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Soil chemical properties and microbiome composition impact N2O emission potential, but the relative importance of these factors as determinants of N2O emission in denitrifying systems is rarely tested. In addition, previous linkages between microbiome composition and N2O emission potential rarely demonstrate causality. Here, we determined the relative impact of bacteriome composition (i.e., soil extracted bacterial cells) and soil water extract (i.e., water extractable chemicals and particles below 0.22 µm) on N2O emission potential utilizing an anoxic cell-based assay system. Cells and water extract for assays were sourced from soils with contrasting N2O/N2O + N2 ratios, combined in various combinations and denitrification gas production was measured in response to nitrate addition. Analysis of 16S amplicon sequencing data revealed similarity in composition between extracted and parent soil bacteriomes. Average directionless effects of cell and water extract on N2O/N2O + N2 (Cell: ∆0.17, soil water extract: ∆0.22) and total N2O hypothetically emitted (Cell: ∆2.62 µmol-N, soil water extract: ∆4.14 µmol-N) across two assays indicated water extract is the most important determinant of N2O emissions. Independent pH differences of just 0.6 points impacted N2O/N2O + N2 on par with independent water extract differences, supporting the dominance of this variable in previous studies. However, impacts on overall N2O hypothetically emitted were smaller, suggesting that soil pH manipulation may not necessarily be a successful approach to mitigate emissions. In addition, we observed increased N2O accumulation and emission potential at the end of incubations concomitant with predicted decreases in carbon (C) availability, suggesting that C limitation increases N2O emission transiently with the magnitude of emission dependent on both chemical and bacteriome controls.

Inoculating agricultural soils with nitrous oxide respiring bacteria (NRB) can reduce N2O-emission, but would be impractical as a standalone operation. Here we demonstrate that digestates obtained after biogas production are suitable substrates and vectors for NRB. We show that indigenous NRB in digestates grew to high abundance during anaerobic enrichment under N2O. Gas-kinetics and meta-omic analyses showed that these NRB’s, recovered as metagenome-assembled genomes (MAGs), grew by harvesting fermentation intermediates of the methanogenic consortium. Three NRB’s were isolated, one of which matched the recovered MAG of a Dechloromonas, deemed by proteomics to be the dominant producer of N2O-reductase in the enrichment. While the isolates harbored genes required for a full denitrification pathway and could thus both produce and sequester N2O, their regulatory traits predicted that they act as N2O sinks in soil, which was confirmed experimentally. The isolates were grown by aerobic respiration in digestates, and fertilization with these NRB-enriched digestates reduced N2O emissions from soil. Our use of digestates for low-cost and large-scale inoculation with NRB in soil can be taken as a blueprint for future applications of this powerful instrument to engineer the soil microbiome, be it for enhancing plant growth, bioremediation, or any other desirable function.

Nonlinear transient convection in a porous rectangle heated from below is studied by an analytically based method. Fourier series for the temperature and streamfunction are applied, where each Fourier coefficient evolves in time according to a coupled set of ordinary differential equations. The mathematical method can be considered as a recursive nonlinear mapping in Fourier space from a given state at a time t to an updated state at time t + dt, with an infinitesimal time increment. This nonlinear evolution in Fourier space requires normal-mode compatible boundary conditions along the entire boundary. Each Fourier coefficient gets three contributions during its updating in time: (i) one decay term due to thermal diffusion, governed by linear theory; (ii) one growth term due to buoyancy, governed by linear theory; and (iii) quadratic nonlinearities from the convection term in the heat equation, involving all pairwise interactions between the Fourier modes. We present numerical computations with a standard Runge–Kutta method, with Rayleigh numbers up to ten times the well-known critical value [Formula: see text]. Our plots are produced with Fourier series truncated to include 15 normal modes in both the horizontal and vertical directions. For validation of our method, we present tables for the Nusselt number of steady convection, with a higher number (up to 20) of normal modes included in the truncated system of equations. Our computations of transient nonlinear convection lead to steady states. A final steady state is not unique for a given geometry, but depends on the initial state and the Rayleigh number. This ambiguity of steady states is depicted by a hysteresis loop. The Malkus hypothesis of maximal heat transfer is put into perspective. This hypothesis does not pick a preferred cell width, but it nevertheless constrains the hysteresis loop.

The rapidly changing global climate due to increased emission of anthropogenic greenhouse gases (GHGs) is leading to an increased occurrence of extreme weather events such as droughts, floods, and heatwaves. The three major GHGs are carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). The major natural sources of CO2 include ocean–atmosphere exchange, respiration of animals, soils (microbial respiration) and plants, and volcanic eruption; while the anthropogenic sources include burning of fossil fuel (coal, natural gas, and oil), deforestation, and the cultivation of land that increases the decomposition of soil organic matter and crop and animal residues. Natural sources of CH4 emission include wetlands, termite activities, and oceans. Paddy fields used for rice production, livestock production systems (enteric emission from ruminants), landfills, and the production and use of fossil fuels are the main anthropogenic sources of CH4. Nitrous oxide, in addition to being a major GHG, is also an ozone-depleting gas. N2O is emitted by natural processes from oceans and terrestrial ecosystems. Anthropogenic N2O emissions occur mostly through agricultural and other land-use activities and are associated with the intensification of agricultural and other human activities such as increased use of synthetic fertiliser (119.4 million tonnes of N worldwide in 2019), inefficient use of irrigation water, deposition of animal excreta (urine and dung) from grazing animals, excessive and inefficient application of farm effluents and animal manure to croplands and pastures, and management practices that enhance soil organic N mineralisation and C decomposition. Agriculture could act as a source and a sink of GHGs. Besides direct sources, GHGs also come from various indirect sources, including upstream and downstream emissions in agricultural systems and ammonia (NH3) deposition from fertiliser and animal manure.

Soils harbour diverse soil faunaand a wide range of soil microorganisms. These fauna and microorganisms directly contribute to soil greenhouse gas (GHG) fluxes via their respiratory and metabolic activities and indirectly by changing the physical, chemical and biological properties of soils through bioturbation, fragmentation and redistribution of plant residues, defecation, soil aggregate formation, herbivory, and grazing on microorganisms and fungi. Based on recent results, the methods and results found in relation to fauna as well as from fungi and plants are presented. The approaches are outlined, and the significance of these hitherto ignored fluxes is discussed.

Agriculture is a significant source of GHGsglobally and ruminant livestock animals are one of the largest contributors to these emissions, responsible for an estimated 14% of GHGs (CH4and N2O combined) worldwide. A large portion of GHG fluxes from agricultural activities is related to CH4 emissions from ruminants. Both direct and indirect methods are available. Direct methods include enclosure techniques, artificial (e.g. SF6) or natural (e.g. CO2) tracer techniques, and micrometeorological methods using open-path lasers. Under the indirect methods, emission mechanisms are understood, where the CH4 emission potential is estimated based on the substrate characteristics and the digestibility (i.e. from volatile fatty acids). These approximate methods are useful if no direct measurement is possible. The different systems used to quantify these emission potentials are presented in this chapter. Also, CH4 from animal waste (slurry, urine, dung) is an important source: methods pertaining to measuring GHG potential from these sources are included.

Methods and techniques are described for automated measurements of greenhouse gases (GHGs) in both the laboratory and the field. Robotic systems are currently available to measure the entire range of gases evolved from soils including dinitrogen (N2). These systems usually work on an exchange of the atmospheric N2with helium (He) so that N2 fluxes can be determined. Laboratory systems are often used in microbiology to determine kinetic response reactions via the dynamics of all gaseous N species such as nitric oxide (NO), nitrous oxide (N2O), and N2. Latest He incubation techniques also take plants into account, in order to study the effect of plant–soil interactions on GHGsand N2 production. The advantage of automated in-field techniques is that GHG emission rates can be determined at a high temporal resolution. This allows, for instance, to determine diurnal response reactions (e.g. with temperature) and GHG dynamics over longer time periods.

Agricultural lands make up approximately 37% of the global land surface, and agriculture is a significant source of greenhouse gas (GHG) emissions, including carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O). Those GHGs are responsible for the majority of the anthropogenic global warming effect. Agricultural GHG emissions are associated with agricultural soil management (e.g. tillage), use of both synthetic and organic fertilisers, livestock management, burning of fossil fuel for agricultural operations, and burning of agricultural residues and land use change. When natural ecosystems such as grasslands are converted to agricultural production, 20–40% of the soil organic carbon (SOC) is lost over time, following cultivation. We thus need to develop management practices that can maintain or even increase SOCstorage in and reduce GHG emissions from agricultural ecosystems. We need to design systematic approaches and agricultural strategies that can ensure sustainable food production under predicted climate change scenarios, approaches that are being called climate‐smart agriculture (CSA). Climate‐smart agricultural management practices, including conservation tillage, use of cover crops and biochar application to agricultural fields, and strategic application of synthetic and organic fertilisers have been considered a way to reduce GHG emission from agriculture. Agricultural management practices can be improved to decreasing disturbance to the soil by decreasing the frequency and extent of cultivation as a way to minimise soil C loss and/or to increase soil C storage. Fertiliser nitrogen (N) use efficiency can be improved to reduce fertilizer N application and N loss. Management measures can also be taken to minimise agricultural biomass burning. This chapter reviews the current literature on CSA practices that are available to reduce GHG emissions and increase soil Csequestration and develops a guideline on best management practices to reduce GHG emissions, increase C sequestration, and enhance crop productivity in agricultural production systems.

GHGemissions are usually the result of several simultaneous processes. Furthermore, some gases such as N2 are very difficult to quantify and require special techniques. Therefore, in this chapter, the focus is on stable isotopemethods. Both natural abundance techniques and enrichment techniques are used. Especially in the last decade, a number of methodological advances have been made. Thus, this chapter provides an overview and description of a number of current state-of-the-art techniques, especially techniques using the stable isotope15N. Basic principles and recent advances of the 15N gasflux method are presented to quantify N2 fluxes, but also the latest isotopologue and isotopomermethods to identify pathways for N2O production. The second part of the chapter is devoted to 15N tracing techniques, the theoretical background and recent methodological advances. A range of different methods is presented from analytical to numerical tools to identify and quantify pathway-specific N2O emissions. While this chapter is chiefly concerned with gaseous N emissions, a lot of the techniques can also be applied to other gases such as methane (CH4), as outlined in Sect. 5.3.

Several approaches exist for measuring greenhouse gases (GHGs), mainly CO2, N2O, and CH4, from soil surfaces. The principle methods that are used to measure GHG from agricultural sites are chamber-based techniques. Both open and closed chamber techniques are in use; however, the majority of field applications use closed chambers. The advantages and disadvantages of different chamber techniques and the principal steps of operation are described. An important part of determining the quality of the flux measurements is the storage and the transportation of the gas samples from the field to the laboratory where the analyses are carried out. Traditionally, analyses of GHGs are carried out via gas chromatographs (GCs). In recent years, optical analysers are becoming increasingly available; these are user-friendly machines and they provide a cost-effective alternative to GCs. Another technique which is still under development, but provides a potentially superior method, is Raman spectroscopy. Not only the GHGs, but also N2, can potentially be analysed if the precision of these techniques is increased in future development. An important part of this chapter deals with the analyses of the gas concentrations, the calculation of fluxes, and the required safety measures. Since non-upland agricultural lands (i.e. flooded paddy soils) are steadily increasing, a section is devoted to the specificities of GHG measurements in these ecosystems. Specialised techniques are also required for GHG measurements in aquatic systems (i.e. rivers), which are often affected by the transfer of nutrients from agricultural fields and therefore are an important indirect source of emission of GHGs. A simple, robust, and more precise methodof ammonia (NH3) emission measurement is also described.

Micrometeorological techniques are useful if greenhouse gas (GHG) emissions from larger areas (i.e. entire fields) should be integrated. The theory and the various techniques such as flux-gradient, aerodynamic, and Bowen ratio as well as Eddy correlation methods are described and discussed. Alternative methods also used are Eddy correlation, mass balance techniques, and tracer-based methods. The analytical techniques with current state-of-the-art approaches as well as the calculation procedures are presented.

Microbial nitrogen (N) transformations in soil, notably denitrification, result in the production of the potent greenhouse and ozone depleting gas nitrous oxide (N2O). Soil chemistry and microbiome composition impact N2O emission potential but the relative importance of these factors as determinants of N2O emission in denitrifying systems is rarely tested. In addition, previous linkages between microbiome composition and N2O emission potential rarely demonstrate causality. Here, we determined the relative impact of microbiome composition (i.e. soil extracted cells) and chemistry (i.e. water extractable chemicals) on N2O emission potential utilizing an anoxic cell based assay system. Cells and chemistry for assays were sourced from soils with contrasting N2O/N2O+N2ratios, combined in various combinations and denitrification gas production was measured in response to nitrate addition. Average directionless effects of cell and chemical extract on N2O/N2O+N2(Cell: Δ0.16, Chemical extract: Δ0.22) and total N2O hypothetically emitted (Cell: Δ2.62 μmol-N, Chemical extract: Δ4.14 μmol-N) indicated chemistry is the most important determinant of N2O emissions. Independent pH differences of just 0.6 points impacted N2O/N2O+N2on par with independent chemical extract differences, supporting the dominance of this variable in previous studies. However, impacts on overall N2O hypothetically emitted were smaller suggesting that soil pH manipulation may not necessarily be a successful approach to mitigate emissions over a fixed time period. In addition, we observed increased N2O accumulation and emission potential at the end of incubations concomitant with predicted decreases in carbon availability suggesting that carbon limitation increases N2O emission transiently with the magnitude of emission dependent on the both chemical and microbiome controls.

Inoculating agricultural soils with N2O-respiring bacteria (NRB) can reduce N2O-emissions, but would be impractical as a standalone operation. Here we demonstrate that digestates obtained after biogas production are suitable substrates and vectors for NRB. We show that indigenous NRB in digestates grew to high abundance during anaerobic enrichment under N2O. Gas-kinetics and meta-omic analyses showed that these NRB's, recovered as metagenome-assembled genomes (MAGs), grew by harvesting fermentation intermediates of the methanogenic consortium. Three NRB's were isolated, one of which matched the recovered MAG of a Dechloromonas, deemed by proteomics to be the dominant producer of N2O-reductase in the enrichment. While the isolates harbored genes required for a full denitrification pathway and could thus both produce and sequester N2O, their regulatory traits predicted that they act as N2O sinks in soil, which was confirmed experimentally. The isolates were grown by aerobic respiration in digestates, and fertilization with these NRB-enriched digestates reduced N2O emissions from soil. Our use of digestates for low-cost and large-scale inoculation with NRB in soil can be taken as a blueprint for future applications of this powerful instrument to engineer the soil microbiome, be it for enhancing plant growth, bioremediation, or any other desirable function.

Soil denitrification produces the potent greenhouse gas nitrous oxide (N2O) and by further reduction of N2O, the harmless inert gas N2. N2O emission is determined by rate and timing of the N2O producing and reducing steps which are sensitive to a series of proximal and distal regulators such as pH and microbial community composition. Microbial community associations to N2O emission potential (N2O/(N2O+N2)) are commonly entangled with pH leaving the true role of community composition unclear. Here, we leverage a set of soil microbiomes strongly linked to rainfall above pH to test the hypothesis that microbiome vs. N2O emission potential (N2O/(N2O+N2)) correlations will be maintained across alternative distal drivers. N2O emission potential (N2O/(N2O+N2)) and denitrification gas (NO, N2O, N2) kinetics were assessed by automated gas chromatography while community composition was assessed by 16S rRNA gene sequencing and qPCR of nosZI and II genes. Analyses revealed a sustained correlation between microbiome and N2O emission potential (N2O/(N2O+N2)) in the absence of a pH effect. Further, a continuum of gas accumulation phenotypes linked to NO accumulation and sensitive to carbon addition are identified. Separate phenotypes carried out N2O production and reduction steps more concurrently or sequentially and thus determined N2O accumulation and emission potential (N2O/(N2O+N2)). Concurrent N2O producing/reducing soils typically contained NO accumulation to a low steady state, while carbon addition manipulations which increased NO accumulation also increased sequentiality of N2O production/reduction and thus emission potential (N2O/(N2O+N2)). These features may indicate a conserved NO inhibitory mechanism across multiple effectors (rainfall, community composition, carbon availability).HighlightsN2O emission potential is linked to microbiome changes associated with rainfall, but not to pH.Sequential vs. concurrent denitrification phenotypes differing in NO and N2O accumulation are identified.High N2O accumulation is associated with increased NO accumulation.Sequentiality of N2O production/reduction determines soil N2O emission potential.Sequentiality of N2O reduction was susceptible to manipulation via carbon addition.

Soil denitrification produces the potent greenhouse gas nitrous oxide (N2O) and by further reduction of N2O, the harmless inert gas N2. N2O emission is determined by rate and timing of the N2O producing and reducing steps which are sensitive to a series of proximal and distal regulators such as pH and microbial community composition. Microbial community associations to N2O emission potential (N2O/(N2O + N2)) are commonly entangled with pH leaving the true role of community composition unclear. Here, we leverage a set of soil microbiomes strongly linked to rainfall above pH to test the hypothesis that microbiome vs. N2O emission potential (N2O/(N2O + N2)) correlations will be maintained across alternative distal drivers. N2O emission potential (N2O/(N2O + N2)) and denitrification gas (NO, N2O, N2) kinetics were assessed by automated gas chromatography while community composition was assessed by 16 S rRNA gene sequencing and qPCR of nosZI and II genes. Analyses revealed a sustained correlation between microbiome and N2O emission potential (N2O/(N2O + N2)) in the absence of a pH effect. Further, a continuum of gas accumulation phenotypes linked to NO accumulation and sensitive to carbon addition are identified. Separate phenotypes carried out N2O production and reduction steps more concurrently or sequentially and thus determined N2O accumulation and emission potential (N2O/(N2O + N2)). Concurrent N2O producing/reducing soils typically contained NO accumulation to a low steady state, while carbon addition manipulations which increased NO accumulation also increased sequentiality of N2O production/reduction and thus emission potential (N2O/(N2O + N2)). These features may indicate a conserved NO inhibitory mechanism across multiple effectors (rainfall, community composition, carbon availability).

Homeostatic control of nitric oxide (NO) at nanomolar concentrations appears common among denitrifying bacteria, often ascribed to synchronized expression of nitrite and nitric oxide reductase (Nir and Nor). We questioned whether this is sufficient: using the reported substrate affinities for cytochrome cd1 nitrite reductase (cNor), our model of batch cultures of Paracoccus denitrificans predicted NO concentrations orders of magnitude higher than measured. We rejected a hypothesis that the homeostatic control is due to a negative feedback by NO on the activity of NirS because the inclusion of such feedback resulted in too slow anaerobic growth and N2 production. We proceeded by determining the kinetic parameters for cNor in vivo by a carefully designed experiment, allowing the estimation of NO concentration at the cell surface while anoxic cultures depleted low headspace doses of NO. With the new parameters for cNor kinetics in vivo {v = vmax /[1 + K2 /(NO) + K1 × K2 /(NO)(2) ]; vmax = 3.56 fmol NO cell(-1) h(-1) , K1

Oxygen is known to repress denitrification at the transcriptional and metabolic levels. It has been a common notion that nitrous oxide reductase (N2 OR) is the most sensitive enzyme among the four N-oxide reductases involved in denitrification, potentially leading to increased N2 O production under suboxic or fluctuating oxygen conditions. We present detailed gas kinetics and transcription patterns from batch culture experiments with Paracoccus denitrificans, allowing in vivo estimation of e(-) -flow to O2 and N2 O under various O2 regimes. Transcription of nosZ took place concomitantly with that of narG under suboxic conditions, whereas transcription of nirS and norB was inhibited until O2 levels approached 0 μM in the liquid. Catalytically functional N2 OR was synthesized and active in aerobically raised cells transferred to vials with 7 vol% O2 in headspace, but N2 O reduction rates were 10 times higher when anaerobic pre-cultures were subjected to the same conditions. Upon oxygen exposure, there was an incomplete and transient inactivation of N2 OR that could be ascribed to its lower ability to compete for electrons compared with terminal oxidases. The demonstrated reduction of N2 O at high O2 partial pressure and low N2 O concentrations by a bacterium not known as a typical aerobic denitrifier may provide one clue to the understanding of why some soils appear to act as sinks rather than sources for atmospheric N2 O.

Modeling the C and N dynamics during decomposition of plant residues depends on robust estimation of the litter pool partitioning (LPP) of residues, i.e. the fraction of their C and N allocated to rapidly and slowly decomposing pools. We searched for ways to estimate LPP by analyzing data on C and N mineralization during decomposition of 60 widely different plant residues, using a simple model with two litter pools and one microbial pool. LPP and relevant global parameters were estimated by model optimization (Levenberg-Marquardt-least-squares algorithm) in one operation. These kinetically-defined LPP values were used in regression analyses against data from stepwise chemical digestion (SCD) and near-infrared reflectance (NIR) analysis of the plant residues. LPP predicted by these regression models resulted in better performance than LPP from measured neutral-detergent-soluble (NDS) C and N when validated on independent data (n = 15 plant residues). The results demonstrated the potential improvement by simultaneous estimation of residue specific LPP and global parameters, and that kinetically-defined LPP can be equally well predicted by NIR as by total N and NDS-C. Model failure for a minority of the plant-residues could partly be removed by altering the microbial C/N ratio (global optimum 7.1) within the range 5–15, possibly reflecting a variable dominance of bacteria or fungi.

Homeostatic control of nitric oxide (NO) at nanomolar concentrations appears common among denitrifying bacteria, often ascribed to synchronized expression of nitrite and nitric oxide reductase (Nir and Nor). We questioned whether this is sufficient: using the reported substrate affinities for cytochrome cd1 nitrite reductase (cNor), our model of batch cultures of Paracoccus denitrificans predicted NO concentrations orders of magnitude higher than measured. We rejected a hypothesis that the homeostatic control is due to a negative feedback by NO on the activity of NirS because the inclusion of such feedback resulted in too slow anaerobic growth and N2 production. We proceeded by determining the kinetic parameters for cNor in vivo by a carefully designed experiment, allowing the estimation of NO concentration at the cell surface while anoxic cultures depleted low headspace doses of NO. With the new parameters for cNor kinetics in vivo {v = vmax /[1 + K2 /(NO) + K1 × K2 /(NO)(2) ]; vmax = 3.56 fmol NO cell(-1) h(-1) , K1 1 nM, and K2 = 34 nM}, the model predicted NO concentrations close to that measured. Thus, enzyme kinetics alone can explain the observed NO homeostasis. Determinations of enzyme kinetic parameters in vivo are not trivial but evidently required to understand and model NO kinetics in denitrifying organisms in soils and aquatic environments.

Oxygen is known to repress denitrification at the transcriptional and metabolic levels. It has been a common notion that nitrous oxide reductase (N2 OR) is the most sensitive enzyme among the four N-oxide reductases involved in denitrification, potentially leading to increased N2 O production under suboxic or fluctuating oxygen conditions. We present detailed gas kinetics and transcription patterns from batch culture experiments with Paracoccus denitrificans, allowing in vivo estimation of e(-) -flow to O2 and N2 O under various O2 regimes. Transcription of nosZ took place concomitantly with that of narG under suboxic conditions, whereas transcription of nirS and norB was inhibited until O2 levels approached 0 μM in the liquid. Catalytically functional N2 OR was synthesized and active in aerobically raised cells transferred to vials with 7 vol% O2 in headspace, but N2 O reduction rates were 10 times higher when anaerobic pre-cultures were subjected to the same conditions. Upon oxygen exposure, there was an incomplete and transient inactivation of N2 OR that could be ascribed to its lower ability to compete for electrons compared with terminal oxidases. The demonstrated reduction of N2 O at high O2 partial pressure and low N2 O concentrations by a bacterium not known as a typical aerobic denitrifier may provide one clue to the understanding of why some soils appear to act as sinks rather than sources for atmospheric N2 O.

Plant‐microbe competition for inorganic N is a common phenomenon in soils, and the traditional view is that microorganisms are much stronger competitors than are plant roots. We challenged this view, hypothesizing that the balance between the two competitors is strongly dependent on the spatial heterogeneity of the soil. We constructed a model to explore this hypothesis. The model was structured to simulate a laboratory experiment where N-limited plants were grown in a soil containing two types of microbial “hotspots”; with high potential for either net N-immobilization (straw particles) or net N mineralization (clover particles). In this experiment (Wang and Bakken, 1997), plant roots appeared to compete successfully with microorganisms for inorganic N, and their competitiveness depended strongly on the distance (3, 6, 9 or 12 mm) between the layers of straw and clover material. Most model parameters were taken from the literature, whilst some parameters were estimated by least square model optimization using selected treatments within the experiment. The parameterized model was then statistically evaluated against treatments not used in the parameterization. The model efficiently simulated the observed transient inorganic N accumulation in unplanted soil and its dependency on distance between hotspots (modelling efficiency, EFa 0.611). It also captured very well the plant N uptake and its dependency of the distance between hotspots (EFa 0.860). The modelling exercise underscored the importance of soil heterogeneity in determining the outcome of the competition between plant roots and microorganisms for inorganic N in soil. Spatial segregation of hotspots with net N-immobilization and net N mineralization is likely to be the rule rather than the exception in natural soils. This has a profound impact on the N dynamics of soil plant systems, and it should be taken into account for the interpretation of experiments (such as 15 N pool dilution experiments) as well as in general models for the C and N dynamics of soil‐plant systems. q 2001

As genomic data for bacteria are unraveled at an increasing speed, there is a need for more efficient and refined techniques to characterize metabolic traits. The regulatory apparatus for denitrification, for instance, has been explored extensively for type strains, but we lack refined observations of how these and wild type denitrifiers respond metabolically to changing environmental conditions. There is a need for new “phenomic” approaches, and the present paper describes one; an automated incubation system for the study of gas kinetics in 15 parallel bacterial cultures. An autosampler with a peristaltic pump takes samples from the headspace, and replaces the sampled gas with He by reversing the pump. The sample flows through the injector of a micro GC (for determination of N2 ,O 2 ,C H4 ,C O2 ,N 2O) to the inlet of a chemoluminescence NO analyzer. The linear range for NO is 0.5–10 4 ppmv (CV=2%, detection limit 0.2 ppmv). The gas leakage of N2 into the system is low and reproducible, allowing the quantification of N2 production (in flasks with He+O2 atmosphere) with a detection limit of 150–200 nmol N2 for a single time increment. The gas loss by each sampling is taken into account, securing mass balance for all gases, thus allowing accurate estimation of electron flows to the various terminal acceptors (O2 ,N O2 , NO, N2O) throughout the culture's depletion of O2 and NOx. We present some experimental results with Agrobacterium tumefaciens, Paracoccus denitrificans and denitrifying communities, demonstrating the system's potential for unraveling contrasting patterns of denitrification gene expression as a function of concentrations of O2 and NO in the medium. © 2007 Elsevier B.V. All rights reserved.