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 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

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.

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.

Environmental Monitoring Program data for aquaculture.

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.

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

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

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.