U.S. flag

An official website of the United States government

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).

    Data from: Invasive forb benefits from water savings by native plants and carbon fertilization under elevated CO2 and warming

      To test the hypothesis that elevated CO2 and warming would strongly influence invasive species success in a semi‐arid grassland as a result of both direct and water‐mediated indirect effects, the invasive forb Linaria dalmatica was transplanted into mixed‐grass prairie treated with free‐air CO2 enrichment and infrared warming, and survival, growth, and reproduction followed over 4 yr. Leaf gas exchange and carbon isotopic composition in L. dalmatica and the dominant native C3 grass Pascopyrum smithii were also measured.

      STARFM

        The STARFM algorithm uses comparisons of one or more pairs of observed Landsat/MODIS maps, collected on the same day, to predict maps at Landsat-scale on other MODIS observation dates. STARFM was initially developed at the NASA Goddard Space Flight Center by Dr. Feng Gao. This version (v1.2) has been greatly improved in computing efficiency (e.g. one run for multiple dates and parallel computing) for large-area processing (Gao et al., 2015). Additional improvements (e.g. Landsat and MODIS images co-registration, daily MODIS nadir BRDF-adjusted reflectance) in the operational data fusion system (Wang et al., 2014) are beyond the STARFM program and are not included in this package. Improvement and continuous maintenance are being undertaken in the USDA-ARS Hydrology and Remote Sensing Laboratory (HRSL), Beltsville, MD by Dr. Feng Gao.

        The Ogallala Agro-Climate Tool

          The Ogallala Agro-Climate Tool is a Visual Basic application that estimates irrigation demand and crop water use over the Ogallala Aquifer region.

          USDA/ARS Kimberly, ID - Furrow Infiltration and Erosion Data, 1998 to 2016

            The data are derived from the field monitoring of irrigated furrows from 1998 to 2016 at the research farm of the USDA/ARS-Northwest Irrigation and Water Research Laboratory in Kimberly, Idaho, USA (south-central Idaho). For each monitored furrow, irrigation inflow rates, outflow rates, and sediment concentrations were recorded periodically during the irrigation. A gated pipe conveyed irrigation water across the plots at the head, or inflow-end, of the furrows and adjustable spigots supplied water to each irrigated furrow.

            USDA-ARS Colorado Maize Water Productivity Dataset 2012-2013

              The USDA-Agricultural Research Service carried out an experiment on water productivity in response to seasonal timing of irrigation of maize (*Zea mays* L.) at the Limited Irrigation Research Farm (LIRF) facility in northeastern Colorado (40°26’ N, 104°38’ W) starting in 2012. Twelve treatments involved different water availability targeted at specific growth-stages. This dataset includes data from the first two years, which were complete years with intact treatments. Data includes canopy growth and development (canopy height, canopy cover and LAI), irrigation, precipitation, and soil water storage measured periodically through the season; daily estimates of crop evapotranspiration; and seasonal measurement of crop water use, harvest index and crop yield. Hourly and daily weather data are also provided from the CoAgMET, Colorado’s network of meteorological information.

              Agricultural Land Management Alternative with Numerical Assessment Criteria (ALMANAC) Simulation Model

                The Agricultural Land Management Alternative with Numerical Assessment Criteria (ALMANAC) model simulates crop growth, competition, light interception by leaves, biomass accumulation, partitioning of biomass into grain, water use, nutrient uptake, and growth constraints such as water, temperature, and nutrient stress. Plant development is temperature driven, with duration of growth stages dependent on degree days. Each plant species has a defined base temperature and optimum temperature.