Page 1
Earth Syst. Dynam., 12, 1037–1055, 2021
https://doi.org/10.5194/esd-12-1037-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research
ar
ticle
Soil organic carbon dynamics from agricultural
management practices under climate change
Tobias Herzfeld, Jens Heinke, Susanne Rolinski, and Christoph Müller
Potsdam Institute for Climate Impact Research, Member of the Leibniz Association,
P.O. Box 60 12 03, 14412 Potsdam, Germany
Correspondence:
Tobias Herzfeld (tobias.herzfeld@pik-potsdam.de)
Received: 19 May 2021 – Discussion started: 15 June 2021
Revised: 1 September 2021 – Accepted: 18 September 2021 – Published: 22 October 2021
Abstract.
Sequestration of soil organic carbon (SOC) on cropland has been proposed as a climate change
mitigation strategy to reduce global greenhouse gas (GHG) concentrations in the atmosphere, which in particular
is needed to achieve the targets proposed in the Paris Agreement to limit the increase in atmospheric temperature
to well below 2
◦
C. We analyze the historical evolution and future development of cropland SOC using the global
process-based biophysical model LPJmL, which was recently extended by a detailed representation of tillage
practices and residue management (version 5.0-tillage2). We find that model results for historical global estimates
for SOC stocks are at the upper end of available literature, with
∼
2650 Pg C of SOC stored globally in the
year 2018,
∼
170 Pg C of which is stored in cropland soils. In future projections, assuming no further changes in
current cropland patterns and under four different management assumptions with two different climate forcings,
RCP2.6 and RCP8.5, results suggest that agricultural SOC stocks decline in all scenarios, as the decomposition of
SOC outweighs the increase in carbon inputs into the soil from altered management practices. Different climate
change scenarios, as well as assumptions on tillage management, play a minor role in explaining differences
in SOC stocks. The choice of tillage practice explains between 0.2 % and 1.3 % of total cropland SOC stock
change in the year 2100. Future dynamics in cropland SOC are most strongly controlled by residue management:
whether residues are left on the field or harvested. We find that on current cropland, global cropland SOC stocks
decline until the end of the century by only 1.0 % to 1.4 % if residue retention management systems are generally
applied and by 26.7 % to 27.3 % in the case of residue harvest. For different climatic regions, increases in
cropland SOC can only be found for tropical dry, warm temperate moist, and warm temperate dry regions in
management systems that retain residues.
1
Introduction
To meet the targets of the Paris Agreement of 2015 to keep
the increase in global mean temperature well below 2
◦
C, and
especially for the ambitious target of below 1.5
◦
C, several
negative emission technologies which remove carbon diox-
ide (CO
2
) from the atmosphere have been proposed (Minx
et al., 2018; Rogelj et al., 2018, 2016). At the same time as
the climate is warming, the global human population is ex-
pected to increase to 9.7 billion people in 2050 and 10.9 bil-
lion by 2100 (United Nations et al., 2019), putting additional
pressure on future food production systems. Food production
alone has to increase by at least 50 % (FAO, 2019) or even
double by the year 2050, depending on dietary preferences,
demographical trends, and climate projections, if global food
demand is to be met (Bodirsky et al., 2015). Different agri-
cultural management practices have been proposed as carbon
(C) sequestration strategies to mitigate climate change and
increase the quality and health of the soil by increasing soil
organic carbon (SOC) content of cropland soils (Lal, 2004),
which also decreases the risk of soil erosion and soil degra-
dation (Lal, 2009).
Tillage influences many biophysical properties, such as
soil temperature and soil hydraulic properties (Snyder et
al., 2009), and can increase different forms of soil degra-
Published by Copernicus Publications on behalf of the European Geosciences Union.
Page 2
1038
T. Herzfeld et al.: Soil organic carbon dynamics from agricultural management practices under climate change
dation (Lal, 1993; Kurothe et al., 2014; Cerdà et al., 2009).
The potential of SOC sequestration for agricultural manage-
ment practices, e.g., the effect of no till, is debated in the
scientific community (Baker et al., 2007; Powlson et al.,
2014). Because tillage management is closely interrelated
with residue management (Guérif et al., 2001; Snyder et al.,
2009), these two practices should always be investigated si-
multaneously. Residue management can affect SOC stocks
and soil water properties, as residues left on the soil surface
can increase soil infiltration, reduce evaporation (Guérif et
al., 2001; Ranaivoson et al., 2017), and add soil organic mat-
ter into the soil (Maharjan et al., 2018). Soil moisture and
therefore plant productivity are also influenced by irrigation.
While irrigated systems generally tend to have higher SOC
stocks due to positive feedbacks on plant productivity, the
feedbacks and mechanisms on SOC development are still not
well understood (Humphrey et al., 2021; Emde et al., 2021).
The effectiveness of irrigation systems for SOC development
is influenced by climate and initial SOC stock, and it tends
to be higher in semiarid regions and less effective in humid
regions (Trost et al., 2013).
Minasny et al. (2017) have proposed the “4 per 1000 Soils
for Food Security and Climate” initiative, which aims to in-
crease global SOC sequestration by 0.4 % per year. They ar-
gue that under best-management practices, this target rate
could be even higher. This approach would translate into a
2–3 Pg C a
−
1
SOC increase in the first 1 m of the soil, which
is equivalent to about 20 %–35 % of global greenhouse gas
(GHG) emissions (Minasny et al., 2017). This proposal has
been criticized, as it overestimates the possible effect of SOC
sequestration potential through agricultural management (de
Vries, 2018; White et al., 2018). Field trials on SOC seques-
tration potentials show results with higher, as well as lower,
sequestration rates but only represent the local soil and cli-
matic conditions for the time of the experiment (Fuss et al.,
2018; Minx et al., 2018), which reduces the likelihood for
their validity on larger scales or over longer time periods.
Global total SOC stocks are estimated between 1500 Pg C
(excluding permafrost regions) (Hiederer and Köchy, 2011)
and up to 2456 Pg C for the upper 200 cm (Batjes, 1996) and
agricultural SOC stocks alone, which are subject to agri-
cultural management, are estimated to be between 140 and
327 Pg C depending on soil depth (Jobbágy and Jackson,
2000; Zomer et al., 2017). Since the beginning of cultiva-
tion by humans approximately 12 000 years ago, global SOC
stocks for the top 200 cm of soil have declined by 116 Pg C
because of agriculture by one estimate (Sanderman et al.,
2017). Management assumptions play an important role in
these estimates; e.g., Pugh et al. (2015) found that residue
removal and tillage effects contribute to 6 % and 8 % of total
land use change (LUC) emissions between the year 1850 and
2012 alone, which translates into biomass and soil C losses
of approximately 13.5 and 16 Pg C, respectively.
In this study, we use a modeling approach to quantify the
historical development of global cropland SOC stocks using
new data for agricultural management such as manure and
residue management, as well as a new dataset of the spatial
distribution of tillage practices. In addition, we investigate
the potential for SOC sequestration under different climate
change scenarios on current cropland.
2
Materials and methods
2.1
The LPJmL5.0-tillage2 model
The LPJmL5.0-tillage2 model combines the dynamic phe-
nology scheme of the natural vegetation (Forkel et al.,
2014), with version 5.0-tillage, which covers the terres-
trial nitrogen cycle (von Bloh et al., 2018), and the
representation of tillage practices and residue manage-
ment (Lutz et al., 2019a). The model code is available
at: https://doi.org/10.5281/zenodo.4625868 (Herzfeld et al.,
2021). All organic matter pools in vegetation, litter, and soil
in LPJmL5.0-tillage2 are represented by C pools and the cor-
responding N pools with variable C
:
N ratios. For soil car-
bon, the slow and fast soil pools are explicitly distributed
over five soil layers (Schaphoff et al., 2013). With the term
“SOC” we refer to the sum of all soil and litter C pools.
After the harvest of crops, root carbon is transferred to the
belowground litter pool. The incorporation of aboveground
residues into the soil is dependent on the chosen management
practices. Different tillage and residue management schemes,
as well as the accounting for direct effects of SOC on soil hy-
draulic properties and thus on soil organic matter (SOM) de-
composition and plant productivity, have been introduced in
the implementation of tillage practices in version 5.0-tillage
(Lutz et al., 2019a) and are thus explicitly considered here
(Fig. 1). The model accounts for an irrigation scheme for
green and blue water consumption (Rost et al., 2008) and
the effects of different irrigation systems (Jägermeyr et al.,
2015). Irrigation water is dynamically calculated and cou-
pled with the overall water balance between soil, vegetation,
and climate properties (Schaphoff et al., 2018).
In LPJmL5.0-tillage2, the amount of carbon in biomass,
which is either harvested or can be left on the field as crop
residue, is dependent on productivity (plant growth). Litter
pool sizes are determined by the amount of biomass that is
left on the field (i.e., not harvested) and the rate at which the
litter is decomposed. At decomposition, the model assumes
a fixed ratio of 40 % of C that is transferred from litter to the
soil carbon pools; the other 60 % of C is emitted to the at-
mosphere as CO
2
, as in von Bloh et al. (2018). N cycling is
included in the model, as explained in detail in von Bloh et
al. (2018), and follows similar principles as SOC decomposi-
tion, reflecting the actual C
:
N ratios of the decomposing ma-
terial. Applied N from manure, which is now explicitly con-
sidered in contrast to the previous model version LPJmL5.0-
tillage, is assumed to consist of equal shares of mineral and
organic N so that 50 % is added to the ammonium pool of
the first soil layer and the rest is added to the aboveground
Earth Syst. Dynam., 12, 1037–1055, 2021
https://doi.org/10.5194/esd-12-1037-2021
Page 3
T. Herzfeld et al.: Soil organic carbon dynamics from agricultural management practices under climate change
1039
Figure 1.
Carbon cycling on cropland and productivity feedbacks from plants to residues as well as soil stocks and soil water, as modeled in
LPJmL5.0-tillage. Arrows indicate fluxes; boxes and circles are stocks.
leaf litter nitrogen pool. While manure composition is highly
variable across animal type, feed, and treatment, a general
ratio of 1
:
2 of ammonium to total N in manure is in prin-
ciple supported by the ranges reported by Van Kessel and
Reeves (2002). The organic leaf litter nitrogen is quickly de-
composed and added to the ammonium pool of the soil. The
C part of the organic manure is allocated to the leaf litter C
pool (i.e., an easily degradable organic pool that can be left
on the soil surface or incorporated into the soil column by
tillage), with a fixed C
:
N ratio of 14.5 (IPCC, 2019). To-
tal fertilizer amounts (i.e., mineral fertilizer and manure) are
applied either completely at sowing or split into two appli-
cations per growing season. Manure is always applied at the
first application event at sowing. Only when total combined
fertilizer inputs (manure and mineral N) exceed 5 g N m
−
2
is half of the total fertilizer applied in a second application
as mineral fertilizer, which is applied after 40 % of the nec-
essary phenological heat sums to reach maturity have been
accumulated.
2.2
Simulation protocol
A list of the simulations carried out for this study is sum-
marized in Table 1. An initial spin-up simulation per general
circulation model (GCM) and Climate Research Unit grid-
ded Time Series (CRU TS) climate data of 7000 years is
conducted to bring SOC stocks into a dynamic prehistoric
equilibrium (SP-GCM/SP-CRU), in which the first 30 years
of weather data are cyclically recycled, mimicking stable cli-
mate conditions. A second GCM-specific spin-up simulation
to introduce land use dynamics starts in 1510 so that crop-
land older than that has reached a new dynamic equilibrium
by 1901 when the actual simulations start and land use his-
tory is accounted for otherwise. Simulations were run for
four groups: (a) historical runs from 1901–2018 using CRU
TS version 4.03 climate input (Harris et al., 2020) and in-
puts on historical management time series (which is subject
to the same spin-up procedures as the GCM-specific sim-
ulations), (b) historical simulations from 1901–2005 with
climate inputs from the four GCMs and historical manage-
ment time series, (c) future simulations using projections of
the four GCMs for the Representative Concentration Path-
ways RCP2.6 (low radiative forcing) and RCP8.5 (high ra-
diative forcing) and four different stylized management set-
tings – conventional tillage and residues retained (T_R), con-
ventional tillage and residues removed (T_NR), no till and
residues retained (NT_R), and no till and residues removed
(NT_NR) – and (d) simulations as in (c) but with [CO
2
] held
constant at the level of the year 2005 (379.8 ppmv) that are
used to quantify the CO
2
effect. All other inputs (land use,
https://doi.org/10.5194/esd-12-1037-2021
Earth Syst. Dynam., 12, 1037–1055, 2021
Page 4
1040
T. Herzfeld et al.: Soil organic carbon dynamics from agricultural management practices under climate change
N fertilizer, manure) for all future simulations were also held
constant at the year 2005 values. In future simulations, we ac-
counted for unlimited water supply from resources available
for irrigation. Additionally, the rainfed-to-irrigated cropland
pattern was held constant at the year 2005 pattern. An ad-
ditional simulation per GCM was conducted for which all
inputs, as well as management assumptions, are static after
2005. These are used to analyze the business-as-usual case
under constant land use (h_cLU). To compare the results to
literature values on the maximum potential of global SOC
stocks without land use, an additional simulation with poten-
tial natural vegetation (PNV) was conducted, with all land
assumed to be natural vegetation with internally computed
vegetation composition and dynamics.
2.3
Model inputs
We created input datasets for an explicit representation of
land use, fertilizer, manure, and residue management using
the MADRaT tool (Dietrich et al., 2020). Historic land use
patterns of shares of physical cropland, also separated into an
irrigated and rainfed area, as well as mineral fertilizer data
(application rate per crop in g N m
−
2
a
−
1
) for the period of
the years 1900 to 2015, are based on the Land-Use Harmo-
nization (LUH2v2) data (Hurtt et al., 2020), which provide
fractional land use patterns for the period of 850–2015 as
part of the Coupled Model Intercomparison Project – CMIP6
(Eyring et al., 2016). Manure application rates for the period
1860–2014 are based on Zhang et al. (2017) and account for
organic N. With MADRaT, we were also able to produce
data on CFT-specific (CFT: crop functional type) fractions
of residue rates left on the field (recycling shares) for the
period 1850–2015. We generated data on residue recycling
shares in 5-year time steps for the period 1965–2015 and in-
terpolated linearly between time steps to get an annual time
series. Between 1850 and 1965, default recycling shares for
cereals of 0.25, for fibrous of 0.3, for non-fibrous of 0.3, and
no use of 0.8 were assigned to 1850 and linearly interpolated
to the values of 1965. Cereals include temperate cereals, rice,
maize, and tropical cereals; fibrous crops include pulses, soy-
bean, groundnut, rapeseed, and sugarcane; non-fibrous crops
include temperate roots, and tropical roots; and no-use crops
include sunflower, others, pastures, bioenergy grasses, and
bioenergy trees. Information on conventional tillage and con-
servation agriculture (no till) management was based on Por-
wollik et al. (2019) for the period 1974–2010. Before 1974,
conventional tillage was assumed as the default management
on all cropland. We assume one tillage event after initial cul-
tivation of natural land, independent of the tillage scenario.
This assumption does not affect the results of future projec-
tions as we constrain our analysis to cropland that is already
cultivated in 2005.
Historical simulations were driven using the CRU TS ver-
sion 4.03 climate input (Harris et al., 2020) from 1901 to
2018. Since this dataset does not provide data before 1901,
the 30-year climate from 1901 to 1930 was used repeat-
edly for spin-up simulations covering the period before 1901.
Data on [CO
2
] were taken from ice core measurements (Le
Quéré et al., 2015) and the Mauna Loa station (Tans and
Keeling, 2021). Future simulations from 2006–2099 used cli-
mate scenarios from four GCMs taken from the Coupled
Model Intercomparison Project Phase 5 (CMIP5) in bias-
adjusted form as provided by the ISIMIP2b project (Frieler
et al., 2017; Hempel et al., 2013): HadGEM2-ES, GFDL-
ESM2M, IPSL-CM5A-LR, and MIROC5 for both a weak
climate forcing (Representative Concentration Pathway –
RCP – 2.6) and a strong climate forcing (RCP8.5) with corre-
sponding [CO
2
] levels. The GCM datasets provide inputs for
air temperature, precipitation, radiation, and [CO
2
]. The his-
toric period for these GCM-specific simulations was based
on bias-adjusted data from the GCMs rather than on CRU
data to avoid inconsistencies at the transition between his-
toric and future periods. Land use change in the future was
not analyzed in this context, as the SOC potential of the cur-
rent agricultural area was the focus of this investigation so
that land use patterns after 2005 were held constant after
2005. All results are presented as averages across the ensem-
ble of climate models per RCP unless stated otherwise. Addi-
tional simulations with constant [CO
2
] for both RCP2.6 and
RCP8.5 allow for the isolation of CO
2
fertilization effects.
Conventional tillage starts in 1700. For the period 1700–
1850, the residue extraction rate of the year 1850 is assumed.
The degree to which tillage affects soil properties and pro-
cesses depends on the tillage intensity, which is a combina-
tion of tillage efficiency and mixing efficiency. The fraction
of residues submerged (tillage efficiency) by tillage is set to
0.95. The mixing efficiency for tillage management is set to
0.9, representing a full inversion tillage practice, also known
as conventional tillage (White et al., 2010). The effects of
both mixing and tillage efficiency are described in detail in
Lutz et al. (2019a). The fraction of residues harvested in the
case of residue extraction is 70 % of all aboveground residues
(with the remaining 30 % of aboveground residues and all
roots left on the field). In the case without residue harvest,
100 % is left on the field, and only the harvested organs (e.g.,
grains) are removed.
2.4
Data analysis and metrics
Our analysis is based on simulated changes in cropland SOC
stocks as well as the contributing processes, including the
turnover rate, heterotrophic respiration, litterfall, and the net
primary production (NPP) of cropland areas. NPP is calcu-
lated following Schaphoff et al. (2018).
The turnover rate for cropland is calculated as
mtr
SOC
,
agr
=
rh
agr
SOC
agr
·
100
,
(1)
Earth Syst. Dynam., 12, 1037–1055, 2021
https://doi.org/10.5194/esd-12-1037-2021
Page 5
T. Herzfeld et al.: Soil organic carbon dynamics from agricultural management practices under climate change
1041
Table 1.
Overview of the different simulations conducted for this study. For more details and the purposes of the simulation, see the text. No
LU: no land use, PNV: potential natural vegetation.
Name
No. of sim.
Years
Climate input
Tillage
Residue
treatment
Fertilizer
Manure
LU dataset
Description
SP_CRU
SP_GCM
5
7000
CRU
TS
4.03/
HadGEM2_ES,
GFDL-
ESM2M,
IPSL-CM5A-
LR, MIROC5
Repeated
1901–1930
No LU
No LU
No LU
No LU
No LU
7000 years of PNV
spin-up until 1509
to compute a pre-
historic
dynamic
SOC equilibrium
SPLU_CRU
SPLU_GCM
5
390
CRU
TS
4.03/
HadGEM2_ES,
GFDL-
ESM2M,
IPSL-CM5A-
LR, MIROC5
Repeated
1901–1930
First-year
values of
Porwollik
et al. (2019)
First-year
values
of
MADRaT
First-year
values
of
LUH2v2
First-year
values
of
Zhang
et
al. (2017)
LUH2v2
(Hurtt
et
al., 2020)
390 years of spin-
up until 1900 to
compute the effects
of
LU
history,
which is used as
the starting point
for all simulations
h_PNV
1
1901–2018
CRU TS 4.03
1901–2018
No LU
No LU
No LU
No LU
No LU
PNV run until 2018
(with 390 years of
spin-up for better
comparability
to
LU runs), starting
from SP_CRU
h_dLU
2
1700–2018
CRU
TS
4.03,
from
1700–
1900 repeated
1901–1930,
1901–2018
afterward
Porwollik
et al. (2019)
MADRaT
(Dietrich et
al., 2020)
LUH2v2
(Hurtt
et
al., 2020)
Zhang
et
al. (2017)
LUH2v2
(Hurtt
et
al., 2020)
Historical
run
with
dynamic
LU, starting from
SPLU_CRU
h_cLU
2
1700–2018
CRU
TS
4.03,
from
1700–
1900 repeated
1901–1930,
1901–2018
afterward
Porwollik
et
al. (2019);
static
at
2005 level
MADRaT
(Dietrich et
al., 2020);
static
at
2005 level
LUH2v2
(Hurtt
et
al., 2020);
static
at
2005 level
Zhang
et
al. (2017);
static
at
2005 level
LUH2v2
(Hurtt
et
al., 2020);
static
at
2005 level
Historical run with
constant land use
(with
390
years
of spin-up as in
SPLU_CRU,
but
with the land use
pattern of 2005),
starting
from
SP_CRU
h_GCM
4
1901–2005
HadGEM2_ES,
GFDL-
ESM2M,
IPSL-CM5A-
LR, MIROC5
Porwollik
et al. (2019)
MADRaT
(Dietrich et
al., 2020)
LUH2v2
(Hurtt
et
al., 2020)
Zhang
et
al. (2017)
LUH2v2
(Hurtt
et
al., 2020)
CMIP5
histori-
cal scenario runs
used, starting from
SPLU_GCM
T_R_26/85
NT_R_26/85
T_NR_26/85
NT_NR_26/85
64
2006–2099
RCP2.6/RCP8.5
HadGEM2_ES,
GFDL-
ESM2M,
IPSL-CM5A-
LR, MIROC5
tillage/no
till
Residues
re-
tained/residues
removed
LUH2v2
(Hurtt
et
al., 2020);
static
at
2005 level
Zhang
et
al. (2017);
static
at
2005 level
LUH2v2
(Hurtt
et
al., 2020);
static
at
2005 level
CMIP5
future
runs with different
management
op-
tions, starting from
h_GCM
TRc05_26
TRc05_85
16
2006–2099
RCP2.6/RCP8.5
HadGEM2_ES,
GFDL-
ESM2M,
IPSL-CM5A-
LR, MIROC5
Porwollik
et
al. (2019);
static
at
2005 level
MADRaT
(Dietrich et
al., 2020);
static
at
2005 level
LUH2v2
(Hurtt
et
al., 2020);
static
at
2005 level
Zhang
et
al. (2017);
static
at
2005 level
LUH2v2
(Hurtt
et
al., 2020);
static
at
2005 level
CMIP5 future runs
with
tillage
and
residue
manage-
ment
constant
at
2005 level, starting
from h_GCM
https://doi.org/10.5194/esd-12-1037-2021
Earth Syst. Dynam., 12, 1037–1055, 2021
Page 6
1042
T. Herzfeld et al.: Soil organic carbon dynamics from agricultural management practices under climate change
with mtr
SOC
,
agr
as the mean turnover rate for cropland SOC
(% a
−
1
), SOC
agr
as the SOC content for cropland (g), and
rh
agr
as the heterotrophic respiration for cropland (g a
−
1
).
Decomposition of organic matter pools follows the first-
order kinetics described in Sitch et al. (2003). Total het-
erotrophic respiration (
R
h
) accounts for 60 % of directly de-
composed litter (
R
h
,
litter
) and respiration of the fast and slow
soil pools (decomposition rate of 0.03 and 0.001 a
−
1
, respec-
tively). From the 40 % remaining litter pool, 98.5 % is trans-
ferred to the fast soil C pool and 1.5 % to the slow soil C
pool.
R
h
,
agr
=
R
h
,
litter
,
agr
+
R
h
,
fastSoil
,
agr
+
R
h
,
slowSoil
,
agr
(2)
Cropland litterfall (
C
litterfall
,
agr
; g C a
−
1
) is calculated by con-
sidering root, stem, and leaf carbon as a function of residue
recycling shares:
C
litterfall
,
agr
=
(
C
root
,
CFT
+
((
C
leaf
,
CFT
+
C
stem
,
CFT
)
·
f
res
,
CFT
))
·
f
cell
,
agr
,
(3)
with
C
root
,
CFT
being the C pools of crop roots per CFT,
C
leaf
,
PFT
the C pool of crop leaves per CFT,
C
stem
,
PFT
the
stems and mobile reserves per CFT,
f
res
,
CFT
the residue frac-
tion which is returned to the soil per CFT, and
f
cell
,
agr
the
fraction of agricultural area of the cell. The h_dLU_cropland
scenario uses the results from the h_dLU simulation and
accounts for the cropland SOC only, by taking the crop-
land area at the specific time point into account. The
h_dLU_area05 scenario, on the other hand, also uses the re-
sults from the h_dLU simulation as described in Table 1 but
accounts for all the area which is either already cropland or
will become cropland at any point in time until 2005. To cal-
culate the historical losses of SOC from land use change in
the h_dLU_area05 scenario, the fraction of SOC under PNV
which will become cropland is combined with the historical
cropland SOC parts and calculated as
SOC
LUC
,t
=
d
SOC
,
pnv
,t
·
area
agr
,
2005
−
area
agr
,t
+
d
SOC
,
agr
,t
·
area
agr
,t
,
(4)
where
d
SOC
,
pnv
,t
is the SOC density (g m
−
2
) for PNV area at
time step
t
, which will become cropland in the future, calcu-
lated as
d
SOC
,
pnv
,t
=
d
SOC
,
cell
,t
·
area
cell
−
d
SOC
,
agr
,t
·
area
agr
,t
area
pnv
,t
,
(5)
where
d
SOC
,
pnv
,t
,
d
SOC
,
cell
,t
, and
d
SOC
,
agr
,t
are the SOC den-
sities (g m
−
2
) for the PNV part within the cell, the density
for the entire cell, and the agricultural part within the cell, re-
spectively, at time step
t
(year); area
pnv
,t
and area
agr
,t
are the
corresponding areas of PNV and agriculture (m
−
2
) at time
step
t
; and area
cell
is the area of the entire cell, which does
not change over time. We considered different climatic re-
gions such as tropical wet, tropical moist, topical dry, warm
temperate moist, warm temperate dry, cold temperate moist,
cold temperate dry, boreal moist, and boreal dry regions, fol-
lowing the IPCC climate zone classification (IPCC, 2006;
Fig. S1 in the Supplement), using averaged climate inputs for
the period between the years 2000 and 2009. Polar dry, polar
moist, and tropical montane regions were excluded from this
analysis, as these regions do not include any cropland.
3
Model performance
Modeled global average SOC stocks (period 2000–2009
and year 2018) are compared with previous model versions
and literature estimates (Table 2). Simulated SOC stocks in
LPJmL5.0-tillage2 exhibit higher SOC content compared to
the LPJmL5.0 (von Bloh et al., 2018) model version and LPJ-
GUESS (Olin et al., 2015), with total average global SOC
stocks of 2640 Pg C for simulations with land use (h_dLU)
and 2940 Pg C for simulations with PNV only and no land
use (h_PNV). The simulated stocks correspond well to es-
timates by Carvalhais et al. (2014) for global averages but
are lower for cropland SOC stocks. Total SOC stocks sim-
ulated by LPJmL5.0-tillage2 are 2640 Pg for the entire soil
column of 3 m, which is 300 Pg higher than estimates pro-
vided by Jobbágy and Jackson (2000). Global SOC for PNV
is 2580 Pg for the upper 2 m, which compares well with esti-
mates between 2376 and 2476 Pg provided by Batjes (1996),
who reported SOC stocks for the upper 2 m of soil. Global
average cropland SOC stocks between the years 2000 and
2009 as well as for the year 2018 for the entire soil col-
umn are estimated to be 170 Pg C, which is higher than es-
timates of 148–151 Pg C by Olin et al. (2015). Zomer et
al. (2017) reported cropland SOC stocks of 140 Pg C for the
upper 0.3 m of soil, which is higher than the cropland SOC
stocks of 75 Pg C simulated for the upper 0.3 m in LPJmL.
Ren et al. (2020) reported cropland SOC stocks for the first
0.5 m of soil to be 115 Pg C for the period 2000–2010, which
is higher than cropland SOC of 95 Pg C for the upper 0.5 m
in LPJmL. Scharlemann et al. (2014) conducted a literature
review on global SOC stock and found a wide range of esti-
mates (504–3000 Pg C) and variability across time and space
as well as a high dependency on soil depth, with a median
global SOC stock of 1460 Pg C. Generally, simulated SOC
stocks by LPJmL5.0-tillage2 correspond well to literature
and other model estimates.
4
Results
4.1
Historical development of cropland NPP and SOC
stocks
During the simulation period, cropland NPP increases in the
dynamic LU simulation (h_dLU) from 0.7 Pg C a
−
1
in 1700
to 4.7 Pg C a
−
1
in 2018, while cropland SOC increases from
18 Pg C to a total of 171 Pg C (Fig. 2a and c) in the year
2018. The increase in cropland SOC can be explained by
Earth Syst. Dynam., 12, 1037–1055, 2021
https://doi.org/10.5194/esd-12-1037-2021
Page 7
T. Herzfeld et al.: Soil organic carbon dynamics from agricultural management practices under climate change
1043
Table 2.
Global SOC pools (Pg C) for the LPJmL5.0-tillage2, LPJmL5.0, and LPJ-GUESS model compared to literature estimates. Values
are averages for the period 2000–2009, for the year 2018, and the upper 0.3, 1, and 2 m of soil. PNV values are simulations with potential
natural vegetation only (no land use); global SOC average includes PNV and land use.
Model estimates
Literature estimates
LPJmL5.0-
tillage2 (this
study)
LPJmL5.0
(von
Bloh
et al., 2018)
LPJ-
GUESS
(Olin et al.,
2015)
Carvalhais
et al. (2014)
Batjes
(1996)
Jobbágy
and
Jack-
son (2000)
Zomer et
al. (2017)
Scharlemann
et al. (2014)
Global SOC PNV only
2940
1
,
a
,
2960
2
,
a
,
2580
b
,
1
,
2185
c
,
1
,
1555
d
,
1
2344
1
,
a
1671
3
–
2376
b
,
4
–
2476
b
,
4
–
–
–
Global SOC average
2640
1
,
a
,
2645
2
,
a
,
2295
b
,
1
,
1910
c
,
1
,
1300
d
,
1
2049
1
,
a
1668
3
2397
4
(1837
x
–
3257
y
)
–
1933
b
,
2344
a
–
1460
(504
d
–
3000
e
)
Cropland SOC
170
1
,
a
,
170
2
,
a
,
145
b
,
1
, 115
c
,
1
,
75
d
,
1
–
148
3
327
4
(242
x
–
460
y
)
–
210
b
,
248
a
140
d
–
Values are estimates for
a
the entire soil column,
b
the upper 2 m of soil,
c
the upper 1 m of soil,
d
the upper 0.3 m of soil, and
e
not indicated.
Year of estimate value:
1
2000–2009,
2
2018,
3
1996–2005,
4
not indicated.
x
Percentile 2.5,
y
percentile 97.5.
an increase in cropland area (Fig. S2B in the Supplement).
During the same time, harvested C increases from 0.1 to
2.0 Pg C a
−
1
. The ratio of harvested C to cropland NPP in-
creases with time, especially after the year 1900 (Fig. 2b),
as more material is harvested compared to cropland NPP.
The aggregated SOC stock on all land that is cropland in
the year 2005 declines substantially, especially after the year
1900 (red line in Fig. 2c), which reflects the decline in crop-
land SOC density (Fig. S2a in the Supplement). We also find
that cropland SOC density steadily increases between 1700
and 1950 and decreases since 1950 (Fig. S2a in the Supple-
ment). Simulations with a constant land use pattern of 2005
(h_cLU) for cropland NPP and cropland SOC show no sub-
stantial dynamics (Fig. 2a and c). These simulations are not
entirely insightful because they do not account for the histor-
ical increase in inputs, e.g., fertilizer.
In contrast to the scenario with dynamic land use and the
ones with constant land use, the h_dLU_area05 scenario de-
scribes a combination of historical cropland SOC and his-
torical SOC of natural vegetation (calculated as described
in Eqs. 4 and 5), which is or has been cropland until the
year 2005. This describes the SOC dynamics of all land that
is subject to historical land use change (LUC) (Fig. 2c). Loss
of historical SOC is calculated as the difference between the
years 1700 and 2018 on the land area that was cropland at
any point in time (Fig. 2c, red line). Through this approach,
we calculate a total historical SOC loss of 215 Pg C. Crop-
land SOC stocks increase over time (Fig. 2c, black line), re-
flecting the increase in cropland area. PNV has a higher SOC
density, and therefore SOC stock, before the conversion to
cropland (Fig. S2a in the Supplement). For the calculation
of SOC loss, we only considered the area that is converted
from PNV to cropland at any point in time between 1700 and
2018 in post-processing according to Eqs. (4) and (5). Be-
cause SOC density is generally lower in cropland compared
to PNV (Fig. S2a in the Supplement), SOC is lost after con-
version (Fig. 2c, red line).
4.2
Future soil carbon development with idealized
management under climate change
Future cropland SOC stock development was analyzed con-
sidering two different radiative forcing pathways (RCPs)
with four different climate scenarios (GCMs) per RCP and
four idealized management assumptions (Table 2). To esti-
mate the SOC sequestration potential on current cropland
and to exclude the influence from LUC, the cropland area
was kept constant at the year 2005 pattern. Results for future
SOC development show that the maximum decrease in SOC
stocks on current global cropland area between the year 2005
and the end of the century occurs in the scenario with no till
applied on global cropland, no residues retained, and RCP8.5
climate (NT_NR_85). Total cropland SOC loss for this sce-
nario is evaluated as 38.4 Pg C, or 28.1 % in relative terms
compared to the SOC stocks in the year 2005. All manage-
ment systems that extract residue from the field show a strong
decrease in cropland SOC stocks, independent of the climate
scenario (Fig. 3b). Differences for cropland SOC develop-
https://doi.org/10.5194/esd-12-1037-2021
Earth Syst. Dynam., 12, 1037–1055, 2021
Page 8
1044
T. Herzfeld et al.: Soil organic carbon dynamics from agricultural management practices under climate change
Figure 2.
Plots for cropland NPP and harvested C
(a)
, percentage of harvested C to cropland NPP in h_dLU
(b)
and SOC for cropland
stocks, and historical SOC losses from LUC
(c)
for the years 1700–2018 for simulations with transient land use (h_dLU), constant land use
of 2005 (h_cLU), transient land use, and SOC development from land use change including cropland area and historical PNV area that will
be converted until the year 2005 (h_dLU_area05).
ment between different tillage systems as well as between
the two radiative forcing pathways RCP2.6 and RCP8.5 are
small. Management systems that retain residue on the field
after harvest show the smallest reduction in cropland SOC
stocks, with a maximum reduction of 5.1 Pg C (equivalent
to 3.8 % decline) in the T_R_26 management system. Dif-
ferences between GCM-specific climate scenarios or radia-
tive forcing pathways (RCPs) were small in comparison to
differences in residue management assumptions for SOC,
turnover rates, and litterfall rates (Fig. 3) but larger than dif-
ferences in assumptions on tillage systems. Only for agri-
cultural NPP (Fig. 3a) were differences in radiative forcing
pathways the main determinant of NPP dynamics, followed
by GCM-specific climate scenarios.
Stocks of cropland SOC and turnover rates (Fig. 3c)
initially increase in systems that retain residues, such as
T_R and NT_R, after the change in management after the
year 2005 (Fig. 3b and c), as more residual C is added to the
soil column in comparison to the historic residue removal
rates (Fig. 3d). Turnover rates are higher for the high radia-
tive forcing pathway RCP8.5 in comparison to RCP2.6. The
simulated cropland NPP (Fig. 3a) is sensitive to the radiative
forcing, as the level of NPP is higher in the high-end RCP8.5
scenario and lower in the lower-end RCP2.6 scenario. This is
because of the strong response of NPP to CO
2
fertilization,
which overcompensates for the climate-driven reduction in
NPP (compare Fig. S3 in the Supplement). NPP is less sen-
sitive to the assumptions on tillage practices in comparison
to the effects of assumptions on residue management. The
no-till and residue system (NT_R) results in the highest NPP
mainly due to water-saving effects caused by the surface lit-
ter cover, which reduces evaporation from the soil surface
and at the same time increases infiltration of water into the
soil. NPP increases steadily until 2099 in RCP8.5 scenarios
because of the CO
2
fertilization effects (compare Fig. S3 in
the Supplement). In RCP2.6, NPP first slightly increases and
then decreases until the end of the century in all tillage and
residue scenarios. However, the ranking of management ef-
fects is insensitive to the radiative forcing pathway: no till
and residues (NT_R) results in the highest NPP, tillage and
no residues (T_NR) results in the lowest values.
4.3
Regional cropland SOC analysis
Simulation results show that globally aggregated SOC stocks
on current cropland decline until the end of the century for
all management systems, but there are regional differences
(Fig. 4). We find that in some regions, cropland SOC can in-
crease until the end of the century, even though global sums
indicate a total decline. For cropland SOC density, increases
between the years 2006 and 2099 can be found for T_R
and NT_R management systems for more than a third of the
global cropland area, most clearly in regions in Europe, In-
dia, Pakistan, Afghanistan, southern Chile, southern Mexico,
eastern China, and the southeastern USA (Fig. 4c and d). His-
torically, regions which have already shown an increase in
cropland SOC density from 1900 until today, such as France
and Pakistan, or a decrease, such as Canada and Argentina,
also tend to continue this development in the future (see plots
in Fig. 4 for exemplary cells). In systems in which residues
are not returned to the soil (T_NR and NT_NR), global crop-
land SOC density change is dominated by a decline.
Results for different climatic regions suggest that the dif-
ference between RCP2.6 and RCP8.5 radiative forcing only
plays a minor role for cropland SOC stock development
(Fig. 5). Findings suggested that a positive median increase
in cropland SOC density between the years 2006 and 2099
can be found in warm temperate moist, warm temperate dry,
Earth Syst. Dynam., 12, 1037–1055, 2021
https://doi.org/10.5194/esd-12-1037-2021
Page 9
T. Herzfeld et al.: Soil organic carbon dynamics from agricultural management practices under climate change
1045
Table 3.
Summary of absolute and relative global cropland SOC stock change between the years 2006 and 2099 for different management
systems for RCP2.5 and RCP8.5 as averages across all four GCMs.
Management
Absolute cropland SOC change
Relative cropland SOC change
2006–2099 (Pg C)
2006–2099 (%)
RCP2.6
RCP8.5
RCP2.6
RCP8.5
Tillage and residues (T_R)
−
5
.
1
−
4
.
4
−
3
.
8
−
3
.
2
Tillage and no residues (T_NR)
−
37
.
6
−
38
.
1
−
27
.
5
−
27
.
8
No till and residues (NT_R)
−
3
.
6
−
3
.
2
−
2
.
6
−
2
.
3
No till and no residues (NT_NR)
−
37
.
8
−
38
.
4
−
27
.
7
−
28
.
1
Tillage and residue constant as in the year 2005 (TRc05)
−
24
.
1
−
24
.
0
−
17
.
6
−
17
.
6
and boreal regions for RCP2.6 (GCM average) for the tillage
and residue (T_R) and the no-till and residue (NT_R) man-
agement systems (Fig. 5a). The total aggregated cropland
SOC change for each climate region depends on the crop-
land extent of the region. The smallest amounts of cropland
are found in boreal moist and dry regions, which results in a
total cropland SOC stock change of negligible size (Fig. 5b
and d). Total increases in cropland SOC stocks can be found
for both RCP2.6 (Fig. 5a and b) and RCP8.5 (GCM average)
(Fig. 5c and d) for tropical dry, warm temperate moist, and
warm temperate dry regions in the tillage and residue (T_R)
and the no-till and residue (NT_R) management systems. For
all regions across all simulations, in management systems in
which residues are not returned to the soil, cropland SOC
stocks decrease. The highest absolute losses of total crop-
land SOC stocks for these systems (T_NR and NT_NR) can
be found in cold temperate dry climates, followed by tropical
moist and warm temperate dry regions, which are the regions
with major cropland shares.
Regional results also indicate stronger differences between
GCM-specific climate scenarios within the same radiative
forcing pathway (RCP). The highest positive cropland SOC
stock response can be found for GCM GFDL-ESM2M in
both RCP2.6 and RCP8.5 for the tillage and residue (T_R)
and the no-till and residue (NT_R) systems for warm temper-
ate dry climates, while the positive response for tropical dry
and warm temperate moist climates is lower compared to the
other three GCMs (compare Fig. S5D and S6D in the Sup-
plement). Results for the IPSL-CM5A-LR climate scenarios
for both RCP2.6 and RCP8.5 generally show the most nega-
tive response for cropland SOC density change and cropland
SOC stock change, followed by HadGEM2_ES.
The comparison of cropland affected in RCP2.6 and
RCP8.5 indicates that most regions show effects with the
same direction of response in SOC density, so either it de-
creases or increases in both RCP2.6 and RCP8.5, which is
highlighted by the blue and orange regions in Fig. 6. Red
cells, which indicate that the effects in both RCPs go in the
opposite direction, can only be found in a few regions, e.g.,
the United States and Turkey. In total, between 50 and 53 mil-
lion hectares (Mha) of cropland show the opposite directions
globally for the tillage and residue (T_R) and the no-till and
residue (NT_R) systems, while this is halved (between 27
and 29 Mha) for the tillage and no residue (T_NR) and no-
till and no residue (NT_NR) management system.
5
Discussion
5.1
SOC development in the past and losses due to
land use change
Historical simulations show that the conversion of natural
land to cropland caused SOC losses of 215 Pg C between
the years 1700 and 2018 (Fig. 2c). Soil C density and NPP
in natural vegetation are higher compared to those found in
croplands, which results in C losses after conversion of nat-
ural land to cropland. NPP in croplands is often lower com-
pared to NPP in natural vegetation, as the cultivated period
is typically shorter than the vegetative period in which nat-
ural vegetation is productive so that cultivated plants have
less time to accumulate C. Further, cropland is cultivated
and crops are harvested, which results in the extraction of
NPP in the form of harvested material; this leads to a fur-
ther decline of SOC stocks. Cropland expansion is the main
driver for increases in total cropland SOC stocks, as cropland
SOC density steadily increased since the year 1700 start-
ing at 7 kg m
−
2
and reached its maximum in the year 1960
at 13 kg m
−
2
, but since then cropland SOC density has de-
creased down to 11 kg m
−
2
today (Fig. S2a in the Supple-
ment). SOC density on cropland showed this trend, even
though fertilizer use increased since the 1960s, which was
found to be able to promote SOC sequestration, especially in
temperate regions (Alvarez, 2005). Since the 1960s, cropland
expansion has slowed down, but global yields have, on aver-
age, more than doubled (Pingali, 2012; Ray et al., 2012; Wik
et al., 2008). Ren et al. (2020) show that historical cropland
SOC increase was mainly attributed to cropland expansion,
which is in agreement with the findings here. The ratio of
harvested C to cropland NPP increases with time (Fig. 2b) so
that the increase in yields does not have a positive effect on
cropland SOC, as more and more C is extracted from the soil
in the form of harvested material.
https://doi.org/10.5194/esd-12-1037-2021
Earth Syst. Dynam., 12, 1037–1055, 2021
Page 10
1046
T. Herzfeld et al.: Soil organic carbon dynamics from agricultural management practices under climate change
Figure 3.
Global sums for cropland for NPP
(a)
, SOC
(b)
, turnover rate
(c)
, and litterfall
(d)
from 2000–2005 for default management inputs
and from 2006–2099 under constant cropland area of 2005 for five different management scenarios and two RCPs. Presented are the mean
values across all four GCMs as lines. The spread across all GCMs is depicted as bars in the year 2100. The numbers _26 and _85 describe the
climate forcing RCP2.6 (e.g., TRc05_26) and RCP8.5 (e.g., TRc05_85). Green: residues retained (R), red: residues removed (NR), dashed:
RCP2.6, solid: RCP8.5, light color: no till (NT), dark color: tillage (T). Tillage and residue management are held constant at 2005 level in
TRc05: tillage and residues left on the field (T_R), tillage and residues removed (T_NR), no till plus residues left on the field (NT_R), and
no till and residues removed (NT_NR). Dynamics prior to 2005 (all scenarios equal) mostly show the expansion of cropland until 2005 so
that total SOC increases because the area increases. Turnover rates between 2000 and 2005 increase because decomposition rates are high
on freshly deforested land.
It was estimated that conversion of natural land to culti-
vated land can result in SOC loss of 30 % to 50 % (Lal, 2001).
Sanderman et al. (2017) estimated historical global SOC
losses of natural land to cropland conversion as 133 Pg C, of
which most of the losses occurred in the last 200 years. Pugh
et al. (2015) modeled C emissions from LUC accounting for
agricultural management, such as harvesting and tillage, and
found maximum C losses in vegetation and SOC of 225 Pg C
since the year 1850. Le Quéré et al. (2018) also estimated
the C flux to the atmosphere due to LUC, including defor-
estation, to be 235 Pg C (
±
95) since the year 1750.
5.2
Future cropland SOC development on current global
cropland
Future SOC stocks on current cropland depend on climate
and management. We find that current cropland remains a
source of C, even though the decline of SOC on current crop-
land can be reduced through management. The most efficient
Earth Syst. Dynam., 12, 1037–1055, 2021
https://doi.org/10.5194/esd-12-1037-2021
Page 11
T. Herzfeld et al.: Soil organic carbon dynamics from agricultural management practices under climate change
1047
Figure 4.
Simulated cropland SOC change (kg m
−
2
) between the years 2006 and 2099 (kg m
−
2
) for RCP2.6 for GCM HadGEM2-ES
for the four different management options (T_R, NT_R, T_NR, and NT_NR). Plots 1–4 show examples of SOC development (kg m
−
2
)
from the years 1900 to 2099 for different explanatory regions as shown on the map
(d)
(NT_R). The difference maps of affected change
categories between RCP2.6 and RCP8.5 are shown in Fig. 5. Maps for GFDL-ESM2M, IPSL-CM5A-LR and MIROC5, and RCP8.5 are in
the Supplement (Figs. S7 to S13).
measure to reduce SOC losses on cropland is residue man-
agement. In the model, SOC is formed by C transfer from lit-
ter to the soil through decomposition fluxes (Schaphoff et al.,
2018), bioturbation, or tillage practices (Lutz et al., 2019a).
Residues left on the field are added to the litter C pool, where
they are subject to decomposition. Root C is added to the be-
lowground litter pool, with a specific decomposition accord-
ing to soil temperature and moisture conditions. Stubbles
and root biomass enter the litter pool after harvest, while the
amount of residue extracted or retained depends on crop pro-
ductivity. The addition of fresh material from crop residues
increases the turnover rate in the soil, as this material is more
easily decomposed than the remaining SOC stocks from the
historical natural ecosystems. In the model, SOC decompo-
sition is only driven by the temperature and moisture of the
litter and soil layers, whereas the chemical composition of
the residues is not taken into account. While the N content of
the available material can strongly influence the decomposi-
tion and humification of residues and the formation of SOM
(Hatton et al., 2015; Averill and Waring, 2018), this effect is
not considered here and should be included in future model
development.
The different management aspects show the same ranking
in importance under both radiative forcing pathways, and the
changes in cropland SOC only differ slightly. Cropland SOC
stocks at the end of the century vary only between the two
https://doi.org/10.5194/esd-12-1037-2021
Earth Syst. Dynam., 12, 1037–1055, 2021
Page 12
1048
T. Herzfeld et al.: Soil organic carbon dynamics from agricultural management practices under climate change
Figure 5.
Box plots of cropland SOC density change (kg m
−
2
) and bar plots of total cropland SOC change (Pg C) between the years 2006
and 2099, averaged across the four GCMs (HadGEM2_ES, GFDL-ESM2M, IPSL-CM5A-LR, MIROC5) in RCP2.6
(a, b)
and RCP8.5
(c,
d)
for the climatic regions classified by the IPCC (2006) and the four management systems T_R, NT_R, T_NR, and NT_NR. The same plots
for each GCM can be found in Figs. S5 and S6 in the Supplement;
n
is the number of cropland cells included in each climate region.
RCPs between
−
0
.
6 % and
+
0
.
6 % for all four management
systems. This is caused by a compensating effect of higher
productivity by elevated CO
2
under RCP8.5, which coun-
teracts the increase in turnover rates at higher temperatures
(see Fig. S3 in the Supplement for comparison with constant
[CO
2
] simulations).
Even though experiments have shown that tillage can re-
duce SOC stocks significantly compared to no till (Abdalla
et al., 2016; Kurothe et al., 2014), tillage management only
has small effects on aggregated global cropland SOC in our
simulations. Tillage practices account for differences in crop-
land SOC stocks of 0.9 % and 1.3 % between T_R and NT_R
in 2099 for RCP8.5 and RCP2.6, respectively, and less than
0.2 % between T_NR and NT_NR for both RCPs. Differ-
ences in SOC stocks on cropland between the tillage sys-
tems decrease if residues are not retained on the field. NPP
responds more strongly to the tillage system, which is likely
to be driven by secondary effects (e.g., no-till management
Earth Syst. Dynam., 12, 1037–1055, 2021
https://doi.org/10.5194/esd-12-1037-2021
Page 13
T. Herzfeld et al.: Soil organic carbon dynamics from agricultural management practices under climate change
1049
Figure 6.
Difference maps of change categories for cropland SOC density change between RCP2.6 and RCP8.5 from the year 2006 until
2099 for GCM HadGEM_ES in each management system. Orange areas indicate a reduction in cropland SOC density between the years 2006
and 2099 in both RCPs, and blue areas show an increase in SOC density; in light yellow areas no change occurs, and for red areas SOC
density change occurs in opposite directions in RCP2.6 and RCP8.5. The numbers in brackets (0 to 3) correspond to the categories in the
histogram.
increases soil moisture and nutrient availability from miner-
alization), but shows no long-term effect on SOC stock de-
velopment.
With the given complexity in responses to tillage, the ap-
plication of no tillage has been discussed ambiguously in
the literature (Chi et al., 2016; Derpsch et al., 2014, 2010;
Dignac et al., 2017; Powlson et al., 2014). The LPJmL5.0-
tillage model is capable of reproducing these process interac-
tions and diversity in results well (Lutz et al., 2019a). Tillage
systems thus need to be selected based on local conditions,
but we find these to be less important than residue man-
agement. Given this dependency of the SOC accumulation
potential on climatic and management conditions, there are
strong regional differences in the response of SOC to changes
in management. In line with Stella et al. (2019), who investi-
gated the contribution of crop residues to cropland SOC con-
servation in Germany and found a decrease in SOC stocks
until 2050, if residues are not returned to the soil, we find
that large parts of western Europe can indeed increase the
SOC stocks under management systems in which residues
are retained on the field. Zomer et al. (2017) analyzed the
global sequestration potential for SOC increase in cropland
soils and found the highest potentials in India, Europe, and
the midwestern USA, results which correspond well to our
findings. Also, the duration of the historical cultivation of the
cropland is an important aspect in the ability to sequester C
in current cropland soils. Stella et al. (2019) find the highest
SOC sequestration potentials in soils with low SOC stocks
(i.e., in highly degraded soils).
5.3
Potential for SOC sequestration on cropland and
recommendations for future analysis
For the past years, there has been an ongoing debate on
how much SOC can be stored in agricultural soils through
adequate management as a climate change mitigation strat-
https://doi.org/10.5194/esd-12-1037-2021
Earth Syst. Dynam., 12, 1037–1055, 2021
Page 14
1050
T. Herzfeld et al.: Soil organic carbon dynamics from agricultural management practices under climate change
egy (Baker et al., 2007; Batjes, 1998; Lal, 2004; Luo et al.,
2010; Stockmann et al., 2013). For example, globally applied
no-till management on cropland was estimated to have an
SOC sequestration potential of 0.4–0.6 Gt CO
2
a
−
1
(Powlson
et al., 2014). Additionally, the sequestration of SOC can be
beneficial to soil quality and productivity and minimize soil
degradation (Lal, 2009, 2004). An increase in cropland ir-
rigation can effectively influence SOC development (Trost
et al., 2013; Bondeau et al., 2007). In our simulations with
LPJmL5.0-tillage2, we find that on current cropland, these
sequestration potentials cannot be achieved by varying tillage
practices and residue removal rates, even though the residue
management system is important for cropland SOC dynam-
ics. At the same time, we account for an unlimited supply
of water resources available for irrigation, reducing the con-
straint on SOC development by limitations from irrigation
water. As such, our estimates of SOC development should
tend to be optimistic in all regions where irrigation is applied
but where water resources are limited.
There is a general uncertainty in how experimental find-
ings can be scaled up, e.g., as demonstrated by a review con-
ducted by Fuss et al. (2018). While process-based modeling
as applied here can take environmental conditions into ac-
count and can compare different management aspects, it is
still subject to various uncertainties. One crucial aspect is
the history of land use systems, including the trend in land
productivity. Karstens et al. (2020) show that global histori-
cal cropland SOC stocks are declining even though cropland
inputs are increasing at the same time. Depending on the
agricultural management option, it is argued that the max-
imum sequestration potential is reached after the soil has
a new higher equilibrium state, which can be reached after
10–100 years, depending on climate, soil type, and SOC se-
questration option (Smith, 2016). The IPCC suggests a de-
fault saturation time of the soil sink of 20 years, after which
the equilibrium is reached, which then has to be maintained
to avoid additional release of CO
2
(IPCC, 2006). Increas-
ing cropland SOC in a first step can be achieved by adding
more C to the soil than is lost by respiration, decomposi-
tion and harvest, and soil disturbance. Maintaining SOC lev-
els on cropland after the soil has reached a new equilibrium
will require the application of management strategies that do
not deplete SOC. The “4 per 1000” initiative requires an-
nual SOC sequestration on croplands of approximately 2 to
3 Pg C a
−
1
in the top 1 m of cropland soils, which was criti-
cized to be unrealistic (de Vries, 2018; White et al., 2018). In
this analysis, only two management options affecting SOC,
tillage treatment and residue management, are considered.
High SOC sequestration potentials on cropland are argued
to only be achieved by applying a variety of management op-
tions, e.g., additional restoration of degraded land (Griscom
et al., 2017; Lal, 2003), agroforestry (Lorenz and Lal, 2014;
Torres et al., 2010), biochar (Smith, 2016), and biowaste
compost (Mekki et al., 2019), which add forms of organic
material that increase turnover times of SOC. A combination
of these different practices is more likely to achieve higher
SOC sequestration rates on cropland (Fuss et al., 2018). Man-
agement options that aim at increasing SOC may also affect
yields, as they can maintain productivity and ensure yield sta-
bility (Pan et al., 2009), but reductions in SOC can also re-
duce yields substantially (Basso et al., 2018). Additionally,
the productivity increase can come with an even stronger in-
crease in harvested material, as demonstrated here, which
can lead to a reduction in total cropland SOC. The conver-
sion from natural land to cropland typically causes substan-
tial SOC losses, which stresses the need to further limit land
use expansion and thus requires an intensification of land
productivity on current cropland. In our analysis, we did not
account for the effects of future LUC, but projections show
an increase in total cropland area in the future (Stehfest et al.,
2019) so that global SOC is expected to further decline.
Further research on agricultural management practices
that influence SOC development at the global scale should
investigate the impact of cover crops, rotations, irrigation
systems, and optimal cultivar choice per region and loca-
tion (e.g., Minoli et al., 2019) as well as different options
for cropland intensification (e.g., Gerten et al., 2020) in a
more explicit manner. SOC stabilization mechanisms, such
as clay mineral protection and forming of macroaggregates
in no-till managed soils (Luo et al., 2016), effects of mi-
croorganisms, such as N fixation and phosphorous acquisi-
tion from fungi and bacteria, which also regulate plant pro-
ductivity and community dynamics (Heijden et al., 2008), as
well as effects of soil structure (Bronick and Lal, 2005) on
SOC dynamics, have not been considered here or in other
global process-based assessments and should be taken into
account. Plants and associated root systems can reduce sur-
face erosion and water runoff (Gyssels et al., 2005), but
losses of SOC from runoff and increased erosion (Kurothe
et al., 2014; Naipal et al., 2018) are not considered here ei-
ther. Residues from plants can influence labile, intermediate,
and stable SOC pools through the C
:
N ratio. Residues with
high C
:
N ratios (e.g., straw) decompose relatively slowly
and can increase SOC but reduce N availability to the plants,
while residues with low C
:
N decompose relatively fast and
can release N to the soil through mineralization (Macdonald
et al., 2018). The speed of residue decomposition can also
influence the effectiveness of residues as a soil cover, with
effects on soil moisture through infiltration. Impacts of bio-
diversity and living fauna such as microorganisms on SOC
sequestration are not modeled in this analysis, even though
they are recognized to have a substantial influence on the dy-
namics of SOC (Chevallier et al., 2001).
The implementation of such effects is desirable but needs
to be assessed with respect to the process understanding, the
availability of input data at the global scale, and the avail-
ability of modeling approaches (Lutz et al., 2019b). Global-
scale modeling approaches, in comparison to local or re-
gional studies, allow for the possibility to identify regional
patterns related to SOC sequestration responses with the po-
Earth Syst. Dynam., 12, 1037–1055, 2021
https://doi.org/10.5194/esd-12-1037-2021
Page 15
T. Herzfeld et al.: Soil organic carbon dynamics from agricultural management practices under climate change
1051
tential to foster experimental studies in areas not investigated
so far but that are relevant for global assessments (Luo et
al., 2016; Nishina et al., 2014). They are needed to upscale
findings from experimental sites so that the potential of such
measures for climate change mitigation can be better under-
stood and climate protection plans can be made with better
estimates.
6
Conclusion
In conclusion, the agricultural management systems ana-
lyzed here are not sufficient to increase global SOC stocks on
current cropland until the end of the 21st century. The inter-
action of SOC sequestration and cropland productivity needs
to be better disentangled. Additional C inputs from, e.g., ma-
nure, cover crops, and rotations are needed and could offset
further SOC losses, but additional research on the potentials
of these cropland management options and available amounts
that could be applied is needed. We find that the potential for
SOC sequestration on current global cropland is too small to
fulfill expectations as a negative emission technology, which
stresses the importance of reducing GHG emissions more
strictly by other means, to reach climate protection targets
as outlined in the 2015 Paris Agreement.
Code and data availability.
The source code is available un-
der the GNU APGL version 3 license. The exact version of the
code described here and the R script used for post-processing
the data from the simulations conducted are archived under
https://doi.org/10.5281/zenodo.4625868 (Herzfeld et al., 2021).
Supplement.
The supplement related to this article is available
online at: https://doi.org/10.5194/esd-12-1037-2021-supplement.
Author contributions.
TH and CM designed the study in discus-
sion with JH and SR. TH conducted all the model simulations and
wrote the paper with support from CM. TH conducted the analysis
and prepared all the figures with input from CM and JH. All authors
edited the paper.
Competing interests.
The contact author has declared that nei-
ther they nor their co-authors have any competing interests.
Disclaimer.
Publisher’s note: Copernicus Publications remains
neutral with regard to jurisdictional claims in published maps and
institutional affiliations.
Acknowledgements.
Tobias Herzfeld and Susanne Rolinski
gratefully thank the German Ministry for Education and Research
(BMBF) for funding this work, which is part of the MACMIT
project (01LN1317A). Jens Heinke thanks the BMBF for funding
through the EXIMO project (01LP1903D). Tobias Herzfeld thanks
Vera Porwollik for the support in preparing input datasets and code
development. We thank the two anonymous referees for their help-
ful comments to improve the paper.
Financial support.
This research has been supported by the
Bundesministerium für Bildung, Wissenschaft, Forschung und
Technologie (grant no. 01LN1317A and 01LP1903D).
The article processing charges for this open-access
publication were covered by the Potsdam Institute
for Climate Impact Research (PIK).
Review statement.
This paper was edited by Kirsten Zickfeld
and reviewed by two anonymous referees.
References
Abdalla, K., Chivenge, P., Ciais, P., and Chaplot, V.: No-tillage
lessens soil CO
2
emissions the most under arid and sandy soil
conditions: results from a meta-analysis, Biogeosciences, 13,
3619–3633, https://doi.org/10.5194/bg-13-3619-2016, 2016.
Alvarez, R.: A review of nitrogen fertilizer and conservation tillage
effects on soil organic carbon storage, Soil Use Manag., 21, 38–
52, https://doi.org/10.1079/SUM2005291, 2005.
Averill, C. and Waring, B.: Nitrogen limitation of decomposition
and decay: How can it occur?, Glob. Chang. Biol., 24, 1417–
1427, https://doi.org/10.1111/gcb.13980, 2018.
Baker, J. M., Ochsner, T. E., Venterea, R. T., and Griffis,
T. J.: Tillage and soil carbon sequestration – What do
we
really
know?,
Agric.
Ecosyst.
Environ.,
118,
1–5,
https://doi.org/10.1016/j.agee.2006.05.014, 2007.
Basso, B., Dumont, B., Maestrini, B., Shcherbak, I., Robertson, G.
P., Porter, J. R., Smith, P., Paustian, K., Grace, P. R., Asseng, S.,
Bassu, S., Biernath, C., Boote, K. J., Cammarano, D., De Sanctis,
G., Durand, J.-L., Ewert, F., Gayler, S., Hyndman, D. W., Kent,
J., Martre, P., Nendel, C., Priesack, E., Ripoche, D., Ruane, A.
C., Sharp, J., Thorburn, P. J., Hatfield, J. L., Jones, J. W., and
Rosenzweig, C.: Soil Organic Carbon and Nitrogen Feedbacks
on Crop Yields under Climate Change, Agric. Environ. Lett., 3,
1–5, https://doi.org/10.2134/ael2018.05.0026, 2018.
Batjes, N. H.: Total carbon and nitrogen in the soils of the world,
Eur. J. Soil Sci., 47, 151–163, https://doi.org/10.1111/j.1365-
2389.1996.tb01386.x, 1996.
Batjes, N. H.: Mitigation of atmospheric CO
2
concentrations by in-
creased carbon sequestration in the soil, Biol. Fertil. Soils, 27,
230–235, https://doi.org/10.1007/s003740050425, 1998.
Bodirsky, B. L., Rolinski, S., Biewald, A., Weindl, I., Popp,
A., and Lotze-Campen, H.: Global Food Demand Sce-
narios for the 21st Century, PLoS One, 10, e0139201,
https://doi.org/10.1371/journal.pone.0139201, 2015.
Bondeau, A., Smith, P. C., Zaehle, S., Schaphoff, S., Lucht, W.,
Cramer, W., Gerten, D., Lotze-Campen, H., Müller, C., Re-
ichstein, M., and Smith, B.: Modelling the role of agricul-
ture for the 20th century global terrestrial carbon balance,
https://doi.org/10.5194/esd-12-1037-2021
Earth Syst. Dynam., 12, 1037–1055, 2021
Page 16
1052
T. Herzfeld et al.: Soil organic carbon dynamics from agricultural management practices under climate change
Glob. Change Biol., 13, 679–706, https://doi.org/10.1111/j.1365-
2486.2006.01305.x, 2007.
Bronick,
C.
J.
and
Lal,
R.:
Soil
structure
and
management:
a
review,
Geoderma,
124,
3–22,
https://doi.org/10.1016/j.geoderma.2004.03.005, 2005.
Carvalhais, N., Forkel, M., Khomik, M., Bellarby, J., Jung, M.,
Migliavacca, M., Mu, M., Saatchi, S., Santoro, M., Thurner, M.,
Weber, U., Ahrens, B., Beer, C., Cescatti, A., Randerson, J. T.,
and Reichstein, M.: Global covariation of carbon turnover times
with climate in terrestrial ecosystems, Nature, 514, 213–217,
https://doi.org/10.1038/nature13731, 2014.
Cerdà, A., Flanagan, D. C., le Bissonnais, Y., and Boardman, J.:
Soil erosion and agriculture, Soil Tillage Res., 106, 107–108,
https://doi.org/10.1016/j.still.2009.10.006, 2009.
Chevallier, T., Blanchart, E., Girardin, C., Mariotti, A., Albrecht,
A., and Feller, C.: The role of biological activity (roots, earth-
worms) in medium-term C dynamics in vertisol under a
Digi-
taria decumbens
(Gramineae) pasture, Appl. Soil Ecol., 16, 11–
21, https://doi.org/10.1016/S0929-1393(00)00102-5, 2001.
Chi, J., Waldo, S., Pressley, S., O’Keeffe, P., Huggins, D., Stöckle,
C., Pan, W. L., Brooks, E., and Lamb, B.: Assessing carbon
and water dynamics of no-till and conventional tillage crop-
ping systems in the inland Pacific Northwest US using the
eddy covariance method, Agric. For. Meteorol., 218–219, 37–49,
https://doi.org/10.1016/j.agrformet.2015.11.019, 2016.
Derpsch, R., Friedrich, T., Kassam, A., and Hongwen, L.:
Current status of adoption of no-till farming in the world
and some of its main benefits, Int. J. Agric. Biol. Eng.,
3, 1–25, https://doi.org/10.3965/j.issn.1934-6344.2010.01.001-
025, 2010.
Derpsch, R., Franzluebbers, A. J., Duiker, S. W., Reicosky,
D. C., Koeller, K., Friedrich, T., Sturny, W. G., Sá, J.
C. M., and Weiss, K.: Why do we need to standard-
ize no-tillage research?, Soil Tillage Res., 137, 16–22,
https://doi.org/10.1016/j.still.2013.10.002, 2014.
de Vries, W.: Soil carbon 4 per mille: a good initiative but let’s
manage not only the soil but also the expectations, Geoderma,
309, 111–112, https://doi.org/10.1016/j.geoderma.2017.05.023,
2018.
Dietrich, J. P., Mishra, A., Weindl, I., Bodirsky, B. L.,
Wang, X., Baumstark, L., Kreidenweis, U., Klein, D.,
Steinmetz, N., Chen, D., Humpenoeder, F., and Wirth,
S.: mrland: MadRaT land data package, Zenodo [code],
https://doi.org/10.5281/zenodo.3822083, 2020.
Dignac, M.-F., Derrien, D., Barré, P., Barot, S., Cécillon, L.,
Chenu, C., Chevallier, T., Freschet, G. T., Garnier, P., Guenet,
B., Hedde, M., Klumpp, K., Lashermes, G., Maron, P.-A.,
Nunan, N., Roumet, C., and Basile-Doelsch, I.: Increasing
soil carbon storage: mechanisms, effects of agricultural prac-
tices and proxies. A review, Agron. Sustain. Dev., 37, 1–14,
https://doi.org/10.1007/s13593-017-0421-2, 2017.
Emde, D., Hannam, K. D., Most, I., Nelson, L. M., and Jones,
M. D.: Soil organic carbon in irrigated agricultural sys-
tems: A meta-analysis, Glob. Change Biol., 27, 3898–3910,
https://doi.org/10.1111/gcb.15680, 2021.
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B.,
Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled
Model Intercomparison Project Phase 6 (CMIP6) experimen-
tal design and organization, Geosci. Model Dev., 9, 1937–1958,
https://doi.org/10.5194/gmd-9-1937-2016, 2016.
FAO: The State of Food and Agriculture 2019 (SOFA). Moving for-
ward on food loss and waste reduction., Food and Agriculture
Organization of the United Nations (FAO), Rome, License: CC
BY-NC-SA 3.0 IGO, 2019.
Forkel, M., Carvalhais, N., Schaphoff, S., v. Bloh, W., Migli-
avacca, M., Thurner, M., and Thonicke, K.: Identifying
environmental controls on vegetation greenness phenology
through model–data integration, Biogeosciences, 11, 7025–
7050, https://doi.org/10.5194/bg-11-7025-2014, 2014.
Frieler, K., Lange, S., Piontek, F., Reyer, C. P. O., Schewe, J.,
Warszawski, L., Zhao, F., Chini, L., Denvil, S., Emanuel, K.,
Geiger, T., Halladay, K., Hurtt, G., Mengel, M., Murakami, D.,
Ostberg, S., Popp, A., Riva, R., Stevanovic, M., Suzuki, T.,
Volkholz, J., Burke, E., Ciais, P., Ebi, K., Eddy, T. D., Elliott, J.,
Galbraith, E., Gosling, S. N., Hattermann, F., Hickler, T., Hinkel,
J., Hof, C., Huber, V., Jägermeyr, J., Krysanova, V., Marcé, R.,
Müller Schmied, H., Mouratiadou, I., Pierson, D., Tittensor, D.
P., Vautard, R., van Vliet, M., Biber, M. F., Betts, R. A., Bodirsky,
B. L., Deryng, D., Frolking, S., Jones, C. D., Lotze, H. K., Lotze-
Campen, H., Sahajpal, R., Thonicke, K., Tian, H., and Yamagata,
Y.: Assessing the impacts of 1.5
◦
C global warming – simula-
tion protocol of the Inter-Sectoral Impact Model Intercompar-
ison Project (ISIMIP2b), Geosci. Model Dev., 10, 4321–4345,
https://doi.org/10.5194/gmd-10-4321-2017, 2017.
Fuss, S., Lamb, W. F., Callaghan, M. W., Hilaire, J., Creutzig, F.,
Amann, T., Beringer, T., Garcia, W. de O., Hartmann, J., Khanna,
T., Luderer, G., Nemet, G. F., Rogelj, J., Smith, P., Vicente, J.
L. V., Wilcox, J., Dominguez, M. del M. Z., and Minx, J. C.:
Negative emissions – Part 2: Costs, potentials and side effects,
Environ. Res. Lett., 13, 063002, https://doi.org/10.1088/1748-
9326/aabf9f, 2018.
Gerten, D., Heck, V., Jägermeyr, J., Bodirsky, B. L., Fetzer, I.,
Jalava, M., Kummu, M., Lucht, W., Rockström, J., Schaphoff,
S., and Schellnhuber, H. J.: Feeding ten billion people is possi-
ble within four terrestrial planetary boundaries, Nat. Sustain., 3,
200–208, https://doi.org/10.1038/s41893-019-0465-1, 2020.
Griscom, B. W., Adams, J., Ellis, P. W., Houghton, R. A., Lomax,
G., Miteva, D. A., Schlesinger, W. H., Shoch, D., Siikamäki, J.
V., Smith, P., Woodbury, P., Zganjar, C., Blackman, A., Campari,
J., Conant, R. T., Delgado, C., Elias, P., Gopalakrishna, T., Ham-
sik, M. R., Herrero, M., Kiesecker, J., Landis, E., Laestadius, L.,
Leavitt, S. M., Minnemeyer, S., Polasky, S., Potapov, P., Putz, F.
E., Sanderman, J., Silvius, M., Wollenberg, E., and Fargione, J.:
Natural climate solutions, P. Natl. Acad. Sci. USA, 114, 11645–
11650, https://doi.org/10.1073/pnas.1710465114, 2017.
Guérif, J., Richard, G., Dürr, C., Machet, J. M., Recous, S., and
Roger-Estrade, J.: A review of tillage effects on crop residue
management, seedbed conditions and seedling establishment,
Soil Tillage Res., 61, 13–32, https://doi.org/10.1016/S0167-
1987(01)00187-8, 2001.
Gyssels, G., Poesen, J., Bochet, E., and Li, Y.: Impact
of
plant
roots
on
the
resistance
of
soils
to
erosion
by water: a review, Prog. Phys. Geogr., 29, 189–217,
https://doi.org/10.1191/0309133305pp443ra, 2005.
Harris, I., Osborn, T. J., Jones, P., and Lister, D.: Version 4 of the
CRU TS monthly high-resolution gridded multivariate climate
Earth Syst. Dynam., 12, 1037–1055, 2021
https://doi.org/10.5194/esd-12-1037-2021
Page 17
T. Herzfeld et al.: Soil organic carbon dynamics from agricultural management practices under climate change
1053
dataset, Sci. Data, 7, 109, https://doi.org/10.1038/s41597-020-
0453-3, 2020.
Hatton, P.-J., Castanha, C., Torn, M. S., and Bird, J. A.: Lit-
ter type control on soil C and N stabilization dynamics
in a temperate forest, Glob. Change Biol., 21, 1358–1367,
https://doi.org/10.1111/gcb.12786, 2015.
Heijden, M. G. A. V. D., Bardgett, R. D., and Straalen, N. M. V.:
The unseen majority: soil microbes as drivers of plant diversity
and productivity in terrestrial ecosystems, Ecol. Lett., 11, 296–
310, https://doi.org/10.1111/j.1461-0248.2007.01139.x, 2008.
Hempel, S., Frieler, K., Warszawski, L., Schewe, J., and Piontek, F.:
Bias corrected GCM input data for ISIMIP Fast Track, GFZ Data
Services, https://doi.org/10.5880/PIK.2016.001, 2013.
Herzfeld, T., Müller, C., Heinke, J., Rolinski, S., and Porwollik,
V.: LPJmL Model Source Code (version 5.0-tillage2), Zenodo
[code], https://doi.org/10.5281/zenodo.4625868, 2021.
Hiederer, R. and Köchy, M.: Global Soil Organic Carbon Estimates
and the Harmonized World Soil Database, EUR 25225 EN, 79,
https://doi.org/10.2788/13267, 2011.
Humphrey, V., Berg, A., Ciais, P., Gentine, P., Jung, M., Reich-
stein, M., Seneviratne, S. I., and Frankenberg, C.: Soil moisture–
atmosphere feedback dominates land carbon uptake variability,
Nature, 592, 65–69, https://doi.org/10.1038/s41586-021-03325-
5, 2021.
Hurtt, G. C., Chini, L., Sahajpal, R., Frolking, S., Bodirsky,
B. L., Calvin, K., Doelman, J. C., Fisk, J., Fujimori, S.,
Klein Goldewijk, K., Hasegawa, T., Havlik, P., Heinimann,
A., Humpenöder, F., Jungclaus, J., Kaplan, J. O., Kennedy, J.,
Krisztin, T., Lawrence, D., Lawrence, P., Ma, L., Mertz, O., Pon-
gratz, J., Popp, A., Poulter, B., Riahi, K., Shevliakova, E., Ste-
hfest, E., Thornton, P., Tubiello, F. N., van Vuuren, D. P., and
Zhang, X.: Harmonization of global land use change and man-
agement for the period 850–2100 (LUH2) for CMIP6, Geosci.
Model Dev., 13, 5425–5464, https://doi.org/10.5194/gmd-13-
5425-2020, 2020.
IPCC: 2006 IPCC Guidelines for National Greenhouse Gas Inven-
tories, Prepared by the National Greenhouse Gas Inventories Pro-
gramme, edited by: Eggleston, H. S., Buendia, L., Miwa, K.,
Ngar,a T., and Tanabe, K., IGES, Japan, 2006.
IPCC: 2019 Refinement to the 2006 IPCC Guidelines for Na-
tional Greenhouse Gas Inventories – Chapter 5 – Cropland –
Volume 4: Agriculture, Forestry and Other Land Use, edited
by: Calvo Buendia, E., Tanabe, K., Kranjc, A., Baasansuren, J.,
Fukuda, M., Ngarize S., Osako, A., Pyrozhenko, Y., Shermanau,
P., and Federici, S., Intergovernmental Panel on Climate Change
(IPCC), Geneva, Switzerland, 2019.
Jägermeyr, J., Gerten, D., Heinke, J., Schaphoff, S., Kummu, M.,
and Lucht, W.: Water savings potentials of irrigation systems:
global simulation of processes and linkages, Hydrol. Earth Syst.
Sci., 19, 3073–3091, https://doi.org/10.5194/hess-19-3073-2015,
2015.
Jobbágy, E. G. and Jackson, R. B.: The Vertical Distribution of
Soil Organic Carbon and Its Relation to Climate and Vege-
tation, Ecol. Appl., 10, 423–436, https://doi.org/10.1890/1051-
0761(2000)010[0423:TVDOSO]2.0.CO;2, 2000.
Karstens, K., Bodirsky, B. L., Dietrich, J. P., Dondini, M., Heinke,
J., Kuhnert, M., Müller, C., Rolinski, S., Smith, P., Weindl, I.,
Lotze-Campen, H., and Popp, A.: Management induced changes
of soil organic carbon on global croplands, Biogeosciences Dis-
cuss. [preprint], https://doi.org/10.5194/bg-2020-468, in review,
2020.
Kurothe, R. S., Kumar, G., Singh, R., Singh, H. B., Tiwari, S. P.,
Vishwakarma, A. K., Sena, D. R., and Pande, V. C.: Effect of
tillage and cropping systems on runoff, soil loss and crop yields
under semiarid rainfed agriculture in India, Soil Tillage Res.,
140, 126–134, https://doi.org/10.1016/j.still.2014.03.005, 2014.
Lal, R.: Tillage effects on soil degradation, soil resilience,
soil quality, and sustainability, Soil Tillage Res., 27, 1–8,
https://doi.org/10.1016/0167-1987(93)90059-X, 1993.
Lal, R.: World cropland soils as a source or sink for atmospheric car-
bon, Adv. Agron., 71, 145–191, https://doi.org/10.1016/S0065-
2113(01)71014-0, 2001.
Lal, R.: Offsetting global CO
2
emissions by restoration of de-
graded soils and intensification of world agriculture and forestry,
Land Degrad. Dev., 14, 309–322, https://doi.org/10.1002/ldr.562,
2003.
Lal, R.: Soil Carbon Sequestration Impacts on Global Cli-
mate Change and Food Security, Science, 304, 1623–1627,
https://doi.org/10.1126/science.1097396, 2004.
Lal,
R.:
Challenges
and
opportunities
in
soil
organic
matter
research,
Eur.
J.
Soil
Sci.,
60,
158–169,
https://doi.org/10.1111/j.1365-2389.2008.01114.x, 2009.
Le Quéré, C., Moriarty, R., Andrew, R. M., Canadell, J. G., Sitch, S.,
Korsbakken, J. I., Friedlingstein, P., Peters, G. P., Andres, R. J.,
Boden, T. A., Houghton, R. A., House, J. I., Keeling, R. F., Tans,
P., Arneth, A., Bakker, D. C. E., Barbero, L., Bopp, L., Chang,
J., Chevallier, F., Chini, L. P., Ciais, P., Fader, M., Feely, R. A.,
Gkritzalis, T., Harris, I., Hauck, J., Ilyina, T., Jain, A. K., Kato,
E., Kitidis, V., Klein Goldewijk, K., Koven, C., Landschützer,
P., Lauvset, S. K., Lefèvre, N., Lenton, A., Lima, I. D., Metzl,
N., Millero, F., Munro, D. R., Murata, A., Nabel, J. E. M. S.,
Nakaoka, S., Nojiri, Y., O’Brien, K., Olsen, A., Ono, T., Pérez,
F. F., Pfeil, B., Pierrot, D., Poulter, B., Rehder, G., Rödenbeck,
C., Saito, S., Schuster, U., Schwinger, J., Séférian, R., Steinhoff,
T., Stocker, B. D., Sutton, A. J., Takahashi, T., Tilbrook, B., van
der Laan-Luijkx, I. T., van der Werf, G. R., van Heuven, S., Van-
demark, D., Viovy, N., Wiltshire, A., Zaehle, S., and Zeng, N.:
Global Carbon Budget 2015, Earth Syst. Sci. Data, 7, 349–396,
https://doi.org/10.5194/essd-7-349-2015, 2015.
Le Quéré, C., Andrew, R. M., Friedlingstein, P., Sitch, S., Hauck,
J., Pongratz, J., Pickers, P. A., Korsbakken, J. I., Peters, G. P.,
Canadell, J. G., Arneth, A., Arora, V. K., Barbero, L., Bastos,
A., Bopp, L., Chevallier, F., Chini, L. P., Ciais, P., Doney, S. C.,
Gkritzalis, T., Goll, D. S., Harris, I., Haverd, V., Hoffman, F. M.,
Hoppema, M., Houghton, R. A., Hurtt, G., Ilyina, T., Jain, A.
K., Johannessen, T., Jones, C. D., Kato, E., Keeling, R. F., Gold-
ewijk, K. K., Landschützer, P., Lefèvre, N., Lienert, S., Liu, Z.,
Lombardozzi, D., Metzl, N., Munro, D. R., Nabel, J. E. M. S.,
Nakaoka, S., Neill, C., Olsen, A., Ono, T., Patra, P., Peregon,
A., Peters, W., Peylin, P., Pfeil, B., Pierrot, D., Poulter, B., Re-
hder, G., Resplandy, L., Robertson, E., Rocher, M., Rödenbeck,
C., Schuster, U., Schwinger, J., Séférian, R., Skjelvan, I., Stein-
hoff, T., Sutton, A., Tans, P. P., Tian, H., Tilbrook, B., Tubiello,
F. N., van der Laan-Luijkx, I. T., van der Werf, G. R., Viovy, N.,
Walker, A. P., Wiltshire, A. J., Wright, R., Zaehle, S., and Zheng,
B.: Global Carbon Budget 2018, Earth Syst. Sci. Data, 10, 2141–
2194, https://doi.org/10.5194/essd-10-2141-2018, 2018.
https://doi.org/10.5194/esd-12-1037-2021
Earth Syst. Dynam., 12, 1037–1055, 2021
Page 18
1054
T. Herzfeld et al.: Soil organic carbon dynamics from agricultural management practices under climate change
Lorenz, K. and Lal, R.: Soil organic carbon sequestration in agro-
forestry systems. A review, Agron. Sustain. Dev., 34, 443–454,
https://doi.org/10.1007/s13593-014-0212-y, 2014.
Luo, Y., Ahlström, A., Allison, S. D., Batjes, N. H., Brovkin, V.,
Carvalhais, N., Chappell, A., Ciais, P., Davidson, E. A., Finzi,
A., Georgiou, K., Guenet, B., Hararuk, O., Harden, J. W., He,
Y., Hopkins, F., Jiang, L., Koven, C., Jackson, R. B., Jones, C.
D., Lara, M. J., Liang, J., McGuire, A. D., Parton, W., Peng, C.,
Randerson, J. T., Salazar, A., Sierra, C. A., Smith, M. J., Tian, H.,
Todd-Brown, K. E. O., Torn, M., Groenigen, K. J. van, Wang, Y.
P., West, T. O., Wei, Y., Wieder, W. R., Xia, J., Xu, X., Xu, X.,
and Zhou, T.: Toward more realistic projections of soil carbon
dynamics by Earth system models, Global Biogeochem. Cycles,
30, 40–56, https://doi.org/10.1002/2015GB005239, 2016.
Luo, Z., Wang, E., and Sun, O. J.: Can no-tillage stimulate
carbon sequestration in agricultural soils? A meta-analysis of
paired experiments, Agric. Ecosyst. Environ., 139, 224–231,
https://doi.org/10.1016/j.agee.2010.08.006, 2010.
Lutz, F., Herzfeld, T., Heinke, J., Rolinski, S., Schaphoff, S.,
von Bloh, W., Stoorvogel, J. J., and Müller, C.: Simulating
the effect of tillage practices with the global ecosystem model
LPJmL (version 5.0-tillage), Geosci. Model Dev., 12, 2419–
2440, https://doi.org/10.5194/gmd-12-2419-2019, 2019a.
Lutz,
F.,
Stoorvogel,
J.
J.,
and
Müller,
C.:
Options
to
model
the
effects
of
tillage
on
N
2
O
emissions
at
the
global
scale,
Ecol.
Model.,
392,
212–225,
https://doi.org/10.1016/j.ecolmodel.2018.11.015, 2019b.
Macdonald, C. A., Delgado-Baquerizo, M., Reay, D. S., Hicks,
L. C., and Singh, B. K.: Soil Nutrients and Soil Car-
bon Storage, in: Soil Carbon Storage, Elsevier, 167–205,
https://doi.org/10.1016/B978-0-12-812766-7.00006-8, 2018.
Maharjan, G. R., Prescher, A.-K., Nendel, C., Ewert, F., Mboh, C.
M., Gaiser, T., and Seidel, S. J.: Approaches to model the impact
of tillage implements on soil physical and nutrient properties in
different agro-ecosystem models, Soil Tillage Res., 180, 210–
221, https://doi.org/10.1016/j.still.2018.03.009, 2018.
Mekki, A., Aloui, F., and Sayadi, S.: Influence of biowaste
compost amendment on soil organic carbon storage under
arid climate, J. Air Waste Manag. Assoc., 69, 867–877,
https://doi.org/10.1080/10962247.2017.1374311, 2019.
Minasny, B., Malone, B. P., McBratney, A. B., Angers, D.
A., Arrouays, D., Chambers, A., Chaplot, V., Chen, Z.-S.,
Cheng, K., Das, B. S., Field, D. J., Gimona, A., Hedley, C.
B., Hong, S. Y., Mandal, B., Marchant, B. P., Martin, M.,
McConkey, B. G., Mulder, V. L., O’Rourke, S., Richer-de-
Forges, A. C., Odeh, I., Padarian, J., Paustian, K., Pan, G.,
Poggio, L., Savin, I., Stolbovoy, V., Stockmann, U., Sulae-
man, Y., Tsui, C.-C., Vågen, T.-G., van Wesemael, B., and
Winowiecki, L.: Soil carbon 4 per mille, Geoderma, 292, 59–86,
https://doi.org/10.1016/j.geoderma.2017.01.002, 2017.
Minoli, S., Müller, C., Elliott, J., Ruane, A. C., Jägermeyr, J., Zabel,
F., Dury, M., Folberth, C., François, L., Hank, T., Jacquemin, I.,
Liu, W., Olin, S., and Pugh, T. A. M.: Global Response Patterns
of Major Rainfed Crops to Adaptation by Maintaining Current
Growing Periods and Irrigation, Earths Future, 7, 1464–1480,
https://doi.org/10.1029/2018EF001130, 2019.
Minx, J. C., Lamb, W. F., Callaghan, M. W., Fuss, S., Hilaire,
J., Creutzig, F., Amann, T., Beringer, T., Garcia, W. de O.,
Hartmann, J., Khanna, T., Lenzi, D., Luderer, G., Nemet, G.
F., Rogelj, J., Smith, P., Vicente, J. L. V., Wilcox, J., and
Dominguez, M. del M. Z.: Negative emissions – Part 1: Re-
search landscape and synthesis, Environ. Res. Lett., 13, 063001,
https://doi.org/10.1088/1748-9326/aabf9b, 2018.
Naipal, V., Ciais, P., Wang, Y., Lauerwald, R., Guenet, B.,
and Van Oost, K.: Global soil organic carbon removal
by water erosion under climate change and land use
change during AD 1850–2005, Biogeosciences, 15, 4459–4480,
https://doi.org/10.5194/bg-15-4459-2018, 2018.
Nishina, K., Ito, A., Beerling, D. J., Cadule, P., Ciais, P., Clark, D.
B., Falloon, P., Friend, A. D., Kahana, R., Kato, E., Keribin, R.,
Lucht, W., Lomas, M., Rademacher, T. T., Pavlick, R., Schaphoff,
S., Vuichard, N., Warszawaski, L., and Yokohata, T.: Quantifying
uncertainties in soil carbon responses to changes in global mean
temperature and precipitation, Earth Syst. Dynam., 5, 197–209,
https://doi.org/10.5194/esd-5-197-2014, 2014.
Olin, S., Lindeskog, M., Pugh, T. A. M., Schurgers, G., Wårlind,
D., Mishurov, M., Zaehle, S., Stocker, B. D., Smith, B., and Ar-
neth, A.: Soil carbon management in large-scale Earth system
modelling: implications for crop yields and nitrogen leaching,
Earth Syst. Dynam., 6, 745–768, https://doi.org/10.5194/esd-6-
745-2015, 2015.
Pan, G., Smith, P., and Pan, W.: The role of soil organic
matter in maintaining the productivity and yield stability
of cereals in China, Agr. Ecosyst. Environ., 129, 344–348,
https://doi.org/10.1016/j.agee.2008.10.008, 2009.
Pingali, P. L.: Green Revolution: Impacts, limits, and the
path ahead, P. Natl. Acad. Sci. USA, 109, 12302–12308,
https://doi.org/10.1073/pnas.0912953109, 2012.
Porwollik, V., Rolinski, S., Heinke, J., and Müller, C.: Generating
a rule-based global gridded tillage dataset, Earth Syst. Sci. Data,
11, 823–843, https://doi.org/10.5194/essd-11-823-2019, 2019.
Powlson, D. S., Stirling, C. M., Jat, M. L., Gerard, B. G., Palm, C.
A., Sanchez, P. A., and Cassman, K. G.: Limited potential of no-
till agriculture for climate change mitigation, Nat. Clim. Change,
4, 678–683, https://doi.org/10.1038/nclimate2292, 2014.
Pugh, T. A. M., Arneth, A., Olin, S., Ahlström, A., Bayer, A.
D., Klein Goldewijk, K., Lindeskog, M., and Schurgers, G.:
Simulated carbon emissions from land-use change are sub-
stantially enhanced by accounting for agricultural management,
Environ. Res. Lett., 10, 124008, https://doi.org/10.1088/1748-
9326/10/12/124008, 2015.
Ranaivoson,
L.,
Naudin,
K.,
Ripoche,
A.,
Affholder,
F.,
Rabeharisoa, L., and Corbeels, M.: Agro-ecological func-
tions of crop residues under conservation agriculture. A review,
Agron. Sustain. Dev., 37, 1–17, https://doi.org/10.1007/s13593-
017-0432-z, 2017.
Ray, D. K., Ramankutty, N., Mueller, N. D., West, P. C., and Foley,
J. A.: Recent patterns of crop yield growth and stagnation, Nat.
Commun., 3, 1–7, https://doi.org/10.1038/ncomms2296, 2012.
Ren, W., Banger, K., Tao, B., Yang, J., Huang, Y., and Tian, H.:
Global pattern and change of cropland soil organic carbon dur-
ing 1901–2010: Roles of climate, atmospheric chemistry, land
use and management, Geography and Sustainability, 1, 59–69,
https://doi.org/10.1016/j.geosus.2020.03.001, 2020.
Rogelj, J., den Elzen, M., Höhne, N., Fransen, T., Fekete, H.,
Winkler, H., Schaeffer, R., Sha, F., Riahi, K., and Mein-
shausen, M.: Paris Agreement climate proposals need a boost
Earth Syst. Dynam., 12, 1037–1055, 2021
https://doi.org/10.5194/esd-12-1037-2021
Page 19
T. Herzfeld et al.: Soil organic carbon dynamics from agricultural management practices under climate change
1055
to keep warming well below 2
◦
C, Nature, 534, 631–639,
https://doi.org/10.1038/nature18307, 2016.
Rogelj, J., Popp, A., Calvin, K. V., Luderer, G., Emmerling, J., Ger-
naat, D., Fujimori, S., Strefler, J., Hasegawa, T., Marangoni, G.,
Krey, V., Kriegler, E., Riahi, K., van Vuuren, D. P., Doelman,
J., Drouet, L., Edmonds, J., Fricko, O., Harmsen, M., Havlik,
P., Humpenöder, F., Stehfest, E., and Tavoni, M.: Scenarios to-
wards limiting global mean temperature increase below 1.5
◦
C,
Nat. Clim. Change, 8, 325–332, https://doi.org/10.1038/s41558-
018-0091-3, 2018.
Rost, S., Gerten, D., Bondeau, A., Lucht, W., Rohwer, J., and
Schaphoff, S.: Agricultural green and blue water consumption
and its influence on the global water system, Water Resour. Res.,
44, W09405, https://doi.org/10.1029/2007WR006331, 2008.
Sanderman, J., Hengl, T., and Fiske, G. J.: Soil carbon debt of
12,000 years of human land use, P. Natl. Acad. Sci. USA, 114,
9575–9580, https://doi.org/10.1073/pnas.1706103114, 2017.
Schaphoff, S., Heyder, U., Ostberg, S., Gerten, D., Heinke,
J., and Lucht, W.: Contribution of permafrost soils to
the global carbon budget, Environ. Res. Lett., 8, 014026,
https://doi.org/10.1088/1748-9326/8/1/014026, 2013.
Schaphoff, S., von Bloh, W., Rammig, A., Thonicke, K., Biemans,
H., Forkel, M., Gerten, D., Heinke, J., Jägermeyr, J., Knauer, J.,
Langerwisch, F., Lucht, W., Müller, C., Rolinski, S., and Waha,
K.: LPJmL4 – a dynamic global vegetation model with managed
land – Part 1: Model description, Geosci. Model Dev., 11, 1343–
1375, https://doi.org/10.5194/gmd-11-1343-2018, 2018.
Scharlemann, J. P., Tanner, E. V., Hiederer, R., and Kapos,
V.: Global soil carbon: understanding and managing the
largest terrestrial carbon pool, Carbon Manag., 5, 81–91,
https://doi.org/10.4155/cmt.13.77, 2014.
Sitch, S., Smith, B., Prentice, I. C., Arneth, A., Bondeau, A.,
Cramer, W., Kaplan, J. O., Levis, S., Lucht, W., Sykes, M.
T., and others: Evaluation of ecosystem dynamics, plant ge-
ography and terrestrial carbon cycling in the LPJ dynamic
global vegetation model, Glob. Chang. Biol., 9, 161–185,
https://doi.org/10.1046/j.1365-2486.2003.00569.x, 2003.
Smith, P.: Soil carbon sequestration and biochar as negative
emission technologies, Glob. Change Biol., 22, 1315–1324,
https://doi.org/10.1111/gcb.13178, 2016.
Snyder, C. S., Bruulsema, T. W., Jensen, T. L., and Fixen, P. E.: Re-
view of greenhouse gas emissions from crop production systems
and fertilizer management effects, Agr. Ecosyst. Environ., 133,
247–266, https://doi.org/10.1016/j.agee.2009.04.021, 2009.
Stehfest, E., van Zeist, W.-J., Valin, H., Havlik, P., Popp, A., Kyle,
P., Tabeau, A., Mason-D’Croz, D., Hasegawa, T., Bodirsky, B.
L., Calvin, K., Doelman, J. C., Fujimori, S., Humpenöder, F.,
Lotze-Campen, H., van Meijl, H., and Wiebe, K.: Key determi-
nants of global land-use projections, Nat. Commun., 10, 2166,
https://doi.org/10.1038/s41467-019-09945-w, 2019.
Stella, T., Mouratiadou, I., Gaiser, T., Berg-Mohnicke, M., Wallor,
E., Ewert, F., and Nendel, C.: Estimating the contribution of crop
residues to soil organic carbon conservation, Environ. Res. Lett.,
14, 094008, https://doi.org/10.1088/1748-9326/ab395c, 2019.
Stockmann, U., Adams, M. A., Crawford, J. W., Field, D. J.,
Henakaarchchi, N., Jenkins, M., Minasny, B., Mcbratney, A.
B., Courcelles, V. D. R. D., Singh, K., Wheeler, I., Abbott,
L., Angers, D. A., Baldock, J., Bird, M., Brookes, P. C.,
Chenu, C., Jastrow, J. D., Lal, R., Lehmann, J., O’Donnell,
A. G., Parton, W. J., Whitehead, D., and Zimmermann, M.:
The knowns, known unknowns and unknowns of sequestration
of soil organic carbon, Agric. Ecosyst. Environ., 164, 80–99,
https://doi.org/10.1016/j.agee.2012.10.001, 2013.
Tans, P. and Keeling, R.: Earth System Research Laboratories
(ESRL) Global Monitoring Laboratory – Carbon Cycle Green-
house Gases, National Oceanic and Atmospheric Administra-
tion (NOAA), US Department of Commerce, available at: https:
//gml.noaa.gov/ccgg/trends/, last access: 30 April 2021.
Torres, A. B., Marchant, R., Lovett, J. C., Smart, J. C. R.,
and Tipper, R.: Analysis of the carbon sequestration costs of
afforestation and reforestation agroforestry practices and the
use of cost curves to evaluate their potential for implementa-
tion of climate change mitigation, Ecol. Econ., 69, 469–477,
https://doi.org/10.1016/j.ecolecon.2009.09.007, 2010.
Trost, B., Prochnow, A., Drastig, K., Meyer-Aurich, A., Ellmer,
F., and Baumecker, M.: Irrigation, soil organic carbon and
N
2
O emissions. A review, Agron. Sustain. Dev., 33, 733–749,
https://doi.org/10.1007/s13593-013-0134-0, 2013.
United Nations, Department of Economic and Social Affairs, and
Population Division: World population prospects 2019: High-
lights, (ST/ESA/SER.A/423), United Nations, Department of
Economic and Social Affairs Population Division, New York,
NY, 2019.
Van Kessel, J. and Reeves, J.: Nitrogen mineralization potential of
dairy manures and its relationship to composition, Biol. Fertil.
Soils, 36, 118–123, https://doi.org/10.1007/s00374-002-0516-y,
2002.
von Bloh, W., Schaphoff, S., Müller, C., Rolinski, S., Waha, K.,
and Zaehle, S.: Implementing the nitrogen cycle into the dy-
namic global vegetation, hydrology, and crop growth model
LPJmL (version 5.0), Geosci. Model Dev., 11, 2789–2812,
https://doi.org/10.5194/gmd-11-2789-2018, 2018.
White, J. W., Jones, J. W., Porter, C., McMaster, G. S., and Sommer,
R.: Issues of spatial and temporal scale in modeling the effects
of field operations on soil properties, Oper. Res., 10, 279–299,
https://doi.org/10.1007/s12351-009-0067-1, 2010.
White, R. E., Davidson, B., Lam, S. K., and Chen, D.:
A
critique
of
the
paper
“Soil
carbon
4
per
mille”
by
Minasny
et
al.
(2017),
Geoderma,
309,
115–117,
https://doi.org/10.1016/j.geoderma.2017.05.025, 2018.
Wik, M., Pingali, P., and Broca, S.: Global Agricultural Perfor-
mance: Past Trends and Future Prospects, Background Paper for
the World Development Report 2008, World Bank, Washington,
DC, USA, 39, 2008.
Zhang, B., Tian, H., Lu, C., Dangal, S. R. S., Yang, J., and
Pan, S.: Global manure nitrogen production and application in
cropland during 1860–2014: a 5 arcmin gridded global dataset
for Earth system modeling, Earth Syst. Sci. Data, 9, 667–678,
https://doi.org/10.5194/essd-9-667-2017, 2017.
Zomer, R. J., Bossio, D. A., Sommer, R., and Verchot, L. V.: Global
Sequestration Potential of Increased Organic Carbon in Cropland
Soils, Sci. Rep.-UK, 7, 15554, https://doi.org/10.1038/s41598-
017-15794-8, 2017.
https://doi.org/10.5194/esd-12-1037-2021
Earth Syst. Dynam., 12, 1037–1055, 2021
Document Outline