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 data shows spatial density of annual crops cultivation in Canada. Regions with higher calculated spatial densities represent agricultural regions of Canada in which annual crops 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 annual crops 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.

In 2021, 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 (RCM) 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 Manitoba, & Quebec; point observations from the PEI Department of Environment, Water and Climate Change; Ontario Ministry of Agriculture, Food and Rural Affairs; University of Guelph - Ridgetown campus; British Columbia Ministry of Agriculture; and data collection supported by our regional AAFC Research and Development Centres in St. John's, Charlottetown, Kentville, Fredericton, Guelph and Summerland. Due to COVID-19 travel restrictions and forest fires, complete sampling coverages in BC was not possible, as a result the general agriculture class (120) is found in this province in areas where there was no ground data collected.

The data, created in ArcGIS, represents an assessment of air quality risk for the agricultural area of Alberta in 2005. Agricultural activities that may have some influence on air quality manure production (odour) and cultivation intensity (particulate matter). The airsheds of the agricultural region of Alberta are considered to be uniform in their physical susceptibility to risk from agricultural activities. Air quality risk is a useful measure for those concerned about health, safety and nuisance issues related the quality of air in agricultural areas. Awareness of where agricultural activities related to livestock production and intensive cultivation are located, may be useful for people with health or nuisance related concerns. Blowing soil can cause respiratory problems and can reduce visibility on roads and highways. Dust from farm traffic can be a concern during peak agricultural activity, such as harvesting or manure hauling. Frequent strong odours can be unpleasant nuisance for neighbours. In areas of greater air quality risk, environmental farm planning can help to address the issues and provide solutions. Practices including pen/barn maintenance, method of manure application, manure storage, composting, adjusting, feed rations and reducing or eliminating tillage can be looked at in an environmental farm plan.

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.

In 2014, 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.

Small area data on field crops show seeded and harvested area, yield and production figures for most principal field crops and some special crops in Canada, at the census agricultural region level (except for Quebec, where small areas are defined by provincial administrative boundaries). The provinces covered are British Columbia, Alberta, Saskatchewan, Manitoba, Ontario and Quebec. The data are available in metric and imperial units of measure, for periods ranging from 1976 to 2024. The data are derived from the results of the November Farm Survey of the preceding year, of which the production estimates were only expressed at the provincial level in early December.

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