This data series was compiled by AAFC and Statistics Canada using a combination of agroclimate data and satellite-derived Normalized Difference Vegetation Index (NDVI) data for the current growing season. The forecast is made based on a statistical model using historical yield, climate and NDVI 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.

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

Akkeri og baujur og áætlaðar staðsetningar fyrir búnað í sjókvíaeldi.

In 2018, 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 BC Ministry of Agriculture, & the Ontario Ministry of Agriculture, Food and Rural Affairs; and data collection supported by our regional AAFC Research and Development Centres in St. John’s, Kentville, Charlottetown, Fredericton, Guelph, and Summerland

This web experience includes four dashboards and graphs that show inspections, the most common food safety violations, and the levels of progressive compliance measures taken by health officers to enforce the law. 1. Inspections: This dashboard includes tables showing inspection data collected by the Food Safety and Inspection Directorate over the past five years. <o:p></o:p>Inspection Violations — Overview (arcgis.com) 2. Food safety violations: This dashboard shows the number of violations observed during the years indicated. The number of violations observed is then classified into the category of critical or non-critical offenses. Critical violations are violations that present an immediate risk to food safety and must be corrected within a specified period of time. Non-critical violations do not present an immediate food safety risk but need to be addressed before they become one. <o:p></o:p>Inspection Violations — Overview (arcgis.com) 3. Top five food safety violations: This dashboard shows charts and tables showing the five most common food safety violations observed during inspections. Each type of violation refers to the Manitoba Food Regulations. The five most common violations are expressed as a percentage of all observed food safety violations. <o:p></o:p>Main offences<o:p></o:p> 4. Progressive compliance measures: This dashboard shows how often health officers applied progressive compliance measures to food processing establishments that did not correct food safety violations within the time frame specified in the inspection. The dashboard explains that food processing establishments have a certain amount of time to correct food safety violations based on the risk associated with them. Progressive compliance measures are only applied if the violation is not corrected within the prescribed timeframe. Progressive compliance measures<o:p></o:p> **This third party metadata element was translated using an automated translation tool (Amazon Translate).**

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.

The “Biomass Inventory Cartographic Layer” dataset provides the information that is used with the Biomass Report Framework to generate a visual representation of the availability of agricultural and forestry biomass and municipal solid waste in Canada. In addition to yield and production information for biomass produced by the agricultural and forestry industries, this dataset also provides information about the demand for agricultural residues for cattle feed and bedding, tillage systems currently in use on agricultural lands, and land suitability for hybrid poplar and willow plantations that are grown specifically to produce biomass. Agricultural information includes the median annual residue yield and available residue amounts. Residue yields were calculated using crop-to-residue ratios. The available residue information includes the amount that is available after adjusting for the estimated demand of straw used for cattle feed and bedding. Forestry estimates include average residue production, based on forestry activities including permitted amounts of harvesting, mills in operation and mill production. Municipal Solid Waste information includes organic waste (food and yard), paper waste and total residential municipal solid waste (which includes organic and paper waste, among others).

The Canada Land Inventory (CLI), 1:000,000, Land Capability for Agriculture dataset illustrates the varying potential of a specific area for agricultural production. Classes of land capability for agriculture are based on mineral soils grouped according to their potential and limitations for agricultural use. The classes indicate the degree of limitation imposed by the soil in its use for mechanized agriculture. The subclasses indicate the kinds of limitations that individually or in combination with others, are affecting agricultural land use. Characteristics of the soil as determined by soil surveys.

The Grain Elevators in Canada - 2021 dataset maps the list of grain elevators in Canada as provided by the Canadian Grain Commission (CGC). The elevators have been located as much as possible to an actual location rather than generalizing to the station name centroid. Additionally car spot information from CN, CP and the grain companies has been added where this has been published. This dataset attempts to provide a temporal and geographical extent of the grain elevators in Canada.