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PECMAN Software

    Peanut Curing Manager (PECMAN) is a decision support system that assists drying facility managers with inventory control. It schedules sampling and removal times, estimates time remaining on dryer and current moisture content. Cooperators have reported reduced drying costs and improved peanut quality.

    Cotton Irrigation Tool

      A Web Application for Estimating Irrigated and Dryland Cotton Profitability using Modeled Yield Data.

      SGA

        Stored Grain Advisor (SGA) is a decision support system for managing insect pests of farm-stored wheat. The program predicts the likelihood of insect infestation, and recommends appropriate preventative actions . It also provides advice on how to sample and identify insect pests of stored wheat. SGA Pro was designed for use in commercial elevators as part of the Areawide IPM Project for stored grain. Grain samples are taken with a vacuum probe and processed over an inclined sieve. SGA Pro analyzes the insect data, grain temperatures and moistures, and determines which bins need to be fumigated.

        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.

          CPM - Cotton Production Model

            A new process-based cotton model, CPM, has been developed to simulate the growth and development of upland cotton (Gossypium hirsutum L.) throughout the growing season with minimal data input. CPM predicts final cotton yield for any combination of soil, weather, cultivar and sequence of management actions.

            RF-CLASS: Remote-sensing-based Flood Crop Loss Assessment Service System

              The Remote-sensing-based Flood Crop Loss Assessment Service System (RF-CLASS) is an Earth Observation (EO) based flood crop loss assessment cyber-service system operated by the Center for Spatial Information Science and Systems (CSISS), George Mason University. RF-CLASS supports flood-related crop statistics and insurance decision-making.

              National Land Cover Database 2011 (NLCD 2011)

                National Land Cover Database 2011 (NLCD 2011) is the most recent national land cover product created by the Multi-Resolution Land Characteristics (MRLC) Consortium, providing the capability to assess national land cover changes and trends across the United States from 2001 to 2011 at a spatial resolution of 30 meters, based primarily on a decision-tree classification of circa 2011 Landsat satellite data.

                Useful to Usable: Developing usable climate science for agriculture

                  Useful to Usable (U2U): Transforming Climate Variability and Change Information for Cereal Crop Producers, was a USDA-funded research and extension project designed to improve the resilience and profitability of U.S. farms in the Corn Belt amid a changing climate. Over a six-year period from April 2011 - April 2017, 122 faculty, staff, graduate students, and undergraduate students from ten Midwestern universities contributed to this interdisciplinary project. Our team integrated expertise in applied climatology, crop modeling, agronomy, cyber-technology, agricultural economics, sociology, Extension and outreach, communication, and marketing to improve the use and uptake of climate information for agricultural decision making. Together, and with members of the agricultural community, we developed a series of decision support tools, resource materials, and training methods to support data-driven decision making and the adoption of climate-resilient practices.

                  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.