Agricultural Water Survey (AWS)
Detailed information for 2018
Every 2 years
The Agricultural Water Survey is conducted to gather information on irrigation water use, irrigation methods and practices, and sources and quality of water used for agricultural purposes on Canadian farms. The results will help farm operators, governments and the Canadian public gain a better understanding of the demand for water and how it is used on Canadian farms.
Data release - September 12, 2019
The Agricultural Water Survey is conducted to gather information on irrigation water use, irrigation methods and practices, and sources and quality of water used for agricultural purposes on Canadian farms.
This survey is part of the Canadian Environmental Sustainability Indicators (CESI) program. The data collected will be used in CESI's reporting activities. The information will also be used by Agriculture and Agri-Food Canada to inform water use policy and development of programs for Canadian irrigators. Statistics Canada will also use the survey results to improve the modelling of irrigation volumes by type of crops and continue to report on total water use by sector in Canada
- Environmental quality
- Land use and environmental practices
Data sources and methodology
The target population for this survey is composed of Canadian agricultural operations that irrigate. The survey frame was created using information collected as part of the 2016 Census of Agriculture (CEAG). The statistical unit is the agricultural operation and any unit which reported irrigating in 2015 on the CEAG became part of the initial survey frame.
The following groups of units were then removed from the initial survey frame, and as such, their water use is not included in the estimates:
- All farms with sales of less than $10,000;
- All institutional farms (for example, government, university and prison farms), Indian reserve farms and community pastures;
- All units which reported greenhouse, sod, nursery, mushroom or Christmas tree operations on the 2016 Census of Agriculture;
- All units that belong to Statistics Canada's Large Agricultural Operations Statistics program. These very large and complex units have special collection agreements with Statistics Canada concerning the surveys for which they will provide data;
- All units for which the 2016 Census of Agriculture irrigation data was completely imputed;
- All units which reported only irrigation area in the "Other" category on the 2016 Census of Agriculture and did not report owning any irrigation equipment;
- All units in the seven most northern of Canada's 25 drainage regions (DRs): Yukon (5), Peace-Athabasca (6), Lower Mackenzie (7), Arctic Coast-Islands (8), Keewatin-Southern Baffin Island (16), Northern Ontario (17) and Northern Quebec (18).
The remaining 9481 units comprised the final survey frame.
This methodology type does not apply to this statistical program.
This is a sample survey with a cross-sectional design.
The survey frame includes all agricultural operations in the 2016 Census of Agriculture that reported irrigated area and that corresponded to the criteria mentioned in the target population section.
The sampling unit is the agricultural operation.
A stratified sample design was used. Geographic strata were defined at the drainage region (DR) level or, when there were small populations within an individual DR, groups of DRs. DR11, which accounts for a large proportion of the total irrigation volume and area in Canada, was divided into two strata, one each for Alberta and Saskatchewan. Within the resulting 15 geographic strata, the population was divided into sub-strata based on their predicted water use for irrigation modeled from the 2011 and 2016 Census of Agriculture data and the 2010, 2012, 2014 and 2016 Agricultural Water Use Surveys. Units were categorized into one of three sub-strata of low, medium and high predicted water use. The thresholds for these sub-strata varied from one geographic stratum to the next.
Sampling and sub-sampling:
The sample was allocated in order to meet predefined coefficient of variation targets for predicted water use at the geographic stratum (DR group). An initial base sample of 1,800 units was allocated among the 15 DRs, then, around 750 other units were distributed into DR groups considered more sensitive because of rainfall data or the importance of the region in relation to irrigation. The total is slightly greater than 2500 to compensate for both the units that will not be sent to collection because of an overlap with AFUS and other units that were eliminated when creating the SIF.
In order to reduce response burden, a sample coordination method known as the microstratum approach was used to select the sample. Within a sub-stratum, units which had recently been selected by other Statistics Canada agricultural surveys had a smaller likelihood of being selected for the AWS.
Once the initial sample was selected, a number of checks were performed to exclude certain units from the final sample, including inactive units of Business Register exclusions, units who were reclassified since the 2016 Census, or who were on a list to be given survey relief. The total sample size was 2,515 units.
Data collection for this reference period: 2018-10-29 to 2018-12-14
Responding to this survey is voluntary.
Data are collected directly from survey respondents.
Prior to collection, a letter explaining the goal and objectives of the survey was sent to the respondents. A Computer Assisted Telephone Interview (CATI) data collection application was developed for this survey and telephone interviews for the AWS were conducted from Statistics Canada's regional office in Sturgeon Falls.
View the Questionnaire(s) and reporting guide(s) .
The CATI application is programmed with a number of capture edits so that inconsistencies are identified and resolved at the time of telephone collection rather than during data processing at Head Office. When important inconsistencies are identified, Statistics Canada personnel attempt to contact the respondent by telephone for clarification and correction if necessary.
Statistical methods are then used on collected data to identify units which appear to have questionable reported values. These values are corrected when appropriate using other information provided; otherwise they are set to be imputed or categorized as an outlier and not used as a donor in the imputation process.
Data imputation is used for important fields when respondents provide incomplete or inconsistent data. Data on irrigated area or water volumes used for irrigation are imputed using an automated, nearest neighbour approach. Data for other fields are not imputed; instead they are left with a "don't know" response. Complete non-respondents are not imputed, but rather accounted for in the estimation step.
Because the AWS is a sample survey, sampling weights are applied to individual respondents according to the number of units in the population that they represent. The initial or design weights are calculated based on the probability of the unit being selected for the sample. As with all surveys there is non-response, so an adjustment is made to the weights of the respondents to account for the non-responding units. Units with very high water use that are considered to be unique are assigned a weight of one. In order to estimate a characteristic for the entire population, this final weight is multiplied by the response value and summed up over the entire population. Direct variance estimation is used to measure the precision of the estimate.
A comparison of AWS's water volume and irrigated area estimates is made with estimates obtained from the previous survey cycle. Data are also compared with administrative data sources where available.
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.
Tabular data suppression techniques are used to prevent such disclosure. Cells in published tables which are considered at risk to identify an individual's data are suppressed (the value replaced with an x). Additional cells in the table may be suppressed in order to eliminate residual disclosure. The at-risk cells are either those having a small number of respondents contributing to a cell estimate, or those identified by an automated approach known as tabular data cell suppression. This technique measures the sensitivity or the risk of disclosure in each cell, identifies the ones at risk and determines if any other cells also need to be suppressed in order to maintain confidentiality.
Estimates of area are rounded to the nearest ten hectares. Volume estimates are reported to the nearest thousand cubic metres. A random rounding approach is used for estimates containing counts, where cell estimates are randomly rounded up or down to a multiple of five. This means that the sums of rounded values and the rounded marginal totals may not necessarily be equal.
Revisions and seasonal adjustment
Revisions are made for the previous survey reference period, with the initial release of the current data, as required. The purpose is to address any significant issues with the data that were found between survey cycles. The actual period of revision depends on the nature of the issue. For the most current data please refer to tables 38-10-0239 to 38-10-0249. The data are not seasonally adjusted.
The statistics contained in this publication are estimates derived from a random sample of Canadian farms and as such are subject to sampling and non-sampling errors. The quality of the estimates thus depends on the combined effect of these types of errors.
These errors arise because observations are made only on a sample and not on the entire population. The sampling error depends on factors such as the size of the sample, the variability of the characteristic of interest in the population, the sampling design and the method of estimation. For example, for a given sample size, the sampling error will depend on the stratification procedure employed, allocation of the sample, choice of the sampling units and method of selection. In sample surveys, since inference is made about the entire population covered by the survey on the basis of data obtained from only a part of the population, the results are likely to be different from those of a complete census taken under the same general survey conditions. The most important feature of probability sampling is that the sampling error can be measured from the sample itself.
Typically the sampling error is measured by the expected variability of the estimate from the true value, expressed as a percentage of the estimate and known as the Coefficient of Variation (CV). Coefficients of variation of the final estimates were computed for the Agricultural Water Survey and are indicated in the statistical tables. The quality of the estimates is classified as follows:
E. Use caution CV is 25.00% to 49.99%
F. Unreliable CV is > 49.99% (data are suppressed)
These errors are present whether a sample or a complete census of the population is taken. Non-sampling errors may be introduced at various stages of data collection (non-response, differences in the interpretation of questions, incorrect information from respondents) and data processing (coding, data entry, editing, weighting, tabulation, etc.). All efforts are undertaken to minimize non-sampling errors through questionnaire testing, extensive edits, quality control steps and data analysis, but some of these errors are outside the control of Statistics Canada.
The 2018 survey estimates for both irrigation volume and irrigated area of land show rates that vary widely across regions. Differences in weather patterns, crop types and farming practices can all lead to these variations.
After performing the editing and imputation steps and excluding the out-of-scope units, the resulting response rate for 2018 was 73.2%.