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Data from: Underestimation of N2O emissions in a comparison of the DayCent, DNDC, and EPIC 1 models

    Process-based models are increasingly used to study mass and energy fluxes from agro-ecosystems, including nitrous oxide (N2O) emissions from agricultural fields. This data set is the output of three process-based models – DayCent, DNDC, and EPIC – which were used to simulate fluxes of N2O from dairy farm soils. The individual models' output and the ensemble mean output were evaluated against field observations from two agricultural research stations in Arlington, WI and Marshfield, WI. These sites utilize cropping systems and nitrogen fertilizer management strategies common to Midwest dairy farms.

    NUOnet (Nutrient Use and Outcome Network) database

      The Nutrient Uptake and Outcomes (NUOnet) database will be able to help establish baselines on nutrient use efficiencies; processes contributing to nutrient losses; and processes contributing to optimal crop yield, nutritional and organoleptic quality. This national database could be used to calculate many different environmental indicators from a comprehensive understanding of nutrient stocks and flows.

      Data from: Comparative farm-gate life cycle assessment of oilseed feedstocks in the Northern Great plains

        This MS Word document contains the oilseed feedstock farm-gate model inventories, results, and uncertainty analyses for the Northern Great Plains discussed in Moeller et. al 2017. Analysis was conducted using IPCC GHG standardized emissions. Methodology is detailed in the associated publication (doi: 10.1007/s41247-017-0030-3). The supplementary information contains the names of the ecoinvent inventories; oilseed yield, seeding rates, and fertilization rates per USDA crop management zone (CMZ); climate change, freshwater eutrophication, and marine eutrophication percent contributions ReCiPe results per CMZ; Monte Carlo uncertainty results per CMZ; and farm-gate energy balance analysis results per CMZ.

        Data from: Soil organic carbon and isotope composition response to topography and erosion in Iowa

          The dataset includes topographic information, soil properties, and 137Cs levels collected from a 15 ha cropland under soybean/maize (C3/C4) rotation in June 2002. The cropland is located in the central-western part of the Walnut Creek watershed, Story County, Iowa. 128 sampling locations were collected and three soil samples were obtained using a 3.2 cm-diameter push probe from the 0 to 30 cm soil layer within a 1 m × 1 m quadrat at each sampling location. Deeper soil samples were collected from 30 to 50 cm layers in locations where sediment deposition was observed. The three samples from each sampling location were mixed and analyzed to determine soil properties, SOC content and its carbon (C) isotope composition (C12 to C13 ratio), and 137Cs levels. For landscape topography of each sampling location, topographic metrics were derived from a digital elevation mode using LiDAR (Light Detection and Ranging) data. These data are useful in investigating the fate of eroded SOC in croplands and its responses to landscape topography.

          Code from: Fast and robust curve skeletonization for real-world elongated objects

            This record contains C++ code as well as a Docker release for performing curve skeletonization of objects, which have a voxel representation. Curve skeletonization is used to convert a three-dimensional digital object or shape to locally one-dimensional parts; in other words, to reduce the shape information to a more easily processed form. Our algorithm does so for objects whose surface may be noisy, which is a common occurrence when working with data acquired under real-world conditions. This record also includes a test dataset for verifying that the code is running correctly and as examples of how to convert from different file types.

            Measured Annual Nutrient loads from AGricultural Environments (MANAGE) database

              The MANAGE (Measured Annual Nutrient loads from AGricultural Environments) database was developed to be a readily-accessible, easily-queried database of site characteristic and field-scale nutrient export data. Initial funding for MANAGE was provided by USDA-ARS to support the USDA Conservation Effects Assessment Project (CEAP) and the Texas State Soil and Water Conservation Board as part of their mission to understand and mitigate agricultural impacts on water quality. MANAGE contains data from a vast majority of published peer-reviewed N and P export studies on homogeneous cultivated, pasture/range, and forested land uses in the US under natural rainfall-runoff conditions, as well as artificially drained agricultural land. Thus MANAGE facilitates expanded spatial analyses and improved understanding of regional differences, management practice effectiveness, and impacts of land use conversions and management techniques, and it provides valuable data for modeling and decision-making related to agricultural runoff.

              Data from: Eleven years of mountain weather, snow, soil moisture and stream flow data from the rain-snow transition zone - the Johnston Draw catchment, Reynolds Creek Experimental Watershed and Critical Zone Observatory, USA. v1.1

                Detailed hydrometeorological data from the mountain rain-to-snow transition zone are present for water years 2004 through 2014. The Johnston Draw watershed (1.8 km2), ranging from 1497 – 1869 m in elevation, is a sub-watershed of the Reynolds Creek Experimental Watershed (RCEW) in southwestern Idaho. The dataset includes continuous hourly hydrometeorological variables across a 372 m elevation gradient, on north- and south-facing slopes, including air temperature, relative humidity and snow depth from 11 sites in the watershed. Hourly measurements of solar radiation, precipitation, wind speed and direction, and soil moisture and temperature are available at selected stations. The dataset includes hourly stream discharge measured at the watershed outlet. These data provide the scientific community with a unique dataset useful for forcing and validating models in interdisciplinary studies and will allow for better representation and understanding of the complex processes that occur in the rain-to-snow transition zone.

                Data from: Soil Water Holding Capacity Mitigates Downside Risk and Volatility in US Rainfed Maize: Time to Invest in Soil Organic Matter?

                  This dataset includes county-level annual data on maize (Zea mays L.) yield, soil physical and chemical characteristics, and mean weather data for 2000 through 2014 for IL, MI, MN and PA. The data were aggregated from public databases, including NASS Quick Stats, NOAA Climate Data Online, and the USDA-NRCS Web Soil Survey. U.S. counties were the experimental unit for this study, and all data are county-level averages. Covariances among county-level maize yield stability and environmental variability were analyzed using structural equation models (SEM) and linear mixed effects (LME) models.

                  USDA LCA Commons Data Submission Guidelines

                    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.

                    Data from: Genomic analyses of dominant US clonal lineages of Phytophthora infestans reveals a shared ancestry for US11 and US18 and a lack of recently shared ancestry for all other US lineages

                      The populations of the potato and tomato late blight pathogen, Phytophthora infestans, in the US are well known for emerging repeatedly as novel clonal lineages. These successions of dominant clones have historically been named US1 through US24, in order of appearance, since their first characterization using molecular markers. Hypothetically, these lineages can emerge by descent from prior lineages or as novel, independent lineages.