Farm Management Survey (FMS)

Detailed information for 2017

Status:

Active

Frequency:

Every 5 years

Record number:

5044

This survey, focusing on both livestock and crop operations, will allow the establishment of base lines and development of updates for an expanded set of agri-environmental indicators, and generate the information to design effective and well targeted policy and program responses.

Data release - Spring 2019

Description

The FMS contributes to Agriculture and Agri-Food Canada's work on measuring management practices on Canadian farms. The information generated from this survey will help measure management practices in the Canadian agriculture industry, address federal and provincial policy needs and support the development of effective agricultural programs. This mandatory survey focuses on both livestock and crop operations; specifically the production of dairy, beef, poultry, pig, field crops, forage crops and vegetable, fruit, berry and nut crops.

This survey is conducted in conjunction with Agriculture and Agri-Food Canada, to ensure that agriculture programs reflect the changing way resources are being managed on today's farms.

Reference period: Calendar year

Subjects

  • Agriculture
  • Environment
  • Environmental protection
  • Land use and environmental practices

Data sources and methodology

Target population

The conceptual universe is made up of all active farms on Statistics Canada's Business Register. The following types of farms were excluded:

. Active farms with agricultural sales in 2016 of less than $10,000;
. Institutional farms (prisons, research stations, colleges);
. Farms located on Indian reserves;
. Farms for whom more than 50% of the gross income in 2016 came from sales of greenhouse, sod and nursery products;
. Farms included on the 2017 Greenhouse, Sod and Nursery Survey frame
. Farms without livestock inventory or crop area at the time of the 2016 Census of Agriculture;
. Farms located in the Yukon, the Northwest Territories and Nunavut.

Instrument design

The Farm Environmental Management Survey questionnaires were designed by a project team made up of Statistics Canada and Agriculture and Agri-Food Canada employees and provincial experts assigned to the project. Questionnaire design specialists were consulted within Statistics Canada. In October 2016, 58 one-on-one in-depth interviews with farm producers in four regions were used to test draft versions of the questionnaires: Lethbridge, Alberta, Steinbach, Manitoba, Barrie, Ontario, and Saint-Hyacinthe, Quebec. Participants represented various types of agricultural operations. The questionnaires were then revised based on results of the questionnaire testing, questionnaire design specialist recommendations and a second round of consultation with Agri-Food Canada employees and provincial focal points or experts.

A second round of testing was done in September 2017. During this test the focus was the functionality of the application. Fifteen one-on-one interviews were conducted with farm producers in two regions; London, Ontario, and Drummondville, Quebec. The application was then updated based on recommendations from the questionnaire design specialists and additional consultations with Agri-Food Canada employees.

Sampling

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

The survey targets large producers in seven specific sub-sectors. As such, seven survey frames are created.

Not all geographic areas are covered by the FMS. Inclusion is based on whether the area is an important contributor to production of one of the seven sub-sector's activities at the national level. Some areas may be in-scope for many sub-sectors while others are not in-scope for any.

The first step is to determine whether each geographic area is an important contributor for the sub-sector. Geographic areas are sorted in descending order based on the amount of activity in the sub-sector, the largest geographic areas are selected until the sum of their size represents at least 90% of the national activity.

Then within each retained geographic area for a sub-sector, a similar process is used to select the farms. Farms are sorted in descending order based on the amount of activity in the sub-sector, and the largest are selected until the sum of their size represents at least 90% of the activity within the geographic area.

Stratification
Within each geographic area for a sub-sector the farms are further stratified in the following manner:
- Beef farms are sub-stratified by cow/calf and finishing operations
- Vegetable and fruit farms are sub-stratified by blueberry, potato and sugar beet, and other fruit and vegetable operations
All strata are then sub-stratified by size and a higher percentage of farms will be selected from strata containing the larger farms.

Sample allocation
The total sample size was 18,000 farms. The sample was allocated to sub-sectors and geographic areas to attain a targeted coefficient of variation.

Sample Selection
Farms were selected within each stratum, with a higher percentage of farms being selected from strata containing the larger farms. The main source of frame under-coverage was new farms that started their activities after the 2016 Census of Agriculture.

Data sources

Data collection for this reference period: 2018-02-15 to 2018-04-18

Responding to this survey is voluntary.

Data are collected directly from survey respondents.

An Electronic Questionnaire has been developed as the data collection method for this survey. However, Computer Assisted Telephone Interview data collection is also available for those choosing not to respond using the Electronic Questionnaire.

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

Error detection

Data were verified using the edits in the capture software. These edits verify that all mandatory cells have been filled in, that certain values lie within acceptable ranges, and that totals equal the sum of their components. If an edit was triggered, collection officers asked follow-up questions to the respondent in order to resolve the edit.

Further data checking was performed by subject matter officers who reviewed the survey data that were identified as outliers and compared them to data from the 2016 Census of Agriculture to determine their validity.

Imputation

No statistical imputation of the survey data was performed to address non-response. Total non-response (that is, when mandatory questions are left unanswered) was dealt with by adjusting the weights assigned to the responding units, such that one responding unit might also represent other non-responding units with similar characteristics (that is, province, ecozone or ecoregion, farm type).

Estimation

The collected survey data were weighted in order to produce unbiased estimates which were representative of the population. These weights reflected the sample design and the non-response observed during collection. Estimates were produced at the province and ecoregion levels where there was sufficient response to produce reliable statistical aggregated data.

The estimates were produced using Statistics Canada's Generalized Estimation System.

Quality evaluation

The data collected from the survey were compared to the Census of Agriculture data. Estimates were also compared to other comparable published estimates and analyzed by subject matter experts. The estimates at the provincial level were also validated by the provincial focal points who were involved in the design of the survey questionnaire and who are knowledgeable about the subject matter on a regional basis. Finally, the results of the survey were also compared, whenever possible, to the previous results of the Farm Environmental Management 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.

To reduce response burden and to ensure more uniform statistics, Statistics Canada has entered into an agreement under section 12 of the Statistics Act with Agriculture and Agri-Food Canada, the Institut de la statistique du Québec, the Ontario Ministry of Agriculture, Food and Rural Affairs and the Alberta Agriculture and Food Ministry. Under Section 12, Statistics Canada will not share any name, address or other identifying respondent information. The information is required to be kept confidential and used only for statistical and research purposes. These various data sharing partners will only have access to respondents within their respective province who agree to share survey information.

Revisions and seasonal adjustment

This methodology type does not apply to this statistical program.

Data accuracy

While non-sampling errors are difficult to quantify, sampling errors can be estimated from the sample itself using the standard error (SE) of estimated values also referred to as an absolute sampling error. For level estimates (e.g. totals and averages), a statistical measure called the coefficient of variation (CV) is normally used. The CV, defined as the standard error divided by the survey estimate, is a measure of precision in relative terms and is expressed as a percentage.

For level estimates, the CV is the appropriate measure of the sampling error. For proportions, however, an absolute sampling error -- such as the SE itself -- is preferred. In the case of the Farm Environmental Management Survey, most of the estimates deal with proportions.

The SE (which is a function of the population size, the sample size, and the estimate), along with the confidence level, can be used to calculate the margin of error. This measure is straightforward to interpret, since it is on the same scale as the estimate itself. For example, an estimated proportion of 80% might have a margin of error of 3%, meaning that we would conclude (with the appropriate confidence level, usually 19 times out of 20) that the true proportion is between 77% and 83%.

Suppose we want to estimate the proportion of Canadian livestock farms that store liquid manure. The estimated proportion is 14% with a standard error of 2.54. It can be deduced that the proportion of farms that do not store liquid manure is 86% and that the quality of the estimate is the same (i.e., the standard error is still 2.54). The standard error is an absolute error that applies to both the 14% and 86% estimates. The CV, being a relative error, would be different for the two estimates. It can even appear good for one proportion (86% for CV1) and bad for the complementary proportion (14% for CV2) as shown below:

CV1 = 100 * 2.54/86 = 3 (for farms which do not store liquid manure)
CV2 = 100 * 2.54/14 = 18 (for farms which store liquid manure)

Though the quality of the estimates is the same, the CV2 implies that the quality of the estimated proportion of farms which store liquid manure is much lower. In this case as with all proportion estimates, the CV can be misleading.

The following is a suggested CV rating system for level estimates, and a standard error (SE) rating system for proportion estimates:

CV Rating

0.01% - 4.99% A -- excellent
5.0% - 9.99% B -- very good
10.0% - 14.99% C -- good
15.0% - 24.99% D -- acceptable
25.0% - 34.99% E -- use with caution
35.0% and more F -- too unreliable to be published

SE Rating

0.01% - 2.49% A -- excellent
2.5% - 4.99% B -- very good
5.0% - 7.49% C -- good
7.5% - 12.49% D -- acceptable
12.5% - 17.49% E -- use with caution
17.5% and more F -- too unreliable to be published

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