Ciais et al 2010 GCB

The European carbon balance. Part 2: croplands

P. C I A I S *, M . WA T T E N B A C H w , N . V U I C H A R D *, P. S M I T H w , S . L . P I A O *, A . D O N z,S . L U Y S S A E R T § , I . A . J A N S S E N S § , A . B O N D E A U } , R . D E C H O W k, A . L E I P **, P C . S M I T H *,

C . B E E R k, G . R . V A N D E R W E R F w w , S . G E R V O I S *, K . V A N O O S T zz, E . T O M E L L E R I k,A . F R E I B A U E R k, E . D . S C H U L Z E k and C A R B O E U R O P E S Y N T H E S I S T E A M *

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

Biological and Environmental Sciences, School of Biological Sciences, University of Aberdeen, Cruickshank Building, St. Machar

Drive, Aberdeen AB24 3UU, UK, zMax-Planck Max-Planck Institute for Biogeochemistry, Hans-Knoell-Strasse 10, 07745 Jena,

Germany, §Department of Biology, University of Antwerpen, Universiteitsplein 1, 2610 Wilrijk, Belgium, }Potsdam Institute for

Climate Impact Research (PIK), Telegrafenberg, PO Box 601203, D-14412 Potsdam, Germany, kMax-Planck Institute for

Biogeochemistry, Hans-Knoell-Strasse 10, 07745 Jena, Germany, **European Commission – DG Joint Research Centre, Institute for

Environment and Sustainability, Ispra, Italy, wwFaculty of Earth and Life Sciences, VU University Amsterdam, Netherlands,

zzDepartement de Geographie, Universite catholique de Louvain, 3 Place Louis Pasteur, 1348 Louvain-la-Neuve, Belgium

Abstract

We estimated the long-term carbon balance [net biome production (NBP)] of European (EU-25)croplands and its component fluxes, over the last two decades. Net primary production (NPP)estimates, from different data sources ranged between 490 and 846 gC m�2 yr�1, and mostlyreflect uncertainties in allocation, and in cropland area when using yield statistics. Inventoriesof soil C change over arable lands may be the most reliable source of information on NBP, butinventories lack full and harmonized coverage of EU-25. From a compilation of inventories weinfer a mean loss of soil C amounting to 17 g m�2 yr�1. In addition, three process-basedmodels, driven by historical climate and evolving agricultural technology, estimate a smallsink of 15 g C m�2 yr�1 or a small source of 7.6 g C m�2 yr�1. Neither the soil C inventory data,nor the process model results support the previous European-scale NBP estimate by Janssensand colleagues of a large soil C loss of 90� 50 gC m�2 yr�1. Discrepancy between measuredand modeled NBP is caused by erosion which is not inventoried, and the burning of harvestresidues which is not modeled. When correcting the inventory NBP for the erosion flux, andthe modeled NBP for agricultural fire losses, the discrepancy is reduced, and cropland NBPranges between �8.3� 13 and �13� 33 g C m�2 yr�1 from the mean of the models andinventories, respectively. The mean nitrous oxide (N2O) flux estimates ranges between 32and 37 g C Eq m�2 yr�1, which nearly doubles the CO2 losses. European croplands act as smallCH4 sink of 3.3 g C Eq m�2 yr�1. Considering ecosystem CO2, N2O and CH4 fluxes provides forthe net greenhouse gas balance a net source of 42–47 g C Eq m�2 yr�1. Intensifying agriculturein Eastern Europe to the same level Western Europe amounts is expected to result in a neardoubling of the N2O emissions in Eastern Europe. N2O emissions will then become the mainsource of concern for the impact of European agriculture on climate.

Keywords: agriculture, ecosystem models, EU-25, green house gas balance, inventory, uncertainty

Received 21 January 2009 and accepted 18 May 2009

Introduction

In their analysis of the European carbon budget, Jans-

sens et al. (2003) concluded that there was a large soil

organic carbon (SOC) loss to the atmosphere from

croplands. This loss was based on extrapolation from

an earlier model study with simple assumptions about

crop yield and farmer practice (Vleeshouwers & Verha-

gen, 2002). In fact, the large and widespread increase in

crop yield observed everywhere in Europe during

Correspondence: S. Luyssaert, e-mail:

[email protected]

*Members of the CARBOEUROPE Synthesis Team: G. Abril,

O. Bouriaud, G. Churkina, J. Grace, M. Jung, G.-J. Nabuurs, J.-D.

Paris, D. Papale, P. Peylin, M. Reichstein, M.-J. Schelhaas, J.-F.

Soussana, M. Vetter, N. Viovy, S. Zaehle.

Global Change Biology (2010) 16, 1409–1428, doi: 10.1111/j.1365-2486.2009.02055.x

r 2009 Blackwell Publishing Ltd 1409

mailto:[email protected]

recent decades, does not seem to have entrained a

parallel increase in soil carbon stocks (Arrouays et al.,

2002). Soil carbon is rather observed to be decreasing in

regions of intensive agriculture (Fardeau et al., 1988;

Walter et al., 1995) and increasing in others, reflecting

changes in management practice (Sleutel et al., 2003),

with some areas showing no change (Dersch & Boehm,

1997; Heidmann et al., 2002).

In addition to CO2 emitted by SOC decomposition,

agricultural soils emit N2O by nitrification and denitri-

fication of mineral nitrogen, which in croplands is

driven predominantly by fertilizer inputs. This N2O

flux, once converted into CO2 radiative forcing equiva-

lent, is a significant component of the European green-

house gas (GHG) balance, roughly 8% in CO2

equivalents of fossil fuel CO2 emissions, according to

the report of each country to the United Nations Frame-

work Convention on Climate Change (UNFCCC, 2000).

Further, for most EU-25 countries, N2O emissions from

cultivated soils are the most uncertain part of GHG

emissions declared to the UNFCCC (Rypdal & Wini-

warter, 2001; Leip et al., 2008).

The GHG balance of European croplands is driven

by agricultural practice both for CO2 and N2O fluxes,

and additionally by the effects of climate change

(Gervois et al., 2008). Agricultural practice affects: (i)

the input of carbon to the soil through manure,

nonharvested and nonburned residues, (ii) the de-

composition of soil carbon, for example through

tillage timing and intensity, the soil mineral N content,

and irrigation, and (iii) N2O emissions from

soils through fertilization practice. Models developed

to quantify long-term soil carbon changes at for

example the European scale must hence account for

regional and temporal differences in these farming

practices.

The goal of this paper is to contribute to a better level

of understanding of the productivity and carbon bal-

ance of European croplands in relation to other terres-

trial ecosystems such as grasslands (Ciais et al.,

unpublished results) and forests (Luyssaert et al.,

2009). Here, we analyze data from ecological cropland

sites, from a compilation of cropland inventories

and repeated measurements, and the output of biogeo-

chemical models in order to address the following

questions:

What is the net primary production (NPP) of crop-

lands in the EU-25, as estimated by each independent

data stream?

What is the fate of the carbon incorporated in biomass

and soils, and its return to the atmosphere?

How does the long-term carbon balance or net biome

production (NBP) relate to NPP?

To what degree do the emissions of nitrous oxide

(N2O) offset the carbon sequestration in cropland

soils?

Components of the carbon balance

A general description of the carbon balance is given in

Ciais et al (unpublished results). In croplands, the bulk

of NPP is allocated to the production of biomass in

foliage, shoots and roots. However, because not all of

the biomass produced remains on site, direct measure-

ments of total NPP are impossible and the biomass

removed needs to be corrected for. Examples of biomass

removal processes include harvest, and herbivory

by insects and mammals. In addition other components

of NPP are rarely measured such as weed production,

seed production, emission of volatile organic com-

pounds (VOC) to the atmosphere, exudation from roots

and carbon transfer to root symbionts. The sum of

all these components is the total ecosystem NPP (see

Ciais et al., unpublished results, for the definitions of

and relationships between the carbon balance

components). However, depending on the ecosystem,

some of these components are difficult to measure or

are of minor importance. In this manuscript NPP of

croplands denotes the sum of NPPfoliage, NPPshoots and

NPProots.

The net ecosystem carbon balance (NECB) is the

term applied to the total rate of organic carbon accu-

mulation in (or loss from) ecosystems (Chapin et al.,

2005). When integrated over time and space the NECB

equals the net biome production (NBP; Schulze &

Heimann, 1998; Buchmann & Schulze, 1999; Chapin

et al., 2005). In this study, NBP of croplands is quantified

as

NBP ¼ NPP� Rh1 �H �D� F� VOC� Eþ I; ð1Þ

where Rh1 is the soil heterotrophic respiration, D is

the C flux of photosynthetic origin loss to hydraulic

conduits and rivers, F is the loss to the atmosphere by

fire disturbance, H the harvested component of NPP,

VOC the NPP component emitted as biogenic volatile

compounds emissions to the atmosphere, E the flux

of C exported from cropland ecosystems by erosion –

but not necessarily lost to the atmosphere, and I the

input to the soil, e.g. via manure applications. Assum-

ing that 100% of H is respired as CO2 after digestion of

crop products by animals and humans, we can identify

H to a component of heterotrophic respiration taking

place outside ecosystems, called Rh2. When summing

up the C balance at continental scale, we need to add

the flux T of respiration by humans and livestock of

1410 P. C I A I S et al.

r 2009 Blackwell Publishing Ltd, Global Change Biology, 16, 1409–1428

crop products imported by trade from outside EU-25.

This gives:

NBP ¼ NPP� Rh1 � Rh2 � T �D� F� VOC� Eþ I: ð2Þ

Materials and methods

Study area

The C fluxes representing the photosynthetic carbon

uptake [gross primary production (GPP)], respiratory

and fire disturbance, are estimated for croplands in

EU-25. Our definition of croplands follows the land

use classification of the underlying data sources: for

ecological site studies we followed the classification of

the principal investigator of the site, for yield statistics

we followed the national classification, and when data

came from ecosystem models we followed the CORINE

land use classification (EEA, 2007). Further, the EU-25

contains member states of the European Union

(31 December 2006) i.e. Austria, Belgium, Cyprus, Czech

Republic, Denmark, Estonia, Finland, France, Germany,

Greece, Hungary, Ireland, Italy, Latvia, Lithuania,

Luxembourg, Malta, Netherlands, Poland, Portugal,

Slovakia, Slovenia, Spain, Sweden and United Kingdom.

In general, the numbers represent mean values for the

EU-25 over the period 1990–1999 (Table 1). For some

data streams, the period is longer (e.g. soil C inven-

tories) or shorter (e.g. MODIS data only after 2000).

However, with the need to better understand regional

details in the carbon cycle’s response to perturbations

and gradual changes, regional information is provided

to complete the EU-25 mean values. Despite the general

nature of the results, estimates at the EU-25 level are

derived from spatially explicit datasets and models.

There are inevitable inconsistencies in the various input

datasets and methodologies that were used, and these

are only, in part, corrected for by scaling the carbon

fluxes to the same spatial domain i.e. the EU-25 crop-

land area (1.08� 106 km2) defined from EEA (2007)

agricultural area, subtracted by the grassland area

diagnosed from the Land Use/Cover Area Frame

Statistical Survey (LUCAS) (Ciais et al., unpublished

results).

Data sources

Most of the results presented here (Fig. 1) are new data

or model results, which were prepared between 2003

and 2008. References to these data or models are given

in Table 2. When other data were needed to complete

the analyses or discussion, in-text citations are given.

Mean EU-25 flux estimates from inventories and mod-

els were reported without uncertainty and a min-max

range is provided for the modeling results. Mean EU-25

flux estimates from ecological site studies were reported

with an uncertainty expressed by the Sd resulting from

propagating site-level uncertainties. Uncertainties of the

summary statistics at the EU-25 level are expressed by

the SD of the mean values of these approaches, thereby

assessing uncertainty arising from comparing the dif-

ferent approaches rather than the uncertainty of each

approach.

Inventories of yield statistics. EU-25 cropland NPP was

estimated by transformation of the FAO (FAO, 2009)

Table 1 Spatial and temporal coverage of the different methods

Method Spatial coverage/upscaled to

Temporal

coverage

FAO national yield

statistics

EU-25 at country scale (1.03� 106 km2 crops) 1961–now

EUROSTAT regional yield

statistics

EU-25 1970–now

River database European watershed draining into the Artic Sea, Baltic Sea, North Sea,

Atlantic Ocean, Mediterranean sea, and Black sea

1980–2000

Site and regional

inventories studies

Austria, Belgium, Finland, UK, Franconia (Germany), France Last 40–20 years

EOS-Terra-MODIS EU-25 at 1 km by 1 km 2000–2006

CASA EU-25 at 11 by 11 driven by MODIS burned area data 1997–2006

ORCHIDEE-STICS Western European subdomain of EU-25 (Fig. 2) with 0.56 106 km2 cropland

coverage

1990–1999

LPJml EU-25 with 1.18� 106 km2 1990–1999

RothC EU-25 1990–1999

T H E E U R O P E A N C A R B O N B A L A N C E : PA R T 2 1411


national yield statistics in two ways. Allometric factors

and conversion ratios were compiled for 19 different

crop varieties to calculate NPP, according to Eqn (3):

NPP ¼ ðH=HIÞ � ð1þ RFÞ �DM� CC; ð3Þ

where H is the yield given by statistics, HI the harvest

index defined as the ratio of yield to aboveground NPP,

RF the root production expressed as a fraction of

aboveground NPP, and DM and CC are factors

converting fresh biomass as reported by FAO into dry

matter and carbon content, respectively. In a first

method, NPP was calculated with global values of HI

and RF from Goudriaan et al. (2001), further called

Goudriaan factors. Corresponding HI values are

0.47–0.49 for maize and wheat, respectively. The

values of DM and CC were from Spitters & Kramer

(1986) and Marcelis et al. (1998). In a second, alternative

method, NPP was calculated by taking crop and region-

specific factors for HI and accounting for the proportion

of NPP unaccounted for by yield statitics (e.g. losses to

pests, disease and herbivory and weed NPP) and the

proportion of NPP occurring below ground, as used by

Haberl et al. (2007), further called the Haberl factors.

Note that the factors of Goudriaan are in line with our

definition of NPP (see ‘Components of the carbon

balance’) whereas the factors of Haberl result in the

total NPP. The third method used an independent

dataset of yield statistics from the NUTS3 level

database of EUROSTAT (2009) and the Goudriaan

factors.

Export of C to rivers. The export of C from cropland soils

into rivers was estimated by taking the average

cropland area of each river basin in the EU-25, for

which values of D were produced in Meybeck & Ragu

(1996) and Ciais et al. (2008). Only C of atmospheric

origin is accounted for in D, not C from geological

pools. The flux D includes losses of dissolved organic

carbon (DIC) of atmospheric origin by weathering, and

the export of dissolved organic carbon (DOC),

particulate organic carbon (POC) from cropland soils.

There is a large uncertainty on the value of D. There is

an even larger uncertainty associated with the

component of D derived from erosion of old soil

organic matter, as opposed to that derived from

carbon recently added to the soil. Most of river

transported carbon originates from ecosystems, but

can have different lifetimes through the river filters,

being either degassed to the atmosphere within a year,

or sequestered in long-lived organic sediments. The

recent data compilation of Ciais et al. (2008) suggests

that a minimum fraction of 70% of the ecosystem carbon

transported by rivers returns rapidly to the atmosphere.

Erosion. Soil C erosion (E) defined as the sum of human

accelerated erosion and the effect of natural processes,

Fig. 1 EU-25 croplands carbon cycle. Fluxes in Tg C yr�1 over a cropland area of 1.08� 106 km2. Rh2 is calculated to close the carbon

balance.



is a significant flux in the European GHG balance

context and cannot be neglected, especially on

croplands. The global map of E created by Van Oost

et al. (2007) showed significant cropland soil erosion

rates in EU-25, of the order of 10–15 g C m�2 yr�1 (see

Fig. 2, Table S2 and S3 in Van Oost et al. (2007)),

compared with arable lands in the rest of the World.

Erosion maps from the Van Oost et al. (2007), modeled

at 10 km spatial resolution, were averaged at EU-25

scale with a mask of the EU-25 croplands based upon

the CORINE land cover.

Fires. Fire emissions (F) from croplands, chiefly from

burning of residues, were calculated for the period

1997–2006 from the global biomass burning emission

dataset GFEDv2 of Van der Werf et al. (2006). The

GFEDv2 emissions data available at 11 by 11 spatial

resolution, combine the CASA model (Potter et al., 1993)

forced by variable climate fields and 10-daily burned

areas from remote sensing (Giglio et al., 2006). We used

a mask of EU-25 croplands based upon CORINE land

cover. Owing to the coarse modeling approach used,

however, our estimate for EU-25 may include some

Table 2 Data sources of the new analyses presented in this study

Site-observations Models Remote sensing Inventories

Net primary

production

(NPP)

ORCHIDEE-STICS

(Gervois et al., 2008)

CASA (Potter et al.,

1993), MODIS 2007

(Zhao et al., 2005)

FAO yield statistics (FAO,

2007), EUROSTAT yield

statistics (EUROSTAT,

2009) with allometry

factors from Haberl et al.

(2007) and Goudriaan et al.

(2001)

Harvest (H) ORCHIDEE-

STICS 1 simple rules

for harvest date and

exported NPP

fraction

FAO yield statistics (FAO,

2007), EUROSTAT yield

statistics (EUROSTAT,

2009)

Fires (F) MODIS burned area

products (Giglio et al.,

2006) coupled to the

CASA biosphere

model (van der Werf

et al., 2006)

Loss to rivers (D) European rivers database

(Meybeck & Ragu, 1996)

upscaled on the basis of

runoff, land cover and

rock types similarities as

in Ciais et al. (2008)

Heterotrophic

respiration (Rh)

ORCHIDEE-STICS from

decomposition of

non harvested NPP

Erosion Spatially explicit

upscaling (Van

Oost et al., 2007)

N2O Fuzzy-logic model on

data of Stehfest &

Bouwman (2006)

UNFCCC (2008) and

information about

fertilizer applications for

different types of land use

Net biome

production

(NBP)

Data compilation of

site studies

(Table 3)

ORCHIDEE-STICS

(Gervois et al., 2008),

RothC 1 soil C inputs

from LPJ (Smith et al.,

2005a), LPJml

(Bondeau et al., 2007)

Regional inventories of soil C

changes in Austria,

Belgium, Denmark, UK,

France, Finland (Table 3)

The data sources and the model structures are described in detail in the tabulated references.



nonagricultural fires. The uncertainty reported on F

corresponds to the SD of interannual variations in

GFEDv2 emissions between 1997 and 2006.

Trade of crop products. The C fluxes of imported and

exported crop products by international trade (T) must

be included in the GHG balance when working at the

EU-25 scale. We used the methodology described in

Ciais et al. (2007) to account for the digestion of C in

imported food and feed products, resulting in a release

of CO2 to the atmosphere. Similarly, the C fluxes of

exported food products will be respired outside the EU-

25 and must be subtracted from the GHG balance. In

other words, the trade of crop products modulates the

heterotrophic respiration component Rh2, that occurs

away from cropland ecosystems. Estimation of the C

source (imports) and C sink (exports) resulting from

trade at EU-25 scale were obtained from FAO trade

statistics. Geospatial estimates of these fluxes are those

provided by Ciais et al. (2008).

Regional soil C change inventories. For the purpose of this

study, we compiled cropland NBP data from regional

inventories over Austria, Belgium, Denmark, Finland,

France, UK and some regions in Germany. Altogether,

these inventoried regions represent 33% of the total EU-

25 cropland area. Details of the compilation are given in

Table 3.

Primary productivity

0

500

1000

1500g

C m

–2 y

r–1g

C m

–2 y

r–1

g C

m–2

yr–1

g C

m–2

yr–1

0

100

200

300

400

500 Harvest removal and respiration

–20

–10

0

10

20 Net carbon balance (NBP)

–30–20–10

01020304050 Disturbance and lateral fluxes

0

10

20

30

40

50

N2O emissions(from UNFCCC)

N2O emissions(from fuzzy logic)

g C

–C

O2

eq m

–2 y

r–1

N2O emissions

(a) (b)

(c) (d)

(e)

Fig. 2 Component fluxes of European cropland C balance. By convention, losses of C by the European continent are o0 and gains are

40. (a) Gross and net primary productivity estimated using models or harvest statistics and allometry data. (b) Harvest removal and

respiration components. (Rh2) respiration by humans and animals is calculated by mass balance (see text). (c) Disturbance, imported food

products emitted to atmosphere, carbon lost to rivers and accumulated over land outside croplands by erosion. (d) Net carbon balance

estimated by three models and by regional soil C inventories with incomplete coverage. White bars are results before correction of C

removed by rivers and of erosion, black bars show uncorrected carbon balance (see text). By convention C losses by croplands are o0

and C gains are 40. (e) N2O emissions to the atmosphere in g C CO2 equivalent units. The Net GHG balance of cropland is the sum of net

carbon balance and N2O fluxes.



Ecosystem models. Cropland NPP and NBP were

independently assessed using the results of spatially

explicit process-based models of cultivated ecosystems:

ORCHIDEE-STICS (Gervois et al., 2008), RothC (Smith

et al., 2005a) and LPJml (Bondeau et al., 2007). In this

context, the generic DGVM vegetation models com-

piled for forest NPP by Luyssaert et al. (2009) are

inappropriate, because they treat crops as natural

grasses, leading to a very different NPP, although not

necessarily smaller than the one observed; crops have a

higher resource use efficiency (LUE) but a shorter

growing season than natural grasses.

ORCHIDEE-STICS is a generic model of cultivated

ecosystems parameterized with three generic crop

varieties widespread in Europe (De Noblet-Ducoudre

et al., 2004; Gervois et al., 2004, 2008; Gervois, 2004;

Smith et al., 2009a, b). These varieties are winter wheat

(Soissons) assumed to represent all other types of winter

cereals and rape seed, grain yield maize (DK-604)

assumed to represent all C4 crops, and soybean

assumed to represent all types of other summer C3

crops. For NPP calculation, the ORCHIDEE-STICS

model was run over the period 1996–2002 with a time

step of 3-h over a western European domain, W,

bounded by 101W–201E and 35–551N (0.56� 106 km2

of croplands) shown in Fig. 3.

The ORCHIDEE-STICS mean annual NPP over

the western domain W of EU-25, called NPPO�STICS,

was extrapolated over the whole EU-25 territory by

using the FAO derived NPP, NPPFAO, according to

Eqn (4):

NPPO�STICSðEU� 25Þ ¼ ½NPPFAOðEU� 25Þ=NPPFAOðWÞ��NPPO�STICSðWÞ: ð4Þ

In doing so, we checked that over the domain W

where they overlap, the mean NPP of ORCHIDEE-

STICS and FAO were in quite good agreement

(within � 8%), so that Eqn (4) did not amplify any

initial bias of the model over this Western domain.

However, in Eqn (4) management types are not

described in Eastern Europe, and gaps have been

filled with data from Western Europe, which is likely

to lead to an overestimation of NPP so that also the

overall estimate is likely to be overestimated.

The ORCHIDEE-STICS model was also used to

simulate NBP between 1901 and 2000. In this version,

the model simulated winter wheat and maize only,

ignoring summer C3 crops (Gervois et al., 2008). The

soil carbon decomposition module of ORCHIDEE-

STICS is similar to the CENTURY model equations

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eco

syst

emC

gai

n.



practice and crop varieties in 1901, the model was

driven in the domain W by rising CO2 and transient

climate fields at a resolution of 10 km, and by evolving

agricultural technology after 1950. The technological

evolution parameterized by Gervois et al. (2008)

included increased yields, increased HI, increased N-

fertilizers and decreased manure applications, maize

irrigation, and increased tillage which accelerated SOC

decomposition.

RothC is a soil carbon model (Smith et al., 2005a). For

this purpose, RothC was prescribed with changing NPP

– discounted for harvest – from the LPJ vegetation

model (Sitch et al., 2003) as the soil carbon input. In a

similar way than ORCHIDEE-STICS, the RothC model

was initialized and run at a resolution of 10 km from

1900 to 2100 (Smith et al., 2005a).

LPJmL (Sitch et al., 2003) is another DGVM that has

been extended to represent the carbon and water cycles

of managed land (Bondeau et al., 2007). The version

used here considers 11 crops functional types, eight of

them being cultivated in the EU-25: temperate cereals

(wheat like), maize, soybean, rice, temperate roots

(sugarbeet like), sunflowers, rapeseed, pulses. The Leff

et al. (2004) crop distribution is used to determine the

grid cell fraction cover of each of these types within the

cropland cover database provided for CarboEurope

(Vetter et al., 2008) for the window [151W–601E;

30–751N]. Remaining crops are put within the

temperate cereals type. For each type and each grid

cell, the model determines from the climatic conditions

the most appropriate variety. The sowing date, the heat

unit requirements, and the base temperature, differ for

Latit

ude

LongitudeNPP

(a) (b)

(c) (d)

(e) (f)

Harvest/NPP

Latit

ude

Latit

ude

Latit

ude

Latit

ude

Latit

ude

LongitudeHarvested C Inputs C

Longitude

Longitude

NBP NBP/NPP

Longitude

Longitude

l

Fig. 3 Distribution of cropland component carbon fluxes from the ORCHIDEE-STICS crop model, after Gervois et al. (2008). The model

is forced with variable CO2, climate and changing farmers practice during the past century. Fluxes are given as area weighted average

values for winter wheat and maize over each grid cell, over the decade 1990–1999. All grid-cells containing less than 20% of croplands

have been masked in white. (a) Net primary production, (b) ratio of yield (harvest exported from ecosystem) to NPP, (c) Yield, (d) input

of C to the soil, defined as NPP – Yield, with burning or harvest of agricultural residues being ignored, (e) Net biome production defined

as the mean annual soil C storage change over 1990–1999, (f) ratio of NBP to NPP.



each crop over the European window. For example,

spring wheat is grown in higher latitudes, while winter

wheat is grown over central and southern Europe.

Several management factors impact the crop cycle

and its productivity. The current version of the model

deals with four of these: fertilization, irrigation, fate of

residues and intercropping. Agricultural productivity

relies to a large extent on the level of fertilizer input. We

used crop-specific data on usage of industrial fertilizer

at the country-level (IFA, 2002): the period 1990–1999

was a period of transition for the eastern countries of

the current EU-25 and far less inputs were used

compared to western Europe. On the other hand, the

use of animal manure was probably more common in

Eastern Europe, but is not considered within the model.

The model may therefore underestimate the crop

productivity of the eastern countries of EU-25. In

order to allow comparison with the results of other

models within the CarboEurope project, irrigation has

been deactivated in the version of LPJmL used here,

which reduces the crop productivity of southern

Mediterranean Europe. We have also considered a

removal of 90% of the crop residues, which are either

burned or used (e.g. for animals), but in all cases the

carbon is returned to the atmosphere during the same

year. Finally, there is no intercropping in the version

used here (the fields remain bare between two crop

cycles).

Using the management options previously

described, LPJmL was run on a 0.251 resolution grid

for the EU-25 window. The regional climate model

(REMO; Jacob & Podzun, 1997), driven with NCEP

reanalysis (Kalnay et al., 1996) at the boundaries of the

European model domain (Feser et al., 2001) provides

meteorological data for the period 1948–2005. The data

are available at http://www.bgc-jena.mpg.de/bgc-

systems/projects/ce_i/index.shtml. In order to build

the soil carbon pool, a 1000-year spin-up of LPJmL

was run using the meteorological data of 1948–1958,

and a transient run for the period 1958–2005 was

performed. In this model run, only the atmospheric

conditions changed during that period; the land use

and the management options remained constant. In

reality, both land use and management have changed,

most likely in a way that leads to increase soil carbon

sequestration (e.g. higher crop productivity).

Contribution of land cover changes to NBP. In order to

assess the contribution of past land use change on NBP

over EU-25, the ORCHIDEE-STICS was used. We first

integrated the model until C pools reached equilibrium,

based on average climate reconstructed data during the

period of 1901–1910 (Mitchell & Jones, 2005), the

atmospheric CO2 concentration, and land cover of

1860. Based on this starting equilibrium state, a

simulation from 1901 to 2000 was carried out, with

prescribed land cover maps combining Ramankutty &

Foley (1999) and Mather et al. (1998). Changing land

cover is prescribed to the model at a time step of 10

years, and the dynamic evolution of C pools and NPP

consecutive to a transition is calculated by ORCHIDEE,

until a new equilibrium is reached. The main trend of

EU-25 land cover change is an abandonment of

croplands, causing C sequestration in regrowing

forests and grasslands. Grid points with disappearing

croplands over the last 20 years are not considered as

cropland NBP however, but rather accounted for as

forest (Luyssaert et al., this thematic issue, Part 3) or

grassland NBP (Ciais et al., this thematic issue, Part 1).

Remote sensing. We derived remote-sensing cropland

NPP estimates over EU-25 from EOS-Terra-MODIS

MODIS-2007 version 5 data product, which use a LUE

approach (Zhao et al., 2005) on a 1 km2 cell basis for the

years 2000–2006. Mean annual NPP per cell was

calculated by using the MODIS-2007 Version 5 mean

NPP with the MODIS land cover cropland area estimate

based on its UMD classification scheme (mod12q1

v004). These values were aggregated to country level

by overlaying the UN country map (FAO, 2007).

An alternative remote-sensing NPP estimate was

derived from the CASA model (Potter et al., 1993)

forced by the AVHRR FAPAR products (Tucker et al.,

2005), and using a different LUE formulation form that

used by MODIS (2007) to calculate NPP. In CASA, the

nonlimited LUE is set to 0.5 mol C/mol FAPAR, and it is

the same for crop and natural vegetation.

N2O

UNFCCC statistics. UNFCCC statistics report national

N2O emissions from agricultural soils by different

sectors. According to the agricultural sector considered,

N2O emitted by cropland and grassland soils were split

as follows. The categories synthetic fertilizer-related

emissions (4.D.1.1 in UNFCCC nomenclature), N2O

emissions falling into the animal manure category

(4.D.1.2) and nitrogen fixation emissions (4.D.1.3) were

split according to rules for region- and crop type-specific

nitrogen demand on cropland with the remainder being

allocated to grassland. This procedure follows state-of-

the-art approaches by Freibauer (2003) and CAPRI-

Dynaspat.

The resulting allocation factors for N input to

cropland vary between 480% in Hungary, Finland and

Sweden to o50% of the N input in Austria, Germany

and the Netherlands. N2O emissions from histosols

(4.D.1.5) were attributed to croplands according to the



http://www.bgc-jena.mpg.de/bgc-systems/projects/ce_i/index.shtml











emissions from cropland and grassland by country in

Drosler et al. (2008). In average, 43% of the total N2O

emissions from agricultural histosols in EU-25 were

assigned to croplands, and the rest attributed to

grasslands.

N2O emissions falling in the other direct and indirect

emissions categories of the UNFCCC were all attributed

to croplands, except for pasture emissions (4.D.2) and

nitrogen fixation emissions (4.D.1.3) that were all

attributed to grasslands. UNFCCC statistics for the

years 1990 until 2000 have been used.

Fuzzy logic model. A fuzzy logic model developed by

R. Dechow (unpublished results) was used to calcu-

late direct N2O emissions. It is a sequence of ‘IF-THEN’

rules that aims to estimate N2O emissions based on a

combination of input factors. Training of the model

finds the most suitable combination of information

about soil properties (texture, organic carbon organic

nitrogen), climatic conditions and management options

(amount of mean applied N, type of applied fertilizer)

in order to match direct annual N2O emissions known

at the site level. Cross-validation was performed by

excluding a subset of sites from the calibration dataset

(R2 5 0.45). Training and cross-validation data come

from 30 sites with 163 variables that were extracted

from the N2O emissions database described in Stehfest

& Bouwman (2006).

Factors used for up-scaling to the EU-25 level were

the amount of applied N, sand content, pH, crop type

(cereals, roots and vegetables, fallow, other), mean

autumn precipitation and winter temperature of the

precedent year. Nitrogen addition via fertilizer in

1990–1999 was extrapolated from CAPRI-Dynaspat

data for the year 2000 (Leip et al., 2008) and country

budgets from the EUROSTAT database.

Seasonal precipitation and temperature were

derived from simulations with the REMO model

(Vetter et al., 2007). Local distribution of cropland

areas for the year 2000 originates from a two-step

regression approach (Leip et al., 2008) taking into

consideration environmental factors (climate, soil

properties, land cover, etc.), statistical data of the

CAPRI database with information at NUTS 2 Level

and the Land Use/Cover Area Frame Statistical

Survey (LUCAS; European Commission; Kempen

et al., 2007). This data was extrapolated to the time

period 1990–1999 using statistics from FAO.

N2O emissions from histosols were calculated using

national emission inventories from Drosler et al. (2008).

Other direct and indirect sources came from UNFCCC

statistics. N2O flux estimate provided is the mean value

over the 1990s decade.

Results and discussion

Net primary production

NPP from yield statistics. The mean NPP over the EU-25

croplands obtained from the FAO yield statistics and

Goudriaan factors is 646 g C m�2 yr�1, and the mean

total NPP from the FAO statistics and Haberl factors is

846 g C m�2 yr�1, using a cropland area of 1.03� 106 km2

to remain consistent with FAO (Table 1). Using the

EUROSTAT statistics and Goudriaan factors, with the

CORINE cropland area (1.06� 106 km2; see ‘Materials

and methods’), the corresponding NPP is 586 g

C m�2 yr�1. The range of cropland NPP estimates at the

scale of EU-25, caused by uncertain area (different

definitions of croplands in FAO vs. this synthesis),

hence appears very small.

NPP from process-oriented models. The NPP distribution

obtained from ORCHIDEE-STICS is provided in Fig. 3a.

NPP is uniformly high over western and central Europe

(approximately 1000 g C m�2 yr�1), but lower NPP

values in Southern Europe (600–700 g C m�2 yr�1). The

mean annual NPPO�STICS extrapolated over EU-25

using Eqn (3) is 585 g C m�2 yr�1, amounting to 43% of

the modeled GPP (Table 4). The NPP value calculated

over EU-25 is much lower than the NPP that was

originally calculated over Western Europe. This

reflects the East-West gradient in FAO statistical data

on crop yields applied in Eqn (3). By means of

sensitivity tests of ORCHIDEE-STICS where the model

parameters are varied (Smith et al., 2008a, b) the

uncertainty of NPPO�STICS was estimated to be on the

order of 15%. Therefore, NPPO�STICS is consistent

within the uncertainty of NPP estimates derived from

yield statistics (Table 4). For the period 1990–1999, an

average cropland NPP of 482 g C m�2 yr�1 is obtained

from LPJml (using a cropland area of 1.18� 106 km2).

This value is certainly a low estimate; a run with

activated irrigation and intercropping would likely

result in a higher value.

NPP from remote sensing models. Cropland NPP from

CASA is 494 g C m�2 yr�1. Note that in CASA the

nonlimited LUE value was not increased for crops

compared with natural vegetation. The mean annual

cropland NPP from MODIS-2007 (Zhao et al., 2005) is

419 g C m�2 yr�1. Both remote sensing-based estimates

are lower than the estimates from yield statistics and

NPPO�STICS. The low MODIS 2007 values are likely due

to a low bias in the LUE parameter (Reichstein, 2006).

Optimizing the MODIS GPP against site-level eddy

covariance observations (Tomelleri et al., 2008)

provides a GPP estimate of 879 g C m�2 yr�1,



translating into a slightly higher NPP of

510 g C m�2 yr�1 with a GPP to NPP ratio of 0.58

(Zhao et al., 2005).

Crops may have a higher LUE than grasses and

forests (e.g. Turner et al., 2003), but their growing

season is also generally shorter, so that the mean

annual crop NPP is not necessarily larger than that of

grasslands, especially for pastures where N is efficiently

recycled. Therefore, the application of a NPP/GPP ratio

of 0.52 (Moureaux et al., 2008) to the upper part of the

estimate range (� 600 g C m�2 yr�1) leads to a GPP of

1150 g C m�2 yr�1 or 1.2 Pg C yr�1 for Europe which is

consistent with the independent estimate for whole

watersheds based on the carbon-water coupling

exploited by Beer et al. (2007).

Controls of NPP. The long term trend of NPP of

croplands is driven by technological changes (490%)

rather than by climate and atmospheric CO2

concentrations (o10%) (Bondeau et al., 2007; Gervois

et al., 2008). However, the interannual variability of

cropland NPP is determined by fluctuating climate

conditions. For example, during the summer 2003

drought and heat wave, yields of summer crops

dropped dramatically, in particular for maize in

France and Italy (Ciais et al., 2005). The magnitude of

such climatically induced crashes in productivity are

also a function of the adaptability of practices (e.g.

irrigation), and specific phenology of each crop. For

instance, Smith et al. (2009) analyzed the impacts on

crop yields for the years 1976 and 2003, and concluded

that winter wheat already had a significant fraction of

dead leaves during the 2003 heatwaves (early July and

early August), and hence its yield was less dramatically

affected than that of summer crops.

Sources of uncertainty. Comparison of cropland NPP for

the EU-25 given by the different approaches allows an

analysis to be made of the largest sources of

discrepancy. A main cause of discrepancies when

assessing NPP from yield statistics are the allocation

factors used to extrapolate aboveground and

belowground NPP components. For instance, varying

allometric factors from the global values used in

Goudriaan et al. (2001) to the crop/region specific

values of Haberl et al. (2007) leads to discrepancy in

mean NPP of 30% (Haberl vs. Goudriaan) (Table 4). This

shows that the definitions of NPP and total NPP are

very important: the NPP that is lost through herbivory

and diseases or the NPP of weeds is partly accounted

for in the Haberl factors, but not in Goudriaan, which

explains the inferred NPP differences. Concerning the

extrapolation of aboveground NPP from yield using HI

information, in his review of literature data, Hay (1995)

Table 4 Cropland component fluxes (values in g C m�2 yr�1) over EU-25 from different methods

Method GPP NPPbiomass NPPtotal Rh1 Fertilizer E H F D

Trade

(net) NBP

Inventories

FAO and Goudriaan 646 275 6.8

FAO and Haberl 846

EUROSTAT and Goudriaan 586 239

Spatially explicit erosion model

(sink)

4

Soil losses to rivers 9.6

Ecological sites 26

Regional soil C inventories (over

33% of EU25 area)

�17 � 33

Remote sensing

EOS-Terra-MODIS (optimized

against eddy sites)

879 510

CASA 494 3.3 � 1

Crop models applied over Europe

ORCHIDEE-STICS 1360 585 � 85 15 � 15

LPJml 482 1.3

Roth C �7.6

Cropland component fluxes were updated with observation and model-based methods. Except the losses to rivers, all flux

components have been estimated with the latest data and model versions. The uncertainties show the SD. NBPcorr is the carbon

sequestration in soil, corrected for F, D and E losses using Eqns (5) and (6). Values in bold were used in Fig. 1.



reported an observed spatial variability in HI of

selected crop species on the order of 35%, with an

additional long-term trend (1 0.4% yr�1) for species

like wheat, and no significant trend for maize. Such

variability of HI and also of belowground NPP

allocation depends on climate and practice, as

illustrated by Fig. 2d with the ORCHIDEE-STICS

results. As a test, applying the modeled spatially

variable maps of yield/NPP ratio to the FAO yield

statistics produces a 30% higher mean NPP for wheat

than using the fixed Goudriaan factors. From these

various combinations, as seen in Table 4, we conclude

that there is a 30% uncertainty on EU-25 cropland NPP

due to uncertain allocation parameters.

The second source of discrepancy in cropland NPP

estimates lies in the estimates of cropland area.

Fortunately, the cropland area definition (LUCAS) we

used in this synthesis is very close to the one of FAO,

but uncertainties in area must not be ignored. Because

of definitional differences (crop rotation, mosaic of

cultivated and natural ecosystems) cropland area

differs, for instance, by 30% (0.3� 106 km2) between

the CORINE database and FAO (CORINE being

higher), which translates into an equivalent difference

in NPP, when using a similar NPP per unit area.

The models used in this study (i.e. ORCHIDEE-

STICS and LPJmL) do not use HI to derive NPP. For

ORCHIDEE-STICS, the uncertainty in yield predictions

from the models is given by the uncertainty in yield and

biomass which has been reported at 15% (Brisson et al.,

2002). LPJmL uses allocation factors (Bondeau et al.,

2007), hence, uncertainties are in the same order of

magnitude as the NPP to yield conversions (i.e. 30%)

For the LUE-approaches (i.e. CASA and MODIS-2007)

we estimated an uncertainty of around 22% based on

the two estimates presented in this paper (‘Net primary

production’). Because the modeled uncertainties in

yield are lower than the uncertainties in the NPP

estimates derived from yield statistics, we recommend

to directly compare modeled with observed yield,

rather than to convert yield data to NPP.

In summary, the largest sources of uncertainty when

estimating NPP from yield inventory statistics are, in

decreasing order of importance: (i) the NPP definition

[35% bias between total NPP and Eqn (1) NPP], (ii)

allometry (30% random error), (iii) cropland area (up to

30% bias between CORINE and FAO areas) and (iv)

input yield data themselves (14% bias between

EUROSTAT and FAO yields). Given these sources of

bias and errors, one must be very careful when cross

validating models with yield statistics (Bondeau et al.,

2007; Gervois et al., 2008). In this respect, we

recommend to strictly use the same area, and to

compare modeled with observed yield in addition to

comparing modeled NPP to NPP derived from yield

data.

Cropland ecosystem carbon losses

Harvest. Over the period 1990–1999, the mean crop

harvest, or yield, (H) over the EU-25 territory was 275

and 239 g C m�2 yr�1, for the FAO and the EUROSTAT

yield datasets, respectively [using the same DM and CC

factors as in Eqn (2)]. From these data, we infer a mean

ratio H/NPP 5 0.4. Figure 3c provides a map of the

spatial distribution of H in ORCHIDEE-STICS, as an

area weighted average of the three modeled crop

varieties in each 10 km cell. Figure 3b provides the

corresponding map of H/NPP ratios. The regional

patterns of H in Fig. 3c result from variable climate

and soils, from variable fractional coverage of each

variety, and from differing amounts of maize

irrigation and of fertilizer application (impacting C

allocation). Although the spatial distribution of H is

linearly related to that of NPP in Fig. 2, there is a

residual variability on the order of 15% in the ratio

H/NPP, which reflects allometry variations and should

be accounted for in refining future data-oriented

estimates of H from NPP.

Heterotrophic respiration. Cropland heterotrophic

respiration (Rh) stems from decomposition of soil

organic matter in croplands (Rh1) and decomposition

of crop products ingested by humans and livestock,

including ingestion of crop products imported from

outside the EU-25 (Rh2). The trade balance respiration

is estimated below. The annual storage of harvest (H)

being a negligible fraction of the total flux, we have

Rh2 5 H. The flux Rh1 is calculated as a residual by

Rh1 5 NPP�Rh2�T 1 E�D�F�NBP. Rh1 is estimated at

299 g C m�2 yr�1, with harvest from FAO statistics and

mean NBPcorr from the ORCHIDEE-STICS, LPJml and

RothC models (see ‘Materials and methods’). This gives

a fraction of 65% of the total NPP being available for soil

respiration, similar to the soil chamber measurements

derived estimate of 60% determined by Moureaux et al.

(2008) over a Belgian winter wheat field.

Because NBP is such a small fraction of NPP in

cultivated lands, the total Rh 5 Rh1 1 Rh2 amounts to

98% of NPP at EU-25 level, but Rh can also exceed NPP

locally in regions behaving as C sources. The

heterotrophic respiration component Rh1 respired by

soils is twofold smaller than Rh2 respired by humans

and animals after ingestion of crop products (Fig. 2).

The carbon sequestration efficiency (CE) of croplands,

defined as the ratio NBP/NPP, ranges from �0.03 to

0.01. This CE range is low compared to the values of

other ecosystems. In grasslands CE equals 0.13, and in



forests CE is 0.2; (Ciais et al. and Luyssaert et al., this

thematic issue). The smaller CE values in croplands

reflect a smaller return of carbon to the soil, coupled

with an accelerated decomposition of soil organic

matter due to plowing (e.g. destruction of soil micro

aggregates and oxygenation). At face value, improved

cropland management can greatly increase cropland

soil C sequestration (Smith et al., 2008a, b).

Human and animal digestion of H corresponds to

most of the heterotrophic respiration component Rh2.

This flux has a nonuniform pattern across the EU-25,

with higher values in cities and regions of intensive

farming, where domestic animals ingest crop feedstock

in addition to local forage (Ciais et al., 2007). Locally, Rh2

can then be a significant source of CO2 to the

atmosphere, reaching up to 100 g C m�2 yr�1 over large

cities (see Fig. 4 in Ciais et al., 2007). When working at

EU-25 scale, we need to correct the Rh2 component for

the digestion and respiration of crop products

associated with trade.

C balance of crop product trade. Across the EU-25

boundaries, the trade, consumption, and respiration of

imported and exported crop products causes a net CO2

flux T, to the atmosphere. The import of food products

slightly exceeds the export, leaving a small source of

CO2 to the atmosphere T 5 6.8 g C m�2 yr�1 over the EU-

25 (Ciais et al., 2008). Note that, although small, the

magnitude of this net source of CO2 due to imported

food/feed is comparable to the NBP. This flux should be

accounted for when comparing the carbon budget from

atmospheric observations against the carbon budget

from upscaled field-observations.

Fires. EU-25 agricultural fire emissions F, amount to

3.3 � 1 g C m�2 yr�1. Most fire emissions now occur in

Eastern European member states, where harvest

residues are more frequently burnt in the field. In EU-

25, the deliberate burning of agricultural residues was

officially banned in 1993 (except for specific crops).

Because agricultural fires are human-prescribed, their

reported inter-annual variation appears quite large,

similar to the one of forest fires for instance, which

themselves are expected to be more sensitive to climatic

variability. Further examination of annual F estimates

reveals that the random interannual variability appears

small, but there is a decreasing trend in F from an earlier

emission of 3.9 Tg C yr�1 in 1991 down to 2.9 Tg C yr�1

in 1993 (start of the residue burning ban) and a further

decrease thereafter (Giglio et al., 2006). From our

emissions dataset, we infer a negative trend in F of

�0.46 Tg C yr�1 (R2 5 0.95) after 2000, indicating

compliance to the fire regulations by the new Eastern

European EU-25 member states who joined the EU at

that time (Fig. 4).

Export of C from cropland soils to rivers. The export of

carbon of atmospheric origin from cropland soils into

rivers (D) is 9.6 g C m�2 yr�1. The corresponding carbon

flux in rivers is 12 Tg C yr�1 over the EU-25 based upon

the CORINE area and includes atmospheric-derived

DIC by mineral alteration and weathering processes,

and export of DOC and POC. To our knowledge, there is

no estimate of how much of the total flux D results from

recently added C (agriculture and domestic waste) and

from older C (erosion of soil organic matter pools, and

from natural ecosystems at steady state). Typically 30%

of D could come from old pools, against 70% from more

recently added C pools, but the anthropogenic fraction

of this latter flux is not known (Ciais et al., 2008).

Export of C from cropland soils by erosion. For the EU-25,

the ‘gross’ C flux of cropland soils displaced by erosion,

estimated from the high-resolution maps of Van Oost

et al. (2007), amounts to 45 g C m�2 yr�1. Van Oost et al.

(2007) indicate that most of this gross flux removed

from arable soils gets re-deposited elsewhere, yielding a

net erosion sink (E) of 4 g C m�2 yr�1. At EU-25 scale, the

erosion must be used to correct NBP derived from soil

carbon inventories. Otherwise, all the soil C change

measured by inventories will be considered as a flux

exchanged with the atmosphere, whereas part of this C

flux is translocated and reburied by erosion processes

(Fig. 1).

Net biome production

Observed NBP from site studies and national and regional

soil C inventories. Eddy covariance observations of the

0

1

2

3

4

5

6

1996 1998 2000 2002 2004 2006 2008

Em

issi

ons

(t g

C y

r–1)

Fig. 4 Annual EU-25 agricultural fire emissions deduced from

MODIS burned area data incorporated in the CASA ecosystem

model by Van der Werf et al. (2006) (GFEDv2). The date after

which fire emissions started to decrease corresponds to a ban on

burning harvest residues implemented in new Eastern European

member states.



net CO2 exchange (NEE) of croplands are becoming

avaiable and support a net CO2 uptake of �34 to

�193 g C m�2 yr�1 for different crops over several

years (Anthoni et al., 2004; Aubinet et al., 2009).

However, exports need to be deducted from the NEE

to obtain NBP. The mean NBP from five European

cropland sites including 20 site years of eddy

covariance measurements was 88 � 56 g C m�2 yr�1.

However, these flux data suffer from lacking

representativity and high year-to-year variability due

to climate and management variability (W.L. Kutsch,

unpublished results), hence, intensive site studies do

not have sufficient coverage to derive an upscaled

estimate of cropland NBP at the EU-25 scale.

A more robust estimation of cropland NBP can be

derived from repeated soil carbon inventories assuming

no net change in biomass stocks. A compilation of NBP

data from long term study sites and national and

regional gridded inventory measurements including

repeated soil C stock measurements in the EU-25 is

given in Table 3. Worth noting is that only two studies

out of ten reported the NBP of croplands to be a net

sink. The positive NBP at a study of field sites in North

Germany (Nieder & Richter, 2000) may be due to

improved management by decreasing the plowing

depth. Gridded inventories have a better spatial

representativity than sites to quantify NBP, but there

is no harmonized soil C change inventory for the EU-25.

Inventories were conducted with different sampling

depth and time periods of repeated sampling.

However, they cover 33% of European croplands with

long-term integration on different crop rotations and

interannual climate variability. Reported soil C stock

changes were between �91 and 19 g C m�2 yr�1.

Highest C losses reported from the Finish inventory

(Makela-Kurtto & Sippola, 2002) may be due to the

cultivation of organic soils (peatlands) which are

especially susceptible to disturbances by tillage and

drainage. Changes in management practice such as

manure application was found to decrease soil C

stocks in Belgium (Sleutel et al., 2007). A detailed

repeated gridded soil inventory over England and

Wales at 5 km resolution (Bellamy et al., 2005) shows

practically no change in cropland soil carbon

concentrations during 1978–2003, in contrast with

larger C losses from other land use types. However,

this inventory lacks data on bulk densities to derive

reliable soil C stocks. Smith et al. (2007) suggested that

only a small percentage of the reported C loss in

England and Wales could be attributed to climate

change. In France, a recent study combining inventory

data (0–30 cm) and 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 16 g C m�2 yr�1 (Arrouays et al.,

2002; Antoni & Arrouays, 2007). The median NBP

value of area-weighted national and regional soil C

inventories as summarized in Table 3, provides a net

soil carbon loss of 17 � 33 g C m�2 yr�1 (range �91

to 1 19 g C m�2 yr�1).

NBP from models. The three process-oriented models

ORCHIDEE-STICS, RothC, and LPJml were used to

derive NBP estimates. The ORCHIDEE-STICS model,

initialized with ancestral farming practice and crop

varieties in 1900 and integrated over the past century,

provides an NBP of 15 � 15 g C m�2 yr�1 over the

period 1990–1999 (small sink). The spatial distribution

of NBP (Fig. 3e) shows common patterns with the

distribution of NPP, with regional sinks in Central and

Eastern Europe and regional sources in the Iberian

Peninsula, the latter reflecting warming and drying

trends. Most of the NBP is explained in this model by

changes in agricultural technology, with a small

contribution of recent climate change over Southern

Europe (Gervois et al., 2008). RothC provides an NBP

of �7.6 g C m�2 yr�1 over 1990–1999 (small source). For

the same period, LPJml estimates NBP a nonsignificant

sink of 1.3 g C m�2 yr�1. Also here, the result is extremely

dependent on assumptions about the management

options employed and how these have changed over

recent decades. For example, reduced tillage, increased

irrigation, and increased fertilizer use, could increase soil

carbon sequestration (toward a sink), but soil erosion

(not accounted for within the model) would increase the

source. In summary, the three process models integrated

over Europe predict mean NBP of different sign, but

agree on the relatively small absolute magnitude of NBP.

All models sensitivity tests point to the strong sensitivity

of NBP to the assumed choice and past history of

management options.

In both ORCHIDEE-STICS and RothC, the simulated

soil C dynamics included the effect of past changes in

agricultural technology on input and tillage, as well as

the effects of climate and atmospheric CO2 changes. The

spatial pattern of NBP shows a similar pattern to NPP

and to NPP changes (Fig. 3). This pattern can be

explained by technology changes (490%) rather than

by climate and CO2 (o10%) (Gervois et al., 2004). The

only regions where climate change seems to have

impacted agricultural soil C storage in comparable

magnitude with technology, via an increase in Rh1

and a precipitation-induced reduction in soil C inputs,

are the Iberian Peninsula and some southern

Mediterranean regions.

Past and future trends in NBP. In the ORCHIDEE-STICS

simulation during the 20th Century, most of the C



initially contained in the slow and passive soil C pools

(mean residence times of 1000–100 years) was quickly

lost between 1950 and 1980 through increased tillage,

but a reversal in NBP occurred in the 1980s. Active C

pools increased since the 1980s in response to increasing

crop NPP, but at a slower rate than the initial loss,

because it takes a long time to replenish the slow C

pools. This is why ORCHIDEE-STICS provides a net

soil C loss during 1950–2000, but a net C gain (NBP40)

during the sub-period 1980–2000. For the future, Smith

et al. (2005a) reported that projected climate change to

2080 might have limited impact on cropland NBP due

to the balancing effects of increased losses due to faster

decomposition and increased inputs due to choice of

crops/harvestable fraction and improved technology.

They attributed uncertainties in future projections

mainly to differences in projected climate (2000–2080)

by four GCMs forced by the four IPCC SRES emission

scenarios, and differing assumptions about the

implementation of technology. Smith et al. (2005a) also

suggested that croplands could become a net carbon

sink under improved technology, but the uncertainty

(due to climate projections and interactions between

decomposition, NPP and technology) was also large,

with a 9% uncertainty due to forecast changes in

technology alone.

Comparison with earlier estimates. Regional inventories

and two out of the three models indicate that croplands

are a net source of CO2 to the atmosphere, but this

source is five times smaller than the large positive flux

(90 � 50 g C m�2 yr�1) given by Janssens et al. (2003,

2005), based upon output from the CEASR model of

Vleeshouwers & Verhagen (2002). ORCHIDEE-STICS,

RothC and LPJml have more detailed soil carbon

decomposition parameterization than CESAR and use

longer initialization phases, reducing the risk of model

drift or transient behavior at the start of the study

period. Most likely, the use of only one soil carbon

pool in CESAR overestimated the soil carbon losses as

reported by Janssens et al. (2003). Moreover, comparison

of carbon sequestration potential in Europe with other

studies (Smith et al., 2005b), suggests that CESAR

produces larger responses than other methods/models.

Agriculture in Europe has changed greatly over the

past two decades with yields increasing (Ewert et al.,

2005) but the carbon returns to the soil are reported not

keeping pace with the yield increase, due to an

opposing trend in harvest index (Hay, 1995). Changes

in agricultural practice, with a shift from organic to

mineral fertilizers, and within the organic fertilizers,

from dry lot manure to liquid slurries due to changes in

animal housing (Sleutel et al., 2003) also decreased the

carbon inputs to cropland soils. These factors act to

drive NBP into a C source to the atmosphere. In the

opposite direction, increasing the area of set-a-side, and

more extensive rotations during the 1990s, have begun

to address this longer term loss of soil C such that

during the 1990s, cropland soils appear to be

approximately in balance (Smith et al., 2005a), in

agreement with our results, and certainly less of a

source than suggested in Janssens et al. (2003).

Contribution of land use change to NBP. We found with the

ORCHIDEE-STICS model prescribed with changes in

land use since 1901 that the abandonment of arable land

Fig. 5 N2O emissions by cropland soils for the EU-25 countries.



over the EU-25 during the past 50 years has caused a

significant net sink of 7.7 g C m�2 yr�1 (sink given per

unit area of land that was formerly under cultivation in

1901). However, there is large uncertainty in this

estimate because the historical land use changes are

poorly quantified in terms of both spatial and temporal

resolution (Jain & Yang, 2005; Houghton, 2007). This

NBP component was not counted as part of cropland

NBP, as it concerns lands that are no longer arable. The

C balance of these forest plantations and grasslands

established over arable fields is counted in the numbers

of Part 1 and Part 3 of this thematic issue.

Bridging the gap between inventory and model NBP

estimates. The inventory and the modeling approach

do not estimate the same components of NBP, because

of differences between the system’s boundaries and

different time periods covered. NBP is therefore by

definition, not consistent between inventoried soil C

changes and process-model calculations. Inventories

only accounts for field-scale soil C changes, implicitly

discounted for the river flux D. However, the erosion

flux E represents C re-deposited and reburied into

deeper horizons or other land use types (Bellamy

et al., 2005).

The DOC and POC flux from croplands, D, is

estimated from river catchment data (Ciais et al.,

2008). The net C flux from erosion, E, is estimated by

Caesium-137 profiles as resulting from the balance of

three processes: (1) removal of old SOC and its

replacement by fresh plant input, (2) burial in deep

soil horizons and (3) enhanced SOC decomposition

during lateral transport. The sum of these three

processes is a net sink, thus implying a net decrease

in decomposition rates of SOC during its transport by

erosion processes. Owing to their respective

methodologies, D does not include E, because E

measures carbon that is removed from cropland soils

but redeposited on land, and D measures carbon removed

from cropland soils but transported to estuaries.

Owing to erosion, a net sink of 4 g C m�2 yr�1 (i.e.

erosion translocates C from fast soil pools to longer

mean residence time pools in sediments where it is not

decomposed; see ‘Cropland ecosystem carbon losses’),

must be added to the inventoried soil C change to allow

comparison with NBP calculated by process-models.

These models assume a ‘vertical’ 1�D closure of their

C budget in each grid cell, implicitly accounting for

local erosion translocation (E), but ignoring losses to

rivers (D). Moreover, in ORCHIDEE-STICS and RothC,

CO2 losses to the atmosphere due to the burning of

straw and stubble residues, F, are not modeled. This

causes an overestimate of C inputs to the soil in these

two models, and subsequently of NBP. LPJml assumes

90% of the residues to be exported, and includes the

burning practice (Bondeau et al., 2007). Therefore, D and

F must be removed from the modeled NBP (except for

LPJml) to allow comparison with NBP measured by

inventories.

In order to consistently compare inventories and

model NBP, we corrected in Table 4 the inventory

NBP by adding the net sink E, and the modeled NBP

by subtracting fire emissions (F) and the flux lost to

rivers (D). Only a fraction (n of the ‘avoided input’ to the

soil which is lost by fires would actually be sequestered

to increase NBP. We used n5 0.05 diagnosed from the

ratio of NBP to soil inputs in ORCHIDEE-STICS.

NBPinventory corr ¼ NBPinventory þ E: ð5Þ

NBPmodel corr ¼ NBPmodel � nF�D: ð6Þ

Equations (5) and (6) provide NBPinventory corr 5

�13 � 33 g C m�2 yr�1 and NBPmodel corr 5�8.3 �11g C m�2 yr�1 from the median value and stdandard

deviation of the three models after correction (Table 4).

The new corrected estimates are consistent with each

other, with an 85% probability of being identical within

their errors (based on a t-test).

Other sources of bias in process model NBP are the

lack of information about practice, and model structural

limitations. In ORCHIDEE-STICS for instance, the

largest source of error according to Gervois et al.

(2008) is the parameterization of tillage effects on soil

C decomposition. A future step to further investigate

the consistency between model and inventories NBP

would be to cross validate the model results against

inventories over the same regions of Europe.

N2O emissions

UNFCCC statistics over croplands and the fuzzy logic

model lead to a mean annual N2O emission by EU-25

cropland soils of 0.44 � 0.07 and 0.48 � 0.07 g

N2O m�2 yr�1, respectively. Direct emissions for mineral

soils and histosols contribute with 0.25 and

0.24 gN2O m�2 yr�1 to N2O emissions from croplands.

Based on their IPCC SAR radiative forcing at 100-year

horizon (IPCC, 2007), direct and indirect N2O emissions

therefore equal 37–41 g C Eq m�2 yr�1 and thus cancel

all the NBP.

At the country level, estimates of direct emissions

based on the fuzzy logic approach are higher for those

countries which are exposed to climatic conditions

favoring freeze–thaw cycles in winter and early spring

(Austria, Baltic States, Poland, Slovakia and Czech

Republic). Fuzzy logic emission estimates are in general

lower than the UNFCCC based estimate for regions



which are rarely exposed to freeze–thaw cycles (Bel-

gium, Netherlands, UK and France). Main disagree-

ment between estimates is observed for Greece with

fertilization-based direct emission coefficients of 1% vs.

0.4% for the fuzzy logic model and the UNFCCC

statistics, respectively. Despite major methodological

differences between the UNFCCC statistics and the

fuzzy logic approach, the mean annual N2O emission

by EU-25 cropland soils of the two approaches matches

very well.

Figure 6 shows lower emissions over eastern EU-25

countries. Compared with high-density areas of plot-

sized measured annual N2O budgets at croplands

(Central and West Europe) those regions are often

characterized by climatic conditions that favor high

emission rates. If these eastern countries continue to

intensify their agriculture in the near future, and soon

apply the same amounts of N-fertilizer as those cur-

rently applied in western countries, we expect a near

doubling of N2O emissions. N2O emissions will then

become the main source of concern in assessing the

impact of agriculture on climate.

Methane

Most agricultural crops in Europe are on aerobic soils,

so methane sources are very low, occurring only from

anaerobic microsites especially after manure applica-

tion. Methanogenesis is restricted to completely anae-

robic soil conditions as occur in rice paddies when the

soils are flooded. These CH4 emissions were estimated

at 1.3 Tg C Eq yr�1 over the EU-25 (Freibauer, 2003).

Indeed cropland soils are known to be a sink for

methane, and provide overall net oxidation of methane,

though the sink capacity of cropland soils is consider-

ably lower than that of aerobic grassland or forest soils

(Willison et al., 2005). Strength of the methane sink was

estimate at 0.05 g CH4 m�2 yr�1 or 4.9 Tg C Eq yr�1 for

the EU-25. At the European scale the net exchange of

methane i.e. 3.3 g C Eq m�2 yr�1 is small compared to

the net CO2 flux from croplands.

Conclusions and recommendations

We have shown in this paper that NBP of three spatially

explicit models and 10 sites or regional inventories

studies agree on the magnitude of the cropland NBP.

Cropland NBP in EU-25 is a small source or sink, and

this is a major revision to the earlier estimated strong

source suggested by Janssens et al. (2003). The major

driver of NBP is likely to be current and past manage-

ment. NBP follows NPP, but the NBP–NPP relationship

is modulated by agricultural management practice im-

pacting the litter input (fate of residues) and the decom-

position of soil organic matter (tillage).

Cropland NPP estimated by models, or inferred from

yield statistics, was found to be in the range

451–595 g C m�2 y�1 (excluding NPPresidual). Compared

with the NPP values compiled for forests and grass-

lands NPP of crops is on average similar to that of

forests, but lower than that of grasslands.

The carbon sequestration efficiency (CE 5 NBP/NPP)

of croplands is very small: CE 5�0.03 to 0.03 compared

with grasslands (CE 5 0.13) and forests (CE 5 0.2). This

is supported by analyses of long-term measurements of

NBP or SOC, which show conversion of either forest or

grassland to croplands results in very significant losses

of SOC (Guo & Gifford, 2002). However, because a

different fraction of NPP is harvested in each of the

three ecosystems, the ratio of NBP to the C input to the

soil (I 5 NPP�H) is more appropriate to compare than

the ratio NBP/NPP. This ratio equals 0.23 for forests,

0.29 for grasslands, and from 6 to 25� 10�4 for crop-

lands. This indicates that per unit of input, the seques-

tration in cropland soils is much less efficient than in

other biomes, possibly as a result of soil tillage.

Latit

ude

Longitude

(a) (b)

Statistic-based emission factor Fuzzy-based emission factor

Latit

ude

Longitude

Fig. 6 N2O emissions per ha of croplands for the EU-25 countries.



We recommend that C cycle models continue to be

improved for describing crops, including spatialized

information on the intensity of tillage, N fertilizer

applications, past land use history. In that context, it

will be particularly useful to test the models against the

newly available eddy covariance flux measurements on

crop fields obtained in CARBOEUROPE-IP. Treating

nitrogen-carbon interactions and nitrogen cycle in mod-

els would also be an advantage, especially for N2O flux

modeling. Moreover, independent Rh1 measurements

such as presented in Moureaux et al. (2008) are needed

to check/improve models, and soil inventories with

independent NBP estimates from component fluxes.

We also recommend that for evaluating models describ-

ing cropland C cycling in a spatially explicit way,

regional yield data should be used. We have shown

that errors in such a comparison will be minimized only

if the same crop area is considered, and if the modeled

yield is directly compared with the data (rather than

using an ‘observational operator’ to transform yields

into NPP by fixed allometric coefficients.

Although being close to carbon neutral today, the sign

of NBP at EU-25 is sensitive to small changes in

management practices, e.g. erosion control, ban on

residue burning, changed tillage practice. For instance

set-aside has been abolished in Europe last year, and in

the most recent years, nonfood production (biofuels)

was even subsidized, which may increase C losses. Our

estimated N2O emissions ranges between 21 and

39 g C Eq m�2 yr�1, which nearly doubles the CO2

losses. Considering CO2, N2O and CH4 fluxes provides

for the net GHG balance a net source of

42–47 g C Eq m�2 yr�1. Intensifying agriculture in East-

ern Europe to the same level Western Europe is ex-

pected to result in a near doubling of the N2O emissions

in Eastern Europe. N2O emissions will then become the

major source of concern for the impact of European

agriculture on climate.

It should be noted that our analysis was limited to the

ecosystem fluxes of CO2, N2O and CH4, hence, the CO2-

emissions from fossil fuel dependency of cropping

practices and the production of fertilizers, pesticides

and tools are not included in the presented GHG

balance. Adding these emissions to the ecosystem

fluxes would result in a GHG-based life-cycle analysis

of cropping in Europe. However, spatially representa-

tive farm-based emission factors are not available yet.

Acknowledgements

We thank all site investigators, their funding agencies and thevarious regional flux networks and the CARBOEUROPE-IPproject, whose support is essential for obtaining the measure-ments without which the type of integrated analyses conducted

in this study would not be possible. Koen Hufkens prepared Fig. 1,artwork courtesy of the Integration and Application Network(http://ian.umces.edu/symbols/), University of MarylandCenter for Environmental Science. PS is a Royal Society-WolfsonResearch Merit Award Holder.

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