unexpected control of soil carbon turnover by soil carbon concentration

ORIGINAL PAPER

Unexpected control of soil carbon turnover by soil carbonconcentration

Axel Don • Christian Rodenbeck • Gerd Gleixner

Received: 18 June 2013 / Accepted: 19 July 2013 / Published online: 29 August 2013

� Springer-Verlag Berlin Heidelberg 2013

Abstract Soils are a key component of the terrestrial

carbon cycle as they contain the majority of terrestrial

carbon. Soil microorganisms mainly control the accumu-

lation and loss of this carbon. However, traditional con-

cepts of soil carbon stabilisation failed so far to account for

environmental and energetic constraints of microorgan-

isms. Here, we demonstrate for the first time that these

biological limitations might have the overall control on soil

carbon stability. In a long-term experiment, we incubated13C-labelled compost with natural soils at various soil

carbon concentrations. Unexpectedly, we found that soil

carbon turnover decreased with lower carbon concentra-

tion. We developed a conceptual model that explained

these observations. In this model, two types of particles

were submitted to random walk movement in the soil

profile: soil organic matter substrate and microbial

decomposers. Soil carbon turnover depended only on the

likelihood of a decomposer particle to meet a substrate

particle; in consequence, carbon turnover decreased with

lower carbon concentration, like observed in the experi-

ment. This conceptual model was able to simulate realistic

depth profiles of soil carbon and soil carbon age. Our

results, which are simply based on the application of a two-

step kinetic, unmystify the stability of soil carbon and

suggest that observations like high carbon ages in subsoil,

stability of carbon in fallows and priming of soil carbon

might be simply explained by the probability to be

decomposed.

Keywords Soil carbon � Carbon isotopes �Michaelis–Menten � Soil microbiology �Carbon turnover

Introduction

Soils are the largest terrestrial buffer for organic carbon

(C), storing 1,500–2,300 Pg carbon, which is more carbon

than in the atmosphere and the biosphere together (Gleix-

ner 2013; IPCC 2000). The actual stocks and the turnover

of soil C are strongly influenced by land-use activities:

27 % of the global soils are under cultivation, and 60 % of

forests are managed or exploited (Bryant et al. 1997;

Cassman and Wood 2005).

Around 30 % of SOC is lost upon cultivation (Don et al.

2011; Poeplau et al. 2011) which adds up to 180–200 Pg C

(DeFries et al. 1999) during the last two centuries, or more

than 50 % of total anthropogenic CO2 emissions (Boden

et al. 2010) In order to improve C management of agri-

cultural and forest soils, it is crucial to understand the

mechanisms of soil C stabilisation and destabilisation.

Recent findings suggest that C turnover in mineral soils is

only weakly controlled by the molecular structure (Mars-

chner et al. 2008): Turnover of lignin or certain biochar,

which were supposed to be stable, was similar to other

organic compounds (Gleixner et al. 1999; Heim and

Schmidt 2007). In contrast, physical stabilisation mecha-

nisms like adsorption on mineral surfaces and occlusion in

aggregates have been suggested to effectively stabilise soil

C (Marschner et al. 2008; Torn et al. 1997). The underlying

mechanisms are still poorly understood and controversially

A. Don

Thunen-Institute of Climate-Smart Agriculture, Bundesallee 50,

38116 Braunschweig, Germany

A. Don � C. Rodenbeck � G. Gleixner (&)

Max Planck Institute for Biogeochemistry,

Hans Knoll Strasse 10, 07745 Jena, Germany

e-mail: gerd.gleixner@bgc-jena.mpg.de

Environ Chem Lett (2013) 11:407–413

DOI 10.1007/s10311-013-0433-3

discussed (Schmidt et al. 2011), in particular for soil C

stabilisation in subsoils (Rumpel and Koegel-Knabner

2011). The simultaneous occurrence of old, i.e. stabilised,

soil C and its high decomposability after extraction (Fon-

taine et al. 2007) finally led to the theory of an abiotically

controlled ‘‘regulatory gate’’ that controls soil C turnover

(Kemmitt et al. 2008).

However, the fact that decomposers, mainly microor-

ganisms, facilitate SOC turnover and decomposition has

been widely ignored. Microorganisms act as catalysts for

SOC decomposition, facilitated by extracellular and intra-

cellular enzymes. But, microbial life in soils is constrained

by the availability and subsequent supply of organic matter

as energy source, the oxygen availability for heterotrophic

decomposition and spatial limitations in water-filled pores

and water films (Blume et al. 2010). In consequence, dif-

ferent living conditions for microorganisms are the basic

drivers of SOC turnover, and this biological control on

SOC turnover should be considered (Dungait et al. 2012;

Ekschmitt et al. 2005).

For aquatic systems, it is well established with the turnover

rate depending on the substrate concentration (English et al.

2006; Walter 2006). Decomposition is a two-step process

following the Michaelis–Menten kinetics: In a first step,

enzyme and substrate form a joint complex, and then the

decomposition reaction is catalysed (Michaelis and Menten

1913). Thus, biological decomposition relies on the encounter

of substrate and the degradation catalyst (Allison 2005).

Lower substrate concentration decreases the likelihood of an

enzyme to hit a substrate molecule, to form an enzyme–sub-

strate complex, and thus to catalyse the reaction. A similar

reaction kinetic is unproven and has been widely ignored for

turnover of C in soils. In soils, the main assumption is that C

decays like radioactive elements, and the biological part of

decomposition is widely ignored (Davidson et al. 2006). Only

recently, attempts have been made to incorporate second-

order kinetics such as Michaelis–Menten into carbon turnover

models (Davidson et al. 2012).

In contrast to aquatic systems, soils are much more com-

plex, representing a three-phase system with half of the vol-

ume being solid, comprised of minerals and organic matter;

roughly one quarter being air-filled pores, and one quarter

being water in the form of surface films and pore water.

Enzymes and microorganisms can only live in the water-filled

pores and water films which restrict their reaction space much

more than in pure aquatic systems (Foster 1988; Lehmann

et al. 2007). Any movement of enzymes and microorganisms

to unlock new substrate sources relies on movement through

water films and water-filled pores. This movement is slow

and works mainly over short distances. Only fungi and

Gram? actinomycetes have the limited ability to move

through the pore space in a target-oriented manner with

hyphen growth (Rubino et al. 2010). Consequently, a faster

and effective movement of substrate and microorganisms

relies on soil disruptions, like ploughing (Koch and Stockfisch

2006), and on seepage water (Chou et al. 2008).

The amount soil microbial carbon is fairly constant at around

1 % of the soil C (Fierer et al. 2009). This implies that in coarse

soils, less than 1 % of the mineral surfaces are covered with

microorganisms; moreover, in fine soils, this value decreases to

10-6 % (Deschesne et al. 2007; Foster 1988; Young and

Crawford 2004). In addition, soil microorganisms are not ran-

domly distributed but live in clusters, leaving vast soil spaces as

microbial deserts (Franklin and Mills 2003; Nunan et al. 2003).

Soil C is also restricted to distinct locations in the soil (Lehmann

et al. 2008). It can be found as particulate organic matter, which

have high carbon concentrations and associated with mineral

surfaces. Thus, even though microbial activity is generally

highly correlated with the soil C content, the substrate avail-

ability, and consequently its turnover, is limited by its acces-

sibility. Here, we suggest for the first time that any decrease in

the C concentration increases the distance between decomposer

and substrate, which consequently decreases accessibility and

soil C turnover.

Experimental

Incubation experiment

The soil samples derived from a grassland site with car-

bonate-free sandstone as parent material. The samples were

taken from the lower part of the former plough horizon

(Ah; 8–20 cm depth) of a well characterised Arenosol

(Table 1a) (Don et al. 2007). The samples were air-dried

and sieved (0.5 mm). Remaining fine roots were manually

removed. The soil samples were rewetted 13 days before

the start of the experiment. Soil moisture was adjusted to

40 % of the field capacity. The sample was pre-incubated

at 4 �C for 10 days and thereafter for 3 days at 21 �C.

The substrate derived from a compost heap of the C4

species Amaranthus retroflexus L., which is naturally

labelled with higher 13C content than C3 soils (Table 1b).

The substrate material was air-dried and ground to powder

using a ball mill (Retsch, Germany) for 2 min.

The incubation experiment was conducted with an

automated flow-through system for continuous soil respi-

ration measurements (Heinemeyer et al. 1989). In brief, the

system allows simultaneous measuring of CO2-production

for 24 incubation tubes with a time resolution of 24 times

per day per tube. The air stream was water-saturated with

humidifiers in order to prevent drying of the soil sample

during the incubation. CO2 was detected with an infrared

gas analyser (ADC, UK).

In each tube, 100 g DW soil sample and 5 g DW sub-

strate was filled. The only difference between the four

408 Environ Chem Lett (2013) 11:407–413

treatments was the degree of mixing between the soil

sample and the substrate (Fig. 1a):

• Undiluted: the soil sample (preincubated) and the

substrate sample (rewetted) were not mixed but incu-

bated adjacently but separately in the same tube.

• 25 % dilution: 25 g of the soil sample was mixed with the

5 g substrate sample using an electronic stir (1 min). The

remaining 75 g of the mineral sample were incubated

adjacently to the 30 g of soil–substrate mixture.

• 50 % dilution: 50 g of the soil sample was mixed with the

5 g substrate sample using an electronic stir (1 min). The

remaining 50 g of the mineral sample were incubated

adjacently to the 55 g of soil–substrate mixture.

• 100 treatment was incubated with % dilution: the soil

sample (100 g) and the substrate (5 g) were completely

mixed (electronic stir 1 min) and incubated.

Each treatment was incubated with four replicates. The

CO2 production was calculated as the product of air stream

and CO2 concentration. Four incubation tubes were left as

blanks without any sample. The incubation was performed

with three measurement periods. During the weeks

between the measurement intervals, the samples were kept

at the same temperature in the same room.

Post-incubation measurements

After 6 months, the incubated samples were homogenised,

air-dried and ground for further analysis. C and N content

was determined with an elemental analyser (VarioMax

Elementar, Germany) (Steinbeiss et al. 2008a). The 13C

content was determined as d13C value relative to the

V-PDB standard using the combination of an elemental

analyser (NA 1110, CE Instruments, Italy) with an isotope

ratio mass spectrometer (Delta C, Finnigan MAT, Ger-

many) (Steinbeiss et al. 2008b). All analyses were per-

formed with four analytical replicates.

The C-loss of the substrate was calculated using a tow

pool model using the organic C content and initial d13C

content and after the 6 months of incubation (postincub.).

Substrate C� loss ½%�

¼substrateCinitial � totalCpostincub

d13Cpostincub�soild13Cinitail

substrated13Cinitail�soild13Cinitail

substrate Cinitial

� 100%

The C-loss of the soil sample was calculated accordingly.

Statistics

The differences in CO2 production among the treatments

were tested using linear mixed models with the four treat-

ments as fixed factor and the four replications as randomTa

Environ Chem Lett (2013) 11:407–413 409

factor. The model was applied to the last of the three

measurement periods. Statistical analysis was performed

using R software.

Conceptual simulation

The basic principles of the Michaelis–Menten kinetic were

applied to simulate the effect of microbial decomposition

on soil depth distributions of soil carbon and carbon age. A

simple model [Soil carbon and microorganism process

model (SCAMP)] was developed that encompasses two

kinds of particles, substrate particles (soil organic C) and

decomposer particles (microorganisms, enzymes) that were

allowed to random walk in a 2-dimensional space (detailed

model parameters see Table 2). The decomposers, for

which represent bacterial and fungal clusters (Pietramellara

et al. 2002), were randomly distributed at the initialisation

of the simulations. Each cluster could ingest substrate from

its ‘‘surrounding’’ (Table 2) and respire it with first-order

kinetics. The size of the ‘‘surrounding’’ was proportional to

the carbon content of the cluster, which simulates the

growth of a cluster (Ekschmitt et al. 2005).

At each time step, substrate particles representing litter and

root exudates were added to the soil surface. Both particles

were allowed to diffuse isotropically in each direction using a

random walk algorithm (Table 2). At the boundaries of the

modelled soil section, a mirror reflected the particles back into

the modelled soil section. The duration of a time step is

arbitrary and independent of unit. Once a substrate particle

enters the ‘‘surrounding’’ of a decomposer, the substrate car-

bon is transformed into decomposer carbon and respired with

a first-order kinetic (Table 2). Thus, all carbon in the model is

decomposed with the same rate and only the ‘‘probability for

decomposition’’ controlled the carbon turnover.

The age of each substrate particle was calculated each

time step starting with its initialisation. The age of a par-

ticle increased with each time step. The mean age of all

substrate particles in a soil layer was calculated from the

mean of all substrate particles per layer.

5 g subst.

100 g soil

75 g soil

50 g soil

Mixture100 g soil

5 g substrate

Mix. 50 g soil+

5 g substrate

Mix. 25 g

soil+ 5 g

substrate

undiluted

Flowout

Flow in5.

25% 50% 100%

undiluted 25% diluted 50% diluted 100% diluted

Fig. 1 Concentration-

dependent decomposition of

carbon. a (upper left) Setup of

the incubation. The same

amount of soil and substrate are

incubated in four different

dilution treatments. b (lower

left) Time course of CO2

evolution the incubation

experiment. c (lower right)

Mean values of liberated CO2

for last incubation phase

Table 2 Parameter of the SCAMP simulation model

Parameter Value Unit

Soil depth 100 cm

Soil section width 500 cm

Substrate particle input 5 Per time step

Random movement of substrate 1 cm per time step

Number of decomposers 250

Random movement of decomposers 1 1 cm per time step

Respiration radius around decomposers 0.4 cm

Fraction of C respired by decomposers 0.02 Per time step

The simulation model was written in GDL (GNU data

language) in order to facilitate easy visualisation. GDL is

an open-source interpreted language aimed at numerical

data analysis and visualisation with a free IDL (Interactive

Data Language) compatible incremental compiler. The

conceptual model is freely available on request.

Results and discussion

Litter incubation

The effect of concentration-controlled C turnover was

explored experimentally with incubations at different C

concentrations using different mixing between substrate

and soil (Fig. 1a). Carbon turnover rates were significantly

higher in undiluted treatments and decreased with

increasing degree of dilution and subsequent decreasing C

concentrations (p [ 0.001; Fig. 1b, c). The total loss of

carbon decreased from 9.4 to 6.4 % (Table 1a) and a Vmax

of *1.8 lg C 9 gC-1 9 day-1 was calculated for the

undiluted compost (Fig. 2b). Sorption to mineral surfaces

affect the activity and kinetic of enzymes. Thus, Km is soil

type specific and depends on the setup of the experiment. If

carbon concentrations are smaller than Km, which is likely

to be the case in subsoils, C turnover logarithmically

depends on the substrate concentration (Fig. 2a). SOC

concentration will therefore most strongly control the C

turnover in C-poor soil such as subsoil horizons. Thus, this

experiment shows for the first time that decreasing soil C

concentration indeed decrease the total soil carbon turnover

rate. Additional incubation experiments, using pure sand

instead of mineral soil, showed that our results cannot be

explained by enhanced carbon stabilisation on mineral

surfaces in the diluted treatments (data not shown).

We calculated isotopic mass balances for the experiment

that allowed a more detailed process understanding. At all

carbon concentration treatments, mainly substrate carbon

contributed to the total soil respiration. On average were

17 % of the substrate and only 2 % of the soil C decom-

posed (Table 1b). Most interestingly, we observed a slight

tendency that at higher dilution, i.e. lower carbon con-

centrations, the loss of soil carbon was lower (negative

priming). This is in line with higher microbial substrate use

efficiencies at lower substrate supply (Gude et al. 2012)

and lower priming efficiencies (Kuzyakov 2002). More

experiments are necessary that focus on the effect of car-

bon and nutrient availability on the microbial community,

in order to gain deeper understanding of the underlying

processes.

Substrate C concentration [%]

0 5 10 150 5 10 15

Substrate C concentration [%]

Labile Carbon

Total Carbon

Carbon age

Soil C content Soil carbon age and

ratio respiration/total C

Respiration/total C

Fig. 2 Measured (a, b) and

modelled (c, d) carbon turnover.

a (upper left) decrease in total

carbon loss with substrate

concentration (R2 = 0.97).

b (upper right) Michaelis–

Menten function fitted through

the experimental data of C

turnover derived from the long-

term incubation experiment of

four substrate concentrations

(pure substrate and substrate

diluted with mineral soil).

c (lower left): simulation of

realistic soil depth profiles of

labile (microbial) carbon and

soil carbon using substrate and

decomposer particles in a

random walk model. d (lower

right): increasing carbon age in

depth profile after simulation

with two particle model. Note

With increasing soil depth

decreases the decomposition

efficiency (respiration/total C),

which explains the apparent

stability of soil carbon

Simulating microbes and C particles in soils

To elucidate the effect of concentration-controlled C

turnover on soil profiles, we performed a conceptual sim-

ulation based on the two-step kinetic (see above). After a

spin-up period of 2000 time steps, equilibrium was reached

between C input via litter and C output of respired C. The

total microbial C pool (labile) was about 9 % of total C,

which is typical for soils (Foster 1988; Haynes 2005). In

line with our expectations, soil C and microorganisms

exponentially declined with soil depth (Fig. 2c). Most

interestingly, also substrate age almost linearly increase

with soil depth (Fig. 2d). Both observations are in line with

values summarised from the literature (Gleixner 2013;

Gleixner et al. 2001). Moreover, the simulations demon-

strate that old carbon particles in deeper soil layers are less

decomposable as the ratio of total respiration to carbon was

declining with depth (Fig. 2d). However, this low turnover

of soil C in deeper soil layers was neither due to different

turnover rates for different C pools nor to stable carbon

pools as used in soil turnover models (Gilmanov et al.

1997; Jenkinson et al. 1987). In our simulation, only carbon

concentration controlled the carbon turnover.

Supporting evidence from the literature

The majority of soil organic molecules are too large to be

directly assimilated by microorganisms (Ekschmitt et al.

2005). Consequently, exoenzymes and extracellular poly-

meric substances (EPS) are produced by the microorgan-

isms to form an external digestion space. In the latter one,

macromolecules are depolymerised into small, assimilable

parts of \600 Da (White 2000). Under conditions of low

substrate concentration and low density of microorganisms,

the effectiveness of exoenzymes and EPS is reduced as

compared to conditions with high C concentration (Ek-

schmitt et al. 2005). As soon as the energetic investment

for decomposition, i.e. biosynthesis of exoenzymes, is

higher than the energy gain from mineralisation, decom-

position will stop and the organism will turn to a dormant

state. Thus, both the deceased C turnover and the lower

contribution of soil derived carbon to respiration of diluted

samples, as found in our experiments, may be a result of

biophysical and energetic constraints of the decomposers.

Any energy investment in the production of exoenzymes in

a low C environment imposes a high risk of failure, i.e. the

enzymes never meet a substrate molecule. In consequence

is the carbon concentration a biological constraint for

carbon turnover.

Our findings suggest that lower carbon concentrations in

deeper plough horizons may yield in larger total carbon

stocks of agricultural soils. This evidently supports long-

term field experiments. During the 1960 and 1970s, a

deepening of the plough layer from \25 to C30 cm depth

was promoted in many parts of Europe. Deeper ploughing

decreased the surface soil C concentrations, but yielded in

higher total soil C stocks over the whole plough horizon

(Nieder and Richter 2000).

Conclusion

Here, we provided for the first time experimental results,

simulations and supporting evidence from the literature

that soil carbon can be stabilised simply by low carbon

concentrations. We provided evidence from first principle

simulations that mainly biological, biophysical and ener-

getic constraints of soil microorganisms determine the

stability of soil carbon. In consequence, two processes

mutualistically explain the observed effect:

1. The likelihood of substrate molecule to be decomposed

decreases with substrate concentration as described by

the Michaelis–Menten equation;

2. The overall energy balance of soil microorganisms

needs to be positive, and decomposition stops at low

substrate concentrations.

In consequence, we have to rethink our current concepts

of ‘‘soil carbon stability’’ and shift to a more ‘‘biological

view of soil carbon turnover’’. This change will have major

implications on the discussion of the vulnerability of soil

carbon and on land management practices.

Acknowledgments We would like to thank Andrea Oehns-Rittg-

erodt for technical help with the incubation experiment and Jens

Schumacher for helping with the statistical analysis.

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