Smart agriculture refers to tools that collect, store and analyze digital data along the agricultural value chain. Geographic Information System (GIS) system software is one of those tools used in the agricultural sector. The GIS System market in Spain had a value of over ** million dollars in 2019.

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.

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.

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.

Global GIS Software in Agriculture Market is segmented by Application (Land Management_ Crop Monitoring_ Soil Analysis_ Water Management_ Precision Farming), Type (Desktop GIS_ Web GIS_ Mobile GIS_ Cloud GIS), and Geography (North America_ LATAM_ West Europe_Central & Eastern Europe_ Northern Europe_ Southern Europe_ East Asia_ Southeast Asia_ South Asia_ Central Asia_ Oceania_ MEA)

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

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.

A Geographic Information System (GIS) shapefile and summary tables of irrigated agricultural land-use are provided for the 15 counties fully within the Northwest Florida Water Management District (Bay, Calhoun, Escambia, Franklin, Gadsden, Gulf, Holmes, Jackson, Leon, Liberty, Okaloosa, Santa Rosa, Wakulla, Walton, and Washington counties). These files were compiled through a cooperative project between the U.S. Geological Survey and the Florida Department of Agriculture and Consumer Services, Office of Agricultural Water Policy. Information provided in the shapefile includes the location of irrigated lands that were verified during field surveying that started in May 2021 and concluded in August 2021. Field data collected were crop type, irrigation system type, and primary water source used. A map image of the shapefile is also provided. Previously published estimates of irrigation acreage for years since 1982 are included in summary tables.

Discover the booming Satellite Remote Sensing Software market! Explore key trends, growth drivers, and regional market shares in our comprehensive analysis. Learn about leading companies and the future of this technology in agriculture, forestry, and beyond. Get the insights you need to make informed decisions.

The size of the GIS Software in Agriculture market was valued at USD XXX million in 2024 and is projected to reach USD XXX million by 2033, with an expected CAGR of XX % during the forecast period.

Geographic Information Systems Market in Agriculture by Offering, Application (Soil & Agricultural Mapping, Crop Monitoring, Yield Prediction, Livestock Monitoring), Sub-sector (Crop Farming, Forestry, Livestock) - Global Forecast to 2032

GIS Software in Agriculture Market Focus on Solution (On-Cloud, On-Premise), Application (Crop Monitoring, Soil Analysis, Irrigation Monitoring), and Region. The report aims at estimating the market size and future growth of GIS Software in Agriculture Market. GIS Software in Agriculture Market to grow at a significant CAGR of 10.41% during the forecast period from 2019 to 2024.

The GIS Software in Agriculture market size reached USD 4.04 Billion in 2020 and revenue is forecasted to reach USD 10.54 Billion in 2028 registering a CAGR of 12.8%. Geographic information system software in agriculture industry report classifies global market by share, trend, growth and based on d...

The GIS shapefile and summary tables provide irrigated agricultural land-use for Hendry and Palm Beach 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 Hendry and Palm Beach Counties, Florida. A map image of the shapefile is provided in the attachment.

Advancing Research on Nutrition and Agriculture (AReNA) is a 6-year, multi-country project in South Asia and sub-Saharan Africa funded by the Bill and Melinda Gates Foundation, being implemented from 2015 through 2020. The objective of AReNA is to close important knowledge gaps on the links between nutrition and agriculture, with a particular focus on conducting policy-relevant research at scale and crowding in more research on this issue by creating data sets and analytical tools that can benefit the broader research community. Much of the research on agriculture and nutrition is hindered by a lack of data, and many of the datasets that do contain both agriculture and nutrition information are often small in size and geographic scope. AReNA team constructed a large multi-level, multi-country dataset combining nutrition and nutrition-relevant information at the individual and household level from the Demographic and Health Surveys (DHS) with a wide variety of geo-referenced data on agricultural production, agroecology, climate, demography, and infrastructure (GIS data). This dataset includes 60 countries, 184 DHS, and 122,473 clusters. Over one thousand geospatial variables are linked with DHS. The entire dataset is organized into 13 individual files: DHS_distance, DHS_livestock, DHS_main, DHS_malaria, DHS NDVI, DHS_nightlight, DHS_pasture and climate (mean), DHS_rainfall, DHS_soil, DHS_SPAM, DHS_suit, DHS_temperature, and DHS_traveltime.

A shapefile of the extent of irrigated agricultural fields which includes an attribute table of the irrigated acreage for the period between January and December 2020 was compiled for Lake, Marion, and Orange Counties, Florida. Attributes for each polygon that represents a field include a general or specific crop type, irrigation system, and primary water source for irrigation.

Global Precision Agriculture Market size is set to expand from $ 10.10 Billion in 2023 to $ 24.62 Billion by 2032, with CAGR of 10.4% from 2024 to 2032.

The agricultural mapping software market is experiencing robust growth, driven by the increasing adoption of precision agriculture techniques and the need for enhanced farm management efficiency. The market, estimated at $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching approximately $7 billion by 2033. This expansion is fueled by several key factors. Firstly, the rising demand for optimized resource utilization, including water and fertilizers, is pushing farmers to adopt data-driven decision-making. Secondly, technological advancements in GPS, GIS, and sensor technologies are making agricultural mapping software more affordable and accessible. Furthermore, government initiatives promoting digital agriculture and precision farming in various regions are significantly contributing to market growth. Key segments within the market include software solutions for crop monitoring, yield prediction, and soil analysis, each contributing to the overall market expansion. Leading companies like Trimble, CNH Industrial, and Geosys are at the forefront of innovation, continuously developing advanced features and functionalities to meet evolving farmer needs. However, challenges remain, including the initial investment costs associated with adopting new technologies and the need for reliable internet connectivity in rural areas, potentially hindering wider adoption in some regions. Despite these restraints, the long-term outlook for agricultural mapping software remains positive. The increasing availability of affordable drones and remote sensing technologies is further enhancing the capabilities of these software solutions, allowing for more accurate and timely data collection. The integration of artificial intelligence (AI) and machine learning (ML) is also paving the way for predictive analytics and automated decision support systems, which will further transform farm management practices. The market is likely to witness increased consolidation as larger companies acquire smaller players, leading to the development of more comprehensive and integrated solutions. The geographical expansion of the market, particularly in developing economies with a large agricultural sector, represents a significant opportunity for growth in the coming years. The market will continue to evolve as technology matures and more farmers embrace precision agriculture as a means of improving productivity and profitability.

This data set consists of a detailed digital map of the areal extent of fields and a summary of the irrigated acreage for the 2018 growing season developed for Lee County, Florida. Selected attribute data that include crop type, irrigation system, and primary water source were collected for each irrigated field.

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.