100+ datasets found
  1. u

    Data from: Grass-Cast Database - Data on aboveground net primary...

    • agdatacommons.nal.usda.gov
    • catalog.data.gov
    html
    Updated May 1, 2025
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    Chris Dorich; Justin Derner; Greg Torell; Jerry Volesky; Jameson Brennan; David Archer; John Blair; Alan Knapp; Jesse Nippert; David Hartnett; Mitchel McClaran; Greg Maurer; Douglas Moore; Pat Clark; William Parton; Dannele Peck; Lauren Kramer; William Kolby Smith; Emile Elias; Brian Fuchs; Walter H. Schacht; John Hendrickson; Keith Harmoney; Scott Collins; Lauren Baur; Lauren Porensky; Lance Vermeire; Kevin Wilcox (2025). Grass-Cast Database - Data on aboveground net primary productivity (ANPP), climate data, NDVI, and cattle weight gain for Western U.S. rangelands [Dataset]. http://doi.org/10.15482/USDA.ADC/1521120
    Explore at:
    htmlAvailable download formats
    Dataset updated
    May 1, 2025
    Dataset provided by
    Ag Data Commons
    Authors
    Chris Dorich; Justin Derner; Greg Torell; Jerry Volesky; Jameson Brennan; David Archer; John Blair; Alan Knapp; Jesse Nippert; David Hartnett; Mitchel McClaran; Greg Maurer; Douglas Moore; Pat Clark; William Parton; Dannele Peck; Lauren Kramer; William Kolby Smith; Emile Elias; Brian Fuchs; Walter H. Schacht; John Hendrickson; Keith Harmoney; Scott Collins; Lauren Baur; Lauren Porensky; Lance Vermeire; Kevin Wilcox
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Western United States, United States
    Description

    Grass-Cast: Experimental Grassland Productivity Forecast for the Great Plains Grass-Cast uses almost 40 years of historical data on weather and vegetation growth in order to project grassland productivity in the Western U.S. More details on the projection model and method can be found at https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecs2.3280.
    Every spring, ranchers in the drought‐prone U.S. Great Plains face the same difficult challenge—trying to estimate how much forage will be available for livestock to graze during the upcoming summer grazing season. To reduce this uncertainty in predicting forage availability, we developed an innovative new grassland productivity forecast system, named Grass‐Cast, to provide science‐informed estimates of growing season aboveground net primary production (ANPP). Grass‐Cast uses over 30 yr of historical data including weather and the satellite‐derived normalized vegetation difference index (NDVI)—combined with ecosystem modeling and seasonal precipitation forecasts—to predict if rangelands in individual counties are likely to produce below‐normal, near‐normal, or above‐normal amounts of grass biomass (lbs/ac). Grass‐Cast also provides a view of rangeland productivity in the broader region, to assist in larger‐scale decision‐making—such as where forage resources for grazing might be more plentiful if a rancher’s own region is at risk of drought. Grass‐Cast is updated approximately every two weeks from April through July. Each Grass‐Cast forecast provides three scenarios of ANPP for the upcoming growing season based on different precipitation outlooks. Near real‐time 8‐d NDVI can be used to supplement Grass‐Cast in predicting cumulative growing season NDVI and ANPP starting in mid‐April for the Southern Great Plains and mid‐May to early June for the Central and Northern Great Plains. Here, we present the scientific basis and methods for Grass‐Cast along with the county‐level production forecasts from 2017 and 2018 for ten states in the U.S. Great Plains. The correlation between early growing season forecasts and the end‐of‐growing season ANPP estimate is >50% by late May or early June. In a retrospective evaluation, we compared Grass‐Cast end‐of‐growing season ANPP results to an independent dataset and found that the two agreed 69% of the time over a 20‐yr period. Although some predictive tools exist for forecasting upcoming growing season conditions, none predict actual productivity for the entire Great Plains. The Grass‐Cast system could be adapted to predict grassland ANPP outside of the Great Plains or to predict perennial biofuel grass production. This new experimental grassland forecast is the result of a collaboration between Colorado State University, U.S. Department of Agriculture (USDA), National Drought Mitigation Center, and the University of Arizona. Funding for this project was provided by the USDA Natural Resources Conservation Service (NRCS), USDA Agricultural Research Service (ARS), and the National Drought Mitigation Center. Watch for updates on the Grass-Cast website or on Twitter (@PeckAgEc). Project Contact: Dannele Peck, Director of the USDA Northern Plains Climate Hub, at dannele.peck@ars.usda.gov or 970-744-9043. Resources in this dataset:Resource Title: Cattle weight gain. File Name: Cattle_weight_gains.xlsxResource Description: Cattle weight gain data for Grass-Cast Database. Resource Title: NDVI. File Name: NDVI.xlsxResource Description: Annual NDVI growing season values for Grass-Cast sites. See readme for more information and NDVI_raw for the raw values. Resource Title: NDVI_raw . File Name: NDVI_raw.xlsxResource Description: Raw bimonthly NDVI values for Grass-Cast sites. Resource Title: ANPP. File Name: ANPP.xlsxResource Description: Dataset for annual aboveground net primary productivity (ANPP). Excel sheet is broken into two tabs, 1) 'readme' describing the data, 2) 'ANPP' with the actual data. Resource Title: Grass-Cast_sitelist . File Name: Grass-Cast_sitelist.xlsxResource Description: This provides a list of sites-studies that are currently incorporated into the Database as well as meta-data and contact info associated with the data sets. Includes a 'readme' tab and 'sitelist' tab. Resource Title: Grass-Cast_AgDataCommons_overview. File Name: Grass-Cast_AgDataCommons_download.htmlResource Description: Html document that shows database overview information. This document provides a glimpse of the data tables available within the data resource as well as respective meta-data tables. The R script (R markdown, .Rmd format) that generates the html file, and can be used to upload the Grass-Cast associated Ag Data Commons data files can be downloaded at the 'Grass-Cast R script' zip folder. The Grass-Cast files still need to be locally downloaded before use, but we are looking to make a download automated. Resource Title: Grass-Cast R script . File Name: R_access_script.zipResource Description: R script (in Rmarkdown [Rmd] format) for uploading and looking at Grass-Cast data.

  2. Z

    Mapping forests with different levels of naturalness using machine learning...

    • data.niaid.nih.gov
    Updated Apr 21, 2023
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    Bubnicki, Jakub Witold (2023). Mapping forests with different levels of naturalness using machine learning and landscape data mining - GRASS GIS DB [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_7847615
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    Dataset updated
    Apr 21, 2023
    Dataset authored and provided by
    Bubnicki, Jakub Witold
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The GRASS GIS database containing the input raster layers needed to reproduce the results from the manuscript entitled:

    "Mapping forests with different levels of naturalness using machine learning and landscape data mining" (under review)

    Abstract:

    To conserve biodiversity, it is imperative to maintain and restore sufficient amounts of functional habitat networks. Hence, locating remaining forests with natural structures and processes over landscapes and large regions is a key task. We integrated machine learning (Random Forest) and wall-to-wall open landscape data to scan all forest landscapes in Sweden with a 1 ha spatial resolution with respect to the relative likelihood of hosting High Conservation Value Forests (HCVF). Using independent spatial stand- and plot-level validation data we confirmed that our predictions (ROC AUC in the range of 0.89 - 0.90) correctly represent forests with different levels of naturalness, from deteriorated to those with high and associated biodiversity conservation values. Given ambitious national and international conservation objectives, and increasingly intensive forestry, our model and the resulting wall-to-wall mapping fills an urgent gap for assessing fulfilment of evidence-based conservation targets, spatial planning, and designing forest landscape restoration.

    This database was compiled from the following sources:

    1. HCVF. A database of High Conservation Value Forests in Sweden. Swedish Environmental Protection Agency.

    source: https://geodata.naturvardsverket.se/nedladdning/skogliga_vardekarnor_2016.zip

    1. NMD. National Land Cover Data. Swedish Environmental Protection Agency.

    source: https://www.naturvardsverket.se/en/services-and-permits/maps-and-map-services/national-land-cover-database/

    1. DEM. Terrain Model Download, grid 50+. Lantmateriet, Swedish Ministry of Finance.

    source: https://www.lantmateriet.se/en/geodata/geodata-products/product-list/terrain-model-download-grid-50/

    1. GFC. Global Forest Change. Global Land Analysis and Discovery, University of Maryland.

    source: https://glad.earthengine.app

    1. LIGHTS. A harmonized global nighttime light dataset 1992–2018. Land pollution with night-time lights expressed as calibrated digital numbers (DN).

    source: https://doi.org/10.6084/m9.figshare.9828827.v2

    1. POPULATION. Total Population in Sweden. Statistics Sweden.

    source: https://www.scb.se/en/services/open-data-api/open-geodata/grid-statistics/

    To learn more about the GRASS GIS database structure, see:

    https://grass.osgeo.org/grass82/manuals/grass_database.html

  3. Spearfish Sample Database

    • zenodo.org
    application/gzip
    Updated Aug 30, 2023
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    Larry Batten; Larry Batten (2023). Spearfish Sample Database [Dataset]. http://doi.org/10.5281/zenodo.8296851
    Explore at:
    application/gzipAvailable download formats
    Dataset updated
    Aug 30, 2023
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Larry Batten; Larry Batten
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Spearfish
    Description

    The spearfish sample database is being distributed to provide users with a solid database on which to work for learning the tools of GRASS. This document provides some general information about the database and the map layers available. With the release of GRASS 4.1, the GRASS development staff is pleased to announce that the sample data set spearfish is also being distributed. The spearfish data set covers two topographic 1:24,000 quads in western South Dakota. The names of the quads are Spearfish and Deadwood North, SD. The area covered by the data set is in the vicinity of Spearfish, SD and includes a majority of the Black Hills National Forest (i.e., Mount Rushmore). It is anticipated that enough data layers will be provided to allow users to use nearly all of the GRASS tools on the spearfish data set. A majority of this spearfish database was initially provided to USACERL by the EROS Data Center (EDC) in Sioux Falls, SD. The GRASS Development staff expresses acknowledgement and thanks to: the U.S. Geological Survey (USGS) and EROS Data Center for allowing us to distribute this data with our release of GRASS software; and to the U.S. Census Bureau for their samples of TIGER/Line data and the STF1 data which were used in the development of the TIGER programs and tutorials. Thanks also to SPOT Image Corporation for providing multispectral and panchromatic satellite imagery for a portion of the spearfish data set and for allowing us to distribute this imagery with GRASS software. In addition to the data provided by the EDC and SPOT, researchers at USACERL have dev eloped several new layers, thus enhancing the spearfish data set. To use the spearfish data, when entering GRASS, enter spearfish as your choice for the current location.

    This is the classical GRASS GIS dataset from 1993 covering a part of Spearfish, South Dakota, USA, with raster, vector and point data. The Spearfish data base covers two 7.5 minute topographic sheets in the northern Black Hills of South Dakota, USA. It is in the Universal Transverse Mercator Projection. It was originally created by Larry Batten while he was with the U. S. Geological Survey's EROS Data Center in South Dakota. The data base was enhanced by USA/CERL and cooperators.

     
  4. GRASS GIS database for CASAS-PBDM (www.casasglobal.org) geospatial mapping...

    • zenodo.org
    zip
    Updated Nov 18, 2024
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    Luigi Ponti; Luigi Ponti; Andrew Paul Gutierrez; Andrew Paul Gutierrez; Markus Neteler; Markus Neteler (2024). GRASS GIS database for CASAS-PBDM (www.casasglobal.org) geospatial mapping and analysis [Dataset]. http://doi.org/10.5281/zenodo.14179438
    Explore at:
    zipAvailable download formats
    Dataset updated
    Nov 18, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Luigi Ponti; Luigi Ponti; Andrew Paul Gutierrez; Andrew Paul Gutierrez; Markus Neteler; Markus Neteler
    License

    Attribution-ShareAlike 4.0 (CC BY-SA 4.0)https://creativecommons.org/licenses/by-sa/4.0/
    License information was derived automatically

    Description

    GRASS GIS database for geospatial mapping and analysis of physiologically based demographic modeling (PBDM) implemented by the Center for the Analysis of Sustainable Agricultural Systems (CASAS, www.casasglobal.org).

    The casas_gis_grass8data.zip archive includes data updated for use with GRASS GIS version 8.

  5. c

    Database of invasive annual grass spatial products for the western United...

    • s.cnmilf.com
    • data.usgs.gov
    • +1more
    Updated Jul 6, 2024
    + more versions
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    U.S. Geological Survey (2024). Database of invasive annual grass spatial products for the western United States January 2010 to February 2021 [Dataset]. https://s.cnmilf.com/user74170196/https/catalog.data.gov/dataset/database-of-invasive-annual-grass-spatial-products-for-the-western-united-states-january-2
    Explore at:
    Dataset updated
    Jul 6, 2024
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Area covered
    Western United States, United States
    Description

    Invasive annual grasses (IAGs) present a persistent challenge for the ecological management of rangelands, particularly the imperiled sagebrush biome in western North America. Cheatgrass (Bromus tectorum), medusahead (Taeniatherum caput-medusae), and Ventenata spp. are spreading across sagebrush rangelands and already occupy at least 200,000 kilometers squared (km sq.) of the intermountain west. The loss and degradation of native plant communities caused by IAGs threatens the persistence of sagebrush obligate species such as the Greater Sage-grouse (Centrocercus urophasianus) and pygmy rabbit (Brachylagus idahoensis). IAGs convert sagebrush landscapes to monocultures of non-native grasslands that substantially increase the risk of wildfire and degrade important ecosystem services including forage production and quality, soil stability, and carbon sequestration. As a result, the economic consequences of IAGs are substantial. Successful management of IAG invasions depends on extensive and accurate geospatial data that is accessible and interpretable by those charged with managing landscapes across the sagebrush biome. The past decade has seen a rapid growth in these products, yet researchers and managers both report a persistent research-implementation gap between the availability of products and their application. To address this problem, we first conducted a systematic literature review to inventory spatial products released over the past decade that map cheatgrass, medusahead, and Ventenata within the western U.S. at regional and national scales. We then developed a series of informational data resources to guide land managers in understanding and selecting the best available spatial data for their management needs. This Excel-readable .xlsx file version database product represents a searchable, filterable, and sortable collection of summary information for each IAG spatial data product, published from January 2010 to February 2021, we summarized as part of our review. An additional, machine-readable .csv file version of the database is also available for users.

  6. Marsh Grass Data - Dataset - NFWF Coastal Resilience Open Data Platform

    • resiliencedata.nfwf.org
    Updated Aug 17, 2022
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    resiliencedata.nfwf.org (2022). Marsh Grass Data - Dataset - NFWF Coastal Resilience Open Data Platform [Dataset]. https://resiliencedata.nfwf.org/dataset/marsh-grass
    Explore at:
    Dataset updated
    Aug 17, 2022
    Dataset provided by
    National Fish and Wildlife Foundationhttp://www.nfwf.org/
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Marsh grass morphometric (height and density) data collected by clipping marsh grass within quadrats at Sections 4 and 7 of the shoreline in September 2020, July 2021 and October 2021.

  7. g

    Grass-Cast Database - Data on aboveground net primary productivity (ANPP),...

    • gimi9.com
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    Grass-Cast Database - Data on aboveground net primary productivity (ANPP), climate data, NDVI, and cattle weight gain for Western U.S. rangelands | gimi9.com [Dataset]. https://gimi9.com/dataset/data-gov_9efc92f51bc64bff5cdf8218eb932febc82aac4a/
    Explore at:
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Western United States, United States
    Description

    Resource Title: NDVI_raw . File Name: NDVI_raw.xlsxResource Description: Raw bimonthly NDVI values for Grass-Cast sites. Resource Title: ANPP. File Name: ANPP.xlsxResource Description: Dataset for annual aboveground net primary productivity (ANPP). Excel sheet is broken into two tabs, 1) 'readme' describing the data, 2) 'ANPP' with the actual data. Resource Title: Grass-Cast_sitelist . File Name: Grass-Cast_sitelist.xlsxResource Description: This provides a list of sites-studies that are currently incorporated into the Database as well as meta-data and contact info associated with the data sets. Includes a 'readme' tab and 'sitelist' tab. Resource Title: Grass-Cast_AgDataCommons_overview. File Name: Grass-Cast_AgDataCommons_download.htmlResource Description: Html document that shows database overview information. This document provides a glimpse of the data tables available within the data resource as well as respective meta-data tables. The R script (R markdown, .Rmd format) that generates the html file, and can be used to upload the Grass-Cast associated Ag Data Commons data files can be downloaded at the 'Grass-Cast R script' zip folder. The Grass-Cast files still need to be locally downloaded before use, but we are looking to make a download automated.

  8. g

    Grass Carp (Ctenopharyngodon idella) egg capture data from Great Lakes...

    • gimi9.com
    • data.usgs.gov
    • +2more
    Updated Apr 2, 2025
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    (2025). Grass Carp (Ctenopharyngodon idella) egg capture data from Great Lakes tributaries, 2021-2022 (ver. 1.1, November 2023) [Dataset]. https://gimi9.com/dataset/data-gov_grass-carp-ctenopharyngodon-idella-egg-capture-data-from-great-lakes-tributaries-2021-2022/
    Explore at:
    Dataset updated
    Apr 2, 2025
    Area covered
    The Great Lakes
    Description

    The data includes dates, places, and times of sampling events for eggs of invasive Grass Carp (Ctenopharyngodon idella) in tributaries to the Great Lakes in 2021 and 2022. Reference data on locations and dates sampled, gears used, and effort are included. Developmental stages for a subset of undamaged, fertilized eggs are provided. Tables include common fields to allow for integration into a relational database to aid data extraction and associating data among tables. First posted: September 2023 Revised: November 2023 (version 1.1)

  9. New Orleans Dataset for GRASS GIS

    • zenodo.org
    • data.niaid.nih.gov
    zip
    Updated Jan 24, 2020
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    Brendan Harmon; Brendan Harmon (2020). New Orleans Dataset for GRASS GIS [Dataset]. http://doi.org/10.5281/zenodo.3359642
    Explore at:
    zipAvailable download formats
    Dataset updated
    Jan 24, 2020
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Brendan Harmon; Brendan Harmon
    License

    ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
    License information was derived automatically

    Area covered
    New Orleans
    Description

    New Orleans Dataset for GRASS GIS
    This geospatial dataset contains raster and vector data for New Orleans, Louisiana, USA. The top level directory new-orleans-dataset is a GRASS GIS location for the North American Datum of 1983 (NAD 83) / Louisiana South State Plane Feet with EPSG code 3452. Inside the location there are the PERMANENT mapset with citywide data, a vieux_carre mapset with data for the French Quarter, Python scripts for data processing, data records, a color table, a license file, and readme file.

    Instructions
    Install GRASS GIS, unzip this archive, and move the location into your GRASS GIS database directory. If you are new to GRASS GIS read the first time users guide.

    Data Sources

    License
    This dataset is licensed under the ODC Public Domain Dedication and License 1.0 (PDDL) by Brendan Harmon. The scripts are licensed under the GNU General Public License 3.0 by Brendan Harmon. The graphics are licensed under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0) by Brendan Harmon.

  10. Global import data of Grass,seed

    • volza.com
    csv
    Updated Jun 30, 2025
    + more versions
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    Volza FZ LLC (2025). Global import data of Grass,seed [Dataset]. https://www.volza.com/imports-india/india-import-data-of-grass-seed-from-united-states-of-america
    Explore at:
    csvAvailable download formats
    Dataset updated
    Jun 30, 2025
    Dataset provided by
    Volza
    Authors
    Volza FZ LLC
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Variables measured
    Count of importers, Sum of import value, 2014-01-01/2021-09-30, Count of import shipments
    Description

    12808 Global import shipment records of Grass,seed with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

  11. Z

    Governor's Island Dataset for GRASS GIS

    • data.niaid.nih.gov
    • zenodo.org
    Updated Aug 25, 2021
    + more versions
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    Harmon, Brendan (2021). Governor's Island Dataset for GRASS GIS [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_3940779
    Explore at:
    Dataset updated
    Aug 25, 2021
    Dataset authored and provided by
    Harmon, Brendan
    License

    ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
    License information was derived automatically

    Area covered
    Governors Island
    Description

    Governor's Island Dataset for GRASS GIS This geospatial dataset contains raster and vector data for Governor's Island, New York City, USA. The top level directory governors_island_for_grass is a GRASS GIS location for NAD_1983_StatePlane_New_York_Long_Island_FIPS_3104_Feet in US Surveyor's Feet with EPSG code 2263. Inside the location there is the PERMANENT mapset, a license file, data record, readme file, workspace, color table, category rules, and scripts for data processing. This dataset was created for the course GIS for Designers.

    Instructions Install GRASS GIS, unzip this archive, and move the location into your GRASS GIS database directory. If you are new to GRASS GIS read the first time users guide.

    Data Sources

    https://orthos.dhses.ny.gov/

    https://data.cityofnewyork.us/

    Maps

    Orthophotographs from 2012, 2014, 2016, 2018, and 2020

    Digital elevation model from 2017

    Digital surface models from 2014 and 2017

    Landcover from 2014

    License This dataset is licensed under the ODC Public Domain Dedication and License 1.0 (PDDL) by Brendan Harmon.

  12. Sichuan Dataset for GRASS GIS

    • zenodo.org
    • explore.openaire.eu
    zip
    Updated Jan 24, 2020
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    Brendan Harmon; Brendan Harmon (2020). Sichuan Dataset for GRASS GIS [Dataset]. http://doi.org/10.5281/zenodo.3359070
    Explore at:
    zipAvailable download formats
    Dataset updated
    Jan 24, 2020
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Brendan Harmon; Brendan Harmon
    License

    ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
    License information was derived automatically

    Description

    Sichuan Dataset for GRASS GIS
    This geospatial dataset contains raster and vector data for Sichuan Province, China. The top level directory sichuan-dataset is a GRASS GIS location for WGS 84 / UTM zone 48N with EPSG code 32648. Inside the location there is the PERMANENT mapset, color tables, category tables, a license file, and readme file.

    Instructions
    Install GRASS GIS, unzip this archive, and move the location into your GRASS GIS database
    directory. If you are new to GRASS GIS read the first time users guide.

    Data Sources

    License
    This dataset is licensed under the ODC Public Domain Dedication and License 1.0 (PDDL) by Brendan Harmon.

  13. u

    Data from: Turfgrass Soil Carbon Change Through Time: Raw Data and Code

    • agdatacommons.nal.usda.gov
    • datasets.ai
    • +1more
    txt
    Updated Feb 21, 2024
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    Claire Phillips; Ruying Wang; Tara L.E. Trammell; Joseph Young; Alec Kowalewski (2024). Turfgrass Soil Carbon Change Through Time: Raw Data and Code [Dataset]. http://doi.org/10.15482/USDA.ADC/1528200
    Explore at:
    txtAvailable download formats
    Dataset updated
    Feb 21, 2024
    Dataset provided by
    Ag Data Commons
    Authors
    Claire Phillips; Ruying Wang; Tara L.E. Trammell; Joseph Young; Alec Kowalewski
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Data Description Managed turfgrass is a common component of urban landscapes that is expanding under current land use trends. Previous studies have reported high rates of soil carbon sequestration in turfgrass, but no systematic review has summarized these rates nor evaluated how they change as turfgrass ages. We conducted a meta-analysis of soil carbon sequestration rates from 63 studies. Those data, as well as the code used to analyze them and create figures, are shared here. Dataset Development We conducted a systematic review from Nov 2020 to Jan 2021 using Google Scholar, Web of Science, and the Michigan Turfgrass Information File Database. The search terms targeted were "soil carbon", "carbon sequestration", "carbon storage", or “carbon stock”, with "turf", "turfgrass", "lawn", "urban ecosystem", or "residential", “Fescue”, “Zoysia”, “Poa”, “Cynodon”, “Bouteloua”, “Lolium”, or “Agrostis”. We included only peer-reviewed studies written in English that measured SOC change over one year or longer, and where grass was managed as turf (mowed or clipped regularly). We included studies that sampled to any soil depth, and included several methodologies: small-plot research conducted over a few years (22 datasets from 4 articles), chronosequences of golf courses or residential lawns (39 datasets from 16 articles), and one study that was a variation on a chronosequence method and compiled long-term soil test data provided by golf courses of various ages (3 datasets from Qian & Follett, 2002). In total, 63 datasets from 21 articles met the search criteria. We excluded 1) duplicate reports of the same data, 2) small plot studies that did not report baseline SOC stocks, and 3) pure modeling studies. We included five papers that only measured changes in SOC concentrations, but not areal stocks (i.e., SOC in Mg ha-1). For these papers, we converted from concentrations to stocks using several approaches. For two papers (Law & Patton, 2017; Y. Qian & Follett, 2002) we used estimated bulk densities provided by the authors. For the chronosequences reported in Selhorst & Lal (2011), we used the average bulk density reported by the author. For the 13 choronosequences reported in Selhorst & Lal (2013), we estimated bulk density from the average relationship between percent C and bulk density reported by Selhorst (2011). For Wang et al. (2014), we used bulk density values from official soil survey descriptions. Data provenance In most cases we contacted authors of the studies to obtain the original data. If authors did not reply after two inquiries, or no longer had access to the data, we captured data from published figures using WebPlotDigitizer (Rohatgi, 2021). For three manuscripts the data was already available, or partially available, in public data repositories. Data provenance information is provided in the document "Dataset summaries and citations.docx". Recommended Uses We recommend the following to data users:

    Consult and cite the original manuscripts for each dataset, which often provide additional information about turfgrass management, experimental methods, and environmental context. Original citations are provided in the document "Dataset summaries and citations.docx". For datasets that were previously published in public repositories, consult and cite the original datasets, which may provide additional data on turfgrass management practices, soil nitrogen, and natural reference sites. Links to repositories are in the document "Dataset summaries and citations.docx". Consider contacting the dataset authors to notify them of your plans to use the data, and to offer co-authorship as appropriate.

  14. Global export data of Artificial Grass

    • volza.com
    csv
    Updated Jun 30, 2025
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    Volza FZ LLC (2025). Global export data of Artificial Grass [Dataset]. https://www.volza.com/exports-united-states/united-states-export-data-of-artificial+grass
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    csvAvailable download formats
    Dataset updated
    Jun 30, 2025
    Dataset provided by
    Volza
    Authors
    Volza FZ LLC
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Variables measured
    Count of exporters, Sum of export value, 2014-01-01/2021-09-30, Count of export shipments
    Description

    990 Global export shipment records of Artificial Grass with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

  15. E

    Growth rates of grass species with contrasting photosynthetic pathways and...

    • catalogue.ceh.ac.uk
    • hosted-metadata.bgs.ac.uk
    • +1more
    zip
    Updated Aug 4, 2020
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    R.N. Wade; P. Seed; E. McLaren; E. Wood; P.A. Christin; K. Thompson; M. Rees; Colin P. Osborne (2020). Growth rates of grass species with contrasting photosynthetic pathways and life histories [Dataset]. http://doi.org/10.5285/cb0d7a37-45c5-4645-b5ef-ba097d92fc20
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    zipAvailable download formats
    Dataset updated
    Aug 4, 2020
    Dataset provided by
    NERC EDS Environmental Information Data Centre
    Authors
    R.N. Wade; P. Seed; E. McLaren; E. Wood; P.A. Christin; K. Thompson; M. Rees; Colin P. Osborne
    Time period covered
    Jun 18, 2016 - Jul 26, 2017
    Dataset funded by
    Natural Environment Research Councilhttps://www.ukri.org/councils/nerc
    Description

    This dataset contains sequential biomass harvests from a plant growth experiment carried out under controlled environmental conditions in Sheffield. The experiment was carried out in three parts in 2016 and 2017, and was designed to investigate differences in growth among grasses with the C3 and C4 photosynthetic pathways, and with annual and perennial life histories. Plants were harvested approximately weekly over a period of five weeks. The data include information on the dry biomass of roots and leaves, and the numbers of roots, leaves and shoot branches. Also included is an independent dataset of leaf anatomical characteristics derived from herbarium specimens, which was used to test how mechanical support scales with leaf size. Finally, the data include the phylogenetic relationships among species, which were used in analyses. The work was funded by NERC standard grant NE/N003152/1.

  16. Grass climatic and anatomical data

    • data.niaid.nih.gov
    • datadryad.org
    zip
    Updated Oct 11, 2024
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    Daniel Spitzer; Troy Ocheltree; Sean Gleason (2024). Grass climatic and anatomical data [Dataset]. http://doi.org/10.5061/dryad.547d7wmhs
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    zipAvailable download formats
    Dataset updated
    Oct 11, 2024
    Dataset provided by
    Colorado State University
    United States Department of Agriculture
    Authors
    Daniel Spitzer; Troy Ocheltree; Sean Gleason
    License

    https://spdx.org/licenses/CC0-1.0.htmlhttps://spdx.org/licenses/CC0-1.0.html

    Description

    Members of the grass family Poaceae have adapted to a wide range of habitats and disturbance regimes across the planet. The cellular structure and arrangements of leaves can help explain how plants survive in different climates, but these traits are rarely measured in grasses. Further, most studies are focused on individual species or distantly related species within Poaceae. While this focus can reveal broad adaptations, it also likely to overlook subtle adaptations within more closely-related groups (subfamilies, tribes). This study therefore investigated the scaling relationships between leaf size, vein density, and vessel size in five genera within the subfamily Pooideae. The relationship between leaf area and major vein number was consistent with previous findings (p < 0.05, slope = 0.72 +/- 0.24), as was the scaling coefficient of VLA (slope= -0.46 +/- 0.21). However, several genera exhibited novel anatomical relationships. In Poa and Elymus, minor vein number and leaf length were uncorrelated, whereas in Festuca these traits were positively correlated (slope = 0.82 +/- 0.8). These findings suggest there is important broad-scale and fine-scale variation in leaf hydraulic traits among grasses. Thus, future studies should consider both narrow and broad phylogenetic gradients. Methods Plant Material Selection and Germination

    Genus

    specific epithet

    Genus

    specific epithet

    Genus

    specific epithet

    Bromus

    anomalus

    Festuca

    altaica

    Hesperostipa

    neomexicana

    Bromus

    inermis

    Festuca

    arizonica

    Poa

    alpina

    Bromus

    laevipes

    Festuca

    californica

    Poa

    arida

    Elymus

    canadensis

    Festuca

    idahoensis

    Poa

    compressa

    Elymus

    elymoides

    Festuca

    roemeri

    Poa

    fendleriana

    Elymus

    hysterix

    Festuca

    rubra

    Poa

    glauca

    Elymus

    lanceolatus

    Hesperostipa

    comata

    Poa

    secunda

    Table 1- list of the species germinated for data collection. Five species were selected from each of five genera: Poa, Hesperostipa, Elymus, Festuca, and Bromus. The species selected in this study are all phylogenetically classified as part of the Pooideae subfamily; this was done to ensure that all species were separated by relatively recent evolutionary divergences. Species were also selected such that their habitats spanned a wide range of temperature and precipitation across North America. Plants in this study were grown from seeds provided by the USDA Germplasm Resource Information Network. Unfortunately, not all species germinated regardless of any pre-treatments we attempted and so we were not able to measure 5 species in all genera. Specimens used for gas exchange measurements were grown during the summer of 2016 in 35-cm-length Deep-pots (D60 series, Stuewe and Sons, Inc, Tangent, USA) while the remaining specimens were grown in 10-cm-length “cone”-tainers (Ray Leach Cells 3, Stuewe and Sons, Inc, Tangent, USA), with a 70% to 30% mix of potting soil (Promix HP, Quakertown, USA) and fritted clay (Greens’ Grade Porous Ceramic Topdressing, Buffalo Grove, USA), respectively. This substrate was fertilized with slow-release fertilizer (Osmocote Plus, Scotts Miracle-Gro Company, Marysville, USA) at a ratio of 10 mL fertilizer L-1 soil substrate. The soil substrate was saturated with water before seeds were planted, and specimens were misted until the full expansion of their 4th leaf, then watered 3 times per week. Anatomical Analysis Whole-leaf samples (base to tip of lamina) were collected from five randomly selected individuals of each species for the purpose of anatomical examination. The third or fourth full leaf was harvested from each plant after full expansion. Samples were stored in a solution of formalin, acetic acid, ethanol, and deionized water (FAA fixative solution). Anatomical samples were collected halfway along the lamina length of each specimen, using a razor to cut a cross-sectional sample ~0.5 mm in thickness. The distance from the cross-section and the leaf tip were also recorded. Each cross-sectional sample was stained using safranin-o and fast green. Microscopic images of vein anatomy were taken of half the total lamina width using a ZEISS Axio Scope.A1 in conjunction with ZEN microscope software (Carl Zeiss Microscopy, Germany). Images were measured using Fiji open-source image analysis software. The number of vein orders of each species was quantified, and 1° and 2° veins were classified as ‘major’ veins and 3° and 4° (if present) were classified as ‘minor’ veins. We defined major veins as having at least two of the three following characteristics: 1) vein was at least 50% larger than the smallest vein, 2) vein had bundle sheath extension, 3) vein had at least two xylary vessel elements (i.e metaxylem lacuna) 100% larger than remaining xylary vessel elements. Although this criteria differs slightly from what others have used to define vein orders (Baird et al. 2021), it was developed to help us to objectively assign vein orders since leaf veins don’t always fall neatly into the orders defined previously. However, we are confident that what we defined as ‘major’ is consistent with previous research. The anatomical traits of up to five major veins and minor veins per leaf were measured, but all veins were counted and classified to a vein order. Vein length density was calculated as total vein length per unit area (cm cm-2) by measuring the width of the leaf and then counting the number of veins in the leaf, then multiplying by the length of the leaf. The diameter of the vein including the bundle sheath (μm) was measured as the distance from the outer edge of the bundle sheath to the opposite outer edge. Vein diameter (Dvein, μm) was measured from the innermost edge of the bundle sheath to the opposite inner edge. Diameter of the bundle sheath cells (DBS) was then estimated by subtracting vein diameter measured from the innermost edge from the vein diameter measured from the outermost edge of the bundle sheath. The diameters of vessel elements (Dvessel, μm) were measured as the distance between the inner edge of the vessel element cell wall to the opposite inner edge. Vessel element wall thickness (WT; μm) was also measured. ‘MAJ’ is added to each subscript when the data reported was measured on major veins and ‘min’ added to subscript when the data represents the minor veins. Climate Envelope Analysis Species distribution data were collected from the Global Biodiversity Information Facility (GBIF). Climate data were obtained from weather stations closest to the location where seed was harvested as reported by GBIF. In addition to weather station observations, gridded and interpolated climate data were retrieved from WorldClim at 0.1 degree resolution. For each reported occurrence of each species in the GBIF database, climate data from the closet grid point was retrieved and added to the data set. If two reported occurrences were equally close to the same grid point, that point was only used once to avoid pseudo-replication. Once this data set was generated for each species, the 5th, 50th, and 95th percentiles of each climate variable (see list of variables in supplement) were calculated and used to define the climate envelope of each species. Because we hypothesized that hydraulic architecture would relate to temperature and precipitation of the climate of origin, we focused our investigation on climate variables that would capture these abiotic stressors: MAP, MAT, temperature of the wettest and driest quarters, and MAP of the warmest quarter. Statistical Analysis Scaling relationships were evaluated using the ‘sma’ function in the ‘smatr’ package to account for variability in both x and y variables. In cases where we expected variation between the x and y variables to be explained by a power function, we log10-transformed both axes. This included relationships between leaf dimensions (width, length) and vessel number (Price et al. 2007. Gleason et al. 2018), and between leaf dimensions (i.e., pathlength ~ leaf length) and conduit diameter (Anfodillo et al. 2006). Axes were also log10-transformed in cases where we wanted to test an expected linear or proportional relationship between x and y variables, e.g., the relationship between conduit diameter and cell wall thickness at a given buckling pressure (Brodribb and Holbrook 2005), as well as conduit diameter (or conduit number) between different vein orders (Gleason et al. 2018). Variables were first tested for differences between genera by including ‘genus’ in a model to test for differences between the coefficients – slope (hereafter “scaling coefficient”) and intercept. If no differences were found, then a single scaling relationship was used and reported. If differences were found, then the genera with unique coefficients were removed and analyses were performed individually on each group.

  17. d

    Remote Sensing Shrub/Grass National Land Cover Database (NLCD) Back-in-Time...

    • catalog.data.gov
    • data.amerigeoss.org
    Updated Jul 6, 2024
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    U.S. Geological Survey (2024). Remote Sensing Shrub/Grass National Land Cover Database (NLCD) Back-in-Time (BIT) Shrub Products for the Western U.S., 1985 - 2018 [Dataset]. https://catalog.data.gov/dataset/remote-sensing-shrub-grass-national-land-cover-database-nlcd-back-in-time-bit-shrub-p-1985
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    Dataset updated
    Jul 6, 2024
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Area covered
    Western United States, United States
    Description

    The need to monitor change in sagebrush steppe is urgent due to the increasing impacts of climate change, shifting fire regimes, and management practices on ecosystem health. Remote sensing provides a cost-effective and reliable method for monitoring change through time and attributing changes to drivers. We report an automated method of mapping rangeland fractional component cover over a large portion of the Northern Great Basin, USA, from 1986 to 2016 using a dense Landsat imagery time series. 2012 was excluded from the time-series due to a lack of quality imagery. Our method improved upon the traditional change vector method by considering the legacy of change at each pixel. We evaluate cover trends stratified by climate bin and assess spatial and temporal relationships with climate variables. Finally, we statistically evaluate the minimum time density needed to accurately characterize temporal patterns and relationships with climate drivers. Over the 30-yr period, shrub cover declined and bare ground increased. While few pixels had >10% cover change, a large majority had at least some change. All fractional components had significant spatial relationships with water year precipitation (WYPRCP), maximum temperature (WYTMAX), and minimum temperature (WYTMIN) in all years. Shrub and sagebrush cover in particular respond positively to warming WYTMIN, resulting from the largest increases in WYTMIN being in the coolest and wettest areas, and respond negatively to warming WYTMAX because the largest increases in WYTMAX are in the warmest and driest areas. These data can be used to answer critical questions regarding the influence of climate change and the suitability of management practices. Component products can be downloaded from www.mrlc.gov.

  18. Belgium Cultivated Area: Grass Area

    • ceicdata.com
    Updated Feb 15, 2025
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    CEICdata.com (2025). Belgium Cultivated Area: Grass Area [Dataset]. https://www.ceicdata.com/en/belgium/cultivated-area/cultivated-area-grass-area
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    Dataset updated
    Feb 15, 2025
    Dataset provided by
    CEIC Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Apr 1, 2010 - Apr 1, 2021
    Area covered
    Belgium
    Variables measured
    Agricultural, Fishery and Forestry Production
    Description

    Belgium Cultivated Area: Grass Area data was reported at 474,629.990 ha in 2022. This records a decrease from the previous number of 476,276.790 ha for 2021. Belgium Cultivated Area: Grass Area data is updated yearly, averaging 499,686.530 ha from Apr 2000 (Median) to 2022, with 23 observations. The data reached an all-time high of 536,011.490 ha in 2002 and a record low of 467,836.530 ha in 2017. Belgium Cultivated Area: Grass Area data remains active status in CEIC and is reported by Directorate-General Statistics - Statistics Belgium. The data is categorized under Global Database’s Belgium – Table BE.B011: Cultivated Area.

  19. United States Hay Price: Grass Hay, U.S. Average

    • ceicdata.com
    Updated Feb 15, 2025
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    CEICdata.com (2025). United States Hay Price: Grass Hay, U.S. Average [Dataset]. https://www.ceicdata.com/en/united-states/cattle-feeding-simulator/hay-price-grass-hay-us-average
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    Dataset updated
    Feb 15, 2025
    Dataset provided by
    CEIC Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 1, 2024 - Dec 1, 2024
    Area covered
    United States
    Description

    United States Hay Price: Grass Hay, U.S. Average data was reported at 143.000 USD/Ton in Feb 2025. This records an increase from the previous number of 140.000 USD/Ton for Jan 2025. United States Hay Price: Grass Hay, U.S. Average data is updated monthly, averaging 169.000 USD/Ton from Mar 2022 (Median) to Feb 2025, with 35 observations. The data reached an all-time high of 186.000 USD/Ton in Aug 2022 and a record low of 140.000 USD/Ton in Jan 2025. United States Hay Price: Grass Hay, U.S. Average data remains active status in CEIC and is reported by Economic Research Service. The data is categorized under Global Database’s United States – Table US.RI021: Cattle Feeding Simulator.

  20. Data from: Fire season and drought influence fire effects on invasive...

    • data.niaid.nih.gov
    • search.dataone.org
    • +1more
    zip
    Updated Mar 14, 2025
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    Charlotte Reemts; Justin Havird; Whitney Behr; Megan Clayton; Jamie Foster; Meagan Lesak; Carolyn Whiting; Caroline Farrior; Amelia Wolf (2025). Fire season and drought influence fire effects on invasive grasses: A meta-analysis [Dataset]. http://doi.org/10.5061/dryad.xpnvx0ks4
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    zipAvailable download formats
    Dataset updated
    Mar 14, 2025
    Dataset provided by
    The Nature Conservancy
    National Park Service
    Texas Parks and Wildlife Department
    The University of Texas at Austin
    Texas A&M University System
    Authors
    Charlotte Reemts; Justin Havird; Whitney Behr; Megan Clayton; Jamie Foster; Meagan Lesak; Carolyn Whiting; Caroline Farrior; Amelia Wolf
    License

    https://spdx.org/licenses/CC0-1.0.htmlhttps://spdx.org/licenses/CC0-1.0.html

    Description

    Fire can shape plant communities when species respond differently to fire characteristics like season and intensity. If invasive plants are more vulnerable than native species to specific aspects of fire, managers could use prescribed fires to control non-native species. We conducted a meta-analysis of fire effects on six closely related Afro-Eurasian and Australian grasses (Bothriochloa bladhii, B. ischaemum, B. pertusa, Dichanthium annulatum, D. aristatum, and D. sericeum, collectively “invasive bluestems”) that have invaded grasslands worldwide. Using data from 31 studies (only 3 from their native range), we evaluated whether these grasses (275 effect sizes) responded differently than native grasses (184 effect sizes) to fire season, fuel load, and drought. Original data included 15 different response variables (e.g., biomass, survival) that were converted to standardized mean difference for analysis. Fires in summer, fall, and early winter had negative effects on invasive bluestems; no fire season had significant positive effects. Most data were for B. ischaemum, but the other bluestems may also be vulnerable to summer fire. Native grasses did not show significant negative responses in any month. Drought (Keetch-Byram Drought Index) in the month before fire increased the negative effects of fire on invasive bluestems but not native grasses. Drought after fire led to similar negative effects on both groups. Unexpectedly, fuel load (which influences fire intensity) did not significantly influence fire effects in any analysis. At the fuel loads examined (~600 – 10,000 kg/ha dried herbaceous biomass), fire intensity may have been too low to cause meristem mortality. Between-study heterogeneity was large in all analyses (I2>80%), suggesting that additional factors beyond those reported in the studies influence fire effects. These factors could include plant phenology, fire behavior, weather conditions during the fire, and soil characteristics. Synthesis and applications: Fires during summer and fall, especially during dry conditions, could harm invasive bluestems relative to native grasses, likely due to subtle differences in heat sensitivity, phenology, and drought resistance. Other invasive species may have similar vulnerabilities to specific fire seasons and rainfall conditions that allow the use of fire as a control method. Methods We searched Web of Science, Agricola, Proquest, and GoogleScholar on July 15, 2023 (Table S1). A separate search was conducted in each database for each of the six focal species (Bothriochloa bladhii, Bothriochloa ischaemum, Bothriochloa pertusa, Dichanthium annulatum, Dichanthium aristatum, and Dichanthium sericeum). The search term included scientific and common names as well as “fire OR burn*” (Table S2); search terms were the same for each database. After de-duplication, these initial searches identified 272 unique documents (including theses, dissertations, and unpublished reports). Table S1: Details of databases searched

    Database

    Details

    Web of Science

    Searched ‘topic’. Web of Science Core Collection included Science Citation Index Expanded (SCI-EXPANDED—1900-present); Social Sciences Citation Index (SSCI)—1900-present; Arts & Humanities Citation Index (AHCI)—1975-present; Conference Proceedings Citation Index—Science (CPCI-S)—1900-present; Conference Proceedings Citation Index—Social Science & Humanities (CPCI-SSH)—1990-present; Book Citation Index—Science (BKCI-S)—2005-present; Book Citation Index—Social Sciences & Humanities (BKCI-SSH)—2005-present; Emerging Sources Citation Index (SCI)—2005-present; Current Chemical Reactions (CCR-EXPANDED)—1985-present; Index Chemicus (IC)—1993-present.

    Agricola

    1967 to present; searched ‘TX All Text Fields’

    ProQuest Dissertations and Theses Global

    1974 to present; searched ‘anywhere but full text-NOFT’

    GoogleScholar

    No date restrictions; used search strings with only scientific names (otherwise the search term was too long). Stopping point was chosen when there were 10 non-relevant search results on a page.

    Table S2: Exact search terms used when searching the databases listed in Table 1.

    (“Bothriochloa bladhii” OR “Bothriochloa caucasica” OR “Andropogon bladhii” OR “Bothriochloa intermedia” OR “Andropogon intermedius” OR “Andropogon caucasicus” OR “Caucasian bluestem” OR “Australian beard grass” OR “forest-bluegrass” OR “plains bluestem” OR “purple plume grass”) AND (fire OR burn*)

    (“Bothriochloa ischaemum” OR “Amphilophis ischaemum” OR “Andropogon ischaemum” OR “Dichanthium ischaemum” OR “King Ranch bluestem” OR “KR bluestem” OR “yellow bluestem” OR “plains bluestem” OR “bearded finger grass” OR “dogstooth grass” OR “Turkestan bluestem”) AND (fire OR burn*)

    (“Bothriochloa pertusa” OR “Andropogon pertusus” OR “Holcus pertusus” OR “Indian-bluegrass” OR “pitted beardgrass” OR “hurricane grass” OR “Indian couch grass” OR “Seymour grass” OR “Barbados sourgrass” OR “Antigua hay” OR “sweet pitted grass” OR “silver grass”) AND (fire OR burn*)

    (“Dichanthium annulatum” OR “Andropogon annulatus” OR “Andropogon nodosus” OR “Kleberg bluestem” OR “marvel grass” OR “Diaz bluestem” OR “Hindi grass” OR “ringed dichanthium” OR “sheda grass” OR “medio bluestem” OR “jargu grass” OR “Delhi grass” OR “vuda bluegrass” OR “two-flowered golden-beard” OR “Santa Barbara grass”) AND (fire OR burn*)

    (“Dichanthium aristatum” OR “Andropogon aristatus” OR “Angleton bluestem” OR “Angelton bluestem” OR “wildergrass”) AND (fire OR burn*)

    (“Dichanthium sericeum” OR “Andropogon sericeus” OR “Silky bluestem” OR “Queensland blue grass” OR “silky bluegrass” OR “slender bluegrass” OR “tassel bluegrass”) AND (fire OR burn*)

    Search results were stored in Rayyan (Ouzzani et al., 2016) and were screened manually by one of the authors. Papers were retained for further screening if they included prescribed fire or wildfire, at least one of the focal grass species (in the native or introduced range), and a measure of fire effects on the focal grass. Title and abstract screening eliminated 168 records. Remaining records underwent full-text screening with additional criteria: studies included a comparison between burned and unburned treatments; no additional treatments (e.g., fertilizer, mowing) were applied to the measured populations; studies provided information needed for a quantitative meta-analysis (response means, sample size, and measures of variation); and studies provided the month or exact dates of the fires. Where the necessary data were not available in the papers, we attempted to contact the authors. Full-text screening eliminated 76 records. We then conducted a forward/backward citation search based on the papers included after full-text screening, as well as articles that were themselves eliminated because data were combined across treatments (e.g., averaged across fertilizer levels, n = 20), no variance was reported (n = 8), or fire history was not available (n = 2). These searches were conducted in August 2023 and added three additional papers for a final total of 31 papers that met all search criteria. We extracted data from tables, figures (using WebPlotDigitizer 4.6, Rohatgi 2022), published supplemental datasets, or data provided by the authors. We extracted means for burn and control (unburned) treatments along with sample sizes and standard deviation or standard error. Original data types included basal area, biomass, change in cover, change in frequency, cover, crown area, dead crown density, density, frequency, number of plants, number of seed heads, number of tillers, survival, and stem count. We extracted data for the focal grass species as well as any native grass species presented in the same papers. When multiple papers were published about the same study, we used the data from the most recent publication, but included additional data from earlier papers if they presented different information (e.g., cover vs biomass; more detailed treatment groupings), taking care to not duplicate data. We compiled additional information to serve as moderators (defined in Table S3) including site, latitude, species name, species range of bluestems (native vs introduced), photosynthesis type of native grasses (C3 and C4 species according to Cerros-Tlatilpa et al. [2011] and Osborne et al. [2014]), seeding with native species (excluding focal bluestems), current grazing, study type (experimental, observational), fire type (prescribed fire, wildfire, burn box/burn barrel), time since fire, date/month of fire, and response type (e.g., frequency, cover). Because we found more sites in the northern hemisphere, fire months from the southern hemisphere were adjusted by adding 6 months, making them seasonally equivalent to northern hemisphere months (e.g., “July” is always summer). We also recorded fuel load (dried herbaceous biomass) and soil depth when available. To examine the influence of drought on fire effects, we calculated the Keetch-Byram Drought Index (KBDI, Keetch, and Byram 1968; Alexander 1990) before and after each fire. This drought index represents the amount of rainfall needed to return the soil to saturation and changes daily based on temperature and rainfall. Values range from 0 (no moisture deficit) to 800. We used this drought index instead of rainfall because the index takes mean annual rainfall into account, allowing comparisons among regions with different climates. To calculate the index, we downloaded temperature and precipitation data from the closest weather station(s) to each study site from National Oceanic and Atmospheric Administration’s National Centers for Environmental Information (for

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Chris Dorich; Justin Derner; Greg Torell; Jerry Volesky; Jameson Brennan; David Archer; John Blair; Alan Knapp; Jesse Nippert; David Hartnett; Mitchel McClaran; Greg Maurer; Douglas Moore; Pat Clark; William Parton; Dannele Peck; Lauren Kramer; William Kolby Smith; Emile Elias; Brian Fuchs; Walter H. Schacht; John Hendrickson; Keith Harmoney; Scott Collins; Lauren Baur; Lauren Porensky; Lance Vermeire; Kevin Wilcox (2025). Grass-Cast Database - Data on aboveground net primary productivity (ANPP), climate data, NDVI, and cattle weight gain for Western U.S. rangelands [Dataset]. http://doi.org/10.15482/USDA.ADC/1521120

Data from: Grass-Cast Database - Data on aboveground net primary productivity (ANPP), climate data, NDVI, and cattle weight gain for Western U.S. rangelands

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Dataset updated
May 1, 2025
Dataset provided by
Ag Data Commons
Authors
Chris Dorich; Justin Derner; Greg Torell; Jerry Volesky; Jameson Brennan; David Archer; John Blair; Alan Knapp; Jesse Nippert; David Hartnett; Mitchel McClaran; Greg Maurer; Douglas Moore; Pat Clark; William Parton; Dannele Peck; Lauren Kramer; William Kolby Smith; Emile Elias; Brian Fuchs; Walter H. Schacht; John Hendrickson; Keith Harmoney; Scott Collins; Lauren Baur; Lauren Porensky; Lance Vermeire; Kevin Wilcox
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Area covered
Western United States, United States
Description

Grass-Cast: Experimental Grassland Productivity Forecast for the Great Plains Grass-Cast uses almost 40 years of historical data on weather and vegetation growth in order to project grassland productivity in the Western U.S. More details on the projection model and method can be found at https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecs2.3280.
Every spring, ranchers in the drought‐prone U.S. Great Plains face the same difficult challenge—trying to estimate how much forage will be available for livestock to graze during the upcoming summer grazing season. To reduce this uncertainty in predicting forage availability, we developed an innovative new grassland productivity forecast system, named Grass‐Cast, to provide science‐informed estimates of growing season aboveground net primary production (ANPP). Grass‐Cast uses over 30 yr of historical data including weather and the satellite‐derived normalized vegetation difference index (NDVI)—combined with ecosystem modeling and seasonal precipitation forecasts—to predict if rangelands in individual counties are likely to produce below‐normal, near‐normal, or above‐normal amounts of grass biomass (lbs/ac). Grass‐Cast also provides a view of rangeland productivity in the broader region, to assist in larger‐scale decision‐making—such as where forage resources for grazing might be more plentiful if a rancher’s own region is at risk of drought. Grass‐Cast is updated approximately every two weeks from April through July. Each Grass‐Cast forecast provides three scenarios of ANPP for the upcoming growing season based on different precipitation outlooks. Near real‐time 8‐d NDVI can be used to supplement Grass‐Cast in predicting cumulative growing season NDVI and ANPP starting in mid‐April for the Southern Great Plains and mid‐May to early June for the Central and Northern Great Plains. Here, we present the scientific basis and methods for Grass‐Cast along with the county‐level production forecasts from 2017 and 2018 for ten states in the U.S. Great Plains. The correlation between early growing season forecasts and the end‐of‐growing season ANPP estimate is >50% by late May or early June. In a retrospective evaluation, we compared Grass‐Cast end‐of‐growing season ANPP results to an independent dataset and found that the two agreed 69% of the time over a 20‐yr period. Although some predictive tools exist for forecasting upcoming growing season conditions, none predict actual productivity for the entire Great Plains. The Grass‐Cast system could be adapted to predict grassland ANPP outside of the Great Plains or to predict perennial biofuel grass production. This new experimental grassland forecast is the result of a collaboration between Colorado State University, U.S. Department of Agriculture (USDA), National Drought Mitigation Center, and the University of Arizona. Funding for this project was provided by the USDA Natural Resources Conservation Service (NRCS), USDA Agricultural Research Service (ARS), and the National Drought Mitigation Center. Watch for updates on the Grass-Cast website or on Twitter (@PeckAgEc). Project Contact: Dannele Peck, Director of the USDA Northern Plains Climate Hub, at dannele.peck@ars.usda.gov or 970-744-9043. Resources in this dataset:Resource Title: Cattle weight gain. File Name: Cattle_weight_gains.xlsxResource Description: Cattle weight gain data for Grass-Cast Database. Resource Title: NDVI. File Name: NDVI.xlsxResource Description: Annual NDVI growing season values for Grass-Cast sites. See readme for more information and NDVI_raw for the raw values. Resource Title: NDVI_raw . File Name: NDVI_raw.xlsxResource Description: Raw bimonthly NDVI values for Grass-Cast sites. Resource Title: ANPP. File Name: ANPP.xlsxResource Description: Dataset for annual aboveground net primary productivity (ANPP). Excel sheet is broken into two tabs, 1) 'readme' describing the data, 2) 'ANPP' with the actual data. Resource Title: Grass-Cast_sitelist . File Name: Grass-Cast_sitelist.xlsxResource Description: This provides a list of sites-studies that are currently incorporated into the Database as well as meta-data and contact info associated with the data sets. Includes a 'readme' tab and 'sitelist' tab. Resource Title: Grass-Cast_AgDataCommons_overview. File Name: Grass-Cast_AgDataCommons_download.htmlResource Description: Html document that shows database overview information. This document provides a glimpse of the data tables available within the data resource as well as respective meta-data tables. The R script (R markdown, .Rmd format) that generates the html file, and can be used to upload the Grass-Cast associated Ag Data Commons data files can be downloaded at the 'Grass-Cast R script' zip folder. The Grass-Cast files still need to be locally downloaded before use, but we are looking to make a download automated. Resource Title: Grass-Cast R script . File Name: R_access_script.zipResource Description: R script (in Rmarkdown [Rmd] format) for uploading and looking at Grass-Cast data.

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