The Grazing Rental Zones is comprised of two polygons which determine which zone a grazing disposition (GRL, FGL, GRP) is in. These zones are used to apply the rental rate that grazing leases (GRL), grazing licenses (FGL) and grazing permits (GRP) pay to the government of Alberta for use of public lands. The Public Lands Modernization (Grazing Lease and Obsolete Provisions) Amendment Act came into force January 1, 2020. Under the new rental rate framework (Ministerial Order 01/2020), there are now two grazing rental rate zones based on the transition of the boreal region of the province. The North Saskatchewan River is the dividing line between the south (Zone 1) and north (Zone 2).

The “Biomass Agriculture Inventory 1-in-10 Probability” dataset is a table that contains the estimated 1-in-10 year low for agricultural residue yield and crop production for each Biomass Report Framework. It provides the tenth percentile values for the years 1985-2016. The table includes straw or stover information for barley, wheat, flax, oats and corn, and crop information for barley, wheat, flax, oats, corn, canola and soybean. This dataset also includes information about the type of tillage used in the area and demand for straw for cattle bedding and feed. These values are derived from Statistics Canada data. Additionally, the dataset includes the amount of agricultural residue calculated as necessary to remain on the field to prevent soil degradation. Soil degradation is determined by the type of tillage in use as well as the landscape of the area.

The cloud-corrected NDVI data extracted from historical MODIS satellite images at 250 metre resolution provides reliable, objective, and timely information on the state of vegetation throughout Canada and the northern United States. The methodology applied to the images has remained the same as for the program formerly known as the Crop Condition Assessment Program (CCAP). Since the 2000 growing season, Statistics Canada has been processing and compiling MODerate-resolution Imaging Spectoradiometer (MODIS) data (250 metre resolution). The Multi-Spectral Instrument (MSI) captures two spectral bands (red and infrared) that have proven to be extremely useful to produce the Normalized Difference Vegetation Index (NDVI) utilized for vegetation monitoring. The original NDVI image composites were produced by Agriculture and Agri-Food Canada (link to original data in the resources section). Additional computations were completed by Statistics Canada to remove the effects of residual clouds and to calculate and extract the NDVI by geographic region. This dataset provides access to the MODIS images from 2000 to present in GeoTIFF format and covers the crop area during the growing season (Julian weeks 15 to 37; mid-April to mid-September). It also provides access to a database that contains the statistical NDVI by geographic regions (Townships, Census Consolidated Subdivisions (CCS), Census Divisions (CD) and Census Agricultural Regions (CAR)) and agricultural masks (Agriculture (AGR), Crop (CROP) and Pasture (PAS)).

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).**

List of freshwater fish species recorded from survey data throughout Nova Scotia.

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

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.

The Canada Land Inventory (CLI), 1 100,000, Land Capability and Limitation 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.

File contains all issued marine aquaculture leases along the coast of Nova Scotia. Fisheries and Aquaculture also provides a mapping tool for this data at: Nova Scotia Aquaculture & Rockweed Map Viewer

This table contains information about the status, actions and outcomes from inspections conducted by Animal Protection Officers (APO) and prosecutions in the Manitoba Animal Welfare Program. This table contains information about the status, actions and outcomes from inspections conducted by Animal Protection Officers (APO) and prosecutions in the Manitoba Animal Welfare Program for each year, starting in 2016, to the most recent quarter. This data is populated by the Provincial Animal Welfare Database for the Manitoba Animal Welfare Program and is displayed in the Manitoba Animal Welfare Program – Case Outcomes dashboard. The table will be updated on a quarterly basis. Fields included [Alias (Field Name): Field description] StatusGroups2 (StatusGroups2): Includes the status, actions or outcomes that have occurred throughout each assigned case DashboardGrouping2 (DashboardGrouping2): Includes the dashboard element under which the statuses need to be grouped for each assigned case Year (Year): Includes the year, beginning in 2016 to the current year (e.g., 2016, 2017, 2018) Month (Month): Includes the numeric value of all months within a calendar year (e.g., 1, 2, 3) Quarter (Quarter): Includes the numeric values of all quarters in a calendar year (e.g., 1, 2, 3, 4), where quarter 1 corresponds with January, February and March, quarter 2 corresponds with April, May and June, quarter 3 corresponds with July, August and September and quarter 4 corresponds with October, November and December YQ (YQ): Includes the year and quarter of the most recent 12 quarters (e.g., 2021 Q1, 2021 Q2 )