cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15482/usda.adc/25988122.v1
title: Data from: Genetic diversity and classification of <i>Colletotrichum sublineola</i> pathotypes using a standard set of sorghum differentials

description: A total of 30 isolates of Colletotrichum sublineola, causal agent of sorghum anthracnose were collected from Georgia, North Carolina, Puerto Rico, and Texas. Data for their virulence patterns on 18 sorghum differentials, RTx2536, SC748-5, Martin (BTx398), TAM428, BTx430, Brandes, SC112-14, Theis, BTx378, SC326-6, SC283, BTx623, SC328C, SC414-12E, PI570841, PI570726, PI569979, and IS18760 were collected from greenhouse trials in College Station, Texas by Louis K. Prom, Ezekiel J. S. Ahn, Ramasamy Perumal, Hugo E. Cuevas, William L. Rooney, Thomas S. Isakeit, and Clint W. Magill between 2014 and 2018. As described by Prom et al. 2012 and 2024, plants were assessed for anthracnose infection twice, 30 days post-inoculation and a week later using disease rating scale 1-5, where 1 = no symptoms or chlorotic flecks on leaves; 2 = hypersensitive reaction (reddening or red spots) on inoculated leaves but no acervuli formation; 3 = lesions on inoculated and bottom leaves with acervuli in the center; 4 = necrotic lesions with acervuli observed on inoculated and bottom leaves with infection spreading to middle leaves, and not yet on the flag leaves; and 5 = most leaves dead due to infection with infection on the flag leaf containing abundant acervuli. The symptom types were then categorized into two reaction classes, resistant = rating 1 or 2; and susceptible = rating 3, 4, or 5. As noted in the dataset, R = resistant response and S = susceptible response.<br>
DOI: 10.15482/usda.adc/25843822
title: Data from: A genome-wide association study of Nigerien and Senegalese sorghum germplasm of Exserohilum turcicum, the causal agent of leaf blight

description: Sorghum supplies the daily calorie needs of millions of inhabitants in Senegal, West Africa. However, <i>Exserohilum </i><i>turcicum</i>, causal agent of leaf blight significantly reduces the crop's productivity and profitability. Data based on a total of 120 sorghum lines (60 lines each from Niger and Senegal) planted in replicated plots in three locations in Senegal, West Africa. The leaf blight incidence and severity were collected by Louis K Prom, Mame Penda Sarr, Cyril Diatta, Coumba Fall, and Clint Magill. Disease incidence was based on the number of plants infected with leaf blight in a row divided by the total number of plants in the same row multiplied by 100. The severity scale 0 to 11 with mid-points, where 1=5.5, 2=15.5, 3=25.5, 4=35.5, 5=45.5, 6=55.5, 7=65.5, 8=75.5, 9=85.5, 10=95.5, and 11=100 previously described by Prom et al. 2023 was used. the dataset was used to perform genome-wide association studies to identify single-nucleotide polymorphisms associated with leaf blight incidence.
DOI: 10.15482/usda.adc/25843822.v1
title: Data from: A genome-wide association study of Nigerien and Senegalese sorghum germplasm of Exserohilum turcicum, the causal agent of leaf blight

description: Sorghum supplies the daily calorie needs of millions of inhabitants in Senegal, West Africa. However, <i>Exserohilum </i><i>turcicum</i>, causal agent of leaf blight significantly reduces the crop's productivity and profitability. Data based on a total of 120 sorghum lines (60 lines each from Niger and Senegal) planted in replicated plots in three locations in Senegal, West Africa. The leaf blight incidence and severity were collected by Louis K Prom, Mame Penda Sarr, Cyril Diatta, Coumba Fall, and Clint Magill. Disease incidence was based on the number of plants infected with leaf blight in a row divided by the total number of plants in the same row multiplied by 100. The severity scale 0 to 11 with mid-points, where 1=5.5, 2=15.5, 3=25.5, 4=35.5, 5=45.5, 6=55.5, 7=65.5, 8=75.5, 9=85.5, 10=95.5, and 11=100 previously described by Prom et al. 2023 was used. the dataset was used to perform genome-wide association studies to identify single-nucleotide polymorphisms associated with leaf blight incidence.
DOI: 10.15482/usda.adc/25707396
title: Data from: Correlations among agronomic traits obtained from sorghum accessions planted in a field infected with three important fungal diseases

description: Data based on a total of 179 sorghum accessions from Ethiopia, Gambia, and Senegal maintained by the USDA-ARS, Plant Genetic Resources Conservation Unit, Griffin, Georgia, USA, and planted in replicated plots in Isabela, Puerto Rico. Data for anthracnose, grain mold and rust infections with various agronomic traits such as anthracnose, grain mold, rust, seed weight, percent germination rate, and grain yield, seed weight, panicle height, panicle length, and flowering time were collected in 2017 and 2018 by L. K. Prom, H. Cuevas, E. Ahn, T. S. Isakeit, and C. W. Magill. The disease rating was based on previously published scale of 1 to 5. Seed weight in grams was based on the weight of 100 randomly selected seeds from each replication. Percent germination based on the number of seeds that germinated out of 100 seeds placed on germination paper. At maturity, plant height was measured from the soil to the top of the plant in centimeters, and panicle length was measured from the first branch with racemes to the top of the panicle. Grain yield in grams was based on three harvested panicles per accession, and each panicle was threshed and weighed in grams. The flowering date was based on Julian days.<br>
DOI: 10.15482/usda.adc/25707396.v1
title: Data from: Correlations among agronomic traits obtained from sorghum accessions planted in a field infected with three important fungal diseases

description: Data based on a total of 179 sorghum accessions from Ethiopia, Gambia, and Senegal maintained by the USDA-ARS, Plant Genetic Resources Conservation Unit, Griffin, Georgia, USA, and planted in replicated plots in Isabela, Puerto Rico. Data for anthracnose, grain mold and rust infections with various agronomic traits such as anthracnose, grain mold, rust, seed weight, percent germination rate, and grain yield, seed weight, panicle height, panicle length, and flowering time were collected in 2017 and 2018 by L. K. Prom, H. Cuevas, E. Ahn, T. S. Isakeit, and C. W. Magill. The disease rating was based on previously published scale of 1 to 5. Seed weight in grams was based on the weight of 100 randomly selected seeds from each replication. Percent germination based on the number of seeds that germinated out of 100 seeds placed on germination paper. At maturity, plant height was measured from the soil to the top of the plant in centimeters, and panicle length was measured from the first branch with racemes to the top of the panicle. Grain yield in grams was based on three harvested panicles per accession, and each panicle was threshed and weighed in grams. The flowering date was based on Julian days.<br>
DOI: 10.15482/usda.adc/25723959
title: Data From: Gamma-Spectroscopy Method for Soil Water Content Estimation in an Agricultural Field

description: Gamma-ray spectroscopy (GRS) enables continuous estimation of soil water content (SWC) at the subfield scale with a noninvasive sensor. Hydrological applications, including hyper-resolution land surface models and precision agricultural decision making, could benefit greatly from such SWC information, but a gap exists between established theory and accurate estimation of SWC from GRS in the field. In response, we conducted a robust three-year field validation study at a well instrumented agricultural site in Nebraska, United States. The study involved 27 gravimetric water content sampling campaigns in maize and soybean and <sup>40</sup>K specific activity (Bq kg<sup>−1</sup>) measurements from a stationary GRS sensor. Our analysis showed that the current method for biomass water content correction is appropriate for our maize and soybean field but that the ratio of soil mass attenuation to water mass attenuation used in the theoretical equation must be adjusted to satisfactorily describe the field data. We propose a calibration equation with two free parameters: the theoretical <sup>40</sup>K intensity in dry soil and a, which creates an “effective” mass attenuation ratio. Based on statistical analyses of our data set, we recommend calibrating the GRS sensor for SWC estimation using 10 profiles within the footprint and 5 calibration sampling campaigns to achieve a cross-validation root mean square error below 0.035 g g<sup>−1</sup>.
DOI: 10.15482/usda.adc/25723959.v1
title: Data From: Gamma-Spectroscopy Method for Soil Water Content Estimation in an Agricultural Field

description: Gamma-ray spectroscopy (GRS) enables continuous estimation of soil water content (SWC) at the subfield scale with a noninvasive sensor. Hydrological applications, including hyper-resolution land surface models and precision agricultural decision making, could benefit greatly from such SWC information, but a gap exists between established theory and accurate estimation of SWC from GRS in the field. In response, we conducted a robust three-year field validation study at a well instrumented agricultural site in Nebraska, United States. The study involved 27 gravimetric water content sampling campaigns in maize and soybean and <sup>40</sup>K specific activity (Bq kg<sup>−1</sup>) measurements from a stationary GRS sensor. Our analysis showed that the current method for biomass water content correction is appropriate for our maize and soybean field but that the ratio of soil mass attenuation to water mass attenuation used in the theoretical equation must be adjusted to satisfactorily describe the field data. We propose a calibration equation with two free parameters: the theoretical <sup>40</sup>K intensity in dry soil and a, which creates an “effective” mass attenuation ratio. Based on statistical analyses of our data set, we recommend calibrating the GRS sensor for SWC estimation using 10 profiles within the footprint and 5 calibration sampling campaigns to achieve a cross-validation root mean square error below 0.035 g g<sup>−1</sup>.
DOI: 10.15482/usda.adc/27682716
title: Data from: Raccoon density estimation from camera traps for rabies virus management

description: During the springs of 2019 and 2020, we conducted camera trapping at 12 sites at the Savannah River Site in South Carolina. These sites were the same locations where Hill et al. (2023) conducted live trapping. The objective of our study was to determine if we could reliably estimate raccoon densities from images of unmarked animals. We conducted the camera trap study in the location where a live-trap mark-recapture study of raccoons was being conducted (Hill et al. 2023) to know what densities at each site should be.At each site we installed camera traps (Reconyx PC900 Hyperfire Professional IR cameras; Reconyx, Holmen, WI, USA) ~150 cm above the ground using stainless steel camera mounts and we angled cameras to ensure our bait was in the center of the frame. We baited each camera with a single plaster tab scented with fish oil, which we replaced each week. We deployed 34 cameras at each site. We used a 25-camera array (5×5 array with cameras at 100-m spacing) located in the same locations where box traps were deployed during live-trapping sessions. In addition, we added a 9-camera array (3×3 array with 200-m spacing) next to the 25-camera array to allow for a broader examination of effort reduction. We set cameras to take 3 pictures per motion-trigger event on a 1-second interval with no quiet period. We ran camera traps from 21-24 days for each site (exception for first round of 2019, which ran for 15 days). We recorded the date and time (DateTimeOriginal), if the photo used infrared (InfraredIlluminator - 1=Yes, 0=No), the species observed and the number of each (ID), the site name designation (Site), the camera name designation (camera), and an indicator if the image was the first in the motion capture burst (firstImage) for each photo. We also gave each image record a unique record ID (RecordID). The species and number of species in the record are denoted by a short species code designation (see SpeciesIDCode table for interpretation). The number of repeats of a species code indicates the number of individuals of that species seen on an image. Related study: Hill, J. E., J. L. Helton, D. A. Bernasconi, W. C. Dixon, M. T. Hamilton, R. B. Chipman, A. T. Gilbert, J. C. Beasley, G. Dharmarajan, and O. E. Rhodes Jr. 2023. Raccoon densities across four land cover types in the southeastern United States. Journal of Wildlife Management 87:e22480.
DOI: 10.15482/usda.adc/27682716.v1
title: Data from: Raccoon density estimation from camera traps for rabies virus management

description: During the springs of 2019 and 2020, we conducted camera trapping at 12 sites at the Savannah River Site in South Carolina. These sites were the same locations where Hill et al. (2023) conducted live trapping. The objective of our study was to determine if we could reliably estimate raccoon densities from images of unmarked animals. We conducted the camera trap study in the location where a live-trap mark-recapture study of raccoons was being conducted (Hill et al. 2023) to know what densities at each site should be.At each site we installed camera traps (Reconyx PC900 Hyperfire Professional IR cameras; Reconyx, Holmen, WI, USA) ~150 cm above the ground using stainless steel camera mounts and we angled cameras to ensure our bait was in the center of the frame. We baited each camera with a single plaster tab scented with fish oil, which we replaced each week. We deployed 34 cameras at each site. We used a 25-camera array (5×5 array with cameras at 100-m spacing) located in the same locations where box traps were deployed during live-trapping sessions. In addition, we added a 9-camera array (3×3 array with 200-m spacing) next to the 25-camera array to allow for a broader examination of effort reduction. We set cameras to take 3 pictures per motion-trigger event on a 1-second interval with no quiet period. We ran camera traps from 21-24 days for each site (exception for first round of 2019, which ran for 15 days). We recorded the date and time (DateTimeOriginal), if the photo used infrared (InfraredIlluminator - 1=Yes, 0=No), the species observed and the number of each (ID), the site name designation (Site), the camera name designation (camera), and an indicator if the image was the first in the motion capture burst (firstImage) for each photo. We also gave each image record a unique record ID (RecordID). The species and number of species in the record are denoted by a short species code designation (see SpeciesIDCode table for interpretation). The number of repeats of a species code indicates the number of individuals of that species seen on an image. Related study: Hill, J. E., J. L. Helton, D. A. Bernasconi, W. C. Dixon, M. T. Hamilton, R. B. Chipman, A. T. Gilbert, J. C. Beasley, G. Dharmarajan, and O. E. Rhodes Jr. 2023. Raccoon densities across four land cover types in the southeastern United States. Journal of Wildlife Management 87:e22480.
DOI: 10.15482/usda.adc/29610539
title: Effects of increasing supplementation rates of extruded distillers’ grain cubes on stocker steers production grazing native range in Bessie, OK and Fort Supply, OK during the summer months in 2021 and 2022

description: The objectives of this study were to determine the response curve of increasing supplementation rate with extruded distillers’ grains (DDG) cubes for animal performance and supplemental efficiency by steers grazing midgrass prairie in western Oklahoma. A 2-year trial was conducted at the Oklahoma Agricultural Experiment Station, Marvin Klemme Range Research Station (Klemme), Bessie, Oklahoma and the USDA-ARS, Southern Plains Experimental Range (SPER), Ft. Supply, Oklahoma, both in western Oklahoma during the summers of 2021 and 2022. At Klemme, steers (n = 133 Yr 1, BW = 247 ± 24.5 kg; Yr 2, n = 134, BW = 264 ± 29.1 kg) were allocated to 6 pastures in Yr 1 and 7 pastures in Yr 2 which were assigned to three daily supplementation rates of DDG cubes: 1) Negative Control (NC)- no supplementation; 2) Low Supplement (LS) - 0.91 kg/steer and 3) Medium Supplementation (MS) – 1.82 kg/steer. At Klemme, steers were stocked at 2.0 ha/steer in yr 1 and 2.8 ha/steer in yr 2. At SPER steers (n=119 each year; Yr 1, BW = 295 ± 28.8 kg; Yr 2, BW = 294 ± 25.7 kg) were allocated to 12 pastures stocked at 2.0 ha/steer with pastures assigned to: 1) NC; 2) LS; 3) MS and 4) High Supplement – daily supplementation rate of 2.72 kg/steer.
DOI: 10.15482/usda.adc/29610539.v1
title: Effects of increasing supplementation rates of extruded distillers’ grain cubes on stocker steers production grazing native range in Bessie, OK and Fort Supply, OK during the summer months in 2021 and 2022

description: The objectives of this study were to determine the response curve of increasing supplementation rate with extruded distillers’ grains (DDG) cubes for animal performance and supplemental efficiency by steers grazing midgrass prairie in western Oklahoma. A 2-year trial was conducted at the Oklahoma Agricultural Experiment Station, Marvin Klemme Range Research Station (Klemme), Bessie, Oklahoma and the USDA-ARS, Southern Plains Experimental Range (SPER), Ft. Supply, Oklahoma, both in western Oklahoma during the summers of 2021 and 2022. At Klemme, steers (n = 133 Yr 1, BW = 247 ± 24.5 kg; Yr 2, n = 134, BW = 264 ± 29.1 kg) were allocated to 6 pastures in Yr 1 and 7 pastures in Yr 2 which were assigned to three daily supplementation rates of DDG cubes: 1) Negative Control (NC)- no supplementation; 2) Low Supplement (LS) - 0.91 kg/steer and 3) Medium Supplementation (MS) – 1.82 kg/steer. At Klemme, steers were stocked at 2.0 ha/steer in yr 1 and 2.8 ha/steer in yr 2. At SPER steers (n=119 each year; Yr 1, BW = 295 ± 28.8 kg; Yr 2, BW = 294 ± 25.7 kg) were allocated to 12 pastures stocked at 2.0 ha/steer with pastures assigned to: 1) NC; 2) LS; 3) MS and 4) High Supplement – daily supplementation rate of 2.72 kg/steer.
DOI: 10.15482/usda.adc/28257980
title: Simulated Runoff Nitrogen and Phosphorus Data

description: Simulated runoff procured in field plots across three different cover crop treatments, using Cornell Infiltrometer Rainfall Simulator. NO3-N, NH4-N, PO4-P, and TN were measured to investigate the nutrient capture effectiveness of each of the cover crop treatments.This data set contains NO3-N, NH4-N, PO4-P, and Total Nitrogen (TN) concentration (mg/L) measurements from liquid runoff samples collected after simulated precipitation events. The runoff analyzed in this dataset was collected in Beltsville, MD at the Central Maryland Research and Education Center (CMREC) (39°00'46.0"N 76°49'35.7"W). Cornell sprinkler infiltrometers were utilized to generate simulated rainfall and runoff during December 2022 to April 2023 on two fields with contrasting soils. The first field, denoted “7E”, was a silt loam to silty clay loam with somewhat poorly drained soil and Russett-Christiana Complex soil map unit (lat 39.027207, lon -76.837643). The second field, denoted “39A”, was less than a mile away from the first field. This field was a sandy loam to loamy sand with excessively drained soil and Downer-Hammonton Complex soil map unit (lat 39.011941,lon -76.831742). During each sampling event five liters of runoff were collected as well as one liter of the deionized water used to create the “rain”, which was to be analyzed as a control in the nutrient analysis. The runoff collection was part of a greater experiment utilizing three different cover crop treatments (No Cover, 3-Way, and Rye) in two different cash crop residues (Corn and Soybean). Across the two field locations there were 36 sample locations spread evenly across cash crop residue and cover crop type. At each sample location 5L of runoff were collected and brought back to the lab at the University of Maryland College Park. A 45mL sub-sample of each liter of runoff collected was filtered using a 0.45μm membrane. After filtration the inorganic N concentrations, NO3-N and NH4-N, were determined colorimetrically using the Salicylate Method and the Endol Blue Method respectively. The inorganic P concentration, PO4-P, was determined using the Molybdate colorimetric test. The filtrate was also analyzed for the TN concentration by mixing the filtrate with a strong alkaline persulfate solution and heating the mixture for 1hr at 120 deg C under 16kpa in an autoclave. Following this, the mixture was analyzed for NO3-N and using the Salicylate Method, giving the total dissolved N concentration. All colorimetric analysis was performed using a DU 7000 Series Beckman Coulter Spectrophotometer and absorbance readings were converted into concentration (mg/L) using equations derived from the standard curves. <br>NO3-N (mg/L) = 135.5*absorbanceNH4-N (mg/L) = 55.7*absorbanceTN (mg/L) = 146.7*absorbance - 1.447PO4-P (mg/L) = 1.764*absorbance + 0.007++<br><br>
DOI: 10.15482/usda.adc/28257980.v1
title: Simulated Runoff Nitrogen and Phosphorus Data

description: Simulated runoff procured in field plots across three different cover crop treatments, using Cornell Infiltrometer Rainfall Simulator. NO3-N, NH4-N, PO4-P, and TN were measured to investigate the nutrient capture effectiveness of each of the cover crop treatments.This data set contains NO3-N, NH4-N, PO4-P, and Total Nitrogen (TN) concentration (mg/L) measurements from liquid runoff samples collected after simulated precipitation events. The runoff analyzed in this dataset was collected in Beltsville, MD at the Central Maryland Research and Education Center (CMREC) (39°00'46.0"N 76°49'35.7"W). Cornell sprinkler infiltrometers were utilized to generate simulated rainfall and runoff during December 2022 to April 2023 on two fields with contrasting soils. The first field, denoted “7E”, was a silt loam to silty clay loam with somewhat poorly drained soil and Russett-Christiana Complex soil map unit (lat 39.027207, lon -76.837643). The second field, denoted “39A”, was less than a mile away from the first field. This field was a sandy loam to loamy sand with excessively drained soil and Downer-Hammonton Complex soil map unit (lat 39.011941,lon -76.831742). During each sampling event five liters of runoff were collected as well as one liter of the deionized water used to create the “rain”, which was to be analyzed as a control in the nutrient analysis. The runoff collection was part of a greater experiment utilizing three different cover crop treatments (No Cover, 3-Way, and Rye) in two different cash crop residues (Corn and Soybean). Across the two field locations there were 36 sample locations spread evenly across cash crop residue and cover crop type. At each sample location 5L of runoff were collected and brought back to the lab at the University of Maryland College Park. A 45mL sub-sample of each liter of runoff collected was filtered using a 0.45μm membrane. After filtration the inorganic N concentrations, NO3-N and NH4-N, were determined colorimetrically using the Salicylate Method and the Endol Blue Method respectively. The inorganic P concentration, PO4-P, was determined using the Molybdate colorimetric test. The filtrate was also analyzed for the TN concentration by mixing the filtrate with a strong alkaline persulfate solution and heating the mixture for 1hr at 120 deg C under 16kpa in an autoclave. Following this, the mixture was analyzed for NO3-N and using the Salicylate Method, giving the total dissolved N concentration. All colorimetric analysis was performed using a DU 7000 Series Beckman Coulter Spectrophotometer and absorbance readings were converted into concentration (mg/L) using equations derived from the standard curves. <br>NO3-N (mg/L) = 135.5*absorbanceNH4-N (mg/L) = 55.7*absorbanceTN (mg/L) = 146.7*absorbance - 1.447PO4-P (mg/L) = 1.764*absorbance + 0.007++<br><br>