Farm Financial Survey (FFS)

Detailed information for 2019




Every 2 years

Record number:


The Farm Financial Survey, an initiative by Agriculture and Agri-Food Canada and Statistics Canada, provides data on farm assets, liabilities, revenues, expenses, capital investments and capital sales.

Data release - April 30, 2021


The Farm Financial Survey provides data on farm assets, liabilities, revenues, expenses, capital investments and capital sales. Agriculture and Agri-Food Canada uses the data to examine the effects of agriculture programs and policies on different types of farm operations by province. Farm Financial Survey (FRFN) data contribute some measures of assets and liabilities to programs within Statistics Canada.

Reference period: Calendar year

Collection period: October-December


  • Agriculture and food (formerly Agriculture)
  • Farm financial statistics

Data sources and methodology

Target population

The target population for the survey consists of all Canadian agriculture operations that are active at the end of the reference year. Specific farms are excluded from the target population, such as farms with less than $25,000 in sales from agricultural activities; institutional farms; community pastures; farms on First Nations reserves; and farms that are part of multi-holding companies.

Instrument design

The original development of the survey questionnaire was based on agricultural financial balance sheet and income statement concepts, and it was pilot tested. There have been few changes to these core items.

Interviewers and collection managers participate in a telephone debriefing, and also provide answers to a set of written debriefing questions. All comments are considered in the following development cycle.


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

Using a list of all the farms from the Business Register, an establishment is assigned to a stratum by province, by farm type and by farm size. The size stratum is determined by the revenues and the assets of the establishment. The sampling unit is the establishment.

A simple random sampling is selected in each stratum. The sample is allocated to different strata using statistical methods to optimize the precision of the resulting estimates.

The initial sample is slightly modified because sample coordination is performed with other agricultural surveys when their collection period overlaps with the Farm Financial Survey. The overlapping units are replaced, as much as possible, by other units of the population.

The sample size is about 10,000 farms.

Data sources

Data collection for this reference period: 2020-10-13 to 2020-12-31

Responding to this survey is mandatory.

Data are collected directly from survey respondents and extracted from administrative files.

Data are collected using an invitation to open, complete and submit an electronic questionnaire. If the questionnaire is not completed on-line by the deadline, the respondent will be contacted for a scheduled telephone interview.

In an effort to reduce the response burden on farmers, Statistics Canada uses data from the Canada Revenue Agency, to replace the following 18 questions on revenues and expenses:

- Total gross farm revenue
- Revenue: Sale of grains, oilseeds, pulse crops and forage seeds
- Revenue: Sale of horticulture products
- Revenue: Sale of cattle
- Revenue: Sale of pigs
- Revenue: Sale of poultry
- Revenue: Sale of milk, cream and other dairy products
- Revenue: Agriculture custom of contract work or machine rentals
- Revenue: All other farm revenue
- Revenue: Total amount received for program payments

- Total farm operating expenses
- Operating expenses: Fertilizer and lime
- Operating expenses: Herbicides, insecticides, fungicides, etc.
- Operating expenses: Seed and plants
- Operating expenses: Feed, supplements and hay
- Operating expenses: Fuel for machinery, trucks and automobiles
- Operating expenses: Total interest paid on farm debt
- Operating expenses: Land rentals

Commencing with the 2015 reference year, data from the Farm Financial Survey will be linked with taxation data at the micro level. Linkage results will be used to produce aggregate estimates for revenues and expenses data.

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

Error detection

The electronic questionnaire used for collection contains range and consistency edits and help text. A set of reports is run to identify problem items early in collection for remedial action. Processing includes checking interviewer notes, manually reviewing significant inconsistencies, and reviewing the top contributors to the unweighted and weighted estimates (for each variable in each province).


If data inconsistencies and errors are not resolved by telephone follow-up, the inconsistent data are imputed along with other missing data. Starting with reference year 2017, total survey non responses are also fully imputed. Imputation is done mainly through donor imputation where the value is taken from a similar record in terms of farm size, farm type and region. In addition to donor imputation, certain fields are imputed using historical information available from the previous occasion, and others are deterministically imputed using other information from the questionnaire. The edited and imputed data are then subject to verifications and adjustments by expert analysts.


Sampling weights are assigned to all selected establishments based on their probability of selection. These weights are used for the tabulation of the estimates.

The precision of the estimates is described through the sampling variance and uses classical statistical formulae for one-stage stratified simple random sampling survey designs.

Once the weights have been calculated for each record, any level of required estimates may be obtained using domain estimation (i.e.: Canada, provincial, farm type, revenue class, etc.).

Quality evaluation

The electronic questionnaire application contains edits used during collection that enables interviewers to correct errors immediately. Each survey step produces reports that are reviewed and that indicate edit failure counts, missing data counts, and percent impact of imputations on the estimates.

The survey results are evaluated through comparisons to other sources of farm financial information, such as the Agricultural Economic Statistics series, and the Census of Agriculture to assess the quality of the data.

Disclosure control

Statistics Canada is prohibited by law from releasing any data which would divulge information obtained under the Statistics Act that relates to any identifiable person, business or organization without the prior knowledge or the consent in writing of that person, business or organization. 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

This methodology does not apply to this survey.

Data accuracy

Sample surveys are designed to provide the highest sampling efficiency (the smallest sample that will produce a sampling error of a given size). This optimization is usually performed for only a few variables, limited by the data items that are available at the time of sample design and selection, the resources available, and the complexity introduced by trying to optimize for many variables at one time. The sample used for these statistics was designed to produce a reasonable level of accuracy for assets and revenue by province and farm type. Consequently, other items may be less accurately estimated.

All surveys are subject to sampling and non-sampling errors. Sampling error occurs because population estimates are derived from a sample of the population rather than the entire population. Non-sampling error is not related to sampling and may occur for various reasons during the collection and processing of data. For example, non-response is an important source of non-sampling error. Under or over-coverage of the population, differences in the interpretations of questions and mistakes in recording, coding and processing data are other examples of non-sampling errors. To the maximum extent possible, these errors are minimized through careful design of the survey questionnaire, verification of the survey data, and follow-up with respondents when needed to maximize response rates.

Measures of sampling error are calculated for each estimate. Starting with reference year 2017, when non-response occurs, it is taken into account and the quality is reduced based on its importance to the estimate. Other indicators of quality are also provided such as the response rate. Both the sampling error and the non-response rate are combined into one quality rating code (starting with reference year 2017). This code uses letters that ranges from A to F where A means the data is of excellent quality and F means it is unreliable.

The Business Register, from which the sample is drawn, is frequently updated. Some under-coverage may exist due to intercensal agricultural frame differences.

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