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

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 36 million dollars in 2019.

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.

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.

The GIS software market for agricultural use in South America was forecast to reach over 113 million U.S. dollars in 2019, up from an estimated 102.7 million dollars a year earlier. Crop monitoring was the largest application in the region, accounting for more than half of the market's value in the period.

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.

A Geographic Information System (GIS) shapefile and summary tables of irrigated agricultural land-use are provided for Glades, Highlands, Martin, Okeechobee, and St. Lucie Counties, Florida. 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 November 2023 and concluded in July 2024. Field data collected included 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 1992 are included in summary tables.

Geospatial data about World Bank Agriculture Percent Land Used for Agricultural. Export to CAD, GIS, PDF, CSV and access via API.

The LTER annual crops (corn, soy and wheat), treatments 1-4, are harvested annually using a combine equipped with a GPS and precision agriculture software to allow detailed yield measurements with coincident GPS latitude and longitude data.. original data source http://lter.kbs.msu.edu/datasets/40 Resources in this dataset:Resource Title: Website Pointer to html file. File Name: Web Page, url: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-kbs&identifier=37 Webpage with information and links to data files for download

The GIS software market in Agriculture in Latin America was estimated at around 130 million U.S. dollars in 2018, and was forecast to surpass 143 million dollars in 2019. In the latter year, Brazil was expected to account for nearly one third of this market, with a value of 47.8 million dollars. Meanwhile, Argentina's market was forecast at 33.3 million dollars in 2019.

The delineation of agricultural field boundaries has a wide range of applications, such as for crop management, precision agriculture, land use planning and crop insurance, etc. Manually digitizing agricultural fields from imagery is labor-intensive and time-consuming. This deep learning model automates the process of extracting agricultural field boundaries from satellite imagery, thereby significantly reducing the time and effort required. Its ability to adapt to varying crop types, geographical regions, and imaging conditions makes it suitable for large-scale operations.Using the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Fine-tuning the modelThis model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.InputSentinel-2 L2A 12-bands multispectral imagery using Bottom of Atmosphere (BOA) reflectance product in the form of a raster, mosaic or image service.OutputFeature class containing delineated agricultural fields.Applicable geographiesThe model is expected to work well in agricultural regions of USA.Model architectureThis model uses the Mask R-CNN model architecture implemented in ArcGIS API for Python.Accuracy metricsThis model has an average precision score of 0.64 for fields.Training dataThis model has been trained on an Esri proprietary agricultural field delineation dataset.LimitationsThis model works well only in areas having farmlands and may not give satisfactory results in areas near water bodies and hilly regions. The results of this pretrained model cannot be guaranteed against any other variation of the Sentinel-2 data.Sample resultsHere are a few results from the model.

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.

Land use data is critically important to the work of the Department of Water Resources (DWR) and other California agencies. Understanding the impacts of land use, crop location, acreage, and management practices on environmental attributes and resource management is an integral step in the ability of Groundwater Sustainability Agencies (GSAs) to produce Groundwater Sustainability Plans (GSPs) and implement projects to attain sustainability. Land IQ was contracted by DWR to develop a comprehensive and accurate spatial land use database for the Water Year 2018, covering over 9.4 million acres of Irrigable agriculture on a field scale and additional areas of urban extent. The primary objective of this effort was to produce a spatial land use database with accuracies exceeding 95% using remote sensing, statistical, and temporal analysis methods. This project is an extension of the 2014 and 2016 land use mapping, which classified over 14 million acres of land into Irrigable agriculture and urban area. Unlike the 2014 and 2016 datasets, the Water Year 2018 dataset includes multi-cropping and incorporates ground-truth data from Siskiyou, Modoc, Lassen and Shasta counties. Land IQ integrated crop production knowledge with detailed ground truth information and multiple satellite and aerial image resources to conduct remote sensing land use analysis at the field scale. Individual fields (boundaries of homogeneous crop types representing true Irrigable area, rather than legal parcel boundaries) were classified using a crop category legend and a more specific crop type legend. A supervised classification algorithm using a random forest approach was used to classify delineated fields and was carried out county by county where training samples were available. Random forest approaches are currently some of the highest performing methods for data classification and regression. To determine frequency and seasonality of multiple-cropped fields, peak growth dates were determined for annual crops. Fields were attributed with DWR crop categories and included citrus/subtropical, deciduous fruits and nuts, field crops, grain and hay, idle, pasture, rice, truck crops, urban, vineyards, young perennials and wetland. These categories represent aggregated groups of specific crop types in the Land IQ dataset. Accuracy was calculated for the crop mapping using both DWR and Land IQ crop legends. The overall accuracy result for the crop mapping statewide was 96.5% using the Land IQ legend and 98.3% using the DWR legend. Accuracy and error results varied among crop types. In particular, some less extensive crops that have very few validation samples may have a skewed accuracy result depending on the number and nature of validation sample points. Revised crops and conditions were encoded using standard DWR land use codes added to feature attributes, and each modified classification is indicated by the value 'r' in the 'DWR_revised' data field. The value ‘n’ in the ‘DWR_REVISE’ data field indicates a Regional Office added a boundary and attributes where none was included in the Land IQ data set. Each polygon classification is consistent with DWR attribute standards, however some of DWR's traditional attribute definitions are modified and extended to accommodate unavoidable constraints within remote-sensing classifications, or to make data more specific for DWR's water balance computation needs. The original Land IQ classifications reported for each polygon are preserved for comparison, and are also expressed as DWR standard attributes. Comments, problems, improvements, updates, or suggestions about local conditions or revisions in the final data set should be forwarded to the appropriate Regional Office Senior Land Use Supervisor. Revisions were made if: - DWR corrected the original crop classification based on local knowledge and analysis, - young versus mature stages of perennial orchards and vineyards were identified (DWR added ‘Young’ to Special Condition attributes), - DWR determined that a field originally classified ‘Idle’ was actually cropped one or more times during the year, - the percent of cropped area was less than 100% of the original acres reported by Land IQ (values indicated in DWR ‘Percent’ column), - DWR determined that the field boundary should have been split to better reflect separate crops within the same polygon (‘Mixed’ was added to the MULTIUSE column; the crop classification and corresponding area percentages were indicated), - DWR determined that the crop was not irrigated. - DWR identified a distinct early or late crop on the field before the main season crop (‘Double’ was added to the MULTIUSE column); if the 1st and 2nd sequential crops occupied different portions of the total field acreage, the area percentages were indicated for each crop). DWR added Adjusted Day Of Year (ADOY) for peak NDVI date corresponding to CROPTYP category. The date received by Land IQ was delivered in a Julian date format (YYYYDDD) and was converted into the ADOY by DWR for statistical purposes. Land use boundaries delineated by Land IQ were not revised by DWR.

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 2021 was compiled for Bay, Calhoun, Escambia, Franklin, Gadsden, Gulf, Holmes, Jackson, Leon, Liberty, Okaloosa, Santa Rosa, Wakulla, Walton, and Washington Counties, Florida. These counties are fully within the Northwest Florida Water Management District boundaries. Attributes for each polygon that represents a field include a general or specific crop type, irrigation system, and primary water source for irrigation.

Geographic Information Software (GIS) in Agriculture market is anticipated to grow at a CAGR of 10% during 2020-25 forecast says MarkNtel Advisors.

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.

A Geographic Information System (GIS) shapefile and summary tables of irrigated agricultural land-use are provided for the fourteen counties that are fully or partially within the Suwannee River Water Management District, Florida 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 trips that started in January 2020 and concluded in December 2020, and the crop type, irrigation system type, and primary water source used. A map image of the shapefile is provided. Previously published estimates of irrigation acreage for years since 1982 are included in summary tables.

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 Asia-Pacific Geographic Information System (GIS) Software for Agriculture Market is expected to grow at a CAGR of around 14% during the forecast period, i.e., 2022-27 says MarkNtel Advisors.

These are distinguished from community gardens in that they are generally not intended for the public to use the space for their own growing activities, and in that many have a commercial focus. These were drawn by the Office of Planning based on ESRI satellite basemap imagery compared against the Urban Agriculture points layer. Note that, because many locations are small (or indoors) and could not be located through this satellite view, and because acreage as calculated by these polygons differs, sometimes significantly, from producers' self-reported acreage (indicating the presence of other, less visible growing space, or out-of-date satellite imagery), this layer should not be considered complete and should be used for internal purposes only.