Interpretive products combine several pieces of information to answer specific questions. Soil and landscape data can be combined with climate, geology and land use data to address a wide variety of agro-environmental issues. Examples include:
The National Agri-Environmental Health Analysis and Reporting Program (NAHARP) assesses and reports on the agriculture sector's environmental performance via a set of Agri-Environmental Indicators (AEI). These indicators are intended to provide reliable, science-based information on the current state and changes in the conditions of the environment in agriculture at a national or regional scale.
The Canada Land Inventory (CLI) is a comprehensive multi-disciplinary land inventory of rural Canada, covering over 2.5 million square kilometers of land and water. In the late 1990's, CanSIS converted the original CLI agriculture datasets to a component-based file structure.
Derived map products are maps displaying observed or measured information.
The Soil Order map of Canada displays the general distribution of soil types across Canada. The soil order is the highest level (broadest grouping) within the Canadian System of Soil Classification. Soils classified at the order level reflect the climate and landscape characteristics associated with the different regions of Canada.
Soil Landscapes of Canada (SLCs) describe the major characteristics of soil and land for the whole country. SLCs were compiled at a scale of 1:1 million, and information is organized according to a uniform national set of soil and landscape criteria based on permanent natural attributes.The full array of attributes that describe a distinct type of soil and its associated landscape, such as surface form, slope, water table depth, permafrost and lakes, is called a soil landscape. SLC polygons may contain one or more distinct soil landscape components and may also contain small but highly contrasting inclusion components. The location of these components within the polygon is not defined. This application will allow you to explore the various SLC attributes, either by dominant component value, or by percent distribution of an attribute class.