The “Agricultural Land Practices Groups of the Canadian Prairies” dataset lays out the areas of the 13 Land Practices Groups of the agricultural portions of the Canadian Prairies. They are represented by vector polygons amalgamated (dissolved) from the Version 1.9 SLC polygons sharing common water resources, land use and farming practices as developed in the “Agricultural Land Practices Groups of the Canadian Prairies by SLC Polygon” of this series. The dataset is based upon selected attributes from the Soil Landscapes of Canada (SLC) and the 1996 Census of Agriculture.Typical attributes including: land in pasture, land in summerfallow, crop mixture, farm size and the level of chemical and fertilizer inputs.

## Purpose The Ministry of Agriculture, Food and Agribusiness (OMAFA) is responsible for Ontario’s provincial soil maps and maintains and update them as necessary. Digital Soil Mapping (DSM) is a modern methodology using spatially explicit soils and environmental data to predict soil variation throughout a landscape at a high, consistent resolution. Digital soil maps are being rolled out throughout Ontario’s agricultural land base to update provincial soil maps. ## Reach Provincial soil maps are used in many decision-making processes including: * land use planning * land evaluation * farming practices * best management practices * ecological monitoring * land resource mapping Potential users of this data include: * farmers * certified crop advisors * conservation authorities * academic researchers * land use planners ## Potential impacts Digital soil maps provide more accurate and precise soils data and enables improved management of soil resources across multiple stakeholders. This allows for better decision making to maximize land use efficiency, improve economic efficiency of soil resources and promote soil health and soil conservation. ## Technical description Digital soil mapping combines geo-referenced soil observations with geo-referenced environmental layers to mathematically model soil variation as a function of environment variation. These models are based on well established, but often complex relationships, between soil properties and topography, biology, geology and hydrology.

Productive forest land base is defined as the total Crown forest area, determined by subtracting the following from the total area of the Cariboo Natural Resource Region: * All non-Crown land * All Crown land committed to non-timber use through a Land Act designation * All non-forest Crown Land, and * All forest area classified as brush or non-commercial cover in the Forest Inventory. See the CCLUP Land Use Order Implementation Direction for more information: https://www2.gov.bc.ca/assets/gov/farming-natural-resources-and-industry/natural-resource-use/land-water-use/crown-land/land-use-plans-and-objectives/cariboo-region/cariboochilcotin-rlup/cclup_land_use_order_implementation_direction_companion.pdf

The data represents the relative amount of manure production in the agricultural area of Alberta. It is an estimate of the degree to which livestock production may contribute to nutrient loading, pathogens and odour. The classes shown on the map are ranked between 0 (lowest) and 1 (highest). This resource was created in 2002 using ArcGIS.

The data represents the relative cultivation intensity in the agricultural area of Alberta. Cultivation intensity refers to the frequency of cultivation associated with the following management systems: no till, conventional tillage and summerfallow. It is an estimate of the degree to which cultivation contributes to wind and water erosion. The classes shown on the map are ranked between 0 (lowest) and 1 (highest).This map was created in 2002 using ArcGIS.

This data set contains Manitoba market cattle prices and volumes sold at local auction marts.Description of Cattle Prices -source

The “Agricultural Major Land Practices Groups of the Canadian Prairies” dataset lays out the areas of the 5 Major Land Practices Groups of the agricultural portions of the Canadian Prairies. They are represented by vector polygons amalgamated (dissolved) from the Version 1.9 SLC polygons sharing common water resources, land use and farming practices as developed in the “Agricultural Land Practices Groups of the Canadian Prairies by SLC Polygon” of this series. The dataset is based upon selected attributes from the Soil Landscapes of Canada (SLC) and the 1996 Census of Agriculture.Typical attributes including: land in pasture, land in summerfallow, crop mixture, farm size and the level of chemical and fertilizer inputs.

A series of relevant spatial datasets were compiled to construct an Agricultural Potential Index model which combines multiple criteria that influence agricultural suitability.  For more information on the Agriculutural Potential Index Model, please see the metadata link.

In 2014, 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.

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).