United States 

Department of

Agriculture

National

Agricultural

Statistics

Service

Research and

Development Division

Washington DC 20250

RDD Research Report

Number RDD-11-01

October 2011

On the Feasibility of 

Using NASS’s Sampling 

List Frame to Evaluate 

Misclassification Errors 

of the June Area Survey

Denise A. Abreu

Andrea C. Lamas

Hailin Sang

Kenneth K. Lopiano

Pam Arroway

Linda J. Young

This report was prepared for limited distribution to the research community outside the United 

States Department of Agriculture. The views expressed herein are not necessarily those of the 

National Agricultural Statistics Service or of the United States Department of Agriculture. 
EXECUTIVE SUMMARY

During the past three years, the National Agricultural Statistics Service (NASS) has made an

effort to address, quantify and adjust for misclassification on its annual June Area Survey (JAS). 

Misclassification occurs (1) when an operating arrangement is identified as a non-farm but 

qualifying agricultural activity is present or (2) when a non-farm arrangement is incorrectly 

identified as a farm. A farm is defined as a place from which $1,000 or more of agricultural 

products were produced and sold, or normally would have been sold, during the year, and the 

computation includes any government agricultural payments received. Misclassification is a 

direct cause of an undercount in the number of farms indication produced annually by the JAS. 

Through a cooperative agreement between NASS and the National Institute of Statistical 

Sciences (NISS), a research team was created in 2009 to review the methodology associated with 

the JAS and to recommend changes that would address this undercount. In 2009, the team 

evaluated the use of the 2007 Census of Agriculture mailing list (CML) to assess 

misclassification on the 2007 JAS. The results indicated that the CML was a rich source to 

account for the undercount of farms on the JAS. However, the CML is only available every five 

years. Because the JAS is an annual survey, misclassification should be assessed each year.

NASS maintains a list of farmers and ranchers, referred to as the list frame, from which the 

yearly list-based surveys’ samples are selected. The list frame is updated on an on-going basis 

and operators are categorized as either active or inactive. Active list records are those assumed 

to have a high likelihood of being farming operations. Inactive records are those such as 

deceased operators, farms no longer in business, idle facilities, landlords, etc. Many of the active 

records represent agricultural establishments that operate land but do not have sufficient 

production to be classified as a farm in a specific year. However, they are maintained on the list 

frame as active records to help ensure high coverage of farms for the Census of Agriculture 

every five years. There are also pure active status inaccuracies that exist on the list frame. That 

is, there are records identified as "active" that are out-of-business or no longer operate any 

agricultural land or facilities. The team is exploring the potential of using the list frame on a 

yearly basis to assess misclassification on the JAS.

After the 2007 Census of Agriculture, the farm/non-farm status of 2007 list frame records was 

evaluated. This analysis showed that 72 percent of the active records on the list frame were 

identified as farms on the census. Thus, 28 percent of the records identified as active on the list 

frame were actually non-farms. This indicates that the census list frame contains active records 

that are not associated with farms (farm status inaccuracies). A similar distribution of list frame 

farm status inaccuracies is anticipated for non-census years. For this work, the 2009 JAS records

were matched to the 2009 list frame and to the 2009 Farm Numbers Research Project (FNRP) 

records. Characteristics associated with matched records that agreed and disagreed in the 

farm/non-farm classification were explored. Subsequent efforts will focus on whether these 

characteristics could be used to effectively model the probability that a list frame record is a 

farm.
The results showed that 2,068 list frame records matched to FNRP records. Of these, 246 

represented operations incorrectly identified as farms (using an algorithm based on active status

and total value of sales) on the list frame. These misclassified operations were mostly active 

ones, and they were spread evenly across the various cultivated strata with only a few occurring 

in the agri-urban or commercial strata. Half were small with less than $10,000 in agricultural 

sales; 46 percent had $10,000 to $250,000 in sales, and the remaining operations had sales 

exceeding $250,000. An additional 61 operations were identified as non-farms on the list frame 

but were farms according to the FNRP. These operations were located primarily on nonagricultural tracts without potential and represented marginal farms with less than $10,000 in 

value of sales. 

Of the 2,068 list frame operations that matched to FNRP, 1,276 had a completed FNRP interview 

and 792 had their FNRP data estimated. The characteristics of FNRP tracts incorrectly identified 

as farms on the list frame were similar for both completed and estimated FNRP surveys. The 

attributes of the list frame records incorrectly identified as non-farms were also similar for tracts 

with completed and estimated surveys. 

The overall results of this study showed that the FNRP was an important tool in the identification 

of the list frame farm status inaccuracies and confirmed the presence of misclassification on the 

list frame. Therefore, if the list frame is used to adjust for misclassification on the JAS without 

considering its farm status inaccuracies, the JAS number of farms indication could be biased 

upwards. 
RECOMMENDATION

1. Research and evaluate potential ways in which the list frame’s farm status 

inaccuracies can be reliably identified and excluded from any adjustments to the June 

Area Survey (JAS). The results of this analysis confirm the presence of some list frame 

farm status inaccuracies. If the list frame is used to adjust for misclassification on the 

JAS without considering its farm status inaccuracies, the JAS number of farms indication 

could be biased upwards. 
1

On the Feasibility of Using NASS’s Sampling List Frame to Evaluate 

Misclassification Errors of the June Area Survey

Denise A. Abreu1, Andrea C. Lamas1, Hailin Sang2, Pam Arroway3, 

Kenneth K. Lopiano4, Linda J. Young4

Abstract

During the past three years, the National Agricultural Statistics Service (NASS) has made an

effort to address, quantify, and adjust for an undercount in the number of farms indication from

its annual June Area Survey (JAS), which is based on an area frame. This undercount is a direct 

result of the misclassification of agricultural tracts as non-agricultural. The 2007 Census of 

Agriculture mailing list (CML) was evaluated as a potential source to assess misclassification on 

the 2007 JAS. The CML was found to be a rich source from which to quantify the undercount of 

farms on the JAS. However, the CML is only available every five years, and misclassification 

on the JAS should be assessed each year. Independently of the area frame, NASS maintains a 

list of agricultural operators, referred to as the list frame. Yearly list-based samples are selected 

from the list frame. In addition, the list frame serves as the foundation for building the CML. 

The list frame is updated on an on-going basis and operators are categorized as either active or 

inactive. Although the CML includes all active records, some of these do not qualify as farming 

operations. This research report explores the potential of using the list frame on a yearly basis to 

assess the misclassification of farms on the JAS.

KEY WORDS: misclassification errors, area frame, list frame, record linkage, re-screening 

survey

 

1

National Agricultural Statistics Service, USDA, 3251 Old Lee Hwy, Fairfax VA 22030

2 National Institute of Statistical Sciences, 19 T.W. Alexander Drive, Research Triangle Park, NC 27709

3

Department of Statistics, North Carolina State University, Raleigh, NC 27695

4 Department of Statistics, University of Florida, Gainesville, FL 32611
2

1. INTRODUCTION

Each year the National Agricultural Statistics Service (NASS) publishes an estimate of the 

number of farms in the United States (U.S.) based on the June Area Survey (JAS). A farm is 

defined as a place from which $1,000 or more of agricultural products were produced and sold, 

or normally would have been sold, during the year, and the computation includes any 

government agricultural payments received. An independent estimate of the number of farms is 

published from the quinquennial Census of Agriculture, which is conducted in years ending in 2

and 7. At the end of each five-year period, the annual estimates based on the JAS number of 

farms indication are adjusted based on intercensal trends. The annual estimate of the number of 

farms from the JAS has been declining steadily between censuses (especially between the 2002 

and 2007 Censuses) as depicted in Figure 1. In 2007, the estimate from the JAS was significantly 

below that from the census; and the required intercensal trend adjustment to the JAS was 

unexpectedly large as shown by the circled area in Figure 1. 

Figure 1: Published estimates of the number of U.S. farms from 2000 to 2009 and bars with a length of 

one standard error on either side of the estimate.

During previous studies conducted by NASS, misclassification was identified as a source of the

underestimation in the JAS (Abreu 2007; Johnson 2000). Misclassification occurs (1) when an 

operating arrangement with qualifying agricultural activity is identified as a non-farm, or (2) 

when a non-farm arrangement is incorrectly identified as a farm. One study of misclassification 

(Abreu, Dickey and McCarthy, 2009) revealed that some agricultural operations were incorrectly 

classified as non-agricultural during JAS pre-screening. These results led to more intensive 

efforts to understand the source and extent of misclassification in the JAS so that it could be 

addressed. One effort was the Farm Numbers Research Project (FNRP), based on an intensive 

post-June survey re-screening in 2009 (Abreu, McCarthy and Colburn, 2010). Concurrently, this 

undercount issue was also addressed by a team of researchers formed to review the methodology 

associated with the JAS and to recommend changes, through a collaborative agreement with the 

National Institute of Statistical Sciences (NISS). This latter team consists of two NASS 

researchers, two university faculty members, a post doctoral fellow, and a graduate student. The 

team has considered several measures to address the issue of misclassification on the JAS. 

Through matching the JAS to the Census of Agriculture list frame, the team evaluated 
3

misclassification on the JAS (Abreu, et al. 2010) and then developed appropriate methodology to 

adjust for misclassification during non-census years (Lamas, et al. 2010). In addition to 

misclassification, the team identified non-response as another source contributing to the JAS

undercount. In Lopiano, et al. (2010), the effect of estimation of agricultural activity for some

JAS sampled units is discussed, and methodology for adjusting for both non-response and 

misclassification is developed. Because the census is only conducted every fifth year, the team 

further proposed a yearly follow-on survey to the JAS called the Annual Land Utilization Survey 

(ALUS) (Arroway et al. 2010). ALUS would make the JAS a two-phase sample. In addition to 

providing information about misclassification of farms and non-farms, it would allow for proper 

assessment of misclassification and result in an improvement in all JAS indications. However, 

because ALUS would lead to greater costs associated with the JAS, alternative methods that 

would not require enumerators to collect further data are attractive. One possibility is to use 

NASS’s annual list frame to assess misclassification in the JAS during non-census years. The 

team’s current effort is focused on evaluating the potential for this approach. The initial results 

are discussed in this report. 

2. THE JUNE AREA SURVEY (JAS)

The June Area Survey (JAS) is based on an area-frame and collects information about U.S. 

crops, livestock, grain storage capacity, and type and size of farms. The distribution of crops and 

livestock can vary considerably within each state in the United States. Therefore, the precision of 

the survey indications can be substantially improved by dividing the land within each state into 

homogeneous groups (strata) and optimally allocating the total sample to the strata. The basic 

stratification employed by NASS involves: (1) dividing the land into land-use strata such as 

intensively cultivated land, urban areas and range land, and (2) further dividing each land-use 

stratum into substrata by grouping areas that are agriculturally similar. The JAS uses a sample 

comprised of designated land areas (segments) selected from this stratification. A typical 

segment is about one square mile (i.e., 640 acres). Each segment is outlined on an aerial photo 

that is provided to the appropriate field enumerator (See red outlined area in Figure 2).

Through field enumeration, a segment is divided into tracts of land, each representing a unique 

land operating arrangement (Refer to blue outlined areas in Figure 2). An area screening form 

that provides an inventory of all tracts within the segment and contains screening questions that 

determine whether or not each tract has agricultural activity is completed for all sample 

segments. Using this form, all land inside the segment is screened for agricultural activity, and 

the screening applies to all land in the identified operating arrangement (both inside and outside 

the segment). Those operations (tracts) that qualify as agricultural are subsequently interviewed 

using the area version questionnaire, which collects detailed agricultural information about the 

operator’s land, again both inside and outside the segment.
4

Figure 2: JAS Segment (outlined in red) and Tract Boundaries (outlined in blue)

The area frame is a theoretically complete sampling frame with every acre of land having a 

known probability of selection. As such, it is used to estimate the number of farms and land in 

farms independently of the list frame. The area frame also provides a measure of incompleteness 

in the list. The JAS uses a replicated sample design. A sample rotation scheme is used to reduce 

respondent burden caused by repeated interviewing and to avoid the expense of selecting a 

completely new area sample each year. Once selected, a segment stays in the sample for five 

years, so that annually, approximately 20 percent of the sampled segments in each land-use 

stratum are replaced with newly selected segments in each land-use stratum. Full descriptions of 

the JAS design and analysis procedures may be found in Davies (2009) and Lamas et al. (2011),

respectively.

3. THE NASS LIST FRAME

Each year, NASS conducts hundreds of list-based surveys. The agency maintains a list of 

farmers and ranchers from which the samples for these list-based surveys are selected. This list 

frame also serves as the foundation for the development of the Census Mail List (CML). NASS 

builds and improves the list on an ongoing basis by obtaining outside source lists. Sources 

include lists from state and federal government agencies, producer associations, seed growers, 

pesticide applicators, veterinarians, marketing associations, and a variety of other agricultural 

sources. NASS also obtains special commodity lists to address specific list deficiencies. These 

outside source lists are matched to the NASS list using record linkage programs. Most names on 

newly acquired lists are already on the NASS list. Records not on the NASS list are treated as 

potential farms until NASS can confirm their existence as a qualifying farm. Each operation on 

the list frame is categorized as active or inactive. Active list records are assumed to have a high 

probability of representing active farming operations. Inactive list records may be associated 

with landlords, deceased operators, farms no longer in business, etc. Many of the active records 

represent agricultural establishments that operate land but do not have sufficient production to be 
5

classified as a farm in a specific year. However, they are maintained on the list frame as active 

records to help ensure high coverage of farms for the Census of Agriculture every five years. 

There are also pure active status inaccuracies that exist on the list frame. That is, there are 

records identified as "active" that are out-of-business or no longer operate any agricultural land 

or facilities. 

The question being considered here is whether the NASS list frame can be used to assess 

misclassification in the JAS in non-census years. After the 2007 Census of Agriculture, the 

farm/non-farm status of 2007 list frame records was evaluated. Seventy-two percent of the active 

list frame records matched to farms on the census. The remaining 28 percent were found to be 

non-farms,

5

indicating that the census list frame contains active records that are not associated 

with farming operations (farm status inaccuracies). If the list frame farm status inaccuracies are

not considered when adjusting for misclassification on the JAS, the adjustment for 

misclassification will be larger than it should be. Thus, for the list frame to be useful in assessing 

misclassification in the JAS, a method of properly accounting for the list frame farm status

inaccuracies must be developed. 

4. THE FARM NUMBERS RESEARCH PROJECT (FNRP)

In 2009, NASS conducted the Farm Numbers Research Project (FNRP). FNRP was a one-time 

follow-on survey to the 2009 JAS for the first-year rotation segments (Abreu, McCarthy and 

Colburn, 2010). Recall the design of the JAS includes rotating in new segments each year, with 

segments staying in the JAS sample for five years. Each year’s sample is comprised of segments 

from each of five rotations. Thus, the 2009 JAS contained segments that were rotated into the 

sample in 2009, 2008, 2007, 2006 and 2005. The FNRP targeted the 20 percent of JAS segments 

that were newly rotated in for 2009 (“2009 segments”). For the FNRP, all tracts in the 2009 

segments that were non-agricultural or estimated in JAS were revisited. In our present 

framework, FNRP information could subsequently be used to verify the farm/non-farm status of 

the 2009 list frame records. That is, FNRP provides the “gold standard” on farm status for 2009 

list frame records with the limitation that the FNRP only constituted 20 percent of the 2009 JAS. 

5. MATCHING 2009 JAS TO THE 2009 LIST FRAME

Probabilistic record linkage was used to match all 2009 JAS agricultural and non-agricultural 

tracts to the 2009 list frame records in 42 states. The analysis excluded the New England states

because those files were not available at the time the match was processed. The JAS is only 

conducted in Hawaii during census years, and Alaska does not have an area frame. Records 

were brought together into link groups, each of which possibly represented the same operation. 

Routinely, link groups are classified into one of three distinct types: definite match, possible 

match or non-match (Broadbent et. al., 1999). Possible matches are identified for Field Office 

(FO) staff to review. However, in the interest of saving time and resources, no FO review was 

conducted. Instead, only two distinct types of matches were identified: match and non-match. 

Eliminating the FO review from the linkage process led to a more conservative approach in 

identification of matches and non-matches. That is, to maximize the quality of the final results, 

 

5

Internal analysis conducted by Thomas Jacob of the Information Management Group.
6

all possible matches were treated as non-matches. Consequently, some true matches became nonmatches. 

From the 83,203 original JAS tracts, 92,152 names and addresses were identified. These were 

prepared and standardized for matching to the list frame. For this linkage, all agricultural and 

non-agricultural tracts were considered. Partner records and records with additional information 

were also included for each JAS tract to maximize matching results. In addition to the name and 

address information, existing area-to-area and area-to-list links were used to bring records 

together. After each June survey, FOs conduct a yearly overlap/non-overlap process in which 

JAS agricultural tracts are overlapped to the list frame. This provides a measure of list 

incompleteness. JAS identification numbers (IDs) are stored for each list frame record 

overlapped to the JAS (area-to-list links). In addition, an unduplication of the area frame records 

is conducted. The ID of any area record matching another area record (area-to-area links) is 

stored. These identification numbers were used during matching to bring records together that 

would not have come together solely based on name and address information. 

From the 2009 list frame, 4,683,345 names and addresses were prepared and standardized for 

matching to the 2009 JAS. This list included both active and inactive records. Every year,

certain records are purged from the list frame, usually because they have been inactive for more 

than five years. The only records excluded were those flagged to be purged from the list frame 

due to extended inactivity. 

When matching, the ideal scenario is to have one area record match one list record. However, 

after the initial matching, some link groups had more than one tract and others had more than one 

list frame record. Although the area file was set up to have only one tract per link group, in some 

cases, more than one tract occurred in a link group, indicating that different tracts matched to the 

same list records. To address this issue, tracts were split into separate groups and all list records 

that matched were assigned to both split groups. When multiple list records matched one tract, 

the list frame records were ranked and based on their active/inactive status, the “best” one was 

selected using the following rules: 

Ranks Used to Assign the Best of Several List Frame Records to a JAS Tract

Rank List Record Type Description

1 Active target Assumed to be farming operations

2 Potential CML Non-respondents to any of the agricultural surveys conducted 

routinely to update active status of the list frame

3 Active partner Partners associated with active target

4 Inactive Deceased operators, farms no longer in business, idle facilities, 

landlords, etc.

5 Other Hired managers, etc.
7

6. RESULTS

The results of the linkage yielded 41,926 matches. Table 1 shows the breakdown of the matched 

tracts by type of agricultural tract as identified in the JAS. The vast majority of the matches 

were to agricultural tracts (86.4 percent). This is not surprising because the list frame is targeted 

for agricultural operations, and agricultural tracts have the most complete name and address 

information. During JAS screening procedures, non-agricultural tracts are classified into the 

following three types: potential for agriculture unknown, having potential for agriculture, and not 

having potential for agriculture. Non-agricultural tracts without potential comprised almost 12 

percent of all the matches. 

Table 1. Matched JAS and List Frame Records by Type of Agriculture as Identified by the JAS 

Type of Agricultural Tract Number Tracts Matched Percent

Agricultural tracts 36,245 86.4

Non-agricultural tracts w/ potential 546 1.3

Non-agricultural tracts w/ unknown potential 240 0.6

Non-agricultural tracts w/out potential 4,895 11.7

Totals 41,926 100

Further evaluating the matches by the rank, Table 2 shows that 87.6 percent of the matches were 

to active list frame records. Additionally, 3.1 percent were matches to potential CML records, 

and 9.1 percent were to inactive list frame records. Routine follow-up for the list frame does not 

include the inactive list frame records so their farm status will be difficult to determine. 

Table 2. Results of “Best” List Records Matched to Area Tracts

List Record Rank Total Tracts Records Percent

Active target - 1 36,725 87.6

Potential CML - 2 1,320 3.1

Active partner - 3 27 0.1

Inactive – 4 3,831 9.1

Other -5 23 0.1

Totals 41,926 100.0

The results of the initial matching of the area tracts and/or list frame records are displayed in 

Table 3 below. Notice that, as described earlier, some link groups had more than one list frame 

record and/or more than one JAS tract. Only link groups with one JAS tract matching to the list 

frame records are considered for evaluating the farm/non-farm status of the list frame records. In 

addition, any tract matching 10 or more list frame records was excluded from further analysis. 

Thus, the highlighted cells in Table 3 represent the 36,439 matched records that are considered 

here (23,951 were 1-to-1 matches, 12,488 were 1-to-many). Again, for the link groups with 2 or 

more list frame records, only the best ranked one was selected. All results that follow focus on 

the 36,439 matches.
8

Table 3. Numbers of Link Groups for Each Combination of Number of JAS Tracts and Number 

of List Frame Records Within a Link Group6

Tracts in 

Link 

Group

Number of List Records per Link Group

Total

1 2 3 4 5 6 7 8 9 10+

1 23,951 6,412 3,008 1,401 784 426 230 130 97 224 36,663

2 1,364 1,028 694 436 836 4,358

3 96 111 92 305 604

4+ 19 40 242 301

Total 23,951 7,776 3,104 2,448 784 1,231 230 606 189 1,607 41,926

7. ASSESSING THE LIST FRAME’S FARM/NON-FARM STATUS

Each JAS agricultural tract was identified as a farm or non-farm in June based on whether it had 

$1,000 in sales of agricultural products or 1,000 points based on the potential for agricultural 

products produced (if sales were less than $1,000), and the computation includes any 

government agricultural payments received. All non-agricultural tracts were considered nonfarms. Identifying farms on the list frame is important because list frame records lack a 

farm/non-farm status. Subject-matter experts from the List Frame Section recommended 

assigning farm status based on the active status code and total value of sales. This approach was 

adopted and led to the following algorithm for assigning farm status to the list frame records: 

1) For active records (AS = 0) records, an operation is identified as a farm if sales exceeded 

$1000; otherwise it is taken to be a non-farm. 

2) All inactive records (AS = 1 - 8, 10 - 19) are identified as non-farms based on 

enumerator information, regardless of any sales value on the records.

3) The census-only records (AS = 9) are generally present so that farms in multiple counties 

or multiple states are represented for census purposes, but not for the survey program. 

These are not considered further.

4) The farm status of the records assigned AS = 30-31 and 33– 36 are unknown. 

5) Operations with AS = 32 are receiving CRP payments and are considered farms.

6) An operation headquartered out of state is assigned AS = 40 and is assumed to be a farm.

7) An operation that has a major name change (AS = 41) is assumed to be a non-farm.

Farm status was assigned to each JAS tract matched to a list record. In Table 4, the number of 

list frame farms, non-farms and unknowns by their agricultural/non-agricultural status on the 

JAS are shown. Recall that the farm status for the list frame records with active status code 30-31 

and 33-36 is not known (“List Farm Status Unknown” column in Table 4 below). Utilizing the 

sales class information to assign farm status would have resulted in 808, or about 68 percent, of 

the 1,185 records being identified as having some agricultural activity. Because the proportion of 

these records that represent farms may differ from the proportion in the other list frame records, 

they are treated separately from the other matched records. The farm/non-farm status of the list 

frame records by rank is displayed in Table 5 below. The farm/non-farm status of matched 

records for both the list frame and the JAS is shown in Table 6 below.

 

6

All results that follow will be focused on the highlighted cells in Table 3.
9

Table 4. Farm Status Assignment for 2009 List Frame Records

Type of Agricultural Tract List NonFarm

List 

Farm

List Farm Status 

Unknown

Total

Agricultural tracts 1,911 28,626 808 31,395

Non-agricultural tracts w/ potential 195 281 39 515

Non-agricultural tracts w/ unknown 

potential

73 128 20 221

Non-agricultural tracts w/out potential 2,108 1,882 318 4,308

Totals 4,287 30,967 1,185 36,439

Table 5. Farm/Non-farm Status of List Frame Records by Rank

Rank List NonFarm List Farm List Farm Status 

Unknown Total

Active target – 1 814 30,711 0 31,525

Potential CML - 2 0 69 1,185 1,254

Active partner – 3 0 24 0 24

Inactive – 4 3,473 156 0 3629

Other -5 0 7 0 7

Total 4,287 30,967 1,185 36,439

Table 6. Farm/Non-farm Status of Matched List Frame and JAS Records

Farm/Non-farm 

Status

List Non-Farm List Farm List Farm Status 

Unknown

Total

JAS Non-Farm 2,729 2,694 464 5,887

JAS Farm 1,558 28,273 721 30,552

Total 4,287 30,967 1,185 36,439

7.1 Assessing the Accuracy of Farm/Non-farm Status Based on the List Frame

As noted earlier, about 28 percent of the 2007 list frame records identified as active were not 

farms for the 2007 Census of Agriculture5. Thus, the farm/non-farm status of the 2009 list frame 

records had some misclassification. If the list frame is to be used to assess misclassification in 

the JAS, then being able to identify the list frame farm status inaccuracies is important. The 

results of the FNRP, discussed earlier, are used here to provide insight into the types of tracts 

that are misclassified as farms or non-farms on the list frame. Because the FNRP only 

constituted 20 percent of the 2009 JAS, this part of the analysis is limited. 

Current NASS procedures define a tract as a unique land operating arrangement; however, for 

densely populated tracts, multiple operations (places of interest) may have been erroneously 

included for any particular tract during the JAS survey enumeration. For the FNRP, the concept 

of subtracts was introduced to address tracts that had multiple places of interest. For a selected 

tract, all places of interest were considered subtracts. For enumeration purposes, if eight or more 

subtracts were present within a tract, these subtracts were sub-sampled at pre-determined rates. 
10

The FNRP sample consisted of 10,204 JAS tracts, which resulted in a total of 17,191subtracts. 

Only 2,226 (about 13 percent) FNRP subtracts matched to the list frame. Of these, 2,068 had 

only one subtract in the JAS tract. The remaining 158 subtracts were from 52 JAS tracts. These 

158 subtracts are not addressed further here because visual inspection is required to link them to 

their corresponding list frame record. 

7.2 Evaluation of FNRP Records and Their Status on the List Frame 

Of the 2,068 matching records, 483 were list non-farms, 1,458 list farms, and 127 list unknowns 

(i.e., AS=30-31, 33-36) (Table 7). As mentioned earlier, the FNRP results are the gold standard 

and assumed to be accurate. Of the 683 tracts identified as non-farms by both JAS and FNRP, 

246 were farms on the list frame. Similarly, 61 operations were incorrectly classified as nonfarms on the list frame and the JAS. Both of these are the list frame farm status inaccuracies and 

confirm the presence of misclassification on the list frame.

Table 7. A Comparison of Farm/Non-Farm Status on 2009 JAS, 2009 List Frame, and FNRP

List nonfarmList farm List Farm Status 

Unknown

Total

JAS nonfarmFNRP non-farm 356 246 81 683

FNRP farm 61 188 12 261

JAS farm FNRP non-farm 18 25 1 44

FNRP farm 48 999 33 1,080

Total 483 1,458 127 2,068

Ninety-eight percent of the 246 operations inaccurately identified as farms on the list frame were 

identified as active operations (rank 1). However, 77 percent were associated with JAS nonagricultural tracts without potential for agriculture. Only a few of the 246 were from agricultural 

tracts. Most of the misclassified operations are in the moderately to highly cultivated strata; very 

few of the misclassified operations were in the agri-urban or commercial strata. Even though 

half of these misclassified operations were small with less than $10,000 in sales, over 46 percent

of the remaining operations were in the $10,000-$250,000 sales categories. See all related tables 

in Appendix A.

Of the 61 operations that were non-farms on the JAS and the list frame but identified as farms by 

FNRP, most were marginal farms with less than $10,000 in value of sales, and they were 

primarily from JAS non-agricultural tracts without potential. See related tables in Appendix B.

When evaluating the characteristics of the 188 operations that were JAS non-farms, but 

identified as farms on both FNRP and the list frame, it is clear that these are correctly classified

on the list frame. Nearly all of them came from JAS tracts identified as non-agricultural without 

potential, and they were in the moderately to highly cultivated strata. See all related tables in 

Appendix C. Because value of sales was available for both FNRP and list frame farms, 

agreement of farming operations from both sources was evaluated. The highlighted cells in 

Table 8 below are where the list sales class and FNRP sales class agree; the two sources only 

agree about a third of the time. Most of the time the list frame value of sales is higher than that 
11

reported in FNRP, indicating that the list frame is over-estimating sales and thus categorizing 

operations as having more sales than was reported in FNRP. It is important to note that the 

current list frame procedures assign value of sales based on the largest reported values. Thus, it 

is not surprising that it overstates sales when compared to FNRP. It does point out that value of 

sales alone should not be used to determine farm/non-farm status on the list frame.

Table 8. A Comparison of Sales Class Values for Matched FNRP and List Frame Records with 

Highlighted Cells Indicating Agreement Between the Two Sources.7

7.3 Evaluation of FNRP Completed Interviews and Their Status on the List Frame

The 2,068 FNRP records that matched the list frame were comprised of 1,276 completed 

interviews and 792 estimated interviews. In this and the next subsection, the characteristics of 

these two groups and how they differ are explored. For the completed interviews, 199 of the 

operations identified as farms on the list frame were non-farms both in FNRP and the JAS (Table 

9). Similarly, 61 operations were incorrectly classified as non-farms on the list frame and the 

JAS.

Table 9. A Comparison of Farm/Non-Farm Status on 2009 JAS, 2009 List Frame, and FNRP for 

Completed FNRP Interviews

List nonfarmList farm List Farm Status 

Unknown

Total

JAS nonfarmFNRP non-farm 314 199 65 578

FNRP farm 61 174 11 246

JAS farm FNRP non-farm 14 21 1 36

FNRP farm 22 379 15 416

Total 411 773 92 1,276

The 199 farming operations that were inaccurately identified as farms on the list frame were 

primarily considered active operations (rank 1), and most of them (80 percent) were nonagricultural tracts without potential for agriculture. The misclassified operations were evenly 

 

7 A $5M row is not present for this table because there were not any list frame records with sales exceeding $5M 

that matched to a FNRP record. The bars displayed on the table represent the percent contribution of the cell to the 

column total. 

FNRP Sales Class

$1-

$999

$1,000-

$2,499

$2,500-

$4,999

$5,000-

$9,999

$10,000-

$24,999

$25,000-

$49,999

$50,000-

$99,999

$100,000-

$249,999

$250,000-

$499,999

$500,000-

$999,999

$1M-

$2.5M $5M+ Total

$1-$999 0 1 0 0 0 0 0 0 0 0 0 0 1

$1,000-$2,499 0 20 2 2 1 1 0 0 0 0 0 0 26

$2,500-$4,999 0 10 3 3 4 0 1 0 0 0 0 0 21

$5,000-$9,999 0 5 3 6 4 1 1 0 1 0 0 0 21

$10,000-$24,999 0 10 8 6 10 1 1 0 0 1 0 0 37

$25,000-$49,999 0 3 7 7 8 5 3 1 0 0 0 0 34

$50,000-$99,999 0 2 1 2 3 3 3 1 0 0 0 0 15

$100,000-$249,999 0 1 1 1 2 1 2 8 1 0 0 0 17

$250,000-$499,999 0 1 0 0 1 0 0 1 2 0 0 0 5

$500,000-$999,999 0 0 0 0 0 1 0 1 1 1 2 1 7

$1M-$2.5M 0 0 0 0 0 0 1 0 0 1 2 0 4

Total 0 53 25 27 33 13 12 12 5 3 4 1 188

List Sales Class
12

spread over the cultivated strata with very few in the agri-urban or commercial strata. Over half 

of these operations were small with less than $10,000 in sales, 44 percent of the operations 

indicated sales of $10,000-$250,000, and a few indicated sales in excess of $250,000. See all 

related tables in Appendix D.

All 61 operations that were non-farms on the JAS and the list frame but were identified as farms 

on FNRP had completed interviews during the FNRP (see Appendix B for characteristics). 

Similarly, the characteristics of the 174 completed interviews that were considered JAS nonfarms but identified as farms on both FNRP and the list frame were the same as those observed 

for all 188 records (see Appendix E). The pattern of agreement of sales from both sources

(Table 10) was also the same as for the overall group.

Table 10. A Comparison of Sales Class Values for Matched FNRP and List Frame Records with 

Highlighted Cells Indicating Agreement Between the Two Sources.7

7.4 Evaluation of FNRP Estimated Interviews and Their Status on the List Frame

Of the 792 estimated FNRP interviews, 47 of the operations identified as farms on the list frame

were non-farms both in FNRP and the JAS (Table 11). These are additional records that are 

likely to be incorrectly identified as farms on the list frame. In contrast, no operations were

incorrectly classified as a non-farm on either the list frame or the JAS.

Table 11. A Comparison of Farm/Non-Farm Status on 2009 JAS, 2009 List Frame, and FNRP

Based on Estimated FNRP Interviews

List non-farm List farm List Farm Status 

Unknown

Total

JAS non-farm FNRP non-farm 42 47 16 105

FNRP farm 0 14 1 15

JAS farm FNRP non-farm 4 4 0 8

FNRP farm 26 620 18 664

Total 72 685 35 792

The 47 list frame farm operations that were non-farms both in FNRP and the JAS possessed 

similar characteristics to those with completed interviews. See all related tables in Appendix F. 

FNRP Sales Class

List Sales Class $1-

$999

$1,000-

$2,499

$2,500-

$4,999

$5,000-

$9,999

$10,000-

$24,999

$25,000-

$49,999

$50,000-

$99,999

$100,000-

$249,999

$250,000-

$499,999

$500,000-

$999,999

$1M-

$2.5M $5M+ Total

$1-$999 0 1 0 0 0 0 0 0 0 0 0 0 1

$1,000-$2,499 0 20 2 2 1 1 0 0 0 0 0 0 26

$2,500-$4,999 0 9 2 2 4 0 1 0 0 0 0 0 18

$5,000-$9,999 0 5 3 4 4 1 1 0 1 0 0 0 19

$10,000-$24,999 0 10 8 6 10 1 1 0 0 1 0 0 37

$25,000-$49,999 0 3 7 7 7 5 3 1 0 0 0 0 33

$50,000-$99,999 0 2 1 1 2 3 2 1 0 0 0 0 12

$100,000-$249,999 0 1 1 1 2 0 2 6 1 0 0 0 14

$250,000-$499,999 0 1 0 0 0 0 0 1 1 0 0 0 3

$500,000-$999,999 0 0 0 0 0 1 0 1 1 1 2 1 7

$1M-$2.5M 0 0 0 0 0 0 1 0 0 1 2 0 4

Total 0 52 24 23 30 12 11 10 4 3 4 1 174
13

The 14 estimated interviews that were JAS non-farms but identified as farms on both FNRP and 

the list frame were correctly treated as farms. They shared the same attributes of those with 

completed interviews. See all related tables in Appendix G. The highlighted cells in Table 12

correspond to agreement in the sales class for the list frame and FNRP. For operations in this 

group, the two sources agreed half the time. Even though the numbers are too sparse to make 

any firm generalizations, the pattern seems to be similar to that of the completed interviews; that 

is, the list frame value of sales is usually higher than what was reported in FNRP indicating that 

the list frame is over-estimating sales, which is not surprising. 

Table 12. A Comparison of Sales Class Values for Matched FNRP and List Frame Records with 

Highlighted Cells Indicating Agreement Between the Two Sources.7

8. CONCLUSIONS AND DISCUSSION

Using the FNRP as the “gold standard” to accurately identify list frame farm status inaccuracies

was informative. Even though the analysis was split by type of FNRP interview (completed or 

estimated), the results showed to be similar for both groups whenever misclassification was 

present. The list frame value of sales is usually higher than what was reported in FNRP 

indicating that the list frame is overestimating sales. Since the current list frame procedures 

assign value of sales based on the largest reported values, sales will tend to be overstated when 

compared to FNRP. Therefore, value of sales alone should not be used to determine farm/nonfarm status of records on the list frame. The farm status inaccuracies are an issue that needs to 

be addressed further if the list frame is used to adjust for misclassification on the JAS. The JAS 

number of farms indication could become biased upwards. Thus, the potential for using the list 

frame for misclassification adjustment of the number of farms indication depends on whether or 

not the list frame farm status inaccuracies can be reliably identified and excluded from the 

adjustment, and this merits further research.

9. RECOMMENDATION

1. Research and evaluate potential ways in which the list frame’s farm status 

inaccuracies can be reliably identified and excluded from any adjustments to the 

June Area Survey (JAS). The results of this analysis confirm the presence of some

$1,000-

$2,499

$2,500-

$4,999

$5,000-

$9,999

$10,000-

$ 24,999

$25,000-

$49,999

$50,000-

$99,999

$100,000-

$ 249,999

$250,000-

$499,999

$1,000-$2,499 0 0 0 0 0 0 0 0 0

$2,500-$4,999 1 1 1 0 0 0 0 0 3

$5,000-$9,999 0 0 2 0 0 0 0 0 2

$10,000-$24,999 0 0 0 0 0 0 0 0 0

$25,000-$49,999 0 0 0 1 0 0 0 0 1

$50,000-$99,999 0 0 1 1 0 1 0 0 3

$100,000-$249,999 0 0 0 0 1 0 2 0 3

$250,000-$499,999 0 0 0 1 0 0 0 1 2

Total 1 1 4 3 1 1 2 1 14

List Sales Class

FNRP Sales Class

Total
14

list frame farm status inaccuracies. If the list frame is used to adjust for 

misclassification on the JAS without considering its farm status inaccuracies, the JAS 

number of farms indication could be biased upwards. 

10. REFERENCES

Abreu, Denise A., Pam Arroway, Andrea C. Lamas, Kenneth K. Lopiano, and Linda J. Young 

(2010). Using the Census of Agriculture List Frame to Assess Misclassification in the June Area 

Survey. Proceedings of the 2010 Joint Statistical Meetings.

Abreu, D. A., J. S. McCarthy, and L. A. Colburn (2010). Impact of the Screening Procedures of 

the June Area Survey on the Number of Farms Estimates. Research and Development Division. 

RDD Research Report #RDD-10-03. Washington, DC: USDA, National Agricultural Statistics 

Service.

Abreu, D. A., N. Dickey and J. McCarthy (2009). 2007 Classification Error Survey for the 

United States Census of Agriculture. RDD Research Report # RDD-09-03. Washington, 

DC:USDA, National Agricultural Statistics Service. 

Abreu, D. A. (2007). Results from the 2002 Classification Error Study. Research and 

Development Division. RDD Research Report #RDD-07-03. Washington, DC:USDA, National 

Agricultural Statistics Service. 

Arroway, Pam, Denise A. Abreu, Andrea C. Lamas, Kenneth K. Lopiano, and Linda J. Young 

(2010). An Alternate Approach to Assessing Misclassification in JAS. Proceedings of the 2010 

Joint Statistical Meetings.

Broadbent, K and Iwig, W. (1999), “Record Linkage at NASS Using Automatch”. 1999 FCSM 

Research Conference, http://www.fcsm.gov/99papers/broadbent.pdf

Davies, Carrie (2009). Area Frame Design for Agricultural Surveys. Research and Development 

Division. RDD Research Report #RDD-09-06. Washington, DC: USDA, National Agricultural 

Statistics Service.

 

Johnson, J.V. (2000). Agricultural Census Classification Error Estimation Using an Area Frame 

Approach. Data Quality Research Section. Unpublished Manuscript. Washington, DC: National 

Agricultural Statistics Service, USDA.

Lamas, Andrea C., Denise A. Abreu, Pam Arroway, Andrea C. Lamas, Kenneth K. Lopiano, and 

Linda J. Young (2010). Modeling Misclassification in the June Area Survey. Proceedings of the 

2010 Joint Statistical Meetings.

Lopiano, Kenneth K., Denise A. Abreu, Pam Arroway, Andrea C. Lamas, and Linda J. Young 

(2010). Modeling Misclassification in the June Area Survey. Proceedings of the 2010 Joint 

Statistical Meetings.
15

Young, Linda J., Denise A. Abreu, Pam Arroway, Andrea C. Lamas, and Kenneth K. Lopiano 

(2010). Precise Estimates of the Number of Farms in the United States. Proceedings of the 2010 

Joint Statistical Meetings.
Appendix A

16

Characteristics of the 246 operations that were farms on the list frame but non-farms on 

both the JAS and FNRP. 

Table A1: Breakdown by Rank on List Frame Records

Rank Number of Tracts Percent

Active target - 1 241 98.0

Potential CML - 2 2 0.8

Inactive - 4 3 1.2

Total 246 100.0

Table A2: Breakdown by Value of Sales on the List Frame

Total Value of Sales Number of Tracts Percent

Less than $999 1 0.4

$1,000-$2,499 42 17.1

$2,500-$4,999 36 14.6

$5,000-$9,999 46 18.7

$10,000-$24,999 45 18.3

$25,000-$49,999 24 9.8

$50,000-$99,999 21 8.5

$100,000-$249,999 22 8.9

$250,000-$499,999 3 1.2

$500,000-$999,999 5 2.0

$1M-$2.5M 1 0.4

Total 246 100.0

Table A3: Breakdown by Type of Agricultural Tract

Type of Agricultural Tract Number of Tracts Percent of Total

Agricultural tracts 7 2.9

Non-agricultural tracts w/ potential 33 13.4

Non-agricultural tracts w/ unknown potential 16 6.5

Non-agricultural tracts w/out potential 190 77.2

Totals 246 100.0
Appendix A

17

 Table A4: Breakdown by Strata 

Strata Number of Tracts Percent

50% + cultivated 88 35.8

15-49% cultivated 73 29.7

Agri-urban/ Commercial 7 2.8

<15% cultivated 78 31.7

Total 246 100.0

 Table A5: Breakdown by Mode of Collection

Collection Mode (Code) Number of Tracts Percent

Known Zeroes (0) 9 3.7

Mail (1) 56 22.8

Telephone (2) 71 28.9

Face-to-Face (3) 84 34.1

CATI (4) 11 4.5

Other (19) 15 6.0

Total 246 100.0
Appendix B

18

Characteristics of the 61 operations that were non-farms on the JAS and list frame but 

identified as farms on FNRP

 Table B1: Breakdown by Rank on List Frame Records

Rank Number of Tracts Percent

Active target - 1 8 13.1

Potential CML - 2 0 0.0

Inactive - 4 53 86.9

Total 61 100.0

 Table B2: Breakdown by Value of Sales on the List Frame

Sales Class Number of Tracts Percent

$1,000-$2,499 18 29.5

$2,500-$4,999 8 13.1

$5,000-$9,999 19 31.2

$10,000-$24,999 10 16.4

$25,000-$49,999 2 3.3

$50,000-$99,999 1 1.6

$100,000-$249,999 2 3.3

$250,000-$499,999 1 1.6

Total 61 100.0

 Table B3: Breakdown by Type of Agricultural Tract

Type of Agricultural Tract Number of Tracts Percent

Agricultural tracts 2 3.3

Non-agricultural tracts w/ potential 6 9.8

Non-agricultural tracts w/ unknown potential 1 1.6

Non-agricultural tracts w/out potential 52 85.3

Totals 61 100.0
Appendix B

19

 Table B4: Breakdown by Strata

Strata Number of Tracts Percent

50% + cultivated 16 26.2

15-49% cultivated 28 45.9

Agri-urban/ Commercial 0 0.0

<15% cultivated 17 27.9

Total 61 100.0

 Table B5: Breakdown by Mode of Collection

Collection Mode (Code) Number of Tracts Percent

Mail (1) 19 31.2

Telephone (2) 16 26.2

Face-to-Face (3) 16 26.2

CATI (4) 9 14.8

Other (19) 1 1.6

Total 61 100.0
Appendix C

20

Characteristics of 188 operations that were JAS non-farms but identified as farms on both 

FNRP and the list frame

 Table C1: Breakdown by Rank on List Frame Records 

Rank Number of Tracts Percent

Active target - 1 184 97.9

Potential CML - 2 1 0.5

Inactive - 4 3 1.6

Total 188 100.0

 Table C2: Breakdown by Strata

Strata Number of Tracts Percent

50% + cultivated 73 38.8

15-49% cultivated 52 27.7

Agri-urban/ Commercial 4 2.1

<15% cultivated 59 31.4

Total 188 100.0

 Table C3: Breakdown by Type of Agricultural Tract

Type of Agricultural Tract Number of Tracts Percent

Agricultural tracts 10 5.3

Non-agricultural tracts w/ potential 22 11.7

Non-agricultural tracts w/ unknown potential 14 7.5

Non-agricultural tracts w/out potential 142 75.5

Totals 188 100.0
Appendix D

21

FNRP Completed Interviews -- Characteristics of the 199 operations that were farms on 

the list frame but non-farms on both the JAS and FNRP. 

 Table D1: Breakdown by Rank on List Frame Records

Rank Number of Tracts Percent

Active target – 1 199 98.0

Potential CML – 2 1 0.5

Inactive – 4 3 1.5

Total 199 100.0

 Table D2: Breakdown by Value of Sales on the List Frame

Total Value of Sales Number of Tracts Percent

Less than $999 1 0.5

$1,000-$2,499 36 18.1

$2,500-$4,999 27 13.6

$5,000-$9,999 41 20.6

$10,000-$24,999 36 18.1

$25,000-$49,999 24 12.1

$50,000-$99,999 13 6.5

$100,000-$249,999 14 7.0

$250,000-$499,999 2 1.0

$500,000-$999,999 4 2.0

$1M-$2.5M 1 0.5

Total 199 100.0

 Table D3: Breakdown by Type of Agricultural Tract

Type of Agricultural Tract Number of Tracts Percent

Agricultural tracts 4 2.0

Non-agricultural tracts w/ potential 26 13.1

Non-agricultural tracts w/ unknown potential 9 4.5

Non-agricultural tracts w/out potential 160 80.4

Totals 199 100.0
Appendix D

22

 Table D4: Breakdown by Strata 

Strata Number of Tracts Percent

50% + cultivated 74 37.1

15-49% cultivated 60 30.2

Agri-urban/ Commercial 5 2.5

<15% cultivated 60 30.2

Total 199 100.0

 Table D5: Breakdown by Mode of Collection

Collection Mode (Code) Number of Tracts Percent

Known Zeroes (0) 9 4.5

Mail (1) 50 25.1

Telephone (2) 64 32.2

Face-to-Face (3) 68 34.2

CATI (4) 9 4.5

Other (19) 1 0.5

Total 199 100.0
Appendix E

23

FNRP Completed Interviews -- Characteristics of 174 operations that were JAS non-farms 

but identified as farms on both FNRP and the list frame

 Table E1: Breakdown by Rank on List Frame Records 

Rank Number of Tracts Percent

Active target - 1 170 97.7

Potential CML - 2 1 0.6

Inactive - 4 3 1.7

Total 174 100.0

 Table E2: Breakdown by Strata

Strata Number of Tracts Percent

50% + cultivated 65 37.4

15-49% cultivated 51 29.3

Agri-urban/ Commercial 3 1.7

<15% cultivated 55 31.6

Total 174 100.0

 Table E3: Breakdown by Type of Agricultural Tract

Type of Agricultural Tract Number of Tracts Percent

Agricultural tracts 9 5.2

Non-agricultural tracts w/ potential 21 12.0

Non-agricultural tracts w/ unknown potential 13 7.5

Non-agricultural tracts w/out potential 131 75.3

Totals 174 100.0
Appendix F

24

FNRP Estimated Interviews -- Characteristics of the 47 operations that were farms on the 

list frame but non-farms on both the JAS and FNRP. 

 Table F1: Breakdown by Rank on List Frame Records

Rank Number of Tracts Percent

Active target – 1 46 97.9

Potential CML – 2 1 2.1

Inactive – 4 0 0.0

Total 47 100.0

 Table F2: Breakdown by Value of Sales on the List Frame

Total Value of Sales Number of Tracts Percent

$1,000-$2,499 6 12.8

$2,500-$4,999 9 19.2

$5,000-$9,999 5 10.6

$10,000-$24,999 9 19.2

$25,000-$49,999 0 0.0

$50,000-$99,999 8 17.0

$100,000-$249,999 8 17.0

$250,000-$499,999 1 2.1

$500,000-$999,999 1 2.1

Total 47 100.0

 Table F3: Breakdown by Type of Agricultural Tract

Type of Agricultural Tract Number of Tracts Percent

Agricultural tracts 3 6.4

Non-agricultural tracts w/ potential 7 14.9

Non-agricultural tracts w/ unknown potential 7 14.9

Non-agricultural tracts w/out potential 30 63.8

Totals 47 100.0

.
Appendix F

25

 Table F4: Breakdown by Strata 

Strata Number of Tracts Percent

50% + cultivated 14 29.8

15-49% cultivated 13 27.7

Agri-urban/ Commercial 2 4.2

<15% cultivated 18 38.3

Total 47 100.0

 Table F5: Breakdown by Mode of Collection

Collection Mode (Code) Number of Tracts Percent

Known Zeroes (0) 2 4.2

Mail (1) 6 12.8

Telephone (2) 7 14.9

Face-to-Face (3) 16 34.0

CATI (4) 2 4.2

Other (19) 14 29.8

Total 47 100.0
Appendix G

26

FNRP Estimated Interviews -- Characteristics of 14 operations that were JAS non-farms 

but identified as farms on both FNRP and the list frame

 Table G1: Breakdown by Rank on List Frame Records 

Rank Number of Tracts Percent

Active target - 1 14 100.0

Total 14 100.0

 Table G2: Breakdown by Strata

Strata Number of Tracts Percent

50% + cultivated 8 57.1

15-49% cultivated 1 7.1

Agri-urban/ Commercial 1 7.1

<15% cultivated 4 28.6

Total 14 100.0

 Table G3: Breakdown by Type of Agricultural Tract

Type of Agricultural Tract Number of Tracts Percent

Agricultural tracts 1 7.1

Non-agricultural tracts w/ potential 1 7.1

Non-agricultural tracts w/ unknown potential 1 7.1

Non-agricultural tracts w/out potential 11 78.6

Totals 14 100.0