AndreaBozzo/ceres-open-data-index huggingface dataset:
Criollo and Crossbred Steer Comparison: Weight Gain, Grazing, Carcass Quality, 2015-2017

Rarámuri Criollo cows have behavioral traits that are desirable for rangelands in arid environments, but calves from this biotype are difficult to market through conventional methods. One strategy to improve marketability is to crossbreed these cows with traditional beef breed bulls. However, it is unclear whether crossbred calves will achieve marketable weights and carcass qualities on rangeland and whether they will retain the desirable grazing behaviors of their mothers. We evaluated these traits for two cohorts of Rarámuri Criollo (JRC), Mexican Criollo (MC), and Criollo × beef-breed crossbred (XC) steers. Final live and carcass weights of XC were greater than JRC and MC, but all three groups were market ready at 30-mo after finishing on grass. Carcass quality and average daily gain did not differ among biotypes. Both JRC and XC steers exhibited grazing patterns similar to those previously observed in JRC cows. These results suggest JRC, MC, and XC steers can achieve desirable slaughter weights in 30 months using a rangeland-based grass-fed protocol, and JRC and XC steers retain desirable grazing behaviors of JRC cows.

Tags: 216, carcass-quality, global-positioning-systems, grazing, grazing-distribution, heritage-genetics, livestock, livestock-movement, ltar, nsf-lterlong-term-ecological-research, phenology, rangelands, raramuri-criollo, usda-ars-natural-resources-and-sustainable-agricultural-systems-national-program-216-agricultur, weight-gain
Organization: Department of Agriculture
Data to explore circular manureshed management in beef supply chains of the United States and western Canada

Circular management of beef supply chains holds great promise for improving sustainability from grazing agroecosystem to dinner plate. In the United States and Canada, one approach to circularity entails transporting manure nutrients from cattle produced in feedlots back to the grazing agroecosystems where they originated to enrich haylands for further grazing cattle production. We provide data to assess this strategy centered around three grazing agroecosystems: Florida, New Mexico, and the provincial assemblage of Manitoba, Saskatchewan, Alberta, British Columbia. We describe four datasets that can be used to estimate the potential nutrient utilization of hay fed to grazing cattle in the three grazing agroecosystems and the magnitudes of feedlot manure nutrients available for transport back to them. We found that although biogeography and management differ among the three grazing agroecosystems, the hay allocated for grazing cattle represented approximately 65% of the total harvested hay produced per agroecosystem after accounting for harvest losses, and that on average all three areas exported about 450,000 cattle annually for feedlot, pasture, and slaughter to states across the US. Although we highlight only three grazingland settings, our approach relies on methods that could ultimately be scaled nationally and internationally, with applicability to other animal industries for which circular management is an aspiration for sustainability outcomes.

Tags: 216, animal-production, crop-livestock-integration, ltar, manureshed, nsf-lterlong-term-ecological-research, supply-chain-management, sustainable-agricultural-intensification, usda-ars-natural-resources-and-sustainable-agricultural-systems-national-program-216-agricultur
Organization: Department of Agriculture
Johnston Draw (Idaho) High Resolution Pre-Fire Vegetation Map 2023

<p dir="ltr">The variability of vegetation in rangelands can be over generalized in spatial representation and vegetation types mapped by moderate resolution vegetation maps. A high resolution (<1 meter), site specific, vegetation map may better represent the diversity and spatial complexity of rangelands – a necessity for analyzing pre-fire conditions. We pansharpened two 8-band VNIR Worldview 2 scenes to map pre-fire vegetation in Johnston Draw (1.8 square kilometers) in the Reynolds Creek Experimental Watershed in Southwest Idaho. The two Worldview 2 scenes represent peak greenness (June 14, 2023) and pre-fire (September 23, 2023) conditions with spatial resolutions of 50 centimeters and 42 centimeters, respectively. A prescribed fire burned the area on October 6, 2023. We trained a pixel based random forest classifier to map 10 site specific vegetation classes at a 50-centimeter spatial resolution. We applied a majority filter to remove speckling. Map accuracy was 83.3% when validated using a test set of 54, 30-meter diameter, plots selected to represent the following dominant vegetation types: deciduous/riparian (classes were collapsed into a single class for validation), living juniper, dead juniper, sagebrush, mixed low sage and bunchgrass, bitterbrush, and annual grasses. Barren and water classes were not validated. Training data was developed through a combination of site visit based knowledge and a training set of 30-meter diameter dominant vegetation class plots. The 1.5-billion-dollar cost of fire prevention, suppression, and restoration is stretched thin over the vast area of wildfire occurrence, where site-specific high-resolution vegetation maps are essential to mitigate fire potential and address post fire recovery. In addition to the pre-fire vegetation map a post fire burn product will also be submitted to Ag Data Commons and the related materials will be updated to reflect these complimentary submissions.</p>

Tags: high-resolution, juniper-treatment, prescribed-fire, random-forest, rangelands, remote-sensing, vegetation-mapping
Organization: Department of Agriculture
2023 USDA Plant Hardiness Zone Map Mean Annual Extreme Low Temperature Rasters

<p dir="ltr">These rasters provide the local mean annual extreme low temperature from 1991 to 2020 in an 800m x 800m grid covering the USA (including Puerto Rico) based on interpolation of data from more than a thousand weather stations. Each location's Plant Hardiness Zone is calculated based on classifying that temperature into 5 degree bands.The classified rasters are then used to create print and interactive maps.</p><p dir="ltr">Temperature station data for the 2023 edition of the USDA Plant Hardiness Zone Map (PHZM) came from many different sources. In the eastern and central United States, Puerto Rico, and Hawaii, data came primarily from weather stations of the National Weather Service and several state networks. In the western United States and Alaska, data from stations maintained by USDA Natural Resources Conservation Service, USDA Forest Service, U.S. Department of the Interior (DOI) Bureau of Reclamation, and DOI Bureau of Land Management also helped to better define hardiness zones in mountainous areas. Environment Canada provided data from Canadian stations, and data from Mexican stations came from the Mexico National Weather Service and the Global Historical Climate Network. The USDA PHZM was produced with PRISM, a highly sophisticated climate mapping technology developed at Oregon State University. The map was produced from a digital computer grid, with each cell measuring about a half mile on a side. PRISM estimated the mean annual extreme minimum temperature for each grid cell (or pixel on the map) by examining data from nearby stations; determining how the temperature changed with elevation; and accounting for possible coastal effects, temperature inversions, and the type of topography (ridge top, hill slope, or valley bottom). Information on PRISM can be obtained from the PRISM Climate Group website https://prism.oregonstate.edu. Once a draft of the map was completed, it was reviewed by a team of climatologists, agricultural meteorologists, and horticultural experts. If the zone for an area appeared anomalous to these expert reviewers, experts doublechecked the draft maps for errors or biases. A detailed explanation of the mapmaking process and a discussion of the horticultural applications of the 2012 PHZM (similar to 2023) are available from the articles listed below. Daly, C., M.P. Widrlechner, M.D. Halbleib, J.I. Smith, and W.P. Gibson. 2012. Development of a new USDA Plant Hardiness Zone Map for the United States. Journal of Applied Meteorology and Climatology, 51: 242-264.Widrlechner, M.P., C. Daly, M. Keller, and K. Kaplan. 2012. Horticultural Applications of a Newly Revised USDA Plant Hardiness Zone Map. HortTechnology, 22: 6-19.</p>

Tags: climate, garden, low-temperature, perennial, plant-hardiness, weather
Organization: Department of Agriculture
Gridded 20-year Parameterization of a Stochastic Weather Generator (CLIGEN) to Fill Gaps in Coverage in the Northern Hemisphere

<p>CLImate GENerator (CLIGEN) is a stochastic weather generator that produces weather time series for soil erosion modeling and various other applications. The generated time series are statistically similar to observed long-term time series. This gridded CLIGEN parameterization with 0.25° spatial resolution complements existing global coverages by filling in remaining gaps that existed in the northern hemisphere (see the map layer *.kmz file with all grid point locations). The coverage is largely represented by Canada, Europe, and Russia and encompasses countries north of ~40°N with no previous known coverage. The CLIGEN inputs may be used to generate daily precipitation, temperature, dewpoint, solar radiation, and wind time series, as well as sub-daily precipitation patterns. The gridded parameterization allows CLIGEN time series to be generated at any point the grid. In particular, the dataset can provide climate drivers for climate-related research in ungauged areas where observed climate records are unavailable.</p>
<p>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=114,150 files corresponding to the total number of grid points. 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>

Tags: ars, climate-change, climate-models, climatologymeteorologyatmosphere, data-gov, hydrology, np211, soil-erosion
Organization: Department of Agriculture
Data from USDA ARS Central Plains Experimental Range (CPER) near Nunn, CO: Cattle weight gains managed with light, moderate and heavy grazing intensities

<p>The USDA-Agricultural Research Service Central Plains Experimental Range (CPER) is a Long-Term Agroecosystem Research (LTAR) network site located ~20 km northeast of Nunn, in north-central Colorado, USA.  In 1939, scientists established the Long-term Grazing Intensity study (LTGI) with four replications of light, moderate, and heavy grazing.  Each replication had three 129.5 ha pastures with the grazing intensity treatment randomly assigned.  Today, one replication remains.  Light grazing occurs in pasture 23W (9.3 Animal Unit Days (AUD)/ha, targeted for 20% utilization of peak growing-season biomass), moderate grazing in pasture 15E (12.5 AUD/ha, 40% utilization), and heavy grazing in pasture 23E (18.6 AUD/ha, 60% utilization). British- and continental-breed yearling cattle graze the pastures season-long from mid-May to October except when forage limitations shorten the grazing season.  Individual raw data on cattle entry and exit weights, as well as weights every 28-days during the grazing season are available from 2000 to 2019.  Cattle entry and exit weights are included in this dataset.  Weight outliers (± 2 SD) are flagged for calculating summary statistics or performing statistical analysis. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Data Dictionary for LTGI Cattle weights on CPER (2000-2019).</p> <p>File Name: LTGI_2000-2019_data_dictionary.csv</p><p>Resource Description: Data dictionary for data from USDA ARS Central Plains Experimental Range (CPER) near Nunn, CO cattle weight gains managed with light, moderate and heavy grazing intensities </p></li><br><li><p>Resource Title: LTGI Cattle weights on CPER (2000-2019).</p> <p>File Name: LTGI_2000-2019_all_weights_published.csv</p><p>Resource Description: Data from USDA ARS Central Plains Experimental Range (CPER) near Nunn, CO cattle weight gains managed with light, moderate and heavy grazing intensities </p></li></ul><p></p>

Tags: ars, beef-cattle, cattle, cattle-production, cattle-weight-gain, data-gov, livestock, np215, rangeland
Organization: Department of Agriculture
Data from USDA ARS High Plains Grasslands Research Station (East Unit) near Cheyenne, WY: Yearling cattle weight gains managed in light, moderate and heavily stocked pastures (1982-2022)

<p>The USDA-Agricultural Research Service High Plains Grasslands Research Station (HPGRS) is located in Cheyenne, Wyoming, USA.  In 1982, a long-term stocking rate study on northern mixed-grass prairie was initiated with season-long (early June to October) grazing. Stocking rates defined as light (35% below NRCS recommended rate, 15 yearlings per 80 ha), moderate (NRCS recommended rate, 4 yearlings per 12ha), and heavy (33% above NRCS recommended rate, 4 yearlings per 9 ha). British- and continental-breed yearling cattle were used throughout the study years. When forage supply was limited due to drought, grazing seasons were shortened or cattle were not grazed for that season. Individual raw data on cattle entry and exit weights are available from 1982 to 2022. No grazing occurred in the years 1989, 2000, and 2002 due to drought conditions. Weight gain outliers (± 2 sd of treatment mean) were removed from the dataset. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Long-Term Grazing Intensity (LTGI) cattle weight gains.</p> <p>File Name: EastUnit_LTGI.csv</p><p>Resource Description: Cattle weight gain data from the Long-Term Grazing Intensity (LTGI) 1982-2022 on the USDA Agricultural Research Service High Plains Grasslands Research Station, near Cheyenne, WY</p></li><br><li><p>Resource Title: Data Dictionary for Long-Term Grazing Intensity (LTGI) cattle weight gains .</p> <p>File Name: EastUnit_LTGI_DataDictionary.csv</p><p>Resource Description: Data dictionary for cattle weight gain data from the Long-Term Grazing Intensity (LTGI) 1982-2022 on the USDA Agricultural Research Service High Plains Grasslands Research Station, near Cheyenne, WY</p></li></ul><p></p>

Tags: ars, beef-cattle, cattle, cattle-weight-gain, data-gov, grazing, np215, rangeland
Organization: Department of Agriculture
Agricultural Collaborative Research Outcomes System (AgCROS)

<p>The Agricultural Collaborative Research Outcomes System (AgCROS) is a growing “network of networks” that presently consists of multiple agricultural data networks: Nutrient Uptake and Outcome Network (NUOnet), the Greenhouse gas Reduction through Agricultural Carbon Enhancement Network (GRACEnet), Resilient Economic Agricultural Practices (REAP), Dairy Agriculture for People and the Planet (DAPP; Dairy Grand Challenge), Soil Health Assessment Network (SHAnet), Agricultural Antibiotic Resistance (AgAR), and the Long-Term Agroecosystem Research (LTAR) Network. By integrating these diverse database networks, AgCROS facilitates the flow of information and increases the cooperation among researchers participating in these networks. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Agricultural Collaborative Research Outcomes System.</p> <p>File Name: Web Page, url: <a href="https://agcros-usdaars.opendata.arcgis.com/">https://agcros-usdaars.opendata.arcgis.com/</a> </p></li></ul><p></p>

Tags: agar, agricultural-antibiotic-resistance, ars, data-gov, gracenet, greenhouse-gas-fluxes, greenhouse-gas-reduction-through-agricultural-carbon-enhancement-network, long-term-agroecosystem-research, np211, np212, np216, nuonet, nutrient-use-and-outcome-network, online-database, reap, resilient-economic-agricultural-practices, soil-biology
Organization: Department of Agriculture
The Bronson Files, Dataset 9, Field 113, 2017 Cotton

<p>Dr. Kevin Bronson provides a dataset representing the second 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.</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 2017 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 high-throughput plant phenotyping.
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 was 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
Manual annotations of Rhyzopertha dominica genome assembly RdoDt3_Drdd8_decomES

<p>This dataset contains manual annotations from Rhyzopertha dominica community curators, based on genome assembly RdoDt3_Drdd8_decomES.fasta.gz. These annotations are direct exports from Apollo 2.6 (<a href="https://doi.org/10.5281/zenodo.5015109">https://doi.org/10.5281/zenodo.5015109</a>), hosted by the i5k Workspace@NAL (<a href="https://i5k.nal.usda.gov/">https://i5k.nal.usda.gov/</a>). Manual annotations are temporary and will be reviewed by the i5k Workspace@NAL and submitted to NCBI's GenBank database after review. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Manual annotations of Rhyzopertha dominica genome assembly RdoDt3_Drdd8_decomES.</p> <p>File Name: Rhyzopertha-dominica-manual-annotations-presubmission.tar.gz</p><p>Resource Description: The files contained within this directory are manual annotations from the Rhyzopertha dominica genome assembly RdoDt3_Drdd8_decomES.fasta.gz. These annotations are direct exports from Apollo 2.6, hosted by the i5k Workspace@NAL. Manual annotations are temporary and will be reviewed by the i5k Workspace@NAL and submitted to NCBI's GenBank database after review. </p>
<ul>
<li>Annotations.cdna.fasta.gz: cDNA sequences of the manual annotations</li>
<li>Annotations.cds.fasta.gz: coding (CDS) sequences of the manual annotations</li>
<li>Annotations.gff3.gz: manual annotations in the GFF3 format.</li>
<li>Annotations.peptide.fasta.gz: polypeptide sequences of the manual annotations.</li>
<li>RdoDt3_Drdd8_decomES.fasta.gz: The genome assembly that the manual annotations are based on.</li></ul><p></p></li>
</ul>

Tags: ars, data-gov, genome-annotation, i5k, insects, rhyzopertha-dominica
Organization: Department of Agriculture
The Bronson Files, Dataset 8, Field 113, 2016

<p>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.</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 2016 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 high-throughput plant phenotyping.
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 was 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, theme-geospatial
Organization: Department of Agriculture