2017 Census of Agriculture Appendix A A - 1

USDA, National Agricultural Statistics Service

Appendix A.

Census of Agriculture Methodology

The purpose of a census is to enumerate all objects 

with a defined characteristic. For the census of 

agriculture, that goal is to account for “any place from 

which $1,000 or more of agricultural products were 

produced and sold, or normally would have been sold, 

during the census year.” To do this, NASS creates a 

Census Mail List (CML) of agricultural operations 

that potentially meet the farm definition, collects 

agricultural information from those operations, 

reviews the data, corrects or completes the requested 

information, and combines the data to provide 

information on the characteristics of farm operations 

and farm producers at the national, State, and county 

levels. In this appendix, these census processes are 

described.

THE CENSUS POPULATION

The Census Mail List

The National Agricultural Statistics Service (NASS) 

maintains a list of farmers and ranchers from which 

the CML is compiled. The goal is to build as complete 

a list as possible of agricultural places that meet the 

farm definition. The CML compilation begins with 

the list used to define sampling populations for NASS 

surveys conducted for the agricultural estimates 

program. Each record on the list includes name, 

address, telephone number, and email plus additional 

information that is used to efficiently administer the 

census of agriculture and agricultural estimates 

programs.

NASS builds and improves the list on an ongoing 

basis by obtaining outside source lists. Sources 

include State and federal government lists, producer 

association lists, seed grower lists, pesticide 

applicator lists, veterinarian lists, marketing 

association lists, and a variety of other agriculturerelated lists. 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 

sources 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. Staff in NASS regional and field offices 

routinely contact these potential farms to determine 

whether they meet the farm definition. For the 2017 

Census of Agriculture, NASS made a concerted effort 

to work with community-based organizations not only 

to improve list coverage for minorities but also to 

increase census awareness and participation.

List building activities for developing the 2017 CML 

started in 2014 by updating list information from 

respondents to the 2012 Census of Agriculture. 

Between 2015 and 2017, NASS conducted a series of 

National Agricultural Classification Surveys (NACS) 

on approximately 1.6 million records, which included 

nonrespondents from the 2012 census and newly 

added records from outside list sources. The NACS 

report forms collected information that was used to 

determine whether an operation met the farm 

definition. If the definition was met, the operation was 

added to the NASS list and subsequently to the CML. 

Addressees that were nonrespondents to a NACS 

were also added to the CML and identified with a 

special status code.

Measures were taken to improve name and address 

quality. Additional record linkage programs were run 

to detect and remove duplicate records both within 

each State and across States. List addresses were 

processed through software programs that utilize the 

United States Postal Service’s National Change of 

Address System and the Locatable Address 

Conversion System to improve mail delivery. 

Records on the list with missing or invalid phone 

numbers were matched against a nationally available 

telephone database to obtain as many phone numbers 

as possible. To reduce costs, operations with 

characteristics that indicated they were unlikely to be 

farms, according to the farm definition, were removed 

from the list.
A - 2 Appendix A 2017 Census of Agriculture

USDA, National Agricultural Statistics Service

The official CML for the 2017 Census of Agriculture 

was established on September 3, 2017. The list 

contained 2,999,098 records. Of these, 2,259,750 

records were thought to meet the NASS farm 

definition and 739,348 were potential farm records, 

which included NACS nonrespondents, other records 

added to the CML by the NASS regional field offices 

after the record linkage process, and late adds to the 

CML that were not included in any previous NACS or 

State screening survey.

Not on the Mail List (NML)

Extensive efforts are directed toward developing a 

CML that includes all farms in the U.S. However, 

some farms are not on the list, and some agricultural 

operations on the list are not farms. NASS uses its

June Area Survey (JAS) to quantify the number and 

types of farms not on the CML. The records in the 

JAS that are not on the CML are said to be in the Noton-the-Mail List (NML) domain. If a JAS record in 

the NML domain is determined to be a farm during 

the census, it is an NML farm. The NML farms are 

used to measure coverage associated with the census.

The JAS is based on an area frame, which covers all 

land in the U.S. and includes all farms. The land in the 

U.S. is stratified by characteristics of the land. A 

probability sample of segments is drawn within each 

stratum for the JAS. Segments of approximately equal 

size are delineated within each stratum and designated 

on aerial photographs. The JAS sample of segments is 

allocated to strata to provide accurate measures of 

acres planted to widely grown crops, farm numbers, 

and inventories of cattle. Sampled segments in the 

JAS are personally enumerated. Each operation 

identified within a segment boundary is known as a 

tract.

The 2017 JAS sample was increased to improve the 

farm counts for operations that produced specialty 

commodities or had socially disadvantaged or 

minority producers. The total JAS sample consisted 

of 13,972 segments of which 3,012 were additional 

segments. This set of additional segments is referred 

to as the Agricultural Coverage Evaluation Survey 

(ACES) segments. The ACES segments were selected 

using a multivariate sampling design that targeted 

specific items at the U.S. level. The 2017 JAS 

consisted of sample segments from all States, with the 

exception of Alaska where NASS does not maintain 

an area frame.

During the JAS/ACES enumeration process, each 

tract is identified as either agricultural or nonagricultural. Each JAS/ACES agricultural tract is 

identified as a farm or non-farm in June based on the 

farm definition of $1,000 of sales or potential sales of 

agricultural products. Non-agricultural tracts are 

further classified into categories: with farm potential, 

with unknown farm potential, or with no farm 

potential. The names and addresses collected in the 

2017 JAS/ACES were matched to the CML. Those 

from the 2017 JAS/ACES that did not match were 

determined to be in the NML domain and sent a 

yellow census report form so that they could be 

differentiated from the green report form sent to those 

addressees on the CML. Instructions on the census 

report form directed any respondent who received 

duplicate forms to complete the CML form and to 

mail all duplicate forms back together. Those who 

returned a CML and an NML form had been 

misclassified as NML and were removed from the 

NML domain.

The initial NML mailout consisted of 42,430 records. 

A total of 41,787 NML records were summarized of 

which 2,799 records were confirmed to be NML and 

in-scope. 

The farm/nonfarm status of each NML domain 

operation was determined based on the reported data 

in the census form. An operation in the NML domain 

that was determined to be a farm is referred to as an 

NML farm. Characteristics of NML farms and their 

producers provided a measure of the undercoverage 

of farms on the CML. The percentage of farms not 

represented on the CML varied by State. In general, 

NML farms tended to be small in acreage, production, 

and sales of agricultural products. Farm operations 

were missing from the CML for various reasons, 

including the possibility that the operation started 

after development of the CML, the operation was so 

small that it did not appear in any agriculture-related 

source list, or the operation was misclassified as a 

nonfarm prior to census mailout. The CML was used 

with the NML in a capture-recapture framework to 

represent all farming operations across all States in 

the JAS sample.
2017 Census of Agriculture Appendix A A - 3

USDA, National Agricultural Statistics Service

DATA COLLECTION OUTREACH AND

PROMOTIONAL EFFORTS

NASS planned and executed a multi-phase strategic 

communications campaign for the 2017 Census of 

Agriculture, to increase the level of awareness and 

response among all U.S. agricultural producers.

 Phase 1 ran from December 2016 − June 2017. It 

raised awareness about the census and list building, 

encouraged producers to sign up in response to 

NASS mailings and at community, association, 

and other stakeholder meetings where NASS 

partners reached out.

 Phase 2 ran from July 2017 − December 2017. It 

notified farm producers and agricultural 

organizations that the census would be mailed in 

December, and encouraged communications 

regarding the census.

 Phase 3 ran from December 2017 – July 2018. It 

focused on census data collection with messaging 

urging response, reminding producers that it was 

not too late to respond.

 Phase 4 ran from August 2018 – February 2019. It 

thanked producers for their participation and 

NASS partners for their support, and informed all 

of the February 2019 data release plan. 

The communications campaign focused on these 

primary areas: partnership building, local-level 

outreach, public relations, media relations, paid 

media, and social media. Some external support was 

provided by a private communications agency (i.e. 

primarily assistance with paid media/advertising 

strategy and ad creation) and a freelance writer.

The unifying force behind the 2017 communications 

campaign was the theme “Your Voice. Your Future. 

Your Opportunity.” This was accompanied by 

supporting messages and artwork that created a 

consistent look and feel for all census 

communications. All messages and materials served 

the purpose of inspiring action: Grow Your Farm 

Future - Shape Your Farm Programs - Boost Your 

Rural Services - Fill out your Census of Agriculture -

Do your part to be counted - The Census of 

Agriculture is Your Voice, Your Future, Your 

Opportunity.

Partnership and Local-Level Outreach

At the national level, NASS officials met with leaders 

from dozens of agricultural organizations, State 

Departments of Agriculture, and other USDA 

agencies to successfully secure their support in 

promoting the census among their constituencies. 

Stakeholders partnered with NASS to promote the 

2017 Census of Agriculture through publications (e.g. 

newsletters), special mailings, speeches, social media, 

websites, and other communications. In addition, 

through grassroots-level outreach and efforts, NASS 

partnered with a number of community-based 

organizations to reach minority and limited-resource 

farmers and ranchers. National-level outreach was 

encouraged and mirrored at the regional, State, and 

local levels. Among the highlights of these 

partnership efforts was the production of multiple 

television and radio public service announcements 

featuring the U.S. Secretary of Agriculture, State 

secretaries, directors, and commissioners of 

agriculture and leaders from community-based 

organizations. 

Coverage of American Indian and Alaska 

Native Farm Producers

To maximize coverage of American Indian and 

Alaska Native agricultural producers, special 

procedures were followed in the census. A concerted 

effort was made to get individual reports from every 

American Indian and Alaska Native farm or ranch 

producer in the country. If this was not possible within 

some reservations, a single reservation-level census 

report was obtained from knowledgeable reservation 

officials. These reports covered agricultural activity 

on the entire reservation. NASS staff reviewed these 

data and removed duplication with any data reported 

by American Indian or Alaska Native producers who 

responded on an individual census report form. 

Additionally NASS obtained, from knowledgeable 

reservation officials, the count of American Indian 

and Alaska Native producers (on reservations) who 

were not counted through individual census report 

forms, but whose agricultural activity was included in 

the reservation-level report form. 
A - 4 Appendix A 2017 Census of Agriculture

USDA, National Agricultural Statistics Service

Table D, American Indian and Alaska Native 

Producers: 2017 provides the number of producers 

(1) reported as American Indian or Alaska Native in 

the race category, either as a single race or in 

combination with other races, on the individual 

census report forms (for up to four per farm) and (2) 

identified as American Indian or Alaska Native 

producers farming on reservations by reservation 

officials. The count from the individual report forms 

is summarized in the “Individually reported” column. 

It includes up to four producers on or off reservations. 

The “Other” column provides counts of producers on 

reservations as reported by a reservation or tribal 

official. The “Total” column is simply a sum of the 

“Individually reported” and the “Other” columns. 

Tables in other parts of the publication count the 

reservation-level reports as single farms.

Public Relations

In the public relations arena, NASS worked with 

internal and external stakeholders to equip them with 

communications tools and resources to deliver the 

census communications message to their audiences. 

NASS utilized its Intranet and the Partner Tools page 

on the census website to deliver materials to the 12 

regional and 46 field offices as well as to external 

stakeholders. The materials included but were not 

limited to: customizable news releases, public service 

announcement scripts, and a PowerPoint template; 

Secretary of Agriculture video public service 

announcements, and drop-in advertisements; 

informational, instructional, and testimonial videos; 

website buttons and banners; brochures in multiple 

languages; flyers; posters; FAQ sheets, talking points, 

and more. In addition, at the national level, NASS 

issued six news releases during data collection (three 

more were produced before data collection to inform 

and prepare producers) citing department and agency 

spokespeople, published half a dozen timely and 

relevant pieces to the USDA blog highlighting the 

census, and conducted three social media campaigns. 

These public relations efforts at the national and locallevels helped ensure that NASS’ message about the 

census was continually in the media, including print 

and online publications, a variety of social media, 

radio, and some television programs. Media outlets 

included both those specializing in agriculture and 

more general outlets. 

Paid Media

Even with increasingly limited budgets and resources, 

NASS was able to apply a small portion of funds 

toward paid media. For the 2017 Census of 

Agriculture, NASS strategically advertised in 

regional print publications, online, and with national 

agriculture news services (i.e. TV, radio) to bolster 

reach both in general and within geographicallyspecific, previously under-represented populations 

and lower response areas. 

DATA COLLECTION

Method of Enumeration

Data collection was accomplished primarily by mail, 

Computer-Assisted Self Interview (CASI) on the 

Internet, and personal enumeration for special classes 

of records in the census operations. Personal 

enumeration (interviewing) involved the use of both 

Computer-Assisted Telephone Interview (CATI) and 

Computer-Assisted Personal Interview (CAPI) data 

collection instruments. Enumerators at the five NASS 

Data Collection Centers conducted CATI data 

collection. In addition, enumerators under contract 

with NASS through the National Association of State 

Departments of Agriculture (NASDA) conducted 

phone and personal interviews with respondents. For 

the 2017 Census of Agriculture, NASS implemented 

a pre-notification strategy in an effort to increase 

awareness, improve overall responses, and encourage 

respondents to report early to avoid continued 

correspondence. All records with an e-mail address 

received an e-mail message marketing the improved 

web form and announcing the census mail packets 

were coming.

Report Forms

Four versions of report forms were used for the 2017 

Census of Agriculture:

 General form (17-A100)

 Short form (17-A200)

 Hawaii form (17-A101)

 American Indian form (17-A300)

The general form facilitated reporting crops and 

livestock most commonly grown and raised in the 

U.S. The short form expedited reporting specific 

crops or livestock for pre-identified farms and ranches 

in the U.S. The Hawaii form targeted crops and 
2017 Census of Agriculture Appendix A A - 5

USDA, National Agricultural Statistics Service

livestock specifically grown or raised on farms and 

ranches in Hawaii. The American Indian form 

focused on crops and livestock for farms and ranches 

on reservations in Arizona, New Mexico, and Utah. 

All of the report forms allowed respondents to write 

in specific commodities that were not prelisted on 

their report form.

Report Form Mailings

Pre-notification of census data collection began on 

November 17, 2017. Approximately 600,000 

producers with an active e-mail address on the census 

mail list received a message informing them of the 

upcoming census data collection period and 

encouraging them to utilize the new census web form. 

Between November 27 and November 30, 2017, 

approximately 1 million producers received a letter 

with their survey code and instructions for completing 

their census online. The letter encouraged producers 

to report online early to avoid receiving mail and 

phone follow-up. Approximately 3 million mail 

packets were mailed in December 2017 and January 

2018. Each packet contained a cover letter, instruction 

sheet, a labeled report form, and a return envelope. 

The Census Bureau’s National Processing Center 

(NPC) in Jeffersonville, IN was contracted to perform 

mail packet preparation, initial mailout, and two 

follow-up mailings to nonrespondents.

The initial mailout was followed by a thank-you 

reminder postcard that was delivered in January 2018 

to all operations that received mail packets. First 

follow-up mail packets were mailed in mid-February 

2018 to approximately 1.5 million nonrespondents. 

Second follow-up mail packets were mailed in midMarch 2018 to approximately 1 million 

nonrespondents.

Nonresponse Follow-up

Operating concurrently with NPC’s mail data 

collection efforts, NASS Data Collection Centers 

targeted selected groups of census nonrespondents for 

telephone enumeration. NASS regional field offices 

targeted selected groups of census nonrespondents for 

in-person enumeration. These efforts were referred to 

as:

 Must Case Follow-up

 American Indian Producer Follow-up

 National Nonresponse Follow-up

 Not on Mail List (NML) Follow-up

Must Case Follow-up. Must cases are known large 

or unique operations, the absence of which could have 

significantly affected the accuracy of census results. 

For the 2017 Census of Agriculture, 125,697 records 

were categorized as Must cases. Each active Must 

operation was accounted for by mail receipt, phone 

interview, or personal enumeration; if an operation 

was no longer in business, its nonfarm status was 

documented. Call centers conducted CATI calling of 

nonrespondent Must cases from March 2018 through 

May 2018, after the initial and first follow-up 

mailings. Following the CATI calling, the remaining 

nonresponse Must cases were assigned to regional 

field offices for personal enumeration. Because of the 

potential importance of Must cases, they were all 

accounted for and therefore not eligible for 

nonresponse weighting adjustment.

American Indian Producer Follow-up. The 

American Indian report form (17-A300) was mailed 

to all operations in Arizona, New Mexico and Utah 

thought to have an American Indian producer. It was 

included in the initial mailout, but due to poor mail 

response, a personal enumeration data collection 

strategy was utilized with no additional mail followup. A concerted effort was made to get individual 

reports from every American Indian farm producer in 

the country. If this was not possible within a 

reservation, a single reservation-level census report 

was obtained from knowledgeable reservation 

officials. These reports covered agricultural activity 

on the entire reservation. NASS staff reviewed these 

data and removed any duplicate data reported by 

American Indian producers from that reservation who 

responded on an individual census report form. 

Additionally NASS obtained, from knowledgeable 

reservation officials, the count of American Indian 

farm producers (on the reservations) who were not 

counted through individual census report forms, but 

whose agricultural activity was included in the 

reservation-level report form.

National Nonresponse Follow-up (Excludes Must 

Records). The National Nonresponse follow-up 

activity was designed to focus nonresponse follow-up 

in a manner that would both reflect the characteristics 
A - 6 Appendix A 2017 Census of Agriculture

USDA, National Agricultural Statistics Service

of the nonresponders and increase response rates. In 

April 2018, a sample of 249,521 nonrespondents was 

selected from the remaining 864,260 nonrespondents 

using a stratified random design. The strata were 

based on State, county, size of farm, type of farm, 

producer race, and propensity to respond. Beginning 

in mid-April 2018 and continuing through July 2018, 

extensive efforts were made to collect data for the 

sampled records, including an additional CASI push, 

autodial calls, CATI, and CAPI. Records in the same 

stratum received the same set of collection methods. 

Of the 80,504 responses, 51,846 records were 

identified as being in-scope, resulting in a weighted 

farm count of 143,847 from the sample. 

Not-on-the-Mail List (NML) Follow-up. To account 

for farming operations not on the CML, NASS used 

its 2017 JAS sample from the NASS area frame, 

augmented with the ACES segments. Because the 

NASS area frame covers all land in the U.S. with the 

exception of Alaska, it includes all farms. As 

previously described, NASS conducted a record 

linkage operation between the CML records and the 

records from the 2017 JAS/ACES. Those 2017 JAS 

records that did not match records on the CML were 

designated as “Not-on-the-Mail List” (NML) records. 

These records were mailed a yellow census form so 

that it could be differentiated from the green forms 

mailed to CML records. The NML records were 

mailed at the same time as the census mailing and 

received the same follow-up procedures as the census 

mailing through the first follow-up in mid-February 

2018. Beginning in March 2018, CATI was used for 

nonresponse follow-up for NML nonrespondents.

REPORT FORM PROCESSING

Data Capture

The Census Bureau’s National Processing Center 

(NPC) in Jeffersonville, IN was contracted to process 

returned mail packets. NASS staff on site at the NPC 

provided technical guidance and monitored NPC 

processing activities. All report forms returned to the 

NPC were immediately checked in, using bar codes 

printed on the mailing label, and removed from 

follow-up report form mailings. All forms with any 

data were scanned and an image was made of each 

page of a report form. Optical Mark Recognition 

(OMR) was used to capture categorical responses and 

to identify the other answer zones in which some type 

of mark was present.

Data entry operators keyed data from the scanned 

images using OMR results that highlighted the areas 

of the report forms with respondent entries. The keyer 

evaluated the contents and captured pertinent 

responses. Ten percent of the captured data were 

keyed a second time for quality control. If differences 

existed between the first keyed value and the second, 

an adjudicator handled resolution. The decision of the 

adjudicator was used to grade the performance of the 

keyers, who were required to maintain a certain 

accuracy level.

The images and the captured data were transferred to 

NASS’s centralized network and became available to 

NASS analysts on a flow basis. The images were 

available for use in all stages of review. 

Editing Data

Captured data were processed through a computer 

formatting program that verified that records were 

valid – that the record ID number was on the list of 

census records, that the reported counties of operation 

and production were valid, and other related criteria. 

Rejected records were referred to analysts for 

correction. Accepted records were sent to a complex 

computer batch edit process. Each execution of the 

computer edit in batch mode consisted of records 

from only one State and flowed as the data were 

received from NPC, the NASS Computer-Assisted 

Self Interview (CASI), or the Computer-Assisted 

Telephone Interview (CATI) applications.

The computer edit determined whether a reporting 

operation met the qualifying criteria to be counted as 

a farm (in-scope). The edit examined each in-scope 

record for reasonableness and completeness and 

determined whether to accept the recorded value for 

each data item or take corrective action. Such 

corrective actions included removing erroneously 

reported values, replacing an unreasonable value with 

one consistent with other reported data, or providing 

a value for an item omitted by the respondent. To the 

extent possible, the computer edit determined a 

replacement value. Strategies for determining 

replacement values are discussed in the next section. 

Operations failing to meet the qualifying criteria for 

being classified as a farm were categorized as out-ofscope for the census. Records that NASS had reason 
2017 Census of Agriculture Appendix A A - 7

USDA, National Agricultural Statistics Service

to believe might have been erroneously classified as 

out-of-scope (indications of recent and/or significant 

agricultural activity reported on NASS surveys, for 

example) were referred to analysts for verification. 

The edit systematically checked reported data sectionby-section with the overall objective of achieving an 

internally consistent and complete report. NASS 

subject-matter experts had previously defined the 

criteria for acceptable data. Problems that could not 

be resolved within the edit were referred to an analyst 

for intervention. Prior to the census mail-out, NASS 

established a group of analysts in a Census Editing 

Unit in the National Operations Center in St. Louis, 

MO who examined the scanned images, consulted 

additional sources of information, and determined an 

appropriate action. Regional field office analysts also 

participated using an interactive version of the edit 

program to submit corrected data and immediately reedit the record to ensure a satisfactory solution. 

Short Form Editing

From the CML, 400,000 records were selected to 

receive a short form; this short form was derived from 

the full census report form by reducing a number of 

sections to a ‘total’ question – for example, instead of 

asking the respondent to report the acreage for each 

specific type of fruit or vegetable, the short form only 

asked for total fruit acreage or total vegetable acreage. 

In some cases, the same questions were asked on the 

general form, in which case the edit treated the short 

form responses as though they were incomplete 

general forms, as described in the previous 

paragraphs. In other cases, several items on the 

general form were collapsed – for example, total acres 

of Christmas trees and short rotation woody crops 

were asked as a single item on the short form, instead 

of separately as on the general form. In such cases, 

different approaches were taken in the edit to create a 

general form item or items from the short-form 

specific items. Any short form record that reported 

values above a certain threshold (in practice this 

threshold was 0 for almost all items) for these shortform-specific questions was ‘flagged’ by the edit; 

these records were later called back and the 

respondent asked for additional information about the 

items reported – for example, a producer reporting 10 

acres of fruit on the short form was called back and 

asked for the total, bearing, and nonbearing acres for 

each type of fruit grown, as was asked on the general 

form. If the producer was successfully contacted and 

these additional data collected, the information was 

added to the record as additional reported data, and 

the edit was ‘reset to original’ – that is, the effects of 

the previous edit were undone – and the record was 

reedited with the new additional information. A flag 

was passed to the edit so that the short form record 

was not flagged for callback in such cases. In many 

cases, of course, it was not possible to recontact the 

respondent. In such cases, a flag was passed to the edit 

system, and the record was unlocked and available for 

review.

Imputing Data

The edit determined the best value to impute for 

reported responses that were deemed unreasonable 

and for required responses that were absent. If an item 

could not be calculated directly from other current 

responses, the edit determined whether acreage, 

production, or inventory items had been reported for 

that farm on a recent NASS crop or livestock survey. 

For producers who had not changed in five years,

demographics such as race and gender were taken 

from the previous census. Administrative data from 

the Farm Service Agency were used for a few items, 

such as Conservation Reserve Program acreage. 

When deterministic edit logic and previouslyreported data sources were unable to provide a current 

value, data from a reporting farm of similar type, size, 

and location were considered. In cases where 

automated imputation was unable to provide a 

consistent report, the record was referred to an analyst 

for resolution.

Separate system processes were established to 

efficiently provide data from a similar farm to the edit 

when donor imputation was required. The farm 

characteristics used to define similarity between a 

recipient record and its donor record were determined 

dynamically by the edit logic. Euclidean distance was 

used for similarity computations, with each 

contributing similarity characteristic scaled 

appropriately. The most similar farm based on this 

criterion (the “nearest neighbor”) was identified and 

returned to the edit for use as a donor. The calculated 

distance between the centroids of the principal 

counties of production of the donor and recipient was 

always included as one of the measures of similarity.
A - 8 Appendix A 2017 Census of Agriculture

USDA, National Agricultural Statistics Service

To provide donors to the automated edit, a pool of 

successfully edited records was maintained for each 

section of the report form. These donor pools began 

with 2012 census data, reconfigured to emulate 2017 

data and then edited using 2017 logic. Data from the 

2015 Census Content Test were similarly remapped 

and edited before being added to the original donor 

pools. As 2017 records were successfully processed, 

they were added to the donor pools, which maintained 

the most recent data for each farm. Donor pools were 

updated approximately every other week, as 

determined by edit processing schedules. After 

several updates, all initial data records were dropped, 

leaving only 2017 records in the donor pools. After 

each update, donor pool records were grouped into 

strata containing farms in the same State of similar 

type and size, using a data-driven algorithm to define 

strata. Certain American Indian farms were treated as 

a separate group, effectively having their own donor 

pool.

In response to each donor request issued by the edit, a 

dedicated system process would search the 

appropriate stratum and respond with the most similar 

donor, while giving preference to more recent donors. 

In relatively rare instances where it was unable to 

provide a donor, the donor selection process issued an 

appropriate failure message to the edit. Imputation 

failures occurred for several different reasons. The 

requirement that an imputed value be positive could 

have ruled out all available donors, as could have the 

necessity for the donor record to satisfy a particular 

constraint – say, that the donor record has cattle, but 

no milk cows. In general, an imputation failure 

occurred if there were no satisfactory donors in the 

same profile as the report being edited. Records with 

imputation failures were either held until more 

records were available in the donor pool or referred to 

an analyst. In addition, when such a failure occurred 

in finding a donor for expenditure data, donor pool 

averages were provided in lieu of an individual donor, 

wherever possible. This “failover” utility was first 

introduced for the 2012 census imputation process, 

and significantly reduced the number of imputation 

failures among the expenditure and labor variables. 

During the early stages of editing, records requiring 

imputation for production (and hence yields) of field 

crops or hay, land values, or certain expenditure 

variables, were set aside or “parked.” These records 

were edited when the donor pools contained only 

2017 records, ensuring that 2017 data were used in the 

imputations for the variables.

After receiving a donor's data, the edit substituted the 

values into the edited record. In many cases, the donor 

record's data value was scaled using another data field 

specified in the edit logic. In such cases, the size of 

the auxiliary field's value in the edited record, relative 

to its value in the donor record, was used to 

appropriately scale the donor record's value for the 

field to be imputed. The imputed data were then 

validated by the same edit logic to which reported data 

were subject. Since imputation was conducted 

independently for each occurrence, reports requiring 

multiple imputations may have drawn from multiple 

donors.

Substantial changes were introduced to the Personal 

Characteristics section of the form in 2017. 

Information on an additional (fourth) producer was 

collected, and several new questions were added for 

each producer – specifically, whether or not the 

person was considered a “principal producer,” 

whether the person was a spouse of a principal 

producer, and whether the person was involved in any 

of five types of decisions with respect to the 

operation. These changes necessitated a new 

imputation process for records reporting three or more 

persons as producers. Records with one or two 

persons reported as producers had these data edited 

and imputed using the decision logic table edit and 

donor pool imputation process. Records with three or 

more persons reported as producers, and for which it 

was determined that these data were inconsistent or 

missing, had these data imputed using a fully 

conditional specification method. During the edit for 

records reporting three or more producers, the items 

needing imputation were marked, and the record was

flagged. Periodically the data for these records (both 

the items needing to be imputed and the other 

variables needed by the model) were pulled and run 

through the imputation program. The resulting 

imputed values were loaded back to the records, and 

the records were made available for review. This 

process was conducted 19 times for the CML, and 6 

times for the NML, during census production editing.

Data Analysis

The complex edit ensured the full internal consistency 

of the record. Successfully completing the edit did not 

provide insight as to whether the report was 
2017 Census of Agriculture Appendix A A - 9

USDA, National Agricultural Statistics Service

reasonable compared to other reports in the county. 

Analysts were provided an additional set of tools, in 

the form of listings and graphs, to review record-level 

data across farms. These examinations revealed 

extreme outliers, large and small, or unique data 

distribution patterns that were possibly a result of 

reporting, recording, or handling errors. Potential 

problems were investigated and, when necessary, 

corrections were made and the record interactively 

edited again.

When NASS summarizes data from the census of 

agriculture, each individual report is typically 

assigned to a single “principal” county. The principal 

county is the county in which the majority of an 

operation’s agricultural products are produced, as 

reported by the producer. For large operations that 

have significant production in multiple counties, their 

reports may be broken up into multiple source 

counties to more accurately summarize the data. 

Similarly, for large farms operating in more than one 

State, separate report forms are completed by State in 

order to assign the proper portion of the farm’s total 

agricultural production to each State in which the 

farm operates.

ACCOUNTING FOR UNDERCOVERAGE,

NONRESPONSE, AND MISCLASSIFICATION

Although much effort was expended making the CML 

as complete as possible, the CML did not include all 

U.S. farms, resulting in list undercoverage. Some 

farm producers who were on the CML did not respond 

to the census, despite numerous attempts to contact 

them. In addition, although each operation was 

classified as a farm or a nonfarm based on the 

responses to the census report form, some were 

misclassified; that is, some nonfarms were classified 

as farms and some farms were classified as nonfarms. 

NASS’s goal was to produce agricultural census totals 

for publication at the county level that were fully 

adjusted for list undercoverage, nonresponse, and 

misclassification.

In 2012 NASS used capture-recapture methodology 

to adjust for undercoverage, nonresponse, and 

misclassification. This same methodology was 

implemented for the 2017 Census of Agriculture. To 

implement capture-recapture methods, two 

independent surveys were required. The 2017 Census 

of Agriculture (based on the CML) and the 2017 JAS 

(based on the area frame) were those two surveys. 

Historically, NASS has been careful to maintain the 

independence of these two surveys.

A second assumption was that the proportion of JAS 

farms with a given set of characteristics captured by 

the census was equal to the proportion of U.S. farms 

with those same characteristics captured by the 

census.

For a farm to be identified as a farm, and thus captured 

by the census, it must be on the CML, respond to the 

census report form and, based on the census response, 

be classified as a farm. Only those nonrespondents 

included in the nonresponse sample had an 

opportunity to be captured and had a probability πS of 

being included in the sample; respondents prior to 

drawing the nonresponse sample had πS = 1. Thus, the 

capture probability πC is of interest:

πC = π(CML, Responded, Farm on Census|Farm) πS

Two types of classification error can occur. First, a 

farm can be misclassified as a nonfarm. This type of 

misclassification is accounted for in determining the 

probability of capture πC. The second type of 

classification error results when a response to the 

census is classified as a farm operation when it does 

not meet the definition of a farm. That is, some farms 

on the CML may be misclassified from their census 

report response and may be nonfarms. To account for 

the misclassification of nonfarms as farms, the 

probability of a farm on the census being classified 

correctly must be estimated; that is,

πCCFC = π(Farm | Farm on Census)

where CCFC represents Correct Census Farm 

Classification. To adjust for undercoverage, 

nonresponse, and misclassification, each CML record 

classified as a farm based on its response to the census 

report form was given a weight of the ratio of the 

estimated probability of correct classification of a 

farm on the census and the estimated probability of 

capture where the hat symbol (^) denotes an 

estimate). To estimate the number of farms with a 

given set of characteristics, the weights of CML 

records responding as farms on the census and having 

that set of characteristics were summed. This 
A - 10 Appendix A 2017 Census of Agriculture

USDA, National Agricultural Statistics Service

estimator is referred to as the capture-recapture 

estimator (CR):

where F is the set of all CML records classified as 

farms based on their responses to the census report 

form.

To estimate the capture and correct census farm 

classification probabilities, a matched dataset 

consisting of JAS records and census records was 

created. Records in the 2017 JAS sample were 

matched to the 2017 census using probabilistic record 

linkage. The CML records that matched with JAS 

tracts represent the Census Sample. 

Note: The Census Sample is a subset of the CML 

records and includes only those records matching a 

JAS tract. Both agricultural and non-agricultural 

tracts were included in the matched dataset. 

Resolving Farm Status

The farm status based on census responses to either 

the CML or NML census data collection and the JAS 

agreed in most cases; these records are referred to as 

having resolved farm status. However, in other cases, 

a record was identified as a farm (nonfarm) on the 

JAS and as a nonfarm (farm) by the census through 

either the CML or the NML. Such records are said to 

have conflicting or unresolved farm status. An 

operation identified as a farm is referred to as inscope; an operation identified as a nonfarm is referred 

to as out-of-scope. From the set of matched records, 

two groups with conflicting farm status were 

identified: 1) in-scope JAS records that were out-ofscope on the census and 2) census in-scope and JAS 

out-of-scope records. The records with conflicting 

farm status were sent to NASS regional field offices 

for review. In each case, efforts were made to 

determine whether (1) the status had changed between 

June and December when the census was conducted, 

(2) the JAS farm status was correct, (3) the census 

farm status was correct, (4) the records were 

incorrectly matched, or (5) the farm status could not 

be resolved. Not all of the records with conflicting 

farm status could be resolved. In 2017, 8.1 percent of 

the records in the Census Sample had unresolved farm 

status. 

The probability an operation is a farm was estimated 

for the records with unresolved farm status. Using the 

2017 matched dataset, a logistic model of the 

probability an operation is a farm based on the records 

with resolved farm status was developed; that is, the 

operations where the farm (or nonfarm) status agreed 

between the JAS and the census were used to develop 

a missing data model, which was then used to resolve 

farm status. The final missing data model was used to 

impute the probability that each of the agricultural 

operations with unresolved farm status is a farm. For 

the resolved farms and nonfarms, the probability of 

the operation being a farm was 1 and 0, respectively. 

Five-fold cross-validation was used to develop and to 

compare competing models. The accuracy of the 

model was thereby not overstated due to fitting and 

evaluating the model on the same set of data. To 

ensure that each of the cross-validation samples 

covered the U.S., the five cross-validation samples of 

JAS segments were drawn within State-stratum 

combinations. Characteristics of the JAS tracts were 

considered as potential covariates in the model. 

Because limited information is available for JAS 

nonfarm tracts, other covariates considered included 

county-level socio-demographic variables from the 

most recent U.S. population census, segment-level

data from the Cropland Data Layer, the county-level 

rural-urban code, state-level response rates, an 

indicator for records that are thought to be out-ofbusiness, and an indicator for records in the national 

nonresponse sample. The sample weight associated 

with each JAS tract was multiplied by the probability 

of being a farm. This adjusted weight was used in all 

subsequent modeling.

Capture Probabilities

Recall that, for a farm to be identified as a farm, and 

thus captured, by the census, it must be on the CML, 

respond to the census report form and, based on the 

census response, be classified as a farm. These 

adjustments are dependent. Further, those 

nonrespondents at the time the nonresponse sample 

was drawn had a known probability πS of being 

included in the sample; respondents before the sample 

was drawn had πS = 1. Therefore, the probability of 

capture πC may be written as
2017 Census of Agriculture Appendix A A - 11

USDA, National Agricultural Statistics Service

πC = π(CML, Responded, Farm on Census|Farm) πS

= π(CML|Farm)π(Responded|CML, Farm)π(Farm 

on Census|CML, Responded, Farm) πS

The probability of being included in the sample πS is 

known for all responding farms. The other terms in 

the probability of capturing a farm depend on the 

characteristics of the farm. Using five-fold crossvalidation, three logistic models were developed 

based on the matched dataset. The first model 

estimated the probability of a farm being on the CML. 

The second model estimated the probability that a 

farm on the CML responded to the census report form. 

The final model estimated the probability that a farm 

that was on the CML and responded to the census was 

identified as a farm based on its response. The 

probability that a farm is captured by the census of 

agriculture is then the product of the three conditional 

probabilities that a farm is on the CML, responds, and 

is identified as a farm.

Note 1: Responses were required for Must cases. 

These operations were only excluded in modeling the 

probability of a farm responding given that it was on 

the CML.

Note 2: Because Alaska is not included in the JAS and 

thus has no area frame, the Alaskan agricultural 

operations were not included in the capture-recapture 

process. No adjustments were made for 

undercoverage or misclassification. To account for 

nonresponse, the CML records were divided into 

three groups: (1) the Must records, (2) the Criteria 

Records, and (3) the remaining CML records. The 

must records received a weight of one, thereby 

receiving no adjustment for nonresponse. The 

probability of response for each of the other two 

groups was the proportion of responders within the 

group. Each record within the group was then given a 

weight equal to the reciprocal of the probability of 

response.

Misclassification

An operation is misclassified if: (1) it meets the 

definition of a farm, but is classified as a nonfarm on 

the census or (2) it does not meet the definition of a 

farm, but is classified as a farm on the census. The 

first type of misclassification is accounted for when 

modeling the probability of capture. An adjustment is 

still needed for the misclassification of nonfarms as 

farms. As with farm status and capture, the probability 

of this misclassification depends on an operation’s 

characteristics. Thus, a final logistic model was 

developed. Given that an operation was classified as 

a farm on the CML, the probability of its being a farm 

was modeled based on its characteristics. Five-fold 

cross-validation was used to ensure that the model 

was not over-fitted.

CALIBRATION

Each operation identified as being in-scope on the 

CML was given a weight equal to the probability of 

misclassification divided by the probability of 

capture. This weight accounted for undercoverage, 

nonresponse, both types of misclassification, and the 

nonresponse sample.

The record weighting processes were initially applied 

at the State level to produce adjusted estimates of farm 

numbers and land in farms for 63 different categories 

of 8 characteristics of the farm operation or the farm 

producer -- value of agricultural sales (9); age (2); 

female; race (3); Hispanic origin of principal farm 

producer; 4 sales categories for each of 10 major 

commodities (40); and farm type groups (7). The 

State-level number of farms and land in farms were 

two additional adjusted estimates, resulting in 65 

categories. To reduce the intercensal variation at the 

State level, the State targets were smoothed by 

averaging the 2017 estimates from capture-recapture 

and the published 2012 State estimates with the 

restrictions that the smoothed targets were within two 

standard errors of the capture-recapture estimates. 

The smoothed State targets were rescaled so that they 

summed to the national capture-recapture estimates.

These State estimates were general purpose in that 

they did not provide any control over expected levels 

of commodity production of the individual farm 

operation. As a result of this limitation, the procedures 

could have over-adjusted or under-adjusted for 

commodity production. To address this, a second set 

of variables, known as commodity targets, was added 

to the calibration algorithm. These targets were 

commodity totals from administrative sources or from 

NASS surveys of nonfarm populations (e.g. USDA 

Farm Service Agency program data, Agricultural 

Marketing Service market orders, livestock slaughter 

data, cotton ginning data). The introduction of these 
A - 12 Appendix A 2017 Census of Agriculture

USDA, National Agricultural Statistics Service

commodity coverage targets strengthened the overall 

adjustment procedure by ensuring that major 

commodity totals remained within reasonable bounds 

of established benchmarks. 

Each State was calibrated separately. The calibration 

algorithm addressed commodity coverage. The 

algorithm was controlled by the 65 State farm 

operation coverage targets and the State commodity 

coverage targets. Because calibration targets are 

estimates subject to uncertainty, NASS allowed some 

tolerance in the determination of the adjusted weights. 

Rather than forcing the total for each calibration 

variable computed using the adjusted weights to equal 

a specific amount, NASS allowed the estimated total 

to fall within a tolerance range. 

Tolerance ranges for the farm operation coverage 

targets were determined differently from the 

commodity targets. The tolerance range for the 65 

State farm operation coverage targets was the 

estimated smoothed State total for the variable plus or 

minus one standard error of the capture-recapture 

estimate. This choice limited the cumulative deviation 

from the estimated total for a variable when State 

totals were summed to a U.S. total. Commodity 

coverage targets with acceptable ranges were 

established based on the administrative source for 

each State. Ranges were not necessarily symmetric 

around the target value.

To ensure that all subdomains for which NASS 

publishes summed to their grand total, integer weights 

were produced by a discrete calibration algorithm. 

This eliminated the need for rounding individual cell 

values and ensured that marginal totals always added 

correctly to the grand total. If a weight was initially 

not in the interval [1,6], it was trimmed so that in was 

in that interval. That is, adjusted weights less than 1 

were set to 1, and those greater than 6 were set to 6. 

The remaining non-integer weights were then 

rounded sequentially to reduce the distance of the 

estimated totals from the targets. 

Calibration adjustments began with the computation 

of a priority index for each record. The priority index 

was the absolute value of the gradient of the relative 

error associated with increasing or decreasing a 

record’s weight by one. The record with the highest 

priority index was then selected as a candidate to 

increase or decrease its weight by one to reduce the 

cumulative distance from the targets as measured by 

the relative error. If the new value produced an 

improvement and satisfied the range restrictions, the 

weight was updated and new priorities were assigned; 

otherwise, the record with the next highest priority 

index was processed. This process was iteratively 

performed until convergence was attained. Because 

census data collection was assumed to be complete for 

very large and unique farms, their weights were 

controlled to 1 during the calibration adjustment 

process. For all other farms, the final census record 

weights were forced to be an integer number in the 

interval [1, 6]. The calibration process considered all 

targets simultaneously through the priority index. 

Although calibration was seldom able to adjust 

weights so that all State targets were met, all targets 

were brought collectively as close to the targets as 

possible. 

The proportions of selected census data items that 

were due to coverage, response, and classification 

adjustments are displayed in Tables A and C. 

DISCLOSURE REVIEW

After tabulation and review of the aggregates, a 

comprehensive disclosure review was conducted. 

NASS is obligated to withhold, under Title 7, U.S. 

Code, any total that would reveal an individual’s 

information or allow it to be closely estimated by the 

public. Farm counts are not considered sensitive and 

are not subject to disclosure controls. Cell suppression 

was used to protect the cells that were determined to 

be sensitive to a disclosure of information. 

Based on agency standards, data cells were 

determined to be sensitive to a disclosure of 

information if they failed either of two rules. The 

threshold rule failed if the data cell contained less than 

three operations. For example, if only one farmer 

produced turkeys in a county, NASS could not 

publish the county total for turkey inventory without 

disclosing that individual’s information. The 

dominance rule failed if the distribution of the data 

within the cell allowed a data user to estimate any 

respondent’s data too closely. For example, if there 

are many farmers producing turkeys in a county and 

some of them were large enough to dominate the cell 

total, NASS could not publish the county total for 

turkey inventory without risking disclosing an 

individual respondent’s data. In both of these 
2017 Census of Agriculture Appendix A A - 13

USDA, National Agricultural Statistics Service

situations, the data were suppressed and a “(D)” was 

placed in the cell in the census publication table. 

These data cells are referred to as primary 

suppressions.

Since most items were summed to marginal totals, 

primary suppressions within these summation 

relationships were protected by ensuring that there 

were additional suppressions within the linear 

relationship that provided adequate protection for the 

primary. A detailed computer routine selected 

additional data cells for suppression to ensure all 

primary suppressions were properly protected. These 

data cells are referred to as complementary 

suppressions. These cells are not themselves sensitive 

to a disclosure of information but were suppressed to 

protect other primary suppressions. A “(D)” was also 

placed in the cell of the census publication table to 

indicate a complementary suppression. A data user 

cannot determine whether a cell with a (D) represents 

a primary or a complementary suppression.

Regional field office analysts reviewed all 

complementary suppressions to ensure no cells had 

been withheld that were vital to the data users. In 

instances where complementary suppressions were 

deemed critically important to a State or county, 

analysts requested an override and a different 

complementary cell was chosen.

CENSUS QUALITY

The purpose of the census of agriculture is to account 

for “any place from which $1,000 or more of 

agricultural products were produced and sold, or 

normally would have been sold, during the census 

year.” To accomplish this, NASS develops a CML 

that contains identifying information for operations 

that have an indication of meeting the census 

definition, develops procedures to collect agricultural 

information from those records, establishes criteria 

for analyst review of the data, creates computer

routines to correct or complete the requested 

information, and provides census estimates of the 

characteristics of farms and farm producers with 

associated measures of uncertainty.

It is not likely that either the CML includes all 

operations that meet the definition of a farm or that all 

those that do meet the definition of a farm respond to 

the census inquiry. The goal is to publish data with a 

high level of quality. The quality of a census may be 

measured in many ways. One of the first indicators 

used is a measure of the response to the census data 

collection as it has generally been thought that a high 

response rate indicates more complete coverage of the 

population of interest. This is a valid assumption if the 

enumeration list, the CML here, has complete 

coverage of the population of interest. In the case of 

the census of agriculture, the definition requiring 

advance knowledge of sales makes achieving a high 

level of coverage difficult. To ensure that the census 

of agriculture is as complete as possible, records are 

included that might not meet the census definition of 

a farm – in fact, almost 50 percent more records than 

the anticipated number of qualifying farm operations 

were included in the 2017 CML. A second indicator 

of quality then is the coverage of the farm population 

by the CML. Other indicators of quality relate to the 

accuracy and completeness of the data, and the 

validity of the procedures used in processing the data.

In some cases, NASS was able to produce measures 

of quality – such as the response rate to the data 

collection, the coverage of the census mail list, and 

the variability of the final adjusted estimates. In other 

cases, measures were not produced but descriptions of 

procedures that NASS used to reduce errors from the 

procedures were subsequently provided.

Census Response Rate

The response rate is one indicator of the quality of a 

data collection. It is generally assumed that if a 

response rate is close to a full participation level of 

100 percent, the potential for nonresponse bias is 

small, although this has been questioned in the 

literature. The response rate for the 2017 Census of 

Agriculture CML was 71.8 percent, as compared with 

the 2012 Census of Agriculture’s response rate of 74.6 

percent and 78.2 percent for the 2007 Census of 

Agriculture.

The 2017 Census of Agriculture’s response rate used 

the fourth response rate formula (RR4) from the 

American Association of Public Opinion Research’s 

Response Rate Standard Definitions manual:
A - 14 Appendix A 2017 Census of Agriculture

USDA, National Agricultural Statistics Service

where

Cadj = number of fully and partially completed 

records, excluding replicated records

R = number of explicit refusals

NC = number of non-contacted operations known to 

be eligible

O = number of other types of nonrespondents

Replicated = number of replicated records

U = number of operations of unknown eligibility

e(U) = estimated number of operations of unknown 

eligibility assumed to be eligible

Records were classified into the above variables 

based on the combination of their active status (AS) 

codes, in-scope status, and replication status. Active 

status refers to the eligibility status of records for 

selection on the CML. All replicated records were 

considered to be a form of nonresponse and were 

classified into other nonrespondents; in-scope status 

was considered immaterial.

Certain active status classifications indicated records 

of unknown agricultural status. These classifications 

included records to be removed from the CML but had 

data from outside sources indicating agricultural 

activity, new records from outside data sources, 

nonrespondents and refusals to the NACS, records for 

regional office handling only, and records with Farm 

Service Agency or Conservation Reserve Program 

data on operations that are not owned by the principal 

producer. These records were stratified (grouped) 

based on their probabilities of being in-scope had they 

responded. The estimated number of in-scope 

nonrespondents was calculated for the hth stratum 

(group) by the following formula:

where

e(Uh) = estimated number of operations of unknown 

eligibility assumed to be eligible in the hth group

Cin-scope,h = the number of completed and in-scope 

census records in the hth group

Ch = the number of completed census records in the 

hth group

Uh = number of operations of unknown eligibility in 

the hth group

Census Coverage

As a side-product of the statistical adjustment used to 

account for undercoverage, nonresponse of farms on 

the CML, and misclassification of responses to the 

census, the proportion of the adjustments due to each 

of those factors can be derived. The percentages of 

final census estimates due to adjustments for 

undercoverage, nonresponse, and misclassification as 

well as the total percent adjustment for selected items 

are displayed in Tables A and C. 

MEASURED ERRORS IN THE CENSUS 

PROCESS

Although the census of agriculture does not inherently 

rely on a sample, NASS used a national nonresponse 

sample as part of its follow-up efforts in 2017. In 

addition to the uncertainty introduced by the 

nonresponse sample, NASS uses statistical 

procedures in compiling the CML, in its data 

collection procedures, in data editing and processing, 

and in compiling the final data. Additionally, it uses 

statistical procedures to both measure errors in the 

various processes and in making adjustments for 

those errors in the final data. One example is the 

statistical process used to account for undercoverage, 

nonresponse of farms on the CML, and 

misclassification of responses to the census. The basis 

of the undercoverage adjustment is the capturerecapture procedure that uses the area sample 

enumeration from the JAS. The largest contributors to 

error in the census estimates are due to the 

adjustments for nonresponse, undercoverage, 

misclassification, calibration, and integerization.

Variability in Census Estimates due to 

Statistical Adjustment

In conducting the 2017 Census of Agriculture, efforts 

were initiated to measure error associated with the 

adjustments for farm operations that were not on the 

CML, for farm operations that were on the CML but 

did not respond to the census report form, and for 

farms and nonfarms that were misclassified as 

nonfarms and farms, respectively, for calibration. 

These error measurements were developed from the 

standard error of the estimates at the national, State, 

and county levels and were expressed as coefficients 

of variation (CVs) at the national and State levels and 
2017 Census of Agriculture Appendix A A - 15

USDA, National Agricultural Statistics Service

as generalized coefficients of variation (GCVs) at the 

county levels.

The standard error of an estimate is an estimate of the 

standard deviation of the sampling distribution of the 

estimator. Because Alaska was modeled separately 

from the other States, the variances of a national-level 

data item for this State was computed separately and 

added to the variance of that data item for the rest of 

the U.S. The standard error was then the square root 

of the total variance. In each case, standard errors 

were computed using an approach based on a 

combination of group jackknife and bootstrap 

methodologies. To conduct the jackknifing, k = 10 

mutually exclusive and exhaustive groups of JAS 

segments were formed. The groups were selected 

using a stratified random design so that each group 

reflected the survey design, including State and 

agricultural strata within a State. The weight of record 

i in jackknife group j is CRi

(j )for j = 1, 2, ..., k. Based 

on these weights, a group jackknife estimator to 

estimate the variance would account for the 

uncertainty associated with modeling the capturerecapture probabilities. To account for the additional 

uncertainty due to calibration, the weights within each 

jackknife group were transformed through bootstrap 

simulation; these transformed weights are called 

calibration-adjusted-jackknife weights. The full 

dataset, which is composed of the records of all 

responding farms on the CML, is calibrated as 

described in the Calibration section, and the final 

calibration-adjusted weight of record i is denoted by

ŵi. For each record i in jackknife group k, the 

calibration-adjusted-jackknife weights of that record 

can be approximated as wi

(j)=ai(j)CRi(j) where ai(j) ~ 

N(1,( ŵi – 1) / ŵi). The bootstrap process simulated the 

value of the adjustment ai

(j) for each record on the 

CML to obtain the calibration-adjusted-jackknife 

weights. For a given data item, such as the number of 

farms, the estimate T

(j) was computed at the specified 

geographical level, such as nation, State, or county, 

using the (k – 1) groups remaining after deleting the 

calibration-adjusted jackknife group j. Estimates of 

the variance and standard error associated with the 

estimator Ti are then, respectively, 

Increasing k improves the estimate of the variance but, 

as k increases, the observations become too sparse to 

reflect the survey design and to provide countrywide 

coverage. Ten (10) calibration-adjusted jackknife 

groups were used to provide standard errors for 2017 

State and national estimates. For the estimate of the 

number of farms with a given set of characteristics, 

only the CML records with those characteristics were 

used to obtain the overall estimate as well as the 

estimates from each calibration-adjusted jackknife 

group.

Note that the calibrated jackknife groups were only 

constructed once, and different subsets of the records 

were used to compute estimates and standard errors 

for the data items.

The CV is a measure of the relative amount of error 

associated with the sample estimate:

where SE(Ti) is the standard error of the capturerecapture estimate for data item i. This relative 

measure allows the reliability of a range of estimates 

to be compared. For example, the standard error is 

often larger for large population estimates than for 

small population estimates, but the large population 

estimates may have a smaller CV, indicating a more 

reliable estimate. For county-level estimates, a 

generalized coefficient of variation (GCV) was 

determined for each estimate within a State. A 

generalized variance function relates a function of the 

variance of an estimator to a function of the estimator. 

Within a State, the standard error of an estimate for a 

data item was often found to be linearly related to the 

estimate of that item with an intercept of zero. Based 

on this modeled relationship, the GCV is the slope of 

the line relating the standard error to the estimate, 

multiplied times 100 to represent the GCV as a 

percentage.

The standard error is the product of the CV (or GCV 

for county estimates) and the estimate divided by 100. 

As an example, if the GCV for a State is 25 percent 

and a county’s estimate is 4, then the standard error is 

25(4)/100 = 1. The standard error of an estimated data 

item from the census provides a measure of the error 

variation in the value of that estimated data item based 

on the possible outcomes of the census collection, 

 

 

 

2

2 2

1 1

1

 ;

k k l

j i

i i i i

j l

k T

T SE T

k k

 

 

  

            

CV 100% i

i

i

SE T

T


A - 16 Appendix A 2017 Census of Agriculture

USDA, National Agricultural Statistics Service

including variants as to who was on the CML, who 

returned a census form, who was misclassified either 

as a farm or as a nonfarm, and the uncertainty 

associated with calibration and integerization. With 

95 percent confidence, an estimate is within two 

standard errors of the true value being estimated. For 

this example, with 95 percent confidence, the estimate 

of 4 is within 2(1) = 2 of the true county value.

Table B presents the fully adjusted estimates with the 

coefficient of variation for selected items. 

NONMEASURED ERRORS IN THE CENSUS 

PROCESS

As noted in the previous section, sampling errors can 

be introduced from the coverage, nonresponse and 

misclassification adjustment procedures. This error is 

measureable. However, nonsampling errors are 

imbedded in the census process that cannot be directly 

measured as part of the design of the census but must 

be contained to ensure an accurate count. Extensive 

efforts were made to compile a complete and accurate 

mail list for the census, to elicit response to the 

census, to design an understandable report form with 

clear instructions, to minimize processing errors 

through the use of quality control measures, to reduce 

matching error associated with the capture-recapture 

estimation process, and to minimize error associated 

with identification of a respondent as a farm operation 

(referred to as classification error). The weight 

adjustment and tabulation processes recognize the 

presence of nonsampling errors; however, it is 

assumed that these errors are small and that, in total, 

the net effect is zero. In other words, the positive 

errors cancel the negative errors.

Respondent and Enumerator Error

Incorrect or incomplete responses to the census report 

form or to the questions posed by an enumerator can 

introduce error into the census data. Steps were taken 

in the design and execution of the census of 

agriculture to reduce errors from respondent 

reporting. Poor instructions and ambiguous 

definitions lead to misreporting. Respondents may not 

remember accurately, may estimate responses, or may 

record an item in the wrong cell. To reduce reporting 

and recording errors, the report form was tested prior 

to the census using industry accepted cognitive testing 

procedures. Detailed instructions for completing the 

report form were provided to each respondent. 

Questions were phrased as clearly as possible based 

on previous tests of the report form. Computerassisted telephone interviewing software included 

immediate integrity checks of recorded responses so 

suspect data could be verified or corrected. In 

addition, each respondent’s answers were checked for 

completeness and consistency by the complex edit 

and imputation system.

Processing Error

Processing of each census report form was another 

potential source of nonsampling error. All mail 

returns that included multiple reports, respondent 

remarks, or that were marked out of business and 

report forms with no reported data were sent to an 

analyst for verification and appropriate action. 

Integrity checks were performed by the imaging 

system and data transfer functions. Standard quality 

control procedures were in place that required that 

randomly selected batches of data keyed from image 

be re-entered by a different operator to verify the work 

and evaluate key entry operators. All systems and 

programs were thoroughly tested before going on-line 

and were monitored throughout the processing period.

Developing accurate processing methods is 

complicated by the complex structure of agriculture. 

Among the complexities are the many places to be 

included, the variety of arrangements under which 

farms are operated, the continuing changes in the 

relationship of producers to the farm operated, the 

expiration of leases and the initiation or renewal of 

leases, the problem of obtaining a complete list of 

agriculture operations, the difficulty of contacting and 

identifying some types of contractor/contractee 

relationships, the producer’s absence from the farm 

during the data collection period, and the producer’s 

opinion that part or all of the operation does not 

qualify and should not be included in the census. 

During data collection and processing of the census, 

all operations underwent a number of quality control 

checks to ensure results were as accurate as possible.

Item Nonresponse

All item nonresponse actions provide another 

opportunity to introduce measurement errors. 

Regardless of whether it was previously reported data, 

administrative data, the nearest neighbor algorithm, 
2017 Census of Agriculture Appendix A A - 17

USDA, National Agricultural Statistics Service

the fully conditional specification method, or 

manually imputed by an analyst, some risk exists that 

the imputed value does not equal the actual value. 

Previously reported and administrative data were used 

only when they related to the census reference period. 

A new nearest neighbor was randomly selected for 

each incident to eliminate the chance of a consistent 

bias.

Record Matching Error

The process of building and expanding the CML 

involves finding new list sources and checking for 

names not on the list. An automated processing 

system compared each new name to the existing CML 

names and “linked” like records for the purpose of 

preventing duplication. New names with strong links 

to a CML name were discarded and those with no 

links were added as potential farms. Names with weak 

links, possible matches, were reviewed by staff to 

determine whether the new name should be added. 

Despite this thorough review, some new names may 

have been erroneously added or deleted. Additions 

could contribute to duplication (overcoverage) 

whereas deletions could contribute to undercoverage. 

As a result, some names received more than one 

report form, and some farm producers did not receive 

a report form. Respondents were instructed to 

complete one form and return all forms so the 

duplication could be removed.

Another chance for error came when comparing June 

Area Survey tract producer names to the CML. Area 

producers whose names were not found on the CML 

were part of the measure of list incompleteness, or 

NML. Mistakes in determining overlap status resulted 

in overcounts (including a tract whose producer was 

on the CML) or undercounts (excluding a tract whose 

producer was not on the CML). All tracts determined 

to not be on the list were triple checked to eliminate, 

or at least minimize, any error. NML tract producers 

were mailed a report form printed in a different color. 

In order to attempt to identify duplication, all 

respondents who received multiple report forms were 

instructed to complete the CML version and return all 

forms so duplication could be removed.

Records in the 2017 JAS were matched to the 2017 

census using probabilistic record linkage. The records 

of operations with differing farm status were sent out 

to be reviewed by NASS regional field offices. If farm 

status could not be resolved, the probability of an 

operation being a farm was imputed using a missing 

data model. The uncertainty associated with this 

estimate, with the exception of model uncertainty, 

was accounted for, but errors not found through this 

process were not.
A - 18 Appendix A 2017 Census of Agriculture

USDA, National Agricultural Statistics Service

Table A. Summary of U.S. Coverage, Nonresponse, and Misclassification Adjustments: 2017

[For meaning of abbreviations and symbols, see introductory text.]

Item Total Standard

error

Adjustment

as percent

of total

Percent of total

adjustment

from coverage

Percent of total

adjustment from

nonresponse

Percent of total

adjustment from

misclassification

Farms .......................................................................................... number 

Land in farms ..................................................................................acres 

 

Farms by size: 

 1 to 9 acres .................................................................................farms 

acres 

 10 to 49 acres .............................................................................farms 

acres 

 50 to 69 acres .............................................................................farms 

acres 

 70 to 99 acres .............................................................................farms 

acres 

 100 to 139 acres .........................................................................farms 

acres 

 140 to 179 acres .........................................................................farms 

acres 

 180 to 219 acres .........................................................................farms 

acres 

 220 to 259 acres .........................................................................farms 

acres 

 260 to 499 acres .........................................................................farms 

acres 

 500 to 999 acres .........................................................................farms 

acres 

 1,000 to 1,999 acres ...................................................................farms 

acres 

 2,000 acres or more ....................................................................farms 

acres 

 

Irrigated land use: 

 Harvested cropland .....................................................................farms 

acres 

 Pastureland and other land .........................................................farms 

acres 

 

Market value of agricultural products 

 sold (see text) ..............................................................................$1,000 

 

Farms by value of sales: 

 Less than $1,000 (see text) .........................................................farms 

$1,000 

 $1,000 to $2,499 .........................................................................farms 

$1,000 

 $2,500 to $4,999 .........................................................................farms 

$1,000 

 $5,000 to $9,999 .........................................................................farms 

$1,000 

 $10,000 to $19,999 .....................................................................farms 

$1,000 

 $20,000 to $24,999 .....................................................................farms 

$1,000 

 $25,000 to $39,999 .....................................................................farms 

$1,000 

 $40,000 to $49,999 .....................................................................farms 

$1,000 

 $50,000 to $99,999 .....................................................................farms 

$1,000 

 $100,000 to $249,999 .................................................................farms 

$1,000 

 $250,000 to $499,999 .................................................................farms 

$1,000 

 $500,000 to $999,999 .................................................................farms 

$1,000 

 $1,000,000 or more .....................................................................farms 

$1,000 

 

Legal status for tax purposes (see text): 

 Family or individual .....................................................................farms 

acres 

 Partnership ..................................................................................farms 

acres 

 Corporation: 

 Family held ..............................................................................farms 

acres 

 Other than family held .............................................................farms 

acres 

 Other - estate or trust, prison farm, grazing association, 

 American Indian Reservation, etc .............................................farms 

acres 

 

Tenure: 

 Full owners ..................................................................................farms 

acres 

 Part owners .................................................................................farms 

acres 

 Tenants .......................................................................................farms 

acres 

 

All principal producer characteristics by 1- 

 Sex of operator: 

 Male ........................................................................................farms 

acres 

 Female ....................................................................................farms 

acres 

 

 Primary occupation: 

 Farming ...................................................................................farms 

 Other .......................................................................................farms 

2,042,220

900,217,576

273,325

1,302,208

583,001

14,787,940

135,126

7,845,508

163,251

13,414,191

149,478

17,343,842

116,908

18,399,918

74,086

14,645,228

57,096

13,586,644

183,835

65,775,717

133,321

92,872,530

87,666

120,680,141

85,127

519,563,709

255,348

53,959,077

64,450

4,054,830

388,522,695

603,752

93,210

187,949

310,520

185,341

662,980

208,074

1,477,595

174,780

2,468,212

53,438

1,181,954

100,490

3,162,749

43,623

1,937,293

119,434

8,477,635

130,932

21,171,316

87,839

31,318,548

69,703

49,338,998

76,865

266,921,684

1,751,126

541,071,476

130,173

158,051,459

104,155

126,671,963

12,685

12,889,821

44,081

61,532,857

1,408,961

310,218,983

493,137

503,138,279

140,122

86,860,314

1,787,998

847,232,627

766,474

238,157,861

1,207,375

1,533,078

43,278

15,031,334

23,216

119,480

27,053

728,067

2,902

169,522

1,480

117,725

2,564

312,035

4,263

669,574

1,276

255,063

1,560

372,774

3,483

1,244,202

1,651

1,164,377

2,592

3,602,322

2,002

16,173,465

7,306

1,097,813

3,314

170,744

2,923,858

26,259

5,223

8,512

13,915

4,777

17,382

5,255

36,707

4,230

57,415

864

18,406

2,235

65,497

645

29,399

2,473

170,770

1,810

275,035

1,376

505,169

1,012

761,022

922

2,573,412

39,037

6,917,125

3,658

3,722,094

1,350

6,438,831

626

1,116,353

2,263

3,091,251

33,785

6,470,335

7,102

8,927,625

6,570

1,990,706

39,842

14,008,443

21,918

6,484,070

18,039

55,236

37.6

22.7

57.2

57.8

42.4

41.3

34.6

34.6

33.3

33.2

32.1

32.1

31.5

31.5

28.2

28.2

27.9

27.9

29.3

29.4

30.1

30.3

30.2

30.2

24.6

16.8

35.2

22.5

44.9

24.8

18.1

51.2

60.6

42.2

42.1

38.5

38.3

37.1

36.9

25.7

25.7

26.4

26.3

26.3

26.3

27.5

27.5

28.0

28.1

27.6

27.9

30.9

31.1

30.9

31.1

20.7

12.6

38.5

27.3

31.6

17.7

31.3

17.1

34.2

11.8

33.7

8.9

39.3

21.9

31.5

22.8

41.5

24.8

36.2

22.4

43.0

25.1

34.1

41.3

15.1

4.4

25.9

25.1

19.1

18.0

14.3

14.2

12.3

12.2

10.9

10.9

9.7

9.7

9.9

9.9

9.5

9.5

8.4

8.3

8.5

8.3

4.2

4.2

2.8

1.9

13.6

2.7

19.6

6.0

3.4

22.1

26.5

20.3

20.2

18.4

18.3

17.0

16.8

9.3

9.3

9.3

9.3

7.4

7.4

7.8

7.8

7.3

7.2

3.8

3.7

3.2

3.1

2.2

2.3

2.9

3.0

15.8

6.1

9.4

2.4

10.2

2.1

12.3

1.7

13.6

2.0

16.9

5.9

8.9

2.9

14.7

5.5

14.1

4.0

17.0

5.2

11.5

16.2

13.9

11.8

17.6

17.0

13.8

13.2

13.4

13.3

13.1

13.1

12.3

12.2

11.5

11.5

13.5

13.6

13.4

13.4

14.9

15.0

17.6

17.9

18.1

18.0

18.2

8.9

15.1

16.0

15.0

12.5

11.1

15.1

18.1

14.0

14.0

12.8

12.8

12.9

12.9

11.1

11.1

11.6

11.6

13.6

13.6

14.2

14.2

15.2

15.4

17.9

18.3

22.9

23.3

25.9

26.0

14.7

6.5

13.9

15.0

14.9

9.3

14.1

8.0

14.3

4.4

11.4

2.5

12.8

8.2

16.8

14.0

20.2

15.6

14.2

12.1

15.0

12.8

14.4

15.5

8.6

6.5

13.8

15.7

9.5

10.1

7.0

7.0

7.9

7.9

8.9

9.0

10.2

10.2

4.8

4.8

5.0

5.0

6.0

6.1

4.1

4.1

7.8

8.0

3.5

6.0

6.6

3.8

10.2

6.3

3.6

14.0

16.1

7.8

7.9

7.2

7.2

7.2

7.2

5.3

5.3

5.4

5.4

5.3

5.3

5.5

5.5

5.5

5.5

5.9

5.9

4.8

4.8

2.8

2.8

3.2

3.2

8.8

6.2

7.3

6.0

7.0

7.0

7.5

5.7

8.7

4.4

9.6

7.8

5.9

5.9

6.7

3.6

7.9

6.3

10.9

7.2

8.1

9.7

See footnote(s) at end of table. --continued
2017 Census of Agriculture Appendix A A - 19

USDA, National Agricultural Statistics Service

Table A. Summary of U.S. Coverage, Nonresponse, and Misclassification Adjustments: 2017 (continued)

[For meaning of abbreviations and symbols, see introductory text.]

Item Total Standard

error

Adjustment

as percent

of total

Percent of total

adjustment

from coverage

Percent of total

adjustment from

nonresponse

Percent of total

adjustment from

misclassification

All principal producer characteristics by 1- - Con. 

 

 Hispanic, Latino, or 

 Spanish origin (see text) ........................................................... farms 

acres 

 

 Race: 

 American Indian or 

 Alaska Native ........................................................................ farms 

acres 

 Asian ....................................................................................... farms 

acres 

 Black or African American ....................................................... farms 

acres 

 Native Hawaiian or 

 Other Pacific Islander ........................................................... farms 

acres 

 White ...................................................................................... farms 

acres 

 More than one race reported .................................................. farms 

acres 

 

 Military service (see text): 

 Never served ................................................................... producers 

 Served ............................................................................. producers 

 

All producers by age group 1: 

 Under 25 years ........................................................................... farms 

 25 to 34 years ............................................................................. farms 

 35 to 44 years ............................................................................. farms 

 45 to 54 years ............................................................................. farms 

 55 to 64 years ............................................................................. farms 

 65 to 74 years ............................................................................. farms 

 75 years and over ....................................................................... farms 

 

Net cash farm income of operations (see text): 

 Farms with gains of 2- 

 Less than $1,000 .................................................................... farms 

$1,000 

 $1,000 to $4,999 ..................................................................... farms 

$1,000 

 $5,000 to $9,999 ..................................................................... farms 

$1,000 

 $10,000 to $24,999 ................................................................. farms 

$1,000 

 $25,000 to $49,999 ................................................................. farms 

$1,000 

 $50,000 or more ..................................................................... farms 

$1,000 

 

 Farms with losses of - 

 Less than $1,000 .................................................................... farms 

$1,000 

 $1,000 to $4,999 ..................................................................... farms 

$1,000 

 $5,000 to $9,999 ..................................................................... farms 

$1,000 

 $10,000 to $24,999 ................................................................. farms 

$1,000 

 $25,000 to $49,999 ................................................................. farms 

$1,000 

 $50,000 or more ..................................................................... farms 

$1,000 

 

Livestock and poultry: 

 Cattle and calves inventory ......................................................... farms 

number 

 Beef cows inventory ................................................................ farms 

number 

 Milk cows inventory ................................................................. farms 

number 

 Hog and pigs inventory ............................................................... farms 

number 

 Layers inventory ........................................................................ farms 

number 

 Broilers sold ................................................................................ farms 

number 

 Aquaculture sold ......................................................................... farms 

$1,000 

 

Selected crops harvested: 

 Corn for grain .............................................................................. farms 

acres 

 Durum wheat for grain ................................................................ farms 

acres 

 Other spring wheat for grain (see text) ....................................... farms 

acres 

 Winter wheat for grain ................................................................. farms 

acres 

 Sorghum for grain ....................................................................... farms 

acres 

 Soybeans for beans .................................................................... farms 

acres 

 Rice ............................................................................................ farms 

acres 

 Cotton ......................................................................................... farms 

acres 

 Peanuts ...................................................................................... farms 

acres 

77,416

26,041,600

39,632

51,095,994

13,904

1,831,229

32,052

3,862,936

2,092

426,068

1,955,737

843,497,615

19,773

6,712,435

2,402,342

338,111

50,943

234,496

390,345

614,654

955,354

757,936

396,106

66,633

31,436

156,683

431,683

103,942

756,426

153,619

2,525,811

114,269

4,097,569

296,183

104,245,583

89,302

45,846

342,608

988,554

256,919

1,854,855

272,079

4,266,566

104,865

3,629,228

85,118

13,380,008

882,692

93,648,041

729,046

31,722,039

54,599

9,539,631

66,439

72,381,007

232,500

368,241,393

32,751

8,889,759,283

5,350

1,778,587

304,801

84,738,562

3,093

2,206,169

20,076

10,419,033

86,596

26,186,417

15,339

5,070,159

303,191

90,149,480

4,637

2,395,054

16,149

11,401,965

6,379

1,786,767

7,488

1,211,639

4,690

1,971,823

1,191

141,253

2,720

288,775

434

228,386

40,615

14,192,177

1,469

319,035

63,141

9,391

8,438

27,511

18,472

27,200

12,836

13,931

7,027

1,074

699

1,875

5,726

2,032

15,498

2,497

39,809

2,015

73,458

3,414

1,009,852

3,454

1,742

13,270

41,411

9,567

71,776

10,940

188,227

3,872

133,023

2,038

254,975

19,877

1,983,371

14,946

809,066

1,722

161,118

3,424

1,322,671

10,221

10,596,560

1,582

248,694,312

201

88,052

4,815

1,097,857

161

117,367

517

511,562

1,103

210,542

345

155,826

3,615

1,746,145

466

638,071

610

316,506

400

134,399

55.9

26.7

52.3

14.6

47.4

27.8

59.9

52.5

47.9

39.9

36.9

23.0

47.7

24.5

38.4

36.1

49.4

50.5

44.0

40.4

35.9

34.4

31.4

35.7

34.5

30.7

30.3

27.1

27.0

26.1

26.1

26.5

26.5

26.9

20.0

42.8

43.4

46.6

47.0

47.3

47.2

45.5

45.3

42.1

42.0

35.7

30.2

36.1

22.8

34.4

24.7

32.8

11.3

42.7

24.5

50.0

1.4

41.8

27.2

28.4

6.1

27.0

24.2

23.2

19.7

27.9

25.5

25.4

21.9

27.0

25.5

27.1

25.3

27.8

19.9

28.0

25.6

33.2

28.1

23.6

7.5

17.2

3.8

14.7

6.0

12.9

7.5

15.3

7.8

15.1

4.4

17.9

4.4

14.2

14.5

13.2

16.8

16.8

13.9

15.0

14.3

12.3

16.6

16.0

13.7

13.4

10.6

10.5

8.4

8.3

6.9

6.8

4.0

3.3

19.3

19.4

20.9

21.0

20.7

20.6

19.1

18.8

16.5

16.3

11.7

8.6

13.5

4.0

12.6

4.2

9.3

2.3

17.2

7.1

21.4

0.5

16.8

7.9

14.1

2.8

5.4

2.3

2.9

1.9

3.8

2.3

5.0

2.7

4.7

2.7

5.6

2.5

2.4

1.1

4.0

2.4

4.8

2.4

21.5

11.1

22.4

6.5

21.6

14.9

31.2

32.6

20.5

23.4

13.4

12.0

19.1

12.8

15.3

11.9

24.3

22.1

20.4

17.7

12.8

9.6

9.2

11.3

11.0

10.3

10.3

10.4

10.5

11.8

11.9

13.7

13.9

18.2

12.9

13.2

13.5

14.5

14.8

15.6

15.6

15.5

15.6

15.7

15.7

16.4

15.3

16.3

13.7

15.6

15.1

20.5

7.7

17.3

8.8

18.0

0.5

17.3

13.0

9.3

1.5

16.9

18.8

17.0

14.7

20.3

19.4

15.9

15.3

17.9

19.1

16.8

19.5

21.3

15.2

20.6

20.4

23.5

22.2

10.7

8.1

12.7

4.3

11.1

6.9

15.7

12.4

12.1

8.7

8.4

6.6

10.8

7.3

8.9

9.7

11.9

11.6

6.8

8.8

8.0

10.5

10.0

7.8

7.5

6.7

6.6

6.0

6.0

5.9

5.9

5.8

5.8

4.7

3.8

10.3

10.5

11.2

11.2

11.0

11.0

11.0

11.0

10.0

10.0

7.6

6.3

6.3

5.1

6.2

5.4

3.0

1.3

8.2

8.5

10.5

0.4

7.8

6.3

4.9

1.7

4.7

3.2

3.3

3.1

3.8

3.8

4.5

3.9

4.4

3.8

4.7

3.3

4.1

3.7

3.4

2.9

5.0

3.5

See footnote(s) at end of table. --continued
A - 20 Appendix A 2017 Census of Agriculture

USDA, National Agricultural Statistics Service

Table A. Summary of U.S. Coverage, Nonresponse, and Misclassification Adjustments: 2017 (continued)

[For meaning of abbreviations and symbols, see introductory text.]

Item Total Standard

error

Adjustment

as percent

of total

Percent of total

adjustment

from coverage

Percent of total

adjustment from

nonresponse

Percent of total

adjustment from

misclassification

Selected crops harvested: - Con. 

 

 Barley ..........................................................................................farms 

acres 

 Oats ............................................................................................farms 

acres 

 

 Forage - land used for all hay and all 

 haylage, grass silage, and 

 greenchop (see text) .................................................................farms 

acres 

 Land in vegetables (see text) ......................................................farms 

acres 

 Potatoes ..................................................................................farms 

acres 

 Tomatoes in the open .............................................................farms 

acres 

 Sweet corn ..............................................................................farms 

acres 

 Lettuce ....................................................................................farms 

acres 

 Land in orchards (see text) .........................................................farms 

acres 

 Apples .....................................................................................farms 

acres 

 Grapes ....................................................................................farms 

acres 

 Oranges ..................................................................................farms 

acres 

 Almonds ..................................................................................farms 

acres 

 Land in berries ............................................................................farms 

acres 

11,188

2,206,808

19,842

814,140

799,627

56,858,622

74,276

3,965,622

16,554

1,133,128

28,673

335,348

20,784

496,096

10,869

342,965

111,955

5,665,600

26,408

381,718

28,387

1,136,155

7,973

602,830

7,954

1,266,160

33,919

302,199

287

111,622

450

23,504

15,837

729,705

4,298

102,015

1,099

36,604

1,806

21,300

1,179

16,260

949

7,049

3,892

210,257

1,296

18,529

911

69,903

267

29,323

348

35,893

1,472

6,508

27.0

22.1

30.9

28.3

34.5

28.2

37.0

10.6

33.7

6.6

37.2

6.2

32.5

13.2

39.3

6.5

36.0

20.1

35.6

14.2

34.3

22.5

36.2

11.3

33.2

20.7

36.4

13.3

4.7

2.1

7.6

4.7

12.9

6.2

14.6

1.8

13.6

1.1

15.2

1.3

11.9

2.3

16.6

2.3

17.3

4.5

17.0

4.2

16.9

4.0

16.7

2.4

10.9

3.9

16.9

4.6

18.0

16.7

17.5

19.0

12.9

16.2

17.2

6.9

15.5

4.4

17.0

3.3

16.3

8.1

17.4

2.0

13.1

11.9

12.9

7.2

12.0

15.0

14.0

6.2

17.4

12.7

14.0

5.9

4.2

3.4

5.8

4.7

8.7

5.9

5.2

1.9

4.6

1.2

5.0

1.6

4.3

2.8

5.2

2.1

5.5

3.8

5.7

2.7

5.3

3.5

5.5

2.7

5.0

4.1

5.6

2.7

1

 Data were collected for a maximum of four producers per farm.

2

 Farms with total production expenses equal to market value of agricultural products sold, government payments, and farm-related income are included as farms with gains of less than $1,000.
2017 Census of Agriculture Appendix A A - 21

USDA, National Agricultural Statistics Service

Table B. Reliability Estimates of U.S. Totals: 2017

[For meaning of abbreviations and symbols, see introductory text.]

Item Total

Coefficient

of variation

(percent)

Item Total

Coefficient

of variation

(percent)

Farms ................................................................................. number 

Land in farms ..........................................................................acres 

 

Farms by size: 

 1 to 9 acres .........................................................................farms 

acres 

 10 to 49 acres .....................................................................farms 

acres 

 50 to 69 acres .....................................................................farms 

acres 

 70 to 99 acres .....................................................................farms 

acres 

 100 to 139 acres .................................................................farms 

acres 

 140 to 179 acres .................................................................farms 

acres 

 180 to 219 acres .................................................................farms 

acres 

 220 to 259 acres .................................................................farms 

acres 

 260 to 499 acres .................................................................farms 

acres 

 500 to 999 acres .................................................................farms 

acres 

 1,000 to 1,999 acres ...........................................................farms 

acres 

 2,000 acres or more ............................................................farms 

acres 

 

Irrigated land use: 

 Harvested cropland .............................................................farms 

acres 

 Pastureland and other land .................................................farms 

acres 

 

Market value of agricultural products 

 sold (see text) .....................................................................$1,000 

 

Farms by value of sales: 

 Less than $1,000 (see text) ................................................farms 

$1,000 

 $1,000 to $2,499 .................................................................farms 

$1,000 

 $2,500 to $4,999 .................................................................farms 

$1,000 

 $5,000 to $9,999 .................................................................farms 

$1,000 

 $10,000 to $19,999 .............................................................farms 

$1,000 

 $20,000 to $24,999 .............................................................farms 

$1,000 

 $25,000 to $39,999 .............................................................farms 

$1,000 

 $40,000 to $49,999 .............................................................farms 

$1,000 

 $50,000 to $99,999 .............................................................farms 

$1,000 

 $100,000 to $249,999 .........................................................farms 

$1,000 

 $250,000 to $499,999 .........................................................farms 

$1,000 

 $500,000 to $999,999 .........................................................farms 

$1,000 

 $1,000,000 or more ............................................................farms 

$1,000 

 

Legal status for tax purposes (see text): 

 Family or individual .............................................................farms 

acres 

 Partnership .........................................................................farms 

acres 

 Corporation: 

 Family held .....................................................................farms 

acres 

 Other than family held .....................................................farms 

acres 

 Other - estate or trust, prison farm, grazing association, 

 American Indian Reservation, etc .....................................farms 

acres 

 

Tenure: 

 Full owners .........................................................................farms 

acres 

 Part owners .........................................................................farms 

acres 

 Tenants ...............................................................................farms 

acres 

 

All principal producer characteristics by 1- 

 Sex of operator: 

 Male ................................................................................farms 

acres 

 Female ............................................................................farms 

acres 

 

 Primary occupation: 

 Farming ..........................................................................farms 

 Other ...............................................................................farms 

2,042,220

900,217,576

273,325

1,302,208

583,001

14,787,940

135,126

7,845,508

163,251

13,414,191

149,478

17,343,842

116,908

18,399,918

74,086

14,645,228

57,096

13,586,644

183,835

65,775,717

133,321

92,872,530

87,666

120,680,141

85,127

519,563,709

255,348

53,959,077

64,450

4,054,830

388,522,695

603,752

93,210

187,949

310,520

185,341

662,980

208,074

1,477,595

174,780

2,468,212

53,438

1,181,954

100,490

3,162,749

43,623

1,937,293

119,434

8,477,635

130,932

21,171,316

87,839

31,318,548

69,703

49,338,998

76,865

266,921,684

1,751,126

541,071,476

130,173

158,051,459

104,155

126,671,963

12,685

12,889,821

44,081

61,532,857

1,408,961

310,218,983

493,137

503,138,279

140,122

86,860,314

1,787,998

847,232,627

766,474

238,157,861

1,207,375

1,533,078

2.1

1.7

8.5

9.2

4.6

4.9

2.1

2.2

0.9

0.9

1.7

1.8

3.6

3.6

1.7

1.7

2.7

2.7

1.9

1.9

1.2

1.3

3.0

3.0

2.4

3.1

2.9

2.0

5.1

4.2

0.8

4.3

5.6

4.5

4.5

2.6

2.6

2.5

2.5

2.4

2.3

1.6

1.6

2.2

2.1

1.5

1.5

2.1

2.0

1.4

1.3

1.6

1.6

1.5

1.5

1.2

1.0

2.2

1.3

2.8

2.4

1.3

5.1

4.9

8.7

5.1

5.0

2.4

2.1

1.4

1.8

4.7

2.3

2.2

1.7

2.9

2.7

1.5

3.6

All principal producer characteristics by 1- - Con. 

 

 Hispanic, Latino, or 

 Spanish origin (see text) .................................................. farms 

acres 

 

 Race: 

 American Indian or 

 Alaska Native ............................................................... farms 

acres 

 Asian .............................................................................. farms 

acres 

 Black or African American .............................................. farms 

acres 

 Native Hawaiian or 

 Other Pacific Islander ................................................... farms 

acres 

 White .............................................................................. farms 

acres 

 More than one race reported .......................................... farms 

acres 

 

 Military service (see text): 

 Never served ........................................................... producers 

 Served ..................................................................... producers 

 

All producers by age group 1: 

 Under 25 years .................................................................. farms 

 25 to 34 years .................................................................... farms 

 35 to 44 years .................................................................... farms 

 45 to 54 years .................................................................... farms 

 55 to 64 years .................................................................... farms 

 65 to 74 years .................................................................... farms 

 75 years and over .............................................................. farms 

 

Net cash farm income of operations (see text): 

 Farms with gains of 2- 

 Less than $1,000 ........................................................... farms 

$1,000 

 $1,000 to $4,999 ............................................................ farms 

$1,000 

 $5,000 to $9,999 ............................................................ farms 

$1,000 

 $10,000 to $24,999 ........................................................ farms 

$1,000 

 $25,000 to $49,999 ........................................................ farms 

$1,000 

 $50,000 or more ............................................................. farms 

$1,000 

 

 Farms with losses of - 

 Less than $1,000 ........................................................... farms 

$1,000 

 $1,000 to $4,999 ............................................................ farms 

$1,000 

 $5,000 to $9,999 ............................................................ farms 

$1,000 

 $10,000 to $24,999 ........................................................ farms 

$1,000 

 $25,000 to $49,999 ........................................................ farms 

$1,000 

 $50,000 or more ............................................................. farms 

$1,000 

 

Livestock and poultry: 

 Cattle and calves inventory ................................................ farms 

number 

 Beef cows inventory ....................................................... farms 

number 

 Milk cows inventory ........................................................ farms 

number 

 Hog and pigs inventory ...................................................... farms 

number 

 Layers inventory ................................................................ farms 

number 

 Broilers sold ....................................................................... farms 

number 

 Aquaculture sold ................................................................ farms 

$1,000 

 

Selected crops harvested: 

 Corn for grain ..................................................................... farms 

acres 

 Durum wheat for grain ....................................................... farms 

acres 

 Other spring wheat for grain (see text) ............................... farms 

acres 

 Winter wheat for grain ........................................................ farms 

acres 

 Sorghum for grain .............................................................. farms 

acres 

 Soybeans for beans ........................................................... farms 

acres 

 Rice .................................................................................... farms 

acres 

 Cotton ................................................................................ farms 

acres 

77,416

26,041,600

39,632

51,095,994

13,904

1,831,229

32,052

3,862,936

2,092

426,068

1,955,737

843,497,615

19,773

6,712,435

2,402,342

338,111

50,943

234,496

390,345

614,654

955,354

757,936

396,106

66,633

31,436

156,683

431,683

103,942

756,426

153,619

2,525,811

114,269

4,097,569

296,183

104,245,583

89,302

45,846

342,608

988,554

256,919

1,854,855

272,079

4,266,566

104,865

3,629,228

85,118

13,380,008

882,692

93,648,041

729,046

31,722,039

54,599

9,539,631

66,439

72,381,007

232,500

368,241,393

32,751

8,889,759,283

5,350

1,778,587

304,801

84,738,562

3,093

2,206,169

20,076

10,419,033

86,596

26,186,417

15,339

5,070,159

303,191

90,149,480

4,637

2,395,054

16,149

11,401,965

9.7

4.7

11.8

3.9

8.6

7.7

8.5

7.5

20.7

53.6

2.1

1.7

7.4

4.8

2.6

2.8

16.6

11.7

4.7

4.4

1.3

1.8

1.8

1.6

2.2

1.2

1.3

2.0

2.0

1.6

1.6

1.8

1.8

1.2

1.0

3.9

3.8

3.9

4.2

3.7

3.9

4.0

4.4

3.7

3.7

2.4

1.9

2.3

2.1

2.1

2.6

3.2

1.7

5.2

1.8

4.4

2.9

4.8

2.8

3.8

5.0

1.6

1.3

5.2

5.3

2.6

4.9

1.3

0.8

2.3

3.1

1.2

1.9

10.1

26.6

3.8

2.8

See footnote(s) at end of table. --continued
A - 22 Appendix A 2017 Census of Agriculture

USDA, National Agricultural Statistics Service

Table B. Reliability Estimates of U.S. Totals: 2017 (continued)

[For meaning of abbreviations and symbols, see introductory text.]

Item Total

Coefficient

of variation

(percent)

Item Total

Coefficient

of variation

(percent)

Selected crops harvested: - Con. 

 

 Peanuts .............................................................................. farms 

acres 

 Barley ................................................................................. farms 

acres 

 Oats ................................................................................... farms 

acres 

 

 Forage - land used for all hay and all 

 haylage, grass silage, and 

 greenchop (see text) ........................................................ farms 

acres 

 Land in vegetables (see text) ............................................. farms 

acres 

 Potatoes ......................................................................... farms 

acres 

 Tomatoes in the open .................................................... farms 

acres 

6,379

1,786,767

11,188

2,206,808

19,842

814,140

799,627

56,858,622

74,276

3,965,622

16,554

1,133,128

28,673

335,348

6.3

7.5

2.6

5.1

2.3

2.9

2.0

1.3

5.8

2.6

6.6

3.2

6.3

6.4

Selected crops harvested: - Con. 

 Land in vegetables (see text) - Con. 

 

 Sweet corn .....................................................................farms 

acres 

 Lettuce ............................................................................farms 

acres 

 Land in orchards (see text) .................................................farms 

acres 

 Apples ............................................................................farms 

acres 

 Grapes ............................................................................farms 

acres 

 Oranges ..........................................................................farms 

acres 

 Almonds .........................................................................farms 

acres 

 Land in berries ....................................................................farms 

acres 

20,784

496,096

10,869

342,965

111,955

5,665,600

26,408

381,718

28,387

1,136,155

7,973

602,830

7,954

1,266,160

33,919

302,199

5.7

3.3

8.7

2.1

3.5

3.7

4.9

4.9

3.2

6.2

3.4

4.9

4.4

2.8

4.3

2.2

1

 Data were collected for a maximum of four producers per farm.

2

 Farms with total production expenses equal to market value of agricultural products sold, government payments, and farm-related income are included as farms with gains of less than $1,000.
2017 Census of Agriculture Appendix A A - 23

USDA, National Agricultural Statistics Service

Table C. Summary of Coverage, Nonresponse, and Misclassification Adjustments by State: 2017

[For meaning of abbreviations and symbols, see introductory text.]

Geographic area Total

(number)

Standard

error

Adjustment

as percent

of total

Percent of total

adjustment

from coverage

Percent of total

adjustment from

nonresponse

Percent of total

adjustment from

misclassification

ALL FARMS (NUMBER) 

 

United States Total 

 

United States ............................................................................................ 

 

States 

 

Alabama ................................................................................................... 

Alaska....................................................................................................... 

Arizona ..................................................................................................... 

Arkansas................................................................................................... 

California .................................................................................................. 

Colorado................................................................................................... 

Connecticut............................................................................................... 

Delaware .................................................................................................. 

Florida....................................................................................................... 

Georgia..................................................................................................... 

 

Hawaii....................................................................................................... 

Idaho......................................................................................................... 

Illinois........................................................................................................ 

Indiana...................................................................................................... 

Iowa.......................................................................................................... 

Kansas...................................................................................................... 

Kentucky................................................................................................... 

Louisiana .................................................................................................. 

Maine........................................................................................................ 

Maryland................................................................................................... 

 

Massachusetts.......................................................................................... 

Michigan ................................................................................................... 

Minnesota ................................................................................................. 

Mississippi ................................................................................................ 

Missouri .................................................................................................... 

Montana.................................................................................................... 

Nebraska .................................................................................................. 

Nevada ..................................................................................................... 

New Hampshire ........................................................................................ 

New Jersey............................................................................................... 

 

New Mexico .............................................................................................. 

New York .................................................................................................. 

North Carolina .......................................................................................... 

North Dakota ............................................................................................ 

Ohio.......................................................................................................... 

Oklahoma ................................................................................................. 

Oregon...................................................................................................... 

Pennsylvania ............................................................................................ 

Rhode Island ............................................................................................ 

South Carolina.......................................................................................... 

 

South Dakota............................................................................................ 

Tennessee................................................................................................ 

Texas........................................................................................................ 

Utah.......................................................................................................... 

Vermont.................................................................................................... 

Virginia...................................................................................................... 

Washington............................................................................................... 

West Virginia ............................................................................................ 

Wisconsin ................................................................................................. 

Wyoming................................................................................................... 

 

LAND IN FARMS (ACRES) 

 

United States Total 

 

United States ............................................................................................ 

 

States 

 

Alabama ................................................................................................... 

Alaska....................................................................................................... 

Arizona ..................................................................................................... 

Arkansas................................................................................................... 

California .................................................................................................. 

Colorado................................................................................................... 

Connecticut............................................................................................... 

Delaware .................................................................................................. 

Florida....................................................................................................... 

Georgia..................................................................................................... 

 

Hawaii....................................................................................................... 

Idaho......................................................................................................... 

Illinois........................................................................................................ 

Indiana...................................................................................................... 

Iowa.......................................................................................................... 

Kansas...................................................................................................... 

Kentucky................................................................................................... 

Louisiana .................................................................................................. 

Maine........................................................................................................ 

Maryland................................................................................................... 

 

Massachusetts.......................................................................................... 

Michigan ................................................................................................... 

Minnesota ................................................................................................. 

Mississippi ................................................................................................ 

Missouri .................................................................................................... 

Montana.................................................................................................... 

Nebraska .................................................................................................. 

2,042,220

40,592

990

19,086

42,625

70,521

38,893

5,521

2,302

47,590

42,439

7,328

24,996

72,651

56,649

86,104

58,569

75,966

27,386

7,600

12,429

7,241

47,641

68,822

34,988

95,320

27,048

46,332

3,423

4,123

9,883

25,044

33,438

46,418

26,364

77,805

78,531

37,616

53,157

1,043

24,791

29,968

69,983

248,416

18,409

6,808

43,225

35,793

23,622

64,793

11,938

900,217,576

8,580,940

849,753

26,125,819

13,888,929

24,522,801

31,820,957

381,539

525,324

9,731,731

9,953,730

1,135,352

11,691,912

27,006,288

14,969,996

30,563,878

45,759,319

12,961,784

7,997,511

1,307,613

1,990,122

491,653

9,764,090

25,516,982

10,415,136

27,781,883

58,122,878

44,986,821

43,278

1,545

13

2,637

1,661

1,896

3,173

464

204

1,426

1,215

560

1,288

1,894

1,822

1,650

2,763

3,436

1,360

1,065

1,107

723

2,276

1,138

2,117

3,297

2,046

1,383

264

352

1,076

2,354

1,263

1,604

787

2,385

3,431

2,687

2,075

186

1,346

717

2,066

8,706

1,780

569

1,037

2,013

1,297

1,665

917

15,031,334

199,954

1,199

1,432,761

310,057

3,248,717

1,722,400

27,934

36,130

344,544

496,845

42,889

703,194

773,609

297,098

515,336

974,716

377,113

580,578

72,510

72,254

30,187

371,324

402,371

251,770

657,610

3,130,653

1,155,962

37.6

39.5

3.2

56.3

37.3

42.0

39.6

49.6

42.5

47.0

36.1

47.4

40.0

26.0

33.5

23.3

33.8

38.6

44.7

45.5

32.0

46.0

38.2

30.3

39.4

33.7

37.5

37.8

50.8

50.1

35.4

49.8

37.0

41.2

37.9

32.2

38.8

40.2

38.3

47.0

44.8

36.5

35.5

42.1

42.1

44.3

39.0

41.8

34.7

35.2

40.3

22.7

27.7

0.2

11.1

23.5

17.7

21.8

30.0

23.7

19.2

24.4

6.3

18.8

20.7

20.6

24.7

26.2

27.3

30.7

27.8

15.7

30.5

21.7

25.1

26.6

26.6

19.8

26.9

15.1

15.3

(NA)

20.5

14.0

18.8

15.4

22.0

16.3

20.5

14.7

21.1

17.9

9.6

13.2

8.3

10.5

15.7

15.8

20.5

13.3

21.4

16.4

10.9

13.7

13.2

13.7

10.4

22.6

22.8

15.7

21.2

15.2

16.5

8.7

13.8

14.0

18.2

16.5

25.4

16.6

8.5

15.2

17.8

18.1

17.6

17.2

20.4

14.7

14.2

15.1

4.4

8.6

(NA)

2.9

5.9

3.7

4.0

11.1

5.6

4.7

7.0

2.1

3.8

3.1

3.1

3.0

3.6

8.3

5.9

10.3

4.9

12.4

5.8

4.1

7.0

6.2

2.3

3.1

13.9

15.1

3.2

22.7

15.1

15.5

14.1

17.2

17.2

16.2

13.2

17.2

12.5

10.4

12.8

9.8

15.6

13.4

18.9

15.4

11.1

14.5

13.3

12.4

16.6

12.7

14.7

20.0

17.1

16.6

11.2

17.0

13.6

14.7

22.0

10.8

15.8

12.7

13.5

11.6

17.6

20.7

11.3

14.5

13.8

17.0

12.2

12.0

10.9

13.2

15.1

11.8

12.1

0.2

4.0

12.3

8.4

11.3

11.3

12.3

7.9

10.9

1.0

9.8

14.4

14.5

18.4

18.0

12.5

18.4

11.1

7.1

10.9

11.3

17.1

13.9

14.5

12.0

18.4

8.6

9.1

(NA)

13.1

8.3

7.7

10.1

10.4

9.1

10.2

8.2

9.1

9.6

5.9

7.5

5.2

7.7

9.6

9.9

9.6

7.7

10.2

8.5

7.0

9.1

7.9

9.1

7.4

11.1

10.7

8.5

11.6

8.2

10.0

7.2

7.5

9.0

9.3

8.3

10.0

10.6

7.3

9.0

9.8

10.3

9.7

9.6

9.4

9.1

7.8

10.2

6.5

7.0

(NA)

4.2

5.3

5.7

6.4

7.5

5.8

6.7

6.5

3.2

5.2

3.2

3.0

3.3

4.6

6.5

6.4

6.4

3.7

7.2

4.7

3.9

5.6

5.8

5.5

5.4

--continued
A - 24 Appendix A 2017 Census of Agriculture

USDA, National Agricultural Statistics Service

Table C. Summary of Coverage, Nonresponse, and Misclassification Adjustments by State: 2017 (continued)

[For meaning of abbreviations and symbols, see introductory text.]

Geographic area Total

(number)

Standard

error

Adjustment

as percent

of total

Percent of total

adjustment

from coverage

Percent of total

adjustment from

nonresponse

Percent of total

adjustment from

misclassification

LAND IN FARMS (ACRES) - Con. 

 

States - Con. 

 

Nevada...................................................................................................... 

New Hampshire......................................................................................... 

New Jersey ............................................................................................... 

New Mexico .............................................................................................. 

New York .................................................................................................. 

North Carolina........................................................................................... 

North Dakota............................................................................................. 

Ohio .......................................................................................................... 

Oklahoma.................................................................................................. 

Oregon ...................................................................................................... 

 

Pennsylvania............................................................................................. 

Rhode Island............................................................................................. 

South Carolina .......................................................................................... 

South Dakota ............................................................................................ 

Tennessee ................................................................................................ 

Texas ........................................................................................................ 

Utah .......................................................................................................... 

Vermont .................................................................................................... 

Virginia ...................................................................................................... 

Washington ............................................................................................... 

 

West Virginia............................................................................................. 

Wisconsin.................................................................................................. 

Wyoming ................................................................................................... 

 

SALES ($1,000) 

 

United States Total 

 

United States............................................................................................. 

 

States 

 

Alabama.................................................................................................... 

Alaska ....................................................................................................... 

Arizona...................................................................................................... 

Arkansas ................................................................................................... 

California................................................................................................... 

Colorado ................................................................................................... 

Connecticut ............................................................................................... 

Delaware................................................................................................... 

Florida ....................................................................................................... 

Georgia ..................................................................................................... 

 

Hawaii ....................................................................................................... 

Idaho......................................................................................................... 

Illinois ........................................................................................................ 

Indiana ...................................................................................................... 

Iowa .......................................................................................................... 

Kansas ...................................................................................................... 

Kentucky ................................................................................................... 

Louisiana................................................................................................... 

Maine ........................................................................................................ 

Maryland ................................................................................................... 

 

Massachusetts .......................................................................................... 

Michigan.................................................................................................... 

Minnesota ................................................................................................. 

Mississippi................................................................................................. 

Missouri..................................................................................................... 

Montana .................................................................................................... 

Nebraska................................................................................................... 

Nevada...................................................................................................... 

New Hampshire......................................................................................... 

New Jersey ............................................................................................... 

 

New Mexico .............................................................................................. 

New York .................................................................................................. 

North Carolina........................................................................................... 

North Dakota............................................................................................. 

Ohio .......................................................................................................... 

Oklahoma.................................................................................................. 

Oregon ...................................................................................................... 

Pennsylvania............................................................................................. 

Rhode Island............................................................................................. 

South Carolina .......................................................................................... 

 

South Dakota ............................................................................................ 

Tennessee ................................................................................................ 

Texas ........................................................................................................ 

Utah .......................................................................................................... 

Vermont .................................................................................................... 

Virginia ...................................................................................................... 

Washington ............................................................................................... 

West Virginia............................................................................................. 

Wisconsin.................................................................................................. 

Wyoming ................................................................................................... 

6,128,153

425,393

734,084

40,659,836

6,866,171

8,430,522

39,341,591

13,965,295

34,156,290

15,962,322

7,278,668

56,864

4,744,913

43,243,742

10,874,238

127,036,184

10,811,604

1,193,437

7,797,979

14,679,857

3,662,178

14,318,630

29,004,884

388,522,695

5,980,595

70,459

3,852,008

9,651,160

45,154,359

7,491,702

580,114

1,465,973

7,357,343

9,573,252

563,803

7,567,439

17,009,971

11,107,336

28,956,455

18,782,726

5,737,920

3,172,978

666,962

2,472,805

475,184

8,220,935

18,395,390

6,195,968

10,525,938

3,520,623

21,983,430

665,758

187,794

1,097,950

2,582,343

5,369,212

12,900,674

8,234,102

9,341,225

7,465,512

5,006,822

7,758,884

57,998

3,008,739

9,721,522

3,798,934

24,924,041

1,838,610

780,968

3,960,501

9,634,461

754,279

11,427,423

1,472,113

512,649

25,211

53,129

4,089,251

167,815

262,971

2,073,608

278,984

865,429

522,836

228,467

3,981

347,006

1,100,330

219,305

3,219,092

247,332

123,507

145,486

291,923

156,944

435,791

3,226,928

2,923,858

173,995

160

84,786

375,713

1,116,572

288,834

25,685

87,913

275,257

413,404

96,131

555,829

425,802

377,238

363,219

278,393

124,641

93,001

49,050

173,825

44,440

264,720

327,929

235,789

333,340

179,935

591,020

112,662

17,904

28,911

68,848

95,711

239,004

457,678

147,955

163,532

145,919

227,164

3,127

139,270

323,047

146,980

417,770

141,700

72,090

86,812

355,103

19,100

318,424

56,575

13.2

31.0

18.3

17.7

23.6

24.0

32.9

20.6

25.2

13.2

28.4

25.7

28.6

29.5

26.0

23.0

9.4

28.3

26.5

13.4

24.6

24.0

14.0

18.1

21.1

0.3

6.4

22.7

15.8

10.3

11.1

37.2

13.2

21.8

10.3

11.3

18.5

17.2

25.1

14.4

13.8

25.6

12.8

19.4

17.6

13.9

25.3

19.9

20.4

24.0

20.5

21.3

10.7

8.5

8.4

13.1

20.4

30.4

16.9

13.9

10.2

17.0

11.7

12.0

29.0

18.5

14.1

12.5

15.7

13.1

10.6

11.5

17.5

17.3

2.4

14.1

6.3

3.2

7.0

6.6

2.9

5.7

5.8

2.7

8.3

12.0

8.5

2.4

8.3

5.8

2.1

7.1

8.7

3.3

10.3

5.8

1.4

3.4

5.5

(NA)

1.8

4.1

3.3

2.5

3.3

9.1

3.0

4.8

3.4

1.8

2.5

2.4

3.4

2.8

3.2

3.8

3.5

5.8

4.1

2.9

3.5

4.2

3.4

2.3

2.6

2.8

3.3

2.7

2.2

2.9

5.1

2.0

3.9

3.2

2.3

3.6

4.7

2.6

2.2

3.3

3.9

2.7

2.3

3.6

2.8

4.7

2.7

1.9

2.7

8.5

7.5

5.5

11.8

11.2

24.7

10.6

13.3

6.1

14.4

6.6

13.3

22.0

11.2

8.3

4.3

14.9

10.8

6.3

7.5

13.8

5.7

11.1

11.7

0.3

3.1

14.5

9.0

4.5

5.7

19.9

6.7

12.9

5.1

7.2

13.1

12.3

17.8

7.9

7.4

18.1

7.4

9.6

10.5

8.2

17.9

12.2

12.9

16.7

13.6

13.4

5.4

3.8

3.8

7.9

10.1

25.0

9.7

6.8

5.9

10.9

4.4

7.2

23.1

11.8

6.1

7.3

11.5

6.4

5.1

3.4

12.5

9.7

8.0

8.4

4.5

9.0

4.7

6.2

5.3

4.4

6.1

4.5

5.8

7.1

6.8

5.0

6.4

8.9

3.0

6.3

7.1

3.8

6.9

4.4

6.9

3.6

4.0

(NA)

1.5

4.1

3.5

3.3

2.1

8.1

3.6

4.1

1.8

2.3

2.9

2.5

3.8

3.7

3.2

3.7

2.0

4.0

3.0

2.8

3.8

3.5

4.1

4.9

4.3

5.1

2.0

1.9

2.4

2.3

5.2

3.5

3.4

3.8

2.0

2.6

2.6

2.2

3.7

3.4

4.1

2.5

2.0

3.0

2.6

3.3

2.3

5.7
2017 Census of Agriculture Appendix A A - 25

USDA, National Agricultural Statistics Service

Table D. American Indian or Alaska Native Producers: 2017

[For meaning of abbreviations and symbols, see introductory text.]

Geographic area

American Indian or Alaska Native farm producers

Geographic area

American Indian or Alaska Native farm producers

Total Individually

reported 1 Other 2 Total Individuallyreported 1 Other 2

United States Total 

 

United States ...................................... 

 

States 

 

Alabama ............................................. 

Alaska................................................. 

Arizona ............................................... 

Arkansas............................................. 

California ............................................ 

Colorado............................................. 

Connecticut......................................... 

Delaware ............................................ 

Florida................................................. 

Georgia............................................... 

 

Hawaii................................................. 

Idaho................................................... 

Illinois.................................................. 

Indiana................................................ 

Iowa.................................................... 

Kansas................................................ 

Kentucky............................................. 

Louisiana ............................................ 

Maine.................................................. 

Maryland............................................. 

 

Massachusetts.................................... 

Michigan ............................................. 

Minnesota ........................................... 

79,597

1,326

88

19,656

1,326

2,538

1,185

55

8

1,027

524

265

461

332

325

229

961

650

523

113

142

66

777

408

79,198

1,326

88

19,481

1,326

2,537

963

55

8

1,027

524

265

461

332

325

229

961

650

523

112

142

66

777

408

399

-

-

175

-

1

222

-

-

-

-

-

-

-

-

-

-

-

-

1

-

-

-

-

States - Con. 

 

Mississippi........................................... 

Missouri............................................... 

Montana.............................................. 

Nebraska............................................. 

Nevada................................................ 

New Hampshire .................................. 

New Jersey ......................................... 

New Mexico ........................................ 

New York ............................................ 

North Carolina..................................... 

 

North Dakota....................................... 

Ohio .................................................... 

Oklahoma............................................ 

Oregon................................................ 

Pennsylvania....................................... 

Rhode Island....................................... 

South Carolina .................................... 

South Dakota ...................................... 

Tennessee .......................................... 

Texas .................................................. 

 

Utah .................................................... 

Vermont .............................................. 

Virginia................................................ 

Washington......................................... 

West Virginia....................................... 

Wisconsin............................................ 

Wyoming............................................. 

321

1,544

2,130

210

315

39

91

8,812

278

1,023

470

530

17,102

1,255

302

5

307

1,242

843

5,663

1,467

90

440

1,202

249

293

389

321

1,544

2,130

210

315

39

91

8,812

278

1,023

470

530

17,102

1,255

302

5

307

1,242

843

5,663

1,467

90

440

1,202

249

293

389

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

1 Data were collected for a maximum of four producers per farm.

2 Data represent American Indian or Alaska Native farm or ranch producers on reservations who did not report individually. Data obtained by reservation officials.