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Floridan Aquifer Collaborative Engagement for Sustainability (FACETS) - Field trial data from University of Georgia Stripling Irrigation Research Park (SIRP): ARDN Products

    ARDN (Agricultural Research Data Network) annotations for "Floridan Aquifer Collaborative Engagement for Sustainability (FACETS) - Field trial data from University of Georgia Stripling Irrigation Research Park (SIRP)". The ARDN project (https://data.nal.usda.gov/ardn) is a network of datasets harmonized and aggregated using the ICASA vocabulary, as recommended by USDA NAL (https://data.nal.usda.gov/data-dictionary-examples) and described in detail here: www.tinyurl.com/icasa-mvl”. The original dataset presents evaluations of different irrigation and fertilization treatments (corn and cotton have three nitrogen fertilization and three irrigation treatments, peanut has nine irrigation treatments and no N fertilizer treatment) at the University of Georgia’s Stripling Irrigation Research Park (SIRP) located near Camilla, Georgia in a 4 ha research field.

    Floridan Aquifer Collaborative Engagement for Sustainability (FACETS) - Field trial data from University of Georgia Stripling Irrigation Research Park (SIRP)

      Data are presented to evaluate different irrigation and fertilization treatments (corn and cotton have three nitrogen fertilization and three irrigation treatments, peanut has nine irrigation treatments and no N fertilizer treatment) at the University of Georgia’s Stripling Irrigation Research Park (SIRP) located near Camilla, Georgia in a 4 ha research field.

      LTAR Upper Mississippi River Basin - Morris - Swan Lake Research Farm Phenocam

      NAL Geospatial Catalog
        The PhenoCam network is collecting color and near infrared images year-round using cameras in fixed positions on agricultural lands including a site located on the Swan Lake Research Farm. The network effort was initiated in 2015 at this long-term, plot-scale research site. The camera at the research farm on focused a plot-scale, replicated research study that was established in 1997 to assess the long-term impacts of various tillage management options on soil organic carbon

        Farming Systems Study for Greenhouse gas Reduction through Agricultural Carbon Enhancement network in Morris, Minnesota

          Tillage is decreasing globally due to recognized benefits of fuel savings and improved soil health in the absence of disturbance. However, a perceived inability to control weeds effectively and economically hinders no-till adoption in organic production systems in the Upper Midwest, USA. A strip-tillage (ST) strategy was explored as an intermediate approach to reducing fuel use and soil disturbance, and still controlling weeds. An 8-year comparison was made between two tillage approaches, one primarily using ST the other using a combination of conventional plow, disk and chisel tillage [conventional tillage (CT)].

          Alternative Biomass Production Study for Resilient Economic Agricultural Practices in Morris, Minnesota

            The Tillage Study was established in 1997 to assess the effect of a variety of tillage intensities on soil C. The initial eight treatments included no-tillage, moldboard + disk tillage, chisel tillage, and fall and spring residue management, with or without strip-tillage and strip-tillage + subsoiling (Archer and Reicosky, 2009). In 2004, treatments were reduced to no-tillage, moldboard tillage, and fall and spring residue management without strip-tillage, but all had an early or late planting date. The last comprehensive set of soil samples were collected in 2006.

            Low-Disturbance Manure Incorporation

              The LDMI experiment (Low-Disturbance Manure Incorporation) was designed to evaluate nutrient losses with conventional and improved liquid dairy manure management practices in a corn silage (*Zea mays*) / rye cover-crop (*Secale cereale*) system. The improved manure management treatments were designed to incorporate manure while maintaining crop residue for erosion control. Field observations included greenhouse gas (GHG) fluxes from soil, soil nutrient concentrations, crop growth and harvest biomass and nutrient content, as well as monitoring of soil physical and chemical properties. Observations from LDMI have been used for parameterization and validation of computer simulation models of GHG emissions from dairy farms (Gaillard et al., submitted). The LDMI experiment was performed as part of the Dairy CAP.