Monthly Inventory Statement of Butter and Cheese (BUCH)

Detailed information for February 2017





Record number:


The purpose of this survey is to produce monthly statistics on stocks of butter and cheese held in cold storage warehouses.

Data release - March 29, 2017


This monthly census collects data from cold storage warehouses on butter and cheeses stocks that are required to produce economic statistics for the dairy processing industry in Canada.
Data collected from cold storage warehouses are aggregated with information from other sources to produce official estimates of national and provincial economic production for the dairy processing industry. Survey estimates are made available to governments, associations, and the public.

The data are used by Agriculture and Agri-Food Canada, the Canadian Dairy Commission, provincial governments and the Dairy Farmers of Canada to assist in the development, administration and evaluation of dairy policies. Numerous government and non-government organizations are involved in the administration of the dairy sector and require detailed information particularly because this sector operates within a supply-management framework that manages farm-level production, imports, exports and prices.

Statistical activity

The survey is administered as part of the Integrated Business Statistics Program (IBSP). The IBSP program has been designed to integrate approximately 200 separate business surveys into a single master survey program. The IBSP aims at collecting industry and product detail at the provincial level while minimizing overlap between different survey questionnaires. The redesigned business survey questionnaires have a consistent look, structure and content. The integrated approach makes reporting easier for firms operating in different industries because they can provide similar information for each branch operation. This way they avoid having to respond to questionnaires that differ for each industry in terms of format, wording and even concepts. The combined results produce more coherent and accurate statistics on the economy.

Reference period: First day of every month

Collection period: Ten days following the reference period


  • Agriculture
  • Food, beverage and tobacco
  • Livestock and aquaculture
  • Manufacturing

Data sources and methodology

Target population

The target Population consists of all cold storage warehouses that keep butter and cheese stocks.

The observed Population is comprised of all statistical establishments on Statistics Canada's Business Register having been identified as being Cold storage warehouses that keep butter and cheese stocks.

Instrument design

The electronic questionnaire was designed by Statistics Canada as part of the Integrated Business Statistics Program. This program incorporates business surveys into a single framework, using questionnaires with a consistent look, structure and content.


This survey is a census.

The frame for this census is a list frame.

The sampling unit is the establishment as defined on the Business Register.

It is geometric stratification by NAICS, Province and Revenue.

The total size of this census is approximately 30 establishments.

Data sources

Data collection for this reference period: 2017-02-01 to 2017-02-10

Responding to this survey is mandatory.

Data are collected directly from survey respondents.

Respondents are contacted by email or letter and given an access code for the electronic questionnaire for the survey, which can be responded to in either official language. Non-response follow -up is conducted via email, telephone or fax. Respondents are required to report all data for products in stock on the first business day of the month.

The survey, on average, takes respondents 15 minutes to complete.

View the Questionnaire(s) and reporting guide(s) .

Error detection

Error detection is an integral part of both collection and data processing activities. Edits are applied to data records during collection to identify reporting and capture errors. These edits identify potential errors based on year-over-year changes in key variables, totals, and ratios that exceed tolerance thresholds, as well as identify problems in the consistency of collected data (e.g. a total variable does not equal the sum of its parts). During data processing, other edits are used to automatically detect errors or inconsistencies that remain in the data following collection. These edits include value edits (e.g. Value > 0, Value > -500, Value = 0), linear equality edits (e.g. Value1 + Value2 = Total Value), linear inequality edits (e.g. Value1 >= Value2), and equivalency edits (e.g. Value1 = Value2). When errors are found, they can be corrected using the failed edit follow up process during collection or via imputation. Extreme values are also flagged as outliers, using automated methods based on the distribution of the collected information. Following their detection, these values are reviewed in order to assess their reliability. Manual review of other units may lead to additional outliers identified. These outliers are excluded from use in the calculation of ratios and trends used for imputation, and during donor imputation. In general, every effort is made to minimize the non-sampling errors of omission, duplication, misclassification, reporting and processing.


When non-response occurs, or when respondents do not completely answer the questionnaire, imputation is used to fill in the missing information. Many methods of imputation may be used to complete a questionnaire, including manual changes made by an analyst. The automated statistical techniques used to impute the missing data include: deterministic imputation, replacement using historical data (with a trend calculated, when appropriate), and replacement using data from a similar unit in the sample (known as donor imputation). Usually, key variables are imputed first and are used as anchors in subsequent steps to impute other, related variables.


All units in the observed population are being surveyed. Estimation of totals is done by simple aggregation of the values of all estimation units that are found in the domain of estimation. Estimates are computed for domains of estimation such as provinces/territories, based on the most recent classification information available for the estimation unit and the survey reference period.

Quality evaluation

Prior to the data release, combined survey results are analyzed for comparability; in general, this includes a detailed review of: individual responses (especially for the largest companies), general economic conditions, coherence with results from related economic indicators, historical trends, and information from other external sources (e.g. associations, trade publications, newspaper articles).

Disclosure control

Statistics Canada is prohibited by law from releasing any information it collects which could identify any person, business, or organization, unless consent has been given by the respondent or as permitted by the Statistics Act. Various confidentiality rules are applied to all data that are released or published to prevent the publication or disclosure of any information deemed confidential. If necessary, data are suppressed to prevent direct or residual disclosure of identifiable data.

Revisions and seasonal adjustment

There is no seasonal adjustment. Data from previous years may be revised based on updated information.

Data accuracy

As this is a small census style survey with rigorous follow-up as required, data quality is deemed to be very high. In the case of a late report, telephone follow-up results in a high response rate. If the data cannot be obtained, imputation methods are applied.

The data accuracy of this survey is high as the response rate is normally over 90% covering over 95% of the industry.

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