Chapter 18
Processes of Soil Carbon Dynamics
and Ecosystem Carbon Cycling
in a Changing World
Felix Heitkamp, Anna Jacobs, Hermann F. Jungkunst, Stefanie Heinze,
Matthias Wendland, and Yakov Kuzyakov
Abstract Climate change is evident and increases of carbon dioxide concentration
(CO2), temperature and extreme weather events are predicted. To predict the effects
of such changes on carbon (C) cycling, the processes and mechanisms determining
the magnitude of C storage and fluxes must be well understood. The biggest
challenge is nowadays to quantify belowground components of the C-cycle. Soil
respiration accounts for ~70% of total annual ecosystem respiration. However, the
CO2 flux from soil originates from several sources, such as root respiration, rhizomicrobial respiration, mineralization of litter and mineralization of soil organic matter
(SOM). Increasing atmospheric CO2 concentrations will generally increase plant
growth, thus C-input to soil. This higher C-input will be accompanied by higher
SOM mineralization due to warming. However, mineralization of more stable pools
F. Heitkamp (*)
Landscape Ecology, Faculty of Geoscience and Geography, Georg August-University,
Goldschmidtstr. 5, 37077 Göttingen, Germany
Carbon Sequestration and Management Center, School of Environment
and Natural Resources, The Ohio State University,
2021 Coffey Road, Cloumbus, OH 43210, USA
e-mail: fheitka@uni-goettingen.de
A. Jacobs
Department of Agronomy, Institute for Sugar Beet Research,
Holtenser Landstr. 77, 37079 Göttingen, Germany
e-mail: jacobs@ifz-goettingen.de
H.F. Jungkunst
Landscape Ecology, Faculty of Geoscience and Geography, Georg August-University,
Goldschmidtstr. 5, 37077 Göttingen, Germany
e-mail: hjungku@uni-goettingen.de
S. Heinze
Department of Soil Science & Soil Ecology, Geographical Institute,
Ruhr-University Bochum, Universitätsstrasse 150, 44780 Bochum, Germany
e-mail: stefanie.heinze@rub.de
R. Lal et al. (eds.), Recarbonization of the Biosphere: Ecosystems
and the Global Carbon Cycle, DOI 10.1007/978-94-007-4159-1_18,
© Springer Science+Business Media B.V. 2012
395
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may be affected more by warming compared to mineralization of labile pools. The
importance of cropland management is demonstrated in a model scenario. Crop
residue incorporation increased C-storage in the soil markedly. However, under the
assumption of a higher temperature sensitivity of mineralization of stable C-pools
the net-sink of C under recommended management practice is severely reduced.
Precise predictions are hampered due to the lack of quantitative, mechanistic
knowledge. It is discussed that a more interdisciplinary scientific approach will
increase the speed in generating urgently needed understanding of belowground
processes of C-cycling.
Keywords Climate change • Respiration • Temperature sensitivity • CO2 fertilization • Soil organic matter • Ecosystem C cycling • Autotrophic organisms • Soil
respiration • Litter decomposition • Priming effect • Rhizosphere respiration
• Rhizodeposition • Mean residence time • Soil fauna • Root litter • Stabilization of
soil organic carbon • Biochemical recalcitrance • Spatial inaccessibility • Organomineral associations • Soil organic matter fractions • Spectroscopic methods
• Thermal stability • Depolymerization • FACE experiments • Temperature sensitivity (Q10) • Van’t Hoff equation • Rate constant • Arrhenius equation • Extreme
weather events • Substrate • Roth C model • SOC dynamics • Residues incorporation • CO2 fertilization effect
Abbreviations
AGBDM
AUR
BIO
C
CH4
CI
CO2
CO2-fert
CON
Aboveground biomass dry matter
Acid unsoluble residue
Microbial biomass, model pool in RothC
Carbon
Methane
Confidence interval
Carbon dioxide
Max-CC and CO2 fertilization of crops, climate scenario for the modelling example
Control treatment in the Puch experiment
M. Wendland
Institut für Agrarökologie, ökologischen Landbau und Bodenschutz,
Bayrische Landesanstalt für Landwirtschaft, Lange Point 12,
85354 Freising, Germany
e-mail: matthias.wendland@lfl.bayern.de
Y. Kuzyakov
Department of Soil Science of Temperate Ecosystems, Büsgen Institute,
Georg August-University, Büsgenweg 2, 37077 Göttingen, Germany
e-mail: kuzyakov@uni-goettingen.de
18
Processes of Soil Carbon Dynamics and Ecosystem Carbon Cycling…
DJF
DPM
ETP
FACE
GHG
GPP
HUM
IOM
IOSDV
JJA
MAM
MAP
MAT
Max-CC
MRT
N
NECB
NEP
No-CC
NPP
OM
ppm
RA
RE
RES
RH
RMSE
RPM
SOC
SOM
SON
18.1
397
December, January, February
Decomposable plant material, model pool in RothC
Evapotranspiration
Free air carbon dioxide enrichment
Greenhouse gas
Gross primary production
Humified organic matter, model pool in RothC
Inert organic matter, model pool in RothC
“Internationale organische Stickstoff-Dauerdüngungsversuche” (German)
International organic long-term nitrogen fertilization experiment
June, July, August
March, April, May
Mean annual precipitation
mean annual temperature
Maximal climate change, climate scenario for the modelling example
Mean residence time
Nitrogen
Net ecosystem carbon balance
Net ecosystem production
No climate change climate, scenario for the modelling example
Net primary production
Organic matter
Parts per million
Respiration by autotrophic organisms
Ecosystem respiration
Residue incorporation treatment in the Puch experiment
Respiration by heterotrophic organisms
Root mean square error
Resistant plant material model pool in RothC
Soil organic carbon
soil organic matter
September October, November
Introduction
It is evident that atmospheric carbon dioxide (CO2) concentrations rose drastically
from 280 ppm during the preindustrial era to about 390 ppm in 2010 (Conway and
Tans 2011). Similar drastic increases of other greenhouse gases (GHGs) are also
evident. The resulting increase in temperature due to radiative forcing was in the
range of 0.10–0.16°C per decade (1956–2005), which is likely the strongest warming since the last 1,300 years (Solomon et al. 2007). Future projections of CO2 concentration increase and warming until 2100 depends on underlying emission
scenarios. The atmospheric concentration of CO2 is projected to increase to up to
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1,000 ppm, and global surface warming is estimated to increase by 1.1–4.0°C, with
higher values over land compared to oceans. Moreover, extreme weather events
such as heat waves, droughts and heavy precipitation are likely to increase in most
regions (Solomon et al. 2007).
Uncertainties in climate change modeling for a given emission scenario result
mainly from unknown feedback effects between warming and the carbon (C) cycle
(Friedlingstein et al. 2006). On one hand, CO2 fertilization of plants results in higher
uptake of CO2 (negative feedback), thereby more biomass and increased storage of
soil organic carbon (SOC) (Heimann and Reichstein 2008). On the other hand,
increasing temperature induced by rising GHG concentrations accelerates mineralization of SOC, which in turn results in higher atmospheric CO2 concentrations
(positive feedback). Moreover, drying and rewetting as well as freezing and thawing
cycles of the soil may increase or decrease in frequency or severity, with uncertain
effects on the global C cycle.
The ecosystem C-cycle begins with C-assimilation by autotrophic organisms,
which are the higher plants in most terrestrial ecosystems. The rate of CO2 uptake
depends mainly on light energy (photosynthetically active radiation) and ambient
CO2 concentration, but also on water availability, temperature, nutritional status and
plant species. The sum of assimilated C in an ecosystem, typically expressed on
annual basis per square meter, is the gross primary production (GPP). A fraction of
the assimilated C is used for growth or reserve, in other words for buildup of the
biomass, which is the net primary production (NPP). Another fraction is respired by
plants to meet energy demands for growth and maintenance. This CO2 flux is known
as respiration by autotrophs (RA). Cannel and Thornley (2000) reported that the portion of NPP as GPP normally ranges between 0.4 and 0.6, especially when observed
over time scales of several weeks or longer.
In natural ecosystems, most of annual NPP enters the decomposition cycle as
leaf litter, root litter, rhizodeposition (exudates, exfoliates) or, woody debris. Most
of the decomposition process takes place on or in the soil. The C cycle is closed by
mineralization of organic C to CO2 by microorganisms. This part of the CO2 flux is
termed respiration by heterotrophic organisms (RH). Thus, the CO2 flux from the
ecosystem back into the atmosphere is the sum of RA and RH, and is termed ecosystem respiration (RE).
A first step to determine if a particular ecosystem gains (“CO2-sink”) or looses
(“CO2-source”) C over time is the balance, or imbalance, between NPP and RH
(equals the balance between GPP and RE). Chapin et al. (2006) defined this balance
as the net ecosystem production (NEP). Valentini et al. (2000) showed for 15 forest
ecosystems (latitudes ranging from 41°N to 64°N) that GPP is similar across all
locations. Thus, NEP is primarily determined by respiration and we will focus on
this topic below. Clearly, the C-balance is also determined by gains and losses not
induced by photosynthesis or respiration. This includes e.g. leaching, fire, harvested
products, methane (CH4) flux, erosion, herbivory and organic fertilization. For net
changes of C in ecosystem the term net ecosystem C balance (NECB) has been
proposed.
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Measuring net C-fluxes in ecosystems is relatively straightforward (although
expensive) by using eddy-flux towers and automated chambers for measuring soil
respiration, and supplementing the data with independent biomass and SOC measurements (Baldocchi 2003). However, predicting the effects of environmental
changes on fluxes and pools of C, necessitate understanding of how the components
of the gross fluxes affect the pools. The biggest challenge in understanding components of C-fluxes is to quantify the belowground processes (Schulze et al. 2009).
Therefore, the focus of this chapter is to provide an overview on the sources of soil
respiration, mechanisms of litter decomposition and processes of C stabilization by
the soil matrix. Finally, a model is also used as an example to illustrate how changes
in temperature and CO2 concentration may influence the SOC dynamics.
18.2
Mechanisms and Processes of Belowground
Carbon Cycling
Soil respiration accounts for the second largest CO2 flux after GPP, and amounts for
~70% of total annual RE (Yuste et al. 2005). Although soil respiration contributes
considerably to annual CO2 emissions there is a lack of knowledge with regards to
the abiotic and biotic impacts on respiratory activity of soils and the true sources of
soil derived CO2 (Kuzyakov 2006; Trumbore 2006).
Soil respiration is highly variable temporarily, but can be measured on very fine
time scales by using automated chambers. However, measured fluxes represent a
mixture of RH and RA with the portions of the sources varying among seasons,
depending on plant state, substrate supply to heterotrophs as well as temperature
and moisture regimes (Ryan and Law 2005). Thus, the biggest challenge in understanding components and fluxes affecting the NECB is quantification of the different sources of soil respiration.
Flux of CO2 from the soil into the atmosphere originates from different sources.
On a basic functional level, respiration is divided into respiration by autotrophs and
by heterotrophs. Dominant autotrophic organisms in terrestrial ecosystems are
plants. Heterotrophic organisms include various animals and microorganisms.
However, contribution of animals is in general of minor importance, only representing a few percent of total respiration by heterotrophs. In general, mean annual RH
accounts for 54% of soil respiration (Hanson et al. 2000).
Quantification of different sources of soil respiration is important, but remains
to be a work in progress (see Box 18.1 for methods). Kuzyakov (2006) identified
basically three main compartments as a source of soil respiration: (i) the rhizosphere, (ii) plant residue or litter and (iii) soil organic matter (SOM). While the
respiration from litter and SOM is mainly driven by heterotrophic organisms, that
from the rhizosphere is driven by C-allocation of plants to roots (Kuzyakov and
Gavrichkova 2010).
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Box 18.1 Overview on Methods for Partitioning of Soil Respiration
For detailed descriptions readers are referred to reviews by e.g. Hanson et al.
(2000) and Kuzyakov (2006). For component integration all compartments of
interest have to be separated by e.g. sieving and handpicking. Commonly,
roots, litter and soil is divided by this method. The components are then measured for their specific flux rate, and their contribution to total soil respiration
is calculated by the mass balance. Clearly, this method is accompanied by
high disturbance of the system, which may lead to a shift in the proportion of
contribution of components to total CO2 flux. This method provides only relative values.
Root exclusion techniques include basically root removal, root trenching
and gap analysis. All techniques have to deal with the problem to alter microclimatic conditions and nutrient budgets within the soil and as well as with
decaying roots, which contribute to respiration
Isotope tracers are used for partitioning of CO2fluxes from soil without
strong disturbance of the system. The principle of all isotopic approaches for
CO2 partitioning is based on differences in C isotopic signature of various
SOM pools and living or dead roots. Both, the radioactive 14C and stable 13C
isotopes as well as their combination are used successfully for partitioning
CO2 fluxes. The differences in isotopic signature of SOM pools may originate
from natural processes (radioactive decay of 14C; natural changes of vegetation) or can be artificially induced. The natural processes usually change the
isotopic signature too slowly and therefore, were seldom used (Kuzyakov
2011). The artificially induced changes of SOM pools and root-derived CO2
were used in the most CO2 partitioning studies up to now and can be grouped
into the following approaches: Continuous or pulse labeling of plants in 13CO2
or 14CO2 enriched or depleted atmosphere (Werth and Kuzyakov 2008), 13C
natural abundance (Heitkamp et al. 2012a; Rochette et al. 1999), and bomb 14C
approach (Wang and Hsieh 2002). The isotopic methods are precise and less
invasive, but are expensive and provide usually results for small areas, only.
18.2.1
Rhizosphere Respiration
The rhizosphere is the soil directly influenced by the root and often comprises
of only a few millimeter distance to the root. The rhizosphere is different from
surrounding soil by the presence of rhizosphere organisms (e.g. mycorrhiza), and
the strong influence of rhizodeposition (Jones et al. 2004; Kuzyakov 2006). The
rhizosphere respiration consists of heterotrophic (rhizomicrobial respiration) and
autotrophic (root respiration) components. However, with current methodology,
these components are hardly distinguishable. Mycorrhizal fungi, for example, are
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located inside and outside the roots, and directly utilize C from plant metabolites.
Therefore, even isotopic labeling fails to identify the source of respiration (Kuzyakov
and Larionova 2005). Rhizomicrobial and root respiration are often lumped together
as rhizosphere respiration due to methodological problems in separating the CO2
fluxes (Chapin et al. 2006).
Rhizodeposition is the release of organic compounds from living roots into the
surrounding soil, the rhizosphere. Rhizodeposition occurs in the intercellular space
of roots (endorhizosphere), on the root surface (rhizoplane) and outside the root
(ectorhizosphere). Released compounds, such as starch, glucose, carboxylic acids
and amino acids, are often low in molecular weight and are easily degradable by
microorganisms (Fischer et al. 2007; Jones et al. 2004; Schenck zu SchweinsbergMickan et al. 2010). Microorganisms in the rhizosphere take up C and N from
exudates and exfoliates of roots quickly within a few millimeter distance to roots
(Schenck zu Schweinsberg-Mickan et al. 2010) and turnover times are within hours
up to weeks (Kuzyakov 2006). The root exudates are mainly produced during daylight through stimulation of photosynthetic plant activity. Dilkes et al. (2004)
showed by 14C labeling that rhizodeposition of wheat (Triticum aestivum L.) was the
highest 3 h after C-uptake. On average, few hours are necessary for grasses and
herbs and about 4 days for mature trees for the release of rhizodeposits from roots
after CO2 assimilation in leafs (Kuzyakov and Gavrichkova 2010).
Due to their low mean residence time (MRT), rhizodeposition does not contribute
significantly to C storage in soil. However, contribution to respiration during
daylight hours might be substantial (Kuzyakov 2006). Furthermore, the labile nature
of rhizodeposits can influence activity and enzyme production of microorganisms
and, therefore, accelerate or retard mineralization from SOM or litter (i.e. priming
effect, Kuzyakov et al. 2000). Priming can significantly alter mineralization kinetics.
For example, Seiffert et al. (2011) showed under laboratory conditions that after
addition of glucose microorganisms incorporated and mineralized black slate, a low
grade metamorphic rock formed from shale. Therefore, increased rhizodeposition
can induce mineralization of the stabilized SOC pool (Fontaine et al. 2007).
18.2.2
Decomposition of Litter
In a broad sense, litter includes all solid debris such as leaves, roots, stems, stalks
and wood (Zhang et al. 2008). However, most research has been conducted on leaf
litter decomposition in forest ecosystems (Prescott 2010), and crop residues (Abiven
et al. 2005; Jensen et al. 2005).
Litter decomposition includes chemical alteration of litter, assimilation by
decomposers and mineralization to CO2. Mass loss from the so termed litter bags
(Box 18.2) without quantification of CO2 flux is the common approach to measure
litter decomposition under field conditions. Exposure of litter bags in the field
includes losses by leaching and export by fauna. Therefore, rates of mass loss are
higher than decomposition or mineralization rates but not vice versa. Besides these
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methodological shortcomings, the mass loss is closely related to C-mineralization,
justifying to use “mass loss” as a proxy for “C-loss” and “C-mineralization” (Cotrufo
et al. 2010).
In most modeling approaches it is accepted that for one litter type under constant
conditions, mass loss or C-mineralization follows decay by the first order kinetics.
Therefore mass loss can be described by Eq. 18.1:
n
Y (t ) = ∑ Yi × e − ki t
(18.1)
i =1
Where, Y(t) is the mass remaining at time t, Yi is the initial mass of compartment
i, ki is the decay constant of compartment i. A model with one compartment (n = 1)
is often successfully used in litter decomposition studies (Zhang et al. 2008), but
two compartment models are also used to account for different decomposition
stages (Gholz et al. 2000). The reciprocal value of the decay constant is termed
MRT. After the time span of the MRT, approximately 2/3 of the initial mass is lost.
In a global meta-analysis including 70 studies at 110 sites, MRT for litter of different
biomes ranged from 0.2 to 10 years with a median of 3.3 years (Zhang et al. 2008).
The wide range of MRTs is a result of climate, litter quality and decomposer
community (Swift et al. 1979).
Climate influences the decomposition rate through the effects of soil temperature
and moisture regimes (Swift et al. 1979). This influence is not always straightforward,
but thresholds exist. For example if mean annual temperature (MAT) is lower than
10°C, the rate of decomposition is slow regardless of litter type. The same is true for
the moisture contents below 30% and above 80% (Prescott 2010). Therefore, in
studies at sites in Canada, MAT was the principal control of decomposition dynamics,
whereas in tropical studies, moisture is relatively more important (Powers et al. 2009;
Trofymow et al. 2002). One limiting factor can hence determine the decomposition
kinetics: in the tropics temperature is high throughout and decomposition is
governed by moisture conditions. Zhang et al. (2008) reported in their meta-analysis that MAT and mean annual precipitation (MAP) only explained 30% of variation
in k-values. However, climate and litter quality are closely linked by the common
vegetation in bioclimatic zones differing in the chemical decomposition of litter.
The chemical composition is often referred to as litter quality. Litter decomposition
rates are often correlated with litter fractions obtained by stepwise chemical digestion,
operationally defined as cellulose, hemicellulose or acid unsoluble residue (AUR,
often referred to as “lignin”), and N-content or other nutrients (Berg and
McClaugherty 2003; Swift et al. 1979). Prescott (2010) pointed out that a good correlation between litter quality and decomposition is likely over a range of intermediate values. If, e.g. the ratio of AUR-to-N is below 10 or above 40, other factors are
likely to control the rate of decomposition and no significant correlation can be find
between AUR-to-N ratio and MRT.
Using a global dataset, Zhang et al. (2008) observed that AUR-to-N, N-content
and C-to-N ratio explained 73% of variation in decomposition rate constants, making
litter quality the most influential factor in decomposition. However, global analyses
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Table 18.1 Average mean residence time (MRT, in years) of above (Zhang et al. 2008) and
belowground litter (Silver and Miya 2001) of broad life form categories
Broadleaf
Conifer
Graminoid
Leaves or needles
1.3 (115)
2.9 (55)
0.9 (15)
Fine roots (< 2 mm)
2.2 (43)
5.9 (10)
0.7 (35)
Mean values across a broad range of biomes
Figures in brackets are the numbers of values (n)
Box 18.2 Methods for Measuring Litter Decomposition (Cotrufo et al. 2010)
Litter bag approach: Litter is placed in synthetic bags with varying mesh sizes
either to include or exclude fauna of various sizes. The litter bags are then
exposed in the field, either on the ground or buried in soil. Mass or nutrient
loss is regularly determined by weighing harvested bags.
Litter input and standing litter can be measured and annual decomposition
can be calculated by dividing input by standing mass. This approach provides
estimates of MRT on annual basis. Furthermore, the calculated MRT integrates input and standing litter from all species present. This approach is only
possible in ecosystems where MRT of litter is longer than 1 year.
Laboratory incubation studies with analysis of CO2 dynamics are especially useful to compare one particular property or process under controlled
conditions. This approach renders interpretation more straightforward than
field studies. However, extrapolation to field conditions is difficult.
Isotope tracers can be used instead of, or in addition to, measuring the
isotopic signal in respired CO2 (Box 18.1). The exposed material is sampled
and directly analyzed.
are subject to intercorrelation: the vegetation type is clearly influenced by climate
and soil conditions. For example, the lowest decomposition rates have been reported
for Tundra ecosystems were decomposition is slow due to low temperatures, frozen
soil and often waterlogged conditions. Furthermore, common Tundra vegetation is
inherently resistant to decomposition. It is important to recognize that a change in
vegetation (i.e. litter quality) due to climate change may affect decomposition rates
of litter (Table 18.1) perhaps as strong as increasing temperature (see also
Sect. 18.3.2).
A still unresolved issue is the influence of fauna on litter decomposition. Whereas
past studies have reported mostly the positive influence on decomposition rate,
recent studies report mostly neutral or even slowing effects (Prescott 2010). This
trend might be due to methodological issues. Moreover, fauna can alter litter composition and increase contact with soil particles by bioturbation. This in turn can
lead to chemical or physical stabilization of litter, but can also increase initial
decomposition due to favorable moisture conditions and higher nutrient availability
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(Jacobs et al. 2011; Potthoff et al. 2005). Knowledge on litter decomposition, mostly
in forest ecosystems, was greatly enhanced by cross-site studies. Wall et al. (2008)
concluded that invertebrate fauna increased decomposition under temperate and
tropical climates. In case of temperature or moisture limited decomposition rates,
faunal effects became neutral. However, the standardized methodology across 30
sites did not allow mixing with soil particles. Therefore, the magnitude and rate of
measured decomposition rates may differ from the true rates. Nevertheless, inclusion
of fauna in decomposition models is an important and a challenging task.
Root litter has often higher MRTs as compared to leaf or needle litter (Table 18.1).
It has been hypothesized that most of SOC is root derived (Rasse et al. 2005). A part
of the higher MRT of roots can be explained by the quality. Roots often contain
higher amounts of recalcitrant compounds, such as lignin, suberin, lignin and tannin.
However, it is likely that physical or chemical protection by the soil matrix contributes to the higher MRT of roots. These mechanisms are discussed in Sect. 18.2.3.
The short overview presented above indicates that climate change may affect
litter decomposition by increasing temperature, especially when rising above the
threshold and by altering the duration of very wet or dry phases. Large effects may
also result from changes in the vegetation pattern (i.e. in litter chemistry) and
accompanying changes in faunal and microbial communities. Decomposition
dynamics can even change without completely changing the vegetation. Jacob et al.
(2010) showed that beech (Fagus sylvatica L.) leaf litter decomposed slower in the
presence of litter from other tree species. Therefore, occurrence or absence of a few
species can influence significantly the C-cycle. Nevertheless, even highly decomposed litter is not intrinsically stable, as has been shown by Harmon et al. (2009):
even after 10 years of decomposition in litter bags the decay rate was an order of
magnitude higher compared to that of SOC in mineral soil. This trend shows that
studies of soil respiration, litter decomposition and stabilization of C in mineral soil
should be linked more closely for better insight in the belowground C cycle (Fierer
et al. 2009; Kuzyakov 2011; Ryan and Law 2005).
18.2.3
Stabilization of Soil Organic Carbon
The stabilization of SOC is defined as all mechanisms that protect it against decomposition and, thus, slow down mineralization (Baldock and Skjemstad 2000; von
Lützow et al. 2006). Destabilization is defined as the reverse of stabilization, increases
the susceptibility of SOC to decomposition (Sollins et al. 1996), and is, thus, one of
the mechanisms regulating CO2 emission from soils. Therefore, stabilization and
destabilization are closely related to each other and a detailed knowledge of the
mechanisms regulating them is required to better predict CO2 efflux from soil
(Schmidt et al. 2011; Trumbore 2006).
Over decades, several studies have been conducted to describe and to distinguish
different mechanisms of SOC stabilization. The traditional theory of SOC stabilization is based on the understanding that dead organic matter once entered the soil is
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either mineralized or humified by the soil microorganisms. These biologically produced “humic substances” were assumed to be resistant to mineralization due to their
biochemical structure (MacCarthy 2001). However, evidence against this hypothesis
emerged (Burdon 2001; MacCarthy 2001; von Lützow et al. 2006). More recent
studies take into account that C can be stabilized in the soil being biochemically relatively unaltered: SOC is in general a mixture of plant and microbial derived compounds (Burdon 2001). The current conceptual model of SOC stabilization is mainly
based on Sollins et al. (1996), and has been synthesized by von Lützow et al. (2006)
who provided an excellent and detailed description. For temperate zones, basically
three main stabilization mechanisms are identified: (i) biochemical recalcitrance, (ii)
spatial inaccessibility and (iii) organo-mineral association. Baldock et al. (2004) proposed that the capacity of the decomposer community must also be considered as a
fourth mechanism which leads, when capacity is limited, to slow mineralization. It
should be noted however, that not any of these stabilization mechanisms explains the
origin and production of the humic substances which are ubiquitous in soil.
All stabilization mechanisms can occur simultaneously (spatially and temporally),
they may affect each other, and co-limitation is possible (Heimann and Reichstein
2008; Wutzler and Reichstein 2008). The relevance of the respective stabilization
mechanism differs among environmental, geographical, and land-use characteristics
(von Lützow et al. 2006). Thus, a general classification and evaluation of the stabilization mechanisms is difficult. The scientific community is challenged to bridge
between conceptual models and ecosystem-specific processes (Heimann and
Reichstein 2008; Schmidt et al. 2011). Moreover, there is currently not method which is
capable to isolate equivalents of the conceptual model pools (Box 18.3). However, the
conceptual model of stabilization (Sollins et al. 1996; von Lützow et al. 2006) is the main
basis of the recent understanding of SOC dynamics and, thus, is presented herein.
Box 18.3 Methods for Isolating Soil Organic Matter Fractions
Physical fractionation procedures separate the SOC due to physical properties, according to particle size, aggregate size, or density (light, heavy, free,
and occluded organic particles). Thereafter, the mass of the fraction and the
respective C concentrations are measured (Christensen 2001). Density fractionation is particularly useful because fractions influenced by the main stabilization mechanisms can be separated (Golchin et al. 1994).
Chemical fractionation means to extract (e.g. hot water, 6 M HCl, H2O2,
NaOCl, Na2S2O8) more labile fractions of total SOC (Balesdent 1996; Helfrich
et al. 2006). The SOC is quantified before and after the procedure. Chemical
treatments are not completely standardized and may differ in terms of concentration, duration, and external energy added making comparisons difficult.
Biological fractionation is applied to determine the CO2 evolved during
incubation of soil samples. The CO2 emitted in a certain time is assumed to
represent a SOC fraction with a respective turnover time. Pool sizes and cor(continued)
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Box 18.3 (continued)
responding turnover times can be analyzed by curve fitting (e.g., Eq. 18.1,
Paul et al. 2006). Because only labile SOC is mineralized in incubation studies, this approach is best supplemented by another fractionation approach
(Haile-Mariam et al. 2008; Heitkamp et al. 2009).
Thermal stability of OM can be used as a measure for resistance to mineralization. By thermogravimetry mass loss of a sample over a range of temperature (typically 20–550°C for release of organic matter) is measured. The
higher the energy needed to induce a reaction (i.e. oxidation of organic C to
CO2), the more stable (recalcitrant) the fraction (Rovira et al. 2008). At least
two peaks, representing SOC of different stability, can be identified by thermogravemetry (Siewert 2004). However, by measuring directly the CO2
release during heating it is possible to identify up to four clear peaks (H. F.
Jungkunst, unpublished data).
Spectroscopic methods, e.g. infrared-spectroscopy or nuclear magnetic
resonance spectroscopy deliver information of the chemical composition of
SOC in a sample (Ellerbrock et al. 1999; Golchin et al. 1995). However, information is only useful when the turnover time of a respective substance or
functional group is known.
Analytical techniques using C isotopes are a very helpful tool for determination of turnover times or the age of SOC fractions (Balesdent 1996; Wang
and Hsieh 2002). Isotopic measurements are the only way to assign respiration
to certain SOC fractions (Kuzyakov 2011).
Biochemically recalcitrant substances have a molecular structure which leads to a
selective discrimination by the soil microorganisms. Such substances are: (i) not
“attractive” to microorganisms since the net gain in energy by depolymerization is low
(Fontaine et al. 2004; Wutzler and Reichstein 2008) and/or (ii) cannot be hydrolyzed
by common enzymes (von Lützow et al. 2006). Biochemical recalcitrance is mainly
caused by a complex macromolecular structure as aromatic and aliphatic compound,
e.g., waxes, lipids, chitin (Derenne and Largeau 2001), while compounds of a lowmolecular weight, e.g., sugars, amino acids, are more easily degradable (Sollins et al.
1996). However, in various experiments e.g., sugars with longer MRT than the SOC
has on average were observed (Schmidt et al. 2011; Thevenot et al. 2010). Overall, no
matter if plant or microbial derived, biochemical recalcitrance of certain SOC compounds leads to a selective preservation compared to easily degradable material (von
Lützow et al. 2006). Recalcitrance is relevant to stabilization in the time frame of up
to a few decades. An exception is so called “black carbon”, which might be stable over
millennial time frames (Hammes et al. 2007; Kuzyakov et al. 2009).
Spatial inaccessibility may be the result of occlusion of organic particles
(particulate organic matter) within aggregates (Balesdent et al. 2000; Oades 1984).
18
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407
Aggregate formation is induced by biotic activity: Organic and mineral particles are
glued together either in the intestinal tract of soil fauna or by excreted metabolites
of microorganisms as well as by root exudates (Elliott 1986; Oades 1984, 1993).
Moreover, fungal hyphae are a major agent in formation of macroaggregates
(>250 mm, Tisdall and Oades 1982). Since microbial activity drives aggregate formation, the latter also depends on litter quality (Martens 2000). Aggregates play a
substantial role in the stabilization of SOC, for the formation and stability of the soil
structure, and, thus, for fertility of cropland soil (Abiven et al. 2009; Trumbore and
Czimczik 2008). The formation of water-repellent surfaces (hydrophobicity) is also
a type of spatial inaccessibility of SOC to microorganisms (Lamparter et al. 2009;
Piccolo et al. 1999).
Chemical reactions of SOM with the mineral surface have been considered to
be strong and durable forms of stabilization since the oldest SOC is found often
in organo-mineral associations (Eusterhues et al. 2003; von Lützow et al. 2006).
Due to their variable or permanent negative charge of the surface, mainly clay
particles, silicates, and oxides act as mineral sorbents (Sollins et al. 1996; Wiseman
and Puttmann 2005). Positively and negatively charged organic groups can bond
to the sorbent by ligand exchange and/or polyvalent cation bridges. The complexation and/or precipitation of SOM with metal ions, mainly Ca2+, Al3+, and Fe3+, is
a further process of inaccessibility (Kiem and Kögel-Knabner 2002; von Lützow
et al. 2006). Further, bonding mechanisms are water bridging and van der Waals
forces which are relatively weak (von Lützow et al. 2006). There is evidence that,
depending on texture and environmental conditions, the capacity for organo-mineral associations in soils is limited (Baldock and Skjemstad 2000; Wiseman and
Puttmann 2005).
Destabilization is the process of reversing physical or chemical protection of
SOC, rendering SOC to microbial attack, i.e. mineralization. External factors, as
ecosystem properties and soil management, are the major agents controlling timing
and kinetics of SOC destabilization (Schmidt et al. 2011; von Lützow et al. 2006).
Thus, the determination and evaluation of destabilization mechanisms is complicated and needs more detailed research (Trumbore and Czimczik 2008). Kuzyakov
(2011) observed that it is crucial to directly link SOC fractions to CO2 fluxes in
future experiments. Thus, physico-chemical factors that control destabilization are
briefly discussed herein.
Depolymerization, dissolution, and desorption are the reactions which reduce
biochemical recalcitrance and organo-mineral associations. Organo-mineral associations may disintegrate due to changes in the pH, the redox potential and cation
concentration (Sollins et al. 1996). Macroaggregates may disrupt when they are
exposed to physical stress, as dry-wetting and thaw-freeze cycles and cropland soil
management (Denef et al. 2002; Navarro-García et al. 2012). The input of easily
degradable organic matter (OM) can initiate the decomposition of recalcitrant
compounds (priming effect) (Kuzyakov et al. 2000; Trumbore 2006). Further, all
factors that are able to enhance microbial activity (climate, availability of easily
degradable compounds, availability of N) increase the susceptibility of SOM to
mineralization.
408
18.3
F. Heitkamp et al.
Potential Alterations of the Carbon Cycle
in a Changing World
In the next sections, an overview of the possible effects of climate change on the
C-cycle is presented, with emphasis on those processes in soil which are the least
understood. Firstly, the effect of elevated CO2 concentrations on NPP and belowground processes is reviewed. Next, uncertainties of how warming can effect
C-mineralization are discussed, and finally the effects of extreme weather events,
(i.e. rewetting and thawing) on C cycling are presented.
18.3.1
Elevated Atmospheric Carbon Dioxide Concentration
A reduction of the increase of atmospheric CO2 concentrations is expected due to the
CO2-fertilization of plants (i.e. negative feedback, Friedlingstein et al. 2006; Heimann
and Reichstein 2008). It has been shown that the light saturated uptake of CO2
increases (in C3 plants) with increasing CO2 concentration (Leakey et al. 2009).
Much effort is going on to test the effects of elevated CO2 concentrations on the
C-cycle. Globally, 36 free air CO2 enrichment (FACE) experiments have been conducted, and some are still running. A list is available online (http://public.ornl.gov/
face/global_face.shtml). FACE plots are surrounded by pipes injecting a CO2 stream
into the air. Concentrations of CO2 of up to 600 ppm are tested by this method in
forest, grassland, cropland and dessert ecosystems (Ainsworth and Long 2005).
Albeit restricted plot size (up to 30 m diameter), this method provides the possibility
to test the effect of elevated CO2 concentration under field conditions. However, no
forest experiments were conducted in boreal and tropical regions, and no FACE
experiment fumigates mature forests (Hickler et al. 2008). Furthermore, many forest
FACE are only running until 2011 (Ledford 2008).
Effects of changing climate and increasing CO2 concentration on NPP are of
high importance. Ainsworth and Long (2005) showed, by summarizing data from
FACE experiments, that biomass and yield of plant species with C4 photosynthetic
pathway are largely unaffected by CO2 concentration. However, most C3 crops and
juvenile trees showed increased aboveground biomass and crop yields (Table 18.2).
FACE experiments have been extremely valuable, but they are implemented only at
a very limited number of sites and for only a few plant species. De Graaff et al.
(2006) summarized data from FACE in a meta-analysis and concluded that belowground biomass may even increase by 34%. Thus, increased biomass production
may increase C-input into soil, enhancing the SOC storage and mitigate the increased
mineralization caused by warming. However, this may only be true if other nutrients
will not limit plant growth (de Graaff et al. 2006).
Considering only the increases in C-input in the ecosystem is only one issue with
respect to global change. For the C balance studies, the amount of C retained in the
ecosystem is crucial. Specifically, if mineralization also increases with C-input the
C balance may be unaffected. It has been documented that microbial growth rates in
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409
Table 18.2 Aboveground dry matter and crop yield changes as affected by increased CO2
concentration (ca. 500–600 ppm) at FACE experiments
Change (%)
No. of species
No. of sites
Mean
Lower CI
Upper CI
ABGDM
34
6
17.0
14.5
19.6
Tree
7
2
28.0
6.4
54.1
C4 crop
1
1
6.7
−2.2
16.6
C3 grass
8
3
10.5
6.5
14.8
Legume
6
3
20.3
13.7
27.3
Crop yield
6
3
17.3
10.2
24.9
Cotton
–
1
42.2
23.7
63.6
Wheat
–
1
14.4
−1.6
33.1
Rice
–
1
10.4
−4.4
30.2
Beet
–
1
12.5
n.d.
n.d.
Data compiled from Ainsworth and Long (2005) and Manderscheid et al. (2010)
ABGDM Aboveground dry matter, CI 95% confidence interval, n.d. not determined
soil increase linearly with increasing atmospheric CO2 concentration (Blagodatskaya
et al. 2010). Bacterial respiration, but not that of saprotrophic fungi is enhanced
under elevated CO2 (Anderson and Heinemeyer 2011). This indicates the preferential increase of fast growing microorganisms (r-strategists), probably due to more
rhizodeposition. Increased microbial growth on labile substrates can induce priming
effects on SOC mineralization (Kuzyakov et al. 2000) and encounter the increased
C-input of plants caused by CO2-fertilization. On the other hand, Rillig and Allen
(1999) showed an increase of glomalin produced by arbuscular mycorrhizal fungi
after 3 years of elevated CO2. Glomalin is a recalcitrant organic glycoprotein which
is preferentially fixed in macroaggregates and, therefore, protected against microbial
breakdown (Rillig and Allen 1999). However, direct quantification of glomalin is
not possible up to now, all available methods having specific drawbacks (Rosier
et al. 2006). Nevertheless, glomalin seems to have a relatively long MRT (few
decades) and can, therefore, account for a significant C-pool in ecosystems (Treseder
and Allen 2000; Treseder and Turner 2007). Whether priming or production of
glomalin affects SOC storage in the long-term is unknown, and is likely to be
ecosystem specific.
In fertilized agroecosystems the effect of priming may be less compared to
N-limited systems such as forests. Anderson et al. (2011) showed that SOC stocks
under cropland use increased by 10% in 6 years under elevated CO2 (550 ppm)
relative to ambient CO2 concentration. In a warm temperate forest (Duke FACE)
Drake et al. (2011) showed that C-fluxes increased under elevated CO2 (ambient
plus 200 ppm). This trend also increased the N-turnover, presumably increasing N
mineralization from SOM. As a consequence, tree biomass increased (2003–2007),
but SOC stocks remained unaffected. Thus, N or other nutrients may become a limiting
factor for biomass increase in non-fertilized ecosystems (de Graaff et al. 2006).
Thus, CO2 fertilization may enhance the ecosystem C-sink, but only to a minor
extent, which could also be offset by warming.
410
18.3.2
F. Heitkamp et al.
Increase in Temperature
Microbial decomposition is, as are all chemical or biochemical reactions, temperature
dependent. Therefore, it implies that rising temperature can induce a positive feedback: C-mineralization will increase with temperature and the higher release of CO2
will cause additional warming. Therefore, it is important to quantify the effect of
temperature on respiration for improved predictions of effects on SOC storage.
Temperature sensitivity is often expressed as Q10 values by the van’t Hoff equation
(Eq. 18.2):
Q10 = (k2 / k1 )(10 /(T2 −T1 ))
(18.2)
Where, k2 and k1 are rate constants of a certain process and T2 and T1 the corresponding temperatures. An often assumed Q10 of 2 means that respiration would increase
twofold by raising the temperature from 10°C to 20°C. While this empirical relationship has been often used (Davidson and Janssens 2006; Vicca et al. 2009; von
Lützow and Kögel-Knabner 2009), the theoretical basis is determined by thermodynamical laws. Arrhenius formulated an equation which relates the reaction rate constant (k) of a certain compound to its bio-chemical stability, i.e. activation energy
(Ea). This relationship is presented in Eq. 18.3:
k = a × e( − Ea / RT )
(18.3)
where, a is a pre-exponential factor, R the gas constant (8.324 J K−1 mol−1) and T
temperature (K). There are two important implications of the Arrhenius equation for
the temperature sensitivity of C-mineralization. Firstly, the relation shows that Q10
values decrease with increase in temperature for a certain compound. That is, Q10
values are not constant even for pure substances over a range of different temperatures. Secondly, substances with higher activation energies (i.e., less reactive and
more recalcitrant) exhibit higher sensitivity to changing temperatures (Fig. 18.1).
This theoretical basis gives rise to the assumption that more stable (i.e. presumably
more decomposed) SOC fractions are affected relatively more by increasing temperatures than labile fractions (Knorr et al. 2005). Figure 18.1 shows an example
adapted from Davidson and Janssens (2006) for reaction of glucose (Ea » 30 kJ mol−1)
and tannin (Ea » 70 kJ mol−1) relative to 10°C. The relative effect of temperature on
k of tannin is much higher. However, the absolute change in reaction speed of glucose in relation to tannin (kGLU/kTAN) is negligible, given the order of magnitude
presented in Fig. 18.1. On the other hand, labile pools or fractions normally form a
minor part of the SOC stocks. Therefore, despite slow turnover, mineralization of
stable pools can contribute significantly to CO2 efflux from SOC to the atmosphere
(Flessa et al. 2000). Concerns that stable SOC pools/fractions are more sensitive to
warming (Knorr et al. 2005) may only be true if stability is induced by recalcitrance
(see Sect. 18.2.3, Schmidt et al. 2011). Moreover, the Arrhenius equation is only
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Processes of Soil Carbon Dynamics and Ecosystem Carbon Cycling…
411
kGLU, relative to 10°C
kTAN, relative to 10°C
Ratio of kGLU to kTAN (x107)
4
3
2
1
0
0
5
10
15
20
25
30
Temperature (°C)
Fig. 18.1 Relative effect of temperature on reaction rate constant (k) of glucose and tannin and the
ratio of the absolute rate constants. The reaction rate at 10°C is accepted as 1
valid under the assumption of unlimited substrate availability. Theoretically, the
Arrhenius equation can be combined with Michaelis-Menten kinetics (i.e. describing reaction rates affected by substrate limitation) but such attempts are difficult in
a complex media such as soil. Therefore, empirical observation and phenomenological description seem to be the only ways to determine temperature sensitivity of
SOC, as well as of its fractions (Kirschbaum 2006).
The determination of Q10 values seem to be straightforward. Measuring respiration rates during laboratory or field studies and application of Eq. 18.2 should be
easy. However, a wide range of Q10 values (1.4–6.9) have been reported (von Lützow
and Kögel-Knabner 2009). Several methodological problems arise in determining
Q10 values under field conditions. First of all, soil respiration consists of several
components, which are not easy to distinguish (see Sect. 18.2.1). Moreover, increasing temperature is often accompanied with decreasing soil moisture. Therefore, respiration may not, or less, increase with temperature because moisture is limiting.
This bias the determination of Q10 towards underestimation. Water logged conditions, freezing-thawing or drying-rewetting cycles and different substrate supply
also hamper determination of temperature sensitivity under field conditions. For this
reason, Kirschbaum (1995, 2006) recommended laboratory incubations under controlled conditions as the best method to determine temperature sensitivity of SOC
mineralization (Box 18.4).
412
F. Heitkamp et al.
Box 18.4 Methodological Considerations for Determination of Q10 Values
During Laboratory Incubations
A common way for determination of Q10 values is incubation of a soil sample at
different temperatures under otherwise equal conditions (i.e. optimal moisture
content) and measuring respiration rate at different times. Using such “parallel
incubations”, result in “apparent” Q10 values which are strongly biased by the
substrate supply. Figure 18.2a illustrates a simplified example with pool sizes
and turnover times taken from Heitkamp et al. (2009). Bulk respiration is
modeled as contributions from pool 1 (0.4 Mg C ha−1; k = 0.059), pool 2
(3.2 Mg C ha−1 k = 0.002) and pool 3 (16.8 Mg C ha−1; k = 0.0001). The Q10 values
of 2, 3 and 4 are assigned to pools 1, 2 and 3, respectively. Figure 18.2a shows
that apparent Q10 values of all pools decrease with time and even fall below 1.
That is the case when pool 1 is exhausted at higher temperature, but still contributes to respiration at lower temperature. A false conclusion from this pattern is
that stable pools (i.e., contributing to respiration at later incubation time) are less
sensitive to temperature than labile pools (i.e., contributing to respiration at early
stages). Reichstein et al. (2000) tried to overcome this problem by determining
pool sizes and decay constants at each temperature. Then, the pool sizes were
hold constant, and the Q10 values were determined for the decay constant. This
approach indeed overcomes the problems with substrate depletion, but the nonlinear curve fitting approach for determining pool size and decay constant is
itself not straightforward and results depend on incubation conditions (Böttcher
2004; Heitkamp et al. 2009; Sierra 1990). By applying the approach to specific
compounds sampled during incubation, Feng and Simpson (2008) showed, in
accordance with the Arrhenius equation, that lignin monomers exhibited higher
temperature sensitivity than n-alkanoic compounds. Nevertheless, a Q10 could
not be calculated for almost 50% of the dataset due to poor model fits.
Another solution is to incubate all samples at the same temperature and
exposing the sample only for short period to different temperatures (Leifeld
and Fuhrer 2005). This approach avoids different substrate supply at different
temperature for the same pool. Nevertheless, pool sizes change with time
(Fig. 18.2b) simultaneously affecting bulk apparent Q10 values (i.e. apparent
Q10 of respired C). In the present example, the bulk intrinsic Q10 (i.e., Q10
value inherent to a compound due to its chemical properties) value is largely
determined by pool 3, due to its large size. Therefore, bulk apparent Q10 values increase towards the bulk intrinsic Q10 value with time, but do not coincide during the short incubation time because respiration is largely determined
by pool 2. The bulk intrinsic Q10 increases with incubation time due to depletion of more labile substrates with lower Q10 values. If there would be any possibility to measure contribution of pools directly, the “rotating incubation”
method would be straightforward and yield intrinsic Q10 values for each pool
at any time. Attempts using 13C natural abundance after C3/C4 vegetation
changes indeed indicate that “old” SOC is more sensitive to temperature compared to “young” SOC (Vanhala et al. 2007; Waldrop and Firestone 2004).
Processes of Soil Carbon Dynamics and Ecosystem Carbon Cycling…
a
413
b
5
Bulk respiration
4
Bulk intrinsic Q10
5.0
Respiration pool 1
Respiration pool 2
Respiration pool 3
Bulk apparent Q10
2.0
Size of pool 1
Size of pool 2
Size of pool 3
4.5
3
Q10
Apparent Q10
1.5
4.0
3.5
1.0
2
3.0
Relative pool size
18
0.5
1
2.5
0.0
2.0
0
0
200
400
600
0
200
400
600
Incubation time (days)
Fig. 18.2 Theoretical effect of incubation time on Q10 values calculated by respiration from
different pools by parallel incubation (a) and relation between remaining pool size and calculated
intrinsic and apparent Q10 value of bulk respiration with rotating incubation (b). Parallel incubation
means incubation of samples at different temperatures throughout, whereas rotating incubation is
incubation at one temperature and exposure to different temperature only for short time frames
Current knowledge indicates that recalcitrance does not lead to stabilization of
SOC on millennial time scales (Kögel-Knabner et al. 2008). If stabilization of C in
soil is a consequence of chemical protection against decomposition, the Arrhenius
equation might not be relevant for temperature sensitivity of stabilized SOC.
However, Craine et al. (2010) showed that physical or chemical stabilization may
happen without altering temperature sensitivity. For example, mineralization data of
soil and litter samples differed in their respiration rate by an order of magnitude (30
and 420 mg C (g C h)−1, respectively), but not in their activation energies (59 kJ mol−1).
Thus, physical and chemical stabilization mechanisms seem to be less sensitive to
temperature compared to biochemical stabilization (i.e. recalcitrance). In contrast,
Gillabel et al. (2010) compared temperature sensitivity of topsoil and subsoil
samples. In subsoil, the amount of chemically stabilized SOC is assumed to be relatively higher compared to topsoil (Rumpel and Koegel-Knabner 2011). Gillabel
et al. (2010) observed that respiration from topsoil samples was in accordance with
the Arrhenius equation, whereas subsoil respiration was not sensitive at all to
temperature. It was hypothesized that chemical protection induced these trends. The
Arrhenius equation only applies to conditions of unlimited substrate availability.
Due to low substrate availability in subsoil, the effect of temperature might be cancelled out by processes described by Michaelis-Menten kinetics in the subsoil (von
Lützow and Kögel-Knabner 2009). Therefore, the apparent temperature sensitivity
is determined by substrate availability (i.e., abundance and availability of substrate
414
F. Heitkamp et al.
and stabilization mechanism), whereas the intrinsic sensitivity (see Box 18.4) is
determined by the chemistry of the compound. Future research is needed to distinguish between the effects of these different processes (Conant et al. 2011).
18.3.3
Frequency of Extreme Weather Events
Besides increasing temperature and changes in precipitation increases in extreme
weather events are also predicted in a future climate (Christensen et al. 2007). Thus,
increasing numbers of drying and wetting and/or freezing and thawing events are
likely in most ecosystems.
A flush of CO2 efflux occurs upon rewetting of a soil. This is termed the “Birch
effect”. Birch (1958) speculated that the CO2 flush is derived from “solid organic
material” and regulated by microbial state before and during rewetting. Death of
microbial cells due to drying and subsequent re-utilization as substrate after
rewetting is another explanation (Kieft et al. 1987). Whereas microbial death and
re-utilization of cell debris after rewetting remain a common explanation, Fierer and
Schimel (2003) reported that the CO2 release can additionally be explained by
accumulation of labile substrate and possibly also of enzymes. Disruption of aggregates, thus exposure of physical protected SOC to mineralization, may be in part
responsible for increased respiration (Navarro-García et al. 2012). However, drying
and rewetting can also increase aggregate stability in the long-term (Denef et al.
2002). Aggregate size and stability ( i.e., soil structure) also determine gas diffusion.
Therefore, oxygen supply and thus microbial activity can be influenced by changes
in soil structure (Jäger et al. 2011).
After several cycles, the CO2 flush after rewetting is reduced, indicating depletion
of substrate affected by rewetting (Fierer and Schimel 2002). Moreover, the short
flush may contribute only a small portion to annual emissions. Muhr et al. (2008)
reported even decreased cumulative CO2 emissions from soil samples with drying
and rewetting cycles compared to continuously moist samples. If cumulative respiration is reduced depends on the duration of the dry phase, where microbial activity is
limited by soil moisture. Furthermore, microbial respiration can be reduced after the
rewetting flush (Fierer and Schimel 2002), probably because of substrate depletion
and acclimation of the microbial community (Lundquist et al. 1999). Whereas fungal
growth was not affected by drying and rewetting cycles, bacterial growth decreased
after exposure to several cycles (Bapiri et al. 2010). An increase of fungal population
may shift the specific respiration (i.e. respiration per unit microbial biomass) to lower
values, since saprotrophic fungi are more effective in substrate utilization than bacteria
(Joergensen and Wichern 2008). Physical, chemical and biological interactions
apparently govern the net-effects of drying and rewetting on SOC mineralization
(Kim et al. 2011). In the long term, the duration of dry phases (Bottner et al. 2000)
and the number of cycles may determine the net effect on annual C-mineralization.
Similar to drying and rewetting cycles, a flush of CO2 is also observed after thawing
of a frozen soil (Kim et al. 2011; Matzner and Borken 2008). The flush is ascribed to
18
Processes of Soil Carbon Dynamics and Ecosystem Carbon Cycling…
415
microbial death and subsequent utilization of cell debris. Also, diffusion barriers
might be involved. Specifically, microbial activity continues at unfrozen microsites
and/or in subsoil. After thawing, gas can diffuse out of the soil (Teepe et al. 2001).
Aggregate stability is often reduced after thawing, depending on the water content
before the freezing event (Dagesse 2011). Reduced physical protection can, therefore,
contribute to the CO2 flush after thawing. Further, biology and chemistry of soil also
changes after freezing and thawing. Schmitt et al. (2008) reported a decrease in fungal
biomass, whereas bacteria were largely unaffected by freezing and thawing cycles.
Besides the observed flush after thawing, the net-effect on soil respiration is not
entirely clear. Matzner and Borken (2008) reported in their review that cropland soils
seem to lose slightly more (<5%) C by respiration after freeze-and-thaw cycles
compared to unfrozen soil. The opposite has been reported for natural vegetation.
Comparison of studies is further complicated by methodological issues, such as freezing
or thawing temperature, sampling time and experimental duration (Hugh 2007).
For both events, the net effect likely depends on the frequency of cycles, but
seem to be small on annual basis (Fierer and Schimel 2002; Matzner and Borken
2008). However, the lack of understanding the processes involved hampers a
general conclusion. Most knowledge is based on laboratory studies, which is also a
consequence of methodological issues in measuring soil respiration in the field.
Studies involving subsequent events of dry-and-rewet and freeze-and-thaw (e.g.,
effects of subsequent freezing and thawing during winter and subsequent drying and
rewetting in spring) seem to be entirely missing, but are most important to elucidate
effects of climate change on SOC mineralization.
18.4
Cropland Management, Elevated Carbon Dioxide,
and Temperature Increase: A Model Scenario
The following section of this chapter exemplifies effects of CO2-fertilization and
warming on SOC stocks for two cropland management options with a modeling
scenario. However, a model can hardly take into account all factors affecting predictions of SOC dynamics over the next 100 years. There will be changes in management, fertilization techniques, and plant cultivars. Further, climate change is not
simply increasing temperature and precipitation but also cause increases in extreme
weather conditions, which may lead to hardly predictable socio-economic and
agro-ecological changes. Therefore, the model was applied as a tool to separate the
possible quantitative influence of selected variables within a certain scenario on
SOC dynamics. This approach is useful to identify the magnitude of some factors
related to climate change.
The Rothamsted Carbon Model 26.3 (RothC) is chosen as a tool because it is useful in simulating SOC dynamics (Smith et al. 1997). Furthermore, RothC works well
at the chosen site (Heitkamp et al. 2012b) and the model can easily be re-parameterized (Gu et al. 2004; Heitkamp et al. 2012b).
416
18.4.1
F. Heitkamp et al.
Site Conditions and Model Setup
By using regional projections of changing temperature and precipitation, the effects
of climate change and residue management on SOC dynamics at a cropland site at
Puch, Germany were modeled. The long-term (1960–1990) MAT of the site is 7.9°C
and MAP is 922 mm. The soil is a Luvisol which developed on loess deposits, thus
silt is the dominating particle size class (9% sand, 73% silt and 18% clay). The fertilization experiment was set up within the network of the “Internationale Organische
Stickstoff Dauerduengungsversuche” (IOSDV) in 1983. Crop rotation consisted of
sugar beet (Beta vulgaris L.), winter wheat and winter barley (Hordeum vulgare L.).
This analysis was based on two out of several treatments: (i) removal of straw and
beet leaves (CON) and (ii) incorporation of straw and beet leaves (RES). All treatments were under conventional tillage. The SOC content was measured episodically,
in 1983 in samples composited among plots with different N-rates; in 1986, 1989 and
2003 bulked among field replicates, and in 1994 and 2004 for individual plots (n = 3).
Ploughing depth was 25 cm and bulk density was assumed to be 1.5 g cm−3 for conversion of SOC concentration into stocks. Nitrogen was applied at a rate (kg N ha−1)
of 100 to beet, 80 to wheat and 60 to barley until 1998 and was raised by 20 for cereals thereafter. Straw for incorporation was weighed until 1998, thereafter a harvest
index of 0.5 was applied. Beet leaves were weighed throughout until 2004. Residue
input by stubbles, roots and rhizodeposition was estimated by linear regression of
yield (grain and beet) and C-input as shown by Eq. 18.4:
I = (Y × F + K ) × R
(18.4)
Where, I is the C-input (Mg ha−1), Y is crop yield (fresh mass for beet, incl. 13%
water in grain; Mg ha−1), F (Mg C (Mg Y)−1) and K (Mg C ha−1) are crop-specific
constants and R is a multiplier to account for rhizodeposition (Franko 1997; Ludwig
et al. 2007). Crop yields were published (Hege and Offenberger 2006), F was set to
0.008 (winter cereals) or 0.0008 (sugar beet), K was set to 0.4 (winter cereals) or
0.16 (sugar beet), and R was set to 1.5 or 1.2 (winter cereals and sugar beet), respectively. The constants are published in Franko (1997) and for rhizodeposition see
Domanski et al. (2001) and Ludwig et al. (2007).
The RothC model was used for modeling SOC dynamics (Coleman and Jenkinson
1999). The model consists of five pools with different turnover, is easy to calibrate
and was proven useful for simulating SOC dynamics (Ludwig et al. 2007; Smith
et al. 1997). Every pool has a specific decay constant which is modified by temperature, moisture, and plant cover. Partitioning between mineralization and humification is influenced by clay content (Coleman and Jenkinson 1999). The original
temperature function of RothC was replaced by Eq. 18.2 to evaluate the effect of
different temperature sensitivity of pools (Gu et al. 2004). The replaced function
with the commonly assumed Q10 of 2 (Davidson and Janssens 2006) was tested
against the original model and only minor differences occurred. In Model A, Eq. 18.2
was used with Q10 = 2 for all pools. As stated above, more stable pools might have a
higher sensitivity to temperature changes. Therefore, for Model B, a Q10 of 2 was
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417
Table 18.3 Maximal projected increases in temperature and precipitation (Christensen et al.
2007) and potential evapotranspiration (Baguis et al. 2010) until the period of 2080–2099 for
Northern Europe
Temperature (°C)
Precipitation (%)
ETP (%)
DJF
8.2
25
40
MAM
5.3
21
20
JJA
5.4
16
33
SON
5.4
13
30
DJF December, January, February, MAM March, April, May, JJA June, July, August; SON
September, October, November, ETP evapotranspiration
used for DPM and BIO (Davidson and Janssens 2006), a Q10 of 3 for RPM
(Wetterstedt et al. 2010) and a Q10 of 4 for the HUM pool (Leifeld and Fuhrer 2005).
The model was initialized with an equilibrium run to 1983. For this purpose, C-input
and size of inert organic matter (IOM) pool were adjusted. From 1984 to 2004, the
model was run with available weather data and C-input was measured or calculated
independently (Eq. 18.4). From 2004 to 2099 different climate change scenarios
were assumed (Table 18.3).
As a reference scenario (No-CC), monthly temperature, precipitation, and actual
evapotranspiration were assumed to be constant at the mean value of the period
1983–2004. This period is close to that (1980–1999) used for modeling of regional
climate change projections for 2080–2099 by the IPCC (Christensen et al. 2007).
The input of C was calculated as mean value between 1984 and 2004. Assuming a
scenario without changing temperature and precipitation was necessary because
SOC stocks were not in equilibrium in 2004 and management effects must be
separated from climate change effects.
The second scenario (Max-CC) represents a regional climate scenario with maximum assumed temperature increase and precipitation changes for Northern Europe
(Christensen et al. 2007). All increases are supposed to be linear and are originally
projected from the period of 1980–1999 to the period of 2080–2099 (Table 18.3).
The third scenario is the same as the second (Max-CC), but with consideration of
CO2 fertilization effects on crop growth. A linear increase in growth of up to 16%
was assumed until 2050 (Leakey et al. 2009). From 2050 to 2100, an additional
increase of 8% was assumed. Therefore the total increase was in the range reported
from FACE (de Graaff et al. 2006).
Modeling of all scenarios was done in monthly resolution. For presentation of
data the modeled SOC stocks were averaged over a crop rotation period of 3 years.
18.4.2
Effect of Residue Incorporation
During the observed period from 1983 to 2004, SOC stocks showed marked changes.
For instance, SOC stocks (Mg ha−1 ± standard error) of CON were 35.3 ± 3.3 and
stocks of RES were 39.9 ± 1.6. Model fits were satisfactory with root mean square
418
F. Heitkamp et al.
55
Model B
Model A
SOC stocks (Mg ha-1)
50
45
Residue incorporation
40
Residue removal
35
Residue incorporation
Residue removal
30
No climate change
Maximum increase of T and P
CO2 fertilization
25
20
2000
2020
2040
2060
2080
RES, measured
CON, measured
2100 2000
2020
2040
2060
2080
2100
Time (years)
Fig. 18.3 Modeled SOC dynamics for residue removal (CON) and incorporation (RES) for the
different climate change scenarios in Puch, Germany. In model A the same temperature sensitivity
for mineralization is assumed for all model pools. In model B, temperature sensitivity increases
with the MRT of pools (see Table 18.4). Measured stocks in 2004 are means with standard errors
errors (RMSE) between 5.0 and 7.8, and with the mean differences between observed
and predicted values (−1.6 to 1.8) well in the range of the standard errors (Smith
et al. 1997).
With both model parameterizations, the increasing gap in predicted SOC storage
between cropland management with residue incorporation and with residue export
(Fig. 18.3) became obvious. The SOC stocks in the RES treatment were about twice
as large as those in CON treatments (Table 18.4) at the end of the modeled period.
In the CON treatment modeled SOC stocks ranged from 26.1 to 27.9 Mg ha−1, loosing
between 7.8 and 9.6 Mg C ha−1. The estimated SOC stocks were low, but considering
the low C-input (Table 18.4) this is in a realistic range. For example, Rühlmann
(1999) summarized SOC stocks for long-term bare fallow experiments. According
to the empirical equation used by Rühlmann (1999), the SOC stock under bare fallow
(i.e., no C-input) at the Puch site is estimated to be 17 Mg ha−1. The modeled data
indicate that SOC stocks of both treatments will not attain equilibrium until 2100,
which contradicts observations of West and Post (2002), who estimated that a new
equilibrium due to enhanced crop rotation will be reached after 40–60 years. One
explanation may be the large difference in C-input between treatments at the Puch
site (Table 18.4).
18.4.3
Effect of Climate Change Scenarios
Evaluation of climate change effects was done in comparison to a scenario where no
climate change will be present. This was done by creating a scenario which used the
18
Processes of Soil Carbon Dynamics and Ecosystem Carbon Cycling…
419
Table 18.4 Model results for the period 2005–2100 for different climate change scenarios and
model parameterization
Model A
Model B
Scenario
C-input
SOC
D SOC
CC-effect
SOC
D SOC
CC-effect
CON
No CC
106
27.5
−8.2
–
25.4
−9.0
–
Max CC
106
26.1
−9.6
−1.4
22.4
−12.0
−3.0
CO2 fert
121
27.9
−7.8
+0.4
23.9
−10.5
−1.5
RES
No CC
340
50.1
8.6
–
46.3
6.3
–
Max CC
340
46.7
5.2
−3.4
39.3
−0.6
−7.0
CO2 fert
387
52.5
10.9
+2.3
44.1
4.1
−2.2
Given SOC stocks are modeled for the year 2100, whereas D SOC is the difference of the measured
stocks in 2004 and the modeled stocks in 2100. Stocks of SOC at the start of the experiment in
1983 were 40.5 Mg ha−1. All figures in Mg ha−1
CON crop residue removed, RES crop residue incorporated, No CC scenario with average temperature and precipitation, no climate change, Max CC maximal climate change, scenario described in
Table 18.3, CO2 fert Max CC but with CO2 fertilization of plants, Model A Q10 = 2 for all pools,
Model B Q10 = 2 for DPM and BIO, Q10 = 3 for RPM and Q10 = 4 for HUM
monthly mean values of the Puch site during the experimental period 1984–2004.
However, during that period an increase of MAP by 0.07°C year−1 was reported
by Hege and Offenberger (2006). Therefore, choosing that period as baseline is
somewhat arbitrary because temperature already increased. However, this approach
coincides with that used in the IPCC for regional climate scenarios (Christensen
et al. 2007).
The data predicted by model A (Q10 = 2 for all pools) showed only small effects
of climate change scenarios on SOC stocks in the CON treatments. Loss of SOC in
case of the Max-CC scenario was predicted to be 1.4 Mg C ha−1. That difference was
cancelled out under the assumption of CO2-fertilization (Fig. 18.3, Table 18.4). The
predicted differences were in a range hardly detectable under field conditions, given
the effects of soil heterogeneity (Ellert et al. 2008; Heinze et al. 2010; Heitkamp
et al. 2011). Predicted outcomes were different for the RES treatment. Modeled
SOC loss induced by climate change (Max-CC) until the year 2100 was 3.4 Mg C ha−1.
Remarkably, there is a tipping point around the year 2075 (at 47.3 Mg C ha−1) after
which SOC stocks in the RES treatment are predicted to decrease. Inclusion of CO2
fertilization in the scenario even increased the C-sink in the soil by 2.3 Mg C ha−1,
as compared to No-CC.
A different sensitivity of pools to temperature was incorporated in model B,
changing the outcome of predictions markedly. Effect of warming (Max-CC) was
predicted to be strong on SOC stocks of the RES treatment (Table 18.4). The tipping
point from sink to source was predicted for the year 2039, turning the soil of the
RES treatment for that scenario over the almost 100 years into a source of CO2.
Assuming CO2 fertilization occurs led to almost identical predictions in SOC stocks
of No-CC and CO2-Fert scenarios until 2075. Therefore, the predicted source-sink
tipping point was procrastinated by 36 years, as compared to the Max-CC scenario.
420
F. Heitkamp et al.
It has to be pointed out that model B predicted in general higher mineralization as
compared to model A.
For the chosen scenarios changes of +2.3 to −7.0 Mg C ha−1, were predicted to be
induced by climate change from 2005 until 2100 for the Puch site. Therefore, the
feedback between climate change and SOC balance is likely positive, as is also
assumed in several global feedback simulations (Friedlingstein et al. 2006).
However, when comparing the predicted (2005–2100) effects of climate change
(+2.3 to −7.0 Mg C ha−1) and residue incorporation or export (+12.6 and
−12.8 Mg C ha−1, respectively) it becomes obvious that appropriate management of
cropland soils is of outstanding importance for reducing CO2 emissions from these
agroecosystems. Nevertheless, SOC accumulation by recommended management
practices may be severely reduced by warming. This effect will be stronger if
sensitivity of mineralization differs between pools of different stability.
18.5
Conclusions and Outlook
Soils are of major importance for C storage and vice versa. CO2 can accumulate in soil
as OM which is beneficial in terms of reduction of atmospheric CO2 concentrations
and improving soil fertility. Despite their outstanding importance for the C cycle, soils
are still treated as a “black box” in most models. To predict feedbacks between soil,
biosphere and atmosphere, progress is needed to shed light into this black box, i.e., to
apply strategies for reducing SOC loss or re-accumulating SOC, quantitative
predictions on the outcome of diverse strategies must be possible. There is no doubt
that soils have the potential to reduce atmospheric CO2 concentrations. However,
many uncertainties in our understanding of C-cycling in soils hamper quantitative
predictions of the sink potential and feedbacks between climate change and SOC
dynamics. It must be certain that soil management does not turn soils into a net-source
of CO2. Therefore, research on this topic must continue, and increase, but it is clear
that the mitigation potential of soil is not well enough understood to rely on it: reducing anthropogenic GHG emissions is essential to mitigate climate change.
The mechanistic understanding of processes and mechanisms on C-cycling in
soils has vastly improved, but many uncertainties still remain. The analysis presented show that the temperature sensitivity of different SOC pools significantly
affects the outcome of model predictions of SOC dynamics. Still, there is no consensus on a general applicable sensitivity of mineralization of SOC. In fact, today
there is a lack of methodological tools to determine temperature effects, and there
exists a strong need for new and well designed experiments (Conant et al. 2011).
With no reliable quantitative model to assess the temperature response of SOC
mineralization, it is difficult to predict the outcome of complex processes such as
drying-and-rewetting or freezing-and-thawing. Such events are likely to increase in
frequency in the future, and it is essential to improve the mechanistic knowledge of
these processes. Even increased productivity of plants by CO2 fertilization can
potentially destabilize SOC by priming effects (Fontaine et al. 2007). Knowledge is
18
Processes of Soil Carbon Dynamics and Ecosystem Carbon Cycling…
421
generated in many different disciplines which are often only weakly linked in terms
of exchanging results. This limits the progress at a time where fast action is required.
However, the speed of progress may be advanced by the following considerations.
18.5.1
Connect Research Communities
Knowledge on C cycling was and is generated by many different disciplines.
Atmospheric science, soil science, plant ecology, forestry, geography and many
more disciplines are working on the topic. When reading the different subchapters,
it becomes obvious that there is different terminology, even in related topics: studies
in “litter decomposition” and “SOC stabilization” evolved largely independent from
each other. Disconnect between “general ecology” and “soil ecology” was demonstrated recently by tracking citation between specialized journals (Barot et al. 2007).
Knowledge transfer between the disciplines is slow, but is beginning to emerge
(Prescott 2010; Schmidt et al. 2011). By exchanging results, concepts and ideas,
scientific knowledge should increase faster and more effectively.
18.5.2
Connect Empirical Result with Models
Advances in mechanistic understanding of SOC stabilization are only poorly incorporated into quantitative models. To date, only one model exists which can simulate
C-dynamics by inclusion of aggregate turnover (Yoo et al. 2011). Soil scientists use
models only sparingly (Barot et al. 2007). Yet, the concepts must be quantified and
validated. By using models as a tool to quantitatively synthesize various processes
and mechanism, it is possible to better predict how changing conditions may influence
the C cycle in general (Schmidt et al. 2011).
18.5.3
Connect Specificity and Generality
Under “specificity” empirical case studies are understood, whereas “generality”
refers to the broad application of result and synthesis in theories. Both “specificity”
and “generality” are essential to the scientific progress, but more merit should be
given for synthesizing existing data. Entering e.g. “litter decomposition” in web of
knowledge (7th November 2011) yielded 12,600 results, the first listed study being
from 1930 (Melin 1930). Entering all these data into a global database and making
it available for the scientific community will likely increase the precision of models
describing litter decomposition across biomes. It is not said that that no experimental
research will be needed after a global synthesis. Rather by developing a more general
and quantitative model upon the vast data already existing it will be possible to
identify gaps in knowledge and concentrate research efforts.
422
18.5.4
F. Heitkamp et al.
Using Long-Term Cross-Site Experiments
One reason why specificity seems to dominate research in soil science is the
heterogeneous nature of soils. A lot of knowledge is generated under controlled
laboratory conditions. However, it is also important to identify or design field studies,
where the same treatment is implemented across several sites. Selecting or laying
out such cross-site studies for long-term research will add to the knowledge generated in laboratory or single-site field studies, increase scientific output in many
disciplines, and create excellent opportunities to test hypothesis and theories
(Leuschner et al. 2009).
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