e c o l o g i c a l m o d e l l i n g 2 2 0 ( 2 0 0 9 ) 71–80

avai lab le at www.sc iencedi rec t .com

journa l homepage: www.e lsev ier .com/ locate /eco lmodel

Carbon sequestration in soils of SW-Germany as affectedby agricultural management—Calibration of theEPIC model for regional simulations

Norbert Billena,∗, Clara Röderb, Thomas Gaiser c, Karl Stahra

a University of Hohenheim, Institute of Soil Science and Land Evaluation, Emil Wolff Strasse 27,D-70593 Stuttgart, Germanyb ENVIRON Germany GmbH, Mühlwiese 9, 65779 Kelkheim, Germanyc University of Bonn, Institute of Crop Science and Resource Conservation, D-53115 Bonn, Germany

a r t i c l e i n f o

Article history:

Received 8 February 2008

Received in revised form

15 August 2008

Accepted 21 August 2008

Published on line 29 October 2008

Keywords:

Soil organic carbon sequestration

Reduced soil tillage

Agricultural management

EPIC model calibration

a b s t r a c t

Global emissions trading allows for agricultural measures to be accounted for the carbon

sequestration in soils. The Environmental Policy Integrated Climate (EPIC) model was tested

for central European site conditions by means of agricultural extensification scenarios.

Results of soil and management analyses of different management systems (cultivation

with mouldboard plough, reduced tillage, and grassland/fallow establishment) on 13 rep-

resentative sites in the German State Baden-Württemberg were used to calibrate the EPIC

model. Calibration results were compared to those of the Intergovernmental Panel on Cli-

mate Change (IPCC) prognosis tool. The first calibration step included adjustments in (a) N

depositions, (b) N2-fixation by bacteria during fallow, and (c) nutrient content of organic fer-

tilisers according to regional values. The mixing efficiency of implements used for reduced

tillage and four crop parameters were adapted to site conditions as a second step of the

iterative calibration process, which should optimise the agreement between measured and

simulated humus changes. Thus, general rules were obtained for the calibration of EPIC

for different criteria and regions. EPIC simulated an average increase of +0.341 Mg humus-

C ha−1 a−1 for on average 11.3 years of reduced tillage compared to land cultivated with

mouldboard plough during the same time scale. Field measurements revealed an average

increase of +0.343 Mg C ha−1 a−1 and the IPCC prognosis tool +0.345 Mg C ha−1 a−1. EPIC simu-

lated an average increase of +1.253 Mg C ha−1 a−1 for on average 10.6 years of grassland/fallow

establishment compared to an average increase of +1.342 Mg humus-C ha−1 a−1 measured

by field measurements and +1.254 Mg C ha−1 a−1 according to the IPCC prognosis tool. The

comparison of simulated and measured humus C stocks was r2 ≥ 0.825 for all treatments.

However, on some sites deviations between simulated and measured results were consid-

erable. The result for the simulation of yields was similar. In 49% of the cases the simulated

yields differed from the surveyed ones by more than 20%. Some explanations could be found

by qualitative cause analyses. Yet, for quantitative analyses the available information from

farmers was not sufficient. Altogether EPIC is able to represent the expected changes by

reduced tillage or grassland/fallow establishment acceptably under central European site

conditions of south-western Germany.

© 2008 Elsevier B.V. All rights reserved.

∗ Corresponding author. Tel.: +49 711 45922117; fax: +49 711 45923117.E-mail address: billen@uni-hohenheim.de (N. Billen).

0304-3800/$ – see front matter © 2008 Elsevier B.V. All rights reserved.doi:10.1016/j.ecolmodel.2008.08.015

l i n g

72 e c o l o g i c a l m o d e l

1. Introduction

Agricultural extensification is known to lead to humus accu-mulation in soils (e.g. review of West and Post, 2002). This ispartly due to improvements in soil fertility (e.g. Doran et al.,1994) and to the effective capture and retention of atmosphericCO2 by the soils (e.g. Freibauer et al., 2004; Lal, 2004). The latterprocess was recognised by the 7th Climate Conference in Mar-rakech; thus, it is now possible to account for the CO2 capturedby agriculture and forestry (e.g. land use change tillage with-out mouldboard ploughing) in the national emission reductiontargets or within the emissions trading system.

Appropriate orientation values to calculate the effects ofland use measures with regard to emission reduction targetsor emission trading systems are to be collected on the regionalstate-level. Yet, the necessary information within the CentralEuropean climate zone is very fragmentary. This gap is meantto be closed in the south-western part of Germany in thestate of Baden-Württemberg by agro-ecological simulations.Because of the diversity of sites in south-western Germanythe corresponding results can certainly also be transferred tocomparable locations in Central Europe.

For the simulation of soil organic carbon in agriculturalsystems several models are available like DAISY (Hansen etal., 1990), DNDC (IEOS, 2002), CERES (Ritchie, 1991), or EPIC(Williams, 1995). After thorough inspection and considerationof the site specific input variables like soil depth and horizon-ing, hydraulic soil parameters, tillage and cropping systems,as well as the output variables (see Table 1), the EPIC model(Williams et al., 1984; Gassman et al., 2005) with an integratedCENTURY-based model (Parton et al., 1994) by Izaurralde et al.(2006) appeared to provide the best adjustment for site condi-tions in south-western Germany. Nevertheless, there is a lackof information about the necessity and scope of model cali-brations regarding the carbon sequestration potential and the

CO2 emissions which are dependant on climatic, edaphic andcropping system conditions. This, indeed, was done success-fully in single cases (Ojima et al., 1993; Izaurralde et al., 2006),but both for those as for recent comparing investigations the

Table 1 – Comparison of main input and output data of agro-ecsoils regarding different site conditions, management systems

Model Maximumsoil depth

Differenttillage systems

Amount of crops(approximately)

C

CENTURY 30 cm No 6

DAISY 200 cm Restricted 12

DNDC 30 cm Yes 22

CERES Variable Yes 6

EPIC Variable Yes 80

2 2 0 ( 2 0 0 9 ) 71–80

natural-climatic conditions (Lugato et al., 2007; Zhang et al.,2007) or the crop specific management (e.g. Wang et al., 2005;Paudel et al., 2006) differ from the Central European situation.Therefore, the aim of the present study was to check and cali-brate the model for Central European site conditions by meansof field- and data analyses of thirteen representative sites ofsouth-western Germany (Chen et al., 2009). On each of the sitesthree management systems (tillage with and without mould-board plough, and conversion to grassland) were examined asfollows:

• Implementation of site specific management factors likesoil tillage, crop rotation, seeding date, fertilisation regime,and duration of the cultivation;

• comparison of the simulation results with measurementsin the field regarding changes of total organic carbon in thesoil;

• adjustment of simulated to measured results by site andcrop specific model calibration, in case decisive differenceswere existing.

Subsequently the results were integrated into the simula-tion of regional and site-specific CO2 sequestration potentialsof soils in Baden-Württemberg and are described in anotherpublication (Gaiser et al., 2008).

2. Material and methods

2.1. General concept

Management systems of reduced tillage and establishmentof grassland were compared simultaneously to convention-ally ploughed neighbouring fields to show the effects on soilorganic carbon (SOC) stocks. For simulating changes in SOCstocks the EPIC model (Version 0509) was chosen. Since the

1980s the model was under continuous development andhad become a widely tested, comprehensive agro-ecosystemmodel (Williams, 1995). Recently, new C and N modules basedon the Century model were integrated into EPIC, which enables

ological models for simulation of carbon sequestration in, and crops.

onsideration ofhydraulic soil

conditions

Soil organic carbonsequestration

References

No Yes Parton et al. (1994)and Gilmanov et al.(1997)

Yes Yes Hansen et al. (1990)and Jensen et al.(1996)

Yes Yes IEOS (2002) andZhang et al. (2002)

Yes Yes Ritchie (1991) andSaarikko (2000)

Yes Yes Williams (1995) andIzaurralde et al.(2006)

n g 2 2 0 ( 2 0 0 9 ) 71–80 73

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lso SOC calculations (Izaurralde et al., 2006). The total carbonool for carbon estimations in the soil consists of five compart-ents: structural litter, metabolic litter, microbial biomass,

low humus, and passive humus. For model calculations inputata about locations, soils, climate, and management areecessary. Thirteen representative locations in the state ofaden-Württemberg of south-western Germany with adja-ent fields of long-term treatment of mouldboard plough,educed tillage and grassland (converted from arable land)ere selected and sampled in autumn 2004. Seven of the

ampled plots with reduced tillage were part of experimentalelds, the remaining six reduced tillage and all grassland plotsere solely production fields. The simulation duration was setccording to the duration of the reduced tillage or grasslandreatment, the corresponding conventional ploughed fieldss reference for SOC changes were simulated with the sameuration. The mouldboard plough simulation was considereds baseline in the final calculations as follows:

Corg-m = Corg-mt − Corg-pt

tm(1)

ith �Corg-m = annual change of the total organic C poolccording to EPIC simulation for the selected managementm) in Mg C ha−1 a−1; Corg-mt = total organic C pool for the dura-ion (t) of the selected management (m); Corg-pt = total organic

pool for the duration (t) of the conv. plough (p) treatment;

m = duration of the management in years.

.2. Data acquisition

equired terrain information (inclination) and soil informa-ion (depth of each soil horizon, bulk density, texture, organicarbon, organic nitrogen, pH, coarse fragment content, andEC = cation exchange capacity) was collected for each loca-

ion. For the different treatments further analyses of SOCere carried out in 0–20 cm depth (0–5, 5–10 and 10–20) as for

his soil horizon highest humus accumulation was expectede.g. West and Post, 2002). Management data were acquiredy questionnaires and interviews from the farmers or fieldanagers.Climate information (long-term monthly average values

or maximum temperature, minimum temperature, precip-tation, days of rain, solar radiation, and relative humidity)as derived from climate stations, which are representative

or the agro-ecological zone of the corresponding location. Anverview about the locations and their main characteristics isiven in Table 2.

.3. Corrections of EPIC output values

n the EPIC output file, the amount for organic carbon isiven without considering the stone content of the soil. Forcomparison of measured C contents (with stone contents

ncluded) with simulated C contents, a correction of the simu-ated amounts is necessary. A further correction is necessary,s structural litter was removed as much as possible from the

oil for C measurements. For arable fields with quite rough lit-er this is relatively successful. However, for soil samples ofrassland plots it is rather impossible and therefore 50–75%f remaining litter is expected. Furthermore, a few sites were

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74 e c o l o g i c a l m o d e l

eliminated of the data set because management informationwas not plausible.

2.4. Comparisons with the IPCC tool

The Intergovernmental Panel on Climate Change (IPCC)offers software (based on IPCC, 2003) for country-specificcalculations of C accumulation potentials after changingmanagement practices on the field. To reach a minimumadaptation to the south-west German soil distribution, Camounts calculated by IPCC were adapted to the prevailing soiltypes in Baden-Württemberg (classified after Neufeldt, 2005).Then, annual C accumulation amounts were calculated forthe cases of management changes from conventional tillagewith plough to reduced tillage on the one hand and grasslandon the other hand. For the final IPCC results for establishinggrassland, results have been weighed according to the actualintensity of production of sampled locations (33.3% inten-sively and 66.7% extensively managed).

2.5. Management practices and model calibration

The results of the inquiry about management practices reflectthe heterogeneous picture of the reality very well. For eachtreatment very detailed data were put into management setsin EPIC, which finally contained 1369 data sets with singleoperations. Farmers managed their fields of reduced tillagesince 4–19 years, grassland plots were established since 4–20years. Crops grown in the rotations were corn, corn for silage,winter wheat, summer and winter barley, oats, rye, rapeseed,sugar beet, durum, triticale, sunflowers, and clover grass. Assome of these crops do not exist in EPIC they had to be simu-lated with a substitute, e.g. winter wheat for triticale, canolafor mustard, or winter pasture for perennial grassland. Outputyields in EPIC are given in dry matter and have to be adaptedto fresh weight before the comparison.

The finally defined EPIC adjustments are the result ofsouth-west German expert interviews and the implementa-tion into the iterative calibration process with the final aimto optimise the agreement between measured and simulatedhumus changes (for final data setup see Table 3).

As the variety of machines and adaptations given bythe farmers was very large, few typical new machines weredefined in EPIC with the major parameters mixing efficiency,tillage depth, and surface random roughness. Tillage depthhas been set according to values given by the farmers. For thenon-turning tillage machines an average mixing efficiency of0.3 for both passive, i.e. machines with non-powered shares ortines, and active, i.e. machines with powered shares or tines,was selected (mouldboard plough has 0.9 by default) as a resultof continuous simulations with the aim to reach best agree-ment between simulated and measured humus contents.

Fertilisers were also newly defined according to the prod-ucts used by the farmers; nutrient contents of organic fertilis-ers were adapted according to Schilling (2000) and KTBL (2005).

For grasslands without additional fertilisation or green

fallow, which were very common in this investigation, Ninputs from other sources are of high importance for biomassproduction (Billen, 1996). Consequently, these other N sourcesare also very important for the potential sequestration of

Tabl

e3

–D

EPIC

-ID

2 10 14 16 19 23 29 38 62 33+

78

aIn

clu

din

gb

Win

ter

anc

Spri

ng

var

dW

inte

rva

eR

ape

(Bra

sf

Mix

ture

of

n g 2 2 0 ( 2 0 0 9 ) 71–80 75

ctttNctpNoG1at1f

edtcfec

2

Twta

M

3

3

At

e c o l o g i c a l m o d e l l i

arbon in the humus. N-depositions were set to 0.8 ppm ofhe precipitation in EPIC. According to Führer et al. (1988)his is considerably more in south-western Germany andherefore this value was adapted to 1.5 ppm. By asymbiotic

2-fixation green fallow or extensively managed grasslandsan gain up to 15 kg N ha−1 a−1 (Haynes, 1986). To considerhis input potential, an additional N fertilisation of 10 kgure N ha−1 a−1 was defined for the respective fields. Furtheris added to green fallow or extensive grasslands via symbi-

tic N2-fixation (legumes). Common clover in south-westernermany has a potential to capture 25 kg N Mg−1 DM (Kahnt,983). With an average yield of 4 Mg DM ha−1 (KTBL, 2005)nd 10% of legumes resulting in 0.4 Mg legume DM ha−1 a−1,he N input of legumes in green fallow can be calculated to0 kg N ha−1 a−1. This amount was also added to the greenallow in the EPIC management sets.

The potential heat unit, an important input parameter forach crop that was not available from external sources, waserived from the simulation result. Herewith, it is guaranteedhat the plant will reach maturity during the simulation pro-ess. For calibration of the model with respect to crop yields,urther single configurations of the parameters biomassnergy ratio, harvest index and minimum harvest index werearried out.

.6. Statistical analysis

he goodness of fit between simulated and measured valuesas evaluated by the coefficient of regression of a linear rela-

ionship. In addition, the mean relative error was calculateds follows:

R = 1n

∑n

i=1

yi − xi

xi(2)

. Results and discussion

.1. Comparison of yields

s crop growth determines the accumulation of biomass andherefore represents a precondition for carbon sequestra-

Table 4 – Results of observed and EPIC-simulated yields (DM = d

Crop or site Measured/estimated (Mg DM h

Average Standard devia

Corn 10.19 4.94Silage corn 16.88 2.71Sugar beet 6.38 2.55Rapeseed (canola) 3.54 0.60

Cereals 6.70 1.19Winter wheat 7.22 1.11Winter barley 7.08 0.34Spring barley 5.45 0.74

All fields 7.05 3.55Investigation plots with

measured yields7.49 4.18

Farmers fields with estimatedyields

6.58 2.69

Fig. 1 – Comparison of estimated/measured yields withsimulated yields by EPIC in Mg of dry matter per hectare.

tion, the model was calibrated for the simulated yields tomatch the actual yields observed by the farmers and fieldmanagers. Fig. 1 shows the comparison of simulated yieldswith measured/estimated yields. In contrast to results ofIzaurralde et al. (2006) in 49% of all cases the deviation ofsimulated yields compared to observed yields was more than20%. This might be due to the sometimes imprecise esti-mations of yields by the farmers and the partly unreliableinformation regarding management practices. However, thecorrelation coefficients for the experimental sites (r2 = 0.653)did not reveal a relevant wider statistical spread for yieldestimations in comparison to the coefficients of the dataprovided by practitioners (r2 = 0.599). Parsons et al. (1995)reported that EPIC predicted mean yields accurately andexplained 55–89% of the measured yield variance for five ofsix treatments (excluding 1986 from four of the treatments)for corn grown during 1978–1993 on three Virginia soil typesfertilised with either inorganic fertiliser or manure applica-tion.

Williams (1989) described the results of a test of EPIC yieldestimations at several U.S. locations and for sites in Asia,France, and South America. The average predicted yields werealways within 7% of the average measured yields, and there

ry matter).

a−1 a−1) Simulated (Mg DM ha−1 a−1) Difference (%)

tion Average Standard deviation

11.20 2.02 +9.919.40 3.88 +15.0

4.45 1.80 −30.23.99 1.28 +12.9

6.54 2.04 −2.46.83 2.16 −5.44.25 1.74 −40.05.83 1.94 +7.0

7.19 4.03 +1.97.68 4.64 +2.6

6.65 3.22 +1.1

l i n g

76 e c o l o g i c a l m o d e l

was no significant difference between any of the simulatedand measured yields. This is contrary to the present study,where mean deviations were found in the range of −40% and+15% (see Table 4). However, regression coefficients betweenthe simulated and measured yields ranged from relativelystrong values of 0.80 and 0.65 for wheat and corn to only 0.20for barley. The low performance of barley was confirmed bythe present study but much more for winter barley than for

spring barley (see Table 4). A major reason for the poor perfor-mance of barley yield simulations may be due to the fact thatno differentiation between winter and summer variety existsin EPIC. Therefore, it would be helpful to establish data sets

Fig. 2 – Comparison of measured and simulated humus C stocksBaden-Württemberg with different management practices; mre =

2 2 0 ( 2 0 0 9 ) 71–80

with plant parameters for winter barley in Central Europe, orat least simulate it provisionally with the more similar win-ter wheat. A further reason for the insufficient agreementbetween simulated and measured or surveyed yields of thisstudy could be related to the climate information, which didnot originate from the investigated sites directly but from theclosest weather stations of other institutions. Thus, climaticdifferences cannot be excluded. Schmid et al. (2004) pointed

out that differences between simulated and observed yieldsmay be a result of different soil types. However, our data anal-yses did not show any obvious correlation between simulatedyields and site properties.

in topsoils of representative locations inmean relative error; n = number of samples.

n g

3o

Ahroortwap(aItbm

RSCcfiaAsomtsn

faEt(sT0

e c o l o g i c a l m o d e l l i

.2. Comparison of measured and simulated soilrganic carbon

n acceptable agreement between measured and simulatedumus C stocks was obtained for mouldboard plough tillage,educed tillage, and conversion to grassland. The coefficientf regression for the relationship between simulated andbserved grassland C stocks was lowest with 0.8255 and aelative deviation of 0.126 with a slight bias towards overes-imating the carbon stocks by 3.66 Mg ha−1 (Fig. 2). In the plotsith reduced tillage the coefficient of regression was 0.92 withmean relative error of 0.062 Mg ha−1. For the mouldboard

lough treatments the coefficient of regression was highest>0.93) but the tendency of EPIC to overestimate at low C stocksnd underestimate at higher C stocks, as also revealed byzaurralde et al. (2006), could be recognised. Yet, for simula-ions of reduced tillage and grassland this tendency could note confirmed. Here, a slight overestimation was observed inost cases (see Fig. 2).Under conventional tillage and similar climatic conditions,

oloff et al. (1998) found that EPIC satisfactorily estimated totalOC content for a long-term spring wheat rotation at Swifturrent, Saskatchewan. A validation of the improved carbonycling routine was performed by Izaurralde et al. (2006) forve Great Plains sites located in Nebraska, Kansas, and Texas,nd for a 60-year rotation experiment located near Breton,lberta. It was concluded from these studies that the modelatisfactorily replicated the soil carbon dynamics over a rangef environmental conditions and cropping/vegetation andanagement systems. Apezteguía et al. (2002) concluded that

he revised EPIC carbon cycling routine performed robustly forimulations of deforested conditions, cropping systems, andative vegetation in the Córdoba region of Argentina.

The annual accumulation rates are of essential importanceor the potential of the evaluated strategies. An accept-ble agreement between measurement and simulation withPIC could be found (Table 5). When establishing reducedillage an average annual accumulation of 0.343 Mg C ha−1 a−1

=1.26 Mg CO2 ha−1 a−1) could be expected according to mea-urements covering a period of 5–18 years after conversion.he result with EPIC simulation was with an average of.345 C ha−1 a−1 (=1.26 Mg CO2 ha−1 a−1) very similar, the over-

Table 5 – Average humus C accumulation in representative soilEPIC simulations after changing from conventional tillage withthe topsoil (0–20 cm).

Parameter Statistic parameter

Humus-C stock (Mg C ha−1) MeanStandard deviationNumberRange of duration [a]Average duration [a]

Humus-C change in comparison toploughsimulated (Mg C ha−1 a−1)

MeanStandard deviationNumberRange of duration [a]Average duration [a]

2 2 0 ( 2 0 0 9 ) 71–80 77

estimation was 0.6%. The observed increase rates were muchlower than the averaged rates of about 1.3 Mg ha−1 simulatedby Balkovic et al. (2006) for reduced tillage in the state ofBaden-Württemberg (SW-Germany). However, that study wascarried out at an European Union scale and the authors admit-ted the relatively low quality of available information withrespect to input data validation and processing. Furthermore,the increase rates of this study were lower than the IPCCrates calculated by Neufeldt (2005) for zero tillage in the sameregion, because reduced tillage was expected to sequester lesscarbon than zero tillage practices. Simulations performed forIowa by Zhao et al. (2004) with the revised EPIC model resultedin an average annual SOC rate of 0.506 Mg ha−1 a−1 in responseto conservation tillage (mulch till and no till), which com-pared favorably with SOC rates reported by Lal et al. (1998)and West and Post (2002) for similar tillage systems. In thiscase, the sequestration rates might be higher as compared tosouth-western Germany because of a more continental cli-mate with hot summers and very cold winters or distinctlylower C amounts in the soil at the time of shifting the tillagesystem. Additionally, the diverse soil depths of the investiga-tions contributed to the described differences. According tothe site settings found, the present study surveyed the topsoilof 0–20 cm including stone content and bulk density. In otherstudies, topsoil of 0–30 cm was investigated; however, oftenthe stone content and bulk density was not considered (IPCC,2003; Neufeldt, 2005; Balkovic et al., 2006). The study of Westand Post (2002) actually included different soil depths into theanalyses.

Beside the general differences of tillage systems in a globalcontext, this study also revealed higher humus amounts infield measurements with mouldboard plough tillage com-pared to long-term treatment with reduced tillage in singlecases. This phenomenon was already observed in other placesas well (e.g. West and Post, 2002). According to results frominterviews with farmers about their management systems,reasons for the described phenomenon were found to include:

• Major differences in management systems like regularorganic fertilisation, increased cropping of intermediatecrops, or harvest residues remaining consequently on theconventionally ploughed field;

s in Baden-Württemberg according to measurements andmouldboard plough to reduced tillage and grassland in

Reduced tillage Grassland

Measured Simulated Measured Simulated

43.9 46.0 53.0 59.818.5 19.2 20.8 24.312 12 11 115–18 5–18 4–20 4–2011.3 11.3 10.6 10.6

0.343 0.345 1.342 1.2530.406 0.433 1.746 0.77612 12 11 115–18 5–18 4–20 4–2011.3 11.3 10.6 10.6

78 e c o l o g i c a l m o d e l l i n g 2 2 0 ( 2 0 0 9 ) 71–80

Table 6 – Average SOC sequestration rates resulting from different evaluation methods after conversion to reduced tillageand grassland for periods of 10–20 years.

Source Reduced tillage (Mg ha−1 a−1) Conversion to grassland (Mg ha−1 a−1)

Own data analysis of 60 publications with 234 pairedtreatments in 16 nations

0.272 0.596

Measurements on 13 sites in SW-Germany withoutnon-plausible management information

0.343 1.342

EPIC-simulation on 13 analysed sites (see above) inSW-Germany

0.341 1.253

IPCC-evaluation tool for reduced tillage and conversionto managed grassland with regard to different soils inSW-Germany

0.345 1.254

IPCC-evaluation tool for no tillage (1st column) andfallow (2nd column) with regard to different soils inSW-Germany

0.597 0.150

Review of West and Post (2002) for no tillage (1stcolumn) and rotations with grass, hay or pasture (2ndcolumn)

• no consequent, i.e. no annual conventional plough tillage;• long-term humus depleting management before converting

to reduced tillage or grassland, respectively.

Also Schmid et al. (2004) postulated that the behaviour offarmers may deviate from the model assumptions and maycause differences among simulated and observed data.

For the establishment of grassland the discrepancybetween simulated and measured carbon stocks was with6.6% higher compared to reduced tillage (Table 5). Accordingto measurements the C accumulation reached 1.342 C ha−1 a−1

on average (=4.92 Mg CO2 ha−1 a−1), the simulation calculatedonly 1.253 C ha−1 a−1 on average (=4.59 Mg CO2 ha−1 a−1), bothfigures referring to a period of 4–20 years after conver-sion. Yet, from high deviations it could be suggested thatthe mean humus accumulation was not significant for thetopsoil. Nevertheless, the mean experiment and simulationresults showed a very good agreement with the accumulationprognoses of +1.254 Mg C ha−1 a−1 (see Table 6), which werecalculated by the IPCC prognosis tool for extensively managedgrassland.

In comparison to IPCC prognoses for fallow land (accumu-lation of +0.150 Mg C ha−1 a−1) and international experimentevaluations like the review of West and Post (2002) (meanaccumulation of +0.190 Mg C ha−1 a−1) respectively, the south-west German accumulation potential for an establishment ofgreen fallow and extensively managed grassland appearedvery high (see Table 6). Obvious reasons could not be con-cluded from the mentioned studies. Probably, south-westernGermany has a more suitable climate for a fast growth onfallow land and increased biomass production compared toother international sites, with the effect that more plantresidues are available for humus accumulation. Yet, alsothe former management system with manifold crop rota-tions and organic fertilisation could leave behind fertile soils(Christensen and Johnston, 1997), which may eventually be ofonly low importance for the establishment of fallow land inthe global comparison. Finally, the C accumulation potential

is also dependant on the historic C loss since the beginning ofcultivation (Follett, 2001). Yet, similar to the review of Conantet al. (2001) with +0.54 Mg ha−1 a−1, own evaluations of liter-ature revealed with +0.596 Mg C ha−1 a−1 a lower difference

0.570 0.190

between locally and globally measured C accumulation undergrassland.

3.3. Conclusion

Altogether the EPIC simulation produced acceptable Csequestration prognoses in comparison to the measured Caccumulation for south-west German soils after some inputparameters were adapted to the south-west German site con-ditions. The high effectiveness of the model calibration pointsat the general possibility to better adjust model parame-ters (N depositions, N2-fixation, nutrients of manure, mixingefficiency of tillage tools, and plant growth parameters—seeTable 3) to other criteria and regions. The dimension of theadjustments has to be decided and evaluated site by site.Despite thorough selection of sites it became obvious thatfalse time series are only partly suitable to display humuschanges after the establishment of a new management sys-tem. This is documented by high deviations of single values,which reach in some cases more than 100% from mean val-ues. There is therefore, a need of real time series for a precisequantification of changes and a corresponding calibration ofsimulation models, however, there is a lack of long-term exper-iments which could provide real time series. Furthermore,regardless of the method, always net changes in humus Cstocks were measured. Even though differential erosion lossesrepresent a source of error when measuring C accumula-tion, possible erosion losses had already taken place and werenot considered in the calculations of the C balance both inthis and in most other studies. On regional and global levelthis effect has only selectively been dealt with to-date (e.g.Grace et al., 1998; Gaiser et al., 2008). However, the discussedmeasures for carbon sequestration cannot be separated fromother important co-benefits like decreased nutrient loads intosurface waters, soil erosion or biodiversity as shown with EPIC-simulations by Feng et al. (2004) and Gaiser et al. (2008).

Acknowledgement

Funding of this research by the environmental research fundof Baden-Württemberg BWPLUS (project ID BWK24001) is

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ratefully acknowledged. Our thanks apply to Dr. H. Chen,r. B. Deller, the state office for environment of Baden-ürttemberg (LUBW), the German Meteorological Service

DWD), the research station Ihinger Hof and all farmersnvolved for the provision of basic input data like soil data,eather data and management information. Finally, we are

rateful to Dr. K. Adam-Schumm for data analyses of morehan 60 publications and to Dr. J.R. Williams and his teamor constant assistance and cooperation in handling the

odel.

e f e r e n c e s

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