POLICY ISSUES ASSOCIATED WITH THE UTILIZATION OF GEOGRAPHIC

INFORMATION SYSTEMS (GIS) IN THE U.S. NATIONAL AGRICULTURAL

STATISTICS SERVICE (NASS)

By

Rich Allen, Associate Administrator, NASS

Ron Bosecker, Director, Research Division, NASS

George Hanuschak, Associate Director, Research Division, NASS

I. ROLE OF GIS IN NASS

1. The National Agricultural Statistics Service (NASS) of the U.S. Department of Agriculture

is responsible for conducting the Census of Agriculture (at 5-year intervals, 1997 is the current

Census year) and a very extensive survey program to support the publishing of all official current

agricultural statistics for the United States (U.S.). In order to fulfill its complex mission, the

NASS staff has utilized numerous statistical and technological tools over the last 135 years of its

existence. Some historic tools were: use of rural postal route and postcard surveys, design and

use of mechanical crop meters which calculated percent of different crops along rural road

routes, construction and utilization of area sampling frames, design and use of objective crop

yield survey techniques, construction and use of a list sampling frame, combination of list and

area sampling and estimation techniques known widely as multiple frame sampling, use of

statistical software systems, development and use of computer assisted data collection techniques

such as computer assisted telephone interviewing, and use of space borne remotely sensed data to

supplement and aid in area frame construction, crop area estimation and crop monitoring. Even

with all these tools added to the tool kit over the decades, the primary source of data remains as

farmer and agribusiness reported data.

2. Geographic information systems technology is one of the more recent tools in the tool kit

and NASS staff are just beginning to tap some of the potential benefits of this new tool. Another

relatively new tool being utilized by NASS staff is data warehousing and extraction software. An

important concept is to not get too caught up with a new tool as a potential panacea. As with any

new tool, it is important to know what problems it can help solve and also to be trained on the

proper use of that tool, as well as all the other necessary tools.

3. The first question to answer is, “What is the appropriate role for GIS technology in the U.S.

agricultural statistics system?” NASS research staff began the search for this answer in 1990 by

procuring GIS software, hardware and peripherals and also acquiring training in GIS principles

and software utilization proficiency. There were two major categories of potential GIS use in

NASS. The two categories were: spatial or geographic data displays, and survey sampling

and/or estimation.

4. The research staff started with the easier of the two categories which was spatial or

geographic data displays. They examined displays for crop monitoring that utilized NASS
sample survey data and estimates, remotely sensed data, and weather data. A number of these

displays were shown in last year’s paper for this ECE GIS workshop. Also, the vegetative index

data maps that are one aid in monitoring crop conditions are located on the NASS Web site at

http://www.nass.usda.gov/research. Data displays are provided every 2 weeks throughout the

crop season and metadata are included as well. The metadata describe the data sources and

features as well as strengths and limitations of the data. The data displays are usually quite

useful in observing vegetative greenness over large areas and comparisons over time. However,

because of spatial resolution (1 sq. km.) and occasional atmospheric interference from clouds or

haze, one must be aware of limitations in the data content. Also available from the NASS Web

site is the summarized data from the Weekly Weather and Crop Report and Crop Condition

Report. These reports are also available on a weekly basis and provide information about crop

stage and condition and weather data. The crop portion comes mainly from expert opinion of

USDA agricultural extension agents across the country. They submit a weekly report to NASS

on crop stage and condition, which NASS staff summarize at the regional, state and sometimes at

the sub-State agricultural statistics district level. A recent addition to the Web site is the

definitions used for these weekly expert opinion surveys. The vegetative index maps and this

weekly expert opinion data complement each other and both have different strengths and 

limitations. This is just another example of the value of good metadata to data users. These

weekly sources of information are supplements to the Monthly Crop Reports, which are based on

large statistically representative samples of farmers who report monthly yields to NASS and on

objective yield survey plant counts and fruiting characteristics throughout the growing season.

5. In addition to the spatial data displays provided to the public at large, such as the vegetative

index maps, some internal displays are created for crop commodity analysts. These displays

depict sample survey data at geographic levels such as county, which due to data confidentiality

protections can only be used for internal crop analyst review. Some examples of this type of

application are: viewing monthly farmer reported yield data from a small sample at the county

level and doing month to month and year to year graphic comparisons as well and viewing the

weekly crop stage and condition data at the county level.

6. NASS is also using GIS in the construction and maintenance of its area sampling frame. 

Percent of land cultivated strata are constructed, using digital map files and digital remote

sensing data, and are geo-coded. Within a land cultivated strata, blocks of land, called count

units, that encompass 10 or so ultimate sampling units, called sample segments, are identified

and geo-coded. For count units selected for the sample, segments are broken out within those

count units and sample segments randomly selected. There are approximately 17,000 sample

segments selected for a June and a Fall Agricultural Survey. For each sample segment, the

approximate centroid is used as the geo-coding. A customized special purpose software system

developed jointly by NASS and the National Aeronautics and Space Administration (NASA)

with GIS like features was used from 1990-1997. Now, the area frame construction and

maintenance process is being converted to commercial off-the-shelf GIS and image analysis

software. PC ARCVIEW and ERDAS are being used for this task.
7. Recently, PC MAPINFO was procured for the NASS operational units, including the 45

State Statistical Offices. Some training has been conducted and is in progress. This brings us to

the second major category of potential GIS applications which would involve sampling and

estimation. One of the remaining challenges is associated with geo-coding NASS’s list universe

and sampling frame. Possible sources of information to geo-code with are: 5 digit postal zip

codes, 9 digit postal zip codes, Census TIGER files, commercial digital map files, 911

emergency services routes and household locations, and any specific locational data collected

with global positioning systems (GPS) devices. One serious problem is that at best one could get

a household location from most of these sources. This, however, doesn’t necessarily provide the

location of the agricultural output associated with that household. There has been some initial

discussion of using GPS devices to locate large economic scale agricultural facilities locations

such as large cattle feedlots, buildings used for hog production and poultry production and

perhaps large grain storage facilities. Some NASS State Statistical Offices have access to a

limited number of such data sets created by State regulatory agencies. At this stage though, there

is not a firm plan and/or infrastructure to accomplish this for all NASS offices. Thus, the geocoding of even a portion of the list frame universe remains a serious challenge. The easiest geocoding would be to use the centroids of the 9 digit zip codes associated with the mail address

households, but the value of that is limited. The mailing address may or may not be in close

geographic proximity to the agricultural production of the farm. In addition, some large and even

moderate size farms involve numerous land parcels that can be spread out and involve land in

several counties or, in some other rarer cases, across States. For example, if one used 9 digit zip

information to sample within a watershed, considerable additional data collection would be

required to identify the land operated only in the sampled watershed. Also to try to identify land

that is in the watershed that wasn’t represented by sampling operators by zip code, additional

procedures would need to be developed. The 9 digit zip locations could be used for national or

State views of certain types of farm operations (i.e., hog farms with greater than 1,000 head of

inventory) or of identified samples, internal to Agency analysts. Even in these cases, metadata on

the limitations and cautions associated with the accuracy of the locational information for a

specified variable of interest should be added.

8. The brief description of GIS applications for NASS provided above leads us into the next

two sections of this paper which are: data confidentiality issues and protections required, and

statistical defensibility of products provided to the public.

II. DATA CONFIDENTIALITY PROTECTIONS

9. The search for data layers which can be used in GIS analyses have created more interest in

NASS geographically located data and challenged the Agency to explore the release of additional

data breakouts. NASS is examining all new requests in light of its confidentiality restrictions. In

some cases, statistical defensibility is also an issue. NASS cannot release any individually

identifiable data about an operation. The general guidelines used to prevent such identification is

that no aggregate is published which represents fewer than three reports or for which one

operation controls more than 60 percent of the total. The paragraphs below highlight the

handling and assumptions that are being used for various data sets.
10. NASS county estimates data definitely meet confidentiality tests and form very logical data

layers for land use or productivity studies. They also have the advantage of being available for

multiple years so users can examine year-to-year variability or normalize data to an average or

standard. County production data are available for most major crops so users can even add crop

area figures together to account for more of the total land area. Crop county estimates data,

however, do not carry any indication of variation from farm to farm within a county. The

prohibition on variation information is done for two reasons. First, providing information on the

range of amount of area in a crop within a county or even the range of average yields might

reveal information about specific producers. Secondly, county estimates are created through the

use of several data sources and a top down from State level, to district level to county approach

so they are not simply composed of data from one survey which expand to county totals and yield

averages.

11. The one new area of interest in NASS geographic data comes from environmental surveys. 

Individuals would like to study the relationship of inputs, such as various fertilizer levels and

pest control methods on yield. Those studies have involved relatively small sample surveys and

it might be easily possible to identify some operations due to size of holdings, combinations of

crops grown, or other key data. In these studies, summary data have usually been released only

as State level totals or averages since samples were drawn by States. State level data do not

provide much sensitivity for GIS analyses. NASS has received numerous inquiries asking for

individual information (which will not be provided due to confidentiality) or small geographic

detail.

12. After review of the environmental survey results, NASS has created some district

(collections of counties within a State) summaries or data sets which had State/district level

identifiers only which provide more useful information. However, another common request is to

summarize data by hydrogeomorphic regions such as river basins. These regions normally take

in multiple counties, often cross State boundaries, and might split counties in some cases. NASS

is normally not able to do anything about the split counties, although in some specialized

situations all agriculture is known to be in a certain part of a county and it is obvious within

which hydrogeomorphic region the data for a county should fall. NASS has done some

resummarization of data into these special regions, if doing so would not reveal data for a

particular county by comparing district level summaries and hydrogeomorphic regions. At the

present time, only a few data sets have been created. Those have not had wide usage since the

data had to be combined by using multiple expansion and nonresponse adjustment factors.

13. The interest in environmental data and the close scrutiny by users, ended up identifying

one data set that NASS could release as a public use file. The sampling procedure for most field

crop chemical use surveys was a probability proportionate to size selection from area frame

expanded crop area data. The selection was in essence a selection of random points within the

State. No confidential farm level data were used in sample selection and none were collected

during the survey process. With no confidentiality restrictions, diskettes of the data were created

and offered as an available NASS product. However, no geo-coding below the State level has

been retained on the public use file. The first reason for limiting the coding was the chance that

identifying a particular chemical being used in a small geographic area might signify one specific
operation.

14. The larger reason for limiting the geo-coding in the public use file was statistical

defensibility. State level sample sizes were relatively small, often only 100 per State. 

Resultantly, primary agriculture districts within a State would have sufficient sample sizes and

quite representative data relationships. However, some districts within nearly every State and for

each crop would have five or fewer reports, including zero in some cases. Thus, including even

district level coding would result in some districts which have actual crop areas would end up

with no corresponding cropping practices data at all and other distracts would likely have

unrepresentative results based on small sample sizes. If NASS was creating cropping practices

estimates, as it does with county yield estimates, it would be a straightforward process to utilize

current data, previous year district-to-district relationships, and year-to-year changes at the State

level to estimate for each district. However, NASS has not yet come up with a standard approach

to use for this public use file. For the present, people using the data sets have been reasonably

satisfied to have State level summaries or averages, with the capability to look at variations in

chemicals used, fertilizer rates, etc., within the State. If NASS would change the policy on geocoding within the State, the policy would need to be easy to implement and standard across all

crop/State comparisons. The only candidate approach that seems straightforward would be to

retain district coding with n or more (with n = 15, 20, or some number determined through

sensitivity analyses) reports and code all other reports as “other.” This solution would not be a

panacea since a specific district might be codeable one year and not the next but it would allow

creation of some additional geographically located data for analyses.

15. In the case of being categorized into crop types,

III. STATISTICAL DEFENSIBILITY OF GIS PRODUCTS PROVIDED TO THE

PUBLIC

16. The next policy issue topic area is the statistical defensibility of the GIS derived products

that are to be released to the public. The products created with GIS need to be informative,

protect individually reported data and also be statistically defensible. Thus, the policy issues

surrounding statistical defensibility are: defining acceptable statistical measures of confidence

for each product, providing good quality metadata about each data layer or source which includes

strengths and limitations, and having the management infrastructure to assure policy compliance.

17. As far as defining acceptable statistical measures of reliability to GIS derived products, the

picture is mixed. The vast majority of NASS published data are supported by large statistically

designed sample survey reports and the Census of Agriculture. Documentation for Ag Census

procedures is readily available and many of the sample survey procedures are described in the

official releases. Thus, if GIS is used to display such information graphically, metadata is

available that describes the statistical procedures used for the underlying data. NASS has

established target coefficients of variation and monitors the survey results for acceptable

precision levels for its major probability based surveys. One desirable feature would be to design

more GIS derived products where at least some of the statistical properties are displayed as well

as the variable(s) of interest. Dr. Dan Carr of George Mason University has done some
interesting development in this area for several Federal agencies that may be the prototype for

some future NASS graphics.

18. The role and value of good quality metadata plays an important role in GIS and for that

matter, an increasing role for Federal statistics products in general. Defining procedures used,

sources of the data, strengths and limitations of the data, dates associated with the data, and

accuracy or precision levels of the data are helpful for data users or potential data users as they

decide whether a given data set is appropriate for their application.

19. The management and systems infrastructure to follow and monitor standards associated

with statistical defensibility of the products and high quality metadata are essential.