Agriculture, Ecosystems and Environment 139 (2010) 336–345
Contents lists available at ScienceDirect
Agriculture, Ecosystems and Environment
journal homepage: www.elsevier.com/locate/agee
The net biome production of full crop rotations in Europe
W.L. Kutsch a,∗ , M. Aubinet b , N. Buchmann c , P. Smith d , B. Osborne e , W. Eugster c , M. Wattenbach d ,
M. Schrumpf f , E.D. Schulze f , E. Tomelleri f , E. Ceschia g , C. Bernhofer h , P. Béziat g , A. Carrara i ,
P. Di Tommasi j , T. Grünwald h , M. Jones k , V. Magliulo j , O. Marloie l , C. Moureaux b , A. Olioso l ,
M.J. Sanz i , M. Saunders e , H. Søgaard m , W. Ziegler f
a
Johann Heinrich von Thünen-Institut, Institute for Agricultural Climate Research, Braunschweig, Germany
Unit of Biosystem Physics, Faculté Universitaire des Sciences Agronomiques, Gembloux, Belgium
c
ETH Zurich, Institute of Plant, Animal and Agroecosystem Sciences, Dept. of Agricultural and Food Sciences, Zurich, Switzerland
d
Institute of Biological and Environmental Sciences, School of Biological Sciences, University of Aberdeen, Cruickshank Building, St Machar Drive, Aberdeen, AB24 3UU Scotland, UK
e
School of Biology and Environmental Science, University College Dublin, Belfield, Dublin 4, Ireland
f
Max-Planck-Institute for Biogeochemistry, Jena, Germany
g
CESBIO, UMR 5126 – CNES-CNRS-UPS-IRD, 18 Avenue Edouard Belin, 31401 Toulouse cedex 9, France
h
Technische Universität Dresden, Department of Meteorology, Tharandt, Germany
i
Fundación CEAM, c/Charles Darwin 14, Parque Tecnológico, 46980 Paterna, Spain
j
CNR Institute for Agricultural and Forest Systems, Via Patacca 85, 80056 Ercolano, Napoli, Italy
k
School of Natural Sciences, Department of Botany, Trinity College Dublin, Dublin 2, Ireland
l
INRA, UMR 1114 INRA-UAPV, Domaine Saint Paul, Site Agroparc, F-84914 Avignon, France
m
Department of Geography and Geology, University of Kopenhagen, Oster Voldgade 10, DK-1350 Copenhagen, Denmark
b
a r t i c l e
i n f o
Article history:
Received 20 July 2009
Received in revised form 22 July 2010
Accepted 27 July 2010
Available online 24 August 2010
Keywords:
Carbon budget
Eddy covariance
Harvest
Organic fertilizer
Good practice guidelines
Agriculture
a b s t r a c t
Data sets of biometeorological measurements of ecosystem CO2 flux, combined with harvest and manure
data from several European cropland were integrated to provide an assessment of the carbon budget.
Sites encompassed different climatic regions and contrasting crop rotations. The influence of different
crops and management practices was also assessed to identify some of the major factors contributing
to the cropland carbon balance. Since crops are rotated and cropping periods do not always follow the
calendar year, net ecosystem production (NEP) as well as net biome production (NBP) sums of full crop
rotations or of at least 4 years of longer-term crop rotations and of monocultures were used. In a second
analysis NBP sums were correlated to soil properties. Finally, the data were combined with additional
data to derive a mean annual GHG balance for the European cropland sites under consideration.
Five crop rotations and two monocultures were integrated over 4 years. During 4 years the average
annual NEP was −240 ± 113 g C m−2 y−1 . On average, 382 ± 117 g C m−2 y−1 were harvested, where as
average carbon inputs by manure and seeding was 47 ± 51 g C m−2 y−1 . The average NBP of the seven
sites under consideration was estimated to be a carbon loss of 95 ± 87 g C m−2 y−1 . The full GHG balance
of the considered sites was estimated to be 160 g C m−2 y−1 in CO2 -equivalents.
These results challenge current good practice guidelines that predict neutral carbon budgets for systems
where the inputs of manure and crop residues are of comparable magnitude to those associated with
the sites examined in this study. Ongoing humus loss in spite of good practice is mainly related to soils
with high carbon concentrations which are not in equilibrium but may also be a result of already ongoing
climate change. A modification in the good practice guidelines to increase carbon inputs may be required.
Results from a representativeness analysis suggest that more than 50 sites are necessary for a European
cropland flux network to adequately represent the variability of climate, soil and management within
the European continent. Thus, the uncertainties due to the network design are currently bigger than the
uncertainty intrinsic in the measurement method.
© 2010 Elsevier B.V. All rights reserved.
1. Introduction
∗ Corresponding author at: Max-Planck-Institute for Biogeochemistry, HansKnöll-Str. 10, 07745 Jena, Germany.
E-mail address: [email protected] (W.L. Kutsch).
0167-8809/$ – see front matter © 2010 Elsevier B.V. All rights reserved.
doi:10.1016/j.agee.2010.07.016
The specific properties of agricultural practice require an
adopted scientific approach for the estimation of carbon or full
greenhouse gas balances of individual cropland ecosystems as well
W.L. Kutsch et al. / Agriculture, Ecosystems and Environment 139 (2010) 336–345
337
as the generalization of any findings from these efforts. Due to differences in crops, cropping systems and agricultural practices the
adoption of site-specific approaches for estimating the full greenhouse gas balance may often be required (Osborne et al., 2010). Not
only does this limit any generalisations, but it also confounds any
interpretation of the causes for differences in greenhouse gas emissions and carbon sequestration. Two differences between croplands
and forests or other less managed ecosystems are important:
we derived NBP sums of full crop rotations or of at least 4 years of
longer-term crop rotations and of monocultures to derive estimates
that are representative of the particular agricultural practice as
well as encompassing inter-annual variability. In a second step we
attempted to analyse the calculated NBP sums, and in the third and
final steps the data were combined with the results from Ceschia
et al. (2010) to derive a mean annual GHG balance for the European
cropland sites under consideration.
(1) In croplands, lateral fluxes, associated with harvesting and the
application of organic fertilizer/manure can severely influence
the ecosystem carbon budget. However, this transfer of organic
carbon across ecosystem boundaries is not quantified by the
eddy covariance method (Baldocchi et al., 1988; Aubinet et al.,
2000) that has become the standard approach to quantify CO2
fluxes at any given site (Smith et al., 2010). Thus, additional
efforts to measure lateral carbon fluxes are necessary to quantify net biome production (NBP) from net ecosystem exchange
(NEE) as measured using eddy covariance techniques.
(2) Crop rotations are important elements of agricultural management in many regions of Europe. Since cropping and fallow
periods are irregular and seldom follow the calendar year and
since different crops and related agricultural practices result in
different carbon inputs into the soils, a realistic carbon budget for agricultural ecosystems under crop rotation can only be
derived when at least a full rotation is considered.
2.1.1. Net ecosystem production (NEP) measurements
NEP was derived from eddy covariance measurements at all
sites. The application of this technique in agriculture is described in
detail by Smith et al. (2010). The turbulent and storage CO2 fluxes
were calculated for 30 min intervals using standard CarboEurope
software (Mauder et al., 2008) and, thereafter, underwent standard
data processing within the CarboEurope database that included
quality checks, u*-filtering and gap-filling (Reichstein et al., 2005;
Papale et al., 2006; Moffat et al., 2007). The resulting, so-called
‘Level 4 ecosystem flux data’ comprised a full data set of half-hourly
fluxes and these were integrated over the full 4-year-period to
derive NEP. The Level 4 data processing also generates derived variables allowing for flux partitioning into gross primary production
(GPP) and ecosystem respiration (Reco ), which were also integrated
for the full crop rotations.
Crop rotations are often considered an important way to
enhance carbon sequestration and increase the sustainability of
agro-ecosystems, although this will depend on the type and complexity of the rotation used (West and Post, 2002). The inclusion of
a legume in the rotation is more likely to result in a build up of soil
organic carbon, whilst reducing the dependence on nutrient inputs.
In contrast, large carbon losses are likely with root crops, such as
sugar beet or where almost all of the crop is removed at harvest,
as is the case for maize grown for silage (Osborne et al., 2010). This
again makes generalisations difficult and argues for a more comprehensive assessment of the impact of different rotations on net
biome productivity.
For the first time, comprehensive data sets of ecosystem CO2
flux measurements from several EU cropland sites encompassing
different climatic regions and contrasting crop rotations were made
available by the CarboEurope-IP cropland project, allowing a comprehensive synthesis of the results. The current study complements
that of Eugster et al. (2010) who focused on the influence of single events and management practices on respiration, Moors et al.
(2010) who quantified gross and net primary production and harvest losses for single cropping periods and Ceschia et al. (2010)
who derived full annual greenhouse gas balances, in order to provide an assessment of the greenhouse gas budgets of contrasting
cropland sites in Europe over that are associated with full crop
rotations.
2. Material and methods
2.1. General approach
Data obtained from sites belonging to the CarboEurope-IP network were used to derive net biome production (NBP) based on
eddy covariance measurements of net ecosystem exchange (NEE)
supplemented with ancillary data on the main lateral carbon fluxes,
specifically, harvest and manure. Since crops are rotated and cropping periods do not always follow the calendar year, the usual
January to December annual flux estimate cannot be used to derive
representative annual estimates of NBP and NEP. For this reason,
2.1.2. Net biome production (NBP)
Net biome production (NBP) was calculated as the integral over
the 4 years of the relevant carbon fluxes for the croplands under
consideration (see also Buchmann and Schulze, 1999; Chapin et al.,
2006). Here, we used a simplified approach (Eq. (1)) that ignored
the ‘minor’ terms of the fully integrated NBP (see Smith et al., 2010;
Osborne et al., 2010):
NBP = NEP − F − H + I
(1)
where F is carbon lost by fires, H is carbon removed by harvest, I is
inputs by organic fertilizer such as manure or slurry.
Carbon fluxes to the ground water as dissolved or particulate organic, or inorganic carbon, volatile organic compounds,
methane fluxes and carbon lost by soil erosion as detailed elsewhere (Osborne et al., 2010) were not considered. Since harvested
material was removed from the site and most of the trans-located
C was respired as CO2 over short time periods, the NBP reflects the
long-term load of CO2 to the atmosphere.
2.1.3. Full green house gas balances
Emissions that result from field operations were finally added
to the NBP to calculate the full greenhouse gas (GHG) balance over
crop rotations. We used the average emissions for fuel, fertilizer
and machinery (32 g C m−2 y−1 in CO2 -equivalents) and additional
N2 O emissions due to agricultural practices (33 g C m−2 y−1 in CO2 equivalents) calculated by Ceschia et al. (2010).
2.1.4. Output/input ratios at field and farm level
Output/input ratios (O/I) at field level are a measure of the efficiency of agricultural practice. The full GHG balance is divided by
the export of carbon with harvested products. If the ratio is 1 the full
amount of harvest is already balanced by emissions and no net gain
is in the field products. A value of 2 means an efficiency of 50%, or
that half of the exported carbon is already balanced by emissions
and only the other half can be accounted for as net relief for the
atmosphere. This value is particularly important for the evaluation
of biofuel.
The farm gate balance is another O/I ratio that simply relates the
energy or carbon equivalents that enter and leave the farm via the
farm gate (e.g. Kutsch et al., 2008a). It is easy to derive but ignores
humus losses and N2 O emissions. Therefore, it has to be seen more
338
W.L. Kutsch et al. / Agriculture, Ecosystems and Environment 139 (2010) 336–345
Table 1
Average annual values of gross primary productivity (GPP), ecosystem respiration (Reco ), net ecosystem production (NEP), harvest, organic inputs and net biome production
(NBP) of the sites under consideration.
Site
Avignon
Klingenberg
Gebesee
Lonzée
Oensingen
Carlow
Risbyholm
Borgo Cioffib
Suecab
Averagec
a
b
c
GPP (g C m−2 y−1 )
Reco (g C m−2 y−1 )
NEP (g C m−2 y−1 )
Harvest (g C m−2 y−1 )
Organic inputsa (g C m−2 y−1 )
NBP (g C m−2 y−1 )
−1176
−1228
−1184
−1490
−1576
−807
−1262
−1624
−1268
1019
1081
897
1029
1387
644
989
1280
616
−157
−147
−287
−461
−189
−163
−273
−344
−652
272
511
465
483
261
246
437
815
357
−5
−113
−122
−23
−54
−5
−5
−86
−5
110
251
56
−1
18
78
159
385
−300
−1246 ± 248
1006 ± 222
−240 ± 113
382 ± 117
−47 ± 51
95 ± 87
Including organic manure and imported carbon by seeds.
These sites were not included in the averaging.
Average ± standard deviation between sites.
as a measure of economic efficiency of the farm than a measure of
climate change mitigation in agriculture.
2.2. Sites
The CarboEurope cropland network comprised 17 sites where
eddy covariance measurements were conducted. Out of these, there
were six sites with a crop rotation and three sites with monocultures that provided enough data (at least 4 years) to be used for
this study. Since many aspects of the sites are described in detail
in other contributions to this special issue, maps by Eugster et al.
(2010) and Moors et al. (2010), descriptions of the crop rotations by
Ceschia et al. (2010) and Moors et al. (2010), only a brief description
of the sites, that will focus on management and soil properties, is
presented here. A list of all the sites is available in Osborne et al.
(2010). Soil properties of the sites are summarized in Table 1.
2.2.1. Full crop rotation sites and sites with long-term crop
rotations
2.2.1.1. Avignon, France. The Avignon ‘crane’ site (‘Flux and Remote
Sensing Observation Site’) at the INRA Research Center near Avignon, France has crops that are typical for the region, such as durum
wheat, corn, sunflower, soybean and sorghum, grown on a silty clay
loam soil. Irrigation is performed when required using an irrigation ramp, ensuring a complete and even coverage of the field in
8 h. The region is characterized by a typical Mediterranean climate
with mean annual temperature of 14 ◦ C and mean annual precipitation of 683 mm. The soil is a Calcaric Fluvisol (FAO) with average
carbon concentration of 13.3 g kg−1 and a C/N-ratio of 9.6 in the
main footprint area.
2.2.1.2. Gebesee, Germany. The station in Gebesee is located in the
Thuringian Plain in Germany. The area is in the lee of a low mountain range and receives only 500–550 mm of annual precipitation
with a mean annual temperature of 7.9 ◦ C (Anthoni et al., 2004).
The soil is classified as Chernozem (FAO) with a silty clay loam
texture (∼30% clay) of granular structure. Total soil organic carbon concentration was 21 ± 1 g C kg−1 in 2001 in the 0–40 cm soil
layer (Anthoni et al., 2004) and total carbon stock down to 60 cm
depth was 131.2 ± 14.2 t C ha−1 (Schrumpf, pers. comm.). The site
was intensively managed until the 1990s and then subsequently
modified to re-establish soil organic matter that was lost during
the period of intensive cultivation. Deep ploughing was omitted or
reduced in some years. In addition, organic carbon was increased
by additions of manure and increasing retention of plant residues.
2.2.1.3. Klingenberg, Germany. The Klingenberg Agricultural Station is located in Saxonia, Germany. The climate is subo-
ceanic/subcontinental with mean annual precipitation of 850 mm
and a mean annual temperature of 7.3 ◦ C. The soil is a Gleysoil (FAO)
which has been drained for agricultural purposes; the organic soil
horizon (Ap) was identified as a medium clayey loam with a mean
thickness of 0.2 m. Within the B horizon, mainly slightly sandy clay
(Ts2) and clayey sandy loam (Lts) were observed. The mean values of C concentration in horizons Ap and B are 42.6 g C kg−1 and
2.9 g C kg−1 , respectively (Schrumpf, pers. comm.). The total soil
carbon stock up to 60 cm depth is 97 t C ha−1 and the mean C/Nratio is 13 (Schrumpf, pers. comm.). In the period between harvest
and sowing of the new crop, the site is left mainly as fallow covered
by re-growing weeds or volunteers seedlings left over after harvest.
Mineral fertilizer and herbicides were applied several times a year.
Organic fertilizer was applied in August 2004 and October 2006.
2.2.1.4. Lonzée, Belgium. The study site at Lonzée is about 45 km SE
of Brussels. The climate is temperate maritime with a mean annual
temperature of ∼10 ◦ C, with an annual precipitation of ∼800 mm.
The soil is a Luvisol (FAO classification) composed of 18–22% clay,
5–10% sand and 68–77% silt. Manure has not been applied since
1996. The depth of tillage was 30 cm. The soil carbon concentration
in the Ap horizon is 10.8 g kg−1 . Total soil carbon stock up to 60 cm
depth is 63.2 t C ha−1 (Schrumpf, pers.comm.). The land has been
cultivated for more than 70 years.
2.2.1.5. Oensingen, Switzerland. The Swiss cropland site at Oensingen is managed by a regional farmer. The documented crop rotation
since 1994 (see Dietiker et al., 2010) shows a typical 4-year cycle
with either rape or potatoes in one of the years, and barley or
wheat in the three other years, which typically leads to full crop
rotation periods of 8–12 years. The current crop rotation period
was started in 2000 (for 1997–1999 temporary grassland was
included in the crop rotation). The management follows the regulations of the Swiss Integrated Pest Management system (IP-Suisse,
http://www.ipsuisse.ch). The climate is temperate humid with a
mean annual precipitation of 1100 mm and an annual mean temperature of 8.1 ◦ C. The soil is a Eutri-Stagnic Cambisol (FAO) that
has developed on clayey alluvial deposits down to a depth of 1.6 m
(Alaoui and Goetz, 2008). The soil contains 28 g C kg−1 organic matter in the top soil layer (0–0.25 m depth) and a pH of 5.5. The soil
texture is a silty clay with 43.0% clay, 47.5% silt and 9.5% sand in
the top layer (Alaoui and Goetz, 2008). The total soil carbon stock
is 128 t C ha−1 (0–70 cm depth). Due to a hail event potatoes were
not harvested in 2006.
2.2.2. Monoculture sites with 4 years of measurements
2.2.2.1. Carlow, Ireland. The Oak Park cropland site near Carlow,
Ireland is part of a long-term conventional cropping system with a
W.L. Kutsch et al. / Agriculture, Ecosystems and Environment 139 (2010) 336–345
339
Fig. 1. Fingerprints of net ecosystem production (NEP) from nine European cropland sites. Red and orange colours mean net carbon dioxide release from the ecosystem to
the atmosphere, while yellow, green and blue means net uptake of CO2 by the ecosystem. The graph (as the subsequent graphs) follows meteorological conventions with
negative numbers meaning net uptake of CO2 by the ecosystem and positive numbers net release of CO2 . ‘R’ means re-growth of the crop from lost seeds during harvest.
340
W.L. Kutsch et al. / Agriculture, Ecosystems and Environment 139 (2010) 336–345
The parent rock of the soil (sandy–clay type) is carbonate,
but most of the material has an alluvial origin, derived from the
nearby Sele River. Average particle-size distribution is 43:36:21
(clay:sand:silt) but highly variable, so that the texture varies from
clay to sandy clay. The soil contains 15 ± 5 g C kg−1 in the 0–40 cm
soil layer and is classified as a Calcic Kastanozem Skeletic (WRB,
2006). The water table depth may be as shallow as 1 m during the
wintertime while it is about 5–6 m during the summer. Since the
influence of these high fluctuations in water table on soil CO2 efflux
was unclear, the site was not considered in the average NBP values.
Fig. 2. Cumulative net ecosystem production (NEP, above) and net ecosystem carbon budget (NBP, below) from eight European cropland sites that were measured
during the observation period between 2004 and 2007.
continuous crop of Spring Barley (Hordeum vulgare) cropping. Climate is maritime with a mean annual temperature of 9.4 ◦ C and
an annual precipitation of 823 mm. The soil is a Eutric Cambisol.
Average carbon concentration in the top soil is 22.8 g kg−1 .
2.2.2.2. Risbyholm, Denmark. The Risbyholm experimental site is
located in an agricultural area in central Zealand, Denmark. The
eddy correlation equipment is located in a drained bog with Histosol soil type. The organic C concentration of the top soil is
35.1 g C kg−1 while the total carbon content is 38.8 g C kg−1 . The
field is part of a slightly undulating landscape covered by glacial
deposits (Weichsel). The monthly mean temperature varies from
−0.1 ◦ C in January to 16.3 ◦ C in July. The annual precipitation is
580 mm with an annual potential evapotranspiration of 490 mm.
The field has been cultivated for more than 20 years. For the ordinary grain crops the soil is ploughed down to a depth of 25 cm,
harrowing and sowing is carried out in one step.
2.2.2.3. Borgo Cioffi, Italy. This site is located in a 18 ha agricultural field in the middle of Piana del Sele, Italy, a river plain
that comprises the largest flatland area of the Campania region.
The alluvial plain in Piana del Sele was re-claimed for agriculture after the Second World War. The mean annual temperature
is 15.5 ◦ C with an annual rainfall of 900 mm, mostly concentrated
from September through April, with a summer dry spell typical of
a southern mediterranean climate. Alfalfa was continuously grown
from 1998 to 2003, then a maize monoculture combined with fennel (2003/2004, 2005/2006, 2006/2007) or rye-grass (2004/2005,
2007/2008) as winter crops. Irrigation was performed during the
summer by means of a centre pivot system and manure–slurry
applications were done regularly during the alfalfa monoculture
and occasionally during the maize monoculture.
2.2.2.4. El Saler-Sueca, Spain. The flux site “El Saler-Sueca” is
located inside the protected wetland area “La Albufera Natural
Park” within a large rice paddy field area (ca. 15,000 ha). The climate is Mediterranean sub-arid with dry and warm summers, with
no or low excess of water in winter. The mean annual temperature
and precipitation in the area are 17.9 ◦ C and 550 mm, respectively.
The soil is a Gleysol. Soil depth is at least 2 m. The top soil layer
(0–30 cm) is loamy, contains 37.6 g C kg−1 .
The site history indicates continuous paddy rice cultivation as a
monoculture for 150–200 years and representative of the paddy
rice fields of this area. The whole “Albufera” paddy rice area is
very homogeneous in terms of timing (crop period, flooding periods) and management. Management is determined by rice farming
practices that did not change significantly for about 200 years,
apart from mechanisation and use of fertilizers, pesticides and herbicides. Fields are drained from March to April, flooded in early
May for sowing, and drained again in August for rice harvest in
early-mid September. Fields are flooded for the winter period
(November–February) in late October after partial burning of straw
residues (the burning practice did not take place at the flux-tower
paddy field in 2006 and in 2007). Since the emissions by burning
as well as expected methane emissions were not fully accounted
for, only an incomplete balance of this site can be presented and,
therefore, was not considered in the average NBP values.
3. Results
Those sites which were measured during the period between
2004 and 2007 were used to derive full 4-year budgets of net
ecosystem production (NEP) and net biome production (NBP). The
patterns of NEP observed (Fig. 1) show that the seasonal variations in croplands are much more irregular and fluctuate more
than those of many natural ecosystems with continuous vegetation cover (not shown). Only the monocultures (Risbyholm, Carlow,
Borgo Cioffi and Sueca) show a more regular pattern. The fluxes are
highly regulated by management and resulting differences in crop
growth and development. At some sites (e.g. Lonzée and Gebesee
between summer 2005 and spring 2006 or Avignon between summer 2006 and spring 2007) long periods of net carbon losses occur
during fallow periods. Regular cover-cropping to improve the carbon budget of the sites examined in this study (see Osborne et al.,
2010) is practised only at few sites (Oensingen and Borgo Cioffi).
At some sites the re-growth of the crop from seeds left over after
harvest together with weed establishment resulted in short periods with net carbon uptake during the daytime (marked with ‘R’ in
Fig. 1).
The different crop species have specific ‘fingerprints’: winter
cereals show small uptake during late autumn and winter followed
by high daytime uptake during spring and early summer. Spring
cereals, maize, potatoes and peas show small periods of intense
uptake during late spring and summer. Sugar beet crop has an
extended growing season with a remarkable uptake until harvest
in autumn.
W.L. Kutsch et al. / Agriculture, Ecosystems and Environment 139 (2010) 336–345
341
Fig. 3. (a) Total ecosystem respiration as obtained from the Level 4 data (Reco ) and net carbon yield (Ynet ) calculated as carbon removed from the site by harvest minus carbon
brought to the site by organic fertilizers plotted against gross primary production (GPP) for five crop rotation and two monoculture sites. Each data point symbolizes the sum
of the 4 years observation period between 2004 and 2007. (b) Net biome production (NBP) related to soil organic carbon concentration in the top soil of each site. Each data
point symbolizes the sum of the 4 years observation period between 2004 and 2007.
The data for the five crop rotations and the four monocultures were integrated over 4 years (Fig. 2, Table 1). However, the
five crop rotations and two of the monocultures were used to
derive averages, while the sites El Saler and Borgo Cioffi were
excluded due to the reasons explained in the site descriptions.
During 4 years the average NEP of the considered sites was
−960 ± 452 g C m−2 (Fig. 2a). This results in average annual NEP of
−240 ± 113 g C m−2 y−1 .
The NBP was calculated from the NEP and the lateral fluxes from
harvest and organic fertilizer. On average, 1528 ± 468 g C m−2 were
harvested. The respective average annual losses associated with
harvest were 382 ± 117 g C m−2 y−1 . Thus, harvest losses exceeded
NEP by more than 60%. Manure, applied at Gebesee, Lonzée, Oensingen, Klingenberg summed up to 167 ± 204 g C m−2 (mean annual
inputs over all sites: 42 ± 51 g C m−2 y−1 + 5 g C m−2 y−1 for seeds).
Avignon, Carlow and Risbyholm did not receive organic inputs apart
from an average input of 5 g C m−2 y−1 due to seeds as calculated
by Ceschia et al. (2010). Thus, the average carbon inputs were
47 ± 51 g C m−2 y−1 .
On average, including all seven sites under consideration, there
was a loss of 380 ± 348 g C m−2 during the 4 observation years
(mean annual NBP: 95 ± 87 g C m−2 y−1 ). The highest losses were
found at Klingenberg (overall NBP: 1004 g C m−2 , mean annual
NBP: 251 g C m−2 y−1 ) and Risbyholm (overall NBP: 636 g C m−2 ,
mean annual NBP: 159 g C m−2 y−1 ), followed by Avignon (overall NBP: 440 g C m−2 , mean annual NBP: 110 g C m−2 y−1 ), Carlow
(overall NBP: 312 g C m−2 , mean annual NBP: 78 g C m−2 y−1 ), Gebesee (overall NBP: 224 g C m−2 , mean annual NBP: 56 g C m−2 y−1 ),
and Oensingen (overall NBP: 72 g C m−2 , mean annual NBP:
18 g C m−2 y−1 ). Lonzée (overall NBP: −4 g C m−2 , mean annual NBP:
−1 g C m−2 y−1 ) was neutral, while only the Sueca site was an apparent sink with −1195 g C m−2 in 4 years, which results in an average
annual NBP of 298 g C m−2 y−1 (not considered in the average).
The biggest source, Borgo Cioffi (overall NBP: 1669 g C m−2 , mean
annual NBP: 417 g C m−2 y−1 ), was also not considered in the average.
Another way to examine the impact of the different European
crop rotations on ecosystem carbon dynamics is to correlate ecosys-
Fig. 4. Schematic representation of the various components of the mean annual carbon balance of the croplands under consideration. The data were compiled as mean
annual value from 4 years of measurements at each site. Two sites of the original ensemble (Borgo Cioffi and Sueca) were excluded from this average. Fluxes are depicted
in g C m−2 y−1 or g C-equiv. m−2 s−1 when other greenhouse gases are involved. Uncertainties are derived as standard deviation between sites from this study. Additional
information from other studies is shown without uncertainties.
342
W.L. Kutsch et al. / Agriculture, Ecosystems and Environment 139 (2010) 336–345
Table 2
Representativeness of the dataset in terms of crops. Number of site years of crop species are compared to their proportion in the EU 27 cropping area and production.
Crop
Site years all
n
Wheat
Barley
Rye
Maize
Rice
Sugar Beet
Oil Seed Rape
Potatoes
Other
10
7
0
5
4
2
2
2
4
Sum
36
Site years in average*
%
27.8
19.4
0.0
13.9
11.1
5.6
5.6
5.6
11.1
100
n
10
7
0
1
0
2
2
2
4
28
%
41.7
29.2
0.0
4.2
0
8.3
8.3
8.3
12.5
100
EU 27 area(1)
EU 27 Production(2)
% in 2007
%
27.9
15.3
3.0
9.2
0.5
2.1
7.5
2.5
27.5
100
25.6
11.4
1.3
11.3
0.6
22.5
3.2
11.4
12.8
100
Sources: (1) FAO stat (http://faostat.fao.org/default.aspx); (2) eurostat (http://epp.eurostat.ec.europa.eu/portal/page/portal/publications/eurostat yearbook).
tem respiration (Reco ) derived from the eddy data and net yield
(harvested biomass – organic fertilizer brought to the field) with
gross primary production (GPP). The slopes of the correlation lines
were 0.806 (Reco ) and 0.302 (net yield), respectively, which means
that on average the losses were about 110% of the carbon gained
by photosynthesis (Fig. 3a) and the sites are net emitters.
Carbon losses from the ecosystem (positive NBP) were correlated with the carbon concentrations in the topsoils (Fig. 3b). Borgo
Cioffi and Sueca are shown in Fig. 3b but were not considered in
the correlation. Oensingen deviated from the correlation mainly
because the potatoes grown in 2006 were not harvested after a
hail event. Avignon had relatively high losses despite low carbon
concentration in the topsoil.
The average annual carbon and GHG balance of seven European
crop sites (Fig. 4) was also determined using data presented by
Ceschia et al. (this issue) and Moors et al. (2010). The full GHG balance sites was estimated to be 160 g C m−2 y−1 in CO2 -equivalents.
Related to the exported carbon, this means an O/I ratio of 2.4 equivalent to ∼42% of the harvested carbon being ‘lost’ by GHG emissions
related to its agricultural production.
For estimating the average farm gate balance the farms were
considered as pure cash crop entities with an internal cycling of
42 g C m−2 y−1 taken from the harvest as manure. Under this considerations the O/I ratio was 9.2, a value that is very close to that
found by Kutsch et al. (2008a) for farms producing only cash crops
in Northern Germany.
4. Discussion
The results of this analysis of eddy covariance-based assessments of NEP and NBP over several years of crop rotations or of
monocultures indicate carbon losses from all of these agricultural
sites, most of which have been converted to croplands many
decades ago. On average, these sites have received organic inputs
as organic fertilizers or crop residues have been retained, of a
similar order of magnitude to those recommended through good
practice guidelines for farmers in several countries (Defra, 2000;
VDLUFA, 2004; Fachstelle Bodenschutz, 2007; FRCA et al., 2008;
DeProft and Bodson, 2009). However, at sites with high carbon
concentrations in the topsoil, in particular, those inputs seem not
to be sufficient for a maintaining a balanced agriculture.
If the results reported here are representative for all EU croplands, this result may be surprising, since cropland soils in Europe
would be expected to be close to equilibrium with respect to
carbon, as they have been managed as croplands for many years
and good practice has largely been applied. Recent modelling
studies have suggested that European cropland soils are close
to equilibrium, being either a small source (Smith et al., 2005;
Bondeau et al., 2007) or a small sink (Gervois et al., 2008; Schulze
et al., 2009, 2010; Ciais et al., 2010). However, before we can
make any comparison with pan-European modelling studies or
assert that European croplands are losing carbon, the uncertainty
associated with the estimates reported here, and whether they
are generally representative of European croplands, needs to be
examined (see also Osborne et al., 2010).
4.1. Measurement uncertainty – are the estimated carbon losses
different from zero?
Uncertainties in NEP measurements by means of eddy covariance arise from a number of systematic as well as random errors
that have been discussed extensively (Hollinger and Richardson,
2005; Richardson and Hollinger, 2005; Loescher et al., 2006;
Richardson et al., 2006; Van Gorsel et al., 2007; Aubinet, 2008;
Finnigan, 2008; Lasslop et al., 2008). Random errors associated with
eddy covariance measurements are not significant at an annual
scale as the annual sums are based on more than 15,000 half-hourly
flux estimates. In contrast, systematic and selective systematic
errors are significant. The highest uncertainties are likely associated with night time fluxes (Goulden et al., 1996; Aubinet et al.,
2000) due to low turbulence and advection effects. However, if
night time flux underestimation is especially important in tall forest
ecosystems or in complex terrain affected by advection (Aubinet,
2008; Kutsch et al., 2008b) it is expected to be less critical in cropland systems, as they have much lower and more homogeneous
canopies and are generally located in flat terrain. Earlier studies
on crops estimated the night time flux error to be of the order
of some tens (0–50) of g C m−2 y−1 (Falge et al., 2001; Moureaux
et al., 2006, 2008), reaching 100 C m−2 y−1 in two cases (Falge et al.,
2001; Anthoni et al., 2004). This systematic bias may however be
removed by applying the so-called u* correction (Aubinet et al.,
2000; Gu et al., 2005). However, this correction is based on several approximations so that a residual uncertainty remains after its
application. The main causes of uncertainty are due to the determination of the filtering threshold or to the choice of the gap-filling
algorithms. Few studies quantify this uncertainty at crop sites.
Moureaux et al. (2008) estimated the former at 10 g C m−2 y−1 at
Lonzée and Falge et al. (2001) found the latter to vary among three
sites from 30 to 40 g C m−2 y−1 . In Béziat et al. (2009), NEP uncertainty due to measurement random errors and to long gaps in the
data set were estimated to range between ±18 g C m−2 y−1 for sunflower at Auradé and ±42 g C m−2 y−1 for triticale and maize at
Lamasquère. Another uncertainty arises from the choice of the integration period. With different integration periods Aubinet et al.
(2009) and Prescher et al. (2010) calculated slightly higher NBP
values for Lonzée and Klingenberg, respectively.
We expect higher uncertainties associated with the quantification of the lateral fluxes. At some sites (e.g. Klingenberg and Borgo
Cioffi) removal of harvested products was taken from the farmer’s
W.L. Kutsch et al. / Agriculture, Ecosystems and Environment 139 (2010) 336–345
book keeping, whereas at other sites (e.g. Lonzée and Gebesee)
actual measurements of NPP and harvested material were taken
(Anthoni et al., 2004; Aubinet et al., 2009; Béziat et al., 2009).
Aubinet et al. (2009) showed that the uncertainty in NPP estimates
due to biomass sampling amounted to 10–90 g C m−2 , i.e., comparable to those affecting eddy covariance measurements. Béziat et al.
(2009) also found larger uncertainties for NBP than for NEP, which
were mostly related to uncertainties in C removal by harvest and
in C inputs as organic fertilisation. Carbon inputs associated with
organic manures were consistently calculated from the information
provided by farmers. Therefore, it is difficult to quantify the uncertainties with any degree of accuracy. Aubinet et al. (2009) quoted
an overall error of ±140 g C m−2 for 4 years of a crop rotation, which
might be exceeded for some of the sites presented in the current
study. Nevertheless, it is important to note that almost all crop rotation sites in Fig. 2 behave as sources. The question arises whether
this fact, as well as the general trend shown in Fig. 3, is statistically
significant.
Using standard statistics, there is only weak evidence that NBP
differs from zero at the cropland sites used in this study (two-sided
t-test, p = 0.237). If we hypothesize that croplands are a net carbon
source (i.e., NBP >0), then the statistical uncertainty is still p = 0.119
(one-sided t-test). The question, however, remains whether the
croplands used in this analysis can be considered a random but
representative sample of the European ensemble of croplands (see
following section). If we question this assumption made by the ttest and simply use a normal distribution fitted to the available NBP
data from all sites to quantify our statistical uncertainty, then our
hypothesis that European croplands are a net carbon source cannot
be established statistically (p = 0.289).
4.2. How representative are the sites examined?
Even though the sites under consideration represent some of
the most important European crops and cropland areas and cover
many of the typical management practices, it is clear that a few eddy
covariance sites can neither represent the diversity of agricultural
practice nor soil or climatic variability. It has to be considered that
the two biggest sources that were used in the averaging, Klingenberg (drained Gleysol) and Risbyholm (drained Histosol) represent
two soils with a high organic matter content, which might be overrepresented in this study compared to the majority of cropland soils
in Europe. The two sites with lower soil organic matter content,
Gebesee (depleted Chernozem) and Lonzée (Luvisol), which might
be more representative of the larger EU cropland area, showed
much lower carbon losses. However, even at Gebesee the goal of
carbon sequestration or at least a neutral carbon balance was not
achieved in spite of the farmer’s efforts with supplementary organic
manure and increased residue incorporation.
In terms of crop representativeness, a comparison with European crop production (Table 2; Eurostat, 2008) shows that our
dataset is quite representative for winter wheat and maize, but
overestimates rice and barley, while sugar beet and potatoes are
under-represented. Thus, the budgets calculated only for the crop
rotations might be more representative.
Statistically, the representativeness of the cropland sites network can be shown by a very simple approximation of the required
sample size based on the required precision if we assume each year
to be an independent sample and NPP to be normally distributed.
We can use the following equation by Zar (1998) to determine the
number of samples required:
n=
2
s2 · t˛(2),(n−1)
d2
(2)
343
where n represents the sample size; s2 is the sample variance; t
is the two-tailed critical value for Student’s t and d is the halfwidth of the desired confidence interval. If we define the standard
deviation of the sample as the spatial variability of the mean cropland annual carbon budget of Europe we are able to determine it
from remote sensing based approaches like the Terra-MODIS NPP
product (Running et al., 2004; Heinsch et al., 2006; Turner et al.,
2006). The sample standard deviation of the 2000–2004 mean is
95.7 g C m−2 y−1 . A trial and error process with progressively more
accurate estimations can be used to set the t value. In our case a
confidence interval commensurate to the above-described uncertainties of 50 g C m−2 y−1 with a confidence level of 95%, would lead
to a network size of 59 sites. This value is far greater than the actual
network size. This suggests that the cropland flux network might
not adequately represent the variability of climate, soil and management within the European continent and the uncertainties due
to the network design are currently bigger than the uncertainty
intrinsic in the measurement method.
Our results confirm an earlier study of the average cropland
NBP made by Janssens et al. (2003) who estimated a carbon loss
of 90 ± 50 g C m−2 y−1 . However, this work contradicts more recent
studies based on modelling and carbon inventories that resulted in
an almost neutral balance for European agriculture (Gervois et al.,
2008; Ciais et al., 2010).
4.3. Comparison of apparent losses reported here with losses
estimated by other methods
4.3.1. Stocktaking methods
The average humus losses for winter wheat without organic
manure and complete removal of straw should be 28 g C m−2 y−1
according to the German consulting agency (VDLUFA, 2004),
whereas we calculated losses of 178 g C m−2 y−1 as the average for
10 site years in our study. Oil seed rape lost 109 g C m−2 y−1 on
average although the straw was left on one site. Maize apparently
lost on average about 430 g C m−2 y−1 although organic fertilizers were applied. Full crop rotations lost, on average, about
95 ± 87 g C m−2 y−1 , even though the average inputs from manure
(around 47 ± 51 g C m−2 y−1 ) and residues were close to the rates
advised by good practice guidelines.
The large differences between the estimates here and those
given in the good practice guidelines may be partly explained by the
different approaches used. Whereas the VDLUFA (2004) estimates
are based on long-term experiments with repeated stocktaking
of soil organic matter, our results may reflect more the current
weather/climatic conditions that might not be representative of
longer cropping periods. On the other hand, flux measurements
may detect changes earlier and may thus perform as an earlywarning system. While the flux-based approach presented here
has, as discussed, a number of uncertainties, the biggest uncertainty of repeated stocktaking is due to the difficulties of detecting
a small change in a large pool (e.g. Rodeghiero et al., 2009). Nevertheless, also some studies that use the stocktaking approach
support our observation that agricultural soils with higher soil
organic carbon content are not balanced and loose carbon (e.g.
Bellamy et al., 2005; Saby et al., 2008; Stevens and van Wesemael,
2008).
4.3.2. Modelling methods
When comparing the present results with modelled estimates,
uncertainty in the modelled estimates also need to be considered.
Sources of uncertainty reported in Smith et al. (2005) included a
lack of knowledge of how much increased production is offset by
increased decomposition in a warmer climate, and uncertainty over
the contribution of technological changes (management, cultivars,
344
W.L. Kutsch et al. / Agriculture, Ecosystems and Environment 139 (2010) 336–345
etc.) on increasing carbon stocks. Other uncertainties arise from
the steady state assumption: many models use a spin-up run at
the beginning of a model run in order to bring soil carbon pools
to equilibrium. Smith et al. (2005) as well as Gervois et al. (2008)
used a nominal equilibrium date of 1900 and the model was run
forward from then, with results reported in Ciais et al. (2010) for
1990–1999. The 10,000 years model spin-up with an average agricultural practice by Gervois et al. (2008) resulted in an average
equilibrium carbon stock of about 56 t C ha−1 which is lower than
the average stocks observed in this study. It is perhaps not surprising therefore that these studies did not predict any further losses
between 1901 and 2000, since sites with higher carbon stocks that
may be far from equilibrium were ignored as they had a shorter
agricultural history than the assumed ten millenia.
Similarly, different models use different assumptions about
yield/productivity change over the 20th Century, (see Gervois et al.,
2008; Smith et al., 2005) and the range of crops considered by the
models differs greatly with just two crops considered by ORCHIDEE
(Gervois et al., 2008), 12 by LPJmL (Bondeau et al., 2007) and generic
crop inputs only considered by RothC (Smith et al., 2005). Other
differences include the implementation of land use change during
the 20th Century and the legacy effects of such change. Given the
fact that all models used almost the same spin-off procedure and
rather limited additional information on soils, it is not surprising,
that three models used by Schulze et al. (2009, 2010) and Ciais et al.
(2010), arrive at rather similar estimates for the annual carbon flux
from croplands ranging from a small sink of 15 ± 15 g C m−2 y−1
over the period 1990–1999 estimated using ORCHIDEE (Gervois
et al., 2008), through a non-significant sink of 1.7 g C m−2 estimated using LPJmL (Bondeau et al., 2007), to a small source of
−7.6 g C m−2 y−1 estimated using RothC (Smith et al., 2005). The
uncertainties associated with the modeled estimates of European
cropland carbon flux are at least as large as those described in this
study.
5. Conclusions
Despite the uncertainties discussed above, we can conclude
from this integrated data-oriented approach that, on average, the
European croplands under consideration in this study appear to
loose carbon, even though the farmers at some of the sites implemented practices such as humus management with low tillage,
organic fertilizer input, inter-cropping and increased straw residue
return that are considered to enhance carbon sequestration.
Information from the sites with a high soil carbon concentration
suggest that cropping on soils with high carbon contents may result
in hotspots of carbon losses. This fact challenges the equilibrium
presumption that is used in many models. In addition, it shows
that soils with high carbon concentration und agriculture should
be targeted in efforts to reduce cropland emissions, while efforts
on other soils should focus on sequestering carbon by building up
soil organic matter.
We cannot conclude from this study whether the management
of European croplands is, in general, appropriate for humus conservation, or whether humus could still be lost in spite of good
practice as a result of already ongoing climate change. The latter
would require a change in the good practice guidelines to increase
carbon inputs or to further reduce carbon to account for such a
trend.
As far as the uncertainty in the fluxes are concerned, it appears
that two factors will push cropland soil carbon in an unfavourable
direction: climate change will likely result in increased mineralization rates, particularly during warmer winters, and efforts to
mitigate climate change by substitution of fossil fuels with biomass
from agriculture could result in less residues available for carbon
input into agricultural soils.
Acknowledgments
We would like to thank our technical staff and the farmers
for giving us access to their fields and for the time they spent
giving us information concerning management. This work was
made possible through the support of the CARBOEUROPE-IP (GOCECT-2003-505572) European FP7 Program, the French Ministry in
charge of Environment (GICC programme), the Centre National
de la Recherche Scientifique (CNRS), the Institut National des Sciences de l’Univers (INSU), the Région Midi-Pyrénées Council and
the EPA (Ireland) through the CCFLUX project. Foundation CEAM
has been supported by the programs CONSOLIDER-INGENIO 2010
(GRACCIE), BALANGEIS (SUM2006-00030-C02) and CARBORED-ES
(CGL2006-14195-C02-01). Pete Smith is a Royal Society-Wolfson
Research Merit Award holder.
References
Alaoui, A., Goetz, B., 2008. Dye tracer and infiltration experiments to investigate
macropore flow. Geoderma 144, 279–286.
Anthoni, P.M., Freibauer, A., Kolle, O., Schulze, E.D., 2004. Winter wheat carbon
exchange in Thuringia, Germany. Agricultural and Forest Meteorology 121,
55–67.
Aubinet, M., 2008. Eddy covariance CO2 flux measurements in nocturnal conditions:
an analysis of the problem. Ecological Applications 18, 1368–1378.
Aubinet, M., Grelle, A., Ibrom, A., Rannik, Ü., Moncrieff, J., Foken, T., Kowalski, A.,
Martin, P., Berbigier, P., Bernhofer, C., Clement, R., Elbers, J., Granier, A., Grünwald, T., Morgenstern, K., Pilegaard, K., Rebmann, C., Snijders, W., Valentini,
R., Vesala, T., 2000. Estimates of the annual net carbon and water exchange
of forests: the EUROFLUX methodology. Advances in Ecological Research 30,
113–175.
Aubinet, M., Moureaux, C., Bodson, B., Dufranne, D., Heinesch, B., Suleau, M., Vancutsem, F., Vilret, A., 2009. Carbon sequestration by a crop over a 4-year sugar
beet/winter wheat/seed potato/winter wheat rotation cycle. Agricultural and
Forest Meteorology 149, 407–418.
Baldocchi, D.D., Hicks, B.B., Meyers, T.P., 1988. Measuring biosphere-atmosphere
exchanges of biologically related gases with micrometeorological methods.
Ecology 69, 1331–1340.
Bellamy, P.H., Loveland, P.J., Bradley, R.I., Lark, R.M., Kirk, G.J.D., 2005. Carbon losses
from all soils across England and Wales 1978–2003. Nature 437, 245–248.
Bondeau, A., Smith, P.C., Zaehle, S., Schaphoff, S., Lucht, W., Cramer, W., Gerten, D.,
2007. Modelling the role of agriculture for the 20th century global terrestrial
carbon balance. Global Change Biology 13, 679–706.
Buchmann, N., Schulze, E.D., 1999. Net CO2 and H2 O fluxes of terrestrial ecosystems.
Global Biogeochemical Cycles 13, 751–760.
Béziat, P., Ceschia, E., Dedieu, G., 2009. Carbon balance of a three crop succession over
two cropland sites in South West France. Agricultural and Forest Meteorology
149, 1628–1645.
Ceschia, E., Béziat, P., Dejoux, J.F., Aubinet, M., Buchmann, N., Carrara, A., Cellier, P.,
Di Tomasi, P., Elbers, J., Eugster, W., Jacob, C., Jans, W., Jones, M., Kutsch, W., Lanigan, G., Moors, E.C.M., Olioso, A., Osborne, B., Saunders, M., Smith, P., Soegaard,
H., Wattenbach, M., 2010. Management effects on NBP and global warming
potential at European crop sites Agriculture. Ecosystems and Environment.
Chapin, F.S., Woodwell, G.M., Randerson, J.T., Rastetter, E.B., Lovett, G.M., Baldocchi, D.D., Clark, D.A., Harmon, M.E., Schimel, D.S., Valentini, R., Wirth, C., Aber,
J.D., Cole, J.J., Goulden, M.L., Harden, J.W., Heimann, M., Howarth, R.W., Matson,
P.A., McGuire, A.D., Melillo, J.M., Mooney, H.A., Neff, J.C., Houghton, R.A., Pace,
M.L., Ryan, M.G., Running, S.W., Sala, O.E., Schlesinger, W.H., Schulze, E.D., 2006.
Reconciling carbon-cycle concepts, terminology, and methods. Ecosystems 9,
1041–1050.
Ciais, P., Wattenbach, M., Vuichard, N., Smith, P., Piao, S.L., Don, A., Luyssaert, S.,
Janssens, I., Bondeau, A., Dechow, R., Leip, A., Smith, P., Beer, C., van der Werf,
G.R., Gervois, S., Van Oost, K., Tomelleri, E., Freibauer, A., Schulze, E.D., 2010. The
European greenhouse gas balance. Part 2: croplands. Global Change Biology 16,
1409–1428.
Defra, 2000. Fertiliser Recommendations for Agricultural and Horticultural Crops
(RB209), Seventh ed, Available at: http://www.defra.gov.uk/farm/environment/
land-manage/nutrient/fert/rb209/index.htm.
DeProft, M., Bodson, B. (Eds.), 2009. Livre blanc Faculté Universitaire des Sciences
agronomiques de Gembloux. Gembloux, Belgium.
Dietiker, D., Buchmann, N., Eugster, W., 2010. Comparing measured CO2 and H2 O
fluxes under real conditions and yield under changing climatic conditions on
cropland with the DNDC model. Agriculture Ecosystems and Environment, 139,
396–401.
Eugster, W., Moffat, A., Ceschia, E., Aubinet, M., Ammann, C., Davis, P.A., Osborne,
B., Smith, P., Jacobs, C., Moors, E., Jans, W., Le Dantec, V., Béziat, P., Grünwald,
T., Rebmann, C., Kutsch, W., Czerný, R., Janouš, D., Moureaux, C., Dufranne,
D., Carrara, A., Magliulo, V., di Tommasi, P., Olesen, J.E., Schelde, K., Olioso, A.,
Bernhofer, C., Cellier, P., Larmanou, E., Loubet, B., Marloie, O., Nagy, Z., Pintér, C., Sanz, M.J., Saunders, M., Buchmann, N., 2010. Management effects on
W.L. Kutsch et al. / Agriculture, Ecosystems and Environment 139 (2010) 336–345
European cropland respiration. Agriculture Ecosystems and Environment 139,
346–362.
Eurostat, 2008. Europa in Zahlen, Eurostat Jahrbuch, Available at: http://epp.
eurostat.ec.europa.eu/portal/page/portal/publications/eurostat yearbook.
Europäische Kommission.
Fachstelle Bodenschutz and Zürich, 2007. Bedeutung und Entwicklung des Humusgehaltes in Böden: Einfluss von Standort, Bewirtschaftung und Klimawandel.
Kanton Zürich, Amt für Landschaft und Natur, Zürich, p. 5.
Falge, E., Baldocchi, D., Olson, R., Anthoni, P., Aubinet, M., Bernhofer, C., Burba, G.,
Ceulemans, G., Clement, R., Dolman, H., Granier, A., Gross, P., Grunwald, T.,
Hollinger, D., Jensen, N.O., Katul, G., Keronen, P., Kowalski, A., Lai, C.T., Law, B.E.,
Meyers, T., Moncrieff, J., Moors, E., Munger, J.W., Pilegaard, K., et al., 2001. Gap
filling strategies for long term energy flux data sets. Agricultural and Forest
Meteorology 107, 71–77.
Finnigan, J., 2008. An introduction to flux measurements in difficult conditions.
Ecological Applications 18, 1340–1350.
RCA, P., AGRO-TRANSFERT, ARVALIS, INRA, LDAR, Chambres d’agriculture de, P.,
2008. Exportation des pailles sans risque pour l’état organique des sols—Guide
de décision à la parcelle, p. 12.
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
– art. no. GB2022. Global Biogeochemical Cycles 22, B2022.
Goulden, M.L., Munger, J.W., Fan, S.-M., Daube, B.C., Wofsy, S.C., 1996. Measurements
of carbon sequestration by long-term eddy covariance: methods and critical
evaluation of accuracy. Global Change Biology 2, 169–182.
Gu, L.H., Falge, E.M., Boden, T., Baldocchi, D.D., Black, T.A., Saleska, S.R., Suni, T.,
Verma, S.B., Vesala, T., Wofsy, S.C., Xu, L.K., 2005. Objective threshold determination for nighttime eddy flux filtering. Agricultural and Forest Meteorology
128, 179–197.
Heinsch, F.A., Zhao, M., Running, S.W., Kimball, J.S., Nemani, R.R., Davis, K.J., Bolstad, P.V., Cook, B.D., Desai, A.R., Ricciuto, D.M., Law, B.E., Oechel, W.C., Kwon, H.,
Luo, H., Wofsy, S.C., Dunn, A.L., Munger, J.W., Baldocchi, D.D., Xu, L., Hollinger,
D.Y., Richardson, A.D., Stoy, P.C., Siqueira, M.B.S., Monson, R.K., Burns, S.P., Flanagan, L.B., 2006. Evaluation of remote sensing based terrestrial productivity from
MODIS using regional tower eddy flux network observations. IEEE Transactions
on Geoscience and Remote Sensing 44, 1908–1923.
Hollinger, D.Y., Richardson, A.D., 2005. Uncertainty in eddy covariance measurements and its application to physiological models. Tree Physiology 25, 873–885.
Janssens, I.A., Freibauer, A., Ciais, P., Smith, P., Nabuurs, G.J., Folberth, G., Schlamadinger, B., Hutjes, R.W.A., Ceulemans, R., Schulze, E.D., Valentini, R., Dolman,
A.J., 2003. Europe’s terrestrial biosphere absorbs 7 to 12% of European anthropogenic CO2 emissions. Science 300, 1538–1542.
Kutsch, W.L., Hörmann, G., Kappen, L., 2008a. Carbon and energy balances of different ecosystems and ecosystem complexes of the Bornhöved Lake District. In:
Fränzle, O., Kappen, L., Blume, H.P., Dierssen, K. (Eds.), Ecosystem Organization
of a Complex Landscape. Long-term Research in the Bornhöved Lake District,
Germany, Ecological Studies 202. Springer, Berlin, pp. 83–100.
Kutsch, W.L., Kolle, O., Rebmann, C., Knohl, A., Ziegler, W., Schulze, E.D., 2008b.
Advection and resulting CO2 exchange uncertainty in a tall forest in Central
Germany. Ecological Applications 18, 1391–1405.
Lasslop, G., Reichstein, M., Kattge, J., Papale, D., 2008. Influences of observation errors
in eddy flux data on inverse model parameter estimation. Biogeosciences 5,
1311–1324.
Loescher, H.W., Law, B.E., Mahrt, L., Hollinger, D.Y., Campbell, J., Wofsy, S.C., 2006.
Uncertainties in, and interpretation of, carbon flux estimates using the eddy
covariance technique - art. no. D21S90. Journal of Geophysical Research – Atmospheres 111, S2190.
Mauder, M., Foken, T., Clement, R., Elbers, J.A., Eugster, W., Grunwald, T., Heusinkveld,
B., Kolle, O., 2008. Quality control of CarboEurope flux data – part 2: intercomparison of eddy-covariance software. Biogeosciences 5, 451–462.
Moffat, A.M., Papale, D., Reichstein, M., Hollinger, D.Y., Richardson, A.D., Barr,
A.G., Beckstein, C., Braswell, B.H., Churkina, G., Desai, A.R., Falge, E., Gove,
J.H., Heimann, M., Hui, D.F., Jarvis, A.J., Kattge, J., Noormets, A., Stauch,
V.J., 2007. Comprehensive comparison of gap-filling techniques for eddy
covariance net carbon fluxes. Agricultural and Forest Meteorology 147,
209–232.
Moors, E.J., Jacobs, C., Jans, W., Supit, I., Kutsch, W.L., Bernhofer, C., Béziat, P., Buchmann, N., Carrara, A., Ceschia, E., Elbers, J., Eugster, W., Kruijt, B., Loubet, B.,
Magliulo, V., Moureaux, C., Olioso, A., Saunders, M., Soegaard, H., 2010. Variability in carbon exchange of European croplands. Agriculture Ecosystems and
Environment, 139, 325–335.
Moureaux, C., Debacq, A., Bodson, B., Heinesch, B., Aubinet, M., 2006. Annual net
ecosystem carbon exchange by a sugar beet crop. Agricultural and Forest Meteorology 139, 25–39.
345
Moureaux, C., Debacq, A., Hoyaux, J., Suleau, M., Tourneur, D., Vancutsem, F., Bodson,
B., Aubinet, M., 2008. Carbon balance assessment of a Belgian winter wheat crop
(Triticum aestivum L.). Global Change Biology 14, 1353–1366.
Osborne, B., Saunders, M., Walmsley, D., Jones, M., Smith, P., 2010. Key questions
and uncertainties associated with the assessment of the cropland greenhouse
gas balance. Agriculture, Ecosystems and Environment, 139, 293–301.
Papale, D., Reichstein, M., Aubinet, M., Canfora, E., Bernhofer, C., Kutsch, W., Longdoz,
B., Rambal, S., Valentini, R., Vesala, T., Yakir, D., 2006. Towards a standardized
processing of Net Ecosystem Exchange measured with eddy covariance technique: algorithms and uncertainty estimation. Biogeosciences 3, 571–583.
Prescher, A.K., Grünwald, T., Bernhofer, C., 2010. Land use regulates carbon budgets
in eastern Germany: From NEE to NBP. Agricultural and Forest Meteorology 150,
1016–1025.
Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet, M., Berbigier, P., Bernhofer, C., Buchmann, N., Gilmanov, T., Granier, A., Grunwald, T., Havrankova, K.,
Ilvesniemi, H., Janous, D., Knohl, A., Laurila, T., Lohila, A., Loustau, D., Matteucci,
G., Meyers, T., Miglietta, F., Ourcival, J.M., Pumpanen, J., Rambal, S., Rotenberg, E.,
et al., 2005. On the separation of net ecosystem exchange into assimilation and
ecosystem respiration: review and improved algorithm. Global Change Biology
11, 1424–1439.
Richardson, A.D., Hollinger, D.Y., 2005. Statistical modeling of ecosystem respiration
using eddy covariance data: maximum likelihood parameter estimation, and
Monte Carlo simulation of model and parameter uncertainty, applied to three
simple models. Agricultural and Forest Meteorology 131, 191–208.
Richardson, A.D., Hollinger, D.Y., Burba, G.G., Davis, K.J., Flanagan, L.B., Katul, G.G.,
Munger, J.W., Ricciuto, D.M., Stoy, P.C., Suyker, A.E., Verma, S.B., Wofsy, S.C., 2006.
A multi-site analysis of random error in tower-based measurements of carbon
and energy fluxes. Agricultural and Forest Meteorology 136, 1–18.
Rodeghiero, M., Heinemeyer, A., Schrumpf, M., Bellamy, P., 2009. Determination
of soil carbon stocks and changes. In: Kutsch, W.L., Bahn, M., Heinemeyer, A.
(Eds.), Soil Carbon Dynamics – An Integrated Methodology. Cambridge University Press, Cambridge, UK, pp. 49–75.
Running, S.W., Nemani, R.R., Heinsch, F.A., Zhao, M., Reeves, M., Hashimoto, H., 2004.
A continuous satellite-derived measure of global terrestrial primary production.
Bioscience 54, 547–560.
Saby, N.P.A., Arrouays, D., Antoni, V., Lemercier, B., Follain, S., Walter, C., Schvartz,
C., 2008. Changes in soil organic carbon in a mountainous French region,
1990–2004. Soil Use and Management 24, 254–262.
Schulze, E.D., Luyssaert, S., Ciais, P., Freibauer, A., Janssens, I.A., et al., 2009. Importance of methane and nitrous oxide for Europe’s terrestrial greenhouse-gas
balance. Nature Geosciences 3, 65–74.
Schulze, E.D., Ciais, P., Luyssaert, S., Schrumpf, M., Janssens, I., Thiruchittampalam,
B., Theloke, J., Saurat, M., Bringezu, S., Lelieveld, J., Lohila, A., Rebmann, C., Jung,
M., Bastviken, D., Abril, G., Grassi, G., Leip, A., Freibauer, A., Kutsch, W., Don,
A., Nieschulze, J., Börner, A., Gash, J., Dolman, A.J., 2010. The European carbon
balance. Part 4: integration of carbon and other trace-gas fluxes. Global Change
Biology 16, 1451–1469.
Smith, J., Smith, P., Wattenbach, M., Zaehle, S., Hiederer, R., Jones, R.J.A., Montanarella, L., Rounsevell, M.D.A., Reginster, I., Ewert, F., 2005. Projected changes
in mineral soil carbon of European croplands and grasslands, 1990–2080. Global
Change Biology 11, 2141–2152.
Smith, P., Lanigan, G., Kutsch, W.L., Buchmann, N., Eugster, W., Aubinet, M., Ceschia,
Béziat, P., Jagadeesh Babu, Y., Osborne, B., Moors, E.J., Brut, A., Wattenbach, M.,
Saunders, M., Jones, M., 2010. Measurements necessary for assessing the net
ecosystem carbon budget of croplands. Agriculture, Ecosystems and Environment, 139, 302-315.
Stevens, A., van Wesemael, B., 2008. Soil organic carbon dynamics at the regional
scale as influenced by land use history: a case study in forest soils from southern
Belgium. Soil Use and Management 24, 69–79.
Turner, D.P., Ritts, W.D., Cohen, W.B., Gower, S.T., Running, S.W., Zhao, M., Costa,
M.H., Kirschbaum, A.A., Ham, J.M., Saleska, S.R., Ahl, D.E., 2006. Evaluation of
MODIS NPP and GPP products across multiple biomes. Remote Sensing of Environment 102, 282–292.
Van Gorsel, E., Leuning, R., Cleugh, H.A., Keith, H., Suni, T., 2007. Nocturnal carbon
efflux: reconciliation of eddy covariance and chamber measurements using an
alternative to the u(*)-threshold filtering technique. Tellus Series B – Chemical
and Physical Meteorology 59, 397–403.
VDLUFA, 2004. Humusbilanzierung – Methode zur Beurteilung und Bemessung
der Humusversorgung von Ackerland. Standpunkte. Verband Deutscher Landwirtschaftlicher Untersuchungs – und Forschungsanstalten, Bonn, p. 12.
West, T.O., Post, W.M., 2002. Soil organic carbon sequestration rates by tillage and
crop rotation: a global data analysis. Soil Science Society of America Journal 66,
1930–1946.
Zar, J.H., 1998. Biostatistical Analysis, 4th ed. Prentice Hall, New Jersey, USA.