Commercial Stocks of Corn and Soybeans Survey (CSCS)
Detailed information for Decembre 31, 2022
3 times per year
The purpose of this survey is to collect data on commercial stocks of raw unprocessed corn for grain, and soybeans, stored in grain elevators.
Data release - February 7, 2023
This survey gathers information on stocks intended for use by grain elevators, or intended for sale to grain or oilseed processing operations for human or animal consumption or for industrial use.
The estimates produced will be used in national supply-disposition analyses to verify production and farm stocks. The data are also used by Agriculture and Agri-Food Canada and by grain analysts in the public and private sectors. The information provided may also be used by Statistics Canada for other statistical and research purposes.
The survey is administered as part of the Integrated Business Statistics Program (IBSP). The IBSP has been designed to integrate approximately 200 separate business surveys into a single master survey program. The IBSP aims at collecting industry and product detail at the provincial level while minimizing overlap between different survey questionnaires. The redesigned business survey questionnaires have a consistent look, structure and content.
The integrated approach makes reporting easier for firms operating in different industries because they can provide similar information for each branch operation. This way they avoid having to respond to questionnaires that differ for each industry in terms of format, wording and even concepts. The combined results produce more coherent and accurate statistics on the economy.
Reference period: At December 31, March 31 and August 31
Collection period: Collected during the two weeks following the reference period.
- Agriculture and food (formerly Agriculture)
- Crops and horticulture
Data sources and methodology
The target population consists of grain elevators, processors, and exporters with stocks of corn for grain and soybeans, as well as industrial users of corn, in Ontario and Quebec.
This is a sample of all known companies that maintain stocks, as determined by the Ontario Ministry of Agriculture, Food and Rural Affairs. It excludes elevators licensed by the Canadian Grain Commission. The total sample size for this survey is approximately 95-100 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 is a sample survey with a cross-sectional design.
A registry of elevators within Ontario and Quebec is maintained by the Ontario Ministry of Agriculture, Food and Rural Affairs. This registry is updated on an annual basis and provided to Statistics Canada to help establish the survey frame. The survey frame consists of about 250 elevator company headquarters and contains information on the elevator capacity, type of elevator (industrial user, feed manufacturer or country elevator) and contact information. Any elevators licensed by the Canadian Grain Commission are removed from the frame. The frame selected for the December survey is also used for the March and August surveys. The frame is then stratified by type of elevator and by capacity. Industrial users and feed manufacturers are grouped in a take-all stratum. The rest of the sample is selected at random within each of the four strata.
Responding to this survey is mandatory.
Data are collected directly from survey respondents and extracted from administrative files.
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 supply-disposition analysis and by monitoring of industry trends.
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.
The sample used for estimation comes from a single-phase sampling process. 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. The final weights are usually either one or greater than one. Sampling units which are "Take-all" (also called "must-take") have sampling weights of one and only represent themselves.
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. An annual review of the estimates is also conducted by Statistics Canada methodologists.
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.
Revisions and seasonal adjustment
There is no seasonal adjustment. Data from previous years may be revised based on updated information. The survey data are not benchmarked.
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 sampling and non-sampling error. Sampling error can be measured by the standard error (or standard deviation) of the estimate. The coefficient of variation (CV) is the estimated standard error percentage of the survey estimate. Estimates with smaller CVs are more reliable than estimates with larger CVs. The CVs for the total stocks are in the range of 3-5%. Generally any estimate with a C.V. value under 5% is considered to be of excellent quality.
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.
A registry of elevators within Ontario and Quebec is provided annually to Statistics Canada by the Ontario Ministry of Agriculture, Food and Rural Affairs. This registry is used to update company information within the BR. Since relatively few companies make up the majority of the stocks, it is generally believed that any under coverage would be small.
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 approximately 90%.
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.