Miller's Annual Report

Detailed information for 2002-2003





Record number:


This survey collects information on grains milled, production of flour and offal, and stock data.

Data release - October 22, 2003


This survey collects annual milling data from the smaller mills in Canada. The data are used in the determination of expansion factors for the monthly data, and to determine the final crop year totals for the previous year. These data are used by Agriculture and Agri-Food Canada, federal and provincial governments and the related associations in the form of supply and disposition tables.

Reference period: Crop Year (August 1 to July 31)

Collection period: July to November


  • Agriculture and food (formerly Agriculture)
  • Crops and horticulture

Data sources and methodology

Target population

All mills in Canada (approximately 20) that process less than 500 metric tons of cereal grains per month.

Instrument design

The questionnaire was developed by subject matter specialists through consultation with the provinces and industry experts. New questions are not pre-tested in the field. However, testing is conducted in-house for flow and consistency. Questions will be changed, added or removed as the need arises. Required changes are usually identified through such means as subject matter specialist research or changes in market trends.


This survey is a census with a cross-sectional design.

This methodology does not apply.

Data sources

Responding to this survey is mandatory.

Data are collected directly from survey respondents.

The data is collected through a mail-out/mail-back process, while providing respondents with the option of telephone or other electronic filing methods. Follow-up procedures are applied when a questionnaire has not been received after a pre-specified period of time: respondents are phoned or sent a fax to remind them to send their questionnaire in order to reach the survey target response rate.

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

Error detection

Plant output is compared to plant input and to standard extraction rates. Data are also compared on a year-to-year basis and to estimates of other plants producing similar products. Since data normally are fairly stable, large variations are reviewed and verified.


Quantities are imputed for any non-responses, for mills that are slow reporting or only partially complete the questionnaire. Data are usually imputed based on previous reports or on industry information. Occasionally, data from similar plants may be used. Data are revised when actual data are received.


This methodology type does not apply to this statistical program.

Quality evaluation

Data quality is maintained by standard editing techniques which are very rigorous. Average extraction rate and grain production and quality aid in data validation. Data discrepancies are either scrutinized by professional staff or the company involved is contacted.

Estimates produced are analyzed in conjunction with the results from the Millers' Monthly Report (IMDB 3403) in national supply-disposition tables. Summary data are compared with milling data from the Canadian Grain Commission and information from the Canadian Wheat Board and other industry sources.

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. The survey data are not benchmarked.

Data accuracy

While considerable effort is made to ensure high standards throughout all stages of collection and processing, the resulting estimates are inevitably subject to a certain degree of non-sampling error. Examples of non-sampling error are coverage error, data response error, non-response error and processing error.

Coverage error can result from incomplete listing and inadequate coverage of the population of mills. However, given the infrastructure and the supplies of grain needed for a mill, it is unlikely that a new plant could start-up undetected. Since the mills on this survey comprise a small portion of the milling universe, it is considered that any coverage error would be temporary and would have only a minimal effect on the resulting estimates. The estimates also include data from the large mills reporting to the Millers' Monthly Report.

Data response error may be due to questionnaire design, the characteristics of a question, inability or unwillingness of the respondent to provide correct information, misinterpretation of the questions or definitional problems. These errors are controlled through careful questionnaire design and the use of simple concepts and consistency checks. This survey has been in place for many years and most respondents are well versed in the survey concepts.

Non-response error is related to respondents that may refuse to answer, are unable to respond or are too late in reporting. In these cases, data are imputed. The extent on any imputation error decreases with increases in the response rate and attempts are therefore made to obtain as high a response rate as possible. Final response for this survey is very high -- approaching 100%; however, slow reporting is often an issue. Analysts keep in contact with the mills and the related industry associations to maintain the high response rate.

Processing error may occur at various stages of processing such as data entry, editing and tabulation. Measures have been taken to minimize these errors. A few trained staff work on this survey. Data entry and edit are performed simultaneously due to the spreadsheet design which allows errors to be quickly seen. Historical ratios also aid in eliminating outliers created by data entry. Tabulation is automated to eliminate human error.

Date modified: