AndreaBozzo/ceres-open-data-index huggingface dataset:
The Bronson Files, Dataset 7, Field 13, 2015

<p>Dr. Kevin Bronson provides a second experiment year of Field 13 nitrogen and water management in cotton agricultural research data 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. </p>
<p>The reflectance data is good. There are some errors in the CS data.</p>
<p>See included README file for operational details and further description of the measured data signals.</p>
<p>Summary:
Active optical proximal cotton canopy sensing spatial data and including additional related metrics 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 2015 cotton season at Maricopa Agricultural Center, Arizona, USA.
High throughput proximal plant phenotyping via electronic sampling and data processing method approach is exampled using a modified high-clearance Hamby spray-rig.
Acquired data conforms to location standard methodologies of the plant phenotyping.
SAS and GIS compute processing output tables, including Excel formatted examples are presented, where data tabulation and analysis is available. Additional data illustration is offered as a report file with annotated time-series charts.
The weekly proximal sensing data collected include the primary canopy reflectance at six wavelengths.  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 with 1-L vented chambers (samples taken at 0, 12, and 24 minutes).  Nitrous oxide was determined by gas chromatography (electron detection detector).</p>

Tags: agroecosystems-environment-weather-and-climate, ars, data-gov, earth-science-agriculture-soils-nitrogen, earth-science-biosphere-vegetation-plant-phenology, field-based-high-throughput-phenotyping, np211, plant-phenotyping
Organization: Department of Agriculture
The Bronson Files, Dataset 2, Field 17, 2013

<p>Dr. Kevin Bronson provides this 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 sensor recorded logger outputs. </p>
<p>This data was collected during the beginning time period of our USDA Maricopa terrestrial proximal high-throughput plant phenotyping tri-metric method generation, where a 5Hz crop canopy height, temperature and spectral signature are recorded coincident to indicate a plant health status. In this early development period, our Proximal Sensing Cart Mark1 (PSCM1) platform supplants people carrying the CropCircle (CC) sensors, and with an improved view mechanical performance result.</p>
<p>See included README file for operational details and further description of the measured data signals.</p>
<p>Summary:
Active optical proximal cotton canopy sensing spatial data and including additional related metrics such as thermal 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, where the first dual sliding arm configuration was deployed and platform clearance raised successfully as design improvements.
SAS and GIS compute processing output tables, including Excel formatted examples are presented, where intermediate 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).</p>

Tags: agroecosystems-environment-weather-and-climate, ars, data-gov, earth-science-agriculture-soils-nitrogen, earth-science-biosphere-vegetation-plant-phenology, field-based-high-throughput-phenotyping, np211, plant-phenotyping
Organization: Department of Agriculture
The Bronson Files, Dataset 1, Field 17, 2012

<p>Dr. Kevin Bronson provides this 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 generated during the experimentation, high resolution plot level data intermediate analysis tables, plus SAS process output intermediate tables, as well as the complete raw sensor recorded outputs. </p>
<p>This data was collected during the beginning time period of our USDA Maricopa terrestrial proximal high-throughput plant phenotyping tri-metric method generation, where a 5Hz crop canopy height, temperature and spectral signature are recorded coincident to indicate a plant health status. In this early development period, our Proximal Sensing Cart Mark1 (PSCM1) platform supplants people carrying the CropCircle (CC) sensors, and with an improved view mechanical performance result.</p>
<p>See included README file for operational details and further description of the measured data signals.</p>
<p>Summary:
Active optical proximal cotton canopy sensing spatial data and including additional related metrics such as thermal 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 2012 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, and via people.
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).</p>

Tags: agroecosystems-environment-weather-and-climate, ars, data-gov, earth-science-agriculture-soils-nitrogen, earth-science-biosphere-vegetation-plant-phenology, field-based-high-throughput-phenotyping, np211, plant-phenotyping
Organization: Department of Agriculture
Data from: Quality controlled research weather data – USDA-ARS, Bushland, Texas

<p>[ NOTE – 2022/05/06: this dataset is superseded by an updated version <a href="https://doi.org/10.15482/USDA.ADC/1526433">https://doi.org/10.15482/USDA.ADC/1526433</a> ]
The dataset contains 15-minute mean weather data from the USDA-ARS Conservation and Production Laboratory (CPRL), Soil and Water Management Research Unit (SWMRU) research weather station, Bushland, Texas (Lat. 35.186714°, Long. -102.094189°, elevation 1170 m above MSL) for all days in 2016. The data are from sensors deployed at standard heights over grass that is irrigated and mowed during the growing season to reference evapotranspiration standards. Sensors are duplicated at each height, and data from a duplicate sensor may be used to fill gaps in data from the primary sensor using appropriate regression relationships. Gap filling may also be accomplished using sensors deployed at one of the four large weighing lysimeters immediately west of the weather station. The primary paper describes details of the sensors used and methods of testing, calibration and use. The weather data include air temperature (C) and relative humidity (%), wind speed (m/s), solar irradiance (W m-2) and precipitation. Because the large (3 m by 3 m surface area) weighing lysimeters are better rain gages than are tipping bucket gages, the 15-minute precipitation data are derived for each lysimeter from changes in lysimeter mass. The land slope is <1% and flat. The mean annual precipitation is ~470 mm, the 20-year pan evaporation record indicates ~2,600 mm Class A pan evaporation per year, and winds are typically from the South and Southwest. The climate is semi-arid with ~70% (350 mm) of the annual precipitation occurring from May to September, during which period the pan evaporation averages ~1520 mm. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: 2016 USDA-ARS Bushland Texas 15-minute research weather data.</p> <p>File Name: 15-min_SWMRU_CPRL_weather_data_2016.xlsx</p><p>Resource Description: These 15-minute data are for every day of year in 2016 from the research weather station of the USDA-ARS Soil and Water Management Research Unit (SWMRU) at the Conservation and Production Research Laboratory, Bushland, Texas USA. Data were quality assured and controlled using procedures described by Evett et al. (2018). Data from duplicate sensors at the research weather station and at nearby (<800 m distance) weather stations were used for data comparisons and gap filling using appropriate regression relationships.</p><p>Resource Software Recommended: Microsoft Excel 2010 or later,url: <a href="https://products.office.com/en-us/excel">https://products.office.com/en-us/excel</a> </p></li><br><li><p>Resource Title: Data Dictionary - 2016 USDA-ARS Bushland Texas 15-minute research weather data.</p> <p>File Name: Data Dictionary - 2016 USDA-ARS Bushland Texas.csv</p></li></ul><p></p>

Tags: 15-minute, air-temperature, ars, data-gov, np211, precipitation, relative-humidity, solar-irradiance, weather, wind-speed
Organization: Department of Agriculture
Data from: Using zoos as sentinels for re-emerging arboviruses: Vector surveillance during an outbreak of epizootic hemorrhagic disease at the Minnesota Zoo

<p dir="ltr">Vector-borne disease prevalence is increasing at a time when surveillance capacity in the United States is decreasing. One way to address this surveillance deficiency is to utilize established infrastructure, such as zoological parks, to investigate animal disease outbreaks and improve our epidemiological understanding of vector-borne pathogens. During fall 2020, an outbreak of epizootic hemorrhagic disease (EHD) at the Minnesota Zoo resulted in morbidity and seroconversion of several collection animals. In response to this outbreak, insect surveillance was conducted, and the collected insects were tested for the presence of epizootic hemorrhagic disease virus (EHDV) by RT-qPCR to better understand the local transmitting vector populations responsible for the outbreak. Six pools of <i>Culicoides</i> biting midges were positive for EHDV, including three pools of <i>Culicoides sonorensis</i>, two pools of <i>Culicoides variipennis</i>, and a pool of degraded <i>C. variipennis</i> complex midges. All three endemic serotypes of EHDV (1, 2, and 6) were detected in both animals and midge pools from the premises. Despite this outbreak, no EHDV cases had been reported in wild animals near the zoo. This highlights the importance and utility of using animal holding facilities, such as zoos, as sentinels to better understand the spatio-temporal dynamics of pathogen transmission.</p>

Tags: culicoides, culicoides-sonorensis, culicoides-variipennis, epizootic-hemorrhagic-disease-virus, surveillance, zoo
Organization: Department of Agriculture
Agronomic Calendars for the Bushland, Texas Sorghum Datasets

<p>This dataset consists of agronomic calendars for each growing season (year) when sorghum [<em>Sorghum bicolor</em> (L.)] was grown for grain or forage at the USDA-ARS Conservation and Production Laboratory (CPRL), Soil and Water Management Research Unit (SWMRU) research weather station, Bushland, Texas (Lat. 35.186714°, Long. -102.094189°, elevation 1170 m above MSL). Sorghum was grown in 1987, 1988, 1991, 1993, 1997 through 1999, 2003 through 2007, 2014, and 2015. Depending on experimental objectives, sorghum was grown on one, two, or four large, precision weighing lysimeters, each in the center of a 4.44 ha square field. The four fields were contiguous. The fields were designated northeast (NE), southeast (SE), northwest (NW), and southwest (SW), and were themselves arranged in a larger square with the fields in four adjacent quadrants of the larger square. In 2003 and 2004, cotton was grown on only one large weighing lysimeter in rotation with sorghum. In 2015, the NE and SE fields were irrigated with subsurface drip lines and the NW and SW fields were irrigated with a linear move sprinkler. Irrigation was by linear move sprinkler system in 1987 through 2007. Sorghum was sometimes grown as a dryland crop, sometimes as a fully irrigated crop, and sometimes as a deficit irrigated crop. Irrigations designated as full were managed to replenish soil water used by the crop on a weekly or more frequent basis as determined by soil profile water content readings made with a neutron probe to 2.4-m depth in the field. Irrigations designated as deficit typically involved full irrigation to establish the crop. A crop calendar for each season lists by date the pertinent agronomic and maintenance operations (e.g., planting, thinning, fertilization, pesticide application, lysimeter maintenance, harvest). For each season there is one crop calendar for each two lysimeters (NE and SE, and/or NW and SW).
These datasets originate from research aimed at determining crop water use (ET), crop coefficients for use in ET-based irrigation scheduling based on a reference ET, crop growth, yield, harvest index, and crop water productivity as affected by irrigation method, timing, amount (full or some degree of deficit), agronomic practices, cultivar, and weather. Prior publications have focused on ET, crop coefficients, and crop water productivity. Crop coefficients have been used by ET networks. The data have utility for testing simulation models of crop ET, growth, and yield and have been used for testing, and calibrating models of ET that use satellite and/or weather data.</p>
<p>See the README for descriptions of each data file.</p>

Tags: agronomic-log, ars, data-gov, evapotranspiration, forage-sorghum, np211, sorghum-yield
Organization: Department of Agriculture
Data from: Comparing three collection methods for pollinating insects within electric transmission rights-of-ways

<p>Insect pollinator community data collected from three types of insect traps/collecting methods (colored pan traps, blue vane traps, targeted sweep netting) from four power line right of ways in Alabama. Data are from one growing season (May-October 2018), and collection methods were employed once per month. Data include: 1) insect pollinator community composition data; 2) relative diversity calculations by insect Order; 3) overall insect pollinator community diversity summary by trap type/collecting method and month. These data reflect the community as sampled through different means in the same time period.</p>
<p>Resources in this dataset:</p>
<ul>
<li>
<p>Resource title: Insect Pollinator Community Composition Matrix
File name: Pollinator communty matrix.csv
Resource description: Pollinator community composition (taxon, abundance) by site, insect trap type, and season. See Supplemental Table 1 in Campbell et al. 2023 for detailed taxa information.</p>
</li>
<li>
<p>Resource title: Insect Pollinator Community Diversity by Order
File name: Pollinator community diversity by Order.csv
Resource description: Insect Pollinator community diversity metrics separated by Order for each site, for each insect trap type and season.</p>
</li>
<li>
<p>Resource title: Summary of Overall Insect Pollinator Community Diversity
File name: Overall Pollinator community diversity.csv
Resource description: Overall Insect Pollinator community diversity summarized by trap type and season.</p>
</li>
<li>
<p>Resource title: Dataset key
File name: Dataset key table.pdf
Resource description: Column titles and variable descriptions for three datasets, of: 1) Pollinator Community Composition; 2) Pollinator Community Diversity by Order; and 3) Overall Pollinator Community Diversity summarized by Trap Type and Season</p>
</li>
</ul>

Tags: ars, beneficial-insects, blue-vane-traps, buprestidae, colored-bowl-traps, conopidae, data-gov, native-bees, np304, pollinators, sweep-net-sampling, syphidae, trapping
Organization: Department of Agriculture
Gridded 20-Year Parameterization of a Stochastic Weather Generator (CLIGEN) to Fill Gaps in Coverage South of the 40th Parallel

<p dir="ltr">CLImate GENerator (CLIGEN) is a stochastic weather generator that produces daily and sub-daily timeseries of weather variables. This gridded CLIGEN parameterization complements existing coverage for South America and Africa by adding new coverage for Central America, the Caribbean, the Middle East, South Asia, Southeast Asia, Australia, New Zealand, and various islands. This parameterization used the methodology and trained machine learning models discussed in a dataset article by Fullhart et al. (2022), <a href="https://doi.org/10.1080/20964471.2022.2136610">https://doi.org/10.1080/20964471.2022.2136610</a>. The primary dataset for South America and Africa may also be found in Ag Data Commons at <a href="https://doi.org/10.15482/USDA.ADC/1524754">https://doi.org/10.15482/USDA.ADC/1524754</a>.</p><p dir="ltr">The data are formatted as CLIGEN .par files, which are the only required input for CLIGEN. The files are contained in the "Grid Files" download with n=37105 files. The files are labeled according to grid point lat/lon coordinates (WGS84) in decimal degrees. The labeling convention uses 'N' and 'E' (north, east) to represent coordinates with a positive sign and 'S' and 'W' (south, west) to represent coordinates with a negative sign.</p><p dir="ltr">Resources in this dataset:</p><ul><li>Resource Title: Grid Files.File Name: Grid Files.zipResource Description: CLIGEN input files (.par)<br></li><li>Resource Title: Summary Table.File Name: SummaryTable.docxResource Description: Summary table that lists CLIGEN parameters and basic dataset characteristics of the gridded parameterization.<br></li><li>Resource Title: Map Layer.File Name: Map Layer.kmzResource Description: Map layer showing point locations of the CLIGEN grid.</li></ul><p><br></p>

Tags: ars, climate-change, climate-models, climatologymeteorologyatmosphere, data-gov, hydrology, np211, soil-erosion
Organization: Department of Agriculture
Data from: High genetic diversity in the landscape suggests frequent seedling recruitment by Euphorbia virgata Waldst. & Kit. (leafy spurge) in the northern U.S.A.

<p>Site information and field-collected data from a 1-year 100-site survey of leafy spurge (<em>Euphorbia virgata/esula</em>) populations in the northern U.S. Data include: 1) estimates of leafy spurge density and relative prevalence of ramets versus genets; 2) the abundance and composition of <em>Aphthona</em> species complex biological control agents; 3) presence/absence of two additional biological control agents (<em>Oberea erythrocephala</em> and <em>Hyles euphorbiae</em>). </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Data from: High genetic diversity in the landscape suggests frequent seedling recruitment by Euphorbia virgata (leafy spurge) in the northern U.S.A..</p> <p>File Name: West et al Leafy Spurge clonality data.csv</p><p>Resource Description: Dataset containing site-level estimates of leafy spurge abundance, clonality, and associated biological control agent community</p></li><br><li><p>Resource Title: Key for Dataset: High genetic diversity in the landscape suggests frequent seedling recruitment by Euphorbia virgata (leafy spurge) in the northern U.S.A..</p> <p>File Name: Key for West et al Leafy Spurge clonality data.csv</p><p>Resource Description: This provides information on the data columns included in the associated dataset.</p></li></ul><p></p>

Tags: aphthona-species, ars, biological-control, clonal-plant-invasions, data-gov, euphorbia-esula, np304, plant-demography
Organization: Department of Agriculture