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Soils in an Amazonian rainforest are observed to release N2O at a rate larger than the global mean by about a factor of 20. Emissions from a New England hardwood forest are approximately 30 times smaller then Brazilian values. Atmospheric methane is consumed by soils in both systems. Tropical forests would provide a major source of atmospheric N2O if the Brazilian results are representative.
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.
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.
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.
Food systems - including food production, distribution, consumption, and waste disposal - are critical to sustaining livelihoods and delivering nutrition worldwide. However, food systems contribute significantly to the climate crisis, accounting for over 30 percent of human-caused global greenhouse gas emissions (e.g., methane, carbon dioxide, and nitrous oxide). Climate change, in turn, has a significant and growing impact on food systems. Climate change increases heat stress and can lead to deteriorating soil health, for example, slowing agricultural productivity and reducing the nutritional content of crops and livestock. The cascading impacts of climate extremes on agricultural production are likely to destabilize global food security, endangering the livelihoods of billions of people and threatening public health.
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.
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.
Biomass burning is a source of greenhouse gases, carbon dioxide, methane, and nitrous oxide. In addition, biomass burning is a source of chemically active gases, including carbon monoxide, nonmethane hydrocarbons, and nitric oxide. These gases, along with methane, lead to the chemical production of tropospheric ozone (another greenhouse gas) as well as control the concentration of the hydroxyl radical, which regulates the lifetime of almost every atmospheric gas. Following biomass burning, biogenic emissions of nitrous oxide, nitric oxide, and methane are significantly enhanced. It is hypothesized that enhanced postburn biogenic emissions of these gases are related to fire-induced changes in soil chemistry and/or microbial ecology. Biomass burning, once believed to be a tropical phenomenon, has been demonstrated by satellite imagery to also be a regular feature of the world's boreal forests. One example of biomass burning is the extensive 1987 fire that destroyed more than 12 million acres of boreal forest in the People's Republic of China and across its border in the Soviet Union. Recent estimates indicate that almost all biomass burning is human-initiated and that it is increasing with time. With the formation of greenhouse and chemically active gases as direct combustion products and a longer-term enhancement of biogenic emissions of gases, biomass burning may be a significant driver for global change.
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.
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.
Nitrous oxide (N2O) is the third most important anthropogenic greenhouse gas (GHG) and a major ozone-depleting substance (ODS). Its main sources include anthropogenic activities (mostly agriculture) and natural emissions from ocean and soils. However, emission estimates for individual sources are highly variable due to uncertainties in N2O lifetime estimates and partitioning among sources. We derive annual global N2O emissions for 1990-2019 using NOAA Global Monitoring Laboratory (GML) surface N2O observations and the N2O lifetime calculated in the NASA GEOS-5 chemistry climate model (CCM). The inferred global mean N2O emissions has gradually increased from ~15.8 TgN/yr in the early 1990s to ~17.8 TgN/yr in the 2010s. This implies that anthropogenic N2O emissions have grown rapidly from ~6.7 TgN/yr in the 1990s to about ~8.7 TgN/yr in the 2010s, a ~30% increase. With specially designed N2O isotopic tracers in 3-D GEOSCCM, we estimate that, on global average, stratospheric enrichment contributes about +7.7‰/yr, +7.6‰/yr, +8.0‰/yr to tropospheric N(sup α)-δ(sup 15), N(sup β)-δ(sup 15), and O-δ(sup 18) budget, respectively. To balance the global mean isotopic signature for pre-industrial terrestrial sources of N(sup α)-δ(sup 15) ~ 6.7‰, N(sup β)-δ(sup 15) ~ -12.6‰, O-δ(sup 18) ~ 35.4‰, our 3-D isotopic budget simulation using the GEOSCCM suggests global mean anthropogenic isotopic signatures in the recent decades are N(sup α)-δ(sup 15) ~ -18‰, N(sup β)-δ(sup 15) ~ -20‰, O-δ(sup 18) ~ 19‰. These anthropogenic isotopic estimates are significantly lighter than results from one-box atmospheric model-based estimates with the largest difference seen for N(sup β)-δ(sup 15). More surface isotopic measurements are needed to better quantify the N2O isotopic signatures.
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.
Nitrous oxide fluxes and soil nitrogen transformations were measured in experimentally-treated high elevation Douglas-fir forests in northwestern New Mexico, USA. On an annual basis, forests that were fertilized with 200 kg N/ha emitted an average of 0.66 kg/ha of N2O-N, with highest fluxes occurring in July and August when soils were both warm and wet. Control, irrigated, and woodchip treated plots did not differ, and annual average fluxes ranged from 0.03 to 0.23 kg/ha. Annual net nitrogen mineralization and nitrate production were estimated in soil and forest floor using in situ incubations; fertilized soil mineralized 277 kg/ha/y in contrast to 18 kg/ha/y in control plots. Relative recovery of 15NH4-N applied to soil in laboratory incubations was principally in the form of NO3-N in the fertilized soils, while recovery was mostly in microbial biomass-N in the other treatments. Fertilization apparently added nitrogen that exceeded the heterotrophic microbial demand, resulting in higher rates of nitrate production and higher nitrous oxide fluxes. Despite the elevated nitrous oxide emission resulting from fertilization, we estimate that global inputs of nitrogen into forests are not currently contributing significantly to the increasing concentrations of nitrous oxide in the atmosphere.
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.
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.
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.
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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.