Feed Grain Purchases

Status:
Active
Frequency:
Biannual
Record number:
5046

The survey addresses a gap in the Western Provinces regarding the value of feed grains.

Detailed information for August 2013 to July 2014

Data release - These data are released internally to Statistics Canada for other surveys or statistical programs to use as part of their data sources.

Description

The survey addresses a gap in the Western Provinces regarding the value of feed grains. The survey data are used to update the quantities and values of grain deliveries. The quantities are used to improve the estimates of unlicensed grain deliveries in farm supply-disposition tables that in turn improve the estimates of crop production and farm stocks. The values are subsequently used to improve the published farm cash receipts and by the Canadian System of National Accounts (CSNA) to calculate the Gross Domestic Product (GDP) and related variables.

Collection period:
Twice a year at the end of the crop year (July 31st) and the calendar year (December 31st).

Subjects

  • Agriculture
  • Crops and horticulture

Data sources and methodology

Target population

Sixteen companies, which report for all of their subsidiary locations across the Western Provinces. These firms are feed mills that buy grain directly from farmers or from grain dealers. The list of mills was obtained from industry discussions and from the Animal Nutrition Association of Canada. The list is maintained from trade sources and from the survey itself. Feed lots are excluded.

Instrument design

The questionnaire was designed in consultation with internal and external specialists, as well as some respondents, before the start of the survey in 2003.

Sampling

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

Data are collected for all units of the target population, therefore no sampling is done.

Data sources

Responding to this survey is mandatory.

Data are collected directly from survey respondents.

The collection is done by mail with mail/facsimile and telephone follow-up.

The questionnaire asks for the crop year to-date quantities of feed grains purchased from farmers and grain dealers by grain. The data are requested for grains originating from individual provinces in the west, for the total east, for other countries and in total.

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

Error detection

Questionnaires are checked prior to data entry to ensure that the geographic distributions add to the total. In July, data are compared to the December reports to ensure the July data are at least equal to the crop year to-date data reported in December. Reported data are also compared to previous reports to ensure that the commodities reported are the same. Any changes are reviewed for reasonableness given the current availability of feed and the number of livestock.

Imputation

Data are not generally imputed. When necessary, imputes are made based on previous reports by the same company and on trends shown by other reporting firms. Information on livestock numbers and the supply and disposition of grains help in the analyses.

Quality evaluation

This is a census and the data quality is maintained by standard editing techniques which are rigorous. Apparent data discrepancies are either scrutinized by professional staff or the company involved is contacted. Supply and disposition trends, used by government and industry stakeholders, help to confirm the results of the survey.

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

Data are revised for non-response or for incorrect reporting when revisions are received. The survey data are not benchmarked.

Data accuracy

Since this is not a sample survey, there is no sampling error.

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. The major sources of non-sampling error for this survey are considered to be coverage error and non-response error.

Coverage error can result from incomplete listing and inadequate coverage of the population of feed mills. While coverage is considered to be very good in Manitoba and Saskatchewan, there have been difficulties identifying the mills in Alberta. There are also many feed mills that purchase only small quantities occasionally. The effect of not including every small mill in the survey is considered to be negligible.

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. However, this survey is relatively new and some respondents not be well versed in the survey concepts which introduces some non-sampling error.

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 generally not imputed. Attempts are therefore made to obtain as high a response rate as possible. Final response for this survey is about 90% annually.

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 and review the estimates. Tabulation is automated to eliminate human error.