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Found 10 records similar to Crop Field Trial Regions

Federal

The Canadian major and minor crop field trial regions were developed following extensive

stakeholder consultation and have been harmonized between the Pest Management

Regulatory Agency (PMRA) and the Environmental Protection Agency of the USA. The identified regions are used for experimental studies in support of residue chemistry data requirements for the registration of new pesticide uses. The regions are based on soil type and climate and do not correspond to plant hardiness zones.

Last Updated: Jul. 6, 2021
Date Published: Jun. 1, 1998
Organization: Agriculture and Agri-Food Canada
Formats: GML FGDB/GDB GEOJSON HTML PDF ESRI REST
Keywords:  Boundaries, pest management, crop, Pesticides
Federal

There are fourteen major and four minor field trial regions in Canada and USA. Each of these regions recognizes physical characteristics, such as soils, and crops and climate, that make the region unique. The subzones address differences within a region, generally reflected in the types of crops grown in that region. The Canadian regions, as much as possible, correspond to the U.S. regions.

Last Updated: Jul. 6, 2021
Date Published: Jun. 1, 1998
Organization: Agriculture and Agri-Food Canada
Formats: GML FGDB/GDB GEOJSON HTML PDF ESRI REST
Keywords:  Boundaries, pest management, crop, Pesticides
Federal

The “Prairie Agricultural Landscapes (PAL)” datasets identify areas of the agricultural portions of the Canadian Prairies with similar land and water resources, land use and farming practices. They are represented by vector polygons. Based on selected attributes from the Soil Landscapes of Canada (SLC) and the 1996 Census of Agriculture, the Prairies were classified into 13 (thirteen) classes of Land Practices Group and five (5) Major Land Practices Groups. Typical attributes used to define the Land Practice Groups include: land in pasture, land in summerfallow, crop mixture, farm size and the level of chemical and fertilizer inputs.

Last Updated: Jul. 27, 2021
Date Published: May 14, 2013
Organization: Agriculture and Agri-Food Canada
Formats: WMS PDF FGDB/GDB ESRI REST GML
Keywords:  Agriculture, Farmlands, Crops, Terrestrial ecosystems, Land cover
Federal

These datasets show the areas where major crops can be expected within the agricultural regions of Canada. Results are provided as rasters with numerical values for each pixel indicating the level of spatial density calculated for a specific crop type in that location. Regions with higher spatial density for a certain crop have higher likelihood to have the same crop based on the previous years mapped crop inventories.

Last Updated: Nov. 22, 2022
Date Published: Apr. 8, 2015
Organization: Agriculture and Agri-Food Canada
Formats: PDF HTML GeoTIF
Keywords:  Agriculture statistics, Crops, Farms
Federal

Beginning with the 2011 grow season, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) started collecting ground truth data via windshield surveys. This observation data is collected in support of the generation of an annual crop inventory digital map. These windshield surveys take place in provinces where AAFC does not have access to crop insurance data. The collection routes driven attempt to maximize not only the geographical distribution of observations but also to target unique or specialty crop types within a given region.

Last Updated: Apr. 14, 2022
Date Published: Aug. 9, 2021
Organization: Agriculture and Agri-Food Canada
Formats: SHP PDF FGDB/GDB GEOJSON ESRI REST
Keywords:  Land cover, Crops, Agriculture, Remote sensing, Crop insurance
Federal

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.

Last Updated: Jul. 27, 2021
Date Published: Nov. 4, 2013
Organization: Agriculture and Agri-Food Canada
Formats: WMS HTML GeoTIF PDF CSV ESRI REST
Keywords:  Satellites, Agriculture, Remote sensing, Crops, Farmlands, Forage crops, Crop insurance, Land cover, Geography
Federal

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.

Last Updated: Nov. 22, 2022
Date Published: Apr. 8, 2015
Organization: Agriculture and Agri-Food Canada
Formats: PDF HTML ESRI REST GeoTIF
Keywords:  Farms, Crops, Agriculture statistics
Federal

In 2013, 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) 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 the BC Ministry of Agriculture and our regional AAFC colleagues.

Last Updated: Jul. 27, 2021
Date Published: Nov. 4, 2013
Organization: Agriculture and Agri-Food Canada
Formats: WMS HTML GeoTIF PDF CSV ESRI REST
Keywords:  Satellites, Agriculture, Remote sensing, Crops, Crop insurance, Forage crops, Farmlands, Geomatics, Land cover
Federal

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.

Last Updated: Jul. 27, 2021
Date Published: Nov. 4, 2013
Organization: Agriculture and Agri-Food Canada
Formats: WMS HTML GeoTIF PDF CSV ESRI REST
Keywords:  Remote sensing, Agriculture, Satellites, Farmlands, Crops, Crop insurance, Forage crops, Land cover, Geomatics
Federal

In 2015, 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) 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 the BC Ministry of Agriculture and our regional AAFC colleagues.

Last Updated: Jul. 27, 2021
Date Published: Feb. 18, 2016
Organization: Agriculture and Agri-Food Canada
Formats: WMS HTML GeoTIF PDF CSV ESRI REST
Keywords:  Remote sensing, Land cover, Geomatics, Geographic information systems, Geographic data, Maps, Geography
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