This group of dashboards describes some characteristics of Agriculture in North Dakota at the state and county level. Some dashboards describe same data with all states for comparison points with North Dakota. Data for all visuals was pulled from the USDA Census of Agriculture of 2022 which is the last to date. Census of Agriculture are published every 5 years.

Use the arrow to scroll through the different tabs. By default, data is represented for the state level. Select a county in the dropdown to view data at the county level. Use ctrl+click to select more than one.

Some charts refer to Precision Agriculture Practices, Food Marketing Practices and Value-added Agricultural Products. These data points are defined in the Census of Agriculture as following:

•  Food marketing practices:
  • Value of food sold directly to consumers. Data represent the value of edible products, including value added products, produced and sold for human consumption directly to consumers at farmers markets, on-farm stores or farm stands, roadside stands or stores, u-pick, CSA (Community Supported Agriculture), online marketplaces, etc.
  • Value of food sold directly to retail markets, institutions, and food hubs for local or regionally branded products: Data represent the value of products, including value added products, produced and sold for human consumption directly to retail markets, institutions or food hubs for locally or regionally branded products. Examples include supermarkets, restaurants, caterers, independently owned grocery stores, food cooperatives, K-12 schools, colleges or universities, hospitals, workplace cafeterias, prisons, food banks, etc.
• Value-added agricultural products: Value of processed or value-added agricultural products sold. Data represent the value of products that originated from crop or livestock commodities produced on the operation. Through further manufacture or processing, these items are transformed into products worth more than the originally produced commodity
• Use of precision agriculture practices: This is a new item for 2022. The use of precision agricultural practices is defined as the use of practices that utilize technology to improve agricultural productivity or efficiency by connecting the practice to a digital environment for crop or livestock production. Several examples of precision agriculture practices are the use of global positioning (GPS) guidance systems, GPS yield monitoring and soil mapping, variable rate input applications, use of drones for scouting fields or monitoring livestock, electronic tagging, precision feeding, and robotic milking. Precision agriculture practices are not limited to these examples.

These dashboards and maps describe food insecurity and its evolution over the years, demographic indicators, population dynamics. and their geographic distribution.

Use the arrow to scroll through the different tabs. By default, data is represented for the state level. Select a county in the dropdown to view data at the county level. Use ctrl+click to select more than one.

These dashboards describe the breakdown of GDP by NAICS code (BEA and QCEW from BLS combined, provided by Lightcast), by county over the years (trends) or detailed by years.

Use the arrow to scroll through the different tabs. By default, data is represented for the state level. Select a county in the dropdown to view data at the county level. Use ctrl+click to select more than one.

Overview

These data points and trends are descriptive in nature and intended to summarize what the data shows. While patterns or correlations may be visible, the dashboards do not demonstrate cause-and-effect relationships but can be used to guide and identify research projects or further analysis. The scatter plots illustrate the presence of (or the absence of ) correlation between Ag jobs and GRP evolution and other variables. Whether a correlation exists or not, it does not establish a causal relationship

Scope, Methodology and Definitions

Indicators, data points and trends mentioned in the below sections were collected at the county level for the state of North Dakota.

• Jobs and GDP in Ag Production by county dataset includes: Subset of NAICS 11 (2 Digit level) Agriculture, Forestry, Fishing and Hunting category from 2015 to 2024 for each county in North Dakota. For this dataset, jobs related to Forestry, Fishing and Hunting have been excluded. Complete list is referenced in Table 1
• Jobs and GDP Agriculture Production, manufacturing and support activities group. This group includes multiple NAICS 4 Digit categories related to Agriculture (crop production, food processing, equipment maintenance, storage, ect.) from 2015 to 2024 for each county in North Dakota. Complete list is referenced in Table 2. This data was assembled and curated by NSF Ag Tech Engine in North Dakota ops team, using Lightcast data. Lightcast data is based primarily on data from the Bureau of Economic Analysis (BEA) and the Quarterly Census of Employment and Wages (QCEW) from the Bureau of Labor Statistics (BLS). In addition to The Agriculture activities, this group includes activities from the Manufacturing (NAICS 31) and Other Services (NAICS81) sectors related to Agriculture as listed in table below.
• Average Size of Farms data: this dataset represent the change in size of farms per county over the 1997-2022 period using Ag Census data from USDA
• Number of Farms data: this dataset represent the change in size of farms per county over the 1997-2022 period using Ag Census data from USDA
• Filters (aka slicers) are present above multiple visuals. Location filter allows users to select one or more specific county. By default all Counties are selected and data aggregated.The Trend filters allow user to narrow data according to the trend for a given variable. Trends consist in up, down and stable option. "Up" applies to all positive change percentage values, "down" applies to all negative change percentage values, "stable" applies to all percentage values comprise between -1% and +1%

These dashboards describe the Labor Force Participation Rate by county over the years (trends) or detailed by years. Data is represented by Prime Age Workers group or Age groups.

Use the arrow to scroll through the different tabs. By default, data is represented for the state level. Select a county in the dropdown to view data at the county level. Use ctrl+click to select more than one.

These dashboards and map describe some health indicators by county, at the state level and the country level

Use the arrow to scroll through the different tabs.