soil carbon sequestration potential as affected by management practices in northern china: a...

Pedosphere 24(4): 529–543, 2014

ISSN 1002-0160/CN 32-1315/P

c© 2014 Soil Science Society of China

Published by Elsevier B.V. and Science Press

Soil Carbon Sequestration Potential as Affected by Management

Practices in Northern China: A Simulation Study∗1

WANG Guo-Cheng1, WANG En-Li2,∗2, HUANG Yao3 and XU Jing-Jing1

1State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric

Physics, Chinese Academy of Sciences, Beijing 100029 (China)2CSIRO Land & Water, Black Mountain, Canberra, ACT 2601 (Australia)3State Key Laboratory of Vegetation and Environmental Change (LVEC), Institute of Botany, Chinese Academy of Sciences, Beijing

100093 (China)

(Received March 6, 2013; revised January 5, 2014)

ABSTRACTSoil has been identified as a possible carbon (C) sink for sequestering atmospheric carbon dioxide (CO2). However, soil organic

carbon (SOC) dynamics in agro-ecosystems is affected by complex interactions of various factors including climate, soil and agricultural

management practices, which hinders our understanding of the underlying mechanisms. The objectives of this study were to use the

Agricultural Production Systems sIMulator (APSIM) model to simulate the long-term SOC dynamics under different management

practices at four long-term experimental sites, Zhengzhou and Xuzhou with double cropping systems and Gongzhuling and Urumqi

with single cropping systems, located in northern China. Firstly, the model was calibrated using information from the sites and

literature, and its performance to predict crop growth and SOC dynamics was examined. The calibrated model was then used to

assess the impacts of different management practices, including fertilizer application, irrigation, and residue retention, on C dynamics

in the top 30 cm of the soil by scenario modelling. Results indicate a significant SOC sequestration potential through improved

management practices of nitrogen (N) fertilizer application, stubble retention, and irrigation. Optimal N fertilization (Nopt) and 100%

stubble retention (R100) increased SOC by about 11.2%, 208.29%, and 283.67% under irrigation at Gongzhuling, Zhengzhou, and

Xuzhou, respectively. Soil organic carbon decreased rapidly at Urumqi under irrigation, which was due to the enhanced decomposition

by increased soil moisture. Under rainfed condition, SOC remained at a higher level. The combination of Nopt and R100 increased

SOC by about 0.46% under rainfed condition at Urumqi. Generally, agricultural soils with double cropping systems (Zhengzhou and

Xuzhou) showed a greater potential to sequester C than those with single cropping systems (Gongzhuling and Urumqi).

Key Words: agro-ecosystems, APSIM model, fertilizer application, irrigation, residue retention, scenario analysis, soil organic carbon

Citation: Wang, G. C., Wang, E. L., Huang, Y. and Xu, J. J. 2014. Soil carbon sequestration potential as affected by management

practices in northern China: A simulation study. Pedosphere. 24(4): 529–543.

INTRODUCTION

Soil organic carbon (SOC) has experienced signifi-cant decreases due to cultivation of natural soils inagro-ecosystems (Davidson and Ackerman, 1993; Lal,2004). It is suggested that adopting improved agricul-tural management such as stubble retention, conser-vation tillage and fertilization has the potential to en-hance SOC accumulation, thereby mitigating the cli-mate change and promoting the soil quality to supportsustainable crop productivity (Smith, 2004; Wang etal., 2013a). However, the effectiveness of any mana-gement practice on agricultural SOC balance is af-fected by the complex interaction between carbon(C) production and decomposition processes as con-

trolled by spatiotemporally changing environmentalconditions, which hampers our ability to extrapolatethe SOC dynamics over time and space (Luo et al.,2011). Process-based soil-plant system models can cap-ture the interaction between C production (input)and decomposition (output), and thus have been usedworldwide to simulate SOC dynamics under diffe-rent agricultural practices and different climatic andedaphic conditions (Smith et al., 1997b; Li et al., 2003;Lugato and Berti, 2008; Liu et al., 2009; Lehuger etal., 2010). For example, Ogle et al. (2010) simulatedthe change of SOC storage in US croplands from 1990to 2000 using the Century model, Wang et al. (2013b)modelled the regional SOC change from 1960 to 2010 inAustralian wheat growing areas based on the Agro-C

∗1Supported by the National Basic Research Program (973 Program) of China (No. 2010CB950604) and the National Natural Science

Foundation of China (No. 41075108).∗2Corresponding author. E-mail: [email protected].

530 G. C. WANG et al.

model, and Tang al. (2006) estimated the nationalSOC storage and its associated changing rates ac-ross Chinese croplands using the DeNitrification-De-Composition (DNDC) model. The advantage of adop-ting modelling method is that once the model has beenproperly validated, it can be used to predict SOC dy-namics as impacted by various management, soil andclimate conditions and their interactions across timeand space, which is impractical, if not impossible, forfield experiments.

The Agricultural Production Systems sIMulator(APSIM) model (Keating et al., 2003) was developedin Australia for simulation of both crop growth andsoil processes by providing the functionality and flexi-bility to specify complex rotation types and mana-gement options. The model has been extensively testedand applied in Australia to study the performanceof agricultural systems under different climatic con-ditions and various management scenarios (Asseng etal., 1998; Probert et al., 2005; Chen et al., 2010a,b; Wang et al., 2010; Luo et al., 2011; Yang et al.,2011). Recently, Chen et al. (2010a) and Wang etal. (2010) found that the APSIM model performed wellfor simulation of crop production under different irri-gation and fertilization treatments in the North ChinaPlain. Yang et al. (2011) also found that the APSIMmodel could reasonably simulate the dynamics of win-ter and spring wheat production in semi-arid areasof Northwest China. Luo et al. (2011) indicated thatthe model performed well in predicting SOC dyna-mics in the top 30 cm soil at an Australian semi-aridwheat belt. So far, there seems to be no study on theperformance of the APSIM model in predicting long-term SOC dynamics in different agro-ecosystems acrossChina.

Most of northern China has a semi-humid or semi-arid climate. Crop (mainly wheat and/or maize) pro-duction is highly constrained by limited water re-sources, relatively low soil fertility, and poor agricul-tural management practices (Wang et al., 2007). Irri-gation where water is available, application of mine-ral fertilizers and/or organic manure, and crop residueretention have been promoted as strategies to in-crease crop productivity and maintain soil fertility.Traditionally, only 15%–25% of the crop residues werereturned to the field after harvest, with the rest re-moved mainly for cooking and heating in the rural a-reas (Wang and Feng, 2004; Li et al., 2005; Wang et al.,2007; Liu et al., 2008). Recently, residue retention isencouraged for promoting SOC sequestration. Howe-ver, the potential impact of residue return on soil Csequestration across regions is unknown.

Fertilization, particularly nitrogen (N) fertilizer ap-plication, has been widely and increasingly adoptedsince the 1970s in China, motivated by the governmentpolicies to promote grain production. However, exces-sive use of N fertilizer has caused serious environmentalproblems (Ju et al., 2009). The impact of N applica-tion on SOC status is still inconclusive. Generally, Napplication enhances crop production in areas with Ndeficiency, resulting in more crop residues incorporatedinto the soil. However, this view has been challengedby Khan et al. (2007), who reported that the intensiveuse of N fertilizers promotes the decomposition of bothcrop residues and original SOC, leading to a decreasein total SOC of the whole profile, although an increaseof C in the top soil has been widely observed. Con-sequently, optimal fertilization management aiming atbalancing the high crop productivity and SOC needsto be identified in different agro-ecosystems.

Irrigation development has lifted crop productivitysignificantly in the past three decades, particularly inthe North China Plain. However, excessive use of ir-rigation has led to cessation of river flows and rapiddepletion of groundwater resources (Wang et al., 2002,2008a). This might have profound implications on bothcrop productivity and SOC. Irrigation not only affectsthe carbon production by crops, but can also influencethe soil water status and SOC decomposition. Litera-ture studies suggest that information about the impactof irrigation change on SOC dynamics is rather lim-ited.

The objectives of this study were to: 1) use ex-perimental data from four sites distributed in nor-thern China to examine the performance of the AP-SIM model for simulation of long-term SOC dyna-mics under cropping systems with different fertilizationand residue management practices, and 2) apply thevalidated APSIM model to investigate SOC dynamicsas influenced by management scenarios with varyingN application rates, stubble retention fractions, andamounts of irrigation.

MATERIALS AND METHODS

Experimental sites and treatments

Four experimental sites from northern China, Go-ngzhuling, Urumqi, Zhengzhou, and Xuzhou, were se-lected for this study (Table I). They belong to theexperimental sites of the National Long-Term Fertili-sation Experimental Network in China (Zhao et al.,2010). Zhengzhou and Xuzhou had been in arablecropping for at least 100 years before the experi-ments started in 1980 and 1990, and Gongzhuling and

SOIL C SEQUESTRATION AND MANAGEMENT PRACTICES 531

Urumqi had been cultivated for at least 50 years be-fore the experiments started in 1990. At each site, cropbiomass and grain yields were recorded at harvest eve-ry year. SOC and soil total N in the 0–20 cm soillayer were also measured at the start of the experi-ment and each year after autumn harvest (i.e., du-ring September–October). At each site, although noreplicates were designed in the experiment, but soilsamples were taken from five randomly selected loca-tions in each plot to get average values. An auger with5-cm internal diameter was used to take soil samplesin the plough layer (0–20 cm) at the five randomlyselected locations. The fresh soil samples were thenmixed completely, air-dried and sieved through a 2.0-mm sieve and stored for further analysis. Soil organiccarbon content in the top 30 cm soil layer was de-rived from the measured top 20-cm SOC, according toSOC vertical distribution (Jobbagy and Jackson, 2000;Mikhailova and Post, 2006; Qin and Huang, 2010), bytimes the later by 1.32. The crop and soil data wereobtained from Zhang et al. (2009, 2010) and Zhao etal. (2010). Table I shows the site information, and Ta-ble II gives the initial soil properties at the start of thefield experiments.

During the experimental period, a single crop-

ping system, continuous mono-cropping of maize atGongzhuling and a maize-wheat-wheat rotation (i.e.,maize cropping for one year and wheat cropping fornext two years) at Urumqi, was adopted. Maize forthese two sites was sowed during late April to earlyMay, spring wheat in mid-April, and winter wheat inlate September of the same year. At Zhengzhou andXuzhou, a double cropping system of winter wheatand summer maize rotation was adopted, with sum-mer maize sowed in late April to early May and win-ter wheat sowed in October of the same year. Due tothe relatively low annual precipitation and high an-nual evaporation, irrigation was routinely applied du-ring the crop growing seasons at the study sites exceptGongzhuling. Weeds were controlled by hand weedingor herbicides.

There were a total of eight treatments at thefour selected sites in the original design for fertili-zer applications, covering combinations of N, phospho-rus (P), and potassium (K) fertilizer applications andstubble management. Data from three treatments atGongzhuling, Urumqi, and Zhengzhou were used totest the APSIM model performance, including non-fertilization control (CK), application of mineral ni-trogen, phosphorus and potassium fertilizers (NPK),

TABLE I

Locations and climate conditions (1961–2010) at the four selected study sites in northern China

Site Latitude Longitude Absolute Climatea) Mean annual Annual Mean annual Study

altitude temperature rainfall radiation peroid

m ◦C mm MJ m−2

Gongzhuling 103◦30′ N 124◦48′ E 220 CT, SH 7.1 608 15.7 1990–2004

Urumqi 43◦58′ N 87◦26′ E 600 MT, SA 7.8 262 16.8 1990–2004

Zhengzhou 34◦47′ N 113◦40′ E 21 WT, SH 15.0 630 13.6 1990–2003

Xuzhou 33◦39′ N 116◦52′ E 20 WT, H 15.0 848 15.1 1980–2003

a)CT = cool-temperate; MT = mild-temperate; WT = warm-temperate; SH = semi-humid; SA = semi-arid; H = humid.

TABLE II

Initial physical and chemical characteristics of the top 30 cm soil at the four selected study sites in northern Chinaa)

Site Soil type Bulk Sand Silt Clay Total pH in Initial soil organic C (0–30 cm)b)

density N waterFOM BIOM HUM Inert C Total C

g cm−3 % g kg−1 t ha−1

Gongzhuling Black 1.19 38 30 32 1.42 7.2 0.40 0.53 33.00 8.45 42.37

Urumqi Grey desert 1.25 19 53 28 0.91 8.1 0.26 0.53 22.57 5.41 28.78

Zhengzhou Fluvo-aquic 1.55 27 60 13 0.67 8.3 0.13 0.26 6.73 14.78 21.91

Xuzhou Yellow fluvo-aquic 1.25 49 43 8 0.66 8.2 0.13 0.26 5.28 15.71 21.38

a)The quantity of soil organic carbon (SOC) in the 20–30 cm layer (SOC20-30) was not reported in the literature, so the observed SOC

density in the topsoil (0–30 cm) (SOC0-30) was corrected (SOC0-30 = 1.32 × SOC0-20), according to Jobbagy and Jackson (2000) and

Qin and Huang (2010).b)In the APSIM model, soil organic matter is divided into three conceptual pools, including fresh organic matter (FOM) pool, a more ac-

tive carbon (BIOM) pool, and a humic (HUM) pool. The FOM pool contains all the fresh organic matter, such as dead crop roots and

incorporated residues, and is further split into three subpools, i.e., carbohydrate-like (CH), cellulose-like (CL), and lignin-like (LIG)

pools. The BIOM pool represents the soil microbial biomass and microbial products. The HUM pool comprises the rest of the soil

organic matter, part of which is considered to be highly resistant to microbial decomposition (Inert C).


and mineral NPK fertilization combined with stub-ble retention (NPKSt). Only CK and NPK were usedat Xuzhou. Under CK and NPK, wheat straw andmaize stover were cut to ground and removed from thefield after the grain harvest each year, while roots andlitters were left in the field for all the study sites. Un-der NPKSt, 7 500 kg ha−1 of maize stover was scat-tered and applied as green manure around mid July atGongzhuling, all crop (maize or wheat) straw was scat-tered and applied to the field after harvest at Urumqi,and only maize stover was scattered and applied beforesowing of wheat in the same year at Zhengzhou. De-tailed information on N, P and K fertilization at eachstudy site is given in Table III.

APSIM model and its parameterisation and validation

The APSIM model (Keating et al., 2003) was usedto simulate both the crop growth and soil C cyclingprocesses. A configuration of APSIM version 7.5 wasused, including modules of Wheat, Maize, SoilWat,SoilN, SurfaceOM, and Manager. More detailed infor-mation on the model was described by Keating et al.(2003) and Luo et al. (2011).

The APSIM model runs at a daily time step, andneeds daily weather data as inputs, including radia-tion, maximum and minimum temperatures, and rain-fall. All the climate data were obtained directly fromthe nearest meteorological stations to the experimentalsites (http://www.cdc.cma.gov.cn/), except the daily

radiation, which was estimated from the daily sunshineduration using the Angstrom formula (Black et al.,1954; Jones, 1992).

In the APSIM model, soil organic matter is dividedinto three conceptual pools, including fresh organicmatter (FOM) pool, a more active carbon (BIOM)pool, and a humic (HUM) pool. The FOM poolcontains all the fresh organic matter, such as deadcrop roots and incorporated residues, and is furthersplit into three subpools, i.e., carbohydrate-like (CH),cellulose-like (CL), and lignin-like (LIG) pools. TheBIOM pool represents the soil microbial biomass andmicrobial products. The HUM pool comprises the restof the soil organic matter, part of which is consideredto be highly resistant to microbial decomposition (In-ert C). Decomposition of each pool and subpool followsa first-order decay process, and each pool has a spe-cific maximum decomposition rate of C. The actual de-composition rate is determined by the maximum ratemodified by soil temperature, moisture, and C:N ratio(Thorburn et al., 2001). Total soil C content at thestart of the experiments was used to initialize the soilC pools in APSIM. The assumptions used to fraction-ate the total soil C in different soil layers and each soilC pool were similar to those of Luo et al. (2011): 1)total SOC, mostly concentrated in the top 20–30 cm,decreases exponentially with soil depth; 2) in deepersoil layers, the proportion of total soil C that is resis-tant to decomposition is higher, and the absolute amo-

TABLE III

Amounts of nitrogen (N), phosphorus (P), and potassium (K) applied in fertilizer under different treatments for cropping of maize or

wheat at the four selected study sites in northern China

Item Treatmenta) Site

Gongzhulingb) Urumqic) Zhengzhoud) Xuzhoue)

Maize Maize Wheat Maize Wheat Maize Wheat

kg ha−1

N CK 0 0 0 0 0 0 0

NPK 165 99/242 99/242 188 165 150 150

NPKSt 112 89/217 89/217 188 50

P CK 0 0 0 0 0 0 0

NPK 36 29/60 29/60 41 36 33 33

NPKSt 36 25/51 25/51 41 36

K CK 0 0 0 0 0 0 0

NPK 68 18/47 18/47 78 68 93 93

NPKSt 68 16/39 16/39 78 68

a)At Gongzhuling, Urumqi, and Zhengzhou, there are three treatments, application of compound inorganic fertilizers (NPK), applica-

tion of inorganic fertilizers and stubble retention (NPKSt), and control (CK). At Xuzhou, there are two treatments, NPK and CK.b)One-third of the N, P, and K fertilizers were applied as base fertilizer before sowing and the rest as topdressing at the jointing stage.c)The first value of N, P, and K is the amount applied from 1990 to 1994 and the second is that since 1995. Two-thirds of the N, P, and

K fertilizers were applied as base fertilizer before sowing and one-third as topdressing at the jointing stage.d)Two-thirds of the N, P, and K fertilizers were applied as base fertilizer before sowing and one-third as topdressing at the jointing

stage.e)One half of the N, P, and K fertilizers were applied as base fertilizer and the other half as topdressing at the jointing stage.


unt of Inert C is constant across the soil layers; and3) the FOM pool mainly distribute in upper soil layersand represent a small proportion of total SOC in thecropland soil with a long history of cultivation.

Soil hydraulic parameters such as the drained up-per limit of soil water content (DUL) and the lowerlimit of soil water content (LL) can be derived fromthe long-term soil moisture measurements at each siteusing the wettest and driest soil water contents. The15Bar lower limit of soil water content (LL15) can bemeasured from soil samples and the saturated watercontent (SAT) was calculated as total soil porosity mi-nus 0.05 (Probert et al., 1998). In this study, all thesevalues were obtained from the literature or throughpersonal communication (Zhang et al., 2009, 2010;Zhao et al., 2010).

For each study site, the model was run for aselected study period (Table I). Agricultural mana-gement such as sowing date, tillage regimes, and fertili-zer application was set according to historical mana-gement records and relevant literature. Crop cultivarparameters that control phenological phases were ad-justed to match the simulated and observed maturitydates. Harvest index (i.e., the harvested proportionof the total aboveground biomass) was used to ad-just grain growth parameters in the model to reachagreement between the observed and simulated yields.We assumed that crop cultivar parameters and har-vest index were the same through different experimen-tal years for each site. Daily C in the top 30 cm soillayers, crop biomass, and grain yields were simulatedand compared with the observed data to assess the per-formance of the APSIM model.

Following Yu et al. (2012), three statistical crite-ria were used to evaluate the model performance. Theroot mean squared error (RMSE) was calculated tomeasure the degree of coincidence between observedand simulated SOC:

RMSE =100O

√√√√n∑

i=1

(Pi − Oi)2

n(1)

where Pi and Oi represent the ith model estimate andfield measurement, respectively; O is the average of ob-served SOC; and n is the total number of observations.The relative mean deviation (RMD) was computed toevaluate the systematic bias of the model:

RMD =100O

n∑i=1

(Pi − Oi)n

(2)

The model efficiency (EF) was calculated to estimate

the model performance in relation to observed SOC.

EF = 1 −

n∑i=1

(Pi − Oi)2

n∑i=1

(O − Oi)2(3)

Linear regression between simulated and observedSOC was also used to evaluate the model performance.All the above analyses were conducted using R 3.0.3(R Development Core Team, 2014).

Scenario analysis

Using the calibrated and validated APSIM model,we assessed the sensitivity of both crop stubble pro-ductivity and SOC dynamics to changes under threeagricultural management scenarios, including N fertili-zation, stubble retention, and irrigation. For each sce-nario, we used the same crop management parame-ters, such as cultivar and sowing rule, and the initialsoil parameters that were used in the model calibra-tion and validation, and ran the model for 100 years.Daily weather data from 1961 to 2010 were repeatedtwice each year to make up a 100-year climate data ateach site; i.e., daily weather data of 1961 were usedas the climate data input of 1961 and 1962, those of1962 used as the input of 1963 and 1964, · · · We didnot take the effects of possible future change of cropcultivars and climate into account in this study. Theaveraged multi-year crop biomass and grain yield pro-duction were analysed to identify the impacts of diffe-rent agricultural management on the crop producti-vity. The trend of SOC change during the 100 years ofsimulation was analysed.

Scenario 1: fertilization. At Gongzhuling andUrumqi, where a single cropping system per year wasadopted, we modelled the effects of 17 levels of N fertili-zer applications (from 0 (N0) to 400 kg N ha−1 year−1

(N400) with an interval of 25 kg N ha−1 year−1) onboth crop residue production and SOC dynamics un-der different stubble and irrigation management sce-narios. While at Zhengzhou and Xuzhou, where a dou-ble cropping system with a rotation of winter wheatand summer maize is dominant, we extended the fertili-zation scenario scale from 0–400 to 0–800 kg N ha−1

year−1 with the same interval of 25 kg N ha−1 year−1.The timing of N application and the rates for base ap-plication (before sowing) and topdressing (at jointingstage) used in the scenario simulations were strictly inaccordance with the experimental conditions at eachsite (Table III). N deposition from atmosphere alsomakes an important contribution to nutrient budgets


in most Chinese agro-ecosystems (He et al., 2010; Liuet al., 2011). We assigned an atmospheric N deposi-tion (from rainfall) of 85 kg ha−1 year−1 at Zhengzhouand Xuzhou and 65 kg ha−1 year−1 at Gongzhulingand Urumqi (He et al., 2007; Lu and Tian, 2007; Heet al., 2010). At each site, the N fertilizer applicationrate under which the SOC content reaches to 95% ofthe highest final value was defined as the optimal Napplication rate (Nopt).

Scenario 2: stubble management. For the fourstudy sites, we simulated and analysed the effects ofthree levels of stubble retention rates, 0% (completestubble removal, R0), 50% (half stubble removal, R50),and 100% (no stubble removal, R100), on SOC dyna-mics under different fertilization and irrigation scena-rios. Different amounts of the stubble after crop harvestwere removed from the field according to the stubbleretention rate. The remaining stubble was incorporatedinto the top 20 cm of soil through tillage.

Scenario 3: irrigation. For the four study sites,the effect of irrigation management on crop biomassand grain yield production and SOC dynamics was

simulated under both full irrigation and rainfed condi-tions. Under irrigation condition, irrigation water wasapplied to bring the soil water content to field capacityif it dropped to 80% of field capacity using the auto-matic irrigation facility in the APSIM model. Underrainfed condition, crop growth was only supported byrainfall and no irrigation was applied.

RESULTS

Model performance

Generally, the calibrated APSIM model could rea-sonably simulate the grain yield of wheat (winter wheatand spring wheat) and maize under various treatmentswith all data put together (Fig. 1). Generally, the AP-SIM model could explain 50%–90% of the variation inthe grain yield of wheat and maize caused by diffe-rent stubble and fertilization treatments at the fourstudy sites. At all sites, observed crop grain productionunder NPK and/or NPKSt was significantly higher(P < 0.001) than that under CK (Fig. 1).

The simulated trends of SOC change to the depth

Fig. 1 Modelled vs. observed yields of wheat and maize under different stubble and fertilization treatments at the four selected study

sites, Gongzhuling (a), Urumqi (b), Zhengzhou (c), and Xuzhou (d), in northern China. NPK = application of compound inorganic

fertilizers; NPKSt = application of inorganic fertilizers and stubble retention; CK = control. Symbols show the measured values and

lines show the modelled values. Dashed lines are the result of wheat, solid lines are the result of maize, and dotted lines are the 1:1

line. ∗∗∗ indicates significance at P < 0.001.


of 30 cm under different treatments generally agreedwell with the observed data during the selected studyperiod at each site (Fig. 2). The APSIM model couldnot accurately capture the observed SOC change underNPK in the last several years at Gongzhuling (Fig. 2a)or the first few years at Urumqi and Zhengzhou(Fig. 2b, c). However, the measurements at Gongzhu-ling and Urumqi showed exceptional variations, indica-ting possible measurement errors. Under CK, SOC ofthe top 30 cm showed an obvious decline at Gongzhu-ling and Urumqi (Fig. 2a, b) and a relatively slowerdecline at Zhengzhou and Xuzhou (Fig. 2c, d). UnderNPK, SOC still decreased at Gongzhuling and Urumqi(Fig. 2a, b), remained relatively stable at Zhengzhou(Fig. 2c), and showed an increasing trend at Xuzhou(Fig. 2d). Under NPKSt, SOC showed a slight de-creasing trend at Gongzhuling (Fig. 2a), remained re-latively stable at Urumqi (Fig. 2b), and kept increasingat Zhengzhou (Fig. 2c). In general, the APSIM modelcould explain more than 90% of the variation in SOCwhen data from all validation sites were pooled to-gether (Fig. 3), indicating an overall good performanceof the calibrated APSIM model.

Scenario analyses

Scenario 1: fertilization. Simulated responses of

crop productivity to nitrogen input were strongly regu-lated by water availability, with much flattened re-sponses under rainfed conditions (Fig. 4). For the twodrier sites (Urumqi and Gongzhuling), N fertilizationonly slightly increased biomass production under rain-fed conditions due to water limitation. Increasing Napplication enhanced SOC at all sites, under both irri-gation and rainfed management (Fig. 5). At each site,the N input for achieving maximum biomass, grainyield, and SOC content was lower under rainfed condi-tion than that under irrigation condition (Figs. 4 and5). The Nopt for SOC sequestration under irrigationcondition was 125 kg N ha−1 year−1 at Gongzhuling(Fig. 5a), 300 kg N ha−1 year−1 at Urumqi (Fig. 5b),450 kg N ha−1 year−1 at Zhengzhou (Fig. 5c), and 400kg N ha−1 year−1 at Xuzhou (Fig. 5d). Under rain-fed condition, it was 75 (Fig. 5a), 100 (Fig. 5b), 200(Fig. 5c), and 200 (Fig. 5d) kg N ha−1 year−1, respec-tively. Under irrigation condition and 100% stubble re-tention, SOC under optimal N addition increased by6.38 t ha−1 (equivalent to about 15% of the initial SOCcontent) at Gongzhuling, 6.53 t ha−1 (about 23%) atUrumqi, 24.21 t ha−1 (about 112%) at Zhengzhou, and39.29 t ha−1 (about 186%) at Xuzhou at the end of the100-year simulation period, as compared with that un-der N0. Under rainfed condition and 100% stubble re-

Fig. 2 Modelled and observed soil organic carbon (SOC) in the surface 30 cm under different stubble and fertilization treatments at

the four selected study sites, Gongzhuling (a), Urumqi (b), Zhengzhou (c), and Xuzhou (d), in northern China. NPK = application of

compound inorganic fertilizers; NPKSt = application of inorganic fertilizers and stubble retention; CK = control. Symbols show the

measured values and lines show the modelled values.


Fig. 3 Modelled vs. observed SOC under different stubble

and fertilization treatments at the four selected study sites,

Gongzhuling, Urumqi, Zhengzhou, and Xuzhou, in northern

China. Symbols show the measured values, solid line shows the

modelled values, and dotted line is the 1:1 line. ∗∗∗ indicates

significance at P < 0.001. RMSE = root mean squared error;

RMD = relative mean deviation; EF = model efficiency.

tention, SOC under optimal N addition increased by3.35 t ha−1 (about 8.03%), 1.86 t ha−1 (about 6.56%),15.25 t ha−1 (about 70.67%), and 27.45 t ha−1 (about130.34%) at the end of the 100-year simulation pe-riod, as compared with that under N0 at Gongzhuling,Urumqi, Zhengzhou, and Xuzhou, respectively. With

all stubble retained, compared with N0, SOC underNopt reached a higher level after 100 years of simulationunder both rainfed and irrigation conditions (Figs. 6and 7).

Scenario 2: stubble management. SOC incre-ased continuously at Gongzhuling under Nopt andR100, but kept relatively stable under N0 and R100 du-ring the 100 years of simulation, regardless of irrigationmanagement (Fig. 7a). At Urumqi, under irrigationcondition, SOC decreased continuously under the com-bination of N0 and R100, but kept relatively stable un-der the combination of Nopt and R100. Under rainfedcondition, SOC remained stable under R100 regardlessof fertilization management (Fig. 7b). At Zhengzhouand Xuzhou, SOC showed an increasing trend underR100, regardless of fertilization and irrigation mana-gements (Fig. 7c, d). Under N0, increasing the rateof stubble retention reduced SOC loss at Gongzhu-ling and Urumqi, and enhanced SOC accumulation atZhengzhou and Xuzhou, regardless of irrigation mana-gement (Fig. 6). The effect of stubble retention on SOCbalance (sink or source) varied with sites. This wouldresult from the difference in potential biomass pro-ductivity among different sites. For example, underN0, from R0 to R100, the reduction in SOC loss atGongzhuling and Urumqi was from 20.85 to 9.36 t ha−1

Fig. 4 Modelled 100-year average annual crop biomass and grain yields under 100% stubble retention with different fertilization and

irrigation managements at the four selected study sites, Gongzhuling (a), Urumqi (b), Zhengzhou (c), and Xuzhou (d), in northern

China.


Fig. 5 Modelled final soil organic carbon (SOC) in the top 30 cm soil layer after 100 years under 100% stubble retention with different

fertilization and irrigation managements at the four selected study sites, Gongzhuling (a), Urumqi (b), Zhengzhou (c), and Xuzhou (d),

in northern China.

Fig. 6 Modelled changes in soil organic carbon (SOC) during the 100-year simulation under different stubble retention rates with

irrigation and zero nitrogen fertilization (a), irrigation and optimal nitrogen fertilization (b), no-irrigation and zero nitrogen fertilization

(c), and no-irrigation and optimal nitrogen fertilization (d) at the four selected study sites, Gongzhuling, Urumqi, Zhengzhou, and

Xuzhou, in northern China. R0 = 0% stubble retention rate (complete stubble removal); R50 = 50% stubble retention rate (half stubble

removal); R100 = 100% stubble retention rate (no stubble removal).


Fig. 7 Modelled long-term changes in soil organic carbon (SOC) under 100% stubble retention with different nitrogen fertilization and

irrigation managements at the four selected study sites, Gongzhuling (a), Urumqi (b), Zhengzhou (c), and Xuzhou (d), in northern

China. N0 = no N fertilizer application; Nopt = the optimal N application (N fertilizer application at a rate under which the SOC con-

tent reaches to 95% of the highest final value at each site).

under irrigation condition and from 22.13 to 4.68 tha−1 under rainfed condition (Fig. 6a, c). At Zheng-zhou and Xuzhou, SOC under R0 and R100 increasedby 17.02 and 19.99 t ha−1 under irrigation condi-tion, respectively, and by 18.72 and 23.82 t ha−1 un-der rainfed condition, respectively (Fig. 6a, c). Un-der Nopt, from R0 to R100, soil sequestered moreC at Zhengzhou and Xuzhou regardless of irriga-tion patterns and turned from a C source into a Csink at Gongzhuling under rainfed condition (Fig. 6b,d). However, soil was a C source at Gongzhuling underirrigation condition and at Urumqi under both irriga-tion and rainfed conditions regardless of stubble mana-gement (Fig. 6b, d). Under the combination of Nopt andR100, SOC reached the highest level of 46.4, 66.53, and80.8 t ha−1 at Gongzhuling, Zhengzhou, and Xuzhouunder irrigation condition (Fig. 5a, c, d), respectively,and a highest level of SOC of 30.6 t ha−1 under rainfedcondition was reached at Urumqi (Fig. 5b). These num-bers are equivalent to about 111%, 100%, 308%, and384% of the initial total SOC at Gongzhuling, Urumqi,Zhengzhou, and Xuzhou, respectively.

Scenario 3: irrigation. Under the combinationof N0 and R100, the crop biomass and grain yield pro-duction under irrigation condition was similar to thoseunder rainfed condition at Zhengzhou and Xuzhou(Fig. 4c, d), but higher than those under rainfed con-dition at Gongzhuling (Fig. 4a) and particularly atUrumqi (Fig. 4b). However, under the same stubbleand fertilization conditions, the simulated SOC after100 years of simulation was higher under rainfed con-dition than under irrigation condition (Fig. 5), with agreat difference at Urumqi (Fig. 5b), a moderate diffe-rence at Gongzhuling (Fig. 5a), and small differences

at Zhengzhou and Xuzhou (Fig. 5c, d). With increa-sing N input to the level of Nopt, the simulated finalSOC content after 100 years of simulation was higherunder irrigation condition than under rainfed conditionat Zhengzhou and Xuzhou (Figs. 5a, c and 7a, c), wascomparable at Gongzhuling (Fig. 5b), and was lower atUrumqi (Fig. 5b).

At Urumqi, the ability of irrigation to enhance SOCdecomposition was larger than that to enhance theamount of carbon input to soil through increasing cropproduction (Figs. 5b and 7b). Compared with rainfedcondition, SOC under the combination of Nopt andR100 increased by 0.03 t ha−1 (equivalent to about0.07% of the initial SOC) at Gongzhuling (Fig. 5a),8.21 t ha−1 (about 38.04%) at Zhengzhou (Fig. 5c),and 7.58 t ha−1 (about 36.99%) at Xuzhou (Fig. 5d),but deceased by 2.13 t ha−1 (about 7.5%) at Urumqi(Fig. 5b) under irrigation condition.

DISCUSSION

Uncertainties in the simulations

Some uncertainties in the modelling process shouldbe considered when interpreting the modelling resultsin this study. Firstly, we did not account for the appli-cation of manure, which was a prevailing agriculturalpractice across many agro-ecosystems in China, andcould possibly cause an underestimation of SOC se-questration potential. Application of manure had beensuggested to have the potential to increase agriculturalSOC content (Smith et al., 1997a; Smith and Powlson,2000) because of the high amount of C in the manure.Based on a national data analysis of spatiotemporalchange of cropland SOC over the period of 1993–2005


in Chinese ago-ecosystems, Huang and Sun (2006) indi-cated a great potential of SOC sequestration throughthe fertilization of organic manure in many parts ofChinese arable land, even if the initial soil had a rela-tively high C content. Li et al. (2003) estimated that110 Tg of C was added into agricultural soils in Chinaper year due to the amendment of organic manure.However, the distribution of Chinese manure resourcesis uneven and it is difficult to accurately identify theamount of available manure across regions (Shen et al.,2010).

Secondly, we assumed that all crop residues wereincorporated into the soil at a certain stubble retentionrate (e.g., 0%, 50% and 100%) after harvest. However,the common way of stubble retention in China’s crop-land was that part of crop residues were incorporatedinto soil after harvest, and the rest was shattered andapplied as green manure before the sowing of the nextcrop (Yan et al., 2007). Past studies on the decompo-sition rate of total SOC as affected by the amount ofincorporated residues showed distinct results. For exa-mple, Angers and Recous (1997) found that smaller-sized residues had a faster decomposition rate, whilea completely opposite result was found by Sørensen etal. (1996). Recently, Chevallier et al. (2011) suggestedthat the effects of scattering residues on the decomposi-tion of SOC do not bias the estimation of SOC change.Furthermore, the possible change in soil bulk densityas a result of residue application was not considered inthe current study.

Thirdly, our scenario analyses were based on thehistoric climate data, neglecting the possible change offuture climate such as increasing atmospheric CO2 con-centration and temperature and changing patterns ofrainfall, which could have some effects on future SOCchange. Increases in atmospheric CO2 concentrationpromote crop production (Wang and Engel, 2002) andthereby provide more SOC input, which would bene-fit the SOC sequestration (Prior et al., 2005). Increa-sing global temperature and air dryness could havea negative impact on crop C assimilation (Wang etal., 2004) and sustained SOC (Fuhrer, 2003; Davidsonand Janssens, 2006). Change in future rainfall patternsmight also increase plant water stress and affect above-ground biomass production and SOC decompositionand thereby SOC dynamics (Knapp et al., 2002).

Finally, C dynamics in the subhorizon soil as af-fected by N fertilization has recently received increasedattention (Neff et al., 2002; Olson et al., 2005; Christo-pher and Lal, 2007; Khan et al., 2007). Increasing N in-put may cause reduced rooting depth as crop demandfor N is met from N uptake in the upper soil layers,

leading to a decrease in SOC in the subsoils (> tillagedepth of 20 cm) (Svoboda and Haberle, 2006). Someof the applied N was taken up by crop, the leftovercould be leached down to deeper horizons and inhibitthe microbial activity, thereby decreasing the decom-position of SOC in the subsurface layers (Jia et al.,2010). Based on a global data synthesis, Khan et al.(2007) indicated that although the application of N fer-tilizers could enhance the SOC in the upper layers, itcaused a less C input in the subhorizons, hence leadingto a net decrease of total SOC in the 0–50 cm profile.Our modelling results did not show reduced SOC, butenhanced SOC or reduced the C loss in deeper layers(Fig. 8). Further investigation is needed regarding theimpact of N application on SOC dynamics in deeperlayers.

SOC dynamics as affected by agricultural managementpractices

Agricultural soil could sequester more C throughadopting improved management practices, but thetime needed to reach a new equilibrium of SOC re-mains uncertain and ranges from a few years to morethan a hundred years (West and Post, 2002; West andSix, 2007; Qin and Huang, 2010). This is mainly dueto the differences in soil and climate conditions, agri-cultural management, and land use history. A globaldata synthesis of 95 long-term agricultural experimentssuggested that SOC sequestration would continue for19 to 98 years before reaching a new equilibrium un-der improved management (Qin and Huang, 2010). Asimilar APSIM modelling work in Australian wheat-belt agro-ecosystem showed that SOC did not reachthe new steady state even after 120 years (Luo et al.,2011). The Rothamsted carbon model (RothC) simula-tions suggested that the duration of SOC sequestrationranges from 50 to 150 years after changing C mana-gement (West and Six, 2007). Our simulation resultsindicated that SOC in most cases seemed to reach anew equilibrium after 100 years, while at Urumqi, SOCunder irrigation condition was still decreasing after 100years of cultivation (Fig. 5).

Our results showed that under complete stubbleremoval, increasing the N fertilization rate from N0 toNopt led to a reduced SOC loss at Gongzhuling andUrumqi, turned soil from a net C source to a C sink atXuzhou, and sequestered more C at Zhengzhou, underboth irrigation and rainfed conditions (Fig. 6). This im-plies that increasing the soil nutrient availability couldfavour the SOC sequestration. We found that SOC con-tent reached the maximum when 100–400 kg N ha−1 offertilizer was applied, depending on the local cropping


Fig. 8 Changes in soil organic carbon (SOC) in each soil layer under 100% stubble retention with different nitrogen fertilization and ir-

rigation managements at the four selected study sites, Gongzhuling (a), Urumqi (b), Zhengzhou (c), and Xuzhou (d), in northern China.

N0 = no N fertilizer application; Nopt = the optimal N application (N fertilizer application at a rate under which the SOC content

reaches to 95% of the highest final value at each site).

systems and soil conditions at each site.Our modelling results suggested that increasing the

stubble retention rate significantly enhanced the SOCcontent, turned soil into a net atmospheric C sink or atleast reduced the SOC loss. This is consistent with thesimulation studies on SOC dynamics across differentChinese agro-ecosystems based on the DNDC model(Zhang et al., 2006; Wang et al., 2008b; Qiu et al.,2009). Irrigation reduces crop water stress, but alsostimulates microbial activities in the soil and SOC de-composition by inducing frequent wetting and drying,which led to a faster C loss than that in rainfed soils(Chan and Hulugalle, 1999). At Urumqi (Fig. 5b), theresults seem to confirm such cases. Although a highercrop biomass production was achieved under irriga-tion than rainfed condition at Urumqi (Fig. 4b), theSOC content under irrigation condition showed a rela-tively lower level (Fig. 5b) regardless of N fertilization.This implies that under more water-limited conditions(e.g., Gongzhuling and Urumqi), addition of water un-der low nutrient levels accelerated the decompositionof the newly added fresh organic matter in the soil.Apparently, whether irrigation has a positive or nega-tive impact on SOC is largely dependent on regionalclimate and soil characteristics.

Under the best scenario (i.e., 100% stubble reten-tion, Nopt, and full irrigation), SOC still showed dis-tinct spatiotemporal variations at the four study sitesin our simulation study (Fig. 5). This is mainly becauseof the differences in the initial SOC status and the cli-matic conditions at different sites.

The initial SOC status when a trial starts signifi-cantly influences the long-term dynamics of SOC (San-derman and Baldock, 2010). For example, if SOC atthe start of the experiment is decreasing sharply due tonew cultivation, higher stubble retention rates wouldcertainly reduce SOC loss, but would not guaranteea net SOC sink. Additionally, if the initial SOC hasalready reached a new equilibrium after a long culti-vation history, the soil would show a greater C seques-tration potential through stubble retention comparedwith the newly cultivated soils. Our results showedthat the simulated final SOC after 100 years of simu-lation at Gongzhuling and Urumqi was lower thanthat at Zhengzhou and Xuzhou. This is due to, firstly,differences in cropping system: a double cropping sys-tem was adopted at Zhengzhou and Xuzhou, while asingle cropping system at Gongzhuling and Urumqi,which caused significant differences in SOC input. Se-condly, there has been a longer cultivation history at


Zhengzhou and Xuzhou, resulting in a greater amountof recalcitrant inert C than that at Gongzhuling andUrumqi.

Even under similar cropping systems, edaphic con-ditions, and land use history, SOC after 100 yearsof simulation at Xuzhou was higher than that atZhengzhou (Fig. 5). This is because of the differe-nce in climatic conditions at these two sites, i.e.,higher annual rainfall and mean annual radiation atXuzhou (Table I), which favoured the growth of cropand caused both higher crop biomass and grain yield(Fig. 4), and hence a higher amount of C addition tothe soil at Xuzhou.

CONCLUSIONS

The calibrated APSIM model performed well insimulating both crop growth and SOC dynamics un-der different stubble and fertilization management atfour study sites in northern China. Scenario analy-sis indicated a significant SOC sequestration potentialthrough improving management practices of N fertili-zer application, stubble retention, and irrigation. Opti-mal N fertilization and 100% stubble retention couldincrease SOC by about 11.2%, 208.3%, and 283.7% un-der irrigation condition at Gongzhuling, Zhengzhou,and Xuzhou, respectively. However, SOC decreasedrapidly at Urumqi if irrigation was adopted becauseof the enhanced decomposition by increased soil mois-ture, while SOC remained at a higher level under rain-fed condition.

REFERENCES

Angers, D. A. and Recous, S. 1997. Decomposition of wheat

straw and rye residues as affected by particle size. Plant Soil.

189: 197–203.

Asseng, S., Keating, B. A., Fillery, I. R. P., Gregory, P. J., Bow-

den, J. W., Turner, N. C., Palta, J. A. and Abrecht, D. G.

1998. Performance of the APSIM-wheat model in Western

Australia. Field Crop. Res. 57: 163–179.

Black, J. N., Bonython, C. W. and Prescott, J. A. 1954. Solar

radiation and the duration of sunshine. Q. J. Roy. Meteor.

Soc. 80: 231–235.

Chan, K. Y. and Hulugalle, N. R. 1999. Changes in some soil

properties due to tillage practices in rainfed hardsetting Al-

fisols and irrigated Vertisols of eastern Australia. Soil Till.

Res. 53: 49–57.

Chen, C., Wang, E. L. and Yu, Q. 2010a. Modelling the effects

of climate variability and water management on crop water

productivity and water balance in the North China Plain.

Agr. Water Manage. 97: 1175–1184.

Chen, C., Wang, E. L., Yu, Q. and Zhang, Y. Q. 2010b. Quanti-

fying the effects of climate trends in the past 43 years (1961–

2003) on crop growth and water demand in the North China

Plain. Climatic Change. 100: 559–578.

Chevallier, T., Blanchart, E., Toucet, J. and Bernoux, M. 2011.

Methods to estimate aggregate protected soil organic car-

bon: 2. Does the grinding of the plant residues affect the

estimations of the aggregate protected soil organic carbon?

Commun. Soil Sci. Plan. 42: 1537–1543.

Christopher, S. F. and Lal, R. 2007. Nitrogen management af-

fects carbon sequestration in North American cropland soils.

Crit. Rev. Plant Sci. 26: 45–64.

Davidson, E. A. and Ackerman, I. L. 1993. Changes in soil car-

bon inventories following cultivation of previously untilled

soils. Biogeochemistry. 20: 161–193.

Davidson, E. A. and Janssens, I. A. 2006. Temperature sensi-

tivity of soil carbon decomposition and feedbacks to climate

change. Nature. 440: 165–173.

Fuhrer, J. 2003. Agroecosystem responses to combinations of ele-

vated CO2, ozone, and global climate change. Agr. Ecosyst.

Environ. 97: 1–20.

He, C. E., Liu, X. J., Fangmeier, A. and Zhang, F. S. 2007.

Quantifying the total airborne nitrogen input into agroe-

cosystems in the North China Plain. Agr. Ecosyst. Environ.

121: 395–400.

He, C. E., Wang, X., Liu, X. J., Fangmeier, A., Christie, P.

and Zhang, F. S. 2010. Nitrogen deposition and its contribu-

tion to nutrient inputs to intensively managed agricultural

ecosystems. Ecol. Appl. 20: 80–90.

Huang, Y. and Sun, W. J. 2006. Changes in topsoil organic

carbon of croplands in mainland China over the last two

decades. Chinese Sci. Bull. 51: 1785–1803.

Jia, S. X., Wang, Z. Q., Li, X. P., Sun, Y., Zhang, X. Q. and

Liang, A. Z. 2010. N fertilization affects on soil respiration,

microbial biomass and root respiration in Larix gmelinii and

Fraxinus mandshurica plantations in China. Plant Soil. 333:

325–336.

Jobbagy, E. G. and Jackson, R. B. 2000. The vertical distribu-

tion of soil organic carbon and its relation to climate and

vegetation. Ecol. Appl. 10: 423–436.

Jones, H. G. 1992. Plants and Microclimate: A Quantitative Ap-

proach to Environmental Plant Physiology. Cambridge Uni-

versity Press, New York.

Ju, X. T., Xing, G. X., Chen, X. P., Zhang, S. L., Zhang, L. J.,

Liu, X. J., Cui, Z. L., Yin, B., Christie, P., Zhu, Z. L. and

Zhang, F. S. 2009. Reducing environmental risk by improving

N management in intensive Chinese agricultural systems. P.

Natl. Acad. Sci. USA. 106: 3041–3046.

Keating, B. A., Carberry, P. S., Hammer, G. L., Probert, M. E.,

Robertson, M. J., Holzworth, D., Huth, N. I., Hargreaves, J.

N. G., Meinke, H., Hochman, Z., Mclean, G., Verburg, K.,

Snow, V., Dimes, J. P., Silburn, M., Wang, E., Brown, S.,

Bristow, K. L., Asseng, S., Chapman, S., McCown, R. L.,

Freebairn, D. M. and Smith, C. J. 2003. An overview of AP-

SIM, a model designed for farming systems simulation. Eur.

J. Agron. 18: 267–288.

Khan, S. A., Mulvaney, R. L., Ellsworth, T. R. and Boast, C.

W. 2007. The myth of nitrogen fertilization for soil carbon

sequestration. J. Environ. Qual. 36: 1821–1832.

Knapp, A. K., Fay, P. A., Blair, J. M., Collins, S. L., Smith,

M. D., Carlisle, J. D., Harper, C. W., Danner, B. T., Lett,

M. S. and McCarron, J. K. 2002. Rainfall variability, car-

bon cycling, and plant species diversity in a mesic grassland.

Science. 298: 2202–2205.

Lal, R. 2004. Soil carbon sequestration to mitigate climate

change. Geoderma. 123: 1–22.

Lehuger, S., Gabrielle, B., Cellier, P., Loubet, B., Roche, R.,

Beziat, P., Ceschia, E. and Wattenbach, M. 2010. Predicting

the net carbon exchanges of crop rotations in Europe with an

agro-ecosystem model. Agr. Ecosyst. Environ. 139: 384–395.


Li, C. S., Zhuang, Y. H., Frolking, S., Galloway, J., Harriss, R.,

Moore III, B., Schimel, D. and Wang, X. K. 2003. Mode-

ling soil organic carbon change in croplands of China. Ecol.

Appl. 13: 327–336.

Li, J. F., Hu, R. Q., Song, Y. Q., Shi, J. L., Bhattacharya, S.

C. and Salam, P. A. 2005. Assessment of sustainable ener-

gy potential of non-plantation biomass resources in China.

Biomass Bioenerg. 29: 167–177.

Liu, D. L., Chan, K. Y. and Conyers, M. K. 2009. Simulation of

soil organic carbon under different tillage and stubble mana-

gement practices using the Rothamsted carbon model. Soil

Till. Res. 104: 65–73.

Liu, H., Jiang, G. M., Zhuang, H. Y. and Wang, K. J. 2008.

Distribution, utilization structure and potential of biomass

resources in rural China: With special references of crop

residues. Renew. Sust. Energ. Rev. 12: 1402–1418.

Liu, X. J., Duan, L., Mo, J. M., Du, E. Z., Shen, J. L., Lu, X.

K., Zhang, Y., Zhou, X. B., He, C. E. and Zhang, F. S. 2011.

Nitrogen deposition and its ecological impact in China: An

overview. Environ. Pollut. 159: 2251–2264.

Lugato, E. and Berti, A. 2008. Potential carbon sequestration

in a cultivated soil under different climate change scenarios:

A modelling approach for evaluating promising management

practices in north-east Italy. Agr. Ecosyst. Environ. 128: 97–

103.

Luo, Z. K., Wang, E. L., Sun, O. J., Smith, C. J. and Probert, M.

E. 2011. Modeling long-term soil carbon dynamics and se-

questration potential in semi-arid agro-ecosystems. Agr. Fo-

rest Meteorol. 151: 1529–1544.

Lu, C. Q. and Tian, H. Q. 2007. Spatial and temporal patterns

of nitrogen deposition in China: Synthesis of observational

data. J. Geophys. Res. 112: D22S05.

Mikhailova, E. A. and Post, C. J. 2006. Organic carbon stocks

in the Russian Chernozem. Eur. J. Soil Sci. 57: 330–336.

Neff, J. C., Townsend, A. R., Gleixner, G., Lehman, S. J., Turn-

bull, J. and Bowman, W. D. 2002. Variable effects of nitro-

gen additions on the stability and turnover of soil carbon.

Nature. 419: 915–917.

Ogle, S. M., Breidt, F. J., Easter, M., Williams, S., Killian,

K. and Paustian, K. 2010. Scale and uncertainty in modeled

soil organic carbon stock changes for US croplands using a

process-based model. Glob. Change Biol. 16: 810–822.

Olson, K. R., Lang, J. M. and Ebelhar, S. A. 2005. Soil or-

ganic carbon changes after 12 years of no-tillage and tillage

of Grantsburg soils in southern Illinois. Soil Till. Res. 81:

217–225.

Prior, S. A., Runion, G. B., Rogers, H. H., Torbert, H. A. and

Reeves, D. W. 2005. Elevated atmospheric CO2 effects on

biomass production and soil carbon in conventional and con-

servation cropping systems. Glob. Change Biol. 11: 657–665.

Probert, M. E., Delve, R. J., Kimani, S. K. and Dimes, J. P.

2005. Modelling nitrogen mineralization from manures: rep-

resenting quality aspects by varying C:N ratio of sub-pools.

Soil Biol. Biochem. 37: 279–287.

Probert, M. E., Dimes, J. P., Keating, B. A., Dalal, R. C. and

Strong, W. M. 1998. APSIM’s water and nitrogen modules

and simulation of the dynamics of water and nitrogen in fal-

low systems. Agr. Syst. 56: 1–28.

Qin, Z. C. and Huang, Y. 2010. Quantification of soil organic

carbon sequestration potential in cropland: A model appro-

ach. Sci. China Life Sci. 53: 868–884.

Qiu, J., Li, C., Wang, L., Tang, H., Li, H. and Van Ranst,

E. 2009. Modeling impacts of carbon sequestration on net

greenhouse gas emissions from agricultural soils in China.

Global Biogeochem. Cy. 23: GB1007.

R Development Core Team. 2014. R: A Language and Environ-

ment for Statistical Computing. R Foundation for Statistical

Computing, Vienna.

Sanderman, J. and Baldock, J. A. 2010. Accounting for soil car-

bon sequestration in national inventories: a soil scientist’s

perspective. Environ. Res. Lett. 5: 034003.

Shen, L., Liu, L. T., Yao, Z. J., Liu, G. and Lucas, M. 2010. De-

velopment potentials and policy options of biomass in China.

Environ. Manage. 46: 539–554.

Smith, P. 2004. Carbon sequestration in croplands: the potential

in Europe and the global context. Eur. J. Agron. 20: 229–

236.

Smith, P. and Powlson, D. S. 2000. Considering manure and

carbon sequestration. Science. 287: 428–429.

Smith, P., Powlson, D., Glendining, M. and Smith, J. 1997a. Po-

tential for carbon sequestration in European soils: prelimi-

nary estimates for five scenarios using results from long-term

experiments. Glob. Change Biol. 3: 67–79.

Smith, P., Smith, J. U., Powlson, D. S., McGill, W. B., Arah,

J. R. M., Chertov, O. G., Coleman, K., Franko, U., Frol-

king, S., Jenkinson, D. S., Jensen, L. S., Kelly, R. H., Klein-

Gunnewiek, H., Komarov, A. S., Li, C., Molina, J. A. E.,

Mueller, T., Parton, W. J., Thornley, J. H. M. and Whit-

more, A. P. 1997b. A comparison of the performance of nine

soil organic matter models using datasets from seven long-

term experiments. Geoderma. 81: 153–225.

Sørensen, P., Ladd, J. N. and Amato, M. 1996. Microbial as-

similation of 14C of ground and unground plant materials

decomposing in a loamy sand and a clay soil. Soil Biol. Bio-

chem. 28: 1425–1434.

Svoboda, P. and Haberle, J. 2006. The effect of nitrogen fertili-

zation on root distribution of winter wheat. Plant Soil Envi-

ron. 52: 308–313.

Tang, H. J., Qiu, J. J., Van Ranst, E. and Li, C. S. 2006. Estima-

tions of soil organic carbon storage in cropland of China

based on DNDC model. Geoderma. 134: 200–206.

Thorburn, P. J., Probert, M. E. and Robertson, F. A. 2001.

Modelling decomposition of sugar cane surface residues with

APSIM-Residue. Field Crop. Res. 70: 223–232.

Wang, G. C., Luo, Z. K., Wang, E. L. and Huang, Y. 2013a.

Contrasting effects of agricultural management on soil or-

ganic carbon balance in different agricultural regions of

China. Pedosphere. 23: 717–728.

Wang, G. C., Huang, Y., Wang, E. L., Yu, Y. Q. and Zhang, W.

2013b. Modeling soil organic carbon change across Australian

wheat growing areas, 1960–2010. PloS One. 8: e63324.

Wang, E. L. and Engel, T. 2002. Simulation of growth, water

and nitrogen uptake of a wheat crop using the SPASS model.

Environ. Modell. Softw. 17: 387–402.

Wang, E. L., Smith, C. J., Bond, W. J. and Verburg, K. 2004.

Estimations of vapour pressure deficit and crop water de-

mand in APSIM and their implications for prediction of crop

yield, water use, and deep drainage. Aust. J. Agr. Res. 55:

1227–1240.

Wang, E. L., Yu, Q., Wu, D. R. and Xia, J. 2008a. Climate, agri-

cultural production and hydrological balance in the North

China Plain. Int. J. Climatol. 28: 1959–1970.

Wang, H. X., Liu, C. M. and Zhang, L. 2002. Water-saving agri-

culture in China: An overview. Adv. Agron. 75: 135–171.

Wang, L. G., Qiu, J. J., Tang, H. J., Li, H., Li, C. S. and Van

Ranst, E. 2008b. Modelling soil organic carbon dynamics in

the major agricultural regions of China. Geoderma. 147: 47–

55.

Wang, X. B., Cai, D. X., Hoogmoed, W. B., Oenema, O. and

Perdok, U. D. 2007. Developments in conservation tillage in


rainfed regions of North China. Soil Till. Res. 93: 239–250.

Wang, X. H. and Feng, Z. M. 2004. Biofuel use and its emission

of noxious gases in rural China. Renew. Sust. Energ. Rev. 8:

183–192.

Wang, Y. C., Wang, E. L., Wang, D. L., Huang, S. M., Ma, Y. B.,

Smith, C. J. and Wang, L. G. 2010. Crop productivity and

nutrient use efficiency as affected by long-term fertilisation

in North China Plain. Nutr. Cycl. Agroecosys. 86: 105–119.

West, T. O. and Post, W. M. 2002. Soil organic carbon seque-

stration rates by tillage and crop rotation: A global data

analysis. Soil Sci. Soc. Am. J. 66: 1930–1946.

West, T. O. and Six, J. 2007. Considering the influence of se-

questration duration and carbon saturation on estimates of

soil carbon capacity. Climatic Change. 80: 25–41.

Yan, H. M., Cao, M. K., Liu, J. Y. and Tao, B. 2007. Potential

and sustainability for carbon sequestration with improved

soil management in agricultural soils of China. Agr. Ecosyst.

Environ. 121: 325–335.

Yang, X., Chen, C., Luo, Q., Li, L. and Yu, Q. 2011. Climate

change effects on wheat yield and water use in oasis cropland.

Int. J. Plant Prod. 5: 83–94.

Yu, Y. Q., Huang, Y. and Zhang, W. 2012. Modeling soil organic

carbon change in croplands of China, 1980–2009. Global Pla-

net. Change. 82–83: 115–128.

Zhang, F., Li, C., Wang, Z. and Wu, H. 2006. Modeling im-

pacts of management alternatives on soil carbon storage of

farmland in Northwest China. Biogeosciences. 3: 451–466.

Zhang, H., Xu, M. and Zhang, F. 2009. Long-term effects of

manure application on grain yield under different cropping

systems and ecological conditions in China. J. Agr. Sci. 147:

31–42.

Zhang, W. J., Wang, X. J., Xu, M. G., Huang, S. M., Liu, H.

and Peng, C. 2010. Soil organic carbon dynamics under long-

term fertilizations in arable land of northern China. Biogeo-

sciences. 7: 409–425.

Zhao, B. Q., Li, X. Y., Li, X. P., Shi, X. J., Huang, S. M., Wang,

B. R., Zhu, P., Yang, X. Y., Liu, H., Chen, Y., Poulton,

P., Powlson, D., Todd, A. and Payne, R. 2010. Long-term

fertilizer experiment network in China: crop yields and soil

nutrient trends. Agron. J. 102: 216–230.

soil carbon sequestration potential as affected by management practices in northern china: a...

Documents