AndreaBozzo/ceres-open-data-index huggingface dataset:
Extreme Weather Indices: Wind

Winds can significantly influence crop growth and yield mainly due to mechanical damage of plant vegetative and reproductive organs, an imbalance of plant-soil-atmosphere water relationships such as evapotranspiration, and pest and disease distributions in agricultural fields. The maximum wind speed and the number of strong wind days over the forecast period represent short term and extended strong wind events respectively.  
  
  Agriculture is an important primary production sector in Canada.  Agricultural production, profitability, sustainability and food security depend on many agrometeorological factors. Extreme weather events in Canada, such as drought, floods, heat waves, frosts and high intensity storms, have the ability to significantly impact field crop production.   
  
  Agriculture and Agri-Food Canada (AAFC) and Environment and Climate Change Canada (ECCC) have together developed a suite of extreme agrometeorological indices based on four main categories of weather factors: temperature, precipitation, heat, and wind.  The extreme weather indices are intended as short-term prediction tools and generated using ECCC’s medium range forecasts to create a weekly index product on a daily basis.

Organization: Agriculture and Agri-Food Canada | Agriculture et Agroalimentaire Canada
Interpolated Census of Agriculture by Ecoregions

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.  
  
  In the “Interpolated census of agriculture”, suppression confidentiality procedures were applied by Statistics Canada to the custom tabulations to prevent the possibility of associating statistical data with any specific identifiable agricultural operation or individual. Confidentiality flags are denoted where "-1" appears in data cell. This indicates information has been suppressed by Statistics Canada to protect confidentiality. Null values/cells simply indicate no data is reported.

Organization: Agriculture and Agri-Food Canada | Agriculture et Agroalimentaire Canada
Growing Degree Days

Growing degree days (GDDs) are used to estimate the growth and development of plants and insects during the growing season. Growing Degree Day are computed by subtracting a base value temperature from the mean daily temperature and are assigned a value of zero if negative. Base temperatures are a point below which development does not occur for the organism in question. Growing Degree Day products are created for base 0, 5, 10 and 15 degrees Celsius.  
  
  GDD values are only accumulated during the Growing Season, April 1 through October 31.

Organization: Agriculture and Agri-Food Canada | Agriculture et Agroalimentaire Canada
Crop (corn) heat units

Crop Heat Units (CHU) are calculated on a daily basis, using the maximum and minimum temperatures in order to account for a crop’s negative response to higher temperatures.  
  
  The formula used to calculate the CHU value for a day is:   
  
  (1.8 × (Minimum Temperature − 4.4) + 3.33 × (Maximum Temperature − 10) − 0.084 × (Maximum Temperature − 10)²) ÷ 2.0  
  
  CHU values are only accumulated during the Growing Season, April 1 through October 31.

Organization: Agriculture and Agri-Food Canada | Agriculture et Agroalimentaire Canada
Land Cover for Agricultural Regions of Canada, circa 2000

The “Land Cover for Agricultural Regions of Canada, circa 2000” is a thematic land cover classification representative of Circa 2000 conditions for agricultural regions of Canada. Land cover is derived from Landsat5-TM and/or 7-ETM+ multi-spectral imagery by inputting imagery and ground reference training data into a Decision-Tree or Supervised image classification process. Object segmentation, pixel filtering, and/or post editing is applied as part of the image classification. Mapping is corrected to the GeoBase Data Alignment Layer. National Road Network (1:50,000) features and other select existing land cover products are integrated into the product. UTM Zone mosaics are generated from individual 30 meter resolution classified scenes. A spatial index is available indicating the Landsat imagery scenes and dates input in the classification. This product is published and compiled by Agriculture and Agri-Food Canada (AAFC), but also integrates products mapped by other provincial and federal agencies; with appropriate legend adaptations. This release includes UTM Zones 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, and 22 for corresponding agricultural regions in Newfoundland, Prince Edward Island, Nova Scotia, New Brunswick, Québec, Ontario, Manitoba, Saskatchewan, Alberta and British Columbia covering approximately 370,000,000 hectares of mapped area. Mapped classes include: Water, Exposed, Built-up, Shrubland, Wetland, Grassland, Annual Crops, Perennial Crops and Pasture, Coniferous, Deciduous and Mixed forests. However, emphasis is placed on accurately delineating agricultural classes, including: annual crops (cropland and specialty crops like vineyards and orchards), perennial crops (including pastures and forages), and grasslands.

Organization: Agriculture and Agri-Food Canada | Agriculture et Agroalimentaire Canada
Extreme Weather Indices: Heat

Cumulative heat-energy satisfies the essential requirement of field crop growth and development towards a high yield and good quality of agricultural crop products.  
  
  Agriculture is an important primary production sector in Canada.  Agricultural production, profitability, sustainability and food security depend on many agrometeorological factors. Extreme weather events in Canada, such as drought, floods, heat waves, frosts and high intensity storms, have the ability to significantly impact field crop production.   
  
  Agriculture and Agri-Food Canada (AAFC) and Environment and Climate Change Canada (ECCC) have together developed a suite of extreme agrometeorological indices based on four main categories of weather factors: temperature, precipitation, heat, and wind.  The extreme weather indices are intended as short-term prediction tools and generated using ECCC’s medium range forecasts to create a weekly index product on a daily basis.

Organization: Agriculture and Agri-Food Canada | Agriculture et Agroalimentaire Canada
Land and Water Area by Province/Territory and Ecoprovince

The National Ecological Framework for Canada's "Land and Water Area by Province/Territory and Ecoprovince” dataset provides land and water area values by province or territory for the Ecoprovince framework polygon, in hectares. It includes codes and their English and French descriptions for a polygon’s province or territory, total area, land-only area and large water body area.

Organization: Agriculture and Agri-Food Canada | Agriculture et Agroalimentaire Canada
Extreme Weather Indices: Temperature

Temperature is a key factor affecting the physiological development of field crops as well as crop yield and agricultural product quality achieved during the growing season. Crop responses to the temperature are characterized by three important cardinal temperature indices; the cardinal minimum temperature, maximum cardinal temperature, and optimum temperature for field crop production at which the plant growth and development can start, stop, and proceed at the maximum rate respectively.  
  
  Agriculture is an important primary production sector in Canada.  Agricultural production, profitability, sustainability and food security depend on many agrometeorological factors. Extreme weather events in Canada, such as drought, floods, heat waves, frosts and high intensity storms, have the ability to significantly impact field crop production.   
  
  Agriculture and Agri-Food Canada (AAFC) and Environment and Climate Change Canada (ECCC) have together developed a suite of extreme agrometeorological indices based on four main categories of weather factors: temperature, precipitation, heat, and wind.  The extreme weather indices are intended as short-term prediction tools and generated using ECCC’s medium range forecasts to create a weekly index product on a daily basis.

Organization: Agriculture and Agri-Food Canada | Agriculture et Agroalimentaire Canada
Surface Material by Ecodistrict

The National Ecological Framework for Canada's "Surface Material by Ecodistrict” dataset provides surface material information within the ecodistrict framework polygon. It provides surface material codes and their English and French language descriptions as well as information about the percentage of the polygon that the component occupies.    
  
  Surface material includes the abiotic material at the earth's surface. The materials can be:  ICE and SNOW - Glacial ice and permanent snow ORGANIC SOIL - Contains more than 30% organic matter as measured by weight ROCK - Rock undifferentiated MINERAL SOIL - Predominantly mineral particles: contains less than 30% organic matter as measured by weight URBAN - Urban areas. Note that only a few major urban area polygons are included on SLC source maps, therefore, do not use for tabulating total urban coverage.

Organization: Agriculture and Agri-Food Canada | Agriculture et Agroalimentaire Canada
Standardized Precipitation Evapotranspiration Index (SPEI)

The Standardized Precipitation Evapotranspiration Index (SPEI) is computed similarly to the SPI. The main difference is that SPI assesses precipitation variance, while SPEI also considers demand from evapotranspiration which is subtracted from any precipitation accumulation prior to assessment.  
  
  Unlike the SPI, the SPEI captures the main impact of increased temperatures on water demand.

Organization: Agriculture and Agri-Food Canada | Agriculture et Agroalimentaire Canada
Blended Index – Short Term

The Blended Index (BI) is a model which employs multiple potential indicators of drought and excess moisture, such as the Palmer drought index, rolling precipitation amounts and soil moisture, and combines them into a weighted, normalized value between 0 and 100. The inputs and weights used in this model are subject to change periodically as it is optimized to best represent extent, duration and severity of impactful weather conditions. The blended index is deployed as two variations; short term (st) focusing on 1 to 3 months, and long term (lt) focusing on 6 months to 5 years.

Organization: Agriculture and Agri-Food Canada | Agriculture et Agroalimentaire Canada
Extreme Weather Indices: Precipitation

Precipitation (moisture availability) establishes the economic yield potential and product quality of field crops. Both dry and wet precipitation extremes have the ability to inhibit proper crop growth. The maximum daily precipitation index covers the risk of excessive precipitation in the short term, while the other indices pertain to longer term moisture availability.  
  
  Agriculture is an important primary production sector in Canada.  Agricultural production, profitability, sustainability and food security depend on many agrometeorological factors. Extreme weather events in Canada, such as drought, floods, heat waves, frosts and high intensity storms, have the ability to significantly impact field crop production.   
  
  Agriculture and Agri-Food Canada (AAFC) and Environment and Climate Change Canada (ECCC) have together developed a suite of extreme agrometeorological indices based on four main categories of weather factors: temperature, precipitation, heat, and wind.  The extreme weather indices are intended as short-term prediction tools and generated using ECCC’s medium range forecasts to create a weekly index product on a daily basis.

Organization: Agriculture and Agri-Food Canada | Agriculture et Agroalimentaire Canada
Circa 1995 Landcover of the Prairies

The ‘Circa 1995 Landcover of the Prairies’dataset is a geospatial raster data layer portraying the rudimentaryland cover types of all grain-growing areas of Manitoba, Saskatchewan, Alberta and northeastern British Columbia at a 30-metre resolution for the 1995 timeframe. It is the collection of all the classified imagery (1993 to 1995) of the Western Grain Transition Payment Program (WGTPP) assembled into a single seamless raster data layer.

Organization: Agriculture and Agri-Food Canada | Agriculture et Agroalimentaire Canada
Departure from Average Precipitation (mm)

Departure from Average Precipitation represents the accumulated precipitation value for a location, subtracted by the long term average value. The long term average value is defined as the average amount over the 1981 – 2010 period. A negative value indicates that the location has received less than the normal amount of precipitation (mm) for that timeframe. A positive value indicates that the location has received more than the normal amount of precipitation (mm). Products are produced for the following timeframes: Agricultural Year, Growing Season, Winter Season as well as rolling products for 30, 60, 90, 180, 270, 365, 730, 1095, 1460 and 1825 days.

Organization: Agriculture and Agri-Food Canada | Agriculture et Agroalimentaire Canada
AAFC Annual Unit Runoff in Canada

The “AAFC Annual Unit Runoff in Canada" data series illustrates runoff trends across the country by isolines of annual unit runoff for a variety of probabilities of exceedence commonly used by decision makers Annual unit runoff is a measure of runoff volume per square kilometre.  This series uses units of cubic decametres (1000 m3) per square kilometre (dam3/km2), which is equivalent to millimetres depth on the landscape. It includes a point data set for the hydrologic stations that were analyzed and seven sets of line work to show the adjusted isolines for 10%, 25%, 50%, 70%, 75%, 80%, and 90% probability of exceedence.

Organization: Agriculture and Agri-Food Canada | Agriculture et Agroalimentaire Canada
Annual Crop Inventory 2018

In 2018, 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 BC Ministry of Agriculture, & the Ontario Ministry of Agriculture, Food and Rural Affairs; and data collection supported by our regional AAFC Research and Development Centres in St. John’s, Kentville, Charlottetown, Fredericton, Guelph, and Summerland

Organization: Agriculture and Agri-Food Canada | Agriculture et Agroalimentaire Canada
Annual Crop Inventory 2009

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 inventory digital maps using satellite imagery. Focusing on the Prairie Provinces, a Decision Tree (DT) based methodology was applied using both optical (AWiFS, Landsat-5) and radar (RADARSAT-2) based satellite imagery, and having a final spatial resolution of 56m.  Methods were also developed to enhance the optical classification with RADARSAT-2 imagery, addressing issues associated with cloud cover. In conjunction with satellite acquisitions, ground-truth information was provided by provincial crop insurance companies and point observations from our regional AAFC colleagues. The overall process for Crop Inventory Map includes: satellite data acquisition; field data acquisition for classification training and accuracy assessment; and, operational implementation of the classification methodology.  
  
  The initial methodology was developed in partnership with AAFC Research Branch, and supported in part by the Canadian Space Agency. The long-term objective of this endeavour is to expand from the Prairies and produce an annual crop inventory of the entire agricultural extent of Canada.

Organization: Agriculture and Agri-Food Canada | Agriculture et Agroalimentaire Canada
Annual Crop Inventory 2022

In 2022, 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 for all Canadian provinces, in support of a national crop inventory. New this year, a map of the agricultural regions in the Yukon Territory was also produced. A Decision Tree (DT) based methodology was applied using optical (Landsat-8, Landsat-9,  
  
  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 Alberta, Saskatchewan, 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, Kentville, Fredericton, Guelph, Summerland and Whitehorse.

Organization: Agriculture and Agri-Food Canada | Agriculture et Agroalimentaire Canada
Annual Crop Inventory 2012

In 2012, 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 (except Newfoundland), in support of a national crop inventory.  A Decision Tree (DT) based methodology was applied using optical (DMC, SPOT) 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 our regional AAFC colleagues.

Organization: Agriculture and Agri-Food Canada | Agriculture et Agroalimentaire Canada
Accumulated Precipitation (mm)

Accumulated Precipitation represents the amount of total precipitation in mm (solid and/or liquid) which has been recorded over a given period of time.   
  
  Products are produced for the following timeframes: Agricultural Year, Growing Season, Winter Season,  as well as rolling products for 7, 14, 30, 60, 90, 180, 270, 365, 730, 1095, 1460 and 1825 days.

Organization: Agriculture and Agri-Food Canada | Agriculture et Agroalimentaire Canada
Chemicals Management Plan - Sediment Monitoring Program

The Chemicals Management Plan (CMP) was introduced in 2006 and consists of various initiatives that aim to quantify and reduce the risk that chemical substances pose to human and environmental health. A key aspect of CMP is ongoing environmental monitoring of Canadian freshwater systems, including the Great Lakes, their tributaries, and other significant lakes and rivers nationally. Pursuant to this, environmental levels of CMP priority substances in sediment are continually assessed, and the resulting data will be added to this Open Data directory continually. This data allows for the determination of long-term trends and spatial distribution of contaminants in sediment, the evaluation of risk management measure efficacy, and assessment of compliance with sediment quality objectives. Please note that Great Lakes data informing CMP initiatives are stored in a distinct Open Data directory (https://data-donnees.az.ec.gc.ca/data/substances/monitor/great-lakes-water-quality-monitoring-and-aquatic-ecosystem-health-data/). 
Per Annex 3 of the Great Lakes Water Quality Agreement (GLWQA), a national sediment bank is maintained by ECCC to allow for the retrospective analysis of contaminants as new technologies and priority substances emerge. Portions of banked sediment samples may be requested for collaborative research, subject to an evaluation of the proposed research and quantity of material available. For inquiries or requests related to banked sediment, please contact tim.pascoe@ec.gc.ca.

Organization: Environment and Climate Change Canada | Environnement et Changement climatique Canada
Interim guide for officials on how to assess consistency with the United Nations Declaration on the Rights of Indigenous Peoples

This Guide is part of a series of resources that support Government of Canada officials in taking steps to ensure consistency of federal laws, policies, programs and other measures with the UN Declaration and to achieve the objectives of the UN Declaration. In addition to the information provided in this Guide and the accompanying Checklist, you may wish to consult your departmental LSU or departmental focal point on the UN Declaration Act, if such a position or unit has been created in your organization.

Organization: Department of Justice Canada | Ministère de la Justice Canada