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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!

Data from: Plant Tissue Characteristics of Miscanthus x giganteus v2

    As part of a study identifying relationships between environmental variables and insect distributions within a bioenergy crop, giant miscanthus (Miscanthus x giganteus) samples were collected in October 2016 at 33 locations within a field in southeast Georgia, USA. This dataset describes the chemical composition of giant miscanthus leaves and stems including the total carbon (TC) and nitrogen (TN) content, total macro- and micronutrients.

    The Bronson Files, Dataset 8, Field 113, 2016

      Dr. Kevin Bronson provides this dataset representing the first of three consecutive years of cotton and nitrogen management experimentation in Field 113. Included, is an intermediate analysis mega-table of correlated and calculated parameters, laboratory analysis results generated during the experimentation, plus high-resolution plot level intermediate data analysis tables of SAS process output, as well as the complete raw data sensor recorded logger outputs.

      The Bronson Files, Dataset 7, Field 13, 2015

        Dr. Kevin Bronson provides a second year of water management in cotton agricultural research experiment dataset for compute, including notation of field events and operations, an intermediate analysis mega-table of correlated and calculated parameters, and laboratory analysis results generated during the experimentation, plus high-resolution plot level intermediate data analysis tables of SAS process output, as well as the complete raw data sensor recorded logger outputs.

        The Bronson Files, Dataset 6, Field 13, 2014

          Dr. Kevin Bronson provides a unique nitrogen and water management in cotton agricultural research dataset for compute, including notation of field events and operations, an intermediate analysis mega-table of correlated and calculated parameters, and laboratory analysis results generated during the experimentation, plus high-resolution plot level intermediate data analysis tables of SAS process output, as well as the complete raw data sensor recorded logger outputs.

          The Bronson Files, Dataset 5, Field 105, 2014

            Active optical proximal wheat canopy sensing spatial data and including additional related metrics such as canopy thermal and height are presented. Agronomic nitrogen and irrigation management related field operations are listed. Unique research experimentation intermediate analysis table is made available, along with the raw data. The raw data recordings, and annotated table outputs with calculated VIs are made available. Plot polygon coordinate designations allow a re-intersection spatial analysis. Data was collected in the 2014 season at Maricopa Agricultural Center, Arizona, USA. High throughput proximal plant phenotyping via electronic sampling and data processing method approach is exampled. Acquired data using USDA Maricopa first mobile platforms, such as the Proximal Sensing Cart Mark1, SAS and GIS compute processing output tables, including Excel formatted examples are presented, where data tabulation and analysis is available. The weekly proximal sensing data collected include canopy reflectance at six wavelengths, ultrasonic distance sensing of canopy height, and infrared thermometry. Ten levels gradient irrigation application from linear move sprinkler system were applied. Soil physical texture and fertility chemistry results are available. Durum wheat data includes in-season biomass and plant N content, final total biomass, grain yield, grain nitrogen, and yellow berry assessment.

            The Bronson Files, Dataset 4, Field 105, 2013

              Active optical proximal wheat canopy sensing spatial data and including additional related metrics such as canopy thermal and height are presented. Agronomic nitrogen and irrigation management related field operations are listed. Unique research experimentation intermediate analysis table is made available, along with the raw data. The raw data recordings, and annotated table outputs with calculated VIs are made available. Plot polygon coordinate designations allow a re-intersection spatial analysis. Data was collected in the 2013 season at Maricopa Agricultural Center, Arizona, USA. High throughput proximal plant phenotyping via electronic sampling and data processing method approach is exampled. Acquired data using USDA Maricopa first mobile platforms, such as the Proximal Sensing Cart Mark1, SAS and GIS compute processing output tables, including Excel formatted examples are presented, where data tabulation and analysis is available. The weekly proximal sensing data collected include canopy reflectance at six wavelengths, ultrasonic distance sensing of canopy height, and infrared thermometry. Ten levels gradient irrigation application from linear move sprinkler system were applied. Soil physical texture and fertility chemistry results are available. Yield and seed information is presented.

              The Bronson Files, Dataset 3, Field 107, 2013

                Small dataset describing a unique rubber bush, in the context of greater published research Active optical proximal cotton canopy sensing spatial data and including additional related metrics canopy thermal and height are presented. Agronomic nitrogen and irrigation management related field operations are listed. Unique research experimentation intermediate analysis table is made available, along with raw data. The raw data recordings, and annotated table outputs with calculated VIs are made available. Plot polygon coordinate designations allow a re-intersection spatial analysis. Data was collected in the 2013 season at Maricopa Agricultural Center, Arizona, USA. High throughput proximal plant phenotyping via electronic sampling and data processing method approach is exampled. Acquired data using USDA Maricopa first mobile platforms, such as the Proximal Sensing Cart Mark 1. SAS and GIS compute processing output tables, including Excel formatted examples are presented, where data tabulation and analysis is available. The weekly proximal sensing data collected include canopy reflectance at six wavelengths, ultrasonic distance sensing of canopy height, and infrared thermometry. Limited soil sampling and final harvest information is included.

                The Bronson Files, Dataset 2, Field 17, 2013

                  Active optical proximal cotton canopy sensing spatial data and including additional related metrics canopy thermal and height are presented. Agronomic nitrogen and irrigation management related field operations are listed. Unique research experimentation intermediate analysis table is made available, along with raw data. The raw data recordings, and annotated table outputs with calculated VIs are made available. Plot polygon coordinate designations allow a re-intersection spatial analysis. Data was collected in the 2013 season at Maricopa Agricultural Center, Arizona, USA. High throughput proximal plant phenotyping via electronic sampling and data processing method approach is exampled. Acquired data using USDA Maricopa first mobile platforms, such as the Proximal Sensing Cart Mark 1. SAS and GIS compute processing output tables, including Excel formatted examples are presented, where data tabulation and analysis is available. The weekly proximal sensing data collected include canopy reflectance at six wavelengths, ultrasonic distance sensing of canopy height, and infrared thermometry. Lint and seed yields, first open boll biomass, and nitrogen uptake were also determined. Soil profile nitrate to 1.8 m depth was determined in 30-cm increments, before planting and after harvest. Nitrous oxide emissions were determined 20 or more weeks in the season with 1-L vented chambers (samples taken at 0, 12, and 24 minutes). Nitrous oxide was determined by gas chromatography (electron detection detector).