Agricultural Water Survey (AWS)

Detailed information for 2010

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 - May 27, 2011; September 19, 2011 (Agricultural Water Use in Canada, 2010 (16-402-X, free))

• Questionnaire(s) and reporting guide(s)
• Description
• Data sources and methodology
• Data accuracy

Description

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) initiative. 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.

Reference period: April 1st to October 31st of the reference year

Subjects

• Agriculture and food (formerly Agriculture)
• Environment
• Environmental quality
• Land use and environmental practices

Data sources and methodology

Target population

The target population for this survey is the Canadian farm operations that irrigate. The survey frame was created using information collected as part of the 2006 Census of Agriculture. The statistical unit was the agricultural operation. Any unit which reported sales of $10,000 or more and reported either irrigating in 2005 or owning irrigation equipment was considered to be part of the initial survey frame.

A number of groups were removed from the initial survey frame.

- All institutional farms (for example, government, university and prison farms), Indian reserve farms, community pastures, pure hatcheries and farms producing only Christmas trees.
- All units which were in the target population for Statistics Canada's Greenhouse, Sod and Nursery survey (record number 3416).
- 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 in the six most northern of Canada's 25 drainage regions (DRs).

The remaining 15,390 units comprised the survey frame.

Instrument design

The 2010 AWS questionnaire was re-designed by Environment Accounts and Statistics Division. Questionnaire design specialists from Statistics Canada's Questionnaire Design Resource Centre (QDRC) were consulted. The questionnaire was tested in November and December, 2009. One-to-one in depth interviews were conducted in Brantford, Ontario (6 respondents), Swift Current, Saskatchewan (4) and Abbotsford, British Columbia (6). The questionnaire was updated based on feedback from testing. The questionnaire was further revised based on recommendations from questionnaire design specialists.

Sampling

This is a sample survey with a cross-sectional design.

The survey frame came from the 2006 Census of Agriculture (CEAG) and included all farm operations that reported irrigated area or ownership of irrigation equipment and that corresponded to the criteria mentioned in the target population section. The sampling unit was the farm 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. This resulted in 14 geographic strata. Within each of these strata, the population was divided into four sub-strata based on their predicted water use for irrigation. This predicted value was derived from a model which used data from the 2006 Census of Agriculture and the 2007 Agricultural Water Use Survey. Units were categorized into one of four sub-strata of zero, low, medium and high predicted water use. The thresholds for these sub-strata varied from one geographic stratum to the next.

The sample was allocated to minimize coefficient of variation targets at the geographic stratum (DR group) level while at the same time not exceeding a maximum sample size of 2,000 units. The targets were not consistent from one DR group to the next. In those DR groups where greater irrigation was anticipated, the targets were lower than those used in other areas. The total sample size was 1,981 units.

In order to reduce the response burden on those farmers who had been selected for recent Statistics Canada surveys, a sample coordination method known as the microstratum approach was used. Within a geographic/size stratum, the units which had recently been least burdened by other Statistics Canada agriculture surveys were more likely to be selected for the Agricultural Water Survey (AWS).

Data sources

Data collection for this reference period: 2010-08-16 to 2010-10-20

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, along with a description of the information asked in the questionnaire, was sent to the respondents. Paper questionnaires were mailed in late August and respondents had one month to complete and return the questionnaires. The target response rate was 70%.

A Computer Assisted Telephone Interview (CATI) data collection application was developed for follow-up for this survey and some telephone interviews for the AWS were conducted from Statistics Canada's regional office in Sturgeon Falls. The follow-up period lasted from September 20th to October 20th, 2010 with up to 85% of the sample receiving either non-response or failed edit follow-up calls.

View the Questionnaire(s) and reporting guide(s) .

Error detection

The paper questionnaires were captured into an electronic format at Statistics Canada's Head Office. The responses from the CATI interviews were downloaded directly to this format. An initial set of important edits was run against the data to identify inconsistencies in the data. Statistical methods were also used to identify units which appeared to have questionable reported values. When important inconsistencies were identified, Statistics Canada personnel attempted to contact the respondent by telephone for clarification and correction if necessary.

Imputation

A set of edits and predetermined actions was used to impute a value when enough information was available to reasonably deduce the response of a missing or inconsistent field. If this information did not exist, then the action depended upon the field. For those fields related to irrigated area or irrigation volume, the missing or inconsistent data was imputed in an automated manner using a nearest neighbour imputation approach. The imputation was done in such a way to minimize the number of changes to the original data. For all other fields, the response was set to the "don't know" value.

Estimation

Because the AWS was a sample survey, sampling weights were applied to individual respondents to represent the number of units in the population that they represent. The initial or design weights were calculated as the probability of the unit being selected for the sample. As with all surveys, there was non-response. An adjustment was made to the weights of the respondents to account for the non-responding units. 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.

Quality evaluation

A comparison of AWS's resulting water volumes and irrigated areas was made with previous estimates obtained from the 2007 AWS. Data were also compared with administrative data sources where available. However, the possibilities to confront data with other published sources were limited.

Disclosure control

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 System (G-Confid). 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

This methodology does not apply to this survey.

Data accuracy

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.

Sampling errors:
These errors arise because observations are made only on a sample and not on the entire population. The sampling error depends on such factors 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 than if a complete census was 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. This measure is expressed as the coefficient of variation (CV). Coefficients of variation of the final estimates were computed for the Agricultural Water Survey and are indicated on the statistical tables. The quality of the estimates was classified as follows:

A. Excellent CV is 0.00% to 4.99%
B. Very good CV is 5.00% to 9.99%
C. Good CV is 10.00% to 14.99%
D. Acceptable CV is 15.00% to 24.99%
E. Use caution CV is 25.00% to 49.99%
F. Unreliable CV is > 49.99% (data are suppressed)

Non-sampling errors:
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 (such as 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.

After performing the editing and imputation steps and excluding the out-of-scope units, the resulting response rate was 56.8%.