2022 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 isto accountfor “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

agriculture-related 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 2022 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 2022 CML

started in 2019 by updating list information from

respondents to the 2017 Census of Agriculture. Between

2017 and 2022, NASS conducted a series of National

Agricultural Classification Surveys (NACS) on over 2.1

million records, which included nonrespondents from the

2017 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.

The official CML for the 2022 Census of Agriculture was

established on September 3, 2022. The list contained

2,879,343 records. Of these, 2,079,333 records were

thought to meet the NASS farm definition and 800,010

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
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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 Not-on-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 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 2022 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 14,015 segments of which

4,933 were additional ACES segments. This set of

additional segments is referred to as the Agricultural

Coverage Evaluation Survey (ACES)segments.TheACES

segments were selected using a multivariate sampling

design that targeted specific items at the U.S. level.

The 2022 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 non-agricultural. Each

JAS/ACES agricultural tract is identified as a farm or nonfarm in June based on the farm definition of $1,000 ofsales

or potentialsales 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 2022

JAS/ACES were matched to the CML. Those from the

2022 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 41,273 records. A

total of 40,775 NML records were analyzed, of which

1,913 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 present

in 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.

DATA COLLECTION OUTREACH AND

PROMOTIONAL EFFORTS

NASS planned and executed a multi-phase strategic

communications campaign for the 2022 Census of

Agriculture, to increase the level of awareness and

response among all U.S. agricultural producers.

 Phase 1 ran from April 2021 − June 2022. 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 2022 − October 2022. It notified

farm producers and agricultural organizations that the

census would be mailed in November and encouraged

communications regarding the census.

 Phase 3 ran from November 2022 – May 2023. It

focused on census data collection with messaging

urging response to remind producers that it was not too

late to respond.

 Phase 4 ran from August 2023 – February 2024. It

thanked producers for their participation and NASS

partners for their support and informed everyone of the

February 2024 data release plan.

The communications campaign focused on these primary

areas: partnership building, local-level outreach, public

relations, media relations, paid media, social media and

some paid advertising. Some external support was

provided by a private communications agency (i.e.

primarily assisted with design and paid advertising).

The unifying force behind the 2022 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: Sign Up

to Be Counted - Show the Value of Your Work - Grow Your
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Farm Future - Shape Farm Policy/Programs - Respond to

the Census of Agriculture - 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 2022 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 producerin the country.Ifthis was not

possible within some reservations, a single reservationlevel 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.

Table D, American Indian and Alaska Native

Producers: 2022 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, national, regional, and local stakeholders to

equip them with communications tools and resources to

deliver the census communications message to their

audiences. NASS utilized its Intranet, the Partner Tools

section on the census webpage, and a regularly scheduled,

newsletter-type email update to deliver materials to staff

across its 12 regions, other USDA agencies and 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; social media posts; 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 local-levels 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

With a very limited budget, NASS was able to apply a

small portion of funds toward paid advertising. For the

2022 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 geographically specific,

previously under-represented populations and lower

response areas.
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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 2022 Census of Agriculture, NASS

implemented a pre-notification strategy 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 2022

Census of Agriculture:

 General form (22 - A100)

 Hawaii form (22 - A101)

 American Indian form (22 - A300)

 Farm Status form (22 - A400)

The general form facilitated reporting crops and livestock

most commonly grown and raised in the U.S. The short

form expedited reporting specific crops orlivestock for preidentified farms and ranches in the U.S. The Hawaii

form targeted crops and 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 report forms allowed respondents to write in

specific commodities that were not prelisted on their report

form.

Report Form Mailings

Census data collection began on November 22, 2022.

Nearly all producers on the CML received a letter inviting

them to report online. They received a unique 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 2022. 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

correspondence in January 2023. This pressure-sealed envelope

reminded respondents of the approaching deadline and that they

could report online. First follow-up mail packets were

mailed in mid-February 2023 to approximately 1.5 million

nonrespondents. Second follow-up mail packets were

mailed in mid-March 2023 to approximately 1 million

nonrespondents. A final mailing went to approximately

800,000 non-respondents. This mailing included a

drastically reduced four-page questionnaire designed to

primarily determine if the operation was a farm or not in

business.

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 2022 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

2023 through May 2023, 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 (22-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
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mailout, but due to poor mail response, a personal

enumeration data collection strategy was utilized with no

additional mail follow-up. 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). In April 2023, a group of records that were not

part of other nonresponse data collection efforts were

identified for additional phone contacts. In total, 82,237

records with specified demographics and/or eligibility for

Census Special Studies (follow-ons) were made available

for nonresponse Computer-Assisted Telephone Interviews

(CATI).

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

farming operations not on the CML, NASS used its 2022

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 2022 JAS/ACES. Those 2022 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 followup in mid-February 2023. Beginning in March 2023, 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 zonesin 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 censusrecords,

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 to

believe might have been erroneously classified as out-of-
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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 section-bysection 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

re-edit the record to ensure a satisfactory solution.

Farm Status Form Editing

From the CML, 883,732 records were selected to receive a

Farm Status form as a final follow-up form; this form was

derived from the full census report form by selecting a

subset of the questions on the full form. Since these

questions were also asked on the general form, the edit was

able to treat the Farm Status form responses as though they

were incomplete general forms, as described in the

previous paragraphs.

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

previously-reported 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 wasreferred 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 mostsimilar 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.

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

2017 census data, reconfigured to emulate 2022 data and

then edited using 2022 logic. Data from the 2020 Census

Content Test were similarly remapped and edited before

being added to the original donor pools. As 2022 records

were successfully processed, they were added to the donor

pools,whichmaintained 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 2022 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 2022 records, ensuring that 2022 data were
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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.

As was done for the 2017 Census, for records reporting

three or more persons as producers, a different imputation

process was used for certain items (specifically the items in

question 3) in the Personal Characteristics Section.

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. At the end of the

data collection period, 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.

Data Analysis

The complex edit ensured the full internal consistency ofthe

record. Successfully completing the edit did not provide

insight as to whether the report was 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 has been expended making the CML

as complete and accurate as possible, it does not include all

U.S. farm operations, resulting in list undercoverage.

Additionally, some farm operations on the CML did not

respond to the census, despite numerous contact attempts.

Finally, although each operation was classified as a farm or

a nonfarm based on their census responses, some were

misclassified; that is, some nonfarms were classified as

farms and some farms were classified as nonfarms.

NASS’s goal is to produce agricultural census totals for

publication at the county level that are fully adjusted for

these factors: list undercoverage, nonresponse, and

misclassification.

In 2017, NASS used a series of models based on a subset of

the responding census and all the JAS records in a capturerecapture framework to separately adjust for

undercoverage, nonresponse, and misclassification. For

the 2022 Census of Agriculture, the capture-recapture

methodology was extended to model the probability of

capture with a single model, thereby allowing the

utilization of all census responses and JAS records in the

adjustments. To implement capture-recapture methods,

two independent samples are required. The 2022 Census of

Agriculture (based on the CML) and the 2022 JAS (based

on the area frame) were those two samples. Historically,

NASS has been careful to maintain the independence of the

CML and the area frame. Thus, the Census of Agriculture

and the JAS were assumed to be independent after

accounting for heterogeneity in the capture probabilities

based on characteristics of records.

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 be classified as a farm on the form. Thus,

the capture probability πC is of interest:

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

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
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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 itsresponse 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 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 these probabilities , the records

in the 2022 JAS sample were matched to the 2022 CML

using probabilistic record linkage allowing the records

only on the CML, JAS, and on both the CML and JAS to be

identified. All CML records and JAS tracts were used to

estimate the capture-recapture probabilities jointly.

Resolving Farm Status

The farm status based on census responses to either the

CML or NML census data collection and the response on

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) on 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 in-scope; an

operation identified as a nonfarm is referred to as out-ofscope. From the set of matched records, two groups with

conflicting farm status were identified: 1) in-scope JAS

records that were out-of-scope 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.

The probability that an operation is a farm was estimated

for census and JAS by using a conditional logistic model.

Only those records identified as a farm based on either

their JAS response or their Census response were used to

develop the model for estimating the probability a record is

associated with a farm. Operations with matching farm

status were considered as certain if the farm status agreed

between the JAS and the CML. If the status between the

JAS and CML was conflicting, then the operation was

treated as uncertain during the modeling stages.

Characteristics of the operations were considered as

potential covariates in the model. Variable selection was

conducted using a stepwise algorithm to maximize the

conditional likelihood. The probability of being a farm is

estimated for each record classified as a farm based on

their JAS or census response. The estimated probability is

used as a weight 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

either the census or JAS report form and, based on that

response, be classified as a farm. Therefore, the probability

of capture πC may be written as

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

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

Census|CML, Responded, Farm)

Terms in the probability of capturing a farm depend on

characteristics of the farm. These terms, as well as the

corresponding terms associated with a farm being captured

by the JAS, were jointly estimated from a single model.

Using all Census and JAS data, model variables were

selected by applying a stepwise variable selection

algorithm and expert opinion. Estimation was based on a

conditional weighted likelihood. The events of a farm

being included in the CML, the JAS or both were included

in the likelihood. The event of a farm not being included in

either the JAS or the CML was excluded from the

likelihood but was accounted for through the model’s

capture-recapture properties. Although the probability of

capture is estimated for both CML and JAS records, only

CML records with a census response are given a census

weight; records with only a JAS response are not given a

census weight or used further to produce census estimates.

Because Alaska is not included in the JAS and thus has no

area frame, the Alaskan agricultural operations were not
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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 conditional logistic

model was developed. Given that a farm on the CML was

classified as a farm in the census, the probability of its

being a farm was modeled based on its characteristics.

CALIBRATION

Each operation identified as being in-scope on the CML

was given a weight equal to the probability of

misclassifying a nonfarm as a farm on the census divided

by the probability of capture. This weight accounted for

undercoverage, nonresponse, and both types of

misclassification.

The record weighting processes were initially applied at

the State level to produce adjusted estimates of farm

numbers, land in farms, and for 64 different categories of

characteristics of the farm operation or the farm producer --

value of agricultural sales (10); age (2); female; race (3);

Hispanic origin; 4 sales categories for each of 10 major

commodities (40); and farm type groups (7). The Statelevel number of farms and land in farms were two

additional adjusted estimates, resulting in 66 categories. To

reduce the intercensal variation at the State level, the State

targets were smoothed by averaging the 2022 estimates

from capture-recapture and the published 2017 State

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 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.

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 it 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 set 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
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weights so that all State targets were met, all targets were

brought collectively as close to the targets as possible.

The proportions ofselected census data itemsthat 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

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

suppressionsto 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 2022 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
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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

2022 Census of Agriculture CML was 61.0 percent, as

compared with the 2017 Census of Agriculture’s response

rate of 71.8 percent and 74.6 percent for the 2012 Census

of Agriculture.

The 2022 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:

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 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 inscope had they responded. The estimated number of inscope 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 percentage 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

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 when adjusting 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 responsesto 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, and

integer calibration.

Variability in Census Estimates due to Statistical

Adjustment

In conducting the 2022 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; for farms and nonfarms that were

misclassified as nonfarms and farms, respectively; and for

integer 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 as generalized coefficients of variation (GCVs)

at the county levels.

The standard error of an estimate is an estimate of the
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standard deviation of the sampling distribution of the

estimator. In each case, standard errors were computed

using an approach based on a delete-a-group jackknife

methodology. To conduct the jackknifing, k = 10 mutually

exclusive and exhaustive groups of records were formed.

The groups were selected using a stratified random design

so that each group reflected capture status by the CML and

the JAS. Based on estimated weights for records in each

group, a delete-a-group jackknife estimator of the variance

would account for the uncertainty associated with

modeling the capture-recapture probabilities and the

uncertainty due to integer calibration. Therefore, the

weights within each jackknife group were computed using

the group-specific models and calibrated to match groupspecific targets. For a given data item i, such as the number

of farms, the estimate was computed at the specified

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

the weights obtained for group j. Estimates of the variance

and standard error associated with the estimator Ti are then,

respectively,

Ten (10) calibration-adjusted jackknife groups were used

to provide standard errors for 2022 State and national

estimates (i.e., k=10). 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

calibrated 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 capture-recapture

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 uncertainty associated

with that estimated data item due to the possible outcomes

of the census collection, including incompleteness of the

CML, nonresponse to the census, misclassification either

as a farm or as a nonfarm, and the integer calibration. 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.

Note: The standard errors and consequently, the CVs tend

to be substantially smaller than those reported for the 2017

Census of Agriculture. For 2017, the model of the

probability of capture incorporated information from the

approximately 40,000 respondents to the 2017 JAS and the

census records matching a JAS record. In contrast, the

models for the 2022 Census of Agriculture relied on

information from the approximately 1 million responding

CML records and the 2022 JAS, some of which were on

both the CML and the JAS. The large increase in the

number of records used in the modeling process led to a

major decrease in the measures of uncertainty (standard

errors and CVs).

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, errors can be introduced

from adjustments for coverage, nonresponse, and

misclassification and from integer calibration. These errors

are measurable. 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
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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 responsesto the censusreport 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. Computer-assisted 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 reentered 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

previously reported data, administrative data, the nearest

neighbor algorithm, the fully conditional specification

method, or manual imputation is used to complete a

nonresponse item, 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. To identify duplication, all respondents

who received multiple report forms were instructed to

complete the CML version and return all forms so
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duplication could be removed.

Records in the 2022 JAS were matched to the 2022 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 apart from model

uncertainty was accounted for, but errors not found

through this process were not.
2022 Census of Agriculture Appendix A A - 15

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Table A. Summary of U.S. Coverage, Nonresponse, and Misclassification Adjustments: 2022

[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 ...................................... $1,000

Farms by value of sales:

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

Farms by legal status for tax purposes:

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

Producers characteristics by1(see text)

Sex of operator:

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

acres

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

acres

Primary occupation:

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

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

1,900,487

880,100,848

234,592

1,084,341

566,912

14,193,479

130,096

7,553,962

153,239

12,601,546

138,398

16,045,194

108,796

17,126,924

69,602

13,747,397

52,177

12,415,492

166,600

59,494,534

120,456

83,738,842

76,311

105,224,602

83,308

536,874,535

254,574

51,155,829

55,833

3,774,534

543,087,166

499,816

56,798

179,054

292,472

166,149

593,713

191,688

1,359,995

168,876

2,386,213

53,378

1,180,735

100,390

3,162,262

43,657

1,938,084

116,220

8,237,216

122,968

19,715,890

85,266

30,549,461

67,641

48,248,980

105,384

425,365,347

1,609,899

508,952,143

125,457

157,864,970

108,693

134,277,030

18,955

16,586,984

37,483

62,419,721

1,356,796

311,935,837

423,889

489,711,406

119,802

78,453,605

1,757,655

844,939,806

1,110,546

407,340,844

1,415,315

1,958,729

36,680

19,889,860

11,486

52,987

14,586

357,922

2,830

162,416

3,782

305,221

2,236

257,848

1,843

292,176

1,496

298,052

1,319

312,408

2,842

991,299

2,337

1,567,226

2,320

3,289,192

2,430

16,985,268

7,709

1,510,122

1,585

172,674

9,776

13,150

2

8,076

13

6,257

22

6,923

49

3,843

56

1,327

30

3,925

127

2,163

97

3,968

290

4,859

765

4,871

1,744

1,712

1,155

3,643

10,445

33,964

9,940,625

1,972

3,465,598

1,933

3,998,735

504

523,257

689

3,612,878

30,698

7,843,514

6,543

10,768,105

2,652

2,369,235

34,110

17,575,966

24,161

10,453,012

22,990

40,935

44.5

27.7

57.8

57.0

50.7

49.2

44.0

43.9

41.7

41.6

40.0

39.9

39.7

39.7

37.7

37.7

36.5

36.5

37.3

37.3

37.4

37.3

34.6

34.4

29.8

21.4

41.3

26.6

47.3

27.1

22.7

57.1

62.7

52.4

52.1

47.2

47.0

45.2

45.0

35.1

35.0

35.6

35.5

36.1

36.1

37.6

37.6

35.9

36.0

35.5

35.4

39.5

39.7

35.2

35.4

26.9

18.8

44.8

32.2

42.9

23.8

41.6

24.1

48.3

20.9

44.2

10.3

47.3

29.0

35.1

26.3

46.4

31.6

44.4

27.7

46.1

29.0

41.6

49.6

15.7

3.0

29.7

29.8

23.3

21.3

15.5

15.4

12.5

12.4

10.7

10.6

8.9

8.9

8.2

8.2

7.0

7.0

5.5

5.4

3.9

3.8

2.7

2.6

1.9

1.4

13.3

2.6

19.0

3.7

4.2

23.2

36.2

23.7

23.6

21.9

21.8

19.2

19.0

12.4

12.3

11.3

11.3

10.5

10.4

10.0

10.0

8.3

8.2

5.7

5.5

4.1

4.1

3.9

3.9

4.6

3.7

17.8

4.5

7.4

1.7

8.5

1.7

8.4

1.6

10.8

1.1

19.0

5.0

4.9

1.5

9.5

2.2

15.0

2.9

18.1

3.9

11.9

15.0

15.4

10.3

16.8

16.4

16.1

16.3

15.3

15.3

14.5

14.5

14.0

14.0

15.6

15.6

14.2

14.2

14.1

14.1

15.7

15.7

17.3

17.3

15.4

15.3

11.2

7.0

14.8

8.4

15.7

12.7

5.5

17.7

16.0

18.0

17.9

15.2

15.2

14.9

14.8

12.7

12.7

13.4

13.4

13.2

13.2

14.2

14.2

13.7

13.8

13.7

13.8

20.5

20.7

17.5

17.5

6.7

3.5

15.0

12.2

17.8

9.0

15.5

7.9

18.9

9.3

13.0

3.3

15.4

10.3

15.2

9.2

18.4

16.2

16.1

10.5

16.9

12.8

15.3

19.6

13.4

14.4

11.3

10.8

11.3

11.6

13.1

13.2

14.6

14.6

15.3

15.3

15.1

15.1

15.3

15.3

15.4

15.4

16.2

16.3

16.3

16.2

16.5

16.4

16.7

13.1

13.2

15.5

12.7

10.8

12.9

16.2

10.5

10.7

10.6

10.1

10.1

11.1

11.2

10.0

10.0

10.9

10.8

12.4

12.5

13.4

13.3

13.9

14.0

16.1

16.1

14.9

14.9

13.8

14.1

15.6

11.6

12.0

15.5

17.7

13.1

17.7

14.5

21.0

10.0

20.5

5.9

12.9

13.7

15.0

15.6

18.5

13.2

13.3

14.3

11.2

12.3

14.3

15.0

See footnote(s) at end of table. --continued
A - 16 Appendix A 2022 Census of Agriculture

USDA, National Agricultural Statistics Service

Table A. Summary of U.S. Coverage, Nonresponse, and Misclassification Adjustments: 2022 (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

Producers characteristics by1(see text) - Con.

Hispanic, Latino, or

Spanish origin ..............................................................................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:

Never served or only on active duty for training

in the Reserves or National Guard (see text) ................ producers

Active duty now or in the past (see text) ............................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:

Farms with gains of2

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 ofLess 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 ..........................................................farms

acres

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

acres

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

acres

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

acres

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

acres

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

acres

83,505

36,866,263

40,621

55,781,477

16,072

2,608,401

28,723

4,318,741

2,779

799,151

1,829,449

823,942,903

24,990

9,661,430

3,068,291

305,753

57,000

239,480

426,616

519,430

837,525

826,931

467,062

49,749

23,703

124,479

344,589

85,880

625,404

130,660

2,150,109

99,992

3,592,062

324,637

175,933,735

62,687

31,899

267,812

790,294

236,036

1,718,557

292,088

4,633,969

122,322

4,229,039

104,145

19,626,105

732,123

87,954,742

622,162

29,214,479

36,024

9,309,855

60,809

73,817,751

240,530

388,509,039

31,877

9,176,809,391

5,961

2,270,147

289,382

80,597,963

2,179

1,611,312

17,307

10,406,490

81,601

25,194,192

13,591

4,692,243

270,851

84,599,236

3,824

2,279,958

14,283

7,740,383

2,844

2,645,621

2,213

3,406,570

500

186,323

1,029

237,837

161

172,632

36,736

18,144,054

832

496,631

56,215

6,572

3,134

13,724

8,577

7,585

16,665

11,553

6,465

1,906

1

4,546

12

2,687

19

3,486

60

2,301

83

5,129

2,671

1,732

1

5,810

16

4,562

32

6,002

94

2,859

99

2,130

704

10,470

1,577,554

7,989

434,384

490

206,563

3,353

13,682,264

11,966

10,505,439

1,554

320,913,347

320

51

13,624

2,096,870

291

180,842

2,799

1,393,355

5,016

1,116,052

593

89,710

10,262

3,817,578

454

194,244

1,705

492,954

53.5

26.4

52.5

14.2

46.1

28.0

58.8

53.2

48.6

23.7

44.1

28.5

48.6

33.3

46.3

45.8

61.2

60.1

52.2

47.8

45.2

41.9

39.7

44.2

43.4

41.1

40.6

37.5

37.3

36.3

36.2

35.7

35.6

33.5

24.2

49.0

49.3

51.3

51.6

52.2

52.2

51.4

51.3

49.7

49.6

44.8

39.1

38.2

25.0

37.8

31.4

28.6

12.2

48.4

25.0

51.9

11.5

46.3

33.1

38.3

17.3

34.8

27.7

36.8

26.7

38.3

33.0

33.5

26.4

35.7

26.9

31.6

28.5

31.1

22.9

36.5

18.2

20.7

2.4

19.4

1.5

19.4

4.8

25.5

8.8

22.5

5.9

15.5

3.1

15.0

3.1

13.6

15.2

12.4

15.1

13.0

14.0

14.0

14.1

14.5

17.2

17.1

14.6

14.1

11.6

11.5

9.7

9.6

8.2

8.1

5.0

4.5

21.0

21.2

23.4

23.7

23.9

24.0

22.4

22.2

18.3

18.1

10.3

6.3

25.1

9.3

24.4

11.5

17.4

4.7

17.1

3.5

22.4

3.1

16.8

9.2

10.0

2.4

5.1

1.7

1.4

0.9

2.0

0.7

3.9

1.3

3.3

1.6

5.5

2.5

1.8

1.7

2.3

0.9

18.9

10.2

12.2

5.6

13.1

10.7

17.3

21.9

13.7

7.3

15.5

10.6

19.1

18.7

18.0

16.8

29.6

26.9

23.1

18.7

16.5

14.0

12.9

16.3

16.1

14.2

14.1

13.7

13.7

13.4

13.3

13.7

13.7

13.1

6.0

15.0

15.4

16.1

16.1

16.3

16.2

16.8

17.0

17.1

17.1

17.4

15.1

7.0

4.6

7.1

9.2

6.8

1.4

17.5

5.3

19.2

1.7

17.8

5.6

12.9

0.9

12.6

8.6

26.2

16.5

18.9

11.6

17.4

14.3

18.3

14.2

17.4

12.4

12.4

5.8

15.1

5.3

13.9

13.7

21.0

7.1

13.7

12.5

15.9

22.5

12.4

10.4

13.1

14.8

14.5

11.5

14.7

13.7

19.2

18.1

16.0

15.2

14.7

13.7

12.3

10.7

10.3

12.3

12.5

12.1

12.1

13.2

13.4

13.7

13.8

15.3

13.7

13.0

12.7

11.9

11.8

12.0

12.0

12.2

12.1

14.3

14.4

17.1

17.7

6.1

11.1

6.3

10.8

4.3

6.2

13.8

16.2

10.3

6.7

11.7

18.4

15.4

14.0

17.0

17.4

9.2

9.4

17.3

20.7

12.2

10.8

14.2

11.1

8.7

13.6

16.8

15.4

19.1

12.0

See footnote(s) at end of table. --continued
2022 Census of Agriculture Appendix A A - 17

USDA, National Agricultural Statistics Service

Table A. Summary of U.S. Coverage, Nonresponse, and Misclassification Adjustments: 2022 (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.

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

acres

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

acres

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

acres

Forage - land used for all hay and haylage,

grass silage, and greenchop ....................................................... farms

acres

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

acres

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

acres

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

acres

Sweet corn (see text) ................................................................ farms

acres

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

acres

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

acres

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

acres

Grapes (including muscadine) (see text) .................................. farms

acres

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

acres

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

acres

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

acres

4,919

1,425,267

9,112

2,430,308

15,538

914,059

694,318

54,159,397

69,452

3,927,522

15,099

1,076,285

25,997

323,808

17,768

467,542

11,187

367,432

110,544

6,203,085

27,463

411,262

27,951

1,108,161

7,010

(D)

8,586

1,632,710

35,331

356,896

790

200,271

482

151,793

2,027

71,019

13,624

692,839

9,225

350,868

1,933

48,022

3,986

176,612

2,837

31,026

1,459

25,947

4,245

482,919

834

11,567

895

194,002

668

(D)

550

141,363

2,057

31,554

33.5

21.3

33.1

29.0

39.8

37.9

38.1

33.0

39.7

14.9

35.1

8.3

38.5

10.5

34.3

14.1

38.6

17.5

45.1

25.0

45.5

23.8

44.6

28.9

45.8

(D)

35.3

26.5

46.3

27.4

3.2

1.7

4.0

1.3

11.4

4.0

19.0

9.2

13.7

1.3

12.1

0.3

13.9

3.1

10.7

1.7

13.4

2.3

18.0

8.0

15.9

4.1

15.5

9.8

22.0

(D)

22.9

11.6

15.2

3.6

13.5

7.6

16.9

13.7

16.0

16.3

12.2

13.2

15.2

2.5

12.8

0.6

14.7

3.6

14.2

2.9

14.8

3.8

15.7

6.6

15.9

3.1

14.9

6.6

18.0

(D)

8.0

5.5

16.7

4.7

16.8

12.0

12.2

14.0

12.4

17.6

6.9

10.5

10.7

11.2

10.1

7.3

9.9

3.8

9.3

9.6

10.4

11.4

11.4

10.4

13.7

16.6

14.1

12.5

5.8

(D)

4.4

9.4

14.5

19.1

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.
A - 18 Appendix A 2022 Census of Agriculture

USDA, National Agricultural Statistics Service

Table B. Reliability Estimates of U.S. Totals: 2022

[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 ..............................$1,000

Farms by value of sales:

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

Farms by legal status for tax purposes:

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

Producers characteristics by1(see text)

Sex of operator:

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

acres

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

acres

Primary occupation:

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

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

1,900,487

880,100,848

234,592

1,084,341

566,912

14,193,479

130,096

7,553,962

153,239

12,601,546

138,398

16,045,194

108,796

17,126,924

69,602

13,747,397

52,177

12,415,492

166,600

59,494,534

120,456

83,738,842

76,311

105,224,602

83,308

536,874,535

254,574

51,155,829

55,833

3,774,534

543,087,166

499,816

56,798

179,054

292,472

166,149

593,713

191,688

1,359,995

168,876

2,386,213

53,378

1,180,735

100,390

3,162,262

43,657

1,938,084

116,220

8,237,216

122,968

19,715,890

85,266

30,549,461

67,641

48,248,980

105,384

425,365,347

1,609,899

508,952,143

125,457

157,864,970

108,693

134,277,030

18,955

16,586,984

37,483

62,419,721

1,356,796

311,935,837

423,889

489,711,406

119,802

78,453,605

1,757,655

844,939,806

1,110,546

407,340,844

1,415,315

1,958,729

1.9

2.3

4.9

4.9

2.6

2.5

2.2

2.2

2.5

2.4

1.6

1.6

1.7

1.7

2.1

2.2

2.5

2.5

1.7

1.7

1.9

1.9

3.0

3.1

2.9

3.2

3.0

3.0

2.8

4.6

1.8

2.6

4.0

4.5

4.3

3.8

3.8

3.6

3.6

2.3

2.3

2.5

2.5

3.9

4.0

5.0

5.0

3.4

3.5

4.0

3.9

5.7

5.7

2.5

2.4

3.5

2.5

2.1

2.0

1.6

2.2

1.8

3.0

2.7

3.2

1.8

5.8

2.3

2.5

1.5

2.2

2.2

3.0

1.9

2.1

2.2

2.6

1.6

2.1

Producers characteristics by1(see text) - Con.

Hispanic, Latino, or

Spanish origin ..................................................................... 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:

Never served or only on active duty for training

in the Reserves or National Guard (see text) ........producers

Active duty now or in the past (see text) ................... 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:

Farms with gains of2

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 ofLess 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 ................................................. farms

acres

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

acres

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

acres

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

acres

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

acres

83,505

36,866,263

40,621

55,781,477

16,072

2,608,401

28,723

4,318,741

2,779

799,151

1,829,449

823,942,903

24,990

9,661,430

3,068,291

305,753

57,000

239,480

426,616

519,430

837,525

826,931

467,062

49,749

23,703

124,479

344,589

85,880

625,404

130,660

2,150,109

99,992

3,592,062

324,637

175,933,735

62,687

31,899

267,812

790,294

236,036

1,718,557

292,088

4,633,969

122,322

4,229,039

104,145

19,626,105

732,123

87,954,742

622,162

29,214,479

36,024

9,309,855

60,809

73,817,751

240,530

388,509,039

31,877

9,176,809,391

5,961

2,270,147

289,382

80,597,963

2,179

1,611,312

17,307

10,406,490

81,601

25,194,192

13,591

4,692,243

270,851

84,599,236

3,824

2,279,958

3.4

7.2

5.4

6.1

3.1

7.1

3.6

5.5

5.8

21.6

2.0

2.2

3.3

5.1

1.8

2.1

5.5

5.7

2.0

1.5

2.0

1.4

1.4

3.8

4.5

3.7

3.5

3.1

3.1

2.7

2.8

2.3

2.3

1.6

1.5

2.8

2.1

2.2

2.0

1.9

1.9

2.1

2.0

2.3

2.3

2.0

3.6

1.4

1.8

1.3

1.5

1.4

2.2

5.5

18.5

5.0

2.7

4.9

3.5

5.4

2.3

4.7

2.6

13.4

11.2

16.2

13.4

6.1

4.4

4.4

1.9

3.8

4.5

11.9

8.5

See footnote(s) at end of table. --continued
2022 Census of Agriculture Appendix A A - 19

USDA, National Agricultural Statistics Service

Table B. Reliability Estimates of U.S. Totals: 2022 (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.

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

acres

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

acres

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

acres

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

acres

Forage - land used for all hay and haylage,

grass silage, and greenchop .............................................. farms

acres

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

acres

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

acres

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

acres

14,283

7,740,383

4,919

1,425,267

9,112

2,430,308

15,538

914,059

694,318

54,159,397

69,452

3,927,522

15,099

1,076,285

25,997

323,808

11.9

6.4

16.1

14.1

5.3

6.2

13.0

7.8

2.0

1.3

13.3

8.9

12.8

4.5

15.3

54.5

Selected crops harvested: - Con.

Land in vegetables (see text) - Con.

Sweet corn (see text) ........................................................farms

acres

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

acres

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

acres

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

acres

Grapes (including muscadine) (see text) ..........................farms

acres

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

acres

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

acres

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

acres

17,768

467,542

11,187

367,432

110,544

6,203,085

27,463

411,262

27,951

1,108,161

7,010

(D)

8,586

1,632,710

35,331

356,896

16.0

6.6

13.0

7.1

3.8

7.8

3.0

2.8

3.2

17.5

9.5

(D)

6.4

8.7

5.8

8.8

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.
A - 20 Appendix A 2022 Census of Agriculture

USDA, National Agricultural Statistics Service

Table C. Summary of Coverage, Nonresponse, and Misclassification Adjustments by State: 2022

[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 ......................................................................................................

1,900,487

37,362

1,173

16,710

37,756

63,134

36,056

5,058

2,158

44,703

39,264

6,569

22,877

71,123

53,599

86,911

55,734

69,425

25,006

7,036

12,550

7,083

45,581

65,531

31,290

87,887

24,266

44,479

3,122

3,949

9,998

20,976

30,650

42,817

25,068

76,009

70,378

35,547

49,053

1,054

22,633

28,299

63,105

230,662

17,386

6,537

38,995

32,076

22,787

58,521

10,544

880,100,848

8,629,101

869,852

25,525,087

13,722,525

24,190,604

30,213,899

372,014

522,834

9,701,400

9,939,313

1,053,302

11,547,963

26,292,041

14,602,240

29,978,165

44,794,702

12,431,190

7,986,381

1,225,046

1,978,036

464,451

9,472,069

25,442,625

10,251,497

27,026,243

57,601,320

36,680

1,408

2

2,892

1,863

4,099

3,988

339

75

2,861

2,072

647

1,266

3,161

993

5,597

3,554

6,407

1,878

389

523

387

6,043

4,913

1,577

1,967

4,530

1,964

147

532

396

1,660

994

3,598

2,281

892

2,177

2,043

1,303

83

1,239

1,690

4,120

10,786

608

700

4,076

2,278

1,578

4,272

281

19,889,860

659,543

(L)

1,787,679

882,987

3,158,639

4,681,646

31,368

16,821

1,486,112

2,713,044

155,904

897,873

464,910

336,018

769,892

1,079,764

656,869

1,296,701

65,109

50,262

35,383

4,493,603

7,766,836

1,069,522

653,952

7,185,564

44.5

48.7

7.7

61.0

42.3

46.7

44.5

56.1

50.8

57.9

47.4

49.9

42.7

37.1

40.9

32.9

44.2

44.0

52.1

53.9

41.3

52.3

47.5

35.6

46.9

41.5

44.7

45.7

45.8

58.2

46.5

54.5

43.6

45.2

48.2

43.2

44.5

45.8

45.8

46.3

53.8

44.6

42.2

48.3

42.3

53.4

42.9

48.4

40.1

37.5

43.9

27.7

39.2

0.6

6.4

31.2

19.9

26.1

36.3

29.8

25.8

34.4

6.2

18.9

27.1

27.0

29.7

33.4

33.9

37.7

38.4

25.1

34.9

27.2

31.9

37.2

33.3

24.0

15.7

11.8

(NA)

21.8

13.5

26.4

15.5

25.0

19.8

24.0

12.3

20.4

15.1

10.4

17.4

8.8

7.9

15.5

19.4

25.1

14.0

23.7

23.0

11.1

13.4

15.9

10.6

7.9

16.8

21.1

17.3

20.1

15.7

13.1

4.9

17.4

13.4

23.0

18.3

22.1

12.8

5.5

14.8

20.2

19.1

15.8

14.2

23.7

16.4

17.5

15.8

3.0

4.9

(NA)

0.5

4.3

6.5

2.7

14.7

6.3

3.1

3.8

0.7

1.3

2.6

2.9

2.0

2.3

7.5

5.2

16.6

5.2

15.5

5.2

1.6

3.7

5.7

1.3

15.4

17.6

7.7

10.5

14.5

11.7

17.3

13.1

17.1

18.7

19.4

15.1

19.1

13.0

14.7

10.3

20.2

16.2

18.2

8.5

10.9

13.8

16.3

12.8

15.8

15.7

21.7

17.7

15.0

7.7

10.5

15.5

10.8

17.9

25.9

14.8

22.1

9.4

11.7

9.5

20.6

21.9

15.6

14.0

15.2

24.0

16.1

9.8

13.6

14.1

17.1

10.3

13.2

0.6

1.0

6.5

7.1

12.1

7.3

8.4

3.6

9.9

4.2

11.4

10.5

8.2

7.2

18.1

12.0

8.5

5.6

6.1

7.6

7.9

5.7

8.3

9.4

10.6

13.4

19.3

(NA)

28.8

14.2

8.7

11.8

18.0

13.9

15.2

15.7

14.4

8.5

13.7

8.9

13.8

16.1

12.2

14.5

20.4

16.5

14.8

8.2

11.7

17.6

10.0

12.4

20.1

14.0

29.4

18.8

18.9

17.1

14.2

17.4

11.1

9.0

13.4

15.8

14.8

20.4

17.3

11.8

14.1

7.9

13.6

12.6

14.9

10.1

5.9

11.0

14.4

21.1

(NA)

4.9

20.5

6.3

11.3

14.3

15.0

19.1

20.8

1.2

6.2

14.0

15.9

20.6

13.1

14.4

24.0

16.2

13.8

11.8

14.0

24.5

25.2

18.3

12.1

--continued
2022 Census of Agriculture Appendix A A - 21

USDA, National Agricultural Statistics Service

Table C. Summary of Coverage, Nonresponse, and Misclassification Adjustments by State: 2022 (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.

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 .....................................................................................................

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 .....................................................................................................

43,975,693

5,896,654

417,187

711,502

39,128,563

6,502,286

8,128,136

38,537,022

13,652,346

32,897,563

15,295,779

7,058,325

59,076

4,553,922

42,304,601

10,732,951

125,471,325

10,494,923

1,173,890

7,309,687

13,855,414

3,549,104

13,784,678

28,776,321

543,087,166

9,035,897

90,850

5,202,714

13,899,149

59,005,675

9,222,516

704,034

2,095,755

10,224,600

13,239,372

673,776

10,892,201

26,417,147

18,029,033

43,935,488

23,985,145

8,005,745

4,807,123

869,526

3,378,072

607,161

12,212,745

28,482,097

8,247,775

14,697,022

4,544,537

29,413,398

959,681

249,304

1,487,751

2,949,036

8,037,292

18,692,574

12,138,799

15,412,137

8,540,529

6,771,166

10,282,392

92,830

4,427,204

12,935,225

5,161,034

32,166,561

2,341,598

1,033,194

5,491,996

12,751,572

947,765

16,698,780

1,600,189

814,578

255,058

64,721

16,084

8,449,483

125,847

562,053

1,193,151

195,852

1,345,687

795,173

181,053

2,310

281,360

1,311,508

462,250

7,392,284

1,812,347

104,541

654,280

1,461,196

97,794

1,381,602

2,171,042

9,776

348

(L)

145

648

3,381

544

32

69

499

934

54

232

190

397

1,728

301

479

1,026

38

101

57

4,353

6,450

674

370

698

740

15

16

32

125

153

2,909

296

251

168

248

214

8

267

365

182

960

108

49

446

703

21

1,231

77

31.8

9.3

42.4

26.6

22.6

29.6

29.3

40.2

27.7

31.5

16.4

36.9

16.4

40.1

34.0

34.5

26.8

11.3

36.0

30.7

16.4

31.0

28.1

18.3

22.7

32.9

1.2

11.3

27.7

24.7

11.4

12.6

37.9

26.4

27.8

13.0

11.1

21.4

23.3

26.3

16.5

18.8

25.8

15.7

21.7

22.1

16.7

29.9

28.0

23.8

23.2

23.5

12.1

17.0

15.5

7.2

17.1

29.0

32.2

20.9

17.4

14.9

22.7

16.9

25.1

26.7

20.9

19.9

10.4

17.5

18.9

14.1

14.0

18.4

15.4

3.3

0.5

13.5

6.4

2.4

8.2

3.8

3.1

4.1

3.6

2.2

9.5

6.4

5.0

2.4

5.6

2.6

1.8

11.6

6.8

3.0

9.8

7.2

2.4

4.2

7.8

(NA)

2.0

3.3

5.3

3.7

8.5

11.8

6.4

7.9

2.5

2.1

3.2

4.5

5.6

2.8

3.2

4.1

7.8

3.6

10.5

3.4

2.3

1.8

4.7

1.1

3.6

1.4

8.9

1.1

2.9

2.0

4.3

3.0

2.8

4.3

1.3

3.3

9.0

7.1

4.1

5.0

7.3

3.8

7.8

5.4

1.7

5.6

4.7

3.4

14.3

6.9

6.8

3.8

7.8

5.6

9.2

25.8

9.1

17.9

7.2

7.8

2.0

15.4

18.3

11.8

8.4

5.3

12.9

10.1

5.0

11.3

11.8

8.4

5.5

5.7

1.2

1.5

4.7

6.2

3.8

2.2

13.1

4.7

3.8

6.7

3.5

8.1

8.0

7.2

3.1

5.0

3.7

2.7

4.3

6.1

2.4

5.9

3.3

5.0

9.7

5.0

3.4

1.9

0.6

1.4

1.1

9.4

20.3

11.0

5.3

1.3

4.0

4.7

6.5

12.3

6.0

3.3

3.7

6.4

4.9

1.4

3.5

6.6

5.5

14.1

2.0

22.2

16.4

12.4

15.8

16.3

11.4

14.5

9.9

7.0

19.6

8.0

19.7

13.3

17.1

15.8

4.2

11.5

13.8

8.5

9.9

9.1

7.5

12.9

19.4

(NA)

7.8

19.8

13.2

3.8

1.8

12.9

15.3

16.2

3.8

5.4

10.1

10.7

13.4

10.7

10.6

18.1

5.2

13.8

5.6

10.9

21.7

23.0

14.1

12.4

14.9

7.3

6.2

13.7

2.8

14.1

15.3

8.9

7.1

7.8

12.4

15.4

3.2

11.5

10.3

9.9

9.3

3.0

3.3

8.6

11.1

5.0

7.1

6.5
A - 22 Appendix A 2022 Census of Agriculture

USDA, National Agricultural Statistics Service

Table D. American Indian or Alaska Native Producers: 2022

[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 ............................................

78,854

1,253

123

17,402

1,363

2,398

1,205

48

11

913

556

264

517

373

344

288

1,195

739

509

123

150

83

747

467

78,316

1,253

123

17,087

1,363

2,397

983

48

11

913

556

264

517

373

344

288

1,195

739

509

123

150

83

747

467

538

-

-

315

-

1

222

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

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 ..............................................

295

1,689

1,796

261

447

48

85

8,479

400

1,219

475

578

18,211

1,318

335

13

355

1,265

952

5,640

1,177

109

526

1,130

312

354

314

295

1,689

1,796

261

447

48

85

8,479

400

1,219

475

578

18,211

1,318

335

13

355

1,265

952

5,640

1,177

109

526

1,130

312

354

314

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

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.