Field Crop Reporting Series
Detailed information for November 2020
5 times per year
The purpose of the field crop surveys is to obtain information on seeded and harvested field crop areas, average yields, production and on-farm stocks at strategic times over the course of a typical crop cycle, which ranges from spring to late fall. Therefore, the field crop surveys are conducted in March, June, July, November and December.
Data release - Scheduled for December 3, 2020 (Production of principal field crops)
- Questionnaire(s) and reporting guide(s)
- Data sources and methodology
- Data accuracy
This is a series of five data collection activities which are used in the release of estimates at pre-scheduled, strategic times during the crop year.
These data are meant to provide accurate and timely estimates of seeding intentions, seeded and harvested area, production, yield and farm stocks of the principal field crops in Canada at the provincial level. The crops surveyed include wheat, oats, barley, rye, flaxseed, canola, corn for grain, soybeans, sunflower seed, dry beans, dry field peas, lentils, mustard seed, Canary seed and chick peas.
NOTE: As of fall of 2017, and between mid-September to following early December of each year, the most current reference year's data in Table 32-10-0359-01 (formerly CANSIM 001-0017) is updated with model-based principal field crop estimates obtained from satellite imagery. The September survey has been replaced with the model-based data, reducing respondent's burden.
In 2019, the field crop survey, for the July publication, used a model-based data for the province of Manitoba to evaluate the yield and production. The model utilized data from low resolution satellite imagery, historical field crop survey estimates, agroclimatic information, and it also used data from crop insurance. This new methodology is the continuity of the effort to reduce respondent's burden and to provide high quality estimated data. For the July 2020 survey, model-based data will be used for all provinces. Note that all the sources enumerated are not available for each province. When administrative data are not available, historical data will be used.
In 2020, on-farm stocks of principal field crops on July 31, 2020 were collected on the 2020 June Field Crop Survey between May 14th and June 11th, 2020.
Please refer to the Integrated Metadatabase Web page (Model-based Principal Field Crop Estimates, record number 5225) for more details about the methodology used to obtain these model-based estimates.
The data are used by Agriculture and Agri-food Canada and other federal departments to develop and administer agricultural policies. This information is also used by provincial departments for production and price analysis and for economic research.
Reference period: Seeded area: all occasions; harvested area, yield and production: July and November; stocks: December (calendar year end), March (financial year end) and June and July (crop year end).
Collection period: Electronic questionnaire collection starts on October. The overall collection ends on November.
- Crops and horticulture
Data sources and methodology
The target population for the field crop surveys includes all farms in Canada enumerated in the Census of Agriculture, except institutional farms, farms on First Nations reserves and farms from the Northwest Territories, Yukon and Nunavut.
Field crop surveys collect data from Quebec, Ontario, Manitoba, Saskatchewan and Alberta at all survey occasions. However, they only collect data twice a year for Newfoundland and Labrador, Prince Edward Island, Nova Scotia, New Brunswick and British Columbia in the June cycle (seeded areas) and the November cycle (final crop production).
The questionnaire was originally designed in 1908, and has evolved over the years in order to address Statistics Canada and Canadian agricultural requirements. Consultations have taken place between subject matter specialists and provincial and industry experts. Testing is conducted in-house for flow and consistency. Questions will be changed, added or removed as the need arises.
As of March 2018, the questionnaire is offered in electronic format for use on Statistics Canada website. The survey can now be self-completed by respondents as well as on the phone with an interviewer.
This is a sample survey with a cross-sectional design.
Probability surveys can use two types of sampling frames: list and area. In the field crop surveys, only the list frame is used in sample selection. This list frame is stratified into homogeneous groups on the basis of Census characteristics (such as farm size and crop area) and sub-provincial geographic boundaries. The sample units (farms) are selected from Statistics Canada's Business Register.
Data collection for this reference period: 2020-10-09 to 2020-11-13
Responding to this survey is mandatory.
Data are collected directly from survey respondents.
For the July 2020 survey, yields are collected by satellite imagery, historical field crop survey estimates, agroclimatic information and data from crop insurance.
This survey data is now collected using a web-based electronic version of the questionnaire. A letter or an email containing a survey access code is sent to respondents to access their survey on-line. Follow-up with an interviewer is also conducted using the computer assisted telephone interview method, in conjunction with the Business Collection Portal. The conducting of the interview is offered in both official languages and takes on average 18 minutes to complete.
View the Questionnaire(s) and reporting guide(s) .
With the electronic questionnaire (EQ) and the computer assisted telephone interview (CATI) method, it is possible to implement edit procedures at the time of survey completion. Computer programmed edit checks in the EQ and CATI systems report possible data errors, which can then be corrected immediately by the respondent and/or the interviewer.
Data are then compared on a year-to-year basis. Analysts cross check each other's work and group reviews are performed. All top contributors are also reviewed.
When non-response occurs, when respondents do not completely answer the questionnaire, or when reported data are considered incorrect during the error detection steps, imputation is used to fill in the missing information and modify the incorrect 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, replacement using historical data (with a trend calculated, when appropriate), replacement using auxiliary information available from other sources, replacement based on known data relationships for the sample unit, and replacement using data from a similar unit in the sample (known as donor imputation). 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 microdata file that covers all survey variables.
Information applicable to all field crop surveys:
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. Estimation of totals is done by simple aggregation of the weighted values of all estimation units that are found in the domain of estimation. Also from two level indicators, it is possible to derive unbiased ratio indicators such as the pair change ratio of the seeded acreage from year to year. These level indicators then undergo a validation process, based on subject matter analysis, before final estimates are published.
Since March 2014, for response burden matters, the operations that have a total crop area lower than a sub-provincial threshold are excluded from field crop surveys samples. This threshold, which varies for each sub-provincial region, is such that all farms having an area greater than the threshold itself represent, altogether, 95% of the total crop acreage of the region. The estimate share of the non-surveyed farms is then modeled and added to the estimation derived from surveyed farms, in order to draw a complete picture of field crop areas or productions.
The field crop surveys conducted in March and June estimate farmers' seeding intentions and actual seeded areas, respectively. The field crop surveys conducted in July and November mainly estimate farmers' yields and production obtained from the crops they seeded. Note that the 2020 July estimates were not collected by survey but instead were modelled with the aid of administrative data. In addition, crop yield and production data is also derived in September of each year, using satellite imagery. For more information on these model-based principal field crop estimates, see the Integrated Metadatabase Web page (Model-based Principal Field Crop Estimates, record number 5225).
The survey data from all occasions are weighted to produce estimates at the provincial and crop district levels.
Information applicable to field crop surveys collecting data on farm stocks:
Farm stocks are estimated at December 31, March 31 and July 31. A major tool used in the verification of these estimates is the Farm supply and disposition of grains (or supply-demand). This table reflects activity on farm only before grain enters the commercial system. The supply and disposition numbers must be equal.
The supply is composed of opening farm stocks and production. The disposition is comprised of deliveries, seed use, closing farm stocks as well as feed, waste and dockage. The production and farm stocks are estimated from field crop surveys conducted by Statistics Canada. Seed use data are based on average seeding rates. A major portion of the deliveries are licensed grain deliveries obtained from the Canadian Grain Commission. The "feed, waste and dockage (fwd)" component is a residual in the balance sheet. Indicators such as the number of grain consuming animal units, harvest conditions affecting grain quality, established ratios of dockage to delivered grain and grain inspections are used to ensure data accuracy.
National supply and disposition tables provide further information to aid in estimating farm stocks.
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).
Statistics Canada is prohibited by law from releasing any information it collects that 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.
In order to prevent any data disclosure, confidentiality analysis is done using the Statistics Canada Generalized Disclosure Control (G-Confid) system. G-Confid is used for primary suppression (direct disclosure) as well as for secondary suppression (residual disclosure). Direct disclosure occurs when the value in a tabulation cell is composed of or dominated by few enterprises while residual disclosure occurs when confidential information can be derived indirectly by piecing together information from different sources or data series.
Revisions and seasonal adjustment
Seeded areas data published from all field crop surveys of a typical crop year represent preliminary estimates until the November instance of the survey, and can be changed over the course of the crop year. Yield and crop production estimates published from the July instance of the survey also represent preliminary estimates until November instance. The areas and production estimates published at the end of the crop year through the November instance of the survey are considered final, but are subject to change for two years, and to intercensal revisions if applicable. The revision period for stocks data also lasts two years.
All surveys are subject to sampling and non-sampling errors. Sampling error occurs because population estimates are derived from a sample of the population rather than the entire population. Non-sampling error is not related to sampling and may occur for various reasons during the collection and processing of data. For example, non-response is an important source of non-sampling error. Undercoverage or overcoverage of the population, differences in the interpretations of questions and mistakes in recording, coding and processing data are other examples of non-sampling errors. 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 to maximize response rates.
Measures of sampling error are calculated for each estimate. Also, when non-response occurs, it is taken into account and the quality is reduced based on its importance to the estimate. Other indicators of quality are also provided such as the response rate.
Both the sampling error and the non-response rate are combined into one quality rating code. This code uses letters that ranges from A to F where A means the data is of excellent quality and F means it is unreliable. These quality rating codes can be requested and should always be taken into consideration.
The field crops statistics are based on a random sample of agricultural operations and, as such, are subject to sampling and non-sampling errors. The overall quality of the estimates depends on the combined effect of these two types of errors.
Sampling errors arise because estimates are derived from sample data and not from the entire population. These errors depend on factors such as sample size, sampling design and the method of estimation. An important feature of probability sampling is that sampling errors can be measured from the sample itself.
Non-sampling errors are errors which are not related to sampling and may occur throughout the survey operation for many reasons. For example, non-response is an important source of non-sampling error. Coverage, differences in the interpretation of questions, incorrect information from respondents, mistakes in recording, coding and processing of data are other examples of non-sampling errors.
Usually by the end of the collection period, 70% of the questionnaires have been fully completed. The refusal rate to the survey is approximately 8% to 9%. The remainder of the sample unaccounted for can be explained by non-contact and non-response. Initial sample weights are adjusted by a process called "raising factor adjustment" in cases of total or partial non-response.
Since July 2013, estimates displayed on data tables or in The Daily for the provinces of Newfoundland and Labrador, Prince Edward Island, Nova Scotia, New Brunswick and British Columbia are derived from survey data in June and November occasions, but are carried over from the most recent time they were surveyed for the other occasions, i.e. from the preceding November occasion for March occasion, and from the preceding June occasion for the July occasions.
- An Integrated Crop Yield Model Using Remote Sensing, Agroclimatic Data and Crop Insurance Data
- Canadian Spring Wheat varieties