AndreaBozzo/ceres-open-data-index huggingface dataset:
Data from: Investigating ungulate site use as a driver of Culicoides (Diptera: Ceratopogonidae) emergence from larval habitats

<p dir="ltr">This data set was generated for a study investigating the influence of three different hooved animals on the emergence of <i>Culicoides </i>biting midges from natural larval habitats. <i>Culicoides </i>often develop in semi-aquatic habitats at the periphery of water bodies. These are also areas that are frequented by animal populations, which may impact biting midge development and emergence from these habitats. The research took place at the Konza Prairie Biological Station (KPBS) near Manhattan, KS, which is a large expanse of native tallgrass prairie with interspersed gallery forests. KPBS has managed populations of cattle and bison present as well as native populations of wild white-tailed deer and elk, all of which were investigated for this study to represent impacts of agricultural (cattle), wild (cervid), and intermediate (bison) animal populations on midge emergence. </p><p dir="ltr">This data set represents paired <i>Culicoides </i>larval data and trail camera vertebrate data at eight sites within KPBS representing stream, pond, and spring environments. Larval data were collected by sampling larval substrate from around the larval habitat and placing the substrate on petri dishes in the lab to monitor for adult midge emergence. At each of these larval habitats, trail cameras were set up to view the area from which larval collections were made to investigate animal use of the habitat in the month preceding the larval substrate collections. These collections were conducted monthly from April to November in 2021 and May to November in 2022 and 2023. Data collected from trail cameras included the days since the last animal event (Days_Last_Activity), last animal recorded (Last_Activity_Species), number of animal activity days (Cow_Days, Deer_Days, Bison_Days, and Total_Days), number of animal activity events (Cow_Events, Deer_Events, Bison_Events, Total_Events), total number of animals (Cows, Deer, Bison, Total_Animals), average number of animals per event (Avg_Cow, Avg_Deer, Avg_Bison), total time spent by animals in minutes (Total_Time_Cow, Total_Time_Deer, Total_Time_Bison, Total Time), and the average time spent per event in minutes (Avg_Time_Cow, Avg_Time_Deer, Avg_Time_Bison). Midge data were recorded as total midge emergence (Total_Midge; male and female combined), number of females that emerged (Total_Females), and then specific emergence for six species, <i>C. crepuscularis, C. haematopotus, C. sonorensis, C. stellifer, C. variipennis, </i>and <i>C. venustus.</i></p>

Tags: biting-midge, host-associations, larval-ecology, larval-emergence, veterinary-entomology
Organization: Department of Agriculture
Data from: Carbon Fluxes from a Spring Wheat-Corn-Soybean Crop Rotation Under No-Tillage Management

<p>The increase in corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] production in rainfed cropping systems of the northern Great Plains has altered the delivery of ecosystem services from agricultural land. A study was conducted to quantify carbon balance of a spring wheat (Triticum aestivum L.)-corn-soybean rotation under no-till management using eddy covariance techniques over a 3-yr period. Paired field sites with the same soil type near Mandan, ND USA were used for the study. Data from the study included fluxes of carbon dioxide and water vapor, precipitation, air temperature, relative humidity, photosynthetically active radiation, soil temperature, soil water content, vegetation phenology, green chromatic coordinate, aboveground biomass, leaf area index, and grain yield. Data were used to generate estimates of net ecosystem exchange, ecosystem respiration, gross ecosystem production, net ecosystem carbon balance, evapotranspiration, vapor pressure deficit, relative greenness of vegetation, and carbon-, water-, and light-use efficiencies. Data are generally applicable to rainfed conditions under a semiarid Continental climate for Temvik-Wilton silt loams (fine silty, mixed, superactive, frigid Typic and Pachic Haplustolls) and related soil types (i.e., Grassna, Linton, Mandan, and Williams).</p>

Tags: aboveground-net-primary-productivity, ars, carbon-use-efficiency, data-gov, gross-primary-productivity, net-ecosystem-production, northern-great-plains, np216
Organization: Department of Agriculture
Data and code from: Cotton stalk management and a cover crop produce minimal effects on cotton leafroll dwarf virus

<p dir="ltr">In 2017, cotton (<i>Gossypium hirsutum</i> L.) leafroll dwarf virus (CLRDV) was first reported in the United States. One CLRDV inoculum source includes the previous year’s cotton stalks, hence destroying cotton stalks could be effective for CLRDV management. However, tillage intensive stalk destruction methods (SDMs) can degrade southeastern soils, but a cover crop may provide short-term benefits and reduce CLRDV incidence. Therefore, we examined three SDMs (Tillage, Pull, Mow) across two cover crop levels [no cover and rye (<i>Secale cereale</i> L.) /clover (<i>Trifolium incarnatum</i> L.) mixture] and two cotton varieties to determine how cotton growth, soil penetration resistance (PR), and two CLRDV incidence sample times (pre-harvest and post-harvest) were affected across six environments during the 2021 and 2022 growing seasons. None of the SDMs affected any factors examined in this experiment, except soil PR and cotton yield. The Pull and Mow SDMs both increased soil PR compared to the Tillage SDM. An 8% yield increase (Pull > Mow) was observed, but the Tillage SDM yield did not differ from Pull or Mow SDMs. The rye/clover mixture also increased soil PR. Although cotton stands were 15% greater with no cover crop, subsequent cotton yield and fiber quality were minimally affected by cover crops. The rye/clover mixture increased post-harvest CLRDV incidence, and cotton yields were equal between cover crops. Pre-harvest CLRDV incidence probability was 0.23, but post-harvest CLRDV incidence probability was 0.71. Continuing to identify and evaluate cultural practices that reduce CLRDV incidence is imperative to prevent negative impacts.</p><p dir="ltr">This dataset contains all data and code required to reproduce the analyses, tables, and figures in the associated manuscript. A list of R packages used to create the aforementioned items can be found in the associated manuscript.</p>

Tags: cotton-aphid, cotton-blue-disease, emergence, soil-compaction, soil-strength, strip-tillage
Organization: Department of Agriculture
Code from: Canopy closure, yield, and quality under heterogeneous plant spacing in sunflower

<p dir="ltr"><b>Abstract</b></p><p dir="ltr">Deviation from uniform target plant population density in sunflower (Helianthus annuus L.) production may negatively affect canopy closure, yield, and grain quality. The objective of this study was to evaluate the effects of plant density heterogeneity on yield, quality, and canopy closure in confectionery and oilseed sunflowers. Field experiments consisted of fixed differences in the number of skips/doubles per plot while maintaining equal total number of plants, and stand gaps with proportional decreasing plant counts.These were conducted in Minnesota, Texas, and Colorado, USA. Experiments revealed that yield was occasionally influenced by heterogeneity, with more uniform stand density yielding higher; however, substantial changes in uniformity of plant density often resulted in no differences in yield. Confectionery sunflowers compensated for yield losses in thinner stands mainly by producing larger seeds (observed range of 0.1 to 46.2 % of seeds over a 9.53 mm sieve), while oilseed sunflowers increased seed production per head (as shown by invariable test weight across treatments within environments). Plants that were unharvestable for any cause (most often due to head rot disease or lodging) were more prevalent in less uniform stands, and faster canopy closure was achieved in uniformly spaced plants. Although sunflower is very resilient to stand uniformity issues, these findings underscore the importance of uniform sunflower plant spacing for effective weed suppression and crop performance, and highlights the need for further research into sunflower yield compensation mechanisms.</p><p dir="ltr"><b>Contents</b></p><ol><li>Neil figures.R: An R script that reads canopy cover data from the "can1920.xls" file for oil and confectionery sunflowers in 2019 and 2020. It creates ggplot visualizations of canopy cover by treatment, with facets for different days after planting (DAP) and color-coding for statistical significance.</li><li>canopy.sas: A SAS script that imports canopy data, performs data transformations (including arcsine transformation), and conducts mixed model analyses. It includes repeated measures analysis, least squares means comparisons with Tukey's adjustment, and residual diagnostics. The script analyzes canopy cover data by sunflower type, year, and time point.</li></ol><p dir="ltr">These scripts are used for statistical analysis and visualization of the canopy cover data collected in the sunflower plant density heterogeneity study.</p>

Tags: crop-growth-and-development, crop-quality, dryland-cropping-system, geophysical-instrumentation, remote-sensing, seed-quality, sunflowers
Organization: Department of Agriculture
Spreadsheet for lysimeter data analysis, Bushland, Texas

<p dir="ltr">A spreadsheet was designed for weighing lysimeter raw relative water storage data analysis and reduction to values of evapotranspiration (ET), dew and frost accumulation, precipitation, irrigation, and drainage tank emptying. A new version of the spreadsheet uploaded in April 2025 includes more facilities for visualization, error checking, and validation of ET values. Algorithms in the spreadsheet automatically identify precipitation, and dew and frost accumulations in the 5-minute data, and places flags appropriately (“P” or “DW”) in a column that is referenced by formulae that separately calculate values for these. Noise can, however, cause false identification of precipitation or frost and dew accumulations, so another column is made available in which the user can enter flags to either nullify (“NO”) false automatic identification, or conversely, identify precipitation or dew and frost accumulations (“P” or “DW”) not automatically identified. This column also serves for entry of flags identifying irrigation, drainage tank emptying, counterweight adjustments, etc. Algorithms in other columns act upon these flags to correct the original raw relative storage values so that the adjusted relative storage reflects only evapotranspiration, while simultaneously computing 5-min values for precipitation, irrigation, dew and frost accumulation, and drainage tank emptying.</p>

Tags: ars, data-gov, dew, evapotranspiration-components, frost, irrigation, np211, ogallala-aquifer, precipitation
Organization: Department of Agriculture
Data from: Rapid formation of abiotic CO2 after adding phenolic gallic acid, to agricultural soils

<p dir="ltr">Abiotic efflux of CO<sub>2</sub> from soil is typically attributed to weathering of carbonates but also may be from basic biological and chemical processes in the soil such as concurrent oxidation of organic matter and reduction of metal oxides. However, little is known about how such reactions vary due to differences in the soil environment . An experiment was conducted to measure CO<sub>2</sub> emissions formed during 4-h incubations of soil from different management systems (n = 5), archived benchmark soils from sites across the U.S. (n = 18), and samples of reagent-grade metal oxides (n = 4). Treatments included water, pH 4 phthalate buffer, glucose (0.029 M), or gallic acid (0.025 M). Gallic acid is a simple phenolic constituent of root exudates found in the soil that can result in redox reactions producing CO<sub>2</sub>. Data may be used to understand processes contributing to abiotic sources of CO<sub>2</sub> from agricultural land.</p>

Tags: carbon-dioxide, environment, gallic-acid, glucose, organic-matter, oxidation, pastures, ph, phthalates, soil
Organization: Department of Agriculture
National Snag Hazard 2026 (Image Service)

Snags are a hazard to firefighters that has traditionally been managed at the field level through scouting, rapid assessment, and mitigation by avoidance or by elimination though felling. Widespread wildfires and insect/disease disturbances have resulted in an accumulation of snags across many forested landscapes, raising the risk of injury or death for firefighters and other forest workers. The National Snag Hazard Map (Riley et al. 2022) is intended to provide a landscape level view of current snag hazard to encourage awareness, assessment, and planning to mitigate snag-related risks. The National Snag Hazard Map is based on the estimated density and median height of snags greater than or equal to 7.9-in diameter at breast height and at least 10-ft tall. Snag density and median snag height are classified into hazard levels based on the logic that hazard increases with snag density and height (Dunn et al. 2019). <div><br /></div><div>Snag hazard is a landscape level decision support tool intended to help firefighters consider the magnitude and spatial distribution of snag hazard in their incident response strategy planning. Valid uses include identifying areas of higher snag hazard on the landscape that may require extra mitigation for safe operation, or that could be avoided to reduce risk to firefighters. The snag hazard map is not meant for tactical planning. A rating of low snag hazard does not mean that no overhead hazards are present and should not be interpreted as judgement that an area is safe to occupy. Conditions should always be verified in the field. Maintaining high situational awareness for overhead hazards is recommended regardless of the snag hazard rating. </div><div><br /></div><div> Dunn CJ, O’Connor CD, Reilly MJ, Calkin DE, Thompson MP (2019) Spatial and temporal assessment of responder exposure to snag hazards in post-fire environments. Forest Ecology and Management 441, 202-2014. DOI:10.1016/j.foreco.2019.03.035

Riley KL, O’Connor CD, Dunn CJ, Haas JR, Stratton RD, Gannon B (2022) A national map of snag hazard to reduce risk to wildland fire responders. Forests 13, 1160. DOI:10.3390/f13081160</div>

Tags: fire, national, open-data, snag, snag-hazard, usda-forest-service, usfs
Organization: Department of Agriculture
BdyDesg Disaster Recov Event (Feature layer)

Spatial data is collected by the National Disaster Recovery Team from each unit point of contact. The spatial locations are pulled from fire occurrence and perimeter datasets, FEMA, States, and local unit disaster response assessments. The polygons are then processed through intersections with geopolitical boundaries such as states, counties, and congressional districts as well as administrative boundaries such as regions and forests.

Tags: environment, open-data
Organization: Department of Agriculture
Activity Project Areas Timber Sale (Feature Layer)

Activity Project Area Timber Sale represents an area (polygon) within which one or more Timber Sale related activities are aggregated or organized. The data comes from the Forest Service's Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS), which is the agency standard for managing information about activities related to fire/fuels, silviculture, and invasive species. FACTS is an activity tracking application for all levels of the Forest Service.<div><br /></div><div>These data are a central source for project area boundaries for use in national information requests and cross unit analysis and makes the project area boundaries and their basic attributes more easily available to field units. It also provides public access to the data during project planning and implementation. <b>Please note that this dataset is not complete and forests continue to improve the quality of the data over time.</b></div><div><b><br /></b></div><div><a href='https://data.fs.usda.gov/geodata/edw/datasets.php?xmlKeyword=ProjectArea' target='_blank' rel='nofollow ugc noopener noreferrer'>Metadata and Downloads</a><br /></div>

Tags: activities, edw, facts, nepa, open-data, project-areas, sai, timber-sale, usda-forest-service
Organization: Department of Agriculture
Activity Project Areas Sale Area Improvement (SAI) Plan (Feature Layer)

Activity Project Area Sale Area Improvement (SAI) Plan represents an area (polygon) within which one or more Sale Area Improvement (SAI) related activities are aggregated or organized. The data comes from the Forest Service's Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS), which is the agency standard for managing information about activities related to fire/fuels, silviculture, and invasive species. FACTS is an activity tracking application for all levels of the Forest Service.<div><br /></div><div>These data are a central source for project area boundaries for use in national information requests and cross unit analysis and makes the project area boundaries and their basic attributes more easily available to field units. It also provides public access to the data during project planning and implementation. <b>Please note that this dataset is not complete and forests continue to improve the quality of the data over time.</b></div><div><b><br /></b></div><div><a href='https://data.fs.usda.gov/geodata/edw/datasets.php?xmlKeyword=ProjectArea' target='_blank' rel='nofollow ugc noopener noreferrer'>Metadata and Downloads</a><br /></div>

Tags: activities, edw, facts, nepa, open-data, project-areas, sai, timber-sale, usda-forest-service
Organization: Department of Agriculture
NorWeST Stream Temperatures 2040s (Feature Layer)

This layer represents modeled stream temperatures derived from the 
NorWeST point feature class (NorWest_TemperaturePoints). NorWeST summer 
stream temperature scenarios were developed for all rivers and streams 
in the western U.S. from the &gt; 20,000 stream sites in the NorWeST 
database where mean August stream temperatures were recorded. The 
resulting dataset includes stream lines (NorWeST_PredictedStreams) and 
associated mid-points NorWest_TemperaturePoints) representing 1 
kilometer intervals along the stream network. Stream lines were derived 
from the 1:100,000 scale NHDPlus dataset (USEPA and USGS 2010; McKay et 
al. 2012). Shapefile extents correspond to NorWeST processing units, 
which generally relate to 6 digit (3rd code) hydrologic unit codes 
(HUCs) or in some instances closely correspond to state borders. The 
line and point shapefiles contain identical modeled stream temperature 
results. The two feature classes are meant to complement one another for
 use in different applications. In addition, spatial and temporal 
covariates used to generate the modeled temperatures are included in the
 attribute tables at 
https://www.fs.usda.gov/rm/boise/AWAE/projects/NorWeST/ModeledStreamTemperatureScenarioMaps.shtml.
 The NorWeST NHDPlusV1 processing units include: Salmon, Clearwater, 
Spokoot, Missouri Headwaters, Snake-Bear, MidSnake, MidColumbia, Oregon 
Coast, South-Central Oregon, Upper Columbia-Yakima, Washington Coast, 
Upper Yellowstone-Bighorn, Upper Missouri-Marias, and Upper Green-North 
Platte. The NorWeST NHDPlusV2 processing units include: Lahontan Basin, 
Northern California-Coastal Klamath, Utah, Coastal California, Central 
California, Colorado, New Mexico, Arizona, and Black Hills.

Tags: analysis, aquatic-vulnerability-assessments, big-data, biota, citizen-science, climate-change, climate-effects, climate-scenarios, climatologymeteorologyatmosphere, covariate-predictors, crowd-sourcing, data-loggers, decision-support, ecology, ecosystems, environment, fish, forest-plant-health, gis, global-warming, habitat-management, health, hobo, hydrology, inlandwaters, invasive-species, inventory, landscape-management, microclimate, modeled-temperature, modeling, monitoring, natural-resource-management-use, norwest, observed-temperature, open-data, river-network, river-temperature-model, river-temperatures, sedimentation, spatial-stream-network, stream-network, stream-temperature-database, stream-temperature-model, stream-temperature-records, stream-temperatures, temperature-model, temperature-sensor, thermographs, topoclimate, water, watersheds, wildlife-or-fauna
Organization: Department of Agriculture
NorWeST Stream Temperatures 1993-2011 (Feature Layer)

This layer represents modeled stream temperatures derived from the NorWeST point feature class (NorWest_TemperaturePoints). NorWeST summer stream temperature scenarios were developed for all rivers and streams in the western U.S. from the &gt; 20,000 stream sites in the NorWeST database where mean August stream temperatures were recorded. The resulting dataset includes stream lines (NorWeST_PredictedStreams) and associated mid-points NorWest_TemperaturePoints) representing 1 kilometer intervals along the stream network. Stream lines were derived from the 1:100,000 scale NHDPlus dataset (USEPA and USGS 2010; McKay et al. 2012). Shapefile extents correspond to NorWeST processing units, which generally relate to 6 digit (3rd code) hydrologic unit codes (HUCs) or in some instances closely correspond to state borders. The line and point shapefiles contain identical modeled stream temperature results. The two feature classes are meant to complement one another for use in different applications. In addition, spatial and temporal covariates used to generate the modeled temperatures are included in the attribute tables at https://www.fs.usda.gov/rm/boise/AWAE/projects/NorWeST/ModeledStreamTemperatureScenarioMaps.shtml. The NorWeST NHDPlusV1 processing units include: Salmon, Clearwater, Spokoot, Missouri Headwaters, Snake-Bear, MidSnake, MidColumbia, Oregon Coast, South-Central Oregon, Upper Columbia-Yakima, Washington Coast, Upper Yellowstone-Bighorn, Upper Missouri-Marias, and Upper Green-North Platte. The NorWeST NHDPlusV2 processing units include: Lahontan Basin, Northern California-Coastal Klamath, Utah, Coastal California, Central California, Colorado, New Mexico, Arizona, and Black Hills.

Tags: analysis, aquatic-vulnerability-assessments, big-data, biota, citizen-science, climate-change, climate-effects, climate-scenarios, climatologymeteorologyatmosphere, covariate-predictors, crowd-sourcing, data-loggers, decision-support, ecology, ecosystems, environment, fish, forest-plant-health, gis, global-warming, habitat-management, health, hobo, hydrology, inlandwaters, invasive-species, inventory, landscape-management, microclimate, modeled-temperature, modeling, monitoring, natural-resource-management-use, norwest, observed-temperature, open-data, river-network, river-temperature-model, river-temperatures, sedimentation, spatial-stream-network, stream-network, stream-temperature-database, stream-temperature-model, stream-temperature-records, stream-temperatures, temperature-model, temperature-sensor, thermographs, topoclimate, water, watersheds, wildlife-or-fauna
Organization: Department of Agriculture
Research Station Facilities (Feature Layer)

These data are a point feature class that provides the location of Research and Development's offices across the United States.<div><br /></div>

Tags: facilities, forest-products-laboratory, headquarters, international-institute-for-tropical-forestry, laboratories, location, national-resource-management-use, northern-research-station, open-data, pacific-northwest-research-station, pacific-southwest-research-station, puerto-rico, research-and-development, research-station, rocky-mountain-research-station, southern-research-station, structure, united-states, usda-forest-service
Organization: Department of Agriculture