NIDRM data can be used to:
For a quick overview of the 2013 - 2027 assessment and to learn more information on the differences between the 2006 and 2012 NIDRMs download the\u00a0executive summary\u00a0(2 MB PDF).
Explore forests vulnerable to attack from major insects and diseases by viewing the\u00a0Interactive Story Map of the National Insect and Disease Risk Map
NIDRM data can be used to:
For a quick overview of the 2013 - 2027 assessment and to learn more information on the differences between the 2006 and 2012 NIDRMs download the\u00a0executive summary\u00a0(2 MB PDF).
Explore forests vulnerable to attack from major insects and diseases by viewing the\u00a0Interactive Story Map of the National Insect and Disease Risk Map
The estimated ground evacuation time layer is intended to provide a broad view of medical care accessibility to inform incident- and regional-level strategic risk assessment and mitigation planning. The estimated ground evacuation times account for hospital locations, driving speeds on the current road network, and off-road walking speeds influenced by topography, vegetation, slash/blowdown, and waterbodies. Evacuation times are binned into broad classes (0-1 hours, 1-2 hours, 2-4 hours, 4-6 hours, and & >6 hours) commensurate with the precision of the analysis. There are several limitations of this estimated ground evacuation time dataset that users should understand. The analysis assumes that the closest general acute care or critical access hospital will be the first stop to stabilize the patient. Incident level safety and medical planning may identify different preferred medical facilities based on their actual travel times and/or capabilities. These estimated evacuation times only account for the travel portion of an evacuation and should therefore be viewed as best-case estimates. Complicated injuries may require additional time for patient evaluation, care, packaging for transport, and/or transfer to a vehicle. Travel times are calculated under the assumptions that the litter crew can travel at the median pedestrian rate (influenced by landscape factors) and that the ambulance or other transport vehicle can travel at the designated road speed limits. Users of this dataset should be thoughtful of factors not accounted for in this analysis that could slow evacuations such as additional landscape characteristics, crew condition, or dynamic factors like weather, traffic, or road closures.
The estimated ground evacuation time layer is intended to provide a broad view of medical care accessibility to inform incident- and regional-level strategic risk assessment and mitigation planning. The estimated ground evacuation times account for hospital locations, driving speeds on the current road network, and off-road walking speeds influenced by topography, vegetation, slash/blowdown, and waterbodies. Evacuation times are binned into broad classes (0-1 hours, 1-2 hours, 2-4 hours, 4-6 hours, and & >6 hours) commensurate with the precision of the analysis. There are several limitations of this estimated ground evacuation time dataset that users should understand. The analysis assumes that the closest general acute care or critical access hospital will be the first stop to stabilize the patient. Incident level safety and medical planning may identify different preferred medical facilities based on their actual travel times and/or capabilities. These estimated evacuation times only account for the travel portion of an evacuation and should therefore be viewed as best-case estimates. Complicated injuries may require additional time for patient evaluation, care, packaging for transport, and/or transfer to a vehicle. Travel times are calculated under the assumptions that the litter crew can travel at the median pedestrian rate (influenced by landscape factors) and that the ambulance or other transport vehicle can travel at the designated road speed limits. Users of this dataset should be thoughtful of factors not accounted for in this analysis that could slow evacuations such as additional landscape characteristics, crew condition, or dynamic factors like weather, traffic, or road closures.
Basal Area (BA). 30 meter pixel resolution. Data represents forest conditions circa 2002.
These data are a product of a multi-year effort by the FHTET (Forest Health Technology Enterprise Team) Remote Sensing Program to develop raster datasets of forest parameters for each of the tree species measured in the Forest Service\u2019s Forest Inventory and Analysis (FIA) program. This dataset was created to support the 2013\u20132027 National Insect and Disease Risk Map (NIDRM) assessment. The statistical modeling approach used data-mining software and an archive of geospatial information to find the complex relationships between GIS layers and the presence/abundance of tree species as measured in over 300,000 FIA plot locations. Unique statistical models were developed from predictor layers consisting of climate, terrain, soils, and satellite imagery. Modeled basal area (BA) and stand density index (SDI) datasets for individual tree species were further post-processed to 1) match BA and SDI histograms of FIA data, 2) ensure that the sum of individual species BA and SDI on a pixel did not exceed separately modeled total for all species BA and SDI raster datasets, 3) derive additional tree parameters like quadratic mean diameter and trees per acre. With Landsat image collection dates ranging from 1985 to 2005, and a mean collection date for treed areas of 2002, and FIA plot data generally ranging from 1999 to 2005, the vintage of the base parameter datasets varies based on location, but can be roughly considered as 2002
Basal Area (BA). 30 meter pixel resolution. Data represents forest conditions circa 2002.
These data are a product of a multi-year effort by the FHTET (Forest Health Technology Enterprise Team) Remote Sensing Program to develop raster datasets of forest parameters for each of the tree species measured in the Forest Service\u2019s Forest Inventory and Analysis (FIA) program. This dataset was created to support the 2013\u20132027 National Insect and Disease Risk Map (NIDRM) assessment. The statistical modeling approach used data-mining software and an archive of geospatial information to find the complex relationships between GIS layers and the presence/abundance of tree species as measured in over 300,000 FIA plot locations. Unique statistical models were developed from predictor layers consisting of climate, terrain, soils, and satellite imagery. Modeled basal area (BA) and stand density index (SDI) datasets for individual tree species were further post-processed to 1) match BA and SDI histograms of FIA data, 2) ensure that the sum of individual species BA and SDI on a pixel did not exceed separately modeled total for all species BA and SDI raster datasets, 3) derive additional tree parameters like quadratic mean diameter and trees per acre. With Landsat image collection dates ranging from 1985 to 2005, and a mean collection date for treed areas of 2002, and FIA plot data generally ranging from 1999 to 2005, the vintage of the base parameter datasets varies based on location, but can be roughly considered as 2002
These data are a product of a multi-year effort by the FHTET (Forest Health Technology Enterprise Team) Remote Sensing Program to develop raster datasets of forest parameters for each of the tree species measured in the Forest Service\u2019s Forest Inventory and Analysis (FIA) program. This dataset was created to support the 2013\u20132027 National Insect and Disease Risk Map (NIDRM) assessment. The statistical modeling approach used data-mining software and an archive of geospatial information to find the complex relationships between GIS layers and the presence/abundance of tree species as measured in over 300,000 FIA plot locations. Unique statistical models were developed from predictor layers consisting of climate, terrain, soils, and satellite imagery. Modeled basal area (BA) and stand density index (SDI) datasets for individual tree species were further post-processed to 1) match BA and SDI histograms of FIA data, 2) ensure that the sum of individual species BA and SDI on a pixel did not exceed separately modeled total for all species BA and SDI raster datasets, 3) derive additional tree parameters like quadratic mean diameter and trees per acre. With Landsat image collection dates ranging from 1985 to 2005, and a mean collection date for treed areas of 2002, and FIA plot data generally ranging from 1999 to 2005, the vintage of the base parameter datasets varies based on location, but can be roughly considered as 2002
These data are a product of a multi-year effort by the FHTET (Forest Health Technology Enterprise Team) Remote Sensing Program to develop raster datasets of forest parameters for each of the tree species measured in the Forest Service\u2019s Forest Inventory and Analysis (FIA) program. This dataset was created to support the 2013\u20132027 National Insect and Disease Risk Map (NIDRM) assessment. The statistical modeling approach used data-mining software and an archive of geospatial information to find the complex relationships between GIS layers and the presence/abundance of tree species as measured in over 300,000 FIA plot locations. Unique statistical models were developed from predictor layers consisting of climate, terrain, soils, and satellite imagery. Modeled basal area (BA) and stand density index (SDI) datasets for individual tree species were further post-processed to 1) match BA and SDI histograms of FIA data, 2) ensure that the sum of individual species BA and SDI on a pixel did not exceed separately modeled total for all species BA and SDI raster datasets, 3) derive additional tree parameters like quadratic mean diameter and trees per acre. With Landsat image collection dates ranging from 1985 to 2005, and a mean collection date for treed areas of 2002, and FIA plot data generally ranging from 1999 to 2005, the vintage of the base parameter datasets varies based on location, but can be roughly considered as 2002
This Hardwoods type dataset portrays 12 forest type groups across the contiguous United States. These data were derived from MODIS composite images from the 2002 and 2003 growing seasons in combination with nearly 100 other geospatial data layers, including elevation, slope, aspect, ecoregions, and PRISM climate data. The dataset was developed as a collaborative effort between the USFS Forest Inventory and Analysis and Forest Health Monitoring programs and the USFS Remote Sensing.
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Objective: Characterize recent impacts to terrestrial ecosystems due to outbreaks of major invasive, non-native, and native forest insects and pathogens.
Data: Data are derived from aerial detection surveys for tree defoliation and mortality from the USFS Forest Health Assessment and Applied Sciences Team (FHAAST) National Forest Pest Conditions Database. Polygons are created by aerial sketch mapping, and coded for defoliation and mortality, in addition to other damage codes. Defoliation and mortality layers were created from the polygon data and the attribute codes. The layers were merged to compensate for difficulties in identifying defoliation separately from mortality in hardwoods vs. conifer forests. Areas that were defoliated during two of the five years in each time period have significant impacts and likely are experiencing mortality, so these polygons were added to the mortality layer. The layer includes areas with mortality classed as \u201cvery light\u201d. There is a one-year lag between the most recent available source data and the TCA Assessment year. For instance, the 2024 TCA Assessment used the data for the years 2014-2028 to calculate the 6-10 year mortality metric, with all other assessment years following this same pattern.
Data Format: Raster data are binary: (1) presence or (0) absence.
Spatial Resolution: 240m.
Source data: USFS FHAAST custom product
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
*if you have trouble viewing the Dashboard, please submit a Tableau Viewer Access Request
Direct Download (Raster Data Gateway)
Objective: Characterize recent impacts to terrestrial ecosystems due to outbreaks of major invasive, non-native, and native forest insects and pathogens.
Data: Data are derived from aerial detection surveys for tree defoliation and mortality from the USFS Forest Health Assessment and Applied Sciences Team (FHAAST) National Forest Pest Conditions Database. Polygons are created by aerial sketch mapping, and coded for defoliation and mortality, in addition to other damage codes. Defoliation and mortality layers were created from the polygon data and the attribute codes. The layers were merged to compensate for difficulties in identifying defoliation separately from mortality in hardwoods vs. conifer forests. Areas that were defoliated during two of the five years in each time period have significant impacts and likely are experiencing mortality, so these polygons were added to the mortality layer. The layer includes areas with mortality classed as \u201cvery light\u201d. There is a one-year lag between the most recent available source data and the TCA Assessment year. For instance, the 2024 TCA Assessment used the data for the years 2014-2028 to calculate the 6-10 year mortality metric, with all other assessment years following this same pattern.
Data Format: Raster data are binary: (1) presence or (0) absence.
Spatial Resolution: 240m.
Source data: USFS FHAAST custom product
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
*if you have trouble viewing the Dashboard, please submit a Tableau Viewer Access Request
Direct Download (Raster Data Gateway)
Objective: Characterize recent impacts to terrestrial ecosystems due to outbreaks of major invasive, non-native, and native forest insects and pathogens.
Data: Data are derived from aerial detection surveys for tree defoliation and mortality from the USFS Forest Health Assessment and Applied Sciences Team (FHAAST) National Forest Pest Conditions Database. Polygons are created by aerial sketch mapping, and coded for defoliation and mortality, in addition to other damage codes. Defoliation and mortality layers were created from the polygon data and the attribute codes. The layers were merged to compensate for difficulties in identifying defoliation separately from mortality in hardwoods vs. conifer forests. Areas that were defoliated during two of the five years in each time period have significant impacts and likely are experiencing mortality, so these polygons were added to the mortality layer. The layer includes areas with mortality classed as \u201cvery light\u201d. There is a one-year lag between the source data and the TCA Assessment year. For instance, the 2024 TCA Assessment used data from 2019-2023 aerial detection surveys to calculate the 0-5 year mortality metric, with all other assessment years following this same pattern.
Data Format: Raster data are binary: (1) presence or (0) absence.
Spatial Resolution: 240m.
Source data: USFS FHAAST custom product
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
*if you have trouble viewing the Dashboard, please submit a Tableau Viewer Access Request
Direct Download (Raster Data Gateway)
Objective: Characterize recent impacts to terrestrial ecosystems due to outbreaks of major invasive, non-native, and native forest insects and pathogens.
Data: Data are derived from aerial detection surveys for tree defoliation and mortality from the USFS Forest Health Assessment and Applied Sciences Team (FHAAST) National Forest Pest Conditions Database. Polygons are created by aerial sketch mapping, and coded for defoliation and mortality, in addition to other damage codes. Defoliation and mortality layers were created from the polygon data and the attribute codes. The layers were merged to compensate for difficulties in identifying defoliation separately from mortality in hardwoods vs. conifer forests. Areas that were defoliated during two of the five years in each time period have significant impacts and likely are experiencing mortality, so these polygons were added to the mortality layer. The layer includes areas with mortality classed as \u201cvery light\u201d. There is a one-year lag between the source data and the TCA Assessment year. For instance, the 2024 TCA Assessment used data from 2019-2023 aerial detection surveys to calculate the 0-5 year mortality metric, with all other assessment years following this same pattern.
Data Format: Raster data are binary: (1) presence or (0) absence.
Spatial Resolution: 240m.
Source data: USFS FHAAST custom product
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
*if you have trouble viewing the Dashboard, please submit a Tableau Viewer Access Request
Direct Download (Raster Data Gateway)
Objective: Characterize recent impacts to terrestrial ecosystems due to outbreaks of major invasive, non-native, and native forest insects and pathogens.
Data: Data are derived from aerial detection surveys for tree defoliation and mortality from the USFS Forest Health Assessment and Applied Sciences Team (FHAAST) National Forest Pest Conditions Database. Polygons are created by aerial sketch mapping, and coded for defoliation and mortality, in addition to other damage codes. Defoliation and mortality layers were created from the polygon data and the attribute codes. The layers were merged to compensate for difficulties in identifying defoliation separately from mortality in hardwoods vs. conifer forests. Areas that were defoliated during three of the five years in each time period have significant impacts and likely are experiencing mortality, so these polygons were added to the mortality layer. The layer includes areas with mortality classed as \u201cvery light\u201d. There is a one-year lag between the source data and the TCA Assessment year. For instance, the 2024 TCA Assessment used data from 2019-2023 aerial detection surveys to calculate the 0-5 year mortality metric, with all other assessment years following this same pattern.
Data Format: Raster data are binary: (1) presence or (0) absence.
Spatial Resolution: 240m.
Source data: USFS FHAAST custom product
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
Direct Download (Raster Data Gateway)
Objective: Characterize recent impacts to terrestrial ecosystems due to outbreaks of major invasive, non-native, and native forest insects and pathogens.
Data: Data are derived from aerial detection surveys for tree defoliation and mortality from the USFS Forest Health Assessment and Applied Sciences Team (FHAAST) National Forest Pest Conditions Database. Polygons are created by aerial sketch mapping, and coded for defoliation and mortality, in addition to other damage codes. Defoliation and mortality layers were created from the polygon data and the attribute codes. The layers were merged to compensate for difficulties in identifying defoliation separately from mortality in hardwoods vs. conifer forests. Areas that were defoliated during three of the five years in each time period have significant impacts and likely are experiencing mortality, so these polygons were added to the mortality layer. The layer includes areas with mortality classed as \u201cvery light\u201d. There is a one-year lag between the source data and the TCA Assessment year. For instance, the 2024 TCA Assessment used data from 2019-2023 aerial detection surveys to calculate the 0-5 year mortality metric, with all other assessment years following this same pattern.
Data Format: Raster data are binary: (1) presence or (0) absence.
Spatial Resolution: 240m.
Source data: USFS FHAAST custom product
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
Direct Download (Raster Data Gateway)
Objective: Characterize recent impacts to terrestrial ecosystems due to outbreaks of major invasive, non-native, and native forest insects and pathogens.
Data: Data are derived from aerial detection surveys for tree defoliation and mortality from the USFS Forest Health Assessment and Applied Sciences Team (FHAAST) National Forest Pest Conditions Database. Polygons are created by aerial sketch mapping, and coded for defoliation and mortality, in addition to other damage codes. Defoliation and mortality layers were created from the polygon data and the attribute codes. The layers were merged to compensate for difficulties in identifying defoliation separately from mortality in hardwoods vs. conifer forests. Areas that were defoliated during three of the five years in each time period have significant impacts and likely are experiencing mortality, so these polygons were added to the mortality layer. The layer includes areas with mortality classed as \u201cvery light\u201d. There is a one-year lag between the most recent available source data and the TCA Assessment year. For instance, the 2024 TCA Assessment used the data for the years 2014-2028 to calculate the 6-10 year mortality metric, with all other assessment years following this same pattern.
Data Format: Raster data are binary: (1) presence or (0) absence.
Spatial Resolution: 240m.
Source data: USFS FHAAST custom product
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
Direct Download (Raster Data Gateway)
Objective: Characterize recent impacts to terrestrial ecosystems due to outbreaks of major invasive, non-native, and native forest insects and pathogens.
Data: Data are derived from aerial detection surveys for tree defoliation and mortality from the USFS Forest Health Assessment and Applied Sciences Team (FHAAST) National Forest Pest Conditions Database. Polygons are created by aerial sketch mapping, and coded for defoliation and mortality, in addition to other damage codes. Defoliation and mortality layers were created from the polygon data and the attribute codes. The layers were merged to compensate for difficulties in identifying defoliation separately from mortality in hardwoods vs. conifer forests. Areas that were defoliated during three of the five years in each time period have significant impacts and likely are experiencing mortality, so these polygons were added to the mortality layer. The layer includes areas with mortality classed as \u201cvery light\u201d. There is a one-year lag between the most recent available source data and the TCA Assessment year. For instance, the 2024 TCA Assessment used the data for the years 2014-2028 to calculate the 6-10 year mortality metric, with all other assessment years following this same pattern.
Data Format: Raster data are binary: (1) presence or (0) absence.
Spatial Resolution: 240m.
Source data: USFS FHAAST custom product
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
This data item consists of the data collection and analysis for creating the 18-item Soil Health Well-Being index. The tool is presented and reported on in: Friedrichsen, C., Hammond Wagner, C. R., Pelinski, H. and Jones, W. D. (2026). Soil Health and Well-Being: Developing a reliable and valid psychometric scale to measure intangible benefits and social outcomes of agroecosystem management. Accepted at Environmental and Sustainability Indicators. Data archival consists of data, R scripts, and R projects for the analysis of two surveys of self-identified agricultural producers who are also federal employees with the United States Department of Agriculture.
Methods for data collection and analysis: Expert opinions were collected in two phases on potential soil health well-being index items to inform item selection to test for inclusion in the index. Then, two rounds of survey data collection were completed with a sample of USDA employees who also self-identify as agricultural producers. The survey included 81 potential index items for the soil health well-being index, existing well-being indices to evaluate agriculture and general well-being, questions on adoption of soil health practices, and farmer demographic questions. The first survey was fielded in June 2024 and the second in November 2024. The first survey dataset served to set the structure of the index tool and was analyzed using exploratory factor analysis. The second survey dataset confirmed the structure of the index tool using confirmatory factor analysis. A number of tests were run for content and construct validity, including differentiation between known groups using t-tests and correlations, as well as comparison with pre-existing tools using correlation analysis to evaluate the robustness of the index.
This data item consists of the data collection and analysis for creating the 18-item Soil Health Well-Being index. The tool is presented and reported on in: Friedrichsen, C., Hammond Wagner, C. R., Pelinski, H. and Jones, W. D. (2026). Soil Health and Well-Being: Developing a reliable and valid psychometric scale to measure intangible benefits and social outcomes of agroecosystem management. Accepted at Environmental and Sustainability Indicators. Data archival consists of data, R scripts, and R projects for the analysis of two surveys of self-identified agricultural producers who are also federal employees with the United States Department of Agriculture.
Methods for data collection and analysis: Expert opinions were collected in two phases on potential soil health well-being index items to inform item selection to test for inclusion in the index. Then, two rounds of survey data collection were completed with a sample of USDA employees who also self-identify as agricultural producers. The survey included 81 potential index items for the soil health well-being index, existing well-being indices to evaluate agriculture and general well-being, questions on adoption of soil health practices, and farmer demographic questions. The first survey was fielded in June 2024 and the second in November 2024. The first survey dataset served to set the structure of the index tool and was analyzed using exploratory factor analysis. The second survey dataset confirmed the structure of the index tool using confirmatory factor analysis. A number of tests were run for content and construct validity, including differentiation between known groups using t-tests and correlations, as well as comparison with pre-existing tools using correlation analysis to evaluate the robustness of the index.
Source insects
Sitophilus zemais were from a field population collected in eastern Kansas in 2019, reared continuously on organic whole corn (Heartland Mills, Marienthal, KS, USA) at the Center for Grain and Animal Health Research in Manhattan, KS. In subculturing S. zeamais, 75 adults were allowed to mate and lay eggs on 200 g of maize in a pint mason jar (950 mL) for 7 d, then were removed and adults were used 4\u20135 weeks later after emergence. For experiments below, adults that were 4\u20136 weeks old were used. Prostephanus truncatus were originally collected from its endemic range in Mexico in 2011 and reared continuously on whole organic corn in an APHIS-approved quarantine facility (permit#526-23-58-76547) at the Center for Grain and Animal Health Research in Manhattan, KS. Individual P. truncatus aged 4\u20136 weeks old were used in the experiments below. Both species were reared at 14:10 L:D, and S. zeamais was reared at 27.5\u00b0C and 65% relative humidity (RH) in an environmental chamber (Percival Scientific, Perry, IA, USA), while P. truncatus was reared in a quarantine space at 23 \u00b1 0.1\u00b0C.
Lights and pheromones
The visual stimuli consisted of 3 mm opening fiber optic light emitting diodes (LEDs) that were labeled blue, green, and warm white (Patikil, Dragonmarts Co. Ltd., Hong Kong, China). Pheromones consisted of monitoring lures with the Sitophilus spp. pheromone, namely (4S,5R)-5-hydroxy-4-methyl-3-heptanone, and the P. truncatus 2-component pheromone, 1-methyiethyl (2E)-2-methyl-2-pentenoate and 1-methylethyl (E,E)-2,4-dimethyl-2,4-heptadienoate (bullet lures IL-703 and IL-953, Insects Limited, Westfield, IN, USA).
Wiring of lights for wavelength and intensity trials
To evaluate the individual wavelengths of lights, we used blue, green, and white LEDs at 100% brightness. These were wired in parallel along a solderless bread board (5.5 \u00d7 17 \u00d7 3.5 cm W:L:H; MB102-830, HandsOn Tech, Johor, Malaysia) and supplied with a 9 volt/1 amp switching AC/DC wall plug that was modified to remove the end connector to expose the internal wires for attaching to the bread board (Figure 1).
To determine the effect of intensity of a wavelength on insect behavior, we used a single wavelength of LED at a time at 1, 2, 10, and 100% of the total brightness. These were wired in parallel with resistors to control the flow of electricity (Figure 1). A 1.1 k\u2126 resistor was used for the 10%, three 1.1 k\u2126 resistors were used for the 2%, and single 10 k\u2126 resistor was used for the 1%. They were wired in the same bread board, and supplied with the same power plug as the single brightness.
Assessment of light spectra
A plug-and-play lux meter/spectrophotometer (AH-300 PAR, AquaHotri ApS, Denmark) was attached to a cell phone (e.g., Apple iPhone) and was controlled with the accompanying AquaHotri application for Apple. In total, n = 4 measurements were taken from each LED described above at a distance of 1 cm away. Spectra were captured from outputted graphs using the autotracer feature with the Plot Digitizer Pro app (Porbital).
Determining preference among light wavelengths using a 4-way choice assay
To evaluate the preference by S. zeamais and P. truncatus among light stimuli, a 4-way choice olfactometer (True Choice Olfactometer, Sigma Scientific LLC, Micanopy, FL, USA) was adapted to evaluate choice among light stimuli instead of olfactory stimuli (Figure 2). Briefly, LEDs were placed into the ends of 8.4 \u00d7 3 cm (L:D) cylindrical tubes attached to each cardinal direction of the inert PTFE body of the choice assay (29.7 \u00d7 29.7 \u00d7 4.5 cm L:W:H) that was elevated by four 15-cm long PTFE legs. For each round of testing, an adult P. truncatus or S. zeamais was added to a 29.6-mL capacity portion cup (3.1 \u00d7 4.4 cm H:D) with 5 mm diameter holes drilled every 2 cm around the circumference at floor level, and had a layer of white tape on the inside and outside to block the light except from the added holes. The release zone allowed insects to perceive all wavelengths of light equally and subsequently egress. A piece of 0.5 cm thick glass (29.7 \u00d7 29.7 cm L:W) was placed over the insect and arena. There was a distance of 12.3 cm from the release center zone to the termination of the internal arena, with a total internal arena area of 423.5 cm2. On each cardinal direction, there was one of four choices: no light (control), blue LED, white LED, or green LED. Adults were tested in an environmental chamber under constant conditions (25\u00b0C, 65% RH; Percival Scientific, Perry, IA, USA) under indirect red light. Sitophilus zeamais and P. truncatus were given 2 or 3 min, respectively, to make a decision, which was considered completed when an adult was less than 6.5 cm from the end of the arena (e.g., marked with tape) towards a treatment. Individuals that did not make a decision in that period were tracked but excluded from the statistical analysis. The chosen treatment was recorded, and the time to decision was noted with a stopwatch that began as soon as the adult left the release zone in the portion cup. The position of the assay was rotated by 90\u00b0 after every insect to prevent positional bias by adults. After every 5 replicates, the arena was rinsed with 70% ethanol and allowed to dry to prevent deposition of chemical cues by adults. In total, 400 4\u20136-wk old, mixed-sex adults were tested, with a total of n = 200 adults per species for the combination of treatments.
Determining preference among different light intensities using a 4-way choice assay
To evaluate preference by these species among different intensities of light, we used an identical 4-way choice as above with the following modifications. Blue, white, and green lights were tested separately. On each cardinal direction of the assay, an LED of the same wavelength at 1, 2, 10, or 100% brightness was used. This corresponded to 50, 100, 480, and 4600 lux, respectively. Sitophilus zeamais or P. truncatus were singly added following the procedure above. In each case, the chosen intensity was recorded, and time to decision was also noted with a stopwatch. In total, 1,200 adults were tested in these assays, with n = 200 adults per species and combination of intensities for a given wavelength of LED.
Assessing synergy of light with pheromones in a wind tunnel assay
To determine whether the combination of visual stimuli has a synergistic effect on attraction of conspecific pheromones by S. zeamais and P. truncatus, a wind tunnel assay was employed. The wind tunnel was identical to that in prior work (Van Winkle et al. 2022). Briefly, a mechanical wind turbine generated airflow at 0.38 m/s, which was forced through three successive sieves: an activated charcoal filter to scrub background volatiles, and two progressively smaller slatted-metal sieves (73 \u00d7 85 \u00d7 0.5 cm L:W:H) to create a laminar flow. A total of 13.5 cm upwind of the stimulus edge of the test arena, a single pheromone lure alone, one of the wavelengths of LEDs (at 100% brightness), or both kinds of stimuli were placed, which was 5 cm from the last sieve. A single S. zeamais or P. truncatus was placed in the center of the 21.6 \u00d7 27.9 cm test arena, and insects were given 2 or 3 min, respectively, to make a decision. The side on which the insects left the test arena was recorded as the stimulus edge (e.g., side closest to the stimulus), anti-stimulus edge (e.g., edge farthest away from the stimulus), or non-stimulus edge (other two sides). In addition, the time to decision was recorded with a stopwatch. For the analysis, the four edges were collapsed into the stimulus edge, and non-stimulus edge (other three sides). Insects that did not make a decision within the timeframe were tracked but excluded from the statistical analysis. In total, 640 adults were tested with n = 40 adults tested per species and stimulus.
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Sitophilus zemais were from a field population collected in eastern Kansas in 2019, reared continuously on organic whole corn (Heartland Mills, Marienthal, KS, USA) at the Center for Grain and Animal Health Research in Manhattan, KS. In subculturing S. zeamais, 75 adults were allowed to mate and lay eggs on 200 g of maize in a pint mason jar (950 mL) for 7 d, then were removed and adults were used 4\u20135 weeks later after emergence. For experiments below, adults that were 4\u20136 weeks old were used. Prostephanus truncatus were originally collected from its endemic range in Mexico in 2011 and reared continuously on whole organic corn in an APHIS-approved quarantine facility (permit#526-23-58-76547) at the Center for Grain and Animal Health Research in Manhattan, KS. Individual P. truncatus aged 4\u20136 weeks old were used in the experiments below. Both species were reared at 14:10 L:D, and S. zeamais was reared at 27.5\u00b0C and 65% relative humidity (RH) in an environmental chamber (Percival Scientific, Perry, IA, USA), while P. truncatus was reared in a quarantine space at 23 \u00b1 0.1\u00b0C.
Lights and pheromones
The visual stimuli consisted of 3 mm opening fiber optic light emitting diodes (LEDs) that were labeled blue, green, and warm white (Patikil, Dragonmarts Co. Ltd., Hong Kong, China). Pheromones consisted of monitoring lures with the Sitophilus spp. pheromone, namely (4S,5R)-5-hydroxy-4-methyl-3-heptanone, and the P. truncatus 2-component pheromone, 1-methyiethyl (2E)-2-methyl-2-pentenoate and 1-methylethyl (E,E)-2,4-dimethyl-2,4-heptadienoate (bullet lures IL-703 and IL-953, Insects Limited, Westfield, IN, USA).
Wiring of lights for wavelength and intensity trials
To evaluate the individual wavelengths of lights, we used blue, green, and white LEDs at 100% brightness. These were wired in parallel along a solderless bread board (5.5 \u00d7 17 \u00d7 3.5 cm W:L:H; MB102-830, HandsOn Tech, Johor, Malaysia) and supplied with a 9 volt/1 amp switching AC/DC wall plug that was modified to remove the end connector to expose the internal wires for attaching to the bread board (Figure 1).
To determine the effect of intensity of a wavelength on insect behavior, we used a single wavelength of LED at a time at 1, 2, 10, and 100% of the total brightness. These were wired in parallel with resistors to control the flow of electricity (Figure 1). A 1.1 k\u2126 resistor was used for the 10%, three 1.1 k\u2126 resistors were used for the 2%, and single 10 k\u2126 resistor was used for the 1%. They were wired in the same bread board, and supplied with the same power plug as the single brightness.
Assessment of light spectra
A plug-and-play lux meter/spectrophotometer (AH-300 PAR, AquaHotri ApS, Denmark) was attached to a cell phone (e.g., Apple iPhone) and was controlled with the accompanying AquaHotri application for Apple. In total, n = 4 measurements were taken from each LED described above at a distance of 1 cm away. Spectra were captured from outputted graphs using the autotracer feature with the Plot Digitizer Pro app (Porbital).
Determining preference among light wavelengths using a 4-way choice assay
To evaluate the preference by S. zeamais and P. truncatus among light stimuli, a 4-way choice olfactometer (True Choice Olfactometer, Sigma Scientific LLC, Micanopy, FL, USA) was adapted to evaluate choice among light stimuli instead of olfactory stimuli (Figure 2). Briefly, LEDs were placed into the ends of 8.4 \u00d7 3 cm (L:D) cylindrical tubes attached to each cardinal direction of the inert PTFE body of the choice assay (29.7 \u00d7 29.7 \u00d7 4.5 cm L:W:H) that was elevated by four 15-cm long PTFE legs. For each round of testing, an adult P. truncatus or S. zeamais was added to a 29.6-mL capacity portion cup (3.1 \u00d7 4.4 cm H:D) with 5 mm diameter holes drilled every 2 cm around the circumference at floor level, and had a layer of white tape on the inside and outside to block the light except from the added holes. The release zone allowed insects to perceive all wavelengths of light equally and subsequently egress. A piece of 0.5 cm thick glass (29.7 \u00d7 29.7 cm L:W) was placed over the insect and arena. There was a distance of 12.3 cm from the release center zone to the termination of the internal arena, with a total internal arena area of 423.5 cm2. On each cardinal direction, there was one of four choices: no light (control), blue LED, white LED, or green LED. Adults were tested in an environmental chamber under constant conditions (25\u00b0C, 65% RH; Percival Scientific, Perry, IA, USA) under indirect red light. Sitophilus zeamais and P. truncatus were given 2 or 3 min, respectively, to make a decision, which was considered completed when an adult was less than 6.5 cm from the end of the arena (e.g., marked with tape) towards a treatment. Individuals that did not make a decision in that period were tracked but excluded from the statistical analysis. The chosen treatment was recorded, and the time to decision was noted with a stopwatch that began as soon as the adult left the release zone in the portion cup. The position of the assay was rotated by 90\u00b0 after every insect to prevent positional bias by adults. After every 5 replicates, the arena was rinsed with 70% ethanol and allowed to dry to prevent deposition of chemical cues by adults. In total, 400 4\u20136-wk old, mixed-sex adults were tested, with a total of n = 200 adults per species for the combination of treatments.
Determining preference among different light intensities using a 4-way choice assay
To evaluate preference by these species among different intensities of light, we used an identical 4-way choice as above with the following modifications. Blue, white, and green lights were tested separately. On each cardinal direction of the assay, an LED of the same wavelength at 1, 2, 10, or 100% brightness was used. This corresponded to 50, 100, 480, and 4600 lux, respectively. Sitophilus zeamais or P. truncatus were singly added following the procedure above. In each case, the chosen intensity was recorded, and time to decision was also noted with a stopwatch. In total, 1,200 adults were tested in these assays, with n = 200 adults per species and combination of intensities for a given wavelength of LED.
Assessing synergy of light with pheromones in a wind tunnel assay
To determine whether the combination of visual stimuli has a synergistic effect on attraction of conspecific pheromones by S. zeamais and P. truncatus, a wind tunnel assay was employed. The wind tunnel was identical to that in prior work (Van Winkle et al. 2022). Briefly, a mechanical wind turbine generated airflow at 0.38 m/s, which was forced through three successive sieves: an activated charcoal filter to scrub background volatiles, and two progressively smaller slatted-metal sieves (73 \u00d7 85 \u00d7 0.5 cm L:W:H) to create a laminar flow. A total of 13.5 cm upwind of the stimulus edge of the test arena, a single pheromone lure alone, one of the wavelengths of LEDs (at 100% brightness), or both kinds of stimuli were placed, which was 5 cm from the last sieve. A single S. zeamais or P. truncatus was placed in the center of the 21.6 \u00d7 27.9 cm test arena, and insects were given 2 or 3 min, respectively, to make a decision. The side on which the insects left the test arena was recorded as the stimulus edge (e.g., side closest to the stimulus), anti-stimulus edge (e.g., edge farthest away from the stimulus), or non-stimulus edge (other two sides). In addition, the time to decision was recorded with a stopwatch. For the analysis, the four edges were collapsed into the stimulus edge, and non-stimulus edge (other three sides). Insects that did not make a decision within the timeframe were tracked but excluded from the statistical analysis. In total, 640 adults were tested with n = 40 adults tested per species and stimulus.
","distribution_titles":["fourway_light_simpson_01152026_final.csv","intensity_blue_1232026.csv","intensity_green_3202026.csv","intensity_white_2172026.csv","wt_light_simpson_0111520026_final.csv","light_spectra_simpson_combined.csv"],"harvest_record":"https://catalog.data.gov/harvest_record/75a16062-7f87-4ec0-866c-8634672954e3","harvest_record_raw":"https://catalog.data.gov/harvest_record/75a16062-7f87-4ec0-866c-8634672954e3/raw","has_download":true,"has_spatial":false,"identifier":"10.15482/USDA.ADC/32591979.v1","keyword":["additive","aggregation pheromone","attraction","blue","cgahr","four-way","four-way olfactometer","green","kansas","larger grain borer","light","light intensity","light stimuli","maize weevil","mexico strain","monitoring","pheromone","phototactic","phototaxis","prostephanus truncatus","push-pull","red","repellency","sitophilus zeamais","synergy","taxis","trapping","wavelength","white","wind tunnel"],"last_harvested_date":"2026-10-09T16:38:21.162988","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"352b4532-793d-4075-a03f-05b778a3c43a","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/usda.png","name":"Department of Agriculture","organization_type":"Federal Government","slug":"usda"},"parent_identifier":null,"popularity":3,"publisher":"Agricultural Research Service","slug":"data-from-preference-for-and-taxis-to-single-and-combined-light-and-pheromonal-stimuli-by-","spatial_centroid":null,"spatial_shape":null,"theme":[],"title":"Data from: Preference for and taxis to single and combined light and pheromonal stimuli by the invasive larger grain borer and cosmopolitan maize weevil","type":"dataset"},{"_score":10.476898,"_sort":[1791563899781,10.476898,4,"5bb4c441-4463-498d-bd22-b095fa3b93fc"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","accessRights":"public","bureauCode":["005:18"],"contactPoint":{"fn":"Callicott, Kenneth A.","hasEmail":"mailto:ken.callicott@usda.gov"},"description":"Aflatoxin contamination is a growing concern in hazelnut production across the Caucasus, particularly due to its health risks and the economic impact in markets with strict safety standards, such as the European Union. This study examined the genetic diversity and population structure of Aspergillus flavus, the main source of aflatoxin, in three key hazelnut regions of Azerbaijan: Zaqatala, Qabala, and Khachmaz. It also assessed the presence of non-aflatoxigenic strains, which are valuable for biological control by outcompeting toxin-producing variants. A total of 710 A. flavus isolates were analyzed using 17 simple sequence repeat (SSR) markers, revealing 377 distinct haplotypes and high genetic diversity across regions, seasons, and production stages. Linkage disequilibrium analysis indicated a primarily clonal reproduction pattern, suggesting that dominant genotypes may persist over time and that introduced non-aflatoxigenic strains could remain stable in the population. Among 477 clone-corrected isolates, several haplotypes closely matched non-aflatoxigenic biocontrol strains MUCL54911 and AF36, the active ingredients in AF-X1 and AF36 Prevail, respectively, meaning that genotypes suitable for aflatoxin biocontrol may already be present and adapted to the Azerbaijani hazelnut agroecosystem. These findings establish a genetic baseline for A. flavus in Azerbaijani hazelnut systems and support the potential use of MUCL54911 or AF36 for local biocontrol efforts aimed at reducing aflatoxin contamination.
","distribution":[{"@type":"dcat:Distribution","accessURL":"https://geodata.nal.usda.gov/geonetwork/srv/api/records/64b6540e-eca9-4b04-948e-c8e3d3175be0/formatters/xml","conformsTo":"[{\"@type\": \"Standard\", \"identifier\": \"https://www.isotc211.org/2005/gmd\"}]","format":"xml","license":"https://creativecommons.org/publicdomain/zero/1.0/","mediaType":"text/xml","title":"Geodata ISO 19139 metadata"},{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/61754083","format":"xlsx","license":"https://creativecommons.org/publicdomain/zero/1.0/","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"SSR_dataset_clonecorrected.xlsx"}],"identifier":"10.15482/USDA.ADC/31298089.v1","keyword":["Aspergillus flavus samples","SSR identification","population data"],"license":"https://creativecommons.org/publicdomain/zero/1.0/","modified":"2026-05-01","programCode":["005:040"],"publisher":{"@type":"org:Organization","name":"Agricultural Research Service"},"spatial":"[{\"@type\": \"Location\", \"prefLabel\": \"{\\\"type\\\": \\\"MultiPoint\\\", \\\"coordinates\\\": [[41.26, 48.45], [41.19, 46.48], [41.32, 48.05], [41.28, 46.37], [41.35, 46.26], [41.42, 46.23], [41.37, 48.37], [40.59, 47.36], [41.35, 46.39]]}\"}]","temporal":"[{\"@type\": \"PeriodOfTime\", \"endDate\": \"2024-10-31\", \"startDate\": \"2022-05-01\"}]","theme":["geospatial"],"title":"SSR profiles of Aspergillus flavus isolates from Azerbaijan hazelnuts"},"description":"Aflatoxin contamination is a growing concern in hazelnut production across the Caucasus, particularly due to its health risks and the economic impact in markets with strict safety standards, such as the European Union. This study examined the genetic diversity and population structure of Aspergillus flavus, the main source of aflatoxin, in three key hazelnut regions of Azerbaijan: Zaqatala, Qabala, and Khachmaz. It also assessed the presence of non-aflatoxigenic strains, which are valuable for biological control by outcompeting toxin-producing variants. A total of 710 A. flavus isolates were analyzed using 17 simple sequence repeat (SSR) markers, revealing 377 distinct haplotypes and high genetic diversity across regions, seasons, and production stages. Linkage disequilibrium analysis indicated a primarily clonal reproduction pattern, suggesting that dominant genotypes may persist over time and that introduced non-aflatoxigenic strains could remain stable in the population. Among 477 clone-corrected isolates, several haplotypes closely matched non-aflatoxigenic biocontrol strains MUCL54911 and AF36, the active ingredients in AF-X1 and AF36 Prevail, respectively, meaning that genotypes suitable for aflatoxin biocontrol may already be present and adapted to the Azerbaijani hazelnut agroecosystem. These findings establish a genetic baseline for A. flavus in Azerbaijani hazelnut systems and support the potential use of MUCL54911 or AF36 for local biocontrol efforts aimed at reducing aflatoxin contamination.
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","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/60752854","format":"csv","license":"https://creativecommons.org/publicdomain/zero/1.0/","mediaType":"text/csv","title":"GMDR_FQ_2019-2023.csv"},{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/60791224","format":"csv","license":"https://creativecommons.org/publicdomain/zero/1.0/","mediaType":"text/csv","title":"DataDictionaryGMDR_FQ_2019-2023.csv"}],"identifier":"10.15482/USDA.ADC/30988474.v1","keyword":["Drought","Fort Keogh","Grassland","Rain-out shelter","Thunder Basin Ecoregion","aboveground net primary production","cattle beef cattle","digestible","forage","legacy effects","northern mixed-grass prairie","ruminant"],"license":"https://creativecommons.org/publicdomain/zero/1.0/","modified":"2026-01-22","programCode":["005:040"],"publisher":{"@type":"org:Organization","name":"Agricultural Research Service"},"spatial":"[{\"@type\": \"Location\", \"prefLabel\": \"{\\\"type\\\": \\\"MultiPoint\\\", \\\"coordinates\\\": [[-105.96666296233131, 46.33533192140868], [-105.1212655511701, 43.24451562356131]]}\"}]","temporal":"[{\"@type\": \"PeriodOfTime\", \"endDate\": \"2023-06-30\", \"startDate\": \"2019-05-01\"}]","title":"Data from: Multi-year drought has persistent forage quality and quantity effects that can be intensified by heavy grazing in semiarid rangelands"},"description":"These data were collected as part of a larger research project, called Grazing Management for Drought Resilience (GMDR), which aims to understand how management strategies impact rangeland ecosystem health across varying drought magnitudes. Growing season droughts have major impacts on grassland vegetation and are predicted to become increasingly frequent in semi-arid prairies of North America, but little is known about how droughts and post-drought legacies interact with grazing management to affect forage quality and quantity. The study was conducted in eastern Montana at the Fort Keogh Livestock and Range Research Laboratory and a private cattle ranch in the Thunder Basin region of northeastern Wyoming. We experimentally manipulated grazing and rainfall reduction in a full factorial design (3 grazing \u00d7 5 precipitation levels). Experimental treatments were used to assess both short and long-term changes in forage quantity and quality. Grazing treatments were implemented in early July in Wyoming and early August in Montana. Forage, consisting of living and dead herbaceous material (e.g. grass, forb) was sampled in May, June, and July for 5 years (2019-2023). Samples were dried and weighed to obtain biomass measurements and subsequently ground for analysis of quality including digestible organic matter and percent fiber. In addition, these data include two composite indices of digestibility: relative feed value (unitless index based on digestibility and fiber content) and digestible forage biomass (g \u00d7 m-2).
","distribution_titles":["GMDR_FQ_2019-2023.csv","DataDictionaryGMDR_FQ_2019-2023.csv"],"harvest_record":"https://catalog.data.gov/harvest_record/8f655ffc-055a-4ec8-b8a9-08a508fae270","harvest_record_raw":"https://catalog.data.gov/harvest_record/8f655ffc-055a-4ec8-b8a9-08a508fae270/raw","has_download":true,"has_spatial":true,"identifier":"10.15482/USDA.ADC/30988474.v1","keyword":["Drought","Fort Keogh","Grassland","Rain-out shelter","Thunder Basin Ecoregion","aboveground net primary production","cattle beef cattle","digestible","forage","legacy effects","northern mixed-grass prairie","ruminant"],"last_harvested_date":"2026-10-09T16:38:18.999327","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"352b4532-793d-4075-a03f-05b778a3c43a","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/usda.png","name":"Department of Agriculture","organization_type":"Federal Government","slug":"usda"},"parent_identifier":null,"popularity":4,"publisher":"Agricultural Research Service","slug":"data-from-multi-year-drought-has-persistent-forage-quality-and-quantity-effects-that-can-b-70c20","spatial_centroid":{"lat":44.789923772485,"lon":-105.54396425675071},"spatial_shape":{"coordinates":[[-105.96666296233131,46.33533192140868],[-105.1212655511701,43.24451562356131]],"type":"MultiPoint"},"theme":[],"title":"Data from: Multi-year drought has persistent forage quality and quantity effects that can be intensified by heavy grazing in semiarid rangelands","type":"dataset"},{"_score":7.09713,"_sort":[1791563898200,7.09713,3,"07aaaf47-0c12-404e-91c4-ef994232d86d"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","accessRights":"public","accrualPeriodicity":"irregular","bureauCode":["005:18"],"contactPoint":{"fn":"Balkcom, Kipling S.","hasEmail":"mailto:kip.balkcom@usda.gov"},"description":"This dataset contains all data and code required to reproduce the analyses, tables, and figures in the associated manuscript published in Field Crops Research titled \"Tillage and N requirements for wheat planted into corn residue\". A list of R packages used to create the aforementioned items can be found in the associated manuscript, but session information is also provided in the analysis_pipeline.html file.
The research was based on the fact that corn can generate significant surface residue requiring intensive surface tillage and increased N fertilizer rates to optimize subsequent wheat production. However, surface tillage degrades soil health benefits, while unnecessary N applications reduce profits. Therefore, identifying the correct surface tillage level and N rate combination is critical to optimize wheat yields and profits following corn production. Tillage systems [conventional tillage (CT), mulch tillage (MT), light disk (LD), and no tillage (NT)] and fall and spring N rate (67, 101, and 134 kg N ha-1) combinations were examined to evaluate early season wheat growth (tiller density, tiller biomass, and tiller N uptake), in-season wheat growth (biomass and N uptake), wheat yields, nitrogen use efficiency (NUE), and net returns. Fall N increased early season wheat growth parameters up to 25%, while in-season N uptake varied across tillage systems with inconsistent responses to split applications across N rates. The CT, MT, and LD systems yielded 23% greater than the NT system across N treatments and growing seasons. Grain NUE averaged 38.7% across growing seasons for all tillage and N treatments. Average net returns were US $733 ha-1 (MT), US $643 ha-1 (LD), US $601 ha-1 (CT), and US $468 ha-1 (NT). Wheat growth and production declined when planted into corn residue as surface tillage decreased. This effect was further exacerbated with low N applications. Fall N applications enhanced early season wheat growth, but a split application of the remaining N did not increase wheat yields compared to the corresponding single rate application. Therefore, split N applications did not benefit wheat production following corn compared to single N applications across N rates and conditions examined in this experiment. Applying 134 kg N ha-1 maximized wheat yields and net returns for NT, but results indicated greater N rates above current recommendations across all tillage systems following corn could be justified.
This dataset contains all data and code required to reproduce the analyses, tables, and figures in the associated manuscript published in Field Crops Research titled \"Tillage and N requirements for wheat planted into corn residue\". A list of R packages used to create the aforementioned items can be found in the associated manuscript, but session information is also provided in the analysis_pipeline.html file.
The research was based on the fact that corn can generate significant surface residue requiring intensive surface tillage and increased N fertilizer rates to optimize subsequent wheat production. However, surface tillage degrades soil health benefits, while unnecessary N applications reduce profits. Therefore, identifying the correct surface tillage level and N rate combination is critical to optimize wheat yields and profits following corn production. Tillage systems [conventional tillage (CT), mulch tillage (MT), light disk (LD), and no tillage (NT)] and fall and spring N rate (67, 101, and 134 kg N ha-1) combinations were examined to evaluate early season wheat growth (tiller density, tiller biomass, and tiller N uptake), in-season wheat growth (biomass and N uptake), wheat yields, nitrogen use efficiency (NUE), and net returns. Fall N increased early season wheat growth parameters up to 25%, while in-season N uptake varied across tillage systems with inconsistent responses to split applications across N rates. The CT, MT, and LD systems yielded 23% greater than the NT system across N treatments and growing seasons. Grain NUE averaged 38.7% across growing seasons for all tillage and N treatments. Average net returns were US $733 ha-1 (MT), US $643 ha-1 (LD), US $601 ha-1 (CT), and US $468 ha-1 (NT). Wheat growth and production declined when planted into corn residue as surface tillage decreased. This effect was further exacerbated with low N applications. Fall N applications enhanced early season wheat growth, but a split application of the remaining N did not increase wheat yields compared to the corresponding single rate application. Therefore, split N applications did not benefit wheat production following corn compared to single N applications across N rates and conditions examined in this experiment. Applying 134 kg N ha-1 maximized wheat yields and net returns for NT, but results indicated greater N rates above current recommendations across all tillage systems following corn could be justified.
Insects
The S. fulvus used in this experiment were progeny of field-collected weevils. As described by Prasifka et al. (2015), adult weevils were collected from wild sunflowers in North Dakota, then used to artificially infest cultivated sunflower heads in Casselton, North Dakota. After S. fulvus oviposition and larval development, sunflower heads were cut and transported to the laboratory. Heads were suspended over plastic tubs to catch exiting weevils, and weevils were placed into moistened sand to overwinter in an environmental chambers set to a fluctuating thermal regime (see \u2018FTR\u2019 treatment description in Prasifka et al. 2015) for at least 90 d. Subsequently, larvae were allowed to pupate and emerge from the moistened sand at room temperature (23\u201325\u00b0C) in the laboratory. Emergence of new adults was checked on a daily basis, and the water balance of the sand was kept consistent by weighing the sand initially after placing on lab bench, and adding water on a weekly basis to ensure weight was constant. The experiments were conducted at the USDA Center for Grain and Animal Health Research in Manhattan, KS and weevils were checked daily for emergence and then used immediately for experiments.
Semiochemical-mediated movement assay
To elucidate the movement of S. fulvus in response to conspecific extracts, video-tracking was used to monitor individual male and female S. fulvus behavior. This was performed by tracking movement with a network camera (Basler AG, Ahrensburg, Germany) coupled with Ethovision XT (v. 14.0, Noldus Software, Leesburg, VA, US). Individuals of each sex were placed individually into arenas consisting of 90 mm (D) Petri dishes lined with 85 mm filter paper (Whatman #1 filter, GE Healthcare, Chicago, IL, USA), and covered with a lid to prevent flight. The camera was placed 80 cm above the arenas, and trials lasted 30 min. Each arena had four treatment zones: a stimulus half, stimulus zone (1.6 cm diameter zone in the center of the stimulus half of the dish with the treatment), control half, and control zone (1.6 cm diameter zone in the center of the control half of the dish without the treatment) (e.g., similar to the setup in Ponce et al. 2023). The control half/zone always contained a blank filter paper, while the stimulus half/zone contained a filter paper with a conspecific volatile extract (as above) or solvent only. To account for cursor bounce, an input filter was applied that discarded the accumulated distance if it was >5 cm per s. Each trial was manually checked for irregularities, and any with unusual patterns in the accumulations of distance were re-run. At the end of the trial, the total distance moved (cm), the average instantaneous velocity (cm/s), frequency of entering each zone, duration in each zone, and latency to finding each zone were recorded. There were a total of n = 16 replicate females and n = 18 replicate males.
Volatile Collection
We used two volatile collection methods to sample cohorts of S. fulvus, and included the following treatments: mixed sex (8 males, and 8 females), female only (12 adult female weevils), male only (12 adult male weevils), empty control (unbaited), sunflower only (freshly cut sunflower head of dwarf sunflower inbred line HA 379), and mixed-sex S. fulvus on sunflowers (8 males and 8 females on a freshly cut dwarf sunflower head). The first method was solid phase micro-extraction (SPME), which utilized a 100 \u03bcm polydimethylsiloxane (PDMS) fiber to collect the headspace volatiles inside a container with a 250 mL capacity and removable PTFE septa for a 24 h period per replicate. Fibers were then inserted directly into the gas chromatograph-mass spectrometer (GC-MS) injection port to be desorbed. Fibers were always pre-conditioned at 230\u00b0C in a GC injection port for 5 min prior to sampling to eliminate any volatiles that were adsorbed prior to the experiment. There were n = 3 control, 3 female, 4 male, 5 mixed, and 3 sunflower only samples collected by SPME. Mixed treatments had 4 males and 4 females, while single-sex treatments had 8 male or female.
For the second volatile collection method, in order to potentially quantify samples, we used a solvent extraction method with a volatile collection trap (VCT) attached to a volatile headspace chamber (500 mL capacity; 10.2 \u00d7 12.7 cm D:H). Central air was filtered and run through chemically inert PTFE tubing with airflow restricted to 1 L/min using a flow meter (Volatile Collection Systems, Gainesville, FL, USA) just prior to the headspace chamber. The headspace volatiles from the S. fulvus treatments (mixed sex, empty control, and sunflower only) were collected for 24 h on a VCT consisting of a drip tip borosilicate glass tube packed with 20 mg of absorbent Porapak-Q\u2122 (Volatile Collection Systems, Gainesville, FL, USA) to adsorb volatiles with a stainless steel screen (No. 316) on one side, and held in place with a borosilicate glass wool plug followed by a PTFE Teflon compression seal. The volatiles on the traps were then eluted with 150 \u00b5L of dichloromethane (Millipore, Billerica, MA, USA) by pushing the solvent through with inert N2 gas into 2-mL glass vials containing vial inserts with polymer feet and screwtop magnetic caps containing Teflon-lined septa. There were n = 7 Control, 6 Mixed male and female weevil, and 4 Sunflower only replicate samples. During processing, all samples for which no peaks were detected were removed, leaving 3 samples for each of the Control, Mixed, and Sunflower treatments. The VCTs were washed between runs with 700 \u03bcl of dichloromethane in triplicate. Samples were then stored at -20\u00b0C until batch analysis. Prior to analysis, 190.5 ng of tetradecane (99% purity, GC analytical grade, Millipore, Billerica, MA, USA) was added as an internal standard.
Gas chromatography coupled with mass spectrometry
All headspace collection sample extracts were run on an Agilent 7890B gas chromatograph (GC) (Agilent Technologies, Inc., Santa Clara, CA, USA) equipped with an Agilent Durabond HP-5 column (30 m length, 0.250 mm diameter and 0.25 \u03bcm film thickness) with helium as carrier gas at a constant 1.2 mL/min flow and 40 cm/s velocity, which was coupled with a single-quadrupole Agilent 5997B mass spectrometer (MS). Samples were injected with an autosampler using splitless mode. The compounds were separated by auto-injecting 1 \u03bcl of each sample into the inlet set at 250\u00b0C with flow rate of 18 ml/min. The oven temperature was programmed at 60\u00b0C and was immediately increased by 10\u00b0C/min to a final temperature of 280\u00b0C, which was held for 6 min. After a solvent delay of 3 min, mass ranges between 35 and 500 atomic mass units were scanned. Compounds were tentatively identified by comparison of spectral data with those from the NIST 17 library and by GC retention index. Compound peak areas relative to that of the internal standard were used to calculate the emission rates (ng/h of collection).
","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/56550827","format":"csv","license":"https://creativecommons.org/publicdomain/zero/1.0/","mediaType":"text/csv","title":"rssw_ethovision_2024.csv"}],"identifier":"10.15482/USDA.ADC/29645753.v1","keyword":["CGAHR","Ethovision","Fargo","GC-MS","Kansas","Northern Great Plains","SPME","Smicronyx fulvus","USDA-ARS","VOCs","behavioral ecology","extracts","headspace","movement","oilseeds","orientation","red sunflower seed weevil","rssw","seed crop","semiochemicals","sunflowers","video-tracking","volatile emissions"],"license":"https://creativecommons.org/publicdomain/zero/1.0/","modified":"2025-08-18","programCode":["005:040"],"publisher":{"@type":"org:Organization","name":"Agricultural Research Service"},"temporal":"[{\"@type\": \"PeriodOfTime\", \"endDate\": \"2024-12-01\", \"startDate\": \"2023-04-01\"}]","title":"Data from: Elucidating the behavioral ecology to conspecific emissions by the native red sunflower seed weevil, Smicronyx fulvus, a resurging pest of sunflower"},"description":"Insects
The S. fulvus used in this experiment were progeny of field-collected weevils. As described by Prasifka et al. (2015), adult weevils were collected from wild sunflowers in North Dakota, then used to artificially infest cultivated sunflower heads in Casselton, North Dakota. After S. fulvus oviposition and larval development, sunflower heads were cut and transported to the laboratory. Heads were suspended over plastic tubs to catch exiting weevils, and weevils were placed into moistened sand to overwinter in an environmental chambers set to a fluctuating thermal regime (see \u2018FTR\u2019 treatment description in Prasifka et al. 2015) for at least 90 d. Subsequently, larvae were allowed to pupate and emerge from the moistened sand at room temperature (23\u201325\u00b0C) in the laboratory. Emergence of new adults was checked on a daily basis, and the water balance of the sand was kept consistent by weighing the sand initially after placing on lab bench, and adding water on a weekly basis to ensure weight was constant. The experiments were conducted at the USDA Center for Grain and Animal Health Research in Manhattan, KS and weevils were checked daily for emergence and then used immediately for experiments.
Semiochemical-mediated movement assay
To elucidate the movement of S. fulvus in response to conspecific extracts, video-tracking was used to monitor individual male and female S. fulvus behavior. This was performed by tracking movement with a network camera (Basler AG, Ahrensburg, Germany) coupled with Ethovision XT (v. 14.0, Noldus Software, Leesburg, VA, US). Individuals of each sex were placed individually into arenas consisting of 90 mm (D) Petri dishes lined with 85 mm filter paper (Whatman #1 filter, GE Healthcare, Chicago, IL, USA), and covered with a lid to prevent flight. The camera was placed 80 cm above the arenas, and trials lasted 30 min. Each arena had four treatment zones: a stimulus half, stimulus zone (1.6 cm diameter zone in the center of the stimulus half of the dish with the treatment), control half, and control zone (1.6 cm diameter zone in the center of the control half of the dish without the treatment) (e.g., similar to the setup in Ponce et al. 2023). The control half/zone always contained a blank filter paper, while the stimulus half/zone contained a filter paper with a conspecific volatile extract (as above) or solvent only. To account for cursor bounce, an input filter was applied that discarded the accumulated distance if it was >5 cm per s. Each trial was manually checked for irregularities, and any with unusual patterns in the accumulations of distance were re-run. At the end of the trial, the total distance moved (cm), the average instantaneous velocity (cm/s), frequency of entering each zone, duration in each zone, and latency to finding each zone were recorded. There were a total of n = 16 replicate females and n = 18 replicate males.
Volatile Collection
We used two volatile collection methods to sample cohorts of S. fulvus, and included the following treatments: mixed sex (8 males, and 8 females), female only (12 adult female weevils), male only (12 adult male weevils), empty control (unbaited), sunflower only (freshly cut sunflower head of dwarf sunflower inbred line HA 379), and mixed-sex S. fulvus on sunflowers (8 males and 8 females on a freshly cut dwarf sunflower head). The first method was solid phase micro-extraction (SPME), which utilized a 100 \u03bcm polydimethylsiloxane (PDMS) fiber to collect the headspace volatiles inside a container with a 250 mL capacity and removable PTFE septa for a 24 h period per replicate. Fibers were then inserted directly into the gas chromatograph-mass spectrometer (GC-MS) injection port to be desorbed. Fibers were always pre-conditioned at 230\u00b0C in a GC injection port for 5 min prior to sampling to eliminate any volatiles that were adsorbed prior to the experiment. There were n = 3 control, 3 female, 4 male, 5 mixed, and 3 sunflower only samples collected by SPME. Mixed treatments had 4 males and 4 females, while single-sex treatments had 8 male or female.
For the second volatile collection method, in order to potentially quantify samples, we used a solvent extraction method with a volatile collection trap (VCT) attached to a volatile headspace chamber (500 mL capacity; 10.2 \u00d7 12.7 cm D:H). Central air was filtered and run through chemically inert PTFE tubing with airflow restricted to 1 L/min using a flow meter (Volatile Collection Systems, Gainesville, FL, USA) just prior to the headspace chamber. The headspace volatiles from the S. fulvus treatments (mixed sex, empty control, and sunflower only) were collected for 24 h on a VCT consisting of a drip tip borosilicate glass tube packed with 20 mg of absorbent Porapak-Q\u2122 (Volatile Collection Systems, Gainesville, FL, USA) to adsorb volatiles with a stainless steel screen (No. 316) on one side, and held in place with a borosilicate glass wool plug followed by a PTFE Teflon compression seal. The volatiles on the traps were then eluted with 150 \u00b5L of dichloromethane (Millipore, Billerica, MA, USA) by pushing the solvent through with inert N2 gas into 2-mL glass vials containing vial inserts with polymer feet and screwtop magnetic caps containing Teflon-lined septa. There were n = 7 Control, 6 Mixed male and female weevil, and 4 Sunflower only replicate samples. During processing, all samples for which no peaks were detected were removed, leaving 3 samples for each of the Control, Mixed, and Sunflower treatments. The VCTs were washed between runs with 700 \u03bcl of dichloromethane in triplicate. Samples were then stored at -20\u00b0C until batch analysis. Prior to analysis, 190.5 ng of tetradecane (99% purity, GC analytical grade, Millipore, Billerica, MA, USA) was added as an internal standard.
Gas chromatography coupled with mass spectrometry
All headspace collection sample extracts were run on an Agilent 7890B gas chromatograph (GC) (Agilent Technologies, Inc., Santa Clara, CA, USA) equipped with an Agilent Durabond HP-5 column (30 m length, 0.250 mm diameter and 0.25 \u03bcm film thickness) with helium as carrier gas at a constant 1.2 mL/min flow and 40 cm/s velocity, which was coupled with a single-quadrupole Agilent 5997B mass spectrometer (MS). Samples were injected with an autosampler using splitless mode. The compounds were separated by auto-injecting 1 \u03bcl of each sample into the inlet set at 250\u00b0C with flow rate of 18 ml/min. The oven temperature was programmed at 60\u00b0C and was immediately increased by 10\u00b0C/min to a final temperature of 280\u00b0C, which was held for 6 min. After a solvent delay of 3 min, mass ranges between 35 and 500 atomic mass units were scanned. Compounds were tentatively identified by comparison of spectral data with those from the NIST 17 library and by GC retention index. Compound peak areas relative to that of the internal standard were used to calculate the emission rates (ng/h of collection).
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Currently there are some HF resistance genes deployed, but mostly in hexaploid winter wheat (Triticum aestivum), with fewer resistance genes identified in durum wheat (Triticum turgidum ssp. durum L.) and other wheat wild relatives. Mapping of additional resistance genes, along with developing markers for these is needed to develop resistant germplasm. ARS researchers in Fargo, ND evaluated the BP025 population under greenhouse and growth chamber conditions to the Great Plains (GP) biotype of Hessian fly (HF, Mayetiola destructor Say). The BP025 population was developed by crossing Ben (PI 596557), a North Dakota hard amber durum variety, with PI 41025, a cultivated emmer (T. turgidum ssp. dicoccum) accession collected near Samara, Russia. The BP025 population consists of 200 RILs developed by single seed-descent and was advanced to the F7:8 generation. The BP025 population was evaluated for stunting score, larval mortality, and the percentage of resistant plants under growth chamber and greenhouse conditions in Fargo, ND (46.893273, -96.807319). Experimental plants were maintained in a greenhouse at 20 \u00b1 2\u00b0 C with an ambient relative humidity of between 40 and 70% and a 16:8 (L:D) photoperiod. Natural lighting was enhanced with the use of 430-watt high pressure sodium lamps. Individual seeds of the mapping population entries were planted in Ray Leach cone-tainer (4 cm diameter \u00d7 21 cm deep, Stuewe & Sons, Inc., Tangent, OR), held in racks (RL98). Plants were grown in potting media (SB100 Professional Growing Mix, Sungro Horticulture, Bellevue, WA), and fertilized at planting with Osmocote Plus 15-9-12 (N-P-K) standard release fertilizer. Each cone was considered an experimental unit. The BP025 population and the parental lines Ben and PI 41025 were screened for HF larval resistance over two greenhouse seasons. All plants were evaluated using a completely randomized design. For the infestations, seedling plants were exposed to egg-laying HF adult females (~ 1 female for each plant) for 24 h. Infestations were timed to occur when seedlings were at the two-leaf growth stage. Three days after exposure to adult females, plants were moved to a high humidity (50-75% RH) growth chamber. High humidity facilitates egg hatching and promotes the successful migration of neonate larvae down the leaf blade to feeding sites at the base of the plant. Following egg hatch, plants were returned to the greenhouse for 10 to 14 days. This provided time for virulent larvae to grow and be differentiated from the small presumably dead avirulent larvae. Detailed observations of plant quality and larval success provided each plant with a score of \u201cresistant\u201d or \u201csusceptible.\u201d Specifically, plants were scored for their growth, with information on the number of leaves and tillers being recorded. Plant health and appearance (i.e., severity of larval-induced stunting), was also scored for each plant. Normal healthy plants were given a score of 0, lightly stunted plants were scored as a 1, moderately stunted plants were given a 2, and severely stunted planted were given the score of 3. Each plant was also dissected using a stereo microscope. At the time of plant dissection, virulent (i.e., successful) larvae were expected to be large and white in color. The number of dead larvae (eg. large, medium, small, and neonate) and live larvae (eg. large, medium, and small) were recorded for each plant. Averages for the plant and insect measurements were derived from the mean score of the 12 to 14 plants evaluated for each entry in the population. Phenotypic data was analyzed using JMP version 15 (SAS Institute, 2015). Prior to analysis, homogeneity of variance was tested using an O-Brien test at p < 0.05 (O\u2019Brien, 1979). The genotypic data used for further QTL analysis is available Peters Haugrud, Amanda; Saini Sharma, Jyoti; Zhang, Qijun; Green, Andrew J.; Xu, Steven S.; Faris, Justin D. (2023). Data from: Identification of robust yield QTL derived from cultivated emmer for durum wheat improvement. Ag Data Commons. Dataset. https://doi.org/10.15482/USDA.ADC/1529118.
1) Resource Title: Averages of the phenotypic data collected from the durum x cultivated emmer wheat recombinant inbred population BP025 infested with Great Plains biotype of Hessian fly. File name: Ben mapping population average phenotyping data.
Resource Description: In the data file, column headings indicate the line, entry # (seed source), total number of plants evaluated, stunting score (mean), Leaves (mean), tillers (mean), total dead larvae (mean), total live larvae (mean), total larvae (mean), larval mortality (%), R (resistant) plants %, and notes from observations during plant dissections. This data can be used for further QTL analysis and evaluating these lines for HF resistance and how HF infestations affect different traits in each genotype.
2) Resource Title: Individual plant phenotypic data collected from the durum x cultivated emmer wheat recombinant inbred population BP025 infested with Great Plains biotype of Hessian fly. File name: Ben mapping population individual plant phenotyping data.
Resource Description: In the data file, column headings indicate the planting date, infestation date, scoring date, line, entry # (seed source), Plant number for that genotype, stunting score, resistance score, Leaves, tillers, coleoptile tillers, number large dead larvae, number medium dead larvae, number small dead larvae, number neonate larvae, total dead larvae, number large living larvae, number medium living larvae, number small living larvae, total living larvae, and total number of larvae. These values can be used for individual replicates for further analysis.
","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/55241630","format":"csv","license":"https://www.usa.gov/publicdomain/label/1.0/","mediaType":"text/csv","title":"Ben mapping population average phenotyping data.csv"},{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/55241633","format":"csv","license":"https://www.usa.gov/publicdomain/label/1.0/","mediaType":"text/csv","title":"Ben Mapping population individual plant phenotyping data.csv"}],"identifier":"10.15482/USDA.ADC/29257448.v1","keyword":["Durum","Hessian fly","Insect pest","NP301","QTLs","emmer wheat","insect resistance traits"],"license":"https://www.usa.gov/publicdomain/label/1.0/","modified":"2025-11-21","programCode":["005:040"],"publisher":{"@type":"org:Organization","name":"Agricultural Research Service"},"spatial":"[{\"@type\": \"Location\", \"prefLabel\": \"{\\\"type\\\": \\\"Point\\\", \\\"coordinates\\\": [-96.807319, 46.893273]}\"}]","temporal":"[{\"@type\": \"PeriodOfTime\", \"endDate\": \"2025-01-01\", \"startDate\": \"2018-01-01\"}]","title":"Data from: Identification and molecular mapping of two quantitative trait loci for Hessian fly resistance in a durum \u00d7 cultivated emmer wheat population"},"description":"Hessian fly (HF, Mayetiola destructor Say) is a major pest on wheat and can cause significant yield losses. Currently there are some HF resistance genes deployed, but mostly in hexaploid winter wheat (Triticum aestivum), with fewer resistance genes identified in durum wheat (Triticum turgidum ssp. durum L.) and other wheat wild relatives. Mapping of additional resistance genes, along with developing markers for these is needed to develop resistant germplasm. ARS researchers in Fargo, ND evaluated the BP025 population under greenhouse and growth chamber conditions to the Great Plains (GP) biotype of Hessian fly (HF, Mayetiola destructor Say). The BP025 population was developed by crossing Ben (PI 596557), a North Dakota hard amber durum variety, with PI 41025, a cultivated emmer (T. turgidum ssp. dicoccum) accession collected near Samara, Russia. The BP025 population consists of 200 RILs developed by single seed-descent and was advanced to the F7:8 generation. The BP025 population was evaluated for stunting score, larval mortality, and the percentage of resistant plants under growth chamber and greenhouse conditions in Fargo, ND (46.893273, -96.807319). Experimental plants were maintained in a greenhouse at 20 \u00b1 2\u00b0 C with an ambient relative humidity of between 40 and 70% and a 16:8 (L:D) photoperiod. Natural lighting was enhanced with the use of 430-watt high pressure sodium lamps. Individual seeds of the mapping population entries were planted in Ray Leach cone-tainer (4 cm diameter \u00d7 21 cm deep, Stuewe & Sons, Inc., Tangent, OR), held in racks (RL98). Plants were grown in potting media (SB100 Professional Growing Mix, Sungro Horticulture, Bellevue, WA), and fertilized at planting with Osmocote Plus 15-9-12 (N-P-K) standard release fertilizer. Each cone was considered an experimental unit. The BP025 population and the parental lines Ben and PI 41025 were screened for HF larval resistance over two greenhouse seasons. All plants were evaluated using a completely randomized design. For the infestations, seedling plants were exposed to egg-laying HF adult females (~ 1 female for each plant) for 24 h. Infestations were timed to occur when seedlings were at the two-leaf growth stage. Three days after exposure to adult females, plants were moved to a high humidity (50-75% RH) growth chamber. High humidity facilitates egg hatching and promotes the successful migration of neonate larvae down the leaf blade to feeding sites at the base of the plant. Following egg hatch, plants were returned to the greenhouse for 10 to 14 days. This provided time for virulent larvae to grow and be differentiated from the small presumably dead avirulent larvae. Detailed observations of plant quality and larval success provided each plant with a score of \u201cresistant\u201d or \u201csusceptible.\u201d Specifically, plants were scored for their growth, with information on the number of leaves and tillers being recorded. Plant health and appearance (i.e., severity of larval-induced stunting), was also scored for each plant. Normal healthy plants were given a score of 0, lightly stunted plants were scored as a 1, moderately stunted plants were given a 2, and severely stunted planted were given the score of 3. Each plant was also dissected using a stereo microscope. At the time of plant dissection, virulent (i.e., successful) larvae were expected to be large and white in color. The number of dead larvae (eg. large, medium, small, and neonate) and live larvae (eg. large, medium, and small) were recorded for each plant. Averages for the plant and insect measurements were derived from the mean score of the 12 to 14 plants evaluated for each entry in the population. Phenotypic data was analyzed using JMP version 15 (SAS Institute, 2015). Prior to analysis, homogeneity of variance was tested using an O-Brien test at p < 0.05 (O\u2019Brien, 1979). The genotypic data used for further QTL analysis is available Peters Haugrud, Amanda; Saini Sharma, Jyoti; Zhang, Qijun; Green, Andrew J.; Xu, Steven S.; Faris, Justin D. (2023). Data from: Identification of robust yield QTL derived from cultivated emmer for durum wheat improvement. Ag Data Commons. Dataset. https://doi.org/10.15482/USDA.ADC/1529118.
1) Resource Title: Averages of the phenotypic data collected from the durum x cultivated emmer wheat recombinant inbred population BP025 infested with Great Plains biotype of Hessian fly. File name: Ben mapping population average phenotyping data.
Resource Description: In the data file, column headings indicate the line, entry # (seed source), total number of plants evaluated, stunting score (mean), Leaves (mean), tillers (mean), total dead larvae (mean), total live larvae (mean), total larvae (mean), larval mortality (%), R (resistant) plants %, and notes from observations during plant dissections. This data can be used for further QTL analysis and evaluating these lines for HF resistance and how HF infestations affect different traits in each genotype.
2) Resource Title: Individual plant phenotypic data collected from the durum x cultivated emmer wheat recombinant inbred population BP025 infested with Great Plains biotype of Hessian fly. File name: Ben mapping population individual plant phenotyping data.
Resource Description: In the data file, column headings indicate the planting date, infestation date, scoring date, line, entry # (seed source), Plant number for that genotype, stunting score, resistance score, Leaves, tillers, coleoptile tillers, number large dead larvae, number medium dead larvae, number small dead larvae, number neonate larvae, total dead larvae, number large living larvae, number medium living larvae, number small living larvae, total living larvae, and total number of larvae. These values can be used for individual replicates for further analysis.
","distribution_titles":["Ben mapping population average phenotyping data.csv","Ben Mapping population individual plant phenotyping data.csv"],"harvest_record":"https://catalog.data.gov/harvest_record/aa5a17dd-2263-4815-8d51-a36ebdb0cbbe","harvest_record_raw":"https://catalog.data.gov/harvest_record/aa5a17dd-2263-4815-8d51-a36ebdb0cbbe/raw","has_download":true,"has_spatial":true,"identifier":"10.15482/USDA.ADC/29257448.v1","keyword":["Durum","Hessian fly","Insect pest","NP301","QTLs","emmer wheat","insect resistance traits"],"last_harvested_date":"2026-10-09T16:38:16.787885","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"352b4532-793d-4075-a03f-05b778a3c43a","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/usda.png","name":"Department of Agriculture","organization_type":"Federal Government","slug":"usda"},"parent_identifier":null,"popularity":3,"publisher":"Agricultural Research Service","slug":"data-from-identification-and-molecular-mapping-of-two-quantitative-trait-loci-for-hessian-","spatial_centroid":{"lat":46.893273,"lon":-96.807319},"spatial_shape":{"coordinates":[-96.807319,46.893273],"type":"Point"},"theme":[],"title":"Data from: Identification and molecular mapping of two quantitative trait loci for Hessian fly resistance in a durum \u00d7 cultivated emmer wheat population","type":"dataset"},{"_score":58.24933,"_sort":[1791563895562,58.24933,1,"5b73bd74-c362-4cd0-937b-6875fff4c25d"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","accessRights":"public","accrualPeriodicity":"irregular","bureauCode":["005:18"],"contactPoint":{"fn":"Hammond Wagner, Courtney R.","hasEmail":"mailto:Courtney.Hammond-Wagner@usda.gov"},"description":"Methodology for data collection and analysis is reported in: Hammond Wagner, C.R., White, A., Darby, H., Ewing, P., Faulkner, J., Fisher, B., Galford, G., Horner, C., Jones, W.D., Neher, D., Ritzenthaler, C., von Wettberg, E.B. & Zeraatpisheh, M. (2025). Holistic systems thinking underpins Vermont soil health practitioners\u2019 preferences and beliefs. Soil Security, 19, 100186. https://doi.org/10.1016/j.soisec.2025.100186
Data archival consists of data, R scripts, and R projects for the analysis of two surveys from Vermont, USA:
Study 1: Vermont Soil Health Metrics Preferences Survey
Study 2: Vermont Farmer and Conservation and Payment for Ecosystem Services Survey
Methodology for data collection and analysis is reported in: Hammond Wagner, C.R., White, A., Darby, H., Ewing, P., Faulkner, J., Fisher, B., Galford, G., Horner, C., Jones, W.D., Neher, D., Ritzenthaler, C., von Wettberg, E.B. & Zeraatpisheh, M. (2025). Holistic systems thinking underpins Vermont soil health practitioners\u2019 preferences and beliefs. Soil Security, 19, 100186. https://doi.org/10.1016/j.soisec.2025.100186
Data archival consists of data, R scripts, and R projects for the analysis of two surveys from Vermont, USA:
Study 1: Vermont Soil Health Metrics Preferences Survey
Study 2: Vermont Farmer and Conservation and Payment for Ecosystem Services Survey
[NOTE: 2026-07-31: Data files added, see README file for descriptions]
Trapping in 2023 with a linear set of dosages of (E)-8-dodecenyl acetate
Field trapping was done according to the methodology in Ruiz et al. 2022. The fields were located in North-Central Kansas at the Land Institute near Salina, KS. No pesticides were applied to these fields during the experiment in 2023. Starting the first week of June, six transects were set out, two in each Silphium integrifolium field. Each transect contained seven 30.4 cm x 30.4 cm sticky card traps (Alpha Scents, Canby, OR, USA) affixed to the top of a 1.27 cm diameter, three foot in length PVC pole that was hammered into the ground until sturdy. The cards were affixed using a 271 cm long sticky card ring holder (Olson Products Inc., Medina, OH, USA) that was bent to a 90\u00b0 angle and placed inside the PVC pipe. Two large binder clips were also used to anchor the sticky card to its card holder.
The sticky traps in each transect were spaced 10 meters apart around the perimeter of the field. Within each transect, traps were baited with a linear increase in concentrations in 2023, including either a control (50 \u00b5l of acetone), a low concentration (50 \u00b5l of a solution made by mixing 5.75 \u00b5l of (E)-8-dodecenyl acetate in 5 ml of acetone), or a doubled concentration (11.5 \u00b5l of (E)-8-dodecenyl acetate diluted in 5 ml of acetone) of (E)-8-dodecenyl acetate (Alfa Chemistry, Ronkonkoma, NY, USA). All lures were added to a 3-ml LDPE dropping bottle (Wheaton, DWK Life Sciences, Millville, NJ, USA). The clear sticky card traps were collected and replaced biweekly until the first E. giganteana adult was caught, then traps were changed weekly. The lures and control bottles were replaced once every two weeks (with lure emissions confirmed out to 14 d in Ruiz et al. 2022) and their position in the field rotated at each change. Each lure was in each position twice over the course of the season.
When collected, the sticky cards were held in a 7.6 L (=2 gal) labeled Ziploc\u00a9 bag transported back to USDA-ARS. All collected sticky traps were placed in a freezer for approximately 24 h. The total number of E. giganteana per trap and their distance from the lure in millimeters was recorded. In addition, the number of nontarget lepidoptera was recorded on each trap. Individual E. giganteana and non-target lepidoptera were only counted if more than half of the specimen was remaining on the sticky trap at the time of counting to ensure positive identification.
Trapping in 2024 with an exponential set of concentrations of (E)-8-dodecenyl acetate
Field trapping in 2024 was conducted similarly to that in 2023 with the following modifications. Three different fields located at the Land Institute were used (Table 1). [HS1] Pesticides were applied once to one of the fields and adjacent to one of the others. Three transects were deployed in each of the three fields. Each transect contained four traps for a total of 36 traps. The traps were assembled similarly to those used in 2023, but a hand-made sticky card was used instead of a manufactured one to improve captures. These sticky cards were made of a laminated 21.6 \u00d7 27.9 cm (=8.5 by 11 in) piece of white cardstock paper (Astrobright, Neenah, WI, USA) coated on both sides with TAD\u24c7 all-weather adhesive (Tr\u00e9c\u00e9 Adhesives Division, Adair, OK, USA). The sticky sides were covered with wax paper for ease of travel. Additionally, the sticky cards had a chicken wire cage placed over them in the field to try to prevent the capture of birds and other nontargets on the traps. Traps in 2024 were baited with an exponential set of concentrations of (E)-8-dodecenyl acetate. In each transect, there was a solvent only control (50 \u00b5l of acetone), a low concentration equivalent to the 2023 treatment (50 \u00b5l of a solution made of 5.75 \u00b5l of (E)-8-dodecenyl acetate diluted in 5 ml of acetone), a medium concentration (50 \u00b5l of a solution made of 78.5 \u00b5l of (E)-8-dodecenyl acetate diluted in 5 ml of acetone), and a high concentration (50 \u00b5l of a solution made of 580.4 \u00b5l of (E)-8-dodecenyl acetate diluted in 5 ml of acetone). The traps were replaced weekly, and the lures were replaced biweekly, as well as rotated positions in the transect. Each lure was in each position twice over the course of the season.
Eucosma giganteana and Silphium integrifolium collections from the field
Eucosma giganteana cannot yet be reared successfully in the laboratory, thus we sourced all specimens from the field. Adult E. giganteana individuals were carefully captured by hand in one of the fields planted to Silphium integrifolium at the Land Institute (38.769622, -97.598576) between 22:00 and 24:00 five times a week from June to August 2024. Moths were immediately sexed and individually placed in small deli cups with appropriate labels. They were brought back to the USDA-ARS Center for Grain and Animal Health (39.1955486, -96.5987334) for the experiments described below. Once in the lab but prior to use in experiments, moths were kept in a quiet environment at approximately 23 \u00b1 0.1\u2103 and 16:8 L:D photoperiod. Importantly, no lures were used to capture insects to avoid biasing the results of the experiments below. Silphium integrifolium flower heads were cut 1 cm below the flower and brought back on a weekly basis during the same timeframe and stored at 4\u00b0C until needed for experiments. Flower heads were never more than 4 days old prior to use.
Headspace Characterization
Headspace was collected from the following treatments: 10 E. giganteana male moths only, 10 E. giganteana female moths only, an even mix of male and female moths (5:5), flower cuttings of S. integrifolium, and a blank control. For the E. giganteana treatments, only alive, healthy adult moths that were collected within five days were used. For the S. integrifolium collections, approximately 25 grams of flower heads cut the same week as collections were used.
For each treatment, E. giganteana or S. integrifolium were placed in a clean 100-mL beaker. To prevent moth escapees, a metal mesh top was constructed and affixed to the opening of the beaker. The beaker was then placed in one of eight 500-mL glass headspace collection containers with a PTFE septum and lid. A Pora-Pak Q volatile collection trap (VCT) was inserted in the output end. The VCT consisted of an angled drip-tip collection point borosilicate glass tube with a mesh (Stainless Steel #316 screen), packed with 20 mg of PoraPak-Q\u2122 chemical absorbent held in place with a borosilicate glass wool plug, and followed by a PTFE Teflon\u2122 compression seal. A PTFE tube spanned from the flow meter (CADS-4CPP, Clean Air Delivery System, Sigma Scientific, LLC, Micanopy, FL, USA) to the input end of the headspace container at a flow rate of 1 L/min. Prior to that, the air was scrubbed with an activated carbon filter and was pumped in using the central air pump for the center. Samples ran for 24 h. Each volatile collection trap was collected and eluted with 150 \u00b5l of dichloromethane in a fume hood into a 2-mL GC vial containing a 250 \u00b5l glass insert with polymer feet. The solvent was gently pushed through the volatile collection trap with N2 gas. At the end of collecting all the samples, 1 \u00b5l of an internal standard, tetradecane (190.5 ng), was added to each of the samples. The samples were then all capped with a magnetic screw top lid and secured with PTFE tape before being placed in a freezer at -20 \u2103 until they could be run. All headspace samples were collected within 5 weeks. After each replication, the headspace collection containers were all washed with methanol and then hexane. VCTs were rinsed in triplicate with dichloromethane. A total of at least n = 5 replicates were tested for each treatment.
Gas Chromatography Coupled with Mass Spectrometry
All headspace collection sample extracts were run on an Agilent 7890B gas chromatograph (GC) equipped with an Agilent Durabond HP-5 column (30 m length, 0.250 mm diameter and 0.25 \u03bcm film thickness) with He as the carrier gas at a constant 1.2 mL/min flow and 40 cm/s velocity. The GC was coupled with a single-quadrupole Agilent 5997B mass spectrometer (MS). The compounds were separated by auto-injecting 1 \u03bcl of each sample under splitless into the GC\u2013MS at room temperature (approximately 23 \u00b0C). The flow rate was 18 ml/min. The GC program consisted of 40 \u00b0C for 1 min followed by 10 \u00b0C/min increases to 300 \u00b0C and then held for 26.5 min. After a solvent delay of 3 min, mass ranges between 50 and 550 atomic mass units were scanned. Compounds were tentatively identified by comparison of spectral data with those from the NIST 14 library and by GC retention index. The samples were normalized according to the following formula: (Pksam \u2013 Pkmin)/(Pkmax \u2013 Pkmin), where Pksam is the peak area from the sample, Pkmin is the global minimum peak area, and Pkmax is the global max peak area.
Electroantennography of E. giganteana
All electroantennogram (EAG) recordings of E. giganteana were taken from 19:00 to 23:00 which corresponded to the peak activity period of E. giganteana based on prior literature (Ruiz et al. 2022). Prior to recordings, the machine and software were powered on and given 30 min to warm up. Only field-captured moths within ...","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/51279737","format":"csv","license":"https://creativecommons.org/publicdomain/zero/1.0/","mediaType":"text/csv","title":"scribner_headspace_volatiles_eucosma_2024.csv"},{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/51279740","format":"csv","license":"https://creativecommons.org/publicdomain/zero/1.0/","mediaType":"text/csv","title":"Eucosma_EAD .csv"},{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/51279743","format":"csv","license":"https://creativecommons.org/publicdomain/zero/1.0/","mediaType":"text/csv","title":"Combined flight mill 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[NOTE: 2026-07-31: Data files added, see README file for descriptions]
Trapping in 2023 with a linear set of dosages of (E)-8-dodecenyl acetate
Field trapping was done according to the methodology in Ruiz et al. 2022. The fields were located in North-Central Kansas at the Land Institute near Salina, KS. No pesticides were applied to these fields during the experiment in 2023. Starting the first week of June, six transects were set out, two in each Silphium integrifolium field. Each transect contained seven 30.4 cm x 30.4 cm sticky card traps (Alpha Scents, Canby, OR, USA) affixed to the top of a 1.27 cm diameter, three foot in length PVC pole that was hammered into the ground until sturdy. The cards were affixed using a 271 cm long sticky card ring holder (Olson Products Inc., Medina, OH, USA) that was bent to a 90\u00b0 angle and placed inside the PVC pipe. Two large binder clips were also used to anchor the sticky card to its card holder.
The sticky traps in each transect were spaced 10 meters apart around the perimeter of the field. Within each transect, traps were baited with a linear increase in concentrations in 2023, including either a control (50 \u00b5l of acetone), a low concentration (50 \u00b5l of a solution made by mixing 5.75 \u00b5l of (E)-8-dodecenyl acetate in 5 ml of acetone), or a doubled concentration (11.5 \u00b5l of (E)-8-dodecenyl acetate diluted in 5 ml of acetone) of (E)-8-dodecenyl acetate (Alfa Chemistry, Ronkonkoma, NY, USA). All lures were added to a 3-ml LDPE dropping bottle (Wheaton, DWK Life Sciences, Millville, NJ, USA). The clear sticky card traps were collected and replaced biweekly until the first E. giganteana adult was caught, then traps were changed weekly. The lures and control bottles were replaced once every two weeks (with lure emissions confirmed out to 14 d in Ruiz et al. 2022) and their position in the field rotated at each change. Each lure was in each position twice over the course of the season.
When collected, the sticky cards were held in a 7.6 L (=2 gal) labeled Ziploc\u00a9 bag transported back to USDA-ARS. All collected sticky traps were placed in a freezer for approximately 24 h. The total number of E. giganteana per trap and their distance from the lure in millimeters was recorded. In addition, the number of nontarget lepidoptera was recorded on each trap. Individual E. giganteana and non-target lepidoptera were only counted if more than half of the specimen was remaining on the sticky trap at the time of counting to ensure positive identification.
Trapping in 2024 with an exponential set of concentrations of (E)-8-dodecenyl acetate
Field trapping in 2024 was conducted similarly to that in 2023 with the following modifications. Three different fields located at the Land Institute were used (Table 1). [HS1] Pesticides were applied once to one of the fields and adjacent to one of the others. Three transects were deployed in each of the three fields. Each transect contained four traps for a total of 36 traps. The traps were assembled similarly to those used in 2023, but a hand-made sticky card was used instead of a manufactured one to improve captures. These sticky cards were made of a laminated 21.6 \u00d7 27.9 cm (=8.5 by 11 in) piece of white cardstock paper (Astrobright, Neenah, WI, USA) coated on both sides with TAD\u24c7 all-weather adhesive (Tr\u00e9c\u00e9 Adhesives Division, Adair, OK, USA). The sticky sides were covered with wax paper for ease of travel. Additionally, the sticky cards had a chicken wire cage placed over them in the field to try to prevent the capture of birds and other nontargets on the traps. Traps in 2024 were baited with an exponential set of concentrations of (E)-8-dodecenyl acetate. In each transect, there was a solvent only control (50 \u00b5l of acetone), a low concentration equivalent to the 2023 treatment (50 \u00b5l of a solution made of 5.75 \u00b5l of (E)-8-dodecenyl acetate diluted in 5 ml of acetone), a medium concentration (50 \u00b5l of a solution made of 78.5 \u00b5l of (E)-8-dodecenyl acetate diluted in 5 ml of acetone), and a high concentration (50 \u00b5l of a solution made of 580.4 \u00b5l of (E)-8-dodecenyl acetate diluted in 5 ml of acetone). The traps were replaced weekly, and the lures were replaced biweekly, as well as rotated positions in the transect. Each lure was in each position twice over the course of the season.
Eucosma giganteana and Silphium integrifolium collections from the field
Eucosma giganteana cannot yet be reared successfully in the laboratory, thus we sourced all specimens from the field. Adult E. giganteana individuals were carefully captured by hand in one of the fields planted to Silphium integrifolium at the Land Institute (38.769622, -97.598576) between 22:00 and 24:00 five times a week from June to August 2024. Moths were immediately sexed and individually placed in small deli cups with appropriate labels. They were brought back to the USDA-ARS Center for Grain and Animal Health (39.1955486, -96.5987334) for the experiments described below. Once in the lab but prior to use in experiments, moths were kept in a quiet environment at approximately 23 \u00b1 0.1\u2103 and 16:8 L:D photoperiod. Importantly, no lures were used to capture insects to avoid biasing the results of the experiments below. Silphium integrifolium flower heads were cut 1 cm below the flower and brought back on a weekly basis during the same timeframe and stored at 4\u00b0C until needed for experiments. Flower heads were never more than 4 days old prior to use.
Headspace Characterization
Headspace was collected from the following treatments: 10 E. giganteana male moths only, 10 E. giganteana female moths only, an even mix of male and female moths (5:5), flower cuttings of S. integrifolium, and a blank control. For the E. giganteana treatments, only alive, healthy adult moths that were collected within five days were used. For the S. integrifolium collections, approximately 25 grams of flower heads cut the same week as collections were used.
For each treatment, E. giganteana or S. integrifolium were placed in a clean 100-mL beaker. To prevent moth escapees, a metal mesh top was constructed and affixed to the opening of the beaker. The beaker was then placed in one of eight 500-mL glass headspace collection containers with a PTFE septum and lid. A Pora-Pak Q volatile collection trap (VCT) was inserted in the output end. The VCT consisted of an angled drip-tip collection point borosilicate glass tube with a mesh (Stainless Steel #316 screen), packed with 20 mg of PoraPak-Q\u2122 chemical absorbent held in place with a borosilicate glass wool plug, and followed by a PTFE Teflon\u2122 compression seal. A PTFE tube spanned from the flow meter (CADS-4CPP, Clean Air Delivery System, Sigma Scientific, LLC, Micanopy, FL, USA) to the input end of the headspace container at a flow rate of 1 L/min. Prior to that, the air was scrubbed with an activated carbon filter and was pumped in using the central air pump for the center. Samples ran for 24 h. Each volatile collection trap was collected and eluted with 150 \u00b5l of dichloromethane in a fume hood into a 2-mL GC vial containing a 250 \u00b5l glass insert with polymer feet. The solvent was gently pushed through the volatile collection trap with N2 gas. At the end of collecting all the samples, 1 \u00b5l of an internal standard, tetradecane (190.5 ng), was added to each of the samples. The samples were then all capped with a magnetic screw top lid and secured with PTFE tape before being placed in a freezer at -20 \u2103 until they could be run. All headspace samples were collected within 5 weeks. After each replication, the headspace collection containers were all washed with methanol and then hexane. VCTs were rinsed in triplicate with dichloromethane. A total of at least n = 5 replicates were tested for each treatment.
Gas Chromatography Coupled with Mass Spectrometry
All headspace collection sample extracts were run on an Agilent 7890B gas chromatograph (GC) equipped with an Agilent Durabond HP-5 column (30 m length, 0.250 mm diameter and 0.25 \u03bcm film thickness) with He as the carrier gas at a constant 1.2 mL/min flow and 40 cm/s velocity. The GC was coupled with a single-quadrupole Agilent 5997B mass spectrometer (MS). The compounds were separated by auto-injecting 1 \u03bcl of each sample under splitless into the GC\u2013MS at room temperature (approximately 23 \u00b0C). The flow rate was 18 ml/min. The GC program consisted of 40 \u00b0C for 1 min followed by 10 \u00b0C/min increases to 300 \u00b0C and then held for 26.5 min. After a solvent delay of 3 min, mass ranges between 50 and 550 atomic mass units were scanned. Compounds were tentatively identified by comparison of spectral data with those from the NIST 14 library and by GC retention index. The samples were normalized according to the following formula: (Pksam \u2013 Pkmin)/(Pkmax \u2013 Pkmin), where Pksam is the peak area from the sample, Pkmin is the global minimum peak area, and Pkmax is the global max peak area.
Electroantennography of E. giganteana
All electroantennogram (EAG) recordings of E. giganteana were taken from 19:00 to 23:00 which corresponded to the peak activity period of E. giganteana based on prior literature (Ruiz et al. 2022). Prior to recordings, the machine and software were powered on and given 30 min to warm up. Only field-captured moths within ...","distribution_titles":["scribner_headspace_volatiles_eucosma_2024.csv","Eucosma_EAD .csv","Combined flight mill data1.csv","Trapping_combined2024.csv","beta_myrcene_NIST_mass_spectra.csv","beta_myrcene_silphium_sample.csv","beta_myrcene_std_sample.csv","beta_phellandrene_NIST_mass_spectra.csv","beta_phellandrene_sample_silphium.csv","beta_phellandrene_std_sample.csv","beta_pinene_NIST_extracted_mass_spectrum.csv","beta_pinene_sample_silphium.csv","beta_pinene_std_sample.csv","camphene_in_silphium_sample_mass_spectrum.csv","camphene_NIST_mass_spectra.csv","camphene_standard_sample.csv","D_limonene_NIST_mass_spectra.csv","D_limonene_silphium_sample.csv","D_limonene_std_sample.csv","Hazel Amalgamated Script.R","README Scribner et al.docx"],"harvest_record":"https://catalog.data.gov/harvest_record/30544399-9804-4cf2-b101-a57d3b98199a","harvest_record_raw":"https://catalog.data.gov/harvest_record/30544399-9804-4cf2-b101-a57d3b98199a/raw","has_download":true,"has_spatial":false,"identifier":"10.15482/USDA.ADC/28055111.v2","keyword":["ars","attract-and-kill","behavior","behavioral ecology","behaviorally-based management","cgahr","cup plant","eag","eucosma giganteana","flight mill","giant eucosma moth","insect behavior","insect flight","integrated pest management","kansas","lepidoptera","manhattan, ks","mating disruption","monitoring","pest","physiology","prairie","semiochemicals","silphium","silphium integrifolium","the land institute","tortricidae","trapping","usda"],"last_harvested_date":"2026-10-09T16:38:14.603251","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"352b4532-793d-4075-a03f-05b778a3c43a","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/usda.png","name":"Department of Agriculture","organization_type":"Federal Government","slug":"usda"},"parent_identifier":null,"popularity":4,"publisher":"Agricultural Research Service","slug":"data-from-behavioral-and-physiological-response-of-ieucosma-giganteana-ito-semiochemicals-","spatial_centroid":null,"spatial_shape":null,"theme":[],"title":"Data from: Behavioral and physiological response of Eucosma giganteana to semiochemicals from conspecifics and Silphium integrifolium","type":"dataset"},{"_score":11.509251,"_sort":[1791563893492,11.509251,12,"dd792e6e-ef57-4e1f-950f-66d98f3e6aca"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","accessRights":"public","accrualPeriodicity":"irregular","bureauCode":["005:18"],"contactPoint":{"fn":"Osborne, Shannon L.","hasEmail":"mailto:Shannon.Osborne@usda.gov"},"description":"
Diversified crop rotations with no-till management are considered fundamental to sustainable agroecosystems; however, associated uncertainty in economic returns may hinder farmers\u2019 decisions to adopt these practices. The research objectives were to evaluate data from a long-term crop rotation experiment to compare economic performance (gross revenue, net revenue, and production cost) among various low-input diversified versus conventional crop rotations. The experiment was initiated in the fall of 2000 with winter wheat (Triticum aestivum L.) planting and planting of remaining crops in the spring of 2001 near Brookings, SD. Economic analysis was performed from data collected during the fifth complete 4-year crop rotational cycle (2017\u20132020) of six crop rotations: (1) 4-year corn (Zea mays L.)-soybean [Glycine max L. (Merr.)]-spring wheat-sunflower (Helianthus annus L.) (CSSwSf), (2) 4 year corn-soybean-spring wheat-pea (Pisum sativum L.) (CSSwP), (3) 4-year corn-pea-winter wheat-soybean (CPWwS), (4) 4-year corn-oat (Avena sativa L.)-winter wheat-soybean (COWwS), (5) 2-year corn-soybean (CS), and (6) continuous corn (CCC, treatment initiated in 2017). Corn yield in CSSwP rotation, where corn followed peas, was higher (p < 0.05) by 20%, 25%, 45%, and 89%, compared to CPWwS, CSSwSf, CS, and CCC rotations, respectively. Similarly, soybean yield following winter wheat was significantly higher by 16%\u201338% in COWwS and 13%\u201338% in CPWwS compared to CSSwP, CSSwSf, and CS. Overall, diversified crop rotations improved both corn and soybean yield and net revenue compared to 2-year CS and monoculture CCC rotations. Moreover, 4-year diversified systems, specifically COWwS, CPWwS, and CSSwP, demonstrated economic resilience by maintaining stable production costs.
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Resources in this dataset:
Resource description: Raw CFU counts for culturable aerobic bacteria (TSA) and suspected coliforms (VRBA) cultured from replica plating of ten-fold serially diluted house fly homogenates.
Resource File: Raw_daily_avg_climate_Jul-Oct 2019.xlsx
Resource description: Raw climate data downloaded from Kansas Mesonet weather stations for sampling period.
Resource File: Metadata_bacterial_isolates.xlsx
Resource description: Spreadsheet gives information linking individual isolate (Isolate ID #) data resources with which fly they originated from and other collection information (fly sex, farm type, collection date, county).
Resource File: Raw_isolates_disk_susceptibility.xlsx
Resource description: Measured inhibition zones (in millimeters) of individual isolates which underwent disk susceptibility testing against 5 antibiotics (Tet, Flo, Enr, Cef, Amp).
Resource file: Raw_isolates_MALDI-TOF_outputs.xlsx
Resource description: Spreadsheet of best and second-best matches of Bruker MALDI Biotyper Identification Results for individual isolates (Sample ID). Each isolate was measured in duplicate.
All trimmed Sanger sequence reads of AMR isolates are publicly available at GenBank (PQ636534 - PQ636762).
The code repository for 16S sequence analysis for this project can be found here:https://github.com/vlpickens04/Sanger_Phred_Code
","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/49368601","format":"xlsx","license":"https://creativecommons.org/licenses/by/4.0/","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Metadata_bacterial_isolates.xlsx"},{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/49368604","format":"xlsx","license":"https://creativecommons.org/licenses/by/4.0/","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Raw_daily_avg_climate_Jul-Oct 2019.xlsx"},{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/49368607","format":"xlsx","license":"https://creativecommons.org/licenses/by/4.0/","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Raw_fly_CFU_counts.xlsx"},{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/49368610","format":"xlsx","license":"https://creativecommons.org/licenses/by/4.0/","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Raw_isolates_disk_susceptibility.xlsx"},{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/49368613","format":"xlsx","license":"https://creativecommons.org/licenses/by/4.0/","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Raw_isolates_MALDI-TOF_outputs.xlsx"}],"identifier":"10.15482/USDA.ADC/27089242.v1","keyword":["AMR","antibiotic resistance","antibiotic susceptibility testing","antimicrobial resistance","bacteria abundance","beef cattle","dairy cattle","house fly"],"license":"https://creativecommons.org/licenses/by/4.0/","modified":"2026-01-26","programCode":["005:040"],"publisher":{"@type":"org:Organization","name":"Agricultural Research Service"},"temporal":"[{\"@type\": \"PeriodOfTime\", \"endDate\": \"2019-10-08\", \"startDate\": \"2019-08-13\"}]","title":"Data from: Bacterial Abundance and Antimicrobial Resistance Prevalence Carried by Adult House Flies (Diptera: Muscidae) at Kansas Dairy and Beef Cattle Operations"},"description":"House flies (Musca domestica L.) are a global pest ubiquitous in urban and agricultural settings. Their dependence on microbe-rich substrates for development, as well as ability to acquire and transmit pathogenic and antimicrobial resistant (AMR) bacteria, make house flies a risk to human and animal health. Large livestock operations, like confined cattle, are environments which are conducive to both house flies and developing AMR due to large accumulations of animal feed and waste. However, little is known about what factors influence bacterial abundances and AMR prevalence carried by house flies in confined cattle operations. Adult house flies (n=6/fly sex/location) were collected on alternating weeks mid-August through early October of 2019 from a dairy and beef feedlot cattle operation in each of three Kansas counties (Riley, Marion, and Washington). We enumerated colony forming units (CFUs) of culturable aerobic bacteria and suspected coliforms (SC) from house fly homogenates on nonselective (tryptic soy agar, TSA) and selective (violet-red bile agar, VRBA) media to investigate factors, such as fly sex, farm type, location, and climate, which may be associated with bacterial abundances carried by house flies. Further, we screened unique morphotypes of SC isolates for tetracycline (Tet) resistance, then tested for additional resistance to florfenicol (Flo), enrofloxacin (Enr) ceftiofur (Cef), and ampicillin (Amp) to identify multi-drug resistant (MDR) isolates. AMR isolates were identified via 16S rRNA Sanger sequencing or, in select cases, matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS).
Resources in this dataset:
Resource description: Raw CFU counts for culturable aerobic bacteria (TSA) and suspected coliforms (VRBA) cultured from replica plating of ten-fold serially diluted house fly homogenates.
Resource File: Raw_daily_avg_climate_Jul-Oct 2019.xlsx
Resource description: Raw climate data downloaded from Kansas Mesonet weather stations for sampling period.
Resource File: Metadata_bacterial_isolates.xlsx
Resource description: Spreadsheet gives information linking individual isolate (Isolate ID #) data resources with which fly they originated from and other collection information (fly sex, farm type, collection date, county).
Resource File: Raw_isolates_disk_susceptibility.xlsx
Resource description: Measured inhibition zones (in millimeters) of individual isolates which underwent disk susceptibility testing against 5 antibiotics (Tet, Flo, Enr, Cef, Amp).
Resource file: Raw_isolates_MALDI-TOF_outputs.xlsx
Resource description: Spreadsheet of best and second-best matches of Bruker MALDI Biotyper Identification Results for individual isolates (Sample ID). Each isolate was measured in duplicate.
All trimmed Sanger sequence reads of AMR isolates are publicly available at GenBank (PQ636534 - PQ636762).
The code repository for 16S sequence analysis for this project can be found here:https://github.com/vlpickens04/Sanger_Phred_Code
","distribution_titles":["Metadata_bacterial_isolates.xlsx","Raw_daily_avg_climate_Jul-Oct 2019.xlsx","Raw_fly_CFU_counts.xlsx","Raw_isolates_disk_susceptibility.xlsx","Raw_isolates_MALDI-TOF_outputs.xlsx"],"harvest_record":"https://catalog.data.gov/harvest_record/a7c70c25-de9d-4498-9861-d61e032798b4","harvest_record_raw":"https://catalog.data.gov/harvest_record/a7c70c25-de9d-4498-9861-d61e032798b4/raw","has_download":true,"has_spatial":false,"identifier":"10.15482/USDA.ADC/27089242.v1","keyword":["AMR","antibiotic resistance","antibiotic susceptibility testing","antimicrobial resistance","bacteria abundance","beef cattle","dairy cattle","house fly"],"last_harvested_date":"2026-10-09T16:38:13.203958","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"352b4532-793d-4075-a03f-05b778a3c43a","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/usda.png","name":"Department of Agriculture","organization_type":"Federal Government","slug":"usda"},"parent_identifier":null,"popularity":2,"publisher":"Agricultural Research Service","slug":"data-from-bacterial-abundance-and-antimicrobial-resistance-prevalence-carried-by-adult-hou","spatial_centroid":null,"spatial_shape":null,"theme":[],"title":"Data from: Bacterial Abundance and Antimicrobial Resistance Prevalence Carried by Adult House Flies (Diptera: Muscidae) at Kansas Dairy and Beef Cattle Operations","type":"dataset"},{"_score":5.1009274,"_sort":[1791563890146,5.1009274,11,"eb778403-0af8-4082-aff1-8c13f1d76e2c"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","accessRights":"public","accrualPeriodicity":"irregular","bureauCode":["005:18"],"contactPoint":{"fn":"Oliver, Jason B.","hasEmail":"mailto:joliver@tnstate.edu"},"description":"The dataset contains the geographic occurrence of Kneallhazia solenopsae (Knell, Allen & Hazard) (Microsporidia: Burenellidae) (formerly Thelohania solenopsae Knell Allen & Hazard) and Solenopsis invicta virues (SINV-1, SINV-2, and SINV-3) pathogens, as well as Pseudacteon spp. (phorid decapitating fly) presence in imported fire ant (Hymenoptera: Formicidae) colonies sampled throughout the entire Tennessee Federal Imported Fire Ant Quarantine (FIFAQ) area in 2015 to 2016. Pathogen and phorid presence were determined by PCR. Colonies were sampled from July to October in 2015 and 2016 (warm period) (n=440 colonies) and January to April in 2016 (winter period) (n=227 colonies) using a sampling grid of 12.1 by 12.1 kilometers placed over all locations in Tennessee within FIFAQ. During the cool period, every other grid was sampled resulting in a lower total sample number. The project goal was to determine geography and seasonality of pathogens and phorids in Tennessee fire ant populations.
\nThe dataset also contains venom alkaloid (IALK) and cuticular hydrocarbon (IHC) values for each fire ant colony sampled as determined by a Shimadzu QP-2010 Gas Chromatograph - Mass Spectrometer (GC-MS), which were used to determine if samples are red imported fire ant (Solenopsis invicta Buren) (RIFA), black imported fire ant (Solenopsis richteri Forel) (BIFA), or the hybrid imported fire ant (HIFA) of the two parental species. The IALK and IHC values were used to calculate an average (IAVG) and adjusted hybridity index value for each colony sampled with the adjusted value being used to determine BIFA, HIFA, or RIFA status.
\nThe dataset also contains a random sub-sample of ant colonies (n=99) across hybridity groupings (described in Pandey et al. [2019]: https://doi.org/10.1093/ee/nvz023 ) analyzed for the colony social form by analysis of the Gp-9 locus for B (monogyne [single queen]) and b (polygyne [multi-queen) alleles.
\nOther dataset items include the sampling date, the colony grid number, the colony county location and general state region (east, middle, west), global positioning system coordinates for each colony sample location, and colony elevation (meters and feet). A general sample site description is also provided for each sample site location.
\nGaps in the dataset include sample grids where imported fire ant colonies could not be located (indicated by \"No_\" in the \u201cSampling_Successful\u201d column). For samples where no fire ant colony could be located, the GPS coordinates and sample elevation are still provided using the grid point location, as well as the date of the attempted colony sampling. The dataset also does not contain any data for areas in-between grid points that were not sampled. In some cases where fire ant colonies could not be located near the grid point location, colonies were collected where they could be found as close to the grid center as possible.
\nFunding provided by:
\nUSDA Animal Plant Health Inspection Service Plant Protection and Quarantine Agreement No. 15-8130-0523-CA entitled \"Distribution of Solenopsis invicta virus (SINV) and Kneallhazia solenopsae in Tennessee and potential relocation of virus into new areas\".
\nUSDA Floriculture and Nursery Research Initiative (U.S. National Arboretum CRIS #8020-21000-086-000D). Agreement No. 58-8020-8-009 entitled \"Methods for Quarantine Certification and Biological Control of Imported Fire Ant in Nursery Production Systems\".
\nUSDA-ARS Southern Horticultural Research Unit (NACA entitled \"Enhancing Sustainability and Efficiency of Nursery Plant Production in the Southeast U.S.\". Agreement number 58-6062-3-003. Parent CRIS #6062-21430-004-00D entitled \"Management of Diseases, Pests, and Pollinators in Horticultural Crops\".
\nUSDA National Institute of Food and Agriculture Evans-Allen (Accession numbers 1004787 and 1014556)
","distribution":[{"@type":"dcat:Distribution","accessURL":"https://tnstate.maps.arcgis.com/sharing/rest/content/items/ef36c68823874b05ab08fcdd1f9e3059/info/metadata/metadata.xml?format=iso19139","conformsTo":"[{\"@type\": \"Standard\", \"identifier\": \"https://www.isotc211.org/2005/gmd\"}]","format":"xml","license":"https://www.usa.gov/publicdomain/label/1.0/","mediaType":"text/xml","title":"ISO 19139 metadata"},{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/47955916","format":"xlsx","license":"https://www.usa.gov/publicdomain/label/1.0/","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Oliver et al - Raw Data for Ag Data Commons Archiving Final (for CSV).xlsx"}],"identifier":"10.15482/USDA.ADC/26381005.v1","keyword":["Aparavirus vallesi","Dicistroviridae","Invictavirus solenopsae","Kneallhazia solenopsae","Picornavirales","Polycipiviridae","Solenopsis saevissima","Solinviviridae","Sopolycivirus solenopsae"],"license":"https://www.usa.gov/publicdomain/label/1.0/","modified":"2025-11-26","programCode":["005:040"],"publisher":{"@type":"org:Organization","name":"Agricultural Research Service"},"spatial":"[{\"@type\": \"Location\", \"prefLabel\": \"{\\\"type\\\": \\\"Polygon\\\", \\\"coordinates\\\": [[[-90.308361, 34.995672], [-89.539245, 36.497944], [-81.6469, 36.61196], [-84.32182, 34.988856], [-90.308361, 34.995672]]]}\"}]","temporal":"[{\"@type\": \"PeriodOfTime\", \"endDate\": \"2016-04-07\", \"startDate\": \"2015-07-30\"}]","theme":["geospatial"],"title":"Data from: Solenopsis invicta Viruses and Kneallhazia solenopsae in Tennessee Imported Fire Ant (Hymenoptera: Formicidae) Populations"},"description":"The dataset contains the geographic occurrence of Kneallhazia solenopsae (Knell, Allen & Hazard) (Microsporidia: Burenellidae) (formerly Thelohania solenopsae Knell Allen & Hazard) and Solenopsis invicta virues (SINV-1, SINV-2, and SINV-3) pathogens, as well as Pseudacteon spp. (phorid decapitating fly) presence in imported fire ant (Hymenoptera: Formicidae) colonies sampled throughout the entire Tennessee Federal Imported Fire Ant Quarantine (FIFAQ) area in 2015 to 2016. Pathogen and phorid presence were determined by PCR. Colonies were sampled from July to October in 2015 and 2016 (warm period) (n=440 colonies) and January to April in 2016 (winter period) (n=227 colonies) using a sampling grid of 12.1 by 12.1 kilometers placed over all locations in Tennessee within FIFAQ. During the cool period, every other grid was sampled resulting in a lower total sample number. The project goal was to determine geography and seasonality of pathogens and phorids in Tennessee fire ant populations.
\nThe dataset also contains venom alkaloid (IALK) and cuticular hydrocarbon (IHC) values for each fire ant colony sampled as determined by a Shimadzu QP-2010 Gas Chromatograph - Mass Spectrometer (GC-MS), which were used to determine if samples are red imported fire ant (Solenopsis invicta Buren) (RIFA), black imported fire ant (Solenopsis richteri Forel) (BIFA), or the hybrid imported fire ant (HIFA) of the two parental species. The IALK and IHC values were used to calculate an average (IAVG) and adjusted hybridity index value for each colony sampled with the adjusted value being used to determine BIFA, HIFA, or RIFA status.
\nThe dataset also contains a random sub-sample of ant colonies (n=99) across hybridity groupings (described in Pandey et al. [2019]: https://doi.org/10.1093/ee/nvz023 ) analyzed for the colony social form by analysis of the Gp-9 locus for B (monogyne [single queen]) and b (polygyne [multi-queen) alleles.
\nOther dataset items include the sampling date, the colony grid number, the colony county location and general state region (east, middle, west), global positioning system coordinates for each colony sample location, and colony elevation (meters and feet). A general sample site description is also provided for each sample site location.
\nGaps in the dataset include sample grids where imported fire ant colonies could not be located (indicated by \"No_\" in the \u201cSampling_Successful\u201d column). For samples where no fire ant colony could be located, the GPS coordinates and sample elevation are still provided using the grid point location, as well as the date of the attempted colony sampling. The dataset also does not contain any data for areas in-between grid points that were not sampled. In some cases where fire ant colonies could not be located near the grid point location, colonies were collected where they could be found as close to the grid center as possible.
\nFunding provided by:
\nUSDA Animal Plant Health Inspection Service Plant Protection and Quarantine Agreement No. 15-8130-0523-CA entitled \"Distribution of Solenopsis invicta virus (SINV) and Kneallhazia solenopsae in Tennessee and potential relocation of virus into new areas\".
\nUSDA Floriculture and Nursery Research Initiative (U.S. National Arboretum CRIS #8020-21000-086-000D). Agreement No. 58-8020-8-009 entitled \"Methods for Quarantine Certification and Biological Control of Imported Fire Ant in Nursery Production Systems\".
\nUSDA-ARS Southern Horticultural Research Unit (NACA entitled \"Enhancing Sustainability and Efficiency of Nursery Plant Production in the Southeast U.S.\". Agreement number 58-6062-3-003. Parent CRIS #6062-21430-004-00D entitled \"Management of Diseases, Pests, and Pollinators in Horticultural Crops\".
\nUSDA National Institute of Food and Agriculture Evans-Allen (Accession numbers 1004787 and 1014556)
","distribution_titles":["ISO 19139 metadata","Oliver et al - Raw Data for Ag Data Commons Archiving Final (for CSV).xlsx"],"harvest_record":"https://catalog.data.gov/harvest_record/367041af-958e-4cae-ac5a-b6d3e9fa0ed3","harvest_record_raw":"https://catalog.data.gov/harvest_record/367041af-958e-4cae-ac5a-b6d3e9fa0ed3/raw","has_download":true,"has_spatial":true,"identifier":"10.15482/USDA.ADC/26381005.v1","keyword":["Aparavirus vallesi","Dicistroviridae","Invictavirus solenopsae","Kneallhazia solenopsae","Picornavirales","Polycipiviridae","Solenopsis saevissima","Solinviviridae","Sopolycivirus solenopsae"],"last_harvested_date":"2026-10-09T16:38:10.146668","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"352b4532-793d-4075-a03f-05b778a3c43a","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/usda.png","name":"Department of Agriculture","organization_type":"Federal Government","slug":"usda"},"parent_identifier":null,"popularity":11,"publisher":"Agricultural Research Service","slug":"data-from-solenopsis-invicta-viruses-and-kneallhazia-solenopsae-in-tennessee-imported-fire","spatial_centroid":{"lat":35.6180208,"lon":-87.2249374},"spatial_shape":{"coordinates":[[[-90.308361,34.995672],[-84.32182,34.988856],[-81.6469,36.61196],[-89.539245,36.497944],[-90.308361,34.995672]]],"type":"Polygon"},"theme":["geospatial"],"title":"Data from: Solenopsis invicta Viruses and Kneallhazia solenopsae in Tennessee Imported Fire Ant (Hymenoptera: Formicidae) Populations","type":"dataset"},{"_score":25.69339,"_sort":[1791563885594,25.69339,2,"4913dc70-e1b7-4a4b-9a44-40796f579523"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","accessRights":"public","bureauCode":["005:18"],"contactPoint":{"fn":"Kim, James Y.","hasEmail":"mailto:james.y.kim@usda.gov"},"description":"GUI-based software coded in PYTHON to promote throughput image processing and analytics of a big dataset of satellite imagery and provide spatiotemporal monitoring of crop health conditions throughout the growing season by automatically illustrating 1) a field map calendar (FMC) with daily thumbnails of vegetation heatmaps in each month and 2) a seasonal Vegetation Index (VI) Profile of the crop fields. Output examples of FMC and VI Profile are found in files named in fmCalendar.jpg and NDVI_Profile.jpg, respectively, which were created satellite imagery on 5/1-10/31 in 2020 from a sugarbeet field in Moorhead, MN.
","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/54202898","format":"jpg","license":"https://www.usa.gov/publicdomain/label/1.0/","mediaType":"image/jpeg","title":"NDVI_Profile.jpg"},{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/54202901","format":"py","license":"https://www.usa.gov/publicdomain/label/1.0/","mediaType":"text/x-script.python","title":"iCalendar.py"},{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/54202904","format":"jpg","license":"https://www.usa.gov/publicdomain/label/1.0/","mediaType":"image/jpeg","title":"fmCalendar.jpg"},{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/54202907","format":"py","license":"https://www.usa.gov/publicdomain/label/1.0/","mediaType":"text/x-script.python","title":"BM_NDVI.py"},{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/54202910","format":"py","license":"https://www.usa.gov/publicdomain/label/1.0/","mediaType":"text/x-script.python","title":"FMC.py"},{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/54202913","format":"py","license":"https://www.usa.gov/publicdomain/label/1.0/","mediaType":"text/x-script.python","title":"SR.py"}],"identifier":"10.15482/USDA.ADC/25797007.v6","keyword":["Crop health monitoring","Satellite Image Time Series","field mapping images","image analytics","software","vegetation index"],"license":"https://www.usa.gov/publicdomain/label/1.0/","modified":"2026-03-25","programCode":["005:040"],"publisher":{"@type":"org:Organization","name":"Agricultural Research Service"},"temporal":"[{\"@type\": \"PeriodOfTime\", \"endDate\": \"2024-04-17\", \"startDate\": \"2024-04-17\"}]","title":"iCalendar: Satellite-based Field Map Calendar"},"description":"GUI-based software coded in PYTHON to promote throughput image processing and analytics of a big dataset of satellite imagery and provide spatiotemporal monitoring of crop health conditions throughout the growing season by automatically illustrating 1) a field map calendar (FMC) with daily thumbnails of vegetation heatmaps in each month and 2) a seasonal Vegetation Index (VI) Profile of the crop fields. Output examples of FMC and VI Profile are found in files named in fmCalendar.jpg and NDVI_Profile.jpg, respectively, which were created satellite imagery on 5/1-10/31 in 2020 from a sugarbeet field in Moorhead, MN.
","distribution_titles":["NDVI_Profile.jpg","iCalendar.py","fmCalendar.jpg","BM_NDVI.py","FMC.py","SR.py"],"harvest_record":"https://catalog.data.gov/harvest_record/2239d7e4-cf87-432c-ac4f-eb45dd694324","harvest_record_raw":"https://catalog.data.gov/harvest_record/2239d7e4-cf87-432c-ac4f-eb45dd694324/raw","has_download":true,"has_spatial":false,"identifier":"10.15482/USDA.ADC/25797007.v6","keyword":["Crop health monitoring","Satellite Image Time Series","field mapping images","image analytics","software","vegetation index"],"last_harvested_date":"2026-10-09T16:38:05.594897","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"352b4532-793d-4075-a03f-05b778a3c43a","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/usda.png","name":"Department of Agriculture","organization_type":"Federal Government","slug":"usda"},"parent_identifier":null,"popularity":2,"publisher":"Agricultural Research Service","slug":"icalendar-satellite-based-field-map-calendar","spatial_centroid":null,"spatial_shape":null,"theme":[],"title":"iCalendar: Satellite-based Field Map Calendar","type":"dataset"},{"_score":6.4067154,"_sort":[1791563884671,6.4067154,11,"982af769-16f8-464a-b013-7c829f0e71df"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","accessRights":"public","bureauCode":["005:18"],"contactPoint":{"fn":"Spackman, Erica","hasEmail":"mailto:erica.spackman@usda.gov"},"description":"Two commercially available vaccines based on the recombinant herpes virus of turkeys (rHVT) vector were tested against a recent North American clade 2.3.4.4b HPAI virus isolate: A/turkey/Indiana/22-003707-003/2022 H5N1 in specific pathogen free white leghorn (WL) chickens and commercial broiler chickens. One rHVT-H5 vaccine encodes a hemagglutinin (HA) gene designed by the computationally optimized broadly reactive antigen method (COBRA-HVT vaccine). The other encodes an HA gene of a clade 2.2 virus (2.2-HVT vaccine). There was 100% survival of both breeds in the COBRA-HVT vaccinated groups and in the 2.2-HVT vaccinated groups there was 94.8% and 90% survival of the WL and broilers respectively. Compared to the 2.2-HVT vaccinated groups, WL in the COBRA-HVT vaccinated group shed significantly lower mean viral titers by the cloacal route and broilers shed significantly lower titers by the oropharyngeal route than broilers. Virus titers detected in oral and cloacal swabs were otherwise similar among both vaccine groups and chicken breeds. To assess antibody-based tests to identify birds that have been infected after vaccination (DIVA-VI), sera collected after the challenge were tested with enzyme-linked lectin assay-neuraminidase inhibition (ELLA-NI) for N1 neuraminidase antibody detection and by commercial ELISA for detection of antibodies to the NP protein. As early as 7 days post challenge (DPC) 100% of the chickens were positive by ELLA-NI. ELISA was less sensitive with a maximum of 75% positive at 10DPC in broilers vaccinated with 2.2-HVT. Both vaccines provided protection from challenge to both breeds of chickens and ELLA-NI was sensitive at identifying antibodies to the challenge virus therefore should be evaluated further for DIVA-VI.
Methods
Viruses. All procedures using infectious material were reviewed and approved by the Institutional Biosafety Committee of US National Poultry Research Center (USNPRC), US Department of Agriculture-Agricultural Research Service, Athens, GA. The HPAI virus isolate A/turkey/Indiana/22-003707-003/2022 H5N1 (TK/IN/22) was provided by Dr. Mia Torchetti, National Veterinary Services Laboratories, US Department of Agriculture-Animal and Plant Health Inspection Service, Ames, IA. The A/Vietnam/1203/2004 H5N1 HPAI virus (Viet/04), A/Whooper Swan/Mongolia/244/2005 H5N1 (WS/Mongolia/05) HPAI virus, and A/Flycatcher/CA/14875-1/1994 H7N1 low pathogenic avian influenza virus isolates were provided by the repository at the USNPRC. Virus isolates were propagated and titrated in SPF embryonating chicken eggs using standard procedures [1]. Titers were determined using the Reed-Muench method [2].
Vaccines. Two commercial rHVT-H5 vaccines were selected because they are licensed in the US (and may be licensed elsewhere) and were supplied by the manufacturers: 2.2-HVT (Vectormune HVT AIV, Ceva Animal Health LLC, Lenexa, KS) (serial 395-134); and COBRA-HVT (Vaxxitek HVT+IBD+H5, Boehringer-Ingelheim Animal Health USA, Ridgefield, CT) (serial EW003). The amino acid similarity between the vaccine antigens and the challenge virus HA1 was 91.7% (COBRA-HVT) and 91.2% (2.2-HVT).
Challenge study design. All animal work was reviewed and approved by the USNPRC Institutional Animal Care and Use Committee. Mixed sex, SPF WL chickens (Gallus gallus domesticus) were obtained at hatch from in-house flocks. Broiler chicken eggs were obtained from a commercial hatchery at 18 days of incubation prior to administration of any in ovo vaccines and were hatched on-site. All birds were randomly assigned to vaccine groups based on breed. Vaccine groups are shown in Table 1. All vaccines were prepared and administered on the day of hatch by the subcutaneous route at the nape of the neck in accordance with the manufacturer\u2019s instructions (0.2ml per chicken). Serum was collected from all chickens 25 days post vaccination to evaluate the antibody response to the vaccines.
Four weeks post vaccination (four weeks of age) chickens were challenged with a target dose 6.0log10 50% egg infectious doses (EID50) per bird of TK/IN/22 in 0.1ml by the intrachoanal route (titration of the challenge virus after dilution confirmed the challenge dose to be 6.7log10 EID50 per bird). Oropharyngeal and CL swabs were collected from all birds at 2-, 4-, and 7-days post challenge (DPC). Swabs were also collected from dead and euthanized birds.
To evaluate antibody-based DIVA-VI tests, serum was collected at 7-, 10- and 14DPC. Mortality and morbidity were recorded for 14DPC. Surviving birds were euthanized at 14DPC. If birds were severely lethargic or presented with neurological signs, they were euthanized and were counted as mortality at the next observation time for mean death time calculations. Euthanasia was performed by cervical dislocation in accordance with American Veterinary Medical Association guidelines.
Quantitative rRT-PCR (qRRT-PCR). RNA was extracted from OP and CL swabs using the MagMax magnetic bead extraction kit (Thermo Fisher Scientific, Waltham, MA) with the wash modifications as described by Das et al., [3]. Quantitative real-time RT-PCR was conducted as described previously [4] on a QuantStudio 5 (Thermo Fisher Scientific) instrument. A standard curve was generated from a titrated stock of TK/IN/22 and was used to calculate titer equivalents using the real time PCR instrument\u2019s software.
Hemagglutination inhibition assay. Hemagglutination inhibition (HI) assays were run in accordance with standard procedures [5]. All pre-challenge sera collected at 25 days post vaccination were tested against the challenge virus and the closest isolates available to the vaccine antigens. The serum from the 2.2-HVT group was tested against WS/Mongolia/05 (99.3% similarity) and the serum from the COBRA-HVT group was tested against Viet/04 (98.2% similarity). Titers of eight or below were considered negative.
Commercial ELISA. A commercial AIV antibody ELISA (AI Ab Test, IDEXX laboratories, Westbrook, ME) was used in accordance with the manufacturer\u2019s instructions. Sera were tested to detect anti-NP antibodies pre-challenge (25days pos-vaccination) and at 7-, 10- and 14DPC.
Enzyme-linked lectin assay (ELLA) for detection of neuraminidase inhibition (NI) antibody. The ELLA was performed as previously described with minor modifications [6, 7]. Briefly, the NA activity of a beta-propiolactone inactivated H7N1 virus (A/Flycatcher/CA/14875-1/1994) was quantified to determine the effective concentration (EC) of antigen. The 98% EC (EC98) of antigen was subsequently used for the ELLA-NI assays. For ELLA-NI assay, the antigen and serum mixture was incubated overnight (approximately18hr) at 37\u00b0C and the NA activity was determined following the procedure as described in Spackman et al. [7]. The average background absorbance value was subtracted from the sample absorbance value then that value was divided by the average values of wells with only NA antigen. This value was multiplied by a factor of 100 to calculate the percent NA activity. The percent NI activity of individual serum samples was determined by subtracting the percent NA activity from 100%. A cut-off value for positive NI activity was determined by adding three standard deviations to the mean NI activity of pre-challenge sera (i.e., NA antibody negative sera) of each corresponding group of chickens at 7-, 10- and 14DPC. Each serum was tested at dilutions of 1:20 and 1:40.
References.
1. Spackman E, Killian ML. Avian Influenza Virus Isolation, Propagation, and Titration in Embryonated Chicken Eggs. Methods Mol Biol. 2020;2123:149-64. Epub 2020/03/15.
2. Reed LJ, Muench H. A simple method for estimating fifty percent endpoints. American Journal of Hygiene. 1938;27:493-7.
3. Das A, Spackman E, Pantin-Jackwood MJ, Suarez DL. Removal of real-time reverse transcription polymerase chain reaction (RT-PCR) inhibitors associated with cloacal swab samples and tissues for improved diagnosis of Avian influenza virus by RT-PCR. Journal of Veterinary Diagnostic Investigation. 2009;21(6):771-8.
4. Spackman E, Senne DA, Myers TJ, Bulaga LL, Garber LP, Perdue ML, et al. Development of a real-time reverse transcriptase PCR assay for type A influenza virus and the avian H5 and H7 hemagglutinin subtypes. Journal of Clinical Microbiology. 2002;40(9):3256-60.
5. Spackman E, Sitaras I. Hemagglutination Inhibition Assay. Methods Mol Biol. 2020;2123:11-28. Epub 2020/03/15.
6. Bernard MC, Waldock J, Commandeur S, Strauss L, Trombetta CM, Marchi S, et al. Validation of a Harmonized Enzyme-Linked-Lectin-Assay (ELLA-NI) Based Neuraminidase Inhibition Assay Standard Operating Procedure (SOP) for Quantification of N1 Influenza Antibodies and the Use of a Calibrator to Improve the Reproducibility of the ELLA-NI With Reverse Genetics Viral and Recombinant Neuraminidase Antigens: A FLUCOP Collaborative Study. Front Immunol. 2022;13:909297. Epub 20220617.
7. Spackman E, Suarez DL, Lee CW, Pantin-Jackwood MJ, Lee SA, Youk S, Ibrahim S. Efficacy of inactivated and RNA particle vaccines against a North American Clade 2.3.4.4b H5 highly pathogenic avian influenza virus in chickens. Vaccine. 2023. Epub 20231104.
","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/45435619","format":"xlsx","license":"https://creativecommons.org/licenses/by/4.0/","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Lee et al HVT HPAI vaccine in chickens data.xlsx"}],"identifier":"10.15482/USDA.ADC/25533952.v1","keyword":["Avian influenza","DIVA test","Enzyne linked lectin assay","HVT vaccine","differentiation of infected from vaccinated animals","herpes virus of turkeys vectored vaccine","highly pathogenic H5N1 avian influenza virus","highly pathogenic avian influenza A virus","poultry vaccine","vectored vaccines","veterinary vaccine"],"license":"https://creativecommons.org/licenses/by/4.0/","modified":"2025-05-02","programCode":["005:040"],"publisher":{"@type":"org:Organization","name":"Agricultural Research Service"},"temporal":"[{\"@type\": \"PeriodOfTime\", \"endDate\": \"2022-01-01\", \"startDate\": \"2022-01-01\"}]","title":"Data from: Efficacy of commercial recombinant HVT vaccines against a North American clade 2.3.4.4b H5N1 Highly Pathogenic Avian Influenza Virus in chickens"},"description":"Two commercially available vaccines based on the recombinant herpes virus of turkeys (rHVT) vector were tested against a recent North American clade 2.3.4.4b HPAI virus isolate: A/turkey/Indiana/22-003707-003/2022 H5N1 in specific pathogen free white leghorn (WL) chickens and commercial broiler chickens. One rHVT-H5 vaccine encodes a hemagglutinin (HA) gene designed by the computationally optimized broadly reactive antigen method (COBRA-HVT vaccine). The other encodes an HA gene of a clade 2.2 virus (2.2-HVT vaccine). There was 100% survival of both breeds in the COBRA-HVT vaccinated groups and in the 2.2-HVT vaccinated groups there was 94.8% and 90% survival of the WL and broilers respectively. Compared to the 2.2-HVT vaccinated groups, WL in the COBRA-HVT vaccinated group shed significantly lower mean viral titers by the cloacal route and broilers shed significantly lower titers by the oropharyngeal route than broilers. Virus titers detected in oral and cloacal swabs were otherwise similar among both vaccine groups and chicken breeds. To assess antibody-based tests to identify birds that have been infected after vaccination (DIVA-VI), sera collected after the challenge were tested with enzyme-linked lectin assay-neuraminidase inhibition (ELLA-NI) for N1 neuraminidase antibody detection and by commercial ELISA for detection of antibodies to the NP protein. As early as 7 days post challenge (DPC) 100% of the chickens were positive by ELLA-NI. ELISA was less sensitive with a maximum of 75% positive at 10DPC in broilers vaccinated with 2.2-HVT. Both vaccines provided protection from challenge to both breeds of chickens and ELLA-NI was sensitive at identifying antibodies to the challenge virus therefore should be evaluated further for DIVA-VI.
Methods
Viruses. All procedures using infectious material were reviewed and approved by the Institutional Biosafety Committee of US National Poultry Research Center (USNPRC), US Department of Agriculture-Agricultural Research Service, Athens, GA. The HPAI virus isolate A/turkey/Indiana/22-003707-003/2022 H5N1 (TK/IN/22) was provided by Dr. Mia Torchetti, National Veterinary Services Laboratories, US Department of Agriculture-Animal and Plant Health Inspection Service, Ames, IA. The A/Vietnam/1203/2004 H5N1 HPAI virus (Viet/04), A/Whooper Swan/Mongolia/244/2005 H5N1 (WS/Mongolia/05) HPAI virus, and A/Flycatcher/CA/14875-1/1994 H7N1 low pathogenic avian influenza virus isolates were provided by the repository at the USNPRC. Virus isolates were propagated and titrated in SPF embryonating chicken eggs using standard procedures [1]. Titers were determined using the Reed-Muench method [2].
Vaccines. Two commercial rHVT-H5 vaccines were selected because they are licensed in the US (and may be licensed elsewhere) and were supplied by the manufacturers: 2.2-HVT (Vectormune HVT AIV, Ceva Animal Health LLC, Lenexa, KS) (serial 395-134); and COBRA-HVT (Vaxxitek HVT+IBD+H5, Boehringer-Ingelheim Animal Health USA, Ridgefield, CT) (serial EW003). The amino acid similarity between the vaccine antigens and the challenge virus HA1 was 91.7% (COBRA-HVT) and 91.2% (2.2-HVT).
Challenge study design. All animal work was reviewed and approved by the USNPRC Institutional Animal Care and Use Committee. Mixed sex, SPF WL chickens (Gallus gallus domesticus) were obtained at hatch from in-house flocks. Broiler chicken eggs were obtained from a commercial hatchery at 18 days of incubation prior to administration of any in ovo vaccines and were hatched on-site. All birds were randomly assigned to vaccine groups based on breed. Vaccine groups are shown in Table 1. All vaccines were prepared and administered on the day of hatch by the subcutaneous route at the nape of the neck in accordance with the manufacturer\u2019s instructions (0.2ml per chicken). Serum was collected from all chickens 25 days post vaccination to evaluate the antibody response to the vaccines.
Four weeks post vaccination (four weeks of age) chickens were challenged with a target dose 6.0log10 50% egg infectious doses (EID50) per bird of TK/IN/22 in 0.1ml by the intrachoanal route (titration of the challenge virus after dilution confirmed the challenge dose to be 6.7log10 EID50 per bird). Oropharyngeal and CL swabs were collected from all birds at 2-, 4-, and 7-days post challenge (DPC). Swabs were also collected from dead and euthanized birds.
To evaluate antibody-based DIVA-VI tests, serum was collected at 7-, 10- and 14DPC. Mortality and morbidity were recorded for 14DPC. Surviving birds were euthanized at 14DPC. If birds were severely lethargic or presented with neurological signs, they were euthanized and were counted as mortality at the next observation time for mean death time calculations. Euthanasia was performed by cervical dislocation in accordance with American Veterinary Medical Association guidelines.
Quantitative rRT-PCR (qRRT-PCR). RNA was extracted from OP and CL swabs using the MagMax magnetic bead extraction kit (Thermo Fisher Scientific, Waltham, MA) with the wash modifications as described by Das et al., [3]. Quantitative real-time RT-PCR was conducted as described previously [4] on a QuantStudio 5 (Thermo Fisher Scientific) instrument. A standard curve was generated from a titrated stock of TK/IN/22 and was used to calculate titer equivalents using the real time PCR instrument\u2019s software.
Hemagglutination inhibition assay. Hemagglutination inhibition (HI) assays were run in accordance with standard procedures [5]. All pre-challenge sera collected at 25 days post vaccination were tested against the challenge virus and the closest isolates available to the vaccine antigens. The serum from the 2.2-HVT group was tested against WS/Mongolia/05 (99.3% similarity) and the serum from the COBRA-HVT group was tested against Viet/04 (98.2% similarity). Titers of eight or below were considered negative.
Commercial ELISA. A commercial AIV antibody ELISA (AI Ab Test, IDEXX laboratories, Westbrook, ME) was used in accordance with the manufacturer\u2019s instructions. Sera were tested to detect anti-NP antibodies pre-challenge (25days pos-vaccination) and at 7-, 10- and 14DPC.
Enzyme-linked lectin assay (ELLA) for detection of neuraminidase inhibition (NI) antibody. The ELLA was performed as previously described with minor modifications [6, 7]. Briefly, the NA activity of a beta-propiolactone inactivated H7N1 virus (A/Flycatcher/CA/14875-1/1994) was quantified to determine the effective concentration (EC) of antigen. The 98% EC (EC98) of antigen was subsequently used for the ELLA-NI assays. For ELLA-NI assay, the antigen and serum mixture was incubated overnight (approximately18hr) at 37\u00b0C and the NA activity was determined following the procedure as described in Spackman et al. [7]. The average background absorbance value was subtracted from the sample absorbance value then that value was divided by the average values of wells with only NA antigen. This value was multiplied by a factor of 100 to calculate the percent NA activity. The percent NI activity of individual serum samples was determined by subtracting the percent NA activity from 100%. A cut-off value for positive NI activity was determined by adding three standard deviations to the mean NI activity of pre-challenge sera (i.e., NA antibody negative sera) of each corresponding group of chickens at 7-, 10- and 14DPC. Each serum was tested at dilutions of 1:20 and 1:40.
References.
1. Spackman E, Killian ML. Avian Influenza Virus Isolation, Propagation, and Titration in Embryonated Chicken Eggs. Methods Mol Biol. 2020;2123:149-64. Epub 2020/03/15.
2. Reed LJ, Muench H. A simple method for estimating fifty percent endpoints. American Journal of Hygiene. 1938;27:493-7.
3. Das A, Spackman E, Pantin-Jackwood MJ, Suarez DL. Removal of real-time reverse transcription polymerase chain reaction (RT-PCR) inhibitors associated with cloacal swab samples and tissues for improved diagnosis of Avian influenza virus by RT-PCR. Journal of Veterinary Diagnostic Investigation. 2009;21(6):771-8.
4. Spackman E, Senne DA, Myers TJ, Bulaga LL, Garber LP, Perdue ML, et al. Development of a real-time reverse transcriptase PCR assay for type A influenza virus and the avian H5 and H7 hemagglutinin subtypes. Journal of Clinical Microbiology. 2002;40(9):3256-60.
5. Spackman E, Sitaras I. Hemagglutination Inhibition Assay. Methods Mol Biol. 2020;2123:11-28. Epub 2020/03/15.
6. Bernard MC, Waldock J, Commandeur S, Strauss L, Trombetta CM, Marchi S, et al. Validation of a Harmonized Enzyme-Linked-Lectin-Assay (ELLA-NI) Based Neuraminidase Inhibition Assay Standard Operating Procedure (SOP) for Quantification of N1 Influenza Antibodies and the Use of a Calibrator to Improve the Reproducibility of the ELLA-NI With Reverse Genetics Viral and Recombinant Neuraminidase Antigens: A FLUCOP Collaborative Study. Front Immunol. 2022;13:909297. Epub 20220617.
7. Spackman E, Suarez DL, Lee CW, Pantin-Jackwood MJ, Lee SA, Youk S, Ibrahim S. Efficacy of inactivated and RNA particle vaccines against a North American Clade 2.3.4.4b H5 highly pathogenic avian influenza virus in chickens. Vaccine. 2023. Epub 20231104.
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Resource Title: Agricultural Collaborative Research Outcomes System.
File Name: Web Page, url: https://agcros-usdaars.opendata.arcgis.com/
The Agricultural Collaborative Research Outcomes System (AgCROS) is a growing \u201cnetwork of networks\u201d 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.
Resource Title: Agricultural Collaborative Research Outcomes System.
File Name: Web Page, url: https://agcros-usdaars.opendata.arcgis.com/
Dr. Kevin Bronson provides a second year of nitrogen and water management in wheat agricultural research dataset for compute. Ten irrigation treatments from a linear sprinkler were combined with nitrogen treatments. This dataset includes notation of field events and operations, an intermediate analysis mega-table of correlated and calculated parameters, including laboratory analysis results generated during the experimentation, plus high resolution plot level intermediate data tables of SAS process output, as well as the complete raw data sensor records and logger outputs.
\nThis proximal terrestrial high-throughput plant phenotyping data examples our early tri-metric field method, where a geo-referenced 5Hz crop canopy height, temperature and spectral signature are recorded coincident to indicate a plant health status. In this development period, our Proximal Sensing Cart Mark1 (PSCM1) platform suspends a single cluster of sensors on a dual sliding vertical placement armature.
\nSee included README file for operational details and further description of the measured data signals.
\nSummary:\nActive optical proximal wheat canopy sensing spatial data and including additional related metrics such as thermal are presented.\nAgronomic nitrogen and irrigation management related field operations are listed.\nUnique research experimentation intermediate analysis table is made available, along with raw data.\nThe raw data recordings, and annotated table outputs with calculated VIs are made available.\nPlot polygon coordinate designations allow a re-intersection spatial analysis.\nData was collected in the 2014 season at Maricopa Agricultural Center, Arizona, USA.\nHigh throughput proximal plant phenotyping via electronic sampling and data processing method approach is exampled.\nAcquired data using USDA Maricopa first mobile platforms, such as the Proximal Sensing Cart Mark 1, where the first cluster sensor bracket design and rickshaw inspired operator's handle were successfully employed.\nSAS and GIS compute processing output tables, including Excel formatted examples are presented, where intermediate data tabulation and analysis is available.\nThe weekly proximal sensing data collected include canopy reflectance at six wavelengths, ultrasonic distance sensing of canopy height, and infrared thermometry.
\nTen levels gradient irrigation application from linear move sprinkler system were applied.\nSoil physical texture and fertility chemistry results are available.\nDurum wheat data includes in-season biomass and plant N content, final total biomass, grain yield, grain nitrogen, and yellow berry assessment.
Dr. Kevin Bronson provides a second year of nitrogen and water management in wheat agricultural research dataset for compute. Ten irrigation treatments from a linear sprinkler were combined with nitrogen treatments. This dataset includes notation of field events and operations, an intermediate analysis mega-table of correlated and calculated parameters, including laboratory analysis results generated during the experimentation, plus high resolution plot level intermediate data tables of SAS process output, as well as the complete raw data sensor records and logger outputs.
\nThis proximal terrestrial high-throughput plant phenotyping data examples our early tri-metric field method, where a geo-referenced 5Hz crop canopy height, temperature and spectral signature are recorded coincident to indicate a plant health status. In this development period, our Proximal Sensing Cart Mark1 (PSCM1) platform suspends a single cluster of sensors on a dual sliding vertical placement armature.
\nSee included README file for operational details and further description of the measured data signals.
\nSummary:\nActive optical proximal wheat canopy sensing spatial data and including additional related metrics such as thermal are presented.\nAgronomic nitrogen and irrigation management related field operations are listed.\nUnique research experimentation intermediate analysis table is made available, along with raw data.\nThe raw data recordings, and annotated table outputs with calculated VIs are made available.\nPlot polygon coordinate designations allow a re-intersection spatial analysis.\nData was collected in the 2014 season at Maricopa Agricultural Center, Arizona, USA.\nHigh throughput proximal plant phenotyping via electronic sampling and data processing method approach is exampled.\nAcquired data using USDA Maricopa first mobile platforms, such as the Proximal Sensing Cart Mark 1, where the first cluster sensor bracket design and rickshaw inspired operator's handle were successfully employed.\nSAS and GIS compute processing output tables, including Excel formatted examples are presented, where intermediate data tabulation and analysis is available.\nThe weekly proximal sensing data collected include canopy reflectance at six wavelengths, ultrasonic distance sensing of canopy height, and infrared thermometry.
\nTen levels gradient irrigation application from linear move sprinkler system were applied.\nSoil physical texture and fertility chemistry results are available.\nDurum wheat data includes in-season biomass and plant N content, final total biomass, grain yield, grain nitrogen, and yellow berry assessment.
Dr. Kevin Bronson provides this unique nitrogen and water management in wheat agricultural research dataset for compute. Ten irrigation treatments from a linear sprinkler were combined with nitrogen treatments. This dataset includes notation of field events and operations, an intermediate analysis mega-table of correlated and calculated parameters, including laboratory analysis results generated during the experimentation, plus high resolution plot level intermediate data tables of SAS process output, as well as the complete raw sensors records and logger outputs.
\nThis 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.
\nSee included README file for operational details and further description of the measured data signals.
\nSummary:\nActive optical proximal wheat canopy sensing spatial data and including additional related metrics such as thermal are presented.\nAgronomic nitrogen and irrigation management related field operations are listed.\nUnique research experimentation intermediate analysis table is made available, along with raw data.\nThe raw data recordings, and annotated table outputs with calculated VIs are made available.\nPlot polygon coordinate designations allow a re-intersection spatial analysis.\nData was collected in the 2013 season at Maricopa Agricultural Center, Arizona, USA.\nHigh throughput proximal plant phenotyping via electronic sampling and data processing method approach is exampled.\nAcquired data using USDA Maricopa first mobile platforms, such as the Proximal Sensing Cart Mark 1, where the first cluster sensor bracket design and rickshaw inspired operator's handle were successfully employed.\nSAS and GIS compute processing output tables, including Excel formatted examples are presented, where intermediate data tabulation and analysis is available.\nThe weekly proximal sensing data collected include canopy reflectance at six wavelengths, ultrasonic distance sensing of canopy height, and infrared thermometry.
\nTen levels gradient irrigation application from linear move sprinkler system were applied.\nSoil physical texture and fertility chemistry results are available.\nYield and seed information is presented.
Dr. Kevin Bronson provides this unique nitrogen and water management in wheat agricultural research dataset for compute. Ten irrigation treatments from a linear sprinkler were combined with nitrogen treatments. This dataset includes notation of field events and operations, an intermediate analysis mega-table of correlated and calculated parameters, including laboratory analysis results generated during the experimentation, plus high resolution plot level intermediate data tables of SAS process output, as well as the complete raw sensors records and logger outputs.
\nThis 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.
\nSee included README file for operational details and further description of the measured data signals.
\nSummary:\nActive optical proximal wheat canopy sensing spatial data and including additional related metrics such as thermal are presented.\nAgronomic nitrogen and irrigation management related field operations are listed.\nUnique research experimentation intermediate analysis table is made available, along with raw data.\nThe raw data recordings, and annotated table outputs with calculated VIs are made available.\nPlot polygon coordinate designations allow a re-intersection spatial analysis.\nData was collected in the 2013 season at Maricopa Agricultural Center, Arizona, USA.\nHigh throughput proximal plant phenotyping via electronic sampling and data processing method approach is exampled.\nAcquired data using USDA Maricopa first mobile platforms, such as the Proximal Sensing Cart Mark 1, where the first cluster sensor bracket design and rickshaw inspired operator's handle were successfully employed.\nSAS and GIS compute processing output tables, including Excel formatted examples are presented, where intermediate data tabulation and analysis is available.\nThe weekly proximal sensing data collected include canopy reflectance at six wavelengths, ultrasonic distance sensing of canopy height, and infrared thermometry.
\nTen levels gradient irrigation application from linear move sprinkler system were applied.\nSoil physical texture and fertility chemistry results are available.\nYield and seed information is presented.
Dr. Kevin Bronson provides a small area nitrogen and water management in Guayule agricultural research dataset for compute, including notation of field events and operations, an intermediate analysis table of correlated and calculated parameters with 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.
\nThis 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.
\nSummary:\nActive optical proximal cotton canopy sensing spatial data and including additional related metrics such as thermal are presented.\nAgronomic nitrogen and irrigation management related field operations are listed.\nUnique research experimentation intermediate analysis table is made available, along with raw data.\nThe raw data recordings, and annotated table outputs with calculated VIs are made available.\nPlot polygon coordinate designations allow a re-intersection spatial analysis.\nData was collected in the 2013 season at Maricopa Agricultural Center, Arizona, USA.\nHigh throughput proximal plant phenotyping via electronic sampling and data processing method approach is exampled.\nAcquired 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.\nSAS and GIS compute processing output tables, including Excel formatted examples are presented, where intermediate data tabulation and analysis is available.\nThe 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).
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\nThis 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.
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\nThis 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.
\nSee included README file for operational details and further description of the measured data signals.
\nSummary:\nActive optical proximal cotton canopy sensing spatial data and including additional related metrics such as thermal are presented.\nAgronomic nitrogen and irrigation management related field operations are listed.\nUnique research experimentation intermediate analysis table is made available, along with raw data.\nThe raw data recordings, and annotated table outputs with calculated VIs are made available.\nPlot polygon coordinate designations allow a re-intersection spatial analysis.\nData was collected in the 2013 season at Maricopa Agricultural Center, Arizona, USA.\nHigh throughput proximal plant phenotyping via electronic sampling and data processing method approach is exampled.\nAcquired 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.\nSAS and GIS compute processing output tables, including Excel formatted examples are presented, where intermediate data tabulation and analysis is available.\nThe 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).
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\nThis 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.
\nSee included README file for operational details and further description of the measured data signals.
\nSummary:\nActive optical proximal cotton canopy sensing spatial data and including additional related metrics such as thermal are presented.\nAgronomic nitrogen and irrigation management related field operations are listed.\nUnique research experimentation intermediate analysis table is made available, along with raw data.\nThe raw data recordings, and annotated table outputs with calculated VIs are made available.\nPlot polygon coordinate designations allow a re-intersection spatial analysis.\nData was collected in the 2012 season at Maricopa Agricultural Center, Arizona, USA.\nHigh throughput proximal plant phenotyping via electronic sampling and data processing method approach is exampled.\nAcquired data using USDA Maricopa first mobile platforms, such as the Proximal Sensing Cart Mark 1, and via people.\nSAS and GIS compute processing output tables, including Excel formatted examples are presented, where data tabulation and analysis is available.\nThe 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).
","distribution_titles":["Data Dictionary3.csv","2012_F017_ActivitiesLog.csv","F17 2012 plot corner point coordinates_0.csv","2012_F17_CottonN_megatable.xlsx","2012_05_18_HS_data.csv","2012_05_29_HS_data.csv","2012_06_04_HS_data.csv","2012_06_12_HS_data.csv","2012_06_13_HS_data.csv","2012_06_19_HS_data.csv","2012_06_22_HS_data.csv","2012_06_25_HS_data.csv","2012_07_02_HS_data.csv","2012_07_02_CS_data.csv","CS_GPS.csv","2012_07_09_HS_data.csv","2012_07_18_HS_data.csv","2012_07_27_HS_data.csv","2012_08_03_HS_data.csv","2012_08_03_CS_data.csv","2012_08_03_CS_GPS.csv","18052012.xlsx","29052012.xlsx","04062012.xlsx","13062012.xlsx","22062012.xlsx","25062012.xlsx","02072012.xlsx","2012_07_02_CS_GPS.xlsx","09072012.xlsx","18072012.xlsx","270712.xlsx","030812.xlsx","2012_08_03_CS_GPS_tableoutput.dat.xlsx","P8030592.JPG","P7020454.MOV","README_The Bronson Files, Dataset 1, Field 17, 2012.txt","file-list_The Bronson Files, Dataset 1, Field 17, 2012.txt"],"harvest_record":"https://catalog.data.gov/harvest_record/af852f29-c3cf-490b-b0af-8e17f6c015fc","harvest_record_raw":"https://catalog.data.gov/harvest_record/af852f29-c3cf-490b-b0af-8e17f6c015fc/raw","has_download":true,"has_spatial":true,"identifier":"10.15482/USDA.ADC/1523019","keyword":["ARS","Agroecosystems & Environment Weather and Climate","EARTH SCIENCE > AGRICULTURE > SOILS > NITROGEN","EARTH SCIENCE > BIOSPHERE > VEGETATION > PLANT PHENOLOGY","Field-based high-throughput phenotyping","NP211","data.gov","plant phenotyping"],"last_harvested_date":"2026-10-09T16:37:58.558190","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"352b4532-793d-4075-a03f-05b778a3c43a","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/usda.png","name":"Department of Agriculture","organization_type":"Federal Government","slug":"usda"},"parent_identifier":null,"popularity":3,"publisher":"Agricultural Research Service","slug":"the-bronson-files-dataset-1-field-17-2012","spatial_centroid":{"lat":33.0810219892186,"lon":-111.97814512252998},"spatial_shape":{"coordinates":[[[-111.97890043259,33.080087064259],[-111.97701215744,33.080087064259],[-111.97701215744,33.082424376658],[-111.97890043259,33.082424376658],[-111.97890043259,33.080087064259]]],"type":"Polygon"},"theme":[],"title":"The Bronson Files, Dataset 1, Field 17, 2012","type":"dataset"},{"_score":22.207844,"_sort":[1791563877519,22.207844,1,"b3cf9167-f268-4272-bdbd-923521cfe697"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","accessRights":"public","bureauCode":["005:18"],"contactPoint":{"fn":"Schneider, Sharon, K.","hasEmail":"mailto:sharon.schneider@usda.gov"},"description":"These data provide field measurements at two geographical sites of plant and soil as affected by (a) tillage and water erosion and (b) replacement of translocated topsoil through soil-landscape rehabilitation. Data include pre-restoration soil properties, a digital elevation model, and tillage and water erosion estimates. Data reported after restoration include annual assessments of crop emergence, biomass and grain yield; soil physical, chemical, and biological properties; weed communities; and weather information. The Stevens County, Minnesota site was a heavily eroded site while the Roberts County, South Dakota site was moderately eroded. The data can be used to develop agronomic best management practices to improve crop production and to protect environmental and soil health. The data also could contribute to meta-analyses describing effects of erosion and soil-landscape rehabilitation (translocating soil from areas of net deposition to areas of net soil loss by erosion) on crop performance and changes in soil properties.
","distribution":[{"@type":"dcat:Distribution","accessURL":"https://geodata.nal.usda.gov/geonetwork/srv/api/records/9eaacb50-e855-46e1-bde4-4a7438ddfb83/formatters/xml","conformsTo":"[{\"@type\": \"Standard\", \"identifier\": \"https://www.isotc211.org/2005/gmd\"}]","format":"xml","license":"https://creativecommons.org/publicdomain/zero/1.0/","mediaType":"text/xml","title":"Geodata ISO 19139 metadata"},{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/44533802","format":"docx","license":"https://creativecommons.org/publicdomain/zero/1.0/","mediaType":"application/vnd.openxmlformats-officedocument.wordprocessingml.document","title":"Soil-Landscape Rehabilitation Study Metadata.docx"},{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/44533943","format":"xlsx","license":"https://creativecommons.org/publicdomain/zero/1.0/","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"User & Publication Information.xlsx"},{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/44533982","format":"xlsx","license":"https://creativecommons.org/publicdomain/zero/1.0/","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Stevens County MN Pre-restoration Soil Data.xlsx"},{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/44534030","format":"xlsx","license":"https://creativecommons.org/publicdomain/zero/1.0/","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Stevens County MN Plant & Soil Data.xlsx"},{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/44534159","format":"xlsx","license":"https://creativecommons.org/publicdomain/zero/1.0/","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Stevens County MN Weather Information.xlsx"},{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/44534222","format":"xlsx","license":"https://creativecommons.org/publicdomain/zero/1.0/","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Roberts County SD Plant & Soil Data.xlsx"},{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/44534387","format":"xlsx","license":"https://creativecommons.org/publicdomain/zero/1.0/","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Roberts County SD Weather Information.xlsx"}],"identifier":"10.15482/USDA.ADC/25206968.v1","keyword":["Corn","Crop production","Crop productivity","Landscape restoration","Plant nutrients","Productivity","Soil carbon","Soil erosion","Soil fertility","Soil health","Soil profile","Soil restoration","Soybean","Spatial variation","Tillage erosion","Water erosion"],"license":"https://creativecommons.org/publicdomain/zero/1.0/","modified":"2026-08-24","programCode":["005:040"],"publisher":{"@type":"org:Organization","name":"Agricultural Research Service"},"temporal":"[{\"@type\": \"PeriodOfTime\", \"endDate\": \"2011-11-30\", \"startDate\": \"2005-10-02\"}]","theme":["geospatial"],"title":"Soil, crop, and weed communities as affected by soil erosion and topsoil replacement in a Mollisol landform (a six-year soil-landscape rehabilitation study)"},"description":"These data provide field measurements at two geographical sites of plant and soil as affected by (a) tillage and water erosion and (b) replacement of translocated topsoil through soil-landscape rehabilitation. Data include pre-restoration soil properties, a digital elevation model, and tillage and water erosion estimates. Data reported after restoration include annual assessments of crop emergence, biomass and grain yield; soil physical, chemical, and biological properties; weed communities; and weather information. The Stevens County, Minnesota site was a heavily eroded site while the Roberts County, South Dakota site was moderately eroded. The data can be used to develop agronomic best management practices to improve crop production and to protect environmental and soil health. The data also could contribute to meta-analyses describing effects of erosion and soil-landscape rehabilitation (translocating soil from areas of net deposition to areas of net soil loss by erosion) on crop performance and changes in soil properties.
","distribution_titles":["Geodata ISO 19139 metadata","Soil-Landscape Rehabilitation Study Metadata.docx","User & Publication Information.xlsx","Stevens County MN Pre-restoration Soil Data.xlsx","Stevens County MN Plant & Soil Data.xlsx","Stevens County MN Weather Information.xlsx","Roberts County SD Plant & Soil Data.xlsx","Roberts County SD Weather Information.xlsx"],"harvest_record":"https://catalog.data.gov/harvest_record/baa0b1b9-18b3-4abf-8820-b278437d5056","harvest_record_raw":"https://catalog.data.gov/harvest_record/baa0b1b9-18b3-4abf-8820-b278437d5056/raw","has_download":true,"has_spatial":true,"identifier":"10.15482/USDA.ADC/25206968.v1","keyword":["Corn","Crop production","Crop productivity","Landscape restoration","Plant nutrients","Productivity","Soil carbon","Soil erosion","Soil fertility","Soil health","Soil profile","Soil restoration","Soybean","Spatial variation","Tillage erosion","Water erosion"],"last_harvested_date":"2026-10-09T16:37:57.519445","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"352b4532-793d-4075-a03f-05b778a3c43a","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/usda.png","name":"Department of Agriculture","organization_type":"Federal Government","slug":"usda"},"parent_identifier":null,"popularity":1,"publisher":"Agricultural Research Service","slug":"bsoil-crop-and-weed-communities-as-affected-by-soil-erosion-and-topsoil-replacement-in-a-m","spatial_centroid":null,"spatial_shape":null,"theme":["geospatial"],"title":"Soil, crop, and weed communities as affected by soil erosion and topsoil replacement in a Mollisol landform (a six-year soil-landscape rehabilitation study)","type":"dataset"},{"_score":3.1318893,"_sort":[1791563877366,3.1318893,13,"2a10a36f-8cb1-46d2-8b79-b45028f4fd1d"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","accessRights":"public","bureauCode":["005:18"],"contactPoint":{"fn":"Parr, Cynthia","hasEmail":"mailto:cynthia.parr@ars.usda.gov"},"description":"United States agricultural researchers have many options for making their data available online. This dataset aggregates the primary sources of ag-related data and determines where researchers are likely to deposit their agricultural data. These data serve as both a current landscape analysis and also as a baseline for future studies of ag research data.
\nAs sources of agricultural data become more numerous and disparate, and collaboration and open data become more expected if not required, this research provides a landscape inventory of online sources of open agricultural data.
\nAn inventory of current agricultural data sharing options will help assess how the Ag Data Commons, a platform for USDA-funded data cataloging and publication, can best support data-intensive and multi-disciplinary research. It will also help agricultural librarians assist their researchers in data management and publication. The goals of this study were to
\nThe National Agricultural Library team focused on Agricultural Research Service (ARS), Natural Resources Conservation Service (NRCS), and United States Forest Service (USFS) style research data, rather than ag economics, statistics, and social sciences data. To find domain-specific, general, institutional, and federal agency repositories and databases that are open to US research submissions and have some amount of ag data, resources including re3data, libguides, and ARS lists were analysed. Primarily environmental or public health databases were not included, but places where ag grantees would publish data were considered.
\nWe first compiled a list of known domain specific USDA / ARS datasets / databases that are represented in the Ag Data Commons, including ARS Image Gallery, ARS Nutrition Databases (sub-components), SoyBase, PeanutBase, National Fungus Collection, i5K Workspace @ NAL, and GRIN. We then searched using search engines such as Bing and Google for non-USDA / federal ag databases, using Boolean variations of \u201cagricultural data\u201d /\u201cag data\u201d / \u201cscientific data\u201d + NOT + USDA (to filter out the federal / USDA results). Most of these results were domain specific, though some contained a mix of data subjects.
\nWe then used search engines such as Bing and Google to find top agricultural university repositories using variations of \u201cagriculture\u201d, \u201cag data\u201d and \u201cuniversity\u201d to find schools with agriculture programs. Using that list of universities, we searched each university web site to see if their institution had a repository for their unique, independent research data if not apparent in the initial web browser search. We found both ag specific university repositories and general university repositories that housed a portion of agricultural data. Ag specific university repositories are included in the list of domain-specific repositories. Results included Columbia University \u2013 International Research Institute for Climate and Society, UC Davis \u2013 Cover Crops Database, etc. If a general university repository existed, we determined whether that repository could filter to include only data results after our chosen ag search terms were applied. General university databases that contain ag data included Colorado State University Digital Collections, University of Michigan ICPSR (Inter-university Consortium for Political and Social Research), and University of Minnesota DRUM (Digital Repository of the University of Minnesota). We then split out NCBI (National Center for Biotechnology Information) repositories.
\nNext we searched the internet for open general data repositories using a variety of search engines, and repositories containing a mix of data, journals, books, and other types of records were tested to determine whether that repository could filter for data results after search terms were applied. General subject data repositories include Figshare, Open Science Framework, PANGEA, Protein Data Bank, and Zenodo.
\nFinally, we compared scholarly journal suggestions for data repositories against our list to fill in any missing repositories that might contain agricultural data. Extensive lists of journals were compiled, in which USDA published in 2012 and 2016, combining search results in ARIS, Scopus, and the Forest Service's TreeSearch, plus the USDA web sites Economic Research Service (ERS), National Agricultural Statistics Service (NASS), Natural Resources and Conservation Service (NRCS), Food and Nutrition Service (FNS), Rural Development (RD), and Agricultural Marketing Service (AMS). The top 50 journals' author instructions were consulted to see if they (a) ask or require submitters to provide supplemental data, or (b) require submitters to submit data to open repositories.
\nData are provided for Journals based on a 2012 and 2016 study of where USDA employees publish their research studies, ranked by number of articles, including 2015/2016 Impact Factor, Author guidelines, Supplemental Data?, Supplemental Data reviewed?, Open Data (Supplemental or in Repository) Required? and Recommended data repositories, as provided in the online author guidelines for each the top 50 journals.
\nWe ran a series of searches on all resulting general subject databases with the designated search terms. From the results, we noted the total number of datasets in the repository, type of resource searched (datasets, data, images, components, etc.), percentage of the total database that each term comprised, any dataset with a search term that comprised at least 1% and 5% of the total collection, and any search term that returned greater than 100 and greater than 500 results.
\nWe compared domain-specific databases and repositories based on parent organization, type of institution, and whether data submissions were dependent on conditions such as funding or affiliation of some kind.
\nA summary of the major findings from our data review:
\nSee included README file for descriptions of each individual data file in this dataset.
\nResource Title: Journals.
File Name: Journals.csv
Resource Title: Journals - Recommended repositories.
File Name: Repos_from_journals.csv
Resource Title: TDWG presentation.
File Name: TDWG_Presentation.pptx
Resource Title: Domain Specific ag data sources.
File Name: domain_specific_ag_databases.csv
Resource Title: Data Dictionary for Ag Data Repository Inventory.
File Name: Ag_Data_Repo_DD.csv
Resource Title: General repositories containing ag data.
File Name: general_repos_1.csv
Resource Title: README and file inventory.
File Name: README_InventoryPublicDBandREepAgData.txt
United States agricultural researchers have many options for making their data available online. This dataset aggregates the primary sources of ag-related data and determines where researchers are likely to deposit their agricultural data. These data serve as both a current landscape analysis and also as a baseline for future studies of ag research data.
\nAs sources of agricultural data become more numerous and disparate, and collaboration and open data become more expected if not required, this research provides a landscape inventory of online sources of open agricultural data.
\nAn inventory of current agricultural data sharing options will help assess how the Ag Data Commons, a platform for USDA-funded data cataloging and publication, can best support data-intensive and multi-disciplinary research. It will also help agricultural librarians assist their researchers in data management and publication. The goals of this study were to
\nThe National Agricultural Library team focused on Agricultural Research Service (ARS), Natural Resources Conservation Service (NRCS), and United States Forest Service (USFS) style research data, rather than ag economics, statistics, and social sciences data. To find domain-specific, general, institutional, and federal agency repositories and databases that are open to US research submissions and have some amount of ag data, resources including re3data, libguides, and ARS lists were analysed. Primarily environmental or public health databases were not included, but places where ag grantees would publish data were considered.
\nWe first compiled a list of known domain specific USDA / ARS datasets / databases that are represented in the Ag Data Commons, including ARS Image Gallery, ARS Nutrition Databases (sub-components), SoyBase, PeanutBase, National Fungus Collection, i5K Workspace @ NAL, and GRIN. We then searched using search engines such as Bing and Google for non-USDA / federal ag databases, using Boolean variations of \u201cagricultural data\u201d /\u201cag data\u201d / \u201cscientific data\u201d + NOT + USDA (to filter out the federal / USDA results). Most of these results were domain specific, though some contained a mix of data subjects.
\nWe then used search engines such as Bing and Google to find top agricultural university repositories using variations of \u201cagriculture\u201d, \u201cag data\u201d and \u201cuniversity\u201d to find schools with agriculture programs. Using that list of universities, we searched each university web site to see if their institution had a repository for their unique, independent research data if not apparent in the initial web browser search. We found both ag specific university repositories and general university repositories that housed a portion of agricultural data. Ag specific university repositories are included in the list of domain-specific repositories. Results included Columbia University \u2013 International Research Institute for Climate and Society, UC Davis \u2013 Cover Crops Database, etc. If a general university repository existed, we determined whether that repository could filter to include only data results after our chosen ag search terms were applied. General university databases that contain ag data included Colorado State University Digital Collections, University of Michigan ICPSR (Inter-university Consortium for Political and Social Research), and University of Minnesota DRUM (Digital Repository of the University of Minnesota). We then split out NCBI (National Center for Biotechnology Information) repositories.
\nNext we searched the internet for open general data repositories using a variety of search engines, and repositories containing a mix of data, journals, books, and other types of records were tested to determine whether that repository could filter for data results after search terms were applied. General subject data repositories include Figshare, Open Science Framework, PANGEA, Protein Data Bank, and Zenodo.
\nFinally, we compared scholarly journal suggestions for data repositories against our list to fill in any missing repositories that might contain agricultural data. Extensive lists of journals were compiled, in which USDA published in 2012 and 2016, combining search results in ARIS, Scopus, and the Forest Service's TreeSearch, plus the USDA web sites Economic Research Service (ERS), National Agricultural Statistics Service (NASS), Natural Resources and Conservation Service (NRCS), Food and Nutrition Service (FNS), Rural Development (RD), and Agricultural Marketing Service (AMS). The top 50 journals' author instructions were consulted to see if they (a) ask or require submitters to provide supplemental data, or (b) require submitters to submit data to open repositories.
\nData are provided for Journals based on a 2012 and 2016 study of where USDA employees publish their research studies, ranked by number of articles, including 2015/2016 Impact Factor, Author guidelines, Supplemental Data?, Supplemental Data reviewed?, Open Data (Supplemental or in Repository) Required? and Recommended data repositories, as provided in the online author guidelines for each the top 50 journals.
\nWe ran a series of searches on all resulting general subject databases with the designated search terms. From the results, we noted the total number of datasets in the repository, type of resource searched (datasets, data, images, components, etc.), percentage of the total database that each term comprised, any dataset with a search term that comprised at least 1% and 5% of the total collection, and any search term that returned greater than 100 and greater than 500 results.
\nWe compared domain-specific databases and repositories based on parent organization, type of institution, and whether data submissions were dependent on conditions such as funding or affiliation of some kind.
\nA summary of the major findings from our data review:
\nSee included README file for descriptions of each individual data file in this dataset.
\nResource Title: Journals.
File Name: Journals.csv
Resource Title: Journals - Recommended repositories.
File Name: Repos_from_journals.csv
Resource Title: TDWG presentation.
File Name: TDWG_Presentation.pptx
Resource Title: Domain Specific ag data sources.
File Name: domain_specific_ag_databases.csv
Resource Title: Data Dictionary for Ag Data Repository Inventory.
File Name: Ag_Data_Repo_DD.csv
Resource Title: General repositories containing ag data.
File Name: general_repos_1.csv
Resource Title: README and file inventory.
File Name: README_InventoryPublicDBandREepAgData.txt
[NOTE - 11/24/2021: this dataset supersedes an earlier version https://doi.org/10.15482/USDA.ADC/1518654 ]
\n
\nResources in this dataset:
[NOTE - 11/24/2021: this dataset supersedes an earlier version https://doi.org/10.15482/USDA.ADC/1518654 ]
\n
\nResources in this dataset:
To determine whether colony populations of Lasioderma serricorne (cigarette beetle, CB) and Sitophilus oryzae (rice weevil, RW) vectored microbes, and to identify possible interactions with dispersal time, a vectoring assay was performed for each species. For the vectoring assay, the impact of dispersal (0, 24, or 72 h) and foraging time (3 or 5 d) on vectoring ability were tested. Briefly, adult L. serricorne or S. oryzae were singly removed from colony containers with sterilized forceps and then placed immediately in the center of Petri dish containing agar for the 0 h dispersal period. Alternatively, some insects were given a 24 or 72 h dispersal period in an autoclaved 4 L-capacity glass container and stored at constant conditions of 25\u00b0C, 60% RH, and 14:10 L:D photoperiod prior to being added to the PDA. Petri dishes were maintained at 30\u00b0C, 60% RH, and 14:10 L:D photoperiod for either 3 or 5 days, then photographed for microbial growth. Transfer of L. serricorne or S. oryzae adults from dispersal containers to agar at the conclusion of the dispersal period was performed inside the biosafety cabinet to prevent contamination of dishes.
\nPictures of the agar dishes and corresponding microbial growth were taken using a DSLR camera (EOS 7D Mark II, Canon, Tokyo, Japan) mounted to 3D imaging StackShot (CogniSys, Inc., Traverse City, MI, USA) equipped with a dual flash (MT-26EX-RT, Canon, Tokyo, Japan). Light was diffused using a partially cut frosted plastic jar (15.2 \u00d7 7.6 cm D:H) making a total of n = 60 replicates per treatment combination (of dispersal time, insect species, and foraging time in patch). The pictures taken were processed using ImageJ 1.53a (Wayne Rasband, National Institutes of Health, USA) to quantify the microbial growth in the agar dishes. The images had their backgrounds subtracted, then were processed using the \"find edges\" tool. Finally, they were converted to binary and either dilated or eroded to conform to the original image parameters. A circle encompassing the Petri dish was created and the mean grayscale, standard deviation of the grayscale value, and count of pixels was measured as a surrogate for microbial growth on the dishes. This allowed a quantitative measure of microbial growth by creating an average in a given image. The mean grayscale value could range from 0 (full white), indicating no microbial growth, to 255 (full black), indicating full microbial growth on the entire dish. Finally, visually, microbial morphospecies (alpha) richness was assigned to each image given the number of unique morphospecies on the plate as a proxy for community complexity.
\nTreatments included those from microbially-enriched environments where Aspergillus flavus had been inoculated on wheat or flour (AF). To prepare the AF, 600 g of grain was added to a stainless-steel pot filled with water and placed on a hot plate at 500\u00b0C. Once boiling for 15 min, the water was drained and the grain was evenly spread out on sterile wipes (38.1 \u00d7 42.5 cm, 3 ply, Tech wipes, Skilcraft, NIB, Alexandria, VA) and allowed to dry inside a laminar fume hood (ca. 3 h). Afterwards, grain was evenly divided (~300 g) and placed in two separate autoclaved mason jars (950-mL capacity). A single hole was pierced through each lid and lined with a cotton ball. The jars were then sealed with aluminum foil and were autoclaved (533LS, Getinge, Rochester, NY, USA) for 30 min. To inoculate with A. flavus, a 3-inch strip of agar containing a pure culture of A. flavus grown on agar for 7 d at 30\u00b0C, 60% RH, and 14:10 L:D photoperiod was placed into each jar containing the grain. AF was then maintained at room temperature for roughly 10 d or until the A. flavus evenly covered as much the grain as possible. Batches of inoculated grain were used within 10\u201315 d of preparation. Grain was never used more than once for each replicate of every trial in each assay experiment to prevent cross contamination. A total of 75 insects were added to 300 g of AF in a 950-ml mason jar and allowed to forage for 2 weeks prior to use in the vectoring experiment. The same dispersal periods (0, 24, 72 h) and time in patch (3 and 5 d) described above were used for this experiment. The mean grayscale value and microbial morphospecies richness was recorded for each image. There were a total of n = 30 replicates per treatment combination.
\nAnother treatment included field-collected individuals. To obtain sufficient numbers of adults, insects were caught at four different field sites around the area of greater Manhattan, KS including: 1) a site with a pre-harvest wheat field bordered by woodlands (39\u00b014'26.2\"N, 96\u00b034'59.1\"W), 2) local apartment complex consisting of end consumers (39\u00b011'43.6\"N, 96\u00b036'07.4\"W), 3) Kansas State University Agronomy Farm with storage silos (39\u00b012'23.7\"N, 96\u00b035'43.2\"W), and 4) a private residence adjacent to a working cattle farm (39\u00b012'23.7\"N, 96\u00b035'43.2\"W). In each location, a total of three 4-funnel Lindgren traps (Bioquip, Rancho Dominguez, CA, USA) were deployed at least 10 m apart at about 1 m height on rebar or hung from a tree along the perimeter of the location site, and were baited with a multi-species lure containing both L. serricorne sex pheromone and Sitophilus spp. pheromone (PTL bullet lure, #IL-108, and Sitophilus spp. bullet lure, #IL-703, Insects Limited, Westfield, IN, USA). In addition, three ground traps were deployed that consisted of commercially-available pitfall traps (Dome\u00ae, Storgard, Tr\u00e9c\u00e9, Adair, OK, USA) with two connectable pieces (Doud and Phillips 2020; Doud et al. 2021), containing a central well where a Sitophilus spp. lure was added along with a 5 g of whole maize as a kairomone bait. Pheromone lures were changed every 60 d. No kill mechanism was added because adults needed to be alive. Traps were checked on a daily basis for capture of new adults and brought immediately back into the laboratory in separate unused, sterilized containers for addition to agar dishes. Stored product insects were identified using taxonomic keys in USDA (1996). Dispersal period at 0 h and time in patch (3 and 5 d) as described above were used for this experiment.
\nResources in this dataset:
\nTo determine whether colony populations of Lasioderma serricorne (cigarette beetle, CB) and Sitophilus oryzae (rice weevil, RW) vectored microbes, and to identify possible interactions with dispersal time, a vectoring assay was performed for each species. For the vectoring assay, the impact of dispersal (0, 24, or 72 h) and foraging time (3 or 5 d) on vectoring ability were tested. Briefly, adult L. serricorne or S. oryzae were singly removed from colony containers with sterilized forceps and then placed immediately in the center of Petri dish containing agar for the 0 h dispersal period. Alternatively, some insects were given a 24 or 72 h dispersal period in an autoclaved 4 L-capacity glass container and stored at constant conditions of 25\u00b0C, 60% RH, and 14:10 L:D photoperiod prior to being added to the PDA. Petri dishes were maintained at 30\u00b0C, 60% RH, and 14:10 L:D photoperiod for either 3 or 5 days, then photographed for microbial growth. Transfer of L. serricorne or S. oryzae adults from dispersal containers to agar at the conclusion of the dispersal period was performed inside the biosafety cabinet to prevent contamination of dishes.
\nPictures of the agar dishes and corresponding microbial growth were taken using a DSLR camera (EOS 7D Mark II, Canon, Tokyo, Japan) mounted to 3D imaging StackShot (CogniSys, Inc., Traverse City, MI, USA) equipped with a dual flash (MT-26EX-RT, Canon, Tokyo, Japan). Light was diffused using a partially cut frosted plastic jar (15.2 \u00d7 7.6 cm D:H) making a total of n = 60 replicates per treatment combination (of dispersal time, insect species, and foraging time in patch). The pictures taken were processed using ImageJ 1.53a (Wayne Rasband, National Institutes of Health, USA) to quantify the microbial growth in the agar dishes. The images had their backgrounds subtracted, then were processed using the \"find edges\" tool. Finally, they were converted to binary and either dilated or eroded to conform to the original image parameters. A circle encompassing the Petri dish was created and the mean grayscale, standard deviation of the grayscale value, and count of pixels was measured as a surrogate for microbial growth on the dishes. This allowed a quantitative measure of microbial growth by creating an average in a given image. The mean grayscale value could range from 0 (full white), indicating no microbial growth, to 255 (full black), indicating full microbial growth on the entire dish. Finally, visually, microbial morphospecies (alpha) richness was assigned to each image given the number of unique morphospecies on the plate as a proxy for community complexity.
\nTreatments included those from microbially-enriched environments where Aspergillus flavus had been inoculated on wheat or flour (AF). To prepare the AF, 600 g of grain was added to a stainless-steel pot filled with water and placed on a hot plate at 500\u00b0C. Once boiling for 15 min, the water was drained and the grain was evenly spread out on sterile wipes (38.1 \u00d7 42.5 cm, 3 ply, Tech wipes, Skilcraft, NIB, Alexandria, VA) and allowed to dry inside a laminar fume hood (ca. 3 h). Afterwards, grain was evenly divided (~300 g) and placed in two separate autoclaved mason jars (950-mL capacity). A single hole was pierced through each lid and lined with a cotton ball. The jars were then sealed with aluminum foil and were autoclaved (533LS, Getinge, Rochester, NY, USA) for 30 min. To inoculate with A. flavus, a 3-inch strip of agar containing a pure culture of A. flavus grown on agar for 7 d at 30\u00b0C, 60% RH, and 14:10 L:D photoperiod was placed into each jar containing the grain. AF was then maintained at room temperature for roughly 10 d or until the A. flavus evenly covered as much the grain as possible. Batches of inoculated grain were used within 10\u201315 d of preparation. Grain was never used more than once for each replicate of every trial in each assay experiment to prevent cross contamination. A total of 75 insects were added to 300 g of AF in a 950-ml mason jar and allowed to forage for 2 weeks prior to use in the vectoring experiment. The same dispersal periods (0, 24, 72 h) and time in patch (3 and 5 d) described above were used for this experiment. The mean grayscale value and microbial morphospecies richness was recorded for each image. There were a total of n = 30 replicates per treatment combination.
\nAnother treatment included field-collected individuals. To obtain sufficient numbers of adults, insects were caught at four different field sites around the area of greater Manhattan, KS including: 1) a site with a pre-harvest wheat field bordered by woodlands (39\u00b014'26.2\"N, 96\u00b034'59.1\"W), 2) local apartment complex consisting of end consumers (39\u00b011'43.6\"N, 96\u00b036'07.4\"W), 3) Kansas State University Agronomy Farm with storage silos (39\u00b012'23.7\"N, 96\u00b035'43.2\"W), and 4) a private residence adjacent to a working cattle farm (39\u00b012'23.7\"N, 96\u00b035'43.2\"W). In each location, a total of three 4-funnel Lindgren traps (Bioquip, Rancho Dominguez, CA, USA) were deployed at least 10 m apart at about 1 m height on rebar or hung from a tree along the perimeter of the location site, and were baited with a multi-species lure containing both L. serricorne sex pheromone and Sitophilus spp. pheromone (PTL bullet lure, #IL-108, and Sitophilus spp. bullet lure, #IL-703, Insects Limited, Westfield, IN, USA). In addition, three ground traps were deployed that consisted of commercially-available pitfall traps (Dome\u00ae, Storgard, Tr\u00e9c\u00e9, Adair, OK, USA) with two connectable pieces (Doud and Phillips 2020; Doud et al. 2021), containing a central well where a Sitophilus spp. lure was added along with a 5 g of whole maize as a kairomone bait. Pheromone lures were changed every 60 d. No kill mechanism was added because adults needed to be alive. Traps were checked on a daily basis for capture of new adults and brought immediately back into the laboratory in separate unused, sterilized containers for addition to agar dishes. Stored product insects were identified using taxonomic keys in USDA (1996). Dispersal period at 0 h and time in patch (3 and 5 d) as described above were used for this experiment.
\nResources in this dataset:
\nThis dataset includes soil health, crop biomass, and crop yield data for a 13-year corn stover harvest trial in central Iowa.
\nFollowing the release in 2005 of the Billion Ton Study assessment of biofuel sources, several soil health assessments associated with harvesting corn stover were initiated across ARS locations to help provide industry guidelines for sustainable stover harvest. This dataset is from a trial conducted by the National Laboratory for Agriculture and Environment from 2007-2021 at the Iowa State University Ag Engineering and Agronomy farm. Management factors evaluated in the trial included the following.
\nThe dataset includes:\n1) Crop biomass and yields for all crop phases in every year.\n2) Soil organic carbon, total carbon, total nitrogen, and pH to 120 cm depth in 2012, 2016, and 2017. Soil cores from 2005 (pre-study) were also sampled to 90 cm depth.\n3) Soil chemistry sampled to 15 cm depth every 1-2 years from 2007 to 2017.\n4) Soil strength and compaction was assessed to 60 cm depth in April 2021.
\nThese data have been presented in several manuscripts, including Phillips et al. (in review), O'Brien et al. (2020), and Obrycki et al. (2018).
\nResource Title: R Script for Phillips et al. 2022.
File Name: Field 70-71 Analysis Script_AgDataCommons.R
Resource Description: This R script includes analysis and figures for Phillips et al. \"Thirteen-year Stover Harvest and Tillage Effects on Soil Compaction in Iowa\". It focuses primarily on the soil compaction and strength data found in \"Field 70-71 ConeIndex_BulkDensityDepths_2021\". It also includes analysis of corn yields from \"Field 70-71 CornYield_2008-2021\" and weather conditions from \"PRISM_MayTemps\" and \"Rainfall_AEA\".
Resource Software Recommended: R version 4.1.3 or higher,url: https://cran.r-project.org/bin/windows/base/
Resource Title: Field 70-71 ConeIndex_BulkDensityDepths_2021.
File Name: Field 70-71 ConeIndex_BulkDensityDepths_2021.csv
Resource Description: This dataset provides an assessment of soil strength (penetration resistance) and soil compaction (bulk density) to 60 cm depth, in continuous corn plots. Penetration resistance was measured in most-trafficked and least-trafficked areas of the plots to assess compaction from increased traffic associated with stover harvest. This spreadsheet also has associated data, including soil water, carbon, and organic matter content. Data were collected in April 2021 and are described in Phillips et al. (in review, 2022).
Resource Title: Field 70-71 CornYield_2008-2021.
File Name: Field 70-71 CornYield_2008-2021_ForR.csv
Resource Description: This dataset provides corn stover biomass and grain yields from 2008-2021. Note that this dataset is just for corn, which were presented in Phillips et al., 2022. Yields for all crop phases, including soybeans, wheat, alfalfa, and winter cover crops, are in the file \"Field 70-71 Crop Yield File 2008-2020\".
Resource Title: PRISM_MayTemps.
File Name: PRISM_MayTemps.csv
Resource Description: Average May temperatures during the study period, obtained from interpolation of regional weather stations using the PRISM climate model (https://prism.oregonstate.edu/). These data were used to evaluate how spring temperatures may have impacted corn establishment.
Resource Title: Rainfall_AEA.
File Name: Rainfall_AEA.csv
Resource Description: Daily rainfall for the study location, 2008-2021. Data were obtained from the Iowa Environmental Mesonet (https://mesonet.agron.iastate.edu/rainfall/). Title: Field 70-71 Plot Status 2007-2021.
File Name: Field 70-71 Plot Status 2007-2021.xlsx
Resource Description: This file contains descriptions of experimental treatments and diagrams of plot layouts as they were modified through several phases of the trial. Also includes an image of plot locations relative to NRCS soil survey map units.
Resource Title: Field 70-71 Deep Soil Cores 2012-2017.
File Name: Field 70-71 Deep Soil Cores 2012-2017.xlsx
Resource Description: Soil carbon, nitrogen, organic matter, and pH to 120 cm depth in 2012, 2016, and 2017.
Resource Title: Field 70-71 Baseline Deep Soil Cores 2005.
File Name: Field 70-71 Baseline Deep Soil Cores 2005.csv
Resource Description: Baseline soil carbon, nitrogen, and pH data from an earlier trial in 2005, prior to stover trial establishment.
Resource Title: Field 70-71 Crop Yield File 2008-2020.
File Name: Field 70-71 Crop Yield File 2008-2020.xlsx
Resource Description: Yields for all crops in all cropping sequences, 2008-2020. Some of the crop sequences have not been summarized in publications.
Resource Title: Field 70-71 Surface Soil Test Data 2007-2021.
File Name: Field 70-71 Surface Soil Test Data 2007-2021.xlsx
Resource Description: Soil chemistry data, 0-15 cm, collect near-annually from 2007 to 2021. Most analyses were performed by Harris Laboratories (now AgSource) in Lincoln, Nebraska, USA. \n
Resource Title: Iowa Stover Harvest Trial Data Dictionary.
File Name: Field 70-71 Data Dictionary.xlsx
Resource Description: Data dictionary for all data files.
This dataset includes soil health, crop biomass, and crop yield data for a 13-year corn stover harvest trial in central Iowa.
\nFollowing the release in 2005 of the Billion Ton Study assessment of biofuel sources, several soil health assessments associated with harvesting corn stover were initiated across ARS locations to help provide industry guidelines for sustainable stover harvest. This dataset is from a trial conducted by the National Laboratory for Agriculture and Environment from 2007-2021 at the Iowa State University Ag Engineering and Agronomy farm. Management factors evaluated in the trial included the following.
\nThe dataset includes:\n1) Crop biomass and yields for all crop phases in every year.\n2) Soil organic carbon, total carbon, total nitrogen, and pH to 120 cm depth in 2012, 2016, and 2017. Soil cores from 2005 (pre-study) were also sampled to 90 cm depth.\n3) Soil chemistry sampled to 15 cm depth every 1-2 years from 2007 to 2017.\n4) Soil strength and compaction was assessed to 60 cm depth in April 2021.
\nThese data have been presented in several manuscripts, including Phillips et al. (in review), O'Brien et al. (2020), and Obrycki et al. (2018).
\nResource Title: R Script for Phillips et al. 2022.
File Name: Field 70-71 Analysis Script_AgDataCommons.R
Resource Description: This R script includes analysis and figures for Phillips et al. \"Thirteen-year Stover Harvest and Tillage Effects on Soil Compaction in Iowa\". It focuses primarily on the soil compaction and strength data found in \"Field 70-71 ConeIndex_BulkDensityDepths_2021\". It also includes analysis of corn yields from \"Field 70-71 CornYield_2008-2021\" and weather conditions from \"PRISM_MayTemps\" and \"Rainfall_AEA\".
Resource Software Recommended: R version 4.1.3 or higher,url: https://cran.r-project.org/bin/windows/base/
Resource Title: Field 70-71 ConeIndex_BulkDensityDepths_2021.
File Name: Field 70-71 ConeIndex_BulkDensityDepths_2021.csv
Resource Description: This dataset provides an assessment of soil strength (penetration resistance) and soil compaction (bulk density) to 60 cm depth, in continuous corn plots. Penetration resistance was measured in most-trafficked and least-trafficked areas of the plots to assess compaction from increased traffic associated with stover harvest. This spreadsheet also has associated data, including soil water, carbon, and organic matter content. Data were collected in April 2021 and are described in Phillips et al. (in review, 2022).
Resource Title: Field 70-71 CornYield_2008-2021.
File Name: Field 70-71 CornYield_2008-2021_ForR.csv
Resource Description: This dataset provides corn stover biomass and grain yields from 2008-2021. Note that this dataset is just for corn, which were presented in Phillips et al., 2022. Yields for all crop phases, including soybeans, wheat, alfalfa, and winter cover crops, are in the file \"Field 70-71 Crop Yield File 2008-2020\".
Resource Title: PRISM_MayTemps.
File Name: PRISM_MayTemps.csv
Resource Description: Average May temperatures during the study period, obtained from interpolation of regional weather stations using the PRISM climate model (https://prism.oregonstate.edu/). These data were used to evaluate how spring temperatures may have impacted corn establishment.
Resource Title: Rainfall_AEA.
File Name: Rainfall_AEA.csv
Resource Description: Daily rainfall for the study location, 2008-2021. Data were obtained from the Iowa Environmental Mesonet (https://mesonet.agron.iastate.edu/rainfall/). Title: Field 70-71 Plot Status 2007-2021.
File Name: Field 70-71 Plot Status 2007-2021.xlsx
Resource Description: This file contains descriptions of experimental treatments and diagrams of plot layouts as they were modified through several phases of the trial. Also includes an image of plot locations relative to NRCS soil survey map units.
Resource Title: Field 70-71 Deep Soil Cores 2012-2017.
File Name: Field 70-71 Deep Soil Cores 2012-2017.xlsx
Resource Description: Soil carbon, nitrogen, organic matter, and pH to 120 cm depth in 2012, 2016, and 2017.
Resource Title: Field 70-71 Baseline Deep Soil Cores 2005.
File Name: Field 70-71 Baseline Deep Soil Cores 2005.csv
Resource Description: Baseline soil carbon, nitrogen, and pH data from an earlier trial in 2005, prior to stover trial establishment.
Resource Title: Field 70-71 Crop Yield File 2008-2020.
File Name: Field 70-71 Crop Yield File 2008-2020.xlsx
Resource Description: Yields for all crops in all cropping sequences, 2008-2020. Some of the crop sequences have not been summarized in publications.
Resource Title: Field 70-71 Surface Soil Test Data 2007-2021.
File Name: Field 70-71 Surface Soil Test Data 2007-2021.xlsx
Resource Description: Soil chemistry data, 0-15 cm, collect near-annually from 2007 to 2021. Most analyses were performed by Harris Laboratories (now AgSource) in Lincoln, Nebraska, USA. \n
Resource Title: Iowa Stover Harvest Trial Data Dictionary.
File Name: Field 70-71 Data Dictionary.xlsx
Resource Description: Data dictionary for all data files.
Biochars are charcoals used as soil amendments, and they have many beneficial effects on soil health. However, one negative effect is biochars often reduce concentrations of soil nitrogen that are available to plants. This is believed to be due to the high carbon and low nitrogen contents of biochars, which deprive soil microbes of nitrogen as they decompose the biochar, and cause microbes to tie up nitrogen from soil. We tested whether we could predict biochar impacts on soil nitrogen from the quantities of carbon and nitrogen in biochar that can be consumed soil microbes. Because biochars are mostly composed of carbon in molecules that can not be consumed by microbes, the microbially-available portion is generally small. We measured the microbially-available carbon and nitrogen in ten biochars, and measured how they impacted nitrogen concentrations in two soils from Oregon.
\nThis dataset includes characteristics of ten biochars and two soils, and measurements from two incubation experiments. In the first experiment we incubated 13C-labeled biochars with two soil for 101 days, and measured production of biochar- and soil-respired CO2 and soil dissolved inorganic nitrogen. In the second experiment we expanded to study ten biochar types, including seven biochars that were not isotopically-labeled. We measured how much dissolved inorganic nitrogen was produced by amended soils over 28 days.
\nSurprisingly, we found all ten biochars increased rather than decreased soil nitrogen concentrations one month after application. We also found that biochars produced at high temperatures, which were more difficult for soil microbes to consume than low-temperature biochars, stimulated more soil decomposition and released more soil nitrogen. It appeared that microbes increased soil decomposition in response to additions of biochar, and this then increased plant-available nitrogen at least temporarily. These unexpected results show that biochar can sometimes have beneficial impacts on soil nitrogen, and that biochar impacts cannot be readily predicted from the qualities of the biochars themselves. These results are relevant to biochar users, and to biochar producers interested in how to make biochars more beneficial for plant growth. These results indicate that biochar users cannot predict nitrogen impacts, and should therefore monitor soil nitrogen concentrations to ensure levels are sufficient for plant growth.
Resource Title: Biochar Properties.
File Name: Biochar Properties.csv
Resource Description: Describes production conditions and provides chemical characteristics of ten biochars used to evaluate impacts of biochar amendment on plant-available nitrogen in soil. Data were collected at the at USDA-ARS National Forage Seed Production Research Unit in Corvallis, OR in 2019-2020.
Resource Title: Experiment 1 Timeseries.
File Name: Experiment 1 Timeseries.csv
Resource Description: Three 13C-labeled barley biochars produced at 350, 500, and 700 degrees Celsius were incubated in two Oregon soils with contrasting levels of organic matter for 101 days to determine impacts of biochar on soil C and N-mineralization. Dataset provides timeseries of CO2 measured with an LGR Ultraportable Greenhouse Gas Analyzer, and nitrate and ammonium measured from 2M KCl extracts using colorimetric methods. Data were collected at the USDA-ARS National Forage Seed Production Research Unit in Corvallis, Oregon in 2019. Isotopic values of respired CO2 were measured from Exetainer samples that were send to the USDA-ARS in Ft.Collins and analyzed using a gas chromatograph-isotope ratio mass spectrometer (Isoprime Inc, UK). Quantities of CO2 respired from biochar and soil were calculated from a 2-member isotopic mixing model.
Resource Title: Experiment 2 Timeseries.
File Name: Experiment 2 Timeseries.csv
Resource Description: Ten biochars were incubated in an Oregon clay-loam soil to test impacts on plant-available soil nitrogen concentration. Biochars were added to a soil at a rate of 2% by mass, wetted to 60% water-filled pore space, and incubated at 23 degrees C under aerobic conditions for 28 days. Soil nitrate and ammonium concentrations were measured on days 0, 14, and 28, using 2M KCl extraction solutions and colorimetric methods. Net N-mineralized on days 14 and 28 was determined by subtracting total inorganic nitrogen on day 0 from values on days 14 and 28, respectively. The impact of biochar amendment was assessed by subtracting net N-mineralized in unamended soils from net N-mineralized in amended soils. Data were collected at the USDA-ARS National Forage Seed Production Research Center in 2020.
Resource Title: Data dictionary for: Towards predicting biochar impacts on plant-available soil nitrogen content.
File Name: DataDictionary_PAN Experiment.xlsx
Resource Description: Provides data descriptions for all resources in the dataset.
Resource Title: Soil Properties.
File Name: Soil Properties.csv
Resource Description: Provides soil properties for two Oregon soils used to evaluate impacts on biochar on plant-available soil nitrogen concentrations. Soils were collected near Corvallis, Oregon and were analyzed at the USDA-ARS National Forage Seed Production Center in 2019.
Biochars are charcoals used as soil amendments, and they have many beneficial effects on soil health. However, one negative effect is biochars often reduce concentrations of soil nitrogen that are available to plants. This is believed to be due to the high carbon and low nitrogen contents of biochars, which deprive soil microbes of nitrogen as they decompose the biochar, and cause microbes to tie up nitrogen from soil. We tested whether we could predict biochar impacts on soil nitrogen from the quantities of carbon and nitrogen in biochar that can be consumed soil microbes. Because biochars are mostly composed of carbon in molecules that can not be consumed by microbes, the microbially-available portion is generally small. We measured the microbially-available carbon and nitrogen in ten biochars, and measured how they impacted nitrogen concentrations in two soils from Oregon.
\nThis dataset includes characteristics of ten biochars and two soils, and measurements from two incubation experiments. In the first experiment we incubated 13C-labeled biochars with two soil for 101 days, and measured production of biochar- and soil-respired CO2 and soil dissolved inorganic nitrogen. In the second experiment we expanded to study ten biochar types, including seven biochars that were not isotopically-labeled. We measured how much dissolved inorganic nitrogen was produced by amended soils over 28 days.
\nSurprisingly, we found all ten biochars increased rather than decreased soil nitrogen concentrations one month after application. We also found that biochars produced at high temperatures, which were more difficult for soil microbes to consume than low-temperature biochars, stimulated more soil decomposition and released more soil nitrogen. It appeared that microbes increased soil decomposition in response to additions of biochar, and this then increased plant-available nitrogen at least temporarily. These unexpected results show that biochar can sometimes have beneficial impacts on soil nitrogen, and that biochar impacts cannot be readily predicted from the qualities of the biochars themselves. These results are relevant to biochar users, and to biochar producers interested in how to make biochars more beneficial for plant growth. These results indicate that biochar users cannot predict nitrogen impacts, and should therefore monitor soil nitrogen concentrations to ensure levels are sufficient for plant growth.
Resource Title: Biochar Properties.
File Name: Biochar Properties.csv
Resource Description: Describes production conditions and provides chemical characteristics of ten biochars used to evaluate impacts of biochar amendment on plant-available nitrogen in soil. Data were collected at the at USDA-ARS National Forage Seed Production Research Unit in Corvallis, OR in 2019-2020.
Resource Title: Experiment 1 Timeseries.
File Name: Experiment 1 Timeseries.csv
Resource Description: Three 13C-labeled barley biochars produced at 350, 500, and 700 degrees Celsius were incubated in two Oregon soils with contrasting levels of organic matter for 101 days to determine impacts of biochar on soil C and N-mineralization. Dataset provides timeseries of CO2 measured with an LGR Ultraportable Greenhouse Gas Analyzer, and nitrate and ammonium measured from 2M KCl extracts using colorimetric methods. Data were collected at the USDA-ARS National Forage Seed Production Research Unit in Corvallis, Oregon in 2019. Isotopic values of respired CO2 were measured from Exetainer samples that were send to the USDA-ARS in Ft.Collins and analyzed using a gas chromatograph-isotope ratio mass spectrometer (Isoprime Inc, UK). Quantities of CO2 respired from biochar and soil were calculated from a 2-member isotopic mixing model.
Resource Title: Experiment 2 Timeseries.
File Name: Experiment 2 Timeseries.csv
Resource Description: Ten biochars were incubated in an Oregon clay-loam soil to test impacts on plant-available soil nitrogen concentration. Biochars were added to a soil at a rate of 2% by mass, wetted to 60% water-filled pore space, and incubated at 23 degrees C under aerobic conditions for 28 days. Soil nitrate and ammonium concentrations were measured on days 0, 14, and 28, using 2M KCl extraction solutions and colorimetric methods. Net N-mineralized on days 14 and 28 was determined by subtracting total inorganic nitrogen on day 0 from values on days 14 and 28, respectively. The impact of biochar amendment was assessed by subtracting net N-mineralized in unamended soils from net N-mineralized in amended soils. Data were collected at the USDA-ARS National Forage Seed Production Research Center in 2020.
Resource Title: Data dictionary for: Towards predicting biochar impacts on plant-available soil nitrogen content.
File Name: DataDictionary_PAN Experiment.xlsx
Resource Description: Provides data descriptions for all resources in the dataset.
Resource Title: Soil Properties.
File Name: Soil Properties.csv
Resource Description: Provides soil properties for two Oregon soils used to evaluate impacts on biochar on plant-available soil nitrogen concentrations. Soils were collected near Corvallis, Oregon and were analyzed at the USDA-ARS National Forage Seed Production Center in 2019.
Attraction Assessment - assessment of different lure sources, including pheromones and kairomones for red flour beetle and lesser grain borer in the wind tunnel and release-recapture experiment under controlled settings. Abbreviations: WGO - wheat germ oil; Tab - Insects Limited SPB tablet bait; NC - negative control (no stimulus); DDGS - dried distiller's grains with soluables. There were a total of n = 12 replicate releases for the release-recapture and n = 30 replicate individuals for the wind tunnel per treatment.
\nDose Dependency Data - evaluation of whether dose-dependency in attraction exists for red flour beetle and lesser grain borer in the wind tunnel and in a release-recapture experiment for the Insects Limited SPB lure. Abbreviations: SPB1 - a single Insects Limited SPB tablet bait; SPB2 - two Insects Limited SPB tablet baits; SPB3 - three Insects Limited SPB tablet baits; Ctrl - negative control (no stimulus); DDGS - dried distiller's grains with soluables. There were a total of n = 24 replicate releases for the release-recapture and n = 30 replicate individuals for the wind tunnel per treatment.
\nSpillage Trap Collections - datasheet for the number and lowest taxonomic unit of insects collected in interception traps with different kill mechanisms and stimuli at three food facilities in Arkansas and Kansas during 2018 and 2019. There were a total of 27 taxa tracked, and captures totaled to near 4,000. Abbreviations: C, control netting only (no stimulus); L, insecticide-netting only (no stimulus); LS, insecticide-netting with a single SPB Insects Limited tab lure; CS, control netting with a single SPB Insects Limited tab lure. State abbreviations: AR - Arkansas, KS - Kansas. There were three transects per site, each with every treatment above represented, thus a total of n = 8-9 replicate deployments in AR and n = 12 deployments in KS.
\nSpillage Trap Progeny- datasheet for progeny production in interception traps after six weeks under constant conditions with different kill mechanisms and stimuli at three food facilities in Arkansas and Kansas during 2018 and 2019. Abbreviations: C, control netting only (no stimulus); L, insecticide-netting only (no stimulus); LS, insecticide-netting with a single SPB Insects Limited tab lure; CS, control netting with a single SPB Insects Limited tab lure. State abbreviations: AR - Arkansas, KS - Kansas. There were three transects per site, each with every treatment above represented, thus a total of n = 8-9 replicate deployments in AR and n = 12 deployments in KS.
\nTrial 1 Recapture - To understand whether the method by which LLIN was deployed affected subsequent commodity infestation and progeny production, pilot-scale warehouses (5.85 \u00d7 2.81 m) in Manhattan, KS were used. At the far end of the warehouse against the back wall, a commodity consisting of a mixture of 210 mL organic, whole wheat kernels and 210 mL of organic, unbleached flour was placed. A total of 100 individuals each of T. castaneum, R. dominica, and T. variabile were released at the opposite end of the warehouse (approx. 5.25 m away). There were n = 12 replicate releases per treatment from 26 April 2019 to 16 August 2019, comprising a total of 3,600 released insects. There were four LLIN deployment methods that were tested (Figure 2). In the \u201changing\u201d treatment, LLIN (2.72 \u00d7 2.41 m) was affixed to the warehouse ceiling and allowed to hang down to the floor, completely bisecting the room. In the \u201ccover\u201d deployment method, LLIN was directly laid over the commodity. In the \u201cpipe\u201d deployment method, a PVC pipe (91 cm length, 5.1 cm I.D.) was bisected halfway with LLIN. These were compared with a \"control\" that used the same PVC pipe design, but without netting. Insects were given 72 h to disperse across the warehouse to the commodity. After this period, insects were collected by pre-designated zones in the warehouse. The zones were noted respective to the location of the commodity, and included \"in commodity\" (inside the commodity), \"partial dispersal\" (0.5 m radius to 4.5 m away), and \"no dispersal\" (4.5 m\u20135.6 m away, e.g. the release zone). The insects were retrieved, and then brought back to the lab where their health condition was assessed as alive, affected, or dead. Abbreviations: RFB - red flour beetle; LGB - lesser grain borer; WHB - warehouse beetle.
\nTrial 1 Progeny Production - as above but, the commodity was held for 6 weeks after deployment under constant environmental chamber conditions to evaluate progeny production. The species and health conditions of the progeny were recorded.Abbreviations: RFB - red flour beetle; LGB - lesser grain borer; WHB - warehouse beetle.
\nTrial 2 Recapture - To understand whether management tactic affected subsequent commodity infestation and progeny production, pilot-scale warehouses (5.85 \u00d7 2.81 m) in Manhattan, KS were used. There were four treatments in total applied to warehouses for this experiment: LLIN alone (L), AK-based interception trap alone (AK), both together (LAK), or neither (e.g. \"control\" that had no netting or interception trap). The zones were similar to the descriptions above, but a Zone 7 was introduced which described insects captured inside the interception traps (e.g. \"trap\"). At the far end of the warehouse against the back wall, a commodity consisting of a mixture of 210 mL organic, whole wheat kernels and 210 mL of organic, unbleached flour was placed. A total of 100 individuals each of T. castaneum, R. dominica, and T. variabile were released at the opposite end of the warehouse (approx. 5.25 m away). There were n = 12 replicate releases per treatment from 23 August 2019 to 8 November 2019, comprising a total of 3,600 released insects. Insects were given 72 h to disperse across the warehouse to the commodity. After this period, insects were collected by pre-designated zones in the warehouse. The zones were noted respective to the location of the commodity, and included \"in commodity\" (inside the commodity), \"partial dispersal\" (0.5 m radius to 4.5 m away), and \"no dispersal\" (4.5 m\u20135.6 m away, e.g. the release zone). The insects were retrieved, and then brought back to the lab where their health condition was assessed as alive, affected, or dead. Abbreviations: RFB - red flour beetle; LGB - lesser grain borer; WHB - warehouse beetle.
\nTrial 2 Progeny Production - as above but, the commodity was held for 6 weeks after deployment under constant environmental chamber conditions to evaluate progeny production. The species and health conditions of the progeny were recorded. Abbreviations: RFB - red flour beetle; LGB - lesser grain borer; WHB - warehouse beetle.
\nThis work was funded, in part, by a United States Department of Agriculture, National Institute of Food and Agriculture, Crop Protection and Pest Management Grant #2017-70006-27262.
\nSee included file list for more information about each individual data file.
Resource Title: Attraction Assessment.
File Name: Attraction Assessment.xlsx
Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel
Resource Title: Dose Dependency Data.
File Name: Dose Dependency Data.xlsx
Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel
Resource Title: Spillage Trap Collections.
File Name: Spillage Trap Collections.xlsx
Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel
Resource Title: Spillage Trap Progeny.
File Name: Spillage Trap Progeny.xlsx
Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel
Resource Title: Trial 1 Progeny Production.
File Name: Trial 1 Progeny Production.xlsx
Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel
Resource Title: Trial 1 Recapture.
File Name: Trial 1 Recapture.xlsx
Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel
Resource Title: Trial 2 Progeny Production.
File Name: Trial 2 Progeny Production.xlsx
Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel
Resource Title: Trial 2 Recapture.
File Name: Trial 2 Recapture.xlsx
Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel
Resource Title: README file list.
File Name: file_list_stored_product_beetles.txt
Attraction Assessment - assessment of different lure sources, including pheromones and kairomones for red flour beetle and lesser grain borer in the wind tunnel and release-recapture experiment under controlled settings. Abbreviations: WGO - wheat germ oil; Tab - Insects Limited SPB tablet bait; NC - negative control (no stimulus); DDGS - dried distiller's grains with soluables. There were a total of n = 12 replicate releases for the release-recapture and n = 30 replicate individuals for the wind tunnel per treatment.
\nDose Dependency Data - evaluation of whether dose-dependency in attraction exists for red flour beetle and lesser grain borer in the wind tunnel and in a release-recapture experiment for the Insects Limited SPB lure. Abbreviations: SPB1 - a single Insects Limited SPB tablet bait; SPB2 - two Insects Limited SPB tablet baits; SPB3 - three Insects Limited SPB tablet baits; Ctrl - negative control (no stimulus); DDGS - dried distiller's grains with soluables. There were a total of n = 24 replicate releases for the release-recapture and n = 30 replicate individuals for the wind tunnel per treatment.
\nSpillage Trap Collections - datasheet for the number and lowest taxonomic unit of insects collected in interception traps with different kill mechanisms and stimuli at three food facilities in Arkansas and Kansas during 2018 and 2019. There were a total of 27 taxa tracked, and captures totaled to near 4,000. Abbreviations: C, control netting only (no stimulus); L, insecticide-netting only (no stimulus); LS, insecticide-netting with a single SPB Insects Limited tab lure; CS, control netting with a single SPB Insects Limited tab lure. State abbreviations: AR - Arkansas, KS - Kansas. There were three transects per site, each with every treatment above represented, thus a total of n = 8-9 replicate deployments in AR and n = 12 deployments in KS.
\nSpillage Trap Progeny- datasheet for progeny production in interception traps after six weeks under constant conditions with different kill mechanisms and stimuli at three food facilities in Arkansas and Kansas during 2018 and 2019. Abbreviations: C, control netting only (no stimulus); L, insecticide-netting only (no stimulus); LS, insecticide-netting with a single SPB Insects Limited tab lure; CS, control netting with a single SPB Insects Limited tab lure. State abbreviations: AR - Arkansas, KS - Kansas. There were three transects per site, each with every treatment above represented, thus a total of n = 8-9 replicate deployments in AR and n = 12 deployments in KS.
\nTrial 1 Recapture - To understand whether the method by which LLIN was deployed affected subsequent commodity infestation and progeny production, pilot-scale warehouses (5.85 \u00d7 2.81 m) in Manhattan, KS were used. At the far end of the warehouse against the back wall, a commodity consisting of a mixture of 210 mL organic, whole wheat kernels and 210 mL of organic, unbleached flour was placed. A total of 100 individuals each of T. castaneum, R. dominica, and T. variabile were released at the opposite end of the warehouse (approx. 5.25 m away). There were n = 12 replicate releases per treatment from 26 April 2019 to 16 August 2019, comprising a total of 3,600 released insects. There were four LLIN deployment methods that were tested (Figure 2). In the \u201changing\u201d treatment, LLIN (2.72 \u00d7 2.41 m) was affixed to the warehouse ceiling and allowed to hang down to the floor, completely bisecting the room. In the \u201ccover\u201d deployment method, LLIN was directly laid over the commodity. In the \u201cpipe\u201d deployment method, a PVC pipe (91 cm length, 5.1 cm I.D.) was bisected halfway with LLIN. These were compared with a \"control\" that used the same PVC pipe design, but without netting. Insects were given 72 h to disperse across the warehouse to the commodity. After this period, insects were collected by pre-designated zones in the warehouse. The zones were noted respective to the location of the commodity, and included \"in commodity\" (inside the commodity), \"partial dispersal\" (0.5 m radius to 4.5 m away), and \"no dispersal\" (4.5 m\u20135.6 m away, e.g. the release zone). The insects were retrieved, and then brought back to the lab where their health condition was assessed as alive, affected, or dead. Abbreviations: RFB - red flour beetle; LGB - lesser grain borer; WHB - warehouse beetle.
\nTrial 1 Progeny Production - as above but, the commodity was held for 6 weeks after deployment under constant environmental chamber conditions to evaluate progeny production. The species and health conditions of the progeny were recorded.Abbreviations: RFB - red flour beetle; LGB - lesser grain borer; WHB - warehouse beetle.
\nTrial 2 Recapture - To understand whether management tactic affected subsequent commodity infestation and progeny production, pilot-scale warehouses (5.85 \u00d7 2.81 m) in Manhattan, KS were used. There were four treatments in total applied to warehouses for this experiment: LLIN alone (L), AK-based interception trap alone (AK), both together (LAK), or neither (e.g. \"control\" that had no netting or interception trap). The zones were similar to the descriptions above, but a Zone 7 was introduced which described insects captured inside the interception traps (e.g. \"trap\"). At the far end of the warehouse against the back wall, a commodity consisting of a mixture of 210 mL organic, whole wheat kernels and 210 mL of organic, unbleached flour was placed. A total of 100 individuals each of T. castaneum, R. dominica, and T. variabile were released at the opposite end of the warehouse (approx. 5.25 m away). There were n = 12 replicate releases per treatment from 23 August 2019 to 8 November 2019, comprising a total of 3,600 released insects. Insects were given 72 h to disperse across the warehouse to the commodity. After this period, insects were collected by pre-designated zones in the warehouse. The zones were noted respective to the location of the commodity, and included \"in commodity\" (inside the commodity), \"partial dispersal\" (0.5 m radius to 4.5 m away), and \"no dispersal\" (4.5 m\u20135.6 m away, e.g. the release zone). The insects were retrieved, and then brought back to the lab where their health condition was assessed as alive, affected, or dead. Abbreviations: RFB - red flour beetle; LGB - lesser grain borer; WHB - warehouse beetle.
\nTrial 2 Progeny Production - as above but, the commodity was held for 6 weeks after deployment under constant environmental chamber conditions to evaluate progeny production. The species and health conditions of the progeny were recorded. Abbreviations: RFB - red flour beetle; LGB - lesser grain borer; WHB - warehouse beetle.
\nThis work was funded, in part, by a United States Department of Agriculture, National Institute of Food and Agriculture, Crop Protection and Pest Management Grant #2017-70006-27262.
\nSee included file list for more information about each individual data file.
Resource Title: Attraction Assessment.
File Name: Attraction Assessment.xlsx
Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel
Resource Title: Dose Dependency Data.
File Name: Dose Dependency Data.xlsx
Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel
Resource Title: Spillage Trap Collections.
File Name: Spillage Trap Collections.xlsx
Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel
Resource Title: Spillage Trap Progeny.
File Name: Spillage Trap Progeny.xlsx
Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel
Resource Title: Trial 1 Progeny Production.
File Name: Trial 1 Progeny Production.xlsx
Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel
Resource Title: Trial 1 Recapture.
File Name: Trial 1 Recapture.xlsx
Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel
Resource Title: Trial 2 Progeny Production.
File Name: Trial 2 Progeny Production.xlsx
Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel
Resource Title: Trial 2 Recapture.
File Name: Trial 2 Recapture.xlsx
Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel
Resource Title: README file list.
File Name: file_list_stored_product_beetles.txt
This Ag Data Commons submission includes the 94 sunflower paired-end sequencing FASTQ files, the corresponding 16S bacterial FASTQ files, and other relevant data to the study described below:
\nHost-microbe interactions are increasingly recognized as important drivers of organismal health, growth, longevity, and community-scale ecological processes. However, less is known about how genetic variation affects hosts' associated microbiomes and downstream phenotypes. We demonstrate that sunflower Helianthus annuus harbors substantial, heritable variation in microbial communities under field conditions. We show that microbial communities co-vary with heritable variation in resistance to root infection caused by the necrotrophic pathogen Sclerotinia sclerotiorum, and that plants grown in autoclaved soil showed almost complete elimination of pathogen resistance. Association mapping suggests at least 59 genetic locations with effects on both microbial relative abundance and Sclerotinia resistance. Although the genetic architecture appears quantitative, we have elucidated previously unexplained genetic variation for resistance to this pathogen. We identify new targets for plant breeding and demonstrate the potential for heritable microbial associations to play important roles in defense in natural and human-altered environments.
\nSee README for details of each table in the spreadsheet and related information.
","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/44541335","format":"docx","license":"https://www.usa.gov/publicdomain/label/1.0/","mediaType":"application/zip","title":"README.docx"},{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/44541338","format":"xlsx","license":"https://www.usa.gov/publicdomain/label/1.0/","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Pogoda et al Molecular Ecology 2023.xlsx"}],"identifier":"10.15482/USDA.ADC/1529803","keyword":["16S and ITS amplicon","ARS","Bacteria","EARTH SCIENCE > AGRICULTURE > AGRICULTURAL PLANT SCIENCE > PLANT BREEDING AND GENETICS","NP301","Sclerotinia sclerotiorum","association mapping","bioinformatics","community ecology","data.gov","genetic variation","helianthus annuus","heritable resistance","host-microbe interactions","microbial community","nectrotrophic pathogen","pathogen resistance","plant breeding","quantitative genetics","soil microbiome","sunflower"],"license":"https://www.usa.gov/publicdomain/label/1.0/","modified":"2025-11-21","programCode":["005:040"],"publisher":{"@type":"org:Organization","name":"Agricultural Research Service"},"spatial":"[{\"@type\": \"Location\", \"prefLabel\": \"{\\\"type\\\": \\\"Point\\\", \\\"coordinates\\\": [-99.121611, 47.507595]}\"}]","temporal":"[{\"@type\": \"PeriodOfTime\", \"endDate\": \"2017-08-01\", \"startDate\": \"2017-08-01\"}]","title":"Data from: Heritable differences in abundance of bacterial rhizosphere taxa are correlated with fungal necrotrophic pathogen resistance"},"description":"This Ag Data Commons submission includes the 94 sunflower paired-end sequencing FASTQ files, the corresponding 16S bacterial FASTQ files, and other relevant data to the study described below:
\nHost-microbe interactions are increasingly recognized as important drivers of organismal health, growth, longevity, and community-scale ecological processes. However, less is known about how genetic variation affects hosts' associated microbiomes and downstream phenotypes. We demonstrate that sunflower Helianthus annuus harbors substantial, heritable variation in microbial communities under field conditions. We show that microbial communities co-vary with heritable variation in resistance to root infection caused by the necrotrophic pathogen Sclerotinia sclerotiorum, and that plants grown in autoclaved soil showed almost complete elimination of pathogen resistance. Association mapping suggests at least 59 genetic locations with effects on both microbial relative abundance and Sclerotinia resistance. Although the genetic architecture appears quantitative, we have elucidated previously unexplained genetic variation for resistance to this pathogen. We identify new targets for plant breeding and demonstrate the potential for heritable microbial associations to play important roles in defense in natural and human-altered environments.
\nSee README for details of each table in the spreadsheet and related information.
","distribution_titles":["README.docx","Pogoda et al Molecular Ecology 2023.xlsx"],"harvest_record":"https://catalog.data.gov/harvest_record/a35ccaa3-564e-4102-b652-e642d9f142f4","harvest_record_raw":"https://catalog.data.gov/harvest_record/a35ccaa3-564e-4102-b652-e642d9f142f4/raw","has_download":true,"has_spatial":true,"identifier":"10.15482/USDA.ADC/1529803","keyword":["16S and ITS amplicon","ARS","Bacteria","EARTH SCIENCE > AGRICULTURE > AGRICULTURAL PLANT SCIENCE > PLANT BREEDING AND GENETICS","NP301","Sclerotinia sclerotiorum","association mapping","bioinformatics","community ecology","data.gov","genetic variation","helianthus annuus","heritable resistance","host-microbe interactions","microbial community","nectrotrophic pathogen","pathogen resistance","plant breeding","quantitative genetics","soil microbiome","sunflower"],"last_harvested_date":"2026-10-09T16:37:29.126319","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"352b4532-793d-4075-a03f-05b778a3c43a","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/usda.png","name":"Department of Agriculture","organization_type":"Federal Government","slug":"usda"},"parent_identifier":null,"popularity":3,"publisher":"Agricultural Research Service","slug":"data-from-heritable-differences-in-abundance-of-bacterial-rhizosphere-taxa-are-correlated-","spatial_centroid":{"lat":47.507595,"lon":-99.121611},"spatial_shape":{"coordinates":[-99.121611,47.507595],"type":"Point"},"theme":[],"title":"Data from: Heritable differences in abundance of bacterial rhizosphere taxa are correlated with fungal necrotrophic pathogen resistance","type":"dataset"},{"_score":7.1010227,"_sort":[1791563848814,7.1010227,3,"71cff138-c5eb-4354-99fb-8a96178dce68"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","accessRights":"public","accrualPeriodicity":"irregular","bureauCode":["005:18"],"contactPoint":{"fn":"Hulke, Brent","hasEmail":"mailto:brent.hulke@usda.gov"},"description":"Within this dataset:
\nLinum lewisii, a perennial blue flax native to North America, holds potential as a sustainable perennial crop for oilseed production due to its ecological adaptability, upright harvestable structure, nutritious seeds, and low insect and disease issues. Its native distribution spans a large geographic range, from the Pacific Coast to the Mississippi River, and from Alaska to Baja California. Tolerant to cold and drought conditions, this species is also important for native ecosystem rehabilitation. Its enhancement of soil health, support for pollinators, and carbon sequestration underscore its agricultural relevance.
\nThis study presents a high-quality, chromosome-scale assembly of the L. lewisii (2n = 2x = 18) genome, derived from PacBio HiFi and Dovetail Omni-C sequencing of the \u201cMaple Grove\u201d variety. The initial assembly contained 642,903,787 base pairs across 2,924 scaffolds. Following HiRise scaffolding, the final assembly contained 643,041,835 base pairs, across 1,713 scaffolds, yielding an N50 contig length of 66,209,717 base pairs. Annotation of the assembly revealed 38,808 genes, including 37,599 protein-coding genes and 7,108 putative transposable elements. Analysis of synteny with other flax species revealed a striking number of chromosomal rearrangements. We also found an intriguing absence of the single-copy TSS1 gene in the L. lewisii genome, potentially linked to its transition from heterostyly to homostyly.
\nTaken together, these findings represent a significant advancement in our understanding of the Linum genus and provide a resource for future domestication efforts and basic research on Lewis flax.
","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/44541020","format":"gz","license":"https://www.usa.gov/publicdomain/label/1.0/","mediaType":"application/gzip","title":"NDSU_LiLewi_1.0.fasta.gz"},{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/44541023","format":"gz","license":"https://www.usa.gov/publicdomain/label/1.0/","mediaType":"application/gzip","title":"NDSU_LiLewi_1.0.annotation.gff_.gz"},{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/44541026","format":"gz","license":"https://www.usa.gov/publicdomain/label/1.0/","mediaType":"application/gzip","title":"preflower-tissue_R1_001.fastq.gz"},{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/44541044","format":"gz","license":"https://www.usa.gov/publicdomain/label/1.0/","mediaType":"application/gzip","title":"preflower-tissue_R2_001.fastq.gz"},{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/44541062","format":"gz","license":"https://www.usa.gov/publicdomain/label/1.0/","mediaType":"application/gzip","title":"post-flower-tissue_R1_001.fastq.gz"},{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/44541065","format":"gz","license":"https://www.usa.gov/publicdomain/label/1.0/","mediaType":"application/gzip","title":"post-flower-tissue_R2_001.fastq.gz"},{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/44541074","format":"gz","license":"https://www.usa.gov/publicdomain/label/1.0/","mediaType":"application/gzip","title":"light-seedlings_R1_001.fastq.gz"},{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/44541086","format":"gz","license":"https://www.usa.gov/publicdomain/label/1.0/","mediaType":"application/gzip","title":"light-seedlings_R2_001.fastq.gz"},{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/44541089","format":"gz","license":"https://www.usa.gov/publicdomain/label/1.0/","mediaType":"application/gzip","title":"dark-seedlings_R1_001.fastq.gz"},{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/44541110","format":"gz","license":"https://www.usa.gov/publicdomain/label/1.0/","mediaType":"application/gzip","title":"dark-seedlings_R2_001.fastq.gz"},{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/44541119","format":"gz","license":"https://www.usa.gov/publicdomain/label/1.0/","mediaType":"application/gzip","title":"flower-tissue_R1_001.fastq.gz"},{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/44541125","format":"gz","license":"https://www.usa.gov/publicdomain/label/1.0/","mediaType":"application/gzip","title":"flower-tissue_R2_001.fastq.gz"},{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/44541134","format":"gz","license":"https://www.usa.gov/publicdomain/label/1.0/","mediaType":"application/gzip","title":"cold-meristem_R1_001.fastq.gz"},{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/44541152","format":"gz","license":"https://www.usa.gov/publicdomain/label/1.0/","mediaType":"application/gzip","title":"cold-meristem_R2_001.fastq.gz"},{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/44541158","format":"gz","license":"https://www.usa.gov/publicdomain/label/1.0/","mediaType":"application/zip","title":"DTG-OmniC-232_R1_001.fastq.gz"},{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/44541167","format":"gz","license":"https://www.usa.gov/publicdomain/label/1.0/","mediaType":"application/zip","title":"DTG-OmniC-232_R2_001.fastq.gz"}],"identifier":"10.15482/USDA.ADC/1529450","keyword":["ARS","HiRise scaffolding","Linum","NP301","S-Locus","crop domestication","data.gov","gene annotation","genome assembly","heterostyly","homostyly","mating system evolution","oilseed crop","perennials","sustainable agriculture","transposable elements"],"license":"https://www.usa.gov/publicdomain/label/1.0/","modified":"2025-11-21","programCode":["005:040"],"publisher":{"@type":"org:Organization","name":"Agricultural Research Service"},"spatial":"[{\"@type\": \"Location\", \"prefLabel\": \"{\\\"type\\\": \\\"Point\\\", \\\"coordinates\\\": [-113.11111450195, 39.035886280382]}\"}]","temporal":"[{\"@type\": \"PeriodOfTime\", \"endDate\": \"2020-12-15\", \"startDate\": \"2020-12-15\"}]","title":"Data from: Chromosome-scale Genome Assembly of Lewis Flax (Linum lewisii Pursh.)"},"description":"Within this dataset:
\nLinum lewisii, a perennial blue flax native to North America, holds potential as a sustainable perennial crop for oilseed production due to its ecological adaptability, upright harvestable structure, nutritious seeds, and low insect and disease issues. Its native distribution spans a large geographic range, from the Pacific Coast to the Mississippi River, and from Alaska to Baja California. Tolerant to cold and drought conditions, this species is also important for native ecosystem rehabilitation. Its enhancement of soil health, support for pollinators, and carbon sequestration underscore its agricultural relevance.
\nThis study presents a high-quality, chromosome-scale assembly of the L. lewisii (2n = 2x = 18) genome, derived from PacBio HiFi and Dovetail Omni-C sequencing of the \u201cMaple Grove\u201d variety. The initial assembly contained 642,903,787 base pairs across 2,924 scaffolds. Following HiRise scaffolding, the final assembly contained 643,041,835 base pairs, across 1,713 scaffolds, yielding an N50 contig length of 66,209,717 base pairs. Annotation of the assembly revealed 38,808 genes, including 37,599 protein-coding genes and 7,108 putative transposable elements. Analysis of synteny with other flax species revealed a striking number of chromosomal rearrangements. We also found an intriguing absence of the single-copy TSS1 gene in the L. lewisii genome, potentially linked to its transition from heterostyly to homostyly.
\nTaken together, these findings represent a significant advancement in our understanding of the Linum genus and provide a resource for future domestication efforts and basic research on Lewis flax.
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