Fruits and Vegetables Survey (FV)

Detailed information for 2021

Status:

Active

Frequency:

Annual

Record number:

3407

This survey collects data to provide estimates of the total cultivated area, harvested area, total production, marketed production and farm gate value of selected fruits and vegetables grown in Canada.

Data release - February 10, 2022; February 14, 2022 (revised data)

Description

This survey collects data to provide estimates of the total cultivated area, harvested area, total production, marketed production and farm gate value of selected fruits and vegetables grown in Canada. The data are used by Agriculture and Agri-Food Canada, other federal departments, provincial organizations and related industries for production and price analysis, and for development of agricultural policies and programs.

On July 14, 2022 we will publish a second release of estimates from the survey representing only those farms which are certified organic.

Reference period: One year

Collection period: End of October to mid December

Subjects

  • Agriculture and food (formerly Agriculture)
  • Crops and horticulture
  • Food and nutrition

Data sources and methodology

Target population

The target population is all farms in the ten provinces of Canada that grow fruit and/or vegetables for sale. The Yukon, the Northwest Territories and Nunavut are excluded from the survey as well as institutional farms and community pastures. Also excluded are farms producing only mushrooms, farms producing only greenhouse vegetables, and farms producing only potatoes.

The observed population consists of those establishments in the target population which have been identified as having fruit or vegetable production on Statistics Canada<s Business Register.

Instrument design

The questionnaires were developed by subject matter experts through consultation with the provinces and industry experts. The Operations and Integration Division and the Agriculture Commodities Section of the Collection, Planning and Research Division and the Enterprise Statistics Division of Statistics Canada conduct in-house testing for flow and consistency.

Subject matter experts may change, add or remove questions. This typically happens because of changes in market trends or because of information in debriefing reports from field staff.

Sampling

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

This is a stratified sample survey of farms with either fruit or vegetable products with a cross-sectional design.

Sampling unit:
The sampling unit is the establishment.

Stratification method:
For each province, small operations that have a total fruit acreage smaller than a provincial threshold for total fruit and have a vegetable acreage smaller than a threshold for total vegetables are excluded from the sample (Take-None); these thresholds are set so that all farms with greater acreage than either threshold together represent 95% of fruit and vegetable acreage in the province. In each province, a threshold for fruit acreage and another for vegetable acreage was defined using the sigma-gap method, based on the importance of that commodity to the provincial or national totals. All operations with acreage above the threshold were selected in the sample (Take-All). The remaining units were firstly divided into fruit or vegetable strata and then, the Cumulative Root F Rule was used to divide the strata into 2 groups based on acreage, the large and medium-sized Take-Some. A random sample was selected among the operations in each Take-Some stratum. For each stratum, a minimum sample size was set to ensure a maximum design weight of two.

Overall, the sample size was determined in order to achieve a target CV of around 0.01 (1%) for total fruit area and total vegetable area at the provincial level.

Sampling and sub-sampling:
A sample consisting of 7004 farms was selected by stratified random sampling.

Data sources

Data collection for this reference period: 2021-10-29 to 2021-12-13

Responding to this survey is mandatory.

Data are collected directly from survey respondents.

Data are collected annually using an e-mail 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 Quebec, aggregated data was extracted from portail Bio Québec website disseminating data for the Conseil des appellations réservées et des termes valorisants (CARTV).

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

Error detection

Automated edits are applied to data records during collection to identify reporting and capture errors. These edits identify potential errors based on key variable totals, outliers reported or inconsistency of collected data (e.g. a total variable does not equal the sum of its parts). During data processing, other edits are used to automatically detect errors or inconsistencies that remain in the data following collection. These edits include value edits, linear equality edits, linear inequality edits, and equivalency edits. When errors are found, they can be corrected using the failed edit follow up process during the collection or via imputation.

Manual review of a subset of responses also takes place.

We employ the following methods:
1) Top 20 contributor analysis (macro/micro);
2) Use of historical data for comparison and possible imputation (micro/macro);
3) Use of various ratios (e.g. price/area) and provincial averages to impute incorrect or missing data (micro);
4) Industry Research - in consultation with LAOS(Large Agricultural Operation Statistics) team, the Internet, provincial and industry specialists.

Imputation

When non-response occurs, when respondents do not completely answer the questionnaire, or when reported data are considered incorrect during the data editing steps, imputation is used to fill in the missing information and modify the correct 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 include: deterministic imputation, replacement using historical data, replacement using auxiliary information available from other sources, replacement based on known data relationships for the sample unit, and replacement using data from a similar unit in the sample (known as donor imputation).

Estimation

The survey data collected are weighted within each stratum in order to produce estimates representative of the population. 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 that is conditional on the realized sample size. The weight calculated for each sampling unit indicates how many other units it represents. Sampling units which are "Take-all" have sampling weights of one and only represent themselves.

Estimation of totals is done by simple aggregation of the weighted values of all estimation units that are found in the domain of estimation.

In the case of the ineligible for sampling portion (also called take-none portion) of the target population, modeling using Census of Agriculture data is done in order to create data for all requested variables for each unit in the take-none portion. These are also simply aggregated to produce the estimate. The overall estimate includes the estimates from both the surveyed portion and the take-none portion.

Analyses of the top contributors and historical comparisons as well as consultations with the Provincial Departments of Agriculture are performed before a final estimate is published.

Quality evaluation

Disseminated data are subject to a certain degree of error such as incorrect information from respondents or mistakes introduced during processing. Reasonable efforts are made to ensure such errors are kept within acceptable limits through careful questionnaire design, editing of data for inconsistencies and subsequent follow-up and quality control of manual processing operations. Extensive consultation with provincial agricultural experts combined with data from various marketing boards assists in the verification of the level estimates obtained through 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.

In order to prevent any data disclosure, confidentiality analysis is done using the Statistics Canada generalized confidentiality 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.

Revisions and seasonal adjustment

This methodology type does not apply to this statistical program.

Data accuracy

The statistics from the Fall Survey of Fruits and Vegetables are based on a random sample of agricultural operations and, as such, are subject to sampling and non-sampling errors. The overall quality of the estimates depends on the combined effect of these two types of errors.

Non-sampling error:
Non-sampling errors are errors which are not related to sampling and may occur throughout the survey operation for many reasons. For example, non-response is an important source of non-sampling error. Population coverage, differences in the interpretation of questions, incorrect information from respondents, mistakes in recording, coding and processing of data are other examples of non-sampling errors.

Both the sampling error and the non-response rate are combined into one quality rating code. 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. These quality rating codes can be requested and should always be taken into consideration. For the Fall survey of Fruits and Vegetables the majority of estimates at the Canada level for the variables that are more frequently reported (area, investment, expenditures, sales, etc.) have quality ratings of A or B which makes them very reliable. Estimates for some variables at the national and provincial level have a wider range of quality ratings. Quality ratings are available upon request.

Response rates:
The response rate is 81%.

Non-response bias:
By the end of the collection period, more than 80% of the questionnaires have been fully completed. The remainder of the sample unaccounted for can be explained by non-contact, non-response and refusal. Initial sample weights are adjusted by a process called "raising factor adjustment" in cases of total or partial non-response.

Coverage error:
Coverage errors consist of omissions, erroneous inclusions, duplications and misclassification of units in the survey frame. Every five years, following the Census of Agriculture, an extensive update of the survey frame takes place. In the intervening period, tax data submissions, survey feedback and subject matter expertise are used to add and remove farms from the frame.

The Business Register (BR) is the common frame for all surveys using the IBSP model. The BR is a data servicecentre updated through a number of sources including administrative data files, feedback received from conducting
Statistics Canada business surveys, and profiling activities including direct contact with companies to obtain
information about their operations and Internet research findings. Using the BR will ensure quality, while avoiding
overlap between surveys and minimizing response burden to the greatest extent possible.

Other non-sampling errors:
Disseminated data are subject to a certain degree of error such as incorrect information from respondents or mistakes introduced during processing. Reasonable efforts are made to ensure such errors are kept within acceptable limits through careful questionnaire design, editing of data for inconsistencies and subsequent follow-up and quality control of manual processing operations. Extensive consultation with provincial agricultural experts combined with data from various marketing boards assists in the verification of the level estimates obtained through the survey.

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