Agriculture's importance is highlighted by the impact that changes in the industry have on a number of sectors of the economy. As a result, the agriculture data collected by Statistics Canada extend well beyond the data requirements of the immediate agriculture sector. It is important to fully understand these interconnections, so that any changes to the current program can be made with confidence recognizing the full implications on government and industry requirements.

The key areas utilizing agriculture statistics are

• health policy
• food security
• food safety
• natural resource use
• renewable energy production
• environmental stewardship and climate change
• crisis management during disease outbreaks and natural disasters
• long-term viability and competitiveness of agri-business and the ag-value chain
• rural development
• international commitments and competitiveness in trade.

A summary of the uses of agriculture data is presented in this review to illustrate the integrated nature of the activities requiring agriculture data. 

2.1 The current situation facing the agriculture industry

The current situation facing the agriculture industry requires special mention since this is the environment in which decisions are being made regarding the future of the agriculture statistics program.

The agriculture industry is presently facing significant volatility. TD Economics recently produced a special report entitled, "Unprecedented Volatility A Hallmark of Agriculture's New Age," which summarizes the issues facing agriculture: "… the sector's biggest challenge – and one that has grown in recent years – is unpredictability." ⁸

For agriculture, unlike other industries, this rate of change is compounded by an increase in adverse climatic phenomena, and crop and livestock disease that impact production either through the destruction of crops and livestock or because agriculture producers have the ability (unlike in other industries) to react to these phenomena by changing production decisions relatively quickly.

Structural changes occurring in the industry, such as the changes recently announced to the Canadian Wheat Board (CWB), will also have an effect, not only on the industry, but also on the collection of data by Statistics Canada.

International trade policies and regulations, such as the US Country of Origin Labelling (COOL), continue to have an impact on Canadian trade and production. The Canadian agriculture industry is largely export-based and therefore very vulnerable to external factors.

International commitments recently made by Canada in an effort to stabilize agricultural commodity markets and record high food prices will have an impact on how Statistics Canada collects data. The G20 Agriculture Ministers met in June 2011 and stressed the importance of "better market information that improves transmission of market signals, more open trade, comprehensive rural development and agricultural policies, and sustained investments [that] would enable agricultural producers to increase production, enhance their income and improve global supply of food and food security." ⁹

To this end, a new Agriculture Market Information System (AMIS) has recently been created and is housed at the FAO.  This initiative includes the use of remote sensing technologies to improve weather and crop production forecasts.  Canada currently meets the requirements for this initiative; however, any changes to the program will have to ensure that these commitments are not jeopardized. ¹⁰

In an attempt to reduce the effects of some of this volatility, the FAO global strategy for agriculture censuses recommends that a CEAG be conducted more frequently than every ten years. The reasoning is that in this volatile environment, countries "may find that structural changes happen quickly, and structural data may be needed more frequently than every ten years." ¹¹

Government support to the industry is significant. In 2009‑10, the provincial and federal governments together spent approximately $8.4 billion supporting the agri-food industry. Producer support programs represented approximately 59%, on average, of total spending on the industry by both levels of government over the last decade.¹²

Tracking changes in a volatile industry will be a challenge requiring a quinquennial CEAG and a strong survey program. The strength of the survey program will depend on the quinquennial CEAG for realigning the survey estimates and for updating the survey frames.

2.2 Agriculture data in legislation and regulation

The legislative and regulatory requirements for agriculture statistics were reviewed. The agriculture statistics program addresses domestic legislative and regulatory requirements in two ways:

1. in fulfilling explicit mentions in legislation and regulation, such as the requirement to conduct a CEAG ¹³ and the requirement to collect data on the matter of agriculture, (which is listed second only to the matter of population in section 22 of the Statistics Act ¹⁴), or
2. in providing the data to support in practice the fulfillment of the requirements or objectives contained in the legislation or regulation, or in the crafting of associated policies, without specifically being identified in the legislation or regulation.

In the case of agriculture data, the majority of legislative and regulatory uses fall into the second category. At the federal level there are many acts pertaining directly to agriculture. In addition to the agriculture acts, there are several federal environmental acts and health acts that use small area data produced by the CEAG to fulfill the legislation's requirements or to assist in crafting the associated policies. Other federal acts that rely on agriculture statistics relate to banking and the federal-provincial transfer of income. It is of particular importance to note the diverse nature of the activities that make use of agriculture data.

2.3 Why a Census of Agriculture is conducted

As set out in the Statistics Act, the CEAG has been conducted nationally in Canada every five years since 1951.¹⁵ The CEAG collects data for livestock and crops, land management practices, farm revenues and expenses, capital values for land, buildings and equipment, as well as information on Canada's producers and how farms are operated. The CEAG is unique in its ability to provide a comprehensive snapshot of the industry and its people, as well as small area data, both of which are instrumental not only to the agriculture industry, but also for meeting the data requirements of environmental programs, health programs, trade and crisis management.

Beyond the legal requirement, however, there are many reasons underlying the conduct of the CEAG. In the report, Improving Information about America's Farms and Ranches: A Review of the Census of Agriculture,¹⁶ the US Council on Food, Agricultural and Resource Economics outlines the five fundamental reasons for conducting a CEAG and the fundamental drivers for its content, all of which also apply in Canada.

The following lists those reasons and provides concrete examples illustrating the importance of the data provided by the quinquennial CEAG to policies and programs. The stakeholders most reliant on the frequency, quality and relevance of the data from the quinquennial CEAG are AAFC, the provincial ministries of finance and agriculture, Health Canada, Environment Canada, and municipal and regional planners. The requirements of these stakeholders would need to be taken into account if any significant changes are made to the quinquennial CEAG.

1) Benchmarking

1a) Aligning crop and livestock survey estimates as well as the agriculture economic statistics and other key indicators

Statistics Canada and key stakeholders in the agriculture statistics program use the CEAG data to re-align the crop and livestock survey estimates and the economic statistics series. This quinquennial re-alignment assures the accuracy and coherence of the data used by the SNA and AAFC and provincial governments for policy and program development and evaluation. In addition, AAFC's ability to meet the reporting requirements of the Federal Sustainable Development Act is contingent upon the accuracy of the data.

Agriculture is a portfolio of shared responsibility between the federal and provincial governments and, therefore, budget and program costs for agriculture are also shared. These resource allocations are based on CEAG and survey data. The frequency of the CEAG (and hence the quality of the program data) will have a direct impact on the accuracy of the calculations used to allocate billions of dollars through the suite of agriculture programs.

This benchmarking function also provides an accurate measure for monitoring the industry at the national and international level. For example, the US Environmental Protection Agency's (EPA) Renewable Fuel Standard (2) regulations require that Canada demonstrate that land used to grow crops for the production of biofuels is not being converted from natural lands. Quinquennial CEAG data are a key component of an aggregate measure used to fulfill this requirement. To obtain permission from the EPA to use the aggregate measure approach (as opposed to the individual record-keeping approach), the EPA had to review the methodology and be satisfied with the reliability of the underlying data. The repercussions of being unable to comply with the aggregate measure approach could be severe. The individual record-keeping requirement for US biofuels processors is sufficiently exigent to effectively halt exports of biofuel-producing crops from Canada to the US. To put the importance of this crop trade into perspective: in 2010 Canada's exports of canola were $3.4 billion CAD, largely exported to the US.

As is the case with many trade issues, the quality of the Canadian agriculture data may come under close scrutiny. The data required in a trade dispute depend on the dispute itself, e.g., subject matter, scope and whether Canada is the complainant or the respondent. AAFC is often implicated in these trade disputes and relies on Statistics Canada data on trade, production, inventories, area harvested, etc. It is difficult to predict future trade disputes or the type of data that may be required, but in past cases both trade data and agriculture data were required.

1b) Provide information necessary for the non-surveyed portion of intercensal surveys

To reduce costs and response burden, smaller farms in the target population are excluded from agriculture surveys. Although these farms are not surveyed, they are nonetheless estimated for. The quinquennial CEAG provides the only source of updated information for identifying and estimating the non-surveyed population.

One of the most promising strategies for reducing response burden is increasing this non-surveyed portion of the target population. The quinquennial CEAG data provide a sound basis for modelling this non-surveyed population, so that they can still be represented in the published estimates. Without a CEAG, the data for this population would have to be collected from surveys or excluded from the estimates. The quinquennial CEAG data are critical to the successful implementation of this strategy.

2) Frame information

The full enumeration of the CEAG provides information necessary to create and maintain the frame for agriculture surveys. This process presents some important challenges. The agriculture industry is unique in that it has a large proportion of unincorporated businesses. In addition, the current program measures the activity (commodities produced) of farm operations and not only economic indicators. Farm operators have the ability to change commodities produced relatively quickly compared with other industries, making the maintenance of the agriculture frame more complex. A poor quality frame increases response burden and costs and decreases the quality of the estimates.

The CEAG is used in frame maintenance in a number of ways:

2a) Identifying new farms, farms that are out of business, and updating structural and status information about existing operations

It is important to be able to identify new farms for completeness of coverage, so that the survey sample and resulting estimates are accurate. In addition, it is important to identify farms that are out of business, so that resources are not wasted during survey collection and response burden is not imposed on non-active agriculture operators. Changes in the structure of farms are important to document for similar reasons.

The quinquennial CEAG is a regular, reliable source of information for the target population from which agriculture survey samples are selected. Again, the frequency with which the CEAG is conducted has a direct impact on the quality of the frame since no other comprehensive source of frame information currently exists in Canada.

The Canadian agriculture frame will move to the Business Register in 2012, and tax data will provide some frame updates. However, experience in jurisdictions with tax-based frames, such as Australia, has demonstrated the continued importance of the CEAG as a major source of agriculture frame updates. Frame deterioration is a challenge in the current program, despite the fact that the CEAG is conducted quinquennially.

2b) Identifying what commodities are produced and the size of operations for efficient sampling

The CEAG is instrumental in obtaining updated information on the commodities produced, practices used and special characteristics of individual farms. This information is essential for efficient sampling for the intercensal surveys. It also provides sample information necessary to identify operations in scope for occasional surveys that target specific, or relatively rare, characteristics. (For example, the Agricultural Water Survey conducted by the Environment Accounts and Statistics Division [EASD] uses the CEAG data to identify farms reporting irrigation practices.) Without the quinquennial CEAG, the quality of the entire intercensal survey program data would be impacted, but the quality of the surveys of operations with relatively rare characteristics would be impacted even further. The impact would be most evident in the increase in response burden as larger samples would have to be selected to account for frame deterioration as the characteristics of farms change over time. In addition, comprehensive frame update surveys would have to be implemented to gather information to maintain frame quality.

For example, between the 2006 CEAG and the 2010 Farm Financial Survey (FFS), 50% of hog farms had either left the agriculture industry or changed production to other commodities. The FFS estimates were consequently re-weighted to adjust accordingly; however, only when the results of the 2011 CEAG become available will it be possible to determine whether this re-weighting strategy was accurate. These estimates are of particular importance to AAFC because of the payments made over recent years that were designed to re-balance the marketplace for hogs. Without a quinquennial CEAG, the difficulties estimating the hog industry's financial position would be exacerbated.

Maintaining up-to-date farm production information becomes increasingly important as AAFC attempts to determine how best to align policies and programs with the longer term competitiveness of the industry. The goal of targeting government support to ensure the sustainability of the industry would be hampered without the quinquennial CEAG data that AAFC relies upon to conduct these analyses.

3) Data for small and custom geographic areas

The key strength of a CEAG is its unique ability to provide comprehensive small area data based on complete enumeration of the target population. These data are not available from any other source. The frequency with which such detailed geographic data are available would directly affect the accuracy of several federal and provincial programs and the frequency that these programs could be conducted.

Several federal and provincial programs and policies rely on the availability of CEAG small area data. For example:

• Health Canada administers the Pest Control Products Act through the Pest Management Regulatory Agency (PMRA). PMRA analyzes the risks associated with pesticide registrations for 80 crops identified using the most recent CEAG to make recommendations for registration and use. Under the Pest Control Products Act, the PMRA's ability to accurately assess pesticide exposure and whether or not a pesticide product should be registered for use in Canada would be impacted by the frequency of the CEAG data.
• Small area data are used for managing crises and developing programs to mitigate the impacts of the event. The quality of this information is affected by the frequency of small area data availability. Some administrative data are available to assist in these cases; however, these data are not available for the entire country and for all commodities and variables. Some recent examples where CEAG data, along with remote sensing and survey data, were used are
  • the Manitoba floods in 2009 and 2011
  • the Golden nematode outbreak in Québec potatoes in 2006
  • the 2003 Bovine Spongiform Encephalopathy (BSE) outbreak.
• CEAG data are used to develop markets and trade. New Brunswick, for example, has a new agriculture and agri-food export marketing initiative that uses CEAG data extensively at the county or parish level to better market agri-food products within the province as well as to increase export revenues and farm incomes.
• The EASD (SNA) requires a large number of small area physical measures from the CEAG for the environmental accounting program. As well, a new inter-departmental Policy Research Data Group with which EASD has recently become involved requires small area CEAG data to calculate ecosystem indicators.
• The Federal Sustainable Development Act requires reporting by government departments at regular intervals and includes the Canadian Environmental Sustainability Indicators program as a means to measure progress. CEAG data are inputs into the reports of several departments including Health Canada, Environment Canada, Natural Resources Canada and AAFC. Many of the requirements are based on small area data that can be tabulated to reflect ecozones, watershed areas, etc. The Act forms the basis for the reporting requirements nationally and internationally.
• Several federal environmental reporting projects (at AAFC and Environment Canada) require small area data from the CEAG, including the National Agri-Environmental Health Analysis & Reporting Program (NAHARP), the National Carbon & Greenhouse Gas Accounting and Verification System (NCGAVS) and the National Agri-Environmental Standards Initiative (NAESI).
• The provinces' calculations feed into the estimates of Canada's greenhouse gas emissions (GHG) and also serve their own purposes. For example, Alberta recently used CEAG data at a custom area level to study GHG offsets in that province.
• CEAG data at small geographic areas (including custom areas) are used extensively by the provinces for development and analysis of provincial policies and programs. CEAG data provide important historical trends as well as data on a consistent and coherent basis that allow for more efficient and effective analytical results. For example:
  • In Alberta, a water policy for the province is under development. CEAG data, at small geographic levels, are relied upon to study trends and forecast agriculture development and water demands. These data are required if the policy is to adequately address current and future water needs. In addition, the province uses CEAG data to produce animal nutrient budgets and maps of manure applications to assess the risk of water contamination.
  • Alberta is establishing land-use framework legislation that will require custom area data on land use across the province on an on-going basis. Cumulative effects' management will be implemented that will require a series of farm management data from the CEAG. The province requires these data to develop and analyze the policy as well as to meet its reporting requirements.
  • In Ontario, small area data from the CEAG are used to determine fair market price to analyze and evaluate claims under acts covering livestock, poultry and honey bee protection. Additionally, custom area data are used to assess and develop drainage policies under the Ontario Drainage Act.
  • In Québec, small area and custom area data from the CEAG are used to create tools for the management of pesticides.
  • Regional conservation authorities use CEAG data to assess watershed characteristics and risks.
  • Several provinces, including Alberta, Saskatchewan, Ontario and New Brunswick, use CEAG data to meet the reporting requirements of AAFC's Growing Forward Agricultural Policy Framework. Small area and custom area data from the CEAG are essential for the provinces to design programs that respond to the needs of farmers under the Growing Forward policy framework.
• CEAG data at small geographic areas (especially custom areas) are used extensively by municipalities and regional authorities for land-use planning. One current example is the comprehensive review being conducted by Kings County, Nova Scotia. Kings County houses the Annapolis Valley, which is one of the most fertile areas of farmland in the country. In 1979, land-use pressures drove the County to establish a formal plan restricting land-use activities. The plan has been reviewed several times since then, relying heavily on the CEAG small area data. The current review is to be the most comprehensive one conducted thus far. With the expertise of the Land Integration Unit at AAFC, the review will look at what has occurred over the last 30 years: what has worked and what has not worked towards achieving the planning goals. The review will look to future issues anticipated until the year 2050. The periodic review of the plan is therefore necessary to ensure the plan continues to meet the varied needs of its residents and businesses. Without a quinquennial CEAG, Kings County will face significant data gaps in this review process.

4) Enumerating rare and emerging commodities

Often, the CEAG is the only available source of information on rare and emerging commodities. The requirements for these data can often be unanticipated, but can nonetheless be important. They have been used for food safety, animal health, pesticide safety regulations and other uses. Quinquennial CEAG data are also used in the context of World Trade Organization (WTO) bilateral and multilateral trade agreements and for the settlement of trade disputes when the survey program does not provide data for the required commodities.

As one example, greenhouse vegetable production would have been considered an emerging commodity ten years ago. The Greenhouse, Sod and Nursery Survey shows that since 2007, the value of greenhouse vegetable production has surpassed that of field vegetable production, including potatoes. The complete picture of this industry, however, will not truly be known until the results of the 2011 CEAG are available. The greenhouse story is one that demonstrates the speed with which production changes can occur in this industry, and therefore the need to track what today is considered a rare commodity, but in less than ten years can become a leading sector.

An example of the unanticipated requirements for data on rare commodities was a requirement to inform wild boar producers of a proposed traceability system in 2007. This traceability system was required to meet animal health, human health and food safety issues. The CEAG was the only complete source of information about wild boar producers.

A third example is the Canadian Food Inspection Agency's (CFIA) need to address a disease in horses. CFIA used the CEAG data because, again, there is no other comprehensive source of data on horses.

The usefulness of the data in all of these examples would have been hindered by the reduced frequency of the CEAG data.

5) Data for cross-tabulations

CEAG data add a powerful dimension to whole farm analysis. Detailed CEAG data give the ability to perform cross-tabulations across a range of data for farms by type, region or sales class. These data are of particular importance to assessing the impacts of policies and programs on the performance of the sector. For example:

• Competitiveness: The successful farms project at AAFC uses cross sectional data with longitudinal data to provide insight into the link between farm decisions and financial performance to understand the key drivers that underpin farm success.
• Other factors used to assess competitiveness also require cross-sectional farm data, including environmental practices, investment decisions, business practices and business models, which contribute to profitability. These types of analysis are also compared internationally and provide benchmark information that the farm community can use.
• AAFC uses land and other capital asset value data from the CEAG to understand the performance of, and investment in, agriculture. Both income and asset value data are tracked over time to understand underlying trends, performance and health of the sector. AAFC uses the data on land values to evaluate
  • the impact (if any) of government programs on land prices
  • the financial well-being of farmers
  • the difficulties facing new farmers entering the agriculture industry.

  If these cross-sectional data were not available, AAFC would require special surveys to fill these data gaps.

• Municipalities and regional authorities rely not only on the custom small area CEAG data, but just as heavily on the ability to cross tabulate these data. By so doing, land-use planners are able to create comprehensive agriculture profiles to assist with land-use decision making. They are also able to quantify the contribution agricultural systems make to their municipalities. The environmental, social and economic contribution to the region and the challenges faced by producers in their area. This information enables municipalities and regional authorities to develop objective land-use plans.

Another important element of policy analysis is the ability to analyze socioeconomic data obtained from linking the CEAG and the Census of Population (CEPOP) / National Household Survey (NHS). For example, the aging of agriculture producers is an increasing concern in the industry. With the ability to cross-tabulate age of producers with farm characteristics and management practices, AAFC can assess business risk management programs. Currently, analyses such as these would be impossible without the CEAG data.