REGULAR ARTICLE

Modeling black carbon degradation and movement in soil

Bente Foereid & Johannes Lehmann & Julie Major

Received: 3 September 2010 /Accepted: 16 March 2011 /Published online: 27 April 2011# Springer Science+Business Media B.V. 2011

Abstract Black carbon (BC), the residue from burningwith insufficient oxygen supply, is assumed to be verystable in the environment. Here we present a simplemodel for BC movement and decomposition in soilsbased on the assumption that BC consists of two fractionswith different turnover time, and that BC can move in theenvironment as well as decompose. Decomposition ratewas calibrated against laboratory data, whilst a recentfield experiment was used to calibrate losses fromdownward movement through the soil profile. Lossesby erosion are still poorly quantified, but mass balanceindicates that they may be one of the most importantfluxes. The model was able to acceptably predict CO2

production from BC as well as BC left in the soil at theend of the experiment, although BC in the subsoil wasunderestimated. The model was sensitive to erosion rate(varied ±50%), moisture and temperature response

function on a 100-year time scale. The model was notsensitive to the decomposition rate of the stable pool ona 100 year time scale, but it was very sensitive to that ona millennial time scale. Implications and directions forfuture research are discussed.

Keywords Black carbon . Decomposition . Simulationmodel . Dissolved black carbon . Biochar

Introduction

Fire occurs in most terrestrial ecosystems (Bowman etal 2009), and has been studied as a major disturbancefactor (Bond and Keeley 2005). Fire consumesvegetation carbon and produces CO2 and othergreenhouse gases, but a fraction of the vegetationcarbon is transformed into black carbon (BC) duringincomplete combustion and most of this BC isinitially left on the soil surface (Forbes et al. 2006;Kuhlbusch and Crutzen 1995; Kuhlbusch, 1998;Schmidt and Noack 2000). Although the productionof BC is small as a percentage of total vegetationcarbon consumed by fire (<10%, Forbes et al.(2006)), it is still important in the global carbon cyclesince it degrades slowly in the environment. Itsformation is often credited as a CO2 sink bytransferring fast-cycling carbon from the atmo-sphere–biosphere system into much slower cyclingcarbon forms that may persist for millennia (Masiello2004; Preston and Schmidt 2006).

Plant Soil (2011) 345:223–236DOI 10.1007/s11104-011-0773-3

Responsible Editor: Eric Paterson.

Electronic supplementary material The online version of thisarticle (doi:10.1007/s11104-011-0773-3) contains supplementarymaterial, which is available to authorized users.

B. Foereid (*) : J. LehmannCornell University,Bradfield Hall,Ithaca, NY 14853, USAe-mail: bf228@cornell.edu

J. Major1555 ch Ste-Claire,Rivière-Beaudette, Qc J0P 1R0, Canada

Soils rich in BC are usually fertile, and charcoalmaterial added to the soil in the Amazon hasdramatically improved fertility in the so-called “TerraPreta” soils compared to adjacent soils (Glaser et al.2002; Lehmann et al. 2003; Major et al. 2005). Theuse of charred material as a soil amendment (“bio-char”) has been suggested also in modern times(Lehmann et al. 2006; Lehmann and Joseph 2009).It is thought that biochar amendment may also be away to sequester carbon in the soil (Gaunt and Cowie2009; Lehmann et al. 2006), but little effort has goneinto mathematically describing its long term stabilityand fate. Better predictions of stability would alsoimprove assessments of the greenhouse gas mitigationeffect of biochar application.

Simulation models can be used to investigatepossible effects of perturbations on a complicatedsystem. Increasingly, models are used to predict theeffect of environmental change (e.g. Betts et al.1997; Cox et al. 2000; Jones et al. 2005). Whenmodels are used to predict biotic feedback to climatechange, the largest uncertainty is in the soil carbonpool (Friedlingstein et al. 2006). Models of soilcarbon dynamics usually assume that soil organicmatter can be conceptually assigned to pools withdifferent turnover time (Jenkinson et al. 1987; Partonet al. 1987). A large part of the pool with the slowestturnover time is probably BC, but this cannot easilybe verified within model structures.

Some attempts have been made at modeling BCdecomposition or including BC in carbon turnovermodels (Lehmann et al. 2008a; Zimmerman 2010),sometimes as an inert pool (Skjemstad et al. 2004). IfBC decomposition was included, it is typicallydescribed as one pool with exponential decay.However, whilst incubation experiments and chro-nosequence studies indicate that BC is degraded tosome extent (Hamer et al. 2004; Kuzyakov et al.2009; Nguyen et al. 2009; Nguyen and Lehmann2009; Zimmerman 2010), incubations with aged BC(Cheng et al. 2008; Liang et al. 2008), budgetanalyses (Lehmann et al. 2008a) and 14C dating ofBC found in soils indicate that at least part of itlasts for millennia in some systems (Passenda et al.2001; Gouveia et al. 2002; Gavin et al. 2003;Schmidt et al. 2002). This suggests that there aredifferent chemical fractions within BC that turn overat different rates. Using a model with only one poolmay therefore not be adequate, and multi-pool

models have been suggested (Lehmann et al. 2009)but have not yet been developed. Hammes et al.(2008) discussed the possibility of using a two- ormulti-pool model, but concluded that not enoughdata were available to parameterize it. However,many experiments on BC turnover have beenconducted in recent years, making at least parame-terization of the pool with the fastest turnover timepossible (Hamer et al. 2004; Kuzyakov et al. 2009;Nguyen et al. 2009; 2010; Nguyen and Lehmann2009; Whitman 2010; Zimmerman 2010).

Nguyen et al. (2009) found that BC in a chronose-quence after fire could be adequately described as atwo-pool model where one pool disappeared withindecades whilst the other did not degrade. However,the stable pool probably does degrade over very longtime-scales, and in fact it should have a very low rateof decomposition. This suggests that BC decomposi-tion dynamics can be described with a two-poolmodel. It is also known that BC can move in thelandscape through losses by horizontal and verticalmovement, with soil texture, physiography, andclimate (and the presence of permafrost) adding tothis complexity (Guggenberger et al. 2008; Hockadayet al. 2007; Leifeld et al. 2007; Major et al. 2010a;Rumpel et al. 2006, 2009). To predict BC content leftin the soil at a certain time, both decomposition andmovement need to be taken into account.

Here we developed a model for BC degradationand movement in soil based on recent evidence fromincubation and field studies. We used similar theoryas used in most soil organic matter models, but weaimed to keep the model as simple as possible, andbase it on experimental evidence as much as possible.The model was calibrated and tested using laboratorydata and a data set where field applied BC wasmonitored in the soil profile. The model was used toassess the importance of BC decomposition versusmovement in the landscape. We also ran two longersimulations and assessed the sensitivities of uncertainparameters.

Materials and methods

Field data and modeling setup

For calibration of BC movement in soils, we useddata from an experiment where a known quantity

224 Plant Soil (2011) 345:223–236

of BC (23.2 kg C/ha) with a distinct isotopicsignature was incorporated into the soil at thebeginning of the dry season and monitored as itmoved and was decomposed (Major et al. 2010a).The experiment was carried out in the ColombianLlanos region (04°10′15.2″N, 72°36′12.9″) over a2 year period. The soil at the site was a sandy clayloam (64% sand, 21% clay, pH 3.9, 0.65% C and0.044% N in top soil). Long-term average annualrainfall measured approximately 200 km northeast ofthe plot is 2,200 mm, and 95% of precipitation fallsbetween April and December. A marked dry seasonoccurs between January and March, and averageannual temperature is 26°C. Downwards movementof water and BC at 0.15 and 0.3 m depth wasmeasured during the rainy season in both years. Theamount of added BC left in the soil profile wasmeasured towards the end of the experiment. Soilrespiration was only measured during the secondyear of the experiment, but a similar experimentalplot was established adjacent to the first one, formonitoring soil respiration with BC in the first yearafter application. Those measurements are used asfirst year respiration here. The data were used both tocalibrate water movement and to run a simple test ofthe decomposition model. In the field study leachedBC was separated into particulate organic matter(> 0.7 μm) and dissolved organic matter (< 0.7 μm),but as we could not find any difference betweenthem in how they behaved in the soil, they are

analyzed together here. The BC model was imple-mented in Microsoft Excel, and all curve fitting wasdone in Excel or Sigmaplot. The model was initiallyimplemented with daily time steps. An implementa-tion of the model is available as online supplemen-tary material. A list of calibrated parameters can befound in Table 1.

We used the DayCent model (Del Grosso et al.2001; Parton et al. 1998) to calculate soil watercontent, temperature and water fluxes. These wereused as inputs to the BC model developed.Necessary climate input (temperature and rainfall)were measured at a site with a distance of 3.6 kmduring the rainy season. During the dry seasonclimate data measured 200 km away, but at similaraltitude, were used after being adapted using alinear relationship between the two measurementsobtained where the two data sets overlap. Soilprofile data (bulk density, water retention, textureorganic matter content, saturated hydraulic conduc-tivity and pH) were measured at the site (Major etal. 2010a). The option “warm season grasses” inDayCent was chosen as plant type to simulate thegrowth of the native savanna grasses that wereallowed to regrow after plot establishment at thesite. Simulated plant productivity was also tuned tomatch observed levels of biomass and growingseason length, and water flux rates predicted by themodel were checked to make sure they werecomparable to those observed in the field.

Table 1 Parameters for the model of BC degradation and movement in soil

Symbol Value used Unit Interpretation Reference/motivation

k1 0.0038 d−1 Maximum decomposition rate for labile pool Whitman (2010)

f 0.122 – Fraction of new BC in labile pool Whitman (Whitman 2010)

k2 1.37E–06 d−1 Maximum decomposition rate for stabile pool Own estimate

a 1.452 – Parameter for temperature response Calculated from Nguyen et al. (2010)

b −2.4599 – Parameter for temperature response Calculated from Nguyen et al. (2010)

c 4 – Parameter for moisture response From the DayCent model

d −6 – Parameter for moisture response From the DayCent model

C1 8e–7 m−1 Constant in BC transport equation in topsoil Fitted to data set from Major et al. (2010a)

C2 0.01 m−1 Constant in BC transport equation in subsoil Fitted to data set from Major et al. (2010a)

fr 0.9 – Fraction of BC available for erosion Nguyen et al. (2009)

r 0.03 d−1 Rate of erosion Total runoff within range found by Rumpel et al.(2009), reaching stable state as Nguyen et al. (2009)and fit final BC to data in Major et al. (2010a)

Plant Soil (2011) 345:223–236 225

BC model and calibration

We assume that the dynamics of BC in the soil can bedescribed as shown in Fig. 1. BC can decompose aswell as leach to a deeper soil layer and eventually outof the profile, or it can be lost by runoff (erosion) onthe surface. Decomposition is described by a two poolmodel:

BCðtÞ ¼ BC0� ðf »e�tk1 þ ð1� f Þ»e�tk2Þ ð1Þ

Where BC(t) is the amount of BC left at time t, BC0is the amount of BC at time 0, f is the fraction of BC0that is labile (with the fastest turnover rate) and k1 andk2 are constants for decomposition rate of the labileand stable (with the slowest turnover rate) poolsrespectively. Climatic conditions are assumed to influ-ence decomposition rates by modifying the k-values asexplained below.

The labile fraction (f) and decomposition rate ofthe labile pool (k1) for different types of BC werebased on data from an experiment where BC wasincubated for 3 years under near optimal conditionsgenerating maximum decomposition rates (Whitman2010). This experiment included multiple plantmaterials which were pyrolysed at various temper-atures. Since the BC in the experiment of Major et al.(2010a) was produced from prunings of mango treepyrolysed at 400–600°C, we calculated constantsfrom the data of Whitman (2010) from materialsproduced at 500°C, using averaged data from two

woody plant materials (pine and oak) as well as datafrom herbaceous material (corn stalks). It wasassumed that within the time frame of this experi-ment, only the decomposition of the labile pool couldbe detected. The stable pool was therefore assumed tobe constant, and Eq. 1 was fitted to the data, assumingk2=0. As there was large variability in the data set,the fit was fair (r2=0.37; N=20; P<0.012). Decom-position rates of the stable fraction cannot beaccurately calibrated using any available data. How-ever, the range of possible values can be somewhatconstrained. 14C dating has found ages from 64 to12,220 years for BC found in the soil (Gavin et al.2003). Indicating that the turnover time of BC is long.However, it is not possible to quantify turnover timefurther from these data, as initial amount of BC is notknown. Lehmann et al. (2008a) calculated turnovertimes based on BC stocks and assumptions on firefrequency and BC formation and obtain a range of718–9,259 year. However, this calculation assumed aone-pool turnover model. Nguyen et al. (2009) couldnot measure any significant change in the stable poolover 100 years, suggesting that the turnover time mustbe significantly longer than 100 years. It can beconcluded from this that the turnover time of thestable pool is probably in the order of thousands ofyears. Here we use a turnover time of 2,000 years as abase scenario, but we also explore sensitivity tovarying this parameter.

Nguyen et al. (2010) measured decompositionrates for BC at different temperatures. The resultsshow that the actual decomposition rate can be

Stable

Stable

Labile

Labile

CO2

Leaching

ErosionErosion

Decomposition

15cm

30cm

CO2

CO2

TemperatureMoistureTemperature

Moisture

Water flow Water flow

Added black carbon

Fig. 1 Flow diagram of thesuggested model of BCdecomposition and transportin soils. Boxes show poolsand arrows show fluxes.Dashed lines show fluxeswhich are affected bytemperature and moisturemodifiers and water flow

226 Plant Soil (2011) 345:223–236

expressed as the product of a maximum decomposi-tion rate and a modifier:

Mt ¼ a» lnðTÞ � b ð2ÞWhere Mt is the modifier, T is temperature and a

and b are empirical constants, values are given inTable 1. The parameters were adapted assuming thestandard decomposition as in Whitman (2010), andthe temperature modifier function from Nguyen et al.(2010). We used the labile fractions and k1 for BC asfound above, assumed that the decomposition mea-sured over this short time period was only from thelabile pool, and calculated the parameters (Fig. 2).The same temperature sensitivity was used for thestable pool, but the effect of changing that assumptionwas investigated in a sensitivity test (see below).

The only data we could find on the effect ofmoisture on BC degradation was by Nguyen andLehmann (2009) where saturated and non-saturatedwater conditions were compared. The results showedlittle effect of water regime in most cases. We foundno data on the effect of water content below fieldcapacity on decomposition rates of BC. We thereforeuse the moisture modifier (Mw) from the DayCentmodel where decomposition rate increases from closeto 0 at dry conditions to a maximum at high watercontent:

Mw ¼ 1

1þ c»eð�d�relative water contentÞ ð3Þ

where c and d are constants.

We assumed that transported BC represented aconstant fraction of BC remaining in the soil on eachday when downwards water flow occurred, and thatBC transport was also modified by temperature andmoisture content in a similar way as decomposition:

BCt ¼ C»BC»Mt»Mw ðwhen Wf > 0Þ ð4Þwhere BCt is the transport of BC out of the soil layer,BC is the amount of BC present in the soil layer, C isan empirical constant, Wf is the water flow out of thesoil layer and Mt and Mw are the temperature andmoisture modifiers, respectively. Climate modifierswere included as a weak correlation between carbondioxide evolution and BC flux was observed (Fig. 3).As there was (surprisingly) no correlation betweenvolume of water and amount of BC leached over anygiven time period (Fig. 4), water flow volume is notincluded in the equation. However, we assumed thatno BC downward movement could take place withoutsome water movement. The transport of each fraction(stable and labile) was assumed to be proportional tothe fraction of each present. The empirical constantswere calibrated so that the total downward movementover the 2-year period was similar in the measuredand simulated results. This model is calibrated for aregion with flat topography, and should not be appliedon strongly sloping land.

We had no direct data on BC lost by runoff/erosion although this may be the most importantflux (Major et al. 2010a; Rumpel et al. 2009). BCis also less dense than other soil components, so it

y=1.452ln(x)-2.4599

r2=0.7094

Temperature (oC)

0 20 40 60

Tem

pera

ture

mod

ifier

-1

0

1

2

3

4

5

Fig. 2 Calibration of temperature modifier based on data fromNguyen et al. (2010)

CO2 efflux (kg CO2-C ha-1)

0 20 40 60 80 100

BC

leac

hing

(g

ha-1

)

0

200

400

600

800

1000

1200

1400

1600

1800BC leaching below 15 cmBC leaching below 30 cmBC below 15 cm r2=0.1329BC below 30 cm r2=0.2649

Figure 3 Observed BC movement downwards in the soilprofile as a function of CO2 fluxes measured over the sametime period. Correlations are shown on graph. Data from Majoret al. (2010a)

Plant Soil (2011) 345:223–236 227

may be expected to move and float more easily.Guggenberger et al. (2008) found that a largerfraction of BC than other soil organic matter waseroded. There is therefore reason to believe thatmodels developed for erosion of other soil compo-nents are not valid for BC. Furthermore, mostmodels of soil erosion in the literature are verydetailed and require a large number of calibratedparameters (e.g. Nearing et al. 2005; Neitsch et al.2005). We can therefore not apply them directly. Itis, however, reasonable to assume that the quantityof BC lost depends on the quantity still left, i.e. asan exponential decay function. Nguyen et al. (2009)found that large pieces of BC reached a stable levelafter about 20 years. We believe the larger pieces aremost prone to erosion. We therefore assume that afraction of BC will not erode, and obtain a function forloss by erosion similar to decomposition:

BCtðtÞ ¼ BC0� ðfr»e�tr þ ð1� frÞÞðwhen er > 0Þ ð5Þ

where BC(t) is the amount of BC left at time t, BC0is the amount of BC at time 0, fr is the fraction ofBC0 that can erode, r is the erosion rate and er iswater runoff flow. Similar to downward movement,we assume that erosion of BC can only take placewhen there is water runoff. Erosion rate is modifiedwith climatic factors in a similar way as decompo-sition. Nguyen et al. (2009) found that the stablefraction was about 10%. We had no direct measure-ment of r (erosion rate), but Rumpel et al. (2009)

found that 7–55% of surface deposited BC afterslash-and-burn with no incorporation can be subjectto horizontal transport when slope was less than 1%,and Nguyen et al. (2009) found that it took about20 years to reach a stable level of larger pieces ofBC. We also tried to fit the model to predict close tothe measured amount of BC at the end of theexperiment of Major et al. (2010a).

Long term simulation and sensitivity

A longer simulation of 100 years was carried outfor the site, based on data from the two year fieldstudy (Major et al. 2010a) repeated in monthlytime-steps. Water flow was averaged over thenumber of days when water flow occurred. Thepredicted longer term decomposition and transportof BC was assessed. Since some parameters in themodel are not based on firm data, individualsensitivity tests were carried out. This applies tothe decomposition rate for the stable pool and themoisture and temperature modifier equations, aswell as erosion rate. The high and low ends of therange of the stable pool turnover times were tested(500 and 5,000 years). For the moisture modifier,the DayCent model has a second set of parameters(c=30, d=−8.5), which were tried in addition to thestandard parameter as in Table 1 (c=4, d=−6). For thetemperature modifier, a simulation was carried out withthe modifier scaled to reach unity at 30°C, thetemperature at which the standard decomposition rate

Topsoil

Water leaching (Lm-2)

0 50 100 150

BC

leac

hing

(gh

a-1 )

0

200

400

600

800

1000

1200

Subsoil

0 20 40 60 80 100

Fig. 4 Downward transport of BC out of the topsoil (left) and out of the subsoil (right) as a function of water flow by downwardmovement over the same time period. Data from Major et al. (2010a)

228 Plant Soil (2011) 345:223–236

was calibrated (Whitman 2010). Erosion rates (r) wereincreased and decreased by 50%.

To assess model behavior in the very long term, along simulation of 2,000 years was also carried out,using monthly time steps as above. Also on this timescale, sensitivities to erosion rate and the turnovertime of the stable pool were assessed, using the samevariations as above. The effect of making the stablepool more sensitive to temperature was also assessed,using the assumptions of Knorr et al. (2005).Activation energy of the pool was assumed to dependon turnover rate as an empirical formula from Knorret al. (2005), where temperature sensitivity increasewith pool stability. The temperature sensitivity of thestable pool was modified assuming Arrhenius kineticsas in Knorr et al. (2005) so that the turnover rate at15°C did not change.

Results

The model could predict the overall pattern of BCdecomposition and downward movement after cali-bration, but not short term variation. CO2 productionwas predicted to fluctuate, presumably with climaticfactors, decline during the measurement period and besomewhat lower during the dry season (Fig. 5).However, substantial CO2 efflux was predicted alsoin the dry season. The levels of CO2 productionpredicted by the model were somewhat higher than

the measured values (Major et al. 2010a), and couldnot track short term variation (Fig. 6). The modelcould not predict temporal variation in BC downwardmovement, but the total BC amounts were calibratedto match observed values. The observed data alsoshow more BC transport out of than into the subsoilduring the first rainy season. This means that someBC may have moved to the subsoil before the rainyseason, further supporting the idea that some move-ment took place during the first dry season. Most ofthe BC remained in the topsoil during the experimen-tal period, since amounts of BC applied reasonablycorresponded with observed values at the end of theexperiment (Fig. 7). The model underestimated theamount of BC in the subsoil at the end of theexperiment (Fig. 7).

The 100-year simulation showed an exponen-tial decrease in the amount of BC in the topsoil,and a rise followed by decline in the subsoil(Fig. 8). Sensitivities are expressed as averagedifference over the 100 year simulation. Changingthe decomposition rate of the stable pool had verylittle effect at this time scale (Fig. 8), less than a 1%difference from the standard on average over the100 years. Changing the parameters of the moisturemodifier had quite a large effect on BC left in thesoil (58%), as had changing the temperaturemodifier (40%) (Fig. 8). Also erosion rate wasimportant (Fig. 8), and increasing the erosion rate

Time since BC application (days)

0 200 400 600

Pre

dict

ed C

O2

prod

uctio

n fr

om B

C (

g C

O2-

C m

-2)

0.0

0.2

0.4

0.6

0.8

1.0

Fig. 5 Model output of CO2 efflux from the added BC duringthe 2-year experiment as a function of time after BCapplication. The dry season is approximately from day 324 today 452 on the figure (November 20th to March 28th)

RMSD=0.24

Time since start of the experiment (days)

100 200 300 400 500 600 700 800

CO

2 pr

oduc

tion

from

BC

(gC

O2-

Cm

- 2)

0

5

10

15

20

25

30

MeasuredSimulated

Fig. 6 Comparison of observed and simulated CO2 efflux fromthe added BC as a function of time after BC application. Fielddata was collected from a “false time series”, where data for firstand second year after BC application were collected during thesame year and therefore both years are simulated with climatedata from the second year. Data from Major et al. (2010a)

Plant Soil (2011) 345:223–236 229

decreased BC content by 13% on average, whiledecreasing the erosion rate increased BC content inthe soil by 37% on average. However, these wereall important mostly on short to medium timescales. Towards the end of the 100 year simulation,neither erosion rate nor the modifiers had mucheffect on BC left in the soil (Fig. 8).

On a 2,000-year time scale, the time when thelabile pool was depleted and erosion stopped caneasily be identified, early in the simulation(Fig. 9). On this time scale the model was verysensitive to the decomposition rate of the stablepool (turnover rate of 500 years resulted in 62% lessBC on average, and turnover rate of 5,000 yearsresulted in 174% greater BC). The model wassensitive to decreased erosion rate (156% increase),but not to increased erosion rate (5% decrease)(Fig. 9). The reason for this is that only a fractioncan erode, and whether this fraction is depletedbefore or after the labile pool is depleted bydecomposition is critical. The model was also

somewhat sensitive to the temperature sensitivityof the stable pool (27% decrease in BC content onaverage) (Fig. 9).

The model predicted that the largest flux of BC(75%) was runoff/erosion, with decompositionbeing second (20%), while losses by downwardmovement were very small in comparison (0.5%).Erosion was proportionally somewhat more impor-tant in the short term than the long term, butremained the most important process on all time-scales (Table 2). Downward movement becameproportionally more important on longer time-scalesthan shorter time scales (0.03% of the total BC lossfrom the site after 2 years and 0.54% of the total BCloss from the site after 2,000 years), but was stillunimportant with respect to total BC loss from thesite on all time-scales (Table 2). A large proportion(47%) of total decomposition over 2,000 years waspredicted to occur during the first 2 years.

0-15 cm

0

500

1000

1500

2000

2500

standardstable pool decomposition rateerosion ratemoisture modifiertemperature modifier

15-30 cm

Time since BC application (years)

0 20 40 60 80 100

BC

in s

oil l

ayer

(g

m-1

)

0.0

0.1

0.2

0.3

Fig. 8 Results from a 100-year simulation using differentvalues for the turnover rate of the stable BC pool (varyingbetween 500 and 5,000 years, standard 2,000 years) anderosion rate increased and decreased by 50%, as well aschanged moisture and temperature modifiers. Standard valuesfor all parameters are given in Table 1

0-15 cm

400

600

800

1000

1200

1400

1600

1800

2000

2200

2400

2600

15-30 cm

Time since BC application (days)

0 200 400 600

Bla

ck c

arbo

n in

laye

r (g

m-2

)

0

5

10

15

20

25 SimulatedMeasured

Fig. 7 Predicted and measured BC content in both topsoil andsubsoil as a function of time after BC application. Data fromMajor et al. (2010a)

230 Plant Soil (2011) 345:223–236

Discussion

Our model could predict observed patterns of BCdynamics at the experimental site to a reasonabledegree. In accordance with measurements (Majoret al. 2010a), the model predicts that decomposi-tion is much more important as a mechanism forcarbon loss from the system than downwardtransport with water. Downward movementaccounted for a slightly larger portion of totalcarbon loss in the long term simulations. This is aconsequence of the assumption chosen here whereerosion and decomposition are both assumed tohave a stable fraction, but downward movement isnot. Even though current data are weak in thisregard, this is a reasonable scenario, as significantamounts of BC are typically found in subsoils whileactual rates of downward movement are low (Dai etal. 2005; Rodionov et al. 2006; Leifeld et al. 2007;Major et al. 2010a). However, losses from down-ward movement are unimportant in terms of massbalance, and a model could ignore them withoutimpacting the accuracy of the estimate of amount ofBC remaining at the end of the simulation. The

model predicted, in agreement with the experimen-tal observation that the majority of the BC remainedat or near the surface for a long time. This impliesthat applied BC may be consumed by subsequentvegetation fires. This pathway of BC loss must alsobe quantified. Experimental evidence indicates thatrecent fire history often does not affect BC contentin the topsoil to a significant extent, indicating thatfires may consume as well as produce BC (Dai et al.2005; Knicker et al. 2006).

One of the main findings from the work of Majoret al. (2010a) was that there was a large flux of BCout of the system that was not accounted for. Major etal. (2010a) speculate that most of this loss can beattributed to surface erosion, the only unmeasuredflux. Our simulation also indicates that decompositionand to some extent downward movement and erosion,can occur during the dry season, when no measure-ments were made by Major et al. (2010a). This isbased on the temperature and moisture content in thesoil and water flux between layers as predicted byDayCent. The conclusion that decomposition happensalso in the dry season will therefore depend on themoisture modifier, which is an uncertain and sensitiveparameter. Furthermore, we also needed to assumelarge erosion losses to achieve predictions of BC leftin the soil at the end of the experiment that are closeto the measured values. This means that erosion, themost poorly quantified BC flux in our model, isprobably the most important pathway for loss of BCfrom the system. This highlights the need for betterquantification of the size of this flux, how erosionoccurs temporally and the fate of eroded BC. BCerosion rate may also depend on soil texture. We havenot included this in our model, as we only have datafrom one site, and therefore cannot parameterize it.Future work should focus on measuring BC erosionon different soil types. Since surface erosion ispotentially such an important loss factor, large-scaledeployment of biochar technology will require thedevelopment of best management practices to mini-mize such losses.

Time since BC application (years)

0 500 1000 1500 2000

BC

in 0

-30

cm s

oil (

g m

-2)

0

100

200

300

400

500

standarderosion ratestable pool decomposition ratetemperature sensitivity

Fig. 9 Results from a 2000-year simulation with the samechanges in stable pool turnover rate and erosion rate as in Fig. 8as well temperature sensitivity of the stable pool increasedaccording to Knorr et al. (2005). Standard values for allparameters are given in Table 1

Decomposition Downward movement Runoff Total

Short term 9.78 0.02 49.06 58.86

Medium term 11.68 0.13 76.09 87.89

Long term 20.65 0.52 74.83 96.00

Table 2 Fluxes of BCduring a 2-year (short term),a 100-year (medium term)and a 2000-year (long term)simulation in percent of thetotal at the beginning

Plant Soil (2011) 345:223–236 231

Many of the shortcomings of the model can bealleviated by experiments where a known quantity ofBC is followed over time in the field, similar to theexperiment of Major et al. (2010a), but where BCerosion is also measured directly. The experimentshould ideally be repeated under different conditionsof soil types and climates, to obtain a betterunderstanding of how the magnitude of fluxesdepends on environmental conditions.

Downward movement in this heavy textured soilwas not an important bulk flux of BC, but may still beneeded to explain that BC is found in subsoils (Dai etal. 2005; Leifeld et al. 2007; Major et al. 2010a).However, more BC was observed in the sub-soil at theend of the experiment than the model and the data ondownward movement from the field predict (Major etal. 2010a). Therefore it seems that an additionalmechanism for vertical transport may be relevant,apart from movement with water. We can onlyspeculate what this mechanism may be, i.e. termitesor other soil fauna or pedoturbation (Czimczik andMasiello 2007; Lehmann et al. 2009). More work isneeded to determine the mechanisms of BC movementin the soil profile. Downward movement may be moreimportant quantitatively in soils with lighter texture.

The model is not fully tested here, because thefield data set was also partly used to calibrate themodel. Only CO2 production measured in the fieldcan be interpreted as an independent test, sincedecomposition rate was calibrated using other datafrom the laboratory. However, we were not been ableto find any other acceptable field data set on the fateof soil-applied BC over time, which would be neededfor proper independent tests of the model.

The model was not sensitive to the turnover rate ofthe stable pool on a 100-year time scale. It follows thaton time scales up to a century, the model should be quitereliable, even when this parameter is very uncertain.This means that the model could potentially be used infuture models for IPCC climate impact assessments,which usually consider a century time scale (IPCC2007). On a millennial time scale, the model wassensitive to the turnover rate of the stable pool, asexpected. On that time scale, erosion will have stoppedif we assume that a fraction of BC is resistant toerosion. Therefore decomposition was the only impor-tant loss function for BC on that time scale.

The model was quite sensitive to the moisturemodifier, as expected. This underlines the need to

conduct more experiments to measure the effect ofmoisture on BC decomposition. This can probablybest be achieved in laboratory incubations, where theeffect of moisture can be studied in isolation fromother factors. Although the extent of BC decomposi-tion during the dry season may change with betterquantification of the moisture modifier, it is likely thatsignificant decomposition occurs during the dry season.Assuming some vertical transport during the dry seasoncan explain why there was more transport of BC out ofthan into the lower layer during the first measurementseason. However, as mentioned above, there must befactors other than only water flow involved in verticaltransport, which may also explain this finding.

The data set used to parameterize maximumdecomposition rates (Whitman 2010) measured alower decomposition rate than the data set used toparameterize temperature sensitivity (Nguyen et al.2010). The temperature modifier therefore reaches 1(unity) at a low temperature, and actual decomposi-tion rates will be increased above the one measuredby Whitman (2010) during the simulation of the fieldexperiment of Major et al. (2010a). This means thatactual decomposition rates that the model predictswill be similar to the high values observed by Nguyenet al. (2010). Comparison with soil respiration rates inthe experiment by Major et al. (2010a) indicates thatsimulated values are somewhat higher than observedvalues. The model is also quite sensitive to this.Decomposition rates and temperature sensitivities stillrequire further study, in particular to identify theimportant difference between apparently similarexperiments. A possible explanation is that theexperiment by Nguyen et al. (2010) lasted for ashorter time period than that by Whitman (2010).Measured turnover times tend to be shorter with ashorter the experimental period if we assume that BCactually consists of a large number of chemicalfractions that all decompose with different turnovertimes (Lehmann et al. 2009).

There was large variability in the data-set ofWhitman (2010) that was used to parameterize thelabile pool parameters. The variability stems only inpart from uncertain estimates, but is largely a result ofdifferent feedstocks and pyrolysis conditions generat-ing BC materials with inherently different k values(Zimmerman 2010). Our model does not explicitlycapture BC materials with different k values, forwhich still too few data are available.

232 Plant Soil (2011) 345:223–236

Since we had no data on the climate sensitivity ofthe most stable pool, we assumed that this sensitivitywas equivalent to that of the labile pool. Thisassumption is also made in most soil organic mattermodels, where the climate sensitivity of differentfractions of soil organic matter is taken to be thesame. The validity of this assumption is, however,hotly debated (Davidson and Janssens 2006; Fang etal. 2005; Knorr et al. 2005; Reichstein et al. 2005;Waldrop and Firestone 2004). Nguyen et al. (2010)found that for BC, there was evidence for steeperresponse to temperature in the more resistant BCtypes. We found that the model was sensitive tochanging the assumption on temperature sensitivityon time scales of millennia, when the turnover of thestable pool becomes important. However, on timescales of up to a century this will be unimportantbecause the turnover of the stable pool is low.

Soil organic matter models usually assume that soiltexture influences decomposition rate or carbonretention in the soil in some way (Coleman andJenkinson 1999; Parton et al. 1988). The degree towhich stability of BC is conferred by physicalstabilization is not yet clear. Using polytungstate fordensity fractionation, Glaser et al. (2000) found mostof the BC in Terra preta soils in the light fraction,whereas Liang et al. (2008) reported 72–90% of theorganic carbon in Terra preta soils to reside in theorgano-mineral fraction using sodium iodine. Severalstudies also report BC to be embedded within micro-aggregates (Glaser et al. 2000; Lehmann et al. 2008b)suggesting stabilization mechanism through aggrega-tion. Also Brodowski (2004) found greater BCdecomposition in incubations conducted in sand thansoil. However, Glaser and Amelung (2003) found thatBC content in soil did not correlate with soilproperties as other soil organic matter fractions do.It is possible that stabilization is more important forthe labile than the stable pool. However, we do nothave sufficient data to parameterize any relationshipbetween soil texture and BC decomposition rates, sowe assume that texture does not influence BC stabilityin our model until further refinements are possible.

As decomposition is a microbial process, it would beexpected that microbes would appear in models ofdecomposition and soil organic matter turnover. Mostsoil organic matter turnover models do not includemicrobes explicitly, although a model pool is oftenassumed to correspond to microbial biomass (Jenkinson

et al. 1987; Parton et al. 1987). It is also mostlyassumed that microbial diversity is large enough for anappropriate microbial community to develop, so thatmicrobial community structure is controlled by sub-strate availability and abiotic factors. Schimel andWeintraub (2003) modeled decomposition usingMichaelis-Menton kinetics, and concluded that in soilsMichaelis-Menton kinetics and exponential decayfunctions produce similar results. Nocentini et al.(2010) found a modest effect of different microbialinoculums on decomposition rates of BC, indicatingthat explicitly including microbes may improve themodel only slightly. However priming, an increaseddecomposition rate as a response to added freshorganic matter has been shown in many experiments,both in the laboratory and in the field (Fontaine et al.2004; Fontaine et al. 2007; Hoosbeek and Scarascia-Mugnozza, 2009; Paterson et al. 2008). Priming hasalso been shown to play a role in BC degradation(Hamer et al. 2004; Kuzyakov et al. 2009; Nocentini etal. 2010). Including priming in soil organic mattermodels, may improve them, particularly their predic-tions of the effect of supplying fresh organic matter,which may not always increase organic matter content(Fontaine et al. 2004; Fontaine et al. 2007; Hoosbeekand Scarascia-Mugnozza, 2009). Including priminginto our model for BC decomposition may alsoimprove it. However, at present there is limited datato quantify the priming effect and how it depends onsubstrate supply. Including priming should however, bea priority both for future modeling of BC degradationand soil organic matter turnover.

The model does not recognize any BC decompo-sition product left in the soil (Lehmann et al. 2009).These may form another stable pool that may changedecomposition rates in the longer term. This ispotentially a weakness of the model, but with thedata sets that are available at present no furthercalibration of microbial metabolites is possible. Itdoes, however, highlight the need for quantitative dataon how BC transforms during degradation in soils.

Here we also ran the BC model independentlyfrom possible effects on other forms of soil carbon(Wardle et al. 2008; Lehmann and Sohi 2008) or plantproductivity (Lehmann and Rondon 2006; Major etal. 2005; 2010a and b; Rondon et al. 2007). This wasdesirable as a starting point for simplicity and usingavailable data, but future modeling efforts will need toaddress this aspect.

Plant Soil (2011) 345:223–236 233

Conclusions

The model developed here should be seen as a firstattempt, and it can be modified and developed furtheras more data become available. Particularly, erosionneeds to be better quantified as mass balance indicatesthat it is the most important loss factor. This can beachieved in field experiments where all fluxes of BC,including erosion, are measured over time. We haveshown that a two-pool model can adequately describeBC dynamics. We found the decomposition of thelabile pool to be the process best constrained byexperimental data. The data we used provide only afirst approximation of the accuracy of the decompo-sition rate of the stable pool. However, we haveshown that this parameter is not important for timescales up to a century. New BC additions can beintegrated into current soil organic matter models e.g.the Century or RothC models by allocating the BCentering various pools based on turnover rates.However, erosion losses may be more important forBC than other types of soil carbon and the modelsmay therefore need to be modified to take erosion intoaccount. Also different BC qualities are not explicitlycaptured and should be included in future iterations ofthe model, as soon as empirical data are available thatallow its parameterization.

Acknowledgements The lead author was funded by NASA-USDA award No. 2008-35615-18961. We thank three anony-mous referees for their constructive comments.

References

Betts RA, Cox PM, Lee SE, Woodward FI (1997) Contrastingphysiological and structural vegetation feedbacks inclimate change simulations. Nature 387:796–799

Bond WJ, Keeley JE (2005) Fire as a global ‘herbivore’: theecology and evolution of flammable ecosystems. TrendsEcol Evol 20:387–394

Bowman DMJS, Balch JK, Artaxo P, Bond WJ, Carlson JM,Cochrane MA, D’Antonio CM, DeFries RS, Doyle JC,Harrison SP, Johnston FH, Keeley JE, Krawchuk MA,Kull CA, Marston JB, Moritz MA, Prentice IC, Roos CI,Scott AC, Swetnam TW, van der Werf GR, Pyne SJ (2009)Fire in the earth system. Science 324:481–484

Brodowski S (2004) Origin, function, and reactivity of blackcarbon in the arable soil environment. Ph.D. thesis,University of Bayreuth, Germany.

Cheng C-H, Lehmann J, Thies JE, Burton SD (2008) Stabilityof black carbon in soils across a climatic gradient. Journalof Geophysical Research 113. doi:10.1029/2007JG000642

Coleman K, Jenkinson DS (1999) ROTHC-26.3 Modeldescription and windows users guide. IACR, Rothamsted

Cox PM, Betts RA, Jones CD, Spal SA, Totterdell IJ (2000)Acceleration of global warming due to carbon-cyclefeedbacks in a coupled climate model. Nature 408:184

Czimczik CI, Masiello CA (2007) Controls on black carbonstorage in soils. Glob Biogeochem Cycles. doi:10.1029/2006GB002798

Dai X, Boutton TW, Glaser B, Ansley RJ, Zech W (2005)Black carbon in a temperate mixed-grass savanna. SoilBiol Biochem 37:1879–1881

Davidson EA, Janssens IA (2006) Temperature sensitivity ofsoil carbon decomposition and feedbacks to climatechange. Nature 440:165–173

Del Grosso SJ, Parton WJ, Mosier A, Hartman MD, Brenner J,Ojima DS, Schimel DS (2001) Simulated interaction ofcarbon dynamics and nitrogen trace gas fluxes using theDAYCENT model. In: Schaffer J, Ma L, Hansen SM (eds)Modelling carbon and nitrogen dynamics for soil manage-ment. Lewis, Boca Raton, pp 303–332

Fang C, Smith P, Moncrieff JB, Smith JU (2005) Similarresponse of labile and resistant soil organic matter pools tochanges in temperature. Nature 433:57–59

Fontaine S, Bardoux G, Abbadie L, Mariotti A (2004) Carboninput to soil may decrease soil carbon content. Ecol Lett7:314–320

Fontaine S, Barot S, Barre P, Bdioui N, Mary B, Rumpel C(2007) Stability of organic carbon in deep soil layerscontrolled by fresh carbon supply. Nature 450:277–280

Forbes MS, Raison RJ, Skjemstad JO (2006) Formation,transformation and transport of black carbon (charcoal)in terrestrial and aquatic ecosystems. Sci Tot Environ370:190–206

Friedlingstein P, Cox P, Betts R, Bopp L, von Bloh W, BrovkinV, Cadule P, Doney S, Eby M, Fung I, Bala G, John J,Jones C, Joos F, Kato T, Kawamiya M, Knorr W, LindsayK, Matthews HD, Raddatz T, Rayner P, Reick C,Roeckner E, Schnitzler KG, Schnur R, Strassmann K,Weaver AJ, Yoshikawa C, Zeng N (2006) Climate-carboncycle feedback analysis: results from the C4MIP modelintercomparison. J Clim 19:3337–3353

Gaunt J, Cowie A (2009) Biochar, greenhouse gas accountingand emission trading. In: Lehmann J, Joseph S (eds)Biochar for environmental management. Earthscan, Lon-don, pp 317–340

Gavin DG, Brubakker LB, Lertzman KP (2003) Holocene firehistory of a coastal temperate rain forest based on soilcharcoal radiocarbon dates. Ecology 84:186–210

Glaser B, Amelung W (2003) Pyrogenic carbon in nativegrassland soils along a climosequence in North America.Glob Biogeoch Cycles. doi:10.1029/2002GB002019

Glaser B, Balashov E, Haumaier L, Guggenberger G, Zech W(2000) Black carbon in density fractions of anthropogenicsoils of the Brazilian Amazon region. Org Geochem31:669–678

Glaser B, Lehmann J, Zech W (2002) Ameliorating physicaland chemical properties of highly weathered soils in thetropics with charcoal—a review. Biol Fertil Soils 35:219–230

Gouveia SEM, Pessenda LCR, Aravena R, Boulet R, Scheel-Ybert R, Bendassoli JA, Ribeiro AS, Freitas HA (2002)

234 Plant Soil (2011) 345:223–236

Carbon isotopes in charcoal and soils in studies ofpaleovegetation and climate changes during the latePleistocene and the Holocene in the southeast andcenterwest regions of Brazil. Glob Planet Change33:95–106

Guggenberger G, Rodionov A, Shibistova O, Grabe M,Kasansky OA, Fuchs H, Mikheyeva N, Zrazhevskaya G,Flessa H (2008) Storage and mobility of black carbon inpermafrost soils of the forest ecotone in Northern Siberia.Glob Change Biol 14:1367–1381

Hamer U, Marschner B, Brodowski S, Amelung W (2004)Interactive priming of black carbon and glucose mineral-isation. Org Geochem 35:823–830

Hammes K, Torn MS, Lapenas AG, Schmidt MWI (2008)Centennial turnover observed in a Russian steppe soil.Biogeosciences 5:1339–1350

Hockaday WC, Grannas AM, Kim S, Hatcher PG (2007) Thetransformation and mobility of charcoal in a fire-impactedwatershed. Geochim Cosmochim Acta 71:3432–3445

Hoosbeek M, Scarascia-Mugnozza G (2009) Increased litterbuild up and soil organic matter stabilization in apoplar plantation after 6 years of atmospheric CO2enrichment (FACE): final results of POP-EuroFACEcompared to other forest FACE experiments. Ecosys-tems 12:220–239

IPCC (2007) Climate Change 2007: The Physical SciencesBasis. Cambridge University Press, Cambridge

Jenkinson DS, Hart PBS, Rayner JH, Parry LC (1987) Modellingthe turnover of organic matter in long-term experiments atRothamsted. INTECOL Bulletin, Rothamsted

Jones C, McConnell C, Coleman K, Cox P, Falloon P,Jenkinson D, Powlson D (2005) Global climate changeand soil carbon stocks; predictions from two contrastingmodels for the turnover of organic carbon in soil. GlobChange Biol 11:154–166

Knicker H, Almendros G, González-Vila FJ, González-PerezJA, Polvillo O (2006) Characteristic alterations of quantityand quality of soil organic matter caused by forest fires incontinental Mediterranean ecosystems: a solid-state 13CNMR study. Europ J Soil Sci 57:558–569

Knorr W, Prentice IC, House JI, Holland EA (2005) Long-termsensitivity of soil carbon turnover to warming. Nature433:298–301

Kuhlbusch TAJ (1998) Black carbon and the carbon cycle.Science 280:1903–1904

Kuhlbusch TAJ, Crutzen PJ (1995) Toward global estimate ofblack carbon in residues of vegetation fires representing asink of atmospheric CO2 and a source of CO2. GlobBiogeochem Cycles 9:491–501

Kuzyakov Y, Subbotina I, Chen H, Bogomolova I, Xu X (2009)Black carbon decomposition and incorporation into soilmicrobial biomass estimated by 14C labeling. Soil BiolBiochem 41:210–219

Lehmann J, Joseph S (2009) Biochar for environmentalmanagement: science and technology. Earthscan, London

Lehmann J, Rondon M (2006) Bio-char soil management onhighly weathered soils in the humid tropics. In: Uphoff Net al (eds) Biological approaches to sustainable soilsystems. CRC, Boca Raton, pp 517–530

Lehmann J, Sohi S (2008) Comment on “Fire-derived charcoalcauses loss of forest humus”. Science 321:1295

Lehmann J, Kern DC, Glaser B, Woods WI (2003) Amazoniandark earths: Origin, properties, management. Kluwer, TheNetherlands

Lehmann J, Gaunt J, Rondon M (2006) Bio-char sequestrationin terrestrial ecosystems—a review. Mitigat Adapt StratGlob Change 11:403–427

Lehmann J, Skjemstad JO, Sohi S, Carter J, Barson M, FalloonP, Coleman K, Woodbury P, Krull ES (2008a) Australianclimate-carbon cycle feedback reduced by soil blackcarbon. Nature Geoscience 1:832–835

Lehmann J, Solomon D, Kinyangi JM, Dathe L, Wirick S,Jacobsen C (2008b) Spatial complexity of soil organicmatter forms at nanometre scales. Nature Geoscience1:238–242

Lehmann J, Czimczik CI, Laird D, Sohi S (2009) Stability ofbiochar in soil. In biochar for environmental management.In: Lehmann J, Joseph S (eds) Biochar for environmentalmanagement. Earthscan, London, pp 183–206

Leifeld J, Fenner S, Muller M (2007) Mobility of black carbonin drained peatland soils. Biogeosciences 4:425–432

Liang B, Lehmann J, Solomon D, Sohi S, Thies JE, SkjemstadJO, Luizo FJ, Engelhard MH, Neves EG, Wirick S (2008)Stability of biomass-derived black carbon in soils. Geo-chim Cosmochim Acta 72:6069–6078

Major J, DiTommaso A, Lehmann J, Falcao NPS (2005) Weeddynamics on Amazonian Dark Earth and adjacent soils ofBrazil. Agric Ecosys Environ 111:1–12

Major J, Lehmann J, Rondon M, Goodale C (2010a) Fate ofsoil-applied black carbon: downward migration, leachingand soil respiration. Glob Change Biol 16:1366–1379

Major J, Rondon M, Molina D, Riha SJ, Lehmann J (2010b)Maize yield and nutrition during 4 years after biocharapplication to a columbian savanna oxisol. Plant Soil333:117–128

Masiello CA (2004) New directions in black carbon organicgeochemistry. Marine Chemistry 92:201–213

Nearing MA, Jetten V, Bafautt C, Cerdan O, Cauturier A,Hermandez M, Le Bissonaiss Y, Nichols MH, Nunes JP,Renschler CS, Souchere V, Van Oost K (2005) Modelingresponse of soil erosion and runoff tochanges in precipi-tation and cover. Catena 61:131–154

Neitsch SL, Arnold JG, Kiniry JR, Williams JR (2005) Soil andwater assesment tool theoretical documentation version2005. Grassland Soil and Water Research Laboratory,Temple

Nguyen BT, Lehmann J (2009) Black carbon decompositionunder varying water regimes. Org Geochem 40:846–853

Nguyen B, Lehmann J, Kinyangi J, Smernik R, Riha S,Engelhard M (2009) Long-term black carbon dynamicsin cultivated soil. Biogeochem 89:295–308

Nguyen B, Lehmann J, Hockaday WC, Joseph S, Masiello CA(2010) Temperature sensitivity of black carbon decompositionand oxidation. Environ Sci Tech. doi:10.1021/es903016y

Nocentini C, Guenet B, Di Mattia E, Certini G, Bardoux G,Rumpel C (2010) Charcoal mineralisation potential ofmicrobial inocula from burned and unburned forest soilwith and without substrate addition. Soil Biol Biochem42:1472–1478

Parton WJ, Schimel DS, Cole CV, Ojima DS (1987) Analysisof factors controlling soil organic-matter levels in Great-Plains grasslands. Soil Sci Soc Am J 51:1173

Plant Soil (2011) 345:223–236 235

Parton WJ, Stewart JWB, Cole CV (1988) Dynamics of C, N, Pand S in grassland soils: a model. Biogeochem 5:109–131

Parton WJ, Hartman M, Ojima D, Schimel D (1998) DAY-CENT and its land surface submodel: description andtesting. Glob Plan Change 19:35–48

Passenda LCR, Gouveia SEM, Aravena R (2001) Radiocarbondating of total soil organic matter and humin fraction andits comparison with 14C ages of fossil charcoal. Radiocar-bon 43:595–601

Paterson E, Thornton B, Midwood AJ, Osborne S, Sim A, MillardP (2008) Atmospheric CO2 enrichment and nutrient addi-tions to planted soil increase mineralisation of soil organicmatter, but do not alter microbial utilisation of plant- andsoil C-sources. Soil Biol Biochem 40:2434–2440

`Preston CM, Schmidt MWI (2006) Black (pyrogenic) carbon: asynthesis of current knowledge and uncertainties with specialconsideration of boreal regions. Biogeosciences 3:397–420

Reichstein M, Kätterer T, Andren O, Cias P, Schulze E-D, CramerW, Papale D, Valantini R (2005) Temperature sensitivity ofdecomposition in relation to soil organic matter pools:Critique and outlook. Biogeoscienses 2:317–321

Rodionov A, Amelung W, Haumaier L, Urusevskaja I, Zech W(2006) Black carbon in the zonal steppe soils of Russia. JPlant Nutr Soil Sci 169:363–369

Rondon M, Lehmann J, Ramirez J, Hurtado M (2007)Biological nitrogen fixation by common beans (Phaseolusvulgaris L.) increases with bio-char additions. Biol FertilSoils 43:699–708

Rumpel C, Alexis M, Chabbi A, Chaplot V, Rasse DP, ValentinC, Mariotti A (2006) Black carbon contribution to soilorganic matter composition in tropical sloping land underslash and burn agriculture. Geoderma 130:35–46

Rumpel C, Ba A, Darboux F, Chaplot V, Planchon O (2009)Erosion budget and process selectivity of black carbon atmeter scale. Geoderma 154:131–137

Schimel J, Weintraub NM (2003) The implications ofexoenzyme activity on microbial carbon and nitrogenin soil: a theoretical model. Soil Biol Biochem 35:549–563

Schmidt MWI, Noack AG (2000) Black carbon in soils andsediments: analysis, distribution, implications, and currentchallenges. Glob Biogeochem Cycles 14:777–793

Schmidt MWI, Skjemstad JO, Jäger C (2002) Carbon isotopegeochemistry and nanomorphology of black carbon: blackchernozemic soils in central Europe originate from ancientburning. Glob Biogeochem Cycles. doi:10.1029/2002GB001939

Skjemstad JO, Spouncer LR, Cowie B, Swift RS (2004)Calibration of the Rothamsted organic carbon turnovermodel (RothC ver. 26.3), using measurable soil organiccarbon pools. Aust J Soil Res 42:79–88

Waldrop MP, Firestone MK (2004) Altered utilization patternsof young and old soil C by microorganisms caused bytemperature shifts and N additions. Biogeochem 67:235–248

Wardle DA, Nilsson MC, Zackrisson O (2008) Fire-derived charcoal causes loss of forest humus. Science320:629

Whitman TL (2010) Biochar as carbon sequestration mecha-nism: decomposition, modelling, and policy. M.Sc. thesis,Cornell University

Zimmerman AR (2010) Abiotic and microbial oxidation oflaboratory-produced black carbon (biochar). Environ SciTechnol 44:1295–1301

236 Plant Soil (2011) 345:223–236