This document provides instructions for editing and submitting unit process or product system models to the USDA LCA Commons life cycle inventory (LCI) database. The LCA Commons LCI database uses the openLCA life cycle modeling tool's database schema. Therefore, this document describes how to import and edit data in openLCA and name and classify flows such that they properly import into and operate in the database. This document also describes metadata or documentation requirements for posting models to the LCA Commons. This document is an evolving standard for LCA Commons data. As USDA-NAL continues to gain experience in managing a general purpose LCI database and global conventions continue to evolve, so too will the LCA Commons Submission Guidelines. Resources in this dataset:Resource Title: LCA Commons Submission Guidelines_12/09/2015. File Name: lcaCommonsSubmissionGuidelines_Final_2015-12-09.pdf

The Current Population Survey Food Security Supplement (CPS-FSS) is the source of national and State-level statistics on food insecurity used in USDA's annual reports on household food security. The CPS is a monthly labor force survey of about 50,000 households conducted by the Census Bureau for the Bureau of Labor Statistics. Once each year, after answering the labor force questions, the same households are asked a series of questions (the Food Security Supplement) about food security, food expenditures, and use of food and nutrition assistance programs. Food security data have been collected by the CPS-FSS each year since 1995. Four data sets that complement those available from the Census Bureau are available for download on the ERS website. These are available as ASCII uncompressed or zipped files. The purpose and appropriate use of these additional data files are described below: 1) CPS 1995 Revised Food Security Status data--This file provides household food security scores and food security status categories that are consistent with procedures and variable naming conventions introduced in 1996. This includes the "common screen" variables to facilitate comparisons of prevalence rates across years. This file must be matched to the 1995 CPS Food Security Supplement public-use data file. 2) CPS 1998 Children's and 30-day Food Security data--Subsequent to the release of the April 1999 CPS-FSS public-use data file, USDA developed two additional food security scales to describe aspects of food security conditions in interviewed households not captured by the 12-month household food security scale. This file provides three food security variables (categorical, raw score, and scale score) for each of these scales along with household identification variables to allow the user to match this supplementary data file to the CPS-FSS April 1998 data file. 3) CPS 1999 Children's and 30-day Food Security data--Subsequent to the release of the April 1999 CPS-FSS public-use data file, USDA developed two additional food security scales to describe aspects of food security conditions in interviewed households not captured by the 12-month household food security scale. This file provides three food security variables (categorical, raw score, and scale score) for each of these scales along with household identification variables to allow the user to match this supplementary data file to the CPS-FSS April 1999 data file. 4) CPS 2000 30-day Food Security data--Subsequent to the release of the September 2000 CPS-FSS public-use data file, USDA developed a revised 30-day CPS Food Security Scale. This file provides three food security variables (categorical, raw score, and scale score) for the 30-day scale along with household identification variables to allow the user to match this supplementary data file to the CPS-FSS September 2000 data file. Food security is measured at the household level in three categories: food secure, low food security and very low food security. Each category is measured by a total count and as a percent of the total population. Categories and measurements are broken down further based on the following demographic characteristics: household composition, race/ethnicity, metro/nonmetro area of residence, and geographic region. The food security scale includes questions about households and their ability to purchase enough food and balanced meals, questions about adult meals and their size, frequency skipped, weight lost, days gone without eating, questions about children meals, including diversity, balanced meals, size of meals, skipped meals and hunger. Questions are also asked about the use of public assistance and supplemental food assistance. The food security scale is 18 items that measure insecurity. A score of 0-2 means a house is food secure, from 3-7 indicates low food security, and 8-18 means very low food security. The scale and the data also report the frequency with which each item is experienced. Data are available as .dat files which may be processed in statistical software or through the United State Census Bureau's DataFerret http://dataferrett.census.gov/. Data from 2010 onwards is available below and online. Data from 1995-2009 must be accessed through DataFerrett. DataFerrett is a data analysis and extraction tool to customize federal, state, and local data to suit your requirements. Through DataFerrett, the user can develop an unlimited array of customized spreadsheets that are as versatile and complex as your usage demands then turn those spreadsheets into graphs and maps without any additional software. Resources in this dataset:Resource Title: December 2014 Food Security CPS Supplement. File Name: dec14pub.zipResource Title: December 2013 Food Security CPS Supplement. File Name: dec13pub.zipResource Title: December 2012 Food Security CPS Supplement. File Name: dec12pub.zipResource Title: December 2011 Food Security CPS Supplement. File Name: dec11pub.zipResource Title: December 2010 Food Security CPS Supplement. File Name: dec10pub.zip

[NOTE - 11/24/2021: this dataset supersedes an earlier version https://doi.org/10.15482/USDA.ADC/1518654 ] Data sources. Time series data on cattle fever tick incidence, 1959-2020, and climate variables January 1950 through December 2020, form the core information in this analysis. All variables are monthly averages or sums over the fiscal year, October 01 (of the prior calendar year, y-1) through September 30 of the current calendar year (y). Annual records on monthly new detections of Rhipicephalus microplus and R. annulatus (cattle fever tick, CFT) on premises within the Permanent Quarantine Zone (PQZ) were obtained from the Cattle Fever Tick Eradication Program (CFTEP) maintained jointly by the United States Department of Agriculture (USDA), Animal Plant Health Inspection Service and the USDA Animal Research Service in Laredo, Texas. Details of tick survey procedures, CFTEP program goals and history, and the geographic extent of the PQZ are in the main text, and in the Supporting Information (SI) of the associated paper. Data sources on oceanic indicators, on local meteorology, and their pretreatment are detailed in SI. Data pretreatment. To address the low signal-to-noise ratio and non-independence of observations common in time series, we transformed all explanatory and response variables by using a series of six consecutive steps: (i) First differences (year y minus year y-1) were calculated, (ii) these were then converted to z scores (z = (x- μ) / σ, where x is the raw value, μ is the population mean, σ is the standard deviation of the population), (iii) linear regression was applied to remove any directional trends, (iv) moving averages (typically 11-year point-centered moving averages) were calculated for each variable, (v) a lag was applied if/when deemed necessary, and (vi) statistics calculated (r, n, df, P<, p<). Principal component analysis (PCA). A matrix of z-score first differences of the 13 climate variables, and CFT (1960-2020), was entered into XLSTAT principal components analysis routine; we used Pearson correlation of the 14 x 60 matrix, and Varimax rotation of the first two components. Autoregressive Integrated Moving Average (ARIMA). An ARIMA (2,0,0) model was selected among 7 test models in which the p, d, and q terms were varied, and selection made on the basis of lowest RMSE and AIC statistics, and reduction of partial autocorrelation outcomes. A best model linear regression of CFT values on ARIMA-predicted CFT was developed using XLSTAT linear regression software with the objective of examining statistical properties (r, n, df, P<, p<), including the Durbin-Watson index of order-1 autocorrelation, and Cook’s Di distance index. Cross-validation of the model was made by withholding the last 30, and then the first 30 observations in a pair of regressions. Forecast of the next major CFT outbreak. It is generally recognized that the onset year of the first major CFT outbreak was not 1959, but may have occurred earlier in the decade. We postulated the actual underlying pattern is fully 44 years from the start to the end of a CFT cycle linked to external climatic drivers. (SI Appendix, Hypothesis on CFT cycles). The hypothetical reconstruction was projected one full CFT cycle into the future. To substantiate the projected trend, we generated a power spectrum analysis based on 1-year values of the 1959-2020 CFT dataset using SYSTAT AutoSignal software. The outcome included a forecast to 2100; this was compared to the hypothetical reconstruction and projection. Any differences were noted, and the start and end dates of the next major CFT outbreak identified. Resources in this dataset: Resource Title: CFT and climate data. File Name: climate-cft-data2.csv Resource Description: Main dataset; see data dictionary for information on each column Resource Title: Data dictionary (metadata). File Name: climate-cft-metadata2.csv Resource Description: Information on variables and their origin Resource Title: fitted models. File Name: climate-cft-models2.xlsx Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel; XLSTAT,url: https://www.xlstat.com/en/; SYStat Autosignal,url: https://www.systat.com/products/AutoSignal/

Hydric soils are defined as those soils that are sufficiently wet in the upper part to develop anaerobic conditions during the growing season. The Hydric Soils section presents the most current information about hydric soils. The lists of hydric soils were created by using National Soil Information System (NASIS) database selection criteria that were developed by the National Technical Committee for Hydric Soils. These criteria are selected soil properties that are documented in Soil Taxonomy (Soil Survey Staff, 1999) and were designed primarily to generate a list of potentially hydric soils from the National Soil Information System (NASIS) database. It updates information that was previously published in Hydric Soils of the United States and coordinates it with information that has been published in the Federal Register. It also includes the most recent set of field indicators of hydric soils. The database selection criteria are selected soil properties that are documented in Soil Taxonomy and were designed primarily to generate a list of potentially hydric soils from soil survey databases. Only criteria 1, 3, and 4 can be used in the field to determine hydric soils; however, proof of anaerobic conditions must also be obtained for criteria 1, 3, and 4 either through data or best professional judgment (from Tech Note 1). The primary purpose of these selection criteria is to generate a list of soil map unit components that are likely to meet the hydric soil definition. Caution must be used when comparing the list of hydric components to soil survey maps. Many of the soils on the list have ranges in water table depths that allow the soil component to range from hydric to nonhydric depending on the location of the soil within the landscape as described in the map unit. Lists of hydric soils along with soil survey maps are good off-site ancillary tools to assist in wetland determinations, but they are not a substitute for observations made during on-site investigations. The list of field indicators of hydric soils — The field indicators are morphological properties known to be associated with soils that meet the definition of a hydric soil. Presence of one or more field indicators suggests that the processes associated with hydric soil formation have taken place on the site being observed. The field indicators are essential for hydric soil identification because once formed, they persist in the soil during both wet and dry seasonal periods. The Hydric Soil Technical Notes — Contain National Technical Committee for Hydric Soils (NTCHS) updates, insights, standards, and clarifications. Users can query the database by State or by Soil Survey Area. Resources in this dataset:Resource Title: Website Pointer to Hydric Soils . File Name: Web Page, url: https://www.nrcs.usda.gov/wps/portal/nrcs/main/soils/use/hydric/ Includes description of Criteria, Query by State or Soil Survey Area, national Technical Committee for Hydric Soils. Technical Notes, and Related Links. Report Metadata:

• Area_Symbol: A symbol that uniquely identifies a single occurrence of a particular type of area (e.g. Dane Co., Wisconsin is WI025).
• Area_Name: The name given to the specified geographic area.
• mukey: A non-connotative string of characters used to uniquely identify a record in the Mapunit table.
• Mapunit_SYM: The symbol used to uniquely identify the soil mapunit in the soil survey.
• Mapunit_Name: Correlated name of the mapunit (recommended name or field name for surveys in progress).
• Comp_Name_phase: Component name - Name assigned to a component based on its range of properties. Local Phase - Phase criterion to be used at a local level, in conjunction with "component name" to help identify a soil component.
• muacres: The number of acres of a particular mapunit.
• Comp_RV_Pct: The percentage of the component of the mapunit.
• majcompflag: Indicates whether or not a component is a major component in the mapunit.
• Comp_Acres: The number of acres of a particular component in a mapunit. ((muacres*comppct_r)/100)
• Comp_Landform: A word or group of words used to name a feature on the earth's surface, expressed in the plural form. Column Physical
• Hydric_Rating: A yes/no field that indicates whether or not a map unit component is classified as a "hydric soil". If rated as hydric, the specific criteria met are listed in the Component Hydric Criteria table.
• Hydric_criteria: Criterion code for the soil characteristic(s) and/or feature(s) that cause the map unit component to be classified as a "hydric soil." These codes are the paragraph numbers in the hydric soil criteria publication.

Criteria:

1. All Histels except Folistels and Histosols except Folists; or
2. Map unit components in Aquic suborders, great groups, or subgroups, Albolls suborder, Historthels great group, Histoturbels great group, or Andic, Cumulic, Pachic, or Vitrandic subgroups that: a. Based on the range of characteristics for the soil series, will at least in part meet one or more Field Indicators of Hydric Soils in the United States, or b. Show evidence that the soil meets the definition of a hydric soil;
3. Map unit components that are frequently ponded for long duration or very long duration during the growing season that: a. Based on the range of characteristics for the soil series, will at least in part meet one or more Field Indicators of Hydric Soils in the United States, or b. Show evidence that the soil meets the definition of a hydric soil; or
4. Map unit components that are frequently flooded for long duration or very long duration during the growing season that: a. Based on the range of characteristics for the soil series, will at least in part meet one or more Field Indicators of Hydric Soils in the United States, or b. Show evidence that the soils meet the definition of a hydric soil.

Subsurface sewage disposal systems (SSDS) consist of a house sewer, a septic tank followed by a leaching system, any necessary pumps and siphons, and a groundwater control system upon which the operation of the leaching system depends. This interpretation focuses mainly on the septic tank leaching field and groundwater control system Soil Potential Ratings Soil potential ratings indicate the relative quality of a soil for a particular use compared to othersoils in a given area, in this case the State of Connecticut.The rating criteria were developed by a committee of State and local sanitarians, engineers, and installers. The soils data was provided by the USDA Natural Resources Conservation Service (NRCS), and the performance and site conditions for a typical system were defined. This information provided a standard against which various combinations of properties of soils within Connecticut could be compared.The engineering and installation practices used to overcome various soil limitations were listed, and their costs estimated. This information was used to identify limitations and costs associated with installing an SSDS on each soil in Connecticut. Soils with no or minor limitations for the installation of an SSDS were rated the highest. Conversely, soils requiring extensive site modification and design were rated the lowest. The ease of system installation, and therefore cost, formed the basis of the rating scheme.Rating ClassesThe rating class definitions refer to installation of an SSDS that meets State and local health code regulations. Soils with high potential have characteristics that meet the performance standard. A typical system can be installed at a cost of "x", which represents the going rate for installing an SSDS. The actual value of x varies depending upon many factors unrelated to soil properties. The cost of installing a leaching field is expressed as a multiple of x and called the cost factor. For example, a cost factor of 3x to and 3.5x means that the estimated cost of installing a leaching field in the particular soil ranges from 3 to 3.5 times m

This dataset contains detailed USDA SSURGO soil information and mapped soil extents for the soils of Accomack and Northampton Counties on Virginia's Eastern Shore, including those areas of focused study by the Virginia Coast Reserve LTER project. This USDA soil data is collected and combined here to make it more accessible to VCRLTER researchers and students, in a more GIS-friendly format, and to supersede previous digitized versions of more generalized soil maps created by the VCRLTER and included as part of the 1995 VCRLTER-Northampton County GIS data archive (dataset VCR14219). Data was downloaded in Jan. 2014 and tabular data for both counties was imported into a MS Access database using the provided standard SSURGO US 2003 template. Spatial data for the two counties was merged together into a single ArcGIS shapefile and selected fields from the MAPUNIT and MUAGGATT tables were joined to the final shapefile's attribute table. Each polygon represents all or part of a SSURGO "mapunit", which may contain multiple component soils; usually very similar soils that grade together or else so heterogeneously mixed together at fine spatial scales to make mapping the component soils individually impractical. Also, each soil typically has multiple vertical soil horizons, each with its own distinct composition (mineral, textural, etc.) and other characteristics. Detailed information about component soils (including typical soil moisture, dry albedo, erodibility indices, taxonomic nomenclature, flooding and ponding characteristics, engineering, crop, forest, and habitat suitability indices and yield tables, and geomorphic descriptions) and component horizons (including horizon depths, grain size distributions, sand/silt/clay fractions, mineral and organic content, and pore space characteristics) is included in the MS Access database but NOT in the combined ArcGIS shapefile. Users interested in exploring or displaying component or horizon information may use the report and query forms within the MS Access database, or they may join selected database tables to the shapefile using the appropriate mukey, cokey, and chkey indices in a one-to-many join within a chosen GIS software.

[Note: Integrated as part of FoodData Central, April 2019.] USDA's Food and Nutrient Database for Dietary Studies (FNDDS) is a database that is used to convert food and beverages consumed in What We Eat In America (WWEIA), National Health and Nutrition Examination Survey (NHANES) into gram amounts and to determine their nutrient values. Because FNDDS is used to generate the nutrient intake data files for WWEIA, NHANES, it is not required to estimate nutrient intakes from the survey. FNDDS is made available for researchers using WWEIA, NHANES to review the nutrient profiles for specific foods and beverages as well as their associated portions and recipes. Such detailed information makes it possible for researchers to conduct enhanced analysis of dietary intakes. FNDDS can also be used in other dietary studies to code foods/beverages and amounts eaten and to calculate the amounts of nutrients/food components in those items.
FNDDS is released every two-years in conjunction with the WWEIA, NHANES dietary data release. The FNDDS is available for free download from the FSRG website. Resources in this dataset:Resource Title: Website Pointer to Food and Nutrient Database for Dietary Studies. File Name: Web Page, url: https://www.ars.usda.gov/northeast-area/beltsville-md-bhnrc/beltsville-human-nutrition-research-center/food-surveys-research-group/docs/fndds/ USDA's Food and Nutrient Database for Dietary Studies (FNDDS) is a database that is used to convert food and beverages consumed in What We Eat In America (WWEIA), National Health and Nutrition Examination Survey (NHANES) into gram amounts and to determine their nutrient values.

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Data of image calculation averages, coefficient of variations, and experimental measurements that were presented in the manuscript, Visualizing Plant Responses: Novel Insights Possible through Affordable Imaging Techniques in the Greenhouse, is provided.Abstract: Global climatic pressures and increased human demands create a modern necessity for efficient and affordable plant phenotyping unencumbered by arduous technical requirements. The analysis and archival of imagery have become easier as modern camera technology and computers are leveraged. This facilitates the detection of vegetation status and changes over time. Using a custom lightbox, an inexpensive camera, and common software, turfgrass pots were photographed in a greenhouse environment over an 8-week experiment period. Subsequent imagery was analyzed for area of cover, color metrics, and sensitivity to image corrections. Findings were compared to active spectral reflectance data and previously reported measurements of visual quality, productivity, and water use. Results indicate that Red Green Blue-based (RGB) imagery with simple controls is sufficient to measure the effects of plant treatments. Notable correlations were observed for corrected imagery, including between a percent yellow color area classification segment (%Y) with human visual quality ratings (VQ) (R = -0.89), the dark green color index (DGCI) with clipping productivity in mg d-1 (mg) (R = 0.61), and an index combination term (COMB2) with water use in mm d-1 (mm) (R = -0.60). The calculation of green cover area (%G) correlated with Normalized Difference Vegetation Index (NDVI) (R = 0.91) and its RED reflectance spectra (R = -0.87). A CIELAB b*/a* chromatic ratio (BA) correlated with Normalized Difference Red-Edge index (NDRE) (R = 0.90), and its Red-Edge (RE) (R = -0.74) reflectance spectra, while a new calculation termed HSVi correlated strongest to the Near-Infrared (NIR) (R = 0.90) reflectance spectra. Additionally, COMB2 significantly differentiated between the treatment effects of date, mowing height, deficit irrigation, and their interactions (p < 0.001). Sensitivity and statistical analysis of typical image file formats and corrections that included JPEG (JPG), TIFF (TIF), geometric lens correction (LC), and color correction (CC) were conducted. Results underscore the need for further research to support image corrections standardization and better connect image data to biological processes. This study demonstrates the potential of consumer-grade photography to capture plant phenotypic traits.