Census of Agriculture

Census of Agriculture
Summary of changes
Other reference periods
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Detailed information for 2026

Status:

Active

Frequency:

Every 5 years

Record number:

3438

The Census of Agriculture (CEAG) provides a comprehensive and integrated profile of the physical, economic, social and environmental aspects of Canada's agriculture sector every five years. It provides a wide range of data at the national, provincial and sub provincial levels, being the only official data source for high-quality statistical information on agriculture for small geographic areas in Canada.

The Census of Agriculture publishes data, such as the number of census farms and farm operators, farm type, business operating arrangements, land tenure, land use (e.g., land in crops, summerfallow, pastures), seeding and tillage, land practices and features, crop residue baled, land inputs or manure, irrigation, mushrooms, greenhouses, number of maple trees taps, organic production, bees, poultry, livestock, technologies, renewable energy, farm capital (including value of land and buildings, value of livestock and value of farm vehicles, machinery and equipment), total operating revenues and expenses, direct sales, succession planning, and paid agricultural labour.

Data release - Scheduled for May 12, 2027 (first in a series of releases)

Description

Statistics Canada is required under the Statistics Act to conduct a Census of Agriculture.

The Census of Agriculture is essential for understanding changes in Canada's agriculture sector over time. Consequently, it serves as a basis for informed public and private decision making, as well as for research and analysis in areas of concern to the people of Canada.

Census of Agriculture data are used by:
• Farm operators, to formulate production, marketing and investment decisions.
• Agricultural producer groups, to inform their members about industry trends and developments, to put the viewpoint of operators before legislators and the Canadian public, and to defend their interests in international trade negotiations.
• Governments, to make policy decisions concerning agricultural credit, crop insurance, farm support, transportation, market services and international trade.
• Statistics Canada, as quinquennial benchmarks and to help determine the sample frame for agricultural surveys to provide Canadians with annual estimates between censuses for the agriculture sector.
• Businesses, to market products and services and to make production and investment decisions.
• Academics, to conduct research on the agriculture sector.
• The media, to portray the agriculture sector to the broader Canadian public.

Subjects

  • Agriculture and food (formerly Agriculture)

Data sources and methodology

Target population

The target population for the Census of Agriculture is all "census farms" operating in Canada. In 2026, a "census farm" is defined as an operation that produces agricultural products during the year of the census for which it will report revenues or expenses to the Canada Revenue Agency for tax purposes. Agricultural products covered by the "census farm" definition include crops (such as grains, oilseeds, pulses, vegetables, fruits, nuts, greenhouse products, cultivated mushrooms, sod, nursery products, cultivated Christmas trees, fodder crops, hemp, maple syrup and other crop products) and livestock (such as dairy cattle, beef cattle, pigs, chickens, turkeys, ducks, geese, other poultry, sheep, goats, horses, donkeys, mules, bison, elk, deer, llamas, alpacas, rabbits, mink, bees, honey, eggs and other animal products).

Note: Cannabis operations are not included in the Census of Agriculture. Due to the complexity of these operations' organizational structures and activities, these respondents are not able to provide responses that precisely capture the agricultural activity of cannabis cultivation in its entirety and/or disassociated from non-agricultural activities.

The Census of Agriculture frame is created by combining information from Statistics Canada's Business Register with information from the latest set of tax remittances. The selection process uses the detailed tax information of the operations on the Business Register to select those that have reported agricultural commodity revenues or expenses, signaling that they are involved in agricultural activities. To ensure complete coverage, additional data and methods are used to include operations which report their fiscal data differently. Because the latest available tax data are from 2024, an additional set of modelled records is added to this population to represent newer farms and reduce under-coverage.

Instrument design

The census questionnaire content is reviewed and updated each cycle to ensure that data collected are relevant and continue to reflect an evolving agriculture sector. Before each census, Statistics Canada conducts an extensive consultation and qualitative testing process that involves performing user consultations, evaluating user feedback, and updating the questionnaire content. When making decisions about whether to keep, remove, or add content for the questionnaire, Statistics Canada aims to maintain data quality standards while reducing response burden.

User consultations
User consultations allow data users and interested parties across Canada to provide feedback on the census. This feedback informs content development and provides a better understanding of the needs of data users. The process includes holding workshops and providing users with a submission form for written feedback.

In 2022, Statistics Canada conducted a national consultation process with data users, including federal government departments and provincial ministries, agricultural associations, and educational institutions. Users submitted recommendations for the content they would like to see on the 2026 Census questionnaire, which were then used to develop the questions and design of the census questionnaire.

Evaluating the suggestions
Submissions from consultations were evaluated on multiple criteria including the following factors:
• Relevance to the agriculture sector.
• Were of national interest.
• Demand for the data.
• Sensitivity of data.
• If collection of the data every 5 years sufficient.
• Level of geography required.

Questionnaire content and development
Although the questionnaire is updated every census to reflect users' changing requirements as identified through the submission process, certain questions — such as those on farm operators, land area, livestock numbers and crop areas — can appear on every census, as they are considered essential by Statistics Canada and other major users of Census of Agriculture data. Repeating key questions allows the census to measure change over time, while adding new questions and dropping others allows the census to adapt to changes in the agriculture sector (e.g., the adoption of new technologies or farming practices).

Changes to the Census of Agriculture for the 2026 census cycle were made with a focus on three objectives: using administrative data to reduce the number of questions required of respondents, reflecting the evolution of the agriculture sector since 2021, and improving ease of completing the questionnaire.

The majority of the 2026 questions remained unchanged compared with 2021. Other questions have been added or deleted to better reflect emerging agricultural products and trends. These emerging categories include, for example, new commodities that were previously captured under an 'other' category and are now explicitly listed on the questionnaire. For 2026, nectarines and hazelnuts are now listed in the Fruits, berries and nuts section, while greenhouse lettuces have been listed under the Greenhouse fruits and vegetables category in the Greenhouse products question. Also, a new livestock technology option, electronic feed monitoring system, has been added in the Technologies question.

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. Participants recruited for testing were prioritized based on the proposed changes to the questionnaire's content, while ensuring enough diverse farms were included to cover all elements of the questionnaire. This testing proved that some questions would not perform well on the census, and that the wording of other questions required refinement. Respondent burden, content-testing results, user priorities and budgets were all taken into consideration in determining the final content of the 2026 Census of Agriculture questionnaire. The questions were approved by Cabinet in Spring 2025.

For more information about the 2026 Census of Agriculture consultation process and its results, and the changes to the 2026 questionnaire, view the Documentation section at the bottom of this page.

Sampling

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: 2026-05-04 to 2026-07-31 (Census Day: May 12, 2026)

Responding to this survey is mandatory.

Data are collected directly from survey respondents.

Data for the Census of Agriculture are primarily collected directly from survey respondents. Administrative data and modelling are used to help reduce respondent burden and fill gaps where appropriate.

In 2026, data are collected directly from survey respondents, with two exceptions:
a) modelled records added to the population to account for operations that entered business since January 1, 2025, and for which no tax signals could be recorded because of the timing of tax filing, and
b) specific questions where administrative data are available and used in place of survey responses.

For modelled records, the data are imputed with information from another farm with similar characteristics.

As in the previous census, the 2026 Census of Agriculture uses comparable and timely administrative data to reduce respondent burden if data is available and usable. For example, operating arrangements, operating revenues and expenses, and paid labour are secured from tax data from the Canada Revenue Agency, and operator data such as age and gender are procured from the Census of Population. In addition, some respondents complete a shorter questionnaire, as administrative data from Statistics Canada's Annual Greenhouse, Sod and Nursery Survey and the Producteurs et productrices acéricoles du Québec is used to replace information on greenhouses or maple taps.

Collection
Throughout the data collection period, the objective is to maximize the number of responses from agricultural operations across Canada. Collection processes include the selection of the Census of Agriculture population and the frame list preparation, sending invitation letters to complete the questionnaire, monitoring collection and conducting follow-up interviews with non-respondents.

In 2026, the Census of Agriculture respondents are encouraged to complete the Census of Agriculture via electronic questionnaire. Invitation letters are delivered to farm operations by Canada Post. Farm operators are asked to complete the 2026 Census of Agriculture online by using the secure access code provided in the invitation letter. Alternatively, respondents can call the Census Help Line (CHL) to complete their questionnaire over the phone. If it is determined that a questionnaire is not received, follow-up is conducted by telephone. For a more detailed description of the collection process, please refer to the Guide to the Census of Agriculture, 2026. See the link to the guide in the Documentation section at the bottom of this page.

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

Error detection

Error detection is an integral part of both collection and data processing activities. For data captured through the electronic questionnaire edits are applied to microdata records during collection to identify reporting errors and data inconsistencies and flag them to respondents for review. For example, totals in key variables that do not equal to the sum of their parts.

Should a paper questionnaire be received, the data are captured internally through the electronic questionnaire application and are 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.

The Census of Agriculture in some cases reaches out to contact respondents to verify information on their questionnaire and resolve potential inconsistencies or errors.

During data processing, additional edits are 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 are found, they are corrected using the data editing and imputation processes, or during the data validation process.

Extreme values are also identified using automated methods based on the distribution of the collected information. Following their detection, these values are reviewed by subject-matter analysts to assess their validity. Macro-level totals are 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 is made to minimize the non-sampling errors of omission, duplication, misclassification, reporting and processing.

Imputation

Non-response occurs when respondents leave parts or all of a questionnaire unanswered, or when reported data are flagged as erroneous during error detection. In these situations, imputation fills in missing data and corrects errors. The BANFF generalized system is used to perform a combination of deterministic imputation, historical imputation, nearest neighbour donor imputation, and ratio imputation.

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.

Estimation

Estimation is a process by which Statistics Canada obtains values for the population of interest so that it can draw conclusions about that population based on information gathered from only a sample of the population. For this survey, the sample used for estimation comes from a single-phase sampling process.

An initial sampling weight (the design weight) is calculated for each unit of the survey and is simply the inverse of the probability of selection. The weight calculated for each sampling unit indicates how many other units it represents.

However, since some of the selected units did not answer the survey, reweighting is performed on the responding units so that their final weights still represent the whole target population. The response mechanism can be considered as a second-phase of the sampling process.

After the reweighting is performed, a calibration process is performed so that the weighted totals per calibration groups equal the population totals.

Estimation of proportions is done using the calibrated weights to calculate the population totals in the domains of interest.

Quality evaluation

Estimates were reviewed to ensure that the findings are logical and quality checks were carried out to ensure that estimates are consistent. Atypical results were flagged for investigation and were corrected as necessary.

Disclosure control

The metadata will be provided upon release.

Revisions and seasonal adjustment

This methodology type does not apply to this survey.

Data accuracy

There are two types of errors which can impact the data: sampling errors and non-sampling errors.

Estimates are subject to sampling error. This error can be expressed as a standard error. For example, the proportion of businesses in the target population that would respond YES to a given question is estimated to be 50%, with a standard error of 4%. In repeated sampling, the estimate would be expected to fall between 42.16% and 57.84%, nineteen times out of twenty (50% +/- 7.84%). The following rules based on the standard error (SE) are used to assign a measure of quality to all of the estimates of percentages.

A = Excellent (0.00% to less than 2.50%)
B = Very good (2.50% to less than 5.00%)
C = Good (5.00% to less than 7.50%)
D = Acceptable (7.50% to less than 10.00%)
E = Use with caution (10.00% to less than 15.00%)
F = Too unreliable to be published (Greater than or equal to 15%, data are suppressed)

Non-sampling errors may occur for various reasons during the collection and processing of the 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 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 and verification of the survey data.

Documentation

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