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GOSSYM

    GOSSYM is a dynamic, process-level simulation model of cotton growth and yield. GOSSYM essentially is a materials balance model which keeps track of carbon and nitrogen in the plant and water and nitrogen in the soil root zone. GOSSYM predicts the response of the field crop to variations in the environment and to cultural inputs. Specifically, the model responds to weather inputs of daily total solar radiation, maximum and minimum air temperatures, daily total wind run, and rainfall and/or irrigation amount. The model also responds to cultural inputs such as preplant and withinseason applications of nitrogen fertilizer, row spacing and within row plant density as they affect total plant population, and cultivation practices.

    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.

        WISDEM

          WISDEM simulates the variation in multi-species weed populations over time in response to crop rotation, tillage system, and specific weed management tactics and the consequent crop yield loss due to weed competition. Population dynamics of individual weed species are predicted from a limited number of parameters that can be derived from literature sources and expert opinion.

          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.

            IPM Images: The Source for Agriculture and Pest Management Pictures

              A joint project of The University of Georgia - Warnell School of Forestry and Natural Resources and College of Agricultural and Environmental Sciences, The Center for Invasive Species and Ecosystem Health, USDA National Institute of Food and Agriculture, Southern Integrated Pest Management Center, Southern Plant Diagnostic Network, and USDA/APHIS Identification Technology Program, [IPM Images](https://www.ipmimages.org/) image categories include: Commodity Groups; Taxonomy; Biological Controls; Damage Types; and Diseases.

              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.

                  Sustainable Corn CAP Research Data (USDA-NIFA Award No. 2011-68002-30190)

                    The Sustainable Corn CAP (Cropping Systems Coordinated Agricultural Project: Climate Change, Mitigation, and Adaptation in Corn-based Cropping Systems) was a multi-state transdisciplinary project supported by the USDA National Institute of Food and Agriculture (Award No. 2011-68002-30190). Research experiments were located through the U.S. Corn Belt and examined farm-level adaptation practices for corn-based cropping systems to current and predicted impacts of climate change.