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Ecological Modelling 252 (2013) 228– 237

Contents lists available at SciVerse ScienceDirect

Ecological Modelling

journa l h o me pa g e: www.elsev ier .com/ locate /eco lmodel

mpact of crop patterns and cultivation on carbon sequestration and globalarming potential in an agricultural freeze zone

ei Ouyanga,∗, Shasha Qia, Fanghua Haoa, Xuelei Wangb, Yushu Shana, Siyang Chena

School of Environment, State Key Laboratory of Water Environment Simulation, Beijing Normal University, Beijing 100875, ChinaSatellite Environmental Center, Ministry of Environmental Protection, Beijing 100094, China

r t i c l e i n f o

rticle history:vailable online 14 June 2012

eywords:oil organic carbonrop rotationlobal warming potentialreeze zoneultivation

a b s t r a c t

Agricultural activity is a primary factor contributing to global warming. In higher latitude freeze zone,agricultural activities pose a more serious threat to global warming than other zones. The crop man-agement practices of various land use types have direct impacts on soil organic carbon (SOC) and globalwarming potential (GWP). Crop variations and cultivation practices are two important factors affectingcarbon sequestration and the exchange of greenhouse gases between soils and the atmosphere. Thisexchange has special characteristics in the freeze zone. In this paper, the impact of crop patterns andcultivation management (i.e., residue return rate, manure amendment, and chemical N fertiliser appli-cation) on SOC and GWP in an agricultural freeze zone was analysed. The Denitrification–Decomposition(DNDC) model was employed to predict the long-term dynamics of nitrous oxide (N2O), carbon dioxide(CO2) and methane (CH4) for dryland and paddy rice systems. The CO2-equivalent index was used to

express the GWP response of N2O, CH4 and CO2. The simulated results indicated that the manure amend-ment and N fertiliser application can improve the SOC, increase crop production and enhance the GWP.The cultivation of returning residue to the soil is the win–win solution for SOC conservation and GWPcontrol. It was found that paddy rice was preferable to dryland for sequestering atmospheric CO2 andmitigating global warming. This analysis also indicated that the DNDC model is a valid tool for predictingthe consequences of SOC and GWP changes in cropland agroecosystems in the freeze zone.

. Introduction

Agricultural activities contribute to approximately 13.5% ofhe total global anthropogenic greenhouse gas (GHG) emissionsLugato et al., 2010). Carbon sequestration is an important approachor improving soil fertility and mitigating the greenhouse gasmpact of atmospheric CO2 by converting it into biotic or abioticarbon that can be sequestered in terrestrial ecosystems and otherinks (Lackner, 2003). Thus, the agricultural area can act as a sourcer a sink of major greenhouse gases, depending on crop cultivationractices (Giltrap et al., 2010). Several studies have examined the

nteractions between cultivation activities with soil organic car-on (SOC) and their global warming potential (GWP) (Farage et al.,007; Thelen et al., 2010). In this paper, we focus on the conse-uences of farmland conversion from dryland to paddy rice in thereeze zone, where this conversion is more sensitive to the soil

arbon cycle. The impact of potential cultivation practices on SOConservation and GWP control are also considered.

∗ Corresponding author. Tel.: +86 10 58802078; fax: +86 10 58802078.E-mail address: wei@itc.nl (W. Ouyang).

304-3800/$ – see front matter © 2012 Elsevier B.V. All rights reserved.ttp://dx.doi.org/10.1016/j.ecolmodel.2012.05.009

© 2012 Elsevier B.V. All rights reserved.

Suitable crop patterns are expected to sequester the optimalamount of atmospheric carbon into the soil (Singh et al., 2011). Pre-vious researchers have discussed possible SOC changes and relatedGHG concentrations in terms of defined crop field patterns (Postand Kwon, 2000; Nishimura et al., 2008). Over the past forty years,large areas of dryland have been converted into paddy rice innortheastern China using advanced irrigation technologies (Jianget al., 2009). Because paddy rice for crop cultivation may cause SOCchanges, studies comparing SOC in fields using consecutive drylandcultivation with those using a paddy rice rotation are required. Thecarbon sequestration and global warming potential modelling is abasic issue during agricultural development.

Predicting the impacts of alternative management on the envi-ronment is drawing great attention in the scientific community(Tixier et al., 2008). Alternative management practices such asimproving the rates of residue returned to fields, reducing fertiliserapplication rates, and using organic fertiliser, including farmyardmanure and compost, have frequently proven to be promising forsequestering soil carbon and reducing GHG emissions (Smith et al.,

2001). For agricultural ecosystems, one of the most cost effectivemethods for reducing GHG emissions is through cultivation man-agement practices (Babu et al., 2006). Nevertheless, only a smallamount of research has been published concerning the impacts

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f management practices on both SOC and GWP under differentgro-ecosystems in the same area.

The seasonal freeze–thaw cycle is a significant meteorologicalvent in high-latitude regions. This cycle controls the evolutionrocesses of different ecosystems, affects soil development, andestricts the pattern of the biology-terrestrial global chemistryycle (Grogan et al., 2004). In this climate zone, the temperatureuctuations of the freeze–thaw cycle may intensify microorganismespiration as well as affect GHG emissions, the carbon balance andhe concentration of available nutrients (Schimel and Clein, 1996;

uller et al., 2002). Intensive agricultural development causesore risk to soil carbon sequestration and variations in GHG emis-

ions. Therefore, it is necessary to identity the dynamics of SOC andWP for farmland in the freeze zone and to assess the impact ofotential cultivation practices.

The Denitrification–Decomposition (DNDC) model is a process-riented biogeochemistry model for agro-ecosystems and waspecifically created for use at the field level. The development of

process-based model allows simulations at both the site andegional levels (Li et al., 1992; Giltrap et al., 2010). After improve-ent and amendment of water and energy circles, the model

an simulate carbon transport and transformation and agriculturalHG emissions. In addition, it is possible to explore potential miti-ation strategies, because the DNDC model is able to determine howtrategies that reduce the emission of one gas will affect emissionsf the other gases. Currently, the DNDC model is one of the mostccurate carbon–nitrogen stimulation models and a useful tool forodelling the environmental impacts of agricultural management

ractices.With the application of DNDC, this study focused on complet-

ng the following goals: (i) model and compare the consequencesf long-term crop pattern variations on SOC and GWP in the freezeone; (ii) estimate SOC and GWP emissions from dryland and paddyice systems under a range of management practices; and (iii)dentify how to optimise SOC and GWP using best farmland man-gement practices.

. Materials and methods

.1. Study area description

The selected case study area is a typical farm (47.25N, 134.02E)hich located in the Sanjiang Plain of northeastern China (Fig. 1).

anjiang Plain is one of the largest agricultural bases in China andas experienced intense agricultural development over last threeecades. The local agriculture is setup in a farmland system that isnlike the small households in other areas, which are cultivatednder the standard guideline (Song et al., 2011). Soybean–cornotation in the dryland was the most popular cropping systemefore the 1980s. After this time period, paddy rice was introducednd developed with the new cultivation methods. Presently, nearlyalf of the dryland has been changed to paddy rice, and this area ishe northernmost site for rice cultivation in the world.

This region of China is in a continental temperate monsoon cli-ate with warm, wet summers and cold winters. Approximately

0% of the precipitation occurs during the period from June toeptember. The interannual variations of the annual precipita-ion and temperature from 1964 to 2010 are presented in Fig. 2.hese long-term patterns indicate that the annual precipitationas decreased, and annual temperature has increased. Using theonthly data for three dry, three normal, and three wet years, the

onthly pattern analysis showed that for half of the period the

emperature was below 0◦ and most of the precipitation occurredn June, July and August. The soil texture of this selected site is siltoam, with bulk density 1.1 g cm−3, pH 5.68 and soil organic carbon

elling 252 (2013) 228– 237 229

(SOC) content 20.8 g C kg−1for dryland and 22.9 g C kg−1 for paddyrice.

2.2. The DNDC model

The DNDC model was originally developed for predicting carbonsequestration and trace gas emissions in non-flooded agroecosys-tems and tested in the temperate zone, but more diverse versionshave been developed (Smith et al., 2010). The DNDC model con-sists of the soil, climate, crop growth, decomposition, nitrification,denitrification and fermentation sub-models. A series of biochem-ical and geochemical reactions, such as that dominate carbon(C), and nitrogen (N) transport and transformation, elementalmechanical movement, oxidation and reduction, dissolution andcrystallization, adsorption and dissimilation, complexation anddecomplexation, assimilation and dissimilation occurring in agro-ecosystems, which have been parameterised by mathematicalformulas in the model. Any change in daily meteorological data, soilproperties or farming management practices will simultaneouslyalter several soil environmental factors including temperature,moisture, Eh, pH, and substrate concentration gradients; thereby,affecting the biochemical and geochemical reactions. SOC dynam-ics are predicted primarily by quantifying the SOC input fromplant residue (i.e., litter) returned to the soil and manure amend-ment, and the SOC output through decomposition. The DNDCpredicts CO2, N2O and CH4 emissions by simulating the processesof decomposition, nitrification, denitrification and fermentation(Brown et al., 2002; Beheydt et al., 2008). A relatively complete setof farming management practices (e.g., tillage, fertilisation, manureamendment, irrigation, flooding, and grazing) have been parame-terised in the DNDC to regulate their impacts on environmental soilfactors (Saggar et al., 2002). It has been used and expanded by manyresearch groups covering SOC dynamics, GHGs emissions in rangeof countries and regions (Pathak et al., 2005; Smith et al., 2008).

In China, the model had been applied across a wide range ofagroecosystems by a number of researchers. By calibrating andvalidating, the model request a number of data sets, they haveobtained promising results (Zhang et al., 2006). With the sup-port of these many validations, the DNDC was employed in thisstudy to model carbon and nitrogen fluxes in cold climate regions.The results have been incorporated into the DNDC Version 9.3(http://www.dndc.sr.unh.edu).

2.3. Database preparation

The DNDC model requires input data sets pertaining to soil char-acteristics, daily climate, and agricultural management (Britz andLeip, 2009). To construct a DNDC input database at the field level,the individual input data sets were obtained from various sourcesin different formats. The soil database includes the following vari-ables: SOC, pH, percentage of sand, percentage of silt, percentage ofclay, bulk density, hydraulic conductivity, saturation, field capac-ity, and wilting point. Climate inputs required by the DNDC includedaily air temperature (minimum and maximum), precipitation, anddaily average wind speed. These inputs were collected from a localweather station for the period 1964–2010.

As the main concern in this paper is agricultural activity, thedetails concerning long-term information for agricultural man-agement are presented in Table 1. Intensive land reclamation hasoccurred four times since the 1960s, and the cultivation of paddyrice had been increasing until the 1990s. Therefore, two sites (dry-

land with a two-year soybean–corn rotation cycle and irrigatedpaddy rice) were selected, and four time periods (see Table 1) weredivided in the period from 1964 to 2010 according to the landreclamation data described above. The details of the agricultural

230 W. Ouyang et al. / Ecological Modelling 252 (2013) 228– 237

Fig. 1. The location of the dryland and paddy rice areas highlighting land use changes over the last decade.

tterns

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Fig. 2. The annual and monthly pa

rop management in this period include data for crop type, tillage,ertiliser, weeding, irrigation and flooding. Additional informationoncerning the management practices of fertiliser use and therop residue incorporation rate (residue returned) is also listed. Noanure was applied during the entire period at both sites.

.4. Model validation

The DNDC model has been successfully used to simulate GHGmissions and mitigation in various agricultural systems in manyountries, such as GHG emissions difference in field bean (Viciaaba L.) and winter wheat (Ludwig et al., 2011), conventional and

able 1ultivation management practices for dryland and paddy rice from 1964 to 2010.

Land Crops Period Planting, date(day/month)

Harvest, date(day/month)

Tillag(day/

Dry land

Soybean 1964–1979 10/5 15/10 20/10Corn–soybeanrotation

1980–1992 10/5 15/10 5/5

Soybean–cornrotation

1993–1999 10/5 15/10 5/5

2000–2010 10/5 15/10 5/5

Paddy rice 1993–1999 24/5 2/10 10/5

2000–2010 24/5 2/10 10/5

of precipitation and temperature.

reduced tillage system in Ireland (Abdalla et al., 2011), grasslandsin Europe (Levy et al., 2007). These successes provided a soundreference for model validation. Several researchers have used andexpanded the DNDC model with field experiment validations andsimulations against data sets from a wide range of agroecosys-tems. In this study, the parameters’ values were redefined basedon the validated model applications in Japan, China and Thailand(Desjardins et al., 2010). The DNDC model can simulate SOC den-

sities in the top 20 cm of soil layer, CO2 fluxes, CH4 fluxes andN2O fluxes. In the study area, we have long-term SOC data for thedryland from 1974 to 2010. With the predetermined values, thesimulated SOC results were compared to the observational data.

e, datemonth)

N fertiliserapplication, date(day/month)

Irrigation/flood,date(day/month)

N fertiliser rate(kg N ha−1 perapplication)

Residuereturned(%)

10/5 No18

1556.1/43.8

10/5, 2/7 53.4, 87.6 2510/5, 2/7 70.6, 7.9 25

24/5, 27/5, 26/7 10/5–10/9 40.92, 14.49,14.49 25

24/5, 27/5, 26/7 10/5–10/9 53.75, 25.07,25.07

W. Ouyang et al. / Ecological Modelling 252 (2013) 228– 237 231

Table 2Verification of simulation results.

Index Crop rotation Simulation results Measured value Relative error

Year Value

SOC density (0–20 cm)(kg C ha−1 year−1)

Dryland

1974 25.60 25.63 −0.121978 25.60 23.99 6.711979 25.70 26.42 −2.731982 23.40 24.08 −2.821984 23.30 24.01 −2.961989 23.40 22.56 3.722010 20.20 20.79 −2.84

Index Crop rotation Average Variation range Average Variation range

CH4 fluxes (kg C ha−1 year−1)Dryland −0.72 −0.84 to 0.57 −0.62b −0.43 to 0.80b

Paddy rice 37.93 16.04–82.43 71.12a 63.72–78.52a

N2O fluxes (kg N ha−1 year−1)Dryland 4.41 0.24–20.78 3.12a 2.15–4.09a

Paddy rice 0.09 0.01–0.59 0.87b 0.54–1.20b

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a Jiang et al. (2009).b Hao (2005).

he simulated results of trace gases (CH4 and N2O) were comparedo a similar modelling case (Cai et al., 2003). The detailed validationata are shown in Table 2. With the support of previous validationsnd historical data, it was determined that the model satisfactorilyimulated annual variations of GHG emissions from crop systemsnd the effects of land management. The validated DNDC modelan assess the effects of different crops rotation and managementractices on SOC and GWP.

.5. Simulation of global warming potential

The validated DNDC model was used to simulate the dynamicsf SOC and GWP from 1964 to 2010 under different cultivation tech-iques to identify the impacts of land-use conversion from drylando paddy rice. The cultivation practices, including fertiliser applica-ion rate and crop residue incorporation rate, can be expressed byhe model. These practices have the potential to mitigate effectivelyhe net impact on global warming by decreasing GHG emissionsHastings et al., 2010). Several alternative scenarios of cultivationractices for both dryland and paddy rice systems were entered

nto the model with the goal of predicting the long-term dynamicsn SOC and GWP.

To assess the potential impacts of the strategies adopted onOC and GWP, three types of alternative management scenariosere chosen (Table 3). The first scenario concerns the crop residue

eturn rate, which was set to 40%, 60% and 80% of abovegroundrop residue (the baseline rate was 25%). The manure applicationncreased from 0 (baseline) to 2000 kg C ha−1. Because overuse ofynthetic fertiliser is becoming a notable social problem for mostgricultural regions in China, the fertiliser rates were set to 50%,0% and 120% of the baseline condition. In order to highlight the

ong-term impact, the DNDC was run with each of the scenariosver three decades. During the prediction simulation, the meteo-ological data from 2010 were used for each simulation year.

Because each of the management alternatives could simultane-usly affect CO2, N2O and CH4 fluxes, a net effect of the scenarion global warming must be quantified. The GWP has been devel-ped in order to evaluate the comprehensive greenhouse effect. Thehanges in emissions of greenhouse gases (except CO2) were con-erted into CO2-equivalents (CO2-e) by means of its GWP (Frolkingt al., 2004). The GWP value for each scenario was calculated as

ollows:

WPi = CO2–Ci × 4412

+ Ni × 4428 × 330

+ CH4 − Ci × 1612 × 21

where GWPi (kg CO2-e ha−1 year−1) is the GWP induced by sce-nario i; CO2–Ci (kg C ha− year−1), CH4–Ci (kg C ha− year−1) andNi (kg N ha−1 year−1) are CO2, CH4 and N2O fluxes, respectively,induced by scenario i.

3. Results

3.1. Effect of long-term crop-pattern variation on SOC

Using the DNDC model, the SOC content of topsoil (0–20 cm)for different crop patterns was simulated from 1964 to 2010. Inthe dryland, the crop was only soybean from 1964 to 1979, andit subsequently changed to a soybean–corn rotation. Beginning in1993, the paddy rice area began to emerge and increased rapidly;the fertilisation level increased beginning in 2000. Compared withthe observational data, the simulation results represented the SOCstatus well. The simulation results indicated that the SOC contentfor the dryland gradually decreased during the 47 years. In the firstperiod, the single soybean cultivation kept the SOC concentration inthe soil at a high level, which was more than 24.3 g C kg−1 and just1.3 g C kg−1 less than the original concentration in 1964. With theintroduction of rotation in the dryland, the SOC started to decreaseand reached 23.1 g C kg−1 in 1992. After 1992, there was a periodwhen N fertiliser was added to increase crop output. In 2010, thesoil SOC concentration decreased to 20.8 g C kg−1. In contrast, theSOC concentration in paddy rice was much more stable. The paddyrice SOC ranged from 22.6 to 23.1 g C kg−1 in the period from 1993 to2010. A comparison of the two different cultivation patterns foundthat the SOC in paddy rice had relatively moderate changes andeven a slightly positive trend. The annually averaged SOC loss forthe paddy rice was one quarter that of the dryland (Fig. 3).

3.2. Effect of long-term crop-pattern variation on GWP

Using the same conditions as for the SOC, the GWP was simu-lated for two different crop patterns. The emissions of CO2, N2O andCH4 for the four periods from 1964 to 2010 are listed in Table 4. Theemission of N2O increased and then decreased over this period fordryland. During the conversion of dryland to paddy rice, the amountof N2O dropped substantially; however, the paddy rice contributedgreater quantities of CH4. After converting the gases to their GWPs,

it was found that the annual average values of the four periodswere 2015.40, 5154.34, 7229.03 and 4732.35 kg CO2-e ha−1 year−1

for 1964–1979, 1980–1992, 1993–1999 and 2000–2010, respec-tively. The total annual mean GWP of the dryland from the

232 W. Ouyang et al. / Ecological Modelling 252 (2013) 228– 237

Table 3Cultivation management scenarios during the prediction model run.

Land use Management practice scenarios Baseline Values for alternative scenarios

Dryland and paddy riceResidue return rate (%) 25 40, 60, 80Manure application (kg C ha−1) 0 500, 1000, 2000Chemical fertiliser (% of baseline rate) 50 80, 100, 120

corn–

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Fig. 3. Observed and simulated SOC in the

964 to 2010 time period (5876.83 kg CO2-e ha−1 year−1) was 2.5imes more than the annual mean for paddy rice (1636.99 kg CO2-

ha−1 year−1) over the 1993–2010 period. The conversion ofryland to paddy rice created a lower GWP and was helpful initigating global warming.In the temporal scale, the most significant difference was that

he crop-pattern shift had a direct impact on the GWP (Fig. 4). Afterwo years of the shift, the GWP became stable again. The emis-ions trend of the GWP first decreased for dryland and subsequentlyncreased with the introduction of corn. In comparison, it was foundhat the GWP of corn was the largest of these three crops and thatoybean was the lowest. It was also concluded that due to a sim-lar temporal pattern, the SOC is closely correlated with GWP inarmland.

.3. Response of SOC to cultivation practices

After identifying the temporal SOC dynamics in dryland andaddy rice, the response of SOC to different cultivation practicecenarios was simulated (Fig. 5). The first simulated cultivationractice concerns long-term SOC dynamics when the percentagef residue returned to the land is varied (Fig. 5A, a). With the con-ideration of the present status and development of biomass, theaseline and the three other scenarios were set to simulate theesponse for the two kinds of farmland. Based on the farm statisti-al data and field investigation, the baseline status is 25% of residueeturned to the land. When the baseline and 40% of crop residueere returned to the dryland, the SOC density gradually decreased

uring the simulated 30 years. When the percentage of the cropesidue incorporation increased to 60% and 80%, the temporal SOCrend developed in a positive direction (Fig. 5A, a). Under theseour scenarios, the SOC concentration for the paddy rice increased

able 4he annually averaged GWP for two crop patterns during four periods.

Crops Period N2O (kg N ha−1 year−1) CH4 (kg C

Dryland

1964–1979 1.39 −0.73

1980–1992 5.06 −0.77

1993–1999 7.97 −0.72

2000–2010 5.78 −0.66

Paddy rice1993–1999 0.11 26.50

2000–2010 0.08 45.21

soybean rotation (dryland) and paddy rice.

between 4.88% and 30.46%, which was approximately five timesthat of dryland, which ranged from 0 to 5.95%. The simulation indi-cated that the more residue that is returned to the land, the higherthe SOC in the soil, and the lower the GWP.

No manure has been added in the study areas; however, withthe consideration of ecological agriculture, there is no doubt thatmanure will eventually be applied, and four scenarios with manurewere simulated in this study (Fig. 5B, b). Compared with the impactof crop residue incorporation, the application of the manure grad-ually increased the SOC content for both the dryland and paddyrice. Even at the lower manure amendment level (500 kg C ha−1),the SOC climbed at a stable rate. The simulation demonstrated thatthe application of manure provides a means for improving the SOCcontent. This is true for dryland and is even obvious for paddy rice.

The simulation of various N fertiliser input scenarios showeda different trend (Fig. 5C, c). In the dryland, the SOC concentra-tion gradually decreased for all of the N fertiliser input scenarios,which indicated that N fertiliser decreases the SOC in dryland. TheN fertiliser in the paddy rice first decreased the SOC concentra-tion and subsequently raised it. When the N fertiliser input wasapproximately 50% of the baseline, the SOC in the later simulationperiod was still less than the beginning level. After the additionalfertiliser was 80% or 120% of the baseline status, the SOC in thepaddy rice recovered and increased. The interaction between thesimulated SOC and the additional N fertiliser showed that inorganicN probably decreased the SOC in dryland.

After finding the yearly SOC value, the average value of theannual SOC change was calculated. This value proved to give a moreinsightful result for each cultivation practice scenario (Fig. 6). The

SOC increased along with the rate of the increase in the percentageof residue return to the land and manure amendment load for boththe dryland and paddy rice systems. Comparing the correspondingSOC concentrations, it was concluded that the paddy rice was more

ha−1 year−1) CO2 (kg C ha−1 year−1) GWP (kg CO2-e ha−1 year−1)

371.13 2015.40738.85 5154.34528.55 7229.03239.11 4732.35

353.86 2091.5511.82 1347.72

W. Ouyang et al. / Ecological Modelling 252 (2013) 228– 237 233

Fig. 4. Simulated GWP for dryland and paddy rice.

2040203020202011

25

26

27

28 A

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2040203020202011

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204020302020201125.0

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204020302020201140

41

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45c

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Dryland SOC for percentage of residue returned Paddy rice SOC for percentage of residue returned

Dryland SOC for amount of manure amendment

Dryland SOC for percentage of N fertilizer input

Paddy rice SOC for amount of manure amendment

Paddy rice SOC for percentage of N fertilizer input

Fig. 5. Long-term SOC dynamics for dryland and paddy rice under different management practices.

-50

0

50

100

150

200

250

300

350

25* 40 60 80 0* 500 1000 2000 50 80 100* 120

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Manure ame ndment (kg C ha-1)

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Paddy Rice

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Residue returned (%) N fertil izer input (%)

Fig. 6. Averaged value of SOC changes (0–20 cm) under management practices for dryland and paddy rice.

234 W. Ouyang et al. / Ecological Modelling 252 (2013) 228– 237

s for d

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Fig. 7. Long-term GWP dynamic

ensitive to the percentage of residue return to the land and theanure amendment. However, unlike the paddy rice, the enhanced

ercentage of N fertiliser input did not change the dryland to a sinkor carbon. For all of the scenarios, the SOC of paddy rice had a pos-tive trend. The average value of the annual SOC change showed alearer trend in paddy rice, which can help in designing suitablentegrated cultivation practices for dryland and paddy rice.

.4. Response of GWP to management practices

Using an approach similar to that for the SOC, the response ofWP for the three types of cultivation practices for dryland andaddy rice were simulated (Fig. 7). The four scenarios for the vari-us percentages of residue returned to the dryland and paddy ricead different impacts on the GWP (Fig. 7D, d). As the soybean andorn were rotated in the dryland, the response of GWP fluctuated.

hen the percentage of returned residue for the soybean rota-ion was approximately 40%, 60% and 80%, the incremental meanWP value increased continuously, and the values were 301.74,05.22, and 1137.76 kg CO2-e ha−1 year−1, respectively. Comparingo the 25% baseline condition, the percentage increased to 40%, 60%,nd 80% can decrease GWP 440.59, 1020.30, and 1597.3 kg CO2-

ha−1 year−1, respectively. The GWP of the paddy rice decreasednd gradually tended to a certain value with the increased ratef returned residue. The long-term simulation showed that anncreased percentage of returned residue can reduce the GWP. Corn

s the priority crop in this freeze zone for minimising GWP.

The simulation showed that more manure amendment resultedn higher GWP for the dryland (Fig. 7 E, e). When the manuremendment increased to 500, 1000, and 2000 kg C ha−1, the

ifferent management practices.

averaged GWP value in ten years increased approximately 1.9, 1.5and 1.6 times the baseline load, respectively. For the paddy rice,the manure amendment can control the GWP for the first threeyears, especially the maximum load addition. However, the con-tinual addition will cause the GWP to be much greater than thebaseline status. The manure amendment only provides a brief ben-efit of GWP minimisation that cannot be maintained.

The interaction between N fertiliser input and GWP was similarto that for manure amendment (Fig. 7F, f). The GWP for drylandgradually increased during the first eight years when the rate ofN fertiliser input was increased from 50% to 120% of the baseline.After that period, the GWP of the soybean rotation maintained theincreased trend. In contrast, the GWP of the corn rotation decreasedat first and subsequently increased with the rising rate of N fertiliserinput. The inflection point was at the 80% rate. These simulationsshowed that the GWP increased when the rate of N fertiliser inputincreased in the paddy rice.

Because the three simulated cultivation practices influencedGWP differently, it was essential to identify the sensitivities tomitigating GWP (Fig. 8). The annual mean GWP increased for bothdryland and paddy rice when the load of the manure amendmentand N fertiliser input were increased. This simulation indicated thatthe proposed practices can improve the SOC, increase crop produc-tion and simultaneously enhance the GWP. For the residue returnpractices, the GWP continued to decrease with little amplitude vari-ation (173.39 kg CO2-e ha−1 year−1) for dryland, while the GWP of

the paddy rice decreased 1807.62 kg CO2-e ha−1 year−1 from thebaseline to 80%. In the paddy rice, the GWP became negative (mak-ing the area a sink for greenhouse gases) when 60% and 80% ofthe residue was added. The analysis identified that the practice of

W. Ouyang et al. / Ecological Modelling 252 (2013) 228– 237 235

0

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Fig. 8. Annual mean GWP for the managemen

eturning residue to the soil is a win–win solution for SOC conser-ation and GWP control.

. Discussion

.1. Impact of crop patterns on SOC and GWP in the freeze zone

It has been reported that the permafrost thaw area provides aigh risk to global climate change and has contributed a huge quan-ity of carbon to the atmosphere (Schuur and Abbott, 2011). In aimilar environment, agricultural activity in a freeze zone can accel-rate carbon emissions and increase risks from global warming. Aecreased SOC leads to high soil heterotrophic respiration rateshat overwhelm the SOC input rate from the crop litter incorpora-ion. Our simulations showed that SOC storage in cultivated soils istrongly dependent on the type of crop pattern used. The SOC lossor the corn rotation was much less than that for the soybean rota-ion since the relatively high crop productivities introduced moreitter into the soils. Meanwhile, the corn–soybean rotation lostequestered carbon at a slower rate than the continuous soybean.n contrast, the paddy rice gained more SOC due to the managementractice of seasonally flooding the paddy soil, which protected theresh litter from rapid decomposition (Witt et al., 2000). Thus, theonversion of dryland to paddy rice elevates the soil fertility by thencrease in SOC storage.

However, the environmental consequence of the conversion ofryland to paddy rice is a principal concern (Batlle-Bayer et al.,010). In this study, the GWPs of the two crop patterns were com-ared, which indicated that paddy rice is more amenable to globalarming mitigation than dryland. The calculated GWP values ofryland did not continuously increase but dropped with the lossf SOC, which is consistent with Wang’s research (Wang et al.,008). The different temporal patterns of SOC and GWP for the tworop patterns showed that the shift of dryland to paddy rice bothontributed to the conservation of SOC and a reduction of GWP.

.2. Implication for management practices with SOC and GWPptimisation

Soil is a major component for GHG mitigation. It can reducehe CH4 and N2O emissions, as well as soil carbon sequestrationn the ecosystem (Witt et al., 2011). Implementation of properultivation practices is the preferred solution for controlling theWP of farmland. In this study, the response of SOC and GWP

o three widely applied cultivation practices was assessed. Cropesidue and manure amendment have been the main sources for

oil organic matter for most Chinese farmlands (Qiu et al., 2009).ased on the simulations, we learnt that dryland became a sink fortmospheric carbon and effectively elevated the SOC storage withhe increasing application rate of residue and manure amendment.

10 00 2000 50 80 10 0* 12 0

tices for the dryland and paddy rice systems.

The DNDC-modelled results demonstrated that an increase in theN fertiliser application could sequester SOC in relatively smalleramounts, thereby contributing to the indirect carbon addition ofhigher crop litter (and crop litter). This conclusion is in agreementwith the results of many researchers (Hernanz and Lopez, 2002).The increased percentage of residue returned to the soil contributedto the GWP decline. The manure amendment and N fertiliser inputdecreased the GWP for both dryland and paddy rice. The analysisshowed that the GWP will increase due to the addition of organic orinorganic N, which is the preferred choice for higher crop produc-tion. The return of crop residue to the soil is a symbol of sustainableagriculture that can decrease the GWP. In the freeze zone, the crop ismuch sensitive to the global warming and the interactions betweentwo aspects merit further observations.

4.3. Uncertainty analysis

The main difficulty in this kind of long-term simulation anal-ysis at the farmland scale is validation (Miehle et al., 2006). Partsof the DNDC simulation are based on predefined parameter values(Norman et al., 2008). Fortunately, there were historical SOC datain this area to improve the modelling accuracy. The possible uncer-tainties that could be induced from the initial settings of some inputparameters such as SOC partitioning. Furthermore, the study areais cultivated in the farmland system, which has detailed recordson cultivation practices. This information improved the simula-tion results. However, there are still uncertainties in this study thatare related not only to the theoretical process but also the inputparameters concerning the cultivation practices and environmen-tal parameters. During the long-term SOC and GWP predictions, theclimate data were set to the historical observational data for thelast three decades. This choice may affect the accuracy of the sim-ulation results, but the differences among the scenarios can still beidentified. The DNDC treats the soil as a series of discrete horizontallayers with uniform properties. The quality of the estimation is alsorelated to the quality of the input data and management factors.These uncertainties can influence the absolute value of the simu-lation, but the conclusions concerning the impact of crop-patternchanges and different cultivation practices were reasonable.

5. Conclusion

The SOC content and GWP of dryland for four time periodsand paddy rice for two time periods were simulated with a bio-geochemical, process-based DNDC model. The results show thatflooded rice is more effective at storing SOC and is more amenable

to mitigating global warming than dryland. The fluxes of N2O, CH4and CO2 were simulated to evaluate the effect of different man-agement strategies (i.e., increase/decrease of N fertiliser, manureapplication and residue return rate) on GWP for two kinds of crops.

2 l Mod

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36 W. Ouyang et al. / Ecologica

n addition, a 30-year simulation of three selected managementractices was performed to predict their long-term impacts onoil carbon sequestration. Of the three management practices, theesults suggested that increased implementation of crop residuencorporation would be the sole way both to elevate SOC contentnd efficiently to mitigate GHG emissions at the two tested agroe-osystems. Higher manure amendment and N fertiliser input ratesmproved the SOC content but resulted in a higher GWP value forhe dryland and paddy rice systems. The increase in the fertiliserpplication rate slowed the SOC content losses but did not converthe soil to a sink of atmospheric CO2.

This study may be helpful for assessing the future policies oranagement strategies that meet the two objectives of recover-

ng SOC and mitigating GWP. The DNDC model is a useful toolo model the environmental consequences of agricultural man-gement systems and to improve the agricultural managementractices when considering GHG emissions. GHG fluxes generallyxhibit large temporal variations, and the DNDC can be applied toevelop and assess mitigation strategies. The complex relationshipetween SOC and GWP makes it difficult to identify the influence ofomprehensive management practices, which merits further study.

cknowledgements

This research was financially supported by the National Naturalcience Foundation of China (Grant Nos. 41001317 and 40930740),he Supporting Programme of the “Twelfth Five-year Plan” for Sci

Tech Research of China (2012BAD15B05), Special Fund for Agro-cientific Research in the Public Interest (201003014), Specializedesearch Fund for the Doctoral Program of Higher Education20100003120030), and the National Science Foundation for Inno-ative Research Group (No. 51121003).

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