Cattle Quality Measures

Cattle Methodology and Quality
Measures
ISSN: 2167-1303

Released January 31, 2020, by the National Agricultural Statistics Service (NASS), Agricultural Statistics Board, United States Department
of Agriculture (USDA).

Scope and Purpose: The January Cattle Survey targets cattle and calf producers in the U.S. The January survey collects
data for total cattle inventory and the components of that total, including beef cows, milk cows, bulls, replacement heifers,
other heifers and steers, calves, and cattle on feed. In addition the January survey collects calf crop (calves born from the
previous year), death loss from the previous year, slaughter for consumption, breeding animal values, and grazing fees
data. Data are also collected for total cattle grazing on small grain pastures in Kansas, Oklahoma, and Texas. A federal
program survey is conducted every five years for Cattle and Calf Predator and Non-Predator Loss and is incorporated as
part of the January Cattle Survey.
Survey Timeline: The reference dates for the January Cattle Survey is January 1 with a data collection period of 15 days,
beginning one day prior to the reference date. Regional Field Offices may begin data collection one day prior to the
reference date. Data collection continues until a scheduled ending date and Regional Field Offices have 4 to 5 business
days to complete editing and analysis, execute the summary, and interpret the survey results. The Agricultural Statistics
Board (ASB) must perform the national review, reconcile state estimates to the national estimates, and prepare the official
estimates for release in 5 to 6 business days. The estimates are released to the public on the last business day in January.
Sampling: For most states the target population for the January Cattle Survey is all agricultural establishments with one
or more head of cattle on the land operated. A lower boundary, such as 20 head is used in a few states to establish the
population. NASS uses a dual frame approach, consisting of list frame and area frame components to provide complete
coverage of this target population. The January Cattle Survey is conducted in every state.
The list frame includes all known agricultural establishments. A profile, known as control data of each establishment 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 cattle control data are included in the list frame
population for the cattle sample. The list frame cattle population covers approximately 88 percent of January cattle
inventory in the U.S.
The area frame contains all land in the United States and as such, is complete. The land is stratified according to intensity
of agriculture using satellite imagery. The land in each stratum is divided into segments of roughly one square mile.
Segments are optimally allocated and sampled to effectively measure crops and livestock. The sampled segments are fully
enumerated in June. All farms and ranches found operating tracts in these segments are checked to see if they are included
in the list frame cattle population. The farms and ranches that are not included in the list frame cattle population, called
nonoverlap tracts are sampled for the January Cattle survey so that the target population is completely represented. The
area frame component of the January Cattle survey covers approximately 12 percent of the January cattle inventory in the
U.S.
The January Cattle Survey list frame sample is selected using a hierarchical stratified sampling design with strata defined
by total cattle and calves, milk cows, and cattle on feed. The sample is designed to achieve a U.S. standard error of
1 percent of the point estimate for total cattle and calves, 2 percent for milk cows, and 2 percent for cattle on feed. The
U.S. list frame sample size for the January Cattle Survey in recent years is approximately 34,000. The January Cattle
Survey nonoverlap sample is a subset of the June Area Survey. The sample (or subsample) uses a stratified sample design
based on the cattle data collected from the June Area Survey. The area frame sample size is approximately 4,000. Each list
frame and area frame sampling unit is assigned a sampling weight which is used to create the survey estimates.

Data Collection and Editing: For consistency across modes, the paper version is considered the master questionnaire and
the Computer Assisted Telephone Interview (CATI), Computed Assisted Self Interview (CASI), and Mobile Computer
Assisted Personal Interview (mCAPI) 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 survey methodologist will pre-test the changes for usability. Prior to the start of data collection, all
modes of instruments are reviewed and the paper, mCAPI, CASI 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 cattle questionnaire 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. Also, 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 only be used for statistical purposes in combination with other producers and a
statement saying that response to the survey is voluntary and not required by law must be on the questionnaire.
In addition to asking the specific cattle items, 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 presurvey letter explaining the survey and that they will be contacted for survey
purposes only. The letter provides the questions to be asked to allow respondents to prepare in advance and also provides
a pass code they can use to complete the survey on the internet (CASI). All modes of data collection are utilized for cattle
surveys. Regional Field Offices are given the option of conducting a mail out/mail back phase. While mail is the least
costly mode of collection, the short data collection period and the uncertainty of postal delivery times limit its
effectiveness. Most of the data are collected by CATI by individual Regional Field Offices and Data Collection Centers.
Limited personal interviewing is done, generally for large operations or those with special handling arrangements. A
program is run to determine if any sampled farms are in multiple on-going surveys, so data collection can be coordinated.
Survey Edit: As survey data are collected and captured, they 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 on the current survey are verified and in certain situations those
items may be compared to data from earlier surveys to make sure certain relationships are logical. The edit will determine
the status of each record to be either “dirty” or “clean”. Dirty records must be updated and reedited or certified by an
analyst to be clean. If updates are needed, they are reedited interactively. Only clean records are eligible for analysis and
summary.
Analysis Tools: Edited cattle data are processed through an interactive analysis tool which displays data for all reports by
item. The tool provides scatter plots, tables, charts, and special tabulations that allow the analyst to compare an individual
record to other similar records within their state. Outliers and unusual data relationships become evident and Regional
Field Office staff will review them to determine if they are correct. The tool also allows comparison to previously
reported data to detect large changes in the operation. Suspect data found to be in error are corrected, while data found to
be correct are kept.
Nonsampling Errors: Nonsampling errors are present in any survey process. These errors include reporting, recording,
editing, and imputation errors. Steps are taken to minimize the impact of these errors, such as questionnaire testing,
comprehensive interviewer training, validation and verification of processing systems, detailed computer edits, and the
analysis tool.
Estimators: Each farm and ranch in the sample has an initial sampling weight. This is the inverse of the sampling
fraction. For example, if a stratum has 1,000 farms in the population and 200 are sampled for this survey, each sampled
farm has a weight of 5. In other words, each sampled farm represents 5 farms. The nonoverlap tracts sampled to measure

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Cattle Methodology and Quality Measures (March 2020)
USDA, National Agricultural Statistics Service

the cattle not accounted for by the list have a weight determined by adjusting their original area frame weight by any
second stage sampling weight.
Response to the January Cattle survey 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 cattle are to be made. For the Cattle survey, nonrespondents are accounted for by adjusting the
weights of the respondents. The adjustment occurs by stratum as the bounded strata represent homogeneous groupings of
similar sized farms. The largest stratum is unbounded and is made up of large and, often unique, farms. Nonrespondents in
this stratum and the nonoverlap tracts must be manually imputed by Regional Field Office statisticians and their weights
are not adjusted. The adjustment is performed by individual item on the questionnaire (total cattle, beef cows, calf crop)
so adjustments for item nonresponse (partial reports) and unit nonresponse (refusals and inaccessibles) are done in a single
calculation. Using the previous example, if 180 of the original 200 respond, the weights of the 180 will be adjusted to
1,000 divided by 180, or 5.56.
Two estimators are used to compute direct measures of the cattle items. The “reweighted” estimator and the “adjusted”
estimator are computationally identical except in how the nonresponse adjustments are made. The reweighted estimator
uses a global weight adjustment across all usable reports. The nonresponse weight adjustment for the adjusted estimator
uses an additional piece of information. When a sampled farm refuses to cooperate, interviewers will probe to determine
the presence of cattle even though the number is not known. This presence/absence indicator is used in the weight
adjustment.
Point estimates, called direct expansions, for both estimators are calculated by multiplying the reported value by the
nonresponse adjusted weight and summing to a stratum total. A variance estimate is also computed at the stratum level.
The nonoverlap tracts are treated as an additional stratum. Totals and variances are additive across strata to form a state
estimate and states are additive to a national estimate.
Ratio estimates are also computed for many items. For example, beef cows can be estimated as a percent of total
inventory. Ratio estimates use the reweighted estimator described above for the numerator and denominator. Both the
numerator and denominator must be reported in order for that record to be used in the ratio estimator.
Estimation: When all samples are accounted for, all responses fully edited, and the analysis material is reviewed, each
Regional Field Office executes the summary for their state. When all Regional Field Office 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 their 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 estimates, such as strata level expansions, response rates, and percent of the expansion from
usable reports.
Regional Field Offices 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 estimates
to the official estimate, Regional Field Offices interpret the survey results and submit a recommended estimate to
Headquarters. The data are viewed in tabular and graphical form and a consensus estimate is established. Regional Field
Offices see their survey results only and do not have access to other states’ results. For some data series, information from
other sources is also utilized in the process of establishing estimates.
For the national estimates, NASS assembles a panel of statisticians to serve as the Agricultural Statistics Board 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 number for total cattle and the cattle classes. The ASB has the advantage of being able to examine results
across stats, compare the state recommendations, and utilize administrative data available only at the United States level.
The same estimators used in the state summaries are produced by the national summary. The ASB follows the same
approach the states do in determining the national estimate. The historical relationship of the survey estimates to the
official estimate is evaluated over time to determine accuracy and bias using tables and graphs. Every 5 years NASS
conducts the Census of Agriculture, which is an exhaustive data collection effort for all known farm operations across the
Cattle Methodology and Quality Measures (March 2020)
USDA, National Agricultural Statistics Service

3

U.S. The information gathered from the Census of Agriculture is used to establish bench mark levels by which the survey
estimators can be compared and bias determined. Survey based estimators can also be impacted by outliers – individual
reports that have “excessive influence” on the results due to either improper classification or extremely unusual data for a
given operation (i.e. operation is not representative of other operations). NASS thoroughly reviews the survey data to
identify these situations and consider their impact on the survey results when establishing the official estimates.
External information (administrative data) is also utilized in the process of setting estimates. In order to be considered,
these data must be deemed to be reliable and come from unbiased sources. The most common administrative data is
commercial slaughter. NASS employs a balance sheet approach whenever possible to ensure that estimates are as accurate
as possible. This approach typically is limited to national level estimates. A balance sheet and its components are
reviewed when the inventory numbers are established. Commercial slaughter is an important element of the balance sheet
at the national level since its high degree of reliability is based on a near-actual count of animals slaughtered. Live U.S.
imports and exports to other countries are also considered.
Subtracting the disposition components of the balance sheet from supply components should, theoretically, give the
current inventory. However, each component of the balance sheet has varying degrees of possible estimation error. To be
most useful as an indication of inventory, therefore, each component is estimated on the basis of all available information.
The supply components of the U.S. balance sheet are the beginning inventory, births, and imports (inshipments for State
balance sheets). From this supply, the disposition components – commercial slaughter (marketings at State level), farm
slaughter, deaths, and exports – are subtracted. The result is the indicated number on hand at the end of the period or year.

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Cattle Methodology and Quality Measures (March 2020)
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Quality Metrics for Cattle
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 measurement of error due to sampling in the current period is irrelevant for a fully enumerated
data series. Non-sampling error is evaluated by response rates and the percent of the estimate from reported data. For the
January Cattle Survey both state-level and US-level estimates are published.
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.
Percent of estimate from usable reports is the ratio of survey data expanded by the original sampling weight
compared to survey data expanded by the nonresponse adjusted weight.
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.
Cattle Survey Sample Size and Response Rate: To assist in evaluating the performance of the estimates in the cattle
report, the sample size and response rates are displayed.

Cattle Survey Sample Size and Response Rate - United States: January 1, 2019-2020
State

Sample size

Response rate

2019

2020

2019

2020

(number)

(number)

(percent)

(percent)

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

37,548

37,421

57.0

58.1

Quality Metrics for Cattle by Class - United States: January 1, 2019-2020
Class

Percent of estimate
from usable reports

Coefficient
of variation

2019

2020

2019

2020

(percent)

(percent)

(percent)

(percent)

Cattle and calves .....................................................................

71.5

70.9

0.9

0.9

Cows and heifers that have calved ..........................................
Beef cows ..........................................................................
Milk cows ...........................................................................

68.3
66.4
75.9

67.5
65.5
75.5

1.0
1.2
1.1

1.0
1.3
1.0

Cattle on feed ..........................................................................

89.8

89.7

1.0

1.5

Calf crop ..................................................................................

67.6

67.4

1.0

1.1

Cattle Methodology and Quality Measures (March 2020)
USDA, National Agricultural Statistics Service

5

Quality Metrics for Cattle Survey - States and United States: January 1, 2019-2020
All cattle and calves
State

Sample size

Response rate

Percent of estimate
from usable reports

Coefficient
of variation

2019

2020

2019

2020

2019

2020

2019

2020

(number)

(number)

(percent)

(percent)

(percent)

(percent)

(percent)

(percent)

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

748
65
287
735
1,285
800
160
82
740
760

747
64
292
717
1,335
829
157
78
786
784

63.2
40.0
50.9
72.9
53.7
48.9
46.9
35.4
55.1
62.0

63.5
50.0
59.9
64.3
45.4
60.0
59.2
32.1
52.2
61.2

72.9
75.6
88.0
80.0
69.6
70.8
82.0
73.2
64.5
76.7

70.4
85.7
84.3
70.9
68.7
81.1
81.1
65.9
62.2
74.8

5.1
7.4
3.2
8.1
4.5
8.2
5.3
10.1
6.8
9.0

5.2
3.4
2.9
7.0
3.7
3.5
8.5
9.4
4.9
17.1

Hawaii ...........................
Idaho .............................
Illinois ............................
Indiana ..........................
Iowa ..............................
Kansas ..........................
Kentucky .......................
Louisiana ......................
Maine ............................
Maryland .......................

198
951
830
892
1,678
1,533
1,045
439
191
255

195
899
815
869
1,698
1,495
1,156
453
201
252

56.6
48.3
58.2
51.8
51.9
48.5
55.2
72.9
59.7
55.3

49.2
57.0
65.0
58.7
57.9
52.3
58.4
61.4
62.7
61.5

84.7
79.9
70.9
66.0
67.8
72.7
65.6
78.6
75.7
75.3

81.7
82.5
70.4
69.9
65.2
77.3
63.0
73.9
77.9
72.3

2.6
2.2
5.7
8.0
3.4
2.5
5.2
10.4
6.8
19.3

3.5
6.7
4.8
7.6
3.3
3.0
4.7
5.6
9.2
11.1

Massachusetts ..............
Michigan .......................
Minnesota .....................
Mississippi ....................
Missouri ........................
Montana ........................
Nebraska ......................
Nevada .........................
New Hampshire ............
New Jersey ...................

162
710
1,291
524
1,432
827
1,949
172
115
168

162
656
1,253
536
1,420
788
1,863
180
121
166

45.1
50.8
53.1
65.1
56.1
61.9
59.8
52.3
51.3
47.0

58.6
53.4
53.2
65.1
54.8
58.1
58.8
52.8
57.0
56.6

67.0
67.5
66.6
72.0
69.0
64.9
76.2
60.3
76.2
66.4

70.9
71.5
66.3
74.4
62.3
64.7
74.4
69.7
71.6
61.3

11.2
3.9
4.2
6.5
4.0
3.8
2.7
5.3
7.1
24.4

11.4
6.7
4.0
5.8
3.5
10.2
3.2
7.7
7.1
13.1

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

497
710
420
953
912
2,466
627
1,344
55
292

536
712
464
957
875
2,464
550
1,369
54
295

65.8
55.2
57.4
46.0
52.2
62.7
53.9
60.7
43.6
56.8

62.9
53.9
63.1
48.9
48.5
65.7
62.0
55.8
31.5
61.4

78.7
67.5
74.0
55.8
60.4
69.3
68.4
67.9
64.3
62.1

74.3
68.2
72.4
60.2
61.5
71.2
74.2
60.7
40.9
61.7

5.4
3.8
9.7
5.6
9.7
3.4
5.9
4.8
37.6
9.7

3.2
10.8
9.4
5.3
7.7
2.6
4.9
4.2
13.5
11.0

South Dakota ................
Tennessee ....................
Texas ............................
Utah ..............................
Vermont ........................
Virginia ..........................
Washington ...................
West Virginia .................
Wisconsin .....................
Wyoming .......................

1,186
799
1,873
434
273
769
468
333
1,325
788

1,166
856
1,825
397
271
767
484
363
1,298
751

51.1
64.7
63.5
79.5
55.7
60.6
50.6
71.5
54.7
61.3

49.4
69.5
62.6
74.3
66.1
63.4
50.6
66.7
56.6
60.3

56.7
74.0
80.3
82.0
63.7
68.4
76.7
77.9
66.1
66.3

54.9
73.2
77.2
79.7
73.5
68.2
78.4
69.7
66.7
67.2

4.1
4.7
4.0
6.2
6.9
4.5
6.6
7.9
3.5
3.0

3.7
4.4
2.6
5.4
3.4
7.0
24.2
7.0
3.8
3.9

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

37,548

37,421

57.0

58.1

71.5

70.9

0.9

0.9

6

Cattle Methodology and Quality Measures (March 2020)
USDA, National Agricultural Statistics Service

Information Contacts
Process
Estimation ....................................
Data Collection ............................
Questionnaires ............................
Sampling and Editing ...................
Summary and Estimators .............
Dissemination ..............................
Media Contact and Webmaster ....

Unit
Livestock 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) 720-3570
(202) 720-3895
(202) 720-6201
(202) 720-5805
(202) 720-4008
(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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