Agriculture Frame Update Survey (AFUS)

Detailed information for 2015

The information from this survey will serve the following purposes: maintain and update the Canadian Business Register, the principal frame for the economic statistics program of Statistics Canada and improve the efficiency of the Agriculture Statistical Program by collecting information on the main products and activities of this business.

Data release - AFUS is a frame maintenance tool, with no direct outputs to the public.

• Questionnaire(s) and reporting guide(s)
• Description
• Data sources and methodology

Description

The business register is a structured list of businesses which produce goods and services in Canada. It includes all agricultural operations as well. It is the primary source and control for the Agriculture Division survey program and Census of Agriculture collection and coverage. The agriculture universe is updated continuously as agriculture is a dynamic industry. The Agriculture Frame Update Survey (AFUS) addresses this issue by providing a mechanism to ensure the accuracy of the Business Register for the agricultural universe.

AFUS is a frame maintenance tool, with no direct outputs to the public. The objective of AFUS is to update the frame and respondent information on the Business Register (BR):
- verifying that the establishment is in business;
- determining that it is active in agriculture;
- in the affirmative, profiling farm activities, leading to efficient sample design.

Subjects

• Agriculture and food (formerly Agriculture)
• Farms and farm operators

Data sources and methodology

Target population

The Agriculture Frame Update Survey targets establishments on Statistics Canada's Business Register that are potentially engaged in agriculture activities, but where this activity has not been confirmed. The observed population is the same as the targeted population.

Instrument design

The questionnaire was developed in 2015 in consultation with subject matter experts and methodologists. Statistics Canada's Questionnaire Design Resource Centre (QDRC) provided feedback on the content.

Sampling

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

The establishments sent to AFUS come from the Agricultural universe on BR. They are not linked to units known by the Census of Agriculture or by the Agriculture Survey Program. The information gathered from Tax data or other sources is not completely sufficient to determine if they belong to the universe and in which stratum they should be. Tax data signals, matched with other signals may be sufficient to positively add or remove them from the universe. If a sufficient number of reliable signals cannot be gathered for a case, these cases remain in the AFUS target population.

The target establishment has an Agricultural NAICS or is involved in agriculture as identified by an external signal. The vast majority of the units will not be part of any BR survey population. The information from different sources does not allow distinguishing between an out-of-scope unit and a valid farm.

Data sources

Data collection for this reference period: 2015-05-04 to 2015-06-30

Responding to this survey is mandatory.

Data are collected directly from survey respondents.

The survey is collected through an electronic questionnaire.

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

Disclosure control

Statistics Canada is prohibited by law from releasing any information it collects that 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.

In order to prevent any data disclosure, confidentiality analysis is done using the Statistics Canada Generalized Disclosure Control System (G-Confid). G-Confid is used for primary suppression (direct disclosure) as well as for secondary suppression (residual disclosure). Direct disclosure occurs when the value in a tabulation cell is composed of or dominated by few enterprises while residual disclosure occurs when confidential information can be derived indirectly by piecing together information from different sources or data series.