In British Columbia, there are over 100 Livestock Districts, Bull Control Areas and Pound Districts established by regulation under the Livestock Act. Livestock Districts, with minor exceptions, are areas where livestock may run at large within the district boundaries. Bull Control Areas are areas within Livestock District, in which Bull Control Committees may make rules respecting the number, breed, breeding, quality and age of bulls allowed to run at large. Pound Districts are areas where keepers, peace officers and others may impound animals at large, and allow for the sale of unclaimed impounded animals and the reimbursement of the keeper. The regulations are published in the British Columbia Gazette Part II. The current Index of Current B.C. Regulations provides a complete list. Search Orders in Council from http://BCLaws.ca to view related Orders in Council including metes and bounds descriptions.

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.

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 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.

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

Table containing information relevant to animal disease investigations in Manitoba from 2012 to present. This table contains information relevant to animal disease investigations in Manitoba from 2012 to present, conducted by the Chief Veterinary Office (CVO). Information includes year, number of sites, number of linked sites, animal species, disease types and results. Updated on a weekly basis.  It is important that users are aware of the following caveats when reviewing data presented in the Animal Disease Investigations Dashboard: 1. Each investigation can have one or more cases involved depending on the number of herds or animals exposed.  Not all disease investigations are handled the same due to a partnership approach.  Diseases can be detected via surveillance, ad hoc reporting, or through other programs. 2. Rabies is a separate program. Please see Manitoba's Provincial Rabies Management Program for data related to Rabies Surveillance.3. Certain zoonotic diseases, such as salmonella or influenza, are also captured in more detail through other means.  The total occurrence of a zoonotic disease represented in this dashboard reflects occurrences where risks or exposures were deemed significant enough to warrant further investigation.  4. Historically, One Health Investigations that were predominantly focused on Public Health issues rather than Animal Health concerns were not captured in this system and will be underrepresented here. Fields included ( Alias (Field Name): Field description.) Year (Year): Year of the disease investigation Number of Sites (Number_of_Sites): Number of investigation sites Number of Linked Sites (Number_of_Linked_Sites): Number of sites linked to investigation sites Species/Class (Species__Class): Group of animal species Disease Type (Disease_Type): The type of disease that is being investigated Result (Result): The outcome (positive/negative) for the corresponding animal disease investigation

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.