Report of Crushing Operations
Detailed information for January 2006
This is a census of plants that crush oilseeds into oil and meal. Data collected are part of supply-disposition statistics of major grains and allow the calculation of the domestic disappearance component.
Data release - February 23, 2006
This is a census of plants that crush oilseeds into oil and meal. Data collected are part of supply-disposition statistics of major grains and allow the calculation of the domestic disappearance component. They are also required to verify grain production and farm stocks. The data are used by the provincial governments, the Food and Agriculture Organization (FAO) and related industries for market analysis, particularly of supply-disposition of grain.
Reference period: Month
Collection period: The first ten days following the reference month.
- Agriculture and food (formerly Agriculture)
- Crops and horticulture
Data sources and methodology
The target population consists of all known plants which crush oilseeds to produce oil and meal. The survey frame excludes plants that crush oilseeds for specialty health food stores. The total sample size for this survey is 8 units.
The electronic questionnaire was designed by Statistics Canada as part of the Integrated Business Statistics Program. This program incorporates business surveys into a single framework, using questionnaires with a consistent look, structure and content.
The questionnaire content was developed by subject matter specialists through consultation with industry experts.
This survey is a census with a cross-sectional design.
This methodology does not apply.
Responding to this survey is mandatory.
Data are collected directly from survey respondents.
Respondents are contacted by email or letter and given an access code for the electronic questionnaire for the survey, which can be responded to in either official language. Non-response follow-up is conducted via telephone.
The survey, on average, takes respondents 15 minutes to complete.
View the Questionnaire(s) and reporting guide(s) .
Error detection is an integral part of both collection and data processing activities. Edits are applied to data records during collection to identify reporting and capture errors. These edits identify potential errors based on year-over-year changes in key variables, and totals, as well as identify problems in the consistency 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 (e.g. Value > 0, Value > -500, Value = 0), linear equality edits (e.g. Value1 + Value2 = Total Value), linear inequality edits (e.g. Value1 >= Value2), and equivalency edits (e.g. Value1 = Value2). When errors are found, they can be corrected with manual corrections during collection. Manual review of other units may lead to the identification of outliers. These outliers are excluded from use in the calculation of ratios and trends used for imputation. In general, every effort is made to minimize the non-sampling errors of omission, duplication, misclassification, reporting and processing.
The data reported by each company are verified by comparison to previous reports, by comparing trends between companies, by validating average oil and meal extraction rates, by supply-disposition analysis and by monitoring of industry trends. Data are also validated against industry reports, particularly those prepared by the Canadian Oilseed Processors Association.
When non-response occurs, or when respondents do not completely answer the questionnaire, imputation is used to fill in the missing 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, as well as replacement using historical data (with a trend calculated, when appropriate). Usually, key variables are imputed first and are used as anchors in subsequent steps to impute other, related variables. Imputation generates a complete and coherent micro data file that covers all survey variables.
This methodology type does not apply to this statistical program.
Prior to the data release, combined survey results are analyzed for comparability; in general, this includes a detailed review of: individual responses (especially for the largest companies), general economic conditions, coherence with results from related economic indicators, historical trends, and information from other external sources (e.g. associations, trade publications, newspaper articles).
Supply and disposition trends, used by government and industry stakeholders, help to confirm the results of the survey. Where anomalies occur they are resolved through analysis at the end of the crop year.
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.
This survey is a census of all units in the observed population and is not subject to sampling error. 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 non-sampling error. Non-sampling error is not related to sampling and may occur for various reasons during the collection and processing of data. To the maximum extent possible, these errors are minimized through careful design of the survey questionnaire, verification of the survey data, and follow-up with non-respondents when needed to maximize response rates. Non-sampling error includes coverage error, data response error, non-response error and processing error.
Coverage errors consist of omissions, erroneous inclusions, duplications and misclassification of units in the survey frame. The Business Register (BR) is the common frame for all surveys using the IBSP model. The BR is a data service centre 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 helps ensure quality, while avoiding overlap between surveys and minimizing response burden to the greatest extent possible.
Given the infrastructure and the oilseed supplies required by crushing plants, it is unlikely that a new facility would operate undetected. As such, it is generally believed that the risk of under coverage is minimal. Administrative files compiled by the Canadian Oilseed Processors Association aids in regularly maintaining company information within the BR.
Data response error may be due to questionnaire design, the characteristics of a question, inability or unwillingness of the respondent to provide correct information, misinterpretation of the questions or definitional problems. These errors are controlled through careful questionnaire design and the use of simple concepts and consistency checks.
Non-response error is related to respondents that may refuse to answer, are unable to respond or are too late in reporting. In these cases, data are imputed. This is considered to be the most likely source of error for this survey. When non-response occurs, it is taken into account and the quality of data is reduced based on its importance to the estimate. Efforts are made to obtain as high a response rate as possible while minimizing the response burden. The response rate for this survey is generally 100%.
Processing error may occur at various stages of processing such as during data entry and tabulation. To the maximum extent possible, these errors are minimized through careful design of the survey questionnaire, verification of the survey data, and follow-up with respondents when needed. Edits are applied to data records during collection to prevent the entry of outliers or inconsistent information by respondents. Data analysis tools within the IBSP permit subject matter analysts to quickly detect apparent anomalies. As such, processing errors are considered to be minimal.