Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
48
datasets available to search
ShareScore release 0.9.0
Dataset results
48 results for “N2O emissions”
A Comprehensive Global Aquatic N2O Emission Database (GANED): Unravelling N2O Emission Patterns from Different Water Bodies, 1980-2023
The Global Aquatic Nitrous Oxide Emission Database (GANED) is a comprehensive synthesis of empirical observations of N2O concentration measurements and flux records, spanning the period 1980-2023. GANED advances N2O research by providing the first global systematic emission mechanisms among the different aquatic system types, including rivers, streams, estuaries, reservoirs, ponds, lakes, open seas and coastal areas. The N2O data in GANED is further interconnected with biogeochemical metadata on dissolved oxygen, dissolved organic carbon, ammonium, nitrate, nitrite, total nitrogen, water temperature, salinity and pH, along with site data (latitude, longitude, codes of channel type, depth, surface area, elevation). The dataset explains the discrepancy that emission of N2O in aquatic bodies is determined mainly by substrate availability, and not by climatic factors, and reveals the systematic biases of concentration-only measurements, which can result in an underestimation of fluxes in effluent water of dynamically changing aquatic waters. Consequently, GANED constitutes a crucial transition “where” emissions occur to understanding “why” they differ across systems, and thus enabling targeted mitigation interventions. GANED includes 5130 records of N2O concentration and 7386 flux measurements from 3,002 unique sites, most of which are resolved to the daily time scale.
A global dataset of specialty crop biomass and N2O emissions
<div> <p>We reviewed global field studies of vineyard, orchard, and vegetable cropping systems, which were also included in a meta-analysis (<a href="https://doi.org/10.1111/gcb.17233">https://doi.org/10.1111/gcb.17233</a>). We narrowed down the studies to those with field measurements of adequate variables (biomass C, N, and N<sub>2</sub>O) covering at least one growing season. As a result, cumulative N₂O emission measurements (per growing rotation, season, or year), along with biomass data of different plant organs from the same regions, were compiled for grape (<em>Vitis vinifera</em>), almond [<em>Prunus dulcis</em> (Mill.) D.A. Webb], peach (<em>Prunus persica</em> L.), walnut (<em>Juglans regia</em>), lettuce (<em>Lactuca sativa</em>), broccoli (<em>Brassica oleracea</em> var. <em>italica </em>P.), cauliflower (<em>Brassica oleracea</em> var. <em>botrytis </em>L.), and tomato (<em>Lycopersicon esculentum</em> L.) planting system. These observations were collected from fields spanning seven Koppen-Geiger climate types and five countries (the United States, Germany, Spain, France, and Australia). When only dry mass was measured, biomass C content for aboveground vegetable crops and berry fruit was assumed at 43%; nut fruit and woody organs of orchard tree at 48%. Area-weighted averages of N<sub>2</sub>O emissions were used (tree/vine row and interrow).</p> <p> </p> <p>Corresponding author: Mu Hong (mu.hong@colostate.edu)</p> </div> <p> </p>
Anthropogenic emissions of CH4, N2O, F-gases and BC from GAINS, for EU-countries plus CH, NO, UK developed under the EYE-CLIMA project - March 2025 update
<p><span>As part of the EYE-CLIMA project, GAINS emission data for CH<sub>4</sub>, N<sub>2</sub>O, BC and selected F-gases (HFC-125, HFC-134a, HFC-143a, HFC-23, HFC-32 and SF<sub>6</sub></span>) were released for all EU-27 countries plus UK, Switzerland, and Norway for the period 1990 to 2020 (with exception of F-gases, from 2005 only, and BC/CH<sub>4</sub> emissions from agricultural waste burning, from 2000). Results have been documented in EYE-CLIMA deliverable D2.8 (<a href="http://folk.nilu.no/~rthompson/eyeclima_reports/EYECLIMA_D2.8.pdf">http://folk.nilu.no/~rthompson/eyeclima_reports/EYECLIMA_D2.8.pdf</a>), and they are publicly available at the Zenodo repository under <a href="https://doi.org/10.5281/zenodo.11032177">https://doi.org/10.5281/zenodo.11032177</a>. All data is available on a 0.1°x0.1° grid and in monthly resolution. Emissions are attributed to the respective source categories according to GNFR.</p> <p>The motivation of an update resulted from the need to extending the emission data time series to 2023. With underlying statistics and national emission data currently available till 2022 only (the latter submitted to UNFCCC only by December 2024), the historical data series also could only be established for 2022. Here we use the GAINS scenario feature to extrapolate between 2022 historical data and the first scenario point, 2025 which is based on IEA’s Word Energy Outlook 2023 (https://www.iea.org/reports/world-energy-outlook-2023). Obviously, this also means that emission results for 2023 are not any more based on robust statistics but represent an extrapolation.</p> <p>Extrapolation of spatially explicit data is only possible when the spatial resolution conveys a realistic signal. For the sector “agricultural waste burning” (files with “AWB” as sector, see notation below) spatial allocation is based on actual observation from satellites. As such data products on agricultural fires have been made available until 2022 only, no spatial or temporal signal exists for 2023. The time series provided thus has to end in 2022. No recommendation can be given to modellers, other than to either use 2022 also for 2023 (understanding that the pattern will be strikingly different) or to use a five-year average (which will remove a lot of spatial specificity).</p> <p>The updated dataset covers files as follows (internally, all files now carry version number V05):</p> <p>ALL_FLUX_ALL_EUR_MOD_MONTH_19900101_20231231_GAINS_IIASA_V05.csv</p> <p>BC_FLUX_ALL_EUR_MOD_MONTH_19900101_20231231_GAINS_IIASA_V05.nc</p> <p>BC_FLUX_AWB_EUR_MOD_MONTH_20000101_20221231_GAINS_IIASA_V05.nc</p> <p>CH4_FLUX_ALL_EUR_MOD_MONTH_19900101_20231231_GAINS_IIASA_V05.nc</p> <p>CH4_FLUX_AWB_EUR_MOD_MONTH_20000101_20221231_GAINS_IIASA_V05.nc</p> <p>HFC_FLUX_ALL_EUR_MOD_YEAR_20050101_20231231_GAINS_IIASA_V05.nc</p> <p>N2O_FLUX_ALL_EUR_MOD_MONTH_19900101_20231231_GAINS_IIASA_V05.nc</p> <p>SF6_FLUX_ALL_EUR_MOD_YEAR_20050101_20231231_GAINS_IIASA_V05.nc</p> <p>This is version 2.0 of the dataset. It extends from version 1.0 by covering into the year 2023, but also benefits from a number of additional GAINS improvements. Emissions of emitted compounds are provided as kg/m²/s. File names follow the notation developed for the H-Europe project EYE-CLIMA, i.e. species _ variable-type _ sector _ region _ method (MOD=model) _ timestep _ fromTime _ toTime _ model _ institute _ version . filetype.</p> <p>This version is available at <a href="https://doi.org/10.5281/zenodo.15536170">https://doi.org/10.5281/zenodo.15536170</a>. The generic address of the dataset is <a href="https://doi.org/10.5281/zenodo.10886780">https://doi.org/10.5281/zenodo.10886780</a>, resolving to the latest update available at Zenodo. No further updates are planned in EYE-CLIMA, so this version is expected to also reflect the final update within the project.</p> <p>Compared to version 1.0, GAINS benefitted from a number of new developments such as the following:</p> <p>*) Previously, GAINS has been available in five-year timesteps only (with the aim of allowing for scenarios at that resolution). For data version 1.0, a makeshift solution was found to convert into annual data. A recent update now allows, for historic data, to store and retrieve information on an annual basis (from 1990).</p> <p>*) The energy data were obtained from IEA’s world energy balances 2024 (July version, https://www.iea.org/data-and-statistics/data-product/world-energy-balances#documentation), extending into 2022 and extrapolated towards 2025, downscaled from IEA to GAINS sectors and sub-sectors. Additionally, the annual activity of industrial production is estimated using a linear approach, based on five-year timestep data.</p> <p>*) Agricultural statistics were retrieved from Eurostat (and from FAO globally) and extended to 2022, extrapolated towards 2025.</p> <p>*) Interpretation of GAINS data was reconfirmed and updated in consultations with national experts of multiple EU countries. While the process resulted in revised emission projections to be used in the Clean Air Outlook 4 (see <a title="Protected by Check Point: https://environment.ec.europa.eu/topics/air/clean-air-outlook_en" href="https://protect.checkpoint.com/v2/r02/___https:/environment.ec.europa.eu/topics/air/clean-air-outlook_en___.YzJlOmlpYXNhOmM6bzoyYzdiNDRhNDI4Njc3ZjI5MGFjMTU1N2I2OWVmNzM2ZTo3OjE5OTM6ZTFiY2IzMDMxZGViNGE0MjI0ODRmNWQ4NzA3ZDY3Njc4M2U2NzUxNmEwNzQ0ODViNDBhODc1NmNhZmMzY2FlMjpoOkY6Tg"><span lang="EN-GB">https://environment.ec.europa.eu/topics/air/clean-air-outlook_en</span></a><span lang="EN-GB">). While the details of improvements on the individual aspects cannot be disclosed, they are useful to describe historic data most adequately, and have been integrated also in this assessment. That not only leads to changes in absolute emissions for a given year, but also affects trends that now are more plausible and confirmed through the exchange with the national experts.</span></p> <p><span lang="EN-GB">*) Technical adjustments have improved the precision of temporal allocation of emissions and the conversion of grid sizes to actual area.</span></p>
Soil greenhouse gas emissions (CO2 and N2O) data and metadata derived from H2020 Diverfarming project
<p>Soil greenhouse gas emissions (CO<sub>2</sub> and N<sub>2</sub>O) data and metadata of an almond crop diversified with <em>Thymus hyemalis </em>(diversification 1) and with<em> Capparis spinosa </em>(diversification 2). This data comes from WP5 "Environmental impact and delivery of ecosystem services by crop diversification", derived from H2020 Diverfarming project. This workpackage has been designed to provide sound and robust scientific understanding of the benefits and drawbacks of the tailored diversified cropping systems for improvement of the environmental quality and delivery of ecosystem services in each pedoclimatic region. http://www.diverfarming.eu</p>
Code and data for "KGML-ag: A Modeling Framework of Knowledge-Guided Machine Learning to Simulate Agroecosystems: A Case Study of Estimating N2O Emission using Data from Mesocosm Experiments "
<p>This is code and data for manuscript: <br> "KGML-ag: A Modeling Framework of Knowledge-Guided Machine Learning to Simulate Agroecosystems: <br> A Case Study of Estimating N<sub>2</sub>O Emission using Data from Mesocosm Experiments"<br> Licheng Liu, Shaoming Xu, Zhenong Jin*, Jinyun Tang, Kaiyu Guan, Timothy J. Griffis, <br> Matt D. Erickson, Alexander L. Frie, Xiaowei Jia, Taegon Kim, Lee T. Miller, Bin Peng, Shaowei Wu, Yufeng Yang, Wang Zhou, Vipin Kumar</p> <p>All the files belong to Prof. Zhenong Jin, University of Minnesota, UA. jinzn@umn.edu<br> "code" foler includes code for data processing, model training, and results plotting.<br> "trained_model_saved" includes all trained model so you can use to reproduce the results showed in the study;<br> "data" includes all data presented in the study. Finetuning data is refering to Miller, L.T. , Griffis, T. J., Erickson, M. D., Turner, P. A., Deventer, M. J., Chen, Z., Yu, Z., Venterea, R.T., Baker, J. M., and Frie, A. L. (2021). Response of nitrous oxide emissions to future changes in precipitation and individual rain events. Journal of Environmental Quality, In review</p>
Measurements of savanna landscap fire emission factors for CO2, CO, CH4 and N2O using a UAV-based sampling methodology
<p>This dataset contains direct measurements of biomass burning emission factors for CO<sub>2</sub>, CO, CH<sub>4</sub> and N<sub>2</sub>O. It includes over 4500 EF bag measurements sampled using an unmanned aerial system (UAS), and measured fuel parameters and fire severity proxies during 129 individual fires. The measurements cover a variety of savanna ecosystems in Brazil, Australia, Botswana, Zambia, South-Africa and Mozambique under different seasonal conditions, sampled over the course of six fire seasons between 2017 and 2022. The table in the included word file explains the individual columns in the excell file. </p> <p> </p>
Data from: Reduced snow cover increases wintertime nitrous oxide (N2O) emissions from an agricultural soil in the upper U.S. Midwest
Throughout most of the northern hemisphere, snow cover decreased in almost every winter month from 1967 to 2012. Because snow is an effective insulator, snow cover loss has likely enhanced soil freezing and the frequency of soil freeze–thaw cycles, which can disrupt soil nitrogen dynamics including the production of nitrous oxide (N2O). We used replicated automated gas flux chambers deployed in an annual cropping system in the upper Midwest US for three winters (December–March, 2011–2013) to examine the effects of snow removal and additions on N2O fluxes. Diminished snow cover resulted in increased N2O emissions each year; over the entire experiment, cumulative emissions in plots with snow removed were 69% higher than in ambient snow control plots and 95% higher than in plots that received additional snow (P < 0.001). Higher emissions coincided with a greater number of freeze–thaw cycles that broke up soil macroaggregates (250–8000 µm) and significantly increased soil inorganic nitrogen pools. We conclude that winters with less snow cover can be expected to accelerate N2O fluxes from agricultural soils subject to wintertime freezing.
Data from: Earthworms do not increase greenhouse gas emissions (CO2 and N2O) in an ecotron experiment simulating a realistic three-crop rotation system
<p><span>Earthworms are known to stimulate soil greenhouse gas (GHG) emissions, but the majority of previous studies have used simplified model systems or lacked continuous high-frequency measurements. To address this, we conducted a two-year study using large lysimeters (</span><span>5 m<sup>2</sup> area and 1.5 m soil depth) </span><span>in an ecotron facility, continuously measuring ecosystem-level CO<sub>2</sub>, N<sub>2</sub>O, and H<sub>2</sub>O fluxes. We investigated the impact of endogeic and anecic earthworms on GHG emissions and ecosystem water use efficiency (WUE) in a simulated agricultural setting. Although we observed transient stimulations of carbon fluxes in the presence of earthworms, cumulative fluxes over the study indicated no significant increase in CO<sub>2</sub> emissions. Endogeic earthworms reduced N<sub>2</sub>O emissions during the wheat culture (-44.6%), but this effect was not sustained throughout the experiment. No consistent effects on ecosystem evapotranspiration or WUE were found. Our study suggests that earthworms do not significantly contribute to GHG emissions over a two-year period in experimental conditions that mimic an agricultural setting. These findings highlight the need for realistic experiments and continuous GHG measurements.</span></p>
Compiled soil N2O emission data from Eastern China forests
<p>We searched the Thomson Reuters Web of Science Core Collection and China National Knowledge Infrastructure (CNKI) theses database for literature published before January 1, 2020, using the terms “forest” AND “greenhouse gas” OR “N<sub>2</sub>O” OR “nitrous oxide” in the titles, abstracts, and keywords; this returned 5,948 records and 590 records, respectively. Considering the different preferences for terminology (e.g., nutrient addition, simulated N deposition), we refined our search manually. The criteria for our refinement were as follows: (a) N addition experiment was conducted in eastern China forests with detailed records of N addition levels; (b) N<sub>2</sub>O flux was observed using the static chamber method so that data from different sites were comparable. A total of 58 papers from 38 research sites met our criteria. </p> <p>We also collected the data of soil N<sub>2</sub>O emission rates under natural conditions. The data were collected from literature that met the following criteria: (a) N<sub>2</sub>O fluxes were observed in eastern China forests using the static chamber method; (b) no manipulated experiments were conducted, and no nitrogen was added except for natural N deposition. There were 42 papers and theses that met these criteria, covering 30 different sites. </p> <p>From the abovementioned papers and theses, the soil N<sub>2</sub>O emission rates and the auxiliary information (coordinates, ecoregion type, natural N deposition rate, multi-year mean annual temperature, multi-year mean annual precipitation, and soil texture) were compiled (ds01).</p> <p>To obtain the soil N<sub>2</sub>O emission rates of Eastern China forests on grid level (10km × 10km), another dataset (ds02) on the environmental factors (ecoregion type, natural N deposition rate, multi-year mean annual temperature, multi-year mean annual precipitation, and soil texture) of each grid was compiled from the spatial datasets, including the Chinese vegetation ecoregion map from the Resources, Environmental Sciences, and Data Center at the Chinese Academy of Sciences (<a href="https://www.resdc.cn/">https://www.resdc.cn/data.aspx?DATAID=133</a>), as well as the soil texture raster data (<a href="https://www.resdc.cn/data.aspx?DATAID=260">https://www.resdc.cn/data.aspx?DATAID=260</a>), mean annual temperature raster data (1995–2015; <a href="https://www.resdc.cn/data.aspx?DATAID=228">https://www.resdc.cn/data.aspx?DATAID=228</a>), and mean annual precipitation raster data (1995–2015; <a href="https://www.resdc.cn/data.aspx?DATAID=229">https://www.resdc.cn/data.aspx?DATAID=229</a>). Land use and land cover raster data (2000–2015) were obtained from the Project on Big Earth Data Science Engineering (<a href="https://data.casearth.cn/sdo/detail/5c19a5650600cf2a3c557aae">https://data.casearth.cn/sdo/detail/5c19a5650600cf2a3c557aae</a>). Finally, annual N deposition rate and multi-year mean N deposition (MAN) data were derived from a N deposition dataset from mainland China previously constructed by our group. </p>
Including non-growing season emissions of N2O in US maize could raise net CO2e emissions 31% annually: Code and supporting data
<p>Buma B. 2024. <em>"Including non-growing season emissions of N2O in US maize could raise net CO2e emissions 31% annually," </em>Agriculture and Environmental Letters.</p> <p>Code, county level nitrogen application data, and maize off-season emission factor ratio. For the citations for both the county level data and the maize data, see the manuscript. Both datasets are also publicly available from their respective citations, but included here for ease of review.</p>
Improving nitrogen cycling in a land surface model (CLM5) to quantify soil N2O, NO and NH3 emissions from enhanced rock weathering with croplands
<p>This repository contains CLM5 model soil nitrogen (N2O, NO and NH3) output and metadata, and soil N<sub>2</sub>O fluxes from the Energy Farm ERW field trials used in the paper "Improving nitrogen cycling in a land surface model (CLM5) to quantify soil N2O, NO and NH3 emissions from enhanced rock weathering with croplands" (https://gmd.copernicus.org/preprints/gmd-2023-47/).</p>
Data from: Reduced snow cover increases wintertime nitrous oxide (N2O) emissions from an agricultural soil in the upper U.S. Midwest
Open the record for dataset details and reuse information.
Machine learning reveals dynamic controls of soil nitrous oxide (N2O) emissions from diverse long-term cropping systems
Open the record for dataset details and reuse information.
Data from: Earthworms do not increase greenhouse gas emissions (CO2 and N2O) in an ecotron experiment simulating a realistic three-crop rotation system
Open the record for dataset details and reuse information.
Data from: Nitrous oxide (N2O) emissions from subsurface soils of agricultural ecosystems
Nitrous oxide (N2O) is a major greenhouse gas and cultivated soils are the most important anthropogenic source. N2O production and consumption are known to occur at depths below the A or Ap horizon but their magnitude in situ is largely unknown. At a site in SW Michigan USA we measured N2O concentrations at different soil depths and used diffusivity models to examine the importance of depth-specific production and consumption. We also tested the influence of crop and management practices on subsurface N2O production in 1) till vs. no-till, 2) a nitrogen fertilizer gradient, and 3) perennial crops including successional vegetation. N2O concentrations below 20 cm exceeded atmospheric concentrations by up to 900 times, and profile concentrations increased markedly with depth except immediately after fertilization when production was intense in the surface horizon, and in winter, when surface emissions were blocked by ice. Diffusivity analysis showed that N2O production at depth was especially important in annual crops, accounting for over 50% of total N2O production when crops were fertilized at recommended rates. At nitrogen fertilizer rates exceeding crop need, subsurface N2O production contributed 25-35% of total surface emissions. Dry conditions deepened the maximum depth of N2O production. Tillage did not. In systems with perennial vegetation, subsurface N2O production contributed <20% to total surface emissions. Results suggest that the fraction of total N2O produced in subsurface horizons can be substantial in annual crops, is low under perennial vegetation, appears to be largely controlled by subsurface nitrogen and moisture, and is insensitive to tillage.
Soil properties shape the heterogeneity of denitrification and N2O emissions across large-scale flooded paddy soils
<p>The dataset's contents include the relative abundance of different denitrifier types at the genus level across eight different flooded paddy soils. This dataset categorizes denitrifier types in its first column labeled 'Type.' The second column, 'Genus,' associates each type with various species at the genus level. Subsequent columns present the relative abundance of these species in diverse sampling locations. Researchers can leverage this dataset to derive more patterns regarding the differences within specific dominant denitrifier species. No ethical considerations are pertinent to the utilization of this dataset.</p>
A global meta-analysis of yield-scaled N2O emissions and its mitigation efforts for maize, wheat, and rice
<p>Maintaining or even increasing crop yields while reducing nitrous oxide (N<sub>2</sub>O) emissions is necessary to reconcile food security and climate change, while the metric of yield-scaled N<sub>2</sub>O emission (i.e., N<sub>2</sub>O emissions per unit of crop yield) is at present poorly understood. Here we conducted a global meta-analysis with more than 6000 observations to explore the variation patterns and controlling factors of yield-scaled N<sub>2</sub>O emissions for maize, wheat, and rice and associated potential mitigation options. Our results showed that the average yield-scaled N<sub>2</sub>O emissions across all available data followed the order wheat (322 g N Mg<sup>-1</sup>, with the 95% confidence interval (CI): 301-346) > maize (211 g N Mg<sup>-1</sup>, CI: 198-225) > rice (153 g N Mg<sup>-1</sup>, CI: 144-163). Yield-scaled N<sub>2</sub>O emissions for individual crops were generally higher in tropical or subtropical zones than in temperate zones, and also showed a trend towards lower intensities from low to high latitudes. This global variation was better explained by climatic and edaphic factors than by N fertilizer management, while their combined effect predicted more than 70% of the variance. Furthermore, our analysis showed a significant decrease in yield-scaled N<sub>2</sub>O emissions with increasing N use efficiency or in N<sub>2</sub>O emissions for production systems with cereal yields > 10 Mg ha<sup>-1</sup> (maize), 6.6 Mg ha<sup>-1</sup> (wheat) or 6.8 Mg ha<sup>-1</sup> (rice), respectively. This highlights that N use efficiency indicators can be used as valuable proxies for reconciling trade-offs between crop production and N<sub>2</sub>O mitigation. For all three major staple crops, reducing N fertilization by up to 30%, optimizing the timing and placement of fertilizer application or using enhanced-efficiency N fertilizers significantly reduced yield-scaled N<sub>2</sub>O emissions at similar or even higher cereal yields. Our data-driven assessment provides some key guidance for developing effective and targeted mitigation and adaptation strategies for the sustainable intensification of cereal production.</p>
Database to: Effectiveness of soil management strategies for mitigation of N2O emissions in European arable land: A meta-analysis
<p>Database to a meta-analysis studing the effects of adding different organic matter inputs (crop residues, green manure, livestock manure, slurry, digestate, compost or biochar) to soils on N2O emissions. Database consists of over 50 field experiments conducted in 15 European countries. Diverse arable crops, mainly cereals, were cultivated in monoculture or in crop rotations on mineral soils. Cumulative N2O emissions per unit land area were monitored during periods of 30 to 1,070 days in treatments, which received organic matter inputs, alone or in combination with mineral N fertiliser; and in controls fertilised with mineral N. The original results appeared in 46 articles published between 1993 and 2022 in peer-reviewed scientific journals, as well as a project report, and a PhD thesis.</p>
N2O and N2 emission from DE and PA agriculture
<p>N2, N2O, CO2, soil moisture (%), and headspace oxygen (%) data from two corn fields in the northeast US; a coastal site in Delawre and an inland site in Pennsylvania. Emissions were measured using the nitrogen free air recirculation method (NFARM). Measurements were made a three time points over the growing season of 2014 (post-fertilization, mid-season, and pre-harvest) from plots treated with five different of fertilizer treatments (control, urea, manure, compost, and biochar).</p>
Global soil moisture–atmosphere feedback and N2O emission dataset
<p><span>Soil moisture is essential to microbial nitrogen (N)-cycling networks in terrestrial ecosystems. Studies have found that soil moisture–atmosphere feedbacks dominate the changes in land carbon fluxes. </span><span>However, the influence of soil moisture–atmosphere feedbacks on the N fluxes changes, and the underlying mechanisms remain highly unsure, leading to uncertainties in climate projections. </span><span>To fill this gap, we utilized in situ observation coupled with gridded and remote sensing data to analyze N<sub>2</sub>O fluxes emissions globally. Here, we investigated the synergistic effects of temperature, hydroclimate on global N<sub>2</sub>O fluxes, as the result of soil moisture–atmosphere feedback impact on N fluxes. We found that soil moisture–temperature feedback dominates land N<sub>2</sub>O emissions by controlling the balance between nitrifier and denitrifier genes. The mechanism is that atmospheric water demand increases with temperature and thereby reduces soil moisture, which increases the dominant N<sub>2</sub>O production nitrifier (containing <em>amoA </em>AOB gene) and decreases the N<sub>2</sub>O consumption denitrifier (containing the<em> nosZ</em> gene), consequently will potential increasing N<sub>2</sub>O emissions. However, we find that the spatial variations of soil–water availability as a result of the nonlinear response of soil moisture to vapor pressure deficit caused by temperature are some of the greatest challenges in predicting future N<sub>2</sub>O emissions. Our data-driven assessment deepens the understanding of the impact of soil moisture-atmosphere interactions on the soil N cycle, which remains uncertain in earth system models. We suggest that the model needs to account for feedback between soil moisture and atmospheric temperature when estimating the response of the N<sub>2</sub>O emissions to climatic change globally, as well as when conducting field-scale investigations of the response of the ecosystem to warming.</span></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.