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Earth Syst. Dynam., 6, 745–768, 2015
www.earth-syst-dynam.net/6/745/2015/
doi:10.5194/esd-6-745-2015
© Author(s) 2015. CC Attribution 3.0 License.
Soil carbon management in large-scale Earth system
modelling: implications for crop yields and nitrogen
leaching
S. Olin
1
, M. Lindeskog
1
, T. A. M. Pugh
2
, G. Schurgers
3
, D. Wårlind
4
, M. Mishurov
1
, S. Zaehle
5
,
B. D. Stocker
6
, B. Smith
1
, and A. Arneth
2
1
Department of Physical Geography and Ecosystem Science, Lund University, 223 62 Lund, Sweden
2
Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research/Atmospheric
Environmental Research, 82467 Garmisch-Partenkirchen, Germany
3
Department of Geosciences and Natural Resource Management, University of Copenhagen,
Øster Voldgade 10, 1350 Copenhagen, Denmark
4
CSIRO Sustainable Agricultural Flagship, CSIRO Agriculture, GPO Box 1666, Black Mountain,
Canberra, ACT 2601, Australia
5
Max Planck Institute for Biogeochemistry – Biogeochemical Integration Department, P.O. Box 60 01 64,
07701 Jena, Germany
6
Department of Life Sciences, Imperial College London, Silwood Park, Ascot, SL5 7PY, UK
Correspondence to: S. Olin (stefan.olin@nateko.lu.se)
Received: 5 May 2015 – Published in Earth Syst. Dynam. Discuss.: 15 June 2015
Revised: 17 October 2015 – Accepted: 26 October 2015 – Published: 30 November 2015
Abstract.
Croplands are vital ecosystems for human well-being and provide important ecosystem services such
as crop yields, retention of nitrogen and carbon storage. On large (regional to global)-scale levels, assessment
of how these different services will vary in space and time, especially in response to cropland management, are
scarce. We explore cropland management alternatives and the effect these can have on future C and N pools
and fluxes using the land-use-enabled dynamic vegetation model LPJ-GUESS (Lund–Potsdam–Jena General
Ecosystem Simulator). Simulated crop production, cropland carbon storage, carbon sequestration and nitrogen
leaching from croplands are evaluated and discussed. Compared to the version of LPJ-GUESS that does not
include land-use dynamics, estimates of soil carbon stocks and nitrogen leaching from terrestrial to aquatic
ecosystems were improved.
Our model experiments allow us to investigate trade-offs between these ecosystem services that can be pro-
vided from agricultural fields. These trade-offs are evaluated for current land use and climate and further explored
for future conditions within the two future climate change scenarios, RCP (Representative Concentration Path-
way) 2.6 and 8.5. Our results show that the potential for carbon sequestration due to typical cropland management
practices such as no-till management and cover crops proposed in previous studies is not realised, globally or
over larger climatic regions. Our results highlight important considerations to be made when modelling C–N in-
teractions in agricultural ecosystems under future environmental change and the effects these have on terrestrial
biogeochemical cycles.
Published by Copernicus Publications on behalf of the European Geosciences Union.
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S. Olin et al.: Soil C management in large-scale Earth system modelling
1
Introduction
Growing population along with rapidly changing dietary
preferences pose one of the key economical and environ-
mental challenges of this century (Gerland et al., 2014; Her-
tel, 2015). According to estimates made by the United Na-
tions Food and Agriculture Organization (FAO), food pro-
duction will need to be doubled by 2050 in order to meet
the global food demand (FAO, 2008). Since the beginning
of the 20th century there has been an increase in crop yields
and overall production, especially since the 1950s (Steffen
et al., 2015), as a result of agricultural intensification driven
by substantial advances in agricultural practices and technol-
ogy, improved crop varieties and an increased application
of N and P fertiliser (Evans, 1999; Spano et al., 2003). In
addition, agricultural land area has expanded globally, with
around 35 % of the total land surface presently being covered
by cropland and pastures (Ramankutty et al., 2008).
Yield increases on existing land may be achieved through
further development of high-yielding varieties or through fur-
ther improvements in the efficiency of agricultural practices,
the latter especially in regions where gaps between actual and
potential yields are large (Licker et al., 2010; Mueller et al.,
2014). The enhanced input of nitrogen (N) into ecosystems,
jointly with other technical developments, has played a ma-
jor role in the large increase in agricultural productivity over
the last 50 years, often termed the “green revolution”.
Due to their large areal extent, agricultural ecosystems
have substantially altered global biogeochemical cycles
(Rockstrom et al., 2009; Vitousek et al., 1997). So far,
most studies focused on the greatest direct impacts of these
changes, e.g. carbon losses following deforestation (Ciais
et al., 2013; Houghton et al., 2012; Le Quéré et al., 2015). It
is estimated that over the last 150–200 years, the conversion
of natural to managed ecosystems, especially croplands, has
released ca. 180 Pg carbon (C; current rate is
∼
1 Pg C yr
−
1
)
from the terrestrial biosphere to the atmosphere by disturb-
ing soils and through the harvesting and burning of biomass
(Le Quéré et al., 2014). This sum is equivalent to around a
third of the anthropogenic CO
2
concentration in the atmo-
sphere today. However, the land-use-related carbon flux is
one of the most uncertain terms in the global carbon budget
(Ciais et al., 2013; Le Quéré et al., 2015), and studies with
dynamic vegetation models (DVMs) incorporating represen-
tations of land-use change (LUC) have shown that the actual
estimate is highly dependent on the management practices
assumed in the model (Bondeau et al., 2007; Levis et al.,
2014; Lindeskog et al., 2013).
However, beyond the importance of land use and land-
use change for understanding the global past and future car-
bon balance, other aspects of crop management also need
to be investigated on large scales since the associated en-
vironmental effects have often been detrimental. Negative
impacts have been noted for biodiversity and water quality
and for the substantial emissions of N trace gases that affect
air quality and climate, such as nitrous oxide (N
2
O), a po-
tent greenhouse gas (Galloway et al., 2004; Rockstrom et al.,
2009; Tilman et al., 2002; Vitousek et al., 1997). A large frac-
tion of the N
2
O emitted to the atmosphere today originates
from terrestrial sources, mostly from fertiliser use on agricul-
tural soils (Zaehle et al., 2011; Park et al., 2012; Ciais et al.,
2013). Fertiliser use also promotes nitrate leaching, which
causes eutrophication and algal blooms in watersheds and
coastal seas, with follow-on effects such as loss of fish pop-
ulations and recreational value and health risks through con-
tamination of drinking water (Cameron et al., 2013). Even in
Europe, where environmental regulations are relatively ad-
vanced, a large portion of the population live in areas with
high levels of nitrate in the drinking water (Grizetti, 2011).
Today’s knowledge about the effects of interactions be-
tween global nitrogen and carbon cycles in terrestrial ecosys-
tems is largely based on simulations with DVMs represent-
ing potential natural vegetation (e.g. Thornton et al., 2009;
Zaehle and Dalmonech, 2011; Smith et al., 2014). The re-
sults obtained with these models suggest that soil N pro-
cesses governing plant-available nitrogen can constrain veg-
etation growth and the strength of the terrestrial carbon sink
(e.g. Zaehle et al., 2011; Wårlind et al., 2014). Only two
global modelling frameworks have been put forward with
both detailed cropland ecosystem functioning and coupled
C–N cycling simulated in a consistent fashion (Arora, 2003;
Drewniak et al., 2013). No study has applied such a model
on a global scale to investigate joint impacts of environmen-
tal change and land management on associated changes in
agricultural yields, water pollution and carbon balance, even
though the production of food and the protection of the en-
vironment often require conflicting strategies and decision
making, for instance between enhanced carbon sequestration
rates (typically higher in forests than in croplands), food pro-
duction (and enhanced nitrogen leaching), and other uses of
land resources (Phalan et al., 2011). These trade-offs between
agricultural production, on the one hand, and carbon seques-
tration and reduction in nitrogen leaching, on the other, have
given rise to a number of mitigation strategies in agricultural
practice that only have a limited impact on production but
contribute to other ecosystem services.
Even though applied on the local to regional scale, land-
management practices often have a large regional to global
impact: via water pollution, greenhouse gas emissions or in-
direct land-use change (Tilman et al., 2002). One important
practise discussed in relation to harvest is residue removal
after harvest (Lal and Bruce, 1999). Removing residues for
use in biofuel production is an appealing measure, as making
multiple use of the existing croplands may be seen as a win-
win situation (Lal, 2004b; Smith et al., 2012). However, not
incorporating residues into soils results in the soils becom-
ing drained of soil organic carbon (SOC); as SOC retains
water and nutrients, reducing SOC reduces the soil fertility
(Lal, 2004b; Smith et al., 2012). Another practice that is of-
ten debated is tillage (Lal, 2004a, 2008). Different forms of
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S. Olin et al.: Soil C management in large-scale Earth system modelling
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tillage have been used for centuries to promote the release
of nutrients from the soil organic matter (SOM) for uptake
by crops. However, the aeration of the soil associated with
the mechanical disturbance of the soil profile increases het-
erotrophic respiration (
R
h
) and thus enhances soil C losses
to the atmosphere (Chatskikh et al., 2009; Lal, 2004b). No-
till management has gained popularity as a potential climate
change mitigation measure, as it can prevent management-
related losses of soil C stocks (Lal, 2004b). However, while
no-till management is expected to favour carbon retention by
agricultural fields, the strength and persistence of any sink is
debated (Lal, 2004a; Schlesinger, 2000). In a review of soil
C sequestration, comparing conventional and no-till manage-
ment, Baker et al. (2007) found that the top soil in no-till
treatments did contain more C, but the C density below the
top soil layer in the conventionally tilled fields was higher
and there was no significant difference in overall C densities
between the two treatments. Moreover, the conversion of N
to plant-available forms is reduced in untilled soils and can
thus lead to lower crop productivity. Although no-till farming
is applied partially to improve water and nutrient retention,
the reduced crop productivity and thus reduced input of new
organic material could also decrease the soil’s organic con-
tent in the long run (Lal, 2004b).
Appropriate nutrient management can increase SOC se-
questration (Lal, 2004b), especially under elevated [CO
2
]
(Van Kessel et al., 2000). However, the N-fertiliser effect on
SOC can be offset by the carbon cost of energy intensive
manufacturing of fertilisers (Lal, 2004a). Application of ma-
nure generally increases the SOC and has a positive effect
on soil fertility (Lal, 2004b). Instead of mitigating climate
change, the negative effects of the use of N fertilisers on agri-
cultural fields (N leaching and volatilisation) can also, if not
managed appropriately, contribute to it through emissions of,
e.g., N
2
O, which is a potent greenhouse gas (Zaehle et al.,
2011).
In this study we employ the land-use-enabled version of
a global DVM, LPJ-GUESS (Lund–Potsdam–Jena General
Ecosystem Simulator) (Lindeskog et al., 2013; Olin et al.,
2015), to explore and quantify the effectuality of alternative
management strategies (such as no-till, cover crops and ma-
nure) that aim to mitigate the negative effects of agriculture
on carbon and nitrogen cycles. To this end, we extended the
model to include N dynamics for crops, and the response of
different N-application rates was evaluated on the local to
regional scale. Management options considered are tillage,
cover crops and manure application. We cannot yet assess
the effects of management on soil N
2
O emissions, as work to
do so is still in progress. We quantify management effects on
soil carbon pools, yields and nitrogen losses through leach-
ing from croplands and evaluate the model globally and for a
representative range of climatic regions. In addition, the per-
sistence and direction of these effects under future climate
change scenarios are explored.
2
Materials and methods
2.1
LPJ-GUESS
LPJ-GUESS (Smith et al., 2014) is a DVM that simulates dy-
namic vegetation response to climate, atmospheric CO
2
lev-
els ([CO
2
]) and N input through competition for light, N, and
water on a daily time step. Vegetation is represented by plant
functional types (PFTs) that differ in their growth form, phe-
nology, life-history strategy, distributional temperature lim-
its and N requirements. C
3
and C
4
photosynthetic pathways
are discriminated for grasses. Leaf-level net photosynthesis
is calculated following a Farquhar-type approach, modified
by Collatz et al. (1991, 1992) and scaled to the canopy fol-
lowing Haxeltine and Prentice (1996). Plant N demand is de-
termined through optimal leaf N content for photosynthesis,
based on the optimisation of the carboxylation capacity of
rubisco (Haxeltine and Prentice, 1996). Canopy conductance
of water vapour and respiration of plant compartments other
than leaves are modelled following Sitch et al. (2003). For
potential natural vegetation, carbon allocation and stand dy-
namics are modelled on a yearly time step. Stand dynam-
ics are based on competition among age classes of trees co-
occurring in a number (here 5) of replicated patches in each
grid cell (Smith et al., 2001; Hickler et al., 2004). Distur-
bance by wildfire and other events such as storms are ac-
counted for. Details of the representation of soil and plant
physiological and growth processes are provided in Smith
et al. (2001, 2014) and Olin et al. (2015).
Soil C–N dynamics in LPJ-GUESS are based on the Cen-
tury model (Parton et al., 1993), in which SOM and litter
are represented by 11 pools that differ in their C-to-N ratios
(C : N), which are dynamic within prescribed limits (Smith
et al., 2014). Mobilisation of mineral N is the result of het-
erotrophic decay and respiration which depends on the C : N
and decay rates (
K
d
) of the SOM pools. Values of
K
d
are dy-
namic and vary between these pools, and they are also modi-
fied by temperature and water content of the soil (Smith et al.,
2014). Organic N is mineralised when transferred SOM, af-
ter a fraction of transferred C is respired (heterotrophic res-
piration), has a higher N content than the receiving pool N
demand. Immobilisation occurs when receiving pools’ N de-
mand exceeds transferred N content and the deficit has to
be meet by available soil mineral N. Mineral N available af-
ter mineralisation and immobilisation is further depleted by
plant N uptake, which is directly proportional to plant fine
root C mass with constraints imposed by the soil mineral N
pool itself, plant N status, and soil temperature (Zaehle and
Friend, 2010). Mineral N leaching is then possible on the re-
maining mineral N and is related to percolation. Leaching of
organic N is also represented in the model and relates to the
decomposition of active SOM, percolation and soil silt and
clay fractions.
The present study uses the managed land version of the
model (Lindeskog et al., 2013; Olin et al., 2015). Land-use
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S. Olin et al.: Soil C management in large-scale Earth system modelling
and land cover change are modelled on a yearly basis based
on the externally supplied fractional area change within a
grid cell. Pastures are modelled to represent a mean grazed
or harvested grassland with a harvest of 50 % of the above-
ground biomass annually; the rest, together with the root
biomass is returned to the soil as litter (Lindeskog et al.,
2013). Crops are represented in the model by crop functional
types (CFTs), which differ in their temperature requirements
for survival, heat requirements for growth and their C allo-
cation patterns. Sowing dates are determined dynamically in
the model based on the prevailing climate in the grid cell
(Lindeskog et al., 2013; Waha et al., 2011), and crops are
harvested when specified heat sum requirements (accumula-
tion of degree days above a crop-specific base temperature)
are fulfilled (Lindeskog et al., 2013). The crops are allowed
to adapt to the local conditions by adjusting the heat sum re-
quirements to the historic climate (Lindeskog et al., 2013),
reflecting a difference in varieties of a given crop grown in
different climatic zones.
The allocation of C and N for the CFTs in the C–N ver-
sion of LPJ-GUESS (Olin et al., 2015) is done on a daily
time step. C allocation depends on the plant’s development
stage (DS), which in turn is based on temperature and day
length, and follows Penning de Vries et al. (1989), as de-
scribed in detail for winter and spring wheat in Olin et al.
(2015) and for maize in Table A1. DS is a number between 0
and 2 and describes the developmental phases in a crop plant;
a DS below 1 represents the period during which the crop
plant allocates most of the assimilates to growth and values
between 1 and 2 correspond to the grain-filling phase. N re-
quirements for the plant vary during the growing period. This
is reflected in the model by applying fertilisers proportionally
at different developmental stages; see the Appendix for more
information on the timing of N-fertiliser application for dif-
ferent CFTs. At harvest, the grains together with a portion of
the residues are removed from the field. In the model, harvest
is not perfect; 10 % of the grain and residue C and N is left
as litter and decomposes (see the section “Residue removal”
below).
At present, the C–N version of LPJ-GUESS is limited to
three CFTs, which are based on wheat and maize growth
characteristics: a C3 crop with dynamic selection between
spring and autumn sowing (represented here by winter wheat,
WW), a C3 crop with sowing carried out in spring (spring
wheat, SW) and a C4 crop (maize, MA). Allocation for SW
and WW is described in Olin et al. (2015); MA-specific al-
location parameters are listed in Tables A1–A2. For compar-
ison with yield data, we adopt these three types to represent
the entire spectrum of crops grown globally. In particular,
wheat and rapeseed that have spring- and autumn-sown vari-
eties were simulated as WW, whereas other C3 crops (beans,
rice, tubers, etc.) were modelled as SW, since these are typ-
ically spring sown. Sorghum and millet were modelled as
MA.
LPJ-GUESS has been evaluated against a range of exper-
imental and observational data types, e.g. CO
2
-fertilisation
experiments (Olin et al., 2015; Smith et al., 2014), ecosystem
dynamics (Smith et al., 2014), vegetation seasonality (Lin-
deskog et al., 2013) and C fluxes on various scales (Ahlström
et al., 2012; Piao et al., 2013; Wramneby et al., 2008). In Olin
et al. (2015), the growth response to N-fertiliser application
on site scale (under ambient and elevated CO
2
) and over a
larger region (western Europe) was evaluated.
2.1.1
Cropland management
The cropland management options implemented in LPJ-
GUESS are sowing, irrigation, tillage, N application, cover
crops and residue management. The latter four options are
relevant for this study and will be described below.
Tillage
Tillage is implemented using a tillage factor (
f
T
), which af-
fects
K
d
for selected SOM pools on croplands. Two tillage
routines were implemented: moderate tillage where
f
T
af-
fects the
K
d
of the surface humis and microbial pools, as
well as the microbial and slow turnover pools of the soil, and
full tillage in which
K
d
for the metabolic and structural sur-
face pools and the passive and metabolic pools of the soil are
also affected. The two tillage levels are not intended to repre-
sent different tillage practices but rather to span uncertainties
in the overall effect of tillage on soil respiration rates. The
value of
f
T
(1.94) is taken from Chatskikh et al. (2009) and
modifies
K
d
(
K
0
d
=
f
T
K
d
) throughout the year.
N application
Fertilisers are applied as mineral N (Olin et al., 2015). The
timing of fertiliser applications in the model roughly coin-
cides with the crucial developmental periods of plants: appli-
cations take place at DSs 0, 0.5 and 0.9 (Olin et al., 2015) in
the CFT-specific amounts listed in Table A2.
Here we have extended the available N-fertiliser applica-
tion management options to also include manure application
in the first of the three events (DS
=
0; sowing).
The amount of manure is derived using the mineral N-
application rate but applying the increase in the metabolic
and structural SOM pools rather than in the mineral N pool,
with a C : N of 30. This means that 30 units of C are also
added for every unit of N. The C : N has been chosen to rep-
resent the C and N content in manure from sources rang-
ing from poultry waste (C : N
∼
15) to straw-rich manure
from livestock (C : N
&
40) (Nieder and Benbi, 2008). As the
metabolic and structural SOM pools have different turnover
(decomposition) rates, the manure-derived N becomes avail-
able for an extended period in the soil.
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Table 1.
Summary of simulation experiments; for abbreviations and further explanations, see text.
Purpose of experiment
Time period
Spin-up period
Land-use change
Climate
N-fertiliser data
Land-use
and [CO
2
]
during spin-up
data
change data
Response to different
1901–2006
500 years,
Started 350 years
CRU
Zaehle et al. (2010a)
Hurtt et al. (2011)
management regimes;
1901
[
CO
2
]
into spin-up, linear
C and N pools and fluxes;
increase to 1901
historical
Yield comparison;
1901–2006
500 years,
Started 350 years
CRU
Zaehle et al. (2010a)
Hurtt et al. (2011)
historical
1901
[
CO
2
]
into spin-up, linear
until 1990; thereafter
increase to 1901
Elliott et al. (2014)
Response to different
1850–2100
450 years,
Started 300 years
CMIP5
Zaehle et al. (2010a)
Hurtt et al. (2011)
management regimes;
1901
[
CO
2
]
into spin-up, linear
future
increase to 1850
C sequestration response;
1901–2100
500 years,
Started 350 years
CRU, after 2006:
Zaehle et al. (2010a)
100 % cropland
soil C comparison; historical
1901
[
CO
2
]
into spin-up, linear
1977–2006
increase to 1901
repeated until 2100
Cover crops
Cover crops are intermediate crops that are grown in-between
the main agricultural growing seasons either as a fallow that
stretches over the subsequent growing season or within the
same year (Follett, 2001). A common practice is to sow N-
fixing plants such as legumes as cover crops, but grasses are
also used. If the cover crop is not harvested but, for example,
ploughed in, some of the captured or retained nutrients, as
well as the carbon content of the crop biomass, are retained
in the soil, enhancing nutrient availability.
In our implementation, cover crops are grown in-between
two growing periods of the generic main crop used if the
crop-free period is longer than 15 days. At the time of sow-
ing of the subsequent main crop, the cover-crop biomass is
added to the soil litter pool. C and N allocation of the cover
crop is done daily, with a leaf-to-root ratio that depends on
the plant water status. In case of water stress, a functional bal-
ance response is introduced and allocation to roots increases
relative to leaves. Cover crops are modelled as grasses, be-
ing “planted” with an initial C mass of 0.01 kg C m
−
2
and
with an N mass that is based on the C : N
min
value for grasses
(C : N
min
=
16). Symbiotic N fixation, such as in legumes,
which are common as cover crops in temperate latitudes, is
not yet implemented.
Residue removal
A measure to increase the soil fertility and decrease the wa-
ter loss, in particular in arid areas, is to leave the residues on
the ground after harvest (Lal, 2004a; Smith et al., 2012). This
practice is represented in our model by removing only a frac-
tion (default set to 75 %) of the biomass remaining following
harvest, thus leaving the rest as litter, which enters the nor-
mal soil-decomposition calculations. While this affects soil
C content, the effect of crop residues on soil evaporation and
hence soil water content is not represented in the model.
2.2
Experimental set-ups
Our study is divided into two parts. In the first part we test
the ability of LPJ-GUESS to simulate present-day soil C and
the yield response to management by comparing simulated
results with data sets of soil C in crop fields, potential C se-
questration after a change in management, and global yield
statistics. In the second part of the study, we investigate the
efficacy of alternative crop management options described in
Sect. 2.1.1 for mitigating climate change through increased
carbon retention in cropland soils. The sensitivity of soil car-
bon sequestration to these management options is first stud-
ied for present-day climate conditions, assessing relative ef-
fects in different regions. Subsequently, we force the model
with general circulation model (GCM)-simulated climate un-
der a 21st-century future climate projection to investigate
combined effects of future changes in multiple ecosystem
drivers on cropland ecosystem carbon balance. Below, the
set-up of the different experiments are explained in detail; a
summary is also available in Table 1.
For the simulation of the recent historic period (1901–
2006), gridded monthly mean observations of precipitation,
air temperature and cloudiness from CRU (Climate Re-
search Institute; Mitchell and Jones, 2005) were used. For
the future-climate simulations, monthly climate data were
adopted from four CMIP5 (Coupled Model Intercompari-
son Project Phase 5) GCMs: CCSM4 (Community Climate
System Model version 4) (Gent et al., 2011), MPI-ESM-
LR (Max Planck Institute Earth System Model, low reso-
lution; e.g. Stevens et al., 2013), IPSL-CM5A-LR (Institut
Pierre Simon Laplace coupled model version 5A, low res-
olution). For the simulation of the recent historic (Dufresne
et al., 2013) and HadGEM2-ES (Hadley Centre Global En-
vironmental Model, version 2, Earth system) (Collins et al.,
2011). The GCM climate data cover period between 1850
and 2100 (Taylor et al., 2011) and were bias corrected against
CRU for monthly means over the period from 1961 to 1990,
as described in Ahlström et al. (2013). Climate data for the
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S. Olin et al.: Soil C management in large-scale Earth system modelling
contrasting RCPs (Representative Concentration Pathways)
2.6 and 8.5 radiative forcing projections (Moss et al., 2010)
were selected based on the availability of projections of fu-
ture N fertilisation.
For all simulations soil C and N pools were initialised
with a “spin-up” using atmospheric [CO
2
] from the first his-
toric year (1901 for the historical CRU-based simulations
and 1850 for the CMIP5 simulations) combined with repeat-
edly cycled, detrended climate input using the first 30 years
of the historic climate data set. The spin-up period for the
CRU simulations was set to 500 years. In order to make
the CMIP5 simulations comparable to the simulations using
CRU, the spin-up was set to 450 years, followed by a sim-
ulation for the years 1850–1901 with dynamic climate but
constant [CO
2
] (using the [CO
2
] for 1901).
N atmospheric deposition was provided as decadally vary-
ing monthly averages from the ACCMIP (Atmospheric
Chemistry and Climate Model Intercomparison Project) data
set (Lamarque et al., 2010) transformed to the resolution of
the climate data following Smith et al. (2014) and Wårlind
et al. (2014).
As N-fertiliser input for the croplands, data from Zaehle
et al. (2010a) were used for the historical time period, start-
ing from 1901 (CRU) and 1850 (CMIP5); for the future
period (2006–2100), a data set described in Stocker et al.
(2013) was used, which expands on the data set from Za-
ehle et al. (2010a) and includes simulated future fertiliser
applications from integrated assessment models (RCP2.6 –
Bouwman et al., 2013; RCP8.5 – Riahi et al., 2011). The to-
tal global N fertiliser applied is visualised in Fig. A1a. In ad-
dition, a simulation using N-fertiliser information from Ag-
GRID (AgMIP GRIDded Crop Modeling Initiative) (Elliott
et al., 2014) was performed for the comparison of yields with
national statistics from the FAO. The Ag-GRID data set pro-
vides a long-term mean N-fertiliser input for each grid cell
representing the present day (approximately the year 2000).
In these simulations the input from Zaehle et al. (2010a) was
used until 1990, subsequently switching over to Ag-GRID
data.
Land cover information was adopted from Hurtt et al.
(2011), with data on historic and future cropland, pasture
and natural vegetation. The additional land cover classes in
the data set (forested, rangeland and urban classes) were
treated as natural land cover. During spin-up, cropland frac-
tion was linearly increased from an assumed baseline of zero
in 1750 to the first historic value (1901 for CRU and 1850 for
CMIP5). The number of years for this transition (150 years
for the CRU-based and 100 years for the CMIP5 simulations)
was chosen to ensure that the soil C and N pools of the nat-
ural vegetation fraction of each grid cell reached steady state
by the beginning of the transition period. The different period
lengths were chosen to make the simulations comparable in
terms of land-use change prior to 1901. While this proce-
dure will likely result in higher SOM pools in areas such as
central Europe, India and the Middle East, where agriculture
has been present for many centuries, it will be most realis-
tic for regions where most agricultural expansion has taken
place over the last 100–200 years. Grid cell fractions of crop
coverage for those grid cells where data on crop species ex-
ist were taken from MIRCA (Monthly Irrigated and Rainfed
Crop Areas) (Portmann et al., 2010) and aggregated to the
three CFTs as described in Sect. 2.1. The relative CFT cover
fractions were conserved over time, and information from the
neighbouring cells was used using a distance weighted mean
for grid cells that lack information in the MIRCA data set.
As soil input, a soil map with fractions of clay, silt and
sand from the WISE 3.0 (Wide-field Infrared Survey Ex-
plorer, version 3) data set (Batjes, 2005) were used. Hy-
drological properties of the soil were calculated following
Eqs. (19)–(20) from Olin et al. (2015).
2.2.1
Soil carbon and management response
To evaluate LPJ-GUESS ability to simulate soil C den-
sity and sequestration, soil columns from croplands in the
WISE 3.0 data set (Batjes, 2005) were compared to mod-
elled cropland soil C. Soil carbon from the top 1.5 m of the
soil columns was associated with a 0.5
◦
grid cell matching
the climate data used in this study. If more than one sam-
ple was available within the approximately 1000 grid cells
that had soil C information, the data were averaged to give a
single number per grid cell. As no detailed information was
available on the management or land-use history for the dif-
ferent soil column sites, the CFT fractions from Portmann
et al. (2010) were used together with N-fertiliser input as de-
scribed above.
In Stockmann et al. (2013), data on long-term soil car-
bon response to the management options (cover crops,
no-tillage and manure application) were divided between
four climatic regions: humid temperate, dry temperate,
humid tropical and dry tropical. In order to compare
our simulated carbon sequestration with the findings of
Stockmann et al. (2013), each simulated grid cell for
which observed soil column data were available was clas-
sified to be either tropical (24
◦
S
>
latitude
<
24
◦
N) or
temperate (24
◦
S
<
latitude
>
24
◦
N and latitude
<
60
◦
N),
as depicted in Fig. A3. These categories were fur-
ther subdivided into dry if the water balance coeffi-
cient (WBC
=
precipitation
−
potential evapotranspiration)
was negative and humid if it was positive. Each of the result-
ing four classes covered approximately 200 grid cells, evenly
spread over the continents (Fig. A3). Some 200 of the grid
cells were either in the boreal zone or not included in the cli-
mate data set. To be able to show the effect of the studied
management practices during the historic period, these were
enabled starting in the year 1990 until the end of the simula-
tion period. For the simulations using CRU climate input, the
last 30 years of climate and [CO
2
] (381 ppmv), N deposition
and fertiliser from the last year were repeated until 2100, the
end of the CMIP5 climate data set, in order to allow soil car-
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S. Olin et al.: Soil C management in large-scale Earth system modelling
751
bon and nitrogen pools to reach a new equilibrium after the
management shift.
2.2.2
Management, global soil C and N leaching
The effect of the different management strategies con-
sidered (no-tillage (NT), manure application (MN), cover
crops (CC), leaving all residues (NR); Table 2.1.1) on sim-
ulated global crop yields, soil C pool size, and N leaching
was tested in a factorial experiment where management types
were turned on at the beginning of the simulation. The simu-
lated yields, soil C and N leaching were then compared with
a baseline simulation (
F
std
, Table 2) with settings as in Lin-
deskog et al. (2013) Smith et al. (2014) and Olin et al. (2015).
To be able to compare our results with previous estimates
of global soil C and N pools and N leaching from LPJ-
GUESS (Smith et al., 2014), a simulation with potential nat-
ural vegetation (PNV) was also conducted. In addition, an
optimised simulation set-up was selected (
F
opt
), in which the
management from Table 2 that yielded the largest increase in
soil carbon per grid cell was selected for the CRU and CMIP5
simulations.
3
Results
3.1
Yield comparison
LPJ-GUESS wheat (C3) and maize (C4) yields were sim-
ulated using the gridded N-fertiliser data set (Elliott et al.,
2014) and compared to reported yields from FAO
1
for the
years 1996–2005 (Fig. 1). The overall model agreement
with reported wheat yields per country was good across
all wheat-producing countries, with a correlation coeffi-
cient of 0.73 and root mean square error (RMSE) value of
1.5 t ha
−
1
yr
−
1
. Maize yields had a lower agreement (correla-
tion coefficient 0.46; RMSE 4.3 t ha
−
1
yr
−
1
), with simulated
yields overestimating the observations for most countries
that have a low maize production (e.g. Mexico, China and
many African countries; Fig. 1). However, with the excep-
tion of China and Mexico, yields in highly productive coun-
tries were captured well, including the largest producer, the
USA, despite the model not being directly calibrated against
these yields.
The total simulated production (wet weight) of all agri-
cultural crops (including cereals, tubers and pulses) of
2.7 Gt was within 30 % of what is reported to the FAO:
3.5 Gt for the period 1996–2005 (cereals, 2.12 Gt; coarse
grain, 0.93 Gt; roots and tubers, 0.28 Gt
2
; pulses, 0.06 Gt; oil
crops, 0.11 Gt). In Fig. A2, a comparison between modelled
crop production and that from FAOSTAT is shown.
1
FAOSTAT, http://faostat3.fao.org/home/E
2
Corrected for moisture content; value from FAOSTAT: 0.68 Gt.
Table 2.
Simulation settings used for the comparison of soil C,
yields and N leaching with different agricultural management types.
For implementation of full vs. moderate tillage, see Sect. 2.1.1. In
the row headed “scenario”, the management types that are included
in the different scenario simulations are indicated. Abbreviations:
NT – no-tillage; MN – manure application; CC – cover crops; and
NR – leaving all residues.
Simulation
F
std
F
NT
F
MN
F
CC
F
MT
F
NR
Tillage
full
no
full
full
modest
full
Manure N
no
no
yes
no
no
no
Cover crops
no
no
no
yes
no
no
Residue removal
75
75
75
75
75
0
Scenario
yes
yes
yes
yes
no
no
Figure 1.
Per-country comparison of simulated yields for
WW (wheat) and MA (maize) against reported yields from FAO
(1996–2005). Marker size indicates each country’s total production.
The top six producer countries of both crops are labelled with the
following abbreviations: ARG – Argentina; BRA – Brazil; CAN –
Canada; CHN – China; FRA – France; IND – India; MEX – Mex-
ico; RUS – Russia; USA – United States.
3.2
Simulated soil C and its response to management
Simulated soil C pools (0–1.5 m) for the selected grid cells
(Sect. 2.2.1) were compared against data from soil cores from
agricultural fields for the four climatic regions (Batjes, 2005).
This comparison did not aim to reproduce observed C values
on the individual field scale, as this would require to capture
individual site meteorology as well as details on land-use his-
tory. Consequently, per-site comparison of simulated vs. ob-
served soil C resulted in low correlations of 0.05–0.14, but
the mean and spread over the climatic zones were captured
by the model (Table 3).
In Fig. 2, the simulated mean soil C sequestration response
to the three management types (no-till, manure and cover
crops) is compared to estimates of potential soil C seques-
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752
S. Olin et al.: Soil C management in large-scale Earth system modelling
Figure 2.
Simulated mean C sequestration following the implementation of management over the CRU historic period on agricultural soils
averaged (thick lines) for the selected grid cells in the four climatic regions, compared to estimates (vertical lines) from Stockmann et al.
(2013). Dotted lines indicate the mean plus 2 SD (standard deviations) from all grid cells in each climatic region. The vertical lines do not
represent specific years but the potential over time to sequester C on cropland soils.
tration from Stockmann et al. (2013) for the simulated cli-
matic regions over the historic period (1990–2006). Besides
the model’s average regional response to the three manage-
ment options, Fig. 2 illustrates how the soil C sequestra-
tion in response to the onset of management (here in the
year 1990; see Sect. 2.2.1) evolves over time. The simu-
lated long-term (100-year) mean soil C sequestration by us-
ing manure on tropical soils was ca. 0.001 kg C m
−
2
yr
−
1
,
declining to negligible levels by the end of the simulated
period. For no-till management, the long-term mean C se-
questration was 0.003 kg C m
−
2
yr
−
1
or higher for all treat-
ments, and levelled off to ca. 0.002 kg C m
−
2
yr
−
1
in the sim-
ulated year 2100. The highest mean C sequestration rates
were found for manure in the humid temperate climatic re-
gions (0.006 kg C m
−
2
yr
−
1
) and for cover crops in the tropi-
cal humid regions (0.008 kg C m
−
2
yr
−
1
), in both cases level-
ling off to below 0.001 kg C m
−
2
yr
−
1
by the end of the sim-
ulation period.
3.3
Global responses to management
The simulated management options resulted in an increase
in cropland soil C, for all climatic regions (Fig. 3), with the
largest global increase, as expected, for the option in which
the management that yielded the largest carbon sequestration
in a given grid cell was chosen (
F
opt
). With the exception
of no residue removal, the simulated management treatments
reduced N leaching (expressed here as negative anomalies),
Table 3.
Soil C pools (0–1.5 m) in four climatic regions, observed
(Batjes, 2014) and simulated, with the 0.05 and 0.95 percentiles
in parenthesis. In the last two columns, the correlation coefficients
and the
p
values are shown to demonstrate that there is a positive
relationship between simulated and observed values.
Soil C (kg C m
−
2
)
Climatic region
Observed
Simulated
Corr.
P
Temperate, dry
5.7 (1.3–12.0)
10.0 (1.4–27.4)
0.14
0.0479
Temperate, humid
8.4 (1.9–21.2)
11.9 (5.2–26.2)
0.05
0.4686
Tropical, dry
6.0 (1.7–12.8)
7.6 (2.5–16.7)
0.07
0.3427
Tropical, humid
11.2 (2.5–28.7)
7.9 (3.7–16.0)
0.13
0.0504
with cover crop resulting in the largest decline. Cover crops
and no-residue removal had opposite effects on both yields
and N leaching. The reduction in N leaching from cover
crops (
∼
15 %) was accompanied by a decline in simulated
global yields of 5 %. The large negative effect of cover crops
on simulated yields in the temperate humid climatic region is
due to the implicit competition for the available N between
the cover crop and the main crop; the low temperature makes
the decomposition of the SOM slow and, in turn, the release
of N more evenly spread throughout the year. The N retained
in the system is locked into SOM and not easily available
for plant uptake; the opposite happens in the tropical regions
and especially so for the humid tropics, where turnover of
the SOM is relatively fast due to the prevailing warm and
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S. Olin et al.: Soil C management in large-scale Earth system modelling
753
Figure 3.
The simulated relative response (%) of soil carbon to management options (Table 2) compared to the standard set-up, averaged
for 1996–2005 and displayed as the global response (filled symbol) and per climatic region. Note the reversed axes for N leaching (all axes
display scales from reduced to enhanced ecosystem services with the upper right corner representing a win-win situation).
moist conditions. Leaving all the residues on the fields (no
residue removal) was the only treatment that increased the
modelled yields both globally and for all climatic regions
but with the environmental “cost” of an increase in N leach-
ing. The increase in both modelled yields and N leaching is
obtained because N becomes available for plant uptake and
transport over a longer period, and nothing grows between
the growing periods that can take up the available nitrogen.
In all treatments, the soil N pools were higher than for the
standard simulation (Table 3), which is caused by the reduc-
tion in leaching and the incorporation of nitrogen in SOM.
In general, the soil C pools simulated with the managed
land version of LPJ-GUESS were slightly larger than simu-
lated with PNV (Table 4), which is due to higher C storage
in pastures compared to the natural vegetation which the pas-
tures have replaced (e.g. central Asia and parts of the Great
Plains of North America) and also in high-productivity crop-
lands that receive high inputs of N fertilisers (e.g. Egypt and
western China; results not shown here).
From the simulations of different cropland management
options, the management combination that yielded the largest
SOC stocks for the period 1996–2005 was chosen for each
grid cell (
F
opt
); the spatial patterns are shown in Fig. 4, with
cover crop and no-till management being the most dominant
and with distinct differences with cover crop mostly in hu-
mid tropical areas and no-till management in subtropical and
temperate regions.
For the future simulations, there were changes in the op-
timal C sequestration management (Table 5), most of these
“transitions” (
∼
7 % for RCP2.6 and
∼
9 % for 8.5) being
from no-till management to the other management type op-
tions (Fig. 6).
Figure 5 depicts the evolution over time of the effects of
implementing the different soil carbon sequestration man-
agement types for two future climate change, CO
2
and land-
use change scenarios. The spread that can be seen around
the simulations with CRU forcing in Fig. 5 originates from
the GCM climate variability, which can be seen also dur-
ing the historic period (Fig. A1b). In the scenarios of land-
use change (Hurtt et al., 2011), there is a steady increase in
cropland area globally, which is most extreme for RCP2.6
(Fig. A1a). Differences between the RCP2.6 and 8.5 cases
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S. Olin et al.: Soil C management in large-scale Earth system modelling
Table 4.
Modelled global, total land and cropland soil C and N stocks and total N leaching (inorganic and organic) for the time period
1996–2005, compared to estimates from the literature. References for the studies and explanations of how some of the values were derived
can be found in the notes of this table. See Table 2 for abbreviations.
Soil C, total (Pg C)
Soil N, total (Pg N)
N leach (Tg N yr
−
1
)
Model
Global
a
Cropland
Global
b
Cropland
Global
Cropland
F
std
1440
148
146
16
55
44
F
CC
1444
151
146
16
24
12
F
MT
1442
150
146
16
54
42
F
NT
1447
154
146
17
53
41
F
MN
1442
150
146
16
54
42
F
NR
1443
151
146
16
66
54
F
PNV
1385
139
18
Other
1993–2456
c
171
e
133–140
f
50
g
14–24
i
studies
1500–2400
d
80
h
23
j
a
These numbers do not include litter; soil C including litter is 1668 and 1671 Pg C for
F
std
and
F
PNV
respectively.
b
These numbers do not include litter; soil N including litter is 147 and 140 Pg N for
F
std
and
F
PNV
respectively.
c
Stockmann et al. (2013).
d
Ciais et al. (2013).
e
Stockmann et al. (2013); the estimate for 0–2 m is 184 Pg C, and that for
0–1 m is 157 Pg C.
f
Batjes (2014).
g
Estimated from Fig. 4 in Boyer et al. (2006): 39–60 Tg N yr
−
1
.
h
Gruber and
Galloway (2008).
i
Smil (1999).
j
Liu et al. (2010).
Figure 4.
Optimal carbon sequestration practice (
F
opt
) around the year 2000, as simulated by LPJ-GUESS, based on the different manage-
ment practices and trade-offs shown in Fig. 3 (see also Table 2 for the abbreviations). The standard set-up (
F
std
, blue) was selected when
none of the other management types gave an increase in the amount of carbon sequestered. The C sequestered compared to
F
std
for choosing
the optimal practice in each grid cell is 7.7 Pg C from 1750 to 2000; the reduction in global N leaching for best C sequestration practices is
11.9 Tg N yr
−
1
.
regarding the effects of management are consistently seen
only for cropland soil C storage, with values being higher for
RCP8.5 than for RCP2.6. Manure and no-tillage did not af-
fect calculated N leaching or yields under future conditions
any more than for present-day forcing. The effect of cover
crops and best carbon management for RCP8.5 was an en-
hanced reduction in yields and enhanced N leaching com-
pared to the standard model set-up.
4
Discussion
Olin et al. (2015) addressed the effect of N-fertiliser appli-
cations on crop yields in Europe. In the present study we
extended this analysis to the global scale and addressed the
effect of additional land-management practices, other than
fertiliser applications, on crop yields and carbon retention in
cropland ecosystems and soils. The management practices
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S. Olin et al.: Soil C management in large-scale Earth system modelling
755
Figure 5.
The simulated response on (a) global C in cropland soils, (b) yields and (c) N leaching with management options implemented in
LPJ-GUESS relative to the standard set-up. The black line represents the response with historic climate (CRU); red and blue show the mean
of simulations using four GCMs (described in Sect. 2.2) for RCPs2.6 and 8.5 respectively. Shaded areas show the mean
±
2 SD. The panels
on the right show the results from choosing the best SOC management compared to the standard for the two RCPs.
explored are widely used approaches that have been recom-
mended as suitable for climate change mitigation and are
claimed to have benefits for a range of ecosystem services.
4.1
Soil carbon and nitrogen
LPJ-GUESS projections of soil N pools agree well with other
estimates, although the soil C pools are at the low end of
generally reported global estimates (Ciais et al., 2013; Stock-
mann et al., 2013). This is to be expected since, for instance,
the present version of the model does not include wetland
and permafrost processes (Miller and Smith, 2012; Tarnocai
et al., 2009; Wania et al., 2009). The accurate modelling of
soil carbon pool sizes and changes is of great importance
when assessing impacts of global environmental change,
since soils are the main long-term terrestrial sink of carbon
(Smith, 2004a). While DVMs and Earth system models still
do not capture all processes that are known to be important
(McGuire et al., 2001; Sitch et al., 2008), an additional com-
plication arises also from the limited availability of obser-
vational soil carbon data. Global estimates of soil carbon and
nitrogen pools are derived by extrapolation of highly variable
point observations from soil inventories or from data-based
modelling. For instance, global C and N densities reported
by Batjes (2014) (see Table 4) were derived by extrapolat-
ing measurements taken in 4353 soil cores across all biomes,
using maps of soil types and land cover.
When making projections on global C pools, the informa-
tion on land-use history is vital (Pongratz et al., 2014). In
our simulations, the assumption that the main – natural to
cropland – conversions started in 1750 could have resulted in
overestimations of C stored in agricultural soils where agri-
culture has been practised for a long time but, conversely, in
underestimations of soil C storage in areas where agriculture
is only a recent feature. We expect this effect to be most pro-
found in areas where agriculture has been practised for many
centuries such as the Middle East, India and central Europe.
The result that the simulations with cropland and pastures
showed a higher global soil C pool than the PNV simulation
(Table 4) can be explained by a higher productivity than the
PNV they replaced, e.g. croplands in Egypt (due to irrigation
and high N input) and major pasture areas, e.g. in Mongolia;
this is consistent with observations (Guo and Gifford, 2002).
When focusing on site data collected for croplands and
grouped by four climate regions (Table 3), simulated average
C pools in LPJ-GUESS were higher than observations, es-
pecially for temperate soils. It is to be expected that many of
these sites, especially in temperate environments, would have
been under land use for very long periods, which could well
lead to lower C pools compared to our modelling assump-
tions. However, we do not have comprehensive information
on present management practices or on how these would
have changed over time, and we are therefore unable to draw
definitive conclusions. Still, the among-grid-cell variations in
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S. Olin et al.: Soil C management in large-scale Earth system modelling
Figure 6.
Grid cells where different management options resulted in the highest soil carbon in 2000 (Fig. 4) compared to 2050: (a) RCP2.6
and (b) 8.5. Grid cells with no cropland in the input data set are shown in white.
C pools were similarly large in observed and simulated data
(Table 3), suggesting that the model response to environmen-
tal and management perturbations is realistic.
Carbon sequestration potential through cropland
management
Global-scale modelling of the impacts of specific land-
management options is in its infancy, but since a number of
future climate and socioeconomic scenarios highlight the im-
portance of land-based mitigation and because of the multi-
ple trade-offs that exist with other ecosystem services, they
are of importance for future research and practical applica-
tions.
In the comparison with potential C sequestration (Fig. 2),
the mean modelled response with 2 standard deviations was
either below or in the lower range of the published values
from Stockmann et al. (2013). One possible reason for the
discrepancy between the modelled C sequestration rate and
that from Stockmann et al. (2013) could be that the number
of sites and the spread over geographic regions – neither of
which are specified in that study – are larger in our simulated
estimates. For tropical soils, as discussed in more detail be-
low, the overestimation of C sequestration could be linked
to the higher productivity with the cover-crop management.
Published estimates of the carbon sequestration potential on
existing cropland due to different types of cropland man-
agement range between 0.34–0.57 Pg C yr
−
1
for present-day
environmental conditions (Lal, 2004b). Globally, our model
estimate when implementing the best practice (from the per-
spective of maximising C storage) is roughly 20 % of that
value. Figure 3 shows a global increase of ca. 5 % in soil C
for the
F
opt
case (ranging from 4 to 9 % between large re-
gions), which equates to an annual uptake of 0.08 Pg C yr
−
1
globally compared to the standard model version. The ex-
act reasons for these low simulated uptake rates are diffi-
cult to assess, but representing land-use history and land-
management practices on a large regional to global scale is a
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757
Table 5.
The relative (%) number of cropland grid cells with a shift
regarding the management practice optimal from a carbon seques-
tration perspective, comparing the highest SOC for 1996–2005 and
for 2046–2055 for RCP2.6 and 8.5. Also listed are the share of the
cropland grid cells with no change in the optimal C sequestration
practice and the percentage of the total number of grid cells that
were cropland around the year 2000 in the data set from Hurtt et al.
(2011). See Table 2 for abbreviations.
Amount of grid cells (%)
From
To
RCP2.6
RCP8.5
F
std
F
CC
0.7
0.7
F
std
F
MN
0.2
0.3
F
std
F
NT
1.0
0.9
F
CC
F
std
0.2
0.1
F
CC
F
MN
0.3
0.5
F
CC
F
NT
2.2
2.5
F
MN
F
std
0.0
0.0
F
MN
F
CC
0.1
0.1
F
MN
F
NT
1.3
0.4
F
NT
F
std
0.2
0.1
F
NT
F
CC
3.8
3.5
F
NT
F
MN
2.9
5.2
No change
87.2
85.6
Cropland cells
69.1
67.2
recognised challenge. In the CLM (Community Land Model)
(Levis et al., 2014), country-specific tillage management has
been implemented, which is not constant over the year but
carried out in connection with harvest. The authors found
that CLM without accounting for tillage practices underes-
timates the emissions caused by agricultural practices. Un-
fortunately, due to the different set-up of our simulations,
it is not possible to compare numbers directly between our
studies. Levis et al. (2014) modelled the effect of a sudden
global introduction of tillage, effectively condensing decades
or centuries of emissions into a 30-year period. However,
if the 0.4 Pg C yr
−
1
of Levis et al. (2014) were distributed
over a 250-year period, equivalent to the 1750–2006 treat-
ment of land use in our simulations, the 0.05 Pg C yr
−
1
thus
derived would be comparable to the estimate in our study of
0.02 Pg C yr
−
1
(
F
NT
−
F
std
). We have chosen to implement
uniform management for tillage in this study, reasoning that
the additional assumptions one would need to make to re-
solve spatially varying tillage would increase the uncertainty
in our model predictions, in particular because of the absence
of available information on future tillage practices.
Another important aspect is productivity during the grow-
ing season and the possibility of multicropping. In many
tropical areas the growing season is not limited to a short pe-
riod of the year, especially in the humid tropics where two or
more crops may be grown in sequence (Francis, 1989). Cur-
rently LPJ-GUESS is restricted to one growing period per
year for the primary crop. Multiple cropping has been im-
plemented in other modelling frameworks, such as LPJmL
(Lund–Potsdam–Jena managed Land)(Waha et al., 2013).
Multiple cropping does not always increase the yields of the
economic crops but results in a more resilient cropping sys-
tem with more than one harvest per year and thus reduces the
risk of complete crop failures, while promoting high net pro-
ductivity (Francis, 1989); it is thus also relevant to consider
it from a carbon cycle perspective. Thus, the simplifications
we necessarily have to include in a global model regarding
some management applications might lead to overall lower
C sequestration compared to other published estimates (Lal,
2004a; Smith, 2004b). However, it also needs to be noted
that these previous estimates are based on empirical mod-
elling, not accounting for process-level interactions between
vegetation, soils and the abiotic environment. In a review of
the potential for countries to fulfil emissions reduction obli-
gations under the Kyoto protocol (IPCC, 1996), Schlesinger
(2000) found only a small or no potential for C sequestra-
tion in cropland soils, while Powlson et al. (2014) argued that
no-tillage management following tillage enhances some im-
portant soil properties but has a small overall effect on total
agricultural soil C.
4.2
Yields
Compared to other measures of global C flows, statistics
on crop production and yields are relatively accessible and
encompass relatively long time series, albeit with differing
qualities between individual countries. While yield is not a
direct measure of the net primary productivity (NPP), it is
a good proxy for trends and variability in carbon flows on
croplands (Haberl et al., 2007) and thus relevant for the esti-
mation of fluxes and pools in agricultural fields. From a food
production perspective, Olin et al. (2015) showed that includ-
ing C–N dynamics and fertiliser input significantly increased
model performance compared to the C-only version of LPJ-
GUESS (e.g. Rosenzweig et al., 2014) for yield modelling
and responses of yields to environmental changes. This was
expected, since the C-only version intentionally represents
a situation not limited by spatial or temporal variations in
nutrient availability. The data sets used in this study were
either designed for crop modelling in the AgMIP (Agricul-
tural Model Intercomparison and Improvement Project) (El-
liott et al., 2014) or for studying global flows of carbon and
nitrogen (rather than yields) (Stocker et al., 2013; Zaehle
et al., 2011). When using the former, Elliott et al. (2014), the
model performance was significantly improved (an increase
in model agreement (
R
2
) with observed yields from 0.25
to 0.53 for WW and from 0.1 to 0.25 for MA). However,
since the AgMIP data set lacks information on temporal vari-
ations and trends, it could not be applied to transient histori-
cal or future simulations of global yields, C and N flows. Pre-
vious global modelling studies incorporating C–N dynam-
ics have reported correlations of simulated yield with FAO
statistics of
R
2
=
0.22 (WW) and
R
2
=
0.39 (MA) for PE-
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Earth Syst. Dynam., 6, 745–768, 2015
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758
S. Olin et al.: Soil C management in large-scale Earth system modelling
GASUS (Predicting Ecosystem Goods And Services Using
Scenarios) (Deryng et al., 2011) and
R
2
=
0.66 (WW) and
R
2
=
0.67 (MA) for DayCent (Daily Century) (Stehfest et al.,
2007).
Our results compare favourably with these studies for WW
but less so for MA. The C–N version of our model has not
yet been evaluated and parameterised against observations of
maize yields, and the lower degree of agreement with data
was expected.
We also compared historical global crop yields with num-
bers found in FAOSTAT. Yields in the early 1960s were
similar (ca. 1.5 t ha
−
1
yr
−
1
), but increases in yields were
faster in the reported statistics compared to the model out-
put. Whether or not this related to a missing process in yield
simulations (e.g. lack of double cropping; Waha et al., 2013)
or uncertainty in the fertiliser hindcast product used needs to
be explored in future work.
The modelling approach taken here to represent all crops
globally with three CFTs introduces an uncertainty into the
estimates of global food production and thus also into the
carbon cycle. We expect that this would be most promi-
nent for crops whose growing seasons, water requirements,
or physiology differ substantially from the functional types
used here, e.g. regions where rice (south-east Asia) or tu-
bers (Africa) are grown over a large section of harvested area.
In many rice-producing regions, a second growing season is
often present each year (Waha et al., 2013), whereas in the
model the number of seasons is limited to one, although the
use of cover crops is expected to decrease the effect of this
limitation on the carbon cycle simulations.
4.3
N leaching
Global estimates of N leaching from terrestrial ecosystems
are uncertain (Gruber and Galloway, 2008), and the esti-
mates with LPJ-GUESS fall well within the broad range
of published annual global totals (Table 4). Only a few
other global studies with DVMs (e.g. Smith et al., 2014;
Stocker et al., 2013; Yang et al., 2009; Zaehle et al.,
2010b) have reported N leached from terrestrial ecosys-
tems. For models that included N-fertiliser applications, we
estimated a range from 63 Tg N yr
−
1
(Yang et al., 2009)
3
to 133 Tg N yr
−
1
(Stocker et al., 2013)
4
. None of these
simulation studies accounted for croplands explicitly; non-
harvested grasslands in Zaehle et al. (2010b) and harvested
grasslands in Stocker et al. (2013) were used as proxies
for croplands. Zaehle et al. (2010b), estimated the total N
leached to aquatic ecosystems from terrestrial sources to be
86 Tg N yr
−
1
, of which 57 Tg N yr
−
1
was attributed to agri-
3
Derived by scaling their average 0.47 g N m
−
2
yr
−
1
by the ice-
free land area of 1.33
×
10
14
m
2
; this is consistent with the esti-
mates elsewhere in this study.
4
Derived from the leaching-associated N
2
O emissions of
0.8 Tg N yr
−
1
equalling to a the constant fraction of 0.6 % of
leached nitrogen
cultural ecosystems. These estimates for the entire land sur-
face are considerably larger than the estimates provided here
(24–66 Tg N yr
−
1
for the simulations including croplands;
Table 4). Among the simulations performed here, the sim-
ulation without residue removal (
F
NR
) was the only one in
which N leached from croplands was of comparable mag-
nitude to the findings of Zaehle et al. (2010b). In our study
fertilisers are applied at specific crop developmental stages
with amounts that match the CFT-specific demand (see Ta-
ble A2), whereas in Zaehle et al. (2010b) three applications
with equal amounts were spread using climate indicators
defining the peak in the growing season. This could lead to
higher leaching when fertiliser application is not timed to co-
incide with the peak of the growing season, when crop N up-
take is highest. Despite its importance for the overall amount
of leached N (Cameron et al., 2013), the timing of fertiliser
applications alone cannot explain the difference between this
study and Zaehle et al. (2010b). By contrast to the three other
DVM studies mentioned above, LPJ-GUESS treats all inor-
ganic N as one pool, as opposed to modelling nitrate and am-
monium separately. A fraction of this pool is leached with-
out any distinction of the nitrogen species, while, in reality,
most of the nitrogen leached is in the form of nitrate (Smil,
1999) and only a small amount is in the form of organic N
or ammonium, the latter mainly in association with extreme
events like floods. During the growing season when crops
(and plants in general) are active, leaching may thus be ex-
pected to be overestimated by our model as nitrates are the
primary nitrogen source for plants (Penning de Vries et al.,
1989), while during the fallow periods with no or only very
little vegetation cover – and consequently a relatively higher
abundance of nitrates than of ammonium – the nitrogen ex-
ported in conjunction with run-off and drainage will tend to
be underestimated.
4.4
Trade-offs and win-win management options
Due to the rising human population, changing lifestyles, as
well as a number of – sometimes conflicting – policies re-
lated to, e.g., climate change mitigation, agriculture, conser-
vation or water regulation, the demand for resources from
land ecosystems is increasing and also constantly changing.
In order to achieve, ultimately, a sustainable use of natural
resources, there is a need to identify strategies that minimise
degradation and wastage of resources while still addressing
society’s growing needs for land-based ecosystem services
including agricultural production. To this end, information
on the trade-offs implicit in different management strategies
but also possible win-win situations is of high value. In our
analysis we attempted to compare three important parame-
ters related to ecosystem functioning (yield, C uptake and
N leaching) in terms of how different forms of crop man-
agement may be expected to influence their relative patterns
of change. From our results (Fig. 3), two general findings
emerge. Firstly, none of the management options explored
Earth Syst. Dynam., 6, 745–768, 2015
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S. Olin et al.: Soil C management in large-scale Earth system modelling
759
lead to a win-win situation in the sense of an increase in all
three of the examined variables. Secondly, general patterns
of change on the global scale were – with some variability
– also seen on the regional scale: we did not (for present-
day conditions) find a situation where a win-win situation on
the global scale was contingent upon trade-offs on a large
regional scale.
All the implemented management options targeting car-
bon benefits resulted in a net increase in simulated soil car-
bon (Fig. 3). Most of these also showed the added benefit of
reduced leaching of N – albeit at the cost of reduced yields.
Avoiding residue removal stands out from this general pat-
tern, resulting in increased soil C and increased yields but
at the same time enhanced N leaching. The largest effects,
at least when taking the regional spread into consideration,
were found when including cover crops as a management
option. The relatively large reduction in yield found in the
F
CC
simulations resulted from indirect competition for wa-
ter and nitrogen, which were not available for the new crops
planted following the cover-crop period. Interestingly, even
though the yields were substantially lower in
F
CC
, the to-
tal vegetation productivity was higher due to the extended
growing period (not shown; global annual total NPP was
+
0.25 Pg C yr
−
1
larger for
F
CC
compared to that of
F
std
).
This higher NPP was also reflected in the enhanced soil C
content. In reality, cover crops are an oft-applied manage-
ment technique to sequester or retain nutrients and carbon
in the field, which is why legumes are a preferred choice.
While LPJ-GUESS correctly simulates enhanced C seques-
tration with cover crops, symbiotic N fixation is not yet im-
plemented in the current crop version of the model. Hence,
the indirect competition for N between cover crop and main
crop discussed above may be overestimated in the model.
Vegetation carbon and nitrogen turnover in the grass PFT
used here for
F
CC
obviously is too slow to make nitrogen
available for the following crops, in particular in the temper-
ate regions, which could also underlie the strong simulated
reduction in leaching.
Absence of residue removal was positive for soil carbon
as well as for yields because of the higher litter input. Sim-
ilar responses of enhanced C storage (up to 30 %) and in-
creased yields (10–30 %) to residue management have also
been found for, e.g., maize and soybean in the US (Wilhelm
et al., 2004) and millet in Niger (Bationo et al., 1993).
Manure (
F
MN
) application had minor effects on all the in-
vestigated processes, both globally and with regard to par-
ticular climatic regions (Fig. 3). The relatively low effect on
soil C might be caused by the relatively small fraction of the
total N applied at sowing (which is the time when manure
was also applied): 8 % for WW and 11 % for SW and MA. In
terms of yield, the relatively high C : N (30) might have re-
duced crop productivity slightly, since the manure N will not
be available for plant uptake at sowing but will be released
from the SOM during the growing season. Still, in some of
the highly productive regions (e.g. north-western Europe and
parts of China), manure application was the most effective
management for carbon sequestration (Fig. 4); these are all
areas where the N application rates in the data set used here
are high (Zaehle et al., 2011), and thus the amount of carbon
added to the soil is relatively large.
By contrast with moderate tillage, complete absence of
tillage resulted in enhanced soil C, with only small to
moderate yield reduction and a small reduction in N loss
through leaching. Depending on the regional climate and
N-fertiliser applications, reductions in crop productivity by
up to 0.5 t ha
−
1
were also reported for maize and winter
wheat grown in the USA in a recent meta-analysis, compar-
ing tillage to no-tillage (Ogle et al., 2012). A larger effect
on C sequestration (at similarly small to moderate effects on
yields) was only found when optimising for carbon seques-
tration also resulted in a moderate reduction in yields while
achieving a reduction in the modelled N leaching by ca. 30 %
(Fig. 3). Considering the high global demand for food today
and in the future, it may be difficult to convince producers
that a 5 % yield reduction is worth a 5 % increase in soil
C and reduced leaching. Avoiding the loss of food produc-
tion would require either further intensification (likely result-
ing in enhanced N losses through leaching) or the expansion
of crop and pasture areas (potentially interfering with other
ecosystem services). In this regard, it is crucial to consider
regional differences. Large vegetation carbon stocks in trop-
ical forest ecosystems motivate the protection of these sys-
tems, limiting the further expansion of managed land in these
ecosystems. Given that tropical areas tend also to have the
largest yield gaps (Licker et al., 2010), a much better strat-
egy in these regions is to invest in sustainable intensification
of existing managed land.
The initial difference between
F
opt
and
F
CC
in Fig. 3,
where
F
CC
had a positive effect on yields until the mid 1960s,
is due to the fact that in the model, the cover crops are being
sown with a finite initial carbon and nitrogen mass. This re-
sults in more available nitrogen in the fields with this man-
agement (basically a fertilisation via the seeds), despite the
indirect competition for nitrogen between the cover crop and
the main crop, which subsequently also results in a relatively
larger nitrogen export through leaching. Cover crops have
been used to revitalise croplands; the result shown here im-
plies that the model partly captures this, but the simulated in-
direct competition is too strong and further studies and model
developments are needed to better represent cover-crop man-
agement. Also, as the cover-crop implementation does not in-
clude symbiotic N fixation, the simulated reduction in yields
with that management could very well have resulted in the
opposite effect but, as was seen for N leaching prior to 1960
and also for the no-residue removal, maybe also in a relative
increase in the N leakage.
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S. Olin et al.: Soil C management in large-scale Earth system modelling
5
Conclusions
We have presented a global model analysis highlighting ef-
fects of alternative crop management strategies for a range
of core ecosystem processes and the services derived from
them, related to interactions of climate change and land-use
change.
Our large-scale approach based on the simplifying as-
sumption of uniform management across regions does not
faithfully represent actual conditions but instead allows
the influence of different management actions to be eval-
uated and geographical differences to be highlighted. The
model is equipped to perform simulations with more detailed
(country-scale or regional) management, and it can thus be
used in applications addressing questions of the environmen-
tal impact from, for instance, policies or trends relating to
agricultural intensification or extensification or climate miti-
gation.
Results demonstrate that the effects of management on
cropland can be beneficial for carbon and nutrient retention
without risking (large) yield losses. Nevertheless, effects on
soil carbon are small compared with extant stocks in natu-
ral and semi-natural ecosystem types and managed forests.
While agricultural management can be targeted towards sus-
tainable goals, from a climate change or carbon sink perspec-
tive avoiding deforestation or reforestation constitutes a far
more effective overall strategy for maintaining and enhanc-
ing global carbon sinks. However, enhanced carbon storage
in agricultural soils could also be seen as a surrogate for en-
hanced soil structure and reduced erosion having additional
(non-climate) environmental benefits.
Earth Syst. Dynam., 6, 745–768, 2015
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761
Appendix A: Allocation
In Olin et al. (2015) relationships between allocation to
leaves (
g
L
), stem (
g
St
), root (
g
R
) and grains (
g
Y
) based on
the allocation model of Penning de Vries et al. (1989) were
established using a logistic growth function, a Richards curve
(Richards, 1959) (Eq. A1):
f
i
=
a
+
b
−
a
1
+
e
−
c
(DS
−
d
)
,
(A1)
where
f
i
is the daily allocation of assimilates to a plant organ
relative to, e.g., the shoot,
a
is the asymptote when DS
→
0,
b
is the upper asymptote when DS
→ ∞
,
c
is the growth rate,
and
d
is the DS of maximum growth.
The relative relationships of daily assimilate allocation to
the organs are described by Eq. (A1):
f
1
=
g
R
g
R
+
g
L
+
g
St
, f
2
=
g
L
g
L
+
g
St
, f
3
=
g
Y
g
R
+
g
L
+
g
St
+
g
Y
.
(A2)
Combining the equations in Eq. (A2) yields
g
R
=
f
1
(1
−
f
3
)
g
L
=
f
2
(1
−
f
1
) (1
−
f
3
)
g
St
=
(1
−
f
2
) (1
−
f
1
) (1
−
f
3
)
g
Y
=
f
3
.
(A3)
See Olin et al. (2015) for more details on how these relation-
ships were derived.
Table A1.
The parameters for the factors
f
1
,
f
2
and
f
3
in the
carbon allocation scheme (Eq. A2) for spring wheat (SW), winter
wheat (WW) and maize (MA).
Parameter
SW
WW
MA
f
1
:
a
0.62
0.53
0.24
b
−
0.02
0
1.22
c
5.80
7.63
18.10
d
0.55
0.55
1.12
f
2
:
a
0.86
0.8
0.68
b
0.19
0.20
−
0.06
c
28.65
13.99
12.48
d
0.55
0.55
0.81
f
3
:
a
0
0
0
b
1
1
1
c
8.27
8.32
28.52
d
1.10
1.15
1.03
Table A2.
CFT-specific parameters of specific leaf area (SLA), min-
imum C : N value of the leaves and the amount of the total N that
is applied at the different developmental stages (DS), where DS
=
0
is sowing and DS
=
0.5 is half way into the vegetative phase. The
remainder of the total fertiliser application (not listed in the table)
is applied at DS
=
0.9, which describes the vegetative phase just
before flowering; see Olin et al. (2015) for more details.
Parameter
SW
WW
MA
Unit
Reference
SLA
35
35
45
m
2
kg
−
1
C
−
1
C : N
min
15
15
15
kg C kg
−
1
N
−
1
N
app
,
DS
=
0
11 %
08 %
11 %
fraction
Olin et al. (2015)
N
app
,
DS
=
0
.
5
50 %
19 %
50 %
fraction
Olin et al. (2015)
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Earth Syst. Dynam., 6, 745–768, 2015
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762
S. Olin et al.: Soil C management in large-scale Earth system modelling
Figure A1.
Input used for the simulations over the historic and scenario RCP2.6 and 85 periods: (a) global cropland cover (%) from Hurtt
et al. (2011) with mean N-fertiliser application rate (Stocker et al., 2013; Zaehle et al., 2010a); (b) [CO
2
] and mean terrestrial temperature
from the four GCMs (Sect. 2.2).
Figure A2.
Global simulated mean crop production using the GCM climate (blue – RCP2.6; red – RCP8.5) compared to statistics from
FAOSTAT (black).
Earth Syst. Dynam., 6, 745–768, 2015
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S. Olin et al.: Soil C management in large-scale Earth system modelling
763
Figure A3.
Climatic regions, as defined in Sect. 2.2.1 (tropical dry, tropical humid, temperate dry, temperate humid, boreal). Black pixels
show the cropland soil column sites from Batjes (2005), used in the soil carbon comparison in this paper.
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Earth Syst. Dynam., 6, 745–768, 2015
Page 20
764
S. Olin et al.: Soil C management in large-scale Earth system modelling
Acknowledgements.
This study contributes to the Strong
Research Environment Land-Use Today and Tomorrow funded
by the Swedish Research Council FORMAS (Contract No. 211-
2009-1682), to the Strategic Research Areas BECC and MERGE,
and to projects within the Lund University Centre for Studies
of Carbon Cycle and Climate Interactions (LUCCI). A. Arneth
and T. A. M. Pugh acknowledge support from the European
Commission in the FP 7 projects OPERAS (grant no. 308393)
and LUC4C (grant no. 603542). M. Lindeskog was funded by the
Mistra Swedish Research Programme for Climate, Impacts and
Adaptation.
Edited by: S. Dekker
References
Ahlström, A., Schurgers, G., Arneth, A., and Smith, B.: Robust-
ness and uncertainty in terrestrial ecosystem carbon response
to CMIP5 climate change projections, Environ. Res. Lett., 7,
044008, doi:10.1088/1748-9326/7/4/044008, 2012.
Ahlström, A., Smith, B., Lindström, J., Rummukainen, M., and
Uvo, C. B.: GCM characteristics explain the majority of uncer-
tainty in projected 21st century terrestrial ecosystem carbon bal-
ance, Biogeosciences, 10, 1517–1528, doi:10.5194/bg-10-1517-
2013, 2013.
Arora, V. K.: Simulating energy and carbon fluxes over win-
ter wheat using coupled land surface and terrestrial ecosystem
models, Agr. Forest Meteorol., 118, 21–47, doi:10.1016/S0168-
1923(03)00073-X, 2003.
Baker,
J.
M.,
Ochsner,
T.
E.,
Venterea,
R.
T.,
and
Griffis, T. J.: Tillage and soil carbon sequestration – what
do we really know?, Agr. Ecosyst. Environ., 118, 1–5,
doi:10.1016/j.agee.2006.05.014, 2007.
Bationo, A., Christianson, C., and Klaij, M.: The effect of crop
residue and fertilizer use on pearl millet yields in Niger, Fert.
Res., 34, 251–258, doi:10.1007/BF00750571, 1993.
Batjes, N. H. .: ISRICE-WISE global data set of derived soil prop-
erties on a 0.5 by 0.5 degree grid (version 3.0), Report 08, ISRIC
– World Soil Information, Wageningen, 2005.
Batjes, N. H.: Total carbon and nitrogen in the soils of the world,
Eur. J. Soil Sci., 65, 10–21, doi:10.1111/ejss.12114_2, 2014.
Bondeau, A., Smith, P. C., Zaehle, S., Schaphoff, S., Lucht, W.,
Cramer, W., and Gerten, D.: Modelling the role of agri-
culture for the 20th century global terrestrial carbon bal-
ance, Global Change Biol., 13, 679–706, doi:10.1111/j.1365-
2486.2006.01305.x, 2007.
Bouwman, L., Goldewijk, K. K., Van Der Hoek, K. W.,
Beusen, A. H. W., Van Vuuren, D. P., Willems, J., Rufino, M. C.,
and Stehfest, E.: Exploring global changes in nitrogen and phos-
phorus cycles in agriculture induced by livestock production over
the 1900–2050 period, P. Natl. Acad. Sci. USA, 110, 20882–
20887, doi:10.1073/pnas.1012878108, 2013.
Boyer, E. W., Howarth, R. W., Galloway, J. N., Dentener, F. J.,
Green, P. A., and Vörösmarty, C. J.: Riverine nitrogen export
from the continents to the coasts, Global Biogeochem. Cy., 20,
GB1S91, doi:10.1029/2005GB002537, 2006.
Cameron, K., Di, H., and Moir, J.: Nitrogen losses from the
soil/plant system: a review, Ann. Appl. Biol., 162, 145–173,
doi:10.1111/aab.12014, 2013.
Chatskikh, D., Hansen, S., Olesen, J. E., and Petersen, B. M.: A sim-
plified modelling approach for quantifying tillage effects on soil
carbon stocks, Eur. J. Soil Sci., 60, 924–934, doi:10.1111/J.1365-
2389.2009.01185.x, 2009.
Ciais, P., Sabine, C., Bala, G., Bopp, L., Brovkin, V., Canadell, J.,
Chhabra, A., DeFries, R., Galloway, J., Heimann, M., Jones, C.,
LeQuere, C., Myneni, R. B., Piao, S., and Thornton, P.: Car-
bon and other biogeochemical cycles, in: Climate Change 2013:
The Physical Science Basis. Contribution of Working Group I to
the Fifth Assessment Report of the Intergovernmental Panel on
Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-
K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y.,
Bex, V., and Midgley, P. M., Cambridge University Press, Cam-
bridge, UK and New York, NY, USA, 465–570, 2013.
Collatz, G., Ball, J., Grivet, C., and Berry, J. A.: Physiological and
environmental regulation of stomatal conductance, photosynthe-
sis and transpiration: a model that includes a laminar bound-
ary layer, Agr. Forest Meteorol., 54, 107–136, doi:10.1016/0168-
1923(91)90002-8, 1991.
Collatz,
G.,
Ribas-Carbo,
M.,
and
Berry,
J.:
Coupled
photosynthesis-stomatal conductance model for leaves of
C
4
plants, Funct. Plant Biol., 19, 519–538, 1992.
Collins, W. J., Bellouin, N., Doutriaux-Boucher, M., Gedney, N.,
Halloran, P., Hinton, T., Hughes, J., Jones, C. D., Joshi, M.,
Liddicoat, S., Martin, G., O’Connor, F., Rae, J., Senior, C.,
Sitch, S., Totterdell, I., Wiltshire, A., and Woodward, S.: Devel-
opment and evaluation of an Earth-System model – HadGEM2,
Geosci. Model Dev., 4, 1051–1075, doi:10.5194/gmd-4-1051-
2011, 2011.
Deryng, D., Sacks, W. J., Barford, C. C., and Ramankutty, N.: Sim-
ulating the effects of climate and agricultural management prac-
tices on global crop yield, Global Biogeochem. Cy., 25, GB2006,
doi:10.1029/2009GB003765, 2011.
Drewniak, B., Song, J., Prell, J., Kotamarthi, V. R., and Jacob, R.:
Modeling agriculture in the Community Land Model, Geosci.
Model Dev., 6, 495–515, doi:10.5194/gmd-6-495-2013, 2013.
Dufresne, J.-L., Foujols, M.-A., Denvil, S., Caubel, A., Marti, O.,
Aumont, O., Balkanski, Y., Bekki, S., Bellenger, H., Benshila, R.,
Bony, S., Bopp, L., Braconnot, P., Brockmann, P., Cadule, P.,
Cheruy, F., Codron, F., Cozic, A., Cugnet, D., de Noblet, N.,
Duvel, J.-P., Ethé, C., Fairhead, L., Fichefet, T., Flavoni, S.,
Friedlingstein, P., Grandpeix, J.-Y., Guez, L., Guilyardi, E.,
Hauglustaine, D., Hourdin, F., Idelkadi, A., Ghattas, J., Jous-
saume, S., Kageyama, M., Krinner, G., Labetoulle, S., Lahel-
lec, A., Lefebvre, M.-P., Lefevre, F., Levy, C., Li, Z., Lloyd, J.,
Lott, F., Madec, G., Mancip, M., Marchand, M., Masson, S.,
Meurdesoif, Y., Mignot, J., Musat, I., Parouty, S., Polcher, J.,
Rio, C., Schulz, M., Swingedouw, D., Szopa, S., Talandier, C.,
Terray, P., Viovy, N., and Vuichard, N.: Climate change projec-
tions using the IPSL-CM5 Earth System Model: from CMIP3
to CMIP5, Clim. Dynam., 40, 2123–2165, doi:10.1007/s00382-
012-1636-1, 2013.
Elliott, J., Müller, C., Deryng, D., Chryssanthacopoulos, J.,
Boote, K. J., Büchner, M., Foster, I., Glotter, M., Heinke, J.,
Iizumi, T., Izaurralde, R. C., Mueller, N. D., Ray, D. K., Rosen-
zweig, C., Ruane, A. C., and Sheffield, J.: The Global Grid-
ded Crop Model Intercomparison: data and modeling proto-
cols for Phase 1 (v1.0), Geosci. Model Dev., 8, 261–277,
doi:10.5194/gmd-8-261-2015, 2015.
Earth Syst. Dynam., 6, 745–768, 2015
www.earth-syst-dynam.net/6/745/2015/
Page 21
S. Olin et al.: Soil C management in large-scale Earth system modelling
765
Evans, L.: Steps towards feeding the ten billion: a crop physiologists
view, Plant Prod. Sci., 2, 3–9, 1999.
FAO: The state of food and agriculture, available at:
http://www.fao.org/docrep/011/i0100e/i0100e00.htm (last ac-
cess: 9 June 2015), 2008.
Follett, R.: Soil management concepts and carbon sequestration in
cropland soils, Soil Till. Res., 61, 77–92, doi:10.1016/S0167-
1987(01)00180-5, 2001.
Francis, C. A.: Biological efficiencies in multiple-cropping systems,
Adv. Agron., 42, 1–42, doi:10.1016/S0065-2113(08)60522-2,
1989.
Galloway, J. N., Dentener, F. J., Capone, D. G., Boyer, E. W.,
Howarth, R. W., Seitzinger, S. P., Asner, G. P., Cleveland, C. C.,
Green, P. A., Holland, E. A., Karl, D. M., Michaels, A. F.,
Porter, J. H., Townsend, A. R., and Vörösmarty, C. J.: Nitrogen
cycles: past, present, and future, Biogeochemistry, 70, 153–226,
doi:10.1007/s10533-004-0370-0, 2004.
Gent, P. R., Danabasoglu, G., Donner, L. J., Holland, M. M.,
Hunke, E. C., Jayne, S. R., Lawrence, D. M., Neale, R. B.,
Rasch, P. J., Vertenstein, M., Worley, P. H., Yang, Z.-L., and
Zhang, M.: The Community Climate System Model Version 4, J.
Climate, 24, 4973–4991, doi:10.1175/2011JCLI4083.1, 2011.
Gerland, P., Raftery, A. E., Ševˇcíková, H., Li, N., Gu, D., Spooren-
berg, T., Alkema, L., Fosdick, B. K., Chunn, J., Lalic, N., Bay, G.,
Buettner, T., Heilig, G. K., and Wilmoth, J.: World popula-
tion stabilization unlikely this century, Science, 346, 234–237,
doi:10.1126/science.1257469, 2014.
Grizetti, B.: Nitrogen as a Threat to European Water Quality, Cam-
bridge University Press, Cambridge, UK and New York, NY,
USA, doi:10.1017/CBO9780511976988.020, 2011.
Gruber, N. and Galloway, J. N.: An Earth-system perspec-
tive of the global nitrogen cycle, Nature, 451, 293–296,
doi:10.1038/nature06592, 2008.
Guo, L. B. and Gifford, R. M.: Soil carbon stocks and land use
change: a meta analysis, Global Change Biol., 8, 345–360,
doi:10.1046/j.1354-1013.2002.00486.x, 2002.
Haberl, H., Erb, K. H., Krausmann, F., Gaube, V., Bondeau, A.,
Plutzar, C., Gingrich, S., Lucht, W., and Fischer-Kowalski, M.:
Quantifying and mapping the human appropriation of net pri-
mary production in earth’s terrestrial ecosystems, P. Natl. Acad.
Sci. USA, 104, 12942–12947, doi:10.1073/pnas.0704243104,
2007.
Haxeltine, A. and Prentice, I. C.: A general model for the light-
use efficiency of primary production, Funct. Ecol., 10, 551–561,
doi:10.2307/2390165, 1996.
Hertel, T.: The challenges of sustainably feeding a growing planet,
Food Security, 7, 185–198, doi:10.1007/s12571-015-0440-2,
2015.
Hickler, T., Smith, B., Sykes, M. T., Davis, M. B., Sugita, S., and
Walker, K.: Using a generalized vegetation model to simulate
vegetation dynamics in northeastern USA, Ecology, 85, 519–
530, doi:10.1890/02-0344, 2004.
Houghton, R. A., House, J. I., Pongratz, J., van der Werf, G. R., De-
Fries, R. S., Hansen, M. C., Le Quéré, C., and Ramankutty, N.:
Carbon emissions from land use and land-cover change, Biogeo-
sciences, 9, 5125–5142, doi:10.5194/bg-9-5125-2012, 2012.
Hurtt, G., Chini, L., Frolking, S., Betts, R., Feddema, J., Fis-
cher, G., Fisk, J., Hibbard, K., Houghton, R., Janetos, A.,
Jones, C., Kindermann, G., Kinoshita, T., Klein Goldewijk, K.,
Riahi, K., Shevliakova, E., Smith, S., Stehfest, E., Thomson, A.,
Thornton, P., van Vuuren, D., and Wang, Y.: Harmonization
of land-use scenarios for the period 1500–2100: 600 years
of global gridded annual land-use transitions, wood harvest,
and resulting secondary lands, Climatic Change, 109, 117–161,
doi:10.1007/s10584-011-0153-2, 2011.
IPCC: Revised 1996 IPCC Guidelines for National Greenhouse Gas
Inventories, Organization for Economic Cooperation and Devel-
opment (OECD), Washington, D.C., 1996.
Lal, R.: Soil carbon sequestration to mitigate climate change, Geo-
derma, 123, 1–22, doi:10.1016/j.geoderma.2004.01.032, 2004a.
Lal, R.: Carbon emission from farm operations, Environ. Int., 30,
981–990, doi:10.1016/j.envint.2004.03.005, 2004b.
Lal, R.: Carbon sequestration, Philos. T. Roy. Soc. Lond. B, 363,
815–830, doi:10.1098/rstb.2007.2185, 2008.
Lal, R. and Bruce, J.: The potential of world cropland soils to se-
quester C and mitigate the greenhouse effect, Environ. Sci. Pol-
icy, 2, 177–185, doi:10.1016/S1462-9011(99)00012-X, 1999.
Lamarque, J.-F., Bond, T. C., Eyring, V., Granier, C., Heil, A.,
Klimont, Z., Lee, D., Liousse, C., Mieville, A., Owen, B.,
Schultz, M. G., Shindell, D., Smith, S. J., Stehfest, E., Van Aar-
denne, J., Cooper, O. R., Kainuma, M., Mahowald, N., Mc-
Connell, J. R., Naik, V., Riahi, K., and van Vuuren, D. P.: His-
torical (1850–2000) gridded anthropogenic and biomass burning
emissions of reactive gases and aerosols: methodology and ap-
plication, Atmos. Chem. Phys., 10, 7017–7039, doi:10.5194/acp-
10-7017-2010, 2010.
Le Quéré, C., Peters, G. P., Andres, R. J., Andrew, R. M., Bo-
den, T. A., Ciais, P., Friedlingstein, P., Houghton, R. A., Mar-
land, G., Moriarty, R., Sitch, S., Tans, P., Arneth, A., Arvani-
tis, A., Bakker, D. C. E., Bopp, L., Canadell, J. G., Chini, L. P.,
Doney, S. C., Harper, A., Harris, I., House, J. I., Jain, A. K.,
Jones, S. D., Kato, E., Keeling, R. F., Klein Goldewijk, K.,
Körtzinger, A., Koven, C., Lefèvre, N., Maignan, F., Omar, A.,
Ono, T., Park, G.-H., Pfeil, B., Poulter, B., Raupach, M. R., Reg-
nier, P., Rödenbeck, C., Saito, S., Schwinger, J., Segschneider, J.,
Stocker, B. D., Takahashi, T., Tilbrook, B., van Heuven, S.,
Viovy, N., Wanninkhof, R., Wiltshire, A., and Zaehle, S.:
Global carbon budget 2013, Earth Syst. Sci. Data, 6, 235–263,
doi:10.5194/essd-6-235-2014, 2014.
Le Quéré, C., Moriarty, R., Andrew, R. M., Peters, G. P.,
Ciais, P., Friedlingstein, P., Jones, S. D., Sitch, S., Tans, P., Ar-
neth, A., Boden, T. A., Bopp, L., Bozec, Y., Canadell, J. G.,
Chini, L. P., Chevallier, F., Cosca, C. E., Harris, I., Hoppema, M.,
Houghton, R. A., House, J. I., Jain, A. K., Johannessen, T.,
Kato, E., Keeling, R. F., Kitidis, V., Klein Goldewijk, K.,
Koven, C., Landa, C. S., Landschützer, P., Lenton, A.,
Lima, I. D., Marland, G., Mathis, J. T., Metzl, N., Nojiri, Y.,
Olsen, A., Ono, T., Peng, S., Peters, W., Pfeil, B., Poulter, B.,
Raupach, M. R., Regnier, P., Rödenbeck, C., Saito, S., Salis-
bury, J. E., Schuster, U., Schwinger, J., Séférian, R., Segschnei-
der, J., Steinhoff, T., Stocker, B. D., Sutton, A. J., Takahashi, T.,
Tilbrook, B., van der Werf, G. R., Viovy, N., Wang, Y.-P., Wan-
ninkhof, R., Wiltshire, A., and Zeng, N.: Global carbon budget
2014, Earth Syst. Sci. Data, 7, 47–85, doi:10.5194/essd-7-47-
2015, 2015.
Levis, S., Hartman, M. D., and Bonan, G. B.: The Community Land
Model underestimates land-use CO
2
emissions by neglecting soil
www.earth-syst-dynam.net/6/745/2015/
Earth Syst. Dynam., 6, 745–768, 2015
Page 22
766
S. Olin et al.: Soil C management in large-scale Earth system modelling
disturbance from cultivation, Geosci. Model Dev., 7, 613–620,
doi:10.5194/gmd-7-613-2014, 2014.
Licker, R., Johnston, M., Foley, J. A., Barford, C., Kucharik, C. J.,
Monfreda, C., and Ramankutty, N.: Mind the gap: how do cli-
mate and agricultural management explain the “yield gap” of
croplands around the world?, Global Ecol. Biogeogr., 19, 769–
782, doi:10.1111/j.1466-8238.2010.00563.x, 2010.
Lindeskog, M., Arneth, A., Bondeau, A., Waha, K., Seaquist, J.,
Olin, S., and Smith, B.: Implications of accounting for land use
in simulations of ecosystem carbon cycling in Africa, Earth Syst.
Dynam., 4, 385–407, doi:10.5194/esd-4-385-2013, 2013.
Liu, J., You, L., Amini, M., Obersteiner, M., Herrero, M., Zehn-
der, A. J. B., and Yang, H.: A high-resolution assessment on
global nitrogen flows in cropland, P. Natl. Acad. Sci. USA, 107,
8035–8040, doi:10.1073/pnas.0913658107, 2010.
McGuire, A. D., Sitch, S., Clein, J. S., Dargaville, R.,
Esser, G., Foley, J., Heimann, M., Joos, F., Kaplan, J., Kick-
lighter, D. W., Meier, R. A., Melillo, J. M., Moore, B.,
Prentice, I. C., Ramankutty, N., Reichenau, T., Schloss, A.,
Tian, H., Williams, L. J., and Wittenberg, U.: Carbon balance
of the terrestrial biosphere in the Twentieth Century: Analy-
ses of CO
2
, climate and land use effects with four process-
based ecosystem models, Global Biogeochem. Cy., 15, 183–206,
doi:10.1029/2000GB001298, 2001.
Miller, P. A. and Smith, B.: Modelling tundra vegetation
response to recent arctic warming, Ambio, 41, 281–291,
doi:10.1007/s13280-012-0306-1, 2012.
Mitchell, T. D. and Jones, P. D.: An improved method of con-
structing a database of monthly climate observations and as-
sociated high-resolution grids, Int. J. Climatol., 25, 693–712,
doi:10.1002/joc.1181, 2005.
Moss, R. H., Edmonds, J. A., Hibbard, K. A., Manning, M. R.,
Rose, S. K., van Vuuren, D. P., Carter, T. R., Emori, S.,
Kainuma, M., Kram, T., Meehl, G. A., Mitchell, J. F. B., Na-
kicenovic, N., Riahi, K., Smith, S. J., Stouffer, R. J., Thom-
son, A. M., Weyant, J. P., and Wilbanks, T. J.: The next gener-
ation of scenarios for climate change research and assessment,
Nature, 463, 747–756, doi:10.1038/nature08823, 2010.
Mueller, N. D., West, P. C., Gerber, J. S., MacDonald, G. K., Po-
lasky, S., and Foley, J. A.: A tradeoff frontier for global nitro-
gen use and cereal production, Environ. Res. Lett., 9, 054002,
doi:10.1088/1748-9326/9/5/054002, 2014.
Nieder, R. and Benbi, D. K.: Carbon and Nitrogen in the Terrestrial
Environment, Springer Science and Business Media, the Nether-
lands, 2008.
Ogle, S. M., Swan, A., and Paustian, K.: No-till manage-
ment impacts on crop productivity, carbon input and soil
carbon sequestration, Agr. Ecosyst. Environ., 149, 37–49,
doi:10.1016/j.agee.2011.12.010, 2012.
Olin, S., Schurgers, G., Lindeskog, M., Wårlind, D., Smith, B.,
Bodin, P., Holmér, J., and Arneth, A.: Modelling the response
of yields and tissue C : N to changes in atmospheric CO
2
and
N management in the main wheat regions of western Europe,
Biogeosciences, 12, 2489–2515, doi:10.5194/bg-12-2489-2015,
2015.
Park, S., Croteau, P., Boering, K. A., Etheridge, D. M., Ferretti, D.,
Fraser, P. J., Kim, K.-R., Krummel, P. B., Langenfelds, R. L., van
Ommen, T. D., Steele, L. P., and Trudinger, C. M.: Trends and
seasonal cycles in the isotopic composition of nitrous oxide since
1940, Nat. Geosci., 5, 261–265, doi:10.1038/ngeo1421, 2012.
Parton, W. J., Scurlock, J. M. O., Ojima, D. S., Gilmanov, T. G.,
Scholes, R. J., Schimel, D. S., Kirchner, T., Menaut, J.-C., Seast-
edt, T., Garcia Moya, E., Kamnalrut, A., and Kinyamario, J. I.:
Observations and modeling of biomass and soil organic mat-
ter dynamics for the grassland biome worldwide, Global Bio-
geochem. Cy., 7, 785–809, doi:10.1029/93GB02042, 1993.
Penning de Vries, F. W. T., Jansen, D. M., Berge, H. F. M.,
and Bakema, A.: Simulation of Ecophysiological Processes of
Growth in Several Annual Crops, Centre for Agricultural Pub-
lishing and Documentation, Wageningen, the Netherlands, 1989.
Phalan, B., Onial, M., Balmford, A., and Green, R. E.: Rec-
onciling food production and biodiversity conservation: land
sharing and land sparing compared, Science, 333, 1289–1291,
doi:10.1126/science.1208742, 2011.
Piao, S., Sitch, S., Ciais, P., Friedlingstein, P., Peylin, P., Wang, X.,
Ahlström, A., Anav, A., Canadell, J. G., Cong, N., Hunting-
ford, C., Jung, M., Levis, S., Levy, P. E., Li, J., Lin, X., Lo-
mas, M. R., Lu, M., Luo, Y., Ma, Y., Myneni, R. B., Poulter, B.,
Sun, Z., Wang, T., Viovy, N., Zaehle, S., and Zeng, N.: Eval-
uation of terrestrial carbon cycle models for their response to
climate variability and to CO
2
trends, Global Change Biol., 19,
2117–2132, doi:10.1111/gcb.12187, 2013.
Pongratz, J., Reick, C. H., Houghton, R. A., and House, J. I.: Ter-
minology as a key uncertainty in net land use and land cover
change carbon flux estimates, Earth Syst. Dynam., 5, 177–195,
doi:10.5194/esd-5-177-2014, 2014.
Portmann, F. T., Siebert, S., and Döll, P.: MIRCA2000 – Global
monthly irrigated and rainfed crop areas around the year
2000: a new high-resolution data set for agricultural and hy-
drological modeling, Global Biogeochem. Cy., 24, GB1011,
doi:10.1029/2008GB003435, 2010.
Powlson, D. S., Stirling, C. M., Jat, M. L., Gerard, B. G.,
Palm, C. A., Sanchez, P. A., and Cassman, K. G.: Limited po-
tential of no-till agriculture for climate change mitigation, Nat.
Clim. Change, 4, 678–683, doi:10.1038/nclimate2292, 2014.
Ramankutty, N., Evan, A. T., Monfreda, C., and Foley, J. A.: Farm-
ing the planet: 1. Geographic distribution of global agricultural
lands in the year 2000, Global Biogeochem. Cy., 22, GB1003,
doi:10.1029/2007GB002952, 2008.
Riahi, K., Rao, S., Krey, V., Cho, C., Chirkov, V., Fischer, G., Kin-
dermann, G., Nakicenovic, N., and Rafaj, P.: RCP 8.5 – a sce-
nario of comparatively high greenhouse gas emissions, Climatic
Change, 109, 33–57, doi:10.1007/s10584-011-0149-y, 2011.
Richards, F. J.: A flexible growth function for empirical use, J. Exp.
Bot., 10, 290–301, doi:10.1093/jxb/10.2.290, 1959.
Rockstrom, J., Steffen, W., Noone, K., Persson, A., Chapin, F. S.,
Lambin, E. F., Lenton, T. M., Scheffer, M., Folke, C., Schellnhu-
ber, H. J., Nykvist, B., de Wit, C. A., Hughes, T., van der
Leeuw, S., Rodhe, H., Sorlin, S., Snyder, P. K., Costanza, R.,
Svedin, U., Falkenmark, M., Karlberg, L., Corell, R. W.,
Fabry, V. J., Hansen, J., Walker, B., Liverman, D., Richard-
son, K., Crutzen, P., and Foley, J. A.: A safe operating space for
humanity, Nature, 461, 472–475, doi:10.1038/461472a, 2009.
Rosenzweig, C., Elliott, J., Deryng, D., Ruane, A. C., Müller, C.,
Arneth, A., Boote, K. J., Folberth, C., Glotter, M., Khabarov, N.,
Neumann, K., Piontek, F., Pugh, T. A. M., Schmid, E., Ste-
hfest, E., Yang, H., and Jones, J. W.: Assessing agricultural risks
Earth Syst. Dynam., 6, 745–768, 2015
www.earth-syst-dynam.net/6/745/2015/
Page 23
S. Olin et al.: Soil C management in large-scale Earth system modelling
767
of climate change in the 21st century in a global gridded crop
model intercomparison, P. Natl. Acad. Sci. USA, 111, 3268–
3273, doi:10.1073/pnas.1222463110, 2014.
Schlesinger, W. H.: Carbon sequestration in soils: some cau-
tions amidst optimism, Agr. Ecosyst. Environ., 82, 121–127,
doi:10.1016/S0167-8809(00)00221-8, 2000.
Sitch, S., Smith, B., Prentice, I. C., Arneth, A., Bondeau, A.,
Cramer, W., Kaplan, J. O., Levis, S., Lucht, W., Sykes, M. T.,
Thonicke, K., and Venevsky, S.: Evaluation of ecosystem dy-
namics, plant geography and terrestrial carbon cycling in the LPJ
dynamic global vegetation model, Global Change Biol., 9, 161–
185, doi:10.1046/j.1365-2486.2003.00569.x, 2003.
Sitch, S., Huntingford, C., Gedney, N., Levy, P., Lomas, M.,
Piao, S., Betts, R., Ciais, P., Cox, P., Friedlingstein, P., Jones, C.,
Prentice, I., and Woodward, F.: Evaluation of the terrestrial car-
bon cycle, future plant geography and climate-carbon cycle feed-
backs using five Dynamic Global Vegetation Models (DGVMs),
Global Change Biol., 14, 2015–2039, 2008.
Smil,
V.:
Nitrogen
in
crop
production:
an
account
of
global
flows,
Global
Biogeochem.
Cy.,
13,
647–662,
doi:10.1029/1999GB900015, 1999.
Smith, B., Prentice, I. C., and Sykes, M. T.: Representation of
vegetation dynamics in the modelling of terrestrial ecosystems:
comparing two contrasting approaches within European climate
space, Global Ecol. Biogeogr., 10, 621–637, 2001.
Smith, B., Wårlind, D., Arneth, A., Hickler, T., Leadley, P., Silt-
berg, J., and Zaehle, S.: Implications of incorporating N cy-
cling and N limitations on primary production in an individual-
based dynamic vegetation model, Biogeosciences, 11, 2027–
2054, doi:10.5194/bg-11-2027-2014, 2014.
Smith, P.: Soils as carbon sinks: the global context, Soil Use Man-
age., 20, 212–218, doi:10.1111/j.1475-2743.2004.tb00361.x,
2004a.
Smith, P.: Carbon sequestration in croplands: the potential in
Europe and the global context, Eur. J. Agron., 20, 229–236,
doi:10.1016/j.eja.2003.08.002, 2004b.
Smith, W., Grant, B., Campbell, C., McConkey, B., Desjardins, R.,
Kröbel, R., and Malhi, S.: Crop residue removal effects on soil
carbon: measured and inter-model comparisons, Agr. Ecosyst.
Environ., 161, 27–38, doi:10.1016/j.agee.2012.07.024, 2012.
Spano, G., Di Fonzo, N., Perrotta, C., Platani, C., Ronga, G.,
Lawlor, D. W., Napier, J. A., and Shewry, P. R.: Physiological
characterization of “stay green” mutants in durum wheat, J. Exp.
Bot., 54, 1415–1420, doi:10.1093/jxb/erg150, 2003.
Steffen, W., Broadgate, W., Deutsch, L., Gaffney, O., and Lud-
wig, C.: The trajectory of the anthropocene: the great accelera-
tion, Anthrop. Rev., 2, 81–98, doi:10.1177/2053019614564785,
2015.
Stehfest, E., Heistermann, M., Priess, J. A., Ojima, D. S.,
and Alcamo, J.: Simulation of global crop production with
the ecosystem model DayCent, Ecol. Model., 209, 203–219,
doi:10.1016/j.ecolmodel.2007.06.028, 2007.
Stevens, B., Giorgetta, M., Esch, M., Mauritsen, T., Crueger, T.,
Rast, S., Salzmann, M., Schmidt, H., Bader, J., Block, K.,
Brokopf, R., Fast, I., Kinne, S., Kornblueh, L., Lohmann, U., Pin-
cus, R., Reichler, T., and Roeckner, E.: Atmospheric component
of the MPI-M Earth System Model: ECHAM6, J. Adv. Model.
Earth Syst., 5, 146–172, doi:10.1002/jame.20015, 2013.
Stocker, B. D., Roth, R., Joos, F., Spahni, R., Steinacher, M., Za-
ehle, S., Bouwman, L., Xu-Ri, and Prentice, I. C.: Multiple
greenhouse-gas feedbacks from the land biosphere under fu-
ture climate change scenarios, Nat. Clim. Change, 3, 666–672,
doi:10.1038/nclimate1864, 2013.
Stockmann,
U.,
Adams,
M.,
Crawford,
J.,
Field,
D.,
Henakaarchchi, N., Jenkins, M., Minasny, B., McBratney, A.,
Courcelles, V., Singh, K., Wheeler, I., Abbott, L., Angers, D.,
Baldock, J., Bird, M., Brookes, P., Chenu, C., Jastrow, J.,
Lal, R., Lehmann, J., O’Donnell, A., Parton, W., Whitehead, D.,
and Zimmermann, M.: The knowns, known unknowns and
unknowns of sequestration of soil organic carbon, Agr. Ecosyst.
Environ., 164, 80–99, doi:10.1016/j.agee.2012.10.001, 2013.
Tarnocai, C., Canadell, J. G., Schuur, E. A. G., Kuhry, P., Mazhi-
tova, G., and Zimov, S.: Soil organic carbon pools in the north-
ern circumpolar permafrost region, Global Biogeochem. Cy., 23,
GB2023, doi:10.1029/2008GB003327, 2009.
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An overview of
CMIP5 and the experiment design, B. Am. Meteorol. Soc., 93,
485–498, doi:10.1175/BAMS-D-11-00094.1, 2011.
Thornton, P. E., Doney, S. C., Lindsay, K., Moore, J. K., Ma-
howald, N., Randerson, J. T., Fung, I., Lamarque, J.-F., Fed-
dema, J. J., and Lee, Y.-H.: Carbon-nitrogen interactions regu-
late climate-carbon cycle feedbacks: results from an atmosphere-
ocean general circulation model, Biogeosciences, 6, 2099–2120,
doi:10.5194/bg-6-2099-2009, 2009.
Tilman, D., Cassman, K. G., Matson, P. A., Naylor, R., and
Polasky, S.: Agricultural sustainability and intensive produc-
tion practices, Nature, 418, 671–677, doi:10.1038/nature01014,
2002.
Van Kessel, C., Horwath, W. R., Hartwig, U., Harris, D.,
and Lüscher, A.: Net soil carbon input under ambient
and elevated CO
2
concentrations: isotopic evidence after
4 years, Global Change Biol., 6, 43–444, doi:10.1046/j.1365-
2486.2000.00318.x, 2000.
Vitousek, P. M., Mooney, H. A., Lubchenco, J., and Melillo, J. M.:
Human domination of earth’s ecosystems, Science, 277, 494–
499, doi:10.1126/science.277.5325.494, 1997.
Waha, K., van Bussel, L. G. J., Muller, C., and Bon-
deau, A.: Climate-driven simulation of global crop sowing
dates, Global Ecol. Biogeogr., 21, 247–259, doi:10.1111/j.1466-
8238.2011.00678.x, 2011.
Waha, K., Müller, C., Bondeau, A., Dietrich, J., Kurukulasuriya, P.,
Heinke, J., and Lotze-Campen, H.: Adaptation to climate change
through the choice of cropping system and sowing date in
sub-Saharan Africa, Global Environ. Change, 23, 130–143,
doi:10.1016/j.gloenvcha.2012.11.001, 2013.
Wania, R., Ross, I., and Prentice, I. C.: Integrating peatlands and
permafrost into a dynamic global vegetation model: 1. Evalua-
tion and sensitivity of physical land surface processes, Global
Biogeochem. Cy., 23, GB3014, doi:10.1029/2008GB003412,
2009.
Wårlind, D., Smith, B., Hickler, T., and Arneth, A.: Nitrogen feed-
backs increase future terrestrial ecosystem carbon uptake in an
individual-based dynamic vegetation model, Biogeosciences, 11,
6131–6146, doi:10.5194/bg-11-6131-2014, 2014.
Wilhelm, W. W., Johnson, J. M. F., Hatfield, J. L., Voorhees, W. B.,
and Linden, D. R.: Crop and soil productivity response to corn
residue removal, this paper is a joint contribution of the USDA-
www.earth-syst-dynam.net/6/745/2015/
Earth Syst. Dynam., 6, 745–768, 2015
Page 24
768
S. Olin et al.: Soil C management in large-scale Earth system modelling
ARS and the Agricultural Research Division of the University
of Nebraska, published as Journal Ser. no. 13949, Agron. J., 96,
1–17, 2004.
Wramneby, A., Smith, B., Zaehle, S., and Sykes, M. T.: Param-
eter uncertainties in the modelling of vegetation dynamics –
effects on tree community structure and ecosystem function-
ing in European forest biomes, Ecol. Model., 216, 277–290,
doi:10.1016/j.ecolmodel.2008.04.013, 2008.
Yang, X., Wittig, V., Jain, A. K., and Post, W.: Integration
of nitrogen cycle dynamics into the Integrated Science As-
sessment Model for the study of terrestrial ecosystem re-
sponses to global change, Global Biogeochem. Cy., 23, GB3474,
doi:10.1029/2009GB003474, 2009.
Zaehle, S. and Dalmonech, D.: Carbon–nitrogen interactions on
land at global scales: current understanding in modelling climate
biosphere feedbacks, Curr. Opin. Environ. Sustain., 3, 311–320,
doi:10.1016/j.cosust.2011.08.008, 2011.
Zaehle, S. and Friend, A. D.: Carbon and nitrogen cycle dynamics
in the O-CN land surface model: 1. Model description, site-scale
evaluation, and sensitivity to parameter estimates, Global Bio-
geochem. Cy., 24, GB1005, doi:10.1029/2009GB003521, 2010.
Zaehle, S., Friedlingstein, P., and Friend, A. D.: Terrestrial nitrogen
feedbacks may accelerate future climate change, Geophys. Res.
Lett., 37, L01401, doi:10.1029/2009GL041345, 2010a.
Zaehle, S., Friend, A. D., Friedlingstein, P., Dentener, F., Peylin, P.,
and Schulz, M.: Carbon and nitrogen cycle dynamics in the O-
CN land surface model: 2. Role of the nitrogen cycle in the his-
torical terrestrial carbon balance, Global Biogeochem. Cy., 24,
GB3522, doi:10.1029/2009GB003522, 2010b.
Zaehle, S., Ciais, P., Friend, A. D., and Prieur, V.: Carbon benefits
of anthropogenic reactive nitrogen offset by nitrous oxide emis-
sions, Nat. Geosci., 4, 601–605, doi:10.1038/ngeo1207, 2011.
Earth Syst. Dynam., 6, 745–768, 2015
www.earth-syst-dynam.net/6/745/2015/
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