Page 1
REGULAR ARTICLE
How much carbon input is required to preserve or increase
projected soil organic carbon stocks in German croplands
under climate change?
Catharina Riggers
&
Christopher Poeplau
&
Axel Don
&
Cathleen Frühauf
&
René Dechow
Received: 13 July 2020 / Revised: 3 December 2020 / Accepted: 9 December 2020
#
The Author(s) 2021
Abstract
Aims
Increasing soil organic carbon (SOC) stocks is
discussed as negative emission technology with the
potential to remove relevant amounts of carbon from
the atmosphere. At the same time, climate change-
driven losses of SOC to the atmosphere might impede
such goals.
Methods
In this study, we used an ensemble of different
SOC models and climate projections to project SOC
stocks in German croplands up to 2099 under different
climate change scenarios. We then estimated the re-
quired increase in organic carbon (OC) input to preserve
or increase SOC stocks.
Results
Projected SOC stocks of German croplands are
estimated to decline under current OC input levels and
management, both with and without climate change.
Depending on the climate scenario, we estimated that
the OC input to the soil in 2099 needs to be between
51% (+ 1.3 Mg ha
−
1
) and 93% (+ 2.3 Mg ha
−
1
) higher
than today to preserve current SOC stock levels. A SOC
stock increase of 34.4% (4
‰
a
−
1
) would even require
an OC input increase of between 221% (+ 5.5 Mg ha
−
1
)
and 283% (+ 7.1 Mg ha
−
1
).
Conclusions
Our study highlights that under climate
change increasing SOC stocks is considerable challeng-
ing since projected SOC losses have to be compensated
first before SOC built up is possible. This would require
unrealistically high OC input increases with drastic
changes in agricultural management.
Keywords
Modeling . Soil organic matter . OC
sequestration . Multi-model ensemble . Representative
concentration pathways . 4 per 1000
Introduction
Climate change could alter the soil organic carbon
(SOC) stocks of arable land (Smith
2012
). The dynamic
of SOC stocks depends on the balance between organic
carbon (OC) input, e.g. as litter, and OC output, e.g. via
decomposition (Smith
2012
). Climate change causes
alterations in temperature, precipitation pattern and car-
bon dioxide (CO
2
) levels, which affect not only the
decomposition of SOC but also the OC input via net
primary production (Conant et al.
2011
; Gottschalk et al.
2012
; Smith
2012
). These changes are variable in time
and space, and thus whether SOC stocks are likely to
increase or decrease will differ from region to region
(Gottschalk et al.
2012
). Past and recent changes in SOC
stocks have been detected at regional to continental
https://doi.org/10.1007/s11104-020-04806-8
Responsible Editor: Simon Jeffery.
C. Riggers (
*
)
:
C. Poeplau
:
A. Don
:
R. Dechow
Thünen Institute of Climate-Smart Agriculture, Bundesallee 65,
38116 Braunschweig, Germany
e-mail: catharina.riggers@thuenen.de
C. Frühauf
Deutscher Wetterdienst, ZAMF, Bundesallee 33,
38116 Braunschweig, Germany
/ Published online: 13 January 2021
Plant Soil (2021) 460:417–433
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scale with repeated soil inventories, while future chang-
es in SOC stocks have been estimated using models
(Jandl et al.
2014
).
Repeated national soil inventories show for example
decreasing SOC stocks for England and Wales (Bellamy
et al.
2005
) and Finland (Heikkinen et al.
2013
), and
increasing SOC stocks in the Netherlands (Reijneveld
et al.
2009
) and Sweden (Poeplau et al.
2015a
). Miscel-
laneous results have been detected for Germany: a SOC
time series of 171 permanent soil monitoring sites on
managed croplands in Germany was analyzed and no
significant changes in SOC stocks were detected
(Umweltbundesamt
2016
). The same result was ob-
served by Höper and Meesenburg (
2012
) in an analysis
of cropland monitoring sites in the federal state of Low-
er Saxony for the period 1997 to 2010. In contrast,
Capriel (
2013
) detected for croplands decreasing SOC
stocks for the federal state of Bavaria between 1986 and
2007. However, these results are not representative for
Germany as a whole.
Lugato et al. (
2014
) and Smith et al. (
2005
) estimated
future changes in SOC stocks using the climate scenar-
ios of the Intergovernmental Panel of Climate Change
(IPCC) and the SOC models Roth-C (Coleman and
Jenkinson
2005
) and Century (Parton et al.
1994
). They
reported increasing SOC stocks for Europe for the end
of the 21st century. Regional differences were identi-
fied, with both studies modeling positive SOC changes
for Germany when considering future increases in OC
input. In Germany, Wiesmeier et al. (
2016
) estimated
significant future SOC losses for the federal state of
Bavaria in the 21st century using Roth-C and the IPCC
’
s
climate change scenario A1B. The model also predicted
that an OC input increase of 29% would be needed to
compensate for higher rates of decomposition and to
keep SOC stocks stable (Wiesmeier et al.
2016
).
A decrease in SOC stocks is associated with increas-
ing atmospheric CO
2
and a loss of soil fertility, while the
opposite is the case when SOC stocks are built up (Lal
2016
). Increasing SOC stocks is currently discussed as
negative emission technology and promoted as a CO
2
sink, for example by the international
“
4 per 1000
”
initiative, with the potential to remove relevant amounts
of CO
2
from the atmosphere (Minasny et al.
2017
).
Various management practices are believed to increase
SOC stocks to a new steady state, covering strategies
both to increase OC input and also reduce OC mineral-
ization (Minasny et al.
2017
). These management prac-
tices need to be continued to maintain the benefit of
increased SOC stocks. However, the accumulation of
SOC stocks is not only affected by global warming and
changed management practices, but also restricted if
recent OC inputs are insufficient to even preserve cur-
rent SOC stocks.
We therefore set up scenarios to investigate the possi-
ble effects of a projected future climate on SOC stocks in
German croplands. Based on SOC stocks and manage-
ment data from the first German Agricultural Soil Inven-
tory (Jacobs et al.
2018
; Vos et al.
2019
) and a multi-
model ensemble (Riggers et al.
2019
), the SOC stocks of
German croplands were evaluated for the period 2014
–
2099. The study focused on the following questions:
1) How are SOC stocks in German croplands expected
to change under different climate change scenarios
and current OC input levels?
2) Which increase in OC input via plant residues is
required to preserve or increase SOC stocks under
different climate change scenarios?
Materials and methods
Dataset
Selection of sites
The dataset for this study comprised 991 cropland
sites across Germany (Fig.
1
). These sites were
sampled as part of the German Agricultural Soil
Inventory from 2011 to 2018. At each site, soil
samples were taken and analyzed in the laboratory
for soil texture, OC and total nitrogen content and
other physicochemical soil properties (Jacobs et al.
2018
; Vos et al.
2019
). The SOC stocks were cal-
culated with the fine soil mass (grain size < 2 mm),
the rock fragment fraction (grain size > 2 mm) and
the fine soil density (fine soil mass per fine soil
volume) as suggested by Poeplau et al. (
2017
). In
addition, a questionnaire for farmers documented
farm management during the previous ten years,
including crop rotations and yield data. In the pres-
ent modeling study, the focus was solely on topsoils
(0
–
30 cm).
The dataset was a subset of the complete inventory-
dataset (1758 cropland sites in May 2018). Owing to the
restrictions and limitations of SOC models, some
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Plant Soil (2021) 460:417–433
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cropland sites in the inventory-dataset had to be exclud-
ed: all organic soils with a SOC content higher than
8.7% were excluded because the applied models were
developed for mineral soils only. Soils with a mean
groundwater level of less than 80 cm belowground were
also omitted because the OC dynamics in hydromorphic
soils differ from those described by the SOC models. In
the northwestern part of Germany in particular, some
soils have a high sand content and an unexpectedly high
OC content, which might be due to historical heathland
and peatland land use (Sleutel et al.
2011
; Vos et al.
2018
). The recalcitrant plant material in these soils
shows a slower OC mineralization that is not considered
in the SOC models (Springob and Kirchmann
2002
).
Sites that fulfilled all the following criteria were there-
fore excluded: carbon-to-nitrogen (C/N) ratio
≥
12 AND
sand content
≥
70% AND soil type classified under the
German soil system (Sponagel et al.
2005
) as podzol or
plaggen soil but not as gley (Jacobs et al.
2018
). Sites
with fewer than five years
’
management data (e.g. on
yields and crop types) were also omitted.
Description of sites
Germany is located in the temperate zone, with
influences of maritime and continental climate. The
long-term (1981
–
2010) mean annual temperature
(MAT) of the 991 sites varied between 6.3 °C and
11.0 °C and mean annual precipitation (MAP) was
between 500 mm and 1293 mm (DWD-CDC
2018c
,
d
). The 991 sites comprised a huge variety of soil
properties (Fig.
2
). The sand content in the topsoil
(0
–
30 cm) varied between 1 and 95%, silt content
between 2% and 86%, and clay content between 2%
and 66%. Based on the German soil classification
system (Sponagel et al.
2005
), 24% of the sites were
classified as loam, 27% as sand, 34% as silt and
15% as clay (Fig.
S1
). Dominant soil classes were
Cambisols (30%), Luvisols (16%), Stagnosols
(16%), Phaeozems (11%) and Regosols (10%),
followed by Chernozems, Gleysols, Athrosols,
Vetisols and Fluvisols according to the German soil
classification system and the proposed equivalent
Reference Soil Groups of the WRB system
(Sponagel et al.
2005
). The C/N ratio of the sites
ranged from 7 to 26 and the SOC content from 0.3
to 7.7%. Maximum C/N ratios of > 20 were relicts
of strongly degraded and disturbed organic soils
mixed with sand. A major strength of this study
was that management data of the 991 sites was
recorded via a questionnaire for farmers covering
the previous ten years before sampling and thus
reflecting current agricultural management in Ger-
many. The crop rotations were dominated by six
main crop types, harvested in 80% of the site years
(number of sites x number of years): winter wheat
(31%), summer wheat (14%), winter oilseed rape
(13%), silage maize (12%), winter rye (5%) and
sugar beet (5%). Cover crops were integrated in
12.7% of the site years. Crop residues were left in
the field in 74% of the site years. Application of
organic fertilization took place in 45% of the site
years and ploughing in 68% of the site years.
Climate scenarios
We used three climate change scenarios based on the
representative concentration pathways (RCPs) used in
the IPCC Fifth Assessment Report. RCPs are prescribed
pathways of greenhouse gas emissions and atmospheric
Fig. 1
Location of the 991 sampled cropland sites investigated in this
study. Shaded areas represent the cropland area in Germany in 2016
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Plant Soil (2021) 460:417–433
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concentrations, air pollutant emissions and land use
(IPCC
2013
). They are characterized by the strength of
radiative forcing at the end of the 21st century (IPCC
2013
; Van Vuuren et al.
2011
):
&
RCP8.5: rising radiative forcing leading to 8.5 W
m
−
2
(1370 ppm CO
2
-eq; CO
2
: 935.9 ppm) by 2100;
no effective climate mitigation
&
RCP4.5: stabilization without overshoot to 4.5 W
m
−
2
(650 ppm CO
2
-eq; CO
2
: 538.4 ppm) at stabili-
zation after 2100; climate mitigation.
&
RCP2.6: peak in radiative forcing at 3.0 W m
−
2
(490 ppm CO
2
-eq) before 2100 and then a decline
to 2.6 W m
−
2
(CO
2
: 420.9 ppm); very strong climate
mitigation with the possibility of achieving the
1.5 °C target.
The predicted development of the climate variables
(temperature, precipitation, global radiation) of the cli-
mate change scenarios were derived from different cli-
mate projections. Each climate projection was the result
of a regional climate model, which was driven by a
global climate model. The selection of climate projec-
tions was based on the core ensemble of the German
Weather Service (DWD), which is intended to cover
90% of the variations in air temperature and 80% of
the variations of all other climate parameters of the
DWD reference ensembles v2018 (
www.dwd.de/ref-
ensemble
, accessed 05.02.2019). Each climate change
scenario was described by a set of climate projections
(multi-model approach, Table
S1
). The multi-model
for climate change scenarios RCP2.6 and RCP8.5
consisted of five climate projections, while there
were six climate projections for RCP4.5. A total of
16 different climate projections were thus applied to
obtain three robust climate change scenarios for each
of the 991 investigated sites. We sampled daily air
temperature, precipitation and global radiation for the
period from January 2014 to December 2099 for each
climate projection. The spatial resolution of the bias-
corrected climate projections was 5 km x 5 km (di-
mension of one grid cell). Each site was represented
by 3 × 3 grid cells with the site in the central grid cell.
We used the arithmetic mean of these nine grid cells
to account for spatial uncertainties.
The daily data were used to calculate the
decomposition-modifying factor r
e
(parameter of the
SOC model ICBM) for every site, climate projection
and year (Andrén and Kätterer
1997
; Poeplau et al.
2015b
). In doing so, we used a uniform calendar format
of 365.25 days per year filling any gaps arising from
different calendar formats (Table
S1
) with the arithmetic
mean of the three previous and three subsequent days.
For the rest of the modeling, daily data were aggregated
to monthly data for every site and every climate projec-
tion using the arithmetic mean (temperature, radiation)
or sum (precipitation).
A fourth climate scenario was defined that assumed
no further climate change in the future, with the future
climate being the same as in recent years. To obtain that,
we repeated the climate data recorded in the period of
the German Agricultural Soil Inventory for every site up
to 2099. The length of the time series repeated for each
site varied (depending on the years reported in the site
related farmers
’
questionnaire), thus no pattern was
detectable when summarizing the climate for the total
study area.
The MAT in all three climate change scenarios in-
creased significantly (p < 0.05) up to 2099 for the study
Fig. 2
Boxplots and histograms illustrating the distribution and
quantiles of the soil properties of the topsoil (0
–
30 cm) from the
991 sites of the study area. The sand, silt and clay contents [%],
current soil organic carbon (SOC) contents [%] and carbon-to-
nitrogen (C/N) ratios at all sites were measured and determined in
the same laboratory
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Plant Soil (2021) 460:417–433
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area (Fig.
3
; Table
1
). The MAT increase between the
reference period 1981
–
2010 and the period 2070
–
2099
was greatest for RCP8.5 at + 3.7 °C, compared with +
1.7 °C for RCP4.5 and + 1.2 °C for RCP2.6. The MAP
only decreased significantly for RCP2.6 and annual
global radiation (GR) only decreased significantly for
RCP4.5. The climatic water balance (MAP - potential
evapotranspiration) decreased significantly for RCP8.5
and RCP2.6. For the scenario assuming no future cli-
mate change, no trend was detected for MAT, MAP or
GR.
Multi-model ensemble
We used a multi-model ensemble for the SOC modeling
(Table
2
) which was developed and analyzed in Riggers
et al. (
2019
). In this previous study, Riggers et al. (
2019
)
compared the performance of six different process-
based multi-compartment SOC models. The OC inputs
were derived from OC input estimation methods. These
empirical equations are used to calculate aboveground
and belowground OC input based on crop yields and
crop-specific allocation coefficients (Keel et al.
2017
).
Riggers et al. (
2019
) selected five OC input estimation
methods from the literature and combined them with the
SOC models (in total 30 model combinations). They
evaluated the model combinations as single model or as
multi-model ensemble on a network of 139 agricultural
permanent soil monitoring sites in Germany. Thereby,
the multi-model ensemble used in the present study
showed an absolute mean error of 0.004 Mg ha
−
1
a
−
1
and a root mean squared error of 0.56 Mg ha
−
1
a
−
1
, and
performed better than the single model combinations
and other ensemble combinations.
The multi-model ensemble consisted of five different
SOC models: C-TOOL (Taghizadeh-Toosi et al.
2014
),
CENTURY (Parton et al.
1994
), ICBM (Andrén and
Kätterer
1997
), ROTH-C (Coleman and Jenkinson
2005
) and YASSO07 (Tuomi et al.
2011
). Each of these
models describes SOC decomposition by first order
kinetics and defines different SOC qualities represented
by interacting SOC pools. Decomposition rates of mod-
el pools are modulated by external factors, e.g. temper-
ature, moisture or soil texture. Models differ with re-
spect to the number of pools, linkage between pools and
the factors that modulate decomposition rates
(Table
S2
). Model initialization was done by assuming
initial equilibrium conditions using proposed methods
from the literature, which have been also applied in
Riggers et al. (
2019
). The original literature contains
details on CENTURY (Parton et al.
1994
), C-TOOL
(Taghizadeh-Toosi et al.
2014
), ICBM (Andrén and
Kätterer
1997
), ROTH-C (Coleman and Jenkinson
2005
) and YASSO07 (Tuomi et al.
2011
). In brief, C-
TOOL had three pools in the topsoil (fresh, humified
and resistant organic matter), which were initialized
with fixed fractions (Taghizadeh-Toosi and Olesen
2016
). The used soil organic matter submodule of CEN-
TURY included eight pools: a structural, metabolic and
active (microbes) pool in the surface layer and a struc-
tural, metabolic, active, slow and passive pool in the soil
layer. Initialization was done following Falloon and
Smith (
2002
). The ICBM version was built up of two
young and one old organic carbon pool, which were
initialized following Andrén and Kätterer (
1997
).
ROTH-C consisted of five different pools: decompos-
able and resistant plant material, microbial biomass,
humified organic matter and inert organic carbon. The
pools were initialized by an analytic solution of Dechow
et al. (
2019
). In YASSO07 the organic carbon was
separated into five pools based on the chemical quality:
soluble in ethanol, soluble in water, hydrolysable in
acid, neither soluble nor hydrolysable and recalcitrant
humus. The initial pool sizes were quantified by fixed
fractions (Riggers et al.
2019
). The SOC models were
implemented and run in R (R Core Team
2018
) using
the SoilR package (Sierra et al.
2012
), as was done in
Riggers et al. (
2019
).
The OC input estimation methods calculated the OC
input from different plant compartments (e.g. straw,
stubble, roots) based on crop yields and crop-specific
parameters. The OC input was then summarized in
aboveground and belowground OC input for the SOC
models (Riggers et al.
2019
). For calculation details on
the OC input estimation methods, see bolinder (Bolinder
et al.
2007
), bze (Jacobs et al.
2018
), ccb (Franko et al.
2011
) and ipcc-nir (Rösemann et al.
2017
).
Soil organic carbon modeling and minimization
The modeling was performed from January 2014 to
December 2099 in a monthly time step. The SOC models
required an initial SOC stock, climate data and OC input
data (Table
S2
). SOC stocks of the German Agricultural
Soil Inventory that were sampled from 2011 to 2018 were
shifted to 2014 (mean of the sampling period) and used as
initial SOC stocks. The climate variables used in the SOC
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Plant Soil (2021) 460:417–433
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modeling were taken from the four described climate
scenarios (three climate change scenarios and one no
future climate change scenario). The OC input was de-
rived from the crop yields reported in the German Agri-
cultural Soil Inventory and repeated for each site up to
2099. We constrained the modelling to soil processes and
did not simulate potential plant growth responses to
climate change.
The modeling study included three steps
(Table
3
): (i) calculating the SOC stocks under
unchanged climate and current OC input levels,
(ii) calculating the SOC stocks under changed
climate and current OC input levels, and (iii) esti-
mating the required OC input to achieve different
predefined SOC stock levels at the end of the
century.
Table 1
Trends of mean annual temperature (MAT), mean annu-
al precipitation (MAP), annual global radiation (GR) and climatic
water balance (MAP
–
potential evapotranspiration (ETP)) for the
study area for the period 2014-2099. The climate scenarios
covered three climate change scenarios based on different repre-
sentative concentration pathways (RCPs) and a scenario of no
future climate change
Climate scenario
MAT
[°C a
−
1
]
MAP
[mm a
−
1
]
GR
[kJ cm
−
2
a
−
1
]
MAP-ETP
[mm a
−
1
]
No future climate change
2.9e-05
-0.039
-3.185
-0.036
Climate change (RCP2.6)
0.003 (*)
-0.461 (*)
65.198
-0.625 (*)
Climate change (RCP4.5)
0.017 (***)
0.322
-70.788 (*)
-0.084
Climate change (RCP8.5)
0.048 (***)
0.181
-30.187
-1.094 (***)
Significance codes of p
slope
: 0 < *** < 0.001 < ** < 0.01 < * < 0.05
Fig. 3
Projected mean annual temperature (MAT), mean annual
precipitation (MAP), annual global radiation (GR) and calculated
climatic water balance (MAP
–
potential evapotranspiration
(ETP)) for the study area for the period 2014
–
2099. The climate
scenarios covered three climate change scenarios based on differ-
ent representative concentration pathways (RCPs) plus a scenario
of no future climate change. This figure displays the mean and the
95% confidence interval summarizing the uncertainty of the cli-
mate projections. The scenario of no future climate change was
based on repeated measurement data and therefore showed no
model uncertainty
422
Plant Soil (2021) 460:417–433
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Soil organic carbon stocks under current organic
carbon input levels
In the first step, we run the SOC models with unchanged
OC input and recent climate data (no future climate
change) for each of the 991 sites. This resulted in 8919
SOC stock time series all across Germany (991 sites x 9
SOC model combinations x 1 climate projection). We
averaged the results of the 991 sites per climate projection
and SOC model combination, resulting in a total of 9 SOC
stock time series (9 SOC model combinations x 1 climate
projection). We then calculated the average and the stan-
dard deviation of the 9 SOC stock time series to receive the
ensemble mean. This ensemble mean is further referred to
as reference model run. The variability between spatially
averaged model estimates represents the model structural
uncertainty of the model ensemble (Riggers et al.
2019
).
Thus, the standard deviation of the ensemble mean repre-
sented only the model ensemble uncertainty and not the
site-specific variability.
In the second step, we run the SOC models with
unchanged OC input and climate variables according
to the three climate change scenarios. This resulted in
142,704 SOC stock time series all across Germany (991
sites x 9 SOC model combinations x 16 climate projec-
tions). Again, we first averaged the SOC stock time
series of the 991 sites per climate projection and SOC
model combination, resulting in a total of 144 SOC
stock time series (9 SOC model combinations x 16
climate projections). Of these 144 SOC stock time se-
ries, 54 were associated with RCP4.5 and 45 were
associated with each of RCP2.6 and RCP8.5. The
SOC stock time series were then averaged per climate
change scenario to obtain one ensemble SOC stock time
series for each climate scenario.
Estimating the required organic carbon input
We estimated the additional OC input via crop res-
idues required to preserve or increase SOC stocks.
In doing so, we increased the aboveground and
belowground OC inputs of the study sites equally
assuming a linear increase between 2014 and 2099.
The relative OC input increase between 2014 and
2099 was determined using one-dimensional optimi-
zation. Minimization was performed using the
optim
function in R with the method
“
Brent
”
, which is
used for one-dimensional problems (Brent
1973
).
The algorithm minimized the difference between a
chosen target SOC stock and the SOC stock of a
model run with increased OC input (
Δ
SOC, Mg ha
−
1
) at the end of the simulation:
Δ
SOC
¼
SOC
target
1
10
X
2099
i
¼
2090
SOC
cinput
;
i
where
SOC
target
is the chosen target SOC stock [Mg ha
−
1
]
and
SOC
cinput
is the SOC stock time series of the model run
with increased OC input [Mg ha
−
1
].
SOC
cinput
was aver-
aged first per climate projection and SOC model combi-
nation and then per climate change scenario, as it was done
above. To avoid errors due to high or low OC input in the
last year, the previous 10 years of the ensemble SOC stock
time series were averaged for the purposes of minimiza-
tion. Lower and upper limits of
optim
were set to a
sufficient range but modified individually for each model
run to reduce the running time. The default settings of
optim
were used to stop the algorithm.
We defined three different target SOC stocks for
the end of the 21st century (Table
4
, Fig.
S2
): (1)
Reference SOC stock: projected SOC stocks in 2095
(the average for the period 2090
–
2099) are at the
same level as the SOC stock of the reference model
run in 2095. (2) Constant SOC stock: projected SOC
stocks in 2095 are the same as in 2014. The 2014
SOC stock was thereby the mean of the SOC stocks
measured in the first German Agricultural Soil In-
ventory (2011
–
2018). (3) 4
‰
-increased SOC stock:
Table 2
Soil organic carbon (SOC) models and organic carbon
(OC) input estimation methods used in the multi-model ensemble
SOC model
OC input estimation method
CENTURY
a
ccb
f
ipcc-nir
g
bze
h
C-TOOL
b
bolinder
i
ipcc-nir
g
ICBM
c
ccb
f
ipcc-nir
g
ROTH-C
d
ccb
f
YASSO07
e
ipcc-nir
g
a
Parton et al. (
1994
),
b
Taghizadeh-Toosi et al. (
2014
),
c
Andrén
and Kätterer (
1997
),
d
Coleman and Jenkinson (
2005
),
e
Tuomi
et al. (
2011
),
f
Franko et al. (
2011
),
g
Rösemann et al. (
2017
),
h
Jacobs et al. (
2018
),
i
Bolinder et al. (
2007
)
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Plant Soil (2021) 460:417–433
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projected SOC stocks in 2095 are increased com-
pared with 2014. Here we increased the SOC stock
of 2014 by 34.4% up to 2099, assuming an annual
linear increase of 4
‰
to test the theoretical feasibil-
ity of increasing SOC stocks, as promoted in the
“
4
per 1000
”
initiative.
Computations
Trends in climate (MAT, MAP, GR) and mean
annual SOC changes were calculated in R (R Core
Team
2018
) with linear regressions using the basic
R function
lm
. The ensemble SOC stock time series
and associated uncertainty were calculated for each
climate scenario. Here, we first averaged the SOC
stock time series of the 991 sites per climate projec-
tion and multi-model ensemble member. We then
calculated the mean and the 95% confidence interval
(CI) across the climate projections and the multi-
model ensemble members. Thus, the 95% CI
showed the model uncertainty of the different cli-
mate projections and SOC models. The relationship
between SOC stock increase in 2095 and required
OC input increase up to 2099 was determined. We
thereby estimated the required OC input increase to
raise SOC stocks in 2095 by 0% (Constant SOC
stock), 10%, 20% and 34.4% (4
‰
-increased SOC
stock) and fitted linear models (
lm
in R) grouped for
the four climate scenarios. All figures were created
using ggplot2 (Wickham
2009
).
Results
Projected soil organic carbon stocks up to 2099
under current OC input levels
On average, the models estimated declining SOC stocks
for German croplands in the period 2014
–
2099, but
revealed a large 95% CI (Fig.
4
). The decline was more
pronounced for the climate change scenario under
RCP8.5 than under RCP2.6. Projected SOC stocks de-
clined by 10% from current SOC stock (58.1 Mg ha
−
1
)
to an average level of 52.5 Mg ha
−
1
[95% CI: 46.0 Mg
ha
−
1
; 59.0 Mg ha
−
1
] for no future climate change and
52.1 Mg ha
−
1
[43.9 Mg ha
−
1
; 60.4 Mg ha
−
1
] for
RCP2.6, and appeared to approximate a new steady
state. Projected SOC stocks for climate change scenarios
RCP4.5 and RCP8.5 declined even more drastically by
between 14% and 18% to a level of 50.1 Mg ha
−
1
[43.6 Mg ha
−
1
; 57.0 Mg ha
−
1
] and 47.8 Mg ha
−
1
[39.3 Mg ha
−
1
; 57.4 Mg ha
−
1
] respectively. Thus, with-
out changes in OC input, estimated SOC stocks declined
up to the end of the 21st century by between 10 and
18%, representing an average annual SOC loss of be-
tween 0.065 and 0.120 Mg ha
−
1
a
−
1
.
SOC losses were highest in the first 10 years and
declined up to 2099 (Table
5
). For the climate change
scenario RCP8.5, SOC change rates were fairly constant
from the period 2024
–
2037 onwards, which might be
due to the strong non-linear warming in this climate
scenario (Fig.
3
). The changes in annual SOC losses
during the 86-year period were especially pronounced
Table 3
Overview of the performed modeling steps with the origin of the climate and the organic carbon (OC) input data used as input data
for the soil organic carbon (SOC) models
Step
Climate data
OC input data
SOC stock
SOC stocks under current OC input levels
and unchanged climate (Reference model
run)
Reported in the German Agricultural
Soil Inventory
Derived from crop yields reported in
the German Agricultural Soil
Inventory
Calculated
SOC stocks under current OC input levels
and changed climate
Derived from RCP2.6, RCP4.5,
RCP8.5
Derived from crop yields reported in
the German Agricultural Soil
Inventory
Calculated
Estimating the required OC input
Reported in the German Agricultural
Soil Inventory (no future climate
change)
+
Derived from RCP2.6, RCP4.5,
RCP8.5 (climate change)
Estimated via one-dimensional opti-
mization
Predefined
RCP = representative concentration pathway
424
Plant Soil (2021) 460:417–433
Page 9
for climate change scenario RCP2.6 and the scenario of
no future climate change, with a decline of nearly 88%.
This, together with the low SOC change rates of -
0.026 Mg ha
−
1
a
−
1
(no future climate change) and
−
0.025 Mg ha
−
1
a
−
1
(RCP2.6) in the period 2057
–
2099,
might indicate that both are moving towards a new
steady state after that period. However, the SOC change
rate for the climate change scenario RCP8.5 decreased
only by 53%, showing an ongoing high loss of SOC at
the end of the 21st century.
Estimated required total organic carbon input in 2099
The total OC input from crop residues in the reference
model run was 2.7 Mg ha
−
1
on average in 2095 (the
annual average for the period 2090
–
2099). As the total
OC input in the reference model run did not change with
time, this value represents the current total OC input
level for the 991 German cropland sites studied. The
total OC input would need to increase by + 5% to +
283%, depending on the climate scenario and target
(Table
6
), in order to achieve the three chosen targets.
Thus, the estimated required total OC input in 2095
varied between 2.8 Mg ha
−
1
and 9.8 Mg ha
−
1
(Fig.
5
).
In the model runs referring to the reference SOC stock,
the annual total OC input increases required were low
(0.002 Mg C ha
−
1
a
−
1
to 0.013 Mg C ha
−
1
a
−
1
),
resulting in an estimated OC input of 2.8 Mg ha
−
1
(RCP2.6), 3.2 Mg ha
−
1
(RCP4.5) and 3.7 Mg ha
−
1
(RCP8.5) in 2095. It should be noted that the reference
SOC stock in 2095 was lower than the current SOC
stock in 2014, and estimated OC input increases would
only compensate for future climate change-driven SOC
losses in this target. A minimum increase in total OC
input of 45% in 86 years would be needed to maintain
SOC stocks in 2095 at the 2014 SOC stock level (con-
stant SOC stock). In the case of climate change scenario
RCP8.5, the required total OC input in 2095 would be
nearly double (5.0 Mg ha
−
1
) of the current input. An
even more drastic increase in total OC input would be
necessary to raise SOC stocks annually by 4
‰
from
58.1 Mg ha
−
1
(2014) to 78.1 Mg ha
−
1
(2099). In this
case, the required total OC inputs were more than three
times the current total OC input, ranging in 2095 from
8.2 Mg ha
−
1
(RCP2.6) to 9.8 Mg ha
−
1
(RCP8.5). The
relative SOC stock increase between 2014 and 2099 was
linearly correlated to the required relative OC input
increase in this period (Fig.
S3
), because decomposition
is described by first order kinetics in all models. The
slopes of this linear correlation did not vary very much
between climate scenarios, but nonetheless indicated
that greater climate change would require a higher OC
input to increase SOC stocks by the same amount com-
pared with no future climate change.
Discussion
Projected changes in soil organic carbon stocks
in German cropland
Under current OC input levels, modeled SOC stocks of
German cropland showed a declining trend up to the end
of the century. These results are in line with the study by
Wiesmeier et al. (
2016
) for Bavaria, which modeled a
16% decline in SOC stocks for croplands in the 21st
century with a mean temperature increase of + 3.3 °C
(SRES-A1B) and unchanged OC input, while we
modeled a decline in SOC stocks of 14% (RCP4.5)
and 18% (RCP8.5) with a temperature increase of +
1.7 °C and + 3.7 °C and unchanged OC input. For
European croplands, Smith et al. (
2005
) estimated
SOC losses of between 10% and 14% up to 2080
compared with the average cropland SOC stock in
1990 without considering changes in net primary pro-
duction. Given that these losses are an average value
across Europe, with higher losses in the north, our
slightly higher SOC losses fit well with the modeling
result of Smith et al. (
2005
). Moreover, the projected
SOC change rates for the timespan 2014
–
2023 are in
Table 4
Overview of the targeted mean soil organic carbon
(SOC) stocks used in the minimization
Target name
Description
SOC
target
[Mg ha
−
1
]
Reference SOC
stock
Projected SOC stocks in 2095 are
at the same level as the SOC
stock of the reference model
run in 2095.
52.7
Constant SOC
stock
Projected SOC stocks in 2095 are
the same as in 2014.
58.1
4
‰
-increased
SOC stock
Projected SOC stocks in 2095 are
increased by 34.4% compared
with 2014.
78.1
NB: 2095 refers to the average for the period 2090
–
2099
425
Plant Soil (2021) 460:417–433
Page 10
good agreement with the SOC change rates of 139
permanent soil monitoring sites for German croplands
for the last 20 years, which showed an average SOC loss
of -0.23 Mg C ha
−
1
a
−
1
(Jacobs et al.
2018
).
The reasons for the projected decline in German
cropland SOC stocks even without future climate
change could be manifold, such as the effects of past
climate change, changes in management practices or
land use changes. The long-term (1981
–
2010) MAT
and MAP of the 991 sites increased by 0.64 °C and
32 mm on average compared with the long-term MAT
and MAP of the reference period 1961
–
1990 (DWD-
CDC
2018a
,
b
,
c
,
d
). At the same time, OC inputs to the
soil could have increased due to increasing yields in the
past 60 years (Wiesmeier et al.
2014
). Thus, increased
OC input could have compensated for the effects of
climate change. However, this would only be true if
belowground OC inputs were correlated to yield, which
is questioned in some recent studies (Hirte et al.
2018
;
Hu et al.
2018
) and belowground residues are not de-
creasing with breeding progress (Siddique et al.
1990
),
especially if the aboveground residues are removed. In
Fig. 4
Projected mean soil organic carbon (SOC) stocks for
German croplands under current organic carbon input levels and
the 95% confidence interval for the ensemble of climate projec-
tions and SOC models. The climate scenarios covered three
climate change scenarios based on different representative con-
centration pathways (RCPs) and a scenario of no future climate
change
Table 5
Soil organic carbon (SOC) change rates with standard errors for different timespans for the climate scenarios of no future climate
change and climate change based on the representative concentration pathways (RCPs) RCP2.6, RCP4.5 and RCP8.5
Climate scenario
SOC change rate [Mg ha
−
1
a
−
1
]
2014
–
2023
2024
–
2037
2038
–
2056
2057
–
2099
No future climate change
-0.218 ± 0.030
-0.088 ± 0.005
-0.049 ± 0.002
-0.026 ± 0.001
Climate change (RCP2.6)
-0.225 ± 0.028
-0.099 ± 0.005
-0.061 ± 0.003
-0.025 ± 0.001
Climate change (RCP4.5)
-0.209 ± 0.027
-0.101 ± 0.004
-0.067 ± 0.002
-0.064 ± 0.001
Climate change (RCP8.5)
-0.223 ± 0.031
-0.110 ± 0.003
-0.093 ± 0.002
-0.104 ± 0.001
426
Plant Soil (2021) 460:417–433
Page 11
addition, several studies indicate that changes in land
management, and especially land use changes, can
greatly affect SOC stocks (Poeplau et al.
2011
) and that
these effects might influence SOC losses more than
climate change (Keel et al.
2019
; Mayer et al.
2019
;
Smith et al.
2007
). Soils under permanent grassland and
forest have higher SOC stocks than soils under arable
land (Freibauer et al.
2004
; Jacobs et al.
2018
; Poeplau
et al.
2011
). Thus, when converting these sites into
cropland, a new equilibrium of SOC stocks develops
Table 6
Required organic carbon (OC) input increase in 2099
compared with 2014 and corresponding yearly OC input increase.
The required OC input was estimated for three target soil organic
carbon (SOC) stocks. The climate scenarios covered three climate
change scenarios based on different representative concentration
pathways (RCPs) and a scenario of no future climate change
Target name
Climate scenario
Required OC input increase in 2099
compared with 2014 [%]
Required yearly OC input
increase [Mg C ha
-1
a
-1
]
Reference SOC stock
Climate change (RCP2.6)
5
0.002
Climate change (RCP4.5)
20
0.006
Climate change (RCP8.5)
41
0.013
Constant SOC stock
No future climate change
45
0.014
Climate change (RCP2.6)
51
0.016
Climate change (RCP4.5)
69
0.021
Climate change (RCP8.5)
93
0.029
4
‰
-increased SOC stock
No future climate change
213
0.066
Climate change (RCP2.6)
221
0.068
Climate change (RCP4.5)
246
0.076
Climate change (RCP8.5)
283
0.088
Fig. 5
Mean estimated required organic carbon (OC) input for the
last decade of the modeling period (2090
–
2099) for the three target
soil organic carbon (SOC) stocks. The climate scenarios covered
three climate change scenarios based on different representative
concentration pathways (RCPs) and a scenario of no future climate
change
427
Plant Soil (2021) 460:417–433
Page 12
that is lower than before. It is not easy to separate out
sites that have experienced land use change in the past.
Land use in Germany has partly been documented in
historical maps, which can date back to 1800 depending
on the federal state. Based on these maps and farmers
’
questionnaires in the German Agricultural Soil Invento-
ry (Jacobs et al.
2018
), land use changes were presumed
for 138 sites (14%) since 1900, and for 118 sites (12%)
since 1950. More than 85% of these sites were formerly
used as grassland. Accordingly, some of the sites might
still lose OC, but the declining SOC trend was found for
more than this fraction of affected sites. When omitting
sites with reported land use change, (i) ensemble SOC
stocks were lower in all four climate scenarios under
current OC input levels, and (ii) SOC change rates for
the first ten years were still negative, but were 14%
lower than the complete dataset of 991 sites. However,
the historical maps only provide snapshots and there is
no guarantee that the land use was constant in the years
between map publications. It is likely that more than
14% of the sites underwent land use change in the last
100 years. In addition, a change in farming system or,
more specifically, changes in input of organic matter can
alter SOC stocks on arable land (Hu et al.
2018
;
Johnston et al.
2009
; Leifeld and Fuhrer
2010
). Howev-
er, sites where land use and management changes had
been identified were not removed from the modeling
since the intention was to provide a realistic picture of
the development of SOC stocks in German croplands.
Projected SOC stocks for German croplands and thus
the estimated OC input increases were associated with
large uncertainties resulting from nine combinations of
SOC models and OC input estimation methods and five
to six different climate projections. Soil organic carbon
dynamics are complex and yet it is not clear, how soils
react to a warmer environment (Bradford et al.
2016
)
and if this reaction changes in the long term (Carey et al.
2016
; Melillo et al.
2017
; Romero-Olivares et al.
2017
).
There is a wide range of sensitivities of decomposition
to temperature estimated from laboratory and field ex-
periments and the range of sensitivities is still a matter of
debate, which is also true for the temperature response
functions implemented in models (Todd-Brown et al.
2018
). Sulman et al. (
2018
) compared the response to
warming and litter additions for different SOC model
concepts and field manipulation experiments and found
a similar variability for models and experiments. More-
over, the experiments showed SOC gains and losses and
were not sufficient to validate model outcomes (Sulman
et al.
2018
). Abramoff et al. (
2019
) found similar results
and concluded that measured total SOC changes did not
help to evaluate the temperature sensitivity of mineral
sorption and/or microbial activity. Bradford et al. (
2016
)
suggest representing these structural uncertainties with
multi-model ensembles to increase the confidence of the
model predictions. In the present multi-model ensemble,
the SOC models showed different temperature re-
sponses (Fig.
S4
). In addition, soil moisture also influ-
ences decomposition rates (Ise and Moorcroft
2006
).
The response to changing moisture conditions reduced
the temperature effects on decomposition rates in some
of the models. However, not all the models included a
moisture response (Riggers et al.
2019
). These argu-
ments collectively support the strength of a multi-
model ensemble compared with a single model
(Hagedorn et al.
2005
; Tebaldi and Knutti
2007
), as it
averages a range of possible temperature and moisture
responses and provides information about resulting
uncertainties.
In summary, SOC stocks in German croplands
are predicted to decline under climate change when
considering current OC input levels and manage-
ment. Soil warming experiments have revealed that
temperature sensitivity might be highly variable and
that the temperature response of the decomposition
rate under long-term warming might be changing.
Thus, the use of multi-models is useful in order to
take this uncertainty into consideration. However,
the full range of possible biotic and abiotic re-
sponses in the soil to climate change might not be
covered by the models used.
Estimated required changes in carbon input in the 21st
century
Based on climate projections and SOC models we esti-
mated future needs of OC input development to preserve
or increase carbon stocks of agricultural soils in Germa-
ny. Our simulations suggest that an OC input increase of
5 to 41% up to 2095 is needed to maintain the reference
SOC stock and thus compensate for future climate
change-driven SOC losses. There are considerable un-
certainties associated with the extrapolation of plant
growth into the future (Asseng et al.
2013
; Iizumi et al.
2017
). Estimated yield changes depend significantly on
the chosen influence factors, which mainly include cli-
matic variables (temperature, precipitation), atmospher-
ic CO
2
concentration, and management adaptations
428
Plant Soil (2021) 460:417–433
Page 13
(Asseng et al.
2013
; Deryng et al.
2014
; Ewert et al.
2005
). Ewert et al. (
2005
) estimated changes in crop
productivity for Europe based on climate change, atmo-
spheric CO
2
levels and technology development. They
estimated increases in crop productivity of + 163% for
the global economic and fossil fuel intensive world
(Special Report on Emissions Scenarios (SRES),
A1F1) and + 43% for the regional environmental world
(SRES B2) for the period 2000
–
2080 when considering
all three factors together. Where the estimation was only
based on climate change, yields decreased slightly or
remained constant up to 2080 (Ewert et al.
2005
).
Müller and Robertson (
2014
) even estimated yields as
decreasing by 12
–
21% (wheat) and 10
–
38% (maize)
from 2000 to 2050 for the climate scenario RCP8.5
when just considering climate factors and leaving out
CO
2
fertilization effects. Kersebaum and Nendel (
2014
)
simulated positive wheat yield changes (0.9-6.0%) for
Germany between the reference period (1961
–
1990)
and a future period (2021
–
2050) in response to climate
change (SRES-A1B) and elevated CO
2
, but the re-
sponse varied across regions and was influenced by soil
properties. As in the other studies, they also found
decreasing (-16%) yields when neglecting the CO
2
ef-
fect (Kersebaum and Nendel
2014
). The effect of tech-
nology development, especially breeding, outweighed
the effects of climate change and rising CO
2
concentra-
tion in the study of Ewert et al. (
2005
). Furthermore,
also declining SOC stocks might affect yields, poten-
tially inducing a feedback loop, since both are
interlinked through soil fertility (Henryson et al.
2018
;
Wiesmeier et al.
2015
). However, with reference to
SOC it will be important that not only yield but also
carbon input is enhanced. This is not necessarily the
case when crop yields are increased, e.g. due to changes
in harvest index (Freibauer et al.
2004
). Also
Taghizadeh-Toosi et al. (
2016
) and Hirte et al. (
2018
)
recently showed, that root biomass of crops is not nec-
essarily a function of shoot biomass. If the required OC
input increase would come from increasing above-
ground biomass only, the required increase in OC input
to achieve the reference SOC stock and compensate for
future climate change-driven SOC losses needs to be
higher (10
–
93%, Fig.
S5
). This is because roots contrib-
ute more to stabilized SOC than aboveground residues
(Kätterer et al.
2011
) which is considered by some
members of the model ensemble.
Although associated with some uncertainties elevat-
ed CO
2
levels and breeding may well lead to an increase
in plant growth and subsequent greater OC input.
However, if SOC losses are not only compensated for
but SOC stocks are also increased, the introduction of
additional management actions will be required. For
example, Poeplau and Don (
2015
) suggest replacing
bare fallow with cover crops during the winter period
to sequester more OC in agricultural soils. Moreover,
with rising temperatures thermal growing seasons also
lengthen (Ruosteenoja et al.
2016
), leading to an in-
crease in the thermal growing season by + 15 to +
90 days in Germany by the end of the century (2071
–
2100) (
https://www.regionaler-klimaatlas.de
, accessed
15.08.2019). A longer thermal growing season of up
to three months might offer the opportunity, with a
sufficient water supply, to add a further crop into the
rotation, thus having three crops in two years, which
would lead to a higher OC input. There are many other
management options such as residue retention,
integrated nutrient management, improved crop
rotation, agroforestry, mixed crop-livestock farming
systems, biochar application to soil or clay addition to
sandy soils that seem to be promising ways of enhancing
SOC stocks (Singh et al.
2018
). Besides increased OC
input, also the availability of sufficient nitrogen for
additional SOC storage needs to be ensured (Hungate
et al.
2003
). This is of particular importance under
elevated atmospheric CO
2
concentration, which has
been shown to increase C/N ratios of plant tissues (Gill
et al.
2002
; Luo et al.
2004
). However, the chosen
management options should be evaluated with respect
to their environmental, societal and economical effects.
Reported SOC change rates of mentioned agricultural
management practices range up to ~ 1 Mg C ha
−
1
a
−
1
(Freibauer et al.
2004
). This is considerably higher than
the projected climate change-driven SOC losses
(Table
5
). Therefore, an overcompensation of those
losses by appropriate agricultural management, i.e. car-
bon sequestration under climate change, is certainly
feasible at farm level. However, at the national level, it
will be more difficult to reach that goal, since climate
change is affecting all soils, while improved manage-
ment will most likely only be conducted on some.
An increase in OC input of 213 to 283%, correspond-
ing to an OC input of 8.1
–
9.8 Mg C ha
−
1
a
−
1
, in order to
increase SOC stocks annually by 4
‰
up to end of the
century seems unrealistic. Even if SOC stocks under
climate change scenario RCP8.5 were increased by 4
per 1000 only for the next 25 years followed by man-
agement to preserve SOC, high OC inputs (8.3 Mg ha
−
1
429
Plant Soil (2021) 460:417–433
Page 14
a
−
1
from 2038 onwards) would be required (data not
shown). Chenu et al. (
2019
) used Roth-C to estimate the
additional OC input to increase SOC stocks
(equilibrium) by 8% in 20 years for a set of pedo-
climatic conditions. In contrast, they calculated an addi-
tional OC input of 24% for temperate crops on sandy
soils and 29% on clayey soils. These values are much
smaller compared to the required OC input increase of
207% estimated in the extra simulation mentioned
above. This difference might be explained by the fact
that the SOC stocks in Chenu et al. (
2019
) were in
equilibrium, while the modeled SOC stocks in our study
are not. It stresses, that an increase of SOC stocks
requires a compensation of SOC losses first. Moreover,
Wiesmeier et al. (
2017
) concluded that a SOC stock
increase of 4 per 1000 is not possible for Bavaria.
Instead, they estimated, without considering climate
change, that a SOC stock increase of 1.2 per 1000 is
achievable for Bavaria if considerable changes in agri-
cultural management are implemented (Wiesmeier et al.
2017
).
To conclude, future SOC dynamics in German agri-
cultural soils under climate change are associated with
considerable uncertainties, which are more robustly rep-
resented with multi-model ensembles. In the literature,
contrasting results are found when extrapolating plant
growth into the future. Nevertheless, it appears possible
that projected warming-induced SOC losses could be
compensated for at least in part by future enhanced plant
growth, while the preservation and increase of current
SOC stocks would require more effort and adaptations
of land management. Increasing SOC stocks, which is
currently discussed as a negative emission technology,
appears to be challenging for German agricultural soils
due to the effects of climate change and the legacy of
former land use and management.
Supplementary Information
The online version contains sup-
plementary material available at
https://doi.org/10.1007/s11104-
020-04806-8
.
Acknowledgements
This study was funded by the German
Federal Ministry of Food and Agriculture as part of the German
Agricultural Soil Inventory. The soil dataset used in this study is
available in the OpenAgrar repository,
https://doi.org/10.3220
/DATA20200203151139
.
Funding
Open Access funding enabled and organized by
Projekt DEAL.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no con-
flict of interest.
Open Access
This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing,
adaptation, distribution and reproduction in any medium or format,
as long as you give appropriate credit to the original author(s) and
the source, provide a link to the Creative Commons licence, and
indicate if changes were made. The images or other third party
material in this article are included in the article's Creative Com-
mons licence, unless indicated otherwise in a credit line to the
material. If material is not included in the article's Creative Com-
mons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain
permission directly from the copyright holder. To view a copy of
this licence, visit
http://creativecommons.org/licenses/by/4.0/
.
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