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.

Crop development stage in a numerical scale. All living organisms move from one stage of development to the next over time. For annual crops, it life cycle (growing season) completed within a year. Crop water use differs from one stage to another mostly due to the differences in the amount of green leaves, thus crop stage is closely related to its water consumption and water stress condition. Crop stages are mostly controlled by growing season heat accumulation and regulated by day-length crop some crops. The crop stages provided here are determined by a biometeorlogical time scale model (Robertson, 1968) for cool season crops (wheat, barley etc.) , and a Crop Heat Unit (Brown and Bootsma, 1993) algorithm for warm season crops (corn and soybean etc.).

This data shows spatial density of pulses cultivation in Canada. Regions with higher calculated spatial densities represent agricultural regions of Canada in which pulses are 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 pulses based on analysis of the 2009 to 2021 AAFC annual crop inventory data. Pulses consist of the following specfic crops types from the AAFC annual crop inventory; Pulses, Beans, Black Beans, Cranberry Beans, Faba Beans, Great Northern Beans, Kidney Beans, Lima Beans, Pinto Beans, Navy Beans, Red Beans, White Beans, Other Beans, Lentils, Peas, Chick Peas, Field Peas, White Peas, Other Peas, and Other Pulse

This data shows spatial density of winter wheat cultivation in Canada. Regions with higher calculated spatial densities represent agricultural regions of Canada in which winter 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 winter wheat based on analysis of the 2009 to 2021 AAFC annual crop inventory data.

This data shows spatial density of rye cultivation in Canada. Regions with higher calculated spatial densities represent agricultural regions of Canada in which rye are 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 rye based on analysis of the 2009 to 2021 AAFC annual crop inventory data.

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.

This table contains information about the number of cases reported, inspections conducted, and non-compliances to The Animal Care Act from 2016 to present. This table contains information about the number of cases reported, inspections conducted, and non-compliances to The Animal Care Act for each year, starting in 2016, to the most recent quarter. These data are populated by the Provincial Animal Welfare Database for the Manitoba Animal Welfare Program and are displayed in the Manitoba Animal Welfare Program – Trends chart. The table will be updated on a quarterly basis. Fields included [Alias (Field Name): Field description] Category (Category): Includes the year, beginning in 2016, to the current year (e.g., 2016, 2017, 2018) # of cases reported (F__of_cases_reported): Includes the total number of cases reported for each year # of inspections conducted (F__of_inspections_conducted): Includes the total number of inspections conducted for each year # of non-compliances found* (F__of _non_compliances_found_): Includes the total number of non-compliances found following an inspection for each year * The number of non-compliances found as a result of an inspection by an Animal Protection Officer (APO) include animals deemed abandoned, issued notice of seizure, custody and distress, Director’s Order issued, surrendered ownership and recommendations for improvements.

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.

In 2019, 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, Sentinel-2) 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 in Alberta, Saskatchewan, Manitoba, & Quebec; point observations from the PEI Department of Environment, Water and Climate Change and data collection supported by our regional AAFC Research and Development Centres in St. John’s, Kentville, Charlottetown, Fredericton, and Guelph.

The Agri-Environmental Spatial Data (AESD) product from the Census of Agriculture provides a large selection of farm-level variables from the Census of Agriculture and uses alternative data sources to improve the spatial distribution of the production activities. Therefore, the AESD database offers clients the possibility to better analyze the impact of agriculture activities on the environment and produce key indicators, or for any applications where accurate location of activities matters. Variables are offered using two types of physical boundaries: by Soil Landscape of Canada polygons and by Sub-sub-drainage areas (watersheds). The focus of the redistribution of the data is on the field crops and land use variables, but the database includes all census variables related to crops, livestock and management practices. This frame can also be used to extract Census of Agriculture data by custom geographic areas. Also, users interested in this version of the Census of Agriculture database using administrative types of regions can request it. In both cases, please contact Statistics Canada. This file was produced by Statistics Canada, Agriculture Division, Remote Sensing and Geospatial Analysis section, 2022, Ottawa.