1997 National Resources Inventory

(revised December 2000)

**A GUIDE FOR USERS OF 1997 NRI DATA FILES**

**CD-ROM Version 1**

**(December 2001)**

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**1997 National Resources Inventory**

**(revised December 2000)**

**A GUIDE FOR USERS OF 1997 NRI DATA FILES**

**Version 1**

**(December 2001)**

**Table of Contents**

[Page]{.underline}

**Chapter 1. Overview**

1.  [Introduction.................................................................................1]{.smallcaps}

[1.2. Basic
Concepts...............................................................................1]{.smallcaps}

3.  [Special
    Considerations...................................................................3]{.smallcaps}

4.  **[Statistical
    Considerations............................................................6]{.smallcaps}**

5.  **[Confidence Intervals --
    Examples...................................................8]{.smallcaps}**

**Chapter 2. The National Resources Inventory Program**

1.  [The Natural Resources Conservation
    Service...................................11]{.smallcaps}

2.  [Historical
    Inventories................................................\..................11]{.smallcaps}

3.  [Initiation of the National Resources
    Inventory................................12]{.smallcaps}

[2.4. The National Resources Inventory
Program.................................\....12]{.smallcaps}

5.  [NRCS Data Collection
    Activities..............................\......................13]{.smallcaps}

[2.6. Meeting Future Needs for
Information.....................\.....................\...13]{.smallcaps}

**Chapter [3.]{.smallcaps} Inventory Procedures**

1.  [Inventory
    Design.........................................................................14]{.smallcaps}

2.  [Data
    Gathering........................................................................\...15]{.smallcaps}

3.  [Quality
    Assurance......................................................................16]{.smallcaps}

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4.  [County Base
    Data..................................................................\......17]{.smallcaps}

3.5. [Confidentiality and Integrity of Sample
Locations...........................19]{.smallcaps}

**Chapter 4. Construction of the NRI Database**

1.  [Introduction............................................................................\....20]{.smallcaps}

**[4.2. Statistical Estimation
Procedure.............................................\....20]{.smallcaps}**

[4.3.
Imputation..................................................................................22]{.smallcaps}

4.  [Development of NRI
    Weights..........................................................25]{.smallcaps}

[4.5. Construction of Pseudo
Points........................................................28]{.smallcaps}

[4.6. Additional
Features........................\..............................................29]{.smallcaps}

**Chapter 5. NRI Database Structure and Characteristics**

NRI

[5.1.
Introduction...............................................................................31]{.smallcaps}

[5.2. Database Structure on
CD-ROM..............................\........................31]{.smallcaps}

[5.3. Database Characteristics
.............................................................32]{.smallcaps}

[5.4. Aggregation of
Data....................................................................33]{.smallcaps}

5.  [Example of Linking Data Across
    Tables...........................................36]{.smallcaps}

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6.  [Importing Raw
    Data......................................................................37]{.smallcaps}

7.  [Recommendations to Users After Data
    Import..................................38]{.smallcaps}

8.  **[Deriving Margins of
    Error..........................................................39]{.smallcaps}**

9.  [Citing Data from the CD-ROM and the 1997 NRI Summary
    Report...\......40]{.smallcaps}

**Chapter 6. NRI Data Analysis**

[6.1.
Introduction.......................................................................\.........41]{.smallcaps}

[6.2. The Analytical
Process.................................................................41]{.smallcaps}

[6.3. Overview of NRI
Applications.........................................................42]{.smallcaps}

[6.4. Know Your
Database....................................................................43]{.smallcaps}

**[6.5. Understanding the Statistical Nature of the
NRI.....................\......44]{.smallcaps}**

6.  [Information
    Presentation..............................................................48]{.smallcaps}

7.  [Work in
    Teams..............................................................................49]{.smallcaps}

[6.8. NRI Analysis Subject
Areas...........................................................50]{.smallcaps}

[6.9. Mapping Results of Data
Analysis...................................................52]{.smallcaps}

**Citations**............................................................................................54

**Appendices**

A.  **CD-ROM Contents: Data, Documentation, and
    Tools..............................A-1**

B.  Important NRI Reference Material on NRI Home Page, World Wide
    Web..........B-1

C.  Understanding the NRI
    Design...........................................................\...C-1

D.  Geographic Information System
    Coverages.......................................\........D-1

E.  Where To Get **Help** and Give
    **Feedback**.............................................\...\...E-1

Sections in **bold** (blue) in Table of Contents contain important
information regarding statistical reliability.

**1. Overview**

**[1.1. Introduction]{.smallcaps}**

The National Resources Inventory (NRI) is a scientifically-designed,
longitudinal panel survey of the Nation's soil, water, and related
resources designed to assess conditions and trends every five years. The
NRI is conducted by the Natural Resources Conservation Service (NRCS),
an agency of the U.S. Department of Agriculture (USDA), in cooperation
with the Iowa State University (ISU) Statistical Laboratory. The 1997
NRI contains data on nonfederal lands and water areas within the 48
conterminous United States, Hawaii, Puerto Rico, and the U.S. Virgin
Islands. The 1997 NRI provides results that are nationally consistent
for all nonfederal lands for four points in time -- 1982, 1987, 1992,
and 1997.

The NRI data can be quite useful in developing statistical estimates of
natural resource conditions, and in conducting geospatial and temporal
analyses of these conditions. The data come from a sample survey and are
different in nature than most data used in geographic information system
(GIS) work. This statistical nature of the data is important to remember
when using the NRI to produce tabulations and analyze resource
conditions and trends.

This document serves as an initial compilation of material developed to
assist and enhance use of NRI data. It provides users of NRI data with
basic information regarding the inventory process, the database, and
proper utilization of the data.

This user guide is referenced as Version 1. A second version of this
document will be developed if it is found that additional documentation
will enhance utilization of the data.

***Users are encouraged to provide feedback as outlined in Appendix
E.***

**[1.2. Basic Concepts]{.smallcaps}**

The NRI database is a statistical database. The data do **not** come in
a summarized or aggregated form. Users of the database decide which
portions of the data are needed, and how to aggregate and summarize the
data most effectively. The data must be aggregated to develop meaningful
statistics and analyses. To use the individual sample point data as
other than a part of the statistical database is inappropriate; for
example, it is a misuse to use the NRI database to attempt to identify a
specific farm and field where certain conservation actions should be
initiated.

Tabulations made from the NRI database are estimates. These tabulations
produce estimates rather than facts because they are based upon sample
data derived from a survey -- rather than data coming from a census, or
complete measurement. Each estimate has some degree of statistical
uncertainty associated with it; this statistical uncertainty affects
analyses of the data and interpretation of results. Statistical
considerations are discussed in Sections 1.4, 4.2, and 6.5.

All data summaries and analyses must take into account the database
weights (expansion factors) and at least some type of geographical
factors. Proper weighting of NRI data is discussed in Sections 4.4 and
6.5. The statistical weights are the data elements (variables) *xfact*
found in the table **point** (see Section 5.2 and Appendix A).
Geographic factors include State, county, hydrologic unit area (HUA),
and Major Land Resource Area (MLRA); various combinations of these can
be used.

The database has been structured so that it is easy to statistically
analyze the data. All NRI data were consolidated into a database with a
straightforward point-type format; this is discussed in Chapter 4,
Construction of the NRI Database. Many characteristics have been
associated with each sample point. This means that the statistical
nature of the database allows aggregation and cross-tabulation of the
specific point data over meaningful entities (domains). Also, it is easy
to link or associate information from other databases with the NRI data
because of this structure.

The NRI data are organized into nine logical subject-matter tables (data
files). Almost all data tabulations and analyses will require data from
two or more of these tables. Data from the various tables are linked
(matched up) using the unique key variable *recordid* (see Section 5.5).
Every point has a unique *recordid* associated with it, and each table
contains the *recordid* variable (field).

-   As an example, consider estimation of the amount of nonfederal
    Forest Land that has become Developed Land between 1982 and 1997 in
    the Northwest (say, Oregon and Washington). This requires using the
    data fields *fips* (geographic factor) and *xfact* (statistical
    weight) from the table **point**, as well as *yr* (year) and *broad*
    (broad land cover/use) from the table **trend**. Conceptually, data
    would be extracted for all points matching the query that:

(1) *fips* corresponds to a county in either Oregon or Washington,

(2) *broad* is the Forest Land code when *yr* is 1982 in the table
    **trend**, and

(3) *broad* is one of the Developed Land codes when *yr* is 1997 in the
    table **trend**.

> The *xfact* variable from the table **point** would be extracted and
> summed for all points meeting all three of these criteria.

It is strongly recommended that users of the NRI database structure
their tabulations in a way that makes it easy to check out new results
with "established" results. This is because it is easy to mis-specify or
incorrectly structure tabulations and analyses using NRI data.
Tabulations can initially be checked against estimates released by NRCS
in the *Summary Report, 1997 National Resources Inventory (Revised
December 2000*) (USDA, 2000) (hereafter referred to as the *1997 NRI
Summary Report*) and on the NRI Web site (see Appendix B). As a data
user's queries become more sophisticated, this checking can be against
results that were previously checked against already established
results.

-   Consider again the above example -- estimating the amount of
    nonfederal Forest Land that has become Developed Land between 1982
    and 1997, for the States of Oregon and Washington. Rather than just
    extracting data for certain points as outlined above, it would be
    best to do a fairly complete cross-tabulation -- this would allow
    replication and checking with estimates that were already derived
    from the 1997 NRI database (see, for example, the State-level
    figures for Oregon and Washington in Tables 1 and 2).

Tabulations and analyses are typically performed in an iterative
fashion. The iterations involve forming different aggregations of the
data, both by changing combinations of element categories and by using
additional factors to better define and refine the results.

-   Consider again the above example -- estimating the amount of
    nonfederal Forest Land that has become Developed Land between 1982
    and 1997, for the States of Oregon and Washington. After looking at
    several cross-tabulations that provide aggregate estimates of these
    conversions, it would be appropriate to make further subdivisions
    and find useful/interesting "messages" in the data. Examples
    include: types of Forest Land being converted, comparisons between
    the three 5-year time periods (1982 -- 1987, 1987 -- 1992, and 1992
    -- 1997), quality of the land being converted, geographical
    distribution of the converted lands as well as those not converted,
    etc. Analysis might involve looking at several of these factors
    simultaneously, in addition to making comparisons with changes in
    human population over these periods of time.

The 1997 NRI database contains data for "imputed" points, or "pseudo
points." The pseudo points should be treated like all other points when
performing tabulations and analyses with the NRI data -- they need to be
included because of the way the database was constructed. Imputation
procedures and construction of pseudo points are discussed in Sections
4.3, 4.4, and 4.5.

The 1997 NRI database contains nationally consistent data for four
points in time: 1982, 1987, 1992, and 1997. All comparisons for two
points in time need to be made using this current NRI database.
Comparisons using data published in 1982, 1987, and/or 1992 can produce
incorrect inferences, because of changes in the statistical estimation
procedure, and because all data were simultaneously reviewed (edited) as
1997 NRI data were collected.

-   As an example, consider estimated average annual sheet and rill
    erosion rates for Cultivated Cropland in Wisconsin. Based upon the
    1992 NRI, the estimates for the years 1982 and 1992 were 5.2 and 4.1
    tons per acre per year, respectively. Based upon revisions made to
    the raw data during 1997 NRI data collection, the corresponding
    estimates derived from the 1997 NRI database are 4.7 and 3.8 tons
    per acre per year. These revisions result from updated technical
    guidance regarding use of the Universal Soil Loss Equation (USLE) --
    these revisions are applied to historical data so that trends
    reported using NRI data reflect true changes in resource conditions
    and not just changes in inventory methodology.

It is important to understand the full nature of each data element used
in tabulations and analyses. Section 1.3 provides insights and guidance
regarding several important components of the NRI database. Data
collection methodology is discussed in Section 3.2; detailed data
collection instructions and a glossary are available in other files on
the CD (see Appendix A). Definitions, protocols, and instructions differ
in some cases from those used by other agencies. These differences need
to be considered when analyzing and interpreting the data.

3.  **[Special Considerations]{.smallcaps}**

**[Land Cover/Use:]{.underline}** All tabulations and analyses need to
account for "Land Cover/Use." NRI uses this term to identify a
classification system that accounts for all the surface area of the
U.S., using mutually exclusive categories. As the name implies, Land
Cover/Use is a hybrid classification system that takes into account both
land cover and land use. The NRI database includes both Specific
Cover/Use and Broad Cover/Use as variables in the database; they are the
data elements *landuse* and *broad* in the table **trend**. There are 12
Broad Cover/Use categories:

  ------- ------------------------ -------- ------------------------------------
  \(1\)   Cultivated Cropland      \(7\)    Urban & Built-up Land

  \(2\)   Noncultivated Cropland   \(8\)    Rural Transportation Land

  \(3\)   Pastureland              \(9\)    Small Water Areas

  \(4\)   Rangeland                \(10\)   Census Water

  \(5\)   Forest Land              \(11\)   Federal Land

  \(6\)   Other Rural Land         \(12\)   Conservation Reserve Program (CRP)
                                            Land
  ------- ------------------------ -------- ------------------------------------

There are 64 Specific Land Cover/Use categories. The NRI data collectors
determined the Specific Cover/Use category; the Broad Cover/Use category
was derived from the Specific Cover/Use category. The system is
hierarchical except for some types of Hayland and Pastureland; if the
hay or pasture is in rotation with cultivated crops, the point is
classified as \"Cultivated Cropland\" rather than \"Noncultivated
Cropland\" or \"Pastureland.\" The 1997 NRI database contains both a
Specific Cover/Use and Broad Cover/Use for each sample point, for each
of the four inventory years -- 1982, 1987, 1992, 1997.

Note that \"Federal Land\" is one of the Broad Cover/Use categories.
Points classified as \"Federal Land\" are included in the database; they
have the statistical weight *xfact* and various geographical codes but
almost no other attributes. They are included in the database for
completeness -- to make a complete accounting of all U.S. surface area.
Note also that there are numerous areas in the U.S. that have either
become Federal Land or gone from Federal to private during the period
1982 to 1997. The 1997 NRI database accounts for all of these dynamic
changes.

It is recommended that tabulations and analyses start with Broad
Cover/Use as one of the baseline variables. That serves several
purposes. It will make it possible to check new results against previous
(established) tabulations of the data, to make sure that the data are
being queried and tabulated properly. It is also helpful to look at the
data this way to uncover idiosyncrasies that may not have been apparent
during previous analysis of the data. It is easy to misuse the data
without taking into account the portion of the landscape that is Federal
Land, or Water Area, or often Developed Land. Certain data elements
(like Prime Farmland designation) do not apply to certain areas -- this
needs to be accounted for when tabulating the data, when making certain
inferences, and when presenting results to policy makers, scientists,
and the general public.

**[Developed Land]{.underline}**: Developed Land contains three Land
Cover/Use categories:

(1) Large Urban & Built-up Areas (greater than 10 acres in size)

(2) Small Built-up Areas (less than 10 acres in size)

(3) Rural Transportation Land (roads, railroads, and associated
    rights-of-way)

As discussed in Section 1.2, the 1997 NRI database contains data that
are nationally consistent for 1982, 1987, 1992, and 1997. Only the 1997
NRI database should be used to analyze issues dealing with conversion of
Rural Land to Developed Land. It is confusing (and incorrect) to compare
the 1997 NRI results with results published for the earlier inventories.
For example: (1) Urban & Built-up Land figures published for the 1982
NRI only included the category Large Urban & Built-up Areas; Small
Built-up Areas were included in a category called Minor Land Cover/Use;
(2) the 1987 NRI Developed Land category included Rural Transportation
Land in addition to Large Urban & Built-up Areas; Small Built-up Areas
were still included under Minor Land Cover/Use; (3) for the 1992 NRI,
Developed Land also included the Small Built-up Areas; (4) for the 1997
NRI, inventory protocols were changed for measurement of Rural
Transportation Land, which required statistical adjustment of figures
for the years 1982, 1987, and 1992. (*Note: Each time a change was made
either in reporting categories or inventory protocols, appropriate
changes were made in the historical data so that the newest inventory
contained data that were comparable over all inventory years.)*

For the NRI, Developed Land areas are considered (permanently) removed
from the Rural Land base. Other agencies and groups that collect
seemingly similar data have different reasons for collecting data. This
means that these other sources of data will show both differences and
similarities with NRI data. Thorough examination and analysis of the
data from two differing sources can be beneficial and strengthen
understanding of the issues, as long as both studies use credible
inventory procedures.

> *Additional documentation regarding Developed Land is provided on the
> NRI web site,*
> [http://www.nhq.nrcs.usda.gov/NRI/1997/national_results.html]{.underline}.
> *Included are discussions on: the amount of uncertainty associated
> with various Developed Land estimates, how to properly use and
> interpret these figures, and how to minimize misuse of these
> estimates. The documentation also provides thousands of estimates and
> their associated margins of error.*

## [Erosion Estimates]{.underline}: Erosion data are a unique and important part of the NRI. The erosion data can not be used to compute the erosion that actually occurred during a particular year; instead, the database contains "factors" that are used as inputs into two different erosion prediction equations (models):

(1) Universal Soil Loss Equation (USLE) -- an erosion model designed to
    predict long-term average annual soil loss (due to sheet and rill
    erosion) from specific field areas in specified cropping and
    management systems. *Sheet and rill erosion* is the removal of
    layers of soil from the land surface by the action of rainfall and
    runoff; it is the first stage in water erosion. The equation is: A =
    RKLSCP, where:

> \- A = Computed (estimated) soil loss per unit area

\- R = *Rainfall and runoff* factor

\- K = *Soil erodibility* factor

\- L = *Slope-length* factor

\- S = *Slope-steepness* factor

\- C = *Cover and management* factor

\- P = Support *practice* factor

(2) Wind Erosion Equation (WEQ) -- an erosion model designed to predict
    long-term average annual soil losses (due to wind erosion) from a
    field having specific characteristics. *Wind erosion* is the process
    of detachment, transport, and deposition of soil by wind. The
    equation is: E = f(I,K,C,L,V), where:

> \- E = Computed (estimated) soil loss per unit area
>
> \- I = *Soil erodibility index*
>
> \- K = Soil *ridge roughness* factor
>
> \- C = *Climatic* factor
>
> \- L = Equivalent *unsheltered distance*
>
> \- V = Equivalent *vegetative cover*

Erosion rates computed from the data are estimated average annual (or
expected) rates based upon the cropping practices, management practices,
and inherent resource conditions. Climatic factors for both USLE and WEQ
are based upon long-term average conditions and not upon one year's
actual events.

The 1997 NRI database contains both computed (estimated) soil loss and
the individual factors, for both the USLE and WEQ, for all points that
are Cropland, Pastureland, or CRP land in a given year. Erosion data are
not given for points that are any other land cover/use. If a sample
point changes land cover/use between two points in time, it has erosion
equation factors for the years it is Cropland, Pastureland, or CRP land
-- but not for any years that is some other land cover/use. This is an
important factor to keep in mind when trying to estimate erosion rates
for a particular area -- to only account for those sample points with a
land cover/use of Cropland, Pastureland, or CRP land. It is incorrect to
average USLE rates over the land area of an entire State, rather than
just some portion of the agricultural land.

Calculating average annual estimated erosion rates for particular areas
and types of land can be confusing. The proper method is to take
"weighted averages" over the appropriate set of NRI sample points (see
Section 6.5). It is also confusing when making comparisons between two
points in time -- to take into account those sample points with changes
in land cover/use, and calculate these averages over a slightly
different set of sample points for the two years.

The NRI database contains all of the individual USLE and WEQ factors
because special use can be made of these factors. Erodibility Index (EI)
is a good example -- this index is a numerical expression of the
potential of a soil to erode, considering climatic factors and the
physical and chemical properties of the soil (the higher the index, the
greater is the investment needed to maintain the sustainability of the
soil resource base if intensively cropped). These factors are also
useful, for example, for analyses of how erosion rates might change if
certain cropping practices are changed. Both of these examples relate to
use of the NRI database for formulation of provisions of the 1985 Food
Security Act.

**[1.4. Statistical Considerations]{.smallcaps}**

*Results derived from the NRI database are estimates -- not absolute
facts.* All NRI results have some degree of uncertainty associated with
them. Interpretation of NRI results requires an understanding of the
amount of this uncertainty associated with each estimate, as well as an
understanding of the inventory procedures. Since the NRI employs
recognized statistical methodology, it is possible to quantify this
statistical uncertainty.

NRI data were collected at more than 800,000 sample sites nationwide
(see Sections 3.1 and 3.2). This is a very large sample, which means
that the data can be legitimately used to analyze issues at many
geographic levels ---- national, regional, State, and sub-State
(multi-county). However, the NRI was **[not]{.underline}** designed for
analyzing issues at the county level (see discussions below).

The precision of NRI estimates depends upon the number of samples within
the region of interest, the distribution of the resource characteristics
across the region, and the sampling procedure. Characteristics that are
common and spread fairly uniformly over an area can be estimated more
precisely than characteristics that are rare or unevenly distributed.

The NRI was originally designed (in 1982) to obtain natural resource
data usable for analysis at a sub-State (multi-county) level. Of
interest were estimates that could be developed for such sub-State
entities as Major Land Resource Areas (MLRA), Soil Conservation Service
Administrative Areas, 4-digit Hydrologic Unit Areas (HUA), and Water
Resources Council aggregated sub-areas (ASA). The sample was selected
giving particular attention to MLRA portions of States. Note, however,
that it was usually too expensive to sample an MLRA of much less than
one million acres at a rate sufficient to meet the NRI standards.

> *A \"Major Land Resource Area\" (MLRA) is a broad geographic area
> characterized by a particular pattern of soils, climate, water
> resources, vegetation, and land use.*
>
> *A \"Hydrologic Unit Area\" (HUA) is a drainage area defined by a
> hierarchical classification system developed by the U.S. Geological
> Survey. The system divides the area of the U.S. and Caribbean into 21
> major regions, 222 subregions, 352 accounting units, and 2,150
> cataloging units that delineate river basins (watersheds) having
> drainage areas usually greater than 700 square miles. Major regions
> and subregions are referred to as 2-digit and 4-digit HUAs,
> respectively; accounting and cataloging units are called 6-digit and
> 8-digit HUAs, respectively.*

This design criterion can also be expressed quantitatively. The sample
was selected so that the margin of error was less than 20 percent for
any estimate of a resource condition that comprised at least 10 percent
of the MLRA land area of the State. Most items could be estimated more
precisely. This criterion also holds for estimates derived from the 1997
NRI database (Nusser and Goebel 1997, Goebel 1998). Note that estimates
of change between two points in time will be less precise (relatively)
because the changes will be occurring on a smaller fraction of the
landscape.

The \"margin of error\" is a commonly used measure of reliability and
can be used to construct a\
95 percent confidence interval for an estimate. The lower bound of the
confidence interval is obtained by subtracting the margin of error from
the estimate; adding the margin of error to the estimate forms the upper
bound. The margin of error is calculated by multiplying the standard
error by the factor 1.96.

These measures of uncertainty (margins of error, standard errors, or
confidence intervals) should be taken into consideration in all data
analyses. Data users who have limited experience in using sample survey
data are advised to seek professional assistance regarding the
statistical implications of the data. As discussed in Section 6.7, it is
advisable to work in teams when utilizing the NRI for a scientific
project. This analysis team should include someone who has statistical
analysis skills, can explain and interpret statistical uncertainty and
sampling error, and can offer guidance for the generation and
interpretation of the NRI estimates.

The NRI design objectives should not be thought of as a restriction to
full analytical use of the NRI database. Examining data at various
geographic levels is a legitimate analytical technique -- it is a
particularly valuable way to gain additional insights into the database,
and in discovering \"messages\" that can be pulled from the data. Users
of the 1997 NRI database can initially analyze the data at a fairly
localized level, paying close attention to the margins of error of the
estimates at those localized levels. Data can then be aggregated to
higher levels for display or presentation purposes; estimates at these
aggregated levels would have sufficiently narrow confidence intervals so
that results would be appropriate for presentation and discussion.

**[1.5. Confidence Intervals -- Examples]{.smallcaps}**

Consider the following example.

-   The estimated amount of Cultivated Cropland for Kentucky in 1997 is
    3,514,000 acres. The estimated standard error for this estimate is
    66,600 acres. This error multiplied by a factor of 1.96 is 130,500,
    which is the estimated margin of error. (Note: the margins of error
    rather than the standard errors are given in Table 1.) A 95 percent
    confidence interval for this estimated acreage is constructed as:

########  Lower bound = (3,514,000 -- 130,500) acres = 3,383,500 acres

####  Upper bound = (3,514,000 + 130,500) acres = 3,644,500 acres

> This means a 95 percent certainty exists that the true amount of
> Cultivated Cropland is
>
> between 3,383,500 and 3,644,500 acres.

As mentioned above, estimates can be derived for many different
geographical units. Notice in the following table how the "reliability"
of estimates varies across geographic units in a predictable manner.

##### **Reliability of Cultivated Cropland Acreage Estimates, 1997**

+-----------------------+---------------+---------------+------------+
| ######                | #######       | Margin of     | Margin of  |
|                       |               |               | [error     |
| ###### Region         | ####### [Es   | [error        | (%)]{.     |
|                       | timated acres | (acres)       | underline} |
|                       | ]{.underline} | ]{.underline} |            |
+-----------------------+---------------+---------------+------------+
|                       |               |               |            |
+-----------------------+---------------+---------------+------------+
| U.S.                  | 326,783,700   | 1,838,300     | 0.6        |
+-----------------------+---------------+---------------+------------+
| Kentucky              | 3,514,000     | 130,500       | 3.7        |
+-----------------------+---------------+---------------+------------+
| MLRA 122 (KY)         | 1,075,900     | 76,200        | 7.1        |
+-----------------------+---------------+---------------+------------+
| Grayson County, KY    | 45,500        | 21,000        | 46.1       |
+-----------------------+---------------+---------------+------------+

Examination of confidence intervals is necessary to interpret the data
correctly. Misleading conclusions can be reached if confidence intervals
are not considered. As an illustration of how confidence intervals
affect what statements can be made, consider Table 1 at the end of this
chapter. There were significant net changes (gains and losses) in
Cultivated Cropland for many States, for the period 1982 to 1997. For
some States, the estimated gains or losses are negligible, i.e., not
statistically significant. For example, for Illinois, there is an
estimated net change of + 60,600 acres, but the estimated margin of
error is 95,600 acres; and for Maine the estimated net change is -5,600
acres, with an estimated margin of error of 29,800 acres. It is
appropriate to describe the net changes in these two States by stating
there were *\"no net changes in Cultivated Cropland in Illinois and
Maine.\"* This statement is true because the 95 percent confidence
intervals are -35,000 to 156,200 for Illinois and -35,400 to 24,200 for
Maine. This means that the observed changes were so close to zero that
we can not be certain if there were small gains or small losses in those
two cases.

The precision of all statistical results should be derived and examined.
The 1997 NRI database has been constructed in a manner that provides
methods for deriving estimated margins of error (or standard errors, or
confidence intervals) for any estimate derived from the database.

<table style="width:100%;">
<colgroup>
<col style="width: 16%" />
<col style="width: 13%" />
<col style="width: 8%" />
<col style="width: 0%" />
<col style="width: 11%" />
<col style="width: 7%" />
<col style="width: 1%" />
<col style="width: 13%" />
<col style="width: 7%" />
<col style="width: 1%" />
<col style="width: 10%" />
<col style="width: 7%" />
</colgroup>
<tbody>
<tr class="odd">
<td colspan="12">Table 1. Estimated acres of Cultivated Cropland and
Developed Land,<br />
with estimated margins of error in parentheses</td>
</tr>
<tr class="even">
<td>State</td>
<td colspan="3"><p>Cultivated</p>
<p>Cropland, 1997</p></td>
<td colspan="3">Change in Cultivated Cropland, 1992 to 1997</td>
<td colspan="3"><p>Developed</p>
<p>Land, 1997</p></td>
<td colspan="2">Change in Developed Land, 1992 to 1997</td>
</tr>
<tr class="odd">
<td></td>
<td colspan="11">- - - - - - - - - - - - - - - - - - - - - - - - - -
1,000 acres - - - - - - - - - - - - - - - - - - - - - - - - - -</td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td></td>
<td colspan="2"></td>
<td></td>
<td colspan="2"></td>
<td></td>
<td colspan="2"></td>
<td></td>
</tr>
<tr class="odd">
<td>Alabama</td>
<td>2,614.1</td>
<td>(155.6)</td>
<td colspan="2">-189.8</td>
<td>(64.3)</td>
<td colspan="2">2,252.3</td>
<td>(139.7)</td>
<td colspan="2">315.3</td>
<td>(31.6)</td>
</tr>
<tr class="even">
<td>Arizona</td>
<td>982.2</td>
<td>(123.7)</td>
<td colspan="2">-34.5</td>
<td>(37.2)</td>
<td colspan="2">1,491.4</td>
<td>(286.4)</td>
<td colspan="2">113.8</td>
<td>(35.9)</td>
</tr>
<tr class="odd">
<td>Arkansas</td>
<td>7,362.4</td>
<td>(257.0)</td>
<td colspan="2">-74.1</td>
<td>(58.0)</td>
<td colspan="2">1,409.1</td>
<td>(130.7)</td>
<td colspan="2">168.9</td>
<td>(34.7)</td>
</tr>
<tr class="even">
<td>California</td>
<td>6,219.7</td>
<td>(558.8)</td>
<td colspan="2">-337.5</td>
<td>(183.3)</td>
<td colspan="2">5,456.1</td>
<td>(303.0)</td>
<td colspan="2">553.4</td>
<td>(76.6)</td>
</tr>
<tr class="odd">
<td>Colorado</td>
<td>7,567.4</td>
<td>(443.7)</td>
<td colspan="2">-107.6</td>
<td>(128.0)</td>
<td colspan="2">1,651.7</td>
<td>(149.7)</td>
<td colspan="2">112.5</td>
<td>(28.2)</td>
</tr>
<tr class="even">
<td>Connecticut</td>
<td>81.0</td>
<td>(20.2)</td>
<td colspan="2">-12.9</td>
<td>(13.3)</td>
<td colspan="2">873.9</td>
<td>(46.6)</td>
<td colspan="2">39.4</td>
<td>(7.3)</td>
</tr>
<tr class="odd">
<td>Delaware</td>
<td>478.4</td>
<td>(35.5)</td>
<td colspan="2">-14.7</td>
<td>(9.0)</td>
<td colspan="2">225.5</td>
<td>(22.7)</td>
<td colspan="2">23.1</td>
<td>(7.4)</td>
</tr>
<tr class="even">
<td>Florida</td>
<td>1,480.3</td>
<td>(184.8)</td>
<td colspan="2">-213.4</td>
<td>(87.2)</td>
<td colspan="2">5,184.8</td>
<td>(224.8)</td>
<td colspan="2">825.2</td>
<td>(91.3)</td>
</tr>
<tr class="odd">
<td>Georgia</td>
<td>4,174.8</td>
<td>(186.8)</td>
<td colspan="2">-539.4</td>
<td>(91.1)</td>
<td colspan="2">3,957.3</td>
<td>(145.4)</td>
<td colspan="2">851.9</td>
<td>(58.4)</td>
</tr>
<tr class="even">
<td>Hawaii</td>
<td>198.0</td>
<td>(35.7)</td>
<td colspan="2">-30.6</td>
<td>(16.9)</td>
<td colspan="2">179.7</td>
<td>(35.3)</td>
<td colspan="2">6.8</td>
<td>(2.9)</td>
</tr>
<tr class="odd">
<td>Idaho</td>
<td>4,541.3</td>
<td>(254.8)</td>
<td colspan="2">-113.9</td>
<td>(145.0)</td>
<td colspan="2">754.9</td>
<td>(64.3)</td>
<td colspan="2">91.9</td>
<td>(21.6)</td>
</tr>
<tr class="even">
<td><strong>Illinois</strong></td>
<td>23,563.4</td>
<td>(266.4)</td>
<td colspan="2"><strong>60.6</strong></td>
<td><strong>(95.6)</strong></td>
<td colspan="2">3,180.9</td>
<td>(125.8)</td>
<td colspan="2">246.5</td>
<td>(37.8)</td>
</tr>
<tr class="odd">
<td>Indiana</td>
<td>12,761.4</td>
<td>(214.6)</td>
<td colspan="2">-162.4</td>
<td>(85.1)</td>
<td colspan="2">2,260.4</td>
<td>(97.0)</td>
<td colspan="2">195.3</td>
<td>(31.9)</td>
</tr>
<tr class="even">
<td>Iowa</td>
<td>24,216.5</td>
<td>(283.8)</td>
<td colspan="2">239.4</td>
<td>(124.7)</td>
<td colspan="2">1,702.1</td>
<td>(86.8)</td>
<td colspan="2">69.1</td>
<td>(17.1)</td>
</tr>
<tr class="odd">
<td>Kansas</td>
<td>24,793.6</td>
<td>(421.6)</td>
<td colspan="2">-293.2</td>
<td>(123.9)</td>
<td colspan="2">1,939.9</td>
<td>(97.4)</td>
<td colspan="2">96.5</td>
<td>(20.0)</td>
</tr>
<tr class="even">
<td><strong>Kentucky</strong></td>
<td><strong>3,514.0</strong></td>
<td><strong>(130.5)</strong></td>
<td colspan="2">-109.6</td>
<td>(103.9)</td>
<td colspan="2">1,737.5</td>
<td>(94.5)</td>
<td colspan="2">237.1</td>
<td>(24.5)</td>
</tr>
<tr class="odd">
<td>Louisiana</td>
<td>5,470.1</td>
<td>(172.7)</td>
<td colspan="2">-323.1</td>
<td>(67.0)</td>
<td colspan="2">1,623.8</td>
<td>(61.5)</td>
<td colspan="2">133.6</td>
<td>(16.9)</td>
</tr>
<tr class="even">
<td><strong>Maine</strong></td>
<td>154.7</td>
<td>(43.5)</td>
<td colspan="2"><strong>-5.6</strong></td>
<td><strong>(29.8)</strong></td>
<td colspan="2">712.0</td>
<td>(76.2)</td>
<td colspan="2">111.1</td>
<td>(17.8)</td>
</tr>
<tr class="odd">
<td>Maryland</td>
<td>1,412.8</td>
<td>(70.6)</td>
<td colspan="2">-85.8</td>
<td>(28.4)</td>
<td colspan="2">1,235.7</td>
<td>(52.7)</td>
<td colspan="2">177.6</td>
<td>(19.0)</td>
</tr>
<tr class="even">
<td>Massachusetts</td>
<td>64.7</td>
<td>(20.4)</td>
<td colspan="2">-11.7</td>
<td>(14.5)</td>
<td colspan="2">1,479.2</td>
<td>(79.2)</td>
<td colspan="2">211.8</td>
<td>(19.2)</td>
</tr>
<tr class="odd">
<td>Michigan</td>
<td>6,558.8</td>
<td>(200.9)</td>
<td colspan="2">-558.1</td>
<td>(109.4)</td>
<td colspan="2">3,545.5</td>
<td>(134.1)</td>
<td colspan="2">364.1</td>
<td>(36.8)</td>
</tr>
<tr class="even">
<td>Minnesota</td>
<td>19,731.2</td>
<td>(395.3)</td>
<td colspan="2">111.8</td>
<td>(173.9)</td>
<td colspan="2">2,185.5</td>
<td>(112.9)</td>
<td colspan="2">231.8</td>
<td>(39.8)</td>
</tr>
<tr class="odd">
<td>Mississippi</td>
<td>4,931.5</td>
<td>(161.5)</td>
<td colspan="2">-550.2</td>
<td>(79.4)</td>
<td colspan="2">1,474.0</td>
<td>(101.5)</td>
<td colspan="2">206.4</td>
<td>(26.5)</td>
</tr>
<tr class="even">
<td>Missouri</td>
<td>10,517.2</td>
<td>(236.0)</td>
<td colspan="2">-477.8</td>
<td>(114.5)</td>
<td colspan="2">2,517.4</td>
<td>(115.1)</td>
<td colspan="2">224.2</td>
<td>(25.9)</td>
</tr>
<tr class="odd">
<td>Montana</td>
<td>12,526.7</td>
<td>(789.5)</td>
<td colspan="2">44.4</td>
<td>(248.1)</td>
<td colspan="2">1,032.3</td>
<td>(107.8)</td>
<td colspan="2">76.3</td>
<td>(20.8)</td>
</tr>
<tr class="even">
<td>Nebraska</td>
<td>17,983.5</td>
<td>(357.3)</td>
<td colspan="2">173.1</td>
<td>(127.8)</td>
<td colspan="2">1,205.9</td>
<td>(90.7)</td>
<td colspan="2">55.1</td>
<td>(12.7)</td>
</tr>
<tr class="odd">
<td>Nevada</td>
<td>121.2</td>
<td>(75.5)</td>
<td colspan="2">-89.9</td>
<td>(48.6)</td>
<td colspan="2">381.4</td>
<td>(65.7)</td>
<td colspan="2">26.7</td>
<td>(9.6)</td>
</tr>
<tr class="even">
<td>New Hampshire</td>
<td>20.7</td>
<td>(10.4)</td>
<td colspan="2">0.0</td>
<td>(4.5)</td>
<td colspan="2">588.6</td>
<td>(47.6)</td>
<td colspan="2">62.6</td>
<td>(9.4)</td>
</tr>
<tr class="odd">
<td>New Jersey</td>
<td>426.7</td>
<td>(48.4)</td>
<td colspan="2">-60.9</td>
<td>(21.0)</td>
<td colspan="2">1,778.2</td>
<td>(60.8)</td>
<td colspan="2">213.6</td>
<td>(19.2)</td>
</tr>
<tr class="even">
<td>New Mexico</td>
<td>1,388.1</td>
<td>(125.8)</td>
<td colspan="2">-72.9</td>
<td>(59.4)</td>
<td colspan="2">1,152.7</td>
<td>(114.7)</td>
<td colspan="2">217.2</td>
<td>(74.5)</td>
</tr>
<tr class="odd">
<td>New York</td>
<td>2,751.6</td>
<td>(146.2)</td>
<td colspan="2">-123.7</td>
<td>(97.2)</td>
<td colspan="2">3,183.6</td>
<td>(117.6)</td>
<td colspan="2">317.6</td>
<td>(27.2)</td>
</tr>
<tr class="even">
<td>North Carolina</td>
<td>5,065.5</td>
<td>(206.2)</td>
<td colspan="2">-483.6</td>
<td>(85.5)</td>
<td colspan="2">3,856.4</td>
<td>(178.2)</td>
<td colspan="2">506.6</td>
<td>(60.6)</td>
</tr>
<tr class="odd">
<td>North Dakota</td>
<td>22,820.7</td>
<td>(378.7)</td>
<td colspan="2">-36.0</td>
<td>(142.1)</td>
<td colspan="2">991.8</td>
<td>(72.1)</td>
<td colspan="2">32.8</td>
<td>(10.6)</td>
</tr>
<tr class="even">
<td>Ohio</td>
<td>10,254.6</td>
<td>(193.3)</td>
<td colspan="2">-598.6</td>
<td>(103.5)</td>
<td colspan="2">3,611.3</td>
<td>(136.0)</td>
<td colspan="2">364.8</td>
<td>(34.3)</td>
</tr>
<tr class="odd">
<td>Oklahoma</td>
<td>9,345.4</td>
<td>(288.7)</td>
<td colspan="2">-289.9</td>
<td>(107.4)</td>
<td colspan="2">1,926.3</td>
<td>(110.2)</td>
<td colspan="2">176.7</td>
<td>(31.4)</td>
</tr>
<tr class="even">
<td>Oregon</td>
<td>2,676.8</td>
<td>(178.6)</td>
<td colspan="2">-104.4</td>
<td>(55.7)</td>
<td colspan="2">1,222.3</td>
<td>(112.7)</td>
<td colspan="2">103.9</td>
<td>(22.7)</td>
</tr>
<tr class="odd">
<td>Pennsylvania</td>
<td>3,629.1</td>
<td>(156.4)</td>
<td colspan="2">-104.4</td>
<td>(100.4)</td>
<td colspan="2">3,983.2</td>
<td>(126.8)</td>
<td colspan="2">545.1</td>
<td>(33.7)</td>
</tr>
<tr class="even">
<td>Rhode Island</td>
<td>4.6</td>
<td>(3.1)</td>
<td colspan="2">-2.6</td>
<td>(2.4)</td>
<td colspan="2">200.6</td>
<td>(16.3)</td>
<td colspan="2">6.6</td>
<td>(2.0)</td>
</tr>
<tr class="odd">
<td>South Carolina</td>
<td>2,319.3</td>
<td>(122.5)</td>
<td colspan="2">-436.2</td>
<td>(56.4)</td>
<td colspan="2">2,097.3</td>
<td>(104.3)</td>
<td colspan="2">362.0</td>
<td>(38.6)</td>
</tr>
<tr class="even">
<td>South Dakota</td>
<td>14,340.0</td>
<td>(345.4)</td>
<td colspan="2">-64.6</td>
<td>(149.7)</td>
<td colspan="2">959.7</td>
<td>(89.2)</td>
<td colspan="2">57.8</td>
<td>(20.8)</td>
</tr>
<tr class="odd">
<td>Tennessee</td>
<td>3,261.7</td>
<td>(132.5)</td>
<td colspan="2">-380.6</td>
<td>(86.4)</td>
<td colspan="2">2,370.6</td>
<td>(108.2)</td>
<td colspan="2">401.9</td>
<td>(37.0)</td>
</tr>
<tr class="even">
<td>Texas</td>
<td>26,330.0</td>
<td>(591.1)</td>
<td colspan="2">-1,225.3</td>
<td>(250.1)</td>
<td colspan="2">8,567.0</td>
<td>(287.3)</td>
<td colspan="2">893.5</td>
<td>(78.2)</td>
</tr>
<tr class="odd">
<td>Utah</td>
<td>705.3</td>
<td>(147.0)</td>
<td colspan="2">-195.4</td>
<td>(159.9)</td>
<td colspan="2">661.6</td>
<td>(74.7)</td>
<td colspan="2">81.3</td>
<td>(24.3)</td>
</tr>
<tr class="even">
<td>Vermont</td>
<td>138.7</td>
<td>(25.3)</td>
<td colspan="2">-6.7</td>
<td>(20.6)</td>
<td colspan="2">317.5</td>
<td>(29.2)</td>
<td colspan="2">11.5</td>
<td>(3.9)</td>
</tr>
<tr class="odd">
<td>Virginia</td>
<td>1,636.6</td>
<td>(112.3)</td>
<td colspan="2">-169.9</td>
<td>(54.7)</td>
<td colspan="2">2,625.8</td>
<td>(102.3)</td>
<td colspan="2">343.5</td>
<td>(29.8)</td>
</tr>
<tr class="even">
<td>Washington</td>
<td>5,577.1</td>
<td>(363.0)</td>
<td colspan="2">-182.8</td>
<td>(119.6)</td>
<td colspan="2">2,065.0</td>
<td>(174.4)</td>
<td colspan="2">240.8</td>
<td>(46.6)</td>
</tr>
<tr class="odd">
<td>West Virginia</td>
<td>166.1</td>
<td>(37.0)</td>
<td colspan="2">-48.3</td>
<td>(19.2)</td>
<td colspan="2">873.6</td>
<td>(65.9)</td>
<td colspan="2">176.8</td>
<td>(21.6)</td>
</tr>
<tr class="even">
<td>Wisconsin</td>
<td>8,752.0</td>
<td>(241.3)</td>
<td colspan="2">-57.7</td>
<td>(153.9)</td>
<td colspan="2">2,417.9</td>
<td>(127.4)</td>
<td colspan="2">188.2</td>
<td>(26.7)</td>
</tr>
<tr class="odd">
<td>Wyoming</td>
<td>978.0</td>
<td>(181.3)</td>
<td colspan="2">3.0</td>
<td>(82.3)</td>
<td colspan="2">643.7</td>
<td>(98.0)</td>
<td colspan="2">34.4</td>
<td>(15.1)</td>
</tr>
<tr class="even">
<td>Caribbean</td>
<td>174.2</td>
<td>(22.7)</td>
<td colspan="2">-108.5</td>
<td>(21.2)</td>
<td colspan="2">506.8</td>
<td>(29.6)</td>
<td colspan="2">112.4</td>
<td>(10.2)</td>
</tr>
<tr class="odd">
<td><strong>Total</strong></td>
<td><strong>326,783.7</strong></td>
<td><strong>(1,838.3)</strong></td>
<td colspan="2">-8,456.5</td>
<td>(741.1)</td>
<td colspan="2">98,251.7</td>
<td>(884.4)</td>
<td colspan="2">11,217.0</td>
<td>(247.9)</td>
</tr>
</tbody>
</table>

  -----------------------------------------------------------------------
  Table 2. Estimated average annual sheet and rill erosion for 1997
  Cultivated Cropland,\
  with estimated margins of error in parentheses (in tons/acre/year)

  -----------------------------------------------------------------------

  -------------------------------- -------------------- -----------------
  State                            Erosion rate         Margin of error

                                                        

  Alabama                          6.7                  (0.32)

  Arizona                          0.7                  (0.07)

  Arkansas                         3.5                  (0.09)

  California                       0.7                  (0.17)

  Colorado                         1.7                  (0.12)

  Connecticut                      5.6                  (1.53)

  Delaware                         2.0                  (0.19)

  Florida                          1.8                  (0.28)

  Georgia                          5.9                  (0.30)

  Hawaii                           2.5                  (0.47)

  Idaho                            3.3                  (0.31)

  Illinois                         4.1                  (0.10)

  Indiana                          3.0                  (0.10)

  Iowa                             4.9                  (0.13)

  Kansas                           2.2                  (0.04)

  Kentucky                         4.4                  (0.23)

  Louisiana                        3.3                  (0.09)

  Maine                            3.9                  (0.93)

  Maryland                         4.4                  (0.41)

  Massachusetts                    4.5                  (1.61)

  Michigan                         2.0                  (0.09)

  Minnesota                        2.1                  (0.07)

  Mississippi                      5.3                  (0.23)

  Missouri                         5.6                  (0.23)

  Montana                          1.9                  (0.13)

                                                        

                                                        

  Nebraska                         2.9                  (0.10)

  Nevada                           0.2                  (0.05)

  New Hampshire                    3.5                  (2.07)

  New Jersey                       5.6                  (0.63)

  New Mexico                       0.9                  (0.08)

  New York                         3.8                  (0.26)

  North Carolina                   5.0                  (0.37)

  North Dakota                     1.4                  (0.04)

  Ohio                             2.6                  (0.10)

  Oklahoma                         2.8                  (0.10)

  Oregon                           3.0                  (0.33)

  Pennsylvania                     5.1                  (0.26)

  Rhode Island                     3.5                  (0.99)

  South Carolina                   3.2                  (0.19)

  South Dakota                     2.0                  (0.07)

  Tennessee                        7.7                  (0.41)

  Texas                            2.6                  (0.05)

  Utah                             1.6                  (0.75)

  Vermont                          3.1                  (0.67)

  Virginia                         5.9                  (0.42)

  Washington                       4.7                  (0.27)

  West Virginia                    4.3                  (1.19)

  Wisconsin                        3.7                  (0.19)

  Wyoming                          1.1                  (0.17)

  Caribbean                        12.2                 (2.71)

  National average                 3.1                  (0.03)

                                                        
  -------------------------------- -------------------- -----------------

**2. The National Resources Inventory Program**

1.  **[The Natural Resources Conservation Service]{.smallcaps}**

The Natural Resources Conservation Service, formerly known as the Soil
Conservation Service, is an agency of the United States Department of
Agriculture. Its mission is "to provide leadership in a partnership
effort to help people conserve, maintain, and improve our natural
resources and environment." The Soil Conservation Service was
established through the Soil Conservation Act of 1935 after the Dust
Bowl catastrophe. This legislation was based, in part, upon results from
the 1934 National Erosion Reconnaissance Survey, which was the first
formal study of erosion conducted in the Nation. Hugh Hammond Bennett,
the agency founder and first administrator, convinced the U.S. Congress
that soil erosion was a national menace and that a permanent agency
within USDA was needed to call landowners\' attention to their land
stewardship opportunities and responsibilities. With the establishment
of the Soil Conservation Service, a nationwide partnership of Federal
agencies, local conservation districts, and communities was developed to
provide assistance to the rural and urban sectors in the conservation of
natural resources. Today, more than 60 years later, NRCS champions the
vitality of the land as USDA\'s lead conservation agency. No other
Federal agency speaks for the health of America\'s private land.

2.  **[Historical Inventories]{.smallcaps}**

Throughout its history, NRCS has conducted periodic inventories of the
Nation\'s natural resources. After the Erosion Reconnaissance Survey of
1934, the agency performed the 1945 Conservation Needs Inventory, which
also was based on reconnaissance methods and the gathering of
information from a variety of sources.

In 1956, NRCS\'s predecessor, the Soil Conservation Service, was
assigned responsibility to lead a cooperative endeavor of 8 Federal
agricultural agencies to develop estimates of the magnitude and urgency
of the various conservation measures needed to maintain and improve the
country's productive capacity. This study used statistical sampling
methodology and was called the 1958 National Inventory of Soil and Water
Conservation Needs, or the 1958 Conservation Needs Inventory (CNI). The
National Inventory of Soil and Water Conservation Needs, 1967, or the
1967 CNI, was conducted primarily to update the 1958 data. To facilitate
data collection and processing while reducing field costs, a two-stage
sample design was implemented. These inventories dealt mostly with land
use, soil type, and conservation treatment needs. The Potential Cropland
Study of 1975 was conducted to determine the quantity, locations, and
characteristics of lands that could be easily converted from noncropland
to cropland uses. This study was motivated by increased global demands
for agricultural products. A national-level sample was easily obtained
by selecting a subset of the 1967 CNI sample points.

3.  **[Initiation of the National Resources Inventory]{.smallcaps}**

The Rural Development Act of 1972 played a critical role in the
initiation of the National Resources Inventory. The Act specified that
information on urban spread and rural economic survival, prime farmland,
flood plains, and conservation practices was needed to devise community
guidance for balanced rural-urban growth. In addition, the Act directed
NRCS to assess the status, condition, and trends of soil, water, and
related resources on the Nation\'s nonfederal lands at intervals of five
years or less. The Soil and Water Resources Conservation Act of 1977 and
other supporting legislation augmented the statutory mandate for
periodic assessment of the Nation\'s natural resources. These demands
for up-to-date information and for additional items related to soil and
water resources led to development of the 1977 National Resources
Inventory. New data elements included water and wind erosion,
conservation practices, incidence of wetlands, and flooding propensity.
For the first time, there was a scientific collection of national
quantitative erosion data based upon the Universal Soil Loss Equation
and the Wind Erosion Equation. The 1977 NRI introduced a number of new
methodologies and is considered the starting point for the current NRI
program.

4.  **[The National Resources Inventory Program]{.smallcaps}**

To adhere to the mandate set forth in the Rural Development Act of 1972,
NRCS began conducting the NRI on a five-year cycle. After the 1977 NRI,
the sample size and scope were significantly expanded for the 1982 NRI,
in response to increased demand for data emphasizing environmental and
ecological concerns. Additional data were collected on soils, irrigation
practices, wetlands, windbreaks, riparian vegetation, wildlife habitat
diversity, critical eroding areas, and many facets of vegetative cover.
Many far-reaching decisions were based upon the 1982 NRI data because of
its comprehensiveness. However, because land use definitions and erosion
estimation protocols were significantly different between the 1977 NRI
and the 1982 NRI, the 1982 NRI has the crucial role of the initial
time-point for the monitoring conducted in more recent inventories.

The scope and size of the 1987 NRI were reduced because of staffing and
funding constraints. Trained data collection teams gathered
approximately 30 percent of the 1987 sample data using remote sensing
and entered the data via computer. Data collection protocols, quality
assurance, estimation techniques, and database construction were all
designed to ensure that the 1987 NRI provided meaningful and legitimate
trending information.

The 1992 and 1997 NRIs are comparable to the 1982 NRI in size and
extent. Some data elements that were collected for the 1982 and/or 1987
NRIs have been dropped, while a few new items were added. However, data
elements, definitions, and protocols were kept as similar as possible
because the main objective has been to develop a time series database
for the years 1982, 1987, 1992, and 1997. Data collection for both the
1992 and 1997 NRIs relied heavily on remote sensing and computer-based
technologies. In 1997, data gatherers recorded information with personal
digital assistants (PDAs) and computer-assisted survey instruments
(CASIs).

Concurrently, there have been several small-scale topical inventories
conducted from 1995 to 1999. These special inventories were designed to
update trends in erosion on highly erodible cropland and investigate
effects of the Federal Agriculture Improvement and Reform Act of 1996
and high commodity prices on conservation practices and cropping
patterns.

5.  **[NRCS Data Collection Activities]{.smallcaps}**

In addition to these recurrent NRI inventories, NRCS also collects large
quantities of field-level natural resources data in support of
conservation planning activities and the Soil Survey Program. Thousands
of NRCS technical specialists, including soil scientists, soil
conservationists, range conservationists, foresters, wildlife
biologists, and agronomists, collect data at the field and farm level in
order to provide conservation assistance to farmers and ranchers in the
development of conservation systems uniquely tailored to the land and
their individual way of doing business. Assistance is also provided to
rural and urban communities to help reduce erosion, conserve and protect
water resources, and solve other resource-related problems. The
information that NRCS collects about natural resources in the United
States is critical for sustaining agriculture, promoting the
conservation and stewardship ethic, and preserving the health of the
Nation\'s natural resources and environment.

6.  **[Meeting Future Needs for Information]{.smallcaps}**

The NRI program is continuing to evolve as cost-effective methods are
developed to collect more timely and relevant information that addresses
emerging agri-environmental issues. These new inventory approaches are
built upon past experiences as well as by incorporating new tools,
methodologies, and technologies. NRCS has developed a new continuous
inventory process, wherein data are collected annually. This process
better addresses priority issues, provides many results on an annual
basis, and more fully utilizes capabilities of highly trained staff and
sophisticated tools and technologies. Efforts are underway to develop a
multi-agency integrated inventory approach from the many Federal
activities that currently attempt to monitor the environment. Other
initiatives include the incorporation of various tools for assessing
resource health and the utilization of inventory data for modeling and
policy analysis.

**3.** **Inventory Procedures**

**[3.1. Inventory Design]{.smallcaps}**

Current NRI statistical procedures are a result of decades of
collaborative research between NRCS and the Iowa State University
Statistical Laboratory. The basic sampling procedures were established
in 1977 and 1982, based upon research conducted in the 1970s. Estimation
techniques have evolved with each successive survey.

The national sample is a stratified two-stage unequal-probability area
sample that can be modified for specific national survey objectives and
used as a frame for special studies. Stratification was developed
county-by-county, utilizing the grid of sections and townships defined
by the Public Land Survey System (PLSS), where possible. A section is a
one-mile square parcel of land, and a township is a 6-mile square area
consisting of 36 sections. Each township was subdivided into three
2-mile by 6-mile strata for sampling purposes. For counties not covered
by the PLSS, strata were developed by: utilizing latitude and longitude,
utilizing the Universal Transverse Mercator grid system, or
superimposing lines analogous to townships and sections over a county
highway map.

Two-stage area samples were selected within each stratum. The first
stage sample unit, or Primary Sampling Unit (PSU), was an area of land;
at the second stage of sampling, one or more sample points were selected
within each sample PSU for observation. Most PSUs corresponded to
quarter-sections and were half-mile squares; three sample points were
selected within most PSUs. Sampling rates varied across strata,
typically being between 2 percent and 6 percent; these PSUs constitute
about a 3 percent sample of all the total land and water area of the 48
conterminous States, Hawaii, and the Caribbean area. There are instances
throughout the U.S. where components of the sample design deviate from
these standard rules. The inventory design for Alaska and the Pacific
Basin is significantly different and analysis for these two areas is
handled separately from the 48 conterminous States, Hawaii, and the
Caribbean. NRI data for Alaska and Pacific Basin are not included in the
CD-ROM release.

Further details are provided in Appendix C, Understanding the NRI
Design. Nusser and Goebel (1997) and Goebel (1998) provide more details
on the specifics of the sampling design, and on historical perspectives.

The national sample consisted of approximately 300,000 PSUs and 800,000
sample points for the 1992 and 1997 National Resources Inventories;
almost all of these were also part of the 1982 NRI. Experience has shown
that it is necessary to return to specific sample points that were in
previous inventories in order to obtain needed data on the dynamics of
change in land use and various natural resource parameters.

**[3.2. Data Gathering]{.smallcaps}**

Data for the 1997 NRI were collected for PSUs and points using
photo-interpretation (PI) and other remote sensing methods and
standards. For the most part, analog PI techniques were employed but GIS
technologies were also tested and evaluated at selected sites. The
agency contracted for the acquisition of aerial photography or obtained
necessary imagery in cooperation with other USDA agencies and partners.
Data gathering for the 1997 NRI occurred from July 1997 through October
1998. This schedule took into account that some aerial photography
needed to be flown during a time period that highlighted late growing
season conditions.

Inventory procedures were developed to ensure that data reflected 1997
growing season conditions, that inventory results are nationally
consistent, and that data recorded for the 1997 NRI are consistent with
1982, 1987, and 1992 determinations. The 1997 NRI protocols specified
that data collected from previous inventory cycles (1982, 1987, and
1992) be reviewed, evaluated, and edited concurrently with the 1997 NRI
data collection effort.

Detailed soils information is essential for the analysis and
interpretation of NRI data. Soils data were provided by the NRCS Soil
Survey Program and were obtained from the NRCS Soil Interpretation
Record database maintained at the Iowa State University Statistical
Laboratory. For the 1992 NRI extensive work was done to match individual
State Soil Survey Databases (SSSD) with each point in the NRI. This
process was designed to verify the accuracy and completeness of the NRI
soils database. This work also provided accurate soils data for use in
the 1997 NRI. Published soil surveys, advanced (pre-publication) soil
mapping field sheets, State-level databases, and ancillary lists of
soils information maintained in field offices were used to provide
critical soils data. Information on soil properties related to soil
erosion and other soil-dependent interpretations (i.e., prime farmland)
were linked to the NRI database.

County Base data is another critical component of the design and
development of the NRI database. This dataset requires periodic editing
and updating in order to establish NRI statistical controls and to
generate certain types of estimates. See Section 3.4 for a discussion of
the design, construction, and purpose of County Base data.

USDA field office records and local NRCS personnel provided information
pertaining to historical cropping and management systems for calculating
long-term erosion rates induced by wind or water, and to determine if
the field at the sample point was enrolled in the Conservation Reserve
Program (CRP). Data gathering protocols incorporated NRCS technical
standards and procedures, records and maps in local USDA offices, and
various Federal publications and standards, primarily the following:

-   NRCS national and field office technical guide (FOTG) publications
    and standards

    relating to the Universal Soil Loss Equation and Wind Erosion
    Equation (WEQ),

    \- NRCS-published or -correlated soil surveys,

    \- NRCS information relating to provisions of the 1985 Food Security
    Act and\
    subsequent farm bills,

    \- U.S. Fish and Wildlife Service wetland maps,

    \- U.S. Fish and Wildlife Service Cowardin wetlands classification
    system,

    \- Society of American Foresters forest classification,

    \- U.S. Geological Survey (USGS) hydrologic and topographic maps,
    and

    \- Bureau of the Census TIGER files and auxiliary information.

Field visits were conducted when available materials were not suitable
for making determinations for one or more data elements. Field visits
were also made for training purposes and other facets of the quality
assurance process. All NRI sample sites were visited on-site for the
1982 NRI. Subsequent on-site visits of selected PSUs also occurred in
1987, 1991, 1992, or 1995.

Twenty-one Inventory Collection and Coordination Sites (ICCS) were
established and assigned oversight and management authorities for data
gathering. These sites were:

  --------------------- ------------------------ ------------------------
  Ames, IA              Amherst, MA              Anchorage, AK

  Auburn, AL            Bismarck, ND             Boise, ID

  Bozeman, MT           Davis, CA                East Lansing, MI

  Lakewood, CO          Lexington, KY            Little Rock, AR

  Madison, WI           Morgantown, WV           Phoenix, AZ

  Portland, OR          Raleigh, NC              Reno, NV

  Salina, KS            Spokane, WA              Temple, TX
  --------------------- ------------------------ ------------------------

The ICCSs were the front-line management structures responsible for
coordinating the day-to-day activities associated with the collection of
data for the 1997 NRI. The ICCS leader trained subordinate staffs,
provided technical support, and managed quality reviews during the
operational phase of data gathering. Full-time, part-time, and temporary
NRCS employees --and in a few places, volunteers -- gathered 1997 NRI
information. The organization of data gathering varied with regional
land use and State staffing patterns. Geographic boundaries of ICCS
organizations ranged from one State to all or portions of several
States. Some collection sites assembled staffs at multiple office
locations, while others assembled staff at one central location. The
State Conservationists assigned various technical specialists to provide
overall support and to work with the ICCS data gathering teams.

**[3.3. Quality Assurance]{.smallcaps}**

Intricate quality assurance procedures were developed to make sure that
year-to-year differences reflect actual changes in resource conditions,
rather than differences in the perspectives of two different data
collection specialists or changes in technologies and protocols.

Data gatherers entered all sample data directly into hand-held computers
called personal digital assistants. All subsequent data quality checking
and evaluations were similarly based on computer forms of survey
information. The PDAs (Apple Computer\'s Newton MessagePad™ models 130,
2000, and 2100) were programmed to provide an intelligent survey
questionnaire with historic information, procedural logic, and single or
multi-variate checking for data completion and consistency. The PDAs
uploaded and downloaded sample records via Internet protocols from a
centralized database server at Iowa State University. The server
controlled and monitored access by \"client\" instruments and protected
survey data from loss, unauthorized access, or accidental disclosure. A
secure Web site allowed database access for purposes of survey
management, review of progress, and data quality evaluation.

Data gatherers received the protocols as well as training and technical
reference materials in printed and electronic forms. Cross-indexed data
gathering instructions, training, and the survey instrument were
specifically designed to foster consistent data gathering standards,
practices, and procedures. Instructions and training articulated the
requirement that trending information be gathered by the same protocols
and for the same locations used in prior studies. The 1997 instrument
included algorithms to evaluate trending data for consistency.

Quality assurance procedures included the use of data validation
software packages on the personal digital assistants. Hundreds of data
collection rules comparing multiple data elements were run on the PDAs
prior to submittal of data to the ISU Statistical Laboratory. The
Statistical Laboratory performed additional data validation and
consistency checks on all data received. After statistical estimation
procedures were completed, tables were generated and sent to technical
specialists at the ICCSs for further review and comment.

Quality assurance of NRI data was monitored at all organizational levels
within the NRI program and was accomplished by several procedures and
protocols. These included consistent training of data collectors in data
collection processes; standardized formalized written data gathering
instructions, documentation, and definitions of data elements;
consistent national rules and methods for data collection; and a
national Help Desk to resolve data collection issues.

National staffs conducted multiple training sessions to support core
work groups. NRCS national program staff in Washington (DC), Fort
Collins (CO), Ames (IA), and Fort Worth (TX) guided and supported the
data gathering activities. State or regional oversight authorities
supported local data gathering staffs in matters relating to quality
assurance. A single point of contact (\"Help Desk\") on the national
staff responded to questions from data gatherers and coordinated
technical responses provided by subject-matter experts from NRCS and the
Iowa State University Statistical Laboratory.

Quality assurance for the NRI process was guided by instructions,
coordinated training, and ongoing technical support. Data quality
checking procedures were embedded in every step, stage, and phase of the
data collection process for the 1997 NRI.

**[3.4. County Base Data]{.smallcaps}**

County Base data are geostatistical data used as surface area control
totals in the NRI statistical weighting process. County Base data for
the 1997 NRI were developed in a different manner than for previous NRI
surveys. New statistical estimation procedures were developed for the
1997 NRI to take advantage of the increased detail available from this
new geostatistical control information.

County Base data for the 1997 NRI were obtained from a Geographic
Information System developed specifically to provide these control data.
Geostatistical information was extracted from this GIS for each "HUCCO"
in the NRI sampling universe. A HUCCO is a polygon formed by the
intersection of 4-digit Hydrologic Unit Areas and county boundaries.
There are about 4,900 HUCCOs in the conterminous U.S., Puerto Rico, and
the Virgin Islands. However, 213 of these were too small to be used in
the NRI estimation process and were combined (collapsed) with an
adjacent HUCCO within the same county. (Note: The data element *mhydro*
in the table **point** specifies this collapsed HUCCO code -- it is
provided in the database for documentation only and would not be used
when creating tabulations from the data. The data element *hydro* in the
table **point** specifies the 8-digit hydrologic unit code that is used
when tabulating the data.)

County boundaries were obtained from U.S. Bureau of the Census TIGER
digital line files released in 1995, corresponding to a 1:100,000 map
scale. The TIGER files were augmented with shoreline data from the
National Oceanic and Atmospheric Administration (NOAA); several
modifications were needed to match NRI land universe definitions.
Spatial data for the 204 4-digit HUAs within the conterminous U.S. were
based upon a 1:250,000 U.S. Geological Survey digital line file;
boundaries were adjusted to conform to the shoreline created for the
county boundary data.

The GIS also included data layers identifying Large Water and Federal
Land areas. The TIGER data were used as a starting point in delineating
water areas; modifications were made using 1992 NRI County Base data,
USGS quadrangle sheets, and other materials. Federal lands were
initially based upon a 1996 USGS 1:2,000,000 scale digital layer
(released in 1998) and the TIGER data files. Modifications were made to
the Large Water and Federal Land data based upon several editing and
checking stages, including NRCS State office reviews. Federal Land data
for several western States were based mostly on databases available to
the State offices. (Note: The NRI definition of Large Water is that used
through 1980 by the Census Bureau, and consists of water bodies 40 acres
and streams 660 feet wide at normal pool.)

Specific data extracted from this GIS were:

(1) Total surface area of each HUCCO

(2) Area of Large Water within each HUCCO, for 1992 and 1997

(3) Federal Land area within each HUCCO, for 1992 and 1997 (Large Water
    is not part of the Federal Land area)

The County Base data used for the 1997 NRI estimation process also used
geostatistical control data for the years 1982 and 1987 (for Large Water
and Federal Land area within HUCCO). These figures were derived from the
1992 NRI County Base data, using statistical adjustments based upon
differences between the 1992 control acres used in 1992 and the 1992
control acres obtained from the GIS. The 1992 NRI County Base data (and
also those for the 1982 and 1987 NRIs) had been supplied by NRCS State
and field offices, were not based upon GIS procedures, and were obtained
for Major Land Resource Area portions of counties rather than HUA
portions.

Use of County Base data within the 1997 NRI estimation process is
briefly discussed in\
Chapter 4; detailed procedures are given by Fuller, Dodd, Wang, and
Peterson (2001). Development of these geostatistical estimation data is
discussed by Nusser, Kienzler, and Fuller (1999); they state that the
general objective of these new procedures was to "provide a more
reliable and efficient source of control information, and to improve the
geospatial properties of imputed data." Updating and maintaining the
accuracy of County Base data is a critical component of NRI statistical
controls and estimation processes.

**[3.5. Confidentiality and Integrity of Sample Locations]{.smallcaps}**

It is USDA and NRCS policy that actual locations of NRI sample sites are
confidential information and are not to be released to the public. As
per policy: *\"Proper security and confidentiality of information and
materials pertaining to locations of data gathering sites must be
maintained. Site locations (identified by coordinate systems, depicted
by maps or photographs, or described by direct observation of survey
location conditions, and other materials assembled for inventories) are
not public information and shall be used only for official inventory
purposes. This critical requirement must be met in order to ensure the
statistical integrity and validity of the NRI survey design, dataset,
and estimates, to prevent intended or unintended sampling bias, and to
maintain the confidentiality and cooperation of landowners and
operators.\"*

There is much at stake regarding the continued confidentiality and
integrity of the NRI sample sites. Many staff years and dollars have
been invested to develop this sophisticated and extensive set of
samples. A great deal is now known about these hundreds of thousands of
samples. Maintaining the confidentiality and integrity of these sample
locations is vital to the continued success of the NRI program.

**4. Construction of the NRI Database**

**[4.1. Introduction]{.smallcaps}**

The NRI database contains millions of pieces of information. It is a
powerful and unique tool, if it is understood and used properly. This
chapter presents information on how the NRI database is constructed --
it provides an overview of various features of the database and of
selected statistical estimation procedures used during creation of the
database. Further information describing the database is contained in
Chapter 5.

**[4.2. Statistical Estimation Procedure]{.smallcaps}**

The NRI statistical estimation procedure is fairly complex, because of
the complexity of the data and because a number of requirements have
been established regarding construction of the database. The goal of the
NRI statistical estimation procedure is to create a final NRI database
that meets the following requirements:

-   *Contains all information collected for the survey* **--** The
    dataset must contain all segment (PSU) and point sample data (for
    1982, 1987, 1992, and 1997). Also, the estimation procedure must
    incorporate certain county and sub-county ancillary data as
    controls; these inputs come from a geostatistical database and are
    called County Base data. (See\
    Section 3.4.)

-   *Produces estimates for previous years that are similar to those
    previously published* **--** The estimation procedure incorporates
    thousands of estimates published in 1992 (for the years 1982, 1987,
    and 1992) as controls. However, there are several complications. For
    example, all historical raw data were reviewed and edited as part of
    the 1997 data collection protocol. Also, county and sub-county
    ancillary data used for controls in 1997 were developed using
    improved (geostatistical) technologies. (See Section 3.4.)

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-   *Is easy to use* **--** Users of the data should be able to produce
    tabulations from the database without needing to understand the
    estimation procedure. All data are transformed into analysis
    variables for the public database. There is a unique weight
    (expansion factor) assigned to each observation.

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-   *Is complete* (for 1982/1987/1992/1997) **--** Imputation procedures
    were used to make sure the database is "fully populated" and has no
    missing values. Many other databases, whether large or small, do
    have missing values, which makes the data more difficult to analyze.

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-   *Produces estimates that agree with known data* -- For example, the
    sum of the weights in County *S* must equal the total area of County
    *S.*

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-   *Contains linkages to other databases* **--** Linkage to NRCS's
    extensive soils database is done directly; many linkages can be made
    geographically (spatially); and other themes, such as land cover/use
    or forest cover type, serve as ways to link with (or tap into) other
    databases.

-   *Produces reasonable estimates for small areas* **--** The NRI was
    designed to support analysis of issues at National, regional, State,
    and sub-State levels -- but **[not]{.underline}** at the county
    level. However, estimates for counties and 8-digit HUAs can be
    derived from the database; such estimates need to be "reasonable,"
    even though they do not meet NRI reliability standards because of
    the small sample sizes for geographic units of that size. This
    characteristic of the database makes users of the database more
    comfortable, and serves to provide more statistically robust results
    when the data are aggregated to higher levels for display,
    presentation, and discussion purposes.

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-   *Allows users of the data to estimate standard errors --* The
    database contains factors identifying the sampling strata and
    clusters. Variance estimates are based upon a number of
    approximations because of the multiple sources of data and
    relatively complex estimation procedure.

-   *Accounts for rare occurrences* **--** This is important for
    large-scale surveys; users of the data expect that the survey will
    provide such estimates (at least at the national or regional level).
    For agri-environmental surveys***,*** the extreme or rare situations
    are often of most interest.

The different types of data collected for the NRI are combined using a
series of statistical estimation procedures. The result is a set of data
tables (or files) that are easy to use and manipulate and represent
consolidated point-type information. This database structure makes it
possible for different users of the data to obtain the same results when
asking the same questions or making the same queries of the data.

Another feature is that many different pieces of information or many
different data themes can be looked at concurrently. In other words,
many pieces of site-specific information describing conditions at each
of the specific sampling sites are available. These hundreds of pieces
of information are site-specific data --- not generalized map and
tabular information. This greatly enhances the user\'s ability to
analyze and correlate the data properly. Overlaying many layers of
generalized data can cause problems when trying to make correlations and
construct models or scenarios.

The point-style data also make it possible to associate non-NRI data
with the NRI database. This can be done much more rigorously, even
though non-NRI data are usually much more generalized; examples would be
county figures on production, chemical use, or precipitation. Analysts
will often treat these data as they do specific NRI data items --- they
will associate or impute these factors onto the specific data points.
The structure of the NRI dataset makes this process more direct and
feasible to implement.

> ***An important requisite of the entire inventory process is that data
> for the four points in time (1982, 1987, 1992, and 1997) must be
> completely comparable and consistent from year to year so that the
> data reflect true trends and temporal analyses can be made. When
> making comparisons with 1982, 1987, and/or 1992 data, the new (1997)
> NRI database must be used. Originally released 1982, 1987, and 1992
> data may not be consistent with the new database --- the historical
> 1982, 1987, and 1992 data were updated to reflect 1997 technical
> criteria and protocols as part of the 1997 NRI inventory process. The
> 1997 NRI database should be considered self-contained in that
> respect.***

The NRI statistical estimation procedures are discussed by Fuller
(1999); extensive details are given by Fuller, Dodd, Wang, and Peterson
(2001). These papers discuss the use of several statistical techniques,
including: small area estimation, imputation, pseudo point generation,
hot deck procedures, ratio adjustment, raking, and rounding procedures.
All statistical techniques have been carefully developed (over many
decades of cooperative research and application) and are accepted by the
scientific community.

**4.3.** **[Imputation]{.smallcaps}**

Data collection protocols, quality assurance processes, estimation
techniques, and database construction were all designed to ensure that
the 1997 NRI provides meaningful and legitimate trending information
(Goebel 1998). Imputation procedures were used during construction of
the NRI database to:

(1) statistically combine information contained in the County Base data,
    PSU data, and point data to produce a complete point dataset with
    one weight for each point; and

(2) statistically fill in any missing data for sample units not observed
    in 1987 and for

> those new to the sample in 1987 and/or 1992.

Imputation is a statistical estimation technique. Imputation processes
use information that was actually collected to predict the values of
missing data elements within the dataset. Because the NRI database is
used in many different ways by many different individuals and groups, it
is important that the database be complete and "fully populated" --
there are no missing values, and every data field for every database
record contains a legitimate value. Statistical imputation techniques
were used in developing the 1992 and 1997 NRI databases as a means of
enhancing the quality of the database.

Data for the 1997 NRI were collected for the same sample sites that were
used for the 1992 NRI. However, only one-third of these was sampled for
the 1987 NRI, because of staffing and funding constraints (Also, some
1992 NRI samples were not part of the 1982 NRI.). For the two-thirds of
the samples that were not inventoried in 1987, data were obtained during
the 1992 NRI using the following process:

(1) If suitable imagery or adequate ancillary information, or both, were
    available for 1987, data gatherers recorded key selected 1987 data
    elements---but not every data element.

(2) If no suitable imagery or adequate ancillary information could be
    found for 1987, data gatherers recorded no data for the 1987
    inventory year; this occurred infrequently.

The key data elements that were determined for those 1992 NRI samples
that were not part of the 1987 NRI were: ownership category, prime
farmland, land cover/use, and irrigation for 1987 for each sample point;
and all PSU data items except for those describing Large Water.

Missing 1987 data were imputed during construction of the 1992 NRI
database. The 1997 NRI database also contains these imputed values. The
following is an example of how the 1992 NRI imputation process handled
missing 1987 data elements dealing with erosion estimation.

Consider first the USLE C-factor, which for Cropland is based upon the
cropping system, particularly the crop rotation, tillage, and residue
management. This is the factor in the USLE that accounts for ground
cover present during the critical eroding period.

The imputation procedure for 1987 USLE C-factor can be thought of as a
stochastic search procedure. This procedure searched for a sample point
that was:

(1) inventoried in 1987

(2) geographically close

(3) similar with regard to 1987 land cover/use history, land capability
    class and subclass, irrigation status, and wind erosion

The search procedure for this "donor point" used a hierarchical scale in
weighing closeness and similarity, and in placing iterative limitations
upon the number of times given points could serve as donors. The search
for a donor first focused on points in the same MLRA and county, then on
points within the same county but in a different MLRA, and then on
points located in adjacent counties. Steps were developed for extreme
situations where no sufficiently similar points existed.

In most cases, the search procedure also determined the imputed 1987
values for the following data elements:

-   cropping history

-   conservation treatment needs

-   P-factor, for USLE

-   K-factors and V-factors, for the Wind Erosion Equation

-   length of rotation, for the Wind Erosion Equation

-   types of conservation treatment needed

-   whether double cropping is present

-   conservation practices

-   range condition

-   apparent trend in range condition

Many regression correlations were examined to determine which variables
should be included and in what order of importance.

> *[WARNING]{.underline}: This type of estimation procedure does not
> produce individual records that are perfect. It is like any prediction
> or modeling technique. Any single predicted value is subject to an
> estimation error. However, the imputation process significantly
> improves the overall quality of the database.*
>
> *Analyses focusing on certain types of changes involving imputed data
> will NOT be legitimate; most of the imputed 1987 data elements dealt
> with erosion estimation, conservation practices, and conservation
> treatment needed. For example, it is not legitimate to estimate number
> of acres where erosion increased by at least two tons/acre/year from
> 1987 to 1992 by looking at specific point data; however it is
> legitimate to examine the overall change in erosion for some set of
> sample points.*
>
> *The imputation process provides defensible scientific estimates for
> overall or aggregated changes --- but not where individual records are
> required to be exact.*

It is necessary to use imputed data values when constructing estimates
from NRI data. For example, imputed data must be used to properly
construct an estimate of 1987 erosion rates. If imputed values are
omitted, estimates will be biased and inconclusive. The 1997 NRI
database contains a data element that indicates which values, if any,
were imputed for the sample point.

The second primary use of imputation for NRI statistical estimation is
generation of "pseudo points" that are needed to transfer the PSU and
County Base data to point-level data. (County Base data are discussed in
Section 3.4.) A goal of NRI is creation of a complete point dataset that
combines the information contained in the County Base data, PSU data,
and point data to produce a complete point dataset with one weight for
each point. A dataset of this form is user-friendly and easy to
tabulate.

The procedures for generating pseudo-points are required because County
Base data and PSU data may contain patterns of change over time that are
not observed at an actual NRI sample point. For example, the County Base
data may indicate a slight increase in Federal land from 1992 to 1997
for a particular county. However, there may be no points in the county
showing a change from nonfederal ownership in 1992 to Federal ownership
in 1997. The approach used by the NRI is to create "pseudo points"
within the county to represent the observed change in Federal ownership.

This imputation approach is used in the NRI to match point estimates for
county total area of Large Water and Federal Land to those values given
in the County Base data. Other examples include area estimation and
change for relatively small geographic areas of interest such as roads,
urban areas, or farmsteads (Fuller 1999).

In Section 4.4, development of weights for point-level data is defined
and examined. In Section 4.5, an example of how pseudo points are
constructed is presented.

**[4.4. Development of NRI Weights]{.smallcaps}**

One of the data fields in the NRI database is the weight, or expansion
factor; it is the data element *xfact* in the table **point**. This
factor specifies the number of acres that a sample point represents when
statistical estimates are derived using the NRI database. This weight
must be used for all tabulations and analyses -- whether estimating
average erosion rates, acreages, percentage figures, or margins of
error; otherwise, results will be biased.

These weights have many desirable features. For example, the weights for
all points in a county sum to the total surface area of the county.
Also, only one set of weights exists within the NRI database. This one
prepared set of weights is used to construct any type of estimate,
regardless of the theme. (Some databases have multiple sets of weights
or do not even have weights constructed. Data analysts then face
confusion and extra work in determining how to properly use the
database.)

A discussion of the development and construction of the NRI weights
follows. It begins with an introduction to the basic concepts and then
describes how other factors affect the calculations. The actual process
used for the 1997 NRI is fairly complex; see Fuller, Dodd, Wang, and
Peterson (2001) for a detailed description.

#### [Basic concept]{.underline}

Consider first the simplest situation:

> -- Only have one year's worth of data
>
> -- Only have point data (and no PSU data)
>
> -- Constant sampling rate across the county (for the first stage
> sampling, or PSUs)
>
> -- Each PSU contains three points

The straightforward way to calculate the weights for this county would
be to assign the same value (acres) to each point. In other words, each
point would represent the same number of acres when using the data to
estimate acres, erosion, and other statistics.

-   For example, if there are 48 PSUs, each with three points, and the
    county is 368,640 acres, then the weight for each of the 144 points
    is: 368,640 acres 144 = 2,560 acres per point.

> Note that for this example sampling was one out of every 48
> quarter-sections, which is a nominal 2 percent sampling rate. Each PSU
> then represents a total of 48 quarter-sections; therefore:

one PSU represents: 48 x 160 acres = 7,680 acres

and each point represents: 7,680 acres 3 = 2,560 acres.

This is the basic concept -- there are five factors that necessitate
modification of this concept:

> \(1\) The sampling rate is not constant for the entire county
>
> \(2\) There are PSU-level data that need to be incorporated into the
> point data
>
> \(3\) County Base data are used as controls
>
> \(4\) Data for four years (1982, 1987, 1992, and 1997) are included in
> this one database
>
> \(5\) Several ancillary data sources are incorporated into the weight
> generation process

It is not surprising that there is not a simple closed-form equation for
a weight. Instead, the weights are developed through a multi-step
process, including several steps that are iterative.

##### Modification -- varying sampling intensities

In simple terms, the value (or size) of the weight for a point is
inversely related to the intensity of the sample. Consider the earlier
example. Suppose the sample had been twice as dense as described. In
other words, the sampling rate was 4 percent rather than 2 percent over
the entire county. Then each weight would be one-half as large, or 1,280
acres per point, rather than 2,560 acres as derived earlier.

Consider next how weights would be determined if exactly half of the
county was sampled at the original 2 percent density and the other half
at double the intensity, or at a 4 percent rate. The points within the 2
percent sample area would have weights of 2,560 acres each. The points
within the 4 percent sample area would have weights of 1,280 acres each.

Note that these weights would sum to the county total --

72 x 2,560 acres + 144 x 1,280 acres = 184,320 acres + 184,320 acres

> = 368,640 acres

Adjustments for PSUs larger or smaller than 160 acres follow logically
from the above discussion.

-   For example, consider the basic concept for the nominal 2 percent
    sample with 640-acre PSUs and three points per PSU. Each point's
    weight would be four times what it is for 160-acre PSUs, because
    each PSU (and hence point) represents four times as much area:

weight = 4 x 2,560 acres =10,240 acres

-   For 40-acre PSUs containing two points each and for a straight 2
    percent rate, the basic concept gives a weight for each point equal
    to:

48 x 40 acres 2 = 960 acres per point

Similar adjustments can be made for PSUs in the Northeastern States.
Those PSUs are 30 seconds of longitude by 20 seconds of latitude,
varying from 97 acres in Maine to 113 acres in Virginia.

##### Modification -- use of PSU-level data

The discussion of the basic concept of a weight omitted several
important factors that are taken into account during the development of
the actual NRI weights. The next consideration is how PSU-level data
affect weights, still omitting the impacts of different years of data,
Geostatistical Control Data, and other ancillary sources.

Area measurements for seven Land Cover/uses are collected at the PSU
level:

(1) Farmsteads

(2) Small Built-up Areas

(3) Large Urban and Built-up Areas

(4) Small Water Bodies (\<40 acres)

(5) Small Streams (\<660 feet wide)

(6) Rural Transportation Land

(7) Census Water

-   Consider the following example for a PSU that is 160 acres in size
    --

  -------------------------------- -- -----------------------------------
  **[PSU Area Data]{.underline}**     **[Point Data]{.underline}**

                                      

  \- 5 acres of Farmsteads            \- 1 point falls on Large Urban &
                                      Built-up

  \- 55 acres of Large Urban &        \- 2 points fall on Cropland
  Built-up                            

  \- 0 acres of the other four        
  categories                          
  -------------------------------- -- -----------------------------------

For this PSU, this means:

(1) 60 acres are in Land Cover/Uses collected at the PSU level

(2) 100 acres are in Land Cover/Uses not covered by PSU-level data

(3) 2 points (out of three) fall on Land Cover/Uses not covered by
    PSU-level data

The logical way to construct weights for the two points falling on
Cropland is to equally allocate the 100 acres. The weight for each of
these points, assuming the nominal 2 percent sampling rate, is:

(100 acres 2) x 48 = 2,400 acres

*(Note that the 48 can be thought of as the PSU expansion factor. Recall
that a nominal 2 percent sample means that 1/48 of the county is covered
by the selected PSUs --- and weights are inversely proportional to
sampling rates.)*

The Urban and Built-up point would be given an initial weight of: (55
acres) x 48 = 2,640 acres.

The acres of Farmsteads are accounted for by creating a pseudo point, as
discussed in\
Section 4.5. Note that Census Water areas are also part of the NRI
County Base data and are handled differently than Small Water areas
during construction of weights.

**[4.5. Construction of Pseudo Points]{.smallcaps}**

The concept of a pseudo point was introduced in the previous example.
The PSU-level data indicated there were 5 acres of Farmsteads on the
PSU, but none of the three real sample points fell on a Farmstead. To
account for these Farmstead acres within the NRI database, a pseudo
point is constructed.

To more fully consider construction of pseudo points, it is necessary to
take into account that all levels of data (County Base, PSU-level, and
point data) are collected for four years (1982, 1987, 1992, 1997) -- and
that the NRI database incorporates all three levels of data for all four
years.

-   For example, consider a PSU where the following data were recorded
    ---

**PSU Data (in acres)**

  ------------------- -------------------- -------------------- -------------------- --------------------
                      [1982]{.underline}   [1987]{.underline}   [1992]{.underline}   [1997]{.underline}

  Small Built-up      2.8                  5.0                  5.0                  7.4

  Large Urban         12                   12                   26                   26

                                                                                     
  ------------------- -------------------- -------------------- -------------------- --------------------

######## 

######## Point Data -- Land Cover/Use

+--------------+-------------+-------------+-------------+-------------+
|              | [1982]{     | [1987]{     | [1992]{     | [1997]{     |
|              | .underline} | .underline} | .underline} | .underline} |
+--------------+-------------+-------------+-------------+-------------+
| Point \# 1   | Corn        | Corn        | Corn        | Corn        |
+--------------+-------------+-------------+-------------+-------------+
| Point \# 2   | Pasture     | Pasture     | Pasture     | Pasture     |
+--------------+-------------+-------------+-------------+-------------+
| Point \# 3   | Corn        | #####       | Corn        | #####       |
|              |             | ## Soybeans |             | ## Soybeans |
+--------------+-------------+-------------+-------------+-------------+
|              |             | #######     |             | #######     |
+--------------+-------------+-------------+-------------+-------------+

Note the following ---

(1) The PSU contains both Small Built-up and Large Urban and Built-up
    areas

(2) Both categories of Built-up are increased during the 15-year period

(3) None of the three points fell on either type of Built-up area

Pseudo points are created to reflect these occurrences within the 1997
NRI database. Five pseudo points need to be created for this PSU -- two
to reflect the acres of Built-Up Land that existed in 1982, and three to
reflect the changes. If the PSU is 160 acres, there are 126.6 acres that
are not in a PSU-level category as of 1997 -- this means the three
points are each given one-third of these acres, or 42.2 acres, as an
initial weight (before applying the PSU expansion factor). This PSU
requires a total of eight "points" (three real points and five pseudo
points), as follows:

Augmented Point Data -- Land Cover/Use & Acres

+----------------+---------+---------+---------+---------+---------+
|                | [198    | [198    | [199    | [199    | [Acre   |
|                | 2]{.und | 7]{.und | 2]{.und | 7]{.und | s]{.und |
|                | erline} | erline} | erline} | erline} | erline} |
+----------------+---------+---------+---------+---------+---------+
|                |         |         |         |         |         |
+----------------+---------+---------+---------+---------+---------+
| Point \# 1 *-  | Corn    | Corn    | Corn    | Corn    | 42.2    |
| real*          |         |         |         |         |         |
+----------------+---------+---------+---------+---------+---------+
| Point \# 2 *-  | Pasture | Pasture | Pasture | Pasture | 42.2    |
| real*          |         |         |         |         |         |
+----------------+---------+---------+---------+---------+---------+
| Point \# 3 *-  | Corn    | ##      | Corn    | ##      | #####   |
| real*          |         | ##### S |         | ##### S | ## 42.2 |
|                |         | oybeans |         | oybeans |         |
+----------------+---------+---------+---------+---------+---------+
| Point \# 4 *-  | Small   | Small   | Small   | Small   | 2.8     |
| pseudo*        | B-U     | B-U     | B-U     | B-U     |         |
+----------------+---------+---------+---------+---------+---------+
| Point \# 5 *-  | Pasture | Small   | Small   | Small   | 2.2     |
| pseudo*        |         | B-U     | B-U     | B-U     |         |
+----------------+---------+---------+---------+---------+---------+
| Point \# 6 *-  | Corn    | ##      | Corn    | Small   | ####    |
| pseudo*        |         | ##### S |         | B-U     | ### 2.4 |
|                |         | oybeans |         |         |         |
+----------------+---------+---------+---------+---------+---------+
| Point \# 7 *-  | L Urban | L Urban | L Urban | L Urban | 12.0    |
| pseudo*        |         |         |         |         |         |
+----------------+---------+---------+---------+---------+---------+
| Point #8 -     | Corn    | ##      | L Urban | #       | #####   |
| *pseudo*       |         | ##### S |         | ######  | ## 14.0 |
|                |         | oybeans |         | L Urban |         |
+----------------+---------+---------+---------+---------+---------+
|                |         | #       |         | #       | #       |
|                |         | ######  |         | ######  | ######  |
+----------------+---------+---------+---------+---------+---------+

The 1982 Land Cover/Use for point 5, the 1982-1992 Land Cover/Uses for
point 6, and the 1982 and 1987 Land Cover/Uses for point 8 were selected
through a randomized search process that is part of the statistical
estimation procedure.

Pseudo points were also created based upon the NRI County Base data.
Recall that these base acreages were developed for each HUCCO portion of
a county.

Consider the following situation:

(1) Federal Land area increased by 200 acres from 1992 to 1997 (for a
    HUCCO portion of a county), and

(2) None of the sample points within this HUCCO changed ownership from
    nonfederal in 1992 to Federal in 1997

A pseudo point with weight of 200 acres would be created to reflect this
change. This pseudo point would be similar to those discussed earlier
that reflect changes in Urban and Built-up Land for a PSU. Statistical
procedures would be used to impute missing data elements for 1982, 1987,
and 1992.

**[4.6. Additional Features]{.smallcaps}**

The NRI database weights were constructed by taking several additional
factors into consideration Most important are controls -- coming from
both the NRI County Base data and several ancillary sources. An example
of a control is the stipulation that the weights for all of the points
within a county must sum to the pre-established total acres for the
county. In other words, a control is placed upon the weights to ensure
that they do sum to the desired total.

The statistical estimation procedure developed weights that met these
controls, to the extent possible. To produce weights that simultaneously
meet all desired controls is not possible. However, by using an
iterative process, it is possible to meet some controls exactly and come
close to meeting most others.

This iterative procedure incorporated a series of regressions and ratio
adjustments. This statistical methodology is the result of over 20 years
of research and development conducted collaboratively by the Iowa State
University Statistical Laboratory and USDA.

The weights were developed so that the sample points meet as closely as
possible the following controls:

(1) Total surface area figures for States, counties, and HUCCO portions
    of counties

(2) Census Water area figures for States, counties, and HUCCO portions
    of counties --- for 1982, 1987, 1992, and 1997

(3) Federal Land area figures for States, counties, and HUCCO portions
    of counties --- for 1982, 1987,1992, and 1997

(4) Nonfederal Land area figures for States, counties, and HUCCO
    portions of counties --- for 1982, 1987, 1992, and 1997

(5) Acres contracted to the Conservation Reserve Program as of the 1992
    and 1997 growing seasons, by county or groupings of counties, and by
    groupings of sign-up periods

(6) Broad cover/use figures developed for the 1982 and 1992 NRI, by
    State

Several other features of the estimation procedure should be mentioned
--

(1) A statistical test was performed to determine if estimates for 1992
    broad cover/uses were significantly different based upon 1997 NRI
    data (compared to the estimates originally published for the 1992
    NRI)

(2) A small area estimation procedure was used to provide better
    estimates of Developed Land cover/uses

(3) Weights in the NRI database are in terms of 100s of acres --
    rounding (to 100s) was done as part of the weight generation process
    so that users of the data will obtain identical results, as long as
    they handle the data appropriately and do no further rounding

> **5. NRI Database Structure and Characteristics**

**[5.1. Introduction]{.smallcaps}**

The NRI database is a statistical database. The data do not come in an
aggregated or summarized form. The data must be aggregated or summarized
in order to be utilized properly and effectively. This chapter describes
the organizational structure of the NRI point data on the CD-ROM and
various characteristics of the database. The user is provided references
to additional documentation and tools on the CD to assist in using the
data.

**[5.2. Database Structure on CD-ROM]{.smallcaps}**

All NRI data are combined into a dataset containing point-type
information. The data are placed in a normalized or relational database
structure so that the data can be efficiently managed and accessed. The
relational database is partitioned into nine logical subject matter
tables as described below. The variables (fields) within each table are
comma delimited.

Every record in each table includes a unique key variable named
*recordid* that is used to match or link data across tables. Every
record in all tables except the table **point** includes a *yr* variable
to indicate the particular inventory year. See an example of linking
records across tables in Section 5.5.

See the \"Point Record Layout and Database Design\" for specific
variables and associated codes, **CD-ROM filename:
Documentation/point_layout.pdf**. A crosswalk from the layout of the
1992 NRI CD point records is provided for users of the previous NRI CD
**at CD-ROM filename: Documentation/crosswalk92_97.pdf**.

1\. Table: **point**

> Data that are not year-specific. This table serves as the \"master
> table\" relative to the relational structure. This master table
> includes two required variables for any data summary: the sample point
> weights or expansion factors (*xfact) and* a geographical factor
> (*fips, hydro, or mlra*). Almost all data tabulations and analyses
> will require data from this master table and one or more of the other
> tables, often the table **trend.**

2\. Table: **trend**

> Trending data that are year-specific and are available for at least
> two inventory years (usually for all four inventory years: 1982, 1987,
> 1992, 1997). Refer to the Point Record Layout and Database Design
> \"Field Name\" column to see what years are available for a particular
> variable. \[**CD-ROM filename: Documentation/point_layout.pdf** \]
> (Includes Average Annual Wind Erosion Equation as opposed to yearly
> equation factors in table **weq**.)

3\. Table: **1997only**

Data that are year-specific, but are only available for 1997.

4\. Table: **crophist**

> Data on cropping history for the three calendar years prior to the
> inventory years (only for those points that have Broad Cover/Uses of
> Cropland, Pastureland, or CRP in a given inventory year).

5\. Table: **weq**

> Wind erosion equation factors for each inventory year plus the three
> prior calendar years (only for those points that have Broad Cover/Uses
> of Cropland, Pastureland, or CRP in a given inventory year). The
> Average Annual Wind Erosion Equation (aaweq) is included in table
> **trend**. \"aaweq\" is the estimated soil loss due to wind erosion
> averaged for the rotation during the four-year period.

6\. Table: **habitatindices**

> Wildlife habitat diversity indices and metrics computed from the raw
> habitat composition and configuration data collected for transects
> associated with the point, for 1997 only. (See \"Applications of
> Habitat Composition and Configuration Data**,\" CD-ROM filename:
> Documentation/habexamp.pdf**.)

7\. Table: **habitatraw**

> Raw habitat composition and configuration data collected for transects
> associated with the point, for 1997 only. There are at least four
> records for each point. A unique record consists of: *recordid*, *yr*,
> *hctransno*, and *hctrsegno*.

8\. Table: **overlandraw**

> Raw overland flow data, for 1997 only (only for those points that have
> Broad Cover/Uses of Cropland, Pastureland, or CRP for 1997). There
> usually are multiple records for each point. A unique record consists
> of: *recordid*, *yr*, and *ofsegno*.

9\. Table: **wetland**

> Wetland and aquatic habitat data for 1997 only.

**[5.3. Database Characteristics]{.smallcaps}**

For the 1997 NRI database on CD-ROM, the table **point** serves as the
\"master table\" relative to the relational structure. The table
**point** contains 1,314,726 point-type records; this includes 814,962
real points. The other records represent pseudo points, except for\
42,690 records added to the dataset to improve estimation of margins of
error for several categories of estimates. These are records identical
to 42,690 real points, except their *recordid* is modified (they are
identified by the split code, element *split*). It is interesting to
note that over half of the real points fell on either Forest Land or
Cropland in 1997--- 224,615 on Forest Land and 216,335 on Cropland.

The number of data records per State varies considerably. Most States
have between 20,000 and 35,000 records. The extremes are Delaware and
Rhode Island with 1,998 and 2,015 records, respectively, and Texas and
California with 88,215 and 46,057 records, respectively. \[See **CD-ROM
filename: Documentation/record_counts.pdf**, Number of Records for 1997
NRI Dataset on CD.\]

**[5.4. Database Elements]{.smallcaps}**

In general terms, the various data elements (variables/fields) will
contain one of the following types of information ---

(1) Data about the location of the point

(2) Data about natural conditions at the point

(3) Data about use and management of the site (describing human
    activities)

(4) Several factors needed to produce statistical estimates from these
    data

(5) A key, or pointer, to the soil interpretations database

(6) Variables needed to provide record uniqueness within the relational
    database structure

All database fields are identified in 1997 NRI -- Point Record Layout
and Database Design \[**CD-ROM filename:
Documentation/point_layout.pdf**\]. The NRI database contains several
data elements that have been derived, in addition to those items
recorded directly by the NRI data gatherers. The following data fields
were [derived]{.underline} rather than directly observed:

\- Weight

\- Stratum

\- Cluster

\- Split Code

\- Imputation Code

\- Imputation 87 Code

> \- Broad Land Cover/Use \[1982/1987/1992/1997\]
>
> (derived from *Specific* Land Cover/Use)

\- Pointer to Soils Database

\- USLE 'K' Factor

\- USLE Flag

\- USLE Soil Loss \[1982/1987/1992/1997\]

\- WEQ 'I' Factor

> \- WEQ Soil Loss, Individual Years \[1979, 1980, 1981, 1982; and

1984, 1985, 1986, 1987; and 1989, 1990, 1991, 1992; and 1994, 1995,
1996, 1997\]

\- Average Annual Soil Loss, WEQ \[1982/1987/1992/1997\]

\- Erodibility Index, EI, Water \[1982/1987/1992/1997\]

\- Erodibility Index, EI, Wind \[1982/1987/1992/1997\]

\- Erodibility Index, EI \[1982/1987/1992/1997\]

\- Habitat Cover Percent \[1997\]

\- Habitat Cover at the Point \[1997\]

\- Habitat Cover Patch \[1997\]

\- Interspersion Index \[1997\]

\- Shannon Diversity Index \[1997\]

\- Shannon Index of Equitability \[1997\]

\- Simple Dominance \[1997\]

\- Dominant Cover Type \[1997\]

\- Cover Type Dominance \[1997\]

\- Mean Length of Segment 1 \[1997\]

\- Segments Per Unit \[1997\]

\- Aquatic Habitat \[1997\]

\- Cowardin System \[1997\]

A number of database elements exist that affect the proper use of the
data. These include the following:

**Federal Land** --- The NRI database does not contain information for
Federal lands. It does contain records for sample points identified as
Federal, but these contain no attributes for years that they are
Federal. These records must be omitted when calculating averages and
other statistics. They do need consideration when looking at land use
changes and other issues involving multiple years of data, because
points do change between Federal and nonfederal. Federal ownership is
determined through examination of the Broad Land Cover/Use (see below).

**Land Cover/Use** --- NRI uses this term to identify a classification
system that accounts for all the surface area of the U.S., using
mutually exclusive categories. The database includes variables for both
a Specific Cover/Use (*landuse)* and Broad Cover/Use (*broad*) in the
table **trend**. There are 12 Broad Cover/Use categories:

\(1\) Cultivated Cropland

\(2\) Noncultivated Cropland

\(3\) Pastureland

\(4\) Rangeland

\(5\) Forest Land

\(6\) Other Rural Land

\(7\) Urban and Built-up Land

\(8\) Rural Transportation

\(9\) Small Water Areas

\(10\) Census Water

\(11\) Federal Land

\(12\) Conservation Reserve Program (CRP) Land

There are 64 Specific Land Cover/Use categories. The NRI data collectors
determined the Specific Cover/Use category; the Broad Cover/Use category
was derived from the Specific Cover/Use category. The system is
hierarchical except for some types of Hayland and Pastureland; if the
hay or pasture is in rotation with cultivated crops, the point is
classified as \"Cultivated Cropland\" rather than \"Noncultivated
Cropland\" or \"Pastureland.\"

Note that \"Federal Land\" is one of the Broad Cover/Use categories.
Points classified as \"Federal Land\" are included in the database; they
have the statistical weight *xfact* and various geographical codes but
almost no other attributes. They are included in the database for
completeness -- to make a complete accounting of all U.S. surface area.

The category Other Rural Land contains farmsteads, other land in farms,
marshland, barren lands, and other rural lands not covered by the first
five categories.

**Developed Land** --- Developed Land contains three Land Cover/Use
categories:

(1) Large Urban & Built-up Areas (greater than 10 acres in size)

(2) Small Built-up Areas (less than 10 acres in size)

(3) Rural Transportation Land (roads, railroads, and associated
    rights-of-way)

Important special considerations regarding Developed Land are discussed
in Section 1.3.

**Soils Data** --- The soil identified for sample points is determined
by standard soil mapping conventions. This means that sample points that
fall on minor soil components are included as part of the primary
mapping unit or mapped soil. The NRI database contains certain soil data
elements, interpretations, and a key or pointer (*nriptr* in table
**point**) that can be used to link the point to additional soil
interpretations information. Note that these additional soil
interpretations are [not]{.underline} provided on the CD-ROM.
(Interested users may request these data by contacting the NRCS
Resources Inventory Division at the address given in Appendix E.)
Efforts are currently underway to link NRI data to a new soil database
management system, the National Soils Information System (NASIS), which
is under development by NRCS.

**Erosion** --- NRI erosion data are discussed in Section 1.3. Note that
the database does not contain erosion data for Broad Cover/Uses other
than Cultivated Cropland, Noncultivated Cropland, Pastureland, and
Conservation Reserve Program lands.

**Erodibility Index** --- The Erodibility Index (EI) expresses the
inherent erodibility at a site. It is only calculated for years when a
point is Cropland. The EI data field can be used to identify highly
erodible Cropland (EI equal to or greater than 8). EI is calculated from
erosion equation factors determined for a transect crossing the sample
point.

**Irrigation** --- For the NRI a site is considered irrigated if
irrigation occurs during the inventory year or for two or more of the
last four years. Consider this when making comparisons with other data
sources. For example, previous Census of Agriculture irrigation figures
were lower than NRI numbers because actual application of water is
required for the year of the census. Historically, problems have existed
with undercounting irrigated acres in the Census of Agriculture.

**Wetlands** --- The Cowardin classification system covers both wetlands
and aquatic deepwater habitats. Wetland and deepwater habitat
determinations are included in the 1997 NRI database for all nonfederal
points for inventory year 1997 only. FSA wetland classifications are
also included. Use the "Cowardin System" data element to replicate
estimates found in Table 16 of the 1997 NRI Summary Report. The
"Cowardin Specific" data element provides a more specific Cowardin
system category.

**Practices** --- Conservation Practices are identified if they were
applied in the area in which the point falls, or the portion of the
field surrounding the point that is considered in conservation planning.
The point does not necessarily fall directly on the practice. There are
22 practices.

**Habitat** --- Measurements of habitat composition and configuration
are new in the 1997 NRI. The makeup of the general cover categories and
their arrangement about a point are useful in analyzing diversity of
habitat in defined geographical areas. Several diversity indices and
metrics can be calculated from these data based upon the needs of the
user. See: \"Applications of Habitat Composition and Configuration
Data**\" \[CD-ROM filename: Documentation/habexamp.pdf**\].

**Overland Flow** --- Overland flow and delivery to water data were
collected only for points classified as a Land Cover/Use category of
Cropland, Pastureland, and CRP land for 1997. Information related to
distance from the point to nearest receiving water, type of receiving
water, cover categories that surface water flows through, and length of
each cover within the water flow route were collected for these points.

**Stratum/Cluster** --- These data fields are needed to properly
estimate margins of error or any other measure of the statistical
uncertainty of an NRI estimate. They were constructed using the NRI
sampling scheme, which is discussed in Chapter 4, Construction of the
NRI Database. SAS programs provide examples of methods used to properly
estimate margins of error **\[CD-ROM filenames: Tools/SAS/table1.sas . .
. table14.sas\]**.

**Imputation Code** --- This code identifies which points are real
points and which are "pseudo points." For pseudo points, this code
identifies the type of imputation. As discussed in Sections 4.3 and 4.5,
these pseudo points are required because County Base data and PSU data
may contain patterns of change over time that are not observed at an
actual NRI sample point.

**Imputation Code for 1987 Data** --- This code identifies situations
where it was necessary to impute 1987 data, either partially or
completely; situations requiring imputation, and methodology, are
discussed in Section 4.3.

**[5.5. Example of Linking Data Across Tables]{.smallcaps}**

-   This example illustrates how to link data across subject matter
    tables using the *recordid* as the unique key. In order to get an
    estimate of acres of nonfederal Cropland in Alabama for all four
    inventory years (Table 2, *1997 NRI Summary Report*, column 1), one
    must query data from two tables.

Table **point**: Table **trend**:

*recordid* \-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\--
*recordid*

*fips yr*

*xfact broad*

> The database or statistical software query must be written to sum the
> expansion factors/weights (*xfact*) for all the points that have a
> *fips* that begins with 01 (for Alabama) and a *broad* cover/use code
> of either 1 or 2 (for Cultivated and Noncultivated Cropland), where
> the *recordid* in table **point** equals or matches the *recordid* in
> table **trend**. In order to summarize the data for each of the
> inventory years, the data would be grouped by the *yr*.

For SAS® users, there are examples of SAS programs that provide
estimates for a sample state for several of the *1997 NRI Summary
Report* tables, including standard errors. \[**CD-ROM filename: Tools/
SAS/table1.sas . . . table14.sas**\]

**[5.6. Importing Raw Data]{.smallcaps}**

Raw data must be imported into database and/or statistical software \*
in order to perform the aggregation mentioned in the preceding sections.
Users may choose from three different options for accessing the raw
data. Further details are provided in the readme file at **CD-ROM
filename: readme.txt**.

1.  Import data for certain table(s) for certain States \*

> Users may choose to import only those table(s) (file(s)) required for
> particular analysis needs. One or multiple States of data for the
> chosen table(s) may be imported.
>
> The CD contains a zip archive of data for each State. Each State
> archive (st.zip) consists of the following comma-delimited files for
> the subject matter tables.
>
> (\"st\" is replaced by the two-letter State abbreviation.)
>
> 1\. st_point.txt
>
> 2\. st_trend.txt
>
> 3\. st_1997only.txt
>
> 4\. st_crophist.txt
>
> 5\. st_weq.txt
>
> 6\. st_habitatindices.txt
>
> 7\. st_habitatraw.txt
>
> 8\. st_overlandraw.txt
>
> 9\. st_wetland.txt
>
> Data header files are also provided.
>
> (A sample MS® ACCESS database software unpopulated database framework
> is provided on the CD.
>
> Sample SAS® programs for importing the raw data are provided on the
> CD.)\*

2.  Import data for certain table(s) for entire country \*

> A user may decide to import only those table(s) or file(s) required
> for particular analysis needs for all the States.
>
> The CD contains a zip archive of the entire Nation (50 States), with
> filenames as listed above in option 1, but with \"national\" as the
> prefix. Data header files are also provided.
>
> (Sample MS® ACCESS and SAS® tools as described above in option 1 are
> provided.)\*

3.  Extract populated MS® Access database \*

> Users who own MS® Access and expect to use all the tables of data for
> most or all of the States may choose to extract the populated Access
> database provided on the CD. The database is a zipped archive that
> contains four files:
>
> 1997NRI_1.mdb
>
> 1997NRI_2.mdb
>
> 1997NRI_3.mdb
>
> National.mdb
>
> National.mdb links to the three 1997\*.mdb files and provides
> relationships between tables. This implementation solves the
> one-gigabyte size limitation of Access at the expense of performance.

\* The MS® Access and SAS® tools are provided as a convenience to users
who already own this software and may be modified to suit individual
needs. Use of product names (or trade names) does not imply endorsement
of products (or software systems) by the USDA Natural Resources
Conservation Service.

7.  **[Recommendations to Users After Data Import]{.smallcaps}**

To make effective use of the NRI database, one must understand NRI
concepts, methods, definitions, and survey design. It is helpful to have
an understanding of the material in the previous chapters of this user
guide. Additionally, it is helpful to continue to use and explore the
data, to compare analyses with results from other databases, and to
discuss and present new results to other data analysts.

After loading the raw data into a database or statistical software
application, users should compare the number of records for each
table(s) and each State(s) against the spreadsheet provided in **CD-ROM
filename: Documentation/record_counts.pdf**.

Users are advised and encouraged to replicate various estimates given in
Tables 1-17 and Appendix Tables 1 and 2 of the *1997 NRI Summary Report*
as a data check \[**CD-ROM filename: Documentation/summary_rpt.pdf**\].
This replication will verify whether (1) the data loaded properly, (2)
the data were aggregated correctly, and (3) the margins of error were
generated properly.

**[5.8. Deriving Margins of error]{.smallcaps}**

The precision of all statistical results should be derived and examined.
The 1997 NRI database has been constructed in a manner that provides
methods for deriving estimated margins of error (or standard errors, or
confidence intervals) for any estimate derived from the database.

-   The data elements *stratum* and *cluster* in the table **point**
    must be used, in order to account for the stratification and
    two-stage sampling used for the NRI, as described in Section 3.1 and
    Appendix C, Understanding the NRI Design.

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-   Several examples of computer programs that appropriately calculate
    estimated standard errors using the statistical software package
    SAS® are included on the CD **\[CD-ROM filename:
    Tools/SAS/table1.sas . . . table14.sas**\].

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-   Such algorithms and procedures provide suitable estimates and should
    be used for all NRI estimates. However, there are some items for
    which these procedures actually provide an overestimate of the
    standard error, because of some of the specialized statistical
    (estimation) techniques used to construct the 1997 NRI database (see
    Chapter 4 for a discussion of these techniques).

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-   Several commercial statistical packages provide the functionality of
    estimating standard errors based upon the NRI stratified cluster
    sampling design: \*\*

> \- SAS^®^ (Release 8.2) (<http://www.sas.com/>)
>
> \- SUDAAN ^®^ (<http://www.rti.org/units/shsp/sud1.cfm>)
>
> \- WesVar (<http://www.westat.com/wesvar/>)

-   PC CARP and SUPERCARP -- DOS software available from: Statistical

> Laboratory, Iowa State University, 219 Snedecor Hall, Ames, Iowa
> 50011;
>
> telephone (515) 294-9773.

\*\* Most common statistical packages will not provide appropriate
estimates for the NRI stratified cluster sampling design. However, the
packages listed above provide this functionality.

**[5.9. Citing Data from the CD-ROM and]{.smallcaps}**

> **[the 1997 NRI Summary Report]{.smallcaps}**

[CD-ROM]{.underline}:

> U.S. Department of Agriculture. 2001. 1997 National Resources
> Inventory (revised December 2000), Natural Resources Conservation
> Service, Washington, DC, and Statistical Laboratory, Iowa State
> University, Ames, Iowa, CD-ROM, Version 1.

*[1997 NRI Summary Report]{.underline}*:

> U.S. Department of Agriculture. 2000. *Summary Report: 1997 National
> Resources Inventory (revised December 2000*), Natural Resources
> Conservation Service, Washington, DC, and Statistical Laboratory, Iowa
> State University, Ames, Iowa,
>
> 89 pages.

.

**6. NRI Data Analysis**

NRI

**6.1.** **[Introduction]{.smallcaps}**

This chapter provides an introduction to the use of the NRI database
from an analyst's perspective. Typical analytical processes and routines
followed to produce NRI estimates are outlined and examples are
presented. The information in this chapter was originally developed to
accompany classroom instruction to internal NRCS staff on the 1992 NRI.
The chapter is written in a tutorial format and some of the information
has been emphasized in other parts of this user guide. The training
module is included in its entirety in this chapter.

**6.2.** **[The Analytical Process]{.smallcaps}**

Data analysis using the NRI---or any other database---must be done
carefully to avoid misusing the data and misrepresenting results to
decision-makers. To help avoid these potential problems, the following
basic steps should be taken in conducting the analyses.

1\. Identify the problem or question.

> All NRI applications are in response to a question or problem that
> requires analysis. The first step in analysis is to identify the
> question as specifically as possible.

2\. Determine if NRI data can be used to address the question.

> The NRI database can be used to address many questions related to
> agriculture and natural resources. However, the NRI database will not
> always be the best information set to use. For example, application of
> analyses to small areas---such as a single county---will not be
> appropriate in most cases.
>
> For some applications satellite imagery data or county-level Census of
> Agriculture data may be better suited to the specific question being
> asked. In other cases the definitions used in the collection of the
> NRI data may not be specific enough to properly address some
> questions. Evaluate the feasibility of using NRI data to address a
> specific question early in the analytical process.

3\. Determine what other kinds of data are needed.

> For many applications, the NRI database will need to be augmented with
> information from other sources. GIS technology provides one means for
> integrating geographic data from other sources. There are also tabular
> databases available that contain geographic codes or descriptions,
> such as Census of Agriculture.

4\. Involve the relevant discipline specialists.

> Involve soil scientists, agronomists, GIS specialists, or other
> discipline specialists as needed to aid in preparing and evaluating
> the analysis.

5\. Define the analytical product precisely.

> After determining that the NRI can be used to address the question and
> the appropriate discipline specialists have been consulted, define the
> precise analytical product. This can range from a simple calculation,
> such as percent area, to complex analysis involving models.

6\. Caveats---know your data.

> Caveats---assumptions---always underlie the analysis. Keep a list of
> them. For example, percent area of Cropland depends specifically upon
> how Cropland was defined. Prevent the audience from misunderstanding
> how the results were derived and from misunderstanding what they
> actually mean.

7\. Prepare tabular and graphical representations of results.

> Use both tables and maps whenever possible. Specifically plan to
> produce these products, as they are the most effective formats for
> communicating findings.

8\. Document methods and results in writing.

> The importance of documentation cannot be overstated.

9.  Present results and documentation to discipline specialists for
    feedback.

10. Adjust analysis and presentation as needed.

11. Present results in written and oral form to decision-makers.

12\. Follow up regarding further questions and concerns.

> Often decision-makers ask new questions after receiving answers to old
> questions. Be
>
> prepared to follow up with additional analysis.

**6.3.** **[Overview of NRI Applications]{.smallcaps}**

The NRI was designed to provide support for natural resources and
environmental policy development and program implementation. The
questions that the NRI can be used to answer typically fall into one of
the following four categories:

1\. Describe the status and trends of natural resources and agricultural
activity.

-   This analytical function is, for the most part, a direct
    representation of the data elements in the NRI.

-   Examples are the geographic extent of a particular land use, the
    spatial variability in land use, or changes in land use over time.

2\. Evaluate the condition of natural resources, using environmental and
ecological indicators.

-   This function involves more sophisticated analysis than the first.

-   An example is generating projected environmental impact indices
    utilizing NRI data and other potential data sources.

3\. Assess environmental and economic implications of changes in
resource use, including

expected impacts associated with shifts in government policies.

-   This function is very complex. It will almost always involve data
    from other sources, will typically be conducted using mathematical
    models, and will involve experts from several disciplines.

4\. Plan and manage the Nation's conservation programs.

-   This analytical function actually incorporates elements from each of
    the previous three functions, but is focused sharply on a narrow
    objective \-- planning and managing programs.

**6.4.** **[Know Your Database]{.smallcaps}**

The key to successfully applying the NRI to answer questions is thorough
knowledge of the database.

Recognize what is in the NRI, and what is not.

\- Is rangeland condition included in the database? (No.)

\- Are there erodibility index values for all land? (No---just Cropland,
Pastureland, and

> CRP.)

Definitions are important.

\- What is Cropland in the NRI? Does it include Hayland? Idle land?
Set-aside acres?

> (See the Glossary in the *1997 NRI Summary Report*.)

\- What is Developed Land? Are farmsteads and barns included as
Developed Land? Are

> airports? Or abandoned railroads? (See the Glossary.)

\- What is prime farmland? How can acres of prime farmland increase? Can
prime

> farmland in Cropland change over time to nonprime farmland in
> Cropland? (Yes---
>
> over one million acres did just that between 1982 and 1992.)

Understand how the data were collected.

\- You should know which attributes were obtained from handbooks and
which were

> obtained from a site visit (and thus are actual data). (See
> \"Instructions for Collecting
>
> 1997 National Resources Inventory Data\" at **CD-ROM filenames:**
>
> **Documentation/collection_instructions/content.pdf . . . through . .
> . ptmod11.pdf**
>
> or at **http://www.nhq.nrcs.usda.gov/NRI/1997/data_gathering.html**.)

\- Understand how PSU data were converted to point data, and which data
were imputed.

For example, pseudo points were used to distribute PSU data to a point
database. Can you ignore or exclude pseudo points in your analysis? (No.
The expansion factors are adjusted so that they sum to control totals
only when pseudo points are included in the sum.)

\- How do you handle imputed points for 1987? Can they be excluded from
your

> analysis? (No.) Why not? (Same answer as above.)

\- Lastly, an important aspect of the NRI is that it is a statistical
database. That means

> that there is some uncertainty about all estimates. The extent of the
> uncertainty is
>
> estimated by the variance, or the margin of error. Margins of error
> should be
>
> calculated for all estimates. To make this point again, look at the
> distribution of NRI
>
> PSUs in a typical Midwest situation. Estimates are made based on this
> sample. The
>
> greater the sample size, the smaller the margin of error.

These topics have all been covered in previous chapters.

**6.5.** **[Understanding the Statistical Nature of the
NRI]{.smallcaps}**

**Important Note: Statistical Reliability**

**NRI data were collected at more than 800,000 sample sites nationwide.
The data, which are gathered using recognized statistical sampling
methods, are statistically reliable for national, regional, State, and
some multi-county sub-State analyses. The standard error, margin of
error, or coefficient of variation should be taken into consideration in
all data analyses. (See Sections 1.4 and 1.5.)**

Information on statistical reliability and limitations is required for:

(1) Reporting results to decision-makers

(2) Comparing results with estimates from other sources

(3) Identifying real differences

-   Area to area

-   Time period to time period

(4) Presentation of results

-   Use of error bars

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```
-   Parsimonious use of classes in maps and tables

(5) Analysis---Aggregation (summing of NRI expansion factor acreages by
    some geographic code, e.g., Hydrologic Unit Areas, MLRAs, States) is
    almost always required

Three kinds of calculations are necessary to properly use NRI data:

(1) Total acres

(2) Weighted totals

(3) Weighted averages

*[Total acres]{.underline}*

-   Sum of Weight over the collection points of interest

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-   Examples:

> -- Acres of corn in Iowa
>
> -- Acres of Cropland in Nation

-   SPECIFIC EXAMPLES:

> The Weight or expansion factor is used to represent the sample point.
> It is ALWAYS used to aggregate.
>
> (The Weight has been defined and discussed previously.)

Example A1\-- Total Acres of Cropland in County A

1997 Land

[Sample Point]{.underline} [Cover/Use]{.underline} [Weight]{.underline}

1 cropland 3,000

2 cropland 2,500

3 cropland 4,000

4 cropland 3,500

5 cropland 3,300

6 cropland 6,000

7 cropland 4,600

8 cropland 2,500

9 cropland 4,000

10 cropland 3,500

11 cropland 3,300

12 cropland 6,000

13 cropland [4,600]{.underline}

Sum = 50,800 acres

-   A percentage as a ratio of two Total Acres estimates can be
    calculated.

Example A2\-- Portion of Total Acres of Cropland that are Prime Farmland
in County A

1997 Land Prime

[Sample Point]{.underline} [Cover/Use]{.underline}
[Farmland]{.underline} [Weight]{.underline}

1 cropland yes 3,000

2 cropland no 2,500

3 cropland yes 4,000

4 cropland yes 3,500

5 cropland yes 3,300

6 cropland no 6,000

7 cropland no 4,600

8 cropland yes 2,500

9 cropland no 4,000

10 cropland no 3,500

11 cropland yes 3,300

12 cropland no 6,000

13 cropland no 4,600

Sum of prime = 19,600 acres

19,600

Prime Farmland Fraction \-\-\-\-\-\-\-\-\-\-\-- = 0.386

50,800

*[Weighted totals]{.underline}*

-   Per acre attributes multiplied times Weight (NRI expansion factor
    acres) and then summed

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-   Example: tons of soil loss from sheet and rill erosion

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-   SPECIFIC EXAMPLE:

> It is not used frequently, but it is important. It is appropriate when
> the attribute is defined in per acre units. Here, it is used to
> calculate soil loss.

Example B \-- Average Rate of Soil loss from Sheet and Rill Erosion on
Cropland in County A

1997 Land USLE Weight Weighed

[Sample Point]{.underline} [Cover/Use]{.underline} [(t/a/y)]{.underline}
[(acres)]{.underline} [Total (t/y)\_]{.underline}

1 cropland 4.0 3,000 12,000

2 cropland 5.0 2,500 12,500

3 cropland 12.1 4,000 48,400

4 cropland 8.5 3,500 29,750

5 cropland 6.8 3,300 22,440

6 cropland 10.1 6,000 60,600

7 cropland 6.1 4,600 28,060

8 cropland 3.0 2,500 7.500

9 cropland 4.0 4,000 16,000

10 cropland 9.2 3,500 32,200

11 cropland 11.3 3,300 37,290

12 cropland 8.4 6,000 50,400

13 cropland 3.9 4,600 17,940

Sum = 375,080

Tons/year

*[Weighted average]{.underline}*

-   Multiply weight (NRI expansion factor acreages) times the attribute,
    sum over the sample points of interest, and then divide by the sum
    of Weight for all sample points in the domain (a geographic area
    unit such as a watershed).

-   Examples:

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-   Average erosion rate, tons per acre per year, for a watershed in
    Texas

-   Average K-factor for the State of Iowa

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-   SPECIFIC EXAMPLE:

Calculation of a weighted average is used frequently.

Example C \--Average Erosion Rate for Acres of Cropland that are Prime
Farmland in County A

Weighted Total

Average USLE = \-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\--

Sum of Weight

375,080

Average USLE = \-\-\-\-\-\-\-\-\-\-- = 7.38 tons/acre/year

50,800

**6.6.** **[Information Presentation]{.smallcaps}**

Once the question has been asked, and the calculation completed, the
challenge is to correctly present what has been found and in a manner
that accurately communicates the findings to the decision-maker.

Some analysts believe this phase of the process is more important than
all the others.

Various analytical presentation forms are possible. The presentation
format should be considered even before the analysis is run.

Often, the presentation medium will dictate aspects of the analysis. It
certainly dictates the kind of numbers that need to be obtained from the
NRI database.

Presentation format is also important because it is the final step in
providing answers to the questions that prompted the analysis. When the
presentation has been planned, the specific objectives of the analysis
become apparent, such as what numbers need to be calculated, and whether
they are total acres, weighted totals, or weighted averages.

Three main presentation formats can be used:

(1) Tables

-   Summary tables for reference

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-   Tables containing large amounts of information usually are not
    helpful in directly answering questions

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-   Tables that make a point

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-   A simple table directly answers a specific question

(2) Charts

-   Pie charts

-   Bar charts

-   Text

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-   A simple text format avoids confusion

(3) Maps

-   Spatial patterns

-   Maps cannot stand alone

-   Easy to mislead \-- consider all aspects carefully

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-   Colors are important. How color is used can greatly influence how
    the map reader will interpret the map. Indeed, by altering colors
    the map creator can cause the map reader to reach different
    conclusions.

Summary:

(1) Whenever possible, use the format that conveys the results visually

(2) Keep it as simple as possible and still answer the question

(3) Always proof your work

**6.7.** **[Work in Teams]{.smallcaps}**

Work in teams \-- do not try to do everything individually.

-   The NRI is a spatial database. GIS tools and expertise are required
    to do some of the analyses and to display the results. A GIS expert
    should be part of your team.

-   Involve discipline specialists (such as soil scientist,
    cartographer, and statistician) to provide scientific credibility to
    your study.

-   Combine efforts across State boundaries whenever possible.

**6.8.** **[NRI Analysis Subject Areas]{.smallcaps}**

**[Display of percentage data aggregated by different geographic
units]{.underline}**

Maps can be used to communicate NRI data differently depending in part
on how the data are geographically aggregated. The NRI database includes
several codes that enable the user to aggregate data by different types
of geographic units. These include States, counties, Major Land Resource
Areas (MLRA), Land Resource Regions (LRR), Bailey's Eco-Regions, and

2-, 4-, 6-, and 8-digit hydrologic unit areas (HUA).

Boundaries such as hydrologic units and MLRAs are often chosen for
portraying NRI data, since they better reflect geographic trends in the
natural environment over broad areas than do political entities such as
counties and States. In many cases, their polygon area sizes encompass a
sufficient number of NRI points to generate acceptable statistical
errors for many common purposes (but be careful about
over-interpretation), and their numbers are such that they communicate
more information than maps with a fewer number of polygons, such as
States.

The 8-digit hydrologic units are usually smaller than MLRAs. They
usually provide more geographic information but error estimates are
generally higher for individual HUAs than for MLRAs.

Data can be aggregated at a 4-digit HUA level, an area that would
typically equate to several 8-digit HUAs. The contrast between maps
produced using 8-digit HUAs and maps produced using the larger 4-digit
HUAs or MLRAs can be striking. When comparing the maps side-by-side, one
may notice that the data properties of a single 8-digit HUA, if it is
highly skewed relative to surrounding HUAs, can skew the visual
impression for the entire 4-digit HUA in which it is contained. In any
case, the choice of data aggregation unit can significantly impact the
impression left on the map reader.

Although smaller areas (8-digit HUAs, MLRAs, or counties) usually have
higher error estimates than larger area analyses, their message is
sometimes less misleading when compared to larger area aggregations of
data.

Multi-county aggregation in the form of Resource Conservation and
Development areas or administrative areas may be used. Various groupings
of this sort are useful if high error estimates are a concern because
larger area aggregations tend to lower the error estimates.

Nevertheless, one should keep in mind that, although the 4-digit HUA
estimates tend to lower the error estimates, they are a concern because
larger data aggregations do not always yield results that more closely
approximate the truth in the geospatial context. Large area data
aggregations, such as States or 2-digit HUAs, while more correctly
generalizing the data (as indicated by lower statistical error
estimates), often fail to reflect legitimate variations within each
unit.

When showing NRI data on polygon maps, it is better to represent the
data as a *percentage* *of total acres* within each polygon that meet a
specific set of criteria rather than as the *actual* *acres* that meet
the criteria within each polygon. The disadvantage of mapping actual
acres within a polygon is that acreages have a direct relationship with
the sizes of the polygons. This will obscure whatever point you are
trying to make with your map theme. Actual acreage totals are better
represented through the use of dot maps, where each dot is randomly
distributed within a geographic unit (such as a hydrologic unit) and
each dot represents a specific number of acres.

When mapping NRI data by polygon, it is also reasonable to use smaller
universes than total acres in the denominator of a percentage
calculation, such as the percent of total nonfederal acres, percent of
total Cropland acres, etc. However, the smaller the universe, the less
accurately it will visually represent the area of each unit as it
appears on a map. Likewise, some units may contain few NRI points for a
specific universe, and the calculation of a percentage in these cases
can create a very misleading impression on a map. For example, few
Cropland points exist in some regions of the West where Rangeland
dominates. Aside from the fact that the statistical error estimate may
be unacceptable in these regions when Cropland is the denominator, it is
probably misleading to represent that Cropland has much significance in
these areas when the map theme emphasizes some more specific Cropland
criteria.

**[Effective use of mapping polygon acreages]{.underline}**

Although polygon maps are better used for representing percentages of
area rather than acreages, representing acres is possible in ways that
may be useful. One way is in the analysis of workload. Workload within a
county may relate to the actual count of acres of Cropland or a variety
of other data elements, or both. For example, the total acres of
Cropland in a geographic area may be directly related to the number of
conservation plans that need to be developed or the number of hours
required for applying pesticides. The sum of the acres from the various
data components, whatever they may be, could be used to make a composite
workload map.

The legend category breaks can be set up as a linear increase in the
width of each class. For example, eight is added to the class width of
each subsequent class so that the first class has a width of 8, the
second has a width of 16, and so on. The cartographer should delineate
class breaks that best highlight the distribution of the data.

Choosing equal-sized class widths is just one of many legitimate methods
that may be employed to establish legend class boundaries. Other
commonly used methods include setting class boundaries at naturally
occurring breaks in the data range or setting boundaries so that
approximately equal numbers of occurrences will occur in each legend
class. How legend breaks are selected will always introduce some bias on
the part of the mapmaker, just as selection of colors and patterns do.

As suggested before, depending on the intended use, it may be more
appropriate to aggregate to multi-county levels to reduce the values of
the error estimates (i.e., reduce the width of the resulting certainty
interval). The trade-off in aggregating to multi-county levels is that
although you may have greater confidence in the data estimates, a map
becomes a less useful means of representing the data. This occurs
especially when the geographic extent of that map is only one State and
is not in the context of all 50 States. When data are aggregated to a
small number of geographic areas, that is less than 10 or 20, tables may
be an equally or more effective means of communication.

For maps with few polygons, the benefit over tables may be marginal.
When considering the trade-off between mapping small polygons and larger
ones, the analyst needs to consider how much statistical variability is
acceptable in the estimates, and what is the cost of being wrong. If you
are just looking for general trends in the data and are not as concerned
with the reliability of the estimate for any given polygon, smaller
polygons are often more useful. If, however, having a high level of
confidence in estimates for individual polygons is critical, then larger
data aggregations are required. The format chosen for display and
analysis will depend upon the question being addressed.

**6.9.** **[Mapping Results of Data Analysis]{.smallcaps}**

Because of its multi-resource approach and consistency across State
borders, the NRI has numerous applications for addressing regional
issues.

For example, resource managers in the Chesapeake Bay watershed are
confronted with decisions that affect water quality of the Bay. The use
of NRI data has increased their ability to provide assistance and
programs, and create new policy that addresses critical issues in this
area.

-   The following examples illustrate a few NRI products that can assist
    managers to better understand natural resource conditions and trends
    in the Chesapeake Bay watershed.

> Understanding change in the cultivation of prime farmland by 8-digit
> hydrologic unit area could be valuable for many reasons. Increases in
> cultivation of prime farmland may indicate a shift towards using the
> best soil resource for this purpose. Such changes may also be
> indicators of long-term impacts of agricultural legislation. Decreases
> in cultivation of prime farmland may be an alert to investigate these
> areas to determine the effect on water quality from these land use
> shifts.
>
> The distribution of cultivated land that is classified as highly
> erodible may indicate areas where special treatment, practices,
> programs, or policies need to be implemented. Such information can be
> useful in assessing conservation workloads and identifying logical
> interstate partnerships to address natural resource concerns.
>
> Mapping the 8-digit HUAs by percentage of Cropland planted to a
> specific crop, such as corn, could be both interesting and useful.
> Because specific pesticides are sometimes linked to specific crops,
> adverse effects on water quality can be predicted. A map locating
> areas where corn is being grown on highly erodible soils can easily be
> linked to a
>
> preceding map for visual comparison or combined using GIS. Such
> analysis can be useful for assessing the potential use of specific
> practices such as conservation tillage.
>
> Some water quality problems may be related to specific land uses, such
> as Pastureland. Such analysis could be useful in understanding issues
> concerning nutrient loading of streams, streambank erosion, or similar
> issues.
>
> The links in the NRI to the Soil Interpretations Records database are
> useful for regional analysis. NRI users have the unique ability to ask
> questions that correlate specific land management practices with the
> soils resources upon which they exist. For example, understanding the
> distribution on a regional basis of soil hydrologic groups and its
> impact on ground water recharge can contribute to understanding water
> quality conditions and planning broad land management strategies.

Citations

Fuller, W.A., K.W. Dodd, J. Wang, and C. Peterson. 2001. Estimation for
the 1997 National Resources Inventory. Unpublished manuscript,
Statistical Laboratory, Iowa State University, 161pp.
http://www.statlab.iastate.edu/survey/nri/est971nr.pdf

Fuller, W.A. 1999. Estimation procedures for the United States National
Resources Inventory. Proceedings of the Survey Methods Section,
Statistical Society of Canada Annual Meeting, June 1999, pp. 39-44.
http://www.statlab.iastate.edu/survey/nri/Fullerpr.html

Goebel, J.J. 1998. The National Resources Inventory and its role in U.S.
agriculture. *In:* Agricultural Statistics 2000, International
Statistical Institute, Voorburg, The Netherlands, pp. 181-192.
http://www.nhq.nrcs.usda.gov/NRI/1997/stat_design.html

Nusser, S.M., J.M. Kienzler, and W.A. Fuller. 1999. Geostatistical
estimation data for the 1997 National Resources Inventory. USDA-Natural
Resources Conservation Service, Washington, D.C.
<http://www.statlab.iastate.edu/survey/nri>

Nusser, S.M., F.J. Breidt, and W.A. Fuller. 1998. Design and estimation
for investigating the dynamics of natural resources. Ecological
Applications. 8(2):234-245. (coming soon to:
http://www.nhq.nrcs.usda.gov/NRI

Nusser, S.M., and J.J. Goebel. 1997. The National Resources Inventory: a
long-term multi-resource monitoring programme. Environmental and
Ecological Statistics. 4:181-204.
http://www.nhq.nrcs.usda.gov/NRI/1997/stat_design.html

**APPENDIX A**

**CD-ROM Contents:**

**Data, Documentation, and Tools**

This appendix describes the organization of the files on the CD_ROM. The
CD filename: readme.txt contains further technical details about the
data and some associated tools provided as a courtesy to users who own
particular commercial software packages.

**The CD top level directory structure is:**

-   Data

-   Documentation

-   readme.txt (plain ASCII text file)

-   Tools

**[CD-ROM filename]{.underline}:**

[Data]{.underline}**:**

-   Nationally aggregated State files

(50 States in each of nine subject matter

table files)

> **Data/National.zip**
>
> **National_point.txt**
>
> **National_trend.txt**
>
> **National_1997only.txt**
>
> **National_crophist.txt**
>
> **National_weq.txt**
>
> **National_habitatindices.txt**
>
> **National_habitatraw.txt**
>
> **National_overlandraw.txt**
>
> **National_wetland.txt**

-   State files (50 files)

> **Data/st.zip**
>
> (where \"st\" is two-letter State abbreviation)
>
> **st_point.txt**
>
> **st_trend.txt**
>
> **st_1997only.txt**
>
> **st_crophist.txt**
>
> **st_weq.txt**
>
> **st_habitatindices.txt**
>
> **st_habitatraw.txt**
>
> **st_overlandraw.txt**
>
> **st_wetland.txt**

-   Header files **Data/header.zip**

> **hdr_point.txt**
>
> **hdr_trend.txt**
>
> **hdr_1997only.txt**
>
> **hdr_crophist.txt**
>
> **hdr_weq.txt**
>
> **hdr_habitatindices.txt**
>
> **hdr_habitatraw.txt**
>
> **hdr_overlandraw.txt**
>
> **hdr_wetland.txt**

**[Documentation]{.underline}:**

-   *A Guide for Users of 1997 NRI Data Files,*

*CD-ROM Version 1 (December 2001)*

\[this document\] **Documentation/user_guide.pdf**

-   *Summary Report, 1997 National Resources Inventory*

*(Revised December 2000)* (includes **Glossary**)

> **Documentation/summary_rpt.pdf**

-   1997 Data Collection Instructions

> **Documentation/collection_instructions/**

Contents **content.pdf**

Introduction **nrintr.pdf**

General Inventory Guidelines **nrigen.pdf**

PSU Module I General Information **psumod1.pdf**

PSU Module II Farmsteads and Developed Areas **psumod2.pdf**

PSU Module III Land Use **psumod3.pdf**

Point Module I Ownership **ptmod1.pdf**

Point Module II Conservation Reserve Program **ptmod2.pdf**

Point Module III Land Use **ptmod3.pdf**

Point Module IV Soils Information **ptmod4.pdf**

Point Module V Habitat Composition and Configuration **ptmod5.pdf**

Point Module VI Overland Flow/Delivery to Water **ptmod6.pdf**

Point Module VII Irrigation **ptmod7.pdf**

Point Module VIII Erosion Data **ptmod8.pdf**

PT Module 8 Erosion Data (Exhibit 1) **ptmod8ex.pdf**

Point Module IX Wetlands **ptmod9.pdf**

Plates 1-10 **pt9ex1-10.pdf**

> Plates 11-20 **pt9ex11-20.pdf**
>
> Plates 21-30 **pt9ex21-30.pdf**
>
> Plates 31-40 **pt9ex31-40.pdf**
>
> Plates 41-50 **pt9ex41-50.pdf**
>
> Plates 51-60 **pt9ex51-60.pdf**
>
> Plates 61-70 **pt9ex61-70.pdf**
>
> Plates 71-80 **pt9ex71-80.pdf**

Plates 81-86 **pt9ex81-90.pdf**

Point Module X Conservation Practices **ptmod10.pdf**

Point Module XI Saline Deposits on Agricultural Land **ptmod11.pdf**

-   Applications of Habitat Composition and Configuration Data

> **Documentation/habexamp.pdf**

-   Point Record Layout and Database Design

> **Documentation/point_layout.pdf**

-   File Sizes

Spreadsheet of file sizes to estimate computer system space requirements

> **Documentation/filesize.pdf**

-   Number of Records for 1997 NRI Dataset on CD

> **Documentation/record_counts.pdf**

-   Data Codes Files

> For reference and/or for user to load into a software application if
> desired.

**Documentation/codes/**

> FIPS **county_codes.txt**
>
> 2-Digit Hydrologic Unit Area **hydro2_codes.txt**
>
> 4-Digit Hydrologic Unit Area **hydro4_codes.txt**
>
> 6-Digit Hydrologic Unit Area **hydro6_codes.txt**
>
> 8-Digit Hydrologic Unit Area **hydro8_codes.txt**
>
> Major Land Resource Area **mlra_codes.txt**
>
> State **state_codes.txt**
>
> Variable**s variable_codes.txt**

-   Crosswalk Between 1992 and 1997 NRI Record Layouts

(for users of 1992 NRI CD-ROM)

> **Documentation/crosswalk_92_97.pdf**

**[Tools]{.underline}:**

-   Sample Import Programs (SAS® \*)

(further details in **CD-ROM filename: readme.txt**)

**Tools/SAS/**

> Import Table **1997only rd97only.sas**

Import Table **crophist rdchist.sas**

Import Table **habitatindices rdhabind.sas**

Import Table **habitatraw rdhabraw.sas**

Import Table **overlandraw rdolraw.sas**

Import Table **point** **rdpoint.sas**

Import Table **trend rdtrend.sas.**

Import Table **wetland rdwetland.sas**

Import Table **weq rdweq.sas**

-   Sample Analysis Programs (SAS®\*)

(further details in **CD-ROM filename: readme.txt**)

**Tools/SAS/**

> Surface area **table1.sas**

Land cover/use **table2.sas**

Changes in land cover/use **table5sas**

> Average annual sheet and rill erosion **table10.sas**
>
> Average annual wind erosion **table11.sas**
>
> Average annual sheet and rill erosion
>
> in relation to T value **table12.sas**
>
> Average annual wind erosion
>
> in relation to T value **table13.sas**
>
> Erodibility index for cropland **table14.sas**

-   User Notes for MS® Access \* Implementation

(Use of **1997NRI.mdb ) Tools/ readme1997NRI.pdf**

-   User Notes for MS® Access \* Implementation

(Use of **NationalNRI.mdb ) Tools/ readmeNationalNRI.pdf**

-   Sample Unpopulated Database Framework (MS® Access \*)

(further details in **CD-ROM filename: readme.txt**)

> **Tools/1997NRI.mdb**

-   Populated National Database (MS® Access \*)

> (further details in **CD-ROM filename: readme.txt**)

**Tools/NationalNRI.zip**

**National_Master.mdb**

**1997NRI_1.mdb**

> **1997NRI_2.mdb**
>
> **1997NRI_3.mdb**

\*There are some limited MS® Access database and SAS® tools provided as
a courtesy to users who already own this software, but the software
itself is not provided.

Use of product names (or trade names) does not imply endorsement of
products

(or software systems) by the USDA Natural Resources Conservation
Service.)

**APPENDIX B**

**Important NRI Reference Materials**

**on NRI Home Page, World Wide Web**

**http://www.nhq.nrcs.usda.gov/NRI \***

There are several technical documents on the statistical design of the
NRI and statistical estimation procedures used in the NRI that may be
helpful in optimizing the utilization of NRI data. Users of NRI data are
encouraged to review these documents and become familiar with the
statistical procedures applied in the NRI. Because of copyright
restrictions, these documents have not been included on this CD-ROM, but
can be found on the NRI Web site or by using the URLs listed below.
Other pertinent documents to which the user may wish to refer are the
*1997 NRI Summary Report* and *Instructions for Collecting 1997 National
Resources Inventory Data*. Both of these items are part of the
documentation on this CD and also can be found on the NRI Web site shown
above.

We will periodically generate additional documentation, further analyses
of NRI data, and/or announcements related to the 1997 NRI. Users are
encouraged to monitor the NRI Web site for updated information.

Statistical Design of the NRI

Goebel, J. J. (1998) \"[The National Resources Inventory and Its Role in
U.S. Agriculture]{.underline},\" *In:* Agricultural Statistics 2000,
International Statistical Institute, Voorburg, The Netherlands, 181-192.

http://www.statlab.iastate.edu/survey/nri/goebel.pdf

Nusser, S. M., and J. J. Goebel (1998) \"[The National Resources
Inventory: A Long-Term Multi-Resource Monitoring
Programme]{.underline},\" Environmental and Ecological Statistics,
4(3):181-204.

http://www.statlab.iastate.edu/survey/nri/nussergoebel.pdf

Statistical Estimation for the NRI

Fuller, W. A. (1999) \"[Estimation procedures for the United States
National Resources Inventory]{.underline},\" 1999 Proceedings of the
Survey Methods Section, Statistical Society of Canada (in press).

http://www.statlab.iastate.edu/survey/nri/fuller.pdf

Nusser, S. M., J. M. Kienzler, and W. A. Fuller (1999) \"[Geostatistical
Estimation Data for the 1997 National Resources
Inventory]{.underline}.\" Working paper.

http://www.statlab.iastate.edu/survey/nri/Summary%20of%20geostat%20info.PDF

Fuller, W. A., K. W. Dodd, J. Wang, and C. Peterson (2001) \"[Estimation
for the 1997 National Resources Inventory]{.underline}.\"

http://www.statlab.iastate.edu/survey/nri/est971nr.pdf

Nusser, S. M., F. J. Breidt, and W. A. Fuller (1998) \"[Design and
Estimation for Investigating the Dynamics of Natural
Resources]{.underline},\" Ecological Applications, 8(2):234-245.

(Web address not yet available)

\* Refer to Appendix E if help is needed regarding Web addresses.

**APPENDIX C**

**Understanding the NRI Design**

**[C.1. INTRODUCTION]{.smallcaps}**

The information in this appendix was originally developed to accompany
classroom instruction to internal NRCS staff on the 1992 NRI. The
appendix is written in a tutorial format and some of the information has
been emphasized in other parts of this user guide. The training module
is included in its entirety in this appendix.

The NRI serves as the Federal Government's principal source of
information on the status, condition, and trends of soil, water, and
related resources in the United States. The assessment of the Nation's
natural resources and environmental condition provided by the NRI
requires a nationally consistent database that allows for manipulation
of various parameters in order to make accurate assessments. For these
manipulations and subsequent analyses to be useful for public policy
considerations, the NRI database ---

-   has a high degree of reliability,

-   looks at natural resources over a period of time, and

-   permits analyses at national, regional, and local levels.

# The NRI is unique because---

-   it provides a nationally consistent database for all nonfederal
    lands,

-   it features data gathered and monitored in 1982, 1987, 1992, and
    1997 by thousands of technical and natural resource data collection
    experts, and

-   it has a direct correlation with soils data, which permits analysis
    of numerous resources in relation to the capability of the land and
    in terms of soil resources and conditions.

Informed and valid decisions are best made when based upon
scientifically derived data. The NRI has been developed to supply this
type of information. The NRI database is powerful, credible, and
scientifically constructed. The present NRI is the result of many
decades of development.

*[Factors influencing the NRI]{.underline}*

Numerous factors influence the methodology and design of each NRI. Some
of the important factors considered when developing an NRI include:

Mandates\--information required/requested by the Rural Development Act
and the Resources Conservation Act, information needed to support recent
Farm Bill legislation, and various laws dealing with environmental
issues.

Data needs\--resource information needed by NRCS, USDA, and legislative
bodies;

information needed by other agencies to examine agricultural, natural
resource, and

environmental issues; modifications to assist the research community;
requirements for

educators and others who disseminate information.

Timing or schedules\--deadlines for product completion, data delivery to
properly support

decision-making.

Available funding and staffing\--resources and staff hours available for
data collection,

individual staff available.

Properly trained and experienced staff.

Impact on staff\--modification of sample to minimize travel by data
collection staff.

Previous inventories\--lessons learned about data collection procedures,
training, data

capture, and processing from both NRIs and Conservation Needs
Inventories; matching the

existing sample to objectives; modifying the existing sample rather than
developing a new

national sample; providing historical data for existing sample locations
for the trending

database.

Available technology\--technologies that are available, suitable for the
data to be collected,

and feasible.

Vision\--new emerging issues, data needs, modifications to the NRI.

*[Goals for the 1997 NRI effort]{.underline}*

The first step in the design process was to identify goals, or
reliability levels for the data. For the 1997 NRI, four general goals
were set:

(1) Provide estimates of 1997 conditions

> Obtain sufficient data to permit analysis of natural resource issues
> at the multi-county or sub-State level such as:

-   areas comparable in size to Major Land Resource Areas,

-   2- or 4-digit hydrologic units such as sub-basins or aggregated
    sub-areas,

-   NRCS Administrative Areas as they existed in the early 1980s, and

-   comparably sized areas.

> Historically, this level has been referred to as MLRA level for the
> NRI.

\(2\) Provide estimates of change between 1982 and 1997

> For many items, estimates of the change from 1982 to 1997 are
> statistically significant at the State and some multi-county sub-State
> levels. Examples include: changes in erosion rates for class II
> cropland, loss of prime farmland to urban and built-up development
> uses.
>
> Some items are rare, occur infrequently, or change slowly over time.
> Detecting changes in these features is difficult unless quite
> different inventory techniques are used. Examples include: changes in
> wetland type and changes from Forest Land to Pastureland for class W
> land. For rare items, significant changes can only be detected at
> regional levels.

(3) Continue to develop the multi-resource concept

> To support ecosystem based planning and assistance, and to properly
> analyze environmental and ecological issues, dozens of site-specific
> pieces of information are collected for each sample site. For the NRI,
> data items covering diverse landscapes and features are included, and
> linkages to databases such as soils, climate, and forest productivity
> can be established.

(4) Enhance opportunities to examine spatial trends

> Analysis using NRI data takes into account the geographical or spatial
> nature of issues related to land use, natural resources, and the
> environment. Spatial analysis is facilitated by geographic information
> systems. Spatial linkage to databases is provided. The use of NRI data
> within a GIS is discussed in Chapter 6, NRI Data Analysis.

**[C.2. PROBABILITY SAMPLING]{.smallcaps}**

*[Alternative data gathering techniques]{.underline}*

Before examining specific NRI techniques, discussing data collection
methods in a more general way is useful. Many factors influence how this
is usually accomplished. Consider the following common methods for
collecting data:

-   Complete enumeration---Census or wall-to-wall data collection; used
    in past years for watershed studies, and by the Census of
    Agriculture.

-   Scientifically designed probability sample.

-   Subjective sampling methods---In this method selected representative
    fields are chosen for observation. A certain amount of bias is
    introduced into the system from the beginning because a somewhat
    arbitrary decision has already been made as to what is a
    representative field. This method will rarely hold up when
    challenged, but research does show there are a few special
    situations where this methodology can be useful and efficient. For
    example, consider determining the average weight of assorted pieces
    of gravel within a bucket. Selecting one or two pieces subjectively
    can provide better results than a completely random selection.
    However, as the chosen sample size increases (becomes five or six
    pieces), random selection can produce better results because it more
    accurately reflects the size ranges within the bucket.

> Also, soil scientists have determined ways to effectively use
> subjective sampling, for example, through the use of typical sites or
> pedons.

-   Assimilation of available information --- compile whatever
    information is available. This involves using information collected
    with varying techniques, definitions, and time frames.

> Examples are available where creative analysts have been able to do a
> good job of developing plausible scenarios, but there are cases where
> questionable conclusions have been reached. For example, the Economic
> Research Service has developed a reputable land use series for several
> decades, using data from various sources. On the other hand, the
> National Agricultural Lands Study developed questionable conclusions
> about the rate of conversions of agricultural land occurring during
> the 1970s.

The NRI employs probability sampling to determine where to collect data.
This means data are collected for only a portion of the possible
locations.

Samples are selected in a scientific rather than subjective manner that
uses scientifically developed and broadly accepted methodology based in
statistical theory.

*[Advantages of sampling]{.underline}*

There are many reasons for using sampling techniques rather than
complete enumeration when collecting resource data. Several of the
obvious and less obvious advantages of sampling are:

-   Costs less to conduct

> Data are collected for only a fraction of the universe or population,
> such as farms, tracts, people, and cities.

-   Involves fewer people

> A smaller number of data gatherers is required because less data are
> collected.
>
> Fewer supervisors and total staff are required. Controlling
> data-gathering activities that involve smaller numbers of staff is
> easier.

-   Takes less time

> A smaller quantity of data is collected, so all steps of the process
> can be accomplished in less time. This means final results are
> produced and published in a more timely manner.

-   Produces more accurate and reliable results

> Samples can produce higher quality data because fewer staff members
> are involved; this means training, supervision, and quality control
> activities are more directly managed and easier to implement.

-   Allows estimation of accuracy

Properly conducted sample surveys provide a means for evaluating the
accuracy of the results. Sampling variability and measurement error can
be estimated with less effort than for a census-type operation.

-   Is statistically defensible

Statistical sampling methods used in the NRI are accepted in the
scientific community as sound inventory methodology.

**[C.3. NRI sampling design]{.smallcaps}**

*[Background]{.underline}*

NRI data are collected at scientifically selected sample sites located
in every county and parish throughout the United States. The sample
design is the result of more than 40 years of research and application.

The goals of the agency's Resources Inventory program have evolved over
decades of data use. As a result, modifications or changes were made to
the sampling procedure, as well as to the inventory procedures and
protocols to meet this evolving need. Data from previous inventories
have been used to adjust and continually refine the survey sample.

It is critical to have a basic understanding of NRI data in order to
fully utilize this extensive data source when examining land use,
natural resources, and related environmental issues.

Knowledge of the principles behind the NRI sampling scheme, as well as
where and how the data were collected, is essential to proper use of the
data. This knowledge enhances an analyst\'s ability to explain the use
of NRI data for examining specific natural resource related issues.

*[Basic concepts]{.underline}*

Samples for the NRI are selected using a stratified, two-stage, area
sampling scheme.

*[Stratification]{.underline}*

Stratification serves to make sampling more efficient by subdividing the
entire population of interest, or data universe, into non-overlapping
portions or layers, or \"strata,\" that are more homogeneous than the
population as a whole.

Administration, data collection, and other operational procedures can be
affected or improved by using stratification.

*[Area sampling]{.underline}*

For an area sample, the sampling units are areas of land.

When drawing a sample of people, farms, fields, or landscapes you can
either:

> Use a list frame---compile a complete list of every person or field,
> and then select a sample from this list; or
>
> Use an area frame---divide the entire landscape area into pieces or
> tracts of smaller areas. Then select a sample of the pieces or areas
> and collect data for those units within these selected areas.
>
> For example, to select a sample of people within a city, it is common
> to select city blocks as the pieces of the city.

For the NRI, this area sample method is used and described in the next
section.

*[Two-stage sampling]{.underline}*

Sample units are selected in two stages. This allows clustering of
sample sites, which usually reduces data collection costs.

For example, to select students for a survey, it would be common to
select schools as a first stage and to then take a sample of students
from each selected school.

Stratification of schools and of students within schools would make
sampling more efficient.

The NRI two-stage selection procedure is discussed in the following
sections.

*[Selection of the sample]{.underline}*

Sample design and selection for the NRI are handled on a
county-by-county basis.

The steps for sample selection include:

(1) Determine design \-- specify the parameters needed to perform
    steps (2) to (4).

(2) Stratify total surface area of the county.

(3) Select first stage sampling units \-- within each stratum, select
    one or more Primary Sampling Units.

(4) Select Sample Points - within each sample PSU, select specific
    locations (points).

The next three sections describe steps (2), (3), and (4) of sample
selection.

*[Stratification]{.underline}*

Each of the 3,100 counties in the United States was first subdivided or
stratified into non-overlapping geographical strata.

In parts of the United States covered by the Public Land Survey system,
geographic stratification was based upon legally described sections and
townships.

Each township was subdivided vertically into three 2- by 6-mile strata
for sampling purposes.

A section is nominally a one-mile square segment of land, and a township
is typically a 6-mile square area consisting of 36 sections. Correction
areas can cause sections and townships to be larger or smaller than the
normal sizes.

In the southeastern half of Ohio and for counties in the Southern and
Southeastern States not covered by the Public Land Survey System, lines
analogous to townships and sections are superimposed on county maps, and
geographic strata were formed from 2-mile by 6-mile tracts of land.

In the 13 Northeastern States, strata were formed using latitude and
longitude. For Louisiana and the northwestern half of Maine, sampling
was based upon the Universal Transverse Mercator grid system.

Some counties were also stratified according to specific resource
conditions and general ownership patterns. This occurred most frequently
in irrigated regions and urbanizing or suburban fringe areas.

Stratification was also altered in relatively remote homogeneous areas
containing large tracts of range, forest or barren land.

In summary \-- stratification is based geographically and uses the
Public Land Survey System, latitude/longitude, or Universal Transverse
Mercator for all counties. Further stratification was employed where it
was advantageous such as where irrigation patterns, urbanization, and
other conditions affect heterogeneity or homogeneity.

*[Selection of Primary Sampling Units]{.underline}*

The Primary Sampling Units, called PSUs, or first stage sampling units
were square plots. In the 34 States that have 2- by 6-mile strata, PSUs
were generally 160-acre square parcels measuring 0.5 mile on each side.

In the Western States, PSUs were often 40- or 640-acre square parcels.
The 40-acre units were used in most irrigated areas, and the larger PSUs
were used in the relatively homogeneous areas containing large tracts of
range, forest, or barren land.

PSUs in portions of the country covered by the Public Land Survey system
are based upon the township/section grid. Therefore, they are frequently
smaller or larger than the nominal sizes of 40, 160, or 640 acres.

In the 13 Northeastern States, PSUs were defined to be 20 seconds of
latitude by 30 seconds of longitude, ranging from 97 acres in Maine to
113 acres in southern Virginia. In Louisiana and part of northwestern
Maine, PSUs were 0.5 kilometer squares or 61.8 acres.

Within each stratum, one or more PSUs were randomly selected.

Note: For counties with 2-mile by 6-mile strata and 160-acre PSUs, there
were 48 possible 160-acre square plots that could have been selected.
Selecting one out of the 48 meant the sampling rate was about 2 percent
for that stratum. Often two or more PSUs were selected within a stratum.
These were color-coded, usually red, blue, green, or purple so that a
ready-made smaller sample already existed if needed.

*[Selection of points within PSUs]{.underline}*

The last step in selecting the sample was to locate three sample points
within each PSU. There were exceptions - two points were selected from
40-acre PSUs and only one point was selected per PSU in Louisiana and
northwestern Maine.

The procedure for selecting the points within a PSU was as follows:

1.  A grid consisting of squares formed with three rows and three
    columns was superimposed on the PSU. Each square was subdivided into
    four equal blocks. The numbers 1 to 12 were assigned to the blocks
    in each row with a number appearing once in each row and once in
    each column No adjoining blocks had the same number.

2.  Two numbers between 1 and X were selected at random, where X is the
    width of the side of the PSU in feet. These two numbers determine
    the coordinates of sample point #1 in feet north and east from the
    PSUs southwest corner.

3.  Points #2 and #3 were located in the blocks with the same label as
    the block for point #1. They were positioned in the same relative
    position within the blocks as point #1. Steps for selection of two
    sample points within a PSU were similar, except the PSU was divided
    into four blocks instead of 36.

*[NRI sample size]{.underline}*

The NRI base sample consists of about 300,000 PSUs. These PSUs
constitute a sample of about 3 percent of all the total land and water
areas of the 48 conterminous States, Hawaii, Puerto Rico, and the U.S.
Virgin Islands.

Sampling rates varied across strata, but typically ranged between 2
percent and 6 percent. They generally were consistent across part or all
of a county. The rate of sampling for any given stratum depended upon
such factors as land use and soils patterns, MLRA and hydrologic unit
distribution, and county size.

The sampling scheme was basically a compromise, attempting
simultaneously to balance the workload among NRCS field offices and
fulfill the design objectives of the study.

*[Features and summary of the sample]{.underline}*

The present NRI sample is the result of over 40 years of research and
development performed collaboratively between NRCS and the Statistical
Laboratory at Iowa State University. Both organizations have well known
experts in probability-based survey sampling and estimation techniques.

In general terms, the NRI samples have been selected using a stratified,
two-stage, area sampling scheme. Nationally, there are 800,000 sample
sites, or point locations, in the current base-line sample.
Probability-based sampling is used to ensure credibility by eliminating
bias and to provide the ability to assess the accuracy of results.

The samples have been developed on a county-by-county basis. This was
done mostly for operational reasons, but this has also allowed use of
information developed and reported by other agencies on a county basis.

Sampling intensity varies from county to county and often within a
county, resulting in unequal probability sampling. Higher sampling rates
occur where land use and resource patterns are more heterogeneous. This
unequal probability sampling easily accommodates special features and
interests. Additional features of the NRI sampling process include:

-   Process is complete---it is for all landscapes and for the entire
    United States.

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-   Design is concurrent and consistent for all data themes and regions.
    This facilitates a multi-resource- or ecosystem-based approach to
    analysis and provides consistent national interpretations.

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-   Flexibility is provided. The sample can be easily modified by
    augmenting or sub-sampling as emphasis changes over time.

**[C.4. CONFIDENTIALITY OF SAMPLE LOCATIONS]{.smallcaps}**

It is Natural Resources Conservation Service policy that actual
locations of NRI sample sites are not public information and are not to
be released to the public. The policy was established to maintain the
integrity of these sample sites so that they will not be affected or
treated differently, and hence lose their randomness. This policy is
also in effect to maintain the privacy, confidentiality, and cooperation
of landowners and operators.

The U.S. Department of Agriculture, NRCS, and the Iowa State Statistical
Laboratory have much at stake regarding the continued confidentiality
and integrity of the NRI sample sites. Many dollars and staff years have
been expended to develop this sophisticated and extensive set of
samples. A great deal is now known about these hundreds of thousands of
samples.

**[C.5. TYPES OF DATA]{.smallcaps}**

There are two types of sampling units -

-   The Primary Sampling Units, and

-   The specific sample points within the selected PSUs

For NRI data collection, taking advantage of these two stages of
sampling units has been beneficial for producing differing types of
statistical estimates. Most data items are collected for the sample
points, but several are collected for the entire PSU.

The NRI database is constructed using:

-   NRI County Base data

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-   Data collected for each first stage sampling unit (PSU)

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-   Data collected at the designated specific points within each sample
    PSU

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-   Ancillary data

The NRI database is constructed by consolidating all of these data into
a convenient format called Point-Type Information.

*[NRI Geostatistical Control Data]{.underline}*

These data were derived from geospatial layers (Fuller et al. 2001,
Nusser et al. 1999, Nusser 1997). Acreages are derived for each control
unit of the sample for the following:

-   The area of each control unit (4-digit HUA intersected with county
    polygon).

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-   The area of \"Census\" water (water bodies 40 acres or more in size,
    streams at least one-eighth mile wide) within each control unit.

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-   The area of Federal land within the control unit for 1992 and 1997.

*[PSU-level data]{.underline}*

These types of data are collected on the entire PSU (Fuller 1999):

-   Farmsteads and ranch headquarters

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-   Small built-up areas

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-   Large urban and built-up areas

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-   Large streams and water bodies

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-   Small streams and water bodies

-   Rural roads

As part of the data collection process, these features were delineated,
labeled, and measured on PSU support maps. The size category for water
bodies was recorded. Area measurements, such as acres or dimensions,
were needed for all of these features.

These features occur as small areas, have linear features, or possess
other unique patterns. Most would be subject to bias if sampled only on
a point basis.

Note: If more detailed data are needed, for example, for water areas or
urbanizing areas, the NRI PSU-level data provide a powerful sampling
frame from which to start.

Major Land Resource Area and 8-digit Hydrologic Unit code are recorded
for the PSU. Also recorded are two factors used in erosion equations:

-   R-factor that is the Rainfall factor for water erosion

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-   C-factor that is the Climate factor for wind erosion

*[Point-specific data]{.underline}*

The third level of data collection was at the designated sample points
located within each sample PSU. Most NRI data elements are collected on
a point basis; this is the most extensive level of data collection. All
land cover/use types are recorded at the points, so in effect data for
farmsteads, water, urban and built-up, and rural transportation are
collected at more than one level.

*[Ancillary data]{.underline}*

Ancillary data are used in the estimation procedures developed and
implemented at the Statistical Laboratory. These data come from other
sources and are used to calibrate NRI results. Further details are
provided in the NRI Database module.

Many types of ancillary materials that were assembled and used during
the NRI data collection process are discussed in the next module.

**[C.6. POINT DATA ELEMENTS]{.smallcaps}**

An important facet of inventory design is determining what data
collection method to use for each particular data item. This is
sometimes referred to as response design, or plot design.

The PSU-level data and NRI County Base data represent two particular
response designs. Collection of point-specific data represents several
more response designs.

Multiple response designs have evolved for point data because natural
resource and land use concepts are not really 0-dimensional or
point-type concepts. They need to be considered spatially, in two or
three dimensions.

> Collection of point data can be thought of as follows. The NRI needs
> to characterize the sample point. What area (or polygon) or spatial
> portion of the landscape does the NRI use?

Three central concepts used by the NRI are:

-   For many features, think of the point falling within a field or land
    unit. Characteristics of this parcel of land are then determined and
    recorded. Examples include Land Cover/Use, and Conservation
    Practices.

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-   Wetlands and soils data items represent a similar response design -
    except these spatial features typically do not occur relative to
    fields or land unit. They have natural boundaries determined by a
    combination of technical definitions and mapping conventions.

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-   For some features, a transect can help characterize the point. The
    NRI uses transects as a response design for erosion determinations
    and rangeland investigation.

Development of appropriate response designs also includes consideration
of such factors as:

> --- accepted and available classification systems
>
> --- utility for those planning to use the data
>
> --- concepts and systems that can be successfully implemented during
> data gathering
>
> --- replication of measurements and determinations by other data
> gatherers

-   providing consistent, scientifically credible information through
    monitoring or remeasurement in subsequent inventories

**APPENDIX D**

**Geographic Information System Coverages**

**Caution for Maps of NRI Data**

NRI sample data are generally \"reliable\" for State and certain broad
sub-State area analyses. Generally, analyses that aggregate data points
by smaller geographic areas and/or more specific criteria result in
fewer data points for each aggregation and therefore less reliable
estimates. Users should generate and refer to the estimated standard
errors or margins of error to determine suitability of the data for a
particular purpose. It is important to remember that values for
individual geographic units are often not sufficiently reliable for
certain analytical purposes. NRI maps reflect broad national patterns
rather than site- specific information.

NRCS has developed some spatial data coverages for mapping 1997 NRI
data. Work is in progress to further test and update the coverages that
exist as of December 2001. For the latest information on what spatial
coverages exist and their status in relation to the 1997 NRI data,
please visit our GIS Coverages Web site at:

**http://www.nhq.nrcs.usda.gov/land/aboutmaps/coverages.html**

GIS coverages include:

-   County and FIPS

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-   Hydrologic Unit

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-   Major Land Resource Area

Other coverages may become available later in 2002.

**APPENDIX E**

**Where to Get Help and Give Feedback**

As [new information]{.underline} regarding NRI data is developed, it
will be posted to the NRI Web site at:

**http://www.nhq.nrcs.usda.gov/NRI** \*

**[[Get Help]{.underline}]{.smallcaps}:**

If assistance is needed while using this CD-ROM, contact the NRI Help
Desk at:

email: **nri@nhq.nrcs.usda.gov**

User messages will be responded to in the order they are received;

response time will vary depending on the volume of requests.

**[[Give Feedback]{.underline}]{.smallcaps}:**

NRCS is interested in comments and suggestions for improvement of this
CD-ROM. In particular, users are encouraged to provide feedback
regarding the documentation. A Version 2 may be developed if additional
documentation will enhance the utilization of the data.

Please send feedback via: email at **nri@nhq.nrcs.usda.gov** or mail to

> NRI Help Desk

Resources Inventory Division

5601 Sunnyside Avenue, Stop Code 5475

Beltsville, MD 20705-5475

\* [Note regarding Web addresses]{.underline}:

NRCS is in the process of a Web modernization effort to improve all of
our Web pages. Efforts are being made to keep current Web addresses from
changing. However, if a \"file not found\" error is received when
locating one of the addresses cited on this CD, please try one of the
following to find us.

1.  **http://192.153.14.2/NRI**

> (if needed, a link will be added to this page to direct user to a new
> location)

2.  **http://www.nrcs.usda.gov**

> Look for \"technical assistance,\" then \"National Resources
> Inventory.\"

3.  Email a message to **nri@nhq.nrcs.usda.gov**