Producer Deliveries of Major Grains, Canada and selected provinces

Detailed information for October 2022

Status:

Active

Frequency:

Monthly

Record number:

5275

The purpose of this program is to collect data on licensed and unlicensed producer deliveries of major grains.

Data release - November 24, 2022

Description

The results of this program are used to produce statistics on the supply and disposition of major grains. These data are used by Agriculture and Agri-food Canada, provincial governments and industry associations.

Producer deliveries destined for a primary elevator, feed mill, crushing plant, flour mill, etc. in exchange for a cash or storage ticket are referred to as a delivery in the context of our publication. The grain does not have to be sold at the time of the delivery.

The information provided may also be used by Statistics Canada for other statistical and research purposes.

Reference period: Month

Collection period: The first fifteen days following the reference month.

Subjects

  • Agriculture and food (formerly Agriculture)
  • Crops and horticulture

Data sources and methodology

Target population

The target population includes all producers in Canada that deliver their grain to a primary elevator, feed mill, crushing plant, or flour mill, in exchange for a cash or storage ticket. Data are available only for the provinces of Quebec, Ontario, Manitoba, Saskatchewan, Alberta, and British Columbia.

Provincial grain delivery data published by the Canadian Grain Commission represents the province where the licensed facilities are located. The data do not represent the province where the grain was grown or where the producers live.

Instrument design

This methodology type does not apply to this statistical program.

Sampling

This methodology type does not apply to this statistical program.

Data sources

Responding to this survey is mandatory.

Data are collected directly from survey respondents and extracted from administrative files.

Statistics Canada collects producer deliveries data from administrative sources, as well as through agriculture surveys, including the Feed Grain Purchases Survey (record number 5046) and the Monthly Crushing Operations Survey (record number 3404). As per the Canadian Grain Commission concepts, grain handlings by primary, process and terminal elevator facilities are considered licensed handlings while grain moved by producer cars, containers and exports to the USA directly from farms are considered unlicensed handlings. As such, administrative data originate from the two sources. Licensed grain deliveries make up the majority of deliveries statistics.

Licensed grain deliveries sources:

Canadian Grain Commission's Grain Statistics Weekly (GSW) report: The Canadian Grain Commission (CGC) is a federal government agency that operates under the authority of the Canada Grains Act. Under the Act, grain dealers and operators of primary, terminal and process elevators must be licensed by the CGC or exempted from licensing. Companies licensed by the CGC are required to provide weekly summaries of cash and storage tickets information. The CGC publishes the licensed grain delivery data on its website on a weekly basis via the GSW report. Provincial grain delivery data published by the CGC represent the province where the licensed facilities are located. The data do not represent the province where the grain was grown or where the producers live.

Unlicensed grain deliveries sources:

CGC's Grain Statistics Weekly (GSW) report: Condominium stock data are also obtained through the GSW report. Condominium storage refers to a space that a producer may own or lease within a licensed primary elevator, but that is separate from licensed primary elevator storage. Because the grain isn't delivered to a licensed facility, but rather to a space leased or provided by a licensed facility, condominium storage deliveries are considered unlicensed. Under the Canada Grains Act, grain dealers and operators of primary, terminal and process elevators must also report condo storage data to the CGC. Stocks in condominium storage may include grain that has physically left the farm but has not been sold.

Statistics Canada's International Accounts and Trade Division (IATD): Since 1990, Canada and the United States have exchanged import data; the import data of one partner country are used to derive the export data of the other. Canada's exports to the United States are compiled using United States import statistics (from the Customs and Border Protection Service via the United States Census Bureau) and account for Canada's export trade in grains to the United States. The data are used to estimate the portion of unlicensed grain delivered directly to the United States, and which is not captured by the CGC. IATD exports data for the current reference month are always estimated, due to timing differences between the availability of trade statistics and that of other producer delivery data sources.

Grain Farmers of Ontario (GFO): The GFO (formed through the amalgamation of the Ontario Corn Producers' Association, the Ontario Soybean Growers and the Ontario Wheat Producers' Marketing Board) provides administrative data on a monthly basis for unlicensed corn, soy and wheat deliveries by Ontario producers.

Ontario Canola Growers' Association (OCGA): The OCGA provides administrative data on a monthly basis regarding unlicensed canola deliveries by Ontario producers.

Les Producteurs de Grains du Québec (PGQ) : The PGQ provides monthly administrative data for unlicensed milling wheat, feed wheat, oats, barley, canola, soybean, and corn deliveries by Quebec producers.

Data collected from surveys administered by Statistics Canada's Agriculture Division are also integrated into the deliveries data:

Monthly Crushing Operations Survey: The survey collects monthly data from oilseed crushing operations, including quantities of raw materials crushed, oil and meal produced, as well as the month-end stocks. Data from this survey are used to help estimate the interprovincial movement of canola, to help determine the canola's province of origin.

Feed Grain Purchases Survey: The survey data are used to update the quantities and values of feed grain deliveries. Data on quantities are used to provide better estimates of unlicensed grain deliveries in farm supply and disposition tables, which in turn improve the quality of the estimates of crop production and farm stocks.

Error detection

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 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.

The data obtained from administrative sources are verified by comparison to previous reports, by preserving coherence between relevant survey programs, by supply-disposition analysis and by monitoring of industry trends. For consistency of collected data, trade data from CGC can be compared with the Canadian International Merchandise Trade Database. The statistical output from this program should also be coherent with data from Field Crop Reporting Series (record number 3401) and Supply and Disposition of Grains in Canada (record number 5223).

Imputation

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 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 program variables.

Replacement using historical data with a calculated trend is used to impute values for grains delivered in Ontario and Quebec. Such imputed data are revised when actual data are received. Data from industry sources may also be used for imputation.

Estimation

All units in the observed population are being surveyed. Estimation of totals is done by simple aggregation of the values of all estimation units that are found in the domain of estimation. Estimates are computed for domains of estimation such as provinces, based on the most recent classification information available for the estimation unit and the reference period.

Quality evaluation

Prior to the data release, combined program results are analyzed for comparability; in general, this includes a detailed review of: 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 data. Where anomalies occur they are resolved through analysis at the end of the crop year.

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.

Revisions and seasonal adjustment

There is no seasonal adjustment. Data from previous years may be revised based on updated information. The program data are not benchmarked.

Data accuracy

This program collects data from 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 verification of the administrative data. 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.

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 verification of the administrative data. Edits are applied to data records during collection to prevent the entry of outliers or inconsistent information. Data analysis tools within the IBSP permit subject matter analysts to quickly detect apparent anomalies. As such, processing errors are considered to be minimal.

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