
A map unit is a collection of soil areas or miscellaneous areas deli-
neated in mapping. A map unit description is generally an aggregate
description of the delineation of many different bodies of a kind of soil
or miscellaneous area but could conceivably consists of one delineated
body. Map units are usually named for taxonomic units.
Depending on the complexity of the soils, the purpose for which the
survey is made and the scale of mapping, the map units may be simple or
complex.
Map units in Maine for this project are associations of soil series.
The soil maps were made at 1:62,500 scale USGS-topographic base. The maps
were made by using existing soil maps if available, air photo interpre-
tation and traversing part of the watershed to confirm map units.
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-2-
Map units can be interpreted by the soil series components of the asso-
ciation. Confidence of interpretations, of course, depends on the purpose
for which the survey is made. At this intensity of mapping the minimum
size delineation can be as small as 40 acres. These maps can be
interpreted for planning: management of forests, watersheds, wildlife
refuges, rangeland and other extensive uses. Broad or general rela-
tionships between soils and their use or reaction to inputs can be made.
The hydrology of the watersheds can be related to SCS hydrologic groups
for each series in the watershed and can be generalized for each map unit
or for the whole watershed.
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Appendix 3 DEVELOPMENT AND USE OF GRAPHICAL SOLUTION OF BINOMIAL
CONFIDENCE LIMITS IN SOIL SURVEY
Richard W. Arnold
Cornell University
INTRODUCTION AND BACKGROUND
The Soil Resource Inventory Group (1978) at Cornell University had been look-
ing for methods to estimate the mininum number of observations required to
check the accuracy of an existing soil survey nap. Dos Santos (1978) and
Perez-Oraa (1979) used a cluster analysis equation and worksheet developed
by the group for analyzing map unit composition. The cluster analysis ap-
proach was based on the brief discussion by Cochran (page 500) in Snedecor's
(1956) book, STATISTICAL. METHODS. It is a method for calculating the stand-
ard deviation of a sarrple proportion of a bincmial distribution where clusters
are of unequal size. Randan transects of varying lengths crossing delinea-
tions of a map unit were considered to be clusters of unequal size. Having
determined the standard deviation it was possible to calculate the standard
error, confidence limits for a given probability level, and even estimate the
nunber of average-length transects required to obtain a state degree of ac-
curacy of the population mean. Although the calculations are not difficult,
they are time-consuming and seem to command too much effort by field men for
the results.
The work by Dos Santos (1978) indicated that sampling strategies for various
types of landscapes could be developed. In some areas closely spaced point
transects were most efficient, while in others it might be line transects,
or points spaced farther apart. In none of his instances did pilot area map-
ping show to be as efficient in determining carposition at the same level of
accuracy.
Several papers came to our attention that provided ideas for a quicker solu-
tion. Van Gendersen et al. (1978) presented a method for allocating a number
of sarrple points in a class of interpreted land use to estimate the classifi-
cation accuracy. The concept incorporates the probability of making incorrect
interpretations at prescribed accuracy levels of interpretation. To us this
sounded similar to inclusions in soil map units. Their method indicates the
number of errors for a particular sanple size using the bincmial expansion and
is useful for determining the minimum number .if randomly selected ground truth
points for an a level of 0.05 (1 in 20 chance of being wrong). I think it was
their discussion of having errors that motivated us to keep trying until we
could reproduce their answers. We eventually realized that the errors are
sinply (1-number observed) listed in Snedecor's tables of confidence inter-
vals for binonial distributions (1956, pages 4 and 5).
A paper by Hord and Brooner (1976) demonstrated how to determine the accuracy
of classification using a quadratic equation to estimate confidence intervals
for a binomial probability function. They prepared tables for sanple nunbers
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2
frcm 50 to 200 in 50 unit intervals which gave limits of accuracy frcm .8
to 1.0. We eventually learned how to solve the quadratic equation on a hand
calculator. The two roots of the equation are the upper and lower confidence
limits for a given sample size with a certain error and at a particular prob-
ability level. This meant we could reconstruct the tables shown by Snedecor.
Graphs of accuracy or confidence limits versus sample sizes are exponential,
clumsy to use, and hard to read. What we wanted was a way to relate the num-
ber of errors (inclusions) to the total sample and estimate the accuracy.
The trick is to take a particular sample size, say 40, and for each error
frcm 0 to 40, calculate the confidence limits using the quadratic equation
approach described by Hord and Brooner. Then you interpolate between values
to obtain the error term associated with a particular accuracy. For exanple,
with a 1 in 10 chance of being wrong (90% probability level) an accuracy of
80% corresponds to about 12.3 errors and an accuracy of 60% corresponds to
about 20.8 errors. We kept doing this until we could draw lines connecting
the same levels of accuracy and they turn out to be straight lines. How con-
venient!
The graphs we have produced use the number of ground truth observations on
the Y axis and the number of "other than" class members on the X axis. Pre-
viously, we called the X axis the errors but realized that inclusions are not
really errors, they are simply observations that belong to classes other than
the one we have called the delineation. The levels of accuracy (maximum =
upper confidence limit and minimum = lower confidence limit) are shown as
straight lines and interpolations can be made between them. The estimates
obtained by this graphical solution are more than adequate for our purposes
in soil survey. In fact, they are adequate for much of our research as well.
USE OF CONFIDENCE LIMITS
When we use classes that are mutually exclusive the decisions about any one
class membership constitute a binomial experiment. We note that an observa-
tion either belongs to the class of interest, or it belongs to sane other
class. It is included or excluded; it is a yes or no decision.
In making probability statements you have trade-offs. For any set of obser-
vations, you can vary the chances of being wrong (probability level) or you
can vary the limits of accuracy (degree of correctness). It is always a ccm—
premise. If you want to be really confident of your statement (say only 1
chance in 100 of being wrong) the limits will be very wide. On the other
hand, if you like to gamble (1 chance in 5 of being wrong), then the stated
limits will be very narrow. If a sample is truly representative of a larger
population about which one wants to say seme thing, then by increasing the
number of samples the limits will beoenve narrower. This is easy to under-
stand when you consider that if we measured all pedons in a map unit we would
have a perfect fit and the answer vrould be absolutely correct. And if we do
that there is no need for statistics!
In the graphs presented there are only two levels of probability. One set is
for 1 in 10 chances of being wrong (90% probability level) and the other set
is for 1 in 20 chances of being wrong (95% probability level). For each prob-
ability level there are 4 graphs; 2 giving confidence limits for samples up
-------
3
to 350. Thus, you have seme flexibility in the size of your sanple and in
the probability level.
A lower confidence limit lets you make an at least statement. When you make
40 observations and 10 belong to other classes, the measured percent is 75%
and graphically you note that at least 62% is estimated to be the sarre class
(1 in 10 chance of error).
An upper confidence limit lets you make an at most statement. With the pre-
vious exanple you note that at most 83% is estimated to be the same class.
All too often we report only our guesstimate of the proportion found in a
sample or suspected of being found. It is more realistic to give ranges
based on the sanple data at our disposal. Every decision we make is based
on our perception of the correctness of the information and on our percep-
tion of the risk or expected consequence of making this decision versus an
alternative decision.
How Many Samples to Take
The minimum number of observations to make varies with the chances of being
wrong (probability level) and the level of accuracy (degree of correctness)
desired.
The graphs for the lower confidence limit can be used to estimate how many
sanples will be needed. Set probability at 90% and assume you want your
estimate to be at least 80% accurate when applying the sample results to the
rest of the map unit. Follow the 80% line for minimum level of classifica-
tion accuracy down to the Y axis where there are 0 "other than" class members
and you note 14. This means you need 14 random observations all belonging to
the same class, that is 14 out of 14. If you expect, or find, 3 observations
that belong to other classes, then go to 3 on the X axis and vertical till
you cross the 80% accuracy level and over on the Y axis where it indicates a
need for about 38 observations. That is with 35 out of 38 observations be-
longing to the same class, you will expect an 80% accuracy of the major com-
ponent.
Another way to think about samples is when you plan to take 200 samples (ob-
servations) then you must not have more than about 27 observations in other
classes if you hope to at least achieve 80% accuracy.
The graphs for upper confidence limits are not applicable to estimate sanple
numbers. By looking at one of the upper limit graphs, you can see that the
lines do not intercept the Y axis above zero, and we do not knew what a nega-
tive sarrple is.
Estimating Composition
Based on the proportion you find in a sanple, you are trying to estimate what
the proportion will be in a larger population. You extrapolate from measure-
ments made in a few delineations to what may be true for the whole map unit.
Assume you made 4 transects having 3, 9, 7, and 11 observations for a total
of 40. Out of that 40 only 30 belonged to the same class. The predicted max-
-------
4
imum accuracy would be about 83% and the miniinum accuracy would be about 62%.
You, therefore, are estimating that the map unit will have between 62 and 83%
of the major component based on your set of observations and assuming a 1 in
10 chance of being wrong.
Estimates of each component can be obtained from the graph, or if necessary,
by extending the graph if you maintain the same intervals on both the X and Y
axes.
This same procedure applies to consociations, complexes, and associations, it
also applies to other features such as stoniness, rock outcrop, and in as many
ways as your mind decides to try.
SOME COMMENTS
1. We live with risk and assessment of correctness of information, but we are
not used to seeing it quantified. This is a matter of training in the ac-
quisition and interpretation of such data.
2. Binomial experiments and decision-making based on such experiments are the
basis for most activities in soil survey.
3. Graphical solutions permit us to concentrate on the nature and precision of
our observations rather than worry about statistical calculations.
4. As field men become familiar with doing data collection as part of their
mapping operations, they will have irrmediate feedback about the degree of
variability they are observing and it will be in a numerical format.
5. Good data collection eases the strain of soil correlation. The facts will
stand up and logical solutions will be more evident.
6. Map unit composition is nothing more than a measure of the correspondence
of two or more sets of features. Ccnposition can be in terms of interpre-
tations just as it can be in terms of taxonomy.
7. Statements of probability with their associated limits on accuracy are the
results of testing the models and hypotheses we use to segment the earth's
surface. They are the tests of taxonomy and they are tests of interpreta-
tions.
8. We have a long way to go in telling others what we do, how we do it, and
how to convey our findings. Gut do not forget, we have come a long way,
Baby!
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5
REFERENCES
Dos Santos, H. G. 1978. Seme strategies of quality control for reconnaissance
soil survey. Unpublished M.S. Thesis, Cornell University, Ithaca, N.Y.
128 p.
Hord, R. M. and W. Brooner. 1976. Land-use map accuracy criteria. Photogram-
metric Engineering and Remote Sensing 42, No. 5:671-677.
Perez-Oraa, J. C. 1979. Quality control and characterization of soil survey
with special reference to Venezuela. Unpublished MPS (Agric.) Project
Report, Cornell University, Ithaca, N.Y. 230 p.
Snedecor, G. W. 1956. Statistical Methods. Iowa State University Press, Ames.
534 p.
Soil Resource Inventory Group. 1978. Guidelines for soil resource inventory
characterization. Unpublished draft, Department of Agronomy, Cornell
University, Ithaca, N.Y. 168 p.
Van Genderen, J. L., B. F. Lock and P. A. Vass. 1978. Remote sensing: statisti-
cal testing of thematic map accuracy. Remote Sensing of Environment 7:3-14.
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50
85%,
95%/ 92%/90%
9
45
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NUMBER OF "OTHER THAN" CLASS MEMBERS
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90%/ 85%/ 80%/ 75%/ 70%
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-------
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60%,
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200
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September 1984
Appendix 4
SUMMARY OF PLANNING MEETING
SOIL/WATER PILOT SURVEY
Orono, Maine
July 23-24, 1984
I. INTRODUCTION
The following institutions (or agencies) were represented:
EPA — ERL-Corvallis
University of Maine
Cornell University
USDA-SCS — Washington, D.C.
USDA-SCS — Maine
USDA-SCS — New York
See attached list for names and addresses of participants.
The group engaged in extensive discussions of various aspects of the
program, including the objectives and the various aspects of the approach,
including site selection, field data collection, field sampling, and
analytical methods. This summary presents the conclusions of the group
concerning the approach and the methods to be used. It is not intended to
be a recapitulation of the discussions.
1
-------
II. OBJECTIVES
A. General Objectives
The general objectives of the pilot program, as presented to the
grpup, are:
1. Conduct pilot survey (including laboratory analyses) to deter-
mine feasibility of using currently available soil association
maps for regional generalization of soil properties relevant to
surface water acidification,
2. Develop and test the organizational structure, field procedures,
and laboratory capability required to carry out an expanded
survey activity in 1985 for the purpose of regional generaliza-
tion of soil characteristics that control the sensitivity of
surface water to acid deposition.
There was no change in the objectives per se as a result of the
discussion. However, the need for some clarification concerning the use
of the term "soil association" became apparent. Soil associations may be
defined at various levels of aggregation or detail. While soil associa-
tion maps at a scale of 1:750,000 are apparently available for all states,
in many of the areas of interest more detailed association maps are also
available. It became apparent from the discussion that the divisions set
forth on the statewide 1:750,000 maps may not always be those best suited
to the present purpose. For instance, in the Adirondack area of New York
the associations as delineated on the 1:750,000 map are aggregated to the
point that the relationships of interest likely would not be discernible.
As more detailed maps (1:62,500) are available for this area, associations
to be tested will be selected from those. It should also be understood
2
-------
that, while aggregation from greater to lesser detail is always possible,
dis-aggregation is not.
B. Specific Objectives
As a result of the discussion at the meeting, a set of more
specific objectives for the Pilot Soil Survey emerged. These may be
summarized as follows.
For each of three or four major soil associations in each of the
states of Maine and New York:
1. Determine the reliability of existing soil association maps in
predicting what soils occur at specific National Surface Water
Survey sites.
2. Determine the homogeneity of soil associations with respect to
soil characteristics that control the sensitivity of surface
water to acid deposition.
3. Determine whether the acid deposition characterizations can be
related to the standard SCS characterization, so that existing
data bases can be used for extrapolation.
III. SITE SELECTION
Approximately 25 small (generally < 30 km2) watersheds containing
lakes to be sampled in the National Surface Water Survey (NSWS) will be
selected in each state. These watersheds will represent three or four
t major soil associations. While the ideal would be an equal number of
sites in each association, on a practical basis this may not always be
possible. However, any soil association selected should be represented by
at least 5 sites.
3
-------
A. Soil Associations
Factors to be considered in selection of associations include:
1. Number of lakes of appropriate size in NSWS sample within the
area represented by the soil associations. ERL-Corval1 is will
furnish a list of lakes within each state selected for the NSWS
sample. The list includes lake name, coordinates, and identifi
cation of USGS maps showing the lake. An estimate of watershed
size (either by area or size class) will also be furnished.
2. Range of Soil Properties
Within the constraints imposed by (1) above the associa-
tions should represent as wide a range of soil properties as
possible. Differences may be due to factors such as bedrock
type, surficial geology (parent material), topography, etc.
Because the factors that will determine the available range of
properties among soil associations will differ in each situa-
tion, no set protocol will be defined for selection of associa-
tions. The selection of associations will be left to the
judgment of the individual state groups.
State groups will clearly identify the criteria used in
selection of associations. A copy of the soil association maps
used for this purpose will be furnished to ERL-Corval1 is along
with a list of the mapping units that fall within each associa-
tion.
4
-------
Watershed Selection (Within Associations)
1. Using the appropriate soil association map and the list
(III.A.l, above), furnished by ERL-Corvallis, plus any other
available information on watershed size, prepare a list of
watersheds less than 50 km2 in area that fall within each of the
selected soil associations. If sufficient candidate watersheds
are available, those larger than 30 km2 may be deleted.
2. Pick random samples (including alternates) from each associa-
tion.
3. Check watershed size on 7-1/2' or 15' maps. Drop those that are
too large, replacing with alternates (note, watersheds may also
be replaced by alternates at later stages if watersheds should
prove unsuitable due to agricultural or urban usage, access
unobtainable, etc.).
Watershed Data -- Pre-visit
1. Check available surveys.
2. Locate and sketch watershed on aerial photos, or on topograph-
ical maps if photos are not available. Preferred scale is
1:62,500.
3. If survey is available, sketch in soils. If survey not avail-
able, make predictions on soils using photo-interpretation
and/or available maps.
4. Justify sampling sites.
Approximately 25 sites representing the major soil series
encountered in the survey will be sampled. While these should be
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drawn from as wide a distribution of the sampled watersheds as
possible, the sampling sites will not necessarily be on a 1
watershed:! sample basis. In some cases a site selected for
sampling a particular series may not actually be within a
watershed (avoid if possible). Number of sampled sites/series
will roughtly reflect the occurrence of the series on the
selected watersheds. Potentially important groups such as
histosols will be represented in the sample, even though they
may not be dominant in any particular watershed. In order to
minimize travel and sampling time, a preliminary selection of
sampling sites should be made during the pre-visit stage.
Site Visit
1. Verify previously prepared sketch, modifying as necessary.
2. Sample and describe profile.
At each site selected for sampling, the profile will be
described (form 232A) at one sample point. Analytical samples
will be composited from the sample point and 9 satellites.
Approximately 2 quarts of sample will be collected from each
horizon as follows:
0 -- Scrape off litter.
A or E -- Sample only if sufficiently thick so that 2 quarts can
be collected.
B -- Sample top one half.
C — If C not present, sample lower B.
Samples will be air dried, crushed, and split (standard SCS
methods) (Soil Survey Investigations Report #1). One half of
-------
each will be retained at the state level, the remainder sub-
mitted to a laboratory designated by ERL-Corvallis.
3.' Describe watershed.
a. Percent of each major (3-5) series.
b. Vegetation types -- Use current SAF descriptors.
c. Topography — Percent slope, average length, configuration,
stream type (i.e., dendritic, etc.) and density.
d. Geology — Type of bedrock, fractioning (Y/N), percent
exposed, type of sampling material (till, outwash, etc.).
Locate site on bedrock and surficial geologic maps (if
available).
IV. ANALYSIS
A. Introduction
The standard procedures referred to below are drawn from four
major sources as follows:
SSIR, Soil Survey Laboratory Methods and Procedures for Soil Samples.
Soil Survey Investigations Report No. 1. USDA Soil Conservation
Service. Washington, D.C. (1972).
MSA 1965, Methods of Analysis. C. A. Black (ed.). American Society
of Agronomy. Madison, WI. Parts 1 and 2 (1965).
MSA 1982, Methods of Soil Analysis. A. L. Page (ed.). American
Society of Agronomy. Madison, WI. Parts 1 and 2 (1982).
NCASI, Field Study Program to Assess the Sensitivity of Soils to
Acidic Deposition Induced Alterations in Soil Productivity.
7
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Technical Bull. No. 104. National Council of the Paper Industry
for Air and Stream Improvement (1983).
Many of the procedures in these references are similar or identical.
In particular, a number of the NCASI procedures are drawn from the other
sources. Procedures recommended in this paper were selected by
ERL-Corvallis on the basis of suitability for the needs of the program and
compatibility with procedures presently in use at the laboratories in the
states that will be participating in the 1984 pilot program. Prior to
final selection, procedures were discussed with participating scientists
at the Orono, Maine, workshop, July 23-24, and in some cases with outside
scientists.
B. Sampling
1. Field Sampling
For this study a small sampling area (one half ha or less)
representative of the site and series to be sampled will be
selected. One sampling point will be selected for profile
description (form 232A). However, the samples collected for
analytical purposes will be composites from the described
sampling point and the 9-10 satellite points (in difficult
sampling situations the number may be reduced but should not be
less than 5). Samples will be collected from the 0 horizon
(after removal of loose litter), the A or E horizon if the
horizon is sufficiently thick so that adequate sampling volume
can be collected, the top half of the B horizon, and the C
horizon. If no C horizon is present the lower half of the B
8
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will be sampled. Two quarts of soil will be collected from each
horizon.
2. Sample Preparation
Follow SSIR procedures 1B1-1B4 (these are apparently the
same or very similar to those in NCASI). Do not grind. After
drying and thorough mixing, one quart will be retained at the
state level for characterization analysis and one quart sub-
mitted to a central laboratory designated by ERL-Corvallis.
C. Physical Analysis
1. Particle Size Distribution
After removal of organic matter the percent sand, silt, and
clay will be determined by the standard pipette method (SSIR
3A1). Fraction sizes (mm) to be recorded are: sand 2-0.50,
silt 0.50-0.002, clay < 0.002. Separation of sand fractions by
sieving is not necessary. If the particle size distribution is
available from other sources, it will not be necessary to
perform this measurement.
2. Bulk Density
Bulk density information is needed by horizons. If
adequate BD information on the series in question is not
available from other sources, we would suggest that the clod
method, using either the paraffin coating (MSA 1965, page 381;
NCASI, page 10) or the Saran coating (SSIR 41Ab, page 15)
applied to air dry clods would be most appropriate.
9
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3. Water Content (Desorption Curves)
These are not a high priority item. However, the 15 bar
desorption curve might be useful if equipment is set up for this
purpose.
D, Chemical Analysis
1. Standard Characterization
a. Organic Carbon
The preferred method is a dry combustion such as that
given in MSA 1965 (page 543). The procedure in NCASI is
identical. The procedures given in SSIR (6Ala or 6A2,
pages 26-27) should also be satisfactory. However, the two
state groups involved in the 1984 pilot program are using
wet combustion procedures. It does not appear feasible for
the state labs to utilize a dry combustion for this
purpose. They should utilize their present procedures,
clearly identifying the method and source. Organic carbon
should also be run by the central lab, using either a dry
combustion or an instrumental combustion method (see
below).
Ordinarily, we would not expect removal of inorganic
carbon to be necessary for samples from the regions
selected for the pilot survey. However, in any areas where
carbonates might possible be encountered, a test for
inorganic carbon should be made (MSA 1982, page 563; or
NCASI, page A25). If carbonates are present they should be
removed (MSA 1982, page 565; NCASI page A25).
10
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NOTE: Some laboratories may be equipped with instru-
ments that simultaneously measure total C and total N.
These instruments can be successfully used on soils if
experienced operators are available and appropriate
precautions are taken. If instruments of this type are to
be used (for example, the Perkin-Elmer 240), the laboratory
should submit detailed procedures along with information on
calibration against dry combustion methods to the EPA
Project Officer for review.
Total Nitrogen
The Kjeldahl method is most widely used. The version
given by Bremmner (MSA 1982, page 610; NCASI, page A29) is
suggested. However, most of the results available in the
current data base have probably been obtained by either the
macro- or semi-micro method given in SSIR (6B, page 29).
Either should be satisfactory. Also, see note on Instru-
mental Methods under Organic Carbon, above).
pH
For this survey pH should be measured in both water
and 0.01 M CaCl2. The procedures used for pH determina-
tions can vary quite substantially. One of the most
significant differences is whether the reading is taken on
a suspension while it is being stirred (MSA 1982, page 20;
SSIR 8C1, 8C13, pages 58-59), or taken in a partially
settled suspension with the glass electrode immersed in the
11
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sediment while the reference electrode is in contact only
with the supernatant liquid (MSA 1965, page 902; NCASI,
page A26).
There probably is no satisfactory answer. The most
extensive analytical data base available for survey
interpretation is that of USDS-SCS, apparently on stirred
suspensions. The state labs are currently using measure-
ments on settled systems. As the lab operators report that
the correspondence of pH measurements made by this method
with the SCS national lab results has been good, use of the
sediment measurement is considered satisfactory. There-
fore, pH will be measured on 1:1 soil:water and 1:1 0.01 M
CaCl2 solutions using current laboratory methods. Again
the exact procedure followed will be reported.
d. Fe and A1
Both Fe and A1 will be determined on three extracts,
i.e., sodium pyrophosphate (SSIR 6C5, page 32), ammonium
oxalate (SSIR 6C6, page 32), and dithionite-citrate-
bicarbonate (SSIR 6C2, page 31; NCASI, page A36). Deter-
mination of the Fe and A1 concentrations in the extracts
will be by atomic absorption, flame photometry or ICP
emission spectroscopy.
A1 will also be determined on a 1 N KC1 extract using
a 30 minute extraction time (SSIR 6G1, page 36). NOTE:
see Extractable Acidity, below.
12
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Exchangeable (Extractable) Acidity
Undoubtedly the most common determination for exchange
acidity is the barium chloride-triethanolamine (pH 8.2)
method. This method probably gives less information
relative to the acid deposition problem than does a non-
buffered extraction such as KC1. However, since results
from analysis by this method are most likely to be avail-
able in current data bases, it should be included in the
characterization. The SSIR method (6H1, page 38) seems to
be essentially the same as that given in MSA 1982 (page
163) and by NCASI (page A30). Either should be satis-
factory.
A measure of neutral salt extractable acidity is
essential for the present purpose. The 30 min KC1 extract
seems appropriate. NCASI recommends the titration method
(MSA 1982, page 163; NCASI, page A32) giving a measure of
total extractable acidity. The most closely corresponding
measurement in the USDA data base would be KC1 extractable
A1. As the extraction appears virtually identical, we
recommend determining A1 by AA or ICP on the extract prior
to titration, so that both measures are available.
CEC and Extractable Base Cations
For our present purpose the most useful estimates of
CEC would appear to be either derived from saturation with
a neutral salt (e.g., NH4CI) or from a summation of
extractable bases and a neutral salt exchange acidity.
13
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Again, we are faced with the problem of compatabi1ity with
currently available data. For the pilot survey it would
seem prudent to include the more commonly available NH4NOAC
(pH 7.0) measurement of CEC (SSIR 5A1, page 22 or 5A6 page
24; MSA 1965, page 894). The extractable bases, i.e., Na,
K, Ca, and Mg shold also be determined on the NH4OAC
extract either by a combination of flame emission (Na and
K) and AA (Ca and Mg), by AA alone, or by ICP.
2. Special Analysis (Central Laboratory)
It is contemplated that a variety of special analyses will
be conducted on a split sample submitted to a central labora-
tory. In addition, at least part of the standard characteriza~
tion analyses will be repeated at this laboratory. Due to the
fact that organic carbon will be determined at the state level
by the use of a wet combustion method, it should definitely be
determined by the central laboratory using either a dry combus-
tion or an instrumental method (see IV.A.6 above).
Special analyses currently under consideration include:
a. Neutral Salt CEC and Extractable Bases
The 1 NH4CI extraction will most likely be used (SSIR
5A7 included in the "addendum"; NCASI, page A29).
b. Lime Potential
While in some soils the pH as measured in 0.01 M CaCl
may be used to estimate the lime potential (pH - 1/2 pCa)
14
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or the H-Ca activity ratio (H/(Ca)l/2), in acid soils with
low permanent charge the amount of Ca-Al exchange that
would take place in this comparatively high Ca environment
is likely to substantially modify the Al-Ca ratios on the
exchange complex. Lime potential should therefore be
measured by equilibrating with a more dilute solution of
CaCl2 (possibly 0.002 M), followed by measurement of both
pH and Ca2+ concentration. Determination of monomeric A1
on this solution could also give an estimate of the H-A13+
activity ratio, providing that the inorganic monomeric A1
can be satisfactorily separated from organically bound A1.
c. Extractable Sulfate
Both water and sodium phosphate extractable sulfate
will be determined on all samples. The procedure will most
likely be that given by NCASI. Ion chromatography will be
required for all sulfate determinations.
d. Sulfate Adsorption Isotherms
Sulfate adsorption isotherms will be determined on all
samples. Tentative plans are to use a series of 6 solu-
tions with 0, 2, 4, 8, 16, and 32 ppm-S (0, 62.5, 125, 250,
500, and 1000 ueq 1-1, respectively), as calcium sulfate,
and a 1:5 soil solution ratio. These will be shaken,
followed by either filtration or centrifugation, and
sulfate determined with by ion chromatography.
15
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FUNDING MECHANISM
Cooperation with the EPA and the existing soil survey community is
essential for both the 1984 Pilot Soil Survey and 1985 Soil Survey. Thus,
the Pilot Soil Survey will proceed through Interagency Agreements (IAG's)
with the state SCS offices in New York and Maine. In turn, these offices
will enter into agreements with cooperators at the corresponding land-
grant universities, Cornell and University of Maine. Also, an IAG with
the SCS in Lincoln, Nebraska, will provide soil analyses by the National
Soil Survey Laboratory. This laboratory will serve as the central labora-
tory for the Pilot Soil Survey.
REPORTS AND MILESTONES
Field work begins 09/10/84
Field data sheets, including sketch maps (each state) 11/30/84
Soil sample analysis data (each state and central lab) 12/31/84
Interpretive workshop 01/15/85
Final reports (each state and central lab) 01/31/85
Final report for Pilot Survey 03/30/85
16
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PLANNING MEETING
SOIL/WATER PILOT SURVEY
Orono, Maine
July 23-24, 1984
Attendees
Richard Babcock
State Soil Scientist
USDA Soil Conservation Service
USDA Building
University of Maine
Orono, Maine 04473
FTS 833-7493
Raymond Bryant
Department of Agronomy
Cornell University
Ithaca, New York 14850
(607) 277-0065
Mark B. David
Corvallis Environmental
Research Laboratory
200 S.W. 35th Street
Corvallis, Oregon 97333
FTS 420-4578
Ivan Fernandez
University of Maine
1 Deering Hall
Orono, Maine 04469
(207) 581-2932
John A. Ferwerda
Soil Consultant
MRB Box 65
Bangor, Maine 04401
(207) 945-2839
Robert V. Joslin
USDA Soil Conservation Service
USDA Building
University of Maine
Orono, Maine 04473
- FTS 833-7393
t
Kenneth J. La Flamme
USDA Soil Conservation Service
USDA Building
University of Maine
Orono, Maine 04473
(207) 866-2132
Jeffrey J. Lee
Corvallis Environmental
Research Laboratory
200 S.W. 35th Street
Corvallis, Oregon 97333
FTS 420-4758
Milton W. Meyer
USDA Soil Conservation Service
P.O. Box 2890
Washington, D.C. 20013
(202) 382-1832
John Reuss
Corvallis Environmental
Research Laboratory
200 S.W. 35th Street
Corvallis, Oregon 97333
FTS 420-4578
Robert V. Rourke
University of Maine
102 Deering Hall
Orono, Maine 04469
(207) 581-2936
R. A. Struchtemeyer
Struchtemeyer Soil Service
RFD 1, Box 307
Old Town, Maine 04468
Keith Wheeler
USDA Soil Conservation Service
Room 771, Federal Building
100 S Clinton Street
Syracuse, New York 13260
FTS 950-5192
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Appendix 5
DRAFT AGENDA TOPICS
PILOT SOIL SURVEY INTERPRETIVE WORKSHOP
Envi ronmental Research Laboratory
Corvallis, Oregon
January 21-25, 1985
I. Overview
A. Soil survey as part of Direct/Delayed Response Project: What is EPA
trying to accomplish? Where? When? Why? Exactly what data are
needed - how will they be used?
B. Proposed statistical structure and analysis; relationship to National
Surface Water Survey.
C. Resources available at ERL-C.
D. List of completed analyses; preliminary list of analyses to be
completed during workshop (iterative).
II. Discussion -- Overview
A. Conclusion on data requirements.
B. Preliminary discussion on statistical structure.
C. Written Summary: Data requirements; statistical structure.
III. PI reports — field operations: What was done? How? Why? What problems/
opportunities were identified? (Discussion at this time limited to points
of clari fication.)
1
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A. Selection of soil associations, watersheds, soil series to be sampled:
How were these chosen? By what criteria?
B. Pre-visit predictions: What resources were available? How useful
were they?
C. Site Visit: How were soil, vegetation sketch maps done? How
difficult, time-consuming? Did you find what you expected to find;
i.e. were pre-visit predictions confirmed? Conclusion on reliability
and utility of existing resources.
D. Soil Sampling: What criteria did you use to choose exact sampling
locations? Exactly how were samples obtained? Labeled? Documented?
Stored? Prepared for analysis? Split? Bring sample of shipping
contai ner.
IV. Discussion -- field operations
A. Pilot protocol: Was it followed? Ambiguous? Logistically manageable?
B. Survey protocol: Changes from approach of pilot? What needs to be
made morfe specific? Implications for QA of field operations?
C. Written Summary: Conclusions on adequacy of pilot protocol and
reliability of existing resources. Specific recommendations for
survey protocol for field operations.
V. Reports -- Lab operations
A. PI reports: What was done? How? Any deviations from pilot protocol?
Problems? How time-consuming?
B. Data management for pilot: What? How? What statistical analyses
have been done and are available? Which statistical analyses can and
should be done during this meeting?
2
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C. Data reports: Inter- and intra-lab comparisons; frequency distribu-
tions of parameters for each soil association; relationships between
parameters; other available analyses.
D. Reports on data analyses performed during this meeting (iterative),
VI. Discussion — Lab operations
A. Pilot protocol: Was it followed? Ambiguous? Other problems?
B. Inter and intra -- lab comparisons: Implications for QA of lab
operati ons.
C. Parameter di stri buti ons: Implications for use of soil associations
as mapping units for soil survey.
D. Parameter relationships: Implications for linking to existing data
bases.
E. Soil survey protocols: Are there parameters which can or should be
eliminated from list of planned analyses? Added to this list? Should
pilot protocols be modified? Made more specific?
F. Written Summary: Conclusions on adequacy of pilot protocols. Con-
clusions on use of soil associations as mapping units, and on linking
to existing data bases. Specific recommendations for parameters and
protocols for survey.
VII. Organizational Issues
A. Statistical structure and analysis revisited: Mechanism for site
selection; proposed analyses.
B. Quality Assurance: Management; information flow; field operations;
data quality objectives; replication; reports.
3
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C. Analytical Lab(s): One or several labs; criteria for selection;
mechanism for selection.
D. Responsibilities: Role of EPA, SCS, Universities; efficient funding
mechanisms; potential cooperators.
E. Written Summary: Conclusions on approaches to statistical structure
and analyses; site selection; QA; selection of analytical labs;
responsibilities of participants.
VI11.Draft Workshop Report: An integrated document synthesized and expanded
from the written summaries produced during the week, and incorporating the
data analyses performed during the week and any additional discussions on
each topic.
4
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Appendix 6
PARTICIPANTS
PILOT SOIL SURVEY INTERPRETIVE WORKSHOP
Corvallis, Oregon
January 21-25, 1984
Cooperators in Pilot Soil Survey:
New York: R. Bryant, W. Waltman, F. Ramos -- Cornell,
K. Wheeler, F. Gilbert -- SCS
Maine: R. Babcock, R. Joslin, K. LaFlamme -- SCS
I. Fernandez, J. Ferwerda, R. Rourke -- UMO
Virginia: R. Webb -- UVA
Nebraska: F. Kaisaki -- SCS (NSSL)
Modelers: J. Reuss — CSU, D. Marmorek -- ESSA, J. Schnoor -- UIA, S. Gherini
— Tetra Tech, K. Thornton -- Ford, Thornton, and Associates
Soil Scientists: D. Johnson, R. Turner -- ORNL, D. Grigal -- IMN,
R. Cline, E. Alexander — USDA-FS
Statistician: D. Stevens -- EOSC
Quality Assurance: G. Meier, P. Arberg, S. Simon, L. Blume -- EMSL-LV
Data Management: P. Kanciruk -- ORNL
State/Regional Respresentatives:
M. Meyer -- SCS (DC)
0. Rice -- SCS (Northeast Region)
L. Rati iff -- SCS (Southeast Region)
L. Langam -- SCS (Western Region)
S. Pilgrim, G. Rosenberg -- SCS (New Hampshire)
D. Van Houten — SCS (Vermont)
E. Sautter -- SCS (Connecticut and Rhode Island)
EPA Headquarters: T. Hinds, R. Linthurst
ERL-C: M. R. Church, M. David, J. Eilers, A. Herstrom, R. Lackey, D. Landers,
J. Lee, S. McCadden, T. Murphy, B. Rochelle, P. Shaffer, J. Sprenger,
R. Wilhour
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Appendix 7
PILOT SOIL SURVEY INTERPRETIVE WORKSHOP
Environmental Research Laboratory
Corvallis, Oregon
January 21-25, 1985
Addresses and Telephone Numbers
Earl Alexander
USDA-FS
630 Sansome Street
San Francisco, California
FTS 556-1564
Phi 1 Arberg
EMSL-LV
P.O. Box 15027
Las Vegas, Nevada 89114
FTS 545-2605
Richard Babcock
USDA - SCS
USDA Building
University of Maine
Orono, Maine 04473
FTS 883-7393
Richard Cline
USDA-FS
Northern Region
P.O. Box 7669
Missoula, Montana 59867
FTS 585-3614
Mark David
ERL-Corvalli s
200 S.W. 35th Street
Corvallis, Oregon 97333
FTS 420-4578
Joe Eilers
ERL-Corval1i s
200 S.W. 35th Street
Corvallis, Oregon 97333
FTS 420-4612
Louis Blume
EMSL-LV
P.O. Box 15027
Las Vegas, Nevada 89114
FTS 545-2241
Ivan Fernandez
University of Maine
1 Deering Hall
Orono, Maine 04469
207-581-2932
Ray Bryant
Department of Agronomy
Cornell University
Ithaca, New York 14850
607-296-3267
Robbins Church
ERL-Corvallis
200 S.W. 35th Street
Corvallis, Oregon 97333
FTS 420-2725
John Ferwerda
MRB Box 65
Banger, Maine 04401
207-945-3829
Steve Gherini
Tetra Tech, Inc.
3746 Mount Diablo Blvd.
Lafayette, California 94549
415-283-3771
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Fred Gi Ibert
USDA - SCS
Room 771, Federal Building
100 S. Clinton Street
Syracuse, New York 13260
FTS 950-5510
David Grigal
Department of Soil Science
University of Minnesota
1529 Gortner Avenue
St. Paul, Minnesota 55108
612-633-1472
Andrew Herstrom
ERL-Corvallis
200 S.W. 35th Street
Corvallis, Oregon 97333
FTS 420-4643
Ted Hinds
US - EPA
401 M Street, S.W. (RD-676)
Washington, DC 20460
FTS 382-5738
Dal e Johnson
Environmental Sciences Division
Oak Ridge National Laboratory
P.O. Box X
Oak Ridge, Tennessee 37830
FTS 624-7362
Robert Joslin
USDA-SCS
USDA Building
University of Maine
Orono, Maine 04473
FTS 833-7393
Frederick Kaisaki
USDA - SCS
National Soil Survey Laboratory
Federal Building, Room 345
100 Centennial Mall North
Lincoln, Nebraska 68508
FTS 541-5363
Paul Kanciruk
Environmental Sciences Division
P.O. Box X
Oak Ridge, Tennessee 37830
Robert Lackey
ERL-Corvalli s
200 S.W. 35th Street
Corvallis, Oregon 97333
FTS 420-4806
Kenneth LaFlamme
USDA-SCS
USDA Building
University of Maine
Orono, Maine 04473
FTS 833-7393
Dixon Landers
ERL-Corvallis
200 S.W. 35th Street
Corvallis, Oregon 97333
FTS 420-4695
Lucien Langan
USDA-SDC
National Technical Center
Federal Building
511 N.S. Broadway, Room 514
Portland, Oregon
FTS 420-2826
Jeffrey Lee
ERL-Corvallis
200 S.W. 35th Street
Corvallis, Oregon 97333
FTS 420-4758
Rick Linthurst
USEPA
Raleigh, North Carolina 27622
914-781-3150
David R. Marmorek
ESSA
678 West Broadway
Vancouver, British Columbia
CANADA V5Z 1G6
604-872-0691
Susan McCadden
ERL-Corvallis
200 S.W. 35th Street
Corvallis, Oregon 97333
FTS 420-4698
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Eugene Meier
EMSL-LV
P.O. Box 15027
Las Vegas, Nevada 89114
FTS 545-2203
Milton Meyer
USDA-SDC
P.O. Box 2890
Washington, DC 20013
FTS 382-1832
Thomas Murphy
ERL-Corval lis
200 S.W. 35th Street
Corvallis, Oregon 97333
FTS 420-4601
Sidney A. L. Pilgrim
USDA - SCS
Federal Building
Durham, New Hampshire 03824
603-868-7581
Frank Ramos
Department of Agronomy
Cornell University
Ithaca, New York 14850
607-256-3267
Larry Ratliff
USDA - SCS
National Technical Center
P.O. Box 6567
Fort Worth, Texas 76133
FTS 334-5224
John Reuss
Department of Agronomy
Colorado State University
Fort Collins, Colorado 80523
303-491-5335
Oliver Rice
USDA - SCS
National Technical Center
160 East 7th Street
Chester, Pennsylvania 19013
215-499-3960
Barry Rochelle
ERL-Corval1i s
200 S.W. 35th Street
Corvallis, Oregon 97333
FTS 420-4662
Gerald Rosenberg
USDA-SCS
Federal Building
Durham, New Hampshire 03824
603-352-3602
Robert Rourke
University of Maine
102 Peering Hall
Orono, Maine 04469
207-581-2936
Edward H. Sautter
USDA - SCS
16 Professional Park Road
Storrs, Conneticut 06268
FTS 244-2547
Jerald Schnoor
University of Iowa
Environmental Engineering, 1152
Iowa City, Iowa 52242
319-353-7262
Paul Shaffer
ERL-Corvallis
200 S.W. 35th Street
Corvallis, Oregon 97333
FTS 420-4665
Steve Simon
EMSL-LV
P.O. Box 15027
Las Vegas, Nevada 89114
Jeffrey Sprenger
ERL-Corvallis
200 S.W. 35th Street
Corvallis, Oregon 97333
FTS 420-4658
Don Stevens
Route 1, Box 116
Summerville, Oregon 97876
503-963-1632
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Kent Thornton
Fort, Thornton, Norton and Assoc.
3 Innwood Circle
Suite 220
Little Rock, Arkansas 72211
501-225-7779
Robb Turner
Environmental Sciences Division
Oak Ridge National Laboratory
P.O. Box X
Oak Ridge, Tennessee 37830
FTS 624-4175
David Van Houten
USDA - SCS
69 Union Street
Winooski, Vermont 05404
FTS 832-6795
Richard Webb
Department of Environmental Sciences
CI ark Hal 1
University of Virginia
Charlottesville, Virginia 22903
804-979-3844
Keith Wheeler
USDA - SCS
Room 771, Federal Building
100 South Clinton Street
Syracuse, New York 13260
FTS 950-5192
Raymond Wi1 hour
ERL-Corvallis
200 S.W. 35th Street
Corvallis, Oregon 97333
FTS 420-4634
Wi11i am Waltman
Department of Agronomy
Cornell University
Ithaca, New York 14850
607-256-3267
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