Carbon sequestration potential of organic agriculture in northern Europe– a modelling approach

Bente Foereid* and Henning Høgh-JensenOrganic Farming Unit, Department of Agricultural Sciences, Royal Veterinary and Agricultural University,Agrovej 10, DK-2630 Taastrup, Denmark; *Author for correspondence (Present address: The MacaulayInstitute, Craigiebuckler, Aberdeen AB15 8QH, UK; e-mail: b.foereid@macaulay.ac.uk; fax:+44-1224-311556)

Received 10 October 2002; accepted in revised form 26 May 2003

Keywords: Carbon sequestration, CENTURY model, Conversion, Denmark, Organic farming

Abstract

Decline in carbon content in agricultural soils contributes both to climate change and to soil fertility problems.The CENTURY element dynamics simulation model was tested and adapted for Northern European agriculturalconditions using long-term datasets from Askov experimental farm in southern Denmark. The part of the modeldealing with decomposition was tested in isolation using a bare fallow experiment and it could predict soil or-ganic matter levels with high accuracy. In the cropping experiments predictions were less accurate. The cropproduction was not accurately predicted. Predictions were more accurate on loamy than on sandy soils. The modelwas used to predict the effect of conversion to organic agriculture on carbon sequestration as soil organic matter.It predicted an increase in soil organic matter during the first 50 years of about 10–40 g C m � 2 y � 1, and astable level after about 100 years. The use of grass-clovers in the rotation and as cover crops was particularlyimportant for the increase in organic matter.

Introduction

Because of concerns about the greenhouse effect, thepossibility of increasing carbon sequestration in ter-restrial ecosystems has received considerable atten-tion recently �Smith et al. 1997a, 1998, 2000;Paustian et al. 1998; Schlesinger 1999, 2000�. Mostattention has focused on forests and natural ecosys-tems as stores of carbon �Valentini et al. 2000�. How-ever, at least in Europe, agricultural production coversa larger part of the area �Eurostat 1995�. In agricul-tural lands plant cover is usually removed every year,so carbon sequestration means an increased carboncontent of the soil �Paustian et al. 1998�. It is, there-fore, of interest to adopt agricultural practices that se-quester carbon as soil organic matter �SOM� inagricultural soils. A high level of SOM is also ben-

eficial for soil fertility and soil structure �Christensenand Johnston 1997; Sylvia et al. 1998�.

Cropping leads to a decrease in SOM content inmost cases �Christensen and Johnston 1997; Paustianet al. 1998�. However, farming practices greatly in-fluence soil carbon storage �Lal and Kimble 1997;Christensen and Johnston 1997�. It is hypothesisedthat organic agriculture stores larger amounts of soilorganic matter than conventional agriculture �Løesand Øgaard 1997; Stolze et al. 2000�, mainly as a re-sult of the use of grass-clover leys in most crop rota-tions, and the recirculation of animal manures andcrop residues. However, the lower production usuallyrecorded in organic agriculture �Halberg and Kris-tensen 1997� due to nutrient limitations works in theopposite direction.

It is difficult to quantify the carbon sequestrationpotential of organic as compared to conventional ag-

© 2004 Kluwer Academic Publishers. Printed in the Netherlands.13Nutrient Cycling in Agroecosystems 68: 13–24, 2004.

riculture, as organic and conventional farming prac-tices are very different. To compare the same rotationunder organic and conventional management makeslittle sense, because crop rotations are different. Wehave, therefore, defined some standard rotations andcompared management practices for conventional andorganic agriculture for some typical production sys-tems.

The main problem in studies of soil carbon seques-tration is the long time scale involved. Under temper-ate conditions it takes 100 years or more to reach anew SOM equilibrium when management is changed�Christensen and Johnston 1997�. It is particularlydifficult to estimate the long-term effects of organicagriculture, as most farms have converted relativelyrecently. One way to establish a fairly accurate pic-ture where insufficient data are available is to usewell-validated dynamic models �Smith et al. 1997b�.

We used the CENTURY model of C, N, P and Sdynamics to evaluate possible long-term effects ofconventional and organic management on SOM. TheCENTURY model has been tested and used undermany conditions �Paustian et al. 1992; Gilmanov etal. 1997; Romanya et al. 2000� and against manylong-term data sets and has performed well undertemperate conditions �Smith et al. 1997b�.

Material and methods

The data set

The datasets from Askov consist of two long-termtrials, one comparing an animal manure/slurry and amineral fertiliser treatment since 1894 �Christensen etal. 1994; Christensen and Trentemøller 1995� and one

comparing crop rotations including bare fallow �noplant cover, weeded� over a 30-year period �Chris-tensen 1990�. The crop rotation in the 30-year experi-ment was winter wheat, fodder beets, spring barleyand grass-clover �Lolium perenne and Trifoliumrepens�. A separate experiment had only bare fallow.In the oldest experiment the crop rotation was slightlychanged over the period, but basically it consisted ofwinter cereals, followed by root crops, followed byspring cereals with undersown grass-clover followedby grass-clover. This experiment was also done bothon sandy and loamy soil. These soils were used astypical sandy and loamy soils. There were four repli-cates of each experiment.

Climate data were available from 1961, and onlyyield data following this date were used in validation.Before that the model was run using the stochasticweather generator in the CENTURY model �Parton etal. 1992� which generates weather from average andstandard deviations of monthly weather over at least10 years. Climate data from the Danish Meteorologi-cal Institute �Scharling 2000� were used as input tothe stochastic weather generator.

As an index for comparing simulated and measuredvalues, modelling efficiency �ME� �Vereecken et al.1991� was calculated as:

ME � 1 ��j�1

N �pj � oj�2

�j�1N �oj � o�2

where pj are the simulated values, oj are the measuredvalues, o is the average of measured values and N isthe number of data pairs.

Table 1. Crop parameter values. The standard parameters used are shown as the crop code from the standard parameter file. As maximumproduction was changed for most of the crops used, the parameter used is shown for all crops. When other parameters were changed from thestandard value, this is shown in the last column.

Crop species Crop Maximum Other changed parameterscode used production

�g C m � 2 month � 1�

Winter cereal W3 337Spring cereal SW3 385Root crop BE 230Legumes ALF 348Oil seed rape SW3 150 Root fraction at start: 0.2; at end: 0.0 after 2 monthsGrass TG 270Grass-clover GCP 348.5 Fraction of full growth rate in first month�1.0

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Model and parameterisation

The CENTURY model has been well described �Par-ton et al. 1987, 1988, 1994�. It was originally devel-oped for simulation of grasslands on the Great Plainsof the US �Parton et al. 1987�, but Kelly et al. �1997�

found CENTURY able to simulate soil organic car-bon across a variety of land use and climate types. Inbrief, the model simulates the dynamics of SOM byusing three pools of soil organic matter and two poolsof organic litter. The model also contains a cropgrowth sub-model. The model operates with time-

Table 2. Conventional and organic rotations for plant, pig and beef/dairy production used in the scenarios, slightly modified from Hansen etal. �2000�. Amounts of nitrogen �kg N ha � 1� are shown, given as mineral fertiliser and slurry, and other practices applied in the organicrotations �straw�straw incorporation, fallow�grass-clover not cut or grazed�. Ploughing was normally done in spring. When tilled in au-tumn, this is indicated.

Conventional Mineralfertiliser

Manure Organic Slurry Other

ArableLoamy Winter wheat 154 0 Spring barley, undersown 150 Straw

Spring barley 117 0 Grass-clover 0 FallowRoot crops 171 0 Winter wheat, catch crop 0 StrawLegumes 0 0 Spring barley, catch crop 100 StrawOil seed rape 107 0 Oats 100 Autumn ploughGrass 307 0

Sandy Winter wheat 157 0 Spring barley, undersown 100 StrawSpring barley 131 0 Grass-clover 0 FallowRoot crops 188 0 Oats, catch 0 StrawLegumes 0 0 Winter wheat, catch crop 100 StrawOil seed rape 129 0 Spring barley 100 Autumn ploughGrass 325 0

PigLoamy Winter wheat 137 0 Spring cereal, undersown 185

Spring barley 0 117 Grass-clover 0 Grazing/cutRoot crops 0 148 Spring cereal, catch crop 218Legumes 0 0 Spring barley, catch crop 157Oil seed rape 0 107 Spring cereal 185 Autumn ploughGrass 198 90

Sandy Winter wheat 144 0 Spring cereal, undersown 173Spring barley 0 131 Grass-clover 0 Grazing/cutRoot crops 0 162 Spring-cereal, catch crop 208Legumes 0 0 Spring barley, catch crop 148Oil seed rape 0 129 Spring cereal 178 Autumn ploughGrass 228 74

Dairy/beefLoamy Winter wheat 144 0 Barley/pea, undersown 81

Spring barley 0 234 Grass-clover 0 CutRoot crops 0 296 Grass-clover 0 GrazingLegumes 0 0 Spring barley, catch crop 157Oil seed rape 0 214 Spring barley, catch crop 157 Autumn ploughGrass 238 96

Sandy Winter wheat 131 0 Barley/pea, undersown 81Spring barley 0 234 Grass-clover 0 CutRoot crops 0 330 Grass-clover 0 GrazingLegumes 0 0 Spring barley, catch crop 157Oil seed rape 0 258 Spring barley, catch crop 157 Autumn ploughGrass 170 258

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steps of 1 month with precipitation and temperatureas climate inputs. This implies that the crop growthmodel is relatively simple, and some site-specific ad-aptation of the crop growth parameters is necessary�Parton et al. 1992�. We used version 4.0 of the model�Metherell et al. 1993�.

Standard parameters were used when available, butwhere yield data were available the parameter formaximum monthly biomass production of the cropwas adapted so that average modelled production fit-ted average observed production levels at the site�Table 1�. Yield data from one of the four replicateswere used in parameterisation; those from the otherreplicates were used in testing. Cereals undersownwith grass-clover were parameterised as the grass-clover grew up after harvest of the cereal, but with anincreased growth rate in the first month. Oilseed rapewas parameterised using standard parameters forspring cereals, with changes according to Boons-Prinset al. �1993�.

Initial C and N levels were set to the measuredvalues of soil organic matter in the field plots atAskov and partitioned among active:slow:passivepools as 3:47:50 following Paustian et al. �1992�. C:Nratios of each of the pools were chosen in the rangesuggested by Parton et al. �1992�, so that observedvalues of total C and N were obtained.

Only nitrogen was considered as a limiting nutri-ent in this study, as this is usually the case in organicagriculture �Lampkin 1990�. If any nutrient is limit-ing in conventional agriculture, it will also usually benitrogen, as mineral nitrogen is easily leached fromsoils. When animal manures were added, standardparameters were used for farmyard manure �lignin25%, C:N ratio of 30�, while the data for liquid ma-nure �lignin 14.5%, C:N ratio of 8.4� were fromSalomonsen �2000�. Tillage was mostly autumn/win-ter ploughing and spring harrowing �Christensen et al.1994�. As tillage was done under cool conditions, theeffect of tillage on decomposition was retained for 2months in the model �Kouegh, Colorado State Uni-versity, personal communication�.

Scenarios

To compare carbon sequestration on organic and con-ventional farms, crop rotations typical of organic andconventional agriculture were compared. The samerotations have previously been used to comparenitrogen leaching in organic and conventional agricul-ture �Hansen et al. 2000�. Because the rule for organic

agriculture has changed slightly since then, some mi-nor changes in fertiliser use were made. The cropsand fertiliser use for each rotation are shown in Table2.

For each production type, the simulations were firstrun with the conventional rotation for 60 years to al-low the pool distribution �active, slow, passive� to getto a level typical for each production type. Subse-quently, two alternatives were applied: either contin-ued conventional practice for 200 years more, or theorganic rotation for 200 years.

To identify the most important practices in organicfarming for soil carbon storage, the different manage-ment components were removed sequentially: slurryapplication, catch crop and straw incorporation anduse of straw-manure instead of slurry. The effect wascompared to that of the standard organic ‘treatment’.Also a treatment with permanent grass-clover asgrazed pastures was included.

Sensitivity tests

Sensitivity tests were performed for maximum cropproduction, initial levels of soil C and N and for cli-matic variation. Maximum crop production waschanged up to 50% in both directions and the effecton soil carbon level was assessed. The effect ofchanging the starting conditions of size and C:N ratiofor each pool of SOM �active, slow, passive� by� 10% was also tested. To test the effect of climaticvariation, the model was run with climate data fromthe sites in Denmark with most and least precipita-tion and with highest and lowest temperature.

Results and discussion

Testing decomposition module

The model mostly reproduced the observed SOM de-velopment in the Askov experiments �Figures 1 and2�. The bare fallow treatment was accurately simu-lated �Figure 1, right�. The decomposition model inCENTURY is valid for the moist conditions and mildwinters characterising coastal areas in northernEurope. Adaptation as suggested by Paustian et al.�1992� appeared unnecessary �Figures 1 and 2�, inagreement with Kelly et al. �1997�. Hence, thedecomposition model has passed the crucial test ofbeing able to simulate soil organic matter transforma-tions under conditions different from those for which

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it was developed. The reason for the discrepancy ofC and N predictions �Figure 1, right� is probably thatthe initial C:N ratios of the pools in the model wereincorrect, as these are particularly difficult to estab-lish �Parton et al. 1992�.

The predictions of soil carbon and nitrogen for the100-year experiment were less accurate, partlybecause the dataset was more noisy than the 30-yeardataset �Figure 2�. The effects of manure/slurry wereoverestimated on sandy soils �Figure 2�.

Problems in the simulation of SOM developmentmay be related to the root:shoot ratio of the crops.This parameter is difficult to measure and thereforenot available for many crops. It may also vary withgrowing conditions, as plants tend to increase alloca-tion to roots when nutrient or water supply is limiting�Cruz et al. 1986; Hirose 1988�. In the CENTURYmodel, the root:shoot allocation factor does notchange with nutrient supply. This may introduce er-rors �Parton and Rasmussen 1994�, particularly when

simulating organic rotations where nutrient availabil-ity, nitrogen in particular, may limit crop growth andtherefore enhance root:shoot ratio.

Testing the crop growth module

Accurate simulation of crop productivity was notpossible under these conditions �Figure 3�. Averageproduction could be tuned to fit observed averageproduction, but variations among years could not bereproduced. Only spring cereals and grass-clover onloamy soils were predicted with a high level of accu-racy. The crop model in CENTURY appears toosimple to simulate crop growth outside the area andclimate for which it was developed, as also found byParton et al. �1996�. Time steps of 1 month and cli-mate data that include only precipitation and temper-ature �not radiation� are probable reasons. Theproblems with the crop model may be particularly se-rious at high latitudes, as the ratio between tempera-

Figure 1. Measured and modelled values of soil nitrogen and carbon content in the top 20 cm soil in a 30-year experiment with a croprotation �left� and bare fallow �right�. Error bars are standard error. Values for modelling efficiency �ME� are also shown.

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ture and radiation is rather different from the situationfor which the model was developed. As correct pre-diction of production level is important for the accu-racy of the soil organic matter predictions, futuredevelopment of the model should be focused on im-proving the crop growth model. Correct productionfor a single year may not be crucial for the accuracyof soil organic matter simulations, however, asobservable differences in SOM take place overdecades rather than years.

Sensitivity to the parameter values

Sensitivity tests showed less than 1% change in soilcarbon in response to a 10% change in starting con-ditions of the active and slow organic pools. For the

passive organic matter pool, changing starting condi-tions by � 10% meant a change of about 3–7% insoil organic C or N. The passive pool will thereforeremain rather unchanged during a simulation of somedecades. However, the change over a period ofdecades seems to be relatively insensitive to startingconditions. We have, therefore, focused on changes insoil organic matter rather than absolute amounts.

Sensitivity analysis with respect to the effect ofclimate showed that the change in organic matter over100 years was 1–5% different from that obtained us-ing average climate data, which suggests that the useof average climate data does not introduce large er-rors.

Sensitivity analysis showed that predicted carbonlevel was relatively insensitive to maximum crop

Figure 2. Measured and modelled values of soil nitrogen and carbon content in a long-term experiment where different sources of fertiliserwere used on loamy and sandy soils. Error bars are standard error. Values for modelling efficiency �ME� are also shown.

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Figure 3. Predicted versus measured biomass production �g C m � 2� for the harvested product �grain�straw, beets�leaves and grass-cloveror legumes�. Values for modelling efficiency �ME� are also shown.

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Figure 4. Sensitivity test of crop maximum production. Percentage change in soil organic carbon over a 200-year simulation period as afunction of percentage change in crop maximum production from the value used in the simulations.

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production levels, except for grass-clovers and to alesser extent spring cereals �Figure 4�. Spring cerealswere included in many of the standard organic rota-tions �Table 2�. Grass-clover contributes particularlylarge amounts of organic matter to the soil �Johnstonet al. 1994; de Neergaard 2000; Høgh-Jensen and

Schjoerring 2001�. Those were also the crops forwhich the predictions were most accurate �Figure 3�,but only on loamy soils. Predictions for sandy soilswere in general less reliable. One reason may be thatthe crop production parameter for grass-clover wascalibrated for loamy soil.

Figure 5. Modelled scenarios comparing carbon sequestration for conventional and organic production over 200 years.

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The model overestimated the effect of added resi-dues, while the sensitivity to crop production wasrelatively low. This may indicate that the amount oforganic matter input in the soil is underestimated inthe model.

Scenarios

The simulations showed an increase in soil organiccarbon for all production types when they were con-verted to organic production practices �Figure 5�. Thelargest amounts of carbon were stored following con-version in the arable production scenarios �Table 3�.This is probably because in plant production the dif-ference between conventional and organic productionis more pronounced than in animal production. As thesimulations started after 60 years of conventionalmanagement of the same production type, soils fromthe arable farms were more depleted in organic mat-

ter, and the relative increase in carbon input waslarger.

The simulations also show that the increase inSOM in the arable organic scenario is not merely dueto manure or slurry imported from conventionalfarms, as withholding manure/slurry applications didnot reduce carbon storage �Table 3�. This is surpris-ing, as withholding organic fertiliser will reduce car-bon storage both through the direct effect on carboninput and through the effect on crop production as aresult of reduced nitrogen availability.

The strong effect predicted from catch crops �Table3� should encourage their use on organic and conven-tional farms alike. The catch crop used here wasgrass-clover, and extending the period of the yearwhen the field has vegetation increases total carboninput. The use of farmyard manure �straw-manure� asopposed to slurry caused a significant increase inSOM content �Table 3�. Surprisingly, smaller carbonsequestration was predicted for permanent grazed

Table 3. Simulated soil carbon increase over a 100-year period �g C m � 2�. In addition to the full organic treatments as given in Table 1, theresults of changing practices in the organic treatments are shown: no use of manure/slurry, no catch crop, no straw incorporation in the plantproduction scenario, adding straw incorporation to the animal production scenarios, and replacing slurry with straw-manure �so that the sameamount of total nitrogen is applied�.

Production type Management Increase over 100 years �g C/m2�

Loam Sand

Plant production Conventional � 351.0 � 106.9Organic 1954.6 2276.8Organic without slurry 1325.8 1966.1Organic without catch crop 841.1 1497.9Organic without straw incorporation 1589.1 2079.9Organic without all the above � 352.0 994.1Organic with straw manure 3047.5 3220.7Permanent grassland 1548.3 1527.9

Pig production Conventional � 322.3 � 23.8Organic 1326.6 960.3Organic without slurry 51.1 � 96.7Organic without catch crop 181.3 9.8Organic without both the above � 914.2 � 852.7Organic with straw incorporation 1711.0 1069.2Organic with straw manure 3545.1 2653.2Permanent grassland 1262.1 960.3

Beef/dairy production Conventional � 278.3 � 68.9Organic 1541.4 1363.6Organic without slurry 765.6 856.9Organic without catch crop 304.4 413.2Organic without both the above � 154.2 � 95.5Organic with straw incorporation 1839.1 1583.6Organic with straw manure 2929.7 2507.7Permanent grassland 1029.9 938.6

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grassland than for organic agriculture �Table 3�. Al-though this should be judged with some caution, itdoes suggest that organic agriculture may act as astrong sink of carbon compared to other land-uses.

The simulations showed a relatively fast increasein the first 50 years �10–40 g C m � 2 y � 1 on aver-age�. The increase then levelled off, and after 100years it had reached an almost stable level �Figure 4�in accordance with experience from long-term exper-iments �Christensen and Johnston 1997�. Hence, con-version to organic agriculture only represents atemporary solution to the problem of carbon dioxideemissions.

Although the simulations described here indicatethat relatively large amounts of carbon per unit areacan be sequestered in the soil following conversionto organic agriculture, this has to be weighed againstthe effect of a reduced and/or changed production.Larger areas may be needed under organic agricul-ture, and so compete with other land uses and naturalvegetation, which will affect the total carbon balance�Smith et al. 2000�.

Acknowledgements

The authors wish to thank Bent T. Christensen at theDanish Institute of Agricultural Sciences for kindlyproviding data, and Bjørn Molt Petersen for advice onthe use of the data, and both are acknowledged forcommenting on this manuscript. Help and advice onthe use of the CENTURY model from Cindy Keoughat Colorado State University is gratefully acknowl-edged.

References

Boons-Prins E.R., de Koning G.H.J., van Diepen C.A. and Penningde Vries F.W.T. 1993. Crop specific simulation parameters foryield forecasting across the European Community. SimulationReports CABO-TT, no. 32, DLO, Wageningen Agricultural Uni-versity, The Netherlands.

Christensen B.T. 1990. Effect of cropping system on soil organicmatter content. II. Field experiment on a sandy loam, 1965–1985�in Danish with English summary�. Tidsskrift for Planteavl 94:161–169.

Christensen B.T., Petersen J., Kjellerup V. and Trentemøller U.1994. The Askov Long-Term Experiments on Animal Manureand Mineral Fertilizers: 1894–1994, SP report, No. 43. DanishInstitute of Plant and Soil Sciences, Lyngby, Denmark.

Christensen B.T. and Johnston A.E. 1997. Soil organic matter andsoil quality – lessons learned from long-term experiments at

Askov and Rothamsted. In: Gregorich E.G. and Carter M.R.�eds�, Soil Quality for Crop Production and Ecosystem Health.Elsevier, Amsterdam, The Netherlands, pp. 399–430.

Christensen B.T. and Trentemøller U. 1995. The Askov Long-TermExperiments on Animal Manure and Mineral Fertilizers, 100thAnniversary Workshop, SP report, No. 29. Danish Institute ofPlant and Soil Science, Lyngby, Denmark.

Cruz R.T., O’Toole J.C., Dingkuhn M., Yambo E.B., Thangaraj M.and De Datta S.K. 1986. Shoot and root responses to water defi-cits in rainfed lowland rice. Austr. J. Plant Physiol. 13: 567–575.

Eurostat, European Commission, European Environment AgencyTask Force, DG XI and PHARE European Commission, UnitedNations Economic Commission for Europe, Organisation forEconomic Cooperation and Development and World Health Or-ganisation 1995. Europe’s Environment: Statistical Compendiumfor the Dobris Assessment. Office for Official Publications of theEuropean Communities, Luxembourg.

Gilmanov T.G., Parton W.J. and Ojima D.S. 1997. Testing the‘CENTURY’ ecosystem level model on data sets from eightgrassland sites in the former USSR representing a wide soil/cli-matic gradient. Ecol. Model. 96: 191–210.

Halberg N. and Kristensen I.S. 1997. Expected crop yield losswhen converting to organic dairy farming in Denmark. Biol.Agric. Horticult. 14: 24–41.

Hansen B., Kristensen E.S., Grant R., Høgh-Jensen H., Simmels-gaard S.E. and Olesen J.E. 2000. Nitrogen leaching from con-ventional versus organic farming systems – a systems modellingapproach. Eur. J. Agron. 13: 65–82.

Hirose T. 1988. Modelling the relative growth rate as a function ofplant nitrogen concentration. Physiol. Plant. 72: 185–189.

Høgh-Jensen H. and Schjoerring J.K. 2001. Rhizodeposition of ni-trogen by red clover, white clover and ryegrass leys. Soil Biol.Biochem. 33: 439–448.

Johnston E., McEwen J., Lane P.W., Hewitt M.V., Poulton P.R. andYeoman D.P. 1994. Effects of one to six year old ryegrass-clo-ver leys on soil nitrogen and on the subsequent yields and fer-tilizer nitrogen requirement of the arable sequence winter wheat,potatoes, winter wheat, winter beans �Vicia faba� grown on asandy loam soil. J. Agric. Sci. 122: 73–89.

Kelly R.H., Parton W.J., Crocker G.J., Grace P.R., Klor J., Korch-ens M., Poulton P.R. and Richter D.D. 1997. Simulating trendsin soil organic carbon in long-term experiments using the CEN-TURY model. Geoderma 81: 75–90.

Lal R. and Kimble J.M. 1997. Conservation tillage for carbon se-questration. Nutr. Cycl. Agroecosyst. 49: 243–253.

Lampkin N. 1990. Organic Farming. Farming Press, Ipswich, UK.Løes A.-K. and Øgaard A.F. 1997. Changes in nutrient content of

agricultural soil on conversion to organic farming in relation tofarm-level nutrient balances and soil contents of clay and organicmatter. Acta Agric. Scand. B: Soil Plant Sci. 47: 201–214.

Metherell A.K., Harding L.A., Cole C.V. and Parton W.J. 1993.CENTURY soil organic matter model environment. Technicaldocumentation. Agroecosystem version 4.0. Great Plains SystemResearch Unit Technical Report No. 4. USDA-ARS, Fort Col-lins, CO.

de Neergaard A. 2000. Rhizosphere processes and below-groundcarbon dynamics in a combined food and energy cropping sys-tem. Ph.D. Thesis. Royal Veterinary and Agricultural University,Copenhagen, Denmark.

23

Parton W.J. and Rasmussen P.E. 1994. Long-term effects of cropmanagement in wheat-fallow: II. CENTURY model simulations.Soil Sci. Soc. Am. J. 58: 530–536.

Parton W.J., Ojima D.S. and Cole C.V. 1994. A general model forsoil organic matter dynamics: sensitivity to litter chemistry, tex-ture and management. In: Bryant R.B. and Arnold R.W. �eds�,Quantitative Modelling of Soil Forming Processes. Soil Sci. Soc.Am. Special Publication, SSSA, Madison, WI, pp. 147–167.

Parton W.J., Ojima D.S. and Schimel D.S. 1996. Models to evalu-ate soil organic matter storage and dynamics. In: Carter M.R. andStewart B.A. �eds�, Structure and Organic Matter Storage in Ag-ricultural Soils. CRC Press, Inc., Boca Raton, FL, pp. 421–448.

Parton W.J., Stewart J.W.B. and Cole C.V. 1988. Dynamics of C,N, P and S in grassland soils: a model. Biogeochemistry 5: 109–131.

Parton W.J., McKeown B., Kirchner V. and Ojima D.S. 1992.CENTURY Users Manual. NREL Publication. Colorado StateUniversity, Fort Collins, CO.

Parton W.J., Schimel D.S., Cole C.V. and Ojima D.S. 1987. Analy-sis of factors controlling soil organic matter levels in great plaingrasslands. Soil Sci. Soc. Am. J. 51: 1173–1179.

Paustian K., Parton W.J. and Persson J. 1992. Modelling soil or-ganic matter in organic-amended and nitrogen-fertilized long-term plots. Soil Sci. Soc. Am. J. 56: 476–488.

Paustian K., Cole V.C., Sauerback D. and Sampson N. 1998. CO2

mitigation by agriculture: An overview. Climatic Change 40:135–162.

Romanya J., Cortina J., Falloon P., Coleman K. and Smith P. 2000.Modelling changes in soil organic matter after plantingfast-growing Pinus radiata on Mediterranean agricultural soils.Eur. J. Soil Sci. 51: 627–641.

Salomonsen K.B. 2000. Potentiale og barrierer for biogasproduk-tion i Danmark ved omfattende økologisk jordbrug med fokuspå dyrkningsjordens kulstofforhold. Ph.D. Thesis. Institut forBygninger og Energi, Danmarks Tekniske Universitet, Lyngby,Denmark.

Scharling M. 2000. Klimagrid – Danmark, normaler 1961–90,måneds- og årsverdier. DMI, Copenhagen, Denmark.

Schlesinger W.H. 1999. Carbon sequestration in soils. Science 284:2095.

Schlesinger W.H. 2000. Carbon sequestration in soils: some cau-tions amidst optimism. Agric. Ecosyst. Environ. 82: 121–127.

Smith P., Powlson D.S., Glendining M.J. and Smith J. 1997a. Po-tential for carbon sequestration in European soils: preliminaryestimates for five scenarios using results from long-term experi-ments. Global Change Biol. 3: 67–79.

Smith P., Smith J.U., Powlson D.S., McGill W.B., Arah J.R.M.,Chertov O.G. et al. 1997b. A comparison of the performance ofnine soil organic matter models using datasets from seven long-term experiments. Geoderma 81: 153–225.

Smith P., Powlson D.S., Glendining M.J. and Smith J.U. 1998.Preliminary estimates of potential for carbon mitigation in Eu-ropean soils through no-till farming. Global Change Biol. 4:679–685.

Smith P., Powlson D.S., Smith J.U., Fallon P. and Coleman K.2000. Meeting Europe’s climate change commitments: quantita-tive estimates of the potential for carbon mitigation by agricul-ture. Global Change Biol. 6: 525–539.

Stolze M., Piorr A., Häring A. and Dabbert S. 2000. The environ-mental impact of organic farming in Europe. In: Organic Farm-ing in Europe: Economics and Policy, Vol. 6, University ofHohenheim, Germany, p. 127.

Sylvia D.M., Fuhrmann J.J., Hartel P.G. and Zuberer D.A. 1998.Principles and Applications of Soil Microbiology. Prentice Hall,Englewood Cliffs, NJ.

Vereecken H., Jansen E.J, Hack-ten Broeke M.J.D., Swerts M.,Engelke R., Fabrewitz S. and Hansen S. 1991. Comparison ofsimulation results of five nitrogen models using differentdatasets. In: Commission of European Communities �ed.�, Soiland Groundwater Research, Report II Nitrate in Soils, Commis-sion of the European Communities, Luxembourg, pp. 321–338.

Valentini R., Matteucci G., Dolman A.J., Schulze E.-D., RebemannC., Moors E.J. et al. 2000. Respiration as the main determinantof carbon balances in European forests. Nature 404: 862–865.

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