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Report No. 1/95

An Assessment of the Existing Information on Agricultural Energy Use in Canada, and New Data Collection Strategy.  Volumes I and II.  By Tulay Yildirim, Tom Allen, Varghese Manaloor and Richard White. June 1995, Saskatoon, Saskatchewan.

Natural Resources Canada (NRCan) announced the creation of the National Energy Use Database (NEUD) initiative in October 1991.  This program, under the umbrella of the Green Plan, aims to reduce green house gas (GHG) emissions by improving energy efficiency in all sectors of the economy.  To achieve this objective, the NEUD initiative gave priority to expanding and improving the existing knowledge about energy consumption and efficiency at the end-use level in all sectors of the economy. The Canadian Agricultural Energy End-Use Data and Analysis Centre (CAEEDAC) was established in the Department of Agricultural Economics at the University of Saskatchewan in May 1994 to accomplish this task for the agricultural sector.  The main objective of the CAEEDAC is to improve the availability, accessibility and compatibility of the existing national databases on agricultural sector energy use.

The objectives of this report are to provide information on the available data, to assess their limitations, and to identify gaps in the existing information.  There has been no comprehensive compilation of this information in order to assess its quality and to determine overlaps and gaps prior to this study.

The CAEEDAC staff spent several months searching for the existing data on agricultural energy use in Canada.  There is a large body of data compiled by Statistics Canada.  There are also some data that could be collected from energy suppliers, public utilities, provincial finance departments' records on tax rebate programs, and provincial surveys conducted by various agencies.  The quality of available data on energy use in Canadian agriculture is, however, presently not of sufficient quality to rigorously support any type of comprehensive analysis, or to be used in monitoring the sector's contribution to GHG emissions.  The existing data can be, and have been, used for less detailed analysis of agricultural energy use.

Limitations and deficiencies in the existing databases should be assessed within the context of the factors affecting energy use in agriculture.  These factors are technological change, relative prices, and technical efficiency at farm level. Technological changes in agricultural production and in input supply sectors have significant implications on energy use, and in input-output ratios.  For instance, research in crop production that results in increased yields inadvertently reduce energy intensity in production, so does the technical change in manufacturing sector through increased device energy efficiency.  Changes in relative input prices induce farmers to substitute away from more expensive inputs, and also to adopt technologies which can save the more expensive inputs.  Changes in relative output prices, through their impact on cropping decisions, also affect energy use in production.  Empirical evidence shows that different levels of output could be produced with the same levels of inputs by following different methods of input application, i.e., by attaining different levels of technical efficiency.  Producers can increase output levels by reorganizing resources at their disposal, and by modifying the method of application. Technical efficiency could be improved through education and extension programs on best practice techniques of input application.  Improvements in technical efficiency, in turn, could result in a costless method of reducing agricultural energy use.

The synergistic nature of multiple inputs in agriculture, where device energy efficiency is one of many factors that affects energy use, requires dynamic analysis to assess changes in input mixes or cropping systems.  Within this context, the following limitations and deficiencies were identified in the existing databases:

Clearly, a new database in agricultural energy use is needed to fill the gaps in the existing data.  The new data collection strategy should be primarily responsive to the main objective of the NEUD, that is to reduce GHG emissions by improving energy efficiency in all sectors.  A set of data collected for a particular purpose may not be suitable for all other uses.  Yet, statistical data sets are public goods and it is desirable to have a data set that benefits the largest number of users.  The new database should include the following variables:

In view of these data requirements CAEEDAC recommends that small annual surveys to be conducted in order to elicit the information listed above.  We propose that survey sample should be the same as other Statistics Canada surveys, such as the National Farm Survey and Farm Financial Survey.  These two surveys provide data on financial characteristics of farms, land use, farm production, machinery and equipment sales and purchases, and farm receipts and expenses, which are essential to analyzing the different aspects of agricultural energy use.  Statistics Canada has also a new pesticide survey that is currently being field tested.  Pesticide information could be obtained from this new survey if the samples were chosen consistently.

Data on the number and use of farm machinery could be collected through the Census of Agriculture.  The Census of Agriculture has already a set of questions regarding the number of selected machinery on farms.  These questions could be expanded to include the use of these machinery.  In this way, database would provide all variables required by the end-use models.  It is also possible to incorporate the questions on farm machinery and equipment in Farm Financial Survey, which is an annual survey. However, it is our opinion that machinery on farms do not change drastically from year to year, hence collecting these data in census years would probably provide enough information for those who work with end-use models.

If a new farm energy survey is designed in a way to allow for cross-referencing with the existing surveys, the questionnaire could be kept shorter and overlaps in survey questionnaires could be avoided, but a complete data set could still be created.  We believe this is the most cost-effective way of creating a complete data set which could be utilized by researchers across all disciplines.

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