Detailed information for October 1, 2000 (Hog Inventories)
The Livestock Survey consists of four survey occasions designed to provide inventories of the number of livestock animals on Canadian farms on four specific dates. The principal data releases include inventories and summarized supply-disposition tables.
Data release - October 23, 2000
The Livestock Survey consists of four survey occasions designed to provide inventories of the number of livestock animals on Canadian farms on four specific dates. The January 1 and July 1 surveys collect data related to cattle, hogs, sheep and other livestock while the April 1 and October 1 surveys focus on hogs. The principal data releases include inventories and summarized supply-disposition tables. These data are used by agricultural industry analysts and producers as they make production and marketing decisions and by government analysts to monitor the livestock industry or develop agricultural policies in Canada. The data are used in the calculation of farm income estimates and flow to the Canadian System of National Accounts. Further, the data are used in the calculation of net farm income projections, produced by Agriculture and Agri-Food Canada in co-operation with Statistics Canada and the provinces.
Reference period: January 1, April 1 (hogs), July 1, October 1 (hogs)
Collection period: March, June, September, December
- Agriculture and food (formerly Agriculture)
- Livestock and aquaculture
Data sources and methodology
The target population for the survey consists of all Canadian agriculture operations that are active at the end of the reference year. Specific farms are excluded from the target population to obtain the survey population such as farms with less than $1,000 in sales from agricultural activities, institutional farms, community pastures and farms on Indian reserves.
The original development of the Computer Assisted Telephone Interview questions was based on the well established Livestock Survey paper questionnaire that it replaced and the livestock module on the Census of Agriculture. The Census of Agriculture questionnaire was tested using focus groups and pilot surveys.
The questions used in the ongoing survey have been tested using focus groups. In addition, staff members observing training and collection, provide observation reports. All survey occasions include debriefing sessions where the results of the testing and observation are incorporated into the development cycle of the next survey, normally to fine-tune the survey.
This is a sample survey with a cross-sectional design.
The population consists of all farms in Canada, excluding the Atlantic, which is surveyed independently (see Atlantic Agriculture Survey, record number 3465). The frame includes all agricultural producers, excluding community pastures and Indian reserves, who reported more than $1,000 on the Census of Agriculture, supplemented by known new hog operations. The survey focuses on cattle, hog and sheep producers during sample selection as the stratification and allocation is multi-variate, by type and size of livestock operation. The sample is selected using a stratified simple random sampling method.
The sample size of the survey depends on the survey occasion.
January Livestock Survey sample size equals 10,000
April Hog Survey sample size equals 2,500
July Livestock Survey sample size equals 22,400
October Hog Survey sample size equals 2,400
Responding to this survey is mandatory.
Data are collected directly from survey respondents.
The survey information is collected by telephone interview in Statistics Canada Regional Offices, using a Computer Assisted Telephone Interview (CATI) application. Farm operators are contacted directly by phone and, in cases where the operator is difficult to contact, there are multiple follow-up telephone calls that ultimately result in a high contact rate.
The CATI application used for collection contains range and consistency edits and help text. A set of reports is run to identify problem items early in collection for remedial action (e.g. variables with a significant number of edits or missing information). Processing includes checking interviewer notes, manually reviewing significant inconsistencies and reviewing the top contributors to the unweighted and weighted estimates (for each variable in each province).
Total non-response (e.g. refusals and no contacts) is accounted for by weighting adjustments to each stratum. Some item non-response is estimated deterministically (using other information in the respondent's questionnaire). Some missing information is imputed manually during the edit process, and others are imputed using a "hot-deck" donor imputation method. The automated imputation system looks for donors within the stratum and then verifies that the donor record and the record to be imputed are acceptable. A final review of the imputed data is then performed.
The response values for sampled units are multiplied by a sampling weight in order to estimate for the entire surveyed population. The sampling weight is calculated using a number of factors, including the probability of the unit being selected in the sample. Raising factor (weight) adjustment is used in the estimation process to account for respondents who could not be contacted or who refused the survey.
Once the weights have been calculated for each record, any level of required estimates may be obtained using domain estimation (i.e. provincial, agricultural region, etc.).
The survey results are evaluated through comparisons to previous estimates and other sources when available. Biological factors affecting livestock are used as a guide when evaluating the data or comparing to other data sets. A primary tool in the evaluation and final determination of the data involves supply-demand analysis and survey-based models that track the supply and demand of the particular type of livestock by province over time.
The survey results are analysed and corrected before the data are used to analyse the industry and fine-tune the estimates. The survey data are reviewed in a board environment before the commodity analyst works with the data primarily using supply-disposition analysis. The results of the industry analysis are reviewed by the board before being sent to the individual provinces. Once the data are finalised they are released to the public and disseminated.
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.
Sample surveys are designed to provide the highest sampling efficiency (the smallest sample that will produce a sampling error of a given size). This optimization is usually performed for only a few variables, limited by the data items that are available at the time of sample design and selection, the resources available, and the complexity introduced by trying to optimize for many variables at one time. The sample used for these statistics was designed to produce a high level of accuracy for provincial level totals.
The following ratings are based on the value of the coefficient of variation (CV). Using this rating system, the vast majority of the total estimates at the provincial level are excellent. This is particularly true in provinces where the livestock type is prevalent. If they are not excellent, the results are normally very good.
Letter Rating CV Rating
A 0.00% to 4.99% Excellent
B 5.00% to 9.99% Very good
C 10.00% to 14.99% Good
D 15.00% to 24.99% Acceptable
E 25.00% to 34.99% Use with caution
F >=35.00% Too unreliable to publish
The variability in the estimates can be obtained by constructing confidence intervals around the estimate using the estimate and the coefficient of variation.
The overall response rate of the survey is very good ranging normally from 97 to 99% and item non-response is low. Overall, the impact of the edits and imputations is small. The data are generally of excellent quality, with some under-coverage due to intercensal frame degradation (i.e. coverage of new operations).
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