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Long-Term Agroecosystem Research Network regions, 2018 version

    The Long-Term Agroecosystem Research Network, consisting of 18+ research locations, is conducting research on the sustainable intensification of agroecosystems. To enable coordinated network level research, a spatial framework is required to facilitate analysis. This dataset contains a geodatabase of three new maps describing regional boundaries for the LTAR Network titled "Long-Term Agroecosystem Research Network regions, 2018 version.”

    Upper Washita River Experimental Watersheds: Nutrient Water Quality Data

    NAL Geospatial Catalog
      Climate variability, changing land use and management, and dynamic policy environments are the main reasons why long-term water quality data sets are needed to understand and predict possible water quality outcomes to alternative future scenarios. Such data sets were acquired by the USDA-ARS in three watersheds in Oklahoma: the Southern Great Plains Research Watershed (SGPRW), the Little Washita River Experimental Watershed (LWREW), and the Fort Cobb Reservoir Experimental Watershed (FCREW).

      Long-term tillage and cropping system experiment for Greenhouse gas Reduction through Agricultural Carbon Enhancement network and Nutrient Use and Outcome Network in Lincoln, Nebraska

        Lincoln NE Long-term Tillage Project Overview of NELITCSE: Long-term Tillage and Cropping System Experiment (Lincoln, NE) The objectives of this experiment is to evaluate the agronomic and environmental impacts of long-term tillage and crop rotation practices in a rainfed agroecosystem. This experiment was initiated in 1981 with continuous corn only under six tillage practices (chisel, tandem disk, moldboard plow, no-till, ridge-tillage, and subsoil tillage). In 1985, the experimental design was modified to include 3 crop rotation systems (continuous corn, corn-soybean, and continuous soybean) under 6 tillage practices. Each year, both the corn phase and soybean phase of the two-year rotation system are present. In 2015, all tillage practices were converted to no-till to evaluate the magnitude, direction, and rate of agronomic and soil changes to this management shift. In addition, the continuous soybean system was converted to continuous corn with a 3-species winter cover crop (hairy vetch, purple-topped radish, and cereal rye).

        Central Plains Experimental Range Study for Long-Term Agroecosystem Research in Nunn, Colorado

          The Central Plains Experimental Range (CPER) is a site with the The Long-Term Agroecosystem Research (LTAR) Network, which consists of 18 sites across the continental United States (US) sponsored by the US Department of Agriculture, Agricultural Research Service, universities and non-governmental organizations. LTAR scientists seek to determine ways to ensure sustainability and enhance food production (and quality) and ecosystem services at broad regional scales. They are conducting common experiments across the LTAR network to compare traditional production strategies (“business as usual or BAU) with aspirational strategies, which include novel technologies and collaborations with farmers and ranchers.

          Data from: Environmental footprints of beef cattle production in the United States

            To quantify important environmental impacts of beef cattle production in the United States, surveys and visits of farms, ranches and feedlots were conducted throughout seven regions (Northeast, Southeast, Midwest, Northern Plains, Southern Plains, Northwest and Southwest). Life cycle environmental impacts of U.S. beef cattle production were determined. Annual carbon emission was 243 ± 26 Tg CO2e (21.3 ± 2.3 kg CO2e/kg carcass weight). Annual fossil energy use was 569 ± 53 PJ (50.0 ± 4.7 MJ/kg carcass weight). Blue water consumption was 23.2 ± 3.5 TL (2034 ± 309 L/kg carcass weight). Reactive nitrogen loss was 1760 ± 136 Gg N (155 ± 12 g N/kg carcass weight).

            Vegetation, rainfall simulation, and overland flow experiments before and after tree removal in woodland-encroached sagebrush steppe: the SageSTEP hydrology study

              Simulated rainfall and overland-flow experiments are useful for enhancing understanding of surface hydrologic and erosion processes, quantifying runoff and erosion rates, and developing and testing predictive quantitative models. This extensive dataset consists of rainfall simulation and overland flow experimental plot data coupled with associated measures of vegetation, ground cover, and surface soil properties across point to hillslope scales. Data were collected at three woodland-encroached sagebrush (*Artemisia* spp.) rangelands in the Great Basin, USA, under undisturbed/untreated conditions and 1 yr to 9 yr following fire and/or mechanical tree-removal treatments.

              RZWQM2

                Root Zone Water Quality Model 2 (RZWQM2) is a whole-system model for studying crop production and environmental quality under current and changing climate conditions. It emphasizes the effects of agricultural management practices on physical, chemical and biological processes. RZWQM2 is a one-dimensional model with a pseudo 2-dimensional drainage flow. Crop simulation options include the generic plant growth model, DSSAT-CSM 4.0 and HERMES SUCROS models. It also can simulate surface energy balance with components from the SHAW model and water erosion from the GLEAMS model. An automated parameter estimation algorithm (PEST) was added to RZWQM2 for objective model calibration and uncertainty analysis.

                GPFARM

                  GPFARM (Great Plains Framework for Agricultural Resource Management) is a simulation model computer application. It incorporates state of the art knowledge in agronomy, animal science, economics, weed science and risk management into a user-friendly, decision support tool. Producers, agricultural consultants, action agencies and scientists can utilize GPFARM to test alternative management strategies that may in turn lead to sustainable agriculture, a reduction in pollution, or maximum economic return. GPFARM Express contains default projects to allow users to quickly set up their operations.