soil organic carbon sequestration under different fertilizer regimes in north and northeast china:...

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Soil organic carbon sequestration under different fertilizerregimes in north and northeast China: RothC simulation

J. WANG1, C. LU

1, M. XU1, P. ZHU

2, S. HUANG3, W. ZHANG

1, C. PENG2, X. CHEN

4 & L. WU5

1Ministry of Agriculture Key Laboratory of Crop Nutrition and Fertilization, Institute of Agricultural Resources and Regional

Planning, Chinese Academy of Agricultural Sciences, Beijing, 100081, China, 2Centre of Agricultural Environment and Resources,

Jilin Academy of Agricultural Sciences, Changchun, 130033, China, 3Institute of Plant Nutrition, Resources and Environment,

Henan Academy of Agricultural Sciences, Zhengzhou, 450002, China, 4College of Resources and Environment, Northwest A&F

University, Yangling, 712100, China, and 5Sustainable Soils and Grassland Systems Department, Rothamsted Research, North

Wyke, Okehampton, Devon EX20 2SB, UK

Abstract

Soil organic carbon (SOC) modelling is a useful approach to assess the impact of nutrient

management on carbon sequestration. RothC was parameterized and evaluated with two long-term

experiments comparing different fertilizer treatments in north (Zhengzhou) and northeast

(Gongzhuling) China. Four nutrient application treatments were used: no fertilizer (Control), mineral

nitrogen–phosphorus–potassium fertilizers (NPK), NPK mineral fertilizer plus manure (NPKM), and

NPK mineral fertilizer plus straw return (NPKS). The comparison between simulated and observed

data showed that the model can adequately simulate SOC contents in the Control, NPK and NPKM

treatments but overestimated in the NPKS treatment at both sites. By changing the value of

decomposable plant material:resistant plant material (DPM:RPM) ratio from the default value to 3.35

for the NPKS treatment at the Zhengzhou site, dynamics of simulated SOC agreed with measured

values. A pseudo-parameter, straw retention factor was introduced to adjust the amount of straw

incorporated into soils. Using the inverse simulation method and the modified value of the ratio, the

best-fitted value was 0.24 for the NPKS treatment at the Gongzhuling site. This result indicated that

retaining straw on the soil surface makes less contribution to carbon sequestration than if it is

incorporated. With this modification for straw, the model produced reasonable predictions for the

two sites. The model was run for another 30 years with the modified parameter values and current

average climatic conditions for different fertilizer treatments at both sites. The results suggested that

the NPK application plus the addition of manure or straw would be better management practices for

carbon sequestration.

Keywords: RothC simulation model, modelling, carbon sequestration, soil organic matter, long-term

experiment

Introduction

Soil organic carbon (SOC) plays an important role in soil

fertility and can contribute to the mitigation of climate change

through the sequestering of carbon (C) in soils. However, the

conversion from natural to agricultural ecosystems and

unsustainable management of agricultural fields have caused a

rapid and significant decline in SOC. It was estimated that the

magnitude of SOC loss from croplands in the Midwestern

United States was between 25 and 40 Mg C ha�1, or about 30–

50% of the antecedent level after about 50 yrs of cultivation

(Lal, 2002). SOC depletion also happened in China, especially

in the black soils in northeast China (Xin et al., 2002) where

the steppe meadow was converted into agricultural land

during the 1950s and has been under cultivation ever since.

Huang (2005) reported that for croplands of the same soil

type, the SOC content in China was less than half of that in

Europe. Of the five major cereal crop regions in China, the

region with the greatest SOC content is in northeast China

(only about 1.0–1.5%) and the region with the second smallest

content is in north China (0.5–0.8%) (Pan & Zhao, 2005). It isCorrespondence: C. Lu. E-mail: [email protected]

Received February 2012; accepted after revision December 2012

182 © 2013 The Authors. Journal compilation © 2013 British Society of Soil Science

Soil Use and Management, June 2013, 29, 182–190 doi: 10.1111/sum.12032

SoilUseandManagement

believed that about 60–70% of SOC lost could be

resequestered by appropriate agricultural management

practices (Lal, 2002). C sequestration in agricultural soils is

recognized as a win–win strategy for food security as well as

CO2 mitigation (Lal, 2002; Smith, 2004) and has been the

focus of research and extension worldwide. However, the

challenge for agronomists is to identify for different regions

the appropriate management practices that can increase and

maintain SOC.

Nutrient sources, such as manure, mineral fertilizers and

harvest residues, such as straw, can affect the quantity and

quality of organic materials in soils and hence the amount of

C sequestered. In China, numerous studies based on long-

term field experiments suggested that balanced application of

mineral fertilizers can maintain and even increase SOC

content owing to increased crop production and residue

return (Cai & Qin, 2006; Fan et al., 2008; Ludwig et al.,

2010). Other reports showed that continuous application of

mineral fertilizers alone would not maintain SOC content

(Su et al., 2006; Zhu et al., 2007). However, the combined

application of mineral fertilizers and manure is an optimal

nutrient management practice for C sequestration (Cai &

Qin, 2006; Guo et al., 2007; Fan et al., 2008; Zhang et al.,

2010; Pathak et al., 2011). In addition, the use of straw as

an amendment could potentially increase C sequestration

(Smith et al., 1997a; Jarecki & Lal, 2003; Lou et al., 2011).

Because of the slow transformation rate of organic materials

in soils, monitoring the influence of organic amendments on

SOC change requires information gained from sampling

programmes carried out over many years.

Long-term field experiments are required to monitor SOC

dynamics in response to management practices, but cost

limits the number of climatic conditions and soil types that

can be investigated and the duration of individual studies.

Consequently, process-based models can be a useful tool to

predict SOC changes in response to changes in management

practices and improve our understanding of C turnover

in soils. In the past decades, a series of SOC models or

SOC embedded models have been developed and tested

(Smith et al., 1997b). Several reviews on the models have

been published (Wu & McGechan, 1998; Ma & Shaffer,

2001).

The RothC model can be used to estimate C sequestration

under different fertilizer application programmes and has

been evaluated against results of long-term experiments from

many parts of the world (Smith et al., 1997b; Shirato &

Taniyama, 2003). However, only limited tests have been

carried out in China (Yang et al., 2003; Guo et al., 2007;

Ludwig et al., 2010; Wan et al., 2011), and repara-

meterization would be required for certain management and

environmental conditions (Skjemstad et al., 2004; Shirato

et al., 2005; Kaonga & Coleman, 2008; Liu et al., 2009;

Todorovic et al., 2010). The aims of this study were to: (i)

evaluate the RothC model against data from two long-term

experiments conducted in north and northeast China,

respectively, and (ii) predict the SOC changes under different

farm management practices.

Materials and methods

Site description

Two long-term field experiments with the application of

manure or straw together with mineral fertilizers were selected

for model evaluation and the assessment of C sequestration.

These experiments began in 1990. One was located in

Zhengzhou (ZZ, hereafter), Henan province, northern China.

The other was in Gongzhuling (GZL, hereafter), Jilin

province, northeast China. Soil types, chemical properties of

the initial soils in 1990 and climatic conditions for the

experimental sites are given in Table 1.

Cropping practices

The long-term experiment had a continuous maize mono-

cropping system at GZL, and a maize–wheat double-cropping

system at ZZ. At ZZ, wheat was sown in mid-October and

harvested around the end of May in the following year, and

then maize was planted in early June and harvested in

Table 1 General description and soil properties (0–20 cm) at the

sites

Zhengzhou (ZZ) Gongzhuling (GZL)

Coordinate 34°47′N,

113°41′E

42°57′N,

148°57′E

Climate Warm-temperate,

semi-humid

Mild-temperate,

semi-humid

Mean annual

precipitation (mm)

645 525

Mean annual

temperature (°C)

14.8 4.5

China soil

classification

Fluvo-aquic soil Black soil

FAO soil

classification

Calcaric Cambisol Luvic Phaeozems

Soil texture Light loam Clay loam

Clay content

(<0.002 mm) (%)

13.4 31.0

Silt (0.002–0.05 mm) (%) 60.7 40.1

Sand (0.05–2 mm) (%) 26.5 28.9

Bulk density (g cm�3)a 1.41 1.24

Soil sample depth (cm) 20 20

SOC (g kg�1) 6.7 13.5

Total N (g kg�1) 0.67 1.42

pH (1:2.5 w/v water) 8.3 7.2

aAverage value of all the treatments measured during the

experimental periods.

© 2013 The Authors. Journal compilation © 2013 British Society of Soil Science, Soil Use and Management, 29, 182–190

Soil C sequestration under long-term fertilizer applications 183

mid-September. Wheat was irrigated 2 or 3 times (ca. 75 mm

each) depending on the amount of precipitation, and maize

was irrigated once at sowing with 75 mm of water. At GZL,

maize was sown in late April and harvested in late September.

At both sites, hand weeding was carried out to control weeds.

Fungicide and pesticides were applied during the growth

season when needed. Grain and straw were air-dried and

weighted separately for yield and aboveground biomass

calculation.

Experimental design

The experiments were in a randomized block design without

replication (plot size 400 m2). Four common treatments for

both sites were selected from the entire experiments: (i)

Control – no fertilizer application; (ii) NPK – mineral

nitrogen (N), phosphorus (P) and potassium fertilizers (K);

(iii) NPKM – mineral NPK fertilizers in combination with

manure; and (iv) NPKS – mineral NPK in combination with

straw.

Nutrient management is shown in Table 2. At each site,

total amount of N applied (i.e. mineral plus organic) was the

same for all treatments except the Control, while the amount

for each crop was determined by local practices, cultivar,

climate and soil conditions. For the NPKM treatment, 70%

of N applied to wheat at ZZ and maize at GZL was from

manure. For the NPKS treatment, the amount of mineral N

added was reduced as straw was returned. The amount of

mineral P and K added was not reduced for the NPKS and

NPKM treatments partially due to difficulty in quantifying

how much P and K from the organic amendments was

available to the crops (Duan et al., 2011). Mineral N, P and

K fertilizers used were urea, calcium superphosphate and

potassium sulphate, respectively. P and K fertilizers were

applied at sowing at both sites. At ZZ, 70% of mineral N

was applied at sowing, and the remaining as top-dressing at

the stem elongation stage. At GZL, one-third of mineral N

was applied at sowing and the rest as top-dressing at the

jointing stage.

On average, 25 Mg ha�1 yr�1 horse or cow manure (fresh

mass, equalled 2.04 Mg C ha�1 yr�1) and 23 Mg ha�1 yr�1

composted farmyard manure (fresh mass, equalled 2.45 Mg

C ha�1 yr�1) were applied once a year as basal fertilizer for

NPKM at ZZ and GZL, respectively. 6 and 7.5 Mg

ha�1 yr�1 of maize straw (dry matter, including 14% water

content), which equalled 2.27 and 2.86 Mg C ha�1 yr�1,

were retained for NPKS at ZZ and GZL, respectively. Straw

was incorporated into soils before wheat was sown at ZZ,

and broadcast in the furrows in mid-July after top-dressing

at GZL. Manure was applied in the same way as straw at

ZZ, but was incorporated into the soils after the harvest of

maize at GZL. Details of complete experimental design at

the two sites have been described elsewhere (Wang et al.,

2010; Zhang et al., 2010; Zhao et al., 2010). Soil samples

from the plough layer (0–20 cm) for soil nutrients and

carbon content were collected annually from each treatment

after maize was harvested.

RothC model

In RothC, including version 26.3 as used, SOC is divided into

four active pools and a small amount of inert organic matter

pool (IOM) (Coleman & Jenkinson, 1999). The four active

compartments are decomposable plant material (DPM),

resistant plant material (RPM), microbial biomass (BIO) and

humified organic matter (HUM). Each compartment

decomposes according to first-order kinetics at a rate chara-

cteristic of that material. The IOM compartment is resistant

to decomposition. Decomposition rates for the active pools

depend on air temperature, soil moisture and vegetation

Table 2 Annual application rate of N, P and K (kg ha�1) for each crop, the source and carbon input by organic amendments

Treatments

Zhengzhou (ZZ) Gongzhuling (GZL)

Maizea Wheat Maize

Mineral

N-P-K

Mineral

N-P-K

Organic amendments

Mineral

N-P-K

Organic amendments

Source N-P-K

Average C

input/Mg C ha�1 Source N-P-K

Average C

input/Mg C ha�1

Control 0 0 – – – 0 – – –

NPK 188-41-78 165-36-68 – – – 165-36-68 – – –

NPKM 188-41-78 50-36-68 HM, CMb 115-66-92c 2.04 50-36-68 FYMb 115-39-77c 2.45

NPKS 188-41-78 121-36-68 Maize straw 42-8-86c 2.27 112-36-68 Maize straw 53-6-58c 2.86

aN, P and K to maize in 1991 and 1992 were 165, 36 and 68 kg ha�1 yr�1, respectively. bThe manure types were horse manure from 1990 to

1998 and cattle manure from 1999 to 2008 at the Zhengzhou site. Manure was not applied in 2007. HM, horse manure; CM, cattle manure;

FYM, farmyard manure mixed with crop residue and soil. cThe nutrient amount added by straw and manure based on Zhao et al. (2010).


184 J. Wang et al.

coverage. Clay content is also used to calculate maximum

moisture deficit in the topsoil (0–20 cm) and affects the

partitioning of C between that lost in gaseous emissions from

the soil and that transferred to BIO + HUM.

Carbon input to soils and driving variables

Grain yield and straw dry matter (DM) for both crops were

measured in all treatments at ZZ, while only grain yield and

limited straw DM were available at GZL. Therefore, straw

DM from maize at GZL was calculated through a mean of

the harvest index from all the treatments measured in 2003,

2006 and 2007. The value was 0.45 � 0.03. The belowground

C input by roots was estimated with a root: aboveground DM

ratio and the proportion of roots in the top 20 cm of the soil.

The values of the two parameters used for maize were 0.35

and 0.86 (Li et al., 1994; Yang et al., 2000), and 0.43 and 0.75

for wheat (Ma, 1987; Li et al., 1994), respectively. Wheat at

ZZ was harvested by combined harvester leaving a stubble

height about 20 cm. By considering the differences of plant

height at harvest between the treatments, C input by wheat

stubble was about 26% of straw for the mineral fertilizer

treatments but 35% for the Control treatment at ZZ. Maize

at both sites was harvested by hand, and stubble was about

3% of straw for all the treatments. The contribution of weeds

to C input was ignored as they were removed manually. For

all the treatments, organic C contents for wheat and maize

were taken as national averaged values, that is, 39.9% and

44.4% (oven-dried basis), respectively (NCATS, 1994).

Monthly average air temperatures and amount of

precipitation between 1989 and 2008 were obtained from the

nearest meteorological stations at both sites. Monthly

evapotranspiration was calculated by the Penman–Monteith

equation (Allen et al., 1998) and then divided by 0.75 to

obtain monthly open-pan evaporation required by the model

(Coleman & Jenkinson, 1999).

In the model, the quality of plant materials added to soils

is distinguished by a DPM: RPM ratio. The larger the ratio,

the faster it decomposes. The ratio was set to 1.44. Manure

was split into DPM (49%), RPM (49%) and HUM (2%),

according to the model defaults. Because no measured data

were available, the IOM in the topsoil was set to 1.39 and

2.68 Mg ha�1 for ZZ and GZL, respectively, based on the

equation set by Falloon et al. (1998). It was assumed that

SOC content was in dynamic equilibrium at the start of the

experiments.

Statistical analysis

The ANOVA and least significant difference (LSD) methods

were applied to compare mean C input in the considered

treatments over the entire experimental period using SPSS

16.0. Model performance was evaluated by the root-mean-

square error (RMSE) and relative error (E), which indicates

the bias in the total difference between simulations and

measurements (Smith et al., 1997b):

RMSE ¼ 100O

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiXni¼1

ðPi � OiÞ2=ns

: ð1Þ

E ¼ 100n

Xni¼1

ðOi � PiÞ=Oi: ð2Þ

where Oi is observed value, Pi is predicted value, O is the

mean of the observed data, and n is the number of paired

values.

Results and discussion

C inputs under long-term fertilizer application

Average annual C input (Mg C ha�1 yr�1) from the crops

(including roots and stubble) under the Control treatment

showed a range from 0.94 at GZL to 1.50 at ZZ (Figure 1).

Application of mineral fertilizer (the NPK treatment)

significantly increased crop biomass (Wang et al., 2010;

Zhang et al., 2010), hence annual C input from the crops, at

both sites (2.45–3.72 Mg C ha�1 yr�1). It has been shown that

the incorporation of straw (the NPKS treatment) and manure

(the NPKM treatment) did not benefit crop biomass

immediately compared with the NPK treatment (Wang et al.,

2010; Zhang et al., 2010). Therefore, annual C input by the

crops showed no significant differences between the NPK,

NPKM and NPKS treatments. As expected, total annual C

input was significant larger in the treatments with straw

returned or manure applied than that in the NPK treatment.

The input under the NPKS and NPKM treatments was

1.50–1.66 times of that under the NPK treatment at ZZ and

2.01–2.16 times at GZL.

Model evaluation and modification

The simulated and measured SOC agreed well, and the

values of RMSE were less than 10% in the Control, NPK

and NPKM treatments at both sites (Figure 2a–c and e–g).

The result is consistent with the reported simulations in

China for similar treatments (Yang et al., 2003; Guo et al.,

2007). However, the SOC content was overestimated

compared with measured data in the NPKS treatment

(Figure 2 d and h). The worst simulation result occurred for

treatment NPKS at GZL.

Overestimation of the SOC content for treatments

involving the incorporation of straw or its retention after

harvest has been reported by Ludwig et al. (2005), Shirato

et al. (2005), Liu et al. (2009) and Heitkamp et al. (2012).



(a) ZZ

CK NPK NPKM NPKSAnn

ual c

arbo

n in

put (

Mg

C/h

a/yr

)

0

2

4

6

8

10

A

BC

a

b b b

C

(b) GZL

CK NPK NPKM NPKS Ann

ual c

arbo

n in

put (

Mg

C/h

a/yr

)

0

2

4

6

8

10

Crop-COrganic amendment C

a

b b bA

B

C C Figure 1 Average annual carbon input from

crops (including roots and stubble) and

organic amendments in the treatments

between 1990 and 2008. Different small letters

(annual C input from crops) and capital

letters (total annual C input) show significant

difference at 0.05 level of Fisher’s LSD for

each site.

(f) GZL: NPK

SO

C (

Mg/

ha)

10

20

30

40

50

60

(e) GZL: Control

10

20

30

40

50

60

(h) GZL: NPKS

1990 1995 2000 2005 2010 10

20

30

40

50

60

(g) GZL: NPKM

10

20

30

40

50

60

(b) ZZ: NPK

SO

C (

Mg/

ha)

10

20

30

40

50

60

(a) ZZ: Control

10

20

30

40

50

60

(d) ZZ: NPKS

Year1990 1995 2000 2005 2010

10

20

30

40

50

60

(c) ZZ: NPKM

10

20

30

40

50

60

RMSE = 4.7%E = 0.8%

RMSE = 8.2%E = –0.2%

RMSE = 6.7%E = –3.2%

RMSE = 13.2%E = –10.4%

RMSE = 8.1%E = 2.5%

RMSE = 7.9%E = –0.6%

RMSE = 7.8%E = –2.0%

RMSE = 24.7%E = –22.5% Figure 2 Comparison between simulated and

observed SOC contents in the topsoil

(0–20 cm) of different treatments at ZZ (a–d)

and GZL (e–h) (● Observed,— Simulated).


186 J. Wang et al.

For example, Ludwig et al. (2005) stated that the simulation

with the default settings and with aboveground residues

incorporated into the soil overestimated the changes in

maize-derived SOC by 1.6-fold after 24 yrs of continuous

maize cropping in a silt-loam soil. Therefore, it could be

deduced that the overestimation of SOC may be attributed

mainly to the C turnover rate for straw being too small,

thereby causing an apparent accumulation of SOC in the

soils.

At ZZ, an overestimation of SOC (E = �10.4%) for the

NPKS treatment could be due to an inappropriate setting of

DPM:RPM. The default value is suitable when root biomass

is the main source of C input to the soil (Figure 2 a, b, e

and f). However, the value should be greater when a large

amount of straw is incorporated into the soil, as straw C has

a shorter residence time than does root C (Rasse et al.,

2005). To improve simulation accuracy for the treatment at

ZZ, a modification of the ratio was made. Following

Ayanaba & Jenkinson (1990), the ratio was set to 3.35,

which improved the prediction of SOC changes (Figure 3).

An independent data set collected at Quzhou site, Hebei

province, northern China, was used to further evaluate the

model (Figure 4).

The model with the modified value of the ratio accurately

predicted SOC dynamics in the topsoil (0–20 cm) under

conventional tillage with straw incorporation. However,

others have reported that there is only a slight improvement

in the simulations with a similar modification to the ratio

value (Ludwig et al., 2005; Shirato et al., 2005). The

discrepancies between the modelling performances highlighted

that further improvement of the model would be needed to

simulate C dynamics in the soil with straw added.

The model was run for treatment NPKS at GZL with the

modified value of the ratio. The simulation was improved

but still overestimated the SOC content (E = �14.5%,

Figure 6), suggesting that simulation of SOC with straw

retention could not be improved, simply by changing the

DPM:RPM ratio. A more accurate estimation of the amount

of surface-retained straw that is incorporated into the soil

also needs to be considered in the simulation. Following the

method proposed by Liu et al. (2009), an extra parameter,

straw retention factor (fs), was introduced to adjust the

amount of straw incorporated into the soil. By using an

inverse simulation technique, the fitted value of the factor

can be determined by taking RMSE as a criterion. To do so,

the value of fs was varied from 0.00 to 1.00 with an

increment of 0.01. The model was repeatedly run for the

whole experimental period with different values of fs. When

the DPM:RPM ratio was set to 3.35, fs was 0.24 while

RMSE was a minimum (Figure 5). With the modifications in

both parameters, simulation for treatment NPKS at GZL

agreed well with the observations (RMSE = 5.6%, Figure 6).

Year

1990 1995 2000 2005 2010

SO

C (

Mg/

ha)

10

15

20

25

30

35

Observed Original, DPM:RPM = 1.44 Modified, DPM:RPM = 3.35

Figure 3 Comparison between simulated and observed SOC

contents in the topsoil (0–20 cm) for treatment NPKS at the ZZ site.

SOC changes were simulated with different DPM:RPM ratios (solid

line for 1.44 and solid line with open cycle for 3.35).

Year1983 1988 1993 1998 2003

SO

C (

Mg/

ha)

5

10

15

20

25

N1P2S0

N2P2S2

Simulated

Original, DPM:RPM = 1.44 Modified, DPM:RPM = 3.35

Figure 4 Simulated and observed SOC contents in the topsoil (0–

20 cm) for two treatments (N1P2S0 and N2P2S2) with conventional

tillage at Quzhou site, Hebei province, northern China. N1P2S0:

112 kg (urea-N) ha�1 yr�1, 150 kg (P2O5) ha�1 yr�1 and no straw

application; N2P2S2: 187 kg (urea-N) ha�1 yr�1, 150 kg (P2O5)

ha�1 yr�1 and 4.5 Mg straw (DM) ha�1 yr�1. Observed data were

extracted from the literature (Niu et al., 2003; Ludwig et al., 2010).

Straw retention factor (fs)0.0 .2 .4 .6 .8 1.0

RM

SE

(%

)

0

5

10

15

20

Figure 5 Changes in root-mean-square error (RMSE) with the straw

retention factor (fs) that varies from 0.00 to 1.00 with an increment

of 0.01. The lowest RMSE is obtained at fs = 0.24.



This result indicated that a large fraction of surface-retained

straw may be lost through wind erosion before entering the

soil because of strong winds and dry soils in spring (Yang

et al., 2006).

SOC prediction under current nutrient managements

We used the reparameterized RothC-26.3 model to predict

the impact of current nutrient managements on SOC

dynamics for 30 yrs from 2009 for both sites. Because of the

difficulty of downscaling projected climatic data to the site

level, average climate data between 1990 and 2008 were used

as the driving variables in the simulations, and average

annual C input between 2004 and 2008 was assumed as

annual C input for the prediction.

The rate of C sequestration at GZL was less than that at

ZZ for the corresponding treatments (Table 3). This can be

explained by differences in initial SOC content and cropping

intensity, which affect the amount of crop residue retention.

The Control treatment (No fertilizer application) resulted in

steadily declining SOC contents at both sites, whereas the

application of mineral fertilizers can maintain SOC content

at GZL and significantly increase SOC content at ZZ. The

application of organic materials in combination with mineral

fertilizers was a more effective way for C sequestration than

using mineral fertilizers alone.

Conclusions

This study tested the suitability of the RothC-26.3 model to

simulate SOC dynamics in north and northeast China

compared with data sets from long-term experimental and

specifically estimated soil C sequestration in response to

different strategies in straw and manure applications.

RothC-26.3 adequately simulated SOC content in the

treatments where roots and manure were the main C input

sources (Control, NPK, NPKM), but overestimated the SOC

content when crop straw was retained (NPKS). By

modifying the default setting for DPM:RPM to 3.35 for

treatment NPKS, the simulated SOC contents agreed well

with the observed values at the ZZ site. Inverse simulation

showed that only 24% of surface-retained straw was

effectively returned to the soil. These results indicated that

RothC-26.3 can be used to simulate SOC dynamics under

different fertilizer regimes in north and northeast China, but

modifications are needed when large quantities of straw are

retained. The application of mineral NPK can maintain and

increase SOC content, but crop straw amendment and

manure application are effective ways to enhance soil C

sequestration in the regions.

Acknowledgements

We acknowledge all colleagues for their unremitting efforts at

the long-term experiments. Dr Wendy Wang, University of

Maryland, made contribution to simulation design. The

research was funded by the Beijing Science Foundation

(6102023), the National Science Foundation of China (4117

1239, 40901141) and the National Basic Research Program

(2011CB100501).

References

Allen, R.G., Pereira, L.S., Raes, D. & Smith, M. 1998. Crop

evapotranspiration: Guidelines for computing crop water requirements.

FAO Irrigation and drainage paper NO.56. FAO, Rome.

Ayanaba, A. & Jenkinson, D.S. 1990. Decomposition of carbon-14

labeled ryegrass and maize under tropical conditions. Soil Science

Society of America Journal, 54, 112–115.

Cai, Z.C. & Qin, S.W. 2006. Dynamics of crop yields and soil

organic carbon in a long-term fertilization experiment in the

Huang-Huai-Hai Plain of China. Geoderma, 136, 708–715.

Table 3 Simulated C sequestration rate (Mg C ha�1 yr�1) in the

topsoil (0–20 cm) under different fertilizer regimes at the two

experimental sites for different periods

Treatments 1990–2008 Next 30 years

ZZ

Control �0.16 �0.07

NPK 0.29 0.19

NPKM 0.55 0.35

NPKS 0.47 0.33

GZL

Control �0.29 �0.14

NPK 0.04 0.04

NPKM 0.67 0.33

NPKS 0.08 0.10

Year1990 1995 2000 2005 2010

SO

C (

Mg/

ha)

10

20

30

40

50

60

Observed Original, DPM:RPM = 1.44 Modified, DPM:RPM = 3.35 Modified, DPM:RPM = 3.35, fs = 0.24

Figure 6 Simulated and observed SOC contents in the topsoil (0–

20 cm) for treatment NPKS at GZL. SOC changes were simulated

with a DPM:RPM ratio of 1.44 (solid line), a ratio of 3.35 (dotted

line) and a ratio of 3.35 and the factor of 0.24 (solid line with open

cycle).


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