Small area data (SAD) on field crops show seeded and harvested area, yield and production estimates for most principal field crops and some special crops in Canada. Most SAD geographies correspond exactly with the Census Agriculture Region (CAR) limits, excepts for some regions of Quebec (where small areas are defined by provincial administrative boundaries), Saskatchewan (where small areas coincide with census divisions boundaries as of 2017) and British Columbia.For exact correspondence between Census Agricultural Regions (CAR) and Small Area Data (SAD) Regions, see the following link:https://www.statcan.gc.ca/eng/statistical-programs/document/3401_D2_V2These regions are associated with Statistics Canada estimates on principal field crops available in the following table: https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=3210000201

This file outlines the boundaries of Manitoba Agriculture's crop reporting regions./This file describes the boundaries of Manitoba Agriculture's crop report regions.This file outlines the boundaries of the five crop reporting regions as specified in Manitoba Agriculture's Crop Report.The Manitoba Agriculture Crop Report provides information on progress of seeding and crop establishment, crop development, any pest activity including weeds, insects and disease, crop activity including weeds, insects and disease, harvest progress, insects and disease, harvest progress, insects and disease, harvest progress, crop yields and grades, fall field work progress, and status of winter cereal crop seeding and establishment. In addition, it provides information on haying progress and estimated yields, as well as pasture conditions.This file describes the boundaries of the five crop report regions as specified in the Agriculture Manitoba crop report.The Manitoba Agriculture crop report provides information on progress in planting and establishing crops, as well as crop establishment, vegetative development, pest activities including weeds, insects, and diseases, harvest progress, yields and crop rankings, progress on field work in the fall, and the status of planting and establishing winter cereal crops. In addition, it provides information on the haymaking process and estimated yields, as well as on pasture conditions.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

These files from Statistics Canada present Census of Agriculture data allocated by standard census geographic polygons: Provinces and Territories (PR), Census Agricultural Regions (CAR), Census Divisions (CD) and Census Consolidated Subdivisions (CCS). Five datasets are provided:1. Agricultural operation characteristics: includes information on farm type, operating arrangements, paid agricultural work and financial characteristics of the agricultural operation.2. Land tenure and management practices: includes information on land use, land tenure, agricultural practices, land inputs, technologies used on the operation and the renewable energy production on the operation.3. Crops: includes information on hay and field crops, vegetables (excluding greenhouse vegetables), fruits, berries, nuts, greenhouse productions and other crops.4. Livestock, poultry and bees: includes information on livestock, poultry and bees.5. Characteristics of farm operators: includes information on age, sex and the hours of works of farm operators.Note: For all the datasets, confidential values have been assigned a value of -1.Correction notice: On January 18, 2023, selected estimates have been corrected for selected variables in the following 2021 Census of Agriculture domains: Direct sales of agricultural products to consumers (Agricultural operations category), Succession plan for the agricultural operation (Agricultural operators category), and Renewable energy production (Use, tenure and practices category).

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 2025. 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 file outlines the boundaries of the 15 risk areas defined by Manitoba Agricultural Services Corporation./This file describes the boundaries of the 15 risk areas defined by the Manitoba Agricultural Services Corporation.This file outlines the boundaries of the 15 risk areas defined by Manitoba Agricultural Services Corporation. Manitoba Agricultural Services Corporation (MASC) divides Manitoba into 15 Risk Areas of similar crop production risks, which are used to determine the premiums a producer country and the coverage that crops receive. For more information, visit MASC's website: https://www.masc.mb.ca/masc.nsf/maps_risk_areas.html This file describes the boundaries of the 15 at-risk areas defined by the Manitoba Agricultural Services Corporation. The Manitoba Agricultural Services Corporation divides Manitoba into 15 high-risk regions where crop production risks are similar. They are used to determine the premium the producer pays and the coverage the crops receive. For more information, visit the Manitoba Agricultural Services Corporation website: https://www.masc.mb.ca/masc_fr.nsf/maps_risk_areas.html**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

This data set contains geographic and descriptive information on buildings and sites subject to the Cultural Heritage Act and located on the territory of the City of Montreal. The proposed polygonal boundaries complement the specific information already provided by the Ministry of Culture and Communications:- [Heritage buildings cited by municipalities] (https://www.donneesquebec.ca/recherche/fr/dataset/immeubles-patrimoniaux-cites-par-les-municipalites-et-les-communautes-autochtones)- [Heritage buildings classified by the Minister of Culture and Communications] (https://www.donneesquebec.ca/recherche/fr/dataset/immeubles-patrimoniaux-classes-par-la-ministre-de-la-culture-et-des-communications)- [Heritage sites cited by municipalities] (https://www.donneesquebec.ca/recherche/fr/dataset/sites-patrimoniaux-cites-par-les-municipalites-et-les-communautes-autochtones) - [National heritage site declared by the Cultural Heritage Act by the Government of Quebec] (https://www.donneesquebec.ca/recherche/fr/dataset/site-patrimonial-national-declare-par-la-loi-sur-le-patrimoine-culturel-par-le-gouvernement-du -)- [Heritage sites classified by the Minister of Culture and Communications] (https://www.donneesquebec.ca/recherche/fr/dataset/sites-patrimoniaux-classes-par-la-ministre-de-la-culture-et-des-communications)WARNING: Approximate demarcations were made from property lines or building right-of-way. The data is provided for information purposes only and, therefore, has no legal effect. Refer to the Cultural Heritage Register, maintained at the Ministry of Culture and Communications by the Registrar of Cultural Heritage, for legal information.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

In 2010 the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) continued 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, DMC) 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.

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.

Following the merger of the Duplessis and Manicouagan regions under the new name Côte-Nord, Quebec now has 21 tourist regions. The delimitation of regions is based on an unofficial division and is provided by Tourisme Québec. The boundaries were made using municipalities or MRCs.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

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.