Impact of crop patterns and cultivation on carbon sequestration and global warming potential in an agricultural freeze zone

Impact of crop patterns and cultivation on carbon sequestration and global warming potential in an agricultural freeze zone

Ecological Modelling 252 (2013) 228–237 Contents lists available at SciVerse ScienceDirect Ecological Modelling journal homepage: www.elsevier.com/l...

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Ecological Modelling 252 (2013) 228–237

Contents lists available at SciVerse ScienceDirect

Ecological Modelling journal homepage: www.elsevier.com/locate/ecolmodel

Impact of crop patterns and cultivation on carbon sequestration and global warming potential in an agricultural freeze zone Wei Ouyang a,∗ , Shasha Qi a , Fanghua Hao a , Xuelei Wang b , Yushu Shan a , Siyang Chen a a b

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

a r t i c l e

i n f o

Article history: Available online 14 June 2012 Keywords: Soil organic carbon Crop rotation Global warming potential Freeze zone Cultivation

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 management practices of various land use types have direct impacts on soil organic carbon (SOC) and global warming potential (GWP). Crop variations and cultivation practices are two important factors affecting carbon sequestration and the exchange of greenhouse gases between soils and the atmosphere. This exchange has special characteristics in the freeze zone. In this paper, the impact of crop patterns and cultivation management (i.e., residue return rate, manure amendment, and chemical N fertiliser application) 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 (N2 O), carbon dioxide (CO2 ) and methane (CH4 ) for dryland and paddy rice systems. The CO2 -equivalent index was used to express the GWP response of N2 O, CH4 and CO2 . The simulated results indicated that the manure amendment 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 GWP control. It was found that paddy rice was preferable to dryland for sequestering atmospheric CO2 and mitigating global warming. This analysis also indicated that the DNDC model is a valid tool for predicting the consequences of SOC and GWP changes in cropland agroecosystems in the freeze zone. © 2012 Elsevier B.V. All rights reserved.

1. Introduction Agricultural activities contribute to approximately 13.5% of the total global anthropogenic greenhouse gas (GHG) emissions (Lugato et al., 2010). Carbon sequestration is an important approach for improving soil fertility and mitigating the greenhouse gas impact of atmospheric CO2 by converting it into biotic or abiotic carbon that can be sequestered in terrestrial ecosystems and other sinks (Lackner, 2003). Thus, the agricultural area can act as a source or a sink of major greenhouse gases, depending on crop cultivation practices (Giltrap et al., 2010). Several studies have examined the interactions between cultivation activities with soil organic carbon (SOC) and their global warming potential (GWP) (Farage et al., 2007; Thelen et al., 2010). In this paper, we focus on the consequences of farmland conversion from dryland to paddy rice in the freeze zone, where this conversion is more sensitive to the soil carbon cycle. The impact of potential cultivation practices on SOC conservation and GWP control are also considered.

∗ Corresponding author. Tel.: +86 10 58802078; fax: +86 10 58802078. E-mail address: [email protected] (W. Ouyang). 0304-3800/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.ecolmodel.2012.05.009

Suitable crop patterns are expected to sequester the optimal amount of atmospheric carbon into the soil (Singh et al., 2011). Previous researchers have discussed possible SOC changes and related GHG concentrations in terms of defined crop field patterns (Post and Kwon, 2000; Nishimura et al., 2008). Over the past forty years, large areas of dryland have been converted into paddy rice in northeastern China using advanced irrigation technologies (Jiang et al., 2009). Because paddy rice for crop cultivation may cause SOC changes, studies comparing SOC in fields using consecutive dryland cultivation with those using a paddy rice rotation are required. The carbon sequestration and global warming potential modelling is a basic issue during agricultural development. Predicting the impacts of alternative management on the environment is drawing great attention in the scientific community (Tixier et al., 2008). Alternative management practices such as improving the rates of residue returned to fields, reducing fertiliser application rates, and using organic fertiliser, including farmyard manure and compost, have frequently proven to be promising for sequestering soil carbon and reducing GHG emissions (Smith et al., 2001). For agricultural ecosystems, one of the most cost effective methods for reducing GHG emissions is through cultivation management practices (Babu et al., 2006). Nevertheless, only a small amount of research has been published concerning the impacts

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of management practices on both SOC and GWP under different agro-ecosystems in the same area. The seasonal freeze–thaw cycle is a significant meteorological event in high-latitude regions. This cycle controls the evolution processes of different ecosystems, affects soil development, and restricts the pattern of the biology-terrestrial global chemistry cycle (Grogan et al., 2004). In this climate zone, the temperature fluctuations of the freeze–thaw cycle may intensify microorganism respiration as well as affect GHG emissions, the carbon balance and the concentration of available nutrients (Schimel and Clein, 1996; Muller et al., 2002). Intensive agricultural development causes more risk to soil carbon sequestration and variations in GHG emissions. Therefore, it is necessary to identity the dynamics of SOC and GWP for farmland in the freeze zone and to assess the impact of potential cultivation practices. The Denitrification–Decomposition (DNDC) model is a processoriented biogeochemistry model for agro-ecosystems and was specifically created for use at the field level. The development of a process-based model allows simulations at both the site and regional levels (Li et al., 1992; Giltrap et al., 2010). After improvement and amendment of water and energy circles, the model can simulate carbon transport and transformation and agricultural GHG emissions. In addition, it is possible to explore potential mitigation strategies, because the DNDC model is able to determine how strategies that reduce the emission of one gas will affect emissions of the other gases. Currently, the DNDC model is one of the most accurate carbon–nitrogen stimulation models and a useful tool for modelling the environmental impacts of agricultural management practices. With the application of DNDC, this study focused on completing the following goals: (i) model and compare the consequences of long-term crop pattern variations on SOC and GWP in the freeze zone; (ii) estimate SOC and GWP emissions from dryland and paddy rice systems under a range of management practices; and (iii) identify how to optimise SOC and GWP using best farmland management practices.

2. Materials and methods 2.1. Study area description The selected case study area is a typical farm (47.25N, 134.02E) which located in the Sanjiang Plain of northeastern China (Fig. 1). Sanjiang Plain is one of the largest agricultural bases in China and has experienced intense agricultural development over last three decades. The local agriculture is setup in a farmland system that is unlike the small households in other areas, which are cultivated under the standard guideline (Song et al., 2011). Soybean–corn rotation in the dryland was the most popular cropping system before the 1980s. After this time period, paddy rice was introduced and developed with the new cultivation methods. Presently, nearly half of the dryland has been changed to paddy rice, and this area is the northernmost site for rice cultivation in the world. This region of China is in a continental temperate monsoon climate with warm, wet summers and cold winters. Approximately 80% of the precipitation occurs during the period from June to September. The interannual variations of the annual precipitation and temperature from 1964 to 2010 are presented in Fig. 2. These long-term patterns indicate that the annual precipitation has decreased, and annual temperature has increased. Using the monthly data for three dry, three normal, and three wet years, the monthly pattern analysis showed that for half of the period the temperature was below 0◦ and most of the precipitation occurred in June, July and August. The soil texture of this selected site is silt loam, with bulk density 1.1 g cm−3 , pH 5.68 and soil organic carbon

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(SOC) content 20.8 g C kg−1 for dryland and 22.9 g C kg−1 for paddy rice. 2.2. The DNDC model The DNDC model was originally developed for predicting carbon sequestration and trace gas emissions in non-flooded agroecosystems and tested in the temperate zone, but more diverse versions have been developed (Smith et al., 2010). The DNDC model consists of the soil, climate, crop growth, decomposition, nitrification, denitrification and fermentation sub-models. A series of biochemical and geochemical reactions, such as that dominate carbon (C), and nitrogen (N) transport and transformation, elemental mechanical movement, oxidation and reduction, dissolution and crystallization, adsorption and dissimilation, complexation and decomplexation, assimilation and dissimilation occurring in agroecosystems, which have been parameterised by mathematical formulas in the model. Any change in daily meteorological data, soil properties or farming management practices will simultaneously alter several soil environmental factors including temperature, moisture, Eh, pH, and substrate concentration gradients; thereby, affecting the biochemical and geochemical reactions. SOC dynamics are predicted primarily by quantifying the SOC input from plant residue (i.e., litter) returned to the soil and manure amendment, and the SOC output through decomposition. The DNDC predicts CO2 , N2 O and CH4 emissions by simulating the processes of decomposition, nitrification, denitrification and fermentation (Brown et al., 2002; Beheydt et al., 2008). A relatively complete set of farming management practices (e.g., tillage, fertilisation, manure amendment, irrigation, flooding, and grazing) have been parameterised in the DNDC to regulate their impacts on environmental soil factors (Saggar et al., 2002). It has been used and expanded by many research groups covering SOC dynamics, GHGs emissions in range of countries and regions (Pathak et al., 2005; Smith et al., 2008). In China, the model had been applied across a wide range of agroecosystems by a number of researchers. By calibrating and validating, the model request a number of data sets, they have obtained promising results (Zhang et al., 2006). With the support of these many validations, the DNDC was employed in this study 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 characteristics, daily climate, and agricultural management (Britz and Leip, 2009). To construct a DNDC input database at the field level, the individual input data sets were obtained from various sources in different formats. The soil database includes the following variables: SOC, pH, percentage of sand, percentage of silt, percentage of clay, bulk density, hydraulic conductivity, saturation, field capacity, and wilting point. Climate inputs required by the DNDC include daily air temperature (minimum and maximum), precipitation, and daily average wind speed. These inputs were collected from a local weather station for the period 1964–2010. As the main concern in this paper is agricultural activity, the details concerning long-term information for agricultural management are presented in Table 1. Intensive land reclamation has occurred four times since the 1960s, and the cultivation of paddy rice had been increasing until the 1990s. Therefore, two sites (dryland with a two-year soybean–corn rotation cycle and irrigated paddy rice) were selected, and four time periods (see Table 1) were divided in the period from 1964 to 2010 according to the land reclamation data described above. The details of the agricultural

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

Fig. 2. The annual and monthly patterns of precipitation and temperature.

crop management in this period include data for crop type, tillage, fertiliser, weeding, irrigation and flooding. Additional information concerning the management practices of fertiliser use and the crop residue incorporation rate (residue returned) is also listed. No manure was applied during the entire period at both sites.

reduced tillage system in Ireland (Abdalla et al., 2011), grasslands in Europe (Levy et al., 2007). These successes provided a sound reference for model validation. Several researchers have used and expanded the DNDC model with field experiment validations and simulations against data sets from a wide range of agroecosystems. In this study, the parameters’ values were redefined based on the validated model applications in Japan, China and Thailand (Desjardins et al., 2010). The DNDC model can simulate SOC densities in the top 20 cm of soil layer, CO2 fluxes, CH4 fluxes and N2 O fluxes. In the study area, we have long-term SOC data for the dryland from 1974 to 2010. With the predetermined values, the simulated SOC results were compared to the observational data.

2.4. Model validation The DNDC model has been successfully used to simulate GHG emissions and mitigation in various agricultural systems in many countries, such as GHG emissions difference in field bean (Vicia faba L.) and winter wheat (Ludwig et al., 2011), conventional and Table 1 Cultivation management practices for dryland and paddy rice from 1964 to 2010. Land

Dry land

Crops

Period

Planting, date (day/month)

Harvest, date (day/month)

Tillage, date (day/month)

Soybean Corn–soybean rotation Soybean–corn rotation

1964–1979 1980–1992

10/5 10/5

15/10 15/10

1993–1999 2000–2010

10/5 10/5

1993–1999 2000–2010

Paddy rice

N fertiliser application, date (day/month)

Irrigation/flood, date (day/month)

N fertiliser rate (kg N ha−1 per application)

Residue returned (%)

20/10 5/5

10/5

No

18 56.1/43.8

15

15/10 15/10

5/5 5/5

10/5, 2/7 10/5, 2/7

53.4, 87.6 70.6, 7.9

25 25

24/5

2/10

10/5

24/5, 27/5, 26/7

10/5–10/9

24/5

2/10

10/5

24/5, 27/5, 26/7

10/5–10/9

40.92, 14.49, 14.49 53.75, 25.07, 25.07

25

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Table 2 Verification of simulation results. Index

Crop rotation

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

Dryland

Simulation results Year

Value

1974 1978 1979 1982 1984 1989 2010

25.60 25.60 25.70 23.40 23.30 23.40 20.20

Measured value

Relative error

25.63 23.99 26.42 24.08 24.01 22.56 20.79

−0.12 6.71 −2.73 −2.82 −2.96 3.72 −2.84

Index

Crop rotation

Average

Variation range

Average

Variation range

CH4 fluxes (kg C ha−1 year−1 )

Dryland Paddy rice

−0.72 37.93

−0.84 to 0.57 16.04–82.43

−0.62b 71.12a

−0.43 to 0.80b 63.72–78.52a

N2 O fluxes (kg N ha−1 year−1 )

Dryland Paddy rice

4.41 0.09

a b

0.24–20.78 0.01–0.59

3.12a 0.87b

2.15–4.09a 0.54–1.20b

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

The simulated results of trace gases (CH4 and N2 O) were compared to a similar modelling case (Cai et al., 2003). The detailed validation data are shown in Table 2. With the support of previous validations and historical data, it was determined that the model satisfactorily simulated annual variations of GHG emissions from crop systems and the effects of land management. The validated DNDC model can assess the effects of different crops rotation and management practices on SOC and GWP.

where GWPi (kg CO2 -e ha−1 year−1 ) is the GWP induced by scenario i; CO2 –Ci (kg C ha− year−1 ), CH4 –Ci (kg C ha− year−1 ) and Ni (kg N ha−1 year−1 ) are CO2 , CH4 and N2 O fluxes, respectively, induced by scenario i.

2.5. Simulation of global warming potential

Using the DNDC model, the SOC content of topsoil (0–20 cm) for different crop patterns was simulated from 1964 to 2010. In the dryland, the crop was only soybean from 1964 to 1979, and it subsequently changed to a soybean–corn rotation. Beginning in 1993, the paddy rice area began to emerge and increased rapidly; the fertilisation level increased beginning in 2000. Compared with the observational data, the simulation results represented the SOC status well. The simulation results indicated that the SOC content for the dryland gradually decreased during the 47 years. In the first period, the single soybean cultivation kept the SOC concentration in the soil at a high level, which was more than 24.3 g C kg−1 and just 1.3 g C kg−1 less than the original concentration in 1964. With the introduction of rotation in the dryland, the SOC started to decrease and reached 23.1 g C kg−1 in 1992. After 1992, there was a period when N fertiliser was added to increase crop output. In 2010, the soil SOC concentration decreased to 20.8 g C kg−1 . In contrast, the SOC concentration in paddy rice was much more stable. The paddy rice SOC ranged from 22.6 to 23.1 g C kg−1 in the period from 1993 to 2010. A comparison of the two different cultivation patterns found that the SOC in paddy rice had relatively moderate changes and even a slightly positive trend. The annually averaged SOC loss for the paddy rice was one quarter that of the dryland (Fig. 3).

The validated DNDC model was used to simulate the dynamics of SOC and GWP from 1964 to 2010 under different cultivation techniques to identify the impacts of land-use conversion from dryland to paddy rice. The cultivation practices, including fertiliser application rate and crop residue incorporation rate, can be expressed by the model. These practices have the potential to mitigate effectively the net impact on global warming by decreasing GHG emissions (Hastings et al., 2010). Several alternative scenarios of cultivation practices for both dryland and paddy rice systems were entered into the model with the goal of predicting the long-term dynamics on SOC and GWP. To assess the potential impacts of the strategies adopted on SOC and GWP, three types of alternative management scenarios were chosen (Table 3). The first scenario concerns the crop residue return rate, which was set to 40%, 60% and 80% of aboveground crop residue (the baseline rate was 25%). The manure application increased from 0 (baseline) to 2000 kg C ha−1 . Because overuse of synthetic fertiliser is becoming a notable social problem for most agricultural regions in China, the fertiliser rates were set to 50%, 80% and 120% of the baseline condition. In order to highlight the long-term impact, the DNDC was run with each of the scenarios over three decades. During the prediction simulation, the meteorological data from 2010 were used for each simulation year. Because each of the management alternatives could simultaneously affect CO2 , N2 O and CH4 fluxes, a net effect of the scenario on global warming must be quantified. The GWP has been developed in order to evaluate the comprehensive greenhouse effect. The changes in emissions of greenhouse gases (except CO2 ) were converted into CO2 -equivalents (CO2 -e) by means of its GWP (Frolking et al., 2004). The GWP value for each scenario was calculated as follows: GWPi =

CO2 –Ci × 44 Ni × 44 CH4 − Ci × 16 + + 12 28 × 330 12 × 21

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

3.2. Effect of long-term crop-pattern variation on GWP Using the same conditions as for the SOC, the GWP was simulated for two different crop patterns. The emissions of CO2 , N2 O and CH4 for the four periods from 1964 to 2010 are listed in Table 4. The emission of N2 O increased and then decreased over this period for dryland. During the conversion of dryland to paddy rice, the amount of N2 O dropped substantially; however, the paddy rice contributed greater quantities of CH4 . After converting the gases to their GWPs, it was found that the annual average values of the four periods were 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, respectively. The total annual mean GWP of the dryland from the

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Table 3 Cultivation management scenarios during the prediction model run. Land use

Management practice scenarios

Baseline

Values for alternative scenarios

Dryland and paddy rice

Residue return rate (%) Manure application (kg C ha−1 ) Chemical fertiliser (% of baseline rate)

25 0 50

40, 60, 80 500, 1000, 2000 80, 100, 120

Fig. 3. Observed and simulated SOC in the corn–soybean rotation (dryland) and paddy rice.

1964 to 2010 time period (5876.83 kg CO2 -e ha−1 year−1 ) was 2.5 times more than the annual mean for paddy rice (1636.99 kg CO2 e ha−1 year−1 ) over the 1993–2010 period. The conversion of dryland to paddy rice created a lower GWP and was helpful in mitigating global warming. In the temporal scale, the most significant difference was that the crop-pattern shift had a direct impact on the GWP (Fig. 4). After two years of the shift, the GWP became stable again. The emissions trend of the GWP first decreased for dryland and subsequently increased with the introduction of corn. In comparison, it was found that the GWP of corn was the largest of these three crops and that soybean was the lowest. It was also concluded that due to a similar temporal pattern, the SOC is closely correlated with GWP in farmland.

3.3. Response of SOC to cultivation practices After identifying the temporal SOC dynamics in dryland and paddy rice, the response of SOC to different cultivation practice scenarios was simulated (Fig. 5). The first simulated cultivation practice concerns long-term SOC dynamics when the percentage of residue returned to the land is varied (Fig. 5A, a). With the consideration of the present status and development of biomass, the baseline and the three other scenarios were set to simulate the response for the two kinds of farmland. Based on the farm statistical data and field investigation, the baseline status is 25% of residue returned to the land. When the baseline and 40% of crop residue were returned to the dryland, the SOC density gradually decreased during the simulated 30 years. When the percentage of the crop residue incorporation increased to 60% and 80%, the temporal SOC trend developed in a positive direction (Fig. 5A, a). Under these four scenarios, the SOC concentration for the paddy rice increased

between 4.88% and 30.46%, which was approximately five times that of dryland, which ranged from 0 to 5.95%. The simulation indicated that the more residue that is returned to the land, the higher the SOC in the soil, and the lower the GWP. No manure has been added in the study areas; however, with the consideration of ecological agriculture, there is no doubt that manure will eventually be applied, and four scenarios with manure were simulated in this study (Fig. 5B, b). Compared with the impact of crop residue incorporation, the application of the manure gradually increased the SOC content for both the dryland and paddy rice. Even at the lower manure amendment level (500 kg C ha−1 ), the SOC climbed at a stable rate. The simulation demonstrated that the application of manure provides a means for improving the SOC content. This is true for dryland and is even obvious for paddy rice. The simulation of various N fertiliser input scenarios showed a different trend (Fig. 5C, c). In the dryland, the SOC concentration gradually decreased for all of the N fertiliser input scenarios, which indicated that N fertiliser decreases the SOC in dryland. The N fertiliser in the paddy rice first decreased the SOC concentration and subsequently raised it. When the N fertiliser input was approximately 50% of the baseline, the SOC in the later simulation period was still less than the beginning level. After the additional fertiliser was 80% or 120% of the baseline status, the SOC in the paddy rice recovered and increased. The interaction between the simulated SOC and the additional N fertiliser showed that inorganic N probably decreased the SOC in dryland. After finding the yearly SOC value, the average value of the annual SOC change was calculated. This value proved to give a more insightful result for each cultivation practice scenario (Fig. 6). The SOC increased along with the rate of the increase in the percentage of residue return to the land and manure amendment load for both the dryland and paddy rice systems. Comparing the corresponding SOC concentrations, it was concluded that the paddy rice was more

Table 4 The annually averaged GWP for two crop patterns during four periods. Crops

Period

N2 O (kg N ha−1 year−1 )

CH4 (kg C ha−1 year−1 )

CO2 (kg C ha−1 year−1 )

GWP (kg CO2 -e ha−1 year−1 )

Dryland

1964–1979 1980–1992 1993–1999 2000–2010

1.39 5.06 7.97 5.78

−0.73 −0.77 −0.72 −0.66

371.13 738.85 528.55 239.11

2015.40 5154.34 7229.03 4732.35

Paddy rice

1993–1999 2000–2010

0.11 0.08

26.50 45.21

353.86 11.82

2091.55 1347.72

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60%

A

80%

26 25 2011

2020

Year

2030

2040

Dryland SOC for amount of manure amendment

27.0

baseline(0)

500

1000

B

2000

26.5 26.0

25.0 2011

26.4

2020

Year

2030

2040

Dryland SOC for percentage of N fertilizer input 50%

80%

t

baseline(100%)

C

120%

57

Paddy rice SOC for percentage of residue returned baseline(25%)

54

40%

60%

80%

a

51 48 45 42 2011 57

2020

Year

2030

2040

t Paddy rice SOC for amount of manure amendment baseline(0)

500

1000

b

2000

54 51 48 45 42 2011

45

2020

Year

2030

2040

Paddy rice SOC for percentage of N fertilizer input 50%

80%

baseline(100%)

120%

c

44

3

26.2 26.0 25.0 24.8 2011

2020

Year

2030

2040

43 42 41 40 2011

2020

Year

2030

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

350

SOC change kg C ha-1 y -1

SOC at 0-20cm (103kgC ha-1)

3

-1

SOC at 0-20cm (10 kgC ha )

27

SOC at 0-20cm (103kgC ha-1)

40%

SOC at 0-20cm (103kgC ha-1)

baseline(25%)

-1

Dryland SOC for percentage of residue returned

3

28

SOC at 0-20cm (10 kgC ha )

-1

SOC at 0-20cm (10 kgC ha )

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

300

Residue returned (%)

Manure amendment (kg C ha-1)

N fertilizer input (%)

250

Dryland

200

Paddy Rice

150

* Baseline status

100 50 0 -50

25*

40

60

80

0*

500

1000

2000

50

80

100*

120

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

2040

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Fig. 7. Long-term GWP dynamics for different management practices.

sensitive to the percentage of residue return to the land and the manure amendment. However, unlike the paddy rice, the enhanced percentage of N fertiliser input did not change the dryland to a sink for carbon. For all of the scenarios, the SOC of paddy rice had a positive trend. The average value of the annual SOC change showed a clearer trend in paddy rice, which can help in designing suitable integrated cultivation practices for dryland and paddy rice. 3.4. Response of GWP to management practices Using an approach similar to that for the SOC, the response of GWP for the three types of cultivation practices for dryland and paddy rice were simulated (Fig. 7). The four scenarios for the various percentages of residue returned to the dryland and paddy rice had different impacts on the GWP (Fig. 7D, d). As the soybean and corn were rotated in the dryland, the response of GWP fluctuated. When the percentage of returned residue for the soybean rotation was approximately 40%, 60% and 80%, the incremental mean GWP value increased continuously, and the values were 301.74, 705.22, and 1137.76 kg CO2 -e ha−1 year−1 , respectively. Comparing to the 25% baseline condition, the percentage increased to 40%, 60%, and 80% can decrease GWP 440.59, 1020.30, and 1597.3 kg CO2 e ha−1 year−1 , respectively. The GWP of the paddy rice decreased and gradually tended to a certain value with the increased rate of returned residue. The long-term simulation showed that an increased percentage of returned residue can reduce the GWP. Corn is the priority crop in this freeze zone for minimising GWP. The simulation showed that more manure amendment resulted in higher GWP for the dryland (Fig. 7 E, e). When the manure amendment increased to 500, 1000, and 2000 kg C ha−1 , the

averaged GWP value in ten years increased approximately 1.9, 1.5 and 1.6 times the baseline load, respectively. For the paddy rice, the manure amendment can control the GWP for the first three years, especially the maximum load addition. However, the continual addition will cause the GWP to be much greater than the baseline status. The manure amendment only provides a brief benefit of GWP minimisation that cannot be maintained. The interaction between N fertiliser input and GWP was similar to that for manure amendment (Fig. 7F, f). The GWP for dryland gradually increased during the first eight years when the rate of N fertiliser input was increased from 50% to 120% of the baseline. After that period, the GWP of the soybean rotation maintained the increased trend. In contrast, the GWP of the corn rotation decreased at first and subsequently increased with the rising rate of N fertiliser input. The inflection point was at the 80% rate. These simulations showed that the GWP increased when the rate of N fertiliser input increased in the paddy rice. Because the three simulated cultivation practices influenced GWP differently, it was essential to identify the sensitivities to mitigating GWP (Fig. 8). The annual mean GWP increased for both dryland and paddy rice when the load of the manure amendment and N fertiliser input were increased. This simulation indicated that the proposed practices can improve the SOC, increase crop production and simultaneously enhance the GWP. For the residue return practices, the GWP continued to decrease with little amplitude variation (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 the baseline to 80%. In the paddy rice, the GWP became negative (making the area a sink for greenhouse gases) when 60% and 80% of the residue was added. The analysis identified that the practice of

GWP change 10 3 kg CO2 -e ha -1 y -1

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235

16 14

Residu e returned (%)

Manure amendment (kg C ha-1)

N fertilizer input (%)

12 10

Dryland

8

Paddy Rice * Baseline status

6 4 2 0 -2

25*

40

60

80

0*

50 0

10 00 2000

50

80

10 0*

12 0

Fig. 8. Annual mean GWP for the management practices for the dryland and paddy rice systems.

returning residue to the soil is a win–win solution for SOC conservation and GWP control. 4. Discussion 4.1. Impact of crop patterns on SOC and GWP in the freeze zone It has been reported that the permafrost thaw area provides a high risk to global climate change and has contributed a huge quantity of carbon to the atmosphere (Schuur and Abbott, 2011). In a similar environment, agricultural activity in a freeze zone can accelerate carbon emissions and increase risks from global warming. A decreased SOC leads to high soil heterotrophic respiration rates that overwhelm the SOC input rate from the crop litter incorporation. Our simulations showed that SOC storage in cultivated soils is strongly dependent on the type of crop pattern used. The SOC loss for the corn rotation was much less than that for the soybean rotation since the relatively high crop productivities introduced more litter into the soils. Meanwhile, the corn–soybean rotation lost sequestered carbon at a slower rate than the continuous soybean. In contrast, the paddy rice gained more SOC due to the management practice of seasonally flooding the paddy soil, which protected the fresh litter from rapid decomposition (Witt et al., 2000). Thus, the conversion of dryland to paddy rice elevates the soil fertility by the increase in SOC storage. However, the environmental consequence of the conversion of dryland to paddy rice is a principal concern (Batlle-Bayer et al., 2010). In this study, the GWPs of the two crop patterns were compared, which indicated that paddy rice is more amenable to global warming mitigation than dryland. The calculated GWP values of dryland did not continuously increase but dropped with the loss of SOC, which is consistent with Wang’s research (Wang et al., 2008). The different temporal patterns of SOC and GWP for the two crop patterns showed that the shift of dryland to paddy rice both contributed to the conservation of SOC and a reduction of GWP. 4.2. Implication for management practices with SOC and GWP optimisation Soil is a major component for GHG mitigation. It can reduce the CH4 and N2 O emissions, as well as soil carbon sequestration in the ecosystem (Witt et al., 2011). Implementation of proper cultivation practices is the preferred solution for controlling the GWP of farmland. In this study, the response of SOC and GWP to three widely applied cultivation practices was assessed. Crop residue and manure amendment have been the main sources for soil organic matter for most Chinese farmlands (Qiu et al., 2009). Based on the simulations, we learnt that dryland became a sink for atmospheric carbon and effectively elevated the SOC storage with the increasing application rate of residue and manure amendment.

The DNDC-modelled results demonstrated that an increase in the N fertiliser application could sequester SOC in relatively smaller amounts, thereby contributing to the indirect carbon addition of higher crop litter (and crop litter). This conclusion is in agreement with the results of many researchers (Hernanz and Lopez, 2002). The increased percentage of residue returned to the soil contributed to the GWP decline. The manure amendment and N fertiliser input decreased the GWP for both dryland and paddy rice. The analysis showed that the GWP will increase due to the addition of organic or inorganic N, which is the preferred choice for higher crop production. The return of crop residue to the soil is a symbol of sustainable agriculture that can decrease the GWP. In the freeze zone, the crop is much sensitive to the global warming and the interactions between two aspects merit further observations. 4.3. Uncertainty analysis The main difficulty in this kind of long-term simulation analysis at the farmland scale is validation (Miehle et al., 2006). Parts of the DNDC simulation are based on predefined parameter values (Norman et al., 2008). Fortunately, there were historical SOC data in this area to improve the modelling accuracy. The possible uncertainties that could be induced from the initial settings of some input parameters such as SOC partitioning. Furthermore, the study area is cultivated in the farmland system, which has detailed records on cultivation practices. This information improved the simulation results. However, there are still uncertainties in this study that are related not only to the theoretical process but also the input parameters concerning the cultivation practices and environmental parameters. During the long-term SOC and GWP predictions, the climate data were set to the historical observational data for the last three decades. This choice may affect the accuracy of the simulation results, but the differences among the scenarios can still be identified. The DNDC treats the soil as a series of discrete horizontal layers with uniform properties. The quality of the estimation is also related to the quality of the input data and management factors. These uncertainties can influence the absolute value of the simulation, but the conclusions concerning the impact of crop-pattern changes and different cultivation practices were reasonable. 5. Conclusion The SOC content and GWP of dryland for four time periods and paddy rice for two time periods were simulated with a biogeochemical, process-based DNDC model. The results show that flooded rice is more effective at storing SOC and is more amenable to mitigating global warming than dryland. The fluxes of N2 O, CH4 and CO2 were simulated to evaluate the effect of different management strategies (i.e., increase/decrease of N fertiliser, manure application and residue return rate) on GWP for two kinds of crops.

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In addition, a 30-year simulation of three selected management practices was performed to predict their long-term impacts on soil carbon sequestration. Of the three management practices, the results suggested that increased implementation of crop residue incorporation would be the sole way both to elevate SOC content and efficiently to mitigate GHG emissions at the two tested agroecosystems. Higher manure amendment and N fertiliser input rates improved the SOC content but resulted in a higher GWP value for the dryland and paddy rice systems. The increase in the fertiliser application rate slowed the SOC content losses but did not convert the soil to a sink of atmospheric CO2 . This study may be helpful for assessing the future policies or management strategies that meet the two objectives of recovering SOC and mitigating GWP. The DNDC model is a useful tool to model the environmental consequences of agricultural management systems and to improve the agricultural management practices when considering GHG emissions. GHG fluxes generally exhibit large temporal variations, and the DNDC can be applied to develop and assess mitigation strategies. The complex relationship between SOC and GWP makes it difficult to identify the influence of comprehensive management practices, which merits further study. Acknowledgements This research was financially supported by the National Natural Science Foundation of China (Grant Nos. 41001317 and 40930740), the Supporting Programme of the “Twelfth Five-year Plan” for Sci & Tech Research of China (2012BAD15B05), Special Fund for Agroscientific Research in the Public Interest (201003014), Specialized Research Fund for the Doctoral Program of Higher Education (20100003120030), and the National Science Foundation for Innovative Research Group (No. 51121003). References Abdalla, M., Kumar, S., Jones, M., Burke, J., Williams, M., 2011. Testing DNDC model for simulating soil respiration and assessing the effects of climate change on the CO2 gas flux from Irish agriculture. Global and Planetary Change 78 (3–4), 106–115. Babu, Y.J., Li, C., Frolking, S., Nayak, D.R., Adhya, T.K., 2006. Field validation of DNDC model for methane and nitrous oxide emissions from rice-based production systems of India. Nutrient Cycling in Agroecosystems 74, 157–174. Batlle-Bayer, L., Batjes, N.H., Bindraban, P.S., 2010. Changes in organic carbon stocks upon land use conversion in the Brazilian Cerrado: a review. Agriculture, Ecosystems & Environment 137 (1–2), 47–58. Beheydt, D., Boeckx, P., Ahmed, H.P., Van Cleemput, O., 2008. N2 O emission from conventional and minimum-tilled soils. Biology and Fertility of Soils 44 (6), 863–873. Britz, W., Leip, A., 2009. Development of marginal emission factors for N losses from agricultural soils with the DNDC–CAPRI meta-model. Agriculture, Ecosystems & Environment 133 (3–4), 267–279. Brown, L., Syed, B., Jarvis, S.C., Sneath, R.W., Phillips, V.R., Goulding, K.W.T., Li, C., 2002. Development and application of a mechanistic model to estimate emission of nitrous oxide from UK agriculture. Atmospheric Enviroment 36 (6), 917–928. Cai, Z., Sawamoto, T., Li, C., Kang, G., Boonjawat, J., Mosier, A., Wassmann, R., Tsuruta, H., 2003. Field validation of the DNDC model for greenhouse gas emissions in East Asian cropping systems. Soil Biology & Biochemistry 36, 641. Desjardins, R.L., Pattey, E., Smith, W.N., Worth, D., Grant, B., Srinivasan, R., MacPherson, J.I., Mauder, M., 2010. Multiscale estimates of N2 O emissions from agricultural lands. Agricultural and Forest Meteorology 150 (6), 817–824. Farage, P.K., Ardö, J., Olsson, L., Rienzi, E.A., Ball, A.S., Pretty, J.N., 2007. The potential for soil carbon sequestration in three tropical dryland farming systems of Africa and Latin America: a modelling approach. Soil and Tillage Research 94 (2), 457–472. Frolking, S., Li, C., Braswell, R., Fuglestvedt, J., 2004. Short- and long-term greenhouse gas and radiative forcing impacts of changing water management in Asian rice paddies. Global Change Biology 7 (10), 1180–1196. Giltrap, D.L., Li, C.S., Saggar, S., 2010. DNDC: a process-based model of greenhouse gas fluxes from agricultural soils. Agriculture, Ecosystems & Environment 136, 292–300. Grogan, P., Michelsen, A., Ambus, P., Jonasson, S., 2004. Freeze–thaw regime effects on carbon and nitrogen dynamics in sub-arctic heath tundra mesocosms. Soil Biology & Biochemistry 36, 641–654.

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