Modeling CH4 and N2O emission patterns and mitigation potential from paddy fields in Shanghai, China with the DNDC model

Agricultural Systems 178 (2020) 102743

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Modeling CH4 and N2O emission patterns and mitigation potential from paddy fields in Shanghai, China with the DNDC model

T

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Zheng Zhaoa, Linkui Caob, , Jia Dengc, Zhimin Shab, Changbin Chua, Deping Zhoua, ⁎ Shuhang Wua, , Weiguang Lva a

Eco-environmental Protection Institute of Shanghai Academy of Agricultural Science, Shanghai 201403, China School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China c Earth Systems Research Center, Institute for the Study of Earth, Oceans and Space, University of New Hampshire, Durham, NH 03824, USA b

A R T I C LE I N FO

A B S T R A C T

Keywords: Paddy field CH4 N2O DNDC model Sensitivity analysis Scenario simulation

The flooded paddy field ecosystem is an important source of CH4 and N2O emissions from agricultural lands. Denitrification-Decomposition (DNDC), a process-based model, was used in this study to evaluate the effects of different field management practices on CH4 and N2O emissions from flooded paddy fields in Shanghai, China. The results indicated that the predicted seasonal patterns of CH4 (R2 = 0.76, ME = 0.71) and N2O (R2 = 0.71, ME = 0.67) emissions were in line with the observations from our experimental paddy field under traditional management practices. The total CH4 and N2O fluxes from paddy fields in the Shanghai region in the 2013 rice season reached 32,300 and 175 tons, respectively, and varied widely across 101 simulated rice-cultivating towns. A sensitivity analysis indicated that CH4 emissions were positively correlated with the organic fertilizer rate, the straw returned fraction, the tillage depth and the soil organic carbon (SOC) content and negatively correlated with the soil clay fraction. N2O emissions had a positive relationship with precipitation, the urea rate, the tillage depth and the SOC content and a negative relationship with the soil pH and the clay fraction. Based on the sensitivity analysis, four field management variables, including the fertilization rate, the irrigation method, the straw returned fraction and the tillage depth, were selected to construct several management scenarios for the DNDC scenario simulation tests. The simulated results indicated that reducing the rate of fertilization by 20% combined with moistening irrigation (keeping the paddy soil saturated with water but not covered with a layer of water) was the best practice for long-term sustainable management of paddy fields. This best management practice could reduce integrated emissions of CH4 and N2O (CO2-equivalent) by 33%, while maintaining optimal rice yields. However, straw returning and deep plowing increased CH4 emissions from paddy fields in Shanghai.

1. Introduction China is the largest rice producer in the world and produces approximately 30% of world's total rice yield in 20% of the global rice planting area (FAO, 2019; Frolking et al., 2002). Paddy rice is one of the primary foods eaten by the Chinese people and is usually cultivated in southern China. Flooded irrigation is the traditional water management practice for Chinese paddy fields, with a stable surface water layer (7–8 cm) maintained for the entire rice season except for the artificially managed mid-season aeration period. With this specific irrigation practice and the high fertilization rate in Chinese paddy fields, the flooded agroecosystem has been confirmed as a major source of greenhouse gas (GHG) emissions (Zhang et al., 2014; Zou et al., 2009). Carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) are three

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important GHGs that are widely considered to be major contributors to global warming. Since the Industrial Revolution, the concentrations of CO2, CH4 and N2O in the atmosphere have increased from 280 ppm to 405 ppm, 715 ppb to 1859 ppb and 270 ppb to 329 ppb, respectively (WMO, 2018). Largely due to the application of synthetic chemical fertilizer after the industrial era, agricultural soils have become a significant anthropogenic source of GHG emissions and have produced approximately 20% of total global GHG emissions (Lokupitiya and Paustian, 2006). Li et al. (2006) pointed out that agricultural sources of CH4 and N2O emissions would increase by 60% in the next two decades if the unlimited application of excessive synthetic fertilizer to agricultural lands continued (Li et al., 2006). Therefore, the Chinese government set a goal to substantially reduce CH4 and N2O emissions from flooded paddy fields (Yan et al., 2009). Scientific fertilization and field

Corresponding authors at: Eco-environmental Protection Institute of Shanghai Academy of Agricultural Science, 1000 Jinqi Rd., Shanghai 201403, PR China. E-mail addresses: [email protected] (L. Cao), [email protected] (S. Wu).

https://doi.org/10.1016/j.agsy.2019.102743 Received 4 December 2018; Received in revised form 3 November 2019; Accepted 3 November 2019 0308-521X/ © 2019 Published by Elsevier Ltd.

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management optimization in the flooded paddy field ecosystem play an important role in achieving this goal. In recent years, process-based models have been developed to trace GHG emissions from the agroecosystem and to evaluate the impacts of alternative field management practices on GHG emissions (Abdalla et al., 2010; Del Grosso et al., 2009; Li et al., 1992a). The modeling approach is an effective tool to optimize field management practices to mitigate GHG emissions from agricultural lands. The DenitrificationDecomposition (DNDC) model is one of the most successful agroecosystem models that can simulate and predict carbon (C) and nitrogen (N) biogeochemical pathways in agricultural lands (Gilhespy et al., 2014; Giltrap et al., 2010). In the past two decades, application of the DNDC model to track CH4 and N2O emissions has been validated across different countries and various agroecosystems (Borzecka-Walker et al., 2012; Lugato et al., 2010; Minamikawa et al., 2016). For example, the DNDC model was utilized in a wheat-maize cropping system in China to evaluate the impacts of alternative management practices on N trace gas emissions, enabling the identification of the best management practice (BMP) suitable for the agroecosystem (Cui et al., 2014). A new version of the model, DNDC-rice, has been validated in Japanese paddy field ecosystems to simulate CH4 and N2O emissions (Katayanagi et al., 2017; Katayanagi et al., 2012). In addition, the DNDC model has also been used in grassland, forest and wetland ecosystems to simulate GHG emissions and their associated biogeochemical processes (Lamers et al., 2007; Levy et al., 2007; Rafique et al., 2011). In general, the DNDC model is an excellent choice for simulating GHG emissions from agricultural lands and for identifying the BMPs for GHG mitigation. Paddy fields are one of the most common agroecosystems in Shanghai. However, the status of CH4 and N2O emissions from flooded paddy fields in Shanghai has not been thoroughly studied to date. It is difficult to study regional CH4 and N2O emissions from paddy fields in Shanghai, as well as the impacts of different management practices on CH4 and N2O emissions, by using traditional field observation experiments. Therefore, the DNDC model was employed in this study to evaluate regional CH4 and N2O emissions from paddy fields under current management practices in Shanghai, China. A sensitivity analysis was conducted with the DNDC model to assess the effects of varying input parameters on CH4 and N2O emissions, thereby determining the best management practice for controlling CH4 and N2O emissions from flooded paddy fields. Based on the results of the sensitivity analysis, several scenarios were created by combining different field management practices (i.e., fertilization rates, irrigation methods, straw returned fraction and plowing depth) and then simulated on a regional scale with the DNDC model. With the DNDC modeling approach, the current field management practices in Shanghai paddy fields were optimized, and these practices are expected to significantly reduce CH4 and N2O emissions while maintaining optimal rice yields.

2.2. DNDC model development The DNDC model is a process-based biogeochemical model that simulates C and N transport and transformation in agroecosystems. The DNDC model was developed by Li et al. (Li et al., 1992a, b) and was originally utilized to simulate soil C sequestration and GHG emissions in agroecosystems. The DNDC model has incorporated a relatively complete suite of biophysical and biogeochemical processes, which enables the DNDC model to simulate crop growth, soil C and N dynamics, and GHG emissions in agroecosystems. The input parameters required to run the model include climate conditions (i.e., precipitation, air temperature), soil properties (i.e., texture, bulk density, pH, SOC, nitrate and ammonium contents) and agricultural management practices (i.e., fertilization, irrigation and tillage). After connecting the DNDC model with a geographic information system (GIS) database, regional simulations can also be conducted. The “anaerobic balloon” concept embedded into the DNDC model provides a feasible framework to track GHG emissions from flooded paddy fields (Li et al., 2004). The DNDC model combines the Nernst equation (Eq. S1) and the Michaelis-Menten equation (Eq. S2) through “anaerobic balloon” to realize the conjugate calculation of the thermodynamic and kinetic conditions of various redox reactions that produce GHGs. Next, the release of GHGs from the soil to the air is calculated using the N2O diffusion equation (Eq. S3) and the CH4 diffusion equation (Eq. S4). In DNDC, methane production is simulated by calculating substrate concentrations (i.e., electron donors and acceptors) and then by tracking a series of reductive reactions between electron donors (i.e., H2 and DOC) and acceptors (i.e., O2, NO3−, Mn4+, Fe3+, SO42−, and CO2). When soil is shifting from unsaturated to saturated conditions under flooding, the soil oxygen is gradually depleted, and additional oxidants (e.g., NO3−, Mn4+, Fe3+, SO42−, and CO2) may become involved in reductive reactions (Deng et al., 2017; Li et al., 2004). The soil redox potential (Eh) gradually decreases along with the consumption of these oxidants, and the changes in the soil Eh is calculated in DNDC using the Nernst equation (Fig. S1). Fermentation and methane production are simulated when the soil Eh is below −150 mV, and the model assumes that these processes are not affected by the soil Eh under this condition. Methane consumption is simulated as an oxidation reaction involving electron exchange between CH4 and oxygen (Li et al., 2004). After draining, the soil shifts from saturated to unsaturated conditions, and the soil Eh increases. The model tracks processes that happen in unsaturated conditions (e.g., CH4 oxidation, nitrification, and denitrification). N2O flux is predicted as an intermediate product by simulating nitrification and denitrification. DNDC simulates the nitrification rate under aerobic conditions and then calculates nitrification-induced N2O production as a proportion of the simulated nitrification rate (Li et al., 1992a). However, nitrification is prohibited under saturated conditions. Denitrification is simulated as sequential reactions from NO3− to N2, and the rate of each step is calculated based on the Michaelis-Menten equation according to the concentrations of the corresponding nitrogenous oxides and DOC (Li et al., 1992a). In summary, the DNDC model uses the Nernst equation to calculate the soil Eh to determine which reaction happens under certain Eh conditions and then calculates reaction rates using the MichaelisMenten equation. In addition, the ability of the DNDC model to predict CH4 emissions from flooded paddy fields has been enhanced by refining CO2-induced and DOC-induced CH4 production (Fumoto et al., 2008; Fumoto et al., 2010). After validating this version for a range of climatic conditions, soil properties and management practices in the paddy fields of Japan, a new version named DNDC-rice was developed (Fumoto et al., 2013; Katayanagi et al., 2016). In our previous study, several modifications were incorporated into DNDC based on the field configurations and water movement characteristics of paddy fields in China. These improvements enhanced the ability of the DNDC model to simulate N loss through surface runoff and leaching from the flooded paddy field

2. Materials and methods 2.1. Study area Shanghai (31° N, 121° E) is located in the Yangtze River Delta in East China and was formed by the river's natural deposition. Rice-wheat rotation is one of the main agricultural systems in Shanghai and usually intensively cultivated to support the food demands of the large population in the city. Rice is generally planted in June and harvested in October, while wheat is sowed in November and harvested the next May. Shanghai has a subtropical humid monsoon climate with a daily average air temperature of 15.6 °C, an annual average precipitation of 1205 mm and an annual frost-free period of approximately 225 days. The paddy soil in this region is classified as Anthrosol based on Chinese Soil Taxonomy.

2

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Second National Soil Survey of China and from actual sampling and lab analysis. The soil data for each town were inputted into the DNDC model with the max and min values to cover the spatial heterogeneity of soil properties. Considering the variation in paddy field management practices across different towns, we utilized the most common fertilization method (as described in Section 2.3) and irrigation method (flooding irrigation) in Shanghai and applied these practices to the whole region to construct the base scenario. In summary, the soil and meteorological data are town specific, and the field management practices are uniform for the whole region for each management scenario. Using the regional simulation, the effects of different management practices on CH4 and N2O emissions on the whole region were evaluated. Similarly, a regional simulation was also conducted for a 3year period (2011–2013) with a 2-year spin-up time.

agroecosystem (Zhao et al., 2016; Zhao et al., 2014). The version adopted in this study (DNDC 9.5) inherited all of these historical modifications and provides an appropriate framework to simulate CH4 and N2O emissions from flooded paddy fields in Shanghai. 2.3. Model validation A field experiment was conducted in the 2013 rice season to measure the CH4 and N2O emissions from a flooded paddy field under traditional local management practices in Shanghai. Rice seedlings were transplanted on Jun. 18, 2013 and harvested on Nov. 04, 2103. The experimental paddy field remained continuously flooded with a water layer 7–8 cm deep throughout the entire rice season except for the mid-season drainage period (Aug. 05–14, 2013). Fertilizer was applied to the experimental paddy field, including basal dressing (60 kg N·ha−1 organic manure and 144 kg N·ha−1 urea were applied on Jun. 16, 2013) and two top-dressings (48 kg N·ha−1 urea was applied on Jul. 22, 2013, and Aug. 14, 2013) for a total of 300 kg N·ha−1 of fertilizer. CH4 and N2O emissions from the experimental paddy field were collected with a static chamber system (Tan et al., 2019) and then measured with a gas chromatograph. CH4 and N2O emissions were measured every day in the first week after fertilization, irrigation flooding and drainage, and then measured once a week until the rice was harvested. The observed data collected from the experimental paddy field were used for the DNDC model validation test. The DNDC model requires soil, climate and field management data to simulate CH4 and N2O emissions from paddy fields. The soil data for the top layer (0–0.1 m) in the experimental site are as follows: soil texture, sandy clay (clay fraction 0.43); bulk density, 1250 kg·m−3; soil pH, 7.05; field capacity, 60% (in water-filled pore space); porosity, 42% (in volume); SOC, 0.015 kg C·kg−1 soil; nitrate, 5 mg·kg−1 soil; and ammonium, 7.25 mg·kg−1 soil. The climate data entered into the DNDC model are daily precipitation (mm), daily max and min temperature. In the 2013 rice season, the total precipitation was 595 mm and the daily average temperature was 26.3 °C. The field management data, such as planting and harvesting date, fertilization method and irrigation method, were set based on the actual management practices in our experimental site (as described above). The flooding and drainage periods were determined based on the flooding and drainage dates of each flooding event during the rice growing season. The DNDC model was run for a 3-year period from 2011 to 2013 in the validation test. The simulations of the first two years were used as the model spin-up to stabilize soil C and N pools, and the simulated data for the 2013 rice season were used for comparison with observed data. Two statistical indexes, the coefficient of determination (R2) and the Nash–Sutcliffe index of model efficiency (ME), were used to evaluate the simulated results (Deng et al., 2011). The R2 value indicates the correlation between the simulations and the observations. The ME value indicates the improvement of the simulations relative to the mean of observations. A positive ME value indicates that the simulations are better than the mean of the observations, and the best model performance has an ME value of 1.

2.5. Sensitivity analysis A sensitivity analysis was used to evaluate how the simulated results responded to the variation in the input parameters. Eight input parameters, including precipitation, urea application rate, organic fertilizer rate, straw returned fraction, tillage depth, soil organic carbon content, clay fraction and pH, were selected for the analysis. Every input parameter was run 500 times in the analysis by randomly varying the selected parameter in a range of ± 20% while keeping the other parameters constant. Simulated results, including rice yields, CH4 emissions and N2O emissions, were selected to evaluate sensitivity to the selected input parameters. A sensitivity index (SI) was calculated to quantify the impacts of the selected parameters on the simulated results. Thus, the key factors that significantly impact CH4 and N2O emissions from paddy fields were identified. The SI was calculated as follows (Eq. (1)):

SI =

(R max −R min)/ Ravg (P max −P min)/ Pavg

(1)

where Pmax, Pmin and Pavg are the maximum, minimum and average values of the selected input parameters, respectively, and Rmax, Rmin and Ravg are the corresponding simulated results. A positive SI value indicates a positive correlation between the simulated results and the selected input parameters, whereas a negative SI value indicates they were negatively correlated. A higher absolute SI value indicates a greater impact of the selected input parameters on the simulated results. 2.6. Scenario simulation Based on the sensitivity analysis results, four field management variables, including fertilization rates, irrigation practices, straw return and plowing methods, were selected to create 11 field management scenarios. Fertilization rates, straw return and plowing depth are sensitivity factors affecting CH4 and N2O emissions from paddy fields. Irrigation is the key factor that regulates soil water content and affects soil redox potential and GHG production. The 11 created simulation scenarios were run with the DNDC model on a regional scale to evaluate CH4 and N2O emissions and mitigation potential in paddy fields in Shanghai. The management details for the 11 simulation scenarios are shown in Table 1. CK was the base scenario using local traditional management practices (the same as the field experiments). The other 10 simulation scenarios were assembled with differences in the four management variables. The objective of the scenario simulation tests is to optimize the current field management practices for flooded paddy fields in Shanghai, which could substantially reduce CH4 and N2O emissions while maintaining optimal rice yields. However, the scenario simulation test was run for only 3 years, which may not be long enough for the soil C and N pools to reach equilibrium and for evaluating the long-term effects on rice yields. Therefore, a long-term (50-year) simulation test was conducted with the DNDC model to evaluate if the

2.4. Region upscaling In this study, regional upscaling was conducted at the town level with the DNDC model. Therefore, a town-level regional database including a climate dataset, a soil properties dataset, and a field management dataset was constructed to support the regional simulation of CH4 and N2O emissions from paddy fields in Shanghai. Climate data at the town level were acquired by interpolation from 9 governmental meteorological stations (district-level) and 6 experimental stations across Shanghai. The DNDC model provides 7 formats of climate data to select, and we choose the most common one, which requires daily precipitation, daily max temperature and min temperature as inputs. The soil properties dataset at the town level was obtained from the 3

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Table 1 The detail management practices of the simulation scenarios. Simulation scenarios

CK 70%CK 50%CK CK + MI 70%CK + 50%CK + 70%CK + 70%CK + 50%CK + 50%CK + 70%CK +

MI MI MI MI MI MI MI

+ + + + +

50%SR 100%SR 50%SR 100%SR 50%SR + DP

Management practices Fertilization rate (kg N·ha−1)

Irrigation method

Straw return

Plowing method

300 210 150 300 210 150 210 210 150 150 210

FI FI FI MI MI MI MI MI MI MI MI

No SR No SR No SR No SR No SR No SR 50% SR 100% SR 50% SR 100% SR 50% SR

NP NP NP NP NP NP NP NP NP NP DP

Note: FI indicates flooding irrigation; MI indicates moistening irrigation; SR indicates straw returning; NP indicates normal plowing (10 cm) and DP indicates deep plowing (30 cm).

optimized scenarios or other potential scenarios (i.e., 80%CK, 80%CK + MI) can sustainably maintain the rice yields. 3. Results and discussion 3.1. Model validation Observed CH4 and N2O emissions from the experimental paddy field in the 2013 rice season were compared with the DNDC simulation results to perform the model validation test. The results indicated that the simulated CH4 (R2 = 0.76, ME = 0.71) and N2O (R2 = 0.71, ME = 0.67) flux pattern was consistent with the observed CH4 and N2O emission dynamics (Figs. 1 and 2). As shown in Fig. 1, two CH4 emission peaks appeared on the 40th and 70th days in both simulations and observations. The lowest CH4 fluxes occurred in the period between the 50th~60th days, which was the drainage aeration period for the paddy field. DNDC simulated the increase in soil Eh when soil oxygen was the dominant electron acceptor following the drainage event. The model thereby simulated low CH4 fluxes due to prohibited CH4 production and increased CH4 oxidation. The simulated CH4 flux varied from −0.02 to 5.48 kg·ha−1·day−1, which agreed well with the observed range of −1.04-5.67 kg·ha−1·day−1. Two observed N2O peaks were captured by the DNDC model (Fig. 2). The first peak with flux of 0.06 kg·ha−1·day−1 appeared after

Fig. 2. Observed and simulated N2O emissions from experimental paddy field (The black arrow indicates fertilization time and the red arrow indicates drainage period).

basal fertilization, and the second peak with the flux up to 0.16 kg·ha−1·day−1 appeared during the aeration period. The corresponding N2O peaks simulated by DNDC were 0.04 and 0.17 kg·ha−1·day−1, respectively, which were very close to the field observations. Both the simulations and the observations showed that the highest N2O peak appeared during the drainage period (Fig. 2). DNDC simulated this peak because both nitrification and denitrification were stimulated due to the conversion of the soil conditions from anaerobic to aerobic and the increased nitrate production driven by the drainage (Li et al., 2005). However, the model did not capture the observed third N2O peak induced by the second topdressing. This discrepancy may be because the second topdressing was applied after reflooding (Fig. 2). While the model simulated anaerobic soil conditions following flooding that prohibited nitrification and N2O production, the O2 concentration in the topsoil layers could be relatively higher, permiting nitrification and hence nitrification-induced N2O production. During the rice season, the observed CH4 total flux reached 172.15 kg·ha−1. The simulated CH4 seasonal flux deviated from observation by 15%. Similarly, the simulated N2O seasonal flux deviated from observation by 19%, while a 1.32 kg·ha−1 N2O seasonal flux was measured in the field observation (Fig. 3). The simulated results showed that the DNDC model slightly overestimated the seasonal CH4 and N2O emissions from the experimental paddy field in the 2013 rice season. In addition, the DNDC model estimated rice yields in the experimental

Fig. 1. Observed and simulated CH4 emissions from experimental paddy field (The black arrow indicates fertilization time and the red arrow indicates drainage period). 4

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N2O emissions were higher for the towns located outside the city border in comparison with the towns inside (Fig. 4) primarily due to the much larger rice cultivation area in the towns outside of the city border. Spatial variations in soil properties also contributed to the regional variation in CH4 and N2O emissions. Simulated rates (in kg C or N ha−1) of CH4 and N2O emissions tend to be high in regions with high SOC because high SOC could facilitate production of DOC, which is a substrate for both methanogenesis and denitrification (Deng et al., 2014; Li et al., 2004). 3.3. Sensitivity analysis Eight input parameters were selected for the sensitivity analysis to evaluate the impacts on rice yields, CH4 and N2O emissions. The SI value was calculated and used to quantify the sensitivity of the simulated results to the selected parameters (Table 2). The results indicated that rice yield was mainly sensitive to fertilization. The urea application rate had a negative impact on rice yield, whereas rice yield was positively correlated with the organic fertilization rate. This results means that a urea application rate over 240 kg N·ha−1 exceeded the rice's actual demand and even depressed rice production. In contrast, if only organic fertilizer is used, then more organic fertilizer (over 300 kg N·ha−1) should be applied to support optimal rice yield because the organic fertilizer is relatively less efficient in supporting rice productivity. The rice yield had slight sensitivity to precipitation and the soil clay fraction. CH4 emissions from paddy fields had a positive relationship with the organic fertilizer rate, the straw returned fraction, the tillage depth and the SOC content and a negative relationship only with the soil clay fraction. N2O emissions were positively correlated with precipitation, the urea rate, the tillage depth and the SOC content and negatively correlated with the soil clay fraction and the soil pH. The sensitivity factors affecting CH4 and N2O emissions were different because of the differences in productive conditions and generative processes for CH4 and N2O (Li et al., 2004). CH4 is produced from the decomposition of SOC and methanogenesis under anaerobic conditions (e.g., flooded paddy soil conditions). DNDC predicted the increases in CH4 emissions would occur when adding more organic fertilizers or straw, increasing SOC content, or increasing tillage depth (Table 2) because these changes can increase the substrates for CH4 production by adding more organic C into the soil or promoting SOC decomposition. N2O is an intermediate product of nitrification and denitrification, and conditions influencing N2O production include SOC and mineral N availability, soil pH, precipitation, and O2 concentration (Biddoccu et al., 2014; Klaus et al., 2013). Therefore, the sensitivity analysis indicated that soil pH, precipitation, SOC content, and the application rate of urea are important factors affecting N2O emissions. According to the results of the sensitivity analysis, field management practices related to these sensitivity factors should significantly affect CH4 and N2O emissions from paddy fields. Therefore, four field management variables, including fertilization rate, irrigation method, straw returned fraction and plowing depth were selected to construct 11 field management scenarios and then evaluate the integrated impact of the different field management practices on CH4 and N2O emissions from paddy fields in Shanghai. Sensitivity analysis is an important functional module of the DNDC model, providing an efficient approach to identify the key field management practices that regulate C and N processes in agroecosystems.

Fig. 3. The observed and simulated seasonal CH4 and N2O flux from the experimental paddy field.

paddy field well, as the observed and simulated rice yields were 8588 and 8541 kg·ha−1, respectively. The validated results indicated that the current version of DNDC 9.5 is able to simulate the emission patterns and seasonal fluxes of CH4 and N2O from typical flooded paddy fields in Shanghai suburbs under traditional management practices. Numerous published studies have demonstrated the validity of the DNDC model in simulating and predicting CH4 and N2O emissions from flooded paddy fields under various agricultural management practices, including different fertilization methods, irrigation methods, residue management practices and soil conditions (Chen et al., 2016; Fumoto et al., 2008; Li et al., 2005). In the past two decades, the DNDC model has been applied to Chinese paddy fields across sites, regional and national scales with encouraging results (Cai et al., 2003; Li et al., 2006; Tian et al., 2018; Yu et al., 2011). The version we utilized in this study includes all of the previous modifications and improvements. Therefore, we assumed that the Version 9.5 DNDC model used in this study is capable of simulating CH4 and N2O emissions from flooded paddy fields in Shanghai in our regional upscaling and scenario simulations. 3.2. CH4 and N2O emissions from paddy fields in Shanghai The regional simulation was conducted at the town level, and we simulated 101 towns with rice cultivation. The CH4 emissions from paddy fields in Shanghai are shown in Fig. 4a. Seasonal CH4 emissions varied from 1 to 1195 tons across the simulated towns. The lowest CH4 emission was simulated for the town of Qianqiao in the district of Fengxian, and the highest emission was simulated for the town of Tinglin in the district of Jinshan. The total CH4 emissions from the paddy fields in Shanghai were 33,495 tons during the rice-growing season in 2013. There were 19 towns where seasonal CH4 emissions exceeded 600 tons, and the total emissions from these towns accounted for approximately 50% of the total CH4 emissions from the paddy fields in Shanghai. Seasonal N2O emissions from the simulated towns ranged from 0 to 10 tons (Fig. 4b). The lowest and highest total seasonal N2O emissions appeared in the towns of Qianqiao and Tinglin, respectively. Total N2O emissions from the simulated paddy fields were 185 tons during the ric- growing season in 2013. There were 11 towns where total seasonal N2O emissions were higher than 5 tons, and the sum of the N2O emissions from these 11 towns accounted for approximately 45% of the total N2O emissions from the simulated paddies. The total simulated seasonal CH4 and N2O emissions from paddy fields varied largely across the 101 simulated towns, with coefficient variations of 95% for CH4 emissions and 125% for N2O emissions. The regional variations in the total seasonal CH4 and N2O emissions were primarily driven by the differences in rice cultivation areas. CH4 and

3.4. Scenario simulation and mitigation of CH4 and N2O emissions In the scenario simulation tests, CH4 and N2O emissions from paddy fields in the Shanghai region were simulated under the 11 different management scenarios using the DNDC model, and the mitigation potential was calculated (Table 3). The simulation results demonstrated that fertilization reduction could significantly reduce N2O emissions while slightly reducing CH4 emissions, and the mitigation potential was 5

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Fig. 4. CH4 (a) and N2O (b) emission map from paddy fields in Shanghai (Unit: ton).

positively correlated with the fertilizer reduction rate. Changing the irrigation method from flooding to moistening irrigation could elevate the soil Eh and increase soil CH4 oxidation because atmospheric oxygen is more easily transported into surface soil layers when the water table fluctuates around the soil surface under moistening irrigation, thereby largely decreasing CH4 emissions. This practice slightly increased N2O emissions because N2O production from denitrification could be elevated under moistening irrigation compared with continuous flooding that usually facilitates N2 production (Li et al., 2005). CH4 and N2O emissions were both significantly reduced by fertilization reduction combined with moistening irrigation in paddy fields. Straw returning was another effective measure that could reduce N2O emissions from paddy fields. However, CH4 emissions would increase under straw return because straw decomposition increases soil DOC concentration and subsequently CH4 production. In addition, the simulation results indicated that deep plowing had no mitigating effect on CH4 and N2O emissions from paddy fields. Reducing fertilization and straw returning

Table 2 Sensitivity analysis with DNDC model. Input parameters

Baseline

Precipitation (mm) Urea rate (kg N·ha−1) OF rate (kg N·ha−1) Straw return (fraction) Tillage depth (cm) SOC content (kg C·kg−1) Soil clay (fraction) Soil pH

1105 300 300 50% 20 0.015 0.43 7.25

Range

SI

884–1326 240–360 240–360 40–60% 16–24 0.012–0.018 0.34–0.52 5.80–8.70

Rice yield

CH4

N2O

−0.05 −0.18 0.15 0.02 0.00 0.03 −0.06 0.00

−0.03 0.00 0.32 0.94 0.34 0.58 −0.21 0.02

1.48 0.51 −0.02 0.02 0.23 0.78 −0.28 −2.37

Note: OF indicates organic fertilizer, SOC indicates soil organic carbon.

Table 3 Mitigation potential of CH4 and N2O emissions from paddy fields in Shanghai with DNDC model scenario simulation tests (Unit: ton). Management scenarios

CK 70%CK 50%CK CK + MI 70%CK + 50%CK + 70%CK + 70%CK + 50%CK + 50%CK + 70%CK +

MI MI MI MI MI MI MI

+ + + + +

50%SR 100%SR 50%SR 100%SR 50%SR + DP

CH4

N2O

Integrated (CO2-equivalent)

Total flux

Mitigation potential

Total flux

Mitigation potential

Total flux

Mitigation potential

33,495 32,270 31,723 22,539 21,356 20,538 24,963 26,726 23,411 24,504 27,810

– 4% 5% 33% 36% 39% 25% 20% 30% 27% 17%

185 156 134 195 164 145 150 148 137 132 172

– 16% 27% −5% 11% 22% 19% 20% 26% 28% 7%

892,468 853,160 833,162 621,442 582,727 556,679 668,827 712,194 626,170 652,074 746,411

– 4% 7% 30% 35% 38% 25% 20% 30% 27% 16%

(94%) (95%) (95%) (91%) (92%) (92%) (93%) (94%) (93%) (94%) (93%)

(6%) (5%) (5%) (9%) (8%) (8%) (7%) (6%) (7%) (6%) (7%)

Note: CK indicates traditional management practices in paddy fields of Shanghai; MI indicates moistening irrigation; SR indicates straw returning; DP indicates deep plowing. Percentage in parentheses indicates the relative contribution of CH4 or N2O to the integrated emissions. 6

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Shanghai could be significantly reduced by 33% while maintaining optimal rice yields. However, only four routine field management practices have been taken into account in this study, and the best management scenario, “80%CK + MI”, is the best only in our constructed scenarios. More factors, such as rotation systems, different fertilizers and cultivars, etc. should be considered to identify the BMP for paddy fields. Moreover, only CH4 and N2O emissions were selected as the criteria for evaluating different management practices in this study. The total balance of soil C and N pools should also be taken into account in identifying the BMP in agroecosystems. To achieve this goal, the process-based biogeochemical DNDC model is an appropriate tool. 4. Summary In this study, we tested the DNDC model against the CH4 and N2O emissions measured from an experimental paddy field in a Shanghai suburb and applied the model to quantify CH4 and N2O emissions from paddy fields in Shanghai. The DNDC model accurately simulated the seasonal patterns of CH4 (R2 = 0.76, ME = 0.71) and N2O (R2 = 0.71, ME = 0.67) fluxes from the experimental paddy fields during the 2013 rice-growing season. According to the regional simulations of the paddy fields in Shanghai, the total seasonal CH4 and N2O emissions during the 2013 rice-growing season were 32,300 and 175 tons, respectively, and showed large variations across the 101 simulated towns. The simulations under different scenarios indicated that alternative field management practices, such as “80%CK + MI”, have strong potential (33%) to reduce integrated emissions of CH4 and N2O from Shanghai paddy fields. However, we note that the model was only tested against the measured CH4 and N2O emissions during one rice-growing season under local traditional practices. Therefore, there remain uncertainties in the simulated regional patterns of CH4 and N2O emissions as well as mitigation efficiencies of different farming management practices due to limited model validation. To verify the simulated results from this study, further studies testing the model against more CH4 and N2O measurements under alternative treatments and different environmental conditions are needed.

Fig. 5. Prediction of rice yields with DNDC model for a 50 years' cultivation with different management practices.

would change the soil C and N pool storage, which are the important substrates for CH4 and N2O production (Wang et al., 2017). Straw return improves the SOC content of paddy soil, which has been shown in various field observations (Bhattacharyya et al., 2012; Cui et al., 2017). Replacing flooded irrigation with moistening irrigation alters the soil water content and further affects the soil redox potential for decomposition and denitrification processes (Wang et al., 2011; Yang et al., 2012). Furthermore, the decomposition of crop straw and deep plowing would affect the soil's physical properties and may influence the release rate of CH4 and N2O from soil into the atmospheric environment. To compare the integrated mitigation potential of the 11 management scenarios on CH4 and N2O emissions, the CO2-equivalent for CH4 (25) and N2O (298) was calculated (Table 3). Given that yield reduction was observed in all “50%CK” scenarios, “70%CK + MI” was deemed the best field management practice among the 11 simulated scenarios, with an integrated mitigation potential of 35%, a CH4 mitigation potential of 36% and a N2O mitigation potential of 11%. However, this best management practice was identified from a 3-year simulation with a spin-up time of 2 years, which may not be long enough to reach equilibrium of the soil C and N pools and to evaluate sustainable management practices. Therefore, we conducted 50-year simulations under different management practices to evaluate if “70%CK + MI” can sustainably maintain rice yields. The long-term simulations indicated that the rice yields were slightly reduced after approximately 10 years under the “70%CK + MI” scenario (Fig. 5). The consistent rice yields during the initial several years were probably due to the large initial soil N pool. For sustainable yields, “80%CK + MI” should better than “70%CK + MI”. The “80%CK + MI” scenario also mitigates GHG emissions and showed an integrated mitigation potential of 33%, a CH4 mitigation potential of 35% and a N2O mitigation potential of 5% in the regional simulation of the 2013 rice season. Based on these results, the current management practices in paddy fields in Shanghai should be adjusted by reducing the N application rate by 20% (total N application rate of 240 kg N/ha) and changing flooding irrigation to moistening irrigation for sustainable long-term cultivation of paddy rice. The over-fertilization in Shanghai suburbs can be attributed to two causes. First, farmers can obtain a certain amount of organic fertilizer for free from the local agricultural department. Second, farmers do not know the optimum fertilization rate for paddy rice under different soil conditions. Therefore, they simply apply as much fertilizer as possible to ensure maximum rice yields. The local agricultural departments of Shanghai should promote the application of these beneficial field management practices in local rice cultivation. As a result, integrated emissions of CH4 and N2O from paddy fields in

Declaration of Competing Interest The authors declared that they have no conflicts of interest to this work. We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the manuscript entitled, “Modeling CH4 and N2O emission patterns and mitigation potential from paddy fields in Shanghai, China with the DNDC model”. Acknowledgements We thank two anonymous reviewers for their constructive comments. This study was supported by the Shanghai Sailing program (19YF1442900), the SAAS Program for Excellent Research Team (SPERT-2017A03) and the National Natural Science Foundation of China (No.31770482). In memory of Prof. Changsheng Li, we especially thank him for his enthusiasm and dedication to DNDC research and education. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.agsy.2019.102743. References Abdalla, M., Jones, M., Yeluripati, J., Smith, P., Burke, J., Williams, M., 2010. Testing

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