This dataset includes global rasters of terrestrial biological nitrogen fixation (BNF) rates (in kgN/(ha*y)) for each major N-fixing niche in natural biomes (trees, shrubs, herbs, soil, litter, dead wood, ground mosses, biocrusts, and epiphytic lichens) and agricultural biomes (legume crops, forage legumes, and rice). It also includes total natural terrestrial, total agricultural, and total terrestrial BNF rasters. The dataset contains rasters with central and confidence interval values at 0.004 and 1-degree resolution.

This dataset includes global rasters of terrestrial biological nitrogen fixation (BNF) rates (in kgN/(ha*y)) for each major N-fixing niche in natural biomes (trees, shrubs, herbs, soil, litter, dead wood, ground mosses, biocrusts, and epiphytic lichens) and agricultural biomes (legume crops, forage legumes, and rice). It also includes total natural terrestrial, total agricultural, and total terrestrial BNF rasters. The dataset contains rasters with central and confidence interval values at 0.004 and 1-degree resolution.

This dataset includes global rasters of terrestrial biological nitrogen fixation (BNF) rates (in kgN/(ha*y)) for each major N-fixing niche in natural biomes (trees, shrubs, herbs, soil, litter, dead wood, ground mosses, biocrusts, and epiphytic lichens) and agricultural biomes (legume crops, forage legumes, and rice). It also includes total natural terrestrial, total agricultural, and total terrestrial BNF rasters. The dataset contains rasters with central and confidence interval values at 0.004 and 1-degree resolution.

This dataset includes field measurements of biological nitrogen fixation (BNF) in all major N-fixing niches across natural terrestrial biomes globally. The dataset comprises 32 variables including site location, biome type, N-fixing niche, sampling year, quantification method, BNF rate (in kgN/(ha*y)), the percentage of nitrogen derived from the atmosphere, N-fixer or N-fixing substrate abundance, BNF rate per unit of N-fixer abundance, and species identity.

This dataset describes the estimates of gridded nitrogen fixation from various sources globally. The global gridded datasets of BNF generated here are available in the ScienceBase repository (https://www.sciencebase.gov/catalog/item/66a97480d34e07a119db3a37). The underlying BNF rate dataset in natural terrestrial biomes is also available in the ScienceBase repository (https://www.sciencebase.gov/catalog/item/66a97365d34e07a119db3a30). All other data are available from the databases cited or are in the main text or the supplementary materials. The links will be made active upon acceptance. Portions of this dataset are inaccessible because: See link above. They can be accessed through the following means: See link above. Format: See link above. This dataset is associated with the following publication: Reis Ely, C.R., S.S. Perakis, C.C. Cleveland, D.N.L. Menge, S.C. Reed, B.N. Taylor, S.A. Batterman, C.M. Clark, T.E. Crews, K.A. Dynarski, M. Gei, M.J. Gundale, D.F. Herridge, S.E. Jovan, S. Kou-Giesbrecht, M.B. Peoples, J. Piipponen, E. Rodríguez-Caballero, V.G. Salmon, F.M. Soper, A.P. Staccone, B. Weber, C.A. Williams, and N. Wurzburger. Global terrestrial nitrogen fixation and its modification by agriculture. NATURE. Nature Portfolio, Berlin, GERMANY, 643(8072): 705-711, (2025).

Biomass weight & nitrogen content for plants collected along transects & dried

,Dr. Kevin Bronson provides a unique nitrogen and water management in cotton agricultural research dataset for compute, including notation of field events and operations, an intermediate analysis mega-table of correlated and calculated parameters, and 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 data sensor recorded logger outputs.,This data was collected using a Hamby rig as a high-throughput proximal plant phenotyping platform.,The Hamby 6000 rig Ellis W. Chenault, & Allen F. Wiese. (1989). Construction of a High-Clearance Plot Sprayer. Weed Technology, 3(4), 659–662. http://www.jstor.org/stable/3987560,Dr. Bronson modified an old high-clearance Hamby 6000 rig, adding a tank and pump with a rear boom, to perform precision liquid N applications. A Raven control unit with GPS supplied variable rate delivery options.,The 12 volt Holland Scientific GeoScoutX data recorder and associated CropCircle ACS-470 sensors with GPS signal, was easy to mount and run on the vehicle as an attached rugged data acquisition module, and allowed the measuring of plants using custom proximal active optical reflectance sensing. The HS data logger was positioned near the operator, and sensors were positioned in front of the rig, on forward protruding armature attached to a hydraulic front boom assembly, facing downward in nadir view 1 m above the average canopy height. A 34-size class AGM battery sat under the operator and provided the data system electrical power supply.,Data suffered reduced input from Conley. Although every effort was afforded to capture adequate quality across all metrics, experiment exterior considerations were such that canopy temperature data is absent, and canopy height is weak due to technical underperformance. Thankfully, reflectance data quality was maintained or improved through the implementation of new hardware by Bronson.,See included README file for operational details and further description of the measured data signals.,Summary: Active optical proximal cotton canopy sensing spatial data and including few additional related metrics and weak low-frequency ultrasonic derived height are presented. Agronomic nitrogen and irrigation management related field operations are listed. Unique research experimentation intermediate analysis table is made available, along with raw data. The raw data recordings, and annotated table outputs with calculated VIs are made available. Plot polygon coordinate designations allow a re-intersection spatial analysis. Data was collected in the 2014 season at Maricopa Agricultural Center, Arizona, USA. High throughput proximal plant phenotyping via electronic sampling and data processing method approach is exampled using a modified high-clearance Hamby spray-rig. Acquired data conforms to location standard methodologies of the plant phenotyping. SAS and GIS compute processing output tables, including Excel formatted examples are presented, where data tabulation and analysis is available. Additional ultrasonic data signal explanation is offered as annotated time-series charts. The weekly proximal sensing data collected include the primary canopy reflectance at six wavelengths. 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 with 1-L vented chambers (samples taken at 0, 12, and 24 minutes). Nitrous oxide was determined by gas chromatography (electron detection detector).,

This EnviroAtlas dataset contains data on the mean cultivated biological nitrogen fixation (C-BNF) in cultivated crop and hay/pasture lands per 12-digit Hydrologic Unit (HUC) in 2006. Nitrogen (N) inputs from the cultivation of legumes, which possess a symbiotic relationship with N-fixing bacteria, were calculated with a recently developed model relating county-level yields of various leguminous crops with BNF rates. We accessed county-level data on annual crop yields for soybeans (Glycine max L.), alfalfa (Medicago sativa L.), peanuts (Arachis hypogaea L.), various dry beans (Phaseolus, Cicer, and Lens spp.), and dry peas (Pisum spp.) for 2006 from the USDA Census of Agriculture (http://www.agcensus.usda.gov/index.php). We estimated the yield of the non-alfalfa leguminous component of hay as 32% of the yield of total non-alfalfa hay (http://www.agcensus.usda.gov/index.php). Annual rates of C-BNF by crop type were calculated using a model that relates yield to C-BNF. We assume yield data reflect differences in soil properties, water availability, temperature, and other local and regional factors that can influence root nodulation and rate of N fixation. We distributed county-specific, C-BNF rates to cultivated crop and hay/pasture lands delineated in the 2006 National Land Cover Database (30 x 30 m pixels) within the corresponding county. C-BNF data described here represent an average input to a typical agricultural land type within a county, i.e., they are not specific to individual crop types. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

This EnviroAtlas dataset contains data on the mean cultivated biological nitrogen fixation (C-BNF) in cultivated crop and hay/pasture lands per 12-digit Hydrologic Unit (HUC) in 2006. Nitrogen (N) inputs from the cultivation of legumes, which possess a symbiotic relationship with N-fixing bacteria, were calculated with a recently developed model relating county-level yields of various leguminous crops with BNF rates. We accessed county-level data on annual crop yields for soybeans (Glycine max L.), alfalfa (Medicago sativa L.), peanuts (Arachis hypogaea L.), various dry beans (Phaseolus, Cicer, and Lens spp.), and dry peas (Pisum spp.) for 2006 from the USDA Census of Agriculture (http://www.agcensus.usda.gov/index.php). We estimated the yield of the non-alfalfa leguminous component of hay as 32% of the yield of total non-alfalfa hay (http://www.agcensus.usda.gov/index.php). Annual rates of C-BNF by crop type were calculated using a model that relates yield to C-BNF. We assume yield data reflect differences in soil properties, water availability, temperature, and other local and regional factors that can influence root nodulation and rate of N fixation. We distributed county-specific, C-BNF rates to cultivated crop and hay/pasture lands delineated in the 2006 National Land Cover Database (30 x 30 m pixels) within the corresponding county. C-BNF data described here represent an average input to a typical agricultural land type within a county, i.e., they are not specific to individual crop types. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

,Dr. Kevin Bronson provides this unique nitrogen and water management in cotton agricultural research dataset for compute, including notation of field events and operations, an intermediate analysis mega-table of correlated and calculated parameters, and 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.,This 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.,See included README file for operational details and further description of the measured data signals.,Summary: Active optical proximal cotton canopy sensing spatial data and including additional related metrics such as thermal are presented. Agronomic nitrogen and irrigation management related field operations are listed. Unique research experimentation intermediate analysis table is made available, along with raw data. The raw data recordings, and annotated table outputs with calculated VIs are made available. Plot polygon coordinate designations allow a re-intersection spatial analysis. Data was collected in the 2013 season at Maricopa Agricultural Center, Arizona, USA. High throughput proximal plant phenotyping via electronic sampling and data processing method approach is exampled. Acquired 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. SAS and GIS compute processing output tables, including Excel formatted examples are presented, where intermediate data tabulation and analysis is available. The 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).,