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Ag Data Commons migration begins October 18, 2023

The Ag Data Commons is migrating to a new platform – an institutional portal on Figshare. Starting October 18 the current system will be available for search and download only. Submissions will resume after the launch of our portal on Figshare in November. Stay tuned for details!

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).

    Data from: All-hazards dataset mined from the US National Incident Management System 1999–2014

      ICS-209-PLUS is a new dataset mined from the public archive (1999–2014) of the U.S. National Incident Management System/Incident Command System Incident Status Summary Form (a total of 124,411 reports for 25,083 incidents, including 24,608 wildfires). This system captures detailed information on incident management costs, personnel, hazard characteristics, values at risk, fatalities, and structural damage.

      Operational Tillage Information System (OpTIS) tillage, residue, and soil health practice dataset

        CTIC has partnered with Applied GeoSolutions and The Nature Conservancy on the development, testing and application of the Operational Tillage Information System (OpTIS), an automated system to map tillage, residue cover, winter cover, and soil health practices using remote sensing data. While OpTIS calculations are performed at the farm-field scale using publicly available data, the privacy of individual producers is fully protected by reporting only spatially-aggregated results at regional and watershed scales. OpTIS-based data are currently available for the years 2005 through 2018 for the US Corn Belt, including all of Illinois, Indiana, and Iowa, as well as parts of: Kansas, Michigan, Minnesota, Missouri, Nebraska, Ohio, Oklahoma, South Dakota, and Wisconsin.

        Nitrogen Decision Aid

          The Nitrogen Decision Aid is a computerized program that predicts the amount of N mineralized from planting to side-dress or 5-leaf growth-stage. By applying just enough N-fertilizer to reach the critical soil nitrate concentration it is possible reduce this waste. This model is designed for Northern climates that will have some frost in the soil during the winter.

          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.

            SHOOTGRO

              SHOOTGRO emphasizes the development and growth of the shoot apex of small-grain cereals such as winter and spring wheat (Triticum aestivum L.) and spring barley (Hordeum vulgare L.). To better incorporate the variability typical in the field, up to six cohorts, or age classes, of plants are followed using a daily time step.

              PhenologyMMS

                PhenologyMMS is a simulation model that outlines and quantifies the developmental sequence of different crops under varying levels of water deficits, provides developmental information relevant to each crop, and is intended to be used either independently or inserted into existing crop growth models.

                Stream Temperature Modeling and Monitoring: Multiple Regression Stream Temperature Model

                  This simple Stream Temperature Modeling and Monitoring approach uses thermograph data and geomorphic predictor variables from GIS software and digital elevation models (DEM). Multiple regression models are used to predict stream temperature metrics throughout a stream network with moderate accuracy (R^2 ~ 0.65). The models can provide basic descriptions of spatial patterns in stream temperatures, suitable habitat distributions for aquatic species, or be used to assess temporal trends related to climate or management activities if multiple years of temperature data are available.