The prediction of nitrous oxide (N2O) and of dinitrogen (N2)
emissions formed by biotic denitrification in soil is notoriously difficult
due to challenges in capturing co-occurring processes at microscopic scales.
N2O production and reduction depend on the spatial extent of anoxic
conditions in soil, which in turn are a function of oxygen (O2) supply
through diffusion and O2 demand by respiration in the presence of an
alternative electron acceptor (e.g. nitrate).
This study aimed to explore controlling factors of complete denitrification
in terms of N2O and (N2O + N2) fluxes in repacked soils by
taking micro-environmental conditions directly into account. This was
achieved by measuring microscale oxygen saturation and estimating the
anaerobic soil volume fraction (ansvf) based on internal air distribution
measured with X-ray computed tomography (X-ray CT). O2 supply and
demand were explored systemically in a full factorial design with soil
organic matter (SOM; 1.2 % and 4.5 %), aggregate size (2–4 and 4–8 mm), and
water saturation (70 %, 83 %, and 95 % water-holding capacity, WHC) as factors. CO2 and N2O
emissions were monitored with gas chromatography. The 15N gas flux
method was used to estimate the N2O reduction to N2.
N gas emissions could only be predicted well when explanatory variables for
O2 demand and O2 supply were considered jointly. Combining
CO2 emission and ansvf as proxies for O2 demand and supply resulted in
83 % explained variability in (N2O + N2) emissions and together
with the denitrification product ratio [N2O/ (N2O+N2)]
(pr) 81 % in N2O emissions. O2 concentration measured by
microsensors was a poor predictor due to the variability in O2 over
small distances combined with the small measurement volume of the
microsensors. The substitution of predictors by independent, readily
available proxies for O2 demand (SOM) and O2 supply
(diffusivity) reduced the predictive power considerably (60 % and 66 %
for N2O and (N2O+N2) fluxes, respectively).
The new approach of using X-ray CT imaging analysis to directly quantify
soil structure in terms of ansvf in combination with N2O and
(N2O + N2) flux measurements opens up new perspectives to estimate
complete denitrification in soil. This will also contribute to improving
N2O flux models and can help to develop mitigation strategies for
N2O fluxes and improve N use efficiency.
Introduction
Predicting emissions of the greenhouse gas nitrous oxide (N2O) is
important in order to develop mitigation strategies. Agriculture accounts
for approximately 60 % of anthropogenic N2O emissions, most likely
because high numbers of substrates for N2O-producing processes result
from nitrogen (N) fertilization on agricultural fields (Syakila and
Kroeze, 2011; Thompson et al., 2019; Tian et al., 2020). The required
process understanding is hindered since various microbial species are
capable of N2O production via several pathways, and these may co-exist
due to different micro-environmental conditions within short distances in
soil (Hayatsu et al., 2008; Braker and Conrad, 2011). Denitrification is
one of the major biological pathways for N2O production, which
describes the reduction in nitrate (NO3-) as the alternative
electron acceptor into the trace gas nitrous oxide (N2O) as an
intermediate and molecular nitrogen (N2) as the final product
(Knowles, 1982; Philippot et al., 2007). Although it is
well known that not all microbial species are capable of performing a denitrification
pathway, it is particularly widespread among bacteria, but also several
fungi and even archaea can denitrify (Shoun et al., 1992; Cabello et al.,
2004).
N2O emissions from soils are often considered to be erratic in nature
due to their high variability in space and time (Butterbach-Bahl et
al., 2013). The low predictability is caused by the mechanisms that regulate
microbial denitrification at the pore scale, which are concealed from
measurement techniques that average across larger soil volumes. This
experimental study is designed to reveal the drivers of oxygen (O2)
supply and demand at the microscale that govern microbial denitrification at
the macroscale.
In general, there are several controlling factors for microbial
denitrification in soil. Proximal factors, such as N and carbon (C), are
needed to ensure the presence of electron acceptors and electron supply. In
addition, the absence of oxygen is required to express the enzymes for the
reduction in reactive nitrogen. Distal factors, i.e. physical and biological
factors like soil structure, soil texture, pH, or microbial community, on the
other hand affect the proximal factors (Groffman and
Tiedje, 1988; Tiedje, 1988). The main physical controlling factors that
regulate O2 supply are water saturation and soil structure because
they determine the pathways through which gaseous and dissolved oxygen but
also NO3- and dissolved organic matter may diffuse towards the
location of their consumption. Likewise they determine the pathways through
which denitrification products may diffuse away from these locations. In
addition, both saturation and soil structure contribute to the regulation
of O2 demand through their impact on substrate accessibility and thus
microbial activity (Keiluweit et al., 2016). Studies have shown
microbial activity, described by microbial respiration, to increase with
increasing water saturation, but it also decreased when water saturation
exceeded a certain optimal value under intermediate conditions (Davidson et
al., 2000; Reichstein and Beer, 2008; Moyano et al., 2012). Low water
saturation causes C substrate limitations, whereas high water saturation
causes limited oxygen diffusion (Davidson et al., 2000).
This observation goes along with an increase in anaerobic respiration in
microbial hot spots when O2 demand exceeded O2 supply, and
denitrification is favoured (Balaine et al., 2015).
These physical processes that govern denitrification at the microscale have
to be effectively described by macroscopic bulk soil properties in order to
improve the predictability of denitrification activity at larger scales. It
has been shown repeatedly that soil diffusivity can be used to predict the
impact of O2 supply on N2O and N2 emissions (Andersen and
Petersen, 2009; Balaine et al., 2016). First N2O emissions increase
with decreasing diffusivity, but then they dramatically decrease due to
N2 production, when diffusivity is extremely low.
Diffusivity is not routinely measured in denitrification studies as it is
more difficult to measure than air content or water saturation, but there
are many empirical models to estimate diffusivity based on air-filled pore
volume (Millington and Quirk, 1960, 1961; Moldrup
et al., 1999; Deepagoda et al., 2011). All of these metrics are only
indirect metrics of the anaerobic soil volume fraction (ansvf) as direct
measurements are difficult to obtain. It is measured either locally via
oxygen sensors with needle-type microsensors (Sexstone et al., 1985;
Højberg et al., 1994; Elberling et al., 2011) or with foils (Elberling
et al., 2011; Keiluweit et al., 2018), which require averaging or extrapolating measured O2 saturation for the entire soil volume. Or it is
estimated for the entire sample volume from pore distances in X-ray computed tomography (X-ray CT)
images of soil structure assuming that there is a direct relationship
between pore distances and anaerobiosis (Rabot et al., 2015; Kravchenko
et al., 2018).
Completeness of denitrification is another important controlling factor that
modulates the relationship between O2 availability and N2O
emissions (Morley et al., 2014), which has previously been neglected in
similar incubation studies (Rabot et al., 2015; Porre et al., 2016;
Kravchenko et al., 2018). Since the N2 background of air (78 %) is
very high, direct N2 measurement from denitrification in soil is very
challenging (Groffman et al., 2006; Mathieu et al., 2006). The 15N-labelling technique is a method successfully applied to determine N2O
and also N2 production from denitrification from 15N-amended
electron acceptors (NO3-) (Mathieu et al., 2006; Scheer et al.,
2020). Complete denitrification generates N2 as the final product,
although it is assumed that 30 % of denitrifying organisms lack the
N2O reductase (Zumft, 1997; Jones et al., 2008; Braker and Conrad,
2011). Thus the denitrification product ratio
[N2O / (N2O + N2)] (pr) was found to be very variable in soil
studies covering the whole range between 0 and 1 (Senbayram et al., 2012;
Buchen et al., 2016). Decreasing pr, i.e. relative increasing N2 fraction
compared to that of N2O, was found with lower oxygen availability as a
consequence of higher water saturations and denitrification activities in
soil (van Cleemput, 1998).
In this paper, we reconcile all these metrics, i.e. soil structure,
bulk respiration, diffusivity, O2 distribution, ansvf, and pr, to assess their
suitability to predict denitrification activity. This requires well-defined
laboratory experiments that either control or directly measure important
distal controlling factors of denitrification activity like microbial
activity, anaerobic soil volume, and denitrification completeness.
To this end the current study presents a comprehensive experimental set-up
with well-defined experimental conditions but also microscale measurements
of oxygen concentrations, soil structure, and the air and water distribution
at the pore scale. The 15N tracer application was used to estimate the
N2O reduction to N2 and the N2O fraction originating from
denitrification. To our knowledge this is the first experimental set-up
analysing N2O and (N2O + N2) fluxes in combination with X-ray-CT-derived structure. Other important factors controlling denitrification
like temperature, pH, nitrate limitation, saturation changes, microbial
community structure, or plant–soil interactions were either controlled or
excluded in this study.
The general objective of the present study is to systematically explore bulk
respiration and denitrification as a function of O2 supply and demand
in repacked soils under static hydraulic conditions. O2 demand was
controlled by incubating soils with different soil organic matter (SOM)
content. O2 supply was controlled by different water saturations and
different aggregate sizes. A novel approach is explored to assess
microscopic O2 supply directly from ansvf estimates based on the
distribution and continuity of air-filled pores within the wet soil matrix.
We hypothesize that the combination of at least one proxy for O2 supply
(e.g. ansvf, diffusivity, air content) and one for O2 demand (CO2
production) is required to predict complete denitrification
(N2O + N2), whereas pr as a proxy for denitrification completeness
is required in addition to predict a single component (N2O). The
specific aims of our study were (a) to investigate the potential of
microscopic metrics for O2 supply, such as ansvf to predict complete
denitrification activity, and (b) to explore the extent to which a substitution of
these predictors by classical, averaged soil properties required for larger-scale denitrification models is acceptable.
Materials and methodsIncubation
Fine-textured topsoil material was collected from two different agricultural
sites in Germany (from a depth of 10–20 cm in Rotthalmünster (RM) and
3–15 cm in Gießen (GI) as representatives for agricultural
mid-European soils; Table 1). To our knowledge, N2O field measurements
only exist for GI soil, which amounted to N2O emissions up to
approximately 160 µg N2O–N m-2 h-1 after fertilization
(Müller et al., 2004; Kammann et al., 2008; Regan et al., 2011).
Denitrification potential, however, exists in both soils, as recently
investigated by Malique et al. (2019) in a laboratory experiment with
both soils. Higher denitrification activity with GI soil was found
compared to that of RM soil (Malique et al., 2019). According to
this, these soils were chosen for the contrast in properties potentially
affecting denitrification and respiration (SOM contents, pH, texture, bulk
density), which induces a large difference in microbial respiration and hence
O2 demand under identical incubation settings. The rationale was that
soil texture and bulk density should mainly govern air content and thus
O2 supply at a certain water saturation, whereas SOM content should
mainly govern microbial activity and thus O2 demand. The soils were
sieved (10 mm), air-dried, and stored at 6 ∘C for several months
before sieving into two different aggregate size fractions in order to
induce variations in O2 supply: small (2–4 mm) and large (4–8 mm). Care
was taken to remove free particulate organic matter (POM) like plant
residues and root fragments during sieving. Other aggregate size classes
were not considered as sieving yielded too low an amount of larger
aggregates that contained too much irremovable POM, whereas smaller
aggregate classes resulted in too fragmented a pore space at the chosen scan
settings.
Basic description of soil materials used for incubation (SOM: soil organic matter; WRB: World Reference Base for the classification of soil).
The soil material was pre-incubated at 50 % water-holding capacity (WHC)
for 2 weeks to induce microbial activity after the long dry spell and let
the flush in carbon mineralization pass that occurs after rewetting the
soil. Three different saturation treatments were prepared for subsequent
incubation experiments (70 %, 83 %, and 95 % WHC) to control the
O2 supply and thus provoke differences in denitrification activity. A
15N solution was prepared by mixing 99 at. % 15N–KNO3
(Cambridge Isotope Laboratories, Inc., Andover, MA, USA) and unlabelled
KNO3 (Merck, Darmstadt, Germany) to reach 50 mg N kg-1 soil with
60 at. % 15N–KNO3 in each water saturation treatment. Hence, for
the two higher water saturations the stock solution was more diluted in
order to reach the same target concentration in the soil. In a first step
the soil was adjusted to 70 % WHC before packing.
This 15N-labelled soil was filled in 2 cm intervals into cylindrical
PVC columns (9.4 cm inner diameter × 10 cm height) (Fig. 1) and compacted to
a target bulk density that corresponded to site-specific topsoil bulk
densities (Jäger et al., 2003; John et al., 2005). Packing in five
vertical intervals achieved a uniform porosity across the column. However,
there were inevitable porosity gradients within intervals (Fig. S4 in the Supplement) that
affected the air and water distribution and thus air continuity at high
water saturations. This packing resulted in 902 and 694 g dry weight of RM
and GI soil, respectively. For the latter two saturation levels, the rest of
the NO3- solution was sprayed sequentially onto each layer after
packing. The incubation of such repacked soils instead of intact soil columns
was chosen to (i) systematically investigate the effect of aggregate size and
to (ii) guarantee thorough mixing of the 15N tracer with the soil.
Schematic of the column for repacked soil showing the dimensions
(10 × 9.4 cm), the lid with inlet and outlet for technical gas (21 %
O2 and 2 % N2 in helium), O2 microsensors (in black), and the temperature sensor (in grey) located in soil core. The outlet of the lid was
directly connected to a gas chromatograph and allowed sampling for
isotope ratio mass spectrometry (IRMS).
In this way, a full factorial design with 12 treatments and three
factors (soil: RM, GI; aggregate size: large, small; saturation: 70 %, 83 %, 95 % WHC) was prepared in triplicates for incubation. WHC was additionally
measured for both soil materials in parallel soil cores. For a better
comparability with previous studies, the results are presented in terms
of water-filled pore space (WFPS), which is derived from the known mass of
soil and water and their respective densities. A detailed description of the
experimental set-up can be found in the Supplement.
The columns containing the packed soil aggregates were closed tightly and
were equipped with an inlet and outlet in the headspace (Fig. 1). To analyse
O2 saturation, needle-type (40×0.8 mm) oxygen microsensors with
<140µm flat-broken sensor tips (NFSG-PSt1, PreSens Precision
Sensing GmbH, Regensburg, Germany) were pinched through sealed holes in the
lid and PVC column at seven well-defined positions. Three sensors were
located at the top by inserting vertically into the soil through the lid and
headspace down to approximately 20 mm depth, whereas four sensors were
inserted laterally at the centre of the column at about 36 mm depth with
angular intervals of 90∘. The microsensors were coupled to a
multi-channel oxygen meter (OXY-10 micro, PreSens Precision Sensing GmbH,
Regensburg, Germany), and O2 measurements were stored in 15 min
intervals. The O2 data were aggregated to 6 h means for further
analysis. The columns were placed in a darkened, temperature-controlled
20 ∘C water bath (JULABO GmbH, Seelbach, Germany). Two flow
controllers (G040, Brooks® Instrument, Dresden, Germany)
served to flush the columns with technical gas (21 % O2 and 2 %
N2 in helium; Praxair, Düsseldorf, Germany) through the inlet of
the columns at a rate of 5 mL min-1. This artificial atmosphere with
low-N2 background concentration was used to increase sensitivity for
N2 fluxes (Lewicka-Szczebak et al., 2017). Initially,
the headspace was flushed with technical gas for approximately 3 to 5 h
under six cycles of mild vacuum (max. 300 mbar) to bring down the N2
concentration within the soil column approximately to that of the technical
gas (2 %) and to ensure comparable initial conditions for incubation.
Incubation time was 192 h. Additional information of a parallel
incubation where atmospheric conditions were switched from oxic to anoxic
conditions to calculate the anaerobic soil volume fraction (ansvfcal) can be
found in the Supplement.
Gas analysisGas chromatography (GC)
The column outlet was directly connected to a gas chromatograph (Shimadzu
14B) equipped with an electron capture detector (ECD) to analyse N2O
and two flame ionization detectors (FIDs) to analyse methane (not reported)
and CO2. GC measurements were taken online every 6.5 min using GC
Solution software (Shimadzu, GCSolution 2.40). The detection limit was
0.25 ppm N2O and 261.90 ppm CO2, with a precision of at least 2 % and
1 %, respectively. The N2O and CO2 data were aggregated to 6 h means for further analysis in order to eliminate the high-frequency
noise from the otherwise gradually changing gas concentrations under static
incubation conditions. The measurements during an equilibration phase of 24 h
were excluded. N2O fluxes derived from GC analysis may include N2O
from processes other than denitrification and are thus referred as the total
net N2O fluxes (N2O_total).
Isotopic analysis
Samples for isotopic analysis of 15N in N2O and N2 were taken
manually after 1, 2, 4, and 8 d of incubation in 12 mL Exetainers® (Labco Limited, Ceredigion, UK). To elute residual air
from the 12 mL Exetainer it was flushed 3 times with helium (helium 6.0,
Praxair, Düsseldorf, Germany) prior to evacuating the air to 180 mbar. The
Exetainers were flushed with headspace gas for 15 min, which amounts to a
sixfold gas exchange of the Exetainer volume. At the end of the incubation,
technical gas was also sampled to analyse the isotopic signature of the
carrier gas.
These gas samples were analysed using an automated gas preparation and
introduction system (GasBench II, Thermo Fisher Scientific, Bremen, Germany;
modified according to Lewicka-Szczebak et al., 2013) coupled to
an isotope ratio mass spectrometer (MAT 253, Thermo Fisher Scientific,
Bremen, Germany) that measured m/z 28 (14N14N), 29
(14N15N), and 30 (15N15N) of N2 and simultaneously
isotope ratios of 29R (29N2/28N2) and 30R
(30N2/28N2). All three gas species (N2O,
(N2O + N2), and N2) were analysed as N2 gas after
N2O reduction in a Cu oven. Details of measurement and calculations for
fractions of different pools (i.e. N in N2O (fp_N2O) or N2
(fp_N2) originating from 15N-labelled NO3- pool) are
described elsewhere and are provided in the Supplement
(Fig. S3) (Spott et al., 2006; Lewicka-Szczebak
et al., 2013; Buchen et al., 2016).
The product ratio (pr) [N2O / (N2O + N2)] was calculated for each
sample:
pr[–]=fp_N2Ofp_N2O+fp_N2.
The calculated average pr [N2O / (N2O + N2)] of each treatment
was also used to calculate the average total denitrification fluxes
(N2O + N2 fluxes) during the incubation:
(N2O+N2)[µgNh-1kg-1]=N2O_totalpr.
Microstructure analysis
Due to the experimental set-up, it was only possible to scan the soil cores
with X-ray CT (X-tek XTH 225, Nikon Metrology) once directly after the
incubation experiment. The temperature sensor was removed, but the oxygen
micro-sensors remained in place during scanning. The scan settings (190 kV,
330 µA, 708 ms exposure time, 1.5 mm Cu filter, 2800 projections, two
frames per projection) were kept constant for all soils and saturations. The
projections were reconstructed into a 3D tomogram with 8 bit precision and a
spatial resolution of 60 µm using the filtered back projection
algorithm in X-tek CT-Pro. Only macropores twice this nominal resolution
were clearly detectable in the soil core images. Hence, at the lowest water
saturation, not all air-filled pores can be resolved, which is discussed
below. The 3D images were processed with the Fiji bundle for ImageJ
(Schindelin et al., 2012) and associated plugins. The raw data were
filtered with a 2D non-local means filter for noise removal. A radial and
vertical drift in grayscale intensities had to be removed
(Iassonov and Tuller, 2010; Schlüter et al., 2016) before
these corrected greyscale images (Fig. 2a) were segmented into multiple
material classes using the histogram-based thresholding methods
(Schlüter et al., 2014). The number of materials varied
between two (air-filled pores, soil matrix) and four (air-filled pores,
water-filled pores, soil matrix, mineral grains), depending on saturation and
soil material. By means of connected-component labelling implemented in the
MorpholibJ plugin (Legland et al., 2016), the air-filled pore space was
further segmented into isolated and connected-air-filled porosity, depending
on whether there was a continuous path to the headspace (Fig. 2b). Average
oxygen supply in the core was estimated by three metrics: (1) visible-air-filled porosity (εvis) and connected-air content
(εcon) determined by voxel counting (Fig. 2b), (2) average air distance derived from the histogram of the Euclidean distances
between all non-air voxels and their closest connected-air voxel (Fig. 2c, d) (Schlüter et al., 2019), and (3) the ansvf which corresponds to the
volume fraction of air distance larger than a certain threshold. Therefore,
in a sensitivity test, air distance thresholds of 0.6, 1.3, 2.5, 3.8, and 5.0 mm were used to estimate the ansvf and to find the best correlation between
ansvf and N2O as well as (N2O + N2) fluxes. This was found with an
ansvf at a critical air distance of 5 mm when pooling GI and RM soils (Fig. 2c, d).
A 2D slice of one soil core packed with large aggregates (4–8 mm)
from Gießen soil (GI) incubated at 75 % WFPS to illustrate grey value
contrast between materials. (a) One oxygen microsensor is shown on the left
(white needle) and the hole of the temperature sensor at the top (black)
within the soil matrix (grey), stones (white), and pores that are filled either with air (black) or water (light grey). (b) Material classes after
segmentation including soil matrix (grey), water (blue), mineral grains
(light grey), connected air (red), and isolated air (rose). The green circle
around the light-grey sensor tip depicts the diameter of 7.2 mm that is used
to characterize its environment. (c) The 3D Euclidean distance to the closest
connected-air voxel (mineral grains are excluded) in each soil matrix or
water voxel. The closest air voxel might be outside of the 2D plane. The
green line depicts the connected-air distance threshold of 5 mm that
differentiates between an anaerobic soil volume fraction (light colours) and
aerated volume (dark colours). (d) Relative frequency of soil volume as a
function of distance to closest connected air [mm] divided into aerobic
(red) and anaerobic (green) soil volume.
In summary, the εcon is a proxy for the supply with
gaseous oxygen coming from the headspace, whereas the connected-air distance
and ansvf are proxies for the supply limitation of dissolved oxygen by diffusive
flux through the wet soil matrix. In addition to these averages for entire
soil cores, both εcon and average air distance were also
computed locally in the vicinity of oxygen sensor tips (Fig. 2b–c) to
compare these metrics with measured oxygen concentrations. Spherical regions
of interest (ROIs) with different diameters from 3.6 to 10.8 mm were tested
with respect to the highest correlation of εcon and average
air distance with average oxygen concentration of individual sensors. This
was found to occur at a diameter of 7.2 mm when centred on the sensor tip.
In addition to scans of the entire core, four individual aggregates (4–8 mm)
of each soil were also scanned with X-ray CT (80 kv, 75 µA, 1 s
exposure time, no filter, 2400 projections, two frames per projection),
reconstructed in 8 bit at a voxel resolution of 5 µm, filtered with a
2D non-local means filter, and segmented into pores and background with the
Otsu thresholding method (Otsu, 1975). The largest cuboid fully inscribed
in an aggregate was cut and used for subsequent diffusion modelling as
described below.
Diffusivity simulations
Diffusivity was simulated for individual aggregates as well as for the
entire soil core (bulk diffusivity) directly on segmented X-ray CT data by
solving the Laplace equation with the DiffuDict module in the GeoDict 2019
software (Math2Market GmbH, Kaiserslautern, Germany). A hierarchical
approach was used to (1) estimate the effective diffusivity of the wet soil
matrix by simulating Laplace diffusion on individual soil aggregates with
the explicit-jump solver (Wiegmann and Bube, 2000; Wiegmann and Zemitis,
2006) and (2) model diffusivity (Dsim) with the explicit-jump solver on
the entire soil core (1550 × 1550 × [1500–1600] voxels). The latter was based on
the visible 3D pore space and using the effective diffusion coefficient of
the soil matrix as obtained from the simulation of soil aggregates. We
assumed an impermeable exterior, impermeable mineral grains (GI only), and
the diffusion coefficient of oxygen in air and water (≥75 % WFPS only)
in the respective material classes (see detailed information in the
Supplement).
Statistical analysis
Statistical analysis was conducted with R (R Core Team, 2018).
Figures were produced with the package ggplot2 (Wickham, 2016). In order to
estimate the correlation between various variables that do not exhibit a
normal distribution (average values of N2O fluxes, (N2O + N2)
fluxes, CO2 fluxes, O2 saturation, Dsim, εcon, ansvf, and pr), Spearman's rank correlations with pairwise deletion of
missing values were performed, pooling data for GI and RM soils. The p values
were corrected for multiple comparison according to Benjamini and
Hochberg (1995) and adjusted p values ≤ 0.05 were considered to be significant.
As described before, there were four missing values for pr due to limitation of
the isotopic measurement at the lowest saturation. For further statistical
analysis of the data set, any missing pr values were imputed using the chained
random forest using more than 100 regression trees in terms of overall
variable pattern as this method can handle nonlinear relationships between
variables (Breiman, 2001; Nengsih et al., 2019). It was also required to
standardize the data of very different value ranges for further analysis.
Since N2O and/or (N2O + N2) were not detectable for a few
samples at the lowest saturation, a constant of 1 was added to N2O and
(N2O + N2) fluxes prior to transformation. This changes the mean
value but not the variance of data. In order to get normal distributions and
linear relationships, a logarithmic transformation was applied to metric
data (CO2, N2O and (N2O + N2) fluxes, Dsim), whereas
a logistic transform logitx=log(x/(1-x))
was applied to dimensionless ratios between 0 and 1 (ansvf).
Since there was a high collinearity among most variables, a partial least
square regression (PLSR) with leave-one-out cross-validated R2 was the
best method to identify the most important independent explanatory variables
(six predictors: CO2 fluxes, O2 saturation, Dsim,
εcon, ansvf, and pr) to predict the response variables N2O or
(N2O + N2) fluxes. It has to be emphasized that N2O fluxes
and pr were measured independently of each other using different measuring
methods (gas chromatography and isotopic analysis), which justifies pr as a
predictor variable for N2O fluxes. In contrast to this,
(N2O + N2) fluxes were calculated from pr, and therefore pr was not
included in PLSR for the response variable (N2O + N2) fluxes
(resulting in five explanatory variables). Bootstrapping was used to provide
confidence intervals that are robust against deviations from normality (R
package boot v. 1.3-24) (Davison and Hinkley, 1997; Canty and
Ripley, 2019). Given the relatively small sample size (36 incubations in
total), the smoothed bootstrap was used by resampling from multivariate
kernel density (R package kernelboot v. 0.1.7) (Wolodzko, 2020). The BCa
bootstrap confidence interval of 95 % of R2 was a measure to explain
the variability in each response variable (Efron, 1987). Components
that best explained N2O and (N2O + N2) fluxes were identified
by permutation testing.
To address the second research question of this study concerning
substitutions of predictors by classical, averaged soil properties,
additional and simplified models with the PLSR approach described above were
performed using various variables to substitute the most important predictors
for N2O or (N2O + N2) fluxes. A detailed description of the
substitution is provided in Sect. 3.4 and Sect. 4.2.
ResultsBulk respiration
Time series of CO2 and N2O fluxes (Fig. S1) show aggregated values for 6 h steps over the complete incubation
time of approximately 192 h, ignoring the first 24 h due to initial
equilibration of the system (i.e. redistribution of water, expression of all
denitrification enzymes, fast mineralization of labile carbon). Averages for
the whole incubation are reported in Fig. 3a, c and in Tables S1 and S4 in the Supplement. The 3.7 times higher SOM content in GI soil
than in RM soil resulted in higher microbial activity so that CO2
fluxes were approximately 3 times higher for all saturations. The
variability in CO2 fluxes between replicates is much higher than the
temporal variability during incubation. This is probably explained by small
differences in the packing of the columns that can have large consequences for
soil aeration. CO2 production in both soils was lowest with the highest
water saturation but was quite similar for both treatments with saturations
<80 % WFPS (Fig. 3a). Aggregate size had a negligible effect on
CO2 production.
(a) Average CO2 fluxes, (b) average O2 saturation, (c) average N2O and (d) (N2O + N2) fluxes, and (e) average
product ratio (pr) [N2O / (N2O + N2)] as a function of water-filled pore space (WFPS) for two repacked aggregate sizes (2–4 and 4–8 mm)
from Rotthalmünster (RM) and Gießen (GI) soil. Symbols depict the
average values for each of the three individual replicates, with error bars
showing the standard error in the mean; standard error in (a) and (c) are of
fluxes measured during incubation, in (b) the standard error from
measurements of seven sensors located within the soil core, and in (d) and (e) of three measurements during incubation time (after 2, 4, and 8 d
with detectable R29 and R30; n=3 for two highest WFPSs). The
number of measurements (n) considered for averaging is displayed in each
subfigure. The lines (dashed and solid) connect the average value of three
replicates at each saturation (large and small aggregates, respectively).
Substantial N2O and (N2O+N2) emissions were detected for
saturations ≥75 % WFPS and were again approximately 3 times
higher in SOM-rich GI soil than in RM soil (Fig. 3c, d). The variability
between replicates is again higher than the temporal variability (e.g. in
Fig. 3d and time series in Fig. S1), and the
effect of aggregate size is inconsistent due to the large variability among
replicates. Mineral N was not analysed after the incubation, and therefore
cumulative (N2O + N2) fluxes were used to estimate the N loss
after 192 h of incubation. Considering the N addition of 50 mg N kg-1 as
NO3- and an average natural NO3- background of 34 mg kg-1, substantial N loss was observed for both soils at ≥75 %
WFPS. The N converted to N2O or N2 represents a proportion equal
to ≤2.6 % with RM soil and ≤8.0 % with GI soil for both
aggregate sizes and saturations.
Average O2 saturation was lowest with the highest water saturation and
roughly the same for saturations <80 % WFPS (Fig. 3b). Some
sensors showed a gradual decline in O2 concentration, whereas some
showed a drastic reduction or increase in a short period of time, probably
due to water redistribution (Fig. S2). The average
of the final 24 h was taken for all subsequent analysis as this probably
best reflects the water distribution scanned with X-ray CT. Standard errors
among the seven O2 microsensors were high in each treatment due to very
local measurement of O2 that probed very different locations in the
heterogeneous pore structure.
The pr, i.e. the N2O / (N2O + N2) as a measure of denitrification
completeness, showed a similar behaviour as a function of water saturation
as N2O release, with a plateau for saturations ≥75 % WFPS at
0.6 and a lower but somewhat more erratic pr for the lowest saturation due to
a generally low 15N gas release (Fig. 3e). Thus, the
(N2O + N2) fluxes at ≤65 % WFPS could only be calculated
for a small number of samples due to lacking data of pr (Tables S1, S4). SOM content and aggregate size had no effect
on pr. Time series of pr showed a gradual reduction for all treatments as the
N2 emissions grew faster than the N2O emissions (Fig. S5). With water saturations >75 % WFPS, the
pr decreased with time and was in most cases <0.5 at the end of
incubation (Fig. S5). In summary, for each soil
all samples with saturation ≥75 % WFPS showed similar pr (Fig. 3e)
and N2O release (Fig. 3c). This agreed well with subsequent X-ray CT
estimates of air connectivity as shown below.
Pore system of soil cores
Due to lower target bulk density in GI soil (1.0 g cm-3) compared to
that of RM soil (1.3 g cm-3), visible-air content (εvis; depicted in red and pink in Fig. 2c) was higher independent of
aggregate size (Fig. 4a). The εvis decreased with
increasing water saturation but not linearly, as would be expected. The air
contents in the very wet range are in fact higher (16 %–17 %) than the
target air saturation of approximately 11 % or 15 % for RM and GI soil,
respectively. It was not possible to remove air more efficiently during
packing, and some ponding water might have accidentally been removed with
vacuum application during purging at the beginning of incubation.
Additionally, the GI soil was rich in vermiculite and swelled upon wetting.
This increase in soil volume at the end of incubation resulted in a relative
decline in water content. For increasing water content the air content that
is connected to the headspace (εcon; depicted in red in
Fig. 2b) was reduced much more strongly as compared to the total
εvis. This was observed for both soils and aggregate sizes
and indicates that a substantial amount of air is trapped (Fig. 4b).
According to this observation, average distance to visible air was very
small (Fig. 4c) and remained below 1.5 mm even for the highest water
saturation, with generally smaller distances for smaller aggregates. Yet, the
average distance to the pore system connected with headspace escalates in
the wet range (Fig. 4d). The huge variability among replicates comes from
the fact that trapping by complete water blockage typically occurs in the
slightly compacted upper part of a packing interval, but the specific
interval where this happens varies among samples (Fig. S4). The different aggregate sizes did not affect the distance to
connected air as the long-range continuity of air is controlled by
bottlenecks in the pore space and not by aggregate size.
(a) Visible-air content (εvis), (b) connected-air content (εcon), (c) average distance to visible air,
(d) average distance to connected visible air, (e) simulated diffusivity
(Dsim), and (f) anaerobic soil volume fraction (ansvf) as a function of water-filled pore space (WFPS) for two repacked aggregate sizes (2–4 and 4–8 mm)
from Rotthalmünster (RM) and Gießen (GI) soil and three replicates
each depicted by symbols. The lines (dashed and solid) connect the average
value of three replicates (large and small aggregates, respectively). The
horizontal grey lines in (e) reflect material properties. The experiment was
performed at 20 ∘C and according to that diffusivity was
calculated at 20 ∘C.
Water saturation had a dramatic impact on Dsim (Fig. 4e), leading to a
reduction by 5 orders of magnitude in a rather small saturation range. At
high saturations it fell below the oxygen diffusion coefficient in pure
water due to the tortuosity of the pore system. The ansvf (Fig. 4f) is directly
linked to connected-air distance and shows the same escalating behaviour at
the highest saturation up to a volume fraction of 50 %–90 %. The ansvf is highly
correlated with CO2 emissions (Spearman's R>-0.7 and
p=0.04), which exhibit the same tipping point behaviour, yet with very
different slopes in the regression for the different soils due to different
microbial activity (Fig. S6). The correlation of ansvf is weaker with N2O
(Spearman's R0.6<R<0.77, p<0.1) and negligible
with (N2O + N2) (p>0.2), suggesting that
denitrification is more complexly controlled. The full regression analysis
of ansvf with different gases and for different soils and aggregate sizes is
presented in the Supplement (Fig. S6).
Microscopic oxygen distribution
The local measurements of O2 using microsensors are demonstrated as an
example for two selected sensors from the same soil column (GI soil
incubated at 75 % WFPS). They are located at the same depth with a
separation distance of <2 cm. Sensor 1 detected low O2
concentrations (18 % air saturation) because it was located in a compact
area with low εcon (4 %) and a rather large distance to
the closest air-filled pore (1.6 mm) (Fig. 5a, b, d). Sensor 2 detected
fairly high O2 concentrations (76 % air saturation) as it happened to
pinch into a macropore with a high εcon (15 %) and a
short distance to connected air (0.8 mm) in its vicinity (Fig. 5a–c). The
green or violet circle with a diameter of 7.2 mm depicts the spherical
averaging volume for εcon and distance to connected air
that correlated best with the average O2 concentrations when lumped
over all soils and saturations (Fig. 5b–d).
Local oxygen distribution in one soil core packed with small
aggregates (2–4 mm) from Gießen soil (GI) incubated at 75 % water-filled pore space (WFPS) to illustrate as an example the very local
measurement of O2. Shown here are (a) O2 saturations measured by
two microsensors as a function of incubation time; (b) a 3D subvolume shown
from the top, with connected air depicted in red and both sensors depicted
with their respective spherical support volume in colours corresponding to (a); and 2D greyscale slices through the sensor tip depicting soil matrix
in light grey, water in dark grey, and air in black for (c) the sensor
measuring high and for (d) the sensor measuring low O2 saturations. The
violet and green circles depict the proximity of the sensor tip (7.2 mm
diameter) used to calculate the averaged local metrics.
The treatment-specific correlations between distance to connected air and
average O2 concentrations are shown in Fig. 6. At the lowest
saturation level there is no correlation at all (Spearman's R-0.4≪R<0.1 and p≥0.38; Fig. 6a, d) because some
unresolved pores (<120µm) within the aggregates are
air-filled so that oxygen availability is not limited by visible air. At the
intermediate saturation level the correlations were best (Spearman's
R<-0.7 and p≤0.02) because all unresolved pores are
water-filled (Fig. 6b, e). At the highest water saturation the correlation
was highest for large aggregates (Spearman's R=-0.6 and p=0.08) because
the local effect of soil structure might become stronger relative to the
non-local effect of air entrapment. With the other three treatments the
correlations were worse again (Spearman's R between -0.01 and -0.3 and p≥0.58; Fig. 6c, f) because distance to connected air ignores all trapped
air, which may still contribute a lot to oxygen supply.
Average O2 saturation (at the end of incubation experiment)
measured with four sensors, each located at the centre of the soil core, as a
function of distance to visible connected air for two repacked aggregate
sizes (2–4 and 4–8 mm) from Gießen (GI; a–c; blue) and
Rotthalmünster (RM; d–f; red) soil. Panels (a) and (d) show results for
the lowest (63 % or 65 % WFPS with GI and RM soil,
respectively), (b, e) for medium (75 % or 78 % WFPS with GI and RM
soil, respectively), and (c, f) for the highest (85 % or 88 % WFPS with GI
and RM soil, respectively) water saturation. The insets in (a), (b), and (d) show a reduced distance range. The distance to visible connected air is
averaged in a spherical region around the sensor tip (7.2 mm diameter). The
Spearman's rank correlation coefficient (R) indicates the extent of monotonic
relation between the ranks of both variables. The associated p values (p)
were corrected for multiple comparison according to Benjamini and
Hochberg (1995).
Explanatory variables for denitrification
So far the correlations among different explanatory variables and between
explanatory variables and N gas release have been shown for individual
treatments, i.e. separately for each combination of soil and aggregate size,
in order to focus on the effect of water saturation. However, the true
potential of explanatory variables to predict denitrification can only be
explored with the entire pooled data set so that the variability in
denitrification is captured more representatively.
The PLSR identified two principal components that best explained N2O
and (N2O + N2) fluxes, while most variables contributed to the first
component (Comp1), and almost exclusively CO2 release contributed to the
second component (Comp2) (see Fig. S8 in the Supplement). These principal
components revealed a vastly different ability of individual explanatory
variables to explain the observed variability in N2O and
(N2O + N2) release. The importance of explanatory variables to
predict N2O and (N2O + N2) fluxes varied as follows: CO2> (pr) > ansvf >Dsim>εcon> O2 (see Fig. S8). Hereinafter pr shown in brackets illustrates its contribution to
PLSR analysis for N2O fluxes only. The explanatory variability,
expressed in the text as R2⋅100 [%], was 82 % for N2O fluxes
and 78 % for (N2O + N2) fluxes when considering the complex model
with all explanatory variables (CO2 flux, O2 saturation,
εcon, Dsim, ansvf (and pr)) (Fig. 7). The resulting
regression equations can be found in the Supplement (Eqs. S7–S8).
Explained variability expressed as R2 with a confidence
interval of 95 % resulting from partial least square regression (PLSR)
with leave-one-out cross-validation and bootstrapping for response variables
N2O (green symbols) or (N2O + N2) fluxes (violet symbols) for
pooled data of both soils (from Rotthalmünster (RM) and Gießen (GI)), WFPS treatments, and aggregate sizes (n=36). The yellow area shows
a complex model including all explanatory variables of the present study
(CO2, O2, connected-air content (εcon),
diffusivity (Dsim), anaerobic soil volume fraction (ansvf), and product ratio
(pr) [N2O / (N2O + N2)]) (all) and a simplified model including
only the most important predictors (CO2+ ansvf (+ pr); predictor (+ pr) was only used
for N2O emissions). The blue area shows additional simplified models
with substitutions of the most important predictor for O2 supply
(ansvf) by Dsim or diffusivity calculated from an empirical model
(Demp) (Deepagoda et al., 2011) or theoretical air content
(εt). The red area shows a simplified model with
substitutions of the most important predictor for O2 demand (CO2)
by soil organic matter (SOM; measured in bulk soil). Substitution of the two most important predictors (CO2 and ansvf) by SOM and Demp is shown in
the violet area.
Starting from this complex model, a series of simplifications and
substitutions of explanatory variables were conducted to assess the extent to which the resulting loss in predictive power is acceptable. Reducing the number of
explanatory variables to the most important variables resulted in CO2
and ansvf for (N2O + N2) release (83 % explained variability,
simplified model in Fig. 7). In other words, the combination of these two
predictors (ansvf and CO2) is crucial as CO2 release explains the
different denitrification rates between the two soils, whereas ansvf explains the
differences within a soil due to different saturations. To predict N2O
emissions, the simplified model with the most important explanatory variables
CO2, ansvf, and pr as a third predictor resulted in 81 % of explained
variability (Fig. 7). Average O2 saturation could be omitted for its
small correlation with N2O or (N2O + N2) release in general,
whereas εcon and Dsim could be omitted because of the
high correlation with ansvf (Fig. S7).
Conceptual scheme of oxygen (O2) supply and demand and its
effect on denitrification. Material classes include soil matrix (grey area),
water (blue), mineral grains (light grey), connected air (red), and isolated
air (rose). The black line divides between aerobic (light-grey area) and
anaerobic (dark-grey area) conditions. O2 supply and demand regulate
the formation of anaerobic soil volume fraction (ansvf) as an imprint of the
spatial distribution of connected air (item number 1), respiration (item
number 2) that would move the boundary between oxic and anoxic zones in the
soil matrix closer towards the pore when soil respiration is high (and vice
versa), and N2O reduction to N2 (expressed by the product ratio
(pr); item number 3). The numbered items show how the explanatory variables
that best describe N2O release affect denitrification.
The regression equations with R2 values and a confidence interval of
95 % in square brackets resulting from PLSR, with CO2 and ansvf (and pr)
identified as the most important explanatory variables to predict N2O or
(N2O + N2) fluxes of the present study for data after log or
logit transformation:
3logN2O=0.65logCO2+0.74logitansvf+0.75pr;R2=0.81[0.67–0.89]4logN2O+N2=1.14logCO2+1.60logitansvf;R2=0.83[0.71–0.90].
Various variables were used to substitute best predictors (CO2 or
ansvf) (Fig. 7) in PLSR. The substitution of CO2 by SOM or ansvf by
εt,Dsim, or empirical diffusivity (Demp) based on
total porosity and air content (Deepagoda et al., 2011) is explained in Sect. 4.2.
DiscussionWhich processes govern denitrification in soil?
The onset and magnitude of denitrification are controlled by O2 supply
and O2 consumption, which in turn depend on processes in soil
occurring at microscopic scales. This study was designed to examine
different levels of O2 consumption by comparing soils with different
SOM contents and different levels of O2 supply by comparing different
aggregate sizes and different water saturations. Other factors that would
have affected O2 demand and energy sources for denitrifiers (quality of
organic matter, temperature, pH, plant–soil interactions), O2 supply
(oxygen concentration in the headspace, temperature), or other drivers of
denitrification (NO3- concentration, pH, denitrifier community
structure) were either controlled or excluded in this study.
N2O release from soil can be low because denitrification does not occur
under sufficient oxygen supply, because it is formed in wet soil but
reduced to N2 before it can escape to the atmosphere, or because it is
trapped in isolated air pockets (Braker and Conrad, 2011). Trapped
N2O is thought to likely be reduced to N2 eventually if gaseous
N2O is not released after a saturation change, which would open up a
continuous path to the headspace. This is shown in the schematic on the
balance between O2 supply and demand and its effect on denitrification
(Fig. 8).
To our knowledge, the experimental set-up of the present study combined for
the first time microstructure analysis of soil (X-ray CT) with measurements
of N2O and (N2O + N2) fluxes to explore controlling factors
of the complete denitrification process including N2 formation. The
explanatory variables that contributed the highest predictive power with
(N2O + N2) release were ansvf and CO2 release (Fig. 8). The
estimated ansvf (item 1) is a sole function of the spatial distribution of
connected air in soil and therefore only reflects soil structural properties
related to O2 supply. The dependence of denitrification on diffusion
constraints was demonstrated by several models that were developed to
predict the formation of anoxic centres within soil aggregates
(Greenwood, 1961; Arah and Smith, 1989; Arah and Vinten, 1995; Kremen et
al., 2005). The distance threshold for anoxic conditions to emerge was set
on an ad hoc basis at 5 mm from connected air at the end of incubation but
is likely to vary with O2 demand by local microbial activity (CO2
release represented by the green fringe area, item 2) during the incubation
(Kremen et al., 2005; Rabot et al., 2015; Ebrahimi and Or, 2018;
Keiluweit et al., 2018; Kravchenko et al., 2018; Schlüter et al., 2019).
Because we could only conduct X-ray CT scans at the end of incubation,
redistribution of water during the incubation time cannot be ruled out. This
could have changed ansvf and thus might explain some of the temporal variability
in gaseous fluxes. In repacked soils it might be distributed rather
uniformly and therefore be correlated with bulk CO2 release (Aon et
al., 2001; Ryan and Law, 2005; Herbst et al., 2016). The fact that aggregate
size had no effect on denitrification indicates that critical distances were
larger than the aggregate radii and rather controlled by air distribution in
the macropore system. When air content was high, all visible macropores
were air-filled so that this critical air distance was hardly exceeded
anywhere. When air content was low (close to full water saturation), the
patchy distribution of air and water in the macropore system was governed by
subtle layering in the pore structure and not by aggregate size. This means
that both aggregate sizes used in the present study might have been too
small to provoke differences in O2 availability and thus in CO2,
N2O, and (N2O + N2) fluxes. The large distance found here is
in contrast to the very short critical distances of 180 µm for
sufficient soil aeration estimated by Kravchenko et al. (2018) and Kravchenko et al. (2019) for intact soil
cores containing crop residues for which soil respiration was not determined
but is likely to be much higher.
A somewhat surprising result is that oxygen concentration measurements did
not have an added value for predicting either N2O release or total
denitrification. The best correlation of local O2 concentration with
εcon was with a radial extent of 3.6 mm used for averaging
around the microsensor (Fig. 6). Thus, with seven microsensors per column
we only probed 0.2 % of the total soil volume. This is too small to
capture aerobic and anaerobic conditions representatively, especially since
they may switch within short distances (Fig. 5). More sensors or sensors
with larger support volume could be a means to improve the predictive power
of local oxygen measurements. However, there is always a trade-off between
retrieving more information and disturbing the soil as little as possible.
If only N2O release is concerned, pr as an independent proxy for N2O
consumption (Fig. 8, item 3) was beneficial to predict N2O emissions
together with CO2 and ansvf (Fig. 7). The N2O reduction to N2
and thus the pr are complexly controlled, where besides physical factors
microbial (the structure of the denitrifier community) and chemical
properties (pH, N oxides, SOM, temperature, salinity) are relevant (Smith
et al., 2003; Clough et al., 2005; Müller and Clough, 2014). With
respect to physical factors, decreasing diffusivity enhances N2O
residence time and N2O concentration in the pore space, thus favouring
N2O reduction. According to this, Bocking and Blyth (2018)
assumed a very small pr in wet soils because N2O may be trapped in the
soil or completely reduced to N2. This assumption may also support
results of the present study, where the average (N2O + N2) fluxes
peaked at the medium water saturation (particularly with GI soil), while
Dsim decreased with increasing water saturations (Fig. 4), which may
indicate an entrapment of (N2O + N2) in isolated soil pores
(Clough et al., 2005; Harter et al., 2016). However, N2 release
increased more strongly with time than the N2O release, resulting in
decreasing pr with time (Fig. S5). The chance of
N2O to be released before it is reduced to N2 depends on the
diffusion distance of dissolved (and gaseous) N2O between its formation
sites and the atmosphere. Although diffusion pathways for O2 and
N2O are similar, just in the opposite direction, ansvf and pr might be a good
combination of proxies to predict N2O emissions to capture physical and
microbial properties.
How to substitute microscale information by bulk
properties
The aims of this study were to find a minimum set of variables that explain
the regulation of microbial denitrification at microscopic scales in a
simplified experimental set-up and to explore the extent to which this microscopic
information can be substituted by readily available bulk properties that are
feasible to measure in a field campaign. The interplay of O2 supply and
O2 demand resulted in CO2 emissions and CT-derived ansvf being the most
important predictors for (N2O + N2) fluxes, while for N2O
fluxes pr was also important (Figs. 7, S8).
Simplified models with the most important predictors only (CO2+ ansvf (+ pr)) were
sufficient to achieve similar explained variabilities (81 % and 83 % for
N2O and (N2O + N2) fluxes, respectively) compared to the
complex models. The downside of using CO2 and CT-derived ansvf as predictors
for denitrification is that these proxies are often unavailable, and
reasonable substitutions by easily available variables would be desirable.
The ansvf could have been replaced with alternative proxies for O2 supply
like Dsim,Demp, and εt, which would have led to a
reduction in explained variability in (N2O + N2) fluxes to
52 %–78 % and an even larger drop for N2O fluxes to 46 %–59 %
(Table S2). The substitution of ansvf by Dsim would
avoid the requirement for an ad hoc definition of a critical pore distance
threshold, but it is gained with the caveat of very time-consuming 3D
simulations or laborious measurements. Therefore, the substitution of
ansvf with diffusivity estimated by empirical models (Demp) seems more
viable. Diffusivity is mainly controlled by soil bulk density and water
saturation (Balaine et al., 2013; Klefoth et al., 2014). These empirical
models predict diffusivity based on empirical relationships with total
porosity (Φ) and air-filled porosity (ε) (Millington
and Quirk, 1961; Moldrup et al., 2000; Resurreccion et al., 2010; Deepagoda
et al., 2011, 2019). As expected the discrepancy between
calculated Demp and simulated Dsim was highest at water saturation
>75 % WFPS, where discontinuity due to packing procedure took
full effect as described earlier (Figs. S9, S4). The substitution of CT-derived ansvf by Demp derived from empirical
models (Fig. 7, Table S2) is perhaps unacceptable
for a genuine understanding of N2O or (N2O + N2) emissions
from individual samples since estimated diffusivity ignores the actual
tortuosity and continuity of the air-filled pore space. However, it may be a
promising approach to reasonably predict average N2O or
(N2O + N2) fluxes under natural conditions with readily available
soil characteristics (Fig. 7, Table S2). In this particular study,
Dsim could even be replaced with the theoretical air content
(εt) adjusted during packing (together with
CO2 (+ pr)) without a reduction in explained variability in N2O and
(N2O + N2) fluxes (Fig. 7, Table S2)
due to the very strong log-linear relationship between the εt and Dsim (Fig. 4e). However, totally neglecting any proxy for
O2 supply (i.e. CO2 only to predict N2O fluxes) was
insufficient to predict N2O fluxes (Table S2).
A different strategy to estimate ansvf from bulk measurements is to switch from
oxic to anoxic incubation by replacing the carrier gas under otherwise
constant conditions. The smaller the ansvf during oxic incubation, the larger the difference in (N2O + N2) release between
the two stages.
Details about the calculation of this ansvfcal can be found in the
Supplement. The ansvfcal assumes that actual denitrification is
linearly related to ansvf and that the specific anoxic denitrification rate is
homogenous, i.e. would be identical at any location within the soil.
Deviations from this assumption could arise from heterogeneity in the
distribution of substrates and microbial communities. However, the actual
soil volume where denitrification may occur, described by the distance to
aerated pores, does not depend only on O2 diffusion but also on
respiration (O2 consumption). Therefore, it could be expected that
ansvf derived from X-ray CT imaging analysis compared to ansvfcal was
overestimated with RM soil or underestimated with GI soil due to the
differences in carbon sources and related O2 consumption. The average
ansvfcal was similar (0.24) to the ansvf (0.21) for RM soil (Table S3). With GI soil, however, the ansvfcal was larger (0.45)
than the image-derived ansvf (0.13). This difference may indeed result from an
underestimation of ansvf of GI soil due to the higher SOM content and respiration
rates. In future experiments it might be recommendable to integrate the
O2 consumption into ansvf estimation. The appeal of this two-stage incubation
is that it can be conducted with larger soil columns as there is no size
restriction as with the application of X-ray CT. Evidently, this two-stage
incubation approach is not feasible for field campaigns, for which we would
recommend to resort to estimated diffusivities instead. However, both
approaches are complementary since both are associated with different
assumptions and thus uncertainties. Therefore, using them both improves the
assessment of ansvf.
The use of CO2 production as a proxy for O2 demand to predict
N2O and (N2O + N2) release is limited as it is not fully
independent of denitrification since anaerobic respiration contributes to
total respiration. Therefore, it is appealing to replace it with estimates
of microbial activity based on empirical relationships with temperature,
SOM, clay, and water content (Smith et al., 2003)
as these properties are routinely measured. When including the SOM measured
before the experiment for the bulk soil (Table 1) to explore N2O or
(N2O + N2) emissions, predictive power for (N2O + N2)
decreased (63 % compared to 83 % with CO2 instead of SOM together
with ansvf), just like it was reduced for predicting N2O emissions (73 %
compared to 81 % with CO2 instead of SOM together with ansvf and pr). The
combination of proxies for O2 supply and demand, SOM and Demp only,
to predict N2O and (N2O + N2) fluxes did not reduce the
explained variability too much beyond that of individual substitutions (60 %
and 66 %, respectively). An improvement might be achieved by accounting
for different quality in SOM, e.g. mineral-associated organic matter, fresh
particulate organic matter, microbial pool, all of which will lead to
different mineralization rates and hence propensity to run into local anoxia
(Beauchamp et al., 1989; Kuzyakov, 2015; Surey et al., 2020) due to the
fact that SOM favours denitrification in several ways
(Beauchamp et al., 1989; Ussiri and Lal, 2013), i.e. by
supplying energy, leading to the consumption of O2 via respiration and the supply of mineral N from mineralization.
Future directions and implications for modelling
In large-scale effective N-cycling models, the ansvf is typically linked to the
partial pressure of oxygen in soil and conveys no explicit spatial
information. In the long run these models like DNDC (DeNitrification and DeComposition), CoupModel (coupled heat and mass transfer), and MicNiT (microbial carbon and nitrogen turnover) (Li et al., 1992; Jansson and Karlberg, 2011; Blagodatsky et al., 2011)
might benefit tremendously from incorporating a spatially explicit ansvf as a
state variable to predict denitrification. The estimation of ansvf can be improved
by taking O2 consumption into account. Knowledge of the spatial
distribution of respiration in combination with pore-scale modelling would
further improve ansvf estimations and could be used to validate our approach with
oxic and anoxic incubation. However, the empirical functions to estimate this
ansvf from readily available properties similar to empirical diffusivity models
have yet to be developed and validated against a whole suite of intact soil
cores with different soil types and vegetation for which oxic and anoxic
incubation and X-ray CT analysis are carried out jointly.
Using intact instead of repacked soils in future experiments will represent
more natural conditions, e.g. larger tortuosity and thus lower diffusivity
in undisturbed compared to sieved soil (Moldrup et al.,
2001). However, in undisturbed soils, diffusivity and soil structure may also
vary locally and as a consequence of this varying O2 supply and demand
affect denitrification. Under field conditions this impact on
denitrification is additionally altered by saturation changes, temperature
variations, atmospheric gas concentrations, microbial community structure,
and plant growth. It would thus be very interesting to also include
different soil types and land use types from various climate zones in future
studies, e.g. paddy soils having high water saturation and that are known to show high denitrification activity, with N2 emissions exceeding those of
N2O emissions.
Conclusions
To our knowledge this is the first experimental set-up combining X-ray-CT-derived imaging and flux measurements of complete denitrification (i.e.
N2O and (N2O + N2) fluxes) to explore the microscopic drivers
of denitrification in repacked soil. We could show that changes in
denitrification within different saturations could be predicted well with
the anaerobic soil volume fraction (ansvf) estimated from image-derived soil
structural properties. The differences in denitrification (i.e. N2O and
(N2O + N2) fluxes) between two investigated soils were triggered
by different respiration rates due to different SOM content. A combination
of CT-derived ansvf and CO2 emission as proxies for oxygen supply and
demand, respectively, is best in predicting (N2O + N2) emission
(83 % explained variability) across a large saturation range and two
different soils. The product ratio (pr), in addition to ansvf and CO2
emissions, was also an important predictor for emissions of only the
greenhouse gas N2O (81 % explained variability).
The ansvf can also be replaced by simulated diffusivity (Dsim) (time-consuming) or by diffusivity from empirical models (Demp) but not
without losing predictive power. A replacement of CO2 fluxes by SOM
also resulted in lower predictive power but is recommended for large-scale
applications since SOM is an independent proxy for microbial activity. The
full substitution of laborious predictors (ansvf, pr, CO2) by readily
available alternatives (SOM, Demp) reduced the explained variability to
60 % and 66 % for N2O and (N2O + N2) fluxes, respectively.
The high explanatory power of image-derived ansvf opens up new perspectives
to make predictions (e.g. by modelling approaches or in pedotransfer
functions) from independent measurements of soil structure using new
techniques (e.g. X-ray CT analysis) available today in combination with
biotic properties, e.g. quantity or quality of SOM. This paves the way for
explicitly accounting for changes in soil structure (e.g. tillage, plants)
and climatic conditions (e.g. temperature, moisture) in denitrification.
Data availability
CT data and gas emission data are available from
the authors on request.
The supplement related to this article is available online at: https://doi.org/10.5194/bg-18-1185-2021-supplement.
Author contributions
HJV, RW, and SS designed the experiment. SS, BA,
and LR carried out the experiment. GMW developed the statistical analysis.
SS and LR prepared the manuscript with contributions from all co-authors.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We thank Jürgen Böttcher from the Institute of Soil
Science, Leibniz University in Hanover, for measurements of soil materials
used for incubation and Anette Giesemann and Martina Heuer from
the Thünen Institute for Climate-Smart Agriculture in Braunschweig, Germany,
for isotopic analysis. Our thanks go to Ines Backwinkel und Jan-Reent Köster from the Thünen Institute for Climate-Smart Agriculture in
Braunschweig, Germany, for conducting parallel incubations under oxic and
anoxic conditions.
Financial support
This research has been supported by the Deutsche Forschungsgemeinschaft through the research unit DFG-FOR 2337: Denitrification in Agricultural Soils: Integrated Control and Modelling at Various Scales (DASIM) (project nos. 270261188 and 290269257).The article processing charges for this open-access publication were covered by a Research Centre of the Helmholtz Association.
Review statement
This paper was edited by Zhongjun Jia and reviewed by three anonymous referees.
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