Timeline / Data.gov — Environment Datasets

changed Source changed

A new raw object was archived. Both versions are preserved. 5856 line(s) added, 5407 line(s) removed.

Evidence

What changed derived

This diff is not evidence. It was produced by civic-memory.diff_engine 1.1.0 at 2026-10-09T18:22:17+00:00 by normalizing the two archived objects above. The objects are authoritative; this reading of them can be regenerated or deleted without loss. 5856 line(s) added, 5407 line(s) removed.

--- previous
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+ "title": "Cropland Data Layer"
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+ "description": "The USDA National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) is an annual raster, geo-referenced, crop-specific land cover data layer produced using satellite imagery and extensive agricultural ground reference data. The program began in 1997 with limited coverage and in 2008 forward expanded coverage to the entire Continental United States. Please note that no farmer reported data are derivable from the Cropland Data Layer.",
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- ],
- "title": "National Multi Order Hydrologic Position (MOHP - High Resolution) Predictor Data for Groundwater and Groundwater-Quality Modeling"
- },
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- "distribution_titles": [
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- "title": "National Multi Order Hydrologic Position (MOHP - High Resolution) Predictor Data for Groundwater and Groundwater-Quality Modeling",
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- "harvest_record_raw": "https://catalog.data.gov/harvest_record/71ce110a-33fc-4786-b02f-80a0e5cadcbd/raw",
+ "harvest_record": "https://catalog.data.gov/harvest_record/2c4ea53b-f50b-40e1-9772-4ccf144b1058",
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  "has_download": true,
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@@ -3507,7 +534,7 @@
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  "health and nutritional"
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- "harvest_record_raw": "https://catalog.data.gov/harvest_record/9e4635a6-2e10-4479-b0db-1b9d6590af93/raw",
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  "has_download": true,
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  "USDA"
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+ "last_harvested_date": "2026-10-09T16:40:36.471250",
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@@ -3635,101 +662,61 @@
  "accessLevel": "public",
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+ "005:13"
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- "fn": "Nakanishi, Brian",
- "hasEmail": "mailto:brian.nakanishi@usda.gov"
- },
- "description": "<p>Recent USDA/ARS patented technologies on bioenergy and the environment that are available for licensing are described, including summary, contact, benefits, and applications. Updated June 2018. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Bioenergy and Environment - Available Technologies, June 2018.</p> <p>File Name: Bioenergy and Environment.pptx</p><p>Resource Description: Slides presenting title, contact, docket number(s), description, image, benefits, and applications of each new technology.</p></li><br><li><p>Resource Title: Patented Technologies Data Dictionary.</p> <p>File Name: patented-technologies-data-dictionary.csv</p><p>Resource Description: Defines fields, data type, allowed values etc. in patented technology tables.</p></li><br><li><p>Resource Title: Bioenergy and Environment - June 2018.</p> <p>File Name: Bioenergy_and_Environment_2018-06.csv</p><p>Resource Description: Listing of technologies to convert materials to bioproducts from agriculture and food production into fuels and other marketable products, and technologies to monitor and conserve the environment and resources. This CSV file provides the title, technology type, docket number, contact, description, and category for each item. Machine-readable content extracted from corresponding slides accompanying this dataset.</p></li></ul><p></p>",
+ "@type": "vcard:Contact",
+ "fn": "Website Administrator",
+ "hasEmail": "mailto:webadmin@ers.usda.gov"
+ },
+ "describedBy": {
+ "accessURL": "http://www.ers.usda.gov/data-products/population-interaction-zones-for-agriculture-(piza)/documentation.aspx"
+ },
+ "description": "Note: Updates to this data product are discontinued.\r\nThe PIZA codes index small geographic areas (the contiguous 48 States divided up into five-kilometer grid cells) according to the size and proximity of population concentrations.\r\n\r\nWidespread conversion of rural lands to urban uses has drawn attention at all levels of government. To provide information useful for projections of future changes in land use, ERS has created a system to classify remaining farmland into \"population-interaction zones for agriculture\" (PIZA). These zones represent areas of agricultural land use in which urban-related activities (residential, commercial, and industrial) affect the economic and social environment of agriculture. In these zones, interactions between urban-related population and farm production activities tend to increase the value of farmland, change the production practices and enterprises of farm operators, and elevate the probability that farmland will be converted to urban-related uses.",
  "distribution": [
  {
  "@type": "dcat:Distribution",
- "downloadURL": "https://ndownloader.figshare.com/files/43633575",
- "format": "pptx",
+ "downloadURL": "https://www.ers.usda.gov/data-products/population-interaction-zones-for-agriculture-piza/",
  "license": "https://creativecommons.org/publicdomain/zero/1.0/",
- "mediaType": "application/vnd.openxmlformats-officedocument.presentationml.presentation",
- "title": "Bioenergy and Environment.pptx"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://ndownloader.figshare.com/files/43633578",
- "format": "csv",
+ "mediaType": "application/vnd.ms-excel",
+ "title": "Web page with links to Excel files"
+ }
+ ],
+ "identifier": "USDA-ERS-00083",
+ "issued": "2005-06-01",
+ "keyword": [
+ "agricultural economics",
+ "land use",
+ "rural",
+ "urban"
+ ],
  "license": "https://creativecommons.org/publicdomain/zero/1.0/",
- "mediaType": "text/plain",
- "title": "patented-technologies-data-dictionary_0.csv"
- },
- {
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- "downloadURL": "https://ndownloader.figshare.com/files/43633581",
- "format": "csv",
- "license": "https://creativecommons.org/publicdomain/zero/1.0/",
- "mediaType": "text/plain",
- "title": "Bioenergy_and_Environment_2018-06_0.csv"
- }
- ],
- "identifier": "10.15482/USDA.ADC/1529221",
- "keyword": [
- "ARS",
- "Ethanol",
- "Water",
- "aerate",
- "bio-oils",
- "biofuels",
- "chars",
- "chemical",
- "data.gov",
- "emissions",
- "energy",
- "fluids",
- "gas",
- "manure",
- "oil",
- "oxygen",
- "rangeland"
- ],
- "license": "https://creativecommons.org/publicdomain/zero/1.0/",
- "modified": "2023-12-14",
+ "modified": "2019-03-26",
  "programCode": [
- "005:040"
+ "005:041"
  ],
  "publisher": {
  "@type": "org:Organization",
- "name": "Agricultural Research Service"
- },
- "title": "USDA Agricultural Research Service - Patented Bioenergy and Environment Technologies"
- },
- "description": "<p>Recent USDA/ARS patented technologies on bioenergy and the environment that are available for licensing are described, including summary, contact, benefits, and applications. Updated June 2018. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Bioenergy and Environment - Available Technologies, June 2018.</p> <p>File Name: Bioenergy and Environment.pptx</p><p>Resource Description: Slides presenting title, contact, docket number(s), description, image, benefits, and applications of each new technology.</p></li><br><li><p>Resource Title: Patented Technologies Data Dictionary.</p> <p>File Name: patented-technologies-data-dictionary.csv</p><p>Resource Description: Defines fields, data type, allowed values etc. in patented technology tables.</p></li><br><li><p>Resource Title: Bioenergy and Environment - June 2018.</p> <p>File Name: Bioenergy_and_Environment_2018-06.csv</p><p>Resource Description: Listing of technologies to convert materials to bioproducts from agriculture and food production into fuels and other marketable products, and technologies to monitor and conserve the environment and resources. This CSV file provides the title, technology type, docket number, contact, description, and category for each item. Machine-readable content extracted from corresponding slides accompanying this dataset.</p></li></ul><p></p>",
+ "name": "Economic Research Service, Department of Agriculture"
+ },
+ "title": "Population-Interaction Zones for Agriculture (PIZA)"
+ },
+ "description": "Note: Updates to this data product are discontinued.\r\nThe PIZA codes index small geographic areas (the contiguous 48 States divided up into five-kilometer grid cells) according to the size and proximity of population concentrations.\r\n\r\nWidespread conversion of rural lands to urban uses has drawn attention at all levels of government. To provide information useful for projections of future changes in land use, ERS has created a system to classify remaining farmland into \"population-interaction zones for agriculture\" (PIZA). These zones represent areas of agricultural land use in which urban-related activities (residential, commercial, and industrial) affect the economic and social environment of agriculture. In these zones, interactions between urban-related population and farm production activities tend to increase the value of farmland, change the production practices and enterprises of farm operators, and elevate the probability that farmland will be converted to urban-related uses.",
  "distribution_titles": [
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- "patented-technologies-data-dictionary_0.csv",
- "Bioenergy_and_Environment_2018-06_0.csv"
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- "harvest_record_raw": "https://catalog.data.gov/harvest_record/aca0ee62-fec3-472c-9277-08364aadbc47/raw",
+ "Web page with links to Excel files"
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+ "harvest_record": "https://catalog.data.gov/harvest_record/8ee6de7a-2c80-4c55-8433-27f60aaba14d",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/8ee6de7a-2c80-4c55-8433-27f60aaba14d/raw",
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- "oxygen",
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+ "identifier": "USDA-ERS-00083",
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+ "agricultural economics",
+ "land use",
+ "rural",
+ "urban"
+ ],
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@@ -3744,22 +731,22 @@
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- "popularity": 2,
- "publisher": "Agricultural Research Service",
- "slug": "usda-agricultural-research-service-patented-bioenergy-and-environment-technologies",
+ "popularity": 3,
+ "publisher": "Economic Research Service, Department of Agriculture",
+ "slug": "population-interaction-zones-for-agriculture-piza",
  "spatial_centroid": null,
  "spatial_shape": null,
  "theme": [],
- "title": "USDA Agricultural Research Service - Patented Bioenergy and Environment Technologies",
+ "title": "Population-Interaction Zones for Agriculture (PIZA)",
  "type": "dataset"
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+ "_score": 13.205969,
  "_sort": [
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@@ -3767,56 +754,68 @@
  "accessLevel": "public",
  "accessRights": "public",
  "bureauCode": [
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+ "005:13"
  ],
  "contactPoint": {
- "fn": "Rotz, C. Alan",
- "hasEmail": "mailto:al.rotz@ars.usda.gov"
- },
- "description": "<p>The need for a research tool that integrates the many physical and biological processes on a farm has led to the development of the Integrated Farm System Model (IFSM). The model has been used to evaluate a wide variety of technologies and management strategies, and these analyses have been reported in the scientific and farm-trade literature. Systems research in dairy and beef production remains as the primary purpose of this tool, but the model also provides an effective teaching aid. With the model, students gain a better appreciation for the complexity of livestock forage systems. The learn how small changes affect many parts of the system, causing unanticipated results. They may also use the model to develop a more optimum food production system. When used in extension type teaching, producers can learn more about their farms and obtain information useful in strategic planning. By testing and comparing different options with the model, those offering the greatest economic benefit with acceptable environmental impact can be found.</p>\n<p>Input information is supplied to the program through three parameter files. The farm parameter file contains data describing the farm such as crop areas, soil type, equipment and structures used, numbers of animals at various ages, harvest, tillage, and manure handling strategies, and prices for various farm inputs and outputs. The machinery file includes parameters for each machine available for use on a simulated farm.</p>\n<p>Simulation output is available in four files, which contain summary tables, report tables, optional tables, and parameter tables. The summary tables provide average performance, environmental impact, costs, and returns for the years simulated. These values consist of crop yields, feeds produced, feeds bought and sold, manure produced, nutrient losses to the environment, production costs, income from products sold, and the net return or profitability of the farm. Values are provided for the average and standard deviation of each over all simulated years. The report tables provide extensive output information including all the data given in the summary tables. In these tables, values are given for each simulated year of weather as well as the mean and variance over all simulated years. Optional tables are available for a closer inspection of how the components of the full simulation are functioning. These tables include very detailed data, often on a daily basis. Parameter tables summarize the input parameters specified for a given simulation. These tables provide a convenient method of documenting the parameter settings used for a simulation.</p>\n<div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Projected Climate Data for IFSM.</p> <p>File Name: Web Page, url: <a href=\"https://www.ars.usda.gov/research/software/download/?softwareid=497&modecode=80-70-05-00\" target=\"_blank\">https://www.ars.usda.gov/research/software/download/?softwareid=497&modecode=80-70-05-00</a> </p><p>Downscaled climate data (1950 to 2100) are available for 78 locations across the United States formatted for use in IFSM. Each location includes 18 climate files created using 9 general circulation models (GCM) and 2 projected emission scenarios. Emission scenarios include Representative Concentration Pathways (RCP) 4.5 and 8.5 where RCP 4.5 represents a somewhat optimistic outlook for reducing greenhouse gas emissions and 8.5 represents continuing the current trend for emissions. </p></li></ul>",
+ "@type": "vcard:Contact",
+ "fn": "Website Administrator",
+ "hasEmail": "mailto:webadmin@ers.usda.gov"
+ },
+ "describedBy": {
+ "accessURL": "http://www.ers.usda.gov/data-products/phytosanitary-regulation/documentation.aspx"
+ },
+ "description": "NOTE: This data product is no longer being updated.\r\nThis data product identifies which countries, under APHIS phytosanitary rules, are eligible to export to the United States the fresh fruits and vegetables that are most important in the American diet.\r\n\r\nIncreased trade in fresh fruits and vegetables provides U.S. consumers with a variety of benefits including the possibility of improved nutrition by making these products available year-round. Imports of these products are regulated by USDA's Animal and Plant Health and Inspection Service (APHIS) to reduce the risk of inadvertent entry of pests and diseases that could harm agriculture, public health, navigation, irrigation, natural resources, or the environment.\r\n\r\nThis data product identifies which countries, under APHIS phytosanitary rules, are eligible to export to the United States the fresh fruits and vegetables that are most important in the American diet. Current data represent country eligibility as of June 2012. Previous data represent eligibility in June of 2008 through 2011 and in February of 2007. Data on the absolute and relative importance of these countries in international production and trade, individually and in aggregate, are also included. This data product supports the objectives of the Program for Research on the Economics of Invasive Species (PREISM) under which ERS funded research to improve the economic basis of decisionmaking concerning invasive species issues, policies, and programs between 2003 and 2008.",
  "distribution": [
  {
  "@type": "dcat:Distribution",
- "downloadURL": "https://www.ars.usda.gov/research/software/download/?softwareid=497&modecode=80-70-05-00",
- "license": "https://www.usa.gov/publicdomain/label/1.0/",
- "mediaType": "text/html",
- "title": "https://www.ars.usda.gov/research/software/download/?softwareid=497&modecode=80-70-05-00"
+ "downloadURL": "http://www.ers.usda.gov/data-products/phytosanitary-regulation.aspx",
+ "license": "https://creativecommons.org/publicdomain/zero/1.0/",
+ "mediaType": "application/vnd.ms-excel",
+ "title": "Web page with links to Excel files"
  }
  ],
- "identifier": "10113/AA7768",
- "keyword": [
- "ARS",
- "IFSM",
- "Integrated Farm System Model",
- "data.gov"
- ],
- "license": "https://www.usa.gov/publicdomain/label/1.0/",
- "modified": "2024-02-09",
+ "identifier": "USDA-ERS-00082",
+ "issued": "2017-05-01",
+ "keyword": [
+ "agricultural economics",
+ "fruits",
+ "health",
+ "phytosanitary",
+ "trade",
+ "vegetables"
+ ],
+ "license": "https://creativecommons.org/publicdomain/zero/1.0/",
+ "modified": "2020-06-15",
  "programCode": [
- "005:040"
+ "005:041"
  ],
  "publisher": {
  "@type": "org:Organization",
- "name": "Agricultural Research Service"
- },
- "title": "Integrated Farm System Model (IFSM)"
- },
- "description": "<p>The need for a research tool that integrates the many physical and biological processes on a farm has led to the development of the Integrated Farm System Model (IFSM). The model has been used to evaluate a wide variety of technologies and management strategies, and these analyses have been reported in the scientific and farm-trade literature. Systems research in dairy and beef production remains as the primary purpose of this tool, but the model also provides an effective teaching aid. With the model, students gain a better appreciation for the complexity of livestock forage systems. The learn how small changes affect many parts of the system, causing unanticipated results. They may also use the model to develop a more optimum food production system. When used in extension type teaching, producers can learn more about their farms and obtain information useful in strategic planning. By testing and comparing different options with the model, those offering the greatest economic benefit with acceptable environmental impact can be found.</p>\n<p>Input information is supplied to the program through three parameter files. The farm parameter file contains data describing the farm such as crop areas, soil type, equipment and structures used, numbers of animals at various ages, harvest, tillage, and manure handling strategies, and prices for various farm inputs and outputs. The machinery file includes parameters for each machine available for use on a simulated farm.</p>\n<p>Simulation output is available in four files, which contain summary tables, report tables, optional tables, and parameter tables. The summary tables provide average performance, environmental impact, costs, and returns for the years simulated. These values consist of crop yields, feeds produced, feeds bought and sold, manure produced, nutrient losses to the environment, production costs, income from products sold, and the net return or profitability of the farm. Values are provided for the average and standard deviation of each over all simulated years. The report tables provide extensive output information including all the data given in the summary tables. In these tables, values are given for each simulated year of weather as well as the mean and variance over all simulated years. Optional tables are available for a closer inspection of how the components of the full simulation are functioning. These tables include very detailed data, often on a daily basis. Parameter tables summarize the input parameters specified for a given simulation. These tables provide a convenient method of documenting the parameter settings used for a simulation.</p>\n<div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Projected Climate Data for IFSM.</p> <p>File Name: Web Page, url: <a href=\"https://www.ars.usda.gov/research/software/download/?softwareid=497&modecode=80-70-05-00\" target=\"_blank\">https://www.ars.usda.gov/research/software/download/?softwareid=497&modecode=80-70-05-00</a> </p><p>Downscaled climate data (1950 to 2100) are available for 78 locations across the United States formatted for use in IFSM. Each location includes 18 climate files created using 9 general circulation models (GCM) and 2 projected emission scenarios. Emission scenarios include Representative Concentration Pathways (RCP) 4.5 and 8.5 where RCP 4.5 represents a somewhat optimistic outlook for reducing greenhouse gas emissions and 8.5 represents continuing the current trend for emissions. </p></li></ul>",
+ "name": "Economic Research Service, Department of Agriculture"
+ },
+ "references": [
+ "http://www.ers.usda.gov/data-products/phytosanitary-regulation/about-this-product.aspx"
+ ],
+ "title": "Phytosanitary Regulation"
+ },
+ "description": "NOTE: This data product is no longer being updated.\r\nThis data product identifies which countries, under APHIS phytosanitary rules, are eligible to export to the United States the fresh fruits and vegetables that are most important in the American diet.\r\n\r\nIncreased trade in fresh fruits and vegetables provides U.S. consumers with a variety of benefits including the possibility of improved nutrition by making these products available year-round. Imports of these products are regulated by USDA's Animal and Plant Health and Inspection Service (APHIS) to reduce the risk of inadvertent entry of pests and diseases that could harm agriculture, public health, navigation, irrigation, natural resources, or the environment.\r\n\r\nThis data product identifies which countries, under APHIS phytosanitary rules, are eligible to export to the United States the fresh fruits and vegetables that are most important in the American diet. Current data represent country eligibility as of June 2012. Previous data represent eligibility in June of 2008 through 2011 and in February of 2007. Data on the absolute and relative importance of these countries in international production and trade, individually and in aggregate, are also included. This data product supports the objectives of the Program for Research on the Economics of Invasive Species (PREISM) under which ERS funded research to improve the economic basis of decisionmaking concerning invasive species issues, policies, and programs between 2003 and 2008.",
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- "harvest_record_raw": "https://catalog.data.gov/harvest_record/dbfca6b3-53a8-4e29-94ee-abf8a5669f60/raw",
+ "Web page with links to Excel files"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/6b9597e9-032d-43f8-8846-8f60bae50ca0",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/6b9597e9-032d-43f8-8846-8f60bae50ca0/raw",
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+ "trade",
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+ ],
+ "last_harvested_date": "2026-10-09T16:40:07.661229",
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Flow and transport can occur in the vertical, horizontal, or a generally inclined direction. The water flow part of the model can deal with (constant or time-varying) prescribed head and flux boundaries, boundaries controlled by atmospheric conditions, as well as free drainage boundary conditions. Soil surface boundary conditions may change during the simulation from prescribed flux to prescribed head type conditions (and vice versa).</p>\n<p>For solute transport the code supports both (constant and varying) prescribed concentration (Dirichlet or first-type) and concentration flux (Cauchy or third-type) boundary conditions. The dispersion coefficient includes terms reflecting the effects of molecular diffusion and tortuosity.</p>\n<p>The Unsaturated Soil Hydraulic Properties are described using van Genuchten [1980], Brooks and Correy [1964] and modified van Genuchten type analytical functions. Modifications were made to improve the description of hydraulic properties near saturation. The HYDRUS code incorporates hysteresis by using the empirical model introduced by Scott et al. [1983] and Kool and Parker [1987]. This model assumes that drying scanning curves are scaled from the main drying curve, and wetting scanning curves from the main wetting curve. </p>\n<p>HYDRUS also implements a scaling procedure to approximate hydraulic variability in a given soil profile by means of a set of linear scaling transformations which relate the individual soil hydraulic characteristics to those of a reference soil. </p>\n<p>Root growth is simulated by means of a logistic growth function. Water and salinity stress response functions can be defined according to functions proposed by Feddes et al. [1978] or van Genuchten [1987]. </p>\n<p>The governing flow and transport equations are solvednumerically using Galerkin type linear finite element schemes. Integration in time is achieved using an implicit (backwards) finite difference scheme for both saturated and unsaturated conditions. Additional measures are taken to improve solution efficiency for transient problems, including automatic time step adjustment and adherence to preset ranges of the Courant and Peclet numbers. The water content term is evaluated using the mass conservative method proposed by Celia et al. [1990]. Possible options for minimizing numerical oscillations in the transport solutions include upstream weighing, artificial dispersion, and/or performance indexing.</p>\n<p>HYDRUS implements a Marquardt-Levenberg type parameter estimation technique for inverse estimation of selected soil hydraulic and/or solute transport and reaction parameters from measured transient or steady-state flow and/or transport data. The procedure permits several unknown parameters to be estimated from observed water contents, pressure heads, concentrations, and/or instantaneous or cumulative boundary fluxes (e.g., infiltration or outflow data). Additional retention or hydraulic conductivity data, as well as a penalty function for constraining the optimized parameters to remain in some feasible region (Bayesian estimation), can be optionally included in the parameter estimation procedure.</p>\n<div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: HYDRUS-1D download page.</p> <p>File Name: Web Page, url: <a href=\"https://www.ars.usda.gov/research/software/download/?softwareid=97&modecode=20-36-15-00\" target=\"_blank\">https://www.ars.usda.gov/research/software/download/?softwareid=97&modecode=20-36-15-00</a> </p></li></ul>",
+ "@type": "vcard:Contact",
+ "fn": "David Nulph",
+ "hasEmail": "mailto:dhulph@ers.usda.gov"
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+ "dataQuality": true,
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- "mediaType": "text/html",
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- "Bayesian theory",
- "Natural Resources Earth and Environmental Sciences",
- "Richards' equation",
- "adsorption",
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- "cations",
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- "solutes",
- "stress response",
- "temperature",
- "transient flow",
- "unsaturated conditions",
- "user interface",
- "water content",
- "water uptake"
- ],
+ "identifier": "USDA-ERS-00098",
+ "issued": "2019-08-20",
+ "keyword": [
+ "APIs",
+ "REST",
+ "XML"
+ ],
+ "landingPage": {
+ "@type": "Document",
+ "accessURL": "https://www.ers.usda.gov/developer/geospatial-apis/",
+ "title": "USDA ERS GIS Map Services and API User Guide"
+ },
  "license": "https://creativecommons.org/publicdomain/zero/1.0/",
- "modified": "2024-02-15",
+ "modified": "2019-08-20",
  "programCode": [
- "005:040"
+ "005:041"
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  "@type": "org:Organization",
- "name": "Agricultural Research Service"
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- "title": "HYDRUS-1D"
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- "description": "<p>HYDRUS-1D is a Microsoft Windows-based modeling environment for analysis of water flow and solute transport in variably saturated porous media. The software package includes the one-dimensional finite element model HYDRUS (version 7.0) for simulating the movement of water, heat, and multiple solutes in variably saturated media. The model is supported by an interactive graphics-based interface for data-preprocessing, discretization of the soil profile, and graphic presentation of the results.</p>\n<p>The HYDRUS program is a finite element model for simulating theone-dimensional movement of water, heat, and multiple solutes in variably saturated media. The program numerically solves the Richards' equation for saturated-unsaturated water flow and Fickian-based advection dispersion equations for heat and solute transport.</p>\n<p>TheFlow equation incorporates a sink term to account for water uptake by plant roots.</p>\n<p>TheHeat transport equation considers conduction as well as convection with flowing water.</p>\n<p>TheSolute transport equations consider advective-dispersive transport in the liquid phase, and diffusion in the gaseous phase.</p>\n<p>The transport equations also include provisions for:</p>\n<p>Nonlinear\nand/orNonequilibrium reactions between the solid and liquid phases,</p>\n<p>Linear equilibrium reactions between the liquid and gaseous phases,\nZero order production, and\nTwoFirst order degradation reactions:\nOne which is independent of other solutes, and\nOne which provides the coupling between solutes involved in sequential first-order decay reactions.\nThe program may be used to analyze water and solute movement in unsaturated, partially saturated, or fully saturated porous media.</p>\n<p>The flow region itself may be composed of nonuniform soils. Flow and transport can occur in the vertical, horizontal, or a generally inclined direction. The water flow part of the model can deal with (constant or time-varying) prescribed head and flux boundaries, boundaries controlled by atmospheric conditions, as well as free drainage boundary conditions. Soil surface boundary conditions may change during the simulation from prescribed flux to prescribed head type conditions (and vice versa).</p>\n<p>For solute transport the code supports both (constant and varying) prescribed concentration (Dirichlet or first-type) and concentration flux (Cauchy or third-type) boundary conditions. The dispersion coefficient includes terms reflecting the effects of molecular diffusion and tortuosity.</p>\n<p>The Unsaturated Soil Hydraulic Properties are described using van Genuchten [1980], Brooks and Correy [1964] and modified van Genuchten type analytical functions. Modifications were made to improve the description of hydraulic properties near saturation. 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The procedure permits several unknown parameters to be estimated from observed water contents, pressure heads, concentrations, and/or instantaneous or cumulative boundary fluxes (e.g., infiltration or outflow data). Additional retention or hydraulic conductivity data, as well as a penalty function for constraining the optimized parameters to remain in some feasible region (Bayesian estimation), can be optionally included in the parameter estimation procedure.</p>\n<div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: HYDRUS-1D download page.</p> <p>File Name: Web Page, url: <a href=\"https://www.ars.usda.gov/research/software/download/?softwareid=97&modecode=20-36-15-00\" target=\"_blank\">https://www.ars.usda.gov/research/software/download/?softwareid=97&modecode=20-36-15-00</a> </p></li></ul>",
+ "name": "Economic Research Service, Department of Agriculture"
+ },
+ "references": [
+ "https://www.ers.usda.gov/developer/"
+ ],
+ "theme": [
+ "geospatial"
+ ],
+ "title": "USDA ERS GIS Map Services and API User Guide"
+ },
+ "description": "All of the ERS mapping applications, such as the Food Environment Atlas and the Food Access Research Atlas, use map services developed and hosted by ERS as the source for their map content. These map services are open and freely available for use outside of the ERS map applications. Developers can include ERS maps in applications through the use of the map service REST API, and desktop GIS users can use the maps by connecting to the map server directly.",
  "distribution_titles": [
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- "harvest_record": "https://catalog.data.gov/harvest_record/afc88812-18f8-4a77-a843-6e753401f014",
- "harvest_record_raw": "https://catalog.data.gov/harvest_record/afc88812-18f8-4a77-a843-6e753401f014/raw",
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- "geometry",
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- "sand",
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- "solutes",
- "stress response",
- "temperature",
- "transient flow",
- "unsaturated conditions",
- "user interface",
- "water content",
- "water uptake"
- ],
- "last_harvested_date": "2026-10-08T22:44:27.672868",
+ "API access page"
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+ "has_download": false,
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+ "identifier": "USDA-ERS-00098",
+ "keyword": [
+ "APIs",
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+ "last_harvested_date": "2026-10-09T16:40:06.946479",
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@@ -3990,159 +928,130 @@
  "slug": "usda"
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- "popularity": 6,
- "publisher": "Agricultural Research Service",
- "slug": "hydrus-1d",
+ "popularity": 4,
+ "publisher": "Economic Research Service, Department of Agriculture",
+ "slug": "usda-ers-gis-map-services-and-api-user-guide",
  "spatial_centroid": null,
  "spatial_shape": null,
- "theme": [],
- "title": "HYDRUS-1D",
+ "theme": [
+ "geospatial"
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+ "title": "USDA ERS GIS Map Services and API User Guide",
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  "accessLevel": "public",
  "accessRights": "public",
+ "accrualPeriodicity": "R/P1Y",
  "bureauCode": [
- "005:18"
+ "005:13"
  ],
  "contactPoint": {
- "fn": "Crouch, Jo Anne",
- "hasEmail": "mailto:joanne.crouch@ars.usda.gov"
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- "description": "<p>Boxwood plants are affected by many different diseases caused by fungi. Some boxwood diseases are deadly and quickly kill the infected plants, but with others, the plant can survive and even thrive when infected. The fungus that causes volutella blight is the most common of these weak boxwood pathogens. Even the healthiest boxwood plants are infected by the volutella fungus, and often there are no signs that the plants are hurt by the infection. In order to understand why the volutella blight fungus is such a weak pathogen and to understand the genetic mechanisms it uses to interact with boxwood, the complete genome of the volutella fungus was sequenced and characterized. These datasets are generated from the genome sequence of <em>Pseudonectria foliicola</em>, strain ATCC13545, the fungus responsible for volutella disease of boxwood. Datasets include the nuclear genome and mitochondrial genome assemblies (sequenced using Illumina technology), the predicted gene model dataset generated using MAKER, the multiple sequence alignment of single-copy orthologs used for phylogenetic analysis, CMAP files generated from SimpleSynteny analysis of mitogenomes, and high quality photographic images. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Ab initio gene prediction for the draft genome assembly of Pseudonectria foliicola ATCC 13545.</p> <p>File Name: Pfoliicola<em>makerRun.all</em>.maker<em>.proteins.txt</em></p><p><em>Resource Description: Ab initio gene prediction for the draft genome assembly of Pseudonectria foliicola ATCC 13545 was performed using the MAKER2 v.2.31.6 annotation pipeline. Gene training was performed according to the program documentation using SNAP and AUGUSTUS v.3.2.1 (Stanke et al. 2004) using Fusarium graminearum as a model organism.</em></p></li><em><br></em><li><em><p>Resource Title: SimpleSynteny CMAP file of Pseudonectria foliicola mitogenome.</p> </em><p><em>File Name: 1VB.cmap</em>.txt</p></li><br><li><p>Resource Title: SimpleSynteny CMAP file of Dactylonectria macrodidyma mitogenome.</p> <p>File Name: 2DM.cmap<em>.txt</em></p></li><em><br></em><li><em><p>Resource Title: SimpleSynteny CMAP file of Fusarium graminearum mitogenome.</p> </em><p><em>File Name: 3FG.cmap</em>.txt</p></li><br><li><p>Resource Title: Genome assembly of Pseudonectria foliicola ATCC 13545 .</p> <p>File Name: Volutella foliicola_ATCC13545_genome assembly.txt</p><p>Resource Description: The genome of <em>Pseudonectria foliicola</em> ATCC 13545 was sequenced on an Illumina MiSeq from gDNA used to construct a TruSeq Nano DNA LT Library. The library was sequenced on an Illumina MiSeq in two independent runs using paired-end 300-cycle reagent cartridge v.3 (Illumina, Inc.). Reads were processed and assembled using CLC Genomics Workbench version 7.5.1 (CLC Bio, Boston, MA, USA). Illumina adapters were trimmed and low quality reads (Phred score <0.05) were removed. Summary statistics for the draft genome were generated using CLC Genomics Workbench, PRINSEQ v.0.20.4 and QUAST. Completeness of the <em>P. foliicola</em> draft genome assembly was evaluated using BUSCO v.1.1b1 </p></li><br><li><p>Resource Title: Photograph of Pseudonectria foliicola growing from boxwood leaf.</p> <p>File Name: Pseudonectria_foliicola_leaf1.jpg</p></li><br><li><p>Resource Title: Photograph of Pseudonectria foliicola growing from boxwood leaf.</p> <p>File Name: Pseudonectria_foliicola_leaf2.jpg</p></li><br><li><p>Resource Title: Multiple sequence alignment of single-copy orthologs.</p> <p>File Name: All_OrthMCL2316_PHYLIP_alignment.txt</p><p>Resource Description: Fourteen publicly available fungal genomes were used to examine the phylogenetic placement of <em>Pseudonectria foliicola</em> through the analysis of single copy orthologous genes. 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The alignment is provided in PHYLIP format.</p></li></ul><p></p>",
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Gene training was performed according to the program documentation using SNAP and AUGUSTUS v.3.2.1 (Stanke et al. 2004) using Fusarium graminearum as a model organism.</em></p></li><em><br></em><li><em><p>Resource Title: SimpleSynteny CMAP file of Pseudonectria foliicola mitogenome.</p> </em><p><em>File Name: 1VB.cmap</em>.txt</p></li><br><li><p>Resource Title: SimpleSynteny CMAP file of Dactylonectria macrodidyma mitogenome.</p> <p>File Name: 2DM.cmap<em>.txt</em></p></li><em><br></em><li><em><p>Resource Title: SimpleSynteny CMAP file of Fusarium graminearum mitogenome.</p> </em><p><em>File Name: 3FG.cmap</em>.txt</p></li><br><li><p>Resource Title: Genome assembly of Pseudonectria foliicola ATCC 13545 .</p> <p>File Name: Volutella foliicola_ATCC13545_genome assembly.txt</p><p>Resource Description: The genome of <em>Pseudonectria foliicola</em> ATCC 13545 was sequenced on an Illumina MiSeq from gDNA used to construct a TruSeq Nano DNA LT Library. The library was sequenced on an Illumina MiSeq in two independent runs using paired-end 300-cycle reagent cartridge v.3 (Illumina, Inc.). Reads were processed and assembled using CLC Genomics Workbench version 7.5.1 (CLC Bio, Boston, MA, USA). Illumina adapters were trimmed and low quality reads (Phred score <0.05) were removed. Summary statistics for the draft genome were generated using CLC Genomics Workbench, PRINSEQ v.0.20.4 and QUAST. 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The genome of the <em>Dactylonectria macrodidyma</em> JAC15-245 (NCBI GenBank accession JYGD00000000 was downloaded and used to generate gene models using the program MAKER. The program OrthoMCL identified 16,356 gene clusters, from which 1,884 orthologous genes were shared across all 15 fungal species. From these shared gene clusters, 1,511 orthologous genes were found as single copy genes and used for the phylogenetic analysis. All proteomes were searched against each other using BLASTp and clustered in orthologous gene sets using OrthoMCL v1.4 in the iPLANT Discovery Environment. Single copy genes found in all 15 fungal proteomes were extracted from the orthologous dataset and amino acid alignments were performed using MUSCLE v3.8.31. Gblocks v.0.91b was used to remove ambiguously aligned regions using less stringent settings. The final aligned dataset after removal of ambiguously aligned regions consists of 388.7 Mb. The alignment is provided in PHYLIP format.</p></li></ul><p></p>",
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