Identifying critical nitrogen application rate for maize yield and nitrate leaching in a Haplic Luvisol soil using the DNDC model

Science of the Total Environment 514 (2015) 388–398

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Identifying critical nitrogen application rate for maize yield and nitrate leaching in a Haplic Luvisol soil using the DNDC model Yitao Zhang a, Hongyuan Wang a, Shen Liu a, Qiuliang Lei a, Jian Liu b, Jianqiang He c, Limei Zhai a, Tianzhi Ren d, Hongbin Liu a,⁎ a

Key Laboratory of Non-point Source Pollution Control, Ministry of Agriculture, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China USDA-Agricultural Research Service, Pasture Systems and Watershed Management Research Unit, University Park, PA 16802, USA c Key Laboratory of Agricultural Soil & Water Engineering in Arid and Semiarid Areas of Ministry of Education, Northwest A&F University, Yangling, Shaanxi 712100, China d Institute of Agro-Environmental Protection, Ministry of Agriculture, Tianjin 300191, China b

H I G H L I G H T S • DNDC model could satisfactorily simulate maize yield in a Haplic Luvisol soil. • DNDC could satisfactorily simulate nitrate leaching in a Haplic Luvisol soil. • DNDC identified critical agronomic and environmental N rates to be 180–240 kg ha− 1.

a r t i c l e

i n f o

Article history: Received 4 November 2014 Received in revised form 21 January 2015 Accepted 6 February 2015 Available online xxxx Editor: Eddy Y. Zeng Keywords: Maize Yield Amount of nitrate leaching DNDC Critical nitrogen application rate

a b s t r a c t Identification of critical nitrogen (N) application rate can provide management supports for ensuring grain yield and reducing amount of nitrate leaching to ground water. A five-year (2008–2012) field lysimeter (1 m × 2 m × 1.2 m) experiment with three N treatments (0, 180 and 240 kg N ha−1) was conducted to quantify maize yields and amount of nitrate leaching from a Haplic Luvisol soil in the North China Plain. The experimental data were used to calibrate and validate the process-based model of Denitrification–Decomposition (DNDC). After this, the model was used to simulate maize yield production and amount of nitrate leaching under a series of N application rates and to identify critical N application rate based on acceptable yield and amount of nitrate leaching for this cropping system. The results of model calibration and validation indicated that the model could correctly simulate maize yield and amount of nitrate leaching, with satisfactory values of RMSE-observation standard deviation ratio, model efficiency and determination coefficient. The model simulations confirmed the measurements that N application increased maize yield compared with the control, but the high N rate (240 kg N ha−1) did not produce more yield than the low one (120 kg N ha−1), and that the amount of nitrate leaching increased with increasing N application rate. The simulation results suggested that the optimal N application rate was in a range between 150 and 240 kg ha−1, which would keep the amount of nitrate leaching below −1 and meanwhile maintain acceptable maize yield above 9410 kg ha−1. Furthermore, 18.4 kg NO− 3 -N ha 180 kg N ha−1 produced the highest yields (9837 kg ha−1) and comparatively lower amount of nitrate leaching −1 ). This study will provide a valuable reference for determining optimal N application rate (10.0 kg NO− 3 -N ha (or range) in other crop systems and regions in China. © 2015 Elsevier B.V. All rights reserved.

1. Introduction Maize (Zea mays L.) is one of the most widely planted crops in the world. According to the Food and Agriculture Organization (FAO), maize was grown for an area of 1.77 × 108 ha in worldwide and 0.35 × 108 ha in China alone in 2012, and it yielded about 8.72 × 108 ⁎ Corresponding author. E-mail address: [email protected] (H. Liu).

http://dx.doi.org/10.1016/j.scitotenv.2015.02.022 0048-9697/© 2015 Elsevier B.V. All rights reserved.

and 2.08 × 108 t grains, respectively (FAO, 2012). The North China Plain is the most important maize production region that provided 35% of the total national maize production in China (NBS, 2012). However, the current high crop yields are mainly obtained through large inputs of nitrogen (N) fertilizer and irrigation (Li et al., 2006). As a result, excessive N application has become a significant source for water contamination (Dinnes et al., 2002; Zhu et al., 2005). A survey in 14 counties in northern China showed that nitrate contents in −1 (or the WHO and groundwater exceeded 11.3 mg NO− 3 -N L

Y. Zhang et al. / Science of the Total Environment 514 (2015) 388–398

European limit for nitrate in drinking water) in about half of the investigated area (280,000 ha) (Zhang et al., 1996). In some areas, nitrate concentration in groundwater in shallow wells has reached as high as −1 (Ju et al., 2006), and the N pollution depth has 274 mg NO− 3 -N L been ever increasing with time (Liu et al., 2005). It is very important to seek double-goal management practices to obtain high maize yield and simultaneously reduce N leaching in northern China. However, both crop yield and nitrate leaching are influenced by environmental and management factors, such as climate (Smith et al., 2013), soil properties (Qiu et al., 2009) and management practices (Li et al., 2006). Field experiments play an important role in monitoring crop yields and nitrate leaching as a basis to evaluate the effects of different management measures (Perego et al., 2013; Zhang et al., 1996), but they are often time- and labor-consuming and the results are difficult to scale up spatially or temporally. Prediction of yield or nitrate leaching at a larger scale has to rely on some successful mathematical formulas (Qiu et al., 2011). Some models based on hydrology are commonly used to predict nutrient loadings in watershed scale, such as SWAT (Wang et al., 2014a) and MIKE SHE (Vansteenkiste et al., 2013). There are also several nutrient-loading prediction models, which include components to simulate not only hydrological processes but also nutrient transport and transformation, such as the Denitrification-Decomposition (DNDC) model (Deng et al., 2011) and STICS soil–crop model (Jego et al., 2008). The DNDC model (Li et al., 1992a,b), which is a process-based biogeochemical model, has been used to evaluate effects of management practices on yield or environment risk (Cui et al., 2014; Li et al., 2010). The model has been well validated by using field monitoring data worldwide (Deng et al., 2011; Tonitto et al., 2007). As DNDC has combined biogeochemical processes with hydrological dynamics, it can calculate water stress and N stress in plant growth period. The DNDC model bases simulations on biogeochemical cycles and can simulate various plants' physiological process, such as N uptake, respiration and assimilate (Zhang et al., 2002). In particular, the DNDC model can simulate denitrification, which is an important reaction for N transformation, accurately compared with many other models, because DNDC considers that denitrification is a sequential reaction driving by soil microbes while the other models simulate denitrification rate by using a linear function (Li et al., 2006). A large number of researches have used the DNDC model to identify best management practices for higher crop yield or lower pollution risk (Gopalakrishnan et al., 2012; Werner et al., 2012). Integrating detailed N transformation with hydrological processes, the model was also regarded to be able to simulate nitrate leaching (Qiu et al., 2011), however, the applicability of the model in this regard has been rarely tested (Li et al., 2006, 2014). Chemical N fertilizer application rates for maize averages 260 kg N ha− 1 in China, which is almost double of needs by most crops (Chen et al., 2011). It has been relatively well demonstrated that N application can increase crop yield within a certain N rate range, but the yield does not remarkably response to further increase when N rate is above a threshold (Cui et al., 2010), and most of the excessive N rate losses to the atmosphere or water (Huang and Tang, 2010). In the North China Plain, planting maize in summer is of a particular concern of nitrate leaching since its growth season is coincided with the rain season during June to September. In order to achieve optimum yields of maize and acceptable amounts of nitrate leaching simultaneously, it is essential to determine critical N application rate or a rate range that can be used in fertilization recommendations for farmers. In this study, critical N application rate is considered as a threshold N rate or a rate range that corresponds (i) to an optimum crop yield (an agronomic critical N rate) and (ii) to a change (or an acceptable) point of nitrate leaching (an environmental critical N rate), for which dramatic increase (or a risky level) in nitrate leaching would occur when N fertilizer application rate is above this critical value. A few studies have identified agronomic critical N rate based on measurements of crop

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yields in the field experiments (Yost et al., 2014), but rare literature has defined an environmental critical N rate (Di and Cameron, 2000; Zhou and Butterbach-Bahl, 2014). This study tested the DNDC model to determine both agronomic and environmental critical N application rates. To evaluate the effects of different N fertilizer rates on yield and amount of nitrate leaching, an experiment of six years was conducted for maize on the typical Haplic Luvisol soil in northern China. Then the experimental data were used to run the DNDC model. The objectives were (1) to evaluate the applicability of DNDC model to simulating yield and amount of nitrate leaching for maize cropping system, and (2) to identify agronomic and environmental critical N rates for the maize cropping system based on DNDC simulations.

2. Materials and methods 2.1. Field experiments Experimental data used in this study were obtained from field experiments conducted from October, 2007 to December, 2012 at an agricultural experimental station (40.22°N, 116.23°E, 43.5 m) of the Chinese Academy of Agricultural Sciences in Changping District, Beijing Municipality, China. The climate is a typical sub-humid temperate continental monsoon climate with warm and wet summer and cold winter. Average monthly meteorological variables measured at the experimental site across the experimental years are presented in Appendix Table A. The mean annual temperature, sum of annual precipitation, and surface evaporation were 11 °C, 630 mm, and 1065 mm, respectively. About 80% of total precipitation occurs from June to October during maize growth, but precipitation is not evenly distributed and irrigation is needed when water demand by maize is large. The average period of frost-free is about 210 days per year. The site has a Haplic Luvisol soil according to the FAO Taxonomy with a bulk density of 1.32 g cm−3 and pH of 8.26. The soil had organic carbon content of 7.9 g kg−1 and total nitrogen content of 1.0 g kg−1 in the top 20-cm soil layer at the start of the experiment. Details about maize planting, irrigations and precipitations in each year during 2008–2012 were listed in Table 1. A randomized complete block experimental design was used in the field. Three treatments (low, high and no rate of N), each with three replicates, were used to evaluate the impacts of N fertilizer on the yield and nitrate leaching (Appendix Table B). The high N rate (T2, 180 kg ha−1 with one-time application for maize in 2008 and 240 kg ha− 1 with two application splits during 2009–2012) represents over-fertilization (Chen et al., 2011), but this rate is still commonly used by local farmers. The low N rate (T1, 120 kg ha− 1 with two application splits during 2009–2012) was half of the rate as used in T2. In addition, the control treatment with no N (CK) was included. In each year, all treatments received mineral fertilizers of 80 kg P ha−1 and 300 kg K ha−1. The plots were separated by concrete down to drain depth (1.2 m) and each plot (lysimeter) had a size of 1 m × 2 m × 1.2 m. In each lysimeter, the soil was backfilled by every 20 cm soil layer and drainage water was conducted via a tube to a collecting bottle. Seepage water was continuously collected as leaching occurred during rain or irrigation. There were 15, 0, 7, 9 and 6 leaching events during 2008 to 2012, respectively. Concentration of nitrate in drainage water was determined for each event and lysimeter, by using a continuous flow analyzer (TRAACS 2000; Bran and Luebbe, Norderstedt, Germany) (Li et al., 2011). The amount of nitrate leached was calculated by multiplying the amount of water with nitrate concentration. Moreover, all plants above the ground (separated into cornstalks and grains) in each plot were collected to determine dry matter content at ripe stage of wheat or maize; N uptake by plant was measured according to the semi-micro-Kjeldahl method and was determined by multiplying biomass (separated into cornstalks and grains) with N concentration (Wang et al., 2014b).

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Table 1 Details about the planting systems, irrigation and precipitation in the field experiments of five years (2008–2012) conducted in Changping District, Beijing. Year

Crop

Sowing date (yyyy.mm.dd)

Harvest date (yyyy.mm.dd)

Irrigation date (yyyy.mm.dd) and depth (mm)

Precipitation (mm year−1)

2008 2008 2009 2010

Wheat Maize Maize Maize

2007.10.19 2008.6.22 2009.5.8 2010.5.15

2008.6.19 2008.9.27 2009.8.27 2010.9.6

655

2011 2012

Maize Maize

2011.5.30 2012.5.30

2011.9.13 2012.9.15

2008.4.3 (100); 2008.4.9 (5) 0 0 2010.6.30 (10); 2010.7.26 (15); 2010.7.30 (10); 2010.8.1 (10); 2010.8.11 (15); 2010.8.14 (10); 2010.8.22 (20); 2010.8.23 (30) 2011.5.27 (150) 2012.5.22 (15); 2012.5.23 (15); 2012.5.28 (15);2012.7.11 (5)

2.2. The DNDC model The DNDC is a model to simulate the biogeochemical cycles of C and N by the primary ecological drivers (Li et al., 2006). Inputting information of daily meteorological data, soil properties, and cropping practices, the model can simulate soil temperature, moisture, pH, Eh, and substrate concentration status as well as nitrification, de-nitrification and fermentation reaction. For site simulation about crop growth and nitrate leaching of DNDC, a number of input parameters are required including N concentration in rainfall, atmospheric NH3 concentration, maximum and minimum temperatures, precipitation, wind speed, humidity, clay content, pH, bulk density, SOC content, as well as tillage, fertilization, manure amendment, irrigation, flooding, weeding, grazing, etc. Any change in input parameters will alter a series of biochemical reactions and soil environmental factors, which will finally determine yields and nitrate leaching from the modeled ecosystems. DNDC has a sub-mode to simulate crop growth. During simulation processes, photosynthesis, respiration, C allocation, water and N uptake are simulated on a daily basis. Capacity of N and water uptake depends on many factors such as concentration in the root zone, soil moisture, root length and soil nitrogen distribution. N demand is calculated based on the optimum daily crop growth and plant C/N ratio but N stress will inhibit plant growth when plant N concentration is below a critical value. Water demand depends on potential transpiration connected with leaf area index and climate conditions while water stress will also inhibit plant growth when actual water supply is lack or potential transpiration is comparatively higher than normal time. The model simulates N transformation including several processes such as urea hydrolysis, ammonium–ammonia equilibrium, nitrification, de-nitrification, decomposition and ammonia volatilization. There are four N sources in the model, inorganic fertilizer or manure rate is determined by farmer. Atmospheric N deposition is determined by rainfall and its N content. The N in irrigation is determined by the amount of irrigation and its N content. N fixation is empirically calculated by using a crop-dependent coefficient. After those parameters are added to the model, N is mineralized to NH+ 4 ions reserving in the living microbial pool, while if the microbes die and the organic matter decomposes,

Table 2 Details of management scenarios for sensitivity analysis. Scenarios

Baseline scenario Alternative scenarios

1 2 3 4 5 6 7 8 9

Tillage depth (cm)

Total fertilizer N input (kg N ha−1)

Fertilizer split

Irrigation (mm)

20 0 10 20 20 20 20 20 20

240 240 240 180 300 240 240 240 240

2 2 2 2 2 1 3 2 2

50 50 50 50 50 50 50 0 30

565 535 582 857

NH+ 4 ions could be released back into the soil liquid phase. The free + NH+ 4 concentration and clay-adsorbed NH4 are in equilibrium, but when NH3 concentration in the soil liquid phase is much higher, NH3 will volatilize into the atmosphere from dissolved ammonia, which is also influenced by soil temperature, moisture, pH and so on. In addition, − − NH+ 4 ions in the soil liquid phase can quickly nitrify to NO3 . Those NO3 ions could be reused by microbes but could not be adsorbed by the soil, so NO− 3 can be relocated as water moves through the soil especially leaches out of the root zone into deeper layers. Detailed descriptions about the equations and parameters of the model can be accessed in previous articles (Li et al., 2006), and the newest version can be downloaded on the internet (http://www.dndc.sr.unh.edu). The version used in the study was DNDC95. 2.3. Model run with field experiment data There were three steps to run the DNDC model with the data collected. First, a data set of 5-year field experiment (2008–2012) of T2 was used to calibrate the parameters of DNDC model. In calibration, parameters of local daily atmospheric and meteorological data, measured soil properties as well as actual farming management measures were input to simulate the crop growth and amount of nitrate leaching. The cultivar- or site-specific crop parameters were parameterized according to field practices and observations; otherwise, some default values were also used. Model outputs of crop yields and amount of nitrate leaching were compared with field observations to determine if the parameterization was suitable. The model parameters were manually calibrated with a trial-and-error method to make the model outputs as close as possible to the field observations. The calibrated parameter values are presented in Appendix Table C. Next, the observed data of treatments CK and T1 were used for model validation. The simulated crop yields and amounts of nitrate leaching were compared with field observations. RMSE-observation standard deviation ratio (RSR; Eq. (1)) (Singh et al., 2004) was used to assess the coincidence between observed and simulated values. RMSE values less than half the standard deviation of observed data can be considered low, but as Moriasi et al. (2007) suggested that the recommended less than 50% RSR value was the most stringent (“very good”) rating and 10% and 20% greater than this value for the “good” and “satisfactory” ratings, respectively. Model efficiency (ME; Eq. (2)) (Cui et al., 2014) indicated the improvement in model predictions relative to the mean of measurements. When RMSE and ME are equal to 0 and 1 respectively, it represents an ideal condition for the model simulation (Miehle et al., 2006). Moreover, the determination coefficient (R2) (Eq. (3)) of the linear regression would be used to evaluate how well the simulations match the observed data.

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n X 2 ðOi −Si Þ

RSR ¼

RMSE i¼1 ¼ sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n  2 STDEVi X Oi −Oavg i¼1

ð1Þ

Y. Zhang et al. / Science of the Total Environment 514 (2015) 388–398 n X

ME ¼ 1− ni¼1 2 X Oi −Oavg i¼1

0

2

ðOi −Si Þ

ð2Þ

n  X

Oi −Oavg

391



Si −Savg



12

C B C B 2 i¼1 ffiC R ¼B Bsffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n  n  2 X 2 C A @ X Oi −Oavg Si −Savg i¼1

ð3Þ

i¼1

Fig. 1. Comparisons of observed and simulated yields of wheat and maize of three treatments of (a) CK, (b) T1, and (c) T2 in the field experiments conducted in Changping District, Beijing, China during 2008 to 2012. The N fertilizer levels were 0 kg N ha−1 in treatment CK and in T1, and 180 kg N ha−1 in treatment T2 in 2008. The N fertilizer levels were 0 kg N ha−1 in treatment CK, 120 kg N ha−1 in T1 and 240 kg N ha−1 in T2 in each year from 2009 to 2012. The error bars are standard deviations of yields (n = 3).

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3. Results and discussions

did not produce much more yields than T1 (120 kg N ha− 1) for maize from 2009 to 2012, which indicated that N application rate of 240 kg ha− 1 for T2 was probably excessive. From 2007 to 2008, the crop system was wheat–maize rotation. No matter wheat or maize in these two years, the N fertilizer was 0 kg N ha−1 in treatment CK and 180 kg N ha−1 in treatment T2. The yields of wheat and maize in CK (1050 and 4000 kg ha−1) were all lower than those in T2 (1680 and 9840 kg ha−1). From 2009 to 2012, the crop system was maize monoculture. Nitrogen also increased the maize yields from 6720 to 9320 kg ha−1 for T1 and from 7640 to 9583 kg ha−1 for T2, comparing with the CK treatment (from 3383 to 5258 kg ha−1). Yields in the three treatments were always the lowest in 2009 (CK: 3380, T1: 7580 and T2: 7690 kg ha− 1) and the highest in 2012 (CK: 5260, 9320 and T2: 9580 kg ha−1), and the two N treatments did not significantly differ in yields in the same year. Crop yield was affected by different N rates in a limited range, which increased with the increase of N application rate, while it would level off at a certain value when N application rate exceeded a threshold (He et al., 2012). A reduction of N application rate within an appropriate range could maintain or even increase grain yields (Chen et al., 2005; Cui et al., 2014), so reducing 240 kg N ha−1 to a reasonable rate should be suggested. Crop yield was also significantly related to rainfall or irrigation and increased with the increasing of water input within a certain range (Chen et al., 2013). Maize yield from 2009 to 2012 showed a growing trend, it maybe because the water (irrigation and precipitation) input increased from 2009 to 2012 (Table 1). The amount of nitrate leaching obviously increased with the increase of fertilizer N applied, although the gaps among treatments were different in different years (Fig. 2), which was consistent with the result of previous publication (He et al., 2012). Both treatments with less N and without N had less nitrate leaching than that with high N (P b 0.05), especially during maize monoculture years from 2009 to 2012. In 2008, N application in T2 (180 kg N ha−1) was higher than in CK and T1 (0 kg N ha−1), and the amount of nitrate leaching in −1 ) was also higher than that in CK and T1 T2 (12.8 kg NO− 3 -N ha − −1 (1.8 kg NO3 -N ha ). From 2009 to 2012, N application rates in CK, T1 and T2 were 0, 120 and 240 kg N ha−1 respectively, while there was no leaching observed in 2009. During 2010 to 2012, amounts of ni−1 −1 in 2010, 40.6 kg NO− trate leaching in T2 (11.4 kg NO− 3 -N ha 3 -N ha −1 in 2011, 27.1 kg NO− -N ha in 2012) were always significantly higher 3 −1 −1 in 2010, 3.7 kg NO− than those in both T1 (2.4 kg NO− 3 -N ha 3 -N ha −1 −1 − − in 2011, 4.5 kg NO3 -N ha in 2012) and CK (0.5 kg NO3 -N ha in −1 −1 in 2011, 0.6 kg NO− in 2012) 2010, 2.0 kg NO− 3 -N ha 3 -N ha (P b 0.05), and the difference in nitrate leaching between T2 and CK −1 ) was the largest in 2011. (39.4 kg NO− 3 -N ha The gaps of nitrate leaching among the three treatments (CK, T1 and T2) differing with years are probably because water input (precipitation and irrigation) differed with years (Table 1). However, previous results from a 12-year study showed that the amount of nitrate leaching was more related to the monthly distribution of precipitation than the total rainfall amount (Nguyen et al., 2013). Other results demonstrated that irrigation also had an important impact on the amount of nitrate leaching (Cui et al., 2014). In this study, 565 mm precipitation with no irrigation in 2009 led to no nitrate leaching in field observation, but 535 mm precipitation with 120 mm irrigation in 2010 resulted in more amount of nitrate leaching than that in 2011 (587 mm precipitation with 150 mm irrigation) and 2012 (857 mm precipitation with 50 mm irrigation), the reason of such phenomenon was not clear. So the relationship between amount of nitrate leaching and precipitation or irrigation needs much more attention in future research by field monitoring as well as model simulation.

3.1. Summary of field experiments

3.2. Model calibration and validation

Nitrogen fertilizer obviously increased the yields of wheat and maize comparing with the treatments without N (Fig. 1). T2 (240 kg N ha−1)

The simulated yields with DNDC model showed a strong correlation with observations for every treatment from 2008 to 2012 (Fig. 1). The

where Oi, Si, Oavg, and Savg were the observed values, the simulated values, the mean of the observed data and the mean of the simulated data, respectively, and n was the number of paired values. Finally, the validated DNDC was run for a sensitivity analysis at the same experimental site but with varied management practices, to identify the most influential factors that could effectively increase the yield and decrease the amount of nitrate leaching. The model simulations conducted by varying the value of a single management parameter within an appropriate range, while keeping all other parameter values fixed. A baseline scenario was formulated as treatment T2: a tillage of 20 cm, a total N fertilizer of 240 kg N ha−1 in two applications (90 kg N ha−1 on May 28 and 150 kg N ha−1 on July 11), and an irrigation of 50 mm during the crop growth (Scenario 1 in Table 2). The other parameters of meteorological data, soil properties, and cropping measures were similar to the observed data in the experimental site in 2012. There were 8 alternative management scenarios (Scenarios 2–9 in Table 2). A total of 9 scenarios were run individually, a relative sensitivity index (Walker et al., 2000) was calculated to evaluate the effects of the input management measures (tillage, fertilizer and irrigation) on crop yields and amount of nitrate leaching (Eq. (4)):

S¼

! ! O2 −O1 I 2 −I1 = Oavg Iavg

ð4Þ

where S is on behalf of the relative sensitivity index; O1 stands for the model output values corresponding to parameter I1 and O2 is the model output values corresponding to parameter I2, and Oavg represents the mean value of O1 and O2. I1 denotes the minimum input value among the parameters of management and I2 is the maximum value among the parameters given, and Iavg is the mean value of I1 and I2. The greater the absolute value of the relative sensitivity index (S), the greater the influence of the input management parameter to the model output of yield or amount of nitrate leaching, while negative value of S shows that there is a negative relationship between the input management parameter and yield or amount of nitrate leaching. 2.4. Identification of agronomic and environmental critical N rates An exploring paradigm with single factor simulations was used to identify agronomic and environmental critical N values. Specifically, seventeen fertilization schedules, which was from 0 to 480 kg N ha−1 with an increment of 30 kg N ha−1 for N fertilizer application rates, were set to explore the optimal N application rates, simultaneously keeping other input management parameters (tillage of 20 cm, two splits of N application, timing of application, and 50-mm irrigation) consistent with the baseline scenario. To assess the effects of different N application rates, we performed the model simulation for each scenario for 5 years with the daily weather data at the experiment site during 2008 to 2012, and calculated the mean of yield and amount of nitrate leaching. By plotting the simulated N leaching against different N application rates, the N rate that did not result in a higher grain yield (the value not changed or decreased with an increasing N application rate) would be considered as the agronomic critical N value. The N application rate that caused lower nitrate leaching (less than 18.4 kg ha−1) was selected as the environmental critical N value. −1 corresponds to human health Nitrogen leaching of 18.4 kg NO− 3 -N ha −1 -N L according to Quality Standard for Ground standard of 20 mg NO− 3 Water GB/T14848-1993 in China.

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Fig. 2. Comparisons of observed and simulated amounts of nitrate leaching of three treatments of (a) CK, (b) T1, and (c) T2 in the field experiments conducted in Changping District, Beijing, China during 2008 to 2012. The N fertilizer levels were 0 kg N ha− 1 in treatment CK and in T1, and 180 kg N ha− 1 in treatment T2 in 2008. The N fertilizer levels were 0 kg N ha− 1 in treatment CK, 120 kg N ha− 1 in T1 and 240 kg N ha− 1 in T2 in each year from 2009 to 2012. The error bars are standard deviations of nitrate leaching (n = 3).

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observed and simulated yields of maize showed similar annual patterns for CK, T1, and T2. The RSR values for CK, T1 and T2 were 0.45, 0.18, and 0.42, respectively; ME values were 0.80, 0.97 and 0.83, respectively; and R2 values were 0.80, 0.97 and 0.83, respectively. These values indicated that the DNDC model was the most stringent (“very good”) to capture the magnitudes and patterns of yield, which was supported by the previous publication (Sansoulet et al., 2014). Similar to yields, the DNDC model was also stringent (“very good”) to capture the magnitudes of plant N uptake, as shown in Fig. 3 for comparisons between the observed and simulated plant N uptake for different treatments from 2008 to 2012. The RSR values for CK, T1 and T2 were 0.62, 0.39, and 0.29, respectively; ME values were 0.61, 0.85 and 0.91, respectively; and R2 values were 0.80, 0.97 and 0.95, respectively. The observed and simulated amounts of nitrate leaching showed similar annual patterns for CK, T1 and T2 from 2008 to 2012 (Fig. 2). Especially for CK and T2, the simulations correctly captured the magnitudes and changes of observed amount of nitrate leaching. The correlation between observed and simulated amounts of nitrate leaching was acceptable for the three treatments (RSR values of 0.66, 0.70 and 0.46; ME values of 0.56, 0.50 and 0.79; R2 values of 0.54, 0.57 and 0.81 for CK, T1 and T2, respectively). These values showed that the DNDC model was at least “satisfactory” to capture the magnitudes and patterns of nitrate leaching in different treatments across five years especially for T2, which was in line with the results of Li et al. (2014). Given the inherently complex processes involved in C and N transformation as well as the yield components and nitrate production– consumption in the field, it was clear that the DNDC model can satisfactorily simulate the amounts of crop yields and nitrate leaching for maize system in a Haplic Luvisol soil in North China Plain, though slightly discrepancies can be found in some year, which could be resolved by further calibration and more sophisticated field observation in the future.

3.3. Effect of different management practices on maize yield and nitrate leaching Different management practices can influence the amount of nitrate leaching and yield (Table 3). Changing conventional tillage in the baseline scenario to no-tillage, the amount of nitrate leaching decreased and yield increased substantially. On the contrary, excessive irrigation during crop growth could not increase crop yields and nitrate leaching increased when irrigation changed from 50 to 0 mm, Li et al. (2006) also demonstrated that reducing irrigation water did not decrease crop yields while benefiting other goal variables such as mitigate greenhouse gas emission and nitrate leaching. Among the management practices, total N application level and split showed notable influences on annual amount of nitrate leaching and yield. When changing total N application level from 240 to 180 and 300 kg N ha−1, nitrate leaching decreased by 31.8% and increased by 32.4% respectively, while the yield increased by 11.1% and 3.4% respectively, which was in line with the results of Cui et al. (2014). Under the same level of total N application, decreasing fertilization splits from two to one, the amount of nitrate leaching and yield decreased by 91.2% and 7.4%, respectively. When fertilizer splits increased from 2 to 3, the amount of nitrate leaching decreased by 51.8%, but its yield increased by 14.6%. This is mainly because appropriate fertilization splits could coincide best with the N need of maize and thus indirectly decreased N leaching during maize growth compared to less splits especially from tasseling to maturity (He et al., 2012). However, nitrate leaching decreased by 91.2% when decreasing fertilization splits from 2 to 1, which indicated that the second fertilization event elevated nitrate leaching as it was close to rainy season in this study. This implies that suitable changes in some management practices could effectively mitigate nitrate leaching and improve grain yield.

3.4. Sensitivity analysis The sensitivity indices indicated that among the four alternative management practices (Table 3), total fertilizer N application (S = 1.28) and fertilization frequency (S = 1.38) had greater influences on nitrate leaching, while tillage (S = 0.09) and irrigation (S = − 0.04) had slight influences on nitrate leaching. Only irrigation had a negatively effect. For yield, total fertilizer N application (S = −0.14) and fertilizer split (S = 0.21) had greater influences than tillage (S = −0.04) and irrigation (S = −0.04). Only fertilizer split had positive effects on yield. Overall, the sensitivity analysis indicated that the nitrate leaching rate was most sensitive to total N application and fertilization splits. The proper management practices should not only efficiently increase crop yield but also mitigate environment degradation stress (Li et al., 2010). Therefore, results of higher yield and lower nitrate leaching based on the single management practice could not be obtained by integration of these best single factors simply. Moreover, the most effective way to explore the optimal practices must adjust measures to local conditions. In this study, although any management practice could affect yield and nitrate leaching, the results of sensitivity analysis indicated that total N application level and split had greater influences on the amount of nitrate leaching. But according to the farmer's management practices, increasing fertilization events would increase labor cost which is unpractical, while only changing N application rate is easier to be accepted by farmers. So the critical N application rate or a rate range, which could ensure optimum yields of maize and acceptable nitrate leaching simultaneously, is the focus in searching appropriate management practice in this study. 3.5. Identification of agronomic and environmental critical N values As climate change and different management measures such as fertilization and irrigation, both crop yield and amount of nitrate leaching varied greatly (Fang et al., 2013; Perego et al., 2013), because any change of these factors could affect C and N transformation processes in air–soil–plant system. Successful model based on element biogeochemical cycling could be used to evaluate effects of complex factors on crop yield and amount of nitrate leaching simultaneously, previous researches had indicated that exploration of possible management options by DNDC was feasible (Cui et al., 2014; Li et al., 2006). The model application to identify agronomic and environmental critical N values was tested, with the aim to find a threshold N application rate or a rate range that would give acceptable yield of maize and acceptable nitrate leaching simultaneously. There was no existing “acceptable yield” available in the current literature or governmental regulation for maize production in China. Thus, the grain yield which was not changed or decreased with an increasing N application rate was considered as the “acceptable yield” in this study. To determine the “acceptable nitrate leaching”, the N application rates were ranked according to their simulated amounts of nitrate leaching. Nitrate mass leached into groundwater was mainly caused by precipitation that if the irrigation was designed to match crop plant demands during the season based on the soil water holding ability (He et al., 2012). The mean annual cumulative drainage from the field reached up to 92 mm during 2008 to 2012, and 91% of drainage water was collected during maize growing season. To meet the human health standard of −1 according to Quality Standard for Ground Water 20 mg NO− 3 -N L GB/T14848-1993 in China, the amount of nitrate leached into groundwater during maize growing season should be at least less −1 . than 18.4 kg NO− 3 -N ha Possible options of N application rate were evaluated for the simulated yields and amount of nitrate leaching (Fig. 4). The result showed that the 5-year average grain yield increased with the N application rate from 0 to 180 kg N ha−1. And then, the yield decreased when N application was above 180 kg ha−1. However, the grain yield leveled off at the simulated grain yield of 9410 kg ha−1 as N application rate was

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Table 3 Influences of different scenarios on the amount of nitrate leaching and yield of varied management practices and their sensitivity indices. Management practice

Tillage depth (cm) Total N application (kg N ha−1 year−1) Fertilizer split

Irrigation (mm)

Baseline

20

240

2

50

Change of management parameter

Amount of nitrate leaching (kg ha−1)

Yield of maize (kg ha−1)

0 10 20 180 240 300 1 2 3 0 30 50

27.5 31.4 33.0 22.5 33.0 43.7 2.9 33.0 15.9 35.7 33.1 33.0

11,285 10,390 10,380 11,533 10,380 10,738 9618 10,380 11,893 11,285 11,295 10,380

Sensitivity index (S) Nitrate leaching

Yield

0.09

−0.04

1.28

−0.14

1.38

0.21

−0.04

−0.04

Fig. 4. Response curves of simulated grain yield (open circle) and amount of nitrate leaching (filled circle) to different N application rates. Each point is the mean value over 5-year continuous weather data (2008–2012).

above 180 kg ha−1. The grain yield of 9410 kg ha−1 could be considered as the acceptable yield as described before and the N fertilizer application rate above 150 kg ha− 1 had achieved acceptable yield. At the same time, average annual nitrate leaching of 5 years steadily increased when more N was applied from 0 to 480 kg N ha−1 and the proportion of amount of nitrate leaching of the total applied N increased from 3% to 13%. For example, nitrate leaching increased from 6.8 to 63.8 kg ha−1 when the N application rate increased from 180 to 480 kg ha−1. Even when no fertilizer was applied, there was still some yield (4294 kg ha−1) and nitrate −1 ). However, the amount of nitrate leaching leaching (0.7 kg NO− 3 -N ha of N application rate from 0 to 240 kg N ha−1 was all in the range of ac−1 ) as described before. ceptable nitrate leaching (b18.4 kg NO− 3 -N ha Considering both the “acceptable yield” and “acceptable nitrate leaching”, the critical N application rate range was between 150 and 240 kg N ha−1, which corresponded to the yield and nitrate leaching −1 . Furin a range of 9410–9837 kg ha−1 and 5.01–15.6 kg NO− 3 -N ha −1 thermore, the N application rate of 180 kg N ha produced the highest yields (9837 kg ha− 1) and comparatively lower nitrate leaching −1 ) among the critical N application rate range. (10.0 kg NO− 3 -N ha

The yield under application of 180 kg N ha−1 was higher than those at 150, 210 and 240 kg ha− 1, while its nitrate leaching (10.0 kg NO− 3 N ha−1) was lower than that at 210 and 240 kg N ha−1. Considering the actual condition of experiment site, the threshold N application rate range that would give acceptable yield of maize and acceptable nitrate leaching was 150–240 kg ha−1, however, the optimal N fertilizer application rate was 180 kg ha−1. The DNDC model was applicable to simulate crop yield and amount of nitrate leaching, but there were still some limitations for wide applications of this tool. For instance, the model assumed that the nitrate leached out of root zone below 50 cm would reach groundwater finally. Thus it simulated the water and nitrate transport only in the 0–50 cm soil, while seepage water was collected at 120 cm in this study. The simulated and measured amounts of nitrate leaching had certain uniformity in variation, and the statistics of model validation (RSR, ME, R2) could be also acceptable. However, the amount of nitrate leaching was zero in field experiment in 2009, but some leaching was simulated by DNDC. Thus, it was better to further adjust parameter or develop model for deeper soil layer (Li et al., 2009). Moreover, the effect of other

Fig. 3. Comparisons of observed and simulated amounts of plant N uptake of (a) CK, (b) T1, and (c) T2 in the field experiments conducted in Changping District, Beijing, China during 2008 to 2012. The N fertilizer levels were 0 kg N ha−1 in treatment CK and in T1, and 180 kg N ha−1 in treatment T2 in 2008. The N fertilizer levels were 0 kg N ha−1 in treatment CK, 120 kg N ha−1 in T1 and 240 kg N ha−1 in T2 in each year from 2009 to 2012. The error bars are standard deviations of plant N uptake (n = 3).

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management practices than fertilization rate on crop yield production and amount of nitrate leaching requires further investigation, and validations of the model for other crop systems and larger scales were also needed in the future. 4. Conclusions This study demonstrated that the DNDC model can be used to assess the effect of N application rates on maize yield and amount of nitrate leaching and to identify the critical N application rate through proper calibration, validation, and sensitivity analysis. The model simulations confirmed the observed results that application of N fertilizer increased crop yield, but the high N rate (240 kg N ha−1) did not produce more yield than the low N rate (120 kg N ha− 1), and that the amount of nitrate leaching increased with increasing N application rate. Both rational N rate and application splits can ensure optimum maize yields and acceptable amount of nitrate leaching simultaneously. However, increasing fertilization frequency would increase labor cost which is unpractical for farmers. Therefore, determination of rational N application rate is more important for recommendations to farmers. The simulation results suggested that the critical N application rate range was between 150 and 240 kg ha−1, which would give an acceptable maize yield ≥ 9410 kg ha − 1 and acceptable amount of nitrate leaching −1 . Within this N rate range, an application of ≤ 18.4 kg NO− 3 -N ha 180 kg N ha− 1 produced the highest yields (9837 kg ha− 1) and com−1 ). This paratively lower amount of nitrate leaching (10.0 kg NO− 3 -N ha study will provide a valuable reference for determination of critical N application rate (or range) in other crop systems and regions in China. Acknowledgments This research was supported by the Special Fund for Agro-scientific Research in the Public Interest from the Ministry of Agriculture, China (Grant No.: 201003014) and the National Natural Science Foundation of China (Grant No.:41301311). We sincerely thank Mr. Bo Yang for his substantial help in field experiment management. Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.scitotenv.2015.02.022. References Chen, L.D., Peng, H.J., Fu, B.J., Qiu, J., Zhang, S.R., 2005. Seasonal variation of nitrogenconcentration in the surface water and its relationship with land use in a catchment of northern China. J. Environ. Sci. (China) 17, 224–231. Chen, X.P., Cui, Z.L., Vitousek, P.M., Cassman, K.G., Matson, P.A., Bai, J.S., et al., 2011. Integrated soil–crop system management for food security. Proc. Natl. Acad. Sci. U. S. A. 108, 6399–6404. Chen, Y., Liu, Y., Wen, X.X., Wu, W., Liao, Y.C., 2013. Effect of conservation tillage on growth and grain yield of wheat under simulated rainfall conditions. Res. Crops 14, 684–691. Cui, Z.L., Zhang, F.S., Chen, X.P., Dou, Z.X., Li, J.L., 2010. In-season nitrogen management strategy for winter wheat: maximizing yields, minimizing environmental impact in an over-fertilization context. Field Crop Res. 116, 140–146. Cui, F., Zheng, X., Liu, C., Wang, K., Zhou, Z., Deng, J., 2014. Assessing biogeochemical effects and best management practice for a wheat–maize cropping system using the DNDC model. Biogeosciences 11, 91–107. Deng, J., Zhou, Z., Zhu, B., Zheng, X., Li, C., Wang, X., et al., 2011. Modeling nitrogen loading in a small watershed in southwest China using a DNDC model with hydrological enhancements. Biogeosciences 8, 2999–3009. Di, H.J., Cameron, K.C., 2000. Calculating nitrogen leaching losses and critical nitrogen application rates in dairy pasture systems using a semi-empirical model. N. Z. J. Agric. Res. 43, 139–147. Dinnes, D.L., Karlen, D.L., Jaynes, D.B., Kaspar, T.C., Hatfield, J.L., Colvin, T.S., et al., 2002. Nitrogen management strategies to reduce nitrate leaching in tile-drained midwestern soils. Agron. J. 94, 153–171. Fang, Q.X., Ma, L., Yu, Q., Hu, C.S., Li, X.X., Malone, R.W., et al., 2013. Quantifying climate and management effects on regional crop yield and nitrogen leaching in the North China Plain. J. Environ. Qual. 42, 1466–1479. FAO, 2012. http://faostat.fao.org/site/567/DesktopDefault.aspx?PageID=567#ancor.

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