cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15482/usda.adc/25343293.v1
title: 2023 USDA Plant Hardiness Zone Map Mean Annual Extreme Low Temperature Rasters

description: These rasters provide the local mean annual extreme low temperature from 1991 to 2020 in an 800m x 800m grid covering the USA (including Puerto Rico) based on interpolation of data from more than a thousand weather stations. Each location's Plant Hardiness Zone is calculated based on classifying that temperature into 5 degree bands.The classified rasters are then used to create print and interactive maps.Temperature station data for the 2023 edition of the USDA Plant Hardiness Zone Map (PHZM) came from many different sources. In the eastern and central United States, Puerto Rico, and Hawaii, data came primarily from weather stations of the National Weather Service and several state networks. In the western United States and Alaska, data from stations maintained by USDA Natural Resources Conservation Service, USDA Forest Service, U.S. Department of the Interior (DOI) Bureau of Reclamation, and DOI Bureau of Land Management also helped to better define hardiness zones in mountainous areas. Environment Canada provided data from Canadian stations, and data from Mexican stations came from the Mexico National Weather Service and the Global Historical Climate Network. The USDA PHZM was produced with PRISM, a highly sophisticated climate mapping technology developed at Oregon State University. The map was produced from a digital computer grid, with each cell measuring about a half mile on a side. PRISM estimated the mean annual extreme minimum temperature for each grid cell (or pixel on the map) by examining data from nearby stations; determining how the temperature changed with elevation; and accounting for possible coastal effects, temperature inversions, and the type of topography (ridge top, hill slope, or valley bottom). Information on PRISM can be obtained from the PRISM Climate Group website https://prism.oregonstate.edu. Once a draft of the map was completed, it was reviewed by a team of climatologists, agricultural meteorologists, and horticultural experts. If the zone for an area appeared anomalous to these expert reviewers, experts doublechecked the draft maps for errors or biases. A detailed explanation of the mapmaking process and a discussion of the horticultural applications of the 2012 PHZM (similar to 2023) are available from the articles listed below. Daly, C., M.P. Widrlechner, M.D. Halbleib, J.I. Smith, and W.P. Gibson. 2012. Development of a new USDA Plant Hardiness Zone Map for the United States. Journal of Applied Meteorology and Climatology, 51: 242-264.Widrlechner, M.P., C. Daly, M. Keller, and K. Kaplan. 2012. Horticultural Applications of a Newly Revised USDA Plant Hardiness Zone Map. HortTechnology, 22: 6-19.
DOI: 10.15482/usda.adc/26207051
title: Data from: Canopy closure, yield, and quality under heterogeneous plant spacing in sunflower

description: <b>Abstract</b>Deviation from uniform target plant population density in sunflower (Helianthus annuus L.) production may negatively affect canopy closure, yield, and grain quality. The objective of this study was to evaluate the effects of plant density heterogeneity on yield, quality, and canopy closure in confectionery and oilseed sunflowers. Field experiments consisted of fixed differences in the number of skips/doubles per plot while maintaining equal total number of plants, and stand gaps with proportional decreasing plant counts.These were conducted in Minnesota, Texas, and Colorado, USA. Experiments revealed that yield was occasionally influenced by heterogeneity, with more uniform stand density yielding higher; however, substantial changes in uniformity of plant density often resulted in no differences in yield. Confectionery sunflowers compensated for yield losses in thinner stands mainly by producing larger seeds (observed range of 0.1 to 46.2 % of seeds over a 9.53 mm sieve), while oilseed sunflowers increased seed production per head (as shown by invariable test weight across treatments within environments). Plants that were unharvestable for any cause (most often due to head rot disease or lodging) were more prevalent in less uniform stands, and faster canopy closure was achieved in uniformly spaced plants. Although sunflower is very resilient to stand uniformity issues, these findings underscore the importance of uniform sunflower plant spacing for effective weed suppression and crop performance, and highlights the need for further research into sunflower yield compensation mechanisms.<b>Contents</b>can1920.xls - Contains canopy cover percentage data for different treatments in oil and confectionery sunflower crops for 2019 and 2020. Includes measurements at various days after planting (DAP) and statistical indicators.canopy_final.xlsx - Appears to be a summary or analysis of canopy cover data, including treatments, crop types (oil/confectionery), dates, years, and statistical significance indicators.Spacing_HarvestData_final.xlsx - Comprehensive dataset with detailed harvest information including location codes (e.g., 19CO, 19MN, 19TX), plot details, treatments, yield data, seed characteristics, and quality measurements for different sunflower types.These files contain the primary data used in the study on plant density heterogeneity effects on sunflower production, covering canopy closure, yield, and grain quality aspects mentioned in the paper abstract.
DOI: 10.15482/usda.adc/26207051.v1
title: Data from: Canopy closure, yield, and quality under heterogeneous plant spacing in sunflower

description: <b>Abstract</b>Deviation from uniform target plant population density in sunflower (Helianthus annuus L.) production may negatively affect canopy closure, yield, and grain quality. The objective of this study was to evaluate the effects of plant density heterogeneity on yield, quality, and canopy closure in confectionery and oilseed sunflowers. Field experiments consisted of fixed differences in the number of skips/doubles per plot while maintaining equal total number of plants, and stand gaps with proportional decreasing plant counts.These were conducted in Minnesota, Texas, and Colorado, USA. Experiments revealed that yield was occasionally influenced by heterogeneity, with more uniform stand density yielding higher; however, substantial changes in uniformity of plant density often resulted in no differences in yield. Confectionery sunflowers compensated for yield losses in thinner stands mainly by producing larger seeds (observed range of 0.1 to 46.2 % of seeds over a 9.53 mm sieve), while oilseed sunflowers increased seed production per head (as shown by invariable test weight across treatments within environments). Plants that were unharvestable for any cause (most often due to head rot disease or lodging) were more prevalent in less uniform stands, and faster canopy closure was achieved in uniformly spaced plants. Although sunflower is very resilient to stand uniformity issues, these findings underscore the importance of uniform sunflower plant spacing for effective weed suppression and crop performance, and highlights the need for further research into sunflower yield compensation mechanisms.<b>Contents</b>can1920.xls - Contains canopy cover percentage data for different treatments in oil and confectionery sunflower crops for 2019 and 2020. Includes measurements at various days after planting (DAP) and statistical indicators.canopy_final.xlsx - Appears to be a summary or analysis of canopy cover data, including treatments, crop types (oil/confectionery), dates, years, and statistical significance indicators.Spacing_HarvestData_final.xlsx - Comprehensive dataset with detailed harvest information including location codes (e.g., 19CO, 19MN, 19TX), plot details, treatments, yield data, seed characteristics, and quality measurements for different sunflower types.These files contain the primary data used in the study on plant density heterogeneity effects on sunflower production, covering canopy closure, yield, and grain quality aspects mentioned in the paper abstract.
DOI: 10.15482/usda.adc/25383307
title: Data from: Diversified grain rotations can be highly and reliably productive in unstable climates

description: Data and analysis from: Ewing, P. M., Chim, B. K., Lehman, R. M., & Osborne, S. L. (2024). Diversified grain rotations can be highly and reliably productive in unstable climates. <i>Field Crops Research</i>, <i>310</i>, 109361.Data was collected from the "Alternative Rotation" managed by Dr. Shannon Osborne at the USDA-ARS North Central Agricultural Research Laboratory in Brookings, SD USA. The study was established in 2000 and is a randomized complete block comparing the corn-soybean rotation with some regionally relevant, diversified rotations. It is still being conducted at the Eastern South Dakota Soil and Water Research Farm, also in Brookings. The presented data covers 2001-2016 and is the yield of eight crops grown in 6 rotations of 2-4 crops and years per rotation. Weather data is included.Analyses in this repository tested whether diversified rotations could a) match the productivity (grain yield) of simplified rotations while b) stabilizing productivity against variable weather in the western Corn Belt. Follow-up analyses look at mechanisms behind observed productivity-stability patterns at the rotation scale.This is an R repository. See README.md for use instructions. A full and possibly easier-to-use version of this repository is on github: https://github.com/PatrickEwing-USDA/NCARL_altrot_yield
DOI: 10.15482/usda.adc/25383307.v1
title: Data from: Diversified grain rotations can be highly and reliably productive in unstable climates

description: Data and analysis from: Ewing, P. M., Chim, B. K., Lehman, R. M., & Osborne, S. L. (2024). Diversified grain rotations can be highly and reliably productive in unstable climates. <i>Field Crops Research</i>, <i>310</i>, 109361.Data was collected from the "Alternative Rotation" managed by Dr. Shannon Osborne at the USDA-ARS North Central Agricultural Research Laboratory in Brookings, SD USA. The study was established in 2000 and is a randomized complete block comparing the corn-soybean rotation with some regionally relevant, diversified rotations. It is still being conducted at the Eastern South Dakota Soil and Water Research Farm, also in Brookings. The presented data covers 2001-2016 and is the yield of eight crops grown in 6 rotations of 2-4 crops and years per rotation. Weather data is included.Analyses in this repository tested whether diversified rotations could a) match the productivity (grain yield) of simplified rotations while b) stabilizing productivity against variable weather in the western Corn Belt. Follow-up analyses look at mechanisms behind observed productivity-stability patterns at the rotation scale.This is an R repository. See README.md for use instructions. A full and possibly easier-to-use version of this repository is on github: https://github.com/PatrickEwing-USDA/NCARL_altrot_yield
DOI: 10.15482/usda.adc/26408356
title: Data and code from: Predictions of Aboveground Herbaceous Production from Satellite-Derived APAR Are More Sensitive to Ecosite than Grazing Management Strategy in Shortgrass Steppe

description: This study set out to accurately estimate the amount of herbaceous biomass grown in rangelands, which is difficult to observe directly. Herbaceous biomass is the total weight of all living plant material, such as grasses, forbs, and small shrubs, some of which are edible forage for livestock. We used a combination of satellite data and ground-based measurements to estimate herbaceous biomass.The data provided here allows others to reproduce our statistical analysis. We also provide cleaned datasets for herbaceous biomass, grazing periods, satellite, and photosynthetic active radiation collected at the Central Plains Experimental Range, which is a Long-Term Agroecosystem Research network site. The time frame for this data spans 2014 – 2022.The .zip archive <code>Peirce_etal_2024_APAR_grazing.zip</code> contains data and code files.This dataset contains all code and data necessary to reproduce the statistical analyses in the manuscript:Peirce, E.S.; Kearney, S.P.; Santamaria, N.; Augustine, D.J.; Porensky, L.M. Predictions of Aboveground Herbaceous Production from Satellite-Derived APAR Are More Sensitive to Ecosite than Grazing Management Strategy in Shortgrass Steppe. Remote Sens. 2024, 16, 2780.https://doi.org/10.3390/rs16152780<br>This research used resources provided by the SCINet project and/or the AI Center of Excellence of the USDA Agricultural Research Service, ARS project numbers 0201-88888-003-000D and 0201-88888-002-000D.
DOI: 10.15482/usda.adc/26408356.v1
title: Data and code from: Predictions of Aboveground Herbaceous Production from Satellite-Derived APAR Are More Sensitive to Ecosite than Grazing Management Strategy in Shortgrass Steppe

description: This study set out to accurately estimate the amount of herbaceous biomass grown in rangelands, which is difficult to observe directly. Herbaceous biomass is the total weight of all living plant material, such as grasses, forbs, and small shrubs, some of which are edible forage for livestock. We used a combination of satellite data and ground-based measurements to estimate herbaceous biomass.The data provided here allows others to reproduce our statistical analysis. We also provide cleaned datasets for herbaceous biomass, grazing periods, satellite, and photosynthetic active radiation collected at the Central Plains Experimental Range, which is a Long-Term Agroecosystem Research network site. The time frame for this data spans 2014 – 2022.The .zip archive <code>Peirce_etal_2024_APAR_grazing.zip</code> contains data and code files.This dataset contains all code and data necessary to reproduce the statistical analyses in the manuscript:Peirce, E.S.; Kearney, S.P.; Santamaria, N.; Augustine, D.J.; Porensky, L.M. Predictions of Aboveground Herbaceous Production from Satellite-Derived APAR Are More Sensitive to Ecosite than Grazing Management Strategy in Shortgrass Steppe. Remote Sens. 2024, 16, 2780.https://doi.org/10.3390/rs16152780<br>This research used resources provided by the SCINet project and/or the AI Center of Excellence of the USDA Agricultural Research Service, ARS project numbers 0201-88888-003-000D and 0201-88888-002-000D.
DOI: 10.15482/usda.adc/28292300
title: Data from: Pennycress reduces potential for nutrient loss in Illinois

description: Nutrient export of nitrogen and phosphorus from row crop agriculture in the Upper US Midwest is a threat to the structure and function of aquatic systems. To meet Environmental Protection Agency (EPA) nutrient reduction goals, the Upper US Midwest needs to implement strategies to reduce nutrient export from agriculture. Studies demonstrate the potential of cover crops to reduce the export of nitrate-nitrogen from the Upper US Midwest. We investigated the impact of the economically viable winter cash cover crop pennycress (<i>Thlaspi arvense</i>) on soil porewater nutrients and soil nutrients and characteristics. We used nine replicated 0.8 ha plots (<i>n </i>= 3 per treatment) at a production scale research farm over 4 years with pennycress and fertilized pennycress (56 kg ha−1 of urea) treatments compared to a fallow reference. Over the study period, soil porewater nitrate-nitrogen was reduced by 53% in pennycress plots and 34% in fertilized pennycress plots relative to the fallow reference at a depth of 45 cm. Early season establishment was crucial in providing nutrient reduction potential. In 2021, poor pennycress establishment resulted in porewater nitrate-nitrogen concentrations 141% higher than in 2022 with excellent pennycress establishment. Following pennycress termination, soil nitrate-nitrogen was reduced by 24% in pennycress and 26% in fertilized pennycress compared to the fallow reference in the top 30 cm of soil. Following 4 years of pennycress planting, nitrate-nitrogen concentrations were significantly reduced with no broad effect on soil characteristics. We conclude that the novel pennycress crop has potential to reduce nutrient loss from row crop agriculture in the Upper US Midwest.
DOI: 10.15482/usda.adc/28292300.v1
title: Data from: Pennycress reduces potential for nutrient loss in Illinois

description: Nutrient export of nitrogen and phosphorus from row crop agriculture in the Upper US Midwest is a threat to the structure and function of aquatic systems. To meet Environmental Protection Agency (EPA) nutrient reduction goals, the Upper US Midwest needs to implement strategies to reduce nutrient export from agriculture. Studies demonstrate the potential of cover crops to reduce the export of nitrate-nitrogen from the Upper US Midwest. We investigated the impact of the economically viable winter cash cover crop pennycress (<i>Thlaspi arvense</i>) on soil porewater nutrients and soil nutrients and characteristics. We used nine replicated 0.8 ha plots (<i>n </i>= 3 per treatment) at a production scale research farm over 4 years with pennycress and fertilized pennycress (56 kg ha−1 of urea) treatments compared to a fallow reference. Over the study period, soil porewater nitrate-nitrogen was reduced by 53% in pennycress plots and 34% in fertilized pennycress plots relative to the fallow reference at a depth of 45 cm. Early season establishment was crucial in providing nutrient reduction potential. In 2021, poor pennycress establishment resulted in porewater nitrate-nitrogen concentrations 141% higher than in 2022 with excellent pennycress establishment. Following pennycress termination, soil nitrate-nitrogen was reduced by 24% in pennycress and 26% in fertilized pennycress compared to the fallow reference in the top 30 cm of soil. Following 4 years of pennycress planting, nitrate-nitrogen concentrations were significantly reduced with no broad effect on soil characteristics. We conclude that the novel pennycress crop has potential to reduce nutrient loss from row crop agriculture in the Upper US Midwest.
DOI: 10.15482/usda.adc/25684290
title: Johnston Draw (Idaho) High Resolution Pre-Fire Vegetation Map 2023

description: The variability of vegetation in rangelands can be over generalized in spatial representation and vegetation types mapped by moderate resolution vegetation maps. A high resolution (&lt;1 meter), site specific, vegetation map may better represent the diversity and spatial complexity of rangelands – a necessity for analyzing pre-fire conditions. We pansharpened two 8-band VNIR Worldview 2 scenes to map pre-fire vegetation in Johnston Draw (1.8 square kilometers) in the Reynolds Creek Experimental Watershed in Southwest Idaho. The two Worldview 2 scenes represent peak greenness (June 14, 2023) and pre-fire (September 23, 2023) conditions with spatial resolutions of 50 centimeters and 42 centimeters, respectively. A prescribed fire burned the area on October 6, 2023. We trained a pixel based random forest classifier to map 10 site specific vegetation classes at a 50-centimeter spatial resolution. We applied a majority filter to remove speckling. Map accuracy was 83.3% when validated using a test set of 54, 30-meter diameter, plots selected to represent the following dominant vegetation types: deciduous/riparian (classes were collapsed into a single class for validation), living juniper, dead juniper, sagebrush, mixed low sage and bunchgrass, bitterbrush, and annual grasses. Barren and water classes were not validated. Training data was developed through a combination of site visit based knowledge and a training set of 30-meter diameter dominant vegetation class plots. The 1.5-billion-dollar cost of fire prevention, suppression, and restoration is stretched thin over the vast area of wildfire occurrence, where site-specific high-resolution vegetation maps are essential to mitigate fire potential and address post fire recovery. In addition to the pre-fire vegetation map a post fire burn product will also be submitted to Ag Data Commons and the related materials will be updated to reflect these complimentary submissions.
DOI: 10.15482/usda.adc/25684290.v1
title: Johnston Draw (Idaho) High Resolution Pre-Fire Vegetation Map 2023

description: The variability of vegetation in rangelands can be over generalized in spatial representation and vegetation types mapped by moderate resolution vegetation maps. A high resolution (&lt;1 meter), site specific, vegetation map may better represent the diversity and spatial complexity of rangelands – a necessity for analyzing pre-fire conditions. We pansharpened two 8-band VNIR Worldview 2 scenes to map pre-fire vegetation in Johnston Draw (1.8 square kilometers) in the Reynolds Creek Experimental Watershed in Southwest Idaho. The two Worldview 2 scenes represent peak greenness (June 14, 2023) and pre-fire (September 23, 2023) conditions with spatial resolutions of 50 centimeters and 42 centimeters, respectively. A prescribed fire burned the area on October 6, 2023. We trained a pixel based random forest classifier to map 10 site specific vegetation classes at a 50-centimeter spatial resolution. We applied a majority filter to remove speckling. Map accuracy was 83.3% when validated using a test set of 54, 30-meter diameter, plots selected to represent the following dominant vegetation types: deciduous/riparian (classes were collapsed into a single class for validation), living juniper, dead juniper, sagebrush, mixed low sage and bunchgrass, bitterbrush, and annual grasses. Barren and water classes were not validated. Training data was developed through a combination of site visit based knowledge and a training set of 30-meter diameter dominant vegetation class plots. The 1.5-billion-dollar cost of fire prevention, suppression, and restoration is stretched thin over the vast area of wildfire occurrence, where site-specific high-resolution vegetation maps are essential to mitigate fire potential and address post fire recovery. In addition to the pre-fire vegetation map a post fire burn product will also be submitted to Ag Data Commons and the related materials will be updated to reflect these complimentary submissions.
DOI: 10.15482/usda.adc/25988122
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>