Grain Stocks - Quality Measures

Grain Stocks Methodology and Quality
Measures
ISSN: 2167-3225

Released February 3, 2023, by the National Agricultural Statistics Service (NASS), Agricultural Statistics Board, United States Department
of Agriculture (USDA).

Special Note
Weighted item response rates for June 2021 have been updated from what was published in last year’s
report due to an error found in the calculation. The updated weighted item response rates are found on
page 15.

Scope and Purpose: Estimates of grain stocks and capacity are derived from the Agricultural Survey and the Off-Farm
Grain Stocks (OFGS) survey. The Agricultural Survey is a quarterly survey (March, June, September, and December)
conducted in all States, except Hawaii, which collects on-farm grain stocks and storage each quarter. Reports received
from individual farmers and ranchers remain confidential and are used only in combination with other reports to arrive at
State and National estimates. The OFGS survey is conducted quarterly in all States, except Alaska, Connecticut, Hawaii,
Nevada, and Rhode Island. For the OFGS survey, elevators, warehouses, and processing facilities are contacted to
determine how much of a commodity is being stored at a certain point in time. Published estimates for the off-farm grain
stocks are used in combination with the on-farm grain stocks estimates to get a complete picture of the amount of grain
stored across the country.
The use of crop acreage, production, and stocks information is extensive and varied. It helps producers find the best
market opportunities for their commodities. Often, recommendations and forecasts presented in agricultural magazines,
news releases, etc. are based on data from the Agricultural Survey and the OFGS surveys found in NASS reports. Uses of
data by farm organizations, financial institutions, insurance companies, agribusinesses, State and National farm policy
makers, and 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 policies. Government agencies at various levels
are important users of statistics. Federal farm programs require information on acreage, production potential, stocks,
prices, and income. Agricultural statistics are used to plan and administer Federal and State programs in areas such as
consumer protection, conservation, foreign trade, education, and recreation.
Timeline: The reference date for the stocks portion of both surveys is the first of the month (March, June, September, and
December) with a data collection period of approximately 15 calendar days. Regional Field Offices (RFOs) may begin
data collection two days prior to the reference date. Data collection continues until a scheduled ending date, and RFOs
have about 4 or 5 business days to complete editing and analysis, execute the summary, and interpret the survey results.
The Agricultural Statistics Board (ASB) conducts the National review, reconciles State estimates to the National
estimates, and prepare the official estimates for release in 5 or 6 business days. The Grain Stocks report is released at the
end of each specified month above except for December. The December 1 stocks estimates are published in early January.
The publication contains quarterly U.S. and State level data for grain stocks for all wheat, barley, corn, Durum wheat,
oats, sorghum, and soybeans. Certain months of the publication contain annual grain stocks data for canola, mustard seed,
rapeseed, rye, and safflower. Additionally, biannual grain stocks data are published for chickpeas, dry edible peas, and
lentils in June and December, and for sunflower in March and September.
Sampling: The target population for the Agricultural Survey is farms with cropland and/or storage capacity. NASS uses a
dual frame approach, consisting of list frame and area frame components, to provide complete coverage of this target
population.

The list frame includes all known farms. Crop acreages and storage capacity of each farm is 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 planted acres or storage capacity of the desired commodities are included in the list
frame population. A lower boundary, such as 50 acres of total cropland or 1,000 bushels of grain storage capacity, is used
for some States to establish the list frame population.
The area frame contains all land in the State and, as such, is complete. The land is stratified according to intensity of
agriculture using satellite imagery and sampled to effectively measure crops and livestock. All sampled land areas are
enumerated in June. The farms found operating in these segments are checked to see if they are included in the list frame
population. The farms that are not included in the list frame sampling population are sub-sampled for the March,
September, and December surveys so that the target population is completely represented. These farms are referred to as
the nonoverlap portion of the area frame (NOL). The area frame portion of the Agricultural Survey sample is selected
from the NOL using a stratified sample design based on data collected in the June Area Survey. A final sampling weight
is assigned to each area frame sampling unit which is used to create the survey estimates.
The Agricultural Survey list frame sample is selected using a multivariate probability proportional to size (MPPS)
sampling scheme. Each list frame record is assigned a measure of size based on the list frame for multiple specified
commodities. The MPPS design makes it very easy to target sample sizes for the commodities of interest, and it is a more
efficient design because farms will have a more optimal probability of selection based upon their individual commodities
and size. A replication scheme is used to reduce respondent burden and to provide indications of change by comparing
reports from the same farm operators. Specific replicates are designated as a stocks panel to accurately measure change in
stocks from quarter to quarter.
After the list frame samples are drawn, the sample weights are calibrated so the sum of the weighted commodities in the
sample equals the sum of the list frame data for the targeted commodities for each quarter. For example, the sum of the
weighted list frame data for storage capacity equals the sum of the population list frame data and is the same for each of
the four quarters. All list frame records in the sample are grouped into strata based on the amount of cropland and capacity
they have on the list frame. These strata are only used for nonresponse adjustments.
For each commodity, target coefficients of variation (CVs) are determined in advance of sampling to provide a certain
level of precision for the stocks estimates. The CV is defined as the ratio of the standard error to the estimate expressed as
a percentage. At the U.S. level, these target CVs range from 2% to 4% for corn, from 2% to 5% for soybeans, and from
3% to 4% for all wheat stocks depending which quarter of the marketing year the survey occurs. As on-farm stocks
become scarce toward the end of the marketing year, the CVs of the stocks estimates generally increase. However, the
standard errors also become smaller as stock levels decline across the marketing year. Each year, the final survey CVs are
examined against the target CVs to see if any modifications to the sampling procedures are needed. CVs at the State level
are expected to be higher than the U.S. level estimates due to the smaller sample sizes, and State level target CVs are set
accordingly. Over the last decade, the U.S. level survey CVs have ranged from 1.5% to 4.5% for corn stocks, from 1.7%
to 11.0% for soybean stocks, and from 2.2% to 5.0% for all wheat stocks.
The OFGS target population is all entities in the United States that can store at least 1,000 bushels of grain (e.g. elevators,
grain and oilseed processing plants, terminals, and any other facilities that store grain or oilseeds excluding peanuts and
rice) off the farm. The OFGS sampling frame is grouped into specialty and non-specialty operations and stratified using
off farm grain storage capacity as a measure of size. The OFGS is a census; hence, stratification is only used for
nonresponse adjustments.
Data Collection: All Regional Field Offices (RFO) use the same standardized questionnaire for data collection. 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 instrument. 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

2

Grain Stocks Methodology and Quality Measures (February 2023)
USDA, National Agricultural Statistics Service

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.
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 storage capacity and stocks 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. While mail and web data collection are the least costly methods of data collection, the short data
collection period and the uncertainty of postal delivery times limits the effectiveness of collecting data by mail. 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.
OFGS Headquarter operations have the option of reporting for each elevator under their control or reporting total levels
for each State in which they operate. If a firm chooses to report for each elevator, they complete a separate report for each
elevator. If an operation chooses to report State totals, a report is completed for each State. Headquarter reports often
account for many individual elevators in a State. The tables on pages 11-14 of this report reflect the counts of reporting
units not the counts of individual elevators.
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.
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.

Grain Stocks Methodology and Quality Measures (February 2023)
USDA, National Agricultural Statistics Service

3

Estimators: Response to both surveys is voluntary. Some producers refuse to participate in the survey. Others cannot be
located during the data collection period, and some submit incomplete reports. These nonrespondents must be accounted
for if accurate estimates of stocks are to be made. For the Agricultural Survey, nonrespondents are accounted for by
imputing data where there are missing values.
For the Agricultural Survey, the imputation program imputes for missing survey data using previously reported survey
data from similar reports with complete data. The algorithm defines “imputation groups” for list frame records as
Agricultural Statistics Districts (ASD) and within the strata assigned at the time of sampling. Operations in the strata with
the most capacity and cropland do not form homogeneous groups and are not eligible for machine imputation. If multiple
follow ups do not produce a response, RFO statisticians are required to manually impute. Area frame records are grouped
for imputation using ASD and similar strata.
Capacity is imputed first for the nonrespondent. When available, previously reported capacity is used. Otherwise, the ratio
of current survey capacity to the list frame data value for capacity is calculated from the respondents in an imputation
group. This ratio is applied to the nonrespondent’s frame capacity to derive the imputed value for the current survey.
When appropriate, if a stocks value is available for the previous quarter, the ratio of the current stocks value to the
previous stocks value is calculated from the respondents in an imputation group. This ratio is applied to the
nonrespondent’s previous quarter stocks value. When a previous quarter stocks value is not available, missing stocks are
imputed similarly to capacity using the respondents’ ratio of stocks to list frame capacity within each imputation group. If
list frame capacity is not available for the nonrespondent, the weighted mean stocks for the imputation group are imputed
for the nonrespondent. An imputation group must have five or more respondents before it is used. List frame records with
insufficient response are collapsed across ASD and, if there is still insufficient response, collapsed with adjacent strata.
NOL records with insufficient response are collapsed across strata and, if there is still insufficient response, collapsed
across ASD.
Two kinds of estimators are used for stocks in the Agricultural Survey: direct expansions and ratio estimators. Direct
expansions are used to estimate totals such as total capacity and stocks. For the list frame, direct expansions are calculated
by summing the reported and imputed commodity values multiplied by the original sample weights. For the NOL sample,
the direct expansion is calculated by summing the total farm data for each tract operation multiplied by the original
sample weights adjusted for the proportion of the operation’s total farmland found in the area sample. The multiple frame
direct expansion is the sum of the direct expansions from the list frame and the area frame NOL component. Variances
and CVs are calculated using non-imputed data only for the direct expansions to measure the precision of the stocks
estimates. U.S. level CVs from the Agricultural Survey for the last eight quarters are displayed in the table on page 15 of
this report.
The ratio estimator takes the form of a ratio of two direct expansions which are calculated by summing over the total
sample (list + NOL), the reported commodity values multiplied by the original sample weights adjusted for usability
status. The ratio estimator is used for all within and across-survey ratios (e.g., Current to Previous Stocks, Stocks to
Production, and Stocks to Capacity). This estimator relies exclusively on reported data. For the survey-to-survey ratios,
both the current and previous survey data must be reported or estimated to be included in the ratio. If either of these
components is not complete, the sampling unit is excluded from the estimate and the weights of the complete records are
adjusted accordingly.
The reweighting of the record level sample weight is made within the strata. The adjustment is calculated by summing the
weights for all sample records within the strata and dividing by the sum of the weights from the usable records. This ratio
is applied to the weights of the usable records. This adjustment assumes that the data of the nonrespondents are similar to
the data of the respondents. CVs are also calculated for any ratio estimates in the summary. One advantage of the ratio
estimator is that the CVs tend to be smaller than those for the direct expansions.
For the OFGS survey, an estimator that uses capacity information is used to calculate the direct expansion for total stocks.
The estimator calculates a nonresponse adjustment by summing the capacity values for all reports and dividing by the sum
of the capacity values for the usable operations in the lower strata. Operations in the higher strata must be manually
imputed to account for any nonresponse. Any errors that may arise from manually imputing records are not captured in
the calculated CVs.
4

Grain Stocks Methodology and Quality Measures (February 2023)
USDA, National Agricultural Statistics Service

The calculated CVs capture the relative uncertainty that originates from sampling the target population and the loss of
sample from nonresponse. However, the CVs do not capture the effect of possible reporting errors or errors that may arise
from nonrespondents making fundamentally different grain storing decisions than respondents within imputation or
nonresponse adjustment groups.
Estimation: When all samples are accounted for, all responses fully edited, and the analysis material is reviewed, each
RFO executes the summary for their States for each survey. When all RFOs have run summaries, Headquarters executes
the National summary. Since all States conduct identical surveys, the samples can be pooled, and National survey results
computed. The summary results provide multiple point estimates and corresponding standard errors for each data series
being estimated. It also provides information used to assess the performance of the current survey and evaluate the quality
of the survey results, such as strata level expansions, response rates, and percent of the expansion from usable reports.
RFO staff are responsible for performing a detailed review of their survey results. Any irregularities revealed by the
summary must be investigated and, if necessary, resolved. Using the historical relationship of the survey results to the
official estimate, RFO staff must interpret the survey results and submit a recommended estimate to Headquarters for any
commodity produced in their States that contributes to the published National estimate. The data are viewed in tabular and
graphical form and a consensus estimate is established. RFO staff see their survey results only and do not have access to
other States’ results. For some data series, information from other sources (administrative data) is also utilized in the
process of establishing estimates.
For the National estimates, NASS assembles a panel of statisticians to serve as the ASB which reviews the National
results and establishes the National estimates. Since larger sample sizes yield more precise results, NASS employs the
“top-down” approach by determining the National estimates first and reconciling the State estimates to the National
estimate. The ASB has the advantage of being able to examine results across States, compare the State recommendations,
and utilize administrative data available only at the U.S. level. The same estimators used in the State summaries are
produced by the National summary. The ASB follows the same approach as the States in determining the National
estimate. The historical relationship of the survey results to the official estimate is evaluated over time to determine
accuracy and bias using tables and graphs. Each ASB member completes an independent interpretation of the survey
results which are shared with the other members. Differing conclusions are discussed and members must explain the logic
behind their estimate. An official National estimate is established only upon ASB consensus. Often the State
recommendations do not sum to the National estimate. ASB members must reexamine the State results and adjust some
States to make the sum of the estimates agree with the National estimate.
External information (administrative data) is also utilized in this process. To be considered, these data must be deemed to
be reliable and come from unbiased sources. The most common administrative data for grain stocks are the outstanding
loan data from USDA’s Farm Service Agency.
For grain stocks, NASS employs a balance sheet approach to corroborate the survey results and official estimates. After
estimates are made for on-farm and off-farm stocks, the totals of these two are combined and evaluated using the balance
sheet. This method utilizes external information to check the reasonableness of the stocks estimates. This external data
will vary some by crop, but includes imports and disappearance data for exports, food use (such as soybeans crushed),
feed use, seed use, and industrial use (such as corn processed to produce ethanol and other by-products). This approach is
typically limited to National level estimates.
Estimates are open to revision on a preannounced schedule only if new information becomes available. On-farm and offfarm stocks are subject to revision the quarter following initial publication and again in the following December 1 Grain
Stocks report published in January each year. Every five years, estimates will also be reviewed following the Census of
Agriculture, which is an exhaustive 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.

Grain Stocks Methodology and Quality Measures (February 2023)
USDA, National Agricultural Statistics Service

5

Quality Metrics for Grain Stocks
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. There is no sampling error present for the OFGS survey since it is a census of all known grain storage
entities. The Agricultural Survey CVs measure the error due to sampling as well as some nonsampling error. Nonsampling
error is also evaluated by examining response rates and the weighted item 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.
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).
Weighted item response rate is a ratio of reported survey data expanded by the original sampling weight
compared to final nonresponse adjusted summary totals.
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 a survey estimator.

6

Grain Stocks Methodology and Quality Measures (February 2023)
USDA, National Agricultural Statistics Service

March 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
Connecticut
Delaware
Florida
Georgia

771
115
310
1,695
2,151
1,923
297
391
650
1,405

781
116
342
1,716
2,150
1,965
297
366
628
1,361

62.4
52.2
69.4
56.6
52.2
46.2
47.8
53.5
47.5
52.7

58.8
53.4
64.3
57.1
43.2
41.1
43.1
44.5
45.9
45.4

Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts

1,797
2,539
2,292
2,609
3,781
1,525
1,046
411
974
310

1,791
2,526
2,254
2,632
3,698
1,515
1,052
393
1,008
292

48.2
53.5
50.8
49.2
43.9
65.4
66.2
56.0
52.9
58.4

51.2
50.1
47.4
42.5
39.3
58.5
64.2
51.9
46.3
56.5

Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico

1,836
2,958
1,347
3,077
2,313
3,523
202
221
402
557

1,744
2,931
1,344
3,156
2,279
3,486
207
213
413
508

57.7
47.6
65.6
47.8
49.6
48.7
63.9
55.7
54.7
54.9

56.8
43.2
60.5
44.0
46.2
38.6
47.3
49.8
54.2
53.0

New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota

1,192
1,642
3,105
1,774
2,382
1,167
1,485
58
887
2,804

1,216
1,623
3,091
1,689
2,271
1,153
1,541
59
929
2,764

60.8
62.1
49.6
48.5
62.8
51.1
50.6
37.9
58.9
48.3

51.3
64.7
42.4
47.5
59.0
52.0
48.7
28.8
55.1
42.3

Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming

1,281
4,542
841
476
1,371
1,763
446
2,009
910

1,255
4,413
830
493
1,385
1,706
468
2,000
904

64.5
58.9
77.6
61.8
65.4
41.1
73.1
55.5
60.0

60.2
53.2
72.7
55.0
61.4
42.8
72.2
51.6
55.4

United States

73,563

72,954

53.7

49.4

Grain Stocks Methodology and Quality Measures (February 2023)
USDA, National Agricultural Statistics Service

7

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

Sample Size

Response Rate

2021

2022

2021

2022

(number)

(number)

(percent)

(percent)

Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Florida
Georgia

1,080
77
271
1,552
1,799
1,715
88
312
417
1,480

1,056
76
284
1,551
1,701
1,643
82
298
405
1,481

50.4
53.2
65.7
47.5
48.7
34.8
48.9
39.7
40.5
40.5

44.6
50.0
58.8
49.8
41.6
35.4
48.8
33.2
37.8
36.2

Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts

1,593
2,122
1,946
2,116
3,851
1,702
1,100
262
892
101

1,612
2,194
2,011
2,141
3,913
1,676
1,052
253
841
96

39.9
42.4
42.7
42.8
33.9
56.6
60.9
43.9
43.0
70.3

42.2
41.9
38.1
39.6
31.1
59.6
57.7
45.5
36.7
49.0

Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico

1,705
2,361
1,218
2,519
1,780
2,993
189
67
366
551

1,721
2,344
1,178
2,512
1,788
3,042
186
64
372
543

48.9
37.9
53.2
37.0
42.7
32.8
31.7
55.2
37.4
53.5

44.7
32.0
56.6
39.1
45.0
33.1
34.4
51.6
41.9
40.5

New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota

1,114
1,452
2,599
1,440
2,194
882
1,372
19
874
2,590

1,109
1,467
2,635
1,515
2,224
891
1,413
19
880
2,660

43.3
59.7
31.7
40.7
56.1
46.9
35.4
31.6
46.3
35.6

43.0
61.6
26.5
37.5
54.6
46.6
44.0
47.4
42.2
32.9

Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming

1,274
3,471
592
183
1,175
1,380
388
2,060
762

1,305
3,459
627
176
1,169
1,373
386
2,053
771

58.0
46.4
69.9
42.6
57.7
40.5
54.6
48.0
55.9

55.6
51.7
57.9
43.8
42.6
39.3
53.4
45.9
40.1

United States

64,046

64,248

44.0

42.2

8

Grain Stocks Methodology and Quality Measures (February 2023)
USDA, National Agricultural Statistics Service

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
Connecticut
Delaware
Florida
Georgia

752
149
304
1,216
1,489
1,152
(NA)
231
439
1,134

782
147
317
1,206
1,440
1,142
(NA)
239
409
1,130

57.3
65.1
66.8
53.3
52.0
42.2
(NA)
43.3
48.5
50.4

61.3
43.5
63.1
53.8
41.0
43.8
(NA)
33.1
48.2
50.4

Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts

1,326
2,184
1,977
2,552
2,552
1,158
878
218
693
(NA)

1,325
2,330
2,028
2,631
2,612
1,142
867
230
699
(NA)

47.1
54.7
47.2
48.0
41.5
69.2
71.1
58.7
50.4
(NA)

40.2
51.4
47.9
45.3
37.9
61.7
67.1
59.1
42.6
(NA)

Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico

1,397
2,178
1,197
2,470
1,838
2,268
(NA)
(NA)
342
494

1,395
2,138
1,181
2,470
1,873
2,246
(NA)
(NA)
332
517

58.4
48.6
67.1
50.4
48.6
44.6
(NA)
(NA)
47.7
59.9

53.2
40.5
63.7
44.0
47.9
45.5
(NA)
(NA)
52.4
46.2

New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota

1,002
1,114
2,370
1,381
2,091
739
1,129
(NA)
898
2,324

927
1,110
2,457
1,420
2,167
741
1,189
(NA)
870
2,348

60.2
72.7
43.7
47.8
66.0
49.4
52.2
(NA)
57.3
45.8

53.8
64.8
42.3
50.2
59.1
41.2
52.1
(NA)
61.5
44.8

Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming

929
3,218
609
(NA)
895
1,307
381
2,047
504

994
3,192
632
(NA)
900
1,307
361
2,076
483

67.5
55.7
77.7
(NA)
62.8
47.7
76.9
57.4
54.8

60.7
52.8
72.2
(NA)
59.6
31.3
75.9
50.1
52.6

United States

55,526

56,002

53.3

49.5

(NA) Not available.

Grain Stocks Methodology and Quality Measures (February 2023)
USDA, National Agricultural Statistics Service

9

December 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
Connecticut
Delaware
Florida
Georgia

1,008
173
397
1,819
2,197
1,666
249
359
797
1,604

982
179
387
1,815
2,054
1,560
229
370
751
1,606

49.2
58.4
67.8
49.5
50.5
41.9
51.0
39.3
44.5
46.0

63.2
45.8
64.9
57.4
41.2
42.4
57.6
35.1
47.3
49.8

Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts

1,654
2,461
2,387
2,823
2,912
1,603
1,451
357
956
288

1,702
2,584
2,489
2,862
2,988
1,572
1,332
340
971
280

49.3
49.8
49.1
43.7
40.1
54.7
67.8
48.7
43.3
63.2

49.7
49.7
45.3
45.8
36.0
56.3
61.6
56.2
42.8
62.1

Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico

1,671
2,780
1,560
3,247
2,140
3,118
202
207
515
606

1,720
2,686
1,493
3,308
2,148
3,202
194
188
491
627

58.5
45.3
63.5
43.9
49.4
37.1
37.6
57.5
44.7
53.5

54.3
41.0
62.6
44.0
47.0
39.4
53.1
52.7
48.3
44.2

New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota

1,120
1,765
2,930
1,657
2,468
873
1,406
63
1,075
2,558

1,089
1,762
2,968
1,737
2,527
874
1,460
57
1,010
2,570

53.1
65.2
42.1
48.9
58.6
48.1
42.5
28.6
53.7
44.7

52.4
57.3
35.2
50.9
54.4
52.6
50.8
24.6
57.2
41.9

Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming

1,401
4,529
793
506
1,367
1,578
551
2,142
614

1,399
4,567
763
501
1,310
1,610
541
2,181
579

61.4
53.4
79.6
52.6
60.4
44.1
70.6
53.0
59.3

51.8
49.0
74.2
51.9
50.1
38.3
75.8
47.4
57.9

United States

72,603

72,615

50.1

48.3

10

Grain Stocks Methodology and Quality Measures (February 2023)
USDA, National Agricultural Statistics Service

March Off Farm Grain Stocks 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
Connecticut
Delaware
Florida
Georgia

48
(NA)
15
52
54
43
(NA)
17
12
88

44
(NA)
15
47
51
48
(NA)
17
10
75

93.8
(NA)
73.3
78.8
75.9
76.7
(NA)
5.9
83.3
92.0

75.0
(NA)
66.7
85.1
54.9
56.3
(NA)
64.7
80.0
80.0

Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts

42
284
169
293
160
133
18
(NA)
33
(NA)

39
275
165
286
158
130
16
(NA)
31
(NA)

83.3
72.5
56.8
90.4
78.1
88.7
66.7
(NA)
51.5
(NA)

71.8
73.1
47.9
85.3
73.4
88.5
75.0
(NA)
61.3
(NA)

Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire 1
New Jersey
New Mexico

107
288
33
163
82
128
(NA)
14
4
4

103
283
29
163
82
126
(NA)
14
4
5

79.4
66.3
78.8
68.7
81.7
75.0
(NA)
57.1
75.0
75.0

87.4
53.0
82.8
64.4
65.9
77.0
(NA)
35.7
50.0
80.0

New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota

32
113
192
150
55
27
110
(NA)
38
112

33
106
188
146
52
28
105
(NA)
33
112

37.5
79.6
74.0
58.7
78.2
70.4
54.5
(NA)
84.2
92.9

48.5
84.0
73.9
50.7
63.5
71.4
57.1
(NA)
84.8
91.1

Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming

113
168
21
(NA)
64
38
8
163
12

108
171
22
(NA)
65
39
8
153
13

91.2
69.0
71.4
(NA)
79.7
86.8
100.0
65.0
83.3

89.8
62.6
72.7
(NA)
90.8
79.5
100.0
64.7
61.5

United States

3,700

3,598

74.6

70.8

(NA) Not available.
1
Includes data for Maine, Massachusetts, New Hampshire, and Vermont.

Grain Stocks Methodology and Quality Measures (February 2023)
USDA, National Agricultural Statistics Service

11

June Off Farm Grain Stocks 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
Connecticut
Delaware
Florida
Georgia

48
(NA)
16
50
54
46
(NA)
17
12
87

44
(NA)
16
46
49
46
(NA)
16
10
75

91.7
(NA)
68.8
82.0
85.2
87.0
(NA)
11.8
83.3
94.3

86.4
(NA)
50.0
91.3
61.2
54.3
(NA)
62.5
70.0
89.3

Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts

45
284
170
292
162
132
18
(NA)
33
(NA)

43
281
162
286
159
135
16
(NA)
31
(NA)

75.6
72.9
53.5
90.1
67.9
87.1
100.0
(NA)
33.3
(NA)

69.8
68.7
53.1
85.0
61.0
88.9
93.8
(NA)
51.6
(NA)

Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire 1
New Jersey
New Mexico

106
289
33
162
85
130
(NA)
14
4
4

103
280
29
161
81
125
(NA)
14
4
6

80.2
62.6
90.9
65.4
80.0
73.1
(NA)
42.9
75.0
100.0

75.7
57.1
89.7
65.8
69.1
76.8
(NA)
42.9
100.0
50.0

New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota

30
112
190
150
51
28
109
(NA)
38
112

32
108
187
145
54
29
106
(NA)
34
113

53.3
75.9
71.6
68.0
70.6
67.9
51.4
(NA)
81.6
94.6

53.1
86.1
70.6
64.8
63.0
65.5
42.5
(NA)
73.5
87.6

Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming

112
170
22
(NA)
64
42
8
162
13

106
170
21
(NA)
62
43
8
151
12

88.4
65.3
77.3
(NA)
65.6
78.6
87.5
63.0
92.3

90.6
65.9
76.2
(NA)
83.9
72.1
87.5
57.6
58.3

United States

3,706

3,599

73.2

70.2

(NA) Not available.
1
Includes data for Maine, Massachusetts, New Hampshire, and Vermont.

12

Grain Stocks Methodology and Quality Measures (February 2023)
USDA, National Agricultural Statistics Service

September Off Farm Grain Stocks 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
Connecticut
Delaware
Florida
Georgia

48
(NA)
16
51
55
47
(NA)
17
12
87

44
(NA)
16
46
49
48
(NA)
16
11
75

91.7
(NA)
68.8
82.4
60.0
68.1
(NA)
29.4
100.0
94.3

97.7
(NA)
37.5
89.1
67.3
47.9
(NA)
68.8
90.9
86.7

Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts

41
275
169
290
158
137
16
(NA)
33
(NA)

39
270
163
283
155
134
16
(NA)
31
(NA)

70.7
71.6
55.0
83.1
73.4
92.0
56.3
(NA)
42.4
(NA)

71.8
77.0
54.0
86.9
74.8
85.8
93.8
(NA)
58.1
(NA)

Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire 1
New Jersey
New Mexico

106
289
31
162
86
129
(NA)
14
4
4

102
278
28
162
77
125
(NA)
14
4
5

90.6
65.7
67.7
60.5
75.6
57.4
(NA)
21.4
75.0
75.0

92.2
65.1
82.1
65.4
61.0
72.8
(NA)
28.6
100.0
60.0

New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota

30
109
192
149
53
28
109
(NA)
38
110

33
105
187
146
53
28
105
(NA)
34
113

66.7
76.1
76.0
63.1
86.8
60.7
64.2
(NA)
89.5
89.1

45.5
80.0
71.1
61.0
64.2
64.3
53.3
(NA)
79.4
88.5

Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming

112
173
23
(NA)
67
38
8
160
11

106
172
21
(NA)
63
39
8
149
13

91.1
61.8
73.9
(NA)
91.0
60.5
100.0
62.5
72.7

91.5
60.5
61.9
(NA)
88.9
64.1
75.0
64.4
30.8

United States

3,687

3,566

72.5

72.2

(NA) Not available.
1
Includes data for Maine, Massachusetts, New Hampshire, and Vermont.

Grain Stocks Methodology and Quality Measures (February 2023)
USDA, National Agricultural Statistics Service

13

December Off Farm Grain Stocks 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
Connecticut
Delaware
Florida
Georgia

47
(NA)
15
50
55
46
(NA)
17
10
82

48
(NA)
16
45
52
47
(NA)
16
13
84

83.0
(NA)
60.0
82.0
54.5
63.0
(NA)
29.4
70.0
87.8

93.8
(NA)
25.0
82.2
59.6
57.4
(NA)
31.3
92.3
86.9

Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts

42
285
166
289
160
137
17
(NA)
31
(NA)

46
268
165
280
149
130
15
(NA)
31
(NA)

73.8
72.3
51.8
89.3
64.4
89.1
82.4
(NA)
41.9
(NA)

60.9
74.3
52.7
88.6
72.5
86.2
93.3
(NA)
41.9
(NA)

Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire 1
New Jersey
New Mexico

106
282
31
164
79
129
(NA)
14
4
5

103
280
28
161
74
127
(NA)
13
4
5

91.5
63.1
90.3
62.8
79.7
72.1
(NA)
21.4
50.0
80.0

93.2
63.9
82.1
64.0
40.5
72.4
(NA)
46.2
75.0
40.0

New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota

33
107
188
147
52
27
106
(NA)
36
111

31
104
179
147
49
30
106
(NA)
35
112

45.5
81.3
70.7
56.5
67.3
74.1
63.2
(NA)
86.1
95.5

45.2
80.8
64.2
57.1
67.3
63.3
52.8
(NA)
74.3
92.9

Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming

105
172
21
(NA)
63
44
8
157
11

108
168
21
(NA)
63
43
8
149
14

93.3
61.0
85.7
(NA)
92.1
77.3
100.0
60.5
72.7

87.0
62.5
52.4
(NA)
90.5
55.8
100.0
63.8
42.9

United States

3,651

3,567

72.2

70.4

(NA) Not available.
1
Includes data for Maine, Massachusetts, New Hampshire, and Vermont.

14

Grain Stocks Methodology and Quality Measures (February 2023)
USDA, National Agricultural Statistics Service

Quality Metrics from the Agricultural Survey by Crop and Date - United States: 2021 and 2022
Date

Weighted Item Response Rate

Coefficient of Variation

2021

2022

2021

2022

(percent)

(percent)

(percent)

(percent)

Corn Stocks
March 1
June 1
September 1
December 1

46.1
33.5
32.6
42.5

40.0
33.2
30.9
41.9

1.8
3.1
3.6
1.6

2.0
2.8
4.1
1.8

Soybeans Stocks
March 1
June 1
September 1
December 1

45.0
34.2
31.2
43.1

39.8
32.6
27.2
43.0

2.7
4.2
5.9
2.1

2.8
5.1
6.4
2.1

All Wheat Stocks
March 1
June 1
September 1
December 1

37.8
26.5
36.1
35.7

34.8
23.7
34.5
33.3

3.0
4.5
2.9
2.6

3.5
4.0
2.6
2.6

Grain Stocks Methodology and Quality Measures (February 2023)
USDA, National Agricultural Statistics Service

15

Quality Metrics from Off Farm Grain Stocks Survey by Crop and Date - United States: 2021 and 2022
Date

Weighted Item Response Rate

Coefficient of Variation

2021

2022

2021

2022

(percent)

(percent)

(percent)

(percent)

Corn Stocks
March 1
June 1
September 1
December 1

83.6
81.2
78.5
80.1

85.0
81.9
80.8
82.5

0.3
0.2
0.4
0.2

0.3
0.2
0.5
0.2

Soybeans Stocks
March 1
June 1
September 1
December 1

86.3
83.4
83.1
84.2

89.5
85.2
83.9
85.8

0.2
0.3
0.4
0.2

0.3
0.3
0.3
0.3

All Wheat Stocks
March 1
June 1
September 1
December 1

80.7
77.6
80.3
81.5

81.6
74.4
80.2
76.9

0.4
0.7
0.4
0.5

0.6
0.8
0.4
0.9

16

Grain Stocks Methodology and Quality Measures (February 2023)
USDA, National Agricultural Statistics Service

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]

Access to NASS Reports
For your convenience, you may access NASS reports and products the following ways:
➢ All reports are available electronically, at no cost, on the NASS web site: www.nass.usda.gov.
➢ Both national and state specific reports are available via a free e-mail subscription. To set-up this free
subscription, visit www.nass.usda.gov and click on “National” or “State” in upper right corner above “search”
box to create an account and select the reports you would like to receive.
➢ Cornell’s Mann Library has launched a new website housing NASS’s and other agency’s archived reports. The
new website, https://usda.library.cornell.edu. All email subscriptions containing reports will be sent from the new
website, https://usda.library.cornell.edu. To continue receiving the reports via e-mail, you will have to go to the
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].