County-level land-use projections for the conterminous United States, 2020-2070, used in the 2020 RPA Assessment

Metadata:
Identification_Information:
Citation:
Citation_Information:
Originator: Mihiar, Christopher M.
Originator: Lewis, David J.
Originator: Coulston, John W.
Publication_Date: 2023
Title:
County-level land-use projections for the conterminous United States, 2020-2070, used in the 2020 RPA Assessment
Geospatial_Data_Presentation_Form: tabular digital data
Publication_Information:
Publication_Place: Fort Collins, CO
Publisher: Forest Service Research Data Archive
Online_Linkage: https://doi.org/10.2737/RDS-2023-0026
Description:
Abstract:
Gross land-use change is projected from 2020-2070 at the county level for the conterminous United States (CONUS) based on an empirical econometric model of observed land-use transitions over the 2001-2012 time period. Land-use transition probability is modeled as a function of starting use, land quality, climate, population, and income. Modeled use categories include urban developed, forest, crop, pasture, and range land. Future projections of land-use are made under alternative scenarios of climate and socio-economic conditions as defined in the 2020 USDA Forest Service, Resources Planning Act (RPA) Assessment. Data are provided for each decade as well as 2012-2020 (calibration period). Net change in land-use in each category can be calculated directly from the gross change tables found in this data publication.
Purpose:
County-level land use change projections were developed in support of the USDA Forest Service, Resources Planning Act (RPA) 2020 Assessment.
Supplemental_Information:
For more information about these data, see Mihiar and Lewis (2023).

These data were published 06/21/2023. Minor metadata updates were made on 07/06/2023 and 09/12/2023.
Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 2012
Ending_Date: 2070
Currentness_Reference:
Ground condition
Status:
Progress: Complete
Maintenance_and_Update_Frequency: As needed
Spatial_Domain:
Description_of_Geographic_Extent:
conterminous United States
Bounding_Coordinates:
West_Bounding_Coordinate: -124.73
East_Bounding_Coordinate: -66.95
North_Bounding_Coordinate: 49.38
South_Bounding_Coordinate: 25.54
Keywords:
Theme:
Theme_Keyword_Thesaurus: ISO 19115 Topic Category
Theme_Keyword: biota
Theme:
Theme_Keyword_Thesaurus: National Research & Development Taxonomy
Theme_Keyword: Climate change
Theme_Keyword: Human response
Theme_Keyword: Environment and People
Theme_Keyword: Impact of people on environment
Theme_Keyword: Natural Resource Management & Use
Theme_Keyword: Economics
Theme_Keyword: Landscape management
Theme:
Theme_Keyword_Thesaurus: None
Theme_Keyword: environmental and resource economics
Theme_Keyword: climate econometrics
Theme_Keyword: land-use change
Theme_Keyword: RPA Assessment
Theme_Keyword: Resources Planning Act Assessment
Place:
Place_Keyword_Thesaurus: None
Place_Keyword: conterminous United States
Place_Keyword: contiguous United States
Place_Keyword: CONUS
Access_Constraints: None
Use_Constraints:
These data were collected using funding from the U.S. Government and can be used without additional permissions or fees. If you use these data in a publication, presentation, or other research product please use the following citation:

Mihiar, Christopher M.; Lewis, David J.; Coulston, John W. 2023. County-level land-use projections for the conterminous United States, 2020-2070, used in the 2020 RPA Assessment. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2023-0026
Point_of_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: USDA Forest Service, Southern Research Station
Contact_Person: Chris Mihiar
Contact_Position: Research Economist
Contact_Address:
Address_Type: physical
Address: Forestry Sciences Laboratory
Address: 3401 E. Cornwallis Rd.
City: Research Triangle Park
State_or_Province: NC
Postal_Code: 27709
Country: USA
Contact_Electronic_Mail_Address: Christopher.Mihiar@usda.gov
Contact Instructions: This contact information was current as of original publication date. For current information see Contact Us page on: https://doi.org/10.2737/RDS.
Data_Set_Credit:
Development of these data was funded by the USDA Forest Service, Southern Research Station (18-JV-11330155-23); USDA Forest Service, Pacific Northwest Research Station (14-JV-11261955-059); USDA National Institute of Food and Agriculture (2017-67023-26275); and the USDA Forest Service, Resources Planning Act (RPA) Assessment.


Author Information:

Christopher M. Mihiar
USDA Forest Service, Southern Research Station, Forest Economics and Policy
https://orcid.org/0000-0002-9832-5262

David J. Lewis
Oregon State University, Department of Applied Economics
https://orcid.org/0000-0002-2161-4189

John W. Coulston
USDA Forest Service, Southern Research Station
Cross_Reference:
Citation_Information:
Originator: Mihiar, Christopher M.
Originator: Lewis, David J.
Publication_Date: 2023
Title:
An empirical analysis of US land-use change under multiple climate change scenarios
Geospatial_Data_Presentation_Form: journal article
Series_Information:
Series_Name: Journal of the Agricultural and Applied Economics Association
Online_Linkage: https://doi.org/10.1002/jaa2.82
Cross_Reference:
Citation_Information:
Originator: Riitters, Kurt
Originator: Coulston, John W.
Originator: Mihiar, Christopher M.
Originator: Brooks, Evan B.
Originator: Greenfield, Eric J.
Originator: Nelson, Mark D.
Originator: Domke, Grant M.
Originator: Mockrin, Miranda
Originator: Lewis, David J.
Originator: Nowak, David J.
Publication_Date: 2023
Title:
Chapter 4: Land Resources
Geospatial_Data_Presentation_Form: document
Other_Citation_Details:
4-1 - 4-37
Online_Linkage: https://doi.org/10.2737/WO-GTR-102-Chap4
Larger_Work_Citation:
Citation_Information:
Originator: U.S. Department of Agriculture, Forest Service
Publication_Date: 2023
Title:
Future of America’s Forest and Rangelands: Forest Service 2020 Resources Planning Act Assessment
Geospatial_Data_Presentation_Form: document
Series_Information:
Series_Name: General Technical Report
Issue_Identification: WO-102
Publication_Information:
Publication_Place: Washington, DC
Other_Citation_Details:
348 p.
Online_Linkage: https://doi.org/10.2737/WO-GTR-102
Analytical_Tool:
Analytical_Tool_Description:
R is a language and environment for statistical computing and graphics.
Tool_Access_Information:
Online_Linkage: https://www.R-project.org/
Tool_Access_Instructions:
see website
Tool_Citation:
Citation_Information:
Originator: R Core Team
Publication_Date: 2021
Title:
R: A language and environment for statistical computing
Geospatial_Data_Presentation_Form: software
Publication_Information:
Publication_Place: Vienna, Austria
Publisher: R Foundation for Statistical Computing
Online_Linkage: https://www.R-project.org/
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Data_Quality_Information:
Attribute_Accuracy:
Attribute_Accuracy_Report:
County-level projections are based on statistical estimates with variation not included in these data, therefore individual data points should be used with caution. These data are not to be taken as a prediction of future land-use, and users are encouraged to summarize projections to courser scales (e.g., state, region, etc.).
Logical_Consistency_Report:
The data are logically consistent. The discrete choice econometric methodology used to generate transition probability requires the probability that a unit land either remains in its current use or transitions to an alternative use must sum to one. Furthermore, the total U.S. land base defined at the starting period remains constant throughout the projection period such that only the share of land-use in each category changes. Historically observed land-use types are used to define the plausible transition types (i.e., forest land is only projected to occur in regions that have historically contained forests). These restrictions were used to verify consistency as part of the quality assurance that occurred during data analysis.
Completeness_Report:
The land base used to generate these projections is restricted to non-federal land, and we further restricted the observations to non-public (i.e., privately owned) land. We implicitly assume that public land-use is held constant throughout the projection period.
Lineage:
Source_Information:
Source_Citation:
Citation_Information:
Originator: PRISM Climate Group, Oregon State University
Publication_Date: Unknown
Title:
PRISM climate data
Geospatial_Data_Presentation_Form: raster digital data
Other_Citation_Details:
Accessed 17 March 2017
Online_Linkage: http://prism.oregonstate.edu
Type_of_Source_Media: Online
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 198101
Ending_Date: 202210
Source_Currentness_Reference:
Publication Date
Source_Citation_Abbreviation:
PRISM
Source_Contribution:
Monthly precipitation and mean temperature were downloaded from the “Recent Years” section of the Prism Data Portal. Geospatial rasters in bil format were processed to calculate total annual precipitation and annual mean temperature at the county level for all years between 1981 and 2012.
Source_Information:
Source_Citation:
Citation_Information:
Originator: Abatzoglou, John T.
Originator: Brown, Timothy J.
Publication_Date: 2012
Title:
A comparison of statistical downscaling methods suited for wildfire applications
Geospatial_Data_Presentation_Form: journal article
Series_Information:
Series_Name: International Journal of Climatology
Issue_Identification: 32(5): 772–780
Online_Linkage: https://doi.org/10.1002/joc.2312
Online_Linkage: https://www.climatologylab.org/maca.html
Type_of_Source_Media: Online
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 1984
Ending_Date: 2070
Source_Currentness_Reference:
Publication Date
Source_Citation_Abbreviation:
Abatzoglou and Brown (2012)
Source_Contribution:
MACAV2 Downscaled climate projections for the United States were obtained from this source. These climate projections were processed to match the annual total precipitation and annual mean temperature variables calculated with the historically observed PRISM climate data. Geospatial rasters in netcdf format were used to calculate county level mean values for the period 2012-2070.
Source_Information:
Source_Citation:
Citation_Information:
Originator: Wear, David N.
Originator: Prestemon, Jeffrey P.
Publication_Date: 2019
Title:
Spatiotemporal downscaling of global population and income scenarios for the United States
Geospatial_Data_Presentation_Form: journal article
Series_Information:
Series_Name: PLOS ONE
Issue_Identification: 14(7): e0219242
Online_Linkage: https://doi.org/10.1371/journal.pone.0219242
Online_Linkage: https://doi.org/10.2737/RDS-2019-0041
Type_of_Source_Media: Online
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 2015
Ending_Date: 2070
Source_Currentness_Reference:
Publication Date
Source_Citation_Abbreviation:
Wear and Prestemon (2019)
Source_Contribution:
Downscaled projections of population and income for the United States were obtained from this source. Data are available from:

Wear, David N.; Prestemon, Jeffrey P. 2019. Socioeconomic data for Forest Service 2020 RPA Assessment. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2019-0041
Source_Information:
Source_Citation:
Citation_Information:
Originator: Mihiar, Christopher M.
Originator: Lewis, David J.
Publication_Date: 2021
Title:
Climate, adaptation, and the value of forestland: A national Ricardian analysis of the United States
Geospatial_Data_Presentation_Form: journal article
Series_Information:
Series_Name: Land Economics
Issue_Identification: 97(41): 911-932
Online_Linkage: https://doi.org/10.3368/le.97.4.011620-0004R1
Online_Linkage: https://www.fs.usda.gov/research/treesearch/64147
Type_of_Source_Media: Online
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 1998
Ending_Date: 2014
Source_Currentness_Reference:
Publication Date
Source_Citation_Abbreviation:
Mihiar and Lewis (2021)
Source_Contribution:
Net economic return to forest used in timber production by county and forest type for the conterminous United States.
Source_Information:
Source_Citation:
Citation_Information:
Originator: U.S. Department of Agriculture, Natural Resources Conservation Service
Publication_Date: Unknown
Title:
2012 National Resources Inventory
Geospatial_Data_Presentation_Form: tabular digital data
Publication_Information:
Publication_Place: Washington, DC (and Iowa State University, Ames, IA)
Publisher: U.S. Department of Agriculture, National Resources Inventory, Natural Resources Conservation Service and Center for Survey and Methodology
Online_Linkage: https://www.nrcs.usda.gov/nri
Type_of_Source_Media: Online
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 1982
Ending_Date: 2012
Source_Currentness_Reference:
Publication Date
Source_Citation_Abbreviation:
NRCS
Source_Contribution:
Observed land-use transitions and soil quality were obtained from an internal source at NRCS. These data are not publicly available.
Source_Information:
Source_Citation:
Citation_Information:
Originator: U.S. Census Bureau
Publication_Date: Unknown
Title:
American Community Survey (ACS) Public Use Microdata Sample (PUMS); Survey of Construction
Geospatial_Data_Presentation_Form: tabular digital data
Series_Information:
Series_Name: ACS 1-Year PUMS; ACS 5-Year PUMS; SOC Microdata
Issue_Identification: 2005-2014 ACS PUMS; 1999-2014 SOC Microdata
Publication_Information:
Publisher: U.S. Census Bureau
Online_Linkage: https://www.census.gov/programs-surveys/acs/microdata/access.html
Online_Linkage: https://www.census.gov/construction/chars/microdata.html
Type_of_Source_Media: Online
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 1999
Ending_Date: 2014
Source_Currentness_Reference:
Publication Date
Source_Citation_Abbreviation:
U.S. Census Bureau
Source_Contribution:
Socio-economic data were obtained from the U.S. Census Bureau’s American Community Survey, and housing construction cost data were obtained from the Survey of Construction.
Source_Information:
Source_Citation:
Citation_Information:
Originator: U.S. Bureau of Economic Analysis
Publication_Date: Unknown
Title:
Regional Economic Accounts
Geospatial_Data_Presentation_Form: tabular digital data
Series_Information:
Series_Name: Personal Income (State and Local)
Issue_Identification: CAINC45: Farm Income and Expenses
Publication_Information:
Publication_Place: Suitland, MD
Publisher: Bureau of Economic Analysis
Online_Linkage: https://apps.bea.gov/regional/downloadzip.cfm
Type_of_Source_Media: Online
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 1998
Ending_Date: 2014
Source_Currentness_Reference:
Publication Date
Source_Citation_Abbreviation:
BEA
Source_Contribution:
Data on farm revenues and costs were obtained from this source.
Process_Step:
Process_Description:
Land-use change projections were developed from secondary data sources and used in an empirical econometric framework to assess the impact of changes in climate and socioeconomic variables on gross land-use change. The process was comprised of three stages 1) estimating the relationship between climate, population, and income and the net economic return to productive land-use, 2) estimating the relationship between net economic returns to alternative land-use systems and the probability of transition between uses, and 3) simulating land-use changes under alternative future scenarios of climate and socio-economic conditions. A diagram outlining the analytical framework is included in this package (\Supplements\process_steps_diagram.pdf).

In the first stage, data from BEA, U.S. Census, and Mihiar and Lewis (2021) are used to construct measures for the net economic returns for cropland, urban developed land, and forest land, respectively. Net returns are normalized to a per acre measure to ensure comparability between land-use types. PRISM climate data are downloaded at the pixel level and aggregated to county level then used as explanatory variables in a regression model unique to each land-use type. Annual temperature and precipitation are used in the forest-climate model, heating/cooling degree days for the urban development-climate model, and seasonal average temperature and total precipitation for the crop-climate model. The urban development model also includes population and income variables obtained from the U.S. Census. These separately estimated models parameterize the relationship between observed climate and socio-economic conditions and the net return to productive land-use.

Secondly, plot level repeated observations of land-use choice are obtained from the NRCS. A discrete choice econometric model is estimated for each starting use, resulting in four choice models: forest, crop, pasture, and range. Due to the rarity of urban developed land transitioning away from developed use in the historically observed period, the land-use model assumes that developed land conversion is irreversible. Pasture and rangeland transitions are driven by land quality measures included in the NRCS data, and the remaining transitions are driven by the net returns estimated in the first stage. The output of this stage are transition probabilities that are a function of climate. In the third and final stage, downscaled climate change projections from Abatzoglou and Brown (2012) and population and income projections from Wear and Prestemon (2019) are used to simulate gross land-use change under alternative future scenarios as defined by the RPA Assessment.


SCENARIOS USED

The RPA Assessment uses a set of scenarios (Langner et al. 2020) of coordinated future climate, population, and socioeconomic change to project resource availability and condition over the next fifty years. These scenarios provide a framework for objectively evaluating a plausible range of future resource outcomes. The 2020 RPA Assessment draws from the global scenarios developed by the Intergovernmental Panel on Climate Change (IPCC) to examine the 2020 to 2070 time period (IPCC 2014). Five general circulation models (climate models) were used for the RPA Assessment: CNRM_CM5, HadGEM2_ES365, IPSL_CM5A_MR, MRI_CGCM3, and NorESM1-M. In addition to the climate models, the RPA Assessment employs four RPA scenarios, which pair two alternative climate futures (Representative Concentration Pathways or RCPs) with four alternative futures (Shared Socioeconomic Pathways or SSPs) in the following combinations of U.S. socioeconomic growth: RCP 4.5 and SSP1 (lower warming-moderate, LM), RCP 8.5 and SSP3 (high warming-low, HL), RCP 8.5 and SSP2 (high warming-moderate, HM), and RCP 8.5 and SSP5 (high warming-high, HH).
Source_Used_Citation_Abbreviation:
1. PRISM

2. Abatzoglou and Brown (2012)

3. Wear and Prestemon (2019)

4. Mihiar and Lewis (2021)

5. NRCS

6. U.S. Census

7. BEA

8. Intergovernmental Panel on Climate Change [IPCC]. 2014. Climate change 2014: Synthesis report. Fifth Assessment Report: Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Geneva, Switzerland: Intergovernmental Panel on Climate Change. 151 p. [Accessed 16 December 2019]. https://ar5-syr.ipcc.ch

9. Langner, Linda L.; Joyce, Linda A.; Wear, David N.; Prestemon, Jeffrey P.; Coulson, David; O'Dea, Claire B. 2020. Future scenarios: A technical document supporting the USDA Forest Service 2020 RPA Assessment. Gen. Tech. Rep. RMRS-GTR-412. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 34 p. https://doi.org/10.2737/RMRS-GTR-412
Process_Date: 2023
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Entity_and_Attribute_Information:
Overview_Description:
Entity_and_Attribute_Overview:
Below you will find a list and description of the files included in this data publication.

VARIABLE DESCRIPTION FILE (1)

1. \Data\_variable_descriptions.csv: Comma-separated values (CSV) file containing a list and description of variables found in the data files. (A description of these variables is also provided in the metadata below.)

Columns include:

Filename = name of data file
Variable = name of variable
Units = units (if applicable)
Precision = precision (if applicable)
Description = description of variable


DATA FILES (2)

1. \Data\county_landuse_projections_RPA.json: JSON (JavaScript Object Notation) file containing county-level land-use projections provided as transition tables grouped by time step and county (each decade from 2020-2070 as well as 2012-2020). Projections are provided for each of 20 climate model/RPA scenario combinations. (Data are also provided in RDS format.)

2. \Data\county_landuse_projections_RPA.rds: R Data Single Object (RDS) file containing county-level land-use projections provided as a multi-dimensional array of transition tables nested by time step and county (each decade from 2020-2070 as well as 2012-2020). Projections are provided for each of 20 climate model/RPA scenario combinations. (Data are also provided in JSON format.)

Data Description:

RPA Scenarios are defined by Global Climate Model (GCM), Representative Concentration Pathway (RCP), and Shared Socioeconomic Pathway (SSP). Climate Models: CNRM_CM5 = “wet” climate model; HadGEM2_ES365 = “hot” climate model; IPSL_CM5A_MR = “dry” climate model; MRI_CGCM3 = “least warm” climate model; and NorESM1_M = “middle” climate model. RPA Scenarios are a combination of Representative Concentration Pathways (RCPs) and Shared Socioeconomic Pathways (SSPs): rcp45_ssp1 = Low emissions forcing, medium growth; rcp85_ssp2 = High emissions forcing, medium growth; rcp85_ssp3 = High emissions forcing, low growth; and rcp85_ssp5 = High emissions forcing, high growth.

RPA Scenario = RPA Scenarios are defined by Global Climate Model (GCM), Representative Concentration Pathway (RCP), and Shared Socioeconomic Pathway (SSP). Climate Models:
CNRM_CM5 = “wet” climate model
HadGEM2_ES365 = “hot” climate model
IPSL_CM5A_MR = “dry” climate model
MRI_CGCM3 = “least warm” climate model
NorESM1_M = “middle” climate model

RPA Scenarios are a combination of Representative Concentration Pathways (RCPs) and Shared Socioeconomic Pathways (SSPs):
rcp45_ssp1 = Low emissions forcing; medium growth
rcp85_ssp2 = High emissions forcing; medium growth
rcp85_ssp3 = High emissions forcing; low growth
rcp85_ssp5 = High emissions forcing; high growth

Time Step = Projections are made in 10-year time steps from 2020-2070. For completeness we have included an additional time step (2012-2020) that was used to calibrate the projections for use in subsequent modeling efforts as part of the 2020 RPA Assessment.

U.S. County Identifier = 5 Digit FIPS (Federal Information Processing Standards) code

[6 x 6 transition tables] = area of land transitioned between land use categories (in 100s of acres), where:
cr = cropland
ps = pasture land
rg = rangeland
fr = forest land
ur = urban developed land
t1 = total area of land in use category at starting year
t2 = total area of land in use category at ending year


SUPPLEMENTAL FILES (1)

1. \Supplements\process_steps_diagram.pdf: Portable Document Format file containing a diagram outlining the analytical framework used (supports the process steps described in the metadata).
Entity_and_Attribute_Detail_Citation:
Mihiar, Christopher M.; Lewis, David J. 2023. An empirical analysis of US land-use change under multiple climate change scenarios. Journal of the Agricultural and Applied Economics Association. https://doi.org/10.1002/jaa2.82
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Distribution_Information:
Distributor:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: USDA Forest Service, Research and Development
Contact_Position: Research Data Archivist
Contact_Address:
Address_Type: mailing and physical
Address: 240 West Prospect Road
City: Fort Collins
State_or_Province: CO
Postal_Code: 80526
Country: USA
Contact_Voice_Telephone: see Contact Instructions
Contact Instructions: This contact information was current as of September 2023. For current information see Contact Us page on: https://doi.org/10.2737/RDS.
Resource_Description: RDS-2023-0026
Distribution_Liability:
Metadata documents have been reviewed for accuracy and completeness. Unless otherwise stated, all data and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. However, neither the author, the Archive, nor any part of the federal government can assure the reliability or suitability of these data for a particular purpose. The act of distribution shall not constitute any such warranty, and no responsibility is assumed for a user's application of these data or related materials.

The metadata, data, or related materials may be updated without notification. If a user believes errors are present in the metadata, data or related materials, please use the information in (1) Identification Information: Point of Contact, (2) Metadata Reference: Metadata Contact, or (3) Distribution Information: Distributor to notify the author or the Archive of the issues.
Standard_Order_Process:
Digital_Form:
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Format_Name: CSV
Format_Version_Number: see Format Specification
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Comma-separated values file
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Computer_Contact_Information:
Network_Address:
Network_Resource_Name: https://doi.org/10.2737/RDS-2023-0026
Digital_Form:
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Format_Version_Number: see Format Specification
Format_Specification:
JavaScript Object Notation file
Digital_Transfer_Option:
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Network_Resource_Name: https://doi.org/10.2737/RDS-2023-0026
Digital_Form:
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Format_Version_Number: see Format Specification
Format_Specification:
R Data Single Object file
Digital_Transfer_Option:
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Network_Resource_Name: https://doi.org/10.2737/RDS-2023-0026
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Format_Version_Number: see Format Specification
Format_Specification:
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Digital_Transfer_Option:
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Computer_Contact_Information:
Network_Address:
Network_Resource_Name: https://doi.org/10.2737/RDS-2023-0026
Fees: None
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Metadata_Reference_Information:
Metadata_Date: 20230912
Metadata_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: USDA Forest Service, Southern Research Station
Contact_Person: Chris Mihiar
Contact_Position: Research Economist
Contact_Address:
Address_Type: physical
Address: Forestry Sciences Laboratory
Address: 3401 E. Cornwallis Rd.
City: Research Triangle Park
State_or_Province: NC
Postal_Code: 27709
Country: USA
Contact_Electronic_Mail_Address: Christopher.Mihiar@usda.gov
Contact Instructions: This contact information was current as of original publication date. For current information see Contact Us page on: https://doi.org/10.2737/RDS.
Metadata_Standard_Name: FGDC Biological Data Profile of the Content Standard for Digital Geospatial Metadata
Metadata_Standard_Version: FGDC-STD-001.1-1999
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