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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Data from: Coupled machine learning-ecosystem ensemble models substantially improve predictions of nitrous oxide (N 2 O) fluxes from US croplands

Nitrous oxide (N₂O) is a potent and persistent greenhouse gas, with rising atmospheric concentrations driven in part by inefficient use of synthetic nitrogen (N) fertilizers in agriculture. Predicting soil N₂O emissions is challenging due to high spatial and temporal variability arising from complex soil biogeochemical processes. Process-based ecosystem models and standalone machine learning (ML) approaches without extensive site-specific calibration often miss high emission episodes. Here, we show how an Ensemble Modeling System (EMS) based on outputs from an ensemble of ecosystem models coupled to an ensemble of ML models can improve predictions and understanding of N2O fluxes from US cropland. Trained and validated on approximately 12,000 N2O chamber measurements at 17 U.S. Midwest sites (six crops, 35 management practices), the EMS accurately predicted daily fluxes of N2O at both training (R² = 0.84, RMSE = 16.4 g N ha⁻¹ d⁻¹) and held-out testing sites (R² = 0.84, RMSE = 6.2 g N ha⁻¹ d⁻¹). Analyses identified six dominant N₂O drivers: soil organic carbon (SOC), NH₄⁺, NO₃⁻, water-filled pore space (WFPS), soil temperature, and biomass production. Wet, warm soils produced large N₂O peaks only with sufficient SOC and mineral N; in low-SOC soils, fluxes remained low. Incorporating these drivers into process-based models might significantly improve their predictive capacity. The EMS demonstrates a strong potential to predict N₂O fluxes at unseen sites, enabling more reliable regional inventories, improved gap-filling where measurements are sparse, and enhanced understanding of mechanisms to advance targeted mitigation strategies in food, feed, and bioenergy crops.

agricultural sciences↗
DOE OSTI2024

Assessing Nutrient and Carbon Responses to Agricultural Conservation Practices in Two Midwest Watersheds

The United States is undertaking efforts to transition to a low-carbon economy in response to the heightened impacts of climate change, which are largely attributed to decades of carbon-intensive development. A concerted effort by the energy sector to achieve net-zero carbon emissions is aimed at strategically reducing the carbon footprint of the energy supply chain, particularly in the production of feedstocks for biofuels. The production of biofuels relies heavily on land use and management practices in biomass cultivation. Among other factors, soil organic carbon (SOC) plays a crucial role in the biofuel carbon cycle, impacting land productivity, greenhouse gas emissions, and water quality. The process of carbon drawdown during plant growth and storage helps to mitigate the release of carbon dioxide (CO 2 ) into the atmosphere. Another key parameter is the release of nitrous oxide (N 2 O) — a potent greenhouse gas with a global warming potential 273 times that of CO 2 — from soil. While prioritizing low-carbon production, sustainable bioenergy also necessitates improved water quality and ecosystem services. Agricultural conservation practices can contribute to enhanced soil health and reduce nutrient and sediment loss into streams. However, studies that explore the broader impact of these practices on soil carbon storage and N 2 O emissions at a watershed scale are limited. Specifically, our understanding of how low-carbon feedstock production and management affect nutrient cycle dynamics is incomplete. This study assesses watershed responses to land management practices in two agriculturally dominant watersheds: the Raccoon River watershed and the Southfork of Iowa River watershed. The study focuses on carbon and nutrient dynamics, characterizing spatial and temporal variations in SOC, N 2 O, and nutrient loadings to examine the relationship between nutrient and carbon responses and agricultural conservation practices. The study employs the newly developed Soil Water Analysis Tool for Carbon (SWAT-C) model, calibrated using 20 years’ of climate and water monitoring data, to simulate and evaluate two agricultural conservation practices: no-till and crop residue harvest with cover crop planting. We compared the calibrated SWAT-C model with a historical baseline model, which allowed us to analyze various aspects of the watershed, including stream flow, suspended sediments, nitrogen, phosphorus, organic carbon, SOC at different depths, and N 2 O emissions from topsoil. Our SWAT-C modeling results indicate that, compared with results from the historical baseline model, no-till practices are positively correlated with SOC accumulation, reduced N 2 O emissions, and decreased soil loss. We also observed no change or slightly increased nitrogen and phosphorus loss to water bodies in the watersheds compared with the baseline. Conversely, crop residue harvest with cover crop planting improved water quality by reducing nutrient and soil losses but also increased N 2 O emissions. This SWAT-C modeling study establishes the groundwork for further smaller-scale and/or sub-basin-level assessments of nitrogen, phosphorus, and carbon cycles. It provides valuable science-based insights to inform policy decisions — particularly in the context of transitioning to biofuel feedstock production in these watersheds — by addressing concerns about nutrient exports to local water bodies and, ultimately, the Mississippi River.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Climate induced microbiome alterations increase cadmium bioavailability in agricultural soils with pH below 7

Climate change and metals independently stress soil microbiomes, but their combined effects remain unresolved. Here we show that future climate affects soil cadmium through altered soil microbiome and nutrient cycles, with soil pH as critical factor. In soils with pH<7 and during summer temperatures, future climate increased porewater cadmium, shifting total and potentially active taxonomic microbiome structures. Microbial ammonium oxidation released protons liberating cadmium through cation exchange from mineral surfaces. When porewater cadmium levels became toxic to non-cadmium-tolerant bacteria, microbial activity, and nutrient cycling decreased, reducing carbon and nitrogen emissions. In contrast, pH>7 soil show no climate impacts on cadmium mobilization, though imprints on microbiome structure were apparent. Subsequent nutrient cycling increased under future climate, stimulating soil respiration and nitrous oxide release. These findings underscore complex interactions between climate change and soil contaminants affecting the soil microbiome and its activity and highlights potential impacts on crop production, groundwater quality, and climate feedback.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Chemical identity of carbon substrates drives differences in denitrification and N 2 O reduction within agricultural soils

Rates of nitrous oxide (N 2 O) production from agricultural soils are highly variable across space and time. Improving predictions of N 2 O emissions will require improving our understanding of the drivers of denitrification and the sources of variability in the rates of N 2 O production between soils and over time. While the amount of available carbon (C) is a known control on denitrification and N 2 O reduction, relatively little attention has been paid to the effect of the chemical identity of C substrates on rates of denitrification and N 2 O reduction. We investigated the effects of twelve different C-substrate additions on the production and reduction of N 2 O in five soils taken from two distinct agricultural locations in Michigan under multiple land uses. We provided additions of glucose, cellulose, N-acetyl-glucosamine, chitin, amino acids, protein, vanillyl alcohol, lignin, citrate, succinate, methanol, and water in laboratory denitrification potential assays to determine the effects of denitrifier C preference on denitrification rates. We found that amino acids, protein, and organic acids stimulated the greatest rates of denitrification potential across all land uses. Similarly, we found these same substrates caused the most N 2 O reduction, resulting in the lowest net concentrations of N 2 O. Soils from agricultural rotations without cover crops had overall lower rates of denitrifier activity, leading to less net N 2 O production compared to soils from other land uses. In general, C-utilization patterns were similar among all soils, and C-substrate identity had a much stronger effect than land use. Here, we demonstrate that the chemical identity of available C gives rise to wide variability in rates of denitrification and N 2 O reduction.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

The climate change mitigation potential of annual grasslands under future climates

Abstract Composted manure and green waste amendments have been shown to increase net carbon (C) sequestration in rangeland soils and have been proposed as a means to help lower atmospheric CO 2 concentrations. However, the effect of climate change on soil organic C (SOC) stocks and greenhouse gas emissions in rangelands is not well understood, and the viability of climate change mitigation strategies under future conditions is even less certain. We used a process‐based biogeochemical model (DayCent) at a daily time step to explore the long‐term effects of potential future climate changes on C and greenhouse gas dynamics in annual grassland ecosystems. We then used the model to explore how the same ecosystems might respond to climate change following compost amendments to soils and determined the long‐term viability of net SOC sequestration under changing climates. We simulated net primary productivity (NPP), SOC, and greenhouse gas fluxes across seven California annual grasslands with and without compost amendments. We drove the DayCent simulations with field data and with site‐specific daily climate data from two Earth system models (CanESM2 and HadGEM‐ES) and two representative concentration pathways (RCP4.5 and RCP8.5) through 2100. NPP and SOC stocks in unamended and amended ecosystems were surprisingly insensitive to projected climate changes. A one‐time amendment of compost to rangeland acted as a slow‐release organic fertilizer and increased NPP by up to 390–814 kg C ha −1 year −1 across sites. The amendment effect on NPP was not sensitive to Earth system model or emissions scenario and endured through the end of the century. Net SOC sequestration amounted to 1.96 ± 0.02 Mg C ha −1 relative to unamended soils at the maximum amendment effect. Averaged across sites and scenarios, SOC sequestration peaked 22 ± 1 years after amendment and declined but remained positive throughout the century. Though compost stimulated nitrous oxide (N 2 O) emissions, the cumulative net emissions (in CO 2 equivalents) due to compost were far less than the amount of SOC sequestered. Compost amendments resulted in a net climate benefit of 69.6 ± 0.5 Tg CO 2 e 20 ± 1 years after amendment if applied to similar ecosystems across the state, amounting to 39% of California's rangeland. These results suggest that the biogeochemical benefits of a single amendment of compost to rangelands in California are insensitive to climate change and could contribute to decadal‐scale climate change mitigation goals alongside emissions reductions.

Mayer, Allegra↗
DOE OSTI2025

NitroNet: Smart System to Quantify Nitrous Oxide Emissions

Agricultural croplands are the largest anthropogenic source of nitrous oxide (N 2 O), the third most important greenhouse gas. Emissions are characterized by “hot spots” and “hot moments”, meaning emissions are highly heterogeneous in space and time. Emissions are driven by nitrification and denitrification processes which depend upon complex factors such as soil characteristics (type, compaction, pH, moisture, topography), management practices (fertilizer type and application, tillage, crop type, field history, irrigation), biogeochemistry (organic carbon, microbial composition), and meteorology (precipitation, temperature, and wind). For these reasons, quantifying cropland emissions by either measurements or modeling is extremely challenging with large uncertainties.

09 BIOMASS FUELS↗
DOE OSTI2025

Greenhouse gas flux response in biochar- and compost-amended urban soils under simulated soil hydrologic dynamics

Understanding greenhouse gas emission dynamics in lawn soils is essential for improving climate change mitigation strategies in urban and suburban environments. This project measured fluxes of carbon dioxide (CO 2 ), methane (CH 4 ), and nitrous oxide (N 2 O) fluxes from turfgrass soil columns amended with biochar, compost, biochar-compost blend, control (no amendment) in a controlled greenhouse mesocosm experiment. This project simulated contrasting water saturation regimes consisting of normal irrigation with sprinkler, transient half or full saturation by water table manipulation, and subsequent drying phase under both sod or seeded grass conditions. Compost-amended columns (compost or biochar-compost blend) exhibited higher average CO 2 fluxes (6 µmol m-2 s-1) compared to biochar or control columns (4.5 – 5.0 µmol m-2 s-1) across all saturation levels under sod conditions. Seeded grass conditions generally resulted in less CO 2 emissions. The CO 2 fluxes were positively correlated with air temperature and negatively correlated with soil moisture. Biochar-amended columns retained high soil moisture (~95 %) throughout experiments, demonstrating superior moisture retention compared to compost. CH 4 and N 2 O fluxes exhibited temporal increases (5-8 nmol m-2 s-1) during saturation and drainage phases, indicating their sensitivity to hydrologic conditions. These findings suggest that temperature and amendment types are primary driver of CO 2 emissions, while CH 4 and N 2 O fluxes are more responsive to water saturation dynamics in lawn soils.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Coupled machine learning–ecosystem ensemble models substantially improve predictions of nitrous oxide (N 2 O) fluxes from US croplands

Nitrous oxide (N 2 O) is a potent and persistent greenhouse gas, with rising atmospheric concentrations driven in part by inefficient use of synthetic nitrogen (N) fertilizers in agriculture. Predicting soil N 2 O emissions is challenging due to high spatial and temporal variability arising from complex soil biogeochemical processes. Process-based ecosystem models and standalone machine learning (ML) approaches without extensive site-specific calibration often miss high-emission episodes. Here, we show how an Ensemble Modeling System (EMS) based on outputs from an ensemble of ecosystem models coupled to an ensemble of ML models can improve predictions and understanding of N 2 O fluxes from US cropland. Trained and validated on ~12,000 N 2 O chamber measurements at 17 US Midwest sites (six crops, 35 management practices), the EMS accurately predicted daily fluxes of N 2 O at both training (R 2 = 0.84, RMSE = 16.4 g N ha −1 d −1 ) and held-out testing sites (R 2 = 0.84, RMSE = 6.2 g N ha −1 d −1 ). Analyses identified six dominant N 2 O drivers: soil organic carbon (SOC), NH 4 + , NO 3 - , water-filled pore space, temperature, and aboveground biomass production. Wet, warm soils produced large N 2 O peaks only with sufficient SOC and mineral N; in low-SOC soils, fluxes remained low. Incorporating these drivers into process-based models might significantly improve their predictive capacity. The EMS demonstrates a strong potential to predict N 2 O fluxes at unseen sites, enabling more reliable regional inventories, improved gap-filling where measurements are sparse, and enhanced understanding of mechanisms to advance targeted mitigation strategies in food, feed, and bioenergy crops.

AI↗
DOE OSTI2021

Bedrock weathering rates, reactive nitrogen influxes and effluxes, and nitrous oxide emissions rates from the Pumphouse Hillslope, East River Watershed, Colorado

Atmospheric nitrous oxide contributes directly to global warming, yet models of the nitrogen cycle do not account for bedrock, the largest pool of terrestrial nitrogen, as a source of nitrous oxide. Although it is known that release rates of nitrogen from bedrock are large, there is an incomplete understanding of the connection between bedrock-hosted nitrogen and atmospheric nitrous oxide. Here, we quantify nitrogen fluxes and mass balances at a hillslope underlain by marine shale. We found that at this site bedrock weathering contributes 78% of the subsurface reactive nitrogen, while atmospheric sources (commonly regarded as the sole sources of reactive nitrogen in pristine environments) account for only the remaining 22%. About 56% of the total subsurface reactive nitrogen denitrifies, including 14% emitted as nitrous oxide. The remaining reactive nitrogen discharges in porewaters to a floodplain where additional denitrification likely occurs. We also found that the release of bedrock nitrogen occurs primarily within the zone of the seasonally fluctuating water table and suggest that the accumulation of nitrate in the vadoes zone, often attributed to fertilization and soil leaching, may also include contributions from weathered nitrogen-rich bedrock. Our hillslope study suggests that under oxygenated and moisture-rich conditions, weathering of deep, nitrogen-rich bedrock makes an important contribution to the nitrogen cycle. The data files are in Excel, which can be accessed using Microsoft Office, and consist of many data sets from the Pumphouse Hillslope PLM (Pumphouse Lower Montane) 1, 2, 3, and 4. They include soil to rock (0-10 meters) solid phase minerals and elements compositions; time- and depth-resolved pore-water chemistry and pore-gas compositions; time- and depth-resolved water table depths and water fluxes; subsurface weathering rates; nitrogen influxes and effluxes and mass balance. The attached paper is in a Word document, which can be accessed using Microsoft Office, and in pdf format.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The influence of tillage and fertilizer on the flux and source of nitrous oxide with reference to atmospheric variation using laser spectroscopy

Nitrous oxide (N 2 O) is the third most important long-lived greenhouse gas and agriculture is the largest source of N 2 O emissions. Curbing N 2 O emissions requires understanding influences on the flux and sources of N 2 O. We measured flux and evaluated microbial sources of N 2 O using site preference ( S P ; the intramolecular distribution of 15 N in N 2 O) in flux chambers from a grassland tilling and agricultural fertilization experiments and atmosphere. We identified values greater than that of the average atmosphere to reflect nitrification and/or fungal denitrification and those lower than atmosphere as increased denitrification. Our spectroscopic approach was based on an extensive calibration with 18 standards that yielded S P accuracy and reproducibility of 0.7 ‰ and 1.0 ‰, respectively, without preconcentration. Chamber samples from the tilling experiment taken ~ monthly over a year showed a wide range in N 2 O flux (0–1.9 g N 2 O-N ha -1 d -1 ) and S P (- 1.8 to 25.1 ‰). Flux and S P were not influenced by tilling but responded to sampling date. Large fluxes occurred in October and May in no-till when soils were warm and moist and during a spring thaw, an event likely representing release of N 2 O accumulated under snow cover. These high fluxes could not be ascribed to a single microbial process as S P differed among chambers. However, the year-long S P and flux data for no-till showed a slight direct relationship suggesting that nitrification increased with flux. The comparative data in till showed an inverse relationship indicating that high flux events are driven by denitrification. Corn ( Zea mays ) showed high fluxes and S P values indicative of nitrification ~ 4 wk after fertilization with subsequent declines in S P indicating denitrification. Although there was no effect of fertilizer treatment on flux or S P in switchgrass ( Panicum virgatum) , high fluxes occurred ~1 month after fertilization. In both treatments, S P was indicative of denitrification in many instances, but evidence of nitrification/fungal denitrification also prevailed. At 2 m atmospheric N 2 O S P had a range of 31.1 ‰ and 14.6 ‰ in the grassland tilling and agricultural fertilization experiments, respectively. These data suggest the influence of soil microbial processes on atmospheric N 2 O and argue against the use of the global average atmospheric S P in isotopic modeling approaches.

Environmental Sciences & Ecology↗
DOE OSTI2024

Position-specific kinetic isotope effects for nitrous oxide: a new expansion of the Rayleigh model

Nitrous oxide (N 2 O) is a potent greenhouse gas and the most significant anthropogenic ozone-depleting substance currently being emitted. A major source of anthropogenic N 2 O emissions is the microbial conversion of fixed nitrogen species from fertilizers in agricultural soils. Thus, understanding the enzymatic mechanisms by which microbes produce N 2 O has environmental significance. Measurement of the 15 N/ 14 N isotope ratios of N 2 O produced by purified enzymes or axenic microbial cultures is a promising technique for studying N 2 O biosynthesis. Typically, N 2 O-producing enzymes combine nitrogen atoms from two identical substrate molecules (NO or NH 2 OH). Position-specific isotope analysis of the central (N α ) and outer (N β ) nitrogen atoms in N 2 O enables the determination of the individual kinetic isotope effects (KIEs) for N α and N β , providing mechanistic insight into the incorporation of each nitrogen atom. Previously, position-specific KIEs (and fractionation factors) were quantified using the Rayleigh distillation equation, i.e., via linear regression of δ 15 N α or δ 15 N β against [–f In f / (1 – f)], where f is the fraction of substrate remaining in a closed system. This approach, however, is inaccurate for N α and N β because it does not account for fractionation at N α affecting the isotopic composition of substrate available for incorporation into the β position (and vice versa). Therefore, we developed a new expansion of the Rayleigh model that includes specific terms for fractionation at the individual N 2 O nitrogen atoms. By applying this Expanded Rayleigh model to a variety of simulated N 2 O synthesis reactions with different combinations of normal, inverse, and/or no KIEs at N α and N β , we demonstrate that our new model is both accurate and robust. We also applied this new model to two previously published datasets describing N 2 O production from NH 2 OH oxidation in a methanotroph culture (Methylosinus trichosporium) and N 2 O production from NO by a purified Histoplasma capsulatum (fungal) P450 NOR, demonstrating that the Expanded Rayleigh model is a useful tool in calculating position-specific fractionation for N 2 O synthesis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

KGML-ag: a modeling framework of knowledge-guided machine learning to simulate agroecosystems: a case study of estimating N<sub>2</sub>O emission using data from mesocosm experiments

Abstract. Agricultural nitrous oxide (N2O) emission accounts for a non-trivial fraction of global greenhouse gas (GHG) budget. To date, estimating N2O fluxes from cropland remains a challenging task because the related microbial processes (e.g., nitrification and denitrification) are controlled by complex interactions among climate, soil, plant and human activities. Existing approaches such as process-based (PB) models have well-known limitations due to insufficient representations of the processes or uncertainties of model parameters, and due to leverage recent advances in machine learning (ML) a new method is needed to unlock the “black box” to overcome its limitations such as low interpretability, out-of-sample failure and massive data demand. In this study, we developed a first-of-its-kind knowledge-guided machine learning model for agroecosystems (KGML-ag) by incorporating biogeophysical and chemical domain knowledge from an advanced PB model, ecosys, and tested it by comparing simulating daily N2O fluxes with real observed data from mesocosm experiments. The gated recurrent unit (GRU) was used as the basis to build the model structure. To optimize the model performance, we have investigated a range of ideas, including (1) using initial values of intermediate variables (IMVs) instead of time series as model input to reduce data demand; (2) building hierarchical structures to explicitly estimate IMVs for further N2O prediction; (3) using multi-task learning to balance the simultaneous training on multiple variables; and (4) pre-training with millions of synthetic data generated from ecosys and fine-tuning with mesocosm observations. Six other pure ML models were developed using the same mesocosm data to serve as the benchmark for the KGML-ag model. Results show that KGML-ag did an excellent job in reproducing the mesocosm N2O fluxes (overall r2=0.81, and RMSE=3.6 mgNm-2d-1 from cross validation). Importantly, KGML-ag always outperforms the PB model and ML models in predicting N2O fluxes, especially for complex temporal dynamics and emission peaks. Besides, KGML-ag goes beyond the pure ML models by providing more interpretable predictions as well as pinpointing desired new knowledge and data to further empower the current KGML-ag. We believe the KGML-ag development in this study will stimulate a new body of research on interpretable ML for biogeochemistry and other related geoscience processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Emergence and disruption of cooperativity in a denitrifying microbial community

Anthropogenic perturbations to the nitrogen cycle, primarily through use of synthetic fertilizers, is driving an unprecedented increase in the emission of nitrous oxide (N 2 O), a potent greenhouse gas and an ozone depleting substance, causing urgency in identifying the sources and sinks of N 2 O. Microbial denitrification is a primary contributor to biotic production of N 2 O in anoxic regions of soil, marine systems, and wastewater treatment facilities. Here, through comprehensive genome analysis, we show that pathway partitioning is a ubiquitous mechanism of complete denitrification within microbial communities. We have investigated mechanisms and consequences of process partitioning of denitrification through detailed physiological characterization and kinetic modeling of a synthetic community of Rhodanobacter thiooxydans FW510-R12 and Acidovorax sp. GW101-3H11. We have discovered that these two bacterial isolates, from a heavily nitrate (NO 3 − ) contaminated superfund site, complete denitrification through the exchange of nitrite (NO 2 − ) and nitric oxide (NO). The process partitioning of denitrification and other processes, including amino acid metabolism, contribute to increased cooperativity within this denitrifying community. We demonstrate that certain contexts, such as high NO 3 − , cause unbalanced growth of community members, due to differences in their substrate utilization kinetics. The altered growth characteristics of community members drives accumulation of toxic NO 2 − , which disrupts denitrification causing N 2 O off gassing.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

High resolution measurements reveal abiotic and biotic mechanisms of elevated nitric oxide emission after wetting dry soil

Wetting dry soil triggers emission of nitric oxide (NO), which can harm air quality and influence ecosystem N balance. NO released after wetting may be derived from two primary mechanisms: (1) abiotic decomposition of nitrite (NO 2 -) present in the dry soil and (2) biologically-mediated production of NO. The relative contributions of these mechanisms to NO emission are challenging to partition. To differentiate the two mechanisms, we made continuous observations of NO emission from a range of soils that varied in texture, pH, and exchangeable cations. Additionally, we coupled NO gas exchange measurements with automated, non-destructive high-resolution sampling of inorganic N species via microdialysis. We identified two distinct phases of NO production (1) an initial phase that reached its maximum less than 30 min after rewetting; and (2) a second phase lasting up to 36 h. The magnitude of the initial phase of NO production was correlated with the initial nitrous acid concentration (calculated from NO 2 - concentrations and soil pH) and soil exchangeable calcium. The second phase of NO emission coincided with a peak in NO 2 - that preceded NO 3 - accumulation. Together, these results suggest that NO emissions following wetting are defined by an initial pulse of abiotic NO formation mediated by soil acidity at mineral surfaces, followed by a prolonged pulse of NO emission driven by a lag between the biological processes of ammonia oxidation and nitrite oxidation. These results can inform mechanistic models of NO emissions from soils to improve predictions of the terrestrial N cycle and atmospheric chemistry.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Sustained bacterial N 2 O reduction at acidic pH

Nitrous oxide (N 2 O) is a climate-active gas with emissions predicted to increase due to agricultural intensification. Microbial reduction of N 2 O to dinitrogen (N 2 ) is the major consumption process but microbial N 2 O reduction under acidic conditions is considered negligible, albeit strongly acidic soils harbor nosZ genes encoding N 2 O reductase. Here, we study a co-culture derived from acidic tropical forest soil that reduces N 2 O at pH 4.5. The co-culture exhibits bimodal growth with a Serratia sp. fermenting pyruvate followed by hydrogenotrophic N 2 O reduction by a Desulfosporosinus sp. Integrated omics and physiological characterization revealed interspecies nutritional interactions, with the pyruvate fermenting Serratia sp. supplying amino acids as essential growth factors to the N 2 O-reducing Desulfosporosinus sp. Thus, we demonstrate growth-linked N 2 O reduction between pH 4.5 and 6, highlighting microbial N 2 O reduction potential in acidic soils.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Metagenomic Characterization of Soil Microbial Communities in the Luquillo Experimental Forest (Puerto Rico) and Implications for Nitrogen Cycling

The phylogenetic and functional diversities of microbial communities in tropical rainforests and how these differ from those of temperate communities remain poorly described but are directly related to the increased fluxes of greenhouse gases such as nitrous oxide (N 2 O) from the tropics. Toward closing these knowledge gaps, in this study we analyzed replicated shotgun metagenomes representing distinct life zones and an elevation gradient from four locations in the Luquillo Experimental Forest (LEF), Puerto Rico. These soils had a distinct microbial community composition and lower species diversity compared to those of temperate grasslands or agricultural soils. In contrast to the overall distinct community composition, the relative abundances and nucleotide sequences of N 2 O reductases (nosZ) were highly similar between tropical forest and temperate soils. However, respiratory NO reductase (norB) was 2-fold more abundant in the tropical soils, which might be relatable to their greater N 2 O emissions. Nitrogen fixation (nifH) also showed higher relative abundance in rainforest than in temperate soils, i.e., 20% versus 0.1 to 0.3% of bacterial genomes in each soil type harbored the gene, respectively. Finally, unlike temperate soils, LEF soils showed little stratification with depth in the first 0 to 30 cm, with ~45% of community composition differences explained solely by location. Collectively, these results advance our understanding of spatial diversity and metabolic repertoire of tropical rainforest soil communities and should facilitate future ecological studies of these ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Climate-carbon feedback tradeoff between Arctic and alpine permafrost under warming

Whether greenhouse gas (GHG) emissions from permafrost will trigger positive climate feedbacks under warming remains unknown. Here, we synthesized the response of growing season carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) emissions to experimentally manipulated warming of ~2°C for permafrost in alpine and Arctic regions. Warming weakened the GHG sink of alpine permafrost, thereby increasing (13%) its global warming potential, but strengthened the GHG sink of Arctic permafrost and decreased (-10%) its global warming potential. When warming caused drying of alpine permafrost soils, the CO2 sink weakened but the CH4 sink increased. In contrast, warming of relatively wet Arctic permafrost increased the CO2 sink and CH4 source. Warming led to much stronger increases of the N2O source in alpine than Arctic permafrost. Although keeping additional warming below 2°C in permafrost regions can avoid the positive permafrost-climate feedback, measures are needed to maintain fragile carbon sink of alpine permafrost ecosystems.

Bao, Tao↗