Chapter

9

By Skye Wills, Candiss Williams, and Cathy Seybold, USDA-NRCS.

Assessing Dynamic Soil 

Properties and Soil Change

Dynamic soil properties (DSPs) are properties that change with 

land use, management, and disturbance over the human time 

scale (decades to centuries). In contrast, inherent soil properties 

(e.g., soil texture) change little, if at all, with changes in land use and 

management. The term “dynamic soil properties” was used by Tugel et 

al. (2005) to describe soil properties that can be documented as a part 

of soil survey activities. The procedures for measuring and recording 

DSPs were later outlined in the Soil Change Guide (Tugel et al., 2008). 

The term DSPs has gained common usage among soil scientists when 

referring to properties that can be changed intentionally or inadvertently 

through human land use and management, either directly (as through 

tillage) or indirectly (as through causing acid rain). While many soil 

properties (such as moisture, temperature, and respiration) are dynamic 

on daily, or smaller, time scales, information about them is not included 

in current soil survey products. The DSPs addressed by soil survey 

include properties that reflect soil functions and can serve as indicators 

of soil quality (or health) or indicators of ecosystem services. Dynamic 

soil properties are more pronounced at or near the soil surface and can 

be used to evaluate changes and departure from a benchmark or set of 

reference soil properties. Conceptually, this allows DSPs to be correlated 

with map unit components used in traditional soil survey (see chapter 4). 

Importance of DSPs

Many land and water conservation programs in the U.S. depend 

upon management of dynamic soil properties. Proven conservation 

practices are used to maintain the soil’s productivity, health, and longterm sustainability. Conservation planning relies on the knowledge of 

the current state of the soil resource and what is achievable through 
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conservation practices. DSP assessments provide a range of potential soil 

property values that define what is achievable.

DSP data is used to document, explain, and predict the effects of land 

use and management on soil and ecosystem functions. It is collected in 

a way that documents both soil properties and classifications along with 

information on land use and management, then stored in an organized 

database. Information about past and current land use and management 

can be used to explain current soil properties. It can also be used, through 

inference or modeling, to predict future soil properties and functions. 

Soil function is a way of describing the role of soil in the environment 

and has been used to define the concept of soil quality and soil health. 

Essential soil functions include nutrient cycling, water storage and 

release, biodiversity and habitat, filtering and buffering, and physical 

stability and support (simplified from Mausbach and Seybold, 1998). 

Soil stores and moderates the cycling of nutrients and other elements. It 

regulates the drainage, flow, and storage of water and solutes (N, P, and 

pesticides). It supports biodiversity and habitat and promotes the growth 

of plants, animals, and microorganisms. It serves as a filter and buffer 

for toxic compounds and excessive nutrients and protects the quality of 

water, air, and other resources. It provides physical stability and support, 

allowing the passage of air and water through its porous structure, serving 

as a medium for plant roots, and providing an anchoring support for 

human structures. While many soil functions are complex and difficult 

to measure, some key soil properties can be considered indicators of 

specific soil functions (fig. 9-1) (Doran et al., 1996; Karlen and Stott, 

1994; Mausbach and Seybold, 1998). These indicator properties are the 

focus of soil survey DSP collection.

The framework of soil survey offers an opportunity to collect and 

disseminate information about how DSPs (and the soil functions they 

support) change with vegetation, land use, and management across space 

and time (Wills et al., 2016). DSP data, such as bulk density values under 

various grazing schemes, enhances soil survey information by providing 

soil property potentials under various land use and management 

scenarios. By combining DSP information with spatially linked soil 

survey information (e.g., soil map unit components), soil survey provides 

spatial context (maps, areas affected, etc.) to land users, researchers, 

and decision makers regarding the expected impacts of changes in land 

use and management. Soil property and function potentials along with 

collated DSP datasets provide greater specificity of soil interpretations, 

target values for soil quality and health assessment, guidelines for 

indicator monitoring, and data for calibration and validation of resource 

modeling.
Soil Survey Manual 4

Figure 9-1

-

β

Relationship between soil functions and some dynamic soil properties (modified from 

Tugel et al., 2008).

How to Collect DSPs for Soil Survey

DSP projects organize data collection and analysis around specific 

soils, soil groups, and land management systems. The scope, specificity, 

and replication of each DSP project depend on the goals for that project. 

The overarching goal of data collection in a DSP project is to document 

the range and central tendencies of DSPs for a given set of soils and 

land management conditions (such as reference and degraded states or 

best and typical crop management practices). The project should provide 

information about typical and potential DSP values for soil map unit 

components and ecological site descriptions. With adequate replication, 

these projects can be conducted as soil change comparison studies (Tugel 

et al., 2008) in which alternate conditions are used in a space-for-time 

substitution framework to make inferences about how soils have changed 

over time under specific management scenarios. In this approach, all 

places with the same soil (or group of soils) are assumed to have had the 

same properties at time zero (i.e., before the specific land management 

practices were applied). The assumption is that any differences observed 

are due to management and not inherent spatial variability. Multi-scale 

replication limits the influence of any spatial variability observed when 

making conclusions about soil change. DSP projects may also seek 

to document baseline conditions (such as ecological site reference 

conditions), best and worst case management scenarios, or alternate 

conditions of interest. 
484 Chapter 

DSP information for soil survey must be collected, organized, and 

used in a way consistent with the soil survey protocols and standards 

used for inherent properties. Data collection for soil survey can be 

characterized in two ways: dispersed and project based. Dispersed DSP 

data collection refers to the integration of DSP data collection with 

other routine soil survey project operations. As a result, DSP and land 

management information is documented throughout a wide range of soil 

survey activities. Efforts are not concentrated on any single land use or 

management system but are dispersed throughout all situations in which 

the soil occurs. In contrast, project-based DSP data collection is designed 

to intensively evaluate specific land management conditions. The most 

robust DSP data collection includes both approaches and so provides both 

spatial and land management representation (from dispersed efforts) and 

detailed comparisons of management scenarios in specific soil landscapes 

(from project-based efforts). DSP data can be used to evaluate the soil 

data representativeness (across land use and management systems) and 

assess spatial variability.

The goal of dispersed DSP collection is to build on other soil survey 

activities and increase the general knowledge of DSPs across all soils 

and land management conditions. In this context, “land management 

condition” is a general term that captures a range of possible situations, 

including ecological states and vegetative communities, land use, and 

specific crop and pasture management systems. Advantages of dispersed 

data collection are that it requires little additional resources and provides 

information on a wide range of soils and conditions to managers, 

modelers, and policy makers. Analysis of this data can be used to group 

soils and land management conditions for further evaluation through 

DSP projects. It can also be used to validate summaries and predictions 

made from completed projects. 

Dispersed DSP Data Collection

At the location of each observation, it is important to record, at 

minimum, information on the site, pedon, and land management 

condition and practice. This data includes any known information about 

general land use, ecological state, type and amount of vegetation, and 

cropping systems; e.g., tillage, crop rotation, and pesticide or fertilizer 

applications. Additional soil properties may be assessed on samples 

near the soil surface, e.g., enzyme activity and aggregate stability. 

Procedures and terminology for recording this information should be 

standardized. Robust soil information systems include data elements 
Soil Survey Manual 4

related to indicators of soil function and land use and management 

condition.

Project-Based DSP Data Collection

Project-based DSP data collection requires thorough planning and 

typically is the most intense type of data collection. The type of project 

determines how data collection will proceed. Projects can be planned to 

meet multiple project goals. Site and pedon replication should be planned 

to meet all project goals on the smallest unit of soil and land management 

condition targeted. It is helpful if all stakeholders of the project (those 

who will collect and use the information) can meet to determine the DSP 

project goal(s) and the target soil(s) and condition(s).

Determining DSP Project Goals

Project goals vary depending on the kind of project. Three kinds of 

projects are described below and examples are given for each.

DSP range study.—The goal of this kind of project is to evaluate the 

entire range of values for DSP properties and so provide soil component 

information regardless of land management or use. A single soil or group 

of closely related soils is selected. Land management conditions are not 

closely controlled (i.e., not specifically targeted in sampling) but should 

be well documented. This type of project requires the least amount of 

replication. Therefore, while results apply across the area of interest (soil 

group), the data typically is not sufficient for statistical comparisons 

between land management conditions. 

Example: The soil of interest occurs in an area used for 

rangeland, pastureland, and cropland. A DSP range study would 

sample a range of management systems across all three land 

uses, including those that are expected to have the smallest and 

highest DSP values.

 Example: A Midwestern U.S. State wanted to know typical 

values of DSPs across a region. For 2 years, all projects included 

sampling for DSPs as well as documentation of land use and 

management information for at least one pedon. The data 

provided a general idea of relative conditions across the region. 

There were no pairs or replications that could be used to make 

statistical comparisons because this was not the purpose of the 

project.

DSP baseline or reference study.—The goal of this kind of project 

is to establish baseline or reference DSP levels for a limited number of 
486 Chapter 

reference or land management conditions. The baselines can be used to 

interpret onsite assessments of soil health as a starting point for modeling 

or monitoring projects. Results apply across an area (a soil or group of 

soils) and the land management conditions of interest. Extrapolation 

beyond these conditions requires expert knowledge and depends on the 

extent and representativeness of the selected land management conditions. 

This type of project requires an intermediate level of replication across 

target soils and land management conditions. 

Example: Kirkland soil has particularly high soil function in 

a grazed native prairie with occasional fire (this is the reference 

condition of its ecological site). A reference DSP study would 

target this condition, and future evaluations and assessments 

could be compared to the baseline, or reference, levels.

DSP soil change study.—The goal of this kind of project is to assess 

soil change using the technique of space-for-time substitution. Instead of 

evaluating the effects of a management system in one location over an 

extended period of time, this technique compares two different locations 

that have had different management systems over the same period of 

time. It assumes that soil properties at the two locations were the same 

before the management system was applied. Typically, this type of study 

also serves as a baseline or reference study for a soil or soil group. In 

addition, soil change studies require the careful selection of land use and 

management conditions that represent a reference state and an alternative 

state. Robust multi-scale replication is required to make statistical 

conclusions about the soil change caused by land management. Pickett 

(1989) gives the theoretical background of space-for-time substitution, 

and Tugel et al. (2008) discuss the implementation of this technique in 

soil survey. 

Example: A group of soil scientists in Michigan wanted to 

investigate dynamic soil properties under two types of wetland 

restoration. They determined that they needed to conduct a DSP 

soil change study that included a baseline or reference state (in 

this case an undisturbed reference wetland) and alternative land 

use conditions with a multi-scale sampling scheme to capture 

variability within individual wetlands and across the project 

area.

Determining the Target Soils and Conditions

Studies can be designed to target soils, ecological sites, or land 

management conditions.
Soil Survey Manual 4

 Soils or ecological sites.—Targeting a specific soil(s) or ecological 

site(s) will determine the extent of the DSP project, where samples and 

observations might be collected, and where the results should be applied. 

Approaches for targeting soils include single soil unit, soil system, and 

ecological site.

Single soil unit.—The smallest unit of the study interest is a map 

unit component represented by a soil series. Benchmark soils that are 

representative of other soils in the area and/or represent important 

resource concerns and ecological processes are selected. 

Example: In an area of Michigan, the organic wetland soil 

Houghton is the most common soil in restored wetlands. The 

Adrian soil is very similar taxonomically and occurs in the same 

landscape positions. Both soils were therefore considered target 

soils for sampling and comparisons. 

Soil system.—A study of a soil system segments the landscape and 

evaluates appropriate hierarchies in a soil system or catena. Soil components 

that represent similar portions of the landscape and/or respond similarly 

to land use and management conditions can be combined for sampling 

purposes. 

Example: In Renville County, Minnesota, the soil landscape 

was segmented into three parts based on topography, hydrology, 

and the reflected taxonomic classes (fig. 9-2). One individual soil 

component was chosen to represent each of the three groups.

Ecological site.—The study of an ecological site groups soil 

components into units that are meaningful for ecological processes and 

land management. 

Land management conditions (for reference, baseline, or comparison).—The land management conditions are selected according 

to the soil and type of project and can include general land cover 

classes (e.g., rangeland or cropland) or specific management systems 

(e.g., 3-year burn cycle with moderate grazing or no-till corn with 

cover crops). For each project, a similar level of variability within the 

specified land management conditions needs to be maintained. For 

example, comparing forested conditions within a reference state to a 

specific cropland management system may be more appropriate than 

comparing all forested conditions under a specific management system. 

When trying to document soil change, the chosen conceptual model 

should partition soil change into discrete frames of reference, conditions 

that can be put into separate categories (Starfield et al., 1993) and that 
488 Chapter 

can be sampled at separate physical locations (using the space-for-time 

technique). The Soil Change Guide (Tugel et al., 2008) recommends 

using common models of soil disturbance and erosion, such as STIR, 

RUSLE2, and SCI (Foster, 2005; Hubbs et al., 2002; USDA-NRCS, 

2003, 2006). Wills et al. (2016) outlined a potential framework for 

grouping management systems by primary production groups and types 

and amount of disturbance. 

Example: A DSP planning team in Michigan determined 

that in order to meet their goals a baseline reference wetland 

needed to be sampled and documented in addition to two 

general types of wetland restoration and typical agricultural 

production. 

Example: In Dodge County, Nebraska, two agricultural 

management systems were chosen as the target conditions. The 

reference condition was the highest functioning agricultural land 

use.

Figure 9-2

Soil

Component

Surface

Parent

Material

Drainage

Taxonomic

Classification

Crooksford

Leen

Okoboji

Lacustrine

over Till

Fine

Loamy Till Lacustrine

over Till

Lacustrine

over Till Lacustrine

over Till

Lacustrine over

Till/Silty

Alluvium

MWD MWD/W

SPD

PD/VPD

VPD

VPD

Hapludoll Hapludolls/

Calciudoll Calciudolls Calciaqolls/

Vertic

Endoaquolls Endoaquolls Vertic

Endoaquolls

A generalized cross-section of a soil landscape near Olivia, Minnesota. A DSP project 

was designed to capture the effect of land use change on the soil system. Crooksford 

components represented relatively well drained Hapludolls, Leen components 

represented Calciudolls and Calciaquolls on depression rims, and Okoboji components 

represented Endoaquolls in depressions and lake plains. (Drainage class abbreviations: 

MDW—moderately well drained, W—well drained, SPD—somewhat poorly drained, 

PD—poorly drained, and VPD—very poorly drained.)
Soil Survey Manual 4

Data Collection Plan

A written plan serves as both a tool for organizing work and a record 

of how the project was conducted for future data use.

Formalizing Project Objectives

Planning decisions are recorded. The project goals and the geographic 

and conditional constraints are clearly defined. This information includes 

identification of which soils and land management conditions will or will 

not be acceptable for sampling.

Gathering Existing Data

Relevant data in soil survey and laboratory databases can be located 

by querying for the target soil taxa or spatial joins or by other means. 

Relevant information may also be located in journal publications, 

extension publications, or graduate student work through nearby 

universities, colleges, or other groups.

Additional Data Collection

All DSP projects need to include a protocol for data collection across 

multiple scales. Sites (independent locations commonly sampled as plots) 

should capture the full range of soils and land management conditions of 

interest. Within each site, a minimum of three pedons should be located 

in a standard layout or in a random fashion. Methods, field forms, and 

equipment for field data collection are discussed in appendix 3 of the Soil 

Change Guide (Tugel et al., 2008). All information should be provided 

as general metadata about how the project was designed and executed.

Determining Sources, Types, and Amount of Variability

Expert knowledge of the system and existing data are used to 

identify sources of variability. Tools such as the Multi-Scale Sampling 

Requirement Evaluation Tool (Tugel et al., 2008) can be used, or 

estimates can be made for the number of sites (independent location) and 

pedons per site needed to meet project objectives.

Designing a Sampling Scheme

The best arrangement of pedons within sites can be determined 

using the information about expected variability. The sampling scheme 

should include multiple sites or locations across the spatial extent of the 
490 Chapter 

study. The design should not under- or over-represent landscapes (e.g., 

hummocks or depressions) or microfeatures (e.g., trails or tree-throw) 

within a site. Figure 9-3 shows a sampling scheme. 

Locating Sites for Data Collection

Field sites should represent both the central concept and the typical 

range of properties for the target soil and land management conditions. 

Care is needed to avoid bias in location selection. GIS techniques, such 

as conditioned Latin hypercube sampling, or other statistical sampling 

techniques can be used. Alternate locations should be chosen in case 

a site cannot be accessed or must be rejected. Brungard and Johanson 

(2015) describe a rigorous plan for substitution.

Developing data collection and sampling plan.—The protocols 

and procedures for DSP project sampling need to be planned. The data 

elements and terminology used must be compatible with the soil system. 

The top image in figure 9-3 shows how sites can be distributed across a 

region. Figure 9-4 shows pedon distribution within a paired site in Dodge 

County, Nebraska. In this project, sites were located as pairs (with both 

target land management conditions present) to limit soil variability and 

improve condition comparisons.

Guidelines for accepting or rejecting a site for sampling.—For 

most soil survey applications, soils and conditions should be verified in 

the field to ensure that sampling will meet project objectives. Guidelines 

should outline the ranges of soils, features, and land management 

conditions that are acceptable for inclusion in the project.

List of data elements for site information.—Management and 

vegetation data are typically collected at the site scale. All data elements 

to be measured or recorded at each site (location or plot) should be 

identified. They may include vegetative cover, residue, site index, or 

other metrics of vegetation or management. Common collection schemes 

for ecological site data in the project area can be used as a starting point. 

Table 9-1 is an example of elements that might be collected at each site, 

location, or plot.

Instructions for locating individual pedons and measurements.—

A clear plan is needed to explain how pedons will be located within 

each site as well as where and how any associated surface properties 

will be measured. It may include a standard plot layout (fig. 9-5), 

randomly positioned pedons within a plot area, or transects (fig. 9-4) 

with pedons positioned at regular intervals along a catena contour. A plan 

for measuring infiltration, hydraulic conductivity, and surface features 

(such as residue, pattern class, and soil crust) before pedons are disturbed 

improves data integrity.
Soil Survey Manual 4

Figure 9-3 

Documentation from the Georgia Longleaf Pine Dynamic Soil Property project 

(unpublished data). Care was taken to include both target land use conditions across 

the study area. Top: Distribution of plots across the major land resource area (MLRA) 

133A. Plots were labeled to designate them as being on the A (Atlantic) or G (Gulf) 

side of the region and as P (pasture) or L (longleaf pine). Bottom left: A longleaf pine 

plot. Bottom right: A pasture plot with a transect tape (for vegetative cover measures). 

County names and boundaries are shown on the map. (Photo courtesy of Dan Wallace)
492 Chapter 

Figure 9-4

Example of pedon placement for a paired site in Dodge County, Nebraska. Each site 

has both target land management conditions (pasture and corn-soybeans with mulch 

tillage). The soil system was captured with three target soils. The central pedon 

location is represented on the map and labeled with the soil name. Two additional 

satellite pedons located along the contour are not shown on the map.

Instructions for pedon sampling and description.—Descriptions 

of pedons to a predetermined depth should follow standard procedures 

(see page 8-2 of Schoeneberger et al., 2012). It is suggested that one 

pedon per site be observed, one pedon per condition be sampled to a 

depth necessary for soil series confirmation, and detailed high-quality 

information, such as bulk density and water retention analysis, be 

collected for those pedons (table 9-2).
Soil Survey Manual 4

Figure 9-5

Soil Change Guide: Procedures for Soil Survey and Resource Inventory 

Version 1.1, 2008 

Stratum soil replicate ID*

Soil sample location, dynamic soil property

Soil sample location, full pedon description

Soil stability subsample

Soil sample location with 3 soil stability 

samples within 25 cm x 25 cm square

* Example: G1-G5 for grass stratum; or S1-

S5 for shrub stratum; etc.

Baseline, 20 m long

Transect, 20 m long

Herbaceous production subplot, 1 m-sq

Woody production subplot, 100 m-sq

G#

Transect 12 3 4 5

0m 5m 10m 15m 20m

20 m

G2 G5

G1 G4

G3

Instructions: The baseline should be positioned obliquely to the slope and 5 

transects should be positioned at approximately 90o from the baseline parallel 

to one another. The individual placing the flags will fill out the “Sample Locations 

and ID” portion of the “Plot Master” field form while identifying and flagging the 

soil sample locations. The flags will be pre-labeled with the Stratum-soil replicate 

ID (e.g., G1-G5). At each soil sample location, stability samples, penetrometer 

readings, bulk density samples, and soil samples for laboratory analysis will be 

collected. Line-point intercept and GAP will be completed along each transect. 

Place herbaceous subplots at meter marks 5 and 15 on each transect. Woody 

subplots are centered at transect 2, meter mark 15 and transect 4, meter mark 5. 

Complete 1 Pedoderm and Pattern Classes form for each plot (Tugel et al., 2008).

Example of detailed plot sampling instructions for a rangeland DSP project in Utah. 

Because the project involved both soil scientists and range scientists, a highly detailed 

plan was developed for sampling. From the Soil Change Guide (Tugel et al., 2008).

Table 9-1

DSP Project Data Elements Collected at Site (Across Plot) Scales

Type of data Property/measurement

Management information Crop rotation

Tillage system

General description

Tillage operations (frequency and 

timing)

Applications and other operations and 

treatments

Grazing management

Forestry management
494 Chapter 

Table 9-1.—continued

Type of data Property/measurement

Vegetation information 

(as appropriate)

Plant biomass or production

Composition

Understory

Overstory

Line-point intercept

Canopy and basal gap

Site index

Forest floor (when present 

in any part of study)

Woody debris

Visual disturbance classes*

Soil surface displacement, compaction, 

litter thickness, crust cover, etc.

Surface

properties

Residue cover/bare soil

Pedoderm and pattern class+

* Page-Dumroese et al., 2012

+ Burkett et al., 2011

Table 9-2

DSP Project Data Elements Collected at Pedons; Multiple 

Locations per Site/Plot

Type of data Property/measurement

Surface properties Aggregate or soil stability

Infiltration 

Single ring

Double ring

Crust description (when present)

Pedoderm and pattern class

Relevant microtopography

Soil surface temperature

Cover/bare soil

Pedon properties Pedon description

Horizon depths, colors, textures, fragment 

estimates
Soil Survey Manual 4

Table 9-2.—continued

Type of data Property/measurement

Pedon properties Agronomic feature (furrow, wheel-track, etc. at 

pedon location)

Soil horizon/depth increment

Temperature

Cover/bare soil

Saturated hydraulic conductivity

Instructions for sample collection.—Collecting a sample from 

a predetermined depth (e.g., 0–5 cm) near the surface helps in making 

comparisons between conditions. The kind of near surface horizon of 

the sample should be noted (see chapter 3). This sample can be treated 

as a subsample of the first horizon or described as a separate horizon. 

All other samples should be collected by genetic horizon to capture 

the most variability within the profile and allow comparisons between 

horizons. Because many DSPs are sensitive to disturbance, walking or 

using heavy equipment on sampling areas should be avoided. A plan 

for labeling samples is needed to keep track of soil, condition, site, and 

pedon replication as well as information on horizons and layers.

Instructions for sample handling.—Many of the properties 

measured in DSP studies are the same as those measured in standard 

soil survey procedures. The emphasis is on targeting, tracking, and 

replicating certain conditions. However, some measures are of particular 

interest for DSP sampling, such as bulk density, aggregate stability, 

and soil biology measures (e.g., enzyme activity). The samples should 

be handled carefully and not exposed to crushing or warming. Samples 

should be air dried as soon as possible if they are to be shipped and/or 

stored for more than 24 hours.

Desired minimal dataset for laboratory samples.—The dataset 

should have information on standard inherent properties to allow for 

correlation and comparisons between soils and sites. It may include 

standard pedon description information (such as horizon thickness, 

texture, and coarse fragments) and laboratory data (such as particle-size 

determination). At minimum, the DSP dataset should include carbon 

(organic and inorganic), pH, EC, bulk density, aggregate stability, 

biological enzymes (β-glucosidase is recommended), particulate organic 

matter, and nutrients (N, P, K, etc., as appropriate). Table 9-3 provides a 

potential list of properties to measure.The Kellogg Soil Survey Laboratory 

currently analyzes standard interpretive and dynamic properties.
496 Chapter 

Table 9-3

Measurements of Dynamic Soil Properties on Individual 

Samples

Type Property/measurement

Standard 

interpretive

Standard laboratory characterization

Particle-size determination

Other properties in lieu of particle size

Minerology (clay or other as appropriate)

CEC

Standard dynamic Organic carbon

Derived from total and inorganic carbon

Inorganic carbon

Derived from calcium carbonate equivalent

pH

EC

Bulk density

Aggregate stability

Water stable aggregates

Total N

P (Mehlich or other as appropriate)

Water retention 

Extractable bases

Extractable acidity

ECEC

Permanganate extractable carbon (POX-C or 

Active C)

Soil enzymes

β-glucosidase

Particulate organic matter (POM)

Supplemental 

as needed and 

available

SAR

Plant available P

Dry sieve aggregates

Potentially mineralizable N
Soil Survey Manual 4

Table 9-3.—continued

Type Property/measurement

Field lab pH

Active C (kit for permanganate extractable C)

Aggregate stability

Advanced soil structure and pore analysis

CO2

 burst and respiration tests

Analyzing Dynamic Soil Property Data

DSP data can be used for many purposes, some directly related to 

soil survey and many others that are indirectly related (fig. 9-6). The first 

and most long-lasting outcome of a DSP project is the collection and 

documentation of soil and vegetation data under various land use and 

management scenarios. This is an immediate product that can serve as 

input for many other products, such as conservation effects modeling and 

general geospatial analysis.

Initial steps for DSP data analysis are the same as those for any 

aggregation of soil survey data. The data compilation is complicated 

by replication across sites and pedons. Good recordkeeping and 

labeling throughout the process help ensure reliable results. To allow 

for improvement over time, all data aggregation should be documented 

through written records, program scripts, and public databases. The 

following outline describes several important steps and considerations in 

analyzing DSP data.

DSP Data Handling

1. Maintain the project’s data collection plan. The data collection 

plan serves as the metadata for the project and will explain to 

future data users how and why the data was collected.

2. Enter and check data for errors. Enter data into required 

programs and databases and examine it for errors. This data 

includes information about the sites, pedons, samples collected, 

and land management systems. Some information (such as 

infiltration rate) may be collected in the field and recorded later 

in a database or other file structure.
498 Chapter 

Figure 9-6

Soil Change Guide: Procedures for Soil Survey and Resource Inventory 

Version 1.1, 2008 

Ecological Sites, Soil Interpretations, and Monitoring Data. A simple conceptual 

model (a) is used to design comparison studies (b). Dynamic soil property data derived 

from these studies are used to populate a point dataset (c). The point data are then 

available to include in ecological site descriptions (d), model dynamic soil property 

values for similar soils (e), develop interpretations (f), and interpret monitoring data 

collected through programs (g), such as the Natural Resources Inventory (NRI), Vital 

Signs, and Forest Inventory and Analysis (FIA).

Soil maps 

and

attribute 

data

Ecological site 

description for group 

of similar soils

Dynamic soil 

property 

point dataset

Interpretations and 

reference data for soil 

quality and soil function

Ecological 

site 

inventory 

“point” data

Soil-site 

correlation

State and transition 

model 

(or cropland model)

Comparison 

studies on 

benchmark 

soils

Modeled 

DSP values 

for map units

Reference data to interpret 

monitoring results (e.g., NRI, Vital Signs)

d

b

c

a

e

f

g

Not a monitoring guide 

Protocols for monitoring resource conditions for decision-making purposes are not 

described in this Guide. Many of the measurement methods, however, are suitable for 

monitoring. Protocols and design for field monitoring programs are addressed in Herric

et al. (2005) and Elzinga et al. (1998). 

Dynamic soil property data in relation to ecological sites, soil interpretations, and 

monitoring data. A simple conceptual model (a) is used to design comparison studies 

(b). Dynamic soil property data derived from these studies are used to populate a point 

dataset (c). The point data are then available to include in ecological site descriptions 

(d), model dynamic soil property values for similar soils (e), develop interpretations 

(f), and interpret monitoring data collected through programs (g), such as the Natural 

Resources Inventory (NRI), Vital Signs, and Forest Inventory and Analysis (FIA). From 

Tugel et al., 2008.

3. Compile or link data across common scales. Link and 

label DSP data, as appropriate, to include site and observation 

information (e.g., vegetation and individual pedons from the 

same site are labeled with the same plot ID). A robust database 

should allow for the association of data elements across 

conditions and locations.

4. Generalize horizons and other units of measure. Data 

collected by samples that are individually labeled, such as by 

genetic horizon, must be grouped into common units so that 

properties can be analyzed and compared. Add other data 

elements (such as comparable layer; Tugel et al., 2008) that 
Soil Survey Manual 4

group all possible horizons in the project. Keep scripts and rules 

as part of the project metadata and documentation.

5. Aggregate individual observations and measurements.

From the smallest individual data element (sample values) to 

the broadest level of interest (soil and land use or management 

system), select meaningful comparisons between conditions. 

Aggregate horizons, pedons, and sites to make comparisons.

a. Create separate data elements for surface samples (0–5 

cm) and comparable layers, such as all A horizons or all 

B horizons or other combinations outlined in the Soil 

Change Guide (Tugel et al., 2008). 

b. Use weighted averages by depth to combine horizons 

into pedon values.

c. Compute statistical measures for plots or sites. 

d. Compute statistical measures for land use or management.

6. Analyze data.

a. Perform data evaluation and graphical comparisons. 

Preliminary data is evaluated to gauge general trends, 

identify errors, and locate any outliers. Graphs should 

include box plots by comparable layers, pedons, and 

sites and depth functions within pedons. Figure 9-7 

shows a summary of two surface layer DSPs for two 

separate DSP projects. Data visualization can be used to 

explore, examine, and share general conclusions about 

the project.

b. Calculate descriptive statistics across soil groups. Initial 

summary statistics include central tendencies (mean, 

median, and mode) as well as measure of dispersion and 

variability (range, standard deviation, etc.). 

c. Calculate descriptive statistics for individual land management conditions (as appropriate). Calculate measures of central tendencies, dispersion, and variability. 

Use site averages or a mixed model to accurately reflect 

any autocorrelation between observations taken at the 

same site.

d. Conduct statistical comparison and ascertain meaningful 

differences. Evaluate statistical differences between 

land management conditions.

i. Use T and F tests for differences. Mixed models 

optimize use of fixed (condition) and random 

(plot replication) factors. 
500 Chapter 

Figure 9-7

Dynamic soil properties of 0–2 cm samples for two DSP projects (Amarillo and 

Kirkland soils) for: a) soil organic carbon (%) measured as total carbon and b) 

water stable aggregates (%). Box plots represent the 25th and 75th percentiles. Note 

that rangeland was used as a reference condition for both projects but that different 

alternate land management systems were used for comparison. The soils also have 

different reference levels of these two DSPs.

ii. Examine literature to determine if described 

differences are meaningful to soil function.

iii. Evaluate sampling sufficiency (e.g., were 

enough samples collected to detect a difference 

if one exists?). If properties are more variable 

than originally anticipated, the sampling design 
Soil Survey Manual 5

may not have the power to detect anything other 

than a very large difference. Additional sites 

can be chosen and samples collected so that 

meaningful statistical comparisons can be made 

to detect smaller (but important) differences in 

DSP values.

7. Make inferences about soil variability, land management 

conditions, and soil change. A final report should summarize 

the project goals, the target soils and land management 

conditions, the data collection process, and the methods of data 

aggregation and analysis. Final conclusions should include 

the most specific level of evaluation and the expected area 

of inference (i.e., other areas where the results might apply). 

This report serves to document the process and support any 

conclusions.

8. Populate soil survey databases (such as information for 

soil map unit components) as appropriate. Depending on the 

nature of the project, report results for the entire extent of the 

soil (or soil group) or report results as being limited to certain 

conditions. 

Care should be taken when incorporating DSP project data into standard 

data aggregation. Consider the distribution and representativeness of data 

when populating general component information, such as representative 

values (RV). If differing management conditions have statistically 

different DSPs, compare the distribution of the conditions assessed to the 

number of pedons available for aggregating. You may need to aggregate 

by land management condition and then weight the conditions by spatial 

prevalence to arrive at an overall value.

Summary of DSPs in Soil Survey

Dynamic soil properties enhance soil survey by providing information 

about soil properties that change with land use and management. 

Information about DSPs improves the ability to document, explain, and 

predict the effects of land use and management on soil and ecosystem 

function. DSP data can be collected as general information or as projects 

designed to detect statistical differences between management and land 

use types. In both approaches, DSPs are collected in a way that documents 

both soil properties and classifications and land use and management 
502 Chapter 

information. Careful planning, sampling, and analysis ensure that DSP 

data enhances soil survey projects and allows for additional use of soil 

information. 

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