Application of the DNDC model to estimate N2O emissions under different types of irrigation in vineyards in Ningxia, China

Agricultural Water Management 163 (2016) 295–304

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Agricultural Water Management journal homepage: www.elsevier.com/locate/agwat

Application of the DNDC model to estimate N2 O emissions under different types of irrigation in vineyards in Ningxia, China Yajie Zhang a , Haishan Niu a,∗ , Shiping Wang b , Kun Xu c , Rui Wang c a b c

College of Resources and Environment, University of Academy of Sciences, No.19A Yuquan Road, Beijing, China Institute of Tibetan Plateau Research, Chinese Academy of Sciences, No.16 Lincui Road, Beijing, China School of Agriculture, Ningxia University, No. 489 Helan Mountain West Road, Ningxia, China

a r t i c l e

i n f o

Article history: Received 21 August 2014 Received in revised form 1 October 2015 Accepted 5 October 2015 Keywords: Nitrous oxide Drip irrigation DNDC model Vineyard Uncertainty analysis

a b s t r a c t Furrow irrigation, which can be combined with fertilisation by dissolving solid fertilisers in irrigation water, is the most common practice in vineyards in the Ningxia Hui Autonomous Region, and drip irrigation and fertigation have been employed in some areas. These irrigation methods and their corresponding fertilisation schemes may affect nitrous oxide (N2 O) emissions from the soil. Therefore, it is important to use a model to simulate differences in N2 O emissions and identify farmland management methods that limit N2 O emissions. During the July–August 2012 and July–September 2013 growing seasons, the denitrification and decomposition (DNDC) model was tested against experimental N2 O emissions data from vineyards in Yuquanying, Yongning, Yinchuan, Ningxia. After model validation, the simulated differences in emissions between furrow irrigation and drip irrigation were 9.86 ± 0.845 and 0.966 ± 0.464 kg ha−1 a−1 in 2012 and 2013, respectively. Thus, the emissions were reduced by approximately 72.5% and 52.4% in 2012 and 2013. In the regional simulation, the Global Warming Potential (GWP) of the annual N2 O emission reductions reached 27,749,760 ± 5,489,160 kg CO2 -equivalents after the vineyards in the Ningxia Autonomous Region were converted from furrow irrigation to drip irrigation. In conclusion, the DNDC model, which proved to be a powerful tool for addressing the efficacy of alternative management practices in vineyards, revealed that N2 O emissions can be reduced by adopting drip irrigation systems rather than traditional furrow irrigation systems. © 2015 Elsevier B.V. All rights reserved.

1. Introduction Nitrous oxide (N2 O) is an important greenhouse gas that occurs in trace amounts in the atmosphere. In the last 100 years, the contributions of N2 O to the greenhouse effect reached 5%. Compared with carbon dioxide (CO2 ), methane (CH4 ) and other greenhouse gases, N2 O has a longer reserve time and greater warming potential. Thus, the potential effect of N2 O on warming over the next 100 years is approximately 190–270 times that of CO2 and 4–21 times that of CH4 (Wang et al., 1994). The most recent report from the Intergovernmental Panel on Climate Change states that the radiation force caused by human activities has reached 2.29 W m−2 , with N2 O emissions of 0.17 W m−2 in 2011 (IPCC, 2013). The concentration of N2 O in the atmosphere has reached 324 ␮g kg−1 , the highest concentration in 800,000 years, is increasing annually at a rate of nearly

∗ Corresponding author. E-mail: [email protected] E-mail address: [email protected] (Y. Zhang). http://dx.doi.org/10.1016/j.agwat.2015.10.006 0378-3774/© 2015 Elsevier B.V. All rights reserved.

0.3%, and is predicted to reach 3.5 × 10−4 − 4.0 × 10−4 mg kg−1 by 2050 (Wang et al., 2000). Agricultural soil is a major source of N2 O emissions (Wang and Jiang, 2012). Globally, estimated N2 O emissions have reached 17.7 Tg N2 O–N a−1 , and the total N2 O emissions from agricultural soils have reached approximately 3.3 Tg a−1 , representing approximately 45% of the total emissions from anthropogenic sources (Mosier and Kroeze, 1998; Bouwman, 1990; Kroeze et al., 1999). Therefore, reducing N2 O emissions from agricultural soils is a subject that requires extensive research. In China, the planting area for grapes reached 570,000 ha in 2012, and Ningxia became one of the major wine-producing regions in the country. In the Ningxia Hui Autonomous Region, more than 34,000 ha are used for cultivating grapes, with the Yellow River irrigation area accounting for approximately 20,000 ha where furrow irrigation is widely used with fertiliser dissolved in the irrigation water. However, in some areas, drip irrigation and fertigation technologies have been implemented. Using furrow irrigation for large areas of land may result in large concentrations of N2 O emissions; therefore, it is particularly important to study the abilities of dif-

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ferent irrigation methods to reduce the effects of N2 O emissions. Previously, some field experiments were conducted to demonstrate that drip irrigation could decrease N2 O emissions while simultaneously improving water and fertiliser use for plant production relative to furrow irrigation in tomato and melon fields (SánchezMartín et al., 2008; Kennedy et al., 2013). Due to the lack of actual observation reports, the use of mathematical models to simulate N2 O emissions under various vineyard soil conditions in Ningxia is a primary method for estimating emission reductions under different irrigation methods. Currently, various mathematical models are used to estimate N2 O emissions from farmland soils. These models include the denitrification and decomposition (DNDC) model, which is a process-based model that has been developed to estimate carbon and nitrogen biogeochemistry in agro-ecosystems (Smith et al., 2010). DNDC was originally developed to simulate N2 O emissions from cropland soils in the US (Li et al., 1992a). From the time of its initial development, classical laws of physics, chemistry and biology and empirical equations generated from laboratory studies have been incorporated into this model to quantify each specific geochemical or biochemical process (Giltrap et al., 2010). Currently, the DNDC model can be used to simulate crop growth, soil temperature, soil moisture content, soil carbon dynamics, nitrogen leaching, and greenhouse gas emissions in various ecosystems around the world and represents a bridge between trace gas emissions and basic ecological drivers (Wang et al., 2008; Li, 2000). By including the climate, soil, crop and agricultural activity input parameters, the DNDC model can be used to simulate daily (drought period) or hourly (rainfall period) N2 O emissions (Wang et al., 2011). In addition, the seasonal N2 O flux variations and agricultural regional soil N2 O emissions can be simulated quarterly or yearly (Xu et al., 2000). In this study, the DNDC model was used to simulate N2 O emissions based on actual measurements and to evaluate whether the DNDC model is suitable for local agro-ecosystems. The objectives of this study were to (1) compare N2 O emissions from drip irrigation and furrow irrigation systems in a vineyard, (2) simulate N2 O emissions from vineyards under different irrigation systems based on model simulations and field observations, (3) validate DNDC model simulations to determine their applicability to local vineyards, (4) assess the efficacy of alternative agricultural practices under different climate scenarios for reducing N2 O emissions and (5) provide a theoretical basis for evaluating the comprehensive eco-environmental effects of different irrigation regimes.

2. Materials and methods 2.1. Natural conditions This experiment was conducted at the Vine and Wine Engineering Research Centre of Ningxia University from July–August 2012 and July–September 2013 (Fig. 1). The site (38◦ 14 21 N, 106◦ 01 43 E) is located in Yuquanying, Yinchuan City, in the Ningxia Hui Autonomous Region, with an elevation of 1,300 m and a mean atmospheric pressure of 887 hPa on sunny days. The soils in the study area, which are mainly derived from alluvial deposits, are classified as sierozems with a sandy loam texture. The main soil physicochemical properties were obtained from literature (see Table 1). The study area also has a typical warm temperate continental monsoon climate, and the mean annual temperature is greater than or equal to 10 ◦ C. The mean annual precipitation is 193.4–202.2 mm, and most of the precipitation occurs during the grape growing season. The mean annual reference evaporation is 1,787.3 mm, the sunshine hours per year is 2,851–3,106 h, and the average daily temperature is 13.6 ◦ C. The large temperature

difference between day and night promotes the synthesis and accumulation of organic matter, which is suitable for crop growth (Su and Wang, 2005). Planting of the vineyard in this area began in the 1980s. 2.2. Experimental design The drip irrigation and furrow irrigation study was based on an experimental design proposed by the Vine and Wine Engineering Research Centre of Ningxia University. A split-plot design, with the irrigation regime as the main plot treatment and fertilisation as the sub-plot treatment, was adopted in the experiment. The DI treatment block (drip irrigation, fertigation) and FI treatment block (furrow irrigation, water dissolved fertiliser) were established on the east and west sides and were completely isolated by a levee. In each block, both the ridge and furrow were sampled. Static chambers were placed on both sides (DI vs FI) for the purpose of block sampling. Five blocks were implemented (Fig. 2). Four-year-old Cabernet Sauvignon grape vines that were grown using a singlearm trellis with continuous cultivation were studied. 2.3. In situ observations In 2012, the closed static chamber method was used to collect N2 O. The height of the chamber was 0.4 m, and the cross-sectional area of the chamber was 0.16 m2 (0.4 m × 0.4 m). The gas samples were collected mid-morning (0900–1100 Greenwich Mean Time +8) when the soil temperature was close to the mean daily soil temperature (Parkin and Kaspar, 2004). Gas measurements were obtained in two zones (ridge and furrow), and a weighted average of the two sampling zones was used to estimate the total crop-bed emissions for each sampling date. During sampling, the temperature in the chamber was measured using a temperature recorder panel, and a large syringe was used to collect 100 ml gas samples from each static chamber 5 times per day at 10-min intervals. The gas samples were then transferred to gas sampling bags for temporary storage and transport. The sampling dates and management events are shown in Appendix A. An Agilent 7890A gas chromatograph equipped with dual gas channels, a hydrogen flame ionisation detector (FID) and a double thermal conductivity detector (TCD) was used by switching a valve to detect the N2 O, CO2 and CH4 concentrations in the gas samples. The rate of N2 O emissions was calculated using the formula F = M/V0 × P/P0 × T0 /T × H × dCt/dt, where F is the N2 O flux per hour (␮g m−2 h−1 ); M is the molar mass of the gas (g mol−1 ); V0 is the molar volume of the gas under standard conditions (22.41 × 10−3 m3 ); T0 and P0 are the absolute air temperature and pressure under standard conditions (273.15 K and 1013.25 hPa); P is the pressure at the sampling site (hPa); T is the absolute temperature (K); dCt/dt is the rate of variation of the N2 O concentration, which can be measured in the static box; and H is the height of the static chamber (m) (Dong et al., 2007; Dai et al., 2011). The underground volumetric soil moisture contents at depths of 0–5 cm and 0–10 cm were tested at each sampling site using TZS-II Global Positioning System Time Domain Reflectometry. These values were measured at three different points near each collar and were converted to the percent of water-filled pore space (%WFPS) using the known bulk density and a particle density value of 2.65 g cm−3 to obtain the pore volume. The 0–10 cm surface soil samples were collected twice during the experiment and were analysed at the Ningxia Agricultural Institute of Survey and Design using an AAIII continuous flow analyser. Temperature and precipitation were obtained from the local meteorological station (see Fig. 3). In 2013, gas samples were acquired mid-morning (0900-1100 Greenwich Mean Time +8), as described above. Samples of 200 ml were collected from each static chamber 4 times at 10-min inter-

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Fig. 1. Location of the Ningxia Hui Autonomous Region and the study site in Ningxia, China.

Table 1 The main soil physicochemical properties. Based on the nutrient classification standards from the second nationwide general soil survey, the soils in the study area have the following characteristics: high pH values and degree of desertification, low field capacities, and low nutrient levels. In particular, the soil organic matter content is less than 5 g kg−1 , which is characteristic of low organic carbon accumulation in semi-arid regions with barren sandy soils (The officer for the Second National Soil Survey of China, 1998). Due to furrow irrigation (FI), fertilisation and repeated tillage in the vineyards, the nitrogen and potassium contents in the subsurface are higher than at the surface (Wang et al., 2012). Depth (cm)

Organic matter (g kg−1 )

Hydrolysable nitrogen (mg kg−1 )

Available phosphorus (mg kg−1 )

Available potassium (mg kg−1 )

pH

Total salt (g kg−1 )

Bulk density (g cm−3 )

Water capacity (%)

0–20 20–40

3.77 1.58

11.20 48.04

18.76 16.66

107.50 115.00

8.18 8.24

0.29 0.29

1.59 1.70

23.39 15.57

Fig. 2. Description of the experimental area and a schematic of the experimental design. The spacing within rows and between rows was 1.0 m × 3.0 m. Boxes represent the placement of collars at a distance of 0.2 m from the plants in the furrows, and the collars were only moved during field operations. The width and depth of the furrows were 1 m and 0.15 m, respectively, and the width of the ridge was 2 m. The drip irrigation belts were tied on the fence frame 0.2 m from the ground.

vals. The sampling dates and management events are shown in Appendix B. The volumetric soil moisture contents at depths of 0–5 cm and 0–10 cm at each sampling site were determined using a portable HS2 TDR. These values were converted to%WFPS. An Agilent 7890A gas chromatograph and AAIII continuous flow analyser were used to analyse the gas samples and the surface soil samples obtained at a depth of 0–10 cm. Temperature and precipitation were provided by the local meteorological station (see Fig. 3).

2.4. Data processing ANOVA was performed to identify the effects of the management system for the corresponding sampling events. Normality was tested using the Shapiro–Wilk method, and the homogeneity of variance was tested during the one-way ANOVA. Data transformations were not required. The differences between the means within each management system were analysed using repeated MANOVA

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Fig. 3. Daily mean air temperatures and mean precipitation during the sampling period in 2012 (a) and 2013 (b).

and Tukey–Kramer pair-wise comparisons in IBM SPSS Statistics 20. Significance was accepted when H0 was rejected at a probability level of P ≤ 0.05. The relationships among the measured parameters were assessed using correlation analyses, and Pearson’s correlation coefficients were calculated on a per-event basis. The coefficient of determinant R2 was calculated to evaluate the simulation results of the DNDC model. As the R2 value approached 1, the fit between the DNDC simulated and measured values improved. Microsoft Excel 2010 was used for data manipulation. 2.5. DNDC model simulation DNDC ver. 9.5 (http://www.dndc.sr.unh.edu/) was used to model N2 O emissions from the vineyard in combination with the daily climate data obtained from the local meteorological station and the farmland management measure information obtained from the Vine and Wine Engineering Research Centre of Ningxia University. This version of DNDC includes a grape parameter named “crop 42”. We fixed the default crop growth period from 10 years to 12 years, which is more consistent with the growth of local grapes. 3. Results and discussion 3.1. Modelling and validation The DNDC model is primarily used to calculate seasonal or yearly N2 O emissions. Therefore, fitting the degree of soil N2 O flux and the variation tendency of the influence factors is a prerequisite for applying the model. While in situ observations revealed a series of soil N2 O emission peaks, the synchronous change in N2 O flux, soil moisture and NO3 − content indicates that the N2 O emitted from the soil mainly originates from the denitrification process, which corresponds with the theoretical assumptions of the DNDC model. For the 2012 simulation, the DNDC model typically captured all N2 O emission peaks that occurred after the irrigation, rainfall and fertilisation events that were measured in situ (see Fig. 4). For DI, the simulated emission peaks emerged on the 188th and 203rd days, which accompanied the irrigation events that occurred on the 181st and 196th days. A simulation emission peak also appeared on day 210 due to rainfall on day 203, and the continuous rainfall following the 211th day resulted in a dramatic increase in the simulated emissions after the 218th day and an emission peak on day 221. Compared with the in situ measurements, the simulation results were delayed by 7 days. This time lag could be

related to uncertainties in the model due to groundwater dynamics or other processes (Nylinder, 2010). Due to the fertilisation event on 6/15 and the interactions between water and fertiliser, the N2 O emissions after the first DI were obviously larger. For FI, the simulated emission peaks coincided with the irrigation and precipitation events without any time lag when compared with the in situ observations. The emissions caused by irrigation were lower than those caused by rainfall, which potentially resulted from the limited N2 O emissions due to runoff during the early flooding period (Qin et al., 2013). When considering the variations in the N2 O flux, the adjusted R2 values of the model-simulated values and the in situ observation values were 0.562 (P < 0.05) and 0.098 (P > 0.05) for the DI and FI treatments, respectively. The correlation coefficient test demonstrated that the former correlation was significant (P < 0.01), which indicated that the N2 O emission trends from the model simulations and the in situ observations were similar under DI. Based on the above results, the DNDC model fit the N2 O emission well under DI; however, this simulation is not ideal. Under FI, the fit between the modelled DNDC values and the measured values requires further validation. Soil N2 O emissions are significantly affected by variations in factors such as soil moisture. Therefore, the degree of fit between the model-simulated values and measured values of soil moisture reflects the effects of climate, agricultural management measures and soil properties on N2 O flux and directly. These findings also illustrate the use of model fitting for elucidating the process of N2 O emissions. The DNDC model uses the water-filled pore space (WFPS) to numerically describe soil moisture. This value is the ratio of the soil moisture to the total pore volume of the soil and effectively reflects the effects of soil moisture on the diffusion rate of the gas and liquid phase (Williams et al., 1992; Le Roux et al., 1995). In the DI and FI treatments, the WFPS (0–10 cm) peaks corresponding to irrigation and precipitation events were simulated using the DNDC model (see Fig. 4). With respect to DI, each WFPS peak on the curve simulated by the DNDC model corresponds to the occurrence of irrigation or rainfall, and no time lag occurred in the simulation. With respect to FI, the degree of fit between the irrigation and rainfall events and the simulated WFPS values obtained from the DNDC model was high, with a 5-days delay. The experimental results revealed that the determination coefficient (R2 ) of the change curve reached 0.539 and 0.391 in the DI and FI treatments, respectively and that the P value was less than 0.05 only in the DI treatment. Consequently, the change tendencies and numerical values of the simulated and observed WFPS were very

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Fig. 4. The simulated and observed nitrous oxide (N2 O) emissions (a) and the simulated and observed water-filled pore space (WFPS, 0–10 cm) (b) under drip irrigation (DI) and furrow irrigation (FI) during the 2012 growing season. Arrows indicate the irrigation dates. The actual date of FI is approximately 2 days later than that indicated by the arrows because the experimental area is located at the far end of the vineyard. The simulation of WFPS (0–5 cm) is not shown. Mod.DI: simulated value of DI; Mod.FI: simulated value of FI; Obsd.DI: observed value of DI; Obsd.FI: observed value of FI.

similar, reflecting the effect of changes in the amount of precipitation, precipitation frequency, and irrigation precipitation on the soil moisture content in the upper 10 cm of soil in the DI treatment. In the simulation for 2013, the DNDC model captured most of the N2 O emission peaks that occurred after the irrigation, rainfall and fertilisation events measured in situ, and the simulated N2 O emission peaks occurred at nearly the same time as the peak N2 O emissions measured in situ (see Fig. 5). Under the DI treatment, all N2 O emission peaks were highly correlated with the precipitation events. With various amounts of precipitation, the simulations were characterised as a series of high or low emission peaks, and the two largest emission peaks resulted from the two heaviest rainfall events. In addition, no time delays were observed. Under the FI treatment, the emission peak caused by weeding on the 215th day was measured and effectively simulated. Under both DI and FI, the synchronous emission peaks caused by weeding resulted in a simulated emission peak on day 235. The emission peak on the 245th day resulted from a synergistic reaction between precipitation and furrow irrigation, and no obvious time lags were observed. For the variations in N2 O flux, the adjusted R2 values between the simulated values and in situ observations were 0.828 (P < 0.01) and 0.953 (P < 0.01) in the DI and FI treatments, respectively. Both treatments showed remarkable correlations (P < 0.01) that were supported by the correlation coefficient test, which indicated that the N2 O emission trends in the model simulations and the in situ observations were similar in the DI and FI treatments. These results indicate that the DNDC model can simulate variations in N2 O emissions under DI and FI well and that the simulation results are superior for DI relative to FI. Therefore, the degree of fit of the DNDC model not only directly indicates the effects of model fit when elucidating the N2 O emissions process but also reflects the effects of climate, agricultural management measures and soil properties on N2 O emissions. Thus, the DNDC model can be used to simulate N2 O emissions from the Ningxia vineyard soil. In the DI and FI treatments, the WFPS (0–10 cm) peaks corresponding to irrigation and precipitation events were simulated using the DNDC model, and no lag times were observed (see Fig. 5). The R2 values reached 0.479 and 0.636 in the DI and FI treatments, respectively, and both of the P values were less than 0.05. Consequently, the results of this simulation regarding the aspects of change and the numerical values of the WFPS reflect the soundness of the DNDC simulation for DI and FI. In summary, all of the above results demonstrate that the DNDC model reflects the trends in N2 O

flux and soil moisture with a higher degree of fit and provides a scientific basis for estimating N2 O emissions from the vineyard soils in Ningxia by using DNDC. 3.2. Sensitivity analysis The sensitivity of the model simulations to variations in certain parameters can be expressed as follows: S=

(O2 − O1 )/Oavg (I2 − I1 )/Iavg

where S is the sensitivity index; I1 and I2 are the minimum and maximum input parameters, respectively; Iavg is the average of I1 and I2 ; O1 and O2 are the output values that correspond to I1 and I2 ; and Oavg is the average of O1 and O2 . When the value of S is 1, the simulated values will change by the same proportion as the mean when the input value changes by a certain proportion relative to the mean. A negative value of S indicates a negative correlation between the simulated values and input parameters; and greater absolute values of S indicate greater effects of the input parameters on the simulation value. The S values can be used to compare the sensitivity of the model under different parameters. Because the effects simulated by the DNDC model with in situ observation data under DI are acceptable, the meteorological data and the agricultural management modes under DI are regarded as the foundations of the model parameter sensitivity analysis for 2013. This research mainly considers the effects of the input parameters related to meteorological factors (nitrogen concentration in precipitation), soil (clay content, bulk density, pH value, and the soil organic carbon content) and agricultural management measures (fertilisation depth, amount of manure amendments, and irrigation volume) on N2 O emissions under drip irrigation. The scenario-based (background) simulation was set up according to the climate, agricultural management of the local test site and edatope. In the alternative scenario (test), one main parameter was modified while the remaining parameters were unchanged and the background simulation was performed. By comparing the simulation results, the patterns and intensity of the responses of the simulated N2 O emissions to each of the input parameters can be obtained under different environmental conditions and when using different farmland management measures. This comparison allows us to provide recommendations for adapting existing irrigation methods and agricultural management modes to achieve

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Fig. 5. The simulated and observed nitrous oxide (N2 O) emissions (a) and the water-filled pore space (WFPS, 0–10 cm) (b) under drip irrigation (DI) and furrow irrigation (FI) in 2013. The WFPS (0–5 cm) data are not shown. Mod.DI: simulated value of DI; Mod.FI: simulated value of FI; Obsd.DI: observed value of DI; Obsd.FI: observed value of FI.

N2 O reduction. The sensitivity index values for main parameters are given in Table 2. Although this sensitivity analysis was not designed to include all environmental or management factors in the Ningxia vineyard, it illustrates the complexity of the relationship between management practices and biogeochemical outputs. The sensitivity analysis that was used in this research is a local sensitivity parameter analysis. The absolute value of the sensitivity index can be used to compare the relative importance of the input parameters (Wei et al., 2007; Walker et al., 2000). The sensitivity index values (absolute) of the parameters that affect N2 O emissions under drip irrigation decreased in the following order: pH value > irrigation amount > depth of chemical fertiliser > bulk density > content of soil organic carbon. In addition, the sensitivity index values of the nitrogen concentrations in the precipitation, the soil clay content and the amounts of organic fertiliser were low. The sensitivity analysis results demonstrate that soil N2 O emissions are mainly affected by the pH, depth of fertilisation, amount of irrigation, and soil organic carbon content. (1) Under neutral conditions, large amounts of N2 O are emitted from the soil. However, these emissions are limited under alkaline conditions because the optimum pH is approximately 8.5 for soil nitrifying bacteria and 6–8 for denitrifying bacteria (Estuardo et al., 2008). (2) As the fertilisation depth was increased from 0.5 cm to 15 cm, the amount of N2 O emissions decreased from 1.197 to 0.517 kg ha−1 a−1 . This decrease occurred because nitrification and denitrification mainly occur in the upper soil layers and because deeper fertilisation hinders these reactions (Estuardo et al., 2008). (3) Within a certain range, N2 O emissions increased as the soil organic carbon content and soil bulk density increased. In addition, N2 O producers increased when the supply of oxygen (O2 ) was insufficient, which resulted in increased denitrification (Weier et al., 1993). (4) N2 O emissions increased as the irrigation volume increased because the largest amounts of N2 O were generally emitted between 77 and 86% WFPS. Below this numerical range, the soil moisture and N2 O emissions were positively correlated (Simojoki and Jaakkola, 2000). In previous studies, factors such as the soil organic carbon content and soil pH were regarded as highly sensitive factors in the DNDC model, which is consistent with our research (Li et al., 1992b). Large N2 O emissions occurred under neutral and acidic conditions and limited emissions occurred under alkaline conditions. As the depth of fertiliser application increased from 5 cm to 25 cm, N2 O emissions decreased from 9.136 kg ha−1 a−1 to 0.344 kg ha−1 a−1 (a

decrease of 96.2%). Within a certain range, the N2 O emissions were positively correlated with the soil organic carbon content, bulk density and amount of irrigation, which suggests that reducing irrigation water use, increasing fertilisation depths and controlling the use of manure are appropriate strategies for regulating surface soil organic carbon based on how plant root systems absorb and utilise water and nutrients, which could be the most effective measures for reducing soil N2 O emissions. These parameters were also used in the uncertainty analysis and scenario analysis. 3.3. Uncertainty analysis Due to the considerable variations in moisture and the physicochemical properties of the soil, large variations were observed between the values measured simultaneously in situ; thus, the approximate uncertainty in the N2 O emissions estimates should be determined. The DNDC model provides tools for quantifying the uncertainties in simulations bounded by the error associated with misestimating some critical parameters. Three soil parameters, bulk density, soil organic carbon content and pH value, were primarily used in the Monte Carlo uncertainty analysis (Table 3). Based on the characteristics of the probability density distributions and the set values of these three parameters, random values for these parameters were extracted to establish the soil parameters before calculating the N2 O emissions to estimate the degrees of uncertainty in these combinations. For a 95% confidence level, the following formulas were used to estimate the uncertainty in N2 O emission rates for each scenario, and the uncertainties in the differences between the two emissions scenarios were determined as follows:



UNCERTAINTYflux

FI

= 1.96 ×

UNCERTAINTYflux

DI

= 1.96 ×

2 sFI

  UNCERTAINTYdiff = 1.96 ×

2 sDI

2 + s2 sFI DI

2

where UNCERTAINTYflux FI and UNCERTAINTYflux DI represent the uncertainty of the N2 O emission estimates in the FI and DI treatments, UNCERTAINTYdiff indicates the uncertainty of the difference 2 , s2 between the N2 O emission estimates for two scenarios, and sFI DI

0.016 0.066 0.119 -4.949 0.108 -0.424 0.095 0.486 1.209, 1.219, 1.229 1.185, 1.192, 1.215, 1.27 1.183, 1.19, 1.207, 1.22 1.808, 1.469, 0.781, 0.426 1.175, 1.17, 1.253, 1.335 1.197, 1.038, 0.731, 0.517 1.105, 1.16, 1.226, 1.255 0.748, 0.878, 1.306, 1.465 1, 2, 3 0.09, 0.14, 0.24, 0.29 1.35, 1.45, 1.65, 1.75 7, 7.5, 8.5, 9 0.0007, 0.0012, 0.0022, 0.0027 0.5, 5, 10, 15 900, 1,800, 3,600, 4,500 0.75, 1.5, 3, 3.75 0 0.19 1.55 8 0.0017 1 2,700 2.25 N concentration in rainfall (mg N L ) Clay fraction (%) Bulk density (g cm−3 ) Soil pH Soil organic carbon in surface soil (kg C kg−1 soil) Fertilisation depth (cm) Amount of manure amendment (kg C ha−1 ) Amount of water applied (cm)

1.197

Sensitivity index Analogue value (kg ha−1 a−1 ) Test value Baseline value (kg ha−1 a−1 ) Background value

Farmland management

Meteorological factor Edaphic factor

−1

Input parameter

Table 2 The sensitivity index values of the main parameters of the DNDC model in the drip irrigation (DI) treatment. Appropriate values are selected as test values for all parameters and are consistent with the actual situation.

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Table 3 The types of the probability density distributions and the degrees of uncertainty for soil parameters in the DNDC model. The degrees of uncertainty for soil parameters were obtained from related project protocol (available via the website http://www.climateactionreserve.org/how/protocols/rice-cultivation/http://www. climateactionreserve.org/how/protocols/rice-cultivation/ date accessed June 3, 2013). Parameter

Probability distribution function

Uncertainty

Bulk density SOC content pH

Log-normal Log-normal Normal

+/− 20% +/− 10% +/− 20%

are the variances in the N2 O emissions for FI and DI, respectively. To compare the model outcomes obtained using the new parameterisation method with the simulations under the default DNDC parameter set, the modified soil and crop library files were used for all DNDC simulations. Based on the model validation and uncertainty analysis, the DNDC model was used to simulate N2 O emissions when different irrigation methods were used. The following N2 O emission results were obtained: 3.742 ± 0.598 kg ha−1 a−1 in the DI treatment and 13.602 ± 1.034 kg ha−1 a−1 in the FI treatment, with a simulated emission difference of 9.86 ± 0.845 kg ha−1 a−1 for 2012. For 2013, the results were 0.878 ± 0.6 kg ha−1 a−1 under DI and 1.844 ± 1.167 kg ha−1 a−1 under FI, with a simulated emission difference of 0.966 ± 0.464 kg ha−1 a−1 . The corresponding results from the in situ observations were 11.62 ± 1.593 kg ha−1 a−1 under DI and 29.861 ± 3.287 kg ha−1 a−1 under FI for 2012 and 0.895 ± 0.197 kg ha−1 a−1 under DI and 4.272 ± 0.936 kg ha−1 a−1 under FI for 2013. The simulated results for daily N2 O fluxes obtained using different methods of irrigation accounted for 32.2% and 45.6% of the observed results during 2012 and 98.1% and 19.8% of the observed results during 2013. Except for the DI data, for which the simulated and observed results fit well in 2013, the other proportions were not in agreement with previously published results (72%) (Li et al., 1994). The simulation values were potentially lower than the observed values because of the weak fit of the model during the high-temperature period from July to September (Wang et al., 2009).

3.4. Scenario analysis By changing inputs and parameters, the DNDC model can be used to simulate the changes in N2 O emissions in agro-ecological systems under different scenarios and provide a basis for maintaining the soil carbon and nitrogen balance by using agricultural management practices that are designed to adapt to environmental changes. To analyse the effects of different amounts of irrigation and fertiliser on the variations of the N2 O fluxes from the Ningxia vineyard soil in 2013, we performed a scenario analysis by increasing or decreasing the quantity of DI by 50% and 100% or by altering the amount of fertiliser by 0.5-, 0.75-, 1.25-, or 1.5- fold while holding the remaining factors constant (Fig. 6). Under normal rainfall, the N2 O flux varied from 0.664 to 1.568 kg ha−1 a−1 as the amount of irrigation increased from −100% to +100% in 2013. Therefore, as the amount of water applied over a certain range increased, the simulated soil N2 O flux increased and was characterised as a smooth curve. A similar phenomenon was also observed with the application of fertiliser. Within a certain range, increasing the amount of fertiliser could increase the N2 O emissions. In addition, reducing the amounts of irrigation water and fertiliser while fulfilling the requirements for adequate grape growth could effectively decrease the soil N2 O emissions.

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Fig. 6. Simulation scenario analysis of the effects of different irrigation and fertiliser quantities on nitrous oxide (N2 O) flux under drip irrigation (DI) in 2013. x: times; fert: fertilisation.

3.5. Regional simulation Recent studies have shown that Yongning, Helan, Shizuishan, Wuzhong, and Qingtongxia are the best grape-growing districts in the Ningxia Autonomous Region. At Helan Mountain, the soils are mainly classified as light sierozems, the frost-free periods are longer than 180 days, the aridity indices are greater than 4.3, and the average amount of precipitation during the growing season is approximately 118 mm. In addition, Helan Mountain is located in the Yellow River irrigated area with convenient access to water (Wang et al., 2010). This study used soil parameter and farmland management data from 2013 and meteorological data from 1994 to 2013 to conduct a regional simulation and calculate the annual N2 O emission reductions that could result from altering the irrigation regimes in the vineyards of Ningxia. The 20-year simulation revealed that the annual N2 O emissions were 0.556 ± 0.174 kg ha−1 a−1 under DI and 5.91 ± 0.434 kg ha−1 a−1 under FI and that the annual difference between DI and FI was 5.353 ± 0.5 kg ha−1 a−1 , ranging from 0.353 kg ha−1 a−1 in 2013 to 9.109 kg ha−1 a−1 in 2000. If the FI vineyards were all converted to drip irrigation, the annual reduction in N2 O emissions would reach 93,120 ± 18,420 kg for the entire territory of Ningxia. The Global Warming Potential (GWP) is used to measure the potential effects of different greenhouse gases on the climate based on their radiative properties. In the 100-year GWP estimation, CO2 is typically considered to be a reference gas, and the warming effect of 1 kg of N2 O is 298 times that of 1 kg of CO2 (IPCC, 2007). Using the formula GWP(N2O) = fN2O × 298, the estimated GWP from reducing the annual N2 O emissions was 27,749,760 ± 5,489,160 kg CO2 -equivalents. Thus, compared with the FI treatment, DI may reduce N2 O emissions and reduce the effects of N2 O on the environment.

4. Conclusions Various irrigation methods may significantly affect soil N2 O emissions. Application of the DNDC model for simulating soil N2 O

emissions under different irrigation methods has become a focus of research. To explore this method, an experiment was conducted at the Vine and Wine Engineering Research Centre of Ningxia University in July–August 2012 and July–September 2013. In this study, the DNDC model performed better for DI during model validation, which indicated that it could be applied in Ningxia vineyards. Following sensitivity and scenario analysis, altering irrigation schemes and their accompanying fertilisation measures could significantly affect N2 O emissions, which exhibited an upward trend within a certain range. Based on in situ observations and model simulations, we determined simulated emission differences of 9.86 ± 0.845 kg ha−1 a−1 (approximately 72.5% reduction) for 2012 and 0.966 ± 0.464 kg ha−1 a−1 (approximately 52.4% reduction) for 2013. Furthermore, based on a regional simulation, the annual reduction in N2 O emissions reached 93,120 ± 18,420 kg after converting vineyards from FI to DI within the Ningxia Autonomous Region, which could also be expressed as the GWP of the annual reduction in N2 O emissions of 27,749,760 ± 5,489,160 kg CO2 -equivalents. Thus, compared with the FI treatment, DI can significantly reduce soil N2 O emissions and increase the emission reduction potential in Ningxia vineyards. However, research in this area remains insufficient, and the application of the DNDC model for Ningxia vineyards requires further verification.

Acknowledgements The authors are indebted to Prof. Shulan Cheng and Linsen Li at the College of Resources and Environment, University of the Chinese Academy of Sciences, Beijing, for providing them with laboratory access to analyse the gas samples. The authors would also like to thank Xudong Wu, Wenting Ma and Xuechun Huang at the School of Agriculture, Ningxia University, Ningxia, for supporting this research and Jing Ge at the Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, for assisting with the data processing. This study was funded by the Poverty and Environment Fund of Asian Development Bank: RETA-6422Calculating Carbon Benefits from Improved Land and Water Resource Management.

Y. Zhang et al. / Agricultural Water Management 163 (2016) 295–304

303

Appendix A. Management events and sampling schedule in 2012. The individual amounts of water applied in the DI and FI systems were 300 m3 ha−1 and 1,200 m3 ha−1 , respectively. The first and last irrigation events were conducted using furrow irrigation across the entire study area. Tillage practices typical of the area were used, and the weeding methods are not indicated in this table. Fertigation events were determined based on the N requirements of the grape crop throughout the season. The DI and FI fertilisation practices differed in timing and total N input. Date

Event

Treatment

4/19/2012 4/25/2012 5/15/2012

Irrigation Irrigation (fertigation) Irrigation (fertigation)

DI & FI DI DI

5/22/2012 6/9/2012 6/15/2012

Irrigation with fertiliser Irrigation Irrigation (fertigation)

FI FI DI

6/29/2012 7/1/2012 7/5/2012 7/14/2012

Irrigation Gas sampling Gas sampling Irrigation with fertiliser Irrigation Gas sampling Gas sampling Gas sampling Gas and soil sampling Gas sampling Gas sampling Soil sampling Irrigation Gas sampling Irrigation (fertigation) Irrigation Irrigation Irrigation (fertigation) Irrigation

DI DI & FI DI & FI FI DI DI & FI DI & FI DI & FI DI & FI DI & FI DI & FI DI & FI FI DI & FI DI FI FI DI DI & FI

7/15/2012 7/20/2012 7/25/2012 8/4/2012 8/5/2012 8/6/2012 8/7/2012 8/8/2012 8/25/2012 8/30/2012 9/26/2012 10/10/2012 11/28/2012

Fertiliser type

kg N ha−1

Urea Urea DAP Compound fertilisera

187.92 111.36 67.5 157.5

Urea DAP

69.6 27

Compound fertilisera

157.5

Manureb

54

Urea

69.6

a

14% N, 16% P, 15% K.

b

Organic matter content ≥40.0%, humic acid content ≥10%, moisture content ≤30%, NPK ≥5%.

kg C ha−1

720

Appendix B. Management events and sampling schedule in 2013. The individual amounts of water applied in DI and FI were 225 m3 ha−1 and 1,200 m3 ha−1 , respectively. The other management actions were the same as those in 2012.

a

Date

Event

Treatment

4/17/2013 4/25/2013

Irrigation Irrigation (fertigation)

DI & FI DI

5/15/2013

Irrigation (fertigation)

DI

5/20/2013 6/10/2013 6/15/2013 6/29/2013 7/2/2013 7/16/2013 7/20/2013 7/22/2013 8/1/2013

Irrigation with fertiliser Irrigation Irrigation (fertigation) Irrigation Irrigation with fertiliser Irrigation Irrigation Gas sampling Irrigation (fertigation)

FI FI DI DI FI DI FI DI & FI DI

8/5/2013 8/7/2013 8/8/2013 8/10/2013 8/15/2013 8/26/2013 8/27/2013 8/28/2013 8/29/2013 8/30/2013 9/15/2013 9/21/2013 9/23/2013 9/24/2013 9/25/2013 9/26/2013 11/25/2013

Gas sampling Gas sampling Gas sampling Irrigation Irrigation Gas sampling Gas and soil sampling Gas sampling Gas sampling Irrigation Irrigation (fertigation) Gas sampling Gas sampling Gas sampling Gas sampling Gas and soil sampling Irrigation

DI & FI DI & FI DI & FI FI DI DI & FI DI & FI DI & FI DI & FI FI DI DI & FI DI & FI DI & FI DI & FI DI & FI DI & FI

14% N, 16% P, 15% K.

b

15% N, 15% P, 15% K.

c

12% N, 15% P, 18% K.

d

Organic matter content ≥40.0%, humic acid content ≥10%, moisture content ≤30%, NPK ≥5%.

Fertiliser type

kg N ha−1

Urea DAP Urea DAP Compound fertilisera

69.6 27 104.4 27 157.5

Compound fertiliserb

56.25

Compound fertilisera

157.5

Compound fertiliserc Calcium nitrate

45 8.25

Manured

202.5

kg C ha−1

2,700

304

Y. Zhang et al. / Agricultural Water Management 163 (2016) 295–304

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