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 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.

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.

Vaccinium is a group of small fruit-bearing shrubs that includes the blueberries, cranberries, and lingonberries which are among the few major crops grown in Canada that are truly native to Canada.Dataset Type: OccurrenceSpecimen Type: Preserved specimens

These products represent crop health indices derived from the Versatile Soil Moisture Budget (VSMB) model using crop specific coefficients and station based precipitation and temperature measurements to simulate crop growth. The VSMB model simulates soil moisture dynamics and water stress conditions based on water availability in the soil profile and simulated evapotranspiration during the crop growing season. Crop phenological stages, which are related to crop water use, are determined by a biometeorlogical time scale model (Robertson, 1968) for cool season crops (wheat, barley etc.) and a Crop Heat Unit (Brown and Bootsma, 1993) algorithm for warm season crops (corn and soybean etc.).

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).

In 2016, 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, Gaofen-1) 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.

In 2017, 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, Gaofen-1) 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

In 2024, 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 and the Yukon Territory, in support of a national crop inventory. 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; and data collection supported by our regional AAFC Research and Development Centres in St. John's, Kentville, Fredericton, Guelph, Summerland and Whitehorse. Forest Fire Perimeter Estimate polygons from Natural Resources Canada’s Canadian Forest Service are used to show burned areas of landcover (Class - 60).

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: provincialcrop 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.