Livestock Farm Practices Survey

Status:
Inactive
Frequency:
One Time
Record number:
5107

The national survey on livestock farm practices is a key aspect of Agriculture and Agri-Food Canada's National Agri-Environmental Health Analysis and Reporting Program (NAHARP).

Detailed information for 2005

Data release - December 7, 2007

Description

The national survey on livestock farm practices is a key aspect of Agriculture and Agri-Food Canada's National Agri-Environmental Health Analysis and Reporting Program (NAHARP). This voluntary survey, focusing on both livestock and poultry operations, collected baseline data on inventory numbers of livestock and poultry in 2005, feeding, housing, manure storage and spreading practices. This information was used to quantify emissions of ammonia into the atmosphere. The information obtained guide researchers to improve efficiency of Nitrogen use on farms. This survey was conducted in conjunction with Agriculture and Agri-Food Canada and Environment Canada.

Reference period:
Calendar year
Collection period:
End of February to the end of March following the reference period.

Subjects

  • Agriculture
  • Environment
  • Land use and environmental practices
  • Livestock and aquaculture

Data sources and methodology

Target population

The conceptual universe is made up of all active farms in the Agriculture Division's Farm Register which contributed to the top 95% of the total head count of each type of livestock and poultry.

The following types of farms were excluded:
. Active farms with agricultural sales in 2001 of less than $10,000;
. Institutional farms (prisons, research stations, colleges);
. Farms located on Indian reserves;
. Farms without livestock or poultry inventory at the time of the 2001 Census of Agriculture;
. Small farms that contributed to the bottom 5% of the total head count of livestock and poultry inventory;
. Farms located in the Yukon, the Northwest Territories and Nunavut.

Instrument design

The Livestock Farm Practices Survey questionnaires were designed by a project team made up of Statistics Canada and Agriculture and Agri-Food Canada employees assigned to the project. Questionnaire design specialists were consulted in Statistics Canada. At the end of September - beginning of October 2005, one-on-one in-depth interviews were used to test draft versions of the questionnaires with 57 farm producers interviewed in five regions: Abbotsford (British Columbia), Lethbridge (Alberta), Winnipeg (Manitoba), Kitchener (Ontario) and Saint-Hyacinthe (Quebec). Participants represented various types of agricultural operations. The questionnaires were then revised based on questionnaire design specialist recommendations and consultation with Agri-Food Canada experts.

Sampling

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

Sampling Plan
There was a requirement to report the survey results for each of 12 ecological regions (described below) defined by similar agronomic, climatic and soil attributes in the Canadian eco-stratification system.

The 12 regions by which the survey frame was stratified are as follows:

1) Atlantic Maritime ecozone
2) St. Lawrence Lowlands ecoregion
3) Manitoulin- Lake Simcoe - Frontenac ecoregions
4) Lake Erie Lowland ecoregion
5) Boreal Shield ecozone
6) Brown Soil Zone (ecoregions Mixed Grassland and Cypress Upland)
7) Dark Brown Soil Zone (ecoregions Moist Mixed Grassland and Fescue Grassland)
8) Black Soil Zone (ecoregions Aspen Parkland and Southwest Manitoba Uplands)
9) Lake Manitoba Plain ecoregion
10) Boreal Plains ecozone
11) Montane Cordillera ecozone
12) Pacific Maritime ecozone

Data sources

Data collection for this reference period: 2006-03-08 to 2006-03-31

Responding to this survey is voluntary.

Data are collected directly from survey respondents.

The survey was conducted from the end of February to the end of March 2006. As suggested by participants during questionnaire testing, this period was chosen for data collection because it had the least effect on farming operations. Due to time constraints, it was not possible to develop a Computer Assisted Telephone Interview data collection technique. As a result a paper and pencil telephone interview method was used to collect the data from respondents, who had received in advance a copy of the questionnaire.

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

Error detection

A systematic approach was used to identify missing or incomplete data as well as to identify outliers. There were consistency and deterministic edits applied. The Ag2000 data processing system was used for editing the data and for identifying outliers. Manual edits were also applied based on AAFC experts' recommendations.

Imputation

Missing values were imputed only if available from other sources for the same farm.

Estimation

Prior to estimation, the data has been edited for completeness and consistency. The data was further adjusted for outliers. Based on the clean response records the initial weights were adjusted to reflect the sample response and estimates could be produced at the province and ecoregion levels upon request where there will be sufficient response to produce reliable statistical aggregated data.

Quality evaluation

The data collected from the survey was validated and analyzed record by record by the subject matter experts and AAFC experts who were involved in the design of the survey questionnaires 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 2001 results of the Farm Environmental Management Survey.

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.

Tabular results are produced using the estimation software "ESTIMEX" developed for Agriculture Division. The tabulation system automatically applies the Statistics Canada standard rules for confidentiality and data not satisfying the rules are suppressed automatically. Manual residual disclosure analysis are also done to ensure that no confidential information is released.

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 AAFC. Under Section 12, Statistics Canada will not share any name, address or other identifying information. The information is required to be kept confidential and used only for statistical and research purposes. The data sharing partner only have access to this survey data of respondents who agree to share survey information.

Revisions can only be approved by subject matter experts from Statistics Canada and AAFC.

Data accuracy

While considerable effort is made to ensure high standards throughout all stages of collection and processing, the resulting estimates are inevitably subject to a certain degree of error. These errors can be broken down into two major types: non-sampling and sampling.

Non-sampling error is not related to sampling and may occur 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, and mistakes in recording, coding, and processing data are other examples of non-sampling errors.

Of the units contributing to the estimate, the response rate was 73.5%.

Sampling error occurs because population estimates are derived from a sample of the population rather than the entire population. Sampling error depends on factors such as sample size, sampling design, and the method of estimation. An important property of probability sampling is that sampling error can be computed from the sample itself by using a statistical measure called the coefficient of variation (CV). The assumption is that over repeated surveys, the relative difference between a sample estimate and the estimate that would have been obtained from an enumeration of all units in the universe would be less than twice the CV, 95 times out of 100. The range of acceptable data values yielded by a sample is called a confidence interval. Confidence intervals can be constructed around the estimate using the CV. First, we calculate the standard error by multiplying the sample estimate by the CV. The sample estimate plus or minus twice the standard error is then referred to as a 95% confidence interval.

CVs were calculated for each estimate.

The qualities of CVs are rated as follows:
. Excellent 0.01% to 4.99%
. Very good 5.00% to 9.99%
. Good 10.00% to 14.99%
. Acceptable 15.00% to 24.99%
. Use with caution 25.00% to 34.99%
. Unreliable 35.00% or higher

Documentation