Understanding the state and trends in agriculture production is essential to combat both short-term and long-term threats to stable and reliable access to food for all, and to ensure a profitable agricultural sector. Starting in 2009, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) began the process of generating annual crop type digital maps. Focusing on the Prairie Provinces in 2009 and 2010, a Decision Tree (DT) based methodology was applied using optical (Landsat-5, AWiFS, DMC) and radar (Radarsat-2) based satellite images. Beginning with the 2011 growing season, this activity has been extended to other provinces in support of a national crop inventory. To date this approach can consistently deliver a crop inventory that meets the overall target accuracy of at least 85% at a final spatial resolution of 30m (56m in 2009 and 2010).

This dataset highlights the network of Research and Development farms associated with Agriculture and Agri-Food Canada that conduct research and collaborate with partners and stakeholders.

This dataset is no longer maintained by Agriculture and Agri-Food Canada and should be considered as an archived product. For current estimates of the agricultural extent in Canada please refer to the Agricultural Ecumeme produced by Statistics Canada. https://www150.statcan.gc.ca/n1/en/catalogue/92-639-XThe Agriculture Extents of Canada derived from the 2001 census of agriculture, based upon soil landscape of Canada polygons (Version 3).

The “Businesses by Census Subdivision” is derived from the Statistics Canada’s Business Register. At the request of Agriculture and Agri-Food Canada, Statistics Canada aggregated the number of businesses per NAICS classification and employment class for each Census Subdivision. The data includes the individual occurrences of a business in each census subdivision by indicating its NAICS classification and employment class.The name, location, and any other identifying information about the businesses has been suppressed by Statistics Canada.

In 2012, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) repeated the process of generating annual crop inventory digital maps using satellite imagery to for all of Canada (except Newfoundland), in support of a national crop inventory. A Decision Tree (DT) based methodology was applied using optical (DMC, SPOT) and radar (RADARSAT-2) based satellite images, and having a final spatial resolution of 30m. In conjunction with satellite acquisitions, ground-truth information was provided by provincial crop insurance companies and point observations from our regional AAFC colleagues.

In 2015, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) repeated the process of generating annual crop inventory digital maps using satellite imagery to for all of Canada, in support of a national crop inventory. A Decision Tree (DT) based methodology was applied using optical (Landsat-8) and radar (RADARSAT-2) based satellite images, and having a final spatial resolution of 30m. In conjunction with satellite acquisitions, ground-truth information was provided by provincial crop insurance companies and point observations from the BC Ministry of Agriculture and our regional AAFC colleagues.

In 2011, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) expanded the process of generating annual crop inventory digital maps using satellite imagery to include British Columbia, Ontario, Quebec, and the Maritime provinces, in support of a national crop inventory. A Decision Tree (DT) based methodology was applied using optical (Landsat-5, DMC) and radar (RADARSAT-2) based satellite images, and having a final spatial resolution of 30m. In conjunction with satellite acquisitions, ground-truth information was provided by provincial crop insurance companies and point observations from our regional AAFC colleagues.

In 2013, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) repeated the process of generating annual crop inventory digital maps using satellite imagery to for all of Canada, in support of a national crop inventory. A Decision Tree (DT) based methodology was applied using optical (Landsat-8) and radar (RADARSAT-2) based satellite images, and having a final spatial resolution of 30m. In conjunction with satellite acquisitions, ground-truth information was provided by provincial crop insurance companies and point observations from the BC Ministry of Agriculture and our regional AAFC colleagues.

In 2014, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) repeated the process of generating annual crop inventory digital maps using satellite imagery to for all of Canada, in support of a national crop inventory. A Decision Tree (DT) based methodology was applied using optical (Landsat-8) and radar (RADARSAT-2) based satellite images, and having a final spatial resolution of 30m. In conjunction with satellite acquisitions, ground-truth information was provided by provincial crop insurance companies and point observations from the BC Ministry of Agriculture and our regional AAFC colleagues.

Many communities in Canada depend to some extent on forestry and the forest sector. The importance of the forest industry to the regional economy can be assessed using the CanEcumene GIS Database. “Ecumene” is a term used by geographers, meaning “inhabited lands.” A forest ecumene refers to areas where human settlement coincides with forested areas, including locations where people depend on the forest for their livelihood. Populated places in the ecumene database are referenced using natural boundaries, as opposed to administrative or census boundaries, and provide a more suitable means for integrating socio-economic data with ecological and environmental data in a region.An analysis of ecumene labour force data and location of mill facilities resulted in a generalized rendering showing regional dependency of the forest industry. The location of mill facilities layer includes information on mill type (i.e., pulp and saw) and ownership. The sensitivity to forest industry layer shows which forest communities and regions are more sensitive to economic impacts in the forest industry.Two layers are provided: the sensitivity of forest communities and regions to forest industry, and the location of mill facilities.