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).

Map showing where custom packaging services in Manitoba are located. This map shows where custom packaging services in Manitoba are located. A contract packager (subcontractor) is a company that manufactures and packages food products sold by other businesses. This list is not exhaustive and does not constitute a recommendation for services. For more information, visit the Manitoba Agriculture website. <o:p></o:p>This map uses the Manitoba contract packaging point layer and is part of the Manitoba contract packaging app. **This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

This data shows spatial density of Cereals cultivation in Canada. Regions with higher calculated spatial densities represent agricultural regions of Canada in which Cereals 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 Cereals based on analysis of the 2009 to 2021 AAFC annual crop inventory data. Cereals consist of the following specific crop types from the AAFC annual crop inventory; Cereals, Barley (including Spring and Winter), Greenfeed / Mixed Cereals, Millet, Oats, Rye (including Spring and Winter), Spelt, Triticale (including Spring and Winter), Wheat (including Spring and Winter), and Other Cereal

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.

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 national agricultural ecumene includes all dissemination areas with 'significant' agricultural activity. Agricultural indicators, such as the ratio of agricultural land on census farms relative to total land area, and total economic value of agricultural production, are used. Regional variations are also taken into account. The ecumene is generalized for small-scale mapping. A new version of the agricultural ecumene is generated every census year (in vector format) since 1986. This file was produced by Statistics Canada, Agriculture Division, Remote Sensing and Geospatial Analysis section, 2022, Ottawa.

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

The “Agricultural Major Land Practices Groups of the Canadian Prairies” dataset lays out the areas of the 5 Major Land Practices Groups of the agricultural portions of the Canadian Prairies. They are represented by vector polygons amalgamated (dissolved) from the Version 1.9 SLC polygons sharing common water resources, land use and farming practices as developed in the “Agricultural Land Practices Groups of the Canadian Prairies by SLC Polygon” of this series. The dataset is based upon selected attributes from the Soil Landscapes of Canada (SLC) and the 1996 Census of Agriculture. Typical attributes including: land in pasture, land in summerfallow, crop mixture, farm size and the level of chemical and fertilizer inputs.

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.