cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15482/usda.adc/26771767
title: Data from: Assessing Nitrate Leaching During Drought and Extreme Precipitation: Exploring Deep Vadose-Zone Monitoring, Groundwater Observations, and Field Mass Balance

description: Our research on nitrate leaching during drought and extreme precipitation involved a multi-faceted methodology, combining field-level mass balance, deep vadose-zone monitoring, and shallow groundwater observation. The study was conducted in a 34-ha field in Yolo County, California, with a focus on a processing tomato-cucumber rotation. Groundwater monitoring wells and a deep vadose-zone monitoring system were used to assess nitrate leaching throughout the growing and rainy seasons.
Field-level water and nitrogen mass balances were calculated using irrigation data, crop evapotranspiration (measured via eddy covariance and remote sensing), and soil water storage changes. Nitrogen inputs and outputs were meticulously recorded, considering fertigation practices, mineralization, and plant uptake.
The study adhered to ethical and legal standards, with all data collection approved by relevant authorities, ensuring compliance with environmental monitoring regulations. Our results highlight the variability and uncertainty in nitrate leaching estimates, underscoring the importance of continuous monitoring to accurately assess agricultural impacts on groundwater quality.
This methodology can be reproduced by following the detailed steps outlined in our data description and published paper.
The necessary context, methodology, and techniques, along with information on legal and ethical adherence, are detailed in the attached files 'Data description' and 'Manuscript'.
DOI: 10.15482/usda.adc/26771767.v1
title: Data from: Assessing Nitrate Leaching During Drought and Extreme Precipitation: Exploring Deep Vadose-Zone Monitoring, Groundwater Observations, and Field Mass Balance

description: Our research on nitrate leaching during drought and extreme precipitation involved a multi-faceted methodology, combining field-level mass balance, deep vadose-zone monitoring, and shallow groundwater observation. The study was conducted in a 34-ha field in Yolo County, California, with a focus on a processing tomato-cucumber rotation. Groundwater monitoring wells and a deep vadose-zone monitoring system were used to assess nitrate leaching throughout the growing and rainy seasons.
Field-level water and nitrogen mass balances were calculated using irrigation data, crop evapotranspiration (measured via eddy covariance and remote sensing), and soil water storage changes. Nitrogen inputs and outputs were meticulously recorded, considering fertigation practices, mineralization, and plant uptake.
The study adhered to ethical and legal standards, with all data collection approved by relevant authorities, ensuring compliance with environmental monitoring regulations. Our results highlight the variability and uncertainty in nitrate leaching estimates, underscoring the importance of continuous monitoring to accurately assess agricultural impacts on groundwater quality.
This methodology can be reproduced by following the detailed steps outlined in our data description and published paper.
The necessary context, methodology, and techniques, along with information on legal and ethical adherence, are detailed in the attached files 'Data description' and 'Manuscript'.
DOI: 10.15482/usda.adc/28507400
title: Multi-Sensor Integration and Machine Learning for High-Resolution Classification of Herbivore Foraging Behavior

description: The study used Random Test-Split (RTS) and Cross-Validation (CV) machine learning methods to test different models to classify cattle behavior foraging behaviors states, foraging activities, posture, and activity by posture, using GPS coupled accelerometer data with 12-hour / days continuous recording observation as supporting ground truth. RTS in XGBoost performing best for general activity state classification, while CV in Random Forest excelled in more detailed foraging activities and activity-posture classifications. Key movement indicators like speed, Actindex and sensor values (x, y, and z) were vital in predicting behaviors, suggesting specific sensors for tracking behaviors of interest to ranchers. The results highlight the benefits of continuous monitoring and advanced data analysis for real-time livestock tracking, leading to better grazing management, improved animal welfare, and more sustainable land use.
DOI: 10.15482/usda.adc/28507400.v1
title: Multi-Sensor Integration and Machine Learning for High-Resolution Classification of Herbivore Foraging Behavior

description: The study used Random Test-Split (RTS) and Cross-Validation (CV) machine learning methods to test different models to classify cattle behavior foraging behaviors states, foraging activities, posture, and activity by posture, using GPS coupled accelerometer data with 12-hour / days continuous recording observation as supporting ground truth. RTS in XGBoost performing best for general activity state classification, while CV in Random Forest excelled in more detailed foraging activities and activity-posture classifications. Key movement indicators like speed, Actindex and sensor values (x, y, and z) were vital in predicting behaviors, suggesting specific sensors for tracking behaviors of interest to ranchers. The results highlight the benefits of continuous monitoring and advanced data analysis for real-time livestock tracking, leading to better grazing management, improved animal welfare, and more sustainable land use.
DOI: 10.15482/usda.adc/26349127.v1
title: <b>Data from: Do species respond to multi-year disturbance rhythms and are we missing the beat?</b>

description: A 15-yr (2006-2020) experiment testing factorially-arranged combinations of fire seasonality (summer, fall, spring) and fire return interval (2, 3, 6-yr) plus non-burned controls was conducted in northern mixed prairie to evaluate effects on <em>Hesperostipa comata</em>. Plots were 20-by-15 m with 3 replicates per treatment. Summer fires were conducted during summer quiescence, fall fires after a &lt;-2.2<sup>o</sup>C frost and spring fires when <em>Bouteloua gracilis</em> initiated growth. Current-year aboveground biomass was estimated annually by clipping four 0.25-m<sup>2</sup> quadrats from each plot during peak biomass. Biomass samples were dried to a constant weight at 60°C and weighed to the nearest 0.01 g before being separated into current and past years’ growth and reweighed to determine current-year biomass as an estimate of productivity.
DOI: 10.15482/usda.adc/26349127
title: <b>Data from: Do species respond to multi-year disturbance rhythms and are we missing the beat?</b>

description: A 15-yr (2006-2020) experiment testing factorially-arranged combinations of fire seasonality (summer, fall, spring) and fire return interval (2, 3, 6-yr) plus non-burned controls was conducted in northern mixed prairie to evaluate effects on <em>Hesperostipa comata</em>. Plots were 20-by-15 m with 3 replicates per treatment. Summer fires were conducted during summer quiescence, fall fires after a &lt;-2.2<sup>o</sup>C frost and spring fires when <em>Bouteloua gracilis</em> initiated growth. Current-year aboveground biomass was estimated annually by clipping four 0.25-m<sup>2</sup> quadrats from each plot during peak biomass. Biomass samples were dried to a constant weight at 60°C and weighed to the nearest 0.01 g before being separated into current and past years’ growth and reweighed to determine current-year biomass as an estimate of productivity.
DOI: 10.15482/usda.adc/26397901
title: Infrared Thermometer Data at the Weighing Lysimeters at Bushland, Texas

description: Directional brightness temperatures of fallow and cropped surfaces that were sensed by wireless infrared thermometers (IRTs) that were deployed at various positions and view angles over the four large weighing lysimeters in 15-minute time steps at the Conservation and Production Research Laboratory, Bushland, Texas, USA.
DOI: 10.15482/usda.adc/26397901.v1
title: Infrared Thermometer Data at the Weighing Lysimeters at Bushland, Texas

description: Directional brightness temperatures of fallow and cropped surfaces that were sensed by wireless infrared thermometers (IRTs) that were deployed at various positions and view angles over the four large weighing lysimeters in 15-minute time steps at the Conservation and Production Research Laboratory, Bushland, Texas, USA.
DOI: 10.15482/usda.adc/26946823
title: UAV image and ground data of citrus 'Bingo' mandarin hybrid (<i>Citrus </i>reticulata, Blanco) rootstock trial

description: The data are UAV ( (Unmanned Aerial Vehicle)   and individual tree ground measurements collected in 2021 of 206 trees that were part of a citrus rootstock trial at the U.S. Horticultural Research Laboratory Picos Road farm site, Ft. Pierce, Florida, USA located at 27.437137958441877, -80.42670096728109. The trees were the Bingo mandarin hybrid scion grafted onto 14 rootstock varieties and were planted in the field in 2018. The UAV images were taken using a DJI Phantom 4 Pro drone flown at an altitude of 46 meters, a speed of 12 kph, and flight lines with 80% side-overlap and 70% forward-overlap. The images are suitable for orthorectification. The images were red-green-blue (sRGB) in a 3:2 format with 5472 x 3648 pixels. The data includes 46 UAV images, a spreadsheet that includes the planting plan and ground measures, and a composite image constructed from the 46 UAV images with the rows and tree numbers labeled. The images are numbered in the order they were taken during the flight. The composite image is useful for general orientation and matching the individual trees to the ground data and other post-processing image analyses.
DOI: 10.15482/usda.adc/26946823.v1
title: UAV image and ground data of citrus 'Bingo' mandarin hybrid (<i>Citrus </i>reticulata, Blanco) rootstock trial

description: The data are UAV ( (Unmanned Aerial Vehicle)   and individual tree ground measurements collected in 2021 of 206 trees that were part of a citrus rootstock trial at the U.S. Horticultural Research Laboratory Picos Road farm site, Ft. Pierce, Florida, USA located at 27.437137958441877, -80.42670096728109. The trees were the Bingo mandarin hybrid scion grafted onto 14 rootstock varieties and were planted in the field in 2018. The UAV images were taken using a DJI Phantom 4 Pro drone flown at an altitude of 46 meters, a speed of 12 kph, and flight lines with 80% side-overlap and 70% forward-overlap. The images are suitable for orthorectification. The images were red-green-blue (sRGB) in a 3:2 format with 5472 x 3648 pixels. The data includes 46 UAV images, a spreadsheet that includes the planting plan and ground measures, and a composite image constructed from the 46 UAV images with the rows and tree numbers labeled. The images are numbered in the order they were taken during the flight. The composite image is useful for general orientation and matching the individual trees to the ground data and other post-processing image analyses.
DOI: 10.15482/usda.adc/26665372
title: Image data of growth of Valencia sweet orange nonembryogenic cells on predicted points from a 5-factor response surface design

description: The data are images of tissue cultures of Valencia sweet orange nonembryogenic callus cells taken in 2006 at the U.S. Horticultural Research Laboratory, Ft. Pierce, Florida, USA to photo document treatments from an experiment designed to determine how well a model developed from a 5-factor response surface methodology (RSM) design predicted callus growth. Growth predictions for thirteen formulations are listed in the spreadsheet file <b>5 factor RSM Design_Predicted.xlsx</b>. Six culture dishes were used to estimate growth for each formulation. A culture dish representative of the growth of each formulation was photographed. Each image is named to match the formulation. For example, the jpg image labeled <b>5-factor RSM_MS prediction 21.JPG</b> is an image of the callus grown on MS formulation #21. This dataset includes 15 files – 14 image files and 1 Excel spreadsheet. Images were captured in JPEG (EXIF 2.2) format with a Nikon Coolpix 5400 digital camera equipped with a 1/1.8” (7.2 x 5.3 mm) CCD sensor at a resolution of 2592 x 1944 pixels. Each image is of a single culture plate with the top lid removed and photographed under cool white, fluorescent lighting.The experimental setup for the 5-factor response surface design is described in - Niedz, R. P. and T. J. Evens (2007). "Regulating plant tissue growth by mineral nutrition." In Vitro Cellular & Developmental Biology - Plant 43(4): 370-381.
DOI: 10.15482/usda.adc/26665372.v1
title: Image data of growth of Valencia sweet orange nonembryogenic cells on predicted points from a 5-factor response surface design

description: The data are images of tissue cultures of Valencia sweet orange nonembryogenic callus cells taken in 2006 at the U.S. Horticultural Research Laboratory, Ft. Pierce, Florida, USA to photo document treatments from an experiment designed to determine how well a model developed from a 5-factor response surface methodology (RSM) design predicted callus growth. Growth predictions for thirteen formulations are listed in the spreadsheet file <b>5 factor RSM Design_Predicted.xlsx</b>. Six culture dishes were used to estimate growth for each formulation. A culture dish representative of the growth of each formulation was photographed. Each image is named to match the formulation. For example, the jpg image labeled <b>5-factor RSM_MS prediction 21.JPG</b> is an image of the callus grown on MS formulation #21. This dataset includes 15 files – 14 image files and 1 Excel spreadsheet. Images were captured in JPEG (EXIF 2.2) format with a Nikon Coolpix 5400 digital camera equipped with a 1/1.8” (7.2 x 5.3 mm) CCD sensor at a resolution of 2592 x 1944 pixels. Each image is of a single culture plate with the top lid removed and photographed under cool white, fluorescent lighting.The experimental setup for the 5-factor response surface design is described in - Niedz, R. P. and T. J. Evens (2007). "Regulating plant tissue growth by mineral nutrition." In Vitro Cellular & Developmental Biology - Plant 43(4): 370-381.
DOI: 10.15482/usda.adc/26663131
title: Image data of growth of Valencia sweet orange nonembryogenic cells on points from a 3-factor response surface design to determine the effects of mineral nutrition on growth

description: The data are images of tissue cultures of Valencia sweet orange nonembryogenic callus cells taken in 2005 at the U.S. Horticultural Research Laboratory, Ft. Pierce, Florida, USA to photo document treatments from an experiment designed to determine the effects of mineral nutrition on callus growth using a 3-factor response surface methodology (RSM) design. The design matrix is presented in the spreadsheet file <b>3 factor RSM Design_7-1-2005.xlsx</b>. The design included 20 runs. Six culture dishes were used to estimate the response for each run. A culture dish representative of each run was photographed. Each image is named to match the run number of the design. For example, the jpg image labeled <b>Run 13_3F RSM_NE_Valencia_7-1-2005.JPG</b> is an image of the callus grown on Run #13 of the 3-factor RSM design for the nonembryogenic Valencia sweet orange callus cells. This dataset includes 21 files – 20 image files and 1 Excel spreadsheet. Images were captured in JPEG (EXIF 2.2) format with a Nikon Coolpix 5400 digital camera equipped with a 1/1.8” (7.2 x 5.3 mm) CCD sensor at a resolution of 2592 x 1944 pixels. Each image is of a single culture plate with the top lid removed and photographed under cool white, fluorescent lighting.The experimental setup for the 5-factor response surface design is described in - Niedz, R. P. and T. J. Evens (2007). "Regulating plant tissue growth by mineral nutrition." In Vitro Cellular & Developmental Biology - Plant 43(4): 370-381.
DOI: 10.15482/usda.adc/26663131.v1
title: Image data of growth of Valencia sweet orange nonembryogenic cells on points from a 3-factor response surface design to determine the effects of mineral nutrition on growth

description: The data are images of tissue cultures of Valencia sweet orange nonembryogenic callus cells taken in 2005 at the U.S. Horticultural Research Laboratory, Ft. Pierce, Florida, USA to photo document treatments from an experiment designed to determine the effects of mineral nutrition on callus growth using a 3-factor response surface methodology (RSM) design. The design matrix is presented in the spreadsheet file <b>3 factor RSM Design_7-1-2005.xlsx</b>. The design included 20 runs. Six culture dishes were used to estimate the response for each run. A culture dish representative of each run was photographed. Each image is named to match the run number of the design. For example, the jpg image labeled <b>Run 13_3F RSM_NE_Valencia_7-1-2005.JPG</b> is an image of the callus grown on Run #13 of the 3-factor RSM design for the nonembryogenic Valencia sweet orange callus cells. This dataset includes 21 files – 20 image files and 1 Excel spreadsheet. Images were captured in JPEG (EXIF 2.2) format with a Nikon Coolpix 5400 digital camera equipped with a 1/1.8” (7.2 x 5.3 mm) CCD sensor at a resolution of 2592 x 1944 pixels. Each image is of a single culture plate with the top lid removed and photographed under cool white, fluorescent lighting.The experimental setup for the 5-factor response surface design is described in - Niedz, R. P. and T. J. Evens (2007). "Regulating plant tissue growth by mineral nutrition." In Vitro Cellular & Developmental Biology - Plant 43(4): 370-381.
DOI: 10.15482/usda.adc/26514073
title: Image data of growth of Valencia sweet orange nonembryogenic cells on points from a 5-factor response surface design to determine the effects of mineral nutrition on growth

description: The data are images of tissue cultures of Valencia sweet orange nonembryogenic callus cells taken in 2006 at the U.S. Horticultural Research Laboratory, Ft. Pierce, Florida, USA to photo document treatments from an experiment designed to determine the effects of mineral nutrition on callus growth using a 5-factor response surface methodology (RSM) design. The design matrix is presented in the spreadsheet file <b>5 factor RSM Design_11-28-2005.xlsx</b>. The design included 46 runs divided into 3 blocks. Six culture dishes were used to estimate the response for each run. A culture dish representative of the run was photographed. Each image is named to match the run of the design. For example, the jpg image labeled <b>Run 04_Blk1_5F RSM_NE_Valencia_11-28-2005.JPG</b> is an image of the callus grown on Run #4 that was part of Block 1 of the 5-factor RSM design for the nonembryogenic Valencia sweet orange callus cells. This dataset includes 47 files – 46 image files and 1 Excel spreadsheet. Images were captured in JPEG (EXIF 2.2) format with a Nikon Coolpix 5400 digital camera equipped with a 1/1.8” (7.2 x 5.3 mm) CCD sensor at a resolution of 2592 x 1944 pixels. Each image is of a single culture plate with the top lid removed and photographed under cool white, fluorescent lighting.The experimental setup for the 5-factor response surface design is described in - Niedz, R. P. and T. J. Evens (2007). "Regulating plant tissue growth by mineral nutrition." In Vitro Cellular & Developmental Biology - Plant 43(4): 370-381.
DOI: 10.15482/usda.adc/26514073.v1
title: Image data of growth of Valencia sweet orange nonembryogenic cells on points from a 5-factor response surface design to determine the effects of mineral nutrition on growth

description: The data are images of tissue cultures of Valencia sweet orange nonembryogenic callus cells taken in 2006 at the U.S. Horticultural Research Laboratory, Ft. Pierce, Florida, USA to photo document treatments from an experiment designed to determine the effects of mineral nutrition on callus growth using a 5-factor response surface methodology (RSM) design. The design matrix is presented in the spreadsheet file <b>5 factor RSM Design_11-28-2005.xlsx</b>. The design included 46 runs divided into 3 blocks. Six culture dishes were used to estimate the response for each run. A culture dish representative of the run was photographed. Each image is named to match the run of the design. For example, the jpg image labeled <b>Run 04_Blk1_5F RSM_NE_Valencia_11-28-2005.JPG</b> is an image of the callus grown on Run #4 that was part of Block 1 of the 5-factor RSM design for the nonembryogenic Valencia sweet orange callus cells. This dataset includes 47 files – 46 image files and 1 Excel spreadsheet. Images were captured in JPEG (EXIF 2.2) format with a Nikon Coolpix 5400 digital camera equipped with a 1/1.8” (7.2 x 5.3 mm) CCD sensor at a resolution of 2592 x 1944 pixels. Each image is of a single culture plate with the top lid removed and photographed under cool white, fluorescent lighting.The experimental setup for the 5-factor response surface design is described in - Niedz, R. P. and T. J. Evens (2007). "Regulating plant tissue growth by mineral nutrition." In Vitro Cellular & Developmental Biology - Plant 43(4): 370-381.
DOI: 10.15482/usda.adc/28628507
title: Grape Vine Shoot Length Data

description: We collected grapevine shoot growth over a growing season of 2024 (April to June) in a vineyard of the horticultural unit 2 farm of the Ohio State University (40.73866822022149, -81.90273359323078). The measurements were made with a measuring tape.
DOI: 10.15482/usda.adc/28628507.v1
title: Grape Vine Shoot Length Data

description: We collected grapevine shoot growth over a growing season of 2024 (April to June) in a vineyard of the horticultural unit 2 farm of the Ohio State University (40.73866822022149, -81.90273359323078). The measurements were made with a measuring tape.
DOI: 10.15482/usda.adc/28705052
title: Survivorship and gut microbial community of <i>Bombus vosnesenskii </i>exposed to an herbicide, a fungicide, an insecticide, or the combination of all three chemicals

description: We investigated site-level differences in survival and microbiome structure of wild bumble bees exposed to multiple pesticides, both individually and in combination. We collected wild <i>Bombus vosnesenskii</i> foragers (<i>N</i> = 175) from an alpine meadow, a valley lake shoreline, and a suburban park and maintained them on a diet containing an herbicide (glyphosate), a fungicide (tebuconazole), an insecticide (imidacloprid), or a combination of these chemicals. The herbicide treatment was 30 ppm of glyphosate (Sigma-Aldrich, USA), the fungicide treatment was 30 ppb of tebuconazole (Sigma-Aldrich, USA), and the insecticide treatment was 30 ppb of imidacloprid (Sigma-Aldrich, USA). These chemicals were selected because they had the highest estimated use for each chemical class (insecticide, herbicide, fungicide) in our region (Washoe county, Nevada, (Wieben 2019)). These concentrations of imidacloprid and tebuconazole approximate the median of those detected in nectar (Zioga et al. 2020). The concentration we chose for glyphosate is within the range of field-realistic exposure from treated plants (Herbert et al. 2014, Thompson et al. 2022, Helander et al. 2023b) although is likely higher than bees would experience foraging from <i>non</i>-target plants (Zioga et al. 2023). We monitored survival daily for 20 days. Every 3 days, we replaced feeders with a clean feeder with 1000 mL of solution. We continued the experiment until bees died or at day 20 (whichever came first) at which point we weighed the bees, measured their intertegular (IT) span to estimate body size (Cane 1987) and dissected out the midgut and hindgut (hereafter ‘gut’) for DNA extractions and 16s sequencing (see below).<br><br>