Small Grains County Estimates - Quality Measures

Small Grains County Estimates
Methodology and Quality Measures
ISSN: 2837-0058

Released June 1, 2023, by the National Agricultural Statistics Service (NASS), Agricultural Statistics Board, United States Department of
Agriculture (USDA).

Scope and Purpose: The National Agricultural Statistics Service (NASS) publishes county-level estimates of planted
acres, harvested acres, production, and yield for small grain crops on an annual basis in select states. Currently, small
grains county estimates are published for barley in 10 states, oats in 18 states, Durum wheat in 4 states, other spring wheat
in 6 states, and winter wheat in 25 states. Information collected from two NASS surveys provide the data used to establish
small grains county estimates: the September Agricultural Survey and the Small Grains County Agricultural Production
Survey (CAPS).
Data from the September Agricultural Survey are first used to set final (end of season) crop estimates for planted and
harvested acres, production, and yield at the State and National level for the small grain commodities listed above. The
methodology for this survey is described in the Small Grains Summary Methodology and Quality Measures report. The
September Agricultural Survey does not provide enough data to set county-level estimates on its own.
A supplemental survey, the Small Grains CAPS, is conducted in 32 states to collect small grains crop data from a
mutually exclusive set of producers from the September Agricultural Survey. The responses from the Small Grains CAPS
are combined with the September Agricultural Survey data. The resulting dataset is summarized and then used along with
administrative data to estimate acreage, yield, and production at the county level.
The uses for county estimates of crop acreage, production, and yield are extensive and varied. County estimates help
producers find the best market opportunities for their commodities. Often, recommendations and forecasts presented in
agricultural magazines, news releases, etc. are based on data found in NASS reports. Uses of data by farm organizations,
financial institutions, insurance companies, agribusinesses, State and National farm policy makers, and foreign buyers of
agricultural products may range from maintaining a basic data series to preparing marketing campaigns and determining
needs and rates on farm loans and insurance. Government agencies at various levels are important users of county
estimates statistics. County yield data resulting from this survey are used by USDA for various programs, including those
administered by USDA’s Farm Service Agency (FSA) and Risk Management Agency (RMA). For example, when a
natural disaster such as drought or flooding impacts crop production, these data are crucial to the agricultural industry.
Other government agencies, universities, and research organizations use county estimate data to determine many
production and economic values on a small area basis. County estimates are the only source of yearly localized estimates.
Timeline: The reference date for the September Agricultural Survey is the first of the month with a data collection period
of approximately 15 days. State and National estimates are published in the Small Grains Annual Summary at the end of
September.
Small Grains CAPS data collection in each state begins between late July to August based on approximate harvesting
date. Data collection for Small Grains CAPS for all states is completed by mid-October. Regional Field Office (RFO)
statisticians have an additional two weeks to review and analyze data with any corrections complete and final summary
run by the end of October. Creation and review of the modeled estimates occurs in November and early December so that
all final county estimates for small grains can be published by mid-December.
Sampling: The target population for the September Agricultural Survey and the Small Grains CAPS is all farms with
cropland and/or storage capacity. The NASS list frame includes all known farms. Crop acreages, storage capacity, and
other agricultural data of each farm are maintained on the list frame to allow NASS to define list frame sampling
populations for specific surveys and to employ efficient sampling designs. Only list frame records with positive cropland
acres, planted acreage of the desired commodities, or storage capacity are included in the list frame population.

The Small Grains CAPS sample is designed to be a supplemental sample to the September Agricultural survey list frame
sample. For that reason, the two samples are mutually exclusive, and the data from both surveys can be combined after
data collection is complete for both surveys.
The September Agricultural Survey and Small Grains CAPS list frame samples are selected using a Multivariate
Probability Proportional to Size (MPPS) sampling scheme. Each list frame record is assigned a measure of size based on
the frame data for multiple specified commodities. The MPPS design allows target sample sizes for the commodities of
interest to be set at the county level. The desired number of samples for each commodity can be controlled with a
minimum overall sample size. The MPPS design makes it easier to change samples to meet the needs of the crops
program changes. The MPPS design is a more efficient design because operations will have a more optimal probability of
selection based upon their individual commodities and size.
After the list frame samples for the September Agricultural Survey and Small Grains CAPS are drawn, the sample weights
are calibrated so the sum of the weighted, targeted commodities in the sample of interest equals the sum of the list frame
data for the targeted commodities. For example, the sum of the weighted winter wheat list frame data equals the sum of
the population list frame data for winter wheat.
Data Collection: All Regional Field Offices (RFO) use the same standardized questionnaire for data collection for each
survey. For consistency across modes, the paper version is considered the master questionnaire and the Computer Assisted
Self Interview (CASI), mobile Computer Assisted Telephone Interview (mCATI), and Computer Assisted Telephone
Interview (CATI) instruments are built to model the paper questionnaire. Questionnaire content and format are evaluated
annually through a specifications process where requests for changes are evaluated and approved or disapproved. Input
may vary from question wording or formatting to a program change involving the deletion or modification of current
questions or addition of new ones. If there are significant changes to either the content or format proposed, a NASS
survey methodologist will pre-test the changes for usability. Prior to the start of data collection, all modes of instruments
are reviewed, and CASI, mCATI, and CATI instruments are thoroughly tested. For consistency across the Agricultural
Survey and corresponding CAPS, a basic rule requires that the acreage and yield questions be asked identically on both
sets of questionnaires.
All federal data collections require approval by the Office of Management and Budget (OMB). NASS must document the
public need for the data, apply sound statistical practice, prove the data does not already exist elsewhere, and ensure the
public is not excessively burdened. The questionnaires must display an active OMB number that gives NASS the
authority to conduct the survey, a statement of the purpose of the survey and the use of the data being collected, a
response burden statement that gives an estimate of the time required to complete the form, a confidentiality statement
that the respondent’s information will be protected from disclosure, and a statement saying that response to the survey is
voluntary and not required by law.
In addition to asking the specific acreage and production questions, all instruments collect information to verify the
sampled unit, determine any changes in the name or address, identify any partners to detect possible duplication, verify
the farm still qualifies for the target population, and identify any additional operations operated by the sampled operator.
Sampled farms and ranches receive a cover letter with the questionnaire mailing explaining the survey and providing
instructions for completing the survey on the internet. The letter also notifies them that they will be contacted for survey
purposes only if they do not return the questionnaire or complete the survey on the web. All modes of data collection are
utilized for each survey. Most of the data are collected by CATI in one of the five Data Collection Centers. Limited
personal interviewing may be done, generally for large operations or those with special handling arrangements. A
coordination tool is available to determine if any sampled farms are in multiple on-going surveys, so data collection can
be coordinated.
After data collection is completed for the September Agricultural Survey but before Small Grains CAPS data collection is
complete, an adaptive survey design program is executed on the combined data collected to date. The purpose is to
prioritize counties to increase commodity coverage and meet publication standards. It is based on individual county crop
profiles and is used to refocus nonresponse follow-up calling in an unbiased and efficient manner. When a county meets
the standards for the number of reports or coverage for all targeted commodities, the adaptive survey design program will
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assign the county to the lowest priority group for phoning. This will leave the county still available for calling but put the
remaining records for the county at the bottom of the call scheduler list. The program is executed several times throughout
the course of nonresponse follow-up calling and reports are generated to gauge the status of data collection.
Survey Edit: As survey data are collected and captured, data are edited for consistency and reasonableness using
automated systems. The edit logic ensures the coding of administrative data follows the methodological rules associated
with the survey design. Relationships between data items (i.e., responses to individual questions) on the current survey are
verified. Some data items in the current survey are compared to data items from earlier surveys to ensure certain
relationships are logical. The edit assigns a status to each record, indicating whether the record passes or fails the edit
requirements for consistency and reasonableness. Records that fail edit requirements must be updated or must be certified
by an analyst to be exempt from the failed edit requirement. All records must pass edit requirements, or be certified
exempt, before further analysis and summary.
Since the Small Grains CAPS data are merged with September Agricultural Survey data to establish county estimates, a
consistent edit logic must be applied to the data of both surveys. That is, September Agricultural Survey and Small Grains
CAPS use the same edit limits and logic.
Analysis Tools: Edited data from both surveys are processed and analyzed separately through standard interactive
analysis tools which display data for all reports by item. The tools provide scatter plots, tables, charts, and special
tabulations that allow the analyst to compare record-level data with previously reported data for the same record and
reported data from similar records. Atypical responses, unusual data relationships, and statistical outliers for all items are
revealed by the analysis tool. RFO and Headquarters staff review such relationships to determine if they are correct. Data
found to be in error are corrected, while accepted data are retained.
Nonsampling Errors: Nonsampling error is present in any survey process. This error includes reporting, recording, and
editing errors, as well as nonresponse error. Steps are taken to minimize the impact of these errors, such as questionnaire
testing, comprehensive interviewer training, validation and verification of processing systems, application of detailed
computer edits, and evaluation of the data via the analysis tool. The respondent pool is monitored and reviewed during
and after data collection, and data collection strategies modified where necessary, to continually minimize nonresponse
error.
Estimators: Response to the September Agricultural Survey and Small Grains CAPS is voluntary. Some producers refuse
to participate in the surveys. Others cannot be located during the data collection period, and some submit incomplete
reports. These nonrespondents must be accounted for if accurate estimates of crop acreage, production, and yield are to be
made. For these surveys, nonrespondents are accounted for by reallocating the sample weights of the nonrespondents to
the respondents. No item-level imputation is performed.
Nonresponse weighting groups are based on operation size and type as well as Agricultural Statistics Districts (ASD). The
nonresponse weighting groups for both samples are based on the frame data items for total cropland and on-farm grain
storage capacity. ASDs are geographically defined groupings of counties in each state. These nonresponse weighting
groups ensure that operation size and location are taken into consideration when reweighting.
When the two surveys are combined for summarization, two kinds of estimators are used for county-level indications:
direct expansions and ratio estimators. Direct expansions are used to estimate totals such as planted and harvested acres
and production. Reweighted direct expansions are calculated by summing the reported commodity values multiplied by
the nonresponse-adjusted sample weights in each nonresponse weighting group. In each weighting group, the adjustment
is calculated by summing the weights for all sampled records and dividing by the sum of the weights from the completed
records. This ratio is applied to the weights of the completed records and assumes that the data of the nonrespondents are
like the data of the respondents.
The ratio estimator takes the form of a ratio of two direct expansions which are calculated by summing the reported
commodity values multiplied by the original sample weights adjusted for nonresponse as described above. Ratio
estimators are used for all within-survey ratios (e.g., yield and harvested to planted acres) and across-survey ratios (e.g.,
current year to previous year planted acreages). Both the numerator and denominator must be complete to be included in
Small Grains County Estimates Methodology and Quality Measures (June 2023)
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3

the ratio. If either of these components is not reported, the sampling unit is excluded from the estimate and the weights of
the completed records are adjusted accordingly. Variances and coefficients of variation (CVs) are calculated for all direct
expansions and ratio estimates to measure the precision of the acreage and production estimates. One advantage of the
ratio estimator is that the CVs tend to be smaller than those for the direct expansions.
Estimation: When all samples are accounted for, all responses fully edited, and the analysis material is reviewed, each
RFO executes the combined summary for their region. The summary results provide multiple point estimates and
corresponding standard errors for each crop being estimated at the county level. It also provides information used to assess
the performance of the current survey and evaluate the quality of the survey results, such as counts of positive reports for
each indication, response rates, and percent of the expansion from completed reports.
Beginning with the 2020 crop year, model-based county-level estimates were incorporated into the NASS estimation
process for small grains county estimates. Bayesian small area estimation models for county-level planted acreage,
harvested acreage, and yield are fit separately for each crop and are processed in batches; common methodologies are
applied nationwide. The planted and harvested acreage models input current year survey expansions, survey standard
errors, and administrative data from FSA and RMA. Yield models input current survey ratios, survey standard errors, and
the National Commodity Crop Productivity Index (NCCPI). County-level production estimates are derived from modelbased yield and harvested acreage estimates. Model based estimates and corresponding standard deviations are calculated
as posterior means and posterior standard deviations, respectively. These county-level estimates are benchmarked against
previously released state-level official estimates established by the Agricultural Statistics Board (ASB). Coefficients of
variation are calculated as ratios of standard deviations and corresponding estimates expressed as a percentage.
Once the model-based county estimates are reviewed by the RFO staff, they are submitted to the ASB. The ASB consists
of both HQ and RFO staff and reviews the estimates for accuracy and consistency across state boundaries and verifies that
proper procedures are followed throughout the entire process.
Estimates are open to revision on a preannounced schedule only if updated information becomes available. If changes are
made to the State-level estimates during the normal annual revision period, then the model-based county estimates are
recreated to ensure that county-level estimates continue to add up to state-level estimates. These previous year revisions
are released at the same time as the estimates for the current year are published. Estimates will also be reviewed following
the 5-year Census of Agriculture, which is an extensive data collection effort of all known farm operations across the U.S.
The information gathered from the Census of Agriculture provides the last chance for revision.
To be published, county estimates for a given crop must meet certain publication standards. These standards include
obtaining a certain number of reports in each county where the respondent reported both harvested acreage and yield or
having the harvested acres from reports with respondent-reported yields account for a certain minimum percent of the
current year's harvested acreage estimate for that county. Estimates that do not meet these standards are combined with
other counties and published as “Other Counties” totals. The county-level estimates are then published to Quick Stats, the
NASS online database of published estimates. The total number of counties published for each crop are displayed in the
table below.

Quality Metrics for Small Grains County Estimates
Purpose and Definitions: Under the guidance of the Statistical Policy Office of the Office of Management and Budget
(OMB), the United States Department of Agriculture’s National Agricultural Statistics Service (NASS) provides data
users with quality metrics for its published data series. The metrics tables below describe the performance data for all
surveys contributing to the publication. The accuracy of data products may be evaluated through sampling and
nonsampling error. The CVs measure the error due to sampling as well as some nonsampling error. Nonsampling error is
also evaluated by examining survey response rates.
Sample size is the number of observations selected from the population to represent a characteristic of the population.
Operations that did not have the item of interest or were out of business at the time of data collection have been excluded.
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Small Grains County Estimates Methodology and Quality Measures (June 2023)
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Response rate is the proportion of the above sample that completed the survey. This calculation follows Guideline 3.2.2
of the OMB Standards and Guidelines for Statistical Surveys (September 2006).
Coefficient of variation provides a measure of the size for the standard error relative to the point estimate and is used to
measure the precision of the results of the model-based estimator. Percentiles of the published model-based CVs for each
crop and estimate type are displayed in the table below. All model-based CVs for published county estimates can be found
using the NASS Quick Stats system.

Small Grains County Estimates Methodology and Quality Measures (June 2023)
USDA, National Agricultural Statistics Service

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September Agricultural Survey Sample Size and Response Rate - States and United States:
2021 and 2022
State

Sample Size
2021

Response Rate
2022

(number)

2021

(number)

2022

(percent)

(percent)

Alabama .......................................
Alaska ...........................................
Arizona .........................................
Arkansas .......................................
California ......................................
Colorado .......................................
Delaware ......................................
Florida ...........................................
Georgia .........................................
Idaho .............................................

752
149
304
1,216
1,489
1,152
231
439
1,134
1,326

782
147
317
1,206
1,440
1,142
239
409
1,130
1,325

57.3
65.1
66.8
53.3
52.0
42.2
43.3
48.5
50.4
47.1

61.3
43.5
63.1
53.8
41.0
43.8
33.1
48.2
50.4
40.2

Illinois ............................................
Indiana ..........................................
Iowa ..............................................
Kansas ..........................................
Kentucky .......................................
Louisiana ......................................
Maine ............................................
Maryland .......................................
Michigan .......................................
Minnesota .....................................

2,184
1,977
2,552
2,552
1,158
878
218
693
1,397
2,178

2,330
2,028
2,631
2,612
1,142
867
230
699
1,395
2,138

54.7
47.2
48.0
41.5
69.2
71.1
58.7
50.4
58.4
48.6

51.4
47.9
45.3
37.9
61.7
67.1
59.1
42.6
53.2
40.5

Mississippi ....................................
Missouri ........................................
Montana ........................................
Nebraska ......................................
New Jersey ...................................
New Mexico ..................................
New York ......................................
North Carolina ...............................
North Dakota .................................
Ohio ..............................................

1,197
2,470
1,838
2,268
342
494
1,002
1,114
2,370
1,381

1,181
2,470
1,873
2,246
332
517
927
1,110
2,457
1,420

67.1
50.4
48.6
44.6
47.7
59.9
60.2
72.7
43.7
47.8

63.7
44.0
47.9
45.5
52.4
46.2
53.8
64.8
42.3
50.2

Oklahoma .....................................
Oregon ..........................................
Pennsylvania ................................
South Carolina ..............................
South Dakota ................................
Tennessee ....................................
Texas ............................................
Utah ..............................................
Virginia ..........................................
Washington ...................................

2,091
739
1,129
898
2,324
929
3,218
609
895
1,307

2,167
741
1,189
870
2,348
994
3,192
632
900
1,307

66.0
49.4
52.2
57.3
45.8
67.5
55.7
77.7
62.8
47.7

59.1
41.2
52.1
61.5
44.8
60.7
52.8
72.2
59.6
31.3

West Virginia .................................
Wisconsin .....................................
Wyoming .......................................

381
2,047
504

361
2,076
483

76.9
57.4
54.8

75.9
50.1
52.6

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

55,526

56,002

53.3

49.5

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Small Grains County Estimates Methodology and Quality Measures (June 2023)
USDA, National Agricultural Statistics Service

Small Grains County Agricultural Production Survey Sample Size and Response Rate - States and
United States: 2021 and 2022
State

Sample Size
2021

Response Rate
2022

(number)

2021

(number)

2022

(percent)

(percent)

Alabama ........................................
Alaska ...........................................
Arizona ..........................................
Arkansas .......................................
California .......................................
Colorado ........................................
Delaware .......................................
Florida ...........................................
Georgia .........................................
Idaho .............................................

(NA)
(NA)
67
397
572
417
(NA)
(NA)
(NA)
802

(NA)
(NA)
55
395
548
394
(NA)
(NA)
(NA)
743

(NA)
(NA)
71.6
59.2
60.8
56.4
(NA)
(NA)
(NA)
64.5

(NA)
(NA)
69.1
67.3
67.3
48.5
(NA)
(NA)
(NA)
59.5

Illinois ............................................
Indiana ..........................................
Iowa ...............................................
Kansas ..........................................
Kentucky ........................................
Louisiana .......................................
Maine ............................................
Maryland ........................................
Michigan ........................................
Minnesota ......................................

1,788
1,171
1,595
3,242
1,338
(NA)
29
375
1,895
3,129

1,690
1,161
1,627
2,848
1,320
(NA)
33
274
1,683
3,059

62.2
56.8
42.9
51.9
74.5
(NA)
62.1
61.3
56.6
44.9

58.9
58.1
62.3
56.4
66.4
(NA)
90.9
53.6
60.0
58.2

Mississippi .....................................
Missouri .........................................
Montana ........................................
Nebraska .......................................
New Jersey ....................................
New Mexico ...................................
New York .......................................
North Carolina ...............................
North Dakota .................................
Ohio ...............................................

182
1,363
1,247
1,604
(NA)
(NA)
820
1,158
1,878
2,752

205
1,411
1,176
1,500
(NA)
(NA)
832
1,270
1,616
2,556

72.0
54.5
60.0
54.4
(NA)
(NA)
59.9
76.8
52.3
51.6

74.1
54.4
54.3
54.3
(NA)
(NA)
63.3
64.1
48.8
56.2

Oklahoma ......................................
Oregon ..........................................
Pennsylvania .................................
South Carolina ...............................
South Dakota .................................
Tennessee .....................................
Texas ............................................
Utah ...............................................
Virginia ..........................................
Washington ...................................

1,500
237
1,792
(NA)
1,410
915
3,716
(NA)
677
271

1,471
214
1,575
(NA)
1,405
844
3,469
(NA)
661
246

73.0
61.6
60.0
(NA)
59.3
77.7
71.4
(NA)
70.3
53.9

66.7
62.6
59.8
(NA)
58.9
68.1
64.3
(NA)
55.5
50.0

West Virginia .................................
Wisconsin ......................................
Wyoming .......................................

(NA)
2,751
151

(NA)
2,143
162

(NA)
53.9
68.9

(NA)
60.8
64.2

United States 1 ...............................

41,241

38,586

58.8

59.5

(NA) Not available.
1
Small Grains County Agricultural Production Survey conducted in 32 states.

Small Grains County Estimates Methodology and Quality Measures (June 2023)
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7

Number of States in the County Estimates Program and Number of Counties Published by
Crop - United States: 2021 and 2022
Number of Counties Published 1

Number of States in
the County Estimates
Program

Crop

2021

(number)

(number)

Barley ....................................
Oats .......................................
Wheat, Durum ........................
Wheat, Other Spring ..............
Wheat, Winter ........................
1

2022

10
18
4
6
25

(number)
102
309
36
177
913

113
318
33
169
875

Number of counties with published estimates includes estimates published for "Other Counties".

Small Grains County Estimates Quality Metrics - United States: 2021 and 2022
Coefficient of Variation
Crop and Estimate
Type

2021

2022

25th percentile

50th percentile

75th percentile

25th percentile

50th percentile

75th percentile

(percent)

(percent)

(percent)

(percent)

(percent)

(percent)

Barley
Planted .................
Harvested .............
Yield ......................
Oats
Planted .................
Harvested .............
Yield ......................
Wheat, Durum
Planted .................
Harvested .............
Yield ......................
Wheat, Other Spring
Planted .................
Harvested .............
Yield ......................
Wheat, Winter
Planted .................
Harvested .............
Yield ......................

0.4
11.2
5.9

0.7
14.3
9.2

1.7
25.0
12.9

0.4
9.5
4.5

0.6
14.8
6.8

1.5
19.8
10.6

0.1
19.5
5.9

0.8
30.1
8.8

3.3
48.0
12.0

0.4
17.3
6.3

0.9
24.2
9.2

3.0
36.9
11.8

0.8
2.2
6.7

1.1
3.3
9.8

2.6
7.6
15.9

0.3
1.5
4.1

0.6
3.1
6.7

2.3
10.7
12.9

(Z)
1.1
4.5

0.2
1.4
6.7

0.6
2.1
10.9

0.3
1.4
4.5

0.6
2.2
6.3

1.0
3.3
9.6

0.1
2.2
3.7

0.2
9.9
5.7

0.8
22.7
8.4

0.1
2.5
4.3

0.2
5.9
6.3

0.6
18.8
9.4

(Z) Less than half of the unit shown.

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Small Grains County Estimates Methodology and Quality Measures (June 2023)
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Information Contacts
Process
Estimation .....................................
Data Collection .............................
Questionnaires .............................
Sampling and Editing ....................
Analysis and Estimators ................
Dissemination ...............................
Media Contact and Webmaster .....

Unit
Crops Branch
Survey Administration Branch
Data Collection Branch
Sampling, Editing, and Imputation Methodology Branch
Summary, Estimation, and Disclosure Methodology Branch
Data Dissemination Office
Public Affairs Office

Telephone
(202) 720-2127
(202) 690-4847
(202) 720-6201
(202) 690-8141
(202) 690-8141
(202) 720-3869
(202) 720-2639

Email
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]

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new website, create a new account and re-subscribe to the reports. If you need instructions to set up an account or
subscribe, they are located at: https://usda.library.cornell.edu/help. You should whitelist [email protected] in your email client to avoid the emails going into spam/junk folders.
For more information on NASS surveys and reports, call the NASS Agricultural Statistics Hotline at (800) 727-9540,
7:30 a.m. to 4:00 p.m. ET, or e-mail: [email protected].
The U.S. Department of Agriculture (USDA) prohibits discrimination against its customers, employees, and applicants for
employment on the basis of race, color, national origin, age, disability, sex, gender identity, religion, reprisal, and where
applicable, political beliefs, marital status, familial or parental status, sexual orientation, or all or part of an individual's
income is derived from any public assistance program, or protected genetic information in employment or in any program
or activity conducted or funded by the Department. (Not all prohibited bases will apply to all programs and/or
employment activities.)
If you wish to file a Civil Rights program complaint of discrimination, complete the USDA Program Discrimination
Complaint Form (PDF), found online at www.ascr.usda.gov/filing-program-discrimination-complaint-usda-customer, or
at any USDA office, or call (866) 632-9992 to request the form. You may also write a letter containing all of the
information requested in the form. Send your completed complaint form or letter to us by mail at U.S. Department of
Agriculture, Director, Office of Adjudication, 1400 Independence Avenue, S.W., Washington, D.C. 20250-9410, by fax
(202) 690-7442 or email at [email protected].