Census of Agriculture

Detailed information for 2021




Every 5 years

Record number:


The Census of Agriculture provides a comprehensive and integrated profile of the physical, economic, social and environmental aspects of Canada's agriculture industry. It provides a snapshot in time and when compared against previous censuses, provides a powerful tool to highlight changes in the industry. It serves as a basis for public and private decision making, as well as research and analysis in areas of concern to the people of Canada.

Every five years, the Census of Agriculture publishes a wide range of data at the national, provincial and sub-provincial levels, such as number of farms and farm operators, farm area, business operating arrangements, land management practices, livestock inventories and crop area, total operating expenses and receipts, farm capital and farm machinery and equipment.

Data release - Feb. 22, 2022 (Reference Maps 2021); Apr. 14, 2022 (Agriculture Guide 2021); May 11, 2022 (Farm Operator Data 2021); June 15, 2022 (Farming across regions); Nov.17, 2022 (Cannabis production); Aug 25, 2023 (Evolving farm population)


The data collected by the Census of Agriculture are used to produce statistics on the full spectrum of the agriculture industry. The information is also used by Agriculture and Agri-Food Canada and provincial governments to develop, administer and evaluate agricultural policies, and by universities and agri-businesses for research and planning.

The census takes place every five years as decreed by the Statistics Act. It provides a historical perspective on Canadian agriculture and on trends in the industry over the years.

Clients: Federal government, provincial and territorial governments, municipal governments; libraries; educational institutions; researchers and academics; private industry; business associations and labour organizations; private citizens; public interest groups.


  • Agriculture and food (formerly Agriculture)

Data sources and methodology

Target population

The target population for the Census of Agriculture is all "census farms" in Canada. In 2021, a "census farm" is defined as an operation that produces at least one agricultural product and will report revenue and/or expenses for that agricultural production to the Canada Revenue Agency. The agricultural product(s) being produced can include any of the following: crops (hay, field crops, tree fruits or nuts, berries or grapes, vegetables, seed); livestock (cattle, pigs, sheep, horses, game animals, other livestock); poultry (hens, chickens, turkeys, chicks, game birds, other poultry); animal products (milk or cream, eggs, wool, furs, meat); or other agricultural products (Christmas trees, greenhouse or nursery products, mushrooms, sod, honey, maple syrup products).

The observed population is selected from Statistics Canada's Business Register in conjunction with information from the latest set of tax remittances. The selection process uses the detailed tax information of the establishments on the Business Register to select those which have reported agricultural commodity revenues and/or expenses, signaling that they are involved in agriculture. To ensure more complete coverage, additional data and methods are used to include establishments which report their fiscal data differently. Because the latest available tax data are from 2019, an additional set of modelled records was added to this population to represent newer farms and reduce undercoverage.

Instrument design

User consultations
A series of workshops were held across Canada in 2017 with users such as federal departments and provincial ministries, agricultural associations, academics and agriculture service providers. Users submitted recommendations for the types of questions they would like to see on the 2021 Census questionnaire which were used to develop the content and design of the census questionnaire.

Evaluating the suggestions
Before going any further, submissions from consultations were evaluated on these key elements:
- Relevance to the agricultural sector;
- Comparability over time;
- Are of national interest;
- Level of geography required;
- Question comprehension and availability of information by farmers;
- Farmer willingness to respond;
- Demand for data;
- Type of questions (e.g., Yes/no versus value);
- Availability of other data sources; and
- Collection frequency.

Questionnaire content and development
Although the questionnaire is updated every census to reflect users' changing requirements as identified through the submission process, certain questions appear on every census. These questions — such as those on farm operators, land area, livestock numbers and crop areas — are considered essential by Statistics Canada and other major users of Census of Agriculture data. Repeating basic questions allows the census to measure change over time, while adding new questions and dropping others allows data to be collected that reflect new technologies and structural changes in the agriculture industry.

The majority of the 2021 questions are identical to questions in the 2016 Census of Agriculture. Changes to the 2021 questionnaire have nonetheless been made to better identify emerging agricultural products and trends. These emerging categories include, for example, hemp, haskaps, kale and ducks. The greenhouse and mushroom questions were also expanded to request areas for additional categories, such as greenhouse tomatoes and specialty mushrooms. Further adjustments were additionally made to content related to renewable energy, technology, direct sales, succession planning, and machinery.

New or changed questions were developed in Head Office in consultation with industry experts and tested a number of times with farm operators across Canada through one-on-one interviews on their farms and in focus groups. Farm operators selected for testing reflected regional diversity in terms of types of agriculture, production techniques, farm size, language and age. This testing proved that some questions would not perform well on the census, and that the wording of other questions would require refinement. Respondent burden, content-testing results, user priorities and budgets were all taken into consideration in determining the final content of the 2021 Census of Agriculture questionnaire. The questions were approved by Cabinet in the spring of 2020.

For more information about the 2021 Census of Agriculture consultation process and its results, and the changes to the 2021 questionnaire, view the Documentation section at the bottom of this page.


This survey is a census with a cross-sectional design.
The Census of Agriculture is designed to obtain complete and accurate data from all farms in Canada. No sampling is done.

Data sources

Data collection for this reference period: 2021-05 to 2021-09 (Census day: May 11, 2021)

Responding to this survey is mandatory.

Data are collected directly from survey respondents.

CEAG data are collected directly from survey respondents, with the exception of those modelled units for which the data are imputed rather than collected.


In 2021, the Census of Agriculture focused on electronic questionnaire collection. Invitation letters were delivered to farm operations by Canada Post. Farm operators were asked to complete the 2021 Census of Agriculture online by using the secure access code provided in the invitation letter. If it was determined that a questionnaire was not received, follow-up was conducted by telephone. For a more detailed description of the collection process, please refer to the Guide to the Census of Agriculture, 2021. See the link to the Guide in the Documentation section at the bottom of this page.

The Census of Agriculture reduces response burden by replacing questionnaire data with administrative data where possible. The utilization of high quality data sources —such as tax data from the Canada Revenue Agency — eliminates the need to ask respondents questions about the operating arrangement, and revenues and expenses because this information can be obtained from their tax forms. Alternative data sources can also be used to populate questionnaire data in some cases.

The age and sex of the farm operators come from what is reported in the Census of Population for each farm operator. The farm operators from the Census of Agriculture are linked to the Census of Population database using a probabilistic linkage method which matches personal and household information provided on both questionnaires (such as name, birthdate, telephone number, etc.). Operators on the Census of Agriculture for which no link is found will have their information imputed with that of another Census of Population person having similar characteristics.

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

Error detection

Error detection is an integral part of both collection and data processing activities. Edits were applied to microdata records during collection to identify reporting and capture errors, as well as data inconsistencies. Totals in key variables that do not equal the sum of their parts and ratios that exceed tolerance thresholds were flagged for respondents to review.

Data from paper questionnaires were captured through the electronic questionnaire application and were subjected to the same rigorous quality control and processing edits as the electronic responses, to identify and resolve problems related to inaccurate, missing or inconsistent data.

During data processing, additional edits were used to automatically detect errors or inconsistencies that remain in the microdata following collection. These edits include value edits (e.g. values which fall outside of expected ranges), linear equality edits (e.g. the sum of parts is equal to the total), linear inequality edits (e.g. a value for one question is always expected to be larger than the value of another), and consistency edits (e.g. an amount is reported for the value of trucks, but no trucks are reported, or the vegetables screening question is flagged as 'yes' but no area is reported for any vegetables). When errors were found, they were corrected using the data editing and imputation processes, or during the data validation process.

Extreme values were also identified using automated methods based on the distribution of the collected information. Following their detection, these values were reviewed by subject-matter analysts in order to assess their validity. Macro-level totals were also reviewed to make sure they line up with expectations and economic market trends. During this process, provincial or agricultural experts are consulted. In general, every effort was made to minimize the non-sampling errors of omission, duplication, misclassification, reporting and processing.


Non-response occurs when respondents do not answer a portion of the questionnaire or the questionnaire as a whole, or when reported data are considered erroneous during the error detection steps. In those situations, imputation is used to fill in the missing information and modify the erroneous 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 mainly include: deterministic imputation and replacement using data from a similar unit in the sample (known as donor imputation). Usually, important variables are imputed first and are used as anchors in subsequent steps to impute other related variables. In some cases, ratio imputation and historical imputation are also used to complete the data for some specific types of units.

Manual imputation of missing data is done only for some cases when the collected data does not align with historical data or with a known data relationship. These are generally done on rare occasions during the data validation process, after thorough investigation.


The Census of Agriculture collects or imputes a set of values for each census farm in the population. For this reason no sampling weights are required for tabulations. Totals and averages can be calculated by simply summing or taking the average value of the variables in question for the records in the database within a desired domain.

The quality of the resulting estimates is represented through the use of a variance measure. In the case of the Census of Agriculture, this variance estimate represents the amount of uncertainty in the point estimate due to the imputation that took place during the data processing step and any additional variance required to maintain the confidentiality of a respondent's data.

Quality evaluation


The initial data validation stage is undertaken by a team of subject-matter analysts. They review the estimates by comparing them to the results of previous censuses or estimates from other data sources. Although the validation of all individual records is not feasible, the analysts review the most important contributors individually, especially when the estimates vary significantly from those of other data sources.

For the Census of Agriculture, the final data validation process is the certification of the data. At this stage, a wider range of analysts and experts review and compare the results to estimates from previous censuses or estimates from other data sources. During data certification, response rates, edit failure rates, coverage rates and a comparison of the data before and after imputation are among the measures used to evaluate the accuracy and coherence of the data and possibly explain differences with other sources. Detailed cross-tabulations are also checked for consistency and accuracy.

Some estimates are not comparable with those of previous censuses. This may be due to wording or conceptual changes in the questions in 2021, or the addition or removal of questions between 2016 and 2021. After thoroughly investigating each case, notes were developed to identify the affected questions and explain the reasons that users should use caution when comparing the results.

Data for cannabis operations were collected for the first time during the 2021 Census of Agriculture. Due to the complexity of these operations' activities and organizational structure, these respondents were not able to provide responses that precisely captured the agricultural activity of cannabis cultivation in its entirety and/or disassociated from non-agricultural activities. Furthermore, the response rate for cannabis operations was low, and this consequently resulted in a high imputation rate. As a result, cannabis operations were excluded from the Census of Agriculture databases and its data releases. As an alternative, the Census of Agriculture published cannabis data extracted from administrative files received from Health Canada. These data provide the number of licensed cannabis cultivators and their production areas at the national and provincial levels of geography. Separate information is available for operations growing cannabis under cover and in open fields. Cannabis operations included in this release are not included in all other Census of Agriculture releases.

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.

Data disclosure occurs when the value in a tabulation cell is composed of or dominated by a few census farms. In order to prevent any data disclosure, all published tables are analysed using a method known as Random Tabular Adjustment. This technique aims to increase the amount of data made available to users compared to traditional data suppression approaches, while protecting the confidentiality of respondents. In cases where the direct publication of an estimate would potentially lead to the disclosure of an individual's data, the estimate is adjusted by a random amount in order to provide additional uncertainty to the estimate of the individual's responses. For a more detailed description of the Random Tabular Adjustment process, please refer to the document at the bottom of this page.

Revisions and seasonal adjustment

This methodology type does not apply to this survey.

Data accuracy

The accuracy of statistical information is the degree to which the information correctly describes the phenomena it was designed to measure. Numerous traditional and enhanced quality assurance steps are put into place to ensure that Census of Agriculture data are as accurate as they can be.

With projects as large and complex as the Census of Agriculture, the estimates produced are inevitably subject to a certain degree of error. Knowing the types of errors that can occur and how they affect specific variables can help users assess the usefulness of the data for their particular applications, as well as assess the risks involved in making conclusions or decisions based on these results.

The quality assurance steps and details about the types of error that can affect the quality of the Census of Agriculture estimates are described in greater detail in the Guide to the Census of Agriculture, 2021 in the Documentation section at the bottom of this page.

In addition to these quality assurance steps, in 2021 for the first time, the Census of Agriculture is providing a quality indicator for most published value estimates. These quality indicators take into account the variance in the estimate resulting from the imputation step during data processing and any extra adjustment required by the tabular disclosure avoidance method to protect the confidentiality of census respondents. Quality indicators are represented by letters—ranging from A through F—with each letter defined by a specific coefficient of variation (CV) range, as seen below:
A < 5.0% Excellent
B 5.0% to 9.99% Very good
C 10.0% to 14.99% Good
D 15.0% to 24.99% Acceptable
E 25.00% to 49.99% Use with caution
F >= 50.00% Too unreliable to be published

Only the letter quality indicator above is published for most estimates included in the Census of Agriculture tabulations; the exact CVs are not disclosed.

The quality indicators for farm count estimates are calculated using a different method than coefficients of variation, but use a similar A to F scale to represent the quality of the estimate.

Quality Indicators
In addition to the quality indicators assigned to an individual estimate, other data quality indicators are computed to measure the reliability and the overall quality of census data.

The 2021 Census of Agriculture farm and operator data are released on May 11, 2022, exactly one year after the May 11, 2021 reference date.

Response rates

Response rates are one of the key data quality measures for the Census of Agriculture. The response rates were calculated at the national level and for each province after the data processing and certification steps. See response rates for the 2021 Census of Agriculture in the "Additional documentation" link provided below.

The response rate between the 2021 census and previous censuses are not directly comparable, due to the inclusion of the modelled records in 2021 that were added to the initial population to represent new census farms since 2019 and reduce undercoverage. Since these census farms were modelled and were not sent a direct questionnaire they are considered to effectively be non-respondents in these calculations. If excluded from the calculations to provide a more equivalent comparison of response rates with previous censuses, the rates look more similar.

These results are presented in the Collection rate table for the 2021 Census of Agriculture in the "Additional documentation" link provided below. This collection rate represents the response rate among those census farms for which direct collection was attempted.

Coverage evaluation

Coverage errors occur when there is difference between the target population and the survey population and they may affect the quality of all estimates. For the Census of Agriculture, coverage errors occur when census farms are missed, incorrectly included or double counted. Estimating these errors is one way to assess the quality of the Census of Agriculture estimates.

The Census of Agriculture processes involved in the creation of the frame, data collection, and data processing are not perfect and can contribute to these coverage errors. For example, when creating the frame, real census farms might be missed because they were simply not part of one of the sources used to create the Census of Agriculture frame. Also, at the end of the collection period, non-responding units might be erroneously classified as census farms or as non-census farms during Census of Agriculture data processing.

The overall coverage of the Census of Agriculture was measured using two components. The first component measured the misclassification of non-respondents; that is, the accuracy of the processing step in which it was decided whether a non-responding unit was a census farm or not. Estimates of both undercoverage (non-enumerated agricultural operations) and overcoverage (units incorrectly enumerated as agricultural operations) were calculated.

The second component measured additional undercoverage (non-enumerated agricultural operations) errors arising from missing census farms on the frame. These were estimated using a post-censal survey called the Agriculture Frame Update Survey. This survey targeted establishments from the Business Register that had some indication of being a census farm, but were not included in the Census of Agriculture frame. Through this survey, additional census farms that had been missed by the Census of Agriculture were identified and used to estimate the total number of such missed farms.

The final net undercoverage estimates combine the estimates of these two components and is calculated using the following formula.

Net undercoverage rate (%) = 100 * (non-enumerated farms - incorrectly enumerated farms) / (enumerated farms + non-enumerated farms - incorrectly enumerated farms)

Similarly, the net undercoverage estimate can be weighted by census variables to estimate the rate of undercoverage of that characteristic. The undercoverage rates for three principal measurements in the Census of Agriculture - the number of census farms, the total farm area and the total operating revenues are presented in the "Additional documentation" link provided below.

Please note that there are no estimates of undercoverage for Yukon, the Northwest Territories and Nunavut as the Agriculture Frame Update Survey was not carried out in the territories.

To improve the accuracy of the coverage estimates in 2021, a more complex and complete estimation approach was used for the components of undercoverage coming from the Agriculture Frame Update Survey, compared to the one used in 2016. This resulted in additional undercoverage which would not have been captured in the 2016 results. Thus, the undercoverage estimates are not directly comparable to those from the 2016 Census of Agriculture.


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