Effects of land use change and management on theEuropean cropland carbon balanceP. C I A I S *, S . G E R V O I S *w , N . V U I C H A R D *z, S . L . P I A O *§ and N . V I O V Y *

*Laboratoire des Sciences du Climat et de l’Environnement, CEA CNRS UVSQ, 91191 Gif sur Yvette, France,

wLaboratoire d’ATmospheres, Milieux et Observations Spatiales, Universite Pierre et Marie Curie, 75005 Paris, France,

zCentre International de Recherche sur l’Environnement et le Developpement, Nogent-sur-Marne, France,

§Department of Ecology, Peking University, Beijing 100871, China

Abstract

We model the carbon balance of European croplands between 1901 and 2000 in response to land use and management

changes. The process-based ORCHIDEE-STICS model is applied here in a spatially explicit framework. We

reconstructed land cover changes, together with an idealized history of agro-technology. These management

parameters include the treatment of straw and stubble residues, application of mineral fertilizers, improvement of

cultivar species and tillage. The model is integrated for wheat and maize during the period 1901–2000 forced by

climate each 1/2-hour, and by atmospheric CO2, land cover change and agro-technology each year. Several tests are

performed to identify the most sensitive agro-technological parameters that control the net biome productivity (NBP)

in the 1990s, with NBP equaling for croplands the soil C balance. The current NBP is a small sink of 0.16 t C ha�1 yr�1.

The value of NBP per unit area reflects past and current management, and to a minor extent the shrinking areas of

arable land consecutive to abandonment during the 20th Century. The uncertainty associated with NBP is large, with a

1-sigma error of 0.18 t C ha�1 yr�1 obtained from a qualitative, but comprehensive budget of various error terms. The

NBP uncertainty is dominated by unknown historical agro-technology changes (47%) and model structure (27%), with

error in climate forcing playing a minor role. A major improvement to the framework would consist in using a larger

number of representative crops. The uncertainty of historical land-use change derived from three different

reconstructions, has a surprisingly small effect on NBP (0.01 t C ha�1 yr�1) because cropland area remained stable

during the past 20 years in all the tested land use forcing datasets. Regional cross-validation of modeled NBP against

soil C inventory measurements shows that our results are consistent with observations, within the uncertainties of

both inventories and model. Our estimation of cropland NBP is however likely to be biased towards a sink, given that

inventory data from different regions consistently indicate a small source whereas we model a small sink.

Keywords: agro-ecosystems, carbon cycle, land-use change

Received 28 August 2009; revised version received 20 July 2010 and accepted 18 August 2010

Introduction

In croplands, both environmental and anthropogenic

drivers influence carbon cycle processes. As a conse-

quence, uncertainty in drivers leads to uncertain esti-

mates of local, regional, and continental scale carbon

budgets. At local-scale, most studies aiming to quantify

and understand the carbon budget of ecosystems using

eddy-covariance flux measurements and biometric ob-

servation, have focused on forests (Jarvis et al., 1997;

Curtiss et al., 2002). More recently, new research was

performed for grasslands, in particular for managed

grasslands in Europe, which revealed the role of

C export and import at ecosystem scale in determining

the C balance (Gilmanov et al., 2007; Soussana et al.,

2007). On the other hand, the scarcity of C budgeting

studies over croplands (Antoni & Arrouays, 2007;

Moureaux et al., 2008; Aubinet et al., 2009) offers a stark

contrast with the abundance of agronomy literature

focused on crop yield measurements and on processes

controlling plant and soil fertility.

The C balance of croplands is a function of past and

present agricultural technology and farming practice.

Technology impacts the input of C to the soil both

directly through the management of harvest residue,

and indirectly through the effect on yield and net

primary productivity (NPP). Technology also impacts

the soil organic carbon (SOC) balance via manure

addition and tillage. Climate change affects simulta-

neously gross primary productivity (GPP) and hetero-

trophic respiration. But the response of each gross fluxCorrespondence: P. Ciais, e-mail: philippe.ciais@lsce.ipsl.fr

Global Change Biology (2011) 17, 320–338, doi: 10.1111/j.1365-2486.2010.02341.x

320 r 2010 Blackwell Publishing Ltd

to climate is different, which makes the sensitivity of the

net carbon balance to climate more difficult to assess.

The effect of climate change on crop productivity has

been investigated using controlled experiments, regio-

nal yield data, and models (e.g. Tubiello et al., 2002;

Long et al., 2006; Lobell et al., 2007). The results of these

studies generally point out to large uncertainties, even

in the sign of the climate response, due to uncertainty

on adaptation potentials (Kenny & Harrison, 1993;

Brisson et al., 2004; Parry et al., 2005) and on climate

variability (Olesen, 2005; Battisti, 2008). Rising atmo-

spheric CO2 is expected to increase crop yield (Jamieson

et al., 2000; Long et al., 2006), to improve plants water

use efficiency, to increase the C : N ratio of litter, hence

decreasing litter quality (Cotrufo et al., 1998), and to

limit root allocation of assimilates (Smith, 2005) thereby

potentially slowing down decomposition under ele-

vated CO2 conditions.

Anthropogenic and environmental changes are affect-

ing the C balance of croplands, and they interact with

each other. Because they enable theoretical separation of

agro-technological (anthropogenic) vs. pedo-climatic

(environmental) drivers and mechanisms, process-

based ecosystem models are valuable tools to study

the C balance of croplands and predict its future evolu-

tion. A number of process-based terrestrial biosphere

models have been enhanced with crop-specific para-

meterizations (Bondeau et al., 2007; Osborne et al., 2007;

Gervois et al., 2008). In parallel, crop models have also

been enhanced to include eco-physiological processes

and full ecosystem C cycling (e.g. Verna and collea-

gues). In a previous study by Gervois et al. (2008)

(GE08), we developed and applied a coupling between

the terrestrial biosphere model ORCHIDEE (Krinner

et al., 2005) and the crop model STICS designed for

site-scale applications (Brisson et al., 1998, 2002, 2003).

The resulting ORCHIDEE-STICS coupled model results

were evaluated against historical yield data for winter

wheat and for maize in Europe. The model was found

to faithfully reproduce the three- to four-fold increase of

yield during the late 20th century, due to intensification

of farming practices, in particular plant selection for

higher harvest index and application of mineral fertili-

zers. The model principal shortcoming is an overesti-

mate of winter wheat yields, and an underestimate of

corn yields in Mediterranean countries (Gervois, 2004;

Smith et al., 2009, 2010a, b). GE08 also performed sensi-

tivity simulations during the period 1901–2000, in a first

attempt to separate the impacts of rising atmospheric

CO2, climate variability and of agricultural technology

on the carbon and water balance of croplands. The

increasing modeled trend in wheat and maize yields

was unambiguously attributed to agro-technology

improvements. Overall, the C balance of cropland soils

was modeled to be out of equilibrium over the current

period. Carbon input from nonharvested NPP exceed

decomposition, hence causing a net carbon sink over

croplands of 0.16 t C ha�1 yr�1 during the 1990s. The

1-sigma uncertainty estimated by GE08 on this sink is

0.15 t C ha�1 yr�1, primarily reflecting errors in pre-

scribed agro-technology drivers.

To have closer look at uncertainties implied by agri-

cultural technologies on the cropland C balance, the

next logical step is to investigate the effect of each driver

separately, using factorial model experiments. Further,

GE08 performed their simulations with fixed cropland

area during the whole 20th Century. This is not realistic

because a strong agricultural abandonment trend has in

reality taken place everywhere in Europe. Overall, this

abandonment of farmland has been mirrored by an

extension of forest and grasslands (Mather et al., 1998;

Ramankutty & Foley, 1999; FAO, 2009). By year 2000, a

relative loss of 14% of the 1901 initial cropland area has

occurred. The consequences of this land use change

(LUC) trend on cropland net biome productivity (NBP)

must be quantified and understood. Here the NBP

system boundary is the area currently under cropland,

which means that indirect LUC sinks created in grass-

lands and forest after abandonment is not counted in

the balance sheet (unless otherwise specified in the

Discussion).

Generally, the legacy of past LUC linked to agricul-

tural expansion or abandonment is a key component of

the slowly varying continental-scale C balance. Across

the United States for instance, abandonment was esti-

mated to induce a net C sink that offset 10–30% of fossil

fuel emissions (0.35 Pg C yr�1), owing mainly to fire

suppression and forest regrowth (Houghton, 1999).

For Europe, we do not have such a continental-wide

estimate of past LUC legacy on the C balance, apart

from not-so-recent global model simulations (e.g. Mc

Guire et al., 2001) in which agriculture is treated in a

highly simplified way.

In this study, we reconstruct historical trends in

agricultural management, as well as annual land cover

changes over a domain comprising western Europe

(156 Mha; same as GE08). Yearly management and land

cover information is used to drive ORCHIDEE-STICS.

The process-based model calculates on a grid the

C budget of croplands using a specific crop module,

and the C balance of forest and grassland as well. The

cropland scheme deals with winter wheat and maize

crops, other crop species being ignored to simplify

the compilation of historical data, which is a limitation.

The simulation period begins in 1901 with ancestral

cultivation practice and ends up in 2000, with mechan-

ized tillage, systematic N-fertilizers applications, high-

harvest index wheat varieties and maize irrigation.

E F F E C T S O F L A N D U S E C H A N G E A N D M A N A G E M E N T 321

r 2010 Blackwell Publishing Ltd, Global Change Biology, 17, 320–338

The goal of this study is to gain understanding about

the following questions:

� What is the effect of each agricultural technology

parameter in controlling the current croplands car-

bon balance (NBP) ?

� What is the effect of past vs. current LUC drivers on

the cropland carbon balance during the 1990s?

� What are the sources of uncertainties in modeled

NBP in response to climate, agro-technology and

LUC, and how can these uncertainties be reduced?

We describe first ORCHIDEE-STICS, together with data

used for its forcing and evaluation (second section).

Results of sensitivity tests performed with different

agricultural technology, and historical LUC datasets

are discussed in third section. The error budget of

NBP during the 1990s is analyzed in fourth section.

Model evaluation against regional soil C inventories in

selected European countries is given in fifth section.

Finally, conclusions are drawn and recommendations

proposed on how to reduce uncertainties on the Euro-

pean cropland C balance in modeling studies.

Material and methods

Land-use reconstructions during 1901–2000

LUC dataset-1. We consider and test three distinct LUC

datasets in this study. These datasets are not independent

one from each other, because of some common input

information, but they rely upon distinct methodologies to

distribute land cover changes spatially, and hence will be

used to derive an uncertainty range on NBP. European LUC

is summarized in Fig. 1 for the three datasets. The first dataset

was created during the CARBOEUROPE-IP project (http://

www.carboeurope.org/). Annual land cover maps are

obtained from historical agricultural census data to hindcast

land cover distributions from modern remote sensing data,

based on Ramankutty et al. (2007). National level data was

collected for 36 countries. Several island nations such as Malta,

Cypress, and Iceland were excluded. National level data was

collected from multiple sources, and when data sources

conflicted, FAOSTAT data was used (FAO, 2009). In addition,

subnational data was collected for 348 EUROSTAT

administrative units from 22 countries (http://epp.eurostat.ec.

europa.eu/portal/page/portal/agriculture/). For all countries

with subnational administrative units, we estimated landcover

by applying the subnational level landcover fraction onto the

FAOSTAT national data. Subnational values were estimated

using subnational proportions from the closest reported year,

making best use of available data. Census data from year 2000

was calibrated to remote sensing land cover data (Ramankutty

et al., 2007). Then, to generate historical gridded land cover

maps, the remote sensing pasture and cropland maps were

multiplied by the normalized historical agricultural data. In

some grid cells, the sum of agricultural land area exceeded the

total area. A spatial filter was applied to smooth these

anomalies. The filter applied a weighted mean to the center

cell where nondiagonal neighboring cells had a weight of 0.125

and the center cell had a weight of 0.5. Cells in which

agricultural area exceeded total land area after filtering were

reclassified to 1. Finally, this LUC dataset-1 was regridded to a

spatial resolution of 0.25 degree.

LUC dataset-2. This second dataset was originally created

during the ENSEMBLES project (http://ensembles-eu.

metoffice.com/) and adapted by Piao et al. (2009) to drive

ORCHIDEE globally. Cropland area from 1860 to 1992 is

prescribed each year from Ramankutty & Foley [1999]. This

cropland dataset is combined with pasture areas from

Goldewijk [2001]. The distribution of natural vegetation in

each grid cell (Loveland et al., 2000) varies with time as a

function of cropland and pasture. Changes in crop and pasture

extent from 1992 till 2002 is derived from the IMAGE 2.2

calculation (http://www.mnp.nl/image/image_products/).

An anomaly procedure is used to ensure consistency

between both datasets. Detailed description on this LUC

dataset can be found for instance, in de Noblet-Ducoudre

et al. (2004, http://www.cnrm.meteo.fr/ensembles/). and in

Piao et al. (2009). The spatial resolution of dataset-2 is 0.5

degree.

LUC dataset-3. For this study, a third LUC dataset is

constructed and used by default in the control model

experiment. The current forest, grassland and cropland land-

cover is prescribed from the CORINE dataset for year 2000

(Buttner et al., 2000; CORINE Land cover (CLC) 2000).

Historical vegetation is reconstructed from national statistics.

Between 1961 and 2000, national statistics from FAOSTAT

(FAO, 2009) are combined with the CLC2000 geospatial

patterns. Between 1901 and 1960, forest land cover is

prescribed from forest area minimum national census, which

were harmonized among European countries (Mather et al.,

1998). For France, additional information is used (Agricultural

Ministry-Agreste, 1995; Mather et al., 1999). For each 10 km

grid point, area not covered by forest is allocated to croplands

and grasslands by using the ratio of cropland to pasture

deduced from Ramankutty & Foley (1999) and Goldewijk

(2001). In dataset-2, reforestation is a dominant process

(Fig. 1). Overall, forest area increased from 26.3 to 41.4 Mha

during the 20th century (total domain area 5 156 Mha). This is

a very large increase of forest (1 57.4%), further evidenced by

changes in rural landscapes across many rural regions of

Europe (Dupouey et al., 1999). Pasture slightly decreased

from 58.8 to 54.5 Mha between 1901 and 1965 (�7.3%)

mainly converted to forest. Pasture area temporarily

reincreased between 1960 and 1975, with arable land being

taken up (Fig. 1b and d), and then decreased after 1975. But the

most ubiquitous LUC transition is the abandonment of arable

land, causing a net loss of 9.83 Mha of croplands between 1901

and 2000. In total, 53.4% of this loss was realized between 1962

and 1972 during the first phase of agricultural intensification.

An additional loss of 31.3% arable land took place between

322 P. C I A I S et al.

r 2010 Blackwell Publishing Ltd, Global Change Biology, 17, 320–338

1985 and 2000. This recent abandonment trend masks however

opposite fluxes of arable land production and consumption,

even within the same country. In the Czech Republic for

instance, cropland abandonment during the transition to

market economy, resulted into a 28% increase in pasture

between 1990 and 2000 (EEA, 1995). In Ireland oppositely,

cropland expansion to produce animal feed, increased by 35%

the cropland area during the 1990s (EEA, 1995).

ORCHIDEE-STICS model

ORCHIDEE is a Dynamic Global Vegetation Model (Krinner

et al., 2005) developed for regional and global applications. In

this study we prescribed the vegetation map, while keeping

active the vegetation dynamics module for letting forests and

grasslands regrow over abandoned farmland at yearly time-

step. It calculates turbulent fluxes of CO2, H2O and energy on a

half hourly time step, and C and water pools dynamics

[allocation, phenology, respiration, mortality, soil organic mat-

ter (SOM) decomposition and soil water balance] on a daily

time step. STICS is an agronomic crop model developed for

site scale applications (Brisson et al., 1998). STICS is however

designed to be generic enough to use a ‘library’ of parameter-

izations describing different crop species. In the ORCHIDEE-

STICS coupled model (de Noblet-Ducoudre et al., 2004; Ger-

vois, 2004; Gervois et al., 2004; Smith, 2008; Smith et al., 2010b),

each day, the leaf area index value (LAI), the nitrogen stress,

and the vegetation height calculated by STICS are sequentially

assimilated into ORCHIDEE, which then updates the evolu-

tion of carbon and water fluxes and pools over a grid.

Climate drivers

In order to account for the effects on NBP of past management

and LUC, we integrate ORCHIDEE-STICS between 1901 and

2000 over a domain going from 35.51N to 54.51N in latitude,

and 9.51W to 19.51E in longitude, at a spatial resolution of

10 km. In this western European domain, maize comprises 11%

of the cropland area, the rest being winter and summer C3

crops (EEA, 1995). Monthly temperature, precipitation, wind

and shortwave radiation (Mitchell et al., 2004) were interpo-

lated into 1/2-hourly time steps using a weather generator

(Richardson & Wright, 1984). Atmospheric CO2 is varied at an

annual time step. The use of a weather generator induces a

Fig. 1 Historical changes in land cover inside the model domain. Three different land-cover reconstructions are prescribed into the

terrestrial biosphere model to estimate uncertainties in C fluxes. Dataset-2 adapted from global cropland and pastures land cover data

used by Piao et al. (2009). Dataset-1 prepared for Carboeurope-IP using historical cropland area data over European countries. Dataset-3

new reconstruction from this study using historical cropland and pasture data together with forest area minimum data. (a) Changes in

forest area in percent of area by year 1901. (b) Changes in grassland area. (c) Changes in cropland area. (d) Time evolution of the

percentage of each type of land cover.

E F F E C T S O F L A N D U S E C H A N G E A N D M A N A G E M E N T 323

r 2010 Blackwell Publishing Ltd, Global Change Biology, 17, 320–338

bias in the meteorology, thus in calculated fluxes. But on the

long time scales investigated here, this bias is not likely to

impact our results for NBP (based upon our experience

comparing of annual fluxes calculated with weather generator

or with numerical weather model fields). The soil depth and

water holding capacity (field capacity minus wilting point) are

prescribed to be the same in each grid point, independent of

vegetation type.

Crop varieties

Each crop variety is assigned a specific vector of parameters,

estimated from cultivars Soisson for wheat and DK-604 for

grain maize. The sowing date, although being function of

climate in the real world, is set here constant over the entire

domain. Only French varieties parameters were available to

STICS. The implications of prescribing uniform French para-

meters was examined by Smith et al. (2010b) who concluded to

a regional underestimation of maize yields in Mediterranean

countries, compared with national statistics. Fodder maize

widespread in Europe north of 521N where the plant cannot

complete its growth cycle, is not accounted for specifically in

this study. But simulations made with ORCHIDEE-STICS at

northern flux tower sites on fodder maize, Langerak (Den-

mark) and Dijkgraaf (the Netherlands), show good perfor-

mances to reproduce the cycle of LAI and the biomass at

harvest (L. Li, N. Vuichard, N. Viovy, P. Ciais, E. Ceschia, W.

Jans, M. Wattenbach, T. Gruenwald, S. Lehugerg, unpublished

results). Hence fodder maize phenology and NPP seem to be

captured correctly by the current parameterization, although

the return of residues may be smaller in reality than assumed

in the model, given that nearly 100% of fodder maize biomass

gets harvested. In Germany and the Netherlands it is compul-

sory to follow maize with other crops, among others to prevent

nutrient leakage during the fallow period. This practice was

not modeled, but should not affect our calculation of maize

productivity since soil fertility is not parameterized. Ignoring

fallow periods may however underestimate the return of C to

the soil compared with reality. Despite these simplifications,

the ORCHIDEE-STICS model was shown to have acceptable

performances in reproducing the seasonal cycle of LAI ob-

served by satellites over the most intensively cultivated Eur-

opean regions, and the mean yield inferred from national

statistics (see Smith et al., 2010a, b for more information).

Modeling LUC impacts on C balance

Land cover maps from LUC datasets 1 to 3 translated into

plant functional types (PFT) maps for ORCHIDEE, is pre-

scribed each year to the model. Over a grid point impacted

by LUC, the NPP of the new vegetation is calculated, and soil C

pools slowly adjust towards their new steady-state equili-

brium conditioned by the ratio of litter input to SOC decom-

position rate (see Vuichard et al., 2007). Wherever the annual

litter input from new vegetation exceeds the one of the former

vegetation, a gain of soil C will take place, else a net source to

the atmosphere. To perform a large number of sensitivity C

balance calculations, a reduced form substitute model of

ORCHIDEE-STICS was constructed. This substitute model

consists in writing the incremental change of soil carbon (dC)

at an annual time step such as dC 5 I(t)�kC where k is the

mean annual decomposition rate, function of climate, and I(t)

the annual carbon input to the soil. We checked that the

reduced form and the full-model version give results that are

comparable within 5%. On average, NPP of crops in ORCH-

IDEE-STICS is of same magnitude – or slightly higher – than

for natural vegetation (Ciais et al., 2010), yet with large regional

differences. But, because most of the NPP is exported from

ecosystems, the input to the soil is always lower for croplands

than for natural vegetation. In addition, tillage enhances SOC

decomposition. Therefore, the establishment of cropland over

natural vegetation nearly always causes a loss of soil C in our

simulations. The converse is true for cropland abandonment to

grassland or forest. Note also that cropland area losses due to

urban sprawl, which increased across Europe, is not accounted

for in this study. Urbanization would likely cause a net

additional CO2 source to the atmosphere.

Modeling protocol

Model initialization

All model simulations are initialized in 1900 by a 10 000

years spin-up to bring water and C pools in equili-

brium. In this spin-up, repeated climate of the 1901–

1905 period, [CO2] of 280 ppm, and a reconstruction of

ancestral farmers practices are applied (GE08). Starting

from the same steady state equilibrium, a control ex-

periment (CNT) and seven other sensitivity tests are

then performed, and described below. The sensitivity

tests are designed to study the individual contributions

of agricultural technology parameters and of land-use

trends. They are summarized in Table 1 (see also Table

S1 in Supporting Information for more details).

Control experiment CNT

The CNT simulation experiment corresponds to our

best estimate of agricultural technology trends during

the period 1901–2000 given by GE08. It includes effects

of LUC, climate changes and rising CO2 as well. From

1901 to 1950, low organic fertilizations values with

2 t ha�1 yr�1 of manure are applied, providing

500 kg C ha�1 yr�1 and 32 kg N ha�1 yr�1. In 1951, it is

assumed that mineral N-fertilizers abruptly replace

manure, although in reality this transition must have

been gradual depending upon the pace of technological

change in each country. From 1951 to 2000, mineral

fertilizers are prescribed to increase linearly with time

from 32 to 150 kg N ha�1 yr�1. Between 1900 and 1950,

the harvest index of crops is set constant to 0.25, but

increases linearly from 0.25 to 0.45 between 1951 and

2000 (Hay, 1995). The assumed fraction of nonharvested

324 P. C I A I S et al.

r 2010 Blackwell Publishing Ltd, Global Change Biology, 17, 320–338

Tab

le1

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mm

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of

the

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om

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ease

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E F F E C T S O F L A N D U S E C H A N G E A N D M A N A G E M E N T 325

r 2010 Blackwell Publishing Ltd, Global Change Biology, 17, 320–338

residues left to the soil is 60% (GE08). Moderate tillage is

assumed, with constant intensity throughout 1901–2000.

Accordingly, the mean residence time (MRT) of carbon

in agricultural soils is set constant to 10% below the one

of natural vegetation in each grid point. Note that this

tillage effect is two times less than parameterized in the

original version of the CENTURY model (Parton et al.,

1988) from which ORCHIDEE is inspired. After a pre-

scribed LUC change at time t0, the dynamics of NPP and

SOC at any time t4t0 in the new vegetation is calculated

with the equations of ORCHIDEE (Krinner et al., 2005) if

the new vegetation is grassland or forest, and with the

equations of ORCHIDEE-STICS if a cropland. As seen

above, dominant LUC is a widespread abandonment of

cropland to forest and grassland, causes sequestration

over these ecosystems. One may wonder if our assump-

tion of manure phase-out in 1951 is realistic. Although

manure phase-out has been more continuous than as-

sumed in CNT, the fact is that manure application is

currently small for cereal fields. Schulze et al. (2009)

estimated manure applications to all croplands over

EU-25 to be 0.26 t C ha�1 yr�1, which is a small flux to

the soil compared with the input of residues each year,

and to the uncertainty in this input (Table 1).

Experiment with no LUC CNT-NOLU

In the CNT-NOLU sensitivity test, agricultural technol-

ogy, CO2 and climate are identical to CNT, but the effect

of LUC is not included. This is achieved by setting the

land cover maps constant as in year 1901 during the

entire simulation period.

Experiments to test the management of harvest residuesS1-LO and S1-HI

These tests study the sensitivity of NBP to the fraction

of residues left to the soil. Between 1901 and 1950, S1-

LO and S1-HI are identical to CNT. From 1951 to 2000,

S1-HI (high C input to the soil) assumes that progres-

sively 100% of nonharvested biomass (straw, leaves and

roots) is left to the soil. Oppositely, S1-LO (low C input

to the soil) assumes that only root biomass returns to the

soil, i.e. a minimum return rate. S1-LO would corre-

spond for instance to burning crop residues after har-

vest. In these two experiments, we prescribed a linear

change in harvest residue management from 1951 and

2000. In the real world, there is an uncertainty on

whether a trend towards increased straw and stubble

export has occurred or not, alongside with intensifica-

tion. Historically, straw production was a priority for

farmers, even making low harvest index a desirable

trait (Sinclair, 1998; Mazoyer, 2002). Therefore, straw

removal may have been already widespread in Europe

before 1950. With respect to residue burning, this prac-

tice was relatively common during the 1970s and the

1980s, but has been phased out in EU-25 countries after

1997. Consequently, a decreasing trend of fire emissions

is observed in satellite burned-area products (Ciais

et al., 2010).

Experiments to test the effect of fertilizer additions S2-HIand S2-LO

These tests study how NBP depends on the amount of

applied manure/mineral fertilizers. The S2-HI (manure

and mineral N) experiment assumes manure applied a

constant rate of 2 t ha�1 yr�1 after 1951 in addition to

mineral N-fertilizers, while the total amount of applied

nitrogen is set to remain the same than in CNT. The

differences between S2-HI and CNT thus only consist of

higher C inputs from manure, leaving N input un-

changed. Oppositely, the S2-LO test (low N additions)

assumes only manure applied at a fixed rate of

2 t ha�1 yr�1 between 1951 and 2000, given a fixed

harvest index fixed to 0.25. This hypothesizes that crop

fields are given the same constant N input throughout

the 20th century. Obviously S2-LO has an hypothetical

(nonrealistic) storyline, but its results are valuable to

define a baseline against which the effect of increased

N-fertilizers on NBP is estimated.

Experiments to test the effect of tillage intensity S3-HIand S3-LO

These tests are designed to study the effects of variable

tillage intensity. The S3-HI (no tillage) experiment as-

sumes that moderate tillage between 1901 and 1950 (see

CNT) is replaced by no-till agriculture between 1951

and 2000. To mimic no-till, the MRT of active, slow and

passive C pools in ORCHIDEE-STICS are set to the

same value than for natural vegetation. Oppositely,

the S3-LO (increased tillage) simulation assumes that

tillage intensity increased between 1951 and 2000, but

reducing MRT of SOC by 20% compared with natural

vegetation value. In both S3-LO and S3-HI experiments,

the MRT change is hypothesized to be abrupt in 1951.

Results: management, climate, land-use effects on

cropland carbon balance

Management effects compared with climate and CO2

effects

Figure 2 provides the modeled evolution of the arable

soil C stocks from 1901 to 2000. The C input to the soil,

that determines NBP is shown in Fig. 3. The mean

cropland soil C pool at the end of the CNT simulation

326 P. C I A I S et al.

r 2010 Blackwell Publishing Ltd, Global Change Biology, 17, 320–338

is rather similar to its initial value. Therefore, we have

only a small cumulative C loss (0.9 t C ha�1) from 1901

to 2000. Under the CNT experiment storyline, arable

soils quickly loose 9% of their C during the first 15 years

or so after shifting from organic to mineral fertilizers.

This loss phase lasts roughly from 1951 to 1965 (Fig. 2),

but gets reversed thereafter with slow accumulation of

soil C until the 1990s. In the model, this build-up of soil

C is determined by increased input to the soil (Fig. 3)

that result from the balance between increasing NPP

and diminished C allocation to stem and roots relative

to grain (i.e. harvest index changes; Table S1). During

the 1990s, the cropland C balance is estimated to be a

small sink of 0.16 g C m�2 yr�1. There are significant

regional differences, however. Arable soils are net C

source to the atmosphere in the Iberian Peninsula, and

small sinks in Central and Eastern Europe (see GE08).

Dealing with effects of climate and CO2 combined

with trends in technology, the simulations performed

by GE08 demonstrated clearly that: (1) agricultural

technology is the dominant driver of current NBP, (2)

the long-term effects of CO2 fertilization on crop NPP

(10% increase since 1901) slightly increases soil C sto-

rage, through a small uniform C sink across Europe, (3)

the effect of warming (1 0.8 1C since 1901) has alone

caused net C losses (Harrison et al., 2008), and (4) the

effects of rainfall changes over western Mediterranean

regions with drier springs and wetter autumns, has also

caused net C losses – locally offset by gains driven by

increasing NPP. In our framework, the impact of in-

creased temperature on production is accounted both

via the STICS phenology based upon growing degree

days functions, and via the temperature sensitivity of a

number of processes in ORCHIDEE. The effect of tem-

perature (Q10 law) and other climate drivers on SOC

decomposition is also accounted for via the decomposi-

tion module of ORCHIDEE (after Parton et al., 1988).

Sensitivity to each technology parameter

In this section, we isolate the effects of different agro-

technology parameters (Table 2) on NBP, by using the

sensitivity tests (Fig. 2). We analyze four output vari-

ables impacted by technology: (1) the cumulated soil C

1910 1920 1930 1940 1950 1960 1970 1980 1990 200075

80

85

90

95

100

105

Soi

l car

bon

stoc

ks (

t C h

a–1)

CNT

1900

CNT NO LU no land use

Fig. 2 Time evolution of soil C stock over arable land in cultivation in 2000. The control simulation in solid red accounts for historical

changes in CO2, climate, land use and agronomy practice from 1901 to 2000. Each other curve is the result of a sensitivity simulation in

which different technology and land-use parameters are varied (see text).

1920 1940 1960 1980 20002

2.5

3

3.5

4

4.5

5

5.5

6

Inpu

t of c

arbo

n to

soi

ls(t

C h

a–1 y

r–1)

1900

Fig. 3 Time evolution of nonharvested biomass input to arable

soil, which creates the potential for sequestration. Same color

coding for each simulation than in Fig. 2.

E F F E C T S O F L A N D U S E C H A N G E A N D M A N A G E M E N T 327

r 2010 Blackwell Publishing Ltd, Global Change Biology, 17, 320–338

storage change between 1951 and 2000, DC, (2) the NBP

during the 1990s, (3) the annual C flux into the soil, I,

and (4) the reversal date after intensification in the early

1950s, at which soil C stocks are modeled to reincrease,

trev. Note that trev is usually negatively correlated with

DC, and to some extent with NBP as well. The earlier

the reversal date, the larger DC is during the period

between trev and year 2000. Some sensitivity experi-

ments produce continuous C losses after intensification

in 1951, such as the S1-LO experiment which assumes

minimum return of harvest residues. For these simula-

tions, a reversal date could not be determined. In the

two tests S2-LO and S2-HI in which manure remains

continuously applied between 1951 and 2000, the re-

versal date is also not defined, because the soil always

gains C (in S2-HI) or always looses C (in S2-LO). A

common storyline between all the simulations but S2-

LO, is an abrupt technology change in 1951. By contrast

in S2-LO, a progressive change is assumed between

1951 and 2000 for mineral fertilizers and harvest index.

This is why the results from S2-LO to S2-HI results do

not show to be symmetric around CNT in Fig. 2. In

ranking the sensitivity tests for their impact on the

model output, we consider DC as main variable. The

results are summarized in Table 1.

Harvest residues. The first agricultural technology that

has the largest impact on DC, and on NBP as well, is the

management of harvest residues. According to S1-HI, it

is predicted that if straw and stubble residues were

progressively returned to the soil since 1951 (i.e. added

to the metabolic and structural litter pools) then both I

and DC would be greater today than in CNT, by a factor

of 8 and 25, respectively. As a result, in S1-HI, the

reversal date trev at which arable lands turn back from

C source to sink occurs only 7 years after intensification

in 1951 (Table 1). Oppositely, exporting progressively all

harvest residues in S1-LO provides a net C source to the

atmosphere, a source that persists until the present,

with no sign of reversal in the near future (Fig. 2).

Tillage. The second most important technology

impacting DC is tillage. This practice is incorporated

in ORCHIDEE-STICS by shortening the MRT of all soil

carbon pools. Shortening MRT by 20% from its nominal

value as in S3-LO, in comparison with assuming no-till

in S3-HI, produces a qualitative change in the SOC

evolution during the 20th Century. In S3-LO, DC is a

net loss seven times higher in absolute value than in

CNT (Table 1). In S3-LO, the timing of the reversal date

trev in shifted to year 1971 compared with year 1965 in

CNT. The higher tillage intensity in S3-LO rapidly

depletes all soil C pools during few years after

intensification, but these effects are eventually offset

by the positive trend of agricultural NPP which forces C

sequestration. Oppositely, no-till experiment S3-HI

produces a net carbon gain (DC40) from 1901 to 2000

(Table 1). Overall, the range of DC obtained between S3-

HI and S3-LO is 11.6 t C ha�1. This DC range is 33%

larger than between S2-LO and S2-HI (fertilizer

sensitivity) but 53% lower than between S1-HI and S1-

LO (residues sensitivity). Remarkably, NBP stays rather

similar between sensitivity tests with very contrasted

tillage scenarios (Table 1). This surprising small

sensitivity of current NBP is due to the fact that in both

S3-LO and S3-HI, the impact of tillage is strong during a

short period following the MRT change applied in 1951

(Fig. 2). Roughly two decades after this shift, a new

regime of soil C balance gets established, and NBP

during the 1990s is similar between S3-LO, S3-HI and

CNT (Fig. 2). The important result here is that the

simulated NBP has therefore little sensitivity to tillage if

tillage intensity does not change over the past 50 years after

initial increase in the early 1950s. On the other hand, DC

Table 2 Summary of European carbon stock, carbon stock change between 1901 and 2000, and mean annual NBP averaged over

our European simulation domain

European average soil carbon stocks* (PgC)

NBP (t C ha�1 yr�1)INIT 1901 Final (period 1991–2000) Difference

CTL 12.74 14.12 1 1.38 0.20

CTL-NOLU 12.74 13.55 1 0.81 0.17

S1-HI 12.74 15.71 1 2.97 0.48

S1-LO 12.74 13.06 1 0.32 0.02

S2-HI 12.74 14.61 1 1.87 0.21

S2-LO 12.74 13.99 1 1.24 0.11

S3-HI 12.74 14.49 1 1.75 0.22

S3-LO 12.74 13.74 1 1.00 0.19

These results are given for all biomes, i.e. not only arable lands.

*Domain size of 156 Mha from 9.51W–19.51E to 35.51N–54.51N.

328 P. C I A I S et al.

r 2010 Blackwell Publishing Ltd, Global Change Biology, 17, 320–338

has a large sensitivity to tillage. Information on past

tillage histories in each region or for each crop type,

was not available to drive ORCHIDEE-STICS. We

expect that a more realistic tillage reconstruction, e.g.

with a progressive increase in plowing depth alongside

with mechanized power in the 1950s and 1960s, would

produce a longer-lasting effect on NBP than obtained

here.

Mineral fertilizers

The third most important technology impacting DC is

the amount of mineral fertilizers, which increases NPP

and subsequently C input to the soil. In the S2-LO

experiment where N-fertilizer during 1951–2000 are

maintained at ancestral rate (32 kg N ha�1 y�1) and har-

vest index fixed to ancestral values as well, the crop

NPP does not increase with time. Therefore, arable soils

always loose C in response to tillage and warming

(DC 5�2.1 t C ha�1). The settings of S2-LO are purpo-

sely not realistic, but they are interesting because they

demonstrate that without a long-term increase in crop

NPP, a large loss of soil C by arable lands would have

occurred. We estimate the impact of tillage from the

difference between S2-HI and CNT. Tillage causes an

extra cumulative loss of 7.5 t C ha�1. We estimate the

impact of increased harvest index and mineral N-ferti-

lizer additions, by taking the difference between CNT

and S2-LO. This provides a cumulative sink of

8.7 t C ha�1, hence a bit higher than that of the no-till

experiment (Fig. 2 and Table 1).

Technology parameters and carbon sequestration efficiency

We study in this section if each agro-technology impacts

NBP through a changes in input to the soil DI (see

Fig. 3), or through a changes in decomposition and

respiration (controlled by the MRT value). The ratio

r5 NBP/DI defines a useful measure of the C balance

per unit of input, or sequestration efficiency. The higher

is the value of r, the more efficient is the sequestration.

Table 1 gives the value of DI during 1901–2000, NBP

during 1991–2000 and r5 NBP/DI. The choice of the

period 1901–2000 for calculating DI is arbitrary. A shorter

time window could have been used as well as a

predictor of the effect of DI on current NBP. But this

choice is not critical because r is simply used as a

measure to intercompare our simulations. Moreover,

taking DI over one century to explain NBP is appro-

priate, because of the long-term carry over effects of

I changes on NBP. On average, r ranges from 0.15 in

S2-LO to 0.6 in S3-LO. It is interesting to see that,

although the sensitivity tests S1-LO and S1-HI differ

strongly in their NBP and DC, they have similar r

values (Fig. 4). Symmetry between S1-LO and S1-HI is

expected from a first order/single pool decay model

describing the soil C response to constant input (Vui-

chard et al., 2007). For the same reason, S2-LO and S2-HI

differing only by their DI value, have similar r values.

In contrast, changing SOC decomposition rates by

modifying tillage, strongly impacts the value of r.

Between S3-LO and S3-HI DI is identical, but a more

intense tillage in S3-LO results into a smaller C seques-

tration efficiency.

Land-use change effects

Figure 5 provides dominant area changes, soil C, and

NBP of the area initially in cropland during 1901. The

main result is that cropland abandonment (Fig. 5a) has

resulted into a net C accumulation in soils of forest and

grasslands that replaced farmland, of 4 t C ha�1 on

average between 1901 and 2000. This is given by the

distance between the dashed and solid red curves in

Fig. 2. This LUC-induced soil carbon sequestration has

increased steadily with time between 1960 and 1980,

because of high abandonment rates during that period

(Fig. 1). After 1980, the effect of LUC on C stocks

become marginal, because cropland area has remained

globally stable (Fig. 1). Besides causing soil C gains in

recovering grasslands and forest, agricultural abandon-

ment has also caused simultaneously a sequestration of

carbon in forest biomass. Because ORCHIDEE does not

simulate forest age classes and the fast growth of young

plantations, the increase in regrowing forests’ biomass

is underestimated. The inventories that countries sub-

mit to the United Nation Framework Convention on

Climate Change (UNFCCC, http://unfccc.int/natio

nal_reports/annex_i_ghg_inventories/national_invento

ries_submissions/items/4771.php) are a valuable alter-

native source of information on LUC. So we used these

reports, as analyzed by Schulze et al. (2010), for estimat-

ing the forest biomass sink over abandoned cropland

abandonment. This biomass sink averaged over 2003–

2007 is small, only 0.03 t C ha�1 yr�1.

Altogether, we estimate a cumulative carbon seques-

tration due to cropland abandonment of 0.6 Gt C

between 1901 and 2000 by taking the difference between

CNT and CNT-NOLU (Table 2). This sequestration has

been caused by reforestation and grassland creation

after 1960 contributing 66% of the LUC induced DC,

and by reforestation before that date contributing 34%

of the DC. This is illustrated by looking at the land cover

change history in Fig. 4. Cumulated over the 20th

century, the effects of LUC in increasing soil C stocks

over abandoned farmland (1 4 t C ha�1) and those of

CO2 and climate over currently active farmland

(1 4.2 t C ha�1 in GE08) are roughly equivalent. In our

E F F E C T S O F L A N D U S E C H A N G E A N D M A N A G E M E N T 329

r 2010 Blackwell Publishing Ltd, Global Change Biology, 17, 320–338

simulations, forests and grasslands tend to always

accumulate carbon, through the rate of arable land

abandonment, and through the increasing forest and

grassland NPP in response to rising CO2 and longer

growing seasons (Harrison et al., 2008). Forest or grass-

land expansion over abandoned croplands is such that

Comparison of soil C input change with NBP

–3

–2

–1

0

1

2

3

CNT S1-LOlow

residuesinput

S1-HIhigh

residuesinput

S2-LOmanure

nomineral

S2-HImanure

andmineral

S3-LOhigh

tillage

S3-HIno

tillage

C fl

ux (

t C h

a–1 y

r–1)

0

0.2

0.4

0.6

0.8

1

Rat

io

Soil C inputchangebetween1990s and1900sNBP during1990s

RatioNBP/soil inputchange

Fig. 4 Comparison of changes C input to the soil between 1901 and 2000 (DInput) and of net biome productivity in the 1990s. Open

circles 5 the ratio of these two quantities on right-hand axis. This ratio defines a measure of the carbon sequestration efficiency, the

amount of C that accumulates into the soil per unit of input.

1920 1940 1960 1980 200010

20

30

40

50

Per

cent

are

as

CropsGrassForest

1920 1940 1960 1980 200050

60

70

80

90

100

110

120

Soi

l car

bon

stoc

ks (

t C h

a–1)

1920 1940 1960 1980 2000

Crop→Grass

Crop→Forest

Grass→Cropl

Grass→Forest

Forest→Crop

Forest→Grass

5W 0 5E 10E 15E

40N

45N

50N

Dom

inan

t con

vers

ion

betw

een

1901

and

200

0

1900

1900

Soi

l car

bon

bala

nce

(t C

ha–1

yr–1

)

1900

–0.3

–0.2

0.2

0.3

0

0.1

–0.1

(a) (b)

(c) (d)

Fig. 5 (a) Time evolution of land cover types from 1901 to 2000. (b) Soil carbon stock evolution for each land cover type. (c) Time

evolution of the net biome productivity of positive sign if ecosystems gain carbon for the three different LUC forcing datasets. (d)

Dominant land use change map color-coded according to the largest land cover transition that occurred from 1901 to 2000 in each grid

point, i.e. the largest change in area between two biomes over each grid point.

330 P. C I A I S et al.

r 2010 Blackwell Publishing Ltd, Global Change Biology, 17, 320–338

soil NBP during the 1990s over the area initially

in cropland in 1901 is a sink of 0.22 t C ha�1 yr�1

(Table 1), against 0.16 t C ha�1 yr�1 only for the area

currently in cropland during the 1990s.

Foremost, the soil carbon gains caused by abandon-

ment have been nearly opposed during the 20th century

by the losses caused by management intensification,

especially mechanized tillage. The overall cropland soil

C increased storage of 0.6 Gt C that we obtain in

response to LUC and intensification represents a very

small 4.5% increase of the initial arable soil C stocks in

1901. Therefore, the sequestration realized by croplands

during the past century is dwarfed by fossil fuel CO2

emissions, barely canceled by 7 months of fossil CO2

emissions over EU25! Schulze et al. (2009) estimated

total fossil fuel CO2 emissions from the agriculture

sector only to be of 22 Tg C yr�1, giving an EU-25

average rate of 0.2 t C ha�1 yr�1. This means that 25

years of fossil fuel emissions associated with current

cropland cultivation technology will negate the carbon

sequestration in soils realized during the past 100 years.

Legacy of past land-use change

In order to quantify the legacy of past LUC on soil C

stock and NBP, we performed several additional runs,

comparing LUC starting in 1901 as in CNT, with LUC

starting in 1910, 1920, etc. The results are shown in

Fig. 6. These simulations of distinct LUC cohorts allow

to better understand how past LUC has a legacy effect

on soil C stocks. The most important result is that LUC

continues to impact soil C stocks long after initial

conversion, typically for 40–50 years, yet with impor-

tant regional differences not shown here. Therefore,

even when ecosystem areas are stabilized like today in

Europe, changes in carbon stocks ‘in the pipeline’ from

former cropland abandonment still drive a positive

NBP. Asymptotic gains of soil C defined by DCt ! infinite

for the conversion of cropland to grassland (Fig. 6a) and

to forest (Fig. 6b) are of 40 and 52 t C ha�1, respectively.

These DCt ! infinite estimates are likely to be an upper

limit because in the reality, forests planted over aban-

doned croplands are regularly harvested making their

C stock lower. Similarly in the real world, grasslands

established over former croplands may not accumulate

C as fast in ORCHIDEE, because of grazing, grass

harvest, and plowing.

Finally, we supposed that European soil carbon

stocks were in equilibrium with climate and land-use

in 1901, which is unlikely to be true given the before-

hand land-use history of Europe (Kaplan et al., 2009).

The available LUC archives (Mather et al., 1998) suggest

however that reforestation during the second part of

19th century in Western Europe mainly concerned

grassland abandonment, thus not affecting cropland

stocks. No data on changes in the intensity of crop

management before 1900 is available, that could allow

a realistic agricultural soil C stock spin-up. Recent

work by Pongratz et al. (2009) based upon reconstructed

LUC since year 800 indicates a cumulative loss of

18 t C ha�1 in Western Europe between 800 and 1850

due to former forest clearing (inferred from their Fig. 3).

This estimate translates into a legated land use source of

0.02 t C ha�1 yr�1 by 1850. Clearly, this is a small legacy

of the pre-20th century LUC to the NBP of cropland

during the 20th century.

0

10

20

30

40

50

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

1910

1920

1930

1940

1950

1960

1970

1980

1990

2000

Years

Yea

r of

con

vers

ion

Conversion : Croplands → Grasslands

0

10

20

30

40

50

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

1910

1920

1930

1940

1950

1960

1970

1980

1990

2000

Years

Yea

r of

con

vers

ion

Conversion : Croplands → Forest(a) (b)

Fig. 6 Legacy of past land use changes on carbon stocks (a) Mean of grid points where cropland was abandoned to create grassland. (b)

Mean of grid points where cropland was abandoned to create forest. The Y-axis shows the result of sensitivity simulations with LUC

cohorts started after 1910, 1920, 1930, . . . thus showing how each decade’s LUC cohort is legating a cumulative change of soil C stock (in

t C ha�1) as shown by the color bar.

E F F E C T S O F L A N D U S E C H A N G E A N D M A N A G E M E N T 331

r 2010 Blackwell Publishing Ltd, Global Change Biology, 17, 320–338

Uncertainty budget

This section is an attempt to evaluate the uncertainty

range of NBP, owing to uncertain (1) input climate

forcing data, (2) agricultural technology drivers, (3)

historical LUC drivers, and (4) structural parameteriza-

tions of the model. All these sources of uncertainties are

considered here for simplicity to be uncorrelated, which

may not be the case in reality. For instance a change in

soil C decomposition parameter value will change the

modeled NBP sensitivity to LUC and to climate forcing

(see Fig. 7) Qualitative analysis and linear scaling, and

expert judgment, are used to infer the uncertainty

budget below.

Uncertain climate forcing

Uncertainty in climate forcing, was found to contribute

a 20% uncertainty on the mean value of GPP by Jung

et al. (2007). We used this relative error on GPP to

estimate an NBP error range of 0.03 t C ha�1 yr�1. This

uncertainty must be understood as the NBP uncertainty

caused by uncertain climate drivers, with every other

model parameter being constant.

Uncertain technology parameters

Three main drivers are considered: the management of

residues, the amount of N-fertilizer, and the effect of

tillage. Concerning residues, the intensity of this prac-

tice depends on regional farming structure, and resi-

dues can be burnt (Yadvinder-Singh et al., 2004), grazed

(Fernandez-Rivera et al., 1989), processed for animal

feed or bedding (Lopez-Guisa et al., 1991; Powell et al.,

2004) or ploughed into the soil (Caviness et al., 1986).

Each residue management option has a distinct impact

on NBP (Fig. 3). We used literature data to estimate an

uncertainty range. In the LPJml model, Bondeau et al.

(2007) adopt a high removed fraction of harvest resi-

dues f 5 90%, against a smaller f 5 60% used here

(GE08). This range of f combined with S1-HI and S1-

LO results, let us to infer an uncertainty range of

0.28 t C ha�1 yr�1 on NBP due to uncertain residues

management.

Uncertainties in N-fertilizers affect also NBP. A trend of

added-N results in the model into a trend of C input to

soil, which exceeds decomposition losses, causing seques-

tration. A recent review for forests suggests that added N

may further decrease soil respiration rates (Janssens et al.,

2010) but this process is not included. We define an

uncertainty range of 90 kg N ha�1 yr�1 on prescribed N-

fertilizer from the difference between two independent

data sources for wheat in France (FAO, 2009). Based on

results from Vuichard et al. (2008) we know that crop NPP

increases by 22 kg C ha�1 yr�1 per extra kg N ha�1 yr�1.

Therefore, an error range of 90 kg N ha�1 yr�1 translates

into a range of NPP of 2 t C ha�1. Given a mean cropland

NPP of 8 t C ha�1 yr�1, this translates into an NBP uncer-

tainty range of 0.05 t C ha�1 yr�1.

An uncertainty range on prescribed tillage can be

estimated from different field experiments results. In a

series of till/no-till experiments, Arrouays et al. (2002)

determined that tillage reduced soil C stores by

3–4 t C ha�1 over 18 years. Vleeshouwers & Verhagen

Uncertainty budget of cropland carbon balance in the 1990s

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Climatedrivers

Land usedrivers

N-fertilizers Tillage Residues COfertilization

Climatechange

response

Modelstructure

Total range

Min

–Max

ran

ge o

f NB

P (

t C h

a–1 y

r–1)

Fig. 7 Uncertainty budget of cropland NBP estimated for different sources of uncertainties, including model climate data and LUC data

forcings, technology parameters using during the 20th century, and model structural uncertainty. Each bar denotes a minimum–

maximum range of uncertainty estimated by expert judgement and literature data. A sigma uncertainty of a uniform distribution can be

obtained by dividing this range by 3.45.

332 P. C I A I S et al.

r 2010 Blackwell Publishing Ltd, Global Change Biology, 17, 320–338

(2002) estimate extra source of 0.25 t C ha�1 yr�1 for

tillage vs. no till. Smith et al. (1997a, b, 2000) estimate a

relative soil C loss rate of 0.73% yr�1 in response to

tillage which, applied to our simulated soil C stocks

gives a till vs. no till extra-source of 0.4 t C ha�1 yr�1.

Overall, these field data provide a range of 0.2–

0.4 t C ha�1 yr�1 for NBP under till vs. no till, compatible

with the obtained difference between S3-LO and CNT

(Table 1). We can adopt 30% of the NBP full range

between till and no-ill as a range of uncertainty on NBP

due to unknown tillage intensity, that is a range of

0.07 t C ha�1 yr�1. In summary, among the three uncer-

tain management parameters that influence NBP, the

largest source of uncertainty is the management of crop

residues, followed by tillage and N-fertilizer applica-

tions, for a total uncertainty range of 0.3 t C ha�1 yr�1.

This result is summarized in Fig. 7.

Uncertain LUC drivers

In order to derive an uncertainty range on NBP from

LUC drivers, we used the three different datasets applied

to ORCHIDEE-STICS (Fig. 1). The most striking differ-

ence between dataset-1 (this study) and dataset-2 stands

for forest area during the period 1900–1960. Dataset-1 has

widespread reforestation with a 5% expansion of forest

during 1901–1960, compared with 2% in dataset-2. In

dataset-3 (Carboeurope) the forest area evolution curve

lies in-between the two others, but it has more variability

during the early 20th century (Fig. 1a). In all three LUC

datasets, arable land area is found to decrease continu-

ously over time, reflecting abandonment. Discrepancies

in cropland area between the LUC datasets mainly mirror

discrepancies in forest area (Fig. 1b). Dataset-1 has the

largest cropland abandonment rate during 1960–2000,

and the largest cropland area loss since 1901 as well.

Because of greater forest area expansion in dataset-1, we

obtain a DC value 0.9 t C ha�1 greater than with dataset-2,

and 0.74 t C ha�1 greater than with dataset-3. The result-

ing uncertainty range on NBP during the 1990s is very

small however, being only of 0.01 t C ha�1 y�1. This sur-

prisingly small error on current NBP is due to the fact

that differences in the three LUC scenarios are either too

‘early’ to legate any effect now, or too small to be seen

during the 1990s. Compared with NBP uncertainties

induced by agro-technology, the important result here is

that historical LUC is only a small source of error in the

total NBP error budget (Fig. 7).

Structural model errors

A full analysis of the full model structural errors (wrong

parameter values or wrong parameterizations) is beyond

the scope of this study (Zaehle et al., 2005; Post et al., 2008).

Indirect information about structural errors is contained

into model-data comparisons (Chevallier et al., 2006), but

the model-data misfit often includes uncertainty in model

drivers, as well as in model structure. An (extreme)

uncertainty range in the modeled response to climate

and CO2 can be estimated from the simulations of GE08.

The NBP uncertainty to CO2 fertilization is obtained from

simulations with and without CO2, hence giving a very

conservative range of 0.06 t C ha�1 yr�1 (Fig. 7). The un-

certainty range of NBP due to of climate change affecting

SOC decomposition is similarly estimated to be

0.06 t C ha�1 yr�1 (Fig. 7). The root mean square error

(RMSE) between ORCHIDEE-STICS modeled and eddy-

covariance observed net ecosystem exchange (NEE) over

wheat and maize sites was found to be 1.3

and 2.0 gC m�2 day�1, respectively (Gervois et al., 2004).

This error is typical of the ORCHIDEE errors when

compared with FLUXNET observations, with temporal

error correlations of � 1 month (Chevallier et al.,

2006). The relative mean bias of modeled NPP

(jNPPobs �NPPmodelj=NPPmodel) was found to be of 7%

across Austria, France, Germany, Italy and Spain, consider-

ing wheat and maize (Smith, 2009). Synthesizing this

information, we estimate an uncertainty range of NBP of

0.17 t C ha�1 y�1 from model structure (Fig. 7) Note that

CO2 and climate response uncertainties are assumed to be

included in this estimate. Therefore, structural uncertainty

is of the same magnitude than agro-technology uncertainty.

Summary of the error budget of cropland C balance atEuropean scale

Figure 7 summarizes how the different ranges of uncer-

tainty combine together to define an overall NBP mini-

mum–maximum range of 0.6 t C ha�1 yr�1. The error

budget of NBP at continental scale is dominated by

uncertainties in agro-technology (47% of the range) and

in model structure (26%), with climate forcing and LUC

drivers induced errors being of smaller importance.

Further ‘unknown–unknowns’ in the model drivers at

the scale of Europe can only make this estimate larger. For

instance, using a larger diversity of crop types compared

with wheat and maize will change NBP to an unknown

extent. This minimum–maximum range can be translated

into the standard deviation of a uniform distribution by

dividing it by 3.45 (1-sigma NBP uncertainty reported in

the abstract of 0.18 t C ha�1 yr�1).

Cross-validation against regional soil carbon

inventories

Because nonmodeled processes such as erosion, and

DOC leaching, and maybe because of ignored uncer-

tainties, cross-validation of our model NBP estimates

E F F E C T S O F L A N D U S E C H A N G E A N D M A N A G E M E N T 333

r 2010 Blackwell Publishing Ltd, Global Change Biology, 17, 320–338

with independent measurements is necessary. Site-scale

measurements provide information about long-term soil C

changes that can be used for model calibration (Smith et al.,

1997a, b), but are not representative for model evaluation

at regional scale. Therefore, we have compiled regional soil

C inventories data in different European countries. These

data indicate soil C losses of 0.24 t C ha�1 yr�1 for Austria

(Dersch & Boehm, 1997), 0.76 t C ha�1 yr�1 for Flanders

respectively (Sleutel et al., 2003). In Germany, mean annual

carbon losses in the range of 0.01–0.26 t C ha�1 yr�1 were

observed at a set of sites in Franconia (Rinklebe & Ma-

keschin, 2003). Other regional inventories recorded a

smaller C loss, e.g. in Denmark (Heidmann et al., 2002).

A detailed repeated gridded upper-soil C inventory over

England and Wales (Bellamy et al., 2005) shows a small C

loss of 0.09 t C ha�1 yr�1 during 1978–2003, in contrast with

larger C losses from other land use types. In France, a

surface soil inventory (0–30 cm) of more than a million

surface samples, suggests a net soil carbon loss from

agriculture of 53 Mt C yr�1 between 1990–1995 and 1999–

2004, equivalent to a loss rate 0.16 t C ha�1 yr�1 (Antoni &

Arrouays, 2007).

The ORCHIDEE-STICS model results were sampled

like in the regional inventories (Table 1). SOC was calcu-

lated over the cropland fraction of grid point in each

inventoried region from 1990 to 1999. Then, NBP was

calculated as the mean change over that period. The

model-data NBP comparison is provided in Fig. 8. All

the simulations give small sinks, whereas all the inven-

tories give small sources, and a big source in Flanders

(Sleutel et al., 2007). However, the simulated NBP remains

consistent with the inventories, within the uncertainty of

both approaches, except for Flanders. The Flanders in-

ventory gives a large positive flux to the atmosphere

(0.76 t C ha�1 yr�1). The only sensitivity test consistent

with the Flanders inventory is the S1-LO experiment

where 100% of harvest residues are assumed exported

away from the field. Sleutel and colleagues attribute their

large source in Flanders to a decrease in manure. Addi-

tional data in the Flanders region (Gabriels et al., 2003) for

40 crop-rotation systems suggest that a mass of 2500

kg DM ha�1 of stubble is left on the field after harvest

for winter wheat and maize cultivation. This translates

into a C input to the soil of 1.3 t C ha�1 yr�1, corresponding

to roughly 15% of total NPP. This observed ratio of input

to NPP is thus close to the one of ORCHIDEE-STICS,

suggesting that the model discrepancy with inventory

should not arise from a model bias in the residues return.

The misfit could be due to the assumed manure history. It

is alternatively possible that climate change (Bellamy et al.,

2005), and soil erosion not accounted in ORCHIDEE

contributed to the observed SOC decrease over Flanders.

In summary, ORCHIDEE-STICS produces a small

sink in arable soils, not confirmed by any of the regional

inventories, but the data are consistent with the model

results within the uncertainty range, and the model

results cannot be falsified by the inventory. Yet, crop-

land NBP in the CNT simulation is likely to be biased

towards a small sink, given that inventories from in-

dependent sources and different regions consistently

indicate a small source.

Non-CO2 gases in the balance

The C balance changes caused by agriculture intensifi-

cation must also be put in perspective with non-CO2

gases emissions caused by intensification and by LUC.

Schulze et al. (2009) showed that N2O emissions and

CH4 emissions from European agriculture altogether

offset the CO2 sinks in all other biomes. Here using the

amount of N applied in the CNT simulation and IPCC

emission factors for N2O emissions per unit of added N

(1.2%), we model low N2O emissions of 0.005 t C–

CO2 Eq ha�1 yr�1 from manure applications between

1901 and 1951, date at which emissions increase pro-

portionally to mineral fertilizer additions to reach

0.23 t C–CO2 Eq ha�1 yr�1 during the 1990s. Therefore,

expressed in CO2 Eq units, N2O emissions by croplands

exceed the small cropland NBP sink of 0.16 t C ha�1

yr�1, and also nearly balance the NBP of the area

initially under cropland in 1901 but now partly trans-

formed to grasslands and forest (0.23 t C ha�1 yr�1). The

period when the ever-increasing N2O emissions began

to exceed in absolute value the CO2 sink in cropland soil

–1

–0.5

0

0.5

1

–1.0 –0.5 0.0 0.5 1.0Regional soil C inventories NBP

(t C ha–1 yr–1)

Mod

eled

NB

P

CTL

S1-HI

S1-LO

S2-HI

S2-LO

S3-HI

S3-LO

Flanders Englandand Wales

France

Germany(elbe)

Austria

Fig. 8 Modeled NBP vs. observed soil C changes from inven-

tories over selected regions of Europe where inventories exist.

Results from the control simulation and different sensitivity tests

are color coded as in Fig. 5 above. Uncertainty in model

estimates are the min-max range given in Fig. 7 and the error

bar in the inventory estimate is defined as 40% of mean value

(see Bellamy et al., 2005).

334 P. C I A I S et al.

r 2010 Blackwell Publishing Ltd, Global Change Biology, 17, 320–338

occurs during the late 1980s. We also accounted for the

decrease of CH4 emissions from grazing livestock on

the grasslands lost to forest and to cropland in each grid

point, given LUC dataset-1. In total, 3.9 Mha of grass-

lands have been lost to cropland during 1901–2000, and

the rest lost to forest (despite locally high rates of

cropland to grassland conversion in the 1970s; Fig.

5d). From EUROSTAT CHRONOS agricultural statis-

tics, we infer a current density of 1.97 head -

livestock ha�1. From Vuichard et al. (2007), we take an

emission rate of 110 kg CH4 per livestock unit per year.

Making the (very crude) assumption that all these

emission factors and animal densities remained con-

stant with time, we infer the decrease in CH4 emission,

hence translated to a sink of CO2 Eq in 1.8 tC–CO2 Eq

ha�1 yr�1, per unit area of grassland that disappeared.

Therefore, the weighted average of the current CO2 sink

over the area initially under cropland in 1901 and of the

avoided CH4 emissions from grasslands within this

area (3.9 Mha – 6% of the initial croplands) is a larger

sink of 0.3 t C–CO2 Eq ha�1 yr�1. Altogether, summing

up N2O emissions directly coming from intensification

and CH4 emissions indirectly coming from intensifica-

tion though the creation of grasslands over abandoned

arable land remains a small sink 0.1 t C–CO2 Eq

ha�1 yr�1. Therefore, as pointed out by Schulze et al.

(2009) and modeled here accounting for LUC, non-CO2

gases emissions play an important role in the radiative

forcing budget of European croplands.

Conclusion and outlook

We have analyzed the controlling drivers of the Eur-

opean cropland carbon balance during the 1990s by

using a process-model forced between 1901 and 2000

by climate, CO2, agricultural technology changes and

land-use change. We confirm the small sink of

0.16 t C ha�1 yr�1 during the period 1991–2000 obtained

by Gervois et al. (2008) who assumed fixed cropland

area over the 20th Century. Therefore, modeling expli-

citly here the effect of LUC has a negligible impact on

the C flux over the area that is currently croplands.

Considering the entire area that was croplands in 1901,

but is now partly covered by grassland and forest

consecutive to abandonment, the sink density is of

0.22 t C ha�1 yr�1. This sink is practically offset by N2O

emissions from extra-fertilizers. Below, we answer to

the questions raised in the introduction:

� What is the effect of each agricultural technology

parameter in controlling the current croplands car-

bon balance? The simulated cropland NBP is highly

sensitive to the assumed historical scenario of agro-

technology, and to the value of technology para-

meters. The most sensitive parameter is the fraction

of harvest residues removed from the field, followed

by tillage and by N-fertilizer additions. Regionally,

the history of manure application could also explain

why the modeled NBP does not fit regional inven-

tories, like in Flanders.

� What is the effect of past vs. current LUC drivers on

the cropland carbon balance during the 1990s?

Because no significant cropland area change has

taken place over the past two decades in Europe,

and because our three LUC reconstruction datasets

are rather similar during the past 20 years, it appears

that uncertainty on past LUC does not affect sig-

nificantly cropland NBP during the 1990s. However,

former LUC activity is still impacting the current

NBP of regrowing forest and grassland (Fig. 6).

� What are the sources of uncertainties in modeled

NBP in response to climate, agro-technology and

LUC, and how can these uncertainties be reduced?

The estimated 1-sigma uncertainty range on NBP is

of 0.18 t C ha�1 yr�1. The uncertainty budget of NBP

is dominated by uncertainties in management para-

meters and model structure, with minor contribu-

tion of climate drivers and surprisingly, of LUC

drivers. In comparison with other factors, uncer-

tainty caused by unknown LUC history is small.

Because the carbon cycle of croplands is modeled

using a consistent framework, systematic error

could be easily corrected in future work, if new

information on agro-technology becomes available.

The priority to reduce uncertainty would be to

reconstruct from regional archives a realistic man-

agement history. Another approach could be to pay

more attention to the current soil C content distribu-

tion of arable land, and adjust the management

history to match this observed distribution (Carval-

hais et al., 2007). This approach looks promising

because it makes use of extensive SOC observations,

but given equifinality in management parameters

affecting NBP (Table 1) it may also deliver a good

match of the current SOC distribution for wrong

reasons, hence knowledge of management history

will remain necessary.

The originality of this work is that historical LUC trends

are coupled to cropland management trends in a spa-

tially explicit manner. The abandonment of arable land

to grassland and forests during the 20th Century has

fostered C sequestration in a nearly opposite amount

than the soil C losses induced by agricultural intensifi-

cation. Therefore, agricultural intensification has caused

simultaneously the sequestration of carbon in forest

biomass and grassland soils (abandonment), and a net

loss from arable soils (management). When N2O emis-

E F F E C T S O F L A N D U S E C H A N G E A N D M A N A G E M E N T 335

r 2010 Blackwell Publishing Ltd, Global Change Biology, 17, 320–338

sions from fertilizers and avoided CH4 emissions from

grasslands lost croplands are accounted for, the small

and uncertain current C sink of 0. 25 � 0.18 t C ha�1 yr�1

is turned into an even smaller sink of 0.1 t C ha�1 yr�1.

Further work is needed to refine this number and

estimate its regional distribution.

Many improvements could be made to our model. In

particular, a larger number of representative crops would

be desirable to reach a realistic description of European

agriculture. In addition, a more process-oriented para-

meterization of tillage effects is desirable (Baker et al.,

2006). For instance one could modify turnover times by a

distinct coefficient in each of the litter and labile, slow

and passive carbon pools, and simulate the vertical

distribution of soil C and its alteration by tillage. It is

also important to bring together the detailed site-level

modeling with more simplified large-scale modeling.

ORCHIDEE-STICS is recently applied at site scale

(L. Li, N. Vuichard, N. Viovy, P. Ciais, E. Ceschia, W. Jans,

M. Wattenbach, T. Gruenwald, S. Lehugerg, unpublished

results) and the results suggest performances not worse

than site-specific models. However, driving the model

with realistic crop rotation and fallow instead of perma-

nent cereals is needed.

In the future, as more data at site-level and regional soil

C inventories will be collected, it is an advantage to use

spatially explicit models like in this study. Also, with better

constraints on initial C stocks and agro-technology

changes, it should be possible to evaluate not only NBP

against inventory data, but also DC – the net change in soil

C until today – at selected sites or regions. It may even be

possible to use the model-data misfit for current NBP and

DC as information to infer initial soil C pool values and

their MRT (Carvalhais et al., 2007). The results of our

sensitivity tests show that NBP and DC are both strongly

sensitive to technology, suggesting that constraining some

technology parameters is achievable. A way forward could

be to perform a larger ensemble of simulations than in this

study in order to sample the technological parameters – or

their combination – that best fits the observations. We

strongly recommend that better historical land use recon-

structions are assembled for Europe. Over the past 30

years, the existence of Earth Observation space-borne

imagers like SPOT or LANDSAT-TM imagers could be

exploited (Masek & Collatz, 2006) to reconstruct land use

and land cover changes at adequate high spatial resolu-

tion. In addition, regional agronomy archives could be

opened up and exploited for detailed information on

management practice affecting cropland carbon cycling.

References

Agreste (1995) Ministere de l’agriculture et de la peche. Foret Bois, Agreste, la

statistique agricole 1995, Resultats 1994, Vol. 4. Statistiques forestieres Ministere

de l’Agriculture, de la peche & de l’Alimentation. Paris, Ministere de l’Agriculture.

pp. 83–78

Antoni V, Arrouays D (2007) Le stock de carbone dans les sols agricoles diminue. Le 4

pages de l’IFEN. 121, Available at: http://www.ifen.fr/uploads/media/de121.pdf

Arrouays D, Balesdent J, Germon JC, Jayet PA, Soussana JF, Stengel P (2002)

Contribution a la lutte contre l’effet de serre. Stocker du carbone dans les sols agricoles

de France? Expertise scientifique collective 332 pp. INRA Paris. Available. http://

www.inra.fr/l_institut/expertise/expertises_realisees/stocker_du_carbone_dans_

les_sols_agricoles_de_france (accessed July 2009).

Aubinet M, Moureaux C, Bodsonb B et al. (2009) Carbon sequestration by a crop over a

4-year sugar beet/winter wheat/seed potato/winter wheat rotation cycle. Agricul-

ture and Forest Meteorology, 149, 407–418.

Baker JM, Ochsner TE, Ventera RT, Griffis TJ (2006) Tillage and soil carbon sequestra-

tion – what do we really know? Agriculture, Ecosystems and Environment, 118, 1–5.

doi: 10.1016/j.agee.2006.05.014.

Battisti A (2008) Forests and climate change – lessons from insects. Forest, 1, 1–5.

Bellamy PH, Loveland PJ, Bradley RI, Lark RM, Kirk GJD (2005) Carbon losses from all

soils across England and Wales 1978–2003. Nature, 437, 245–248.

Bondeau A, Smith PC, Zaehle S et al. (2007) Modelling the role of agriculture for the 20th

century global terrestrial carbon balance. Global Change Biology, 13, 679–706.

Brisson N, Bussiere F, Ozier-Lafontaine H, Tournebize R, Sinoquet H (2004) Adapta-

tion of the crop model STICS to intercropping. Theoretical basis and parameterisa-

tion. Agronomie, 24, 409–421.

Brisson N, Gary C, Justes E et al. (2003) An overview of the crop model STICS. European

Journal of Agronomy, 18, 309–332.

Brisson N, Mary B, Ripoche D et al. (1998) STICS a generic model for the simulation of

crops and their water and nitrogen balances. I Theory and parameterization

applied to wheat and maize. Agronomie, 18, 311–346.

Brisson N, Ruget F, Gate P et al. (2002) STICS: a generic model for the simulation of

crops and their water and nitrogen balance. II Model validation for wheat and

maize. Agronomie, 22, 69–93.

Buttner G, Feranec J, Jaffrain G (2000) Corine Land Cover Update (2000): Technical

Guidelines, Tech. Rep. 89, Eur. Environ. Agency, Copenhagen. Available at: http://

reports.eea.eu.int/technical_report_2002_89/enl

Carvalhais N, Reichstein M, Seixas J et al. (2007) Implications of the Carbon Cycle

Steady State Assumption for Biogeochemical Modeling Performance and Inverse

Parameter Retrieval. Glob. Biogeochemical Cycles, 22, 2007, doi: 10.1029/2007GB00

3033.

Caviness CE, Collins FC, Sullivan M (1986) Effect of wheat residue on early growth of

soybean. Arkansas Farm Research, 35, 8.

Chevallier F, Viovy N, Reichstein M, Ciais P (2006) On the assignment of prior errors in

Bayesian inversions of CO2 surface fluxes. Geophysical Research Letters, 33, L13802,

doi: 10.1029/2006GL026496.

Ciais P, Wattenbach M, Vuichard N et al. (2010) The European carbon balance. Part 2:

croplands. Global Change Biology, 16, 1409–1428.

CORINE Land cover (2000) EEA online publications. Available at: http://reports.

eea.eu.int/COR0-landcover/en

Cotrufo MP, Ineson P, Scott A (1998) Elevated CO2 reduces the nitrogen concentration

of plant tissue. Global Change Biology, 4, 43–54.

Curtiss PS, Hanson PJ, Bolstad P, Barford C, Randolph JC, Schmid HP, Wilson KB

(2002) Biometric and eddy-covariance based estimates of ecosystem carbon storage

in five eastern North American deciduous forests. Agricultural and Forest Meteorol-

ogy, 113, 3–19.

De Noblet-Ducoudre N, Gervois S, Ciais P, Viovy N, Brisson N, Seguin B, Perrier A

(2004) Coupling the soil-vegetation-atmosphere-transfer scheme ORCHIDEE to the

agronomy model STICS to study the influence of croplands on the European carbon

and water budgets. Agronomie, 24, 397–407.

Dersch G, Boehm K (1997) Austrian agriculture’s share in the emission oftrace gases

affecting the climate. Die Bodenkultur, 48, 115–129.

Dupouey JL, Pignard G, Badeau V et al. (1999) Stocks et flux de carbone dans

les forets francaises. Comptes Rendus de l’Academie d’Agriculture de France, 85, 293–

310.

EEA (1995) CORINE land cover. Report of the European Environment Agency, 163pp.

FAO (2009) Food and Agriculture Organization Agricultural Database. Available at:

http://faostat.fao.org/ (accessed July 2009).

Fernandez-Rivera S, Lewis M, Klopfenstein TJ et al. (1989) A simulation model of

forage yield, quality and intake and growth of growing cattle grazing cornstalks.

Journal of Animal Science, 67, 581–589.

Gabriels D, Ghekiere G, Schiettecatte W, Rottiers I (2003) Assessment of USLE cover-

management C-factors for 40 crop rotation systems on arable farms in the

Kemmelbeek watershed, Belgium. Soil and Tillage Research, 74, 47–53.

336 P. C I A I S et al.

r 2010 Blackwell Publishing Ltd, Global Change Biology, 17, 320–338

Gervois S (2004) Les zones agricoles en Europe: evaluation de leur role sur les bilans d’eau

et de carbone a l’echelle de l’Europe; sensibilite de ces bilans aux changements environ-

nementaux sur le vingtieme siecle. Ph.D. Thesis, University Paris VI. 209 pp (in

French).

Gervois S, Ciais P, de Noblet-Ducoudre N, Brisson N, Vuichard N, Viovy N (2008)

Carbon and water balance of European croplands throughout the 20th century.

Global Biogeochemical Cycles, 22, GB2022, doi:10.1029/2007GB003018.

Gervois S, de Noblet-Ducoudre N, Viovy N, Ciais P, Brisson N, Seguin B, Perrier A

(2004) Including croplands in a global biosphere model: methodology and evalua-

tion at specific sites. Earth Interactions, 8, 1–25.

Gilmanov TG, Soussana JF, Aires L et al. (2007) Partitioning European grassland net

ecosystem CO2 exchange into gross primary productivity and ecosystem respira-

tion using light response function analysis. Agriculture, Ecosystems and Environment,

121, 93–120.

Goldewijk KK (2001) Estimating global land-use over the past 300 years: the HYDE

database. Global Biogeochemical Cycles, 15, 417–433.

Harrison RG, Jones CD, Hughes JK (2008) Competing roles of rising CO2 and climate

change in the contemporary European carbon balance. Biogeosciences, 5, 1–10.

Hay RKM (1995) Harvest index – a review of its use in plant-breeding and crop

physiology. Annals of Applied Biology, 126, 197–216.

Heidmann T, Christensen BT, Olesen SE (2002) Changes in soil C and N content in

different cropping systems and soil types. In: Greenhouse Gas Inventories for

Agriculture in the Nordic Countries (eds Petersen SO, Olesen JE), pp. 77–86. Ministry

of Food, Agriculture and Fisheries, Danish Institute of Agricultural Sciences,

Foulum, Denmark.

Houghton RA (1999) The annual net flux of carbon to the atmosphere from changes in

land use 1850–1990. Tellus B, 51, 298–313.

Jamieson PD, Berntsen J, Ewert F et al. (2000) Modelling CO2 effects on wheat with

varying nitrogen supplies. Agriculture, Ecosystems and Environment, 82, 27–37.

Janssens IA, Dieleman W, Luyssaert S et al. (2010) Reduction of forest soil respiration in

response to nitrogen deposition. Nature Geosciences, 3, 315–322.

Jarvis PG, Massheder JM, Hale SE, Moncrieff JB, Rayment M, Scott SL (1997) Seasonal

variation of carbon dioxide, water vapor, and energy exchanges of a boreal black

spruce forest. Journal of Geophysical Research, 102, 28953–28966.

Jung M, Vetter M, Herold M et al. (2007) Uncertainties of modeling gross primary

productivity over Europe: a systematic study on the effects of using different

drivers and terrestrial biosphere models. Global Biogeochemical Cycles, 21, GB4021,

doi:10.1029/2006GB002915.

Kaplan JO, Krumhardt KM, Zimmermann N (2009) The prehistoric and preindustrial

deforestation of Europe. Quaternary Science Reviews, 28, 3016–3034.

Kenny GJ, Harrison PA (1993) Analysis of effects of climate change on broadscale

patterns of agroclimate in Europe. In: The Effect of Climate Change on Agricultural and

Horticultural Potential in Europe (eds Kenny GJ, Harrison PA, Parry ML), pp. 201–

224. University of Oxford, Oxford, UK.

Krinner G, Viovy N, de Noblet-Ducoudre N et al. (2005) A dynamic global vegetation

model for studies of a coupled atmosphere-biosphere system. Global Biogeochemical

Cycles, 19, GB1015, doi: 1029/2003GB002199.

Lal R (2004) Soil Carbon sequestration impact on global climate change and food

security. Science, 304, 1623–1627.

Lobell DB, Cahill KN, Field CB (2007) Historical effects of temperature and precipita-

tion on California crop yields. Climatic Change, 81, 187–203.

Long SP, Ainsworth EA, Leakey ADB, Nosberger J, Ort DR (2006) Food for thought:

lower-than-expected crop yield stimulation with rising CO2 concentrations. Science,

312, 1918–1921.

Lopez-Guisa JM, Satter LD, Panciera M (1991) Utilization of ensiled corn crop residues

by Holstein heifers. Journal of Dairy Science, 74, 3160–3166.

Loveland TR, Reed BC, Brown JF, Ohlen DO, Zhu Z, Yang L (2000) Development of a

global land cover characteristics database and IGBP DISC over from 1 km AVHRR

data. International Journal of Remote Sensing, 21, 1303–1330.

Masek JG, Collatz GJ (2006) Estimating forest carbon fluxes in a disturbed south-

eastern landscape: integration of remote sensing, forest inventory, and biogeo-

chemical modeling. Journal of Geophysical Research-Biogeosciences, 111, G01006, doi:

10.1029/2005JG000062.

Mather AS, Fairnbairn J, Needle C (1999) The course and drivers of the Forest

transition: the case of France. Journal of Rural Studies, 15, 65–90.

Mather AS, Needle C, Fairnbairn J (1998) The human drivers of global land cover

change: the case of forests. Hydrological Processes, 12, 1983–1994.

Mazoyer M (2002) Larousse agricole: le monde paysan au XXeme siecle. Larousse, Paris.

McGuire AD, Sitch S, Clein JS et al. (2001) Carbon balance of the terrestrial biosphere in

the twentieth century: analyses of CO2, climate and land use effects with four

processbased ecosystem models. Global Biogeochemical Cycles, 15, 183–206.

Mitchell TD, Carter TR, Jones PD, Hulme M, New M (2004) A comprehensive set of

high resolution grids of monthly climate for Europe and the globe: the observed

record (1901–2000) and 16 scenarios (2001–2100). Working paper 55 (Tyndall Centre for

Climate Change Research, July 2004). Available at: http://www.tyndall.ac.uk/pub

lications/working_papers/wp55.pdf (accessed January 2009).

Moureaux C, Bodson B, Aubinet M (2008) Mesure des flux de CO2 et bilan carbone de

grandes cultures: etat de la question et methodologie. Biotechnology Agronomy

Society and Environment, 12, 303–315.

Olesen JE (2005) Climate change and CO2 effects on productivity of Danish agricul-

tural systems. Journal of Crop Improvement, 13, 257–274.

Osborne TM, Lawrence DM, Challinor AJ, Slingo JM, Wheeler TR (2007) Development

and assessment of a coupled crop-climate model. Global Change Biology, 13, 169–183.

Parry MC, Rosenzweig C, Livermore M (2005) Climate change, and risk global food

supply of hunger. Philosophical Transactions of The Royal Society, B-Biological Sciences,

360, 2125–2138.

Parton WJ, Stewart JBW, Cole CV (1988) Dynamics of C, P and S in Grasslands soils: a

model. Biogeochemistry, 5, 109–131.

Piao SL, Ciais P, Friedlingstein P, de Noblet-Ducoudre N, Cadule P, Viovy N, Wang T

(2009) Spatiotemporal patterns of terrestrial carbon cycle during the 20th century.

Global Biogeochemical Cycles, 23, 4026, doi: 10.1029/2008GB003339.

Pongratz J, Reick CH, Raddatz T, Claussen M (2009) Effects of anthropogenic land

cover change on the carbon cycle of the last millennium. Global Biogeochemical

Cycles, 23, GB4001, doi: 10.1029/2009GB003488.

Post J, Conradt T, Suckow F, Krysanova V, Weshsung F, Hattermann FF (2008) Soil carbon

sensitivity to climate change in a central European river basin: integrated eco-

hydrological modelling using SWIM. Hydrological Sciences Journal, 53, 1043–1058.

Powell JM, Pearson RA, Hiernaux PH (2004) Crop-livestock interactions in the West

African drylands. Agronomy Journal, 96, 469–483.

Ramankutty N, Foley JA (1999) Estimating historical changes in global land cover:

croplands from from 1700 to 1992. Global Biogeochemical Cycles, 13, 997–1027.

Ramankutty N, Hertel T, Lee HL, Rose SK (2007) Global agricultural land use data for

integrated assessment modeling. In: Human-Induced Climate Change: An Interdisci-

plinary Assessment (ed. Schlesinger M), pp. 252–265. Cambridge University Press,

New York, USA.

Richardson C, Wright D (1984) A Model for Generating Daily Weather Variables, Technical

Report. U. S. Department of Agriculture. Agric. Research Services, Washington, DC.

Rinklebe J, Makeschin F (2003) Der Einfluss von Acker- und Waldnutzung auf Boden

und Vegetation - ein Zeitvergleich nach 27 Jahren. Forstwissenschaftliches Central-

blatt, 122, 81–98.

Schulze ED, Ciais P, Luyssaert S et al. (2010) The European carbon balance. Part 4:

integration of carbon and other trace-gas fluxes. Global Change Biology, 16, 2399–

2399.

Schulze ED, Luyssaert S, Ciais P et al. (2009) Importance of methane and nitrous oxide

for Europe’s terrestrial greenhouse-gas balance. Nature Geoscience, 2, 842–850.

Sinclair TR (1998) Historical changes in harvest index and crop nitrogen accumulation.

Crop Science, 38, 638–643.

Sleutel S, De Neve S, Hofman G (2007) Assessing causes of recent organic carbon

losses from cropland soils by means of regional-scaled input balances for the case of

Flanders (Belgium). Nutrient Cycling in Agroecosystems, 28, 265–278.

Sleutel S, De Neve S, Hofman G et al. (2003) Carbon stock changes and carbon

sequestration potential of Flemish cropland soils. Global Change Biology, 9, 1193–1203.

Smith P (2005) Limited increase of agricultural soil carbon and nitrogen stocks due to

increased atmospheric CO2 concentrations. Journal of Crop Production, 13, 393–399.

Smith P, Milne R, Powlson DS et al. (2000) Revised estimates of the carbon mitigation

potential of UK agricultural land. Soil Use and Management, 16, 293–295.

Smith P, Powlson DS, Glendining MJ et al. (1997a) Opportunities and limitations for C

sequestration in European agricultural soils through changes in management. In:

Management of Carbon Sequestration in Soil (eds Lal R, Kimble JM, Follett RF et al.),

pp. 143–152. Lewis Publishers, Boca Raton, FL, USA.

Smith P, Smitha JU, Powlsona DS et al. (1997b) A comparison of the performance of

nine soil organic matter models using datasets from seven long-term experiments.

Geoderma, 81, 153–225.

Smith PC (2008) Modelisation des cultures europeennes au sein de la biosphere:

phenologie, productivite et flux de CO2 These Universite Pierre et Marie Curie

soutenue le 30 mai. Univ. Paris-6, Paris, France (in French).

Smith PC, Ciais P, Peylin P, De Noblet-Ducoudre N, Viovy N, Meurdesoif Y, Bondeau

A (2010a) European-wide simulations of present croplands using an improved

terrestrial biosphere model. Part 2: interannual yields and anomalous CO2 variability

and carbon fluxes in 2003. Journal of Geophysical Research Biogeosciences (in press).

Smith PC, De Noblet-Ducoudre N, Ciais P et al. (2010b) European-wide simulations

of present croplands using an improved terrestrial biosphere model. Part 1:

E F F E C T S O F L A N D U S E C H A N G E A N D M A N A G E M E N T 337

r 2010 Blackwell Publishing Ltd, Global Change Biology, 17, 320–338

phenology and productivity. Journal of Geophysical Research Biogeosciences, 115,

G01014, doi:10.1029/2008JG000800.

Soussana JF, Allard V, Pilegaard K et al. (2007) Full accounting of the greenhouse gas

(CO2, N2O, CH4) budget of nine European grassland sites. Agriculture, Ecosystems

and Environment, 121, 121–134.

Tubiello FN, Rosenzweig C, Goldberg RA, Jagtap S, Jones JW (2002) Effects of climate

change on US crop production: simulation results using two different GCM

scenarios. Part I: Wheat, potato, maize, and citrus. Climate Research, 20, 259–270.

Vleeshouwers LM, Verhagen A (2002) Carbon emission and sequestration by agricul-

tural land use: a model study for Europe. Global Change Biology, 8, 519–530.

Vuichard N, Ciais P, Belleli L, Smith PC, Valentini R (2008) Carbon sequestration due to

the abandonment of agriculture in the former USSR since 1990. Global Biogeochem-

ical Cycles, 22, 4018, doi: 1029/GB003212.

Vuichard N, Ciais P, Viovy N, Calanca P, Soussana JF (2007) Estimating the greenhouse

gas fluxes of European grasslands with a process-based model: 2. Simulations at the

continental level. Global Biogeochemical Cycles, 21, GB1005, doi: 10.1029/2005GB00

2612.

Yadvinder-Singh BS, Ladha JK, Khind CS (2004) Effects of residue decomposition on

productivity and soil fertility in rice–wheat rotation. Soil Science Society of America

Journal, 68, 854–864.

Zaehle S, Sitch S, Smith B, Hatterman F (2005) Effects of parameter uncertainties on the

modeling of terrestrial biosphere dynamics. Global Biogeochemical Cycles, 19, 3020,

doi: 10.1029/2004GB002395.

Supporting Information

Additional Supporting Information may be found in the

online version of this article:

Table S1. Summary of the historical technology sensitivity

tests carried out in this study.

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authors. Any queries (other than missing material) should be

directed to the corresponding author for the article.

338 P. C I A I S et al.

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