Crop Protection Survey
Detailed information for 2005
The 2005 pilot survey on crop protection was designed to provide key information for Agriculture and Agri-Food Canada (AAFC)'s Pesticide Risk Reduction Program of the Pest Management Centre.
Data release - July 7, 2008
- Questionnaire(s) and reporting guide(s)
- Data sources and methodology
- Data accuracy
The 2005 pilot survey on crop protection was designed to provide key information for Agriculture and Agri-Food Canada (AAFC)'s Pesticide Risk Reduction Program of the Pest Management Centre. This voluntary survey, focusing on apple, grape and carrot operations, collected for the first time baseline data on quantities and types of pesticide and pest management practices used in 2005. This information could be used by researchers, government agencies and farm organizations to document and track changes in pesticide use and pest management systems. This survey was conducted for AAFC, Environment Canada (EC) and Health Canada (HC).
Reference period: calendar year
Collection period: End of February to the end of March following the reference period.
- Agriculture and food (formerly Agriculture)
- Crops and horticulture
- Land use and environmental practices
Data sources and methodology
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 acreage of each type of crop (apple, grape and carrot) in each region (Atlantic Provinces, Quebec, Ontario and British Columbia).
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;
. Small farms that contributed to the bottom 5% of the total acreage of each type of crop;
. Remote farms that could not be visited by interviewers with reasonable traveling distance and costs;
. Farms located in the Prairie Provinces, the Yukon, the Northwest Territories and Nunavut.
The questionnaires were designed by a project team made up of Statistics Canada and Agriculture and Agri-Food Canada employees assigned to the project. Statistics Canada's questionnaire design specialists were consulted. In March 2005, one-on-one in-depth interviews were used to test draft versions of the questionnaires with 50 farm producers interviewed in four regions: Saskatoon (Saskatchewan), Niagara Region (Ontario), Lanaudière Region (Quebec), Kings County (Nova Scotia). Participants represented various types of agricultural operations (fruit and vegetables, field crop and greenhouse operations). The questionnaires were then revised based on questionnaire design specialist recommendations and consultation with AAFC's experts.
This is a sample survey with a cross-sectional design.
The survey frame was updated with acreage of apple, grape and carrot operations for the subset of farm operations that also responded to the 2003, 2004 and 2005 Fruit and Vegetable Survey. The survey frame was split into overlapping family of crops (e.g., orchards, leaf greens, berries) and the objective was to select farms for only one type of crop. Each group was stratified by region and by size of operation based on acreage (large, medium and small operations).
Based on the budget, the expected response rate of 80%, a targeted level of precision and AAFC requirements, the total sample size was set at 1,255 farms.
Farms were randomly selected within each stratum. Largest farms were definitely included in the sample (stratum of sample's subgroup of "take-all" farms). Other farms were selected with a known probability of inclusion in the sample ("take-some" farms). The sampling strategy ensured that a farm with more than one type of crop could be selected for only one type.
Data collection for this reference period: 2006-01-16 to 2006-03-10
Responding to this survey is voluntary.
Data are collected directly from survey respondents.
The survey was conducted from the beginning of January 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 Personal Interview data collection technique. As a result a paper and pencil interview method was used to collect the data from respondents in person on the premise of the farm operations. To reduce response burden, detailed information were required on a field randomly selected by the interviewers. This method was required due to the complexity of collecting the information for a randomly selected field and to improve the quality of the pesticide use information from an exhaustive list of products that can be reported. The survey was well received with an overall response rate of more than 89.9%.
View the Questionnaire(s) and reporting guide(s).
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, EC and HC experts' recommendations.
Missing values were imputed only if available from other sources for the same farm.
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 were produced at the province level where there is sufficient response to produce reliable statistical aggregated data.
The data collected from the survey was validated and analyzed record by record by the subject matter experts and AAFC and EC experts who were involved in the design of the survey questionnaires and who are knowledgeable about the subject matter.
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 were produced using the SAS statistical software. Statistics Canada standard rules for confidentiality were applied and data not satisfying the rules were suppressed automatically. Manual residual disclosure analysis was also done to ensure that no confidential information was 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, EC and HC. 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 had access to this survey data of respondents who agree to share survey information.
Revisions and seasonal adjustment
This methodology does not apply to this survey.
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 89.9%.
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. Coefficient of variation values for this pilot survey are available upon request.
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
- Crop Protection Survey - Organization letters