REGULAR ARTICLE

Degradation kinetics of biochar from pyrolysisand hydrothermal carbonization in temperate soils

Mo Bai & Burkhard Wilske & Franz Buegger & Jürgen Esperschütz &

Claudia Irene Kammann & Christian Eckhardt & Martin Koestler &

Philipp Kraft & Martin Bach & Hans-Georg Frede & Lutz Breuer

Received: 5 October 2012 /Accepted: 26 April 2013# Springer Science+Business Media Dordrecht 2013

AbstractBackground and Aims Estimates of biochar residencetimes in soils range over three orders of magnitude. Wepresent the first direct comparison between the biodegra-dation of a char from hydrothermal carbonization (htcBC)and pyrolysis (pyrBC) with high temporal resolution.Methods Mineralization of the biochars and theirshared Miscanthus feedstock in three soils was deter-mined directly by the 13CO2 efflux using a novelmethod incorporating wavelength scanned cavityring-down spectroscopy. Biochar half-life (t1/2) wasestimated with three empirical models.Results (1) The htcBC was readily biodegradable,whereas pyrBC was more recalcitrant. (2) Cumulative

degradation of both biochars increased with soil organiccarbon and nitrogen content. (3) The corrected Akaikeinformation criterion (AICC) showed an overall prefer-ence for the double exponential model (DEM) reflectinga labile and a recalcitrant C-pool, over the first-orderdegradation model (FODM) and a logarithmic model.(4) The DEM resulted in t1/2 ranging from 19.7–44.5,0.7–2.1 and 0.8–1.3 years for pyrBC, htcBC and feed-stock, respectively.Conclusion The degradation was rather similar be-tween feedstock and htcBC but one order of magnitudeslower for pyrBC. The AICC preferred FODM in twocases, where the DEM parameters indicated no distinc-tion between a labile and recalcitrant carbon pool.

Keywords Char . HTC . Soil amendment . Recalcitrantcarbon . Biodegradation . 13CO2 efflux

Introduction

Biochar has been reinvented as a multi-tool raisingexpectations to enhance bioenergy production, soilwater availability, crop growth, and the carbon seques-tration of soils (Glaser et al. 2000; Lehmann 2007;Gaunt and Lehmann 2008; Atkinson et al. 2010;Zimmerman 2010; Hammond et al. 2011; Matovic2011). Most of the recent studies were fairly positivesuggesting that biochar amendments can indeed (a)improve the water availability in soils (Karhu et al.2011), (b) increase both the water use efficiency and

Plant SoilDOI 10.1007/s11104-013-1745-6

Responsible Editor: Eric Paterson.

M. Bai :B. Wilske (*) :M. Koestler : P. Kraft :M. Bach :H.<G. Frede : L. BreuerInstitute for Landscape Ecology and ResourcesManagement (ILR), Research Centre for BioSystems, LandUse and Nutrition (IFZ), Justus Liebig University, Gießen,Germanye-mail: brkwils@yahoo.com

F. Buegger : J. EsperschützInstitute of Soil Ecology, German Research Centerfor Environmental Health, Helmholtz Zentrum München,Neuherberg, Germany

C. I. Kammann : C. EckhardtDepartment of Plant Ecology, Research Centrefor BioSystems, Land Use and Nutrition (IFZ), Justus LiebigUniversity, Gießen, Germany

nitrogen use efficiency and thereby the growth ofplants (Kammann et al. 2011a; Prendergast-Miller etal. 2011; Vaccari et al. 2011), (c) reduce emissions ofmajor greenhouse gases (e.g., N2O, CH4) from agri-cultural land (Karhu et al. 2011; Kammann et al.2011b; Knoblauch et al. 2011), and (d) amplify thepool of recalcitrant carbon (C) in soils to enhance theirC sequestration (Vaccari et al. 2011; Knoblauch et al.2011). Other effects of biochar include the reductionof herbicide efficiency (Nag et al. 2011) and the in-creased retention of heavy metals, respectively (Cao etal. 2011; Uchimiya et al. 2011; Buss et al. 2011). Fewobservations point to possible caveats in our understand-ing of biochar functions in the terrestrial environment,e.g., one study suggested increased humus loss in thepresence of char from wildfire (Wardle et al. 2008).

Biochar includes a wide spectrum of materials with alarge variability of individual properties (e.g., Lehmann2007; Libra et al. 2011; Liu et al. 2010). Some proper-ties, or the modifications of the same, are dependent onthe conversion process from biomass to char(Schimmelpfennig and Glaser 2011).Many studies haveconsidered biochar from pyrolysis (hereinafter pyrBC),which represents the traditional thermal conversionwithout oxygen. Hydrothermal carbonization (HTC),also termed wet pyrolysis (Libra et al. 2011), is a rela-tively new process providing biochar (hereinafterhtcBC) from a slurry of biomass and water subjectedto autogenous pressure (e.g., 1 MPa at about 200 °C).With respect to the variety of available biomass, HTCcomplements the process of pyrolysis, because htcBCcan be produced from biomass involving much higherwater contents. Similar to pyrBC, htcBC has been con-sidered a potentially useful soil amendment (Libra et al.2011; Kammann et al. 2011b), and positive effects onthe root colonization of fungal symbionts were reported(Rillig et al. 2010). However, residence times of pyrBCand htcBC must be studied in detail to pursue long-termC sequestration (Fuertes et al. 2010). This expands to agreater task, as the property of biochar stability (orresidence time in soil) does not only depend on thefeedstock and production process, but also on the soilconditions (Zimmerman et al. 2011).

While a large variety of biochar products is preparedto enter the market (Meyer et al. 2011), the estimates ofbiochar residence times in soils stretch over three ordersof magnitude (i.e., 4–1,000 years) (Lehmann et al. 2009;Kuzyakov et al. 2000; Steinbeiss et al. 2009; Chenget al. 2008). Hence, efficient methods are required to

investigate the stability and/or the C sequestration po-tential of many different biochars in a variety of soils.The objectives of the present study were to (1) investi-gate the biodegradation of one pyrBC, one htcBC, andtheir shared feedstock (Miscanthus) in relation to theincubation in three common agricultural soils, and (2)estimate the half-life of biochar based on existingmodels that were previously applied to describe itsmineralization. To our knowledge, this is the first directcomparison of the biodegradation of these two pro-foundly different biochars.

At the same time, we present a novel and efficientmethod to measure biochar degradation. Biodegradationof the pyrBC and the htcBC was based on the 13CO2

efflux measurements from biochar-soil incubationsusing wavelength-scanned cavity ring-down spectrosco-py (WS-CRDS). Both 13C- and 14C-labeling can beemployed to measure CO2 evolution from biodegrada-tion of recalcitrant carbon compounds such as blackcarbon or biochar (Kuzyakov et al. 2009; Jones et al.2011). However, the 13C-labeling has the advantage ofnot being subjected to special laboratory standards andenvironmental concerns. Coupling the WS-CRDS tech-nique to auto-sampling (Bai et al. 2011), we were able todirectly measure 13CO2 emissions from a series of sam-ple incubations. The novel method increases the fre-quency and flexibility in accessing degradation rateswith high accuracy, and it reduces simultaneously thesystematic or shifting errors owing to sample handling.

A temporal resolution of weekly measurements pro-vided the basis to examine the degradation kinetics andto calculate biochar half-lives (t1/2) as a benchmark forthe related carbon sequestration potential (Zimmerman2010; Gaunt and Lehmann 2008; Qayyum et al. 2011).Three models were used to estimate half-life: (1) thefirst-order decay rate model (FODM, Bolan et al. 2012),(2) the double exponential model (DEM, Lehmann et al.2009; Zimmerman et al. 2011), and (3) a logarithmicmodel (LOGM, Zimmerman 2010).

Anderson et al. (2011) suggested that the degrada-tion of more recalcitrant carbon pools requires suffi-cient diversity within the soil microbial population.Although biochar can increase soil microbial activityand affect soil enzyme activities (Bailey et al. 2011;Wardle et al. 2008), the microbial activity prior to anytreatment depends greatly on native soil organic car-bon (SOC) (Pascual et al. 1997; Insam and Domsch1988). As litter-derived inputs to the three soils wereequivalent over many years, we suggest that main

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differences between soils were in SOC amount, ratherthan SOC quality. Therefore, the degradation kinet-ics of the chars were expected to reflect biochar–soil interaction, with SOC content and/or micro-bial activity being the key players. Thus, we setout from the hypothesis that for each of thebiochars, the rate of degradation was mainly pro-portional to the different SOC contents of thethree soils tested.

Materials and methods

Biochars and soils

Pyrolysis biochar and char from hydrothermal car-bonization were produced from straw feedstock ofMiscanthus x giganteus at highest temperaturetreatments of about 575±25 °C and 200 °C, re-spectively (Table 1). Total process time was30 min for the pyrBC, whereas process time andpressure were 120 min and 1.6 MPa for thehtcBC. The pH and ash content of both feedstockand biochars were measured according to vanReeuwijk (2002) and based on the loss on ignitionmethod (Heiri et al. 2001), respectively. Processingincreased the pH from feedstock (6.8) to htcBC(10.1) but lowered it to pyrBC (5.1). The ashcontent increased from feedstock (2.5 %) tohtcBC (2.7 %) and further to pyrBC (29.8 %).

Utilization of the C4 species Miscanthus signifi-cantly enriched the 13C content in the biochars ascompared to the soils used for incubation (Table 1).The δ13C and organic carbon contents of the biochars,the feedstock, and the soils were determined using anIR-MS (delta V Advantage, Thermo Finnigan,Bremen, Germany) coupled with an elemental analyz-er (Euro EA, Eurovector, Milano, Italy) at theHelmholtz Centre Munich. Each treatment, the twobiochars and the feedstock, was crushed to a particlesize ≤1.5 cm.

Three soils were obtained from the state research insti-tute for agriculture (Landwirtschaftliche Untersuchungs-und Forschungs-Anstalt LUFA Speyer; www.lufa-speyer.de). The soils from LUFA 1 to LUFA 3 representa gradient from sand to loam texture. In contrast, both soilorganic carbon and nitrogen (N) contents increase fromLUFA 1 to LUFA 3 to LUFA 2 (Table 1). The soils wereair dried and passed through a 2-mm sieve.

Biochar degradation

Degradation is the successive breakdown and massloss of a substance and biodegradation implies thesame is caused by biological means (organisms, en-zymes, etc.). Biodegradation of a substance in a matrixsuch as soil will thus depend on the soil biologicalactivity, particularly if the substance can be presumedbeing not readily biodegradable. As a first approxima-tion, biological activity in turn will depend on sub-strate availability, i.e. the amount of SOC (Insam andDomsch 1988). Hence, native SOC was used as abasis to adjust the amount of biochar application tothe soils. In each treatment biochar equal to 50 % ofthe native SOC was mixed with 300 g soil (corre-sponding to 3.4, 9.7 and 5.0 g C kg−1 added to soilsin LUFA 1, 2 and 3, respectively). The biochar addedcorresponded to 19 to 66 t ha−1 depending on thedifferent soils. These amounts comply with theGerman by-law for the application of biodegradablewaste to agricultural land (Bioabfallverordnung 2012),which provides ranges equaling 20–75 % of the nativeSOC.

Biochar incubation

The two biochars and the feedstock were incubatedwith the three soils over a period of 200 days. Sampleswere transferred to 1-L vessels and incubated con-stantly at 25 °C and 40 % soil water holding capacity(WHC). The water content was adjusted weekly.Between measurements, the samples were kept in adark climate chamber at 70 % relative air humidity toavoid photosynthetic activity and drying of soils.Samples of pure soil were used as references, i.e.the 13CO2 efflux of the reference was subtractedfrom the treatment to obtain the 13CO2 net efflux.Each treatment in each soil (n=3×3), and thereference samples for each soil (n=3), were testedin four replicates (total n=48).

Measurement of biochar degradation

Biochar degradation was measured based on the13CO2 efflux from the samples each day from day 1to day 7, and every week thereinafter up to 200 days.The study was conducted with a novel automatedsystem that couples a batch of samples via two rowsof microprocessor-controlled valves to a WS-CRDS

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13CO2 analyzer (G1101-i, Picarro, Sunnyvale, CA,USA). Details of the method and the system have beendescribed elsewhere (Bai et al. 2011). Briefly, the WS-CRDS technique provides direct quantification of the13C stable isotope in the sample gas, i.e. the mixingratio of 13CO2 (μmol mol−1). The recently developedmethod is based on the open dynamic chamber modewhich has proven to provide reliable results to deter-mine CO2 efflux from soils (Baldocchi and Meyer1991; Pumpanen et al. 2004). Samples are consecu-tively connected through to the WS-CRDS, while theother samples are flushed with ambient air by anexternal pump at the same rate. When a sample isconnected to the analyzer, carry-over effects areflushed out during the first 180 s, and the 13CO2 effluxis averaged over six records during the following 60 s.Subsequently, the 13CO2 concentration of the ambientair is measured for 180 s. Calibration of the measure-ments was checked regularly using at least two 13CO2

isotope standards enclosing the target mixing ratios(e.g., −20‰ V-PDB in CO2 totals of 200 μmolmol−1 and 1,000 μmol mol−1; DEUSTE SteiningerGmbH, Mühlhausen, Germany).

Calculation and statistics

The 13CO2 efflux rate of both the treatment (soil+biochar) and the reference sample (soil only) wascalculated as the difference between the 13CO2 con-centration in ambient air before and after passing thechamber. The 13CO2 efflux rate F (μg d−1) is

F ¼ U � 10�6 � Cout � Cinð Þ � bMV

� 60 � 24 ð1Þ

where is U the gas flow rate with 22 mL min−1, Cout

(μmol mol−1) the 13CO2 concentration in the chamber,Cin (μmol mol−1) the 13CO2 concentration in ambientair (μmol mol−1), b the molar mass of 13C (g mol−1),

Table 1 Properties of the LUFA soils 1–3, Miscanthus feedstock, and the biochars from pyrolysis (pyrBC) and hydrothermalcarbonization (htcBC)

Property Soil Feedstock Biochar

LUFA 1 Inceptisol LUFA 2 Mollisol LUFA 3 Inceptisol-Aquept Miscanthus htcBC pyrBC

Soil texturea Sand Loamy sand Sandy loam

Corg (%) 0.68 1.93 0.99 48.95 52.30 76.10

Total N (%) 0.04 0.17 0.08 0.17 0.31 0.63

C/N ratio 17 11 12 295 169 121

δ13C −27.6 −27.8 −25.2 −11.9 −12.4 −14.2Sand (%)a 87.6 81.3 62.5 – – –

Silt (%)a 9.3 12.1 28.7 – – –

Clay (%)a 3.0 6.6 8.9 – – –

pHb,c 7.1 5.1 5.5 6.8 10.1 5.1

HTTd (°C)a – – – – 200 575e

Process.f (min)a – – – – 120 30

Ash content (%) – – – 2.5 2.7 29.8

Particle size (mm) <2 <2 <2 <15 <15 <15

WHCg (%)a 31.4 45.2 35.6 – – –

Bulk density (g/L)a 1455 1247 1291 – – –

a Parameter values as per the supplierb Soil pH (0.01 M CaCl2)c pH of biochar and feedstock in H2OdHTT: Highest temperature treatmente +/−25 °Cf Processing timegWater holding capacity

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and MV the temperature- and pressure adjusted molarvolume (m3 mol−1). The initial weight of soil wassimilar in sample and reference, and hence, the net13C efflux rate Fnet (μg d−1) due to biochar degrada-tion is

Fnet ¼ Ftreatment � Fsoil ð2Þwhere Ftreatment and Fsoil are

13C efflux rates (μg d−1)from treatment and reference soil, respectively.

Fnet (μg d−1) was referenced to the initial 13C ap-plication (g) resulting in Fdeg (μg g−1 d−1) by

Fdeg ¼ Fnet � a � c

100

� ��1

� 1þ d � 1þ e

1000

� �� ��1� �

ð3Þ

where is a the biochar initial weight (g), c the biocharorganic carbon content (%), d the 13C/12C-ratio of theinternational standard VPDB (i.e. 0.0111802) (Wernerand Brand 2001), and e the biochar δ13C.

For further analysis, the mean degradation curvefrom the efflux of degraded 13C of four replicates perday was used to fit different equations to describe Cdegradation. The cumulative 13C mineralization13Clost:

13Clost¼13Ct�1 þ Fdeg;t þ Fdeg;t�1

2

� �� t � t � 1ð Þð Þ

ð4Þwhere 13Clost (μg g−1 applied 13C) is the cumulative13C mineralization after t (days), 13Ct−1 is the cumu-lative 13C mineralization of the previous day, Fdeg,t isthe degradation 13C in t days, and Fdeg,t−1 is thedegradation of the previous day. Note that the miner-alization between two sampling is assumed as linear.

For the mathematical description of biochar degra-dation the carbon remaining (13Crem, calculated to μgμg−1) from the initial carbon applied (13C0) at the startof experiment is required. The remaining 13Crem ininitial weight is:

13Crem ¼ 13C0 � 13Clost ð5Þ

From this, the half-life of biochar in years t1/2 (yr)can be computed. We compared three modelssuggested by previous studies: (1) Bolan et al.(2012) used the first-order decay rate equation(FODM; Eq. 6) to model the rapid and slower

biodegradation of poultry manure compost and poul-try manure biochar, respectively. (2) A double expo-nential model (DEM; Eq. 7) was used to estimate themineralization of both an htcBC (Qayyum et al. 2011)and pyrBCs (Lehmann et al. 2009; Zimmerman etal. 2011) assuming there are two carbon poolsinvolved: One rapidly degrading C-pool and one slow-ly degrading or recalcitrant C-pool. (3) Zimmerman(2010) proposed a logarithmic model (LOGM, Eq. 9)to predict the long-term biodegradation of pyrBC.

The FOD model is:

13Crem;FODM ¼ 13C0 � e�k�t ð6Þwhere 13Crem,FODM is the 13C weight remaining fromthe initial weight after a certain number of incubationdays and the value k is the rate constant of decrease(d−1).

The DEM considers two carbon pools 13C1 and13C2 with different turnover rates k1 and k2, respec-tively:

13Crem;DEM ¼ 13C1 � e�k1�t þ 13C2 � e�k2�t ð7ÞIf k is known, the half-life t1/2 can be calculated

with Eq. (8) for the FODM and DEM, respectively.The value k2 can be used for the half-life calculationbased on DEM, if C2 >> C1 and k2 << k1, (Qayyum etal. 2011).

The biochar half-life (yr) is:

t1=2 ¼ lnð2Þk � 365 ð8Þ

The two following equations (Eqs. 9 and 10) wereused to calculate the cumulative C mineralization13Clost after any given period time t, and the half-lifebased on the LOGM. The term for 13C mineralizationis:

13Clost ¼13C0 � ebmþ 1

� �� tmþ1 ð9Þ

The term for half-life estimation based on LOGMis:

t1=2 ¼ mþ 1

2 � eb� � 1

mþ1ð Þ

� �

ð10Þ

where m and b are calculated via regression anal-ysis with ln(Fdeg/365)=m ⋅ ln(yr)+b, and Fdeg is13C degradation rate (μg g−1 13C d−1). Note that

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the degradation rate has to be converted to timesteps as fraction of year (yr).

The degradation rates of biochar and feedstockwere analyzed using the repeated measurement analy-sis of variance (RM-ANOVA). The soil type, treat-ment and time were used as fixed factors and theirinteraction. The normal distribution for all variableswas tested using the Shapiro-Wilk test. Two factorialANOVA was used to distinguish between htcBC,pyrBC and feedstock. We analyzed results of the samesoil or tested between three soils containing the sameapplication at 200 days of the incubation (p<0.05).The two factors were soil type and treatment, i.e. theaddition of different biochars or feedstock. Non-linearregression and linear regression analyses were used tocalculate the parameter k for both FODM and DEM,and m and b for LOGM, respectively. Statistics werecomputed using SPSS (IBM SPSS Version 20, NY,USA). Root mean square deviation (RMSD) was cal-culated to characterize the goodness of fit betweenmeasured and modeled data. The Akaike informationcriterion (AIC) was used to indicate the model perfor-mance, i.e., the goodness of model fit in relation to thenumber of estimated model parameters, of the threemodels for each treatment (Akaike 1981). AIC valuesprovide a mean for model selection by weighing thegoodness of fit versus the number of parameters in-cluded in a model, thereby penalizing overfitting. Weused the corrected AIC (AICC), which corrects forsmall n (Burnham and Anderson 2004). AIC can rangefrom positive to negative values, whereby the lowestvalue indicates the best approximating model.

Results

The degradation of biochars

The degradation was significantly different betweenfeedstock and pyrBC, and between htcBC and pyrBC,respectively after 200 incubation days (p<0.0001,Fig. 1). Until at least day 100, degradation rates ofMiscanthus feedstock and htcBC were mostly similarin the LUFA 1 soil (p=0.84) and from day 7 to day100 in the LUFA 2 soil (p=0.18) (Fig. 1a, b). Only inthe LUFA 3 soil, degradation was larger for the feed-stock than the htcBC (p<0.0001). After 100 days, theslope of htcBC degradation curves flattened, i.e. thedegradation rates decreased, as compared to the curves

of the feedstock (Fig. 1b). Among the htcBC curves,the htcBC degradation in LUFA 1 showed anotherflattening of the slope after about day 150. As a result,the mean 200 day degradation rates for htcBC were269.7 mg g−1 (27 %), 304.9 mg g−1 (30 %), and299.4 mg g−1 (30 %) in LUFA 1, LUFA 2, and

Fig. 1 Remaining 13C in initial weight of the applications(13Crem) of feedstock (Miscanthus), htcBC and pyrBC in threeLUFA soils

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LUFA 3, respectively (Table 2). The mean 200 daydegradation rates for the feedstock were slightlyhigher with 32 %, 35 %, and 37 % in LUFA 1, 2,and 3, respectively.

The degradation of pyrBC within the three soilsshowed diverging curves (Fig. 1c). After 3 weeks ofincubation, pyrBC degradation was clearly larger inLUFA 2 than in the two other soils. Virtual increasesin initial weight of pyrBC in LUFA 1 and 3 (Fig. 1c)were a result of repeated negative efflux rates, i.e. theefflux from the treatment sample was smaller thanfrom the reference sample. The mean 200 day degra-dation rate of pyrBC was 25.8 mg g−1 (<3 %) and11.4 mg g−1 (1 %) in LUFA 2 and LUFA 3, respec-tively (Table 2). The 200 day mean degradation ofpyrBC in LUFA 1 resulted in a negative value(−5.6 mg g−1), which means that less 13C was degrad-ed from the pyrBC samples than from the reference(“soil only”). However, the mean was not significantlydifferent from zero.

The two factorial analysis confirmed that the200 day degradation rates were different betweenboth the biochar types and the biochars in differentsoils, whereas there were no significant differencesfrom the interaction of soils and biochar types (p=0.059, Table 2). In each of the three soils, the200 day degradation of htcBC was significantlysmaller, although only by 12–18 %, than those ofthe feedstock. In contrast, the pyrBC degradationrates were an order of magnitude lower as compared tothose of htcBC.

The 200 day degradation of Miscanthus feedstockappeared to increase along the texture gradient fromsand to sandy loam (LUFA 1 to 2 to 3), with the LUFA2 result being not significantly different from either ofthe two others (Table 2). The degradation of bothbiochars increased with increasing native SOC and N

content from LUFA 1 to 3 to 2 (Table 1). This resultwas significant throughout for pyrBC (Table 2),whereas for htcBC, it was only significant for the stepfrom LUFA 1 to LUFA 3. This first increase in bothSOC and N was relatively smaller than the secondfrom LUFA 3 to 2, but it marked the larger decreasein C/N ratio (Table 1).

The fit of model data

Employment of the three kinetic models FODM,DEM and LOGM resulted in a superior curvefitting, i.e., the lowest RMSDs, for the DEMacross the three soil and two BC types (Fig. 2a–f).Furthermore, RMSDs were lower for the FODM thanthe LOGM in all but one case. For the htcBC in LUFA 1(sand), FODM and LOGM overestimated the degrada-tion by about 0.05 μg μg−1 13C application(RMSD=0.022) and 0.1 μg μg−1 13C application(RSMD=0.127) as compared to measured data,respectively (Fig. 2a). Also for the degradation ofhtcBC in LUFA 2 (loamy sand) and LUFA 3(sandy loam), the DEM model matched the mea-sured data best; although differences in RMSDbetween the three models were less pronouncedas compared to LUFA 1 (Fig. 2b, c). The absolutedifferences in RMSD were generally smaller incase of the pyrBC applications (Fig. 2d–f), andFODM and LOGM performed almost equal asDEM in case of LUFA 2 and 3. However, theDEM model showed a substantially lower RMSDwith 0.001 for LUFA 1, where especially LOGMoverestimated the degradation of biochar.

Only from the charts but not the RMSD itbecame obvious that some differences betweenmeasured and modeled increased towards the endof the observation period (see e.g., Fig. 2b, DEM).

Table 2 Cumulative 200 day biodegradation of BC and feedstock (mg13C g−1 applied 13C). Values are mean ± SD (n=4, p<0.05)

Soil Miscanthus Biochar

htcBC pyrBC

LUFA 1 323.7 ±7.7 a A 269.7 ±10.0 a B −5.6 ±7.4 a C

LUFA 2 346.7 ±18.2 ab A 304.9 ±11.1 b B 25.8 ±4.3 b C

LUFA 3 367.3 ±19.6 b A 299.4 ±13.9 b B 11.4 ±3.4 c C

Capital letters indicate significant differences in results between BC and feedstock relative to the same soil; small letters indicatesignificant differences between similar applications (BC type or feedstock) in different soils, respectively

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The half-life of biochar

Calculated half-lives of htcBC in three soils, basedon the three models FODM, DEM and LOGM,ranged between 0.7 and 2.1 years (Table 3).They were only slightly higher than the half-livesof pure Miscanthus feedstock, ranging between 0.7to 1.3 years. In most cases, LOGM estimated a4 month longer half-life than the FODM model forhtcBC. The DEM, which was fitting best to theexisting data of htcBC degradation, suggested half-lives of 0.7, 1.1, and 2.1 years for htcBC in LUFA1, LUFA 2, and LUFA 3, respectively (Table 3).In sand, loamy sand, and sandy loam the t1/2 re-sults compared between the models as LOGM >FODM > DEM, LOGM > DEM > FODM, andDEM > LOGM > FODM, respectively.

Model predictions for the half-life of the pyrBCranged between 11.1 and 110.7 years (Table 3,LOGM). Overall, the results for t1/2 of pyrBC in-creased from LUFA 2 (loamy sand) to LUFA 3 (sandyloam) to LUFA 1 (sand), along with the decrease inSOC and N content. Results of pyrBC t1/2 using allthree models for the different soils varied in roughcomparison between 11 and 15 (LUFA 2), 19–45(LUFA 3) and 20–111 years (LUFA 1). In one andtwo cases, the k value of FODM and the k2 values ofDEM were not significantly different from zero, re-spectively. Overall, the three models did not reveal asimilar and thus clear pattern with regard to estimatedhalf-lives of pyrBC in the different soils. However, wefound a clear tendency for substantially longer half-lives of pyrBC in relation to htcBC and Miscanthusfeedstock in the order of a magnitude and even more.

Fig. 2 Comparison betweenthe remaining 13C in initialweight (13Crem) of htcBCand pyrBC in three LUFAsoils based on the averagemeasured degradation curve,the first order degradationmodel (FODM), the doubleexponential model (DEM)and the logarithmic model(LOGM). RMSD is the rootmean square deviation be-tween measured andmodeled data

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The AICC values assessed better model perfor-mances for the DEM than the FODM, and further,better performances for the FODM than the LOGM(Table 3). DEM and FODM showed the best perfor-mance in seven of nine and two of nine cases, respec-tively. In those two cases, where the DEMperformance was only second best, the k-values forthe labile and recalcitrant pool were identical. Whilethe LOGM performance was always assessed the leastbest among the models, the difference in AICC to thenext better one ranged between 1 and 120 points.

Discussion

Biochar properties and degradation

The 200 day degradation of both feedstock and htcBCwere one order of magnitude larger than the degrada-tion of pyrBC in all of the soils tested. In contrast, thedegradation was not significantly different betweenfeedstock and htcBC in two of three soils. Amongthe property data available for the feedstock and thebiochars (Table 1), it was the ash content that corre-lated with the large difference in degradation between

pyrBC and the other two treatments. Also Chen et al.(2008) found that ash content increases linearly withpyrolysis temperature for pyrBC derived from pineneedle litter. Spokas (2010) suggested H/C and O/Cratios as stability indicators, both of which decreasedlinearly with increasing ash content. Previous studieshave also shown that both H/C and O/C ratios weremuch lower in pyrBC than in htcBC (Fuertes et al.2010; Schimmelpfennig and Glaser 2011). Furtherparameters mainly showed a gradual increase (Corg,total N) and decrease (C/N ratio) from feedstock viahtcBC to pyrBC, and no clear relationship withbiochar degradation. Overall, these relations suggestthat the much slower degradation of pyrBC as com-pared to htcBC was related to a greater extent ofcarbonization (Liu et al. 2010) including higher ashcontent and lower H/C and O/C ratios.

Biochar degradation in different soils

A recent study proposed that environmental and bio-logical controls rather than material properties pre-dominate in controlling the stability of soil organicmatter (Schmidt et al. 2011). The results of the presentstudy showed differences between the 200 day

Table 3 Model parameters and half-lives of biochar andMiscanthus feedstock in three soils. Half-life (t1/2, yr) is esti-mated by three models (first-order degradation FODM, double

exponential DEM, and logarithmic model LOGM). Model per-formance is assessed by the corrected Akaike information crite-rion (AICc)

Soil Application FODM DEM LOGM

k AICc t1/2 C1 k1 C2 k2 AICc t1/2 m b AICc t1/2

LUFA 1 Miscanthus 2.16E-03 −307 0.9 9.19E-05 −0.03a 0.99 2,30E-03 −412 0.8 −0.35 −1.13 −255 1.0

htcBC 1.95E-03 −249 1.0 7.66E-03 −0.02a 0.99 2.60E-03 −376 0.7 −0.73 −2.10 −129 1.4

pyrBC −8.41E-06a

−413 –b 0.01 −6.64E-03a 0.99 9.64E-05a −467 19.7 −0.29 −4.14 −300 110.7c

LUFA 2 Miscanthus 2.19E-03 −311 0.9 – –d 1.00 2.19E-03 −306 0.9 −0.29 −0.96 −296 0.9

htcBC 2.03E-03 −278 0.9 0.04 0.10 0.96 1.74E-03 −346 1.1 −0.42 −1.39 −277 1.3

pyrBC 1.31E-04 −460 14.5 0.17 1.26E-03 0.83 −8.30E-05a −468 –b −0.10 −3.38 −411 11.1c

LUFA 3 Miscanthus 2.56E-03 −258 0.7 0.16 0.02 0.84 1.44E-03 −408 1.3 −0.44 −1.17 −223 0.8

htcBC 2.03E-03 −275 0.9 0.20 0.01 0.80 8.91E-04 −371 2.1 −0.44 −1.41 −251 1.3

pyrBC 4.27E-05 −418 44.5 – –d 1.00 4.27E-05 −413 44.5 0.36 −3.08 −382 18.7c

Note that C2=1−C1a Not significantly different from 0bNot calculated because k<0 or k2<0c Not reliable based on Zimmermann (2010) since linear regression yields R2 <0.4d Because k1 identical to k2

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biodegradation rates of the feedstock, the htcBC andthe pyrBC in the different soils. Initially high rates ofdegradation were observed for both chars and wereprobably due the decomposition of remaining volatilesand soluble compounds (Luo et al. 2011; Smith et al.2010). In spite of the differences in the magnitude ofthe 200 day degradation, the degradation of bothhtcBC and pyrBC increased with increasing nativeSOC and N content. The degradation curves ofhtcBC were very similar in their course (Fig. 1).Where they diverged, it was between the applicationin LUFA 1 and the other two soils. Both SOC and Ncontent changed along the sequence LUFA 1 to 3 to 2.However, the largest difference between LUFA 1 andthe other two soils was the wider C/N ratio. Thisrelationship suggests htcBC degradation is likely toincrease following N-fertilization.

Degradation of pyrBC was very low (LUFA 2 and3) or not detectable (LUFA 1), but significantly differ-ent between LUFA soils and also to htcBC (Table 2).Luo et al. (2011) studied the biodegradation of 700 °Cand 350 °C pyrolysis biochar (pyrBC700, pyrBC350)produced from Miscanthus. They report a 87 day bio-degradation of 0.14 % and 0.18 % of pyrBC700 and0.61 % and 0.84 % of pyrBC350. At day 91, thepyrBC used in the present study (produced at a tem-perature of 575 °C) showed a similar, low degradationof the same magnitude with 0.11 %, 1.24 % and0.31 % in the LUFA soils 1–3, respectively.

Incubation of pyrBC in LUFA 1 even lead to neg-ative degradation rates, i.e. a higher 13CO2 emission ofthe control compared to soil incubated with pyrBC.Negative degradation rates can be explained as fol-lowing: the application of 13C enriched C4-plant de-rived biochar (in our case Miscanthus biochar) usuallyresults in increased 13CO2 emission rates relative to acontrol soil (in our case a soil that is characterized byplant residues stemming from C3-plants). Higheremission rates of the control compared to that of thebiochar amended soil can be explained by a suppres-sion of the microbial degradation of native SOC, e.g.through N immobilization (Zavalloni et al. 2011),adsorption of soil-borne CO2 or of otherwise easydegradable volatile organic compounds. Previousstudies suggested that pyrBC produced at high tem-peratures can exert negative priming effects duringearly and later periods of incubation (Zimmerman etal. 2011; Luo et al. 2011; Cross and Sohi 2011), aneffect that could explain our observations. A clear

separation of the different carbon pools (here nativeSOC and biochar) contributing to observed 13CO2

emissions is not possible following the analyticalmethod by Bai et al. (2011), which we followed in thisstudy. Applying well-known mixing model approachesusing δ13C signatures (Paterson et al. 2009) couldresolve this limitation. But this is currently notfeasible with the first generation WS-CRDS ana-lyzer we used here due to the relative low preci-sion of the δ13C signal provided by the analyzer.In theory, the analyzer could provide higher accu-racies, but for this longer incubation times of upto 1 h are needed, contradicting our aim of rapidlymeasuring a large number of samples (Bai et al.2011). For further details see the recently pub-lished performance check of the Picarro G-1101-ianalyzer by Vogel et al. (2013), the same instru-ment we used here. Nevertheless, all this is notlimiting the value of our study as we do not focuson the investigation of priming effects of biocharapplication but rather on the relative degradationof different biochar types relative to their feed-stock in different soils.

Increasing biochar degradation from LUFA 1 to 3to 2 did not correlate with the texture or the claycontent of the soils. Similarly, Saggar et al. (1996)found that degradation of organic matter in soil wasnot correlated to the clay content but related to thesurface area of clays. Hassink (1994) reported that themicrobial biomass activity in clay was lower than insandy and loamy soil. Baldock and Skjemstad (2000)linked the two prior findings by suggesting that thebest condition for the decomposer consists of goodwater supply and sufficient oxygen supply throughopen pore space. Reviewing mechanisms for blackcarbon stabilization in soil, Knicker (2011) consideredthat association and/or microaggregate formation withsoil minerals can provide another protection from deg-radation. Thus, although the clay contents were rela-tively low in all of the three LUFA soils, a retardingeffect of biochar degradation through its interactionwith clay minerals may be still possible over longertime periods.

The rapid degradation of the tested htcBC suggeststhat it is not suitable for enhancing the long-term carbonsequestration of soils (Woolf et al. 2010). The htcBCmight still be considered a valuable soil amendmentwith respect to its higher N-content as compared to thefeedstock. However, a recent study pointed to the htcBC

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treatments potentially including the risk of increasedN2O emissions (Kammann et al. 2011b), which lowersexpectations htcBC might involve generally reducedemissions of potent greenhouse gases as compared tounprocessed biomass.

Measuring biochar degradation

A general standard method has yet to be defined forthe analysis of biochar degradation in soil (Koide et al.2011). The present study used a method, which facil-itates characterization and comparison of the degrada-tion of diverse biochar products under constanttemperature and moisture conditions. It is well suitedfor standardized, early assessment of biochar degrada-tion and to complement long-term experiments underfield conditions (Bai et al. 2011). Soil moisture is amajor regulator of seasonal microbial biomass in soilsamples (Van Gestel et al. 1992), and proportions ofbiochar incubation in soil and the soil water contentapplied were similar to other studies (Luo et al. 2011;Peng et al. 2011).

The temperature and constant soil moisture condi-tions of the present laboratory study were conducive tobiodegradation, which suggests that degradation ratesare overestimated when compared to the seasonallyvarying conditions in the field (Kuzyakov et al. 2009).While supporting the characterization of the individualbiodegradability of different biochar products, the deg-radation rates reported in this study may not relate di-rectly to those under field conditions. Field amendmentsof biochar are usually mixed with manure or the like toavoid nutrient capturing by fresh char (Prendergast-Miller et al. 2011; Gajić and Koch 2012). Also annuallyrenewed inputs of organic matter and nutrients frombiomass have far-reaching effects on biochar degradation(Kammann et al. 2011b). There are also limits to theperiod, over which the results of a laboratory study canbe useful to draw conclusions on biodegradation in-situ.For example, differences to field conditions may increasedue to the depletion of microbial composition andcompetition during long-term incubation and with-out seasonal changes in soil temperature, andtriggering cycles of dry-wet conditions (Potts etal. 2006). Overall, the multitude of drivers sug-gests that the average field conditions do notexist, and the extent to which laboratory resultsover- or underestimate half-lives of biochar in thefield varies from site to site and or year to year.

Biodegradation models

Biodegradation of organic carbon compounds isusually not constant (Janssen 1984). We testedthree empirical models to predict biochar degra-dation, i.e. the first-order decay rate model, thedouble exponential model (Lehmann et al. 2009;Zimmerman et al. 2011), and a logarithmic model(Zimmerman 2010). All models facilitate curvesimulation and half-life estimation, although withquite variable results as shown by the presentstudy. Increasing model complexity often aims atinterpreting results along the concept of multiplepools, e.g., total biochar degradation may involveone labile and one recalcitrant carbon pool. Weapplied RMSD as a first approximation for thegoodness of fit of model data to measured data.As per its meaning, RMSD was able to compare theoverall matching of different models by pointing tomajor mismatches between measured and model data.Over the period of 200 days, it appeared that the doubleexponential model followed by the first-order decay ratemodel were the best matches for the degradation curves.

To assess the model performance, i.e., thegoodness of fit weighed against the degree ofparameterization (Burnham and Anderson 2004),we employed the corrected Akaike informationcriterion (AICC). The AICC showed overall pref-erences for the DEM over the FODM and for theFODM over the LOGM. However, the DEM wasnot always the best, and the difference in AICC

between FODM and LOGM were sometimessmall. Thus, the sum of results suggest that ratherthan calculating half-lives with only one model,an ensemble of models better captures the uncer-tainty inherent of empirical models. For a bettermodel evaluation and improved process under-standing, we need to investigate variously carbon-ized biochars and conduct more mechanisticstudies on the early, midterm, and late degrada-tion dynamics.

Acknowledgments Research contributing to this study wasfunded by the Deutsche Bundesstiftung Umwelt DBU. Theauthors are grateful to Rainer Georg Joergensen, University ofKassel, for his time invested to pre-review the manuscript. Wethank Sonja Schimmelpfennig for sharing her analysis ofbiochar and feedstock pH and ash content, and BeateLindenstruth and Ina Plesca for their technical support anddiscussion during the manuscript preparation.

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