This dataset contains shape files and supporting files for the most up-to-date (as of the published date) land use map at the UBC Farm. The best uses of these maps are: 1) to visualize locations of field codes in other UBC Farm datasets; 2) to visualize field codes for UBC Farm research projects, and 3) to understand the general layout of the Farm.

This statistic shows the percentage of agricultural operations in Canada using GIS mapping technology in 2015, by farm size. In that year, 52.7 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.

Agricultural Mapping Software Market Outlook

The global agricultural mapping software market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach around USD 3.4 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 12.5% during the forecast period. This promising growth is driven by increasing adoption of precision farming techniques and the need for efficient agricultural management practices. Advances in technology, coupled with rising demand for food production, are significant factors propelling the agricultural mapping software market.

One of the primary growth factors for the agricultural mapping software market is the increasing need for precision farming. Precision farming techniques rely on detailed data collection and analysis, which is facilitated by advanced agricultural mapping software. These tools help farmers make informed decisions about planting, watering, and harvesting, thereby maximizing crop yield and resource efficiency. The emphasis on data-driven farming is expected to drive significant adoption of mapping software across the globe.

Another crucial growth factor is the rising global population, which directly correlates with the increasing demand for food. As the world population continues to grow, the pressure on agricultural systems becomes more intense. Agricultural mapping software aids in optimizing land use, monitoring crop health, and predicting yields, thus playing a pivotal role in meeting the escalating food demands. The software's ability to enhance productivity and sustainability is highly appealing to stakeholders in the agricultural sector.

Technological advancements in GIS (Geographic Information Systems) and remote sensing are also propelling the market. The integration of satellite imagery, drones, and IoT (Internet of Things) devices with agricultural mapping software enables real-time data acquisition and analysis. These technologies provide farmers with detailed insights into their fields, enabling them to detect issues early and take corrective action promptly. The continuous innovation in these technologies is expected to further boost market growth.

From a regional perspective, North America is anticipated to hold the largest market share due to the high adoption rate of advanced farming technologies and substantial investments in agricultural research. Europe follows closely, driven by stringent agricultural policies and a strong focus on sustainable farming practices. The Asia Pacific region is expected to witness the fastest growth, attributed to increasing government initiatives to modernize agriculture and substantial investments in agritech startups. Latin America and the Middle East & Africa also present significant growth opportunities due to expanding agricultural activities and adoption of modern farming techniques.

Crop Monitoring Software plays a pivotal role in the agricultural mapping software market by providing farmers with the tools necessary to maintain and enhance crop health. This software allows for continuous observation and analysis of crops, ensuring that any potential issues such as diseases, pest infestations, or nutrient deficiencies are identified early. By leveraging real-time data, farmers can make informed decisions that lead to improved crop yields and quality. The integration of Crop Monitoring Software with other agricultural technologies further enhances its capabilities, making it an indispensable tool for modern farming practices. As the demand for efficient and sustainable agriculture grows, the adoption of such software is expected to rise, contributing significantly to the market's expansion.

Component Analysis

The agricultural mapping software market by component is divided into two primary segments: software and services. The software segment encompasses a range of solutions tailored to various agricultural needs, including GIS software, remote sensing software, and farm management software. These tools are designed to collect, analyze, and interpret data to support decision-making processes in farming operations. The sophistication and variety of available software solutions are continually expanding, driven by ongoing research and development efforts in agritech.

In contrast, the services segment includes consulting, training, maintenance, and support services that complement the software solutions. As more farmers and agricultural enterprises adopt mapp

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

[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 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.

The global agricultural mapping software market is experiencing robust growth, driven by increasing demand for precision agriculture techniques and the rising adoption of technology in farming practices. This market is projected to reach a substantial size, with a Compound Annual Growth Rate (CAGR) reflecting significant expansion. While the exact market size and CAGR figures are not provided, based on industry reports and observed trends in related sectors like agricultural technology and precision farming, a reasonable estimate would place the 2025 market value at approximately $2.5 billion, growing at a CAGR of 15% from 2025 to 2033. This growth is fueled by several factors, including the increasing need for efficient resource management (water, fertilizers, pesticides), improved crop yields, and enhanced farm profitability. Farmers are increasingly adopting cloud-based solutions for their ease of use and accessibility, leading to a significant segment of the market focused on cloud-based software. Furthermore, the integration of GPS, GIS, and remote sensing technologies into these platforms is boosting market expansion, allowing for precise field monitoring, data analysis, and informed decision-making. The market is segmented by deployment type (cloud-based and on-premise) and application (personal farms and animal husbandry companies). The cloud-based segment is expected to maintain a dominant share owing to its scalability and cost-effectiveness. The competitive landscape comprises established players like Trimble and CNH Industrial, alongside specialized agricultural technology companies such as Agrivi and Xfarm. These companies are constantly innovating and expanding their product offerings to cater to diverse farming needs and geographical locations. Regional market penetration varies, with North America and Europe currently holding significant shares due to advanced agricultural practices and higher technology adoption rates. However, rapidly developing economies in Asia-Pacific and other regions are showing promising growth potential, fuelled by increasing government initiatives promoting digital agriculture and the rising awareness of precision farming techniques. Challenges remain, such as the need for robust internet connectivity in remote areas and the digital literacy gap among some farmers, but overall market projections remain positive, indicating a strong future for agricultural mapping software.

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.

NOTICE TO PROVISIONAL 2023 LAND USE DATA USERS: Please note that on December 6, 2024 the Department of Water Resources (DWR) published the Provisional 2023 Statewide Crop Mapping dataset. The link for the shapefile format of the data mistakenly linked to the wrong dataset. The link was updated with the appropriate data on January 27, 2025. If you downloaded the Provisional 2023 Statewide Crop Mapping dataset in shapefile format between December 6, 2024 and January 27, we encourage you to redownload the data. The Map Service and Geodatabase formats were correct as posted on December 06, 2024.

Thank you for your interest in DWR land use datasets.

The California Department of Water Resources (DWR) has been collecting land use data throughout the state and using it to develop agricultural water use estimates for statewide and regional planning purposes, including water use projections, water use efficiency evaluations, groundwater model developments, climate change mitigation and adaptations, and water transfers. These data are essential for regional analysis and decision making, which has become increasingly important as DWR and other state agencies seek to address resource management issues, regulatory compliances, environmental impacts, ecosystem services, urban and economic development, and other issues. Increased availability of digital satellite imagery, aerial photography, and new analytical tools make remote sensing-based land use surveys possible at a field scale that is comparable to that of DWR’s historical on the ground field surveys. Current technologies allow accurate large-scale crop and land use identifications to be performed at desired time increments and make possible more frequent and comprehensive statewide land use information. Responding to this need, DWR sought expertise and support for identifying crop types and other land uses and quantifying crop acreages statewide using remotely sensed imagery and associated analytical techniques. Currently, Statewide Crop Maps are available for the Water Years 2014, 2016, 2018- 2022 and PROVISIONALLY for 2023.

Historic County Land Use Surveys spanning 1986 - 2015 may also be accessed using the CADWR Land Use Data Viewer: https://gis.water.ca.gov/app/CADWRLandUseViewer.

For Regional Land Use Surveys follow: https://data.cnra.ca.gov/dataset/region-land-use-surveys.

For County Land Use Surveys follow: https://data.cnra.ca.gov/dataset/county-land-use-surveys.

For a collection of ArcGIS Web Applications that provide information on the DWR Land Use Program and our data products in various formats, visit the DWR Land Use Gallery: https://storymaps.arcgis.com/collections/dd14ceff7d754e85ab9c7ec84fb8790a.

Recommended citation for DWR land use data: California Department of Water Resources. (Water Year for the data). Statewide Crop Mapping—California Natural Resources Agency Open Data. Retrieved “Month Day, YEAR,” from https://data.cnra.ca.gov/dataset/statewide-crop-mapping.

Having updated knowledge of cropland extent is essential for crop monitoring and food security early warning. Previous research has proposed different methods and adopted various datasets for mapping cropland areas at regional to global scales. However, most approaches did not consider the characteristics of farming systems and applied the same classification method in different agroecological zones (AEZs). Furthermore, the acquisition of in situ samples for classification training remains challenging. To address these knowledge gaps and challenges, this study applied a zone-specific classification by comparing four classifiers (random forest, the support vector machine (SVM), the classification and regression tree (CART) and minimum distance) for cropland mapping over four different AEZs in the Zambezi River basin (ZRB). Landsat-8 and Sentinel-2 data and derived indices were used and synthesized to generate thirty-five layers for classification on the Google Earth Engine platform. Training samples were derived from three existing landcover datasets to minimize the cost of sample acquisitions over the large area. The final cropland map was generated at a 10 m resolution.

The information here presented was imported from a published paper with the title ''Comparison of Different Cropland Classification Methods under Diversified Agroecological Conditions in the Zambezi River Basin'' which its reference is shown below. The dataset here presented was created based on the results of this study.

Bofana, J.; Zhang, M.; Nabil, M.; Wu, B.; Tian, F.; Liu, W.; Zeng, H.; Zhang, N.; Nangombe, S.S.; Cipriano, S.A.; Phiri, E.; Mushore, T.D.; Kaluba, P.; Mashonjowa, E.; Moyo, C. Comparison of Different Cropland Classification Methods under Diversified Agroecological Conditions in the Zambezi River Basin. Remote Sens. 2020, 12, 2096. https://doi.org/10.3390/rs12132096

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).

The Atlanta Regional Commission aggregates data of garden and farm sites in the 10 county area on behalf of regional partners. Using information provided by the regional food community such as Atlanta Local Food Initiative, the Atlanta Community Food Bank, Captain Planet Foundation, Food Well Alliance, Georgia Organics, Metro Atlanta Urban Farm, the University of Georgia, and Concrete Jungle to show where small to medium scale growing is taking place in the region. Attributes:Name = Name of garden, farm or orchardAddress = Address of garden, farm or orchardCounty = CountyCity = City of addressZIP = 5 digit ZIP codeSource = Source of original dataSubtype = Finer grained segmentation of type, such as community, school, etc.Type = Garden, farm or OrchardDate: 2016Source: Atlanta Regional Commission, community partners including Atlanta Local Food Initiative, the Atlanta Community Food Bank, Captain Planet Foundation, Food Well Alliance, Georgia Organics, Metro Atlanta Urban Farm, the University of Georgia, and Concrete JungleFor additional information, please visit the Atlanta Regional Commission at www.atlantaregional.com

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).

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).

This map features recent high-resolution National Agriculture Imagery Program (NAIP) imagery for the United States and is optimized for display quality and performance. The map also includes a reference layer.This NAIP imagery is from the USDA Farm Services Agency. The NAIP imagery in this layer has been visually enhanced and published as a tile layer for optimal display performance.NAIP imagery collection occurs on an annual basis during the agricultural growing season in the continental United States. Approximately half of the US is collected each year and each state is typically collected every other year. The NAIP program aims to make the imagery available to governmental agencies and to the public within a year of collection.This layer will be updated each year, as the latest imagery is received and processed. Currently, it is primarily composed of NAIP imagery from 2018 and 2019.Use the NAIP Imagery Metadata layer as an overlay to access detailed information about each image in this tile layer. With the metadata layer, a user can point and click any location within the continental US to access information such as collection date and resolution for the imagery at that location.While this tile layer is intended for visualization, the Living Atlas also provides the following NAIP layers for image analysis:USA NAIP Imagery: Natural ColorUSA NAIP Imagery: Color InfraredUSA NAIP Imagery: NDVI

The NAIP Imagery Hybrid (US Edition) web map features recent high-resolution National Agriculture Imagery Program (NAIP) imagery for the United States and is optimized for display quality and performance. The map also includes a reference layer. This NAIP imagery is from the USDA Farm Services Agency. The NAIP imagery in this map has been visually enhanced and published as a raster tile layer for optimal display performance.NAIP imagery collection occurs on an annual basis during the agricultural growing season in the continental United States. Approximately half of the US is collected each year and each state is typically collected every other year. The NAIP program aims to make the imagery available to governmental agencies and to the public within a year of collection.This basemap is available in the United States Vector Basemaps gallery and uses NAIP Imagery and World Imagery (Firefly) raster tile layers. It also uses the Hybrid Reference (US Edition) and Dark Gray Base (US Edition) vector tile layers.The vector tile layers in this web map are built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.Use this MapThis map is designed to be used as a basemap for overlaying other layers of information or as a stand-alone reference map. You can add layers to this web map and save as your own map. If you like, you can add this web map to a custom basemap gallery for others in your organization to use in creating web maps. If you would like to add this map as a layer in other maps you are creating, you may use the tile layers referenced in this map.

Agricultural Mapping Services Market Outlook

The global Agricultural Mapping Services market size was valued at approximately USD 2.5 billion in 2023 and is anticipated to grow significantly to reach around USD 5.8 billion by 2032, reflecting a Compound Annual Growth Rate (CAGR) of approximately 9.8%. The primary growth driver for this market is the increasing demand for precision agriculture practices worldwide, which necessitate the use of detailed mapping services to maximize crop yield and optimize resource utilization. The convergence of technology with agriculture has catalyzed a significant transition in farming methodologies, empowering farmers to make data-driven decisions and thereby enhancing productivity and sustainability.

A major growth factor contributing to the expansion of the Agricultural Mapping Services market is the increasing awareness and adoption of precision farming techniques. Precision agriculture relies heavily on accurate and timely mapping services to monitor and manage field variability in crops. Factors such as climate change and unpredictable weather patterns have also intensified the need for sophisticated agricultural mapping to ensure food security and optimize crop production. Furthermore, government initiatives and subsidies promoting the adoption of advanced agricultural technologies are providing an additional impetus to this market, encouraging both small and large-scale farmers to invest in mapping services.

Another significant factor propelling market growth is the technological advancements in Geographic Information System (GIS), remote sensing, and drone technologies. These advanced tools facilitate the collection and analysis of critical agricultural data, enabling more precise crop monitoring and management. The integration of Artificial Intelligence (AI) and machine learning into mapping technologies further enhances the accuracy and efficiency of agricultural mapping services, providing actionable insights that help in predictive analysis and risk management. As a result, farmers and agronomists are increasingly turning to these technologies to gain a competitive edge and improve their agricultural outputs.

The rising global population and the consequent increase in food demand are also pivotal growth drivers for the Agricultural Mapping Services market. As the world population continues to grow, there is mounting pressure on the agricultural sector to enhance productivity to meet food supply needs. Agricultural mapping services play a crucial role in this context by optimizing land use and improving crop yields. Additionally, the trend towards sustainable agriculture and the need to manage resources more judiciously are fueling the demand for mapping services, which help minimize environmental impact while maximizing crop production.

The integration of GIS Software In Agriculture has revolutionized the way farmers approach precision agriculture. By utilizing GIS technology, farmers can create detailed maps that illustrate various aspects of their fields, such as soil types, crop health, and water availability. This spatial data is crucial for making informed decisions about planting, fertilization, and irrigation, ultimately leading to improved crop yields and resource efficiency. GIS software allows for the layering of different data sets, providing a comprehensive view of the agricultural landscape that helps in identifying patterns and trends. As a result, farmers can optimize their operations, reduce waste, and enhance sustainability, making GIS an indispensable tool in modern agriculture.

Regionally, North America is anticipated to dominate the Agricultural Mapping Services market, owing to the early adoption of advanced agricultural technologies and strong government support. Europe follows closely, with significant investments in agricultural innovation and a focus on sustainable farming practices. The Asia Pacific region, however, is projected to witness the fastest growth during the forecast period, driven by the increasing penetration of precision agriculture practices and the rapid development of the agricultural sector in countries like China and India. Latin America and the Middle East & Africa are also expected to experience substantial growth as these regions strive to enhance agricultural productivity and security.

Service Type Analysis

The Agricultural Mapping Services market is segmented by service type into Soil Mapping, Yield Mapping, Crop Health Monitoring, and Othe

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".

Cropland Index The Cropland Index evaluates lands used to produce crops based on the following input datasets: Revised Storie Index, California Important Farmland data, Electrical Conductivity (EC), and Sodium Adsorption Ratio (SAR). Together, these input layers were used in a suitability model to generate this raster. High values are associated with better CroplandsCalifornia Important Farmland data – statistical data used for analyzing impacts on California’s agricultural resources from the Farmland Mapping and Monitoring Program. Agricultural land is rated according to soil quality and irrigation status. The maps are updated every two years (on even numbered years) with the use of a computer mapping system, aerial imagery, public review, and field reconnaissance. Cropland Index Mask - This is a constructed data set used to define the model domain. Its footprint is defined by combining the extent of the California Important Farmland data (2018) classifications listed above and the area defined by California Statewide Crop Mapping for the state of California.Prime Farmland – farmland with the best combination of physical and chemical features able to sustain long term agricultural production. This land has the soil quality, growing season, and moisture supply needed to produce sustained high yields. Land must have been used for irrigated agricultural production at some time during the four years prior to the mapping date.Farmland of Statewide Importance – farmland similar to Prime Farmland but with minor shortcomings, such as greater slopes or less ability to store soil moisture. Land must have been used for irrigated agricultural production at some time during the four years prior to the mapping date. Unique Farmland – farmland of lesser quality soils used for the production of the state’s leading agricultural crops. This land is usually irrigated but may include Non irrigated orchards or vineyards as found in some climatic zones in California. Land must have been cropped at some time during the four years prior to the mapping date. Gridded Soil Survey Geographic Database (gSSURGO) – a database containing information about soil as collected by the National Cooperative Soil Survey over the course of a century. The information can be displayed in tables or as maps and is available for most areas in the United States and the Territories, Commonwealths, and Island Nations served by the USDA-NRCS. The information was gathered by walking over the land and observing the soil. Many soil samples were analyzed in laboratories. California Revised Storie Index - is a soil rating based on soil properties that govern a soil’s potential for cultivated agriculture in California. The Revised Storie Index assesses the productivity of a soil from the following four characteristics: Factor A, degree of soil profile development; factor B, texture of the surface layer; factor C, slope; and factor X, manageable features, including drainage, microrelief, fertility, acidity, erosion, and salt content. A score ranging from 0 to 100 percent is determined for each factor, and the scores are then multiplied together to derive an index rating.Electrical Conductivity - is the electrolytic conductivity of an extract from saturated soil paste, expressed as Deci siemens per meter at 25 degrees C. Electrical conductivity is a measure of the concentration of water-soluble salts in soils. It is used to indicate saline soils. High concentrations of neutral salts, such as sodium chloride and sodium sulfate, may interfere with the adsorption of water by plants because the osmotic pressure in the soil solution is nearly as high as or higher than that in the plant cells. Sodium Adsorption Ratio - is a measure of the amount of sodium (Na) relative to calcium (Ca) and magnesium (Mg) in the water extract from saturated soil paste. It is the ratio of the Na concentration divided by the square root of one-half of the Ca + Mg concentration. Soils that have SAR values of 13 or more may be characterized by an increased dispersion of organic matter and clay particles, reduced saturated hydraulic conductivity (Ksat) and aeration, and a general degradation of soil structure.