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.

Seasonal and annual trends of mean surface air temperature change (degrees Celsius) for 1948-2016 based on Canadian gridded data (CANGRD) are available at a 50km resolution across Canada. Temperature trends represent the departure from a mean reference period (1961-1990). CANGRD data are interpolated from adjusted and homogenized climate station data (i.e., AHCCD datasets). Homogenized climate data incorporate adjustments to the original station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation.

Understanding the state and trends in agriculture production is essential to combat both short-term and long-term threats to stable and reliable access to food for all, and to ensure a profitable agricultural sector. Starting in 2009, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) began the process of generating annual crop type digital maps. Focusing on the Prairie Provinces in 2009 and 2010, a Decision Tree (DT) based methodology was applied using optical (Landsat-5, AWiFS, DMC) and radar (Radarsat-2) based satellite images. Beginning with the 2011 growing season, this activity has been extended to other provinces in support of a national crop inventory. To date this approach can consistently deliver a crop inventory that meets the overall target accuracy of at least 85% at a final spatial resolution of 30m (56m in 2009 and 2010).

Monthly, seasonal and annual trends of mean wind speed change (kilometres per hour) based on homogenized station data (AHCCD) are available. Trends are calculated using the Theil-Sen method using the station’s full period of available data. The availability of surface wind speed trends will vary by station; if more than 5 consecutive years are missing data or more than 10% of the data within the time series is missing, a trend was not calculated.

Monthly, seasonal and annual trends of total precipitation change (millimetres) based on adjusted station data (AHCCD) are available. Trends are calculated using the Theil-Sen method using the station’s full period of available data. The availability of precipitation trends will vary by station; if more than 5 consecutive years are missing data or more than 10% of the data within the time series is missing, a trend was not calculated.

Monthly, seasonal and annual trends of daily minimum, mean and maximum surface air temperature change (degrees Celsius) based on homogenized station data (AHCCD) are available. Trends are calculated using the Theil-Sen method using the station’s full period of available data. The availability of temperature trends will vary by station; if more than 5 consecutive years are missing data or more than 10% of the data within the time series is missing, a trend was not calculated.

Monthly, seasonal and annual trends of mean hourly sea level and station pressure change (hectopascals) based on homogenized station data (AHCCD) are available. Trends are calculated using the Theil-Sen method using the station’s full period of available data. The availability of surface pressure trends will vary by station; if more than 5 consecutive years are missing data or more than 10% of the data within the time series is missing, a trend was not calculated.

Seasonal and annual trends of relative total precipitation change (%) for 1948-2012 based on Canadian gridded data (CANGRD) are available, at a 50km resolution across Canada. The relative trends reflect the percent change in total precipitation over a period from the baseline value (defined as the average over 1961-1990 as the reference period). CANGRD data are interpolated from adjusted and homogenized climate station data (i.e., AHCCD datasets). Adjusted precipitation data incorporate adjustments to the original station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation.

CANGRD is a set of Canadian gridded annual, seasonal, and monthly temperature and precipitation anomalies, which were interpolated from stations in the Adjusted and Homogenized Canadian Climate Data (AHCCD); it is used to produce the Climate Trends and Variations Bulletin (CTVB).

The datasets contain water surface evaporation (PET, in mm of H2O) over Canada's landmass at a spatial resolution of 10-km and temporal intervals of a month and a year over a 24-year period of 2000-2023. The PET was produced by the Land Surface Model EALCO (Ecological Assimilation of Land and Climate Observations) developed at Natural Resources Canada. The PET algorithm in EALCO integrates the dynamic surface evolutions of liquid water, ice, and snow-on-ice for a waterbody into the Penman Equation. The PET was simulated at a daily time step. The monthly (or annual) PET in the datasets is the sum of the daily PET values in a month (or a year). Dew and frost formations simulated by EALCO are included in the PET as negative values, so the PET represents the net water flux between water surface and the atmosphere. Details of the dataset and the EALCO PET modelling algorithms can be found in Li, Wang, and Li (2020, Spatial variations and long‑term trends of potential evaporation in Canada. Scientific Reports, 10: 22089, doi.org/10.1038/s41598-020-78994-9).