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).

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.

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).

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

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.

Mapping of agricultural production zoning in the urban planning code (CDU) on the territory of Laval.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

This 4th edition Plant Hardiness Zones map shows updated zones related to perennial plant survival in Canada. The map is based on a formula using seven climate variables that influence plant survival: 1. Monthly mean of the daily minimum temperatures of the coldest month. 2. Mean frost-free period above 0°C in days.3. Amount of rainfall from June to November.4. Monthly mean of the daily maximum temperatures of the warmest month. 5. A winter harshness index related to rainfall in January.6. Mean maximum snow depth.7. Maximum wind gust in 30 year period.The original map was developed by Agriculture and Agri-Food Canada in the early 1960s based on average climate values from 1930 to 1960. This new map uses 1991 to 2020 averages. The map shown to the left of this map shows an alternative plant hardiness zone approach using just one climate variable: average extreme minimum temperature for the period 1991 to 2020. This was originally developed by scientists at the United States Department of Agriculture (see https://www.usna.usda.gov/science/plant-hardiness-zone-map/). The development of these maps was made possible through a collaborative effort by scientists at Natural Resources Canada’s Canadian Forest Service, Environment Canada, and Agriculture and Agri-Food Canada. A paper describing the research, “Updated plant hardiness zones for Canada and assessment of change over time”, can be found in Scientific Reports, Vol. 15(1), 22774 ( https://doi.org/10.1038/s41598-025-00931-5).These maps were produced by the Canada Centre for Mapping and Earth Observation, Natural Resources Canada.To view an interactive version of this map and for more information on plant hardiness zones in Canada, please go to: https://www.planthardiness.gc.ca.

“Biomass Inventory Mapping and Analysis – Business Data” provides a number of datasets related to the yield and production of residues from the agricultural and forestry industry, agricultural crops, and municipal solid wastes across Canada. The datasets contain agricultural residue production information (i.e., straw or stover) for barley, wheat, flax, oats and corn, and crop production information for barley, wheat, flax, oats, corn, canola and soybean. They also include information about amounts of straw required for cattle bedding and feeding, the type of tillage used in an area, and the amount of residue needed for soil conservation purposes. Datasets in the series provide the yield, production and other information for the median year and 1-in-10 year and 1-in-20 year lows. The forestry inventory dataset provides information about the location and quantity of residues from the forestry industry, as well as urban wood waste and potential sites and productivity of plantations of fast-growing trees that are grown as feedstock. Forestry residues include material left at the roadside after harvesting and excess and waste materials from mills. The municipal solid waste inventory dataset provides information about the approximate location and quantity of different types of municipal solid wastes, such as organics (including food and yard), paper and total. A transportation network dataset and datasets that are used to calculate cost to harvest and transport biomass are also included in this series.

The data represents the relative expense of farm chemicals (herbicides, insecticides and fungicides) in the agricultural area of Alberta. It is an estimate of the degree to which crop production agriculture may contribute to surface or groundwater contamination.Agriculture production that makes greater use of herbicides, insecticides and pesticides in generally considered more intensive. Presenting the relative farm chemical expenses by SLC polygons reveals where the most intensive agricultural production in the province occurs. Chemical use is part of an equation to determine a measure of surface water quality risk. If an area is known to have certain risk factors that would affect not only surface, but groundwater quality as well, a higher chemical expense index ranking in that same area may be of concern. Where risks of surface or groundwater contamination exist, environmental farm planning can help to minimize them.

Background:More than 80% of the heat produced in the Earth's crust comes from granitoid rocks. When granitoid rocks form they naturally concentrate radioactive elements such as U, Th, and K, and the radiogenic decay of these elements is an exothermic reaction. The radioactive decay of these elements within a granitoid body may generate local heat anomalies and elevated geothermal gradient at relatively shallow crustal levels. In combination with other local rock properties (e.g, porosity, permeability, thermal conductivity), radiogenic heat has the potential to generate a geothermal resource. The decay of radioactive elements converts mass into radiation energy, which in turn gets converted to heat. While all naturally radioactive isotopes generate some heat, significant heat generation only occurs from the decay of 238 U ,235 U ,232 Th and 40 K. Therefore, potential heat production is governed by the concentrations of U ,Th and K in the rock. In igneous rocks, radiogenic heat production is dependent on the bulk chemistry of the rock and decreases from acidic (e.g. granite) through basic to ultra basic rock types. Therefore, granites with anomalously high concentrations of U ,Th and K are targets for calculating potential radiogenic heat production. Potential radiogenic heat production (A)from plutonic rocks can be calculated using this equation:A (\\u03BCW/m 3 )=10 -5 \\u1D29 (9.52c u +2.56c K +3.48c Th )where "c" is the concentration of radioactive elements "U" and "Th" in ppm, and "K" in %; and "\\u1D29" is the rock density. Heat production constants of the natural radio-elements U, Th, K are 9.525x10 -5 , 2.561x10 -5 and 3.477x10 -9 W/kg, respectively.Data and Methods:Geochemical data from \~1760 samples of plutonic rocks from Yukon are used to calculate potential heat production. The calculated values for radiogenic heat production (A) are plotted over the mapped distribution of Paleozoic and younger plutonic rocks and major crustal faults are also shown for reference.