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.
Resource Title: Bioenergy and Environment - Available Technologies, June 2018.
File Name: Bioenergy and Environment.pptx
Resource Description: Slides presenting title, contact, docket number(s), description, image, benefits, and applications of each new technology.
Resource Title: Patented Technologies Data Dictionary.
File Name: patented-technologies-data-dictionary.csv
Resource Description: Defines fields, data type, allowed values etc. in patented technology tables.
Resource Title: Bioenergy and Environment - June 2018.
File Name: Bioenergy_and_Environment_2018-06.csv
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.
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.
Resource Title: Bioenergy and Environment - Available Technologies, June 2018.
File Name: Bioenergy and Environment.pptx
Resource Description: Slides presenting title, contact, docket number(s), description, image, benefits, and applications of each new technology.
Resource Title: Patented Technologies Data Dictionary.
File Name: patented-technologies-data-dictionary.csv
Resource Description: Defines fields, data type, allowed values etc. in patented technology tables.
Resource Title: Bioenergy and Environment - June 2018.
File Name: Bioenergy_and_Environment_2018-06.csv
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.
The\u00a0need\u00a0for\u00a0a\u00a0research\u00a0tool\u00a0that\u00a0integrates\u00a0the\u00a0many\u00a0physical\u00a0and\u00a0biological\u00a0processes\u00a0on\u00a0a 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.
\nInput 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.
\nSimulation 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.
\nResource Title: Projected Climate Data for IFSM.
File Name: Web Page, url: https://www.ars.usda.gov/research/software/download/?softwareid=497&modecode=80-70-05-00
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.
The\u00a0need\u00a0for\u00a0a\u00a0research\u00a0tool\u00a0that\u00a0integrates\u00a0the\u00a0many\u00a0physical\u00a0and\u00a0biological\u00a0processes\u00a0on\u00a0a 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.
\nInput 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.
\nSimulation 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.
\nResource Title: Projected Climate Data for IFSM.
File Name: Web Page, url: https://www.ars.usda.gov/research/software/download/?softwareid=497&modecode=80-70-05-00
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.
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.
\nThe 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.
\nTheFlow equation incorporates a sink term to account for water uptake by plant roots.
\nTheHeat transport equation considers conduction as well as convection with flowing water.
\nTheSolute transport equations consider advective-dispersive transport in the liquid phase, and diffusion in the gaseous phase.
\nThe transport equations also include provisions for:
\nNonlinear\nand/orNonequilibrium reactions between the solid and liquid phases,
\nLinear 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.
\nThe 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).
\nFor 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.
\nThe 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.
\nHYDRUS 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.
\nRoot 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].
\nThe 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.
\nHYDRUS 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.
\nResource Title: HYDRUS-1D download page.
File Name: Web Page, url: https://www.ars.usda.gov/research/software/download/?softwareid=97&modecode=20-36-15-00
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.
\nThe 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.
\nTheFlow equation incorporates a sink term to account for water uptake by plant roots.
\nTheHeat transport equation considers conduction as well as convection with flowing water.
\nTheSolute transport equations consider advective-dispersive transport in the liquid phase, and diffusion in the gaseous phase.
\nThe transport equations also include provisions for:
\nNonlinear\nand/orNonequilibrium reactions between the solid and liquid phases,
\nLinear 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.
\nThe 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).
\nFor 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.
\nThe 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.
\nHYDRUS 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.
\nRoot 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].
\nThe 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.
\nHYDRUS 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.
\nResource Title: HYDRUS-1D download page.
File Name: Web Page, url: https://www.ars.usda.gov/research/software/download/?softwareid=97&modecode=20-36-15-00
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 Pseudonectria foliicola, 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.
Resource Title: Ab initio gene prediction for the draft genome assembly of Pseudonectria foliicola ATCC 13545.
File Name: PfoliicolamakerRun.all.maker.proteins.txt
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.
Resource Title: SimpleSynteny CMAP file of Pseudonectria foliicola mitogenome.
File Name: 1VB.cmap.txt
Resource Title: SimpleSynteny CMAP file of Dactylonectria macrodidyma mitogenome.
File Name: 2DM.cmap.txt
Resource Title: SimpleSynteny CMAP file of Fusarium graminearum mitogenome.
File Name: 3FG.cmap.txt
Resource Title: Genome assembly of Pseudonectria foliicola ATCC 13545 .
File Name: Volutella foliicola_ATCC13545_genome assembly.txt
Resource Description: The genome of Pseudonectria foliicola 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 P. foliicola draft genome assembly was evaluated using BUSCO v.1.1b1
Resource Title: Photograph of Pseudonectria foliicola growing from boxwood leaf.
File Name: Pseudonectria_foliicola_leaf1.jpg
Resource Title: Photograph of Pseudonectria foliicola growing from boxwood leaf.
File Name: Pseudonectria_foliicola_leaf2.jpg
Resource Title: Multiple sequence alignment of single-copy orthologs.
File Name: All_OrthMCL2316_PHYLIP_alignment.txt
Resource Description: Fourteen publicly available fungal genomes were used to examine the phylogenetic placement of Pseudonectria foliicola through the analysis of single copy orthologous genes. For this analysis, the predicted proteomes of Aspergillus nidulans FGSC A4 (ASM114v1), Botrytis cinerea BcDW1 (Assembly GCA000349525), Fusarium graminearum PH-1 (GCA000240135), Macrophomina phaseolina MS6 (GCA000302655), Magnaporthe oryzeae 70-15 (MG8), Neurospora crassa (GCA000786625), Penicillium oxalicum 114-2 (GCA000346795), Pyrenophora tritici-repentis (GCA000149985), Sclerotinia sclerotiorum 1980 UF-70 (ASM1469v1), Trichoderma reesei RUT C-30 (GCA000513815), Ustilago maydis 521 (UM1), Verticillium dahliae JR2 (GCA000400815) and Yarrowia lipolytica CLIB122 (GCA000002525) were downloaded from the EnsemblFungi database (https://fungi.ensembl.org/index.html). The genome of the Dactylonectria macrodidyma 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.
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 Pseudonectria foliicola, 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.
Resource Title: Ab initio gene prediction for the draft genome assembly of Pseudonectria foliicola ATCC 13545.
File Name: PfoliicolamakerRun.all.maker.proteins.txt
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.
Resource Title: SimpleSynteny CMAP file of Pseudonectria foliicola mitogenome.
File Name: 1VB.cmap.txt
Resource Title: SimpleSynteny CMAP file of Dactylonectria macrodidyma mitogenome.
File Name: 2DM.cmap.txt
Resource Title: SimpleSynteny CMAP file of Fusarium graminearum mitogenome.
File Name: 3FG.cmap.txt
Resource Title: Genome assembly of Pseudonectria foliicola ATCC 13545 .
File Name: Volutella foliicola_ATCC13545_genome assembly.txt
Resource Description: The genome of Pseudonectria foliicola 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 P. foliicola draft genome assembly was evaluated using BUSCO v.1.1b1
Resource Title: Photograph of Pseudonectria foliicola growing from boxwood leaf.
File Name: Pseudonectria_foliicola_leaf1.jpg
Resource Title: Photograph of Pseudonectria foliicola growing from boxwood leaf.
File Name: Pseudonectria_foliicola_leaf2.jpg
Resource Title: Multiple sequence alignment of single-copy orthologs.
File Name: All_OrthMCL2316_PHYLIP_alignment.txt
Resource Description: Fourteen publicly available fungal genomes were used to examine the phylogenetic placement of Pseudonectria foliicola through the analysis of single copy orthologous genes. For this analysis, the predicted proteomes of Aspergillus nidulans FGSC A4 (ASM114v1), Botrytis cinerea BcDW1 (Assembly GCA000349525), Fusarium graminearum PH-1 (GCA000240135), Macrophomina phaseolina MS6 (GCA000302655), Magnaporthe oryzeae 70-15 (MG8), Neurospora crassa (GCA000786625), Penicillium oxalicum 114-2 (GCA000346795), Pyrenophora tritici-repentis (GCA000149985), Sclerotinia sclerotiorum 1980 UF-70 (ASM1469v1), Trichoderma reesei RUT C-30 (GCA000513815), Ustilago maydis 521 (UM1), Verticillium dahliae JR2 (GCA000400815) and Yarrowia lipolytica CLIB122 (GCA000002525) were downloaded from the EnsemblFungi database (https://fungi.ensembl.org/index.html). The genome of the Dactylonectria macrodidyma 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.
Phenotypic, genotypic, and environment data for the 2015 field season: The data is stored in CyVerse.
\nData types in this directory tree are: hybrid and inbred agronomic and performance traits; inbred genotypic data; and environmental (soil, weather) data collected from the Genomes To Fields (G2F) project cooperators. G2F is an umbrella initiative to support translation of maize (Zea mays) genomic information for the benefit of growers, consumers and society. This public-private partnership is building on publicly funded corn genome sequencing projects to develop approaches to understand the functions of corn genes and specific alleles across environments. Ultimately this information will be used to enable accurate prediction of the phenotypes of corn plants in diverse environments. There are many dimensions to the over-arching goal of understanding genotype-by-environment (GxE) interactions, including which genes impact which traits and trait components, how genes interact among themselves (GxG), the relevance of specific genes under different growing conditions, and how these genes influence plant growth during various stages of development.
Resource Title: CyVerse Genomes To Fields 2015 dataset download.
File Name: Web Page, url: http://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/Carolyn_Lawrence_Dill_G2F_Mar_2017
Dataset (csv) and metadata (BibTex, Endnote) data downloads. See _readme.txt for file contents.
Phenotypic, genotypic, and environment data for the 2015 field season: The data is stored in CyVerse.
\nData types in this directory tree are: hybrid and inbred agronomic and performance traits; inbred genotypic data; and environmental (soil, weather) data collected from the Genomes To Fields (G2F) project cooperators. G2F is an umbrella initiative to support translation of maize (Zea mays) genomic information for the benefit of growers, consumers and society. This public-private partnership is building on publicly funded corn genome sequencing projects to develop approaches to understand the functions of corn genes and specific alleles across environments. Ultimately this information will be used to enable accurate prediction of the phenotypes of corn plants in diverse environments. There are many dimensions to the over-arching goal of understanding genotype-by-environment (GxE) interactions, including which genes impact which traits and trait components, how genes interact among themselves (GxG), the relevance of specific genes under different growing conditions, and how these genes influence plant growth during various stages of development.
Resource Title: CyVerse Genomes To Fields 2015 dataset download.
File Name: Web Page, url: http://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/Carolyn_Lawrence_Dill_G2F_Mar_2017
Dataset (csv) and metadata (BibTex, Endnote) data downloads. See _readme.txt for file contents.
Phenotypic, genotypic, and environment data for the 2016 field season: The data is stored in CyVerse.
\nData types in this directory tree are: hybrid and inbred agronomic and performance traits; inbred genotypic data; and environmental (soil, weather) data collected from the Genomes To Fields (G2F) project cooperators. G2F is an umbrella initiative to support translation of maize (Zea mays) genomic information for the benefit of growers, consumers and society. This public-private partnership is building on publicly funded corn genome sequencing projects to develop approaches to understand the functions of corn genes and specific alleles across environments. Ultimately this information will be used to enable accurate prediction of the phenotypes of corn plants in diverse environments. There are many dimensions to the over-arching goal of understanding genotype-by-environment (GxE) interactions, including which genes impact which traits and trait components, how genes interact among themselves (GxG), the relevance of specific genes under different growing conditions, and how these genes influence plant growth during various stages of development.
Resource Title: CyVerse Genomes To Fields 2016 dataset download.
File Name: Web Page, url: http://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/GenomesToFields_G2F_2016_Data_Mar_2018
Dataset (csv) and metadata (BibTex, Endnote) data downloads. See _readme.txt for file contents.
Phenotypic, genotypic, and environment data for the 2016 field season: The data is stored in CyVerse.
\nData types in this directory tree are: hybrid and inbred agronomic and performance traits; inbred genotypic data; and environmental (soil, weather) data collected from the Genomes To Fields (G2F) project cooperators. G2F is an umbrella initiative to support translation of maize (Zea mays) genomic information for the benefit of growers, consumers and society. This public-private partnership is building on publicly funded corn genome sequencing projects to develop approaches to understand the functions of corn genes and specific alleles across environments. Ultimately this information will be used to enable accurate prediction of the phenotypes of corn plants in diverse environments. There are many dimensions to the over-arching goal of understanding genotype-by-environment (GxE) interactions, including which genes impact which traits and trait components, how genes interact among themselves (GxG), the relevance of specific genes under different growing conditions, and how these genes influence plant growth during various stages of development.
Resource Title: CyVerse Genomes To Fields 2016 dataset download.
File Name: Web Page, url: http://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/GenomesToFields_G2F_2016_Data_Mar_2018
Dataset (csv) and metadata (BibTex, Endnote) data downloads. See _readme.txt for file contents.
Phenotypic, genotypic, and environment data for the 2014 field season: The data is stored in CyVerse.
\nData types in this directory tree are: dimension and width profile data collected from scanned images of ears, cobs, and kernels collected from the Genomes To Fields (G2F) project cooperators. G2F is an umbrella initiative to support translation of maize (Zea mays) genomic information for the benefit of growers, consumers and society. This public-private partnership is building on publicly funded corn genome sequencing projects to develop approaches to understand the functions of corn genes and specific alleles across environments. Ultimately this information will be used to enable accurate prediction of the phenotypes of corn plants in diverse environments. There are many dimensions to the over-arching goal of understanding genotype-by-environment (GxE) interactions, including which genes impact which traits and trait components, how genes interact among themselves (GxG), the relevance of specific genes under different growing conditions, and how these genes influence plant growth during various stages of development.
Resource Title: CyVerse Genomes To Fields 2014 dataset download.
File Name: Web Page, url: http://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/Carolyn_Lawrence_Dill_G2F_Nov_2016_V.3
Dataset (csv, h5, gz) and metadata (BibTex/Endnote) downloads. See _readme.txt for file contents.
Phenotypic, genotypic, and environment data for the 2014 field season: The data is stored in CyVerse.
\nData types in this directory tree are: dimension and width profile data collected from scanned images of ears, cobs, and kernels collected from the Genomes To Fields (G2F) project cooperators. G2F is an umbrella initiative to support translation of maize (Zea mays) genomic information for the benefit of growers, consumers and society. This public-private partnership is building on publicly funded corn genome sequencing projects to develop approaches to understand the functions of corn genes and specific alleles across environments. Ultimately this information will be used to enable accurate prediction of the phenotypes of corn plants in diverse environments. There are many dimensions to the over-arching goal of understanding genotype-by-environment (GxE) interactions, including which genes impact which traits and trait components, how genes interact among themselves (GxG), the relevance of specific genes under different growing conditions, and how these genes influence plant growth during various stages of development.
Resource Title: CyVerse Genomes To Fields 2014 dataset download.
File Name: Web Page, url: http://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/Carolyn_Lawrence_Dill_G2F_Nov_2016_V.3
Dataset (csv, h5, gz) and metadata (BibTex/Endnote) downloads. See _readme.txt for file contents.
A subset of ~30 inbreds were evaluated in 2014 and 2015 to develop an image based ear phenotyping tool. The data is stored in CyVerse.
\nData types in this directory tree are: dimension and width profile data collected from scanned images of ears, cobs, and kernels collected from the Genomes To Fields (G2F) project cooperators. G2F is an umbrella initiative to support translation of maize (Zea mays) genomic information for the benefit of growers, consumers and society. This public-private partnership is building on publicly funded corn genome sequencing projects to develop approaches to understand the functions of corn genes and specific alleles across environments. Ultimately this information will be used to enable accurate prediction of the phenotypes of corn plants in diverse environments. There are many dimensions to the over-arching goal of understanding genotype-by-environment (GxE) interactions, including which genes impact which traits and trait components, how genes interact among themselves (GxG), the relevance of specific genes under different growing conditions, and how these genes influence plant growth during various stages of development.
Resource Title: CyVerse Genomes To Fields Inbred Ear Imaging 2017 dataset download.
File Name: Web Page, url: http://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/Edgar_Spalding_G2F_Inbred_Ear_Imaging_June_2017
Dataset (csv, tar.gz) and metadata (BibTex/Endnote) downloads. See _readme.txt for file contents.
A subset of ~30 inbreds were evaluated in 2014 and 2015 to develop an image based ear phenotyping tool. The data is stored in CyVerse.
\nData types in this directory tree are: dimension and width profile data collected from scanned images of ears, cobs, and kernels collected from the Genomes To Fields (G2F) project cooperators. G2F is an umbrella initiative to support translation of maize (Zea mays) genomic information for the benefit of growers, consumers and society. This public-private partnership is building on publicly funded corn genome sequencing projects to develop approaches to understand the functions of corn genes and specific alleles across environments. Ultimately this information will be used to enable accurate prediction of the phenotypes of corn plants in diverse environments. There are many dimensions to the over-arching goal of understanding genotype-by-environment (GxE) interactions, including which genes impact which traits and trait components, how genes interact among themselves (GxG), the relevance of specific genes under different growing conditions, and how these genes influence plant growth during various stages of development.
Resource Title: CyVerse Genomes To Fields Inbred Ear Imaging 2017 dataset download.
File Name: Web Page, url: http://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/Edgar_Spalding_G2F_Inbred_Ear_Imaging_June_2017
Dataset (csv, tar.gz) and metadata (BibTex/Endnote) downloads. See _readme.txt for file contents.
myPhyloDB is an open-source software package aimed at developing a user-friendly web-interface for accessing and analyzing all of your laboratory's microbial ecology data (currently supported project types: soil, air, water, microbial, and human-associated). The storage and handling capabilities of myPhyloDB archives users' raw sequencing files, and allows for easy selection of any combination of projects/samples from all of your projects using the built-in SQL database. The data processing capabilities of myPhyloDB are also flexible enough to allow the upload, storage, and analysis of pre-processed data or raw (454 or Illumina) data files using the built-in versions of Mothur and R. myPhyloDB is designed to run as a local web-server, which allows a single installation to be accessible to all of your laboratory members, regardless of their operating system or other hardware limitations. myPhyloDB includes an embedded copy of the popular Mothur program and uses a customizable batch file to perform sequence editing and processing. This allows myPhyloDB to leverage the flexibility of Mothur and allow for greater standardization of data processing and handling across all of your sequencing projects.
\nmyPhyloDB also includes an embedded copy of the R software environment for a variety of statistical analyses and graphics. Currently, myPhyloDB includes analysis for factor or regression-based ANcOVA, principal coordinates analysis (PCoA), differential abundance analysis (DESeq), and sparse partial least-squares regression (sPLS).
Resource Title: Website Pointer to myPhyloDB.
File Name: Web Page, url: https://myphylodb.azurecloudgov.us/myPhyloDB/home/
Provides information and links to download latest version, release history, documentation, and tutorials including type of analysis you would like to perform (Univariate: ANCOVA/GLM; Multivariate: DiffAbund, PcoA, or sPLS).
myPhyloDB is an open-source software package aimed at developing a user-friendly web-interface for accessing and analyzing all of your laboratory's microbial ecology data (currently supported project types: soil, air, water, microbial, and human-associated). The storage and handling capabilities of myPhyloDB archives users' raw sequencing files, and allows for easy selection of any combination of projects/samples from all of your projects using the built-in SQL database. The data processing capabilities of myPhyloDB are also flexible enough to allow the upload, storage, and analysis of pre-processed data or raw (454 or Illumina) data files using the built-in versions of Mothur and R. myPhyloDB is designed to run as a local web-server, which allows a single installation to be accessible to all of your laboratory members, regardless of their operating system or other hardware limitations. myPhyloDB includes an embedded copy of the popular Mothur program and uses a customizable batch file to perform sequence editing and processing. This allows myPhyloDB to leverage the flexibility of Mothur and allow for greater standardization of data processing and handling across all of your sequencing projects.
\nmyPhyloDB also includes an embedded copy of the R software environment for a variety of statistical analyses and graphics. Currently, myPhyloDB includes analysis for factor or regression-based ANcOVA, principal coordinates analysis (PCoA), differential abundance analysis (DESeq), and sparse partial least-squares regression (sPLS).
Resource Title: Website Pointer to myPhyloDB.
File Name: Web Page, url: https://myphylodb.azurecloudgov.us/myPhyloDB/home/
Provides information and links to download latest version, release history, documentation, and tutorials including type of analysis you would like to perform (Univariate: ANCOVA/GLM; Multivariate: DiffAbund, PcoA, or sPLS).
2D finite element water, solute, and heat mover model for plant models.
\nMost crops are grown in rows and this introduces spatial variability in soil processes with respect to the row. However, this variability can be exploited to reduce chemical transport to groundwater or improve management of irrigation water. Unless a model can account for variability perpendicular to crop rows as well as vertically into the soil profile it will not be able to fully evaluate all possible management practices that can be used to make agriculture more efficient and less harmful to the environment. To address this concern we developed 2DSOIL, the first comprehensive, modular, two-dimensional soil simulator that can simulate the major physical, chemical and biological processes in soil. Fully implemented, principles of modular modeling facilitate the addition and replacement of modules, as well as the reuse of existing code. The modularity of 2DSOIL has been designed to make it easy to modify the model and to make it easy to incorporate into plant models. 2DSOIL was used to simulate the effect of several water and nitrogen management practices and was incorporated into ARS potato and cotton models, into the Root Zone Water Quality Model, and into the USGS Modular Modeling System.
","distribution":[],"identifier":"10113/AA22745","keyword":["Root Zone Water Quality Model","computer software","cotton","crop models","crops","finite element analysis","groundwater","heat","interphase","irrigation management","irrigation water","models","nitrogen","potatoes","root growth","simulation models","soil","soil profiles","solutes"],"license":"https://creativecommons.org/publicdomain/zero/1.0/","modified":"2024-02-15","programCode":["005:040"],"publisher":{"@type":"org:Organization","name":"Agricultural Research Service"},"title":"2DSOIL version 03"},"description":"2D finite element water, solute, and heat mover model for plant models.
\nMost crops are grown in rows and this introduces spatial variability in soil processes with respect to the row. However, this variability can be exploited to reduce chemical transport to groundwater or improve management of irrigation water. Unless a model can account for variability perpendicular to crop rows as well as vertically into the soil profile it will not be able to fully evaluate all possible management practices that can be used to make agriculture more efficient and less harmful to the environment. To address this concern we developed 2DSOIL, the first comprehensive, modular, two-dimensional soil simulator that can simulate the major physical, chemical and biological processes in soil. Fully implemented, principles of modular modeling facilitate the addition and replacement of modules, as well as the reuse of existing code. The modularity of 2DSOIL has been designed to make it easy to modify the model and to make it easy to incorporate into plant models. 2DSOIL was used to simulate the effect of several water and nitrogen management practices and was incorporated into ARS potato and cotton models, into the Root Zone Water Quality Model, and into the USGS Modular Modeling System.
","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/e6d71f5c-c3ab-4598-8f5e-bbb97c424f23","harvest_record_raw":"https://catalog.data.gov/harvest_record/e6d71f5c-c3ab-4598-8f5e-bbb97c424f23/raw","has_download":false,"has_spatial":false,"identifier":"10113/AA22745","keyword":["Root Zone Water Quality Model","computer software","cotton","crop models","crops","finite element analysis","groundwater","heat","interphase","irrigation management","irrigation water","models","nitrogen","potatoes","root growth","simulation models","soil","soil profiles","solutes"],"last_harvested_date":"2026-10-08T22:43:53.952633","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"352b4532-793d-4075-a03f-05b778a3c43a","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/usda.png","name":"Department of Agriculture","organization_type":"Federal Government","slug":"usda"},"parent_identifier":null,"popularity":1,"publisher":"Agricultural Research Service","slug":"2dsoil-version-03","spatial_centroid":null,"spatial_shape":null,"theme":[],"title":"2DSOIL version 03","type":"dataset"},{"_score":16.81406,"_sort":[1791499429908,16.81406,2,"a102f2c9-ac2c-4245-ac7c-0acf50d09387"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","accessRights":"public","bureauCode":["005:18"],"contactPoint":{"fn":"Timlin, Dennis","hasEmail":"mailto:Dennis.Timlin@usda.gov"},"description":"GOSSYM is a dynamic, process-level simulation model of cotton growth and yield. GOSSYM essentially is a materials balance model which keeps track of carbon and nitrogen in the plant and water and nitrogen in the soil root zone. GOSSYM predicts the response of the field crop to variations in the environment and to cultural inputs. Specifically, the model responds to weather inputs of daily total solar radiation, maximum and minimum air temperatures, daily total wind run, and rainfall and/or irrigation amount. The model also responds to cultural inputs such as preplant and withinseason applications of nitrogen fertilizer, row spacing and within row plant density as they affect total plant population, and cultivation practices.
","distribution":[],"identifier":"10113/AA22747","keyword":["air temperature","carbon","computer software","cotton","crop management","crop models","farms","field crops","irrigation rates","models","nitrogen","nitrogen fertilizers","plant density","rain","rhizosphere","row spacing","simulation models","soil","solar radiation","weather","wind"],"license":"https://creativecommons.org/publicdomain/zero/1.0/","modified":"2024-02-15","programCode":["005:040"],"publisher":{"@type":"org:Organization","name":"Agricultural Research Service"},"title":"GOSSYM"},"description":"GOSSYM is a dynamic, process-level simulation model of cotton growth and yield. GOSSYM essentially is a materials balance model which keeps track of carbon and nitrogen in the plant and water and nitrogen in the soil root zone. GOSSYM predicts the response of the field crop to variations in the environment and to cultural inputs. Specifically, the model responds to weather inputs of daily total solar radiation, maximum and minimum air temperatures, daily total wind run, and rainfall and/or irrigation amount. The model also responds to cultural inputs such as preplant and withinseason applications of nitrogen fertilizer, row spacing and within row plant density as they affect total plant population, and cultivation practices.
","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/de8200a3-3da8-4a17-9a2b-495a7d4ef5f5","harvest_record_raw":"https://catalog.data.gov/harvest_record/de8200a3-3da8-4a17-9a2b-495a7d4ef5f5/raw","has_download":false,"has_spatial":false,"identifier":"10113/AA22747","keyword":["air temperature","carbon","computer software","cotton","crop management","crop models","farms","field crops","irrigation rates","models","nitrogen","nitrogen fertilizers","plant density","rain","rhizosphere","row spacing","simulation models","soil","solar radiation","weather","wind"],"last_harvested_date":"2026-10-08T22:43:49.908450","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"352b4532-793d-4075-a03f-05b778a3c43a","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/usda.png","name":"Department of Agriculture","organization_type":"Federal Government","slug":"usda"},"parent_identifier":null,"popularity":2,"publisher":"Agricultural Research Service","slug":"gossym","spatial_centroid":null,"spatial_shape":null,"theme":[],"title":"GOSSYM","type":"dataset"},{"_score":11.8364525,"_sort":[1791499424117,11.8364525,2,"1732f566-9546-4559-be01-0dd064a64b5e"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","accessRights":"public","bureauCode":["005:18"],"contactPoint":{"fn":"Acosta-Martinez, Veronica","hasEmail":"mailto:Veronica.Acosta-Martinez@ars.usda.gov"},"description":"To help enhance USA soil health, and ensure a robust living soil component that sustains essential functions for healthy plants, animals, and environment, and ultimately provides food for a healthy society, the GRACEnet Soil Biology group are working together with the larger USDA-ARS GRACEnet community to provide soil biology component measurements across regions and to eliminate data gaps for GRACEnet and REAP efforts. The Soil Biology group is focused on efforts that foster method comparison and meta-analyses to allow researchers to better assess soil biology and soil health indicators that are most responsive to agricultural management and that reflect the ecosystems services associated with a healthy, functioning soil.
\nThe GRACEnet Soil Biology mission is to produce the soil biology data, including methods of identifying and quantifying specific organisms and processes they govern, that are needed to evaluate impacts on agroecosystems and sustainable agricultural practices. This data collection effort is being accomplished in a highly structured manner to support current and future soil health and antimicrobial resistance research initiatives. The outcomes of the efforts of this team will provide a common biological data platform for several ARS databases, including: GRACEnet/REAP, Nutrient Use and Outcome Network (NUOnet), Long-Term Agroecosystem Research (LTAR) network, soil biology (e.g., MyPhyloDB) databases, and others.
\nResource Title: Soil Biology Data Search.
File Name: Web Page, url: https://agcros-usdaars.opendata.arcgis.com/datasets?group_ids=091b86e9e44a4e948ef2aeae3c916ca5
To help enhance USA soil health, and ensure a robust living soil component that sustains essential functions for healthy plants, animals, and environment, and ultimately provides food for a healthy society, the GRACEnet Soil Biology group are working together with the larger USDA-ARS GRACEnet community to provide soil biology component measurements across regions and to eliminate data gaps for GRACEnet and REAP efforts. The Soil Biology group is focused on efforts that foster method comparison and meta-analyses to allow researchers to better assess soil biology and soil health indicators that are most responsive to agricultural management and that reflect the ecosystems services associated with a healthy, functioning soil.
\nThe GRACEnet Soil Biology mission is to produce the soil biology data, including methods of identifying and quantifying specific organisms and processes they govern, that are needed to evaluate impacts on agroecosystems and sustainable agricultural practices. This data collection effort is being accomplished in a highly structured manner to support current and future soil health and antimicrobial resistance research initiatives. The outcomes of the efforts of this team will provide a common biological data platform for several ARS databases, including: GRACEnet/REAP, Nutrient Use and Outcome Network (NUOnet), Long-Term Agroecosystem Research (LTAR) network, soil biology (e.g., MyPhyloDB) databases, and others.
\nResource Title: Soil Biology Data Search.
File Name: Web Page, url: https://agcros-usdaars.opendata.arcgis.com/datasets?group_ids=091b86e9e44a4e948ef2aeae3c916ca5
PhotoSim: Leaf Photosynthesis Model for Floriculture Crops program models the photosynthetic response of 13 floriculture crops to light, temperature, or carbon dioxide (CO2) and allows users to estimate the impact of adjusting their greenhouse environment. You can predict the impact on photosynthesis for different management changes (shading, supplemental high pressure sodium lighting, CO2 injection,or heating or cooling).
Resource Title: PhotoSim .
File Name: Web Page, url: https://www.ars.usda.gov/midwest-area/wooster-oh/application-technology-research/docs/photosim-leaf-photosynthesis-model-for-floriculture-crops/
download page: https://www.ars.usda.gov/research/software/download/?softwareid=447
PhotoSim: Leaf Photosynthesis Model for Floriculture Crops program models the photosynthetic response of 13 floriculture crops to light, temperature, or carbon dioxide (CO2) and allows users to estimate the impact of adjusting their greenhouse environment. You can predict the impact on photosynthesis for different management changes (shading, supplemental high pressure sodium lighting, CO2 injection,or heating or cooling).
Resource Title: PhotoSim .
File Name: Web Page, url: https://www.ars.usda.gov/midwest-area/wooster-oh/application-technology-research/docs/photosim-leaf-photosynthesis-model-for-floriculture-crops/
download page: https://www.ars.usda.gov/research/software/download/?softwareid=447
Geopack is a comprehensive, user-friendly software package containing several computer programs for carrying out geostatistical analyses of spatially correlated data. The programs were written so that they can be used by scientists, engineers or regulators with little experience in geostatistical techniques and still satisfy the requirements of more advanced users. Using the programs, and spending a little time becoming familiar with geostatistics, the end-user should be able to include the geostatistical techniques in their work and research environment.
Resource Title: GEOPACK download page.
File Name: Web Page, url: https://www.ars.usda.gov/research/software/download/?softwareid=95&modecode=20-36-15-00
Geopack is a comprehensive, user-friendly software package containing several computer programs for carrying out geostatistical analyses of spatially correlated data. The programs were written so that they can be used by scientists, engineers or regulators with little experience in geostatistical techniques and still satisfy the requirements of more advanced users. Using the programs, and spending a little time becoming familiar with geostatistics, the end-user should be able to include the geostatistical techniques in their work and research environment.
Resource Title: GEOPACK download page.
File Name: Web Page, url: https://www.ars.usda.gov/research/software/download/?softwareid=95&modecode=20-36-15-00
The dataset for Range Permits is needed to implement the solution for the NRM Range Permit Map modernization. The current legacy custom solution is being modernized with an out of the box solution leveraging ArcGIS Online and ArcPro. This internal read-only dataset for Range Permits and associated Allotments will be the input to this modernized solution for the NRM Range Permit Map. This will deliver value to the Range Management program and make generating official range permit maps easier and more efficient. This provides benefit to the government overall by leveraging Commercial Off The Shelf (COTS) products over custom development to migrate the current legacy application from ArcMap and legacy Citrix to ArcGIS Pro in VDI. This modernized solution will be accessible via web browser in any environment to Range Permit Administrators on the FS network. Sensitive data regarging permit holder information has been removed from this dataset.
The dataset for Range Permits is needed to implement the solution for the NRM Range Permit Map modernization. The current legacy custom solution is being modernized with an out of the box solution leveraging ArcGIS Online and ArcPro. This internal read-only dataset for Range Permits and associated Allotments will be the input to this modernized solution for the NRM Range Permit Map. This will deliver value to the Range Management program and make generating official range permit maps easier and more efficient. This provides benefit to the government overall by leveraging Commercial Off The Shelf (COTS) products over custom development to migrate the current legacy application from ArcMap and legacy Citrix to ArcGIS Pro in VDI. This modernized solution will be accessible via web browser in any environment to Range Permit Administrators on the FS network. Sensitive data regarging permit holder information has been removed from this dataset.