Wiesmeier et al. (2019). Soil organic carbon storage as a key function of soils - A review of drivers and indicators at various scales

https://doi.org/10.1016/j.geoderma.2018.07.026

Blum, 1993

). Although there is

no agreed upon de

ﬁ

nition, the main ecological functions of soils, be-

yond technical and cultural aspects, are: biomass production, storage
and

ﬁ

ltration of water, storage and recycling of nutrients, habitat for

biological activity and carbon storage. The latter can be regarded as a
key function of soils, as it is not only decisive for climate regulation, but
strongly a

ﬀ

ects all other functions as well. In recent years, the rising

integration of soil functions in environmental policy making has

Received 20 November 2017; Received in revised form 10 July 2018; Accepted 20 July 2018

⁎

Corresponding author at: Chair of Soil Sciences, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany.

E-mail address:

wiesmeier@wzw.tum.de

(M. Wiesmeier).

Geoderma 333 (2019) 149–162

created a need for more detailed and quantitative information on spe-
ci

ﬁ

c soil functions (

Baveye et al., 2016

;

Lehmann and Stahr, 2010

;

Schulte et al., 2015

). This is a prerequisite to assess the impact of land

management changes on soil functionality, as it is intended by the

“

BonaRes

”

funding initiative in Germany (

www.bonares.de

). However,

a direct quanti

ﬁ

cation of soil functions requires various cost- and labour

intensive analyses and, hence, is typically not feasible, especially when
large spatial scales are targeted. An alternative approach could be to
identify measureable biotic or abiotic properties that contain su

ﬃ

cient

information to be used as indicators for the quanti

ﬁ

cation of soil

functions (

;

). Because of the soil's multifunctionality and com-

plexity, an indicator system is needed that integrates the relevant soil
processes and their interactions. Such indicators can be derived from

“

functional characteristics

”

as recently proposed by

Vogel et al. (2018)

,

which have to be measurable in a time- and cost-e

ﬃ

cient way and

should be reproducible on a relatively low level of expertise.

The main objective of this review is to identify a set of indicators

that enables a quanti

ﬁ

cation of soil organic carbon (SOC) storage at

di

ﬀ

erent spatial scales. This set of indicators should allow for not only a

prediction of current SOC storage from local to regional scales but also
for a spatial estimation of the SOC storage potential, which is an im-
portant aspect for land management decisions and for reponses to en-
vironmental disturbances. A quanti

ﬁ

cation of current and potential

SOC storage via a set of suitable indicators could be a promising al-
ternative to precise and accurate determination of SOC storage via
(expensive and time-consuming)

ﬁ

eld work and laboratory analyses.

Moreover, for many purposes, an assessment of SOC storage and its role
in ecosystem functioning requires less precision. For example, an in-
dicator-based estimation of SOC is probably su

ﬃ

cient to help farmers

decide which agricultural measures are appropriate to optimize the
SOC level of a speci

ﬁ

c

ﬁ

eld. Similarly, landscape planners, adminis-

trative bodies or ecologists may wish to identify hotspots which are
particularly valuable for conservation, without the need for precise
measurements of SOC storage. To this end, the use of indicators of SOC
storage is appropriate. Such indicators integrate one or several en-
vironmental and soil properties which are known to be related with
SOC storage, allowing comparative assessment of soils at di

ﬀ

erent lo-

cations with regard to expected SOC storage. The so-called

“

SCORPAN

approach

”

of

McBratney et al. (2003)

, building up on the factorial

equation of

Jenny (1941)

, aims to describe any soil property as a

function of climate, organisms, topography, parent material, time,
space and soil information itself. This approach provides a framework
for an identi

ﬁ

cation of factors controlling SOC storage that could serve

as indicators. However, this is impeded by the fact that these factors are
scale-dependent, indicating a

“

hierarchy of controls

”

Manning et al.,

2015

). Important factors at the micro-scale (from particles to pedons),

such as metal oxides or speci

ﬁ

c surface area (SSA), are less relevant at

larger spatial scales, where SOC storage can be related to climate, to-
pography, parent material, vegetation, land use and management
(

Manning et al., 2015

Meier and Leuschner, 2010

Moni et al., 2010

). In this review, we present an

overview of the importance of these factors for SOC storage with re-
spect to scale. We further dicuss their suitability as indicators for SOC
storage and provide an outlook on an indicator system to estimate ac-
tual and potential SOC storage.

2. Drivers and indicators of soil organic carbon storage

2.1. Climate

Climatic conditions, namely temperature and precipitation, are key

drivers of SOC storage globally as well as at broad (sub-)regional scales,
a

ﬀ

ecting both C input into the soil and SOC decomposition.

Precipitation determines net primary productivity (NPP) in many
(water-limited) terrestrial environments and thus the input of C into the
soil. Furthermore, humid conditions favour the formation of SOC-

stabilizing mineral surfaces by intensi

ﬁ

ed weathering of the parent

material (

Chaplot et al., 2010

;

Doetterl et al., 2015

) and often cause soil

acidi

ﬁ

cation leading to reduced decomposition of soil organic matter

(SOM) (

). Temperature largely a

ﬀ

ects the

microbial decomposition of SOM as its complex molecular attributes
have a high intrinsic temperature sensitivity (

Conant et al., 2011

;

Davidson and Janssens, 2006

von Lützow and Kögel-Knabner, 2009

Although this relationship is governed by multiple constraints, nu-
merous studies have indicated a decrease of SOC with increasing tem-
peratures (e.g.,

;

;

;).

As a result of these combined in

ﬂ

uences, SOC stocks are generally

highest under cool humid conditions and decrease under warmer and
drier climates both at the global scale (

;

Post

et al., 1982

) and at the (sub-)regional scale (

Alvarez and Lavado, 1998

;

de Brogniez et al., 2014

;

;

;

;

Viscarra-Rossel et al.,

2014

). However, numerous studies have indicated that the relative

importance of climate for SOC storage diminishes with increasing soil
depth, where factors controlling the stabilization of SOM (see

Section

2.5

) become more important (

Badgery et al., 2013

;

Gray et al., 2016

;

;

). At the regional scale,

climate e

ﬀ

ects may be masked in topsoils by land use/management,

particularly in cropland soils, where intensive management (fertiliza-
tion, irrigation etc.) can counterbalance climate e

ﬀ

ects (

;

Wiesmeier et al., 2013

). Moreover, climate

variability at local scales is frequently small, so that climatic control of
SOC storage is less relevant in contrast to other factors. In a regional
study conducted in the conterminous USA by

Homann et al. (2007)

,

mean annual precipitation, evapotranspiration and clay content were
positively correlated to SOC in the top 20 cm. The observed di

ﬀ

erences

among regions were attributed to more localized di

ﬀ

erences in vege-

tation, clay mineralogy and other processes.

As climate constitutes a major controlling factor for SOC storage at

regional to global scales, mean annual air temperature (MAT) has
proven to be a suitable indicator that could serve as indicator for SOC
storage due to its global availability in free climate databases. Closely
related, vapor pressure de

ﬁ

cit has also been shown to be closely asso-

ciated with SOC storage (

Allen et al., 2013

). Although mean annual

precipitation (MAP) has been shown to have a stronger association with
SOC and its depth distribution in some studies at regional (

Hobley et al.,

2015

) and global scales (

Marin-Spiotta and Sharma, 2013

), other studies

have reported stronger relationships between SOC with temperature
than with precipitation (

Allen et al., 2013

;

Wang et al., 2004

). In a

review of tropical plantation and successional forests, MAT was a better
predictor of SOC than precipitation in a regression tree model, with
cooler sites containing greater SOC (

As such, these relationships between climate drivers and SOC storage
are not always easy to decipher.

To reconcile these con

ﬂ

icting reports, it has been suggested that the

relative importance of either temperature or precipitation on SOC sto-
rage depends on the main limiting factors of SOC production and
turnover in the study region (

Lützow and Kögel-Knabner, 2009

). In arid or semi-arid

environments, water availability limits NPP, so that the system, and
therefore SOC storage, is input limited (

Hobley et al., 2016

). In con-

trast, in regions with su

ﬃ

cient water-availability but colder tempera-

tures, microbial activity is limited to a greater degree than NPP (

von

), so that the system and SOC storage

is output limited. These patterns result in an accumulation of SOC in
colder, moister areas, moderate SOC storage in warmer, moist regions
and lower SOC storage in drier, hotter regions.

In this context, elevation above sea level may be a better indicator

than climatic variables alone, as it integrates the e

ﬀ

ects of temperature

and precipitation on NPP and decomposition and re

ﬂ

ects erosional and

depositional processes which in

ﬂ

uence spatial distribution of soil types

M. Wiesmeier et al.

Geoderma 333 (2019) 149–162

150

as well as depth of soil. In regional studies, SOC stocks have been found
to be highly correlated with elevation (

de Brogniez et al., 2014

;

). Derived from digital elevation models

with high accuracy and ease, elevation is therefore probably the most
promising indicator for SOC storage at the regional scale. However, this
may not hold true for continental climates in relatively

ﬂ

at areas, where

elevation is not a good proxy for temperature and precipitation. A
further exception to expected trends of SOC decreases with increasing
elevation may occur when precipitation and temperature trends are
decoupled across an elevation gradient, as may ocur in some tropical
mountains, and topographic e

ﬀ

ects on soil moisture become more im-

portant (

McGroddy and Silver, 2000

).

2.2. Topography

Topographical features can play a crucial role for SOC storage as

they control precipitation, water

ﬂ

ow paths, water accumulation, and

discharge and therefore contribute signi

ﬁ

cantly to erosional processes.

Sites with a high contributing area, low inclination and concave cur-
vatures favour water accumulation, whereas steep slopes and convex
curvatures lead to higher water discharge. Soil moisture controls not
only NPP and therefore SOC input, but also microbial activity and SOC
output. Both low and high soil moisture contents reduce microbial ac-
tivity due to reduced substrate mobility/oxygen availability, favouring
the accumulation of SOC. Thus, soil moisture was identi

ﬁ

ed as a deci-

sive factor for the spatial distribution of SOC in several regional studies
(

Meersmans et al., 2008

;

;

). Moreover, water

ﬂ

ow paths are key de-

terminants triggering soil erosion and redistribution, which in turn may
have profound e

ﬀ

ects on SOC storage in dynamic landscapes (

Doetterl

et al., 2016

;

Seibert et al., 2007

). Topographical features further in-

ﬂ

uence the local microclimate which in turn may a

ﬀ

ect SOC storage at

local scales. Di

ﬀ

erences in SOC depth pro

ﬁ

les along a

catena

in tropical

mountains of Puerto Rico were attributed to poor drainage and redu-
cing conditions (

Johnson et al., 2015

Schulp and Veldkamp, 2008

Silver et al., 1999

). Similarly,

variability in SOC storage along an elevational gradient in precipitation
in Hawaii was better explained by the e

ﬀ

ect of redox and oxygen

availability rather than climate, NPP or soil mineralogy (

Schuur et al.,

2001

).

Despite these observations, there is no general agreement on the

importance of topography-related factors for SOC storage, which seems
to be partly explained by the various scales of observation. Analyses of
di

ﬀ

erent SOC data sets from global to subcontinental and regional

scales revealed that topographical features, represented by e.g. topo-
graphic position, slope gradient and aspect, were of minor importance
for SOC variability (

Garten and Ashwood, 2002

;

;

;

;

;

Tan et al.,

2004

;

Viscarra-Rossel et al., 2014

;

Xiong et al., 2014

). Local terrain

attributes such as slope, aspect and curvature appear to in

ﬂ

uence SOC

storage only at small spatial scales (< 100 m) and landscape position
may become important at larger scales (> 100 m) (

Powers and Veldkamp, 2005

;

Minasny et al., 2013

). In fact, regional terrain attributes were the most

important controlling factors for SOC variation within a relatively small
study area in Panama, followed by local factors such as slope and
curvature (

Grimm et al., 2008

). Furthermore, in a study on the SOC

distribution in Laos, a signi

ﬁ

cant proportion of SOC variability was

explained by slope gradient (

Chaplot et al., 2010

). In a study on vol-

canic soils in Costa Rica, slope explained 27% of the variation in the
magnitude and direction of di

ﬀ

erences in SOC between paired pastures

and forests, even though at the regional scale, land use did not a

ﬀ

ect

average SOC stocks (

). The authors at-

tributed these elevational di

ﬀ

erences to di

ﬀ

erences in soil weathering,

with the fraction of non-crystalline clay content being a signi

ﬁ

cant

predictor of SOC. It therefore appears that topography is relevant at
local (hillslope) scales where the soil properties do not vary a lot, but
less relevant across larger landscapes, where SOC storage is averaged

over a large variety of soil properties so that other factors become
dominant.

In terms of topographical features which are indicative of the soil

moisture-induced accumulation of SOC, the topographic wetness index
(TWI) is a promising factor, which can easily be calculated from digital
elevation models. Large TWI values are usually found at lower areas of
a landscape with large contributing areas and indicate increased like-
lihood of saturated conditions and/or accumulation of eroded material,
resulting in high SOC stocks (

Sorensen et al., 2006

). In a regional SOC

inventory in Germany, TWI was identi

ﬁ

ed as an important factor for the

spatial variability of SOC in agricultural soils due to its indicative power
for SOC accumulation in groundwater-a

ﬀ

ected soils (

Wiesmeier et al.,

2013

). However, at a larger sub-continental scale, TWI was not im-

portant as a predictor of SOC stocks and content (

This suggests that TWI, similar to general topographic parameters, is
relevant at smaller rather than larger scales.

2.3. Parent material

The role of parent material as a predictor of SOC storage has been

examined in several studies from sub-regional to global scale. The po-
tential in

ﬂ

uence of parent material is mainly attributed to its control on

soil mineralogy, texture and fertility, which a

ﬀ

ect NPP and the stabi-

lization of SOM (see

Sections 2.5.2 and 2.5.6

) (

). Moreover, parent material is stongly linked to soil type,

which is associated with SOC storage (see

Section 2.5.1

). However,

there is no consistency on the importance of parent material to SOC
storage. A global analysis of the relationships between environmental
factors and SOC based on 4382 soil pro

ﬁ

les revealed only a weak cor-

relation between parent material classes and SOC (

Gray et al., 2009

However, the authors concluded that assignment of parent material into
classes assuming speci

ﬁ

c silica contents may be a major simpli

ﬁ

cation.

In SOC inventories at the regional and catchment scale under di

ﬀ

erent

climatic conditions, parent material was also of minor importance for
the spatial distribution of SOC (

Grimm et al., 2008

;

;

Wiesmeier et al., 2014a

). However, in a study in Eastern Australia,

classes of parent material became important for SOC storage in the
subsoil (

). Here again, it appears that the scale of

investigation in

ﬂ

uences the relative importance of di

ﬀ

erent drivers

controlling SOC storage, as well as the depth of investigation.

This is con

ﬁ

rmed by results from other studies, which found in-

dications for a clear e

ﬀ

ect of parent material on SOC stocks (

). In a landscape study in France, dif-

ferences in SOC between forests and crops were smaller in loess-derived
soils than on other parent materials (

Barre et al., 2017

). In a semi-arid

region in Turkey, parent material also mediated the e

ﬀ

ect of land use

on SOC stocks within a soil order (

). SOC stocks were

greatest under grazing on alluvial and lacustrine soil sites, whereas
cropland agriculture had greater SOC stocks on limestone terraces. The
importance of parent material on SOC may be due to di

ﬀ

erences in

silica (Si) contents (

Badgery et al., 2013

) based on geologic source and

weathering stage. In two large subcontinental studies in Australia and
Chile, Si was a key predictor for SOC storage, with increasing SOC
stocks with increasing ma

ﬁ

c character of the parent material and con-

siderably lower stocks in soils developed from siliceous material
(

Doetterl et al., 2015

Gray et al., 2016

). This may be due to a coarser

soil texture and lower surface reactivity, associated with a lower ferti-
lity and C storage capacity.

Doetterl et al. (2015)

argued that climate

may be of secondary importance for SOC storage from a long-term
perspective, and that geochemical predictors, speci

ﬁ

cally Si con-

centrations, are more important. However, at larger superregional to
global scales, studies clearly indicate the importance of climate for SOC
storage in soils of widely varying parent material (

;

), suggesting that the control of climate on

production of SOC and its turnover cannot be neglected.

M. Wiesmeier et al.

Geoderma 333 (2019) 149–162

151

2.4. Organisms

2.4.1. Natural vegetation

At the (sub-)regional scale with more uniform climatic conditions,

the level of SOC stocks is strongly a

ﬀ

ected by vegetation and land use

(see

Section 2.4.2

). Among di

ﬀ

erent climatic ranges of the world, sig-

ni

ﬁ

cantly di

ﬀ

erent SOC stocks and depth distributions were found be-

tween plant functional types (shrubland, grassland, forest) due to dif-
ferent C allocation patterns (

). Studies at the

continental and regional scale in various climatic zones con

ﬁ

rmed that

vegetation type a

ﬀ

ects SOC storage by controlling both the input and

decomposition of C. In a North American study that included a latitu-
dinal gradient from tropical forests to arctic tundra, an estimate of the C
input into the mineral pool was the best predictor of SOC stocks in
topsoils (

Frank et al., 2012

). The analysis of factors controlling SOC

stocks in Eastern Australia con

ﬁ

rmed that vegetation (captured by ve-

getation cover) mainly a

ﬀ

ects SOC in the topsoil and that its in

ﬂ

uence

declines with depth (

Gray et al., 2016

). Plant diversity and functional

composition may also a

ﬀ

ect SOC storage at di

ﬀ

erent spatial scales,

particularly in natural grassland ecosystems under semi-arid conditions,
but also in temperate environments (

). At the (sub-

)regional scale, SOC storage among di

ﬀ

erent grassland types can di

ﬀ

er

signi

ﬁ

cantly, e.g., at the Qinghai-Tibetan Plateau (

Liu et al., 2016

).

However, di

ﬀ

erent grassland types are probably coupled with di

ﬀ

erent

climatic conditions (particularly precipitation), which largely a

ﬀ

ect

NPP. Vegetation-type e

ﬀ

ects on SOC often have been attributed to

di

ﬀ

erences in plant litter chemistry, yet the molecular recalcitrance of

plant inputs is strongly modulated by environmental factors, and there
is little evidence that such intrinsic properties are important controls on
SOC over timescales of decades to millennia (

).

At the local scale, plant diversity has been found to be positively cor-
related with SOC accumulation, mainly due to higher root biomass
production induced by speci

ﬁ

c plant functional traits (e.g., positive

interactions between N-

ﬁ

xing legumes and grasses; enhanced N mi-

neralization in species-rich grasslands without legumes) (

Cong et al.,

2014

;

Fornara and Tilman, 2008

;

Lange et al., 2014

;

Rees et al., 2005

;

Steinbeiss et al., 2008

). For forests, there is no consensus on the e

ﬀ

ect of

tree species on SOC storage. Under temperate climatic conditions, SOC
stocks under coniferous species may be relatively high, particularly due
to an accumulation of acidic litter in the organic layer (

;

;

), yet other studies have

failed to identify signi

ﬁ

cant di

ﬀ

erences between SOC stocks from whole

soil pro

ﬁ

les among forest types (

Vesterdal et al., 2008

Viscarra-Rossel et al., 2014

2.4.2. Land use and management

Land use change is assumed to be the most dynamic factor of SOC

changes (

;

;

). In temperate regions, the storage of

SOC increases in the order cropland < forest < grassland (

Martin

et al., 2011

Schulp and Veldkamp, 2008

Meersmans et al., 2008

). However, depending on site and

climatic conditions, forest SOC stocks can be higher than grassland
stocks (

Leifeld et al., 2005

;

Lettens et al., 2005

). A large number of

studies and meta-analyses found strong evidence for a large SOC de-
cline of 30 to 80% when forests or grasslands were converted to crop-
land (

;

).

The loss of SOC in cultivated soils has been attributed to erosion, lower
C inputs in croplands, a reduced stabilization of SOM due to deterio-
rated aggregation, and a subsequent mineralization promoted by in-
creased soil temperature and aeration (

Balesdent et al., 2000

). Contrasting with these losses,

land use extensi

ﬁ

cation, particularly the conversion of cropland to

grasslands or forests, often leads SOC increases (

Post and Kwon, 2000

;

). Overall, land use information constitutes

an important additional indicator for SOC storage from local to sub-
continental scales.

SOC storage is also in

ﬂ

uenced by land management, though e

ﬀ

ects

can be di

ﬃ

cult to detect, especially deep in the soil pro

ﬁ

le. Agricultural

management systems with crop rotations generally reveal higher
amounts of SOC than monoculture systems (

Jarecki and Lal, 2003

Increasing crop rotational diversity has been shown to play a major role
in increasing SOC storage, driven by enhanced root C input, soil mi-
crobial diversity, and soil aggregate stability (

Tiemann et al., 2015

Moreover, crop systems that are dominated by plants with distinctive
and deep root systems promote SOC storage, as retention of root de-
rived C in soil has been found to be 2.3 times higher than for above-
ground residues (

Kätterer et al., 2011

). In particular, agricultural sys-

tems with perennial crops/forages and cover crops reveal distinctly
higher SOC stocks (

Jarecki and Lal, 2003

;

Paustian et al., 1997

Poeplau

and Don, 2015

). Crop rotational diversity, the use of organic amend-

ments or perennial cropping systems may be indicative for SOC accu-
mulation compared to conventional cropping systems. The indicative
value of reduced tillage systems may be low, as there is evidence that
reduced or no-tillage does not a

ﬀ

ect SOC storage but only leads to re-

distribution of SOC within the soil pro

ﬁ

le (

Luo et al., 2010

;

Powlson

et al., 2014

).

In contrast to cropland management, less is known in terms of

grassland management e

ﬀ

ects on SOC storage due to limited data

availability and large spatial variability of SOC in grassland soils
(

). In global meta-analyses on grazing e

ﬀ

ects on

SOC storage in grassland soils, no uniform trend was found (

McSherry

and Ritchie, 2013

Milchunas and Lauenroth, 1993

McSherry and Ritchie, 2013

Pineiro et al., 2010

;

Smith et al., 2008

). High grazing intensities caused a decrease of SOC in

C3-dominated grasslands, but an increase in C4 and C4-C3 mixed
grasslands (

). In temperate grasslands, there

are indications that the intensity of the grassland management, parti-
cularly in terms of number of cuttings and N fertilization, strongly af-
fects SOC storage (

Conant et al., 2001

). Speci

ﬁ

-

cally, an intermediate level of management intensity with regard to N
fertilizer application and cutting times/stocking rates seems to be as-
sociated with higher SOC stocks compared to lower amounts in both
extensively and intensively managed grasslands (

;

Ward et al., 2016

). Grassland management e

ﬀ

ects are not restricted to

the topsoil but also detected in the subsoil (

Ward et al., 2016

). In forest

soils, management aspects such as thinning, extension of the rotation
period, fertilization, liming,

ﬁ

re, storm and insect management, harvest

management and input of harvest residues as well as stand age can
a

ﬀ

ect SOC storage (

;

;

;

;

). Due to the complexity of

management practices on cropland, grassland and forest soils the suit-
ability of management information as a indicator for SOC storage seems
to be limited. A classi

ﬁ

cation of management systems or the intensity

level of management could be a promising strategy to re

ﬁ

ne an in-

dicator system for SOC storage.

2.4.3. Soil biota
2.4.3.1. Microorganisms

. Heterotrophic soil microorganisms (Bacteria,

Eucarya (fungi) and Archaea) mediate turnover of SOM to cover their
nutrient and energy needs for maintenance and growth. Their central
role in the mineralization-immobilization-turnover of SOM was linked
to the

‘

eye of the needle

’

or the

‘

microbial carbon pump

’

through which

carbon and nutrient transformations are mediated (

Jenkinson, 1977

;

Liang et al., 2017

). The fact that microbial residues and exudates may

contribute up to 80% of C in stable soil OM fractions (

Gleixner et al.,

2002

Kiem and Kögel-Knabner, 2002

;

Liang and Balser, 2011

;

) indicates, that the soil microbial

biomass (SMB) plays a dual role in SOM turnover, balancing SOM
mineralization and SOM stabilization processes.

Because most soil systems are resource (C, N, etc.) limited (exept

peat/organic soils), the pool size of SMB is therefore usually positively
related to OM input (NPP) and the SOM content in arable, grassland
and forest ecosystems (

Anderson and Domsch, 1980

). Strongest relationships between

SMB-C and SOC have been demonstrated in soils with < 2.5% SOC

M. Wiesmeier et al.

Geoderma 333 (2019) 149–162

152

(

). As a living and active soil compartment

with short turnover times of 1

–

5 years (

Jenkinson and Ladd, 1981

), the

SMB pool changes more rapidly than total amounts of SOC. Therefore
changes in the SMB-C pool and the ratio of SMB-C to SOC had been
suggested as useful and sensitive indicators for monitoring changes in
SOC e.g. due to changes in management (

Powlson et al., 1987

;

Powlson

and Jenkinson, 1981

), also because small gradual changes in SOC are

di

ﬃ

cult to detect in the short term due to generally high background C

levels. The ratio of SMB-C to SOC will increase for a time if the input of
OM increased and decreases for a time if the input is decreased. Con-
stancy of the SMB-C to SOC ratio is an indication that the soil ecosystem
is in equilibrium (

Anderson and Domsch, 1980

). The establishment of a

higher SMB-C to SOC ratio can further be discussed in a more e

ﬃ

cient

microbial C turnover.

A wide spectrum of SMB-C to SOC ratios ranging from 0.3 to over

7.0% was reportet in the literature (

von Lützow et al., 2007

). This wide

range can be attributed to di

ﬀ

erent soil management ecosystems

(arable, grassland, forest) (

Anderson and Domsch, 1980

;

)

and environmental factors that in

ﬂ

uence C-input, especially macro-

climate variables, soil texture and soil pH (

Insam and Domsch, 1988

;

). On local and global spatial scales the SMB-C to SOC

ratio is neutrally or negatively correlated with SOC and the soil C/N
ratio (

), indicating that microbial C limitation declines as

SOC content increases and that in many soil ecosystems soil N rather
than soil C may in

ﬂ

uence the immobilization of OM by the microbial

biomass. The lower proportion in temperate forest soils than in most
other ecosystems is probably a result of N limitation and low pH. How
constraints on microbial physiology may in

ﬂ

uence SOC storage is de-

monstrated by increased C losses due to mineralization despite greater
C inputs, when microbes are nutrient limited (

Allison et al., 2010

;

Craine et al., 2007

;

Fontaine et al., 2004

Kirkby et al., 2014

). This

‘

microbial nutrient mining

’

may produce predictions for ecosystem C

storage that are opposite to those of basic stoichiometric decomposition
theory. Therefore, to maintain a speci

ﬁ

c level of SOM and to predict

SOC storage changes, maintenance requirements of the SMB and an-
thropogenic changes in nutrient availability for microbes have to be
considered (

Craine et al., 2007

In conclusion, monitoring the SMB pool size can be used as an early

indicator for changes in SOC contents e.g. due to changes in soil
management. The SMB pool is easily measurable and a unique and non-
composite fraction that can be equated to a model pool (

;

). The measurement of SMB is

based on chloroform-fumigation methods that were developed by

Jenkinson (1976)

and

Vance et al. (1987)

. It is a cheep and precise

method to estimate the microbial biomass pool size (C, N, P,

…

).

Moreover, simpli

ﬁ

ed methods are now available e.g. the freeze-dried

soil extraction method as proposed by

Islam et al. (1997)

. Changes in

the ratio of SMB-C to SOC indicate if a soil ecosystem is loosing or
accumulating C. Correlations of SMB-C/SOC with soil chemical para-
meters (e.g. soil C/N content) may identify limiting factors of SOM
turnover.

2.4.3.2. Soil fauna

. Soil fauna and their interactions, both with the

biotic and the abiotic environment, are involved in a number of
processes that promote C stabilization or mineralization. SOM is
incorporated into the soil pro

ﬁ

le by bioturbation, where it becomes

available to the soil microbial community (

Lavelle et al., 1997

;

Scheu

and Parkinson, 1994

) or is protected against mineralization by mixing

with soil particles (

;

). Soil fauna regulate microbial populations (

Hedlund and

Augustsson, 1995

Scheu and Parkinson, 1994

;

Scheu et al., 2002

),

inhibit or stimulate microbial activity by grazing (

Hättenschwiler and

Bretscher, 2001

Mikola and Setala, 1998

Scheu and Parkinson, 1994

Scheu et al., 2002

) and alter

the microbial community composition by selective grazing or inoculum
dispersal (i.e. passive transport of microorganisms) (

Lavelle et al., 1997

;

;

Scheu et al., 2002

Al-Maliki and Scullion, 2013

Furthermore, soil fauna a

ﬀ

ects SOC storage by a

ﬀ

ecting soil struc-

ture. Soil (macro)porosity is changed by the burrowing activity of
earthworms and enchytraeids (

Falco and Coviella, 2016

;

van Vliet

et al., 1993

). Earthworm feeding activities have been suggested to in-

duce long-term stabilization of SOC as C is protected from microbial
attack by the formation of microaggregates within macroaggregates in
earthworm casts (

;

;

). While there is evidence that earthworms a

ﬀ

ect aggregate for-

mation and aggregate size distribution (

;

Coq et al., 2007

;

Falco and Coviella, 2016

;

Sanchez-de Leon et al., 2014

), a recent meta-analysis

comprising 21 studies did not

ﬁ

nd an overall e

ﬀ

ect of earthworms on

SOC stocks (

Lubbers et al., 2013

). However, these authors noted that

the time frame of many studies may have been too short to detect
earthworm stimulation of C sequestration. Studies suggest that micro-
bial activity and thus mineralization rates increase in the

ﬁ

rst days after

casting and dcreases over time, indicating that the physical protection
within earthworm casts may only be apparent in dried or aged casts
(

Kawaguchi et al., 2011

;

Marinissen and Dexter, 1990

;

Shipitalo and

Protz, 1988

). Aggregate stability within casts furthermore seems to

depend on the soil texture (

Angst et al., 2017

Shaw and Pawluk, 1986

),

the type of OM ingested (

Flegel et al., 1998

) and earthworm species or

functional group (

Flegel et al., 1998

Shaw and Pawluk, 1986

The role of soil fauna other than earthworms on the physical pro-

tection of organic material by aggregate formation generally received
less attention (

Maass et al., 2015

) although millipedes (

Fujimaki et al.,

2010

), springtails (

Siddiky et al., 2012a

Siddiky et al., 2012b

), mites

(

Barois et al., 1998

) and enchytraeid worms (

Barois et al., 1998

Dawod

and Fitzpatrick, 1993

Didden, 1990

Marinissen and Didden, 1997

)

were shown to facilitate soil aggregation through the production of
fecal pellets. In fact, fecal pellets are important components of soil and
may constitute a large amount of the soil in some ecosystems (

Barois

et al., 1998

Dawod and Fitzpatrick, 1993

;

Marinissen and Didden,

1997

Phillips and FitzPatrick, 1999

). Within these fecal pellets, C may

be accumulated (

Schrader et al., 1997

) and protected against water and

microbial decomposition, e.g. by a silicon-rich coating or poly-
saccharides (

Barois et al., 1998

;

Fujimaki et al., 2010

). The potential of

fecal pellets to protect C depends on additional factors such as soil
texture, which can lead to di

ﬀ

erent results: Enchytraeidae for instance

were shown to decrease mineralization in loamy soils, whereas in-
creased mineralization was found in sandy soils (

Koutika et al., 2001

Overall, the amount of comparative data for the impact of soil fauna

on SOC storage is insu

ﬃ

cient, especially as the faunal contribution to

SOC dynamics depends on a number of additional factors. As soil faunal
(functional) groups di

ﬀ

er in their feeding strategies and microhabitat,

they are involved in di

ﬀ

erent processes within soils and can thus lead to

the protection of C. This impact furthermore shows a temporal varia-
bility with stimulated OM mineralization in the short term and C sta-
bilization in the longer term (

Dignac et al., 2017

). Site-speci

ﬁ

c prop-

erties such as soil texture, OM quality and distribution (

;

), land use (

Pulleman et al., 2005

van Vliet et al., 1993

) or agricultural

management (

Fonte et al., 2007

Pulleman et al., 2005

) may largely

in

ﬂ

uence the magnitude and direction of soil faunal e

ﬀ

ects. Due to

these complex interactions, the suitability of soil fauna as an indicator
for SOC storage is limited. Furthermore, the identi

ﬁ

cation and quanti-

ﬁ

cation of soil fauna is labour-intensive and time-consuming, as re-

peated sampling is needed to account for the seasonality and climate-
sensitivity of soil fauna.

2.5. Soil properties

2.5.1. Soil type

There is broad consensus that soil type is strongly associated with

SOC storage at multiple scales and under di

ﬀ

erent climatic conditions.

During pedogenesis chemical weathering reactions lead to shifts in the

M. Wiesmeier et al.

Geoderma 333 (2019) 149–162

153

composition of soil mineralogy which strongly in

ﬂ

uence the bulk re-

active mineral surface area and C storage (

Percival et al., 2000

;

Torn

et al., 1997

), and the resultant characteristics are categorically de-

scribed by soil types. Generally, primary minerals weather to form
secondary minerals including Fe-oxides (e.g. ferrihydrite and goethite),
Al-oxyhydroxides (e.g. gibbsite), short range order minerals (e.g. allo-
phane and imogolite) and phyllosilicates (e.g. kaolinite). In contrast to
climate, vegetation and parent material, which may rather serve as
indicators for SOC storage at larger scales, SOC stocks can be strati

ﬁ

ed

according to soil type even at smaller (local to subregional) scales. In
most soil classi

ﬁ

cation systems, SOC is a major di

ﬀ

erentiation criterion

(

Micheli et al., 2014

). Numerous studies reported a predominant in-

ﬂ

uence of soil type on SOC stocks both in top- and subsoils (

;

;

;

;

). Interestingly, evidence was found that land

use e

ﬀ

ects on SOC storage, which are often assumed to be main drivers,

are strongly di

ﬀ

erentiated within soil type (

;

Wiesmeier et al., 2015b

).

However, soil type is not an independent controlling factor but in-

tegrates a number of climate, parent material and topography related
properties which directly a

ﬀ

ect the potential of soils to store C, parti-

cularly soil moisture regime and soil texture (see

Sections 2.2 and

2.5.2

). For example, Gleysols often store large quantities of SOC, but

this is not an intrinsic feature of the soil type Gleysol but the result of
the topographical situation of Gleysols associated with high soil
moisture contents (

Edwards and Bremner, 1967

Wiesmeier et al., 2013

). How-

ever, if precise information on soil properties such as texture, soil
moisture etc. is not available, soil type can be a suitable indicator for
SOC storage integrating a wide range of decisive factors.

2.5.2. Soil aggregation

Physical protection of SOM by aggregates is considered to be an

important mechanism for C stabilization. Besides abiotic formation of
aggregates, soil biota and the material that they produce play an im-
portant role in formation and stabilization of soil structure and are
strongly involved in the process of OM occlusion by aggregation (see

) (

). The

protective capacity of aggregates is mainly related to a spatial separa-
tion of substrate and microorganisms as well as to a reduced microbial
activity due to a reduced di

ﬀ

usion of oxygen into aggregates (

Mikutta

et al., 2006

;

Six et al., 2002b

). Macroaggregates (soil particles > 250

μ

m) provide a minimal physical protection (

Krull et al., 2003

;

Six et al.,

2002b, 2004

), while microaggregates play a decisive role for the long-

term SOM stabilization (

Totsche et al., 2017

). However, because mi-

croaggregates are formed within macroaggregates (

;

;

;

), macroaggregation

is the

ﬁ

rst step for long-term sequestration of fresh C input (

). The physical protection of C provided by intra-

aggregate occlusion can also be expressed from the perspective of the
pore network, which regulates the availability of oxygen, water, and
other resources for OM decomposition (

Ananyeva et al., 2013

;

Kravchenko et al., 2015

;

Strong et al., 2004

;

Toosi et al., 2017

). Small

pores may also provide physical protection for OM by reducing acces-
sibility to decomposers (

Kravchenko et al., 2015

;

Negassa et al., 2015

).

Soil cultivation in

ﬂ

uences aggregation and thus physically stabi-

lized C pools. Macroaggregates are sensitive to land management and
mechanical disruptive forces of cultivation because they are formed
from plant input products and much less stable than microaggregates
(

Beare et al., 1994

;

;

Oades, 1984

). Long-

term tillage with frequent plowing accelerates the turnover rate of
macroaggregates and limits the physical protection of labile SOM
compounds associated with soil aggregates (

;

Six et al., 1998, 1999

). Similarly, reduced plant

inputs due to harvesting of agricultural products reduces macro-
aggregate formation (

Oades, 1984

). Thus, macroaggregates may be

good predictors of potential SOC responses to tillage or land use change
because of their importance for storing more labile SOM (

Angers and

Giroux, 1996

;

Jastrow et al., 1996

). The microaggregate fraction and

microaggregates-within-macroaggregates were additionally proposed
as diagnostic fractions for the SOC sequestration potential in sustain-
able agroecosystems (

Kong et al., 2005

). Di

ﬀ

erent studies have shown

that the C in that fraction is sensitive to management-induced changes
in a wide range of soil types and under di

ﬀ

erent climates (

;

Six and Paustian, 2014

). These

ﬁ

ndings sug-

gested that microaggregates-within-macroaggregates may be indicative
for changes in total SOC in response to changes in tillage and other
management practices (

Denef et al., 2004

Six et al., 2000

). In a recent

work,

Segoli et al. (2013)

successfully incorporated the explicit re-

presentation of the turnover of soil aggregates into the

AggModel

to

decipher the in

ﬂ

uence on SOM dynamics. However, a major drawback

is that the characterization of the aggregate size distribution is very
sensitive to the soil sample pretreatment and to the methodology ap-
plied (e.g. type of sieving, its duration, etc.) (

;

2.5.3. Silt and clay content

Among the major SOM stabilization mechanisms, interaction of OM

with mineral surfaces is regarded as quantitatively most important in a
wide range of soils (

Sollins et al., 1996

Kaiser and Guggenberger, 2000

). This is

indicated by a strong correlation of SOC stocks with clay contents ob-
served in numerous studies at di

ﬀ

erent spatial scales (

Zinn et al.,

2007

). Therefore, soil texture is probably one of the most promising

factors to be used as indicator for SOC storage over a wider range of
scales. Soil texture data is available for many regions and there also
global data sets with speci

ﬁ

c information on clay-size minerals (

;

Furthermore, on the basis of the stabilizing capacity of the

ﬁ

ne

mineral fraction (medium/

ﬁ

ne silt and clay particles < 20/ < 50

μ

m),

simple regression models have been developed to estimate the (stable)
SOC storage potential (

Table 1

). As the

ﬁ

ne mineral fraction contains a

large proportion of total SOC in most soils, particularly in cropland
soils, it serves as a useful measure for potential SOC storage (

Beare

et al., 2014

Wiesmeier et al., 2014b

). In his pioneer work,

Hassink

(1997)

found a strong correlation between the content of

ﬁ

ne soil

particles < 20

μ

m and

ﬁ

ne fraction SOC in a wide range of un-

cultivated and grassland topsoils of temperate and tropical regions and
proposed that this correlation could be used to estimate the degree of
saturation of stable SOC. Although Hassink's equation proved to be
suitable to estimate the C sequestration potential of agricultural soils in
temperate regions (

;

;

Wiesmeier et al., 2014b

Zhao

et al., 2006

), more speci

ﬁ

c approaches have been developed for soils

with di

ﬀ

erent clay types, land uses and climatic regions.

Feller and

Beare (1997)

delineated regression models based on the

ﬁ

ne frac-

tion < 20

μ

m for cultivated and non-cultivated soils and soils with

di

ﬀ

erent mineralogy in tropical environments. Further signi

ﬁ

cant pro-

gress in this direction was achieved by

Six et al. (2002a)

who expanded

the analysis of

Hassink (1997)

and developed speci

ﬁ

c regression models

for cropland, grassland and forest soils with di

ﬀ

erent clay types (1:1 vs.

2:1) and size delimitations of the

ﬁ

ne fraction (< 20

μ

m vs. < 50

μ

m).

Their results reveal that there are signi

ﬁ

cant di

ﬀ

erences among land

uses, clay types and

ﬁ

ne fraction size classes. Generally higher inter-

cepts (which represent the baseline C storage in particles at or below
the size delimitation) of the regressions based on

ﬁ

ne fractions <

50

μ

m compared to < 20

μ

m were explained by either additional C

binding of organomineral associations into larger silt-sized aggregates
or the presence of silt-sized particulate organic matter. The latter may
also have caused the relatively high intercept in Hassink's equation, as
POM was not separated from the

ﬁ

ne fraction, e.g. by density fractio-

nation (

Wiesmeier et al., 2015a

). It should be noted, however, that

M. Wiesmeier et al.

Geoderma 333 (2019) 149–162

154

these intercepts are mathematically derived and can be negative, so
they should be interpreted with caution. In the study of

Six et al.

(2002a)

, further di

ﬀ

erences were found among clay types (lower C

saturation in soils dominated by 1:1 clays compared to 2:1 clays, see

Section 2.5.4

) and land uses (higher slopes in forest soils compared to

grassland and cropland). These results indicate that it is not just the
quantity of the

ﬁ

ne fraction, but also its quality which drives SOC re-

tention in soils.

Feng et al. (2013)

further developed the estimation of C stabiliza-

tion by the

ﬁ

ne fraction. They argued that predictions based on least-

squares linear regressions are limited as data sets may contain high
proportions of soils which are not saturated or over-saturated. They
proposed that either speci

ﬁ

c surface area (SSA) and OC loading (see

Section 2.5.4

) should be incorporated into the models or that boundary

line analysis should be used including thus only soils close to C sa-
turation, e.g. by selecting only the upper 10th percentile of the data.
The estimates of C stabilization in soils from 32 published studies based
on boundary line analysis were substantially higher compared to linear
regression approaches. Similar to the results of

found general di

ﬀ

erences among land uses and clay types.

In addition to the 10th percentile approach proposed by

Feng et al.

(2013)

, quantile regression analysis was used to estimate the C storage

capacity of grassland soils in New Zealand (

). In this

study, a model that included extractable Al, pH and SSA predicted SOC
stabilization capacity much better than models solely based on the

ﬁ

ne

fraction content. This could partly be related to the andic properties of
soils in New Zealand (

Matus et al., 2014

). A comparison of allophanic

and non-allophanic soils of New Zealand revealed that the best model
for the prediction of the SOC storage capacity included SSA and ex-
tractable Al, although di

ﬀ

erent coe

ﬃ

cients between these two soil units

were found (

). For soils in drier environments,

Hassink (1997)

found signi

ﬁ

cantly lower OC contents in the

ﬁ

ne frac-

tion of natural grassland soils, probably due to a lower C input caused
by a relatively low NPP in water-limited environments (

Chan, 2001

).

Liang et al. (2009)

and

Wiesmeier et al. (2015a)

used natural steppe

relicts in Northern China to derive regression models for

ﬁ

ne fraction

SOC saturation similar to existing approaches. Lower intercepts were
attributed to lower C inputs due to climate limitations, and potentially
due to di

ﬀ

erent clay mineralogy, indicating that where SOC storage is

limited by other factors, soil texture may not be as important for SOC
stabilization.

Overall, evidence for a global dependence of the SOC storage

capacity on the

ﬁ

ne fraction content was found, although this re-

lationship strongly depends on climatic conditions, land use, clay type
and size limits of the

ﬁ

ne fraction. Certainly, there are limitations,

particularly in soils where recalcitrance or other stabilizing agents such
as Al/Fe oxides play an important role. Furthermore, one has to bear in
mind that such approaches are not suitable to estimate the total SOC
storage capacity of soils, as C associated with particles > 20/50

μ

m, e.g.

in macro- and microaggregates are not accounted for (

Baldock and

Skjemstad, 2000

). Nevertheless, it was demonstrated that the

ﬁ

ne

fraction content is a useful and widely available indicator for the SOC
storage capacity in many soils.

2.5.4. Clay mineralogy and speci

ﬁ

c surface area

The widely reported association of SOC with soil mineralogy

(

Bosatta and Agren, 1997

Kaiser and Guggenberger, 2000

Balabane and Plante, 2004

Kiem and

Kögel-Knabner, 2002

) is generally attributed to the large speci

ﬁ

c sur-

face area (SSA) of

ﬁ

ne soil minerals such as phyllosilicates (known as

clay minerals), which promotes sorption of OC to the mineral matrix
(

Tipping, 1981

;

Wagai and Mayer, 2007

). Hence, there is generally a positive re-

lationship between SSA and SOC storage, and soils dominated by clay
minerals with high SSA are expected to adsorb more humic substances
than soils dominated by minerals with low SSA (

Tate and Theng, 1980

Clay mineralogy (clay type) a

ﬀ

ects the SSA available for OC sorption

(

;

;

). In particular, expandable phyllosilicates, e.g., clay

minerals such as smectites and vermiculites have a large impact on OC
stabilization in soils (

;

Ransom et al., 1998

) and

provide a high surface area up to 800 m

2

g

−

1

Carter et al., 1986

),

whereas SSA of non-expanding silicates (e.g., kaolinite, mica) ranges
from 10 to 70 m

2

g

−

1

Dixon, 1977

). Additionally, short range-order

aluminosilicate clays, e.g., allophane and imogolite-type minerals have
large surface areas (

;

;

), so that allophanic soils generally have a

high SSA and large portions of SOC are associated with

ﬁ

ne soil frac-

tions. Illustrating the importance of allophane surfaces for C retention
(

;

),

Percival et al. (2000)

found a

positive relationship between SOC stocks and allophane, but not be-
tween SOC and total clay content in New Zealand grasslands. However,
the covariance of these parameters (allophane and

ﬁ

ne texture) makes

it di

ﬃ

cult to di

ﬀ

erentiate their importance as a driver of SOC storage,

as high SSA of silt and clay fractions can be attributed to the presence of

Table 1

Overview on approaches to estimate the SOC storage capacity for soils under di

ﬀ

erent climates, land uses and clay types based on linear regressions with the

ﬁ

ne

mineral fraction (silt and clay particles < 20/ < 50

μ

m).

Study

Climate

Land use

Clay type

Soil depth

n

Fine fraction

Linear regression

r

Hassink, 1997

Temperate and tropical

Grassland

Diverse

0

–

10 cm

33

< 20

μ

m

4.09 + 0.37 ×

ﬀ

< 20

0.89

Feller and Beare, 1997

Tropical

Uncultivated

1:1

0

–

20 cm

25

< 20

μ

m

−

2.3 + 0.49 ×

ﬀ

< 20

0.91

Cultivated

25

3.2 + 0.29 ×

ﬀ

< 20

0.95

Six et al., 2002a

Diverse

Cropland

Diverse

–

146

< 20

μ

m

4.38 + 0.26 ×

ﬀ

< 20

0.41

Grassland

2.21 + 0.42 ×

ﬀ

< 20

0.44

Forest

−

2.51 + 0.63 ×

ﬀ

< 20

0.55

Cropland

Diverse

66

< 50

μ

m

7.18 + 0.20 ×

ﬀ

< 50

0.54

Grassland

16.33 + 0.32 ×

ﬀ

< 50

0.35

Forest

16.24 + 0.24 ×

ﬀ

< 50

0.35

Diverse

1:1

47

< 20

μ

m

1.22 + 0.30 ×

ﬀ

< 20

0.74

2:1

93

3.86 + 0.41 ×

ﬀ

< 20

0.39

Diverse

1:1

10

< 50

μ

m

5.50 + 0.26 ×

ﬀ

< 50

0.38

2:1

52

14.76 + 0.21 ×

ﬀ

< 50

0.07

Zhao et al., 2006

Temperate

Cropland

–

0

–

15 cm

12

< 20

μ

m

−

0.52 + 0.22 ×

ﬀ

< 20

0.88

Liang et al., 2009

Temperate

Grassland

2:1

0

–

30 cm

27

< 20

μ

m

0.36 ×

ﬀ

< 20

0.51

Feng et al., 2013

Diverse

1:1

–

101

< 20

μ

m

1.04 + 0.23 ×

ﬀ

< 20

0.72

2:1

241

< 20

μ

m

1.68 + 0.32 ×

ﬀ

< 20

0.52

Temperate

Grassland

Allophanic

0

–

15 cm, 15

–

30 cm

29

< 20

μ

m

1.35 ×

ﬀ

< 20

–

Non-allophanic

184

0.62 ×

ﬀ

< 20

–

Wiesmeier et al., 2015a

Semi-arid

Grassland

2:1

0

–

10 cm

14

< 20

μ

m

−

0.23 + 0.36 ×

ﬀ

< 20

0.98

M. Wiesmeier et al.

Geoderma 333 (2019) 149–162

155

smectite and mixed-layer illite

–

smectite minerals (

Wiseman and

Puttmann, 2005

Wiseman and Puttmann (2005)

There is no general agreement on the importance of clay mineral

surfaces for SOC storage. It has been demonstrated that SOC stocks in

kaolinitic soil pro

ﬁ

les in the tropics are similar to those in temperate,

less-weathered soils (

Sanchez and Logan, 1992

), despite faster decom-

position rates associated with higher temperatures. (

Wattel-Koekkoek

et al., 2001

) also showed that SOC contents of kaolinitic and smectitic

soils did not di

ﬀ

er to any signi

ﬁ

cant extent in soils from seven di

ﬀ

erent

countries.

indicated that the two soil

pro

ﬁ

les with the highest SSA and percentage of clay and smectite

concentration do not have signi

ﬁ

cantly higher OC concentrations re-

lative to the other soils. (

Mayer and Xing, 2001

), for instance, found

that SSA did not correlate well with SOC in acid soils in the USA, and

Hassink (1997)

proposed that the surface area of clay minerals is not a

good indicator for the potential of a soil to sorb SOC in tropical and
temperate regions across the globe. Although many authors have de-
scribed a positive correlation between SSA and SOC concentration,
predicting SOC from SSA remains di

ﬃ

cult. This may be because SOM is

distributed in heterogenous patches within the soil microstructure
(

), limiting the direct relationship

between SSA and SOC concentration. Although recently developed re-
gression models for estimating the SOC storage capacity included SSA
(

;

), most of these models were based on estimated SSA (

Feng et al.,

Fig. 1.

Overview on the scale-dependent hierarchy of drivers and indicators for SOC storage (MAT = mean annual temperature, MAP = mean annual precipitation,

TWI = topographic wetness index, SSA = speci

ﬁ

c surface area).

Table 2

Site-typical SOC contents for cropland soils of Bavaria as determined by soil
texture class and elevation as proxy for climate conditions (

Capriel, 2010

,

adapted).

Soil texture class

Elevation (m a.s.l.)

SOC (mg g

−

1

)

Sand

< 350

0.70

–

1.37

350

–

550

0.75

–

1.57

Silt

< 350

0.96

–

1.56

350

–

550

1.17

–

1.88

> 550

1.20

–

2.19

Clay

< 350

1.23

–

2.16

350

–

550

1.31

–

2.52

> 550

2.33

–

4.19

Loam

< 350

1.00

–

1.64

350

–

550

1.16

–

2.32

> 550

1.80

–

3.55

M. Wiesmeier et al.

Geoderma 333 (2019) 149–162

156

2013

) or calculated values from the water content of air-dried soil

(

Baldock and Skjemstad, 2000

). An accurate determination of

the SOC storage capacity based on SSA requires the accurate mea-
surement of SSA for soil samples from a wide-range of diverse soils
(

Feng et al., 2013

), limiting the suitability of SSA as a widely-adopted

indicator for SOC storage. The suitability of mineralogy and SSA as
indicators is further limited because the measurement of these para-
meters is labour-intensive and time-consuming. Although identi

ﬁ

cation

and quanti

ﬁ

cation of phyllosilicates, as well as quanti

ﬁ

cation of their

surface properties (CEC, SSA) has been recommended to address their
role in SOC turnover and stabilization (

Barre et al., 2014

), the wide-

spread adoption of clay mineralogy and associated parameters as an
indicator for SOM is unlikely due to laboratory constraints.

2.5.5. Oxides of aluminum (Al) and iron (Fe)

In addition to phyllosilicates, metallic sesquioxides (oxides, hydro-

xides, and oxyhydroxides of Al and Fe) signi

ﬁ

cantly contribute to SSA

and to OM stabilization in soils (

Kaiser and Guggenberger, 2003

Sollins

et al., 1996

). Additionally, Al- and Fe-oxides have a potentially greater

a

ﬃ

nity for SOM than other mineral surfaces such as clay minerals, and

thus may be more important as drivers of SOC storage than other soil
mineral components (

Jones and Edwards, 1998

;

Kaiser et al., 2002

;

Kaiser and Zech, 1998

). Fe-oxides and hydroxides have particularly

high SSA (

;

Yong et al., 1992

), resulting

in positive relationships between SOC and Fe-oxide (

Kaiser and

Guggenberger, 2003

;

Kleber et al., 2005

). As cations, Fe

3+

and Al

2+

can bind with organic ligands in acidic soils (e.g., carboxylic groups) to
form organo

–

mineral complexes (

Boudot et al., 1989

Higashi, 1983

)

and stabilize SOM via cation bridges (

) through a direct bond formation between OM and either Al

3+

or

Fe

3+

at the surface of a short range-ordered mineral ((

Duckworth et al.,

2008

;

Mikutta et al., 2007

). Whereas Fe

3+

exists in a range of oxides

with amphoteric surfaces on which organic anions are adsorbed (

Oades

and Tipping, 1986

), the interaction of Al

3+

with organic materials is

more likely to be an electrostatic interaction (

Oades, 1988

). In addition

to their direct role in OC sorption, Fe- and Al-oxides can help to sta-
bilize aggregates (

Rasmussen et al., 2005

), resulting in occlusion of OM

at the clay microstructure level (< 20

μ

m) or the coating of phyllosi-

licates by Fe- and Al-oxides (

Mayer et al., 2004

), a phenomenon fre-

quently described for kaolinite (

) and supported by the widespread occurrence of kaolinite-oxides

microaggregates in Oxisols (

Balbino et al., 2004

;

Oades, 1993

). Thus,

soil mineralogy, in particular Fe and Al content, controls aggregate
formation and stability through both mineral-mineral interactions and
organo-mineral associations (

Oades and Waters, 1991

;

Six et al., 2000

).

Numerous studies have highlighted the importance of extractable Fe

and Al in stabilizing SOC in top- and subsoils (

;

;

Schneider et al., 2010

;

Wiseman and

Puttmann, 2005

), and Fe-oxides have been found to be the most re-

levant mineral phases for the formation of organo-mineral associations
in the subsoil of acidic forest soils (

Eusterhues et al., 2005

). Here, it is

likely that speci

ﬁ

c components of OC (O/N-alkyl C and microbially-

derived sugars) are preferentially stabilized by association with Fe- and
Al-minerals (

Schoening et al., 2005

;

Spielvogel et al., 2008

). For soils of

volcanic origin (e.g., allophanic soils), it has been shown that ex-
tractable Al was much more important than Fe (

) or

the mass proportion of

ﬁ

ne particles or clay content (

;

Percival et al., 2000

), likely a

result of the dominance of Al in the

ﬁ

ne mineral phase. As such, whe-

ther Al, Fe or clay content is more important for SOC sorption is likely a
product of their presence, absence and relative proportions in a given
soil.

The positive correlations of soil Fe and Al phases with SOC across a

wide range of soils (

Kaiser and Guggenberger, 2000

;

;

;

Tiessen et al., 1984

) suggest that signi

ﬁ

cant

amounts of SOC are stabilized by interactions with Fe and Al (hydr-)

oxides in soils (

Bruun et al., 2010

) and that Fe and Al could be suitable

indicators for SOC storage. In a diverse landscape in South Africa, Fe
oxides was a better predictor of SOC storage than clay content along
topographic gradients and di

ﬀ

erent lithologies, despite di

ﬀ

erences in

mean annual precipitation (

Khomo et al., 2017

). However, time-con-

suming and costly soil fractionation procedures are required to isolate

ﬁ

ne mineral fractions and determine their contents of Fe- and Al-oxides.

Similar to clay mineralogy and SSA, sesquioxides thus represent only a
moderately applicable indicator for SOC storage because of the high
workload to extract them from soil samples.

2.5.6. Available Ca

2+

and Mg

2+

cations

The in

ﬂ

uence of the exchangeable cation concentration on the be-

havior of clay minerals, the biological availability of OC (

Baldock and

Nelson, 2000

), and SOM stabilization is well documented (

;

;

;

;

). Speci

ﬁ

cally, the presence of

polyvalent cations in soil produces electrostatic bridging between soil
mineral surfaces (clay minerals) and SOM (OM-cation-mineral: cation
bridges), yielding an important mechanism of SOM stabilization
(

Muneer and Oades, 1989b

;

). Sorption of negatively charged organic groups through cation

bridging to negatively charged clay minerals is possibly the most
common mechanism that stabilizes OM against microbial decomposi-
tion (

Oades, 1988

;

Sollins et al., 1996

). In addition to the interaction

with soil mineral surfaces via cation bridges, OM can be stabilized
against microbial decomposition via chelation or cross-linking of OM
with polyvalent cations (

Kaiser et al., 2012

Masiello et al., 2004

) or by

promoting aggregation (

Oades, 1988

;

Paul et al., 2003

Although stabilization of OM due to the presence of Mg in arable

soils of neutral pH has been documented (

Kaiser et al., 2012

), the

presence of Ca, in particular, is more frequently attributed to enhanced
OM stabilization. As a result, Ca has been found to be an important
predictor of SOC accumulation across di

ﬀ

erent landscapes and land-

uses, e.g. under forests (

Morris et al., 2007

), pastures (

Fornara et al.,

2011

), and cultivated soils (

Kaiser et al., 2012

O'Brien et al. (2015)

reported that exchangeable soil Ca

2+

was the strongest predictor of

SOC stocks in restored grasslands, indicated by a strong relationship
(R

2

= 0.74) between Ca

2+

and SOC, followed by clay content

(R

2

= 0.52), highlighting the potentially large role Ca can play in sta-

bilizing SOM. A recent analysis of 5500 soil proiles found that ex-
changeable calcium was a better predictor of SOC content than soil
texture (

Rasmussen et al., 2017

Oades (1988)

reported higher OM

contents and longer turnover times for OC of calcareous soils compared
with adjacent non-calcareous, acidic soils, which may be partly due to
Ca-humates. Other studies have also shown a decreased solubility of OC
in the presence of Ca

2+

(

Muneer and Oades, 1989b

), a reduced mi-

neralization of native organic materials and organic substrates with the
addition of Ca

2+