The Census of Agriculture is disseminated by Statistics Canada's standard geographic units (boundaries). Since these census units do not reflect or correspond with biophysical landscape units (such as ecological regions, soil landscapes or drainage areas), Agriculture and Agri-Food Canada in collaboration with Statistics Canada's Agriculture Division, have developed a process for interpolating (reallocating or proportioning) Census of Agriculture information from census polygon-based units to biophysical polygon-based units. In the “Interpolated census of agriculture”, suppression confidentiality procedures were applied by Statistics Canada to the custom tabulations to prevent the possibility of associating statistical data with any specific identifiable agricultural operation or individual. Confidentiality flags are denoted where "-1" appears in data cell. This indicates information has been suppressed by Statistics Canada to protect confidentiality. Null values/cells simply indicate no data is reported.

[Archived] Deployment locations and configuration details of Acoustic Doppler Current Profilers (ADCPs) included in the Centre for Marine Applied Research’s (CMAR) “Current Data” county datasets. This data has not been maintained or updated. Users looking for the latest information should refer to Nova Scotia Current and Wave Data: Deployment Information https://data.novascotia.ca/d/uban-q9i2.

This data shows spatial density of Wheat cultivation in Canada. Regions with higher calculated spatial densities represent agricultural regions of Canada in which Wheat is more expected. Results are provided as rasters with numerical values for each pixel indicating the spatial density calculated for that location. Higher spatial density values represent higher likelihood to have Wheat based on analysis of the 2009 to 2021 AAFC annual crop inventory data. Wheat consists of all types of wheat including winter wheat from the AAFC annual crop inventory.

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.

This data shows spatial density of Canola cultivation in Canada. Regions with higher calculated spatial densities represent agricultural regions of Canada in which Canola is more expected. Results are provided as rasters with numerical values for each pixel indicating the spatial density calculated for that location. Higher spatial density values represent higher likelihood to have Canola based on analysis of the 2009 to 2021 AAFC annual crop inventory data.

The 2006 Derived Interpolated Census of Agriculture by Soil Landscapes of Canada takes a subset of attributes from the 2006 Agricultural Census and creates new derived attributes that show the proportionate contribution of a variable to the total.

These datasets show the areas where major crops can be expected within the agricultural regions of Canada. Results are provided as rasters with numerical values for each pixel indicating the level of spatial density calculated for a specific crop type in that location. Regions with higher spatial density for a certain crop have higher likelihood to have the same crop based on the previous years mapped crop inventories.

The Estimated Historical Crop Yields in Canada by SLC (kg\hectare) dataset is produced by combining three complementary sources of yield information at the Soil Landscapes of Canada (SLC) level. First, provincial-level yields for seven major crops are allocated down to SLC polygons using area-based adjustments and statistical scaling. Second, forage yields (alfalfa, improved pasture, unimproved pasture) are taken directly from the EPIC model, which already outputs data at the SLC level. Third, insurance yield datasets (for the Prairies) are linked to SLC polygons and incorporated where available. These three sources are harmonized, ensuring consistency across crops and regions, resulting in a comprehensive SLC-level crop yield dataset for national-scale modeling and reporting. This dataset was created primarily in support of Statistics Canada’s Census of Environment. This data can also be used for national scale modelling, calibration of agricultural models, and reporting for the agri-environmental indicators.

This data shows spatial density of canary seed cultivation in Canada. Regions with higher calculated spatial densities represent agricultural regions of Canada in which canary seed is more expected. Results are provided as rasters with numerical values for each pixel indicating the spatial density calculated for that location. Higher spatial density values represent higher likelihood to have canary seed based on analysis of the 2009 to 2021 AAFC annual crop inventory data.

This data shows spatial density of mustard cultivation in Canada. Regions with higher calculated spatial densities represent agricultural regions of Canada in which mustard is more expected. Results are provided as rasters with numerical values for each pixel indicating the spatial density calculated for that location. Higher spatial density values represent higher likelihood to have mustard based on analysis of the 2009 to 2021 AAFC annual crop inventory data.