Discover the booming market for agricultural mapping software! Learn about its $2.5 billion (2025 est.) value, 15% CAGR, key drivers, trends, and leading companies shaping precision agriculture. Explore regional market shares and future growth projections in this comprehensive analysis.

This statistic shows the percentage of agricultural operations in Canada using GIS mapping technology in 2015, by farm size. In that year, **** percent of Canadian farms with 10,000 or more acres of land reported using GIS mapping.

Date : First published: January 16, 2017Managed and Published by: TRCSubject: Boundary, Land ManagementPurpose: To be utilized for Open Data within Web Maps on Local Maps, MyTRC, and other platforms.Language: EnglishContent:Hill Country Farm BoundaryRiparian Farm BoundaryCoverage: Top (Latitude) -38.668783, Bottom (Latitude) -39.879076, Left (Longitude) 173.745239, Right (Longitude) 175.103509Full ExtentXMin: 1664817.7994YMin: 5585462.085XMax: 1770565.862125YMax: 5714793.51325Spatial Reference: 2193 (2193)Spatial Coverage: Taranaki Region, New ZealandProjection: New Zealand Transverse Mercator 2000 (NZTM2000)Description: This item is a group layer displaying Land Management Farm Plans in Taranaki Region, including Plan Boundaries for Hill Country and Riparian Farms. The purpose of Land Management Farm Plans is to ensure sustainable agricultural practices, protect natural resources, and maintain environmental quality across rural landscapes. Please refer to each layer for specific metadata regarding data collection, capture, publication, and distribution. This group hosted feature layer is utilized in Local Maps and Open Data Portal, covering the Taranaki Region. It was created by the TRC GIS Team on February 01, 2023. The dataset undergoes continuous daily updates through an automated process. Relation: Land Management Webmap - https://trcnz.maps.arcgis.com/home/item.html?id=822fa8a58aa64f6cb5772a2d52cee37410m Contours Webmap - https://trcnz.maps.arcgis.com/home/item.html?id=5c507b10e0a6406dad4625d00ab6ded7Source: Refer to layerIdentifier: 9d00ec4882ea46058cc564dd2f3c6e96Version Control: None. Users should take note of the date on which they downloaded the data.

ArcGIS Map Packages and GIS Data for Gillreath-Brown, Nagaoka, and Wolverton (2019)

**When using the GIS data included in these map packages, please cite all of the following:

Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, 2019. PLoSONE 14(8):e0220457. http://doi.org/10.1371/journal.pone.0220457

Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. ArcGIS Map Packages for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al., 2019. Version 1. Zenodo. https://doi.org/10.5281/zenodo.2572018

OVERVIEW OF CONTENTS

This repository contains map packages for Gillreath-Brown, Nagaoka, and Wolverton (2019), as well as the raw digital elevation model (DEM) and soils data, of which the analyses was based on. The map packages contain all GIS data associated with the analyses described and presented in the publication. The map packages were created in ArcGIS 10.2.2; however, the packages will work in recent versions of ArcGIS. (Note: I was able to open the packages in ArcGIS 10.6.1, when tested on February 17, 2019). The primary files contained in this repository are:

• Raw DEM and Soils data
  • Digital Elevation Model Data (Map services and data available from U.S. Geological Survey, National Geospatial Program, and can be downloaded from the National Elevation Dataset)
    • DEM_Individual_Tiles: Individual DEM tiles prior to being merged (1/3 arc second) from USGS National Elevation Dataset.
    • DEMs_Merged: DEMs were combined into one layer. Individual watersheds (i.e., Goodman, Coffey, and Crow Canyon) were clipped from this combined DEM.
  • Soils Data (Map services and data available from Natural Resources Conservation Service Web Soil Survey, U.S. Department of Agriculture)
    • Animas-Dolores_Area_Soils: Small portion of the soil mapunits cover the northeastern corner of the Coffey Watershed (CW).
    • Cortez_Area_Soils: Soils for Montezuma County, encompasses all of Goodman (GW) and Crow Canyon (CCW) watersheds, and a large portion of the Coffey watershed (CW).
• ArcGIS Map Packages
  • Goodman_Watershed_Full_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the full Goodman Watershed (GW).
  • Goodman_Watershed_Mesa-Only_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the mesa-only Goodman Watershed.
  • Crow_Canyon_Watershed_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the Crow Canyon Watershed (CCW).
  • Coffey_Watershed_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the Coffey Watershed (CW).

For additional information on contents of the map packages, please see see "Map Packages Descriptions" or open a map package in ArcGIS and go to "properties" or "map document properties."

LICENSES

Code: MIT year: 2019
Copyright holders: Andrew Gillreath-Brown, Lisa Nagaoka, and Steve Wolverton

CONTACT

Andrew Gillreath-Brown, PhD Candidate, RPA
Department of Anthropology, Washington State University
andrew.brown1234@gmail.com – Email
andrewgillreathbrown.wordpress.com – Web

The agricultural mapping software market is experiencing robust growth, driven by the increasing adoption of precision agriculture techniques and the need for efficient farm management. The market, estimated at $1.5 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching approximately $4.2 billion by 2033. This expansion is fueled by several key factors. Firstly, the rising demand for higher crop yields and improved resource utilization is compelling farmers to adopt technology-driven solutions. Agricultural mapping software provides crucial insights into field conditions, allowing for optimized planting, fertilization, and irrigation strategies, leading to significant cost savings and increased profitability. Secondly, advancements in sensor technology, GPS accuracy, and data analytics are enhancing the capabilities of agricultural mapping software, making it more accessible and user-friendly. Finally, government initiatives promoting precision agriculture and digital farming are further stimulating market growth. The market is segmented by software type (e.g., cloud-based, on-premise), application (e.g., yield mapping, soil analysis), and farm size. Key players like Trimble, CNH Industrial, and Geosys are actively shaping the market through continuous innovation and strategic partnerships. Despite the significant growth potential, certain challenges remain. High initial investment costs for software and hardware can act as a barrier to entry for small-scale farmers. Furthermore, the reliance on robust internet connectivity and technical expertise can hinder adoption in regions with limited infrastructure. However, ongoing technological advancements, coupled with the increasing availability of affordable solutions and training programs, are gradually addressing these limitations. The market will continue to witness consolidation through mergers and acquisitions, as larger players seek to expand their market share and offerings. Future growth will be particularly driven by the integration of artificial intelligence and machine learning into agricultural mapping software, enabling more predictive and insightful analytics for improved farm management.

[Metadata] Description: Agricultural Land Use Maps (ALUM) for islands of Kauai, Oahu, Maui, Molokai, Lanai and Hawaii as of 1978-1980. Sources: State Department of Agriculture; Hawaii Statewide GIS Program, Office of Planning. Note: August, 2018 - Corrected one incorrect record, removed coded value attribute domain.For more information on data sources and methodologies used, please refer to complete metadata at https://files.hawaii.gov/dbedt/op/gis/data/alum.pdf or contact Hawaii Statewide GIS Program, Office of Planning and Sustainable Development, State of Hawaii; PO Box 2359, Honolulu, HI 96804; (808) 587-2846; email: gis@hawaii.gov; Website: https://planning.hawaii.gov/gis.

The agricultural mapping software market is booming, projected to reach $8 billion by 2033. Discover key trends, drivers, and restraints shaping this rapidly evolving sector, featuring leading companies like Trimble and CNH Industrial. Explore market size, CAGR, and regional breakdowns in this comprehensive analysis.

The GIS shapefile and summary tables provide irrigated agricultural land-use for Citrus, Hernando, Pasco, and Sumter Counties, Florida through a cooperative project between the U.S Geological Survey (USGS) and the Florida Department of Agriculture and Consumer Services (FDACS), Office of Agricultural Water Policy. Information provided in the shapefile includes the location of irrigated land field verified for 2019, crop type, irrigation system type, and primary water source used in Citrus, Hernando, Pasco, and Sumter Counties, Florida. A map image of the shapefile is provided in the attachment.

Discover the booming agricultural mapping services market! Learn about its $2 billion (estimated 2025) valuation, impressive CAGR, key drivers, trends, and challenges. Explore precision agriculture technology and leading companies shaping the future of farming.

The China County-Level Data on Population (Census) and Agriculture, Keyed To 1:1M GIS Map consists of census, agricultural economic, and boundary data for the administrative regions of China for 1990. The census data includes urban and rural residency, age and sex distribution, educational attainment, illiteracy, marital status, childbirth, mortality, immigration (since 1985), industrial/economic activity, occupation, and ethnicity. The agricultural economic data encompasses rural population, labor force, forestry, livestock and fishery, commodities, equipment, utilities, irrigation, and output value. The boundary data are at a scale of one to one million (1:1M) at the county level. This data set is produced in collaboration with the University of Washington as part of the China in Time and Space (CITAS) project, University of California-Davis China in Time and Space (CITAS) project, and the Center for International Earth Science Information Network (CIESIN).

This dataset consists of all the farmlots in both the US Military and the Virginia Military Survey Districts of Delaware County.

The Digital Bedrock Geologic-GIS Map of Weir Farm National Historical Park and Vicinity, Connecticut is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (wefa_bedrock_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (wefa_bedrock_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (wefa_bedrock_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (wefa_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (wefa_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (wefa_bedrock_geology_metadata_faq.pdf). Please read the wefa_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: Connecticut Geological and Natural History Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (wefa_bedrock_geology_metadata.txt or wefa_bedrock_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:125,000 and United States National Map Accuracy Standards features are within (horizontally) 63.5 meters or 208.3 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

Turn on your phones location services to view your location withing the farm.

The Digital Geologic-GIS Map of the Bell Farm Quadrangle and parts of the Barthell SW Quadrangle, Kentucky is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (befa_geology.gdb), and a 2.) Open Geospatial Consortium (OGC) geopackage. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (befa_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (befa_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (biso_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (biso_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (befa_geology_metadata_faq.pdf). Please read the biso_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. QGIS software is available for free at: https://www.qgis.org/en/site/. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: Kentucky Geological Survey and U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (befa_geology_metadata.txt or befa_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

Crop growth and yield monitoring over agricultural fields is an essential procedure for food security and agricultural economic return prediction. The advances in remote sensing have enhanced the process of monitoring the development of agricultural crops and estimating their yields. Therefore, remote sensing and GIS techniques were employed, in this study, to predict potato tuber crop yield on three 30 ha center pivot irrigated fields in an agricultural scheme located in the Eastern Region of Saudi Arabia. Landsat-8 and Sentinel-2 satellite images were acquired during the potato growth stages and two vegetation indices (the normalized difference vegetation index (NDVI) and the soil adjusted vegetation index (SAVI)) were generated from the images. Vegetation index maps were developed and classified into zones based on vegetation health statements, where the stratified random sampling points were accordingly initiated. Potato yield samples were collected 2–3 days prior to the harvest time and were correlated to the adjacent NDVI and SAVI, where yield prediction algorithms were developed and used to generate prediction yield maps. Results of the study revealed that the difference between predicted yield values and actual ones (prediction error) ranged between 7.9 and 13.5% for Landsat-8 images and between 3.8 and 10.2% for Sentinel-2 images. The relationship between actual and predicted yield values produced R2 values ranging between 0.39 and 0.65 for Landsat-8 images and between 0.47 and 0.65 for Sentinel-2 images. Results of this study revealed a considerable variation in field productivity across the three fields, where high-yield areas produced an average yield of above 40 t ha-1; while, the low-yield areas produced, on the average, less than 21 t ha-1. Identifying such great variation in field productivity will assist farmers and decision makers in managing their practices.

The Digital Surficial Geologic-GIS Map of Weir Farm National Historical Park and Vicinity, Connecticut is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (wefa_surficial_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (wefa_surficial_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (wefa_surficial_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (wefa_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (wefa_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (wefa_surficial_geology_metadata_faq.pdf). Please read the wefa_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (wefa_surficial_geology_metadata.txt or wefa_surficial_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

This dataset contains farm and pasture features within Montgomery County. These features are classified as either pasture land or farm land. This data was captured for use in general mapping at a scale of 1:100. Countywide data updated Spring 2020. For more information, contact: GIS Manager Information Technology & Innovation (ITI) Montgomery County Planning Department, MNCPPC T: 301-650-5620

Discover the booming GIS Data Collector market! This comprehensive analysis reveals a $2.5B market in 2025, projected to reach $4.2B by 2033, fueled by precision agriculture, infrastructure development, and technological advancements. Explore key trends, drivers, restraints, and leading companies shaping this dynamic sector.

A map showing the Farm to Market routes in Iowa.

The Cropland Data Layer (CDL), hosted on CropScape, provides a raster, geo-referenced, crop-specific land cover map for the continental United States. The CDL also includes a crop mask layer and planting frequency layers, as well as boundary, water and road layers. The Boundary Layer options provided are County, Agricultural Statistics Districts (ASD), State, and Region. The data is created annually using moderate resolution satellite imagery and extensive agricultural ground truth. Users can select a geographic area of interest or import one, then access acreage statistics for a specific year or view the change from one year to another. The data can be exported or added to the CDL. The information is useful for issues related to agricultural sustainability, biodiversity, and land cover monitoring, especially due to extreme weather events. Resources in this dataset:Resource Title: CropScape and Cropland Data Layer - National Download. File Name: Web Page, url: https://www.nass.usda.gov/Research_and_Science/Cropland/Release/index.php Downloads available as zipped files at https://www.nass.usda.gov/Research_and_Science/Cropland/Release/index.php --

National CDL's -- by year, 2008-2020. Cropland Data Layer provides a raster, geo-referenced, crop-specific land cover map for the continental United States. The CDL also includes a crop mask layer and planting frequency layers, as well as boundary, water and road layers. The Boundary Layer options provided are County, Agricultural Statistics Districts (ASD), State, and Region. National Cultivated Layer -- based on the most recent five years (2013-2020). National Frequency Layer -- the 2017 Crop Frequency Layer identifies crop specific planting frequency and are based on land cover information derived from the 2008 through 2020CDL's. There are currently four individual crop frequency data layers that represent four major crops: corn, cotton, soybeans, and wheat. National Confidence Layer -- the Confidence Layer spatially represents the predicted confidence that is associated with that output pixel, based upon the rule(s) that were used to classify it. Western/Eastern/Central U.S.

Visit https://nassgeodata.gmu.edu/CropScape/ for the interactive map including tutorials and basic instructions. These options include a "Demo Video", "Help", "Developer Guide", and "FAQ".