Suitability of the DNDC model to simulate yield production and nitrogen uptake for maize and soybean intercropping in the North China Plain

Journal of Integrative Agriculture 2018, 17(12): 2790–2801 Available online at www.sciencedirect.com

ScienceDirect

RESEARCH ARTICLE

Suitability of the DNDC model to simulate yield production and nitrogen uptake for maize and soybean intercropping in the North China Plain ZHANG Yi-tao1, 2, 3*, LIU Jian4*, WANG Hong-yuan1, LEI Qiu-liang1, LIU Hong-bin1, ZHAI Li-mei1, REN Tian-zhi5, ZHANG Ji-zong1 1

Key Laboratory of Non-point Source Pollution Control, Ministry of Agriculture, Beijing 100081, P.R.China Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China 3 Earth Systems Research Center, Institute for the Study of Earth, Oceans and Space, University of New Hampshire, New Hampshire 03824, USA 4 School of Environment and Sustainability, Global Institute for Water Security, University of Saskatchewan, SK S7N 0X4, Canada 5 Agro-Environmental Protection Institute, Ministry of Agriculture, Tianjin 300191, P.R.China 2

Abstract Intercropping is an important agronomic practice. However, assessment of intercropping systems using field experiments is often limited by time and cost. In this study, the suitability of using the DeNitrification DeComposition (DNDC) model to simulate intercropping of maize (Zea mays L.) and soybean (Glycine max L.) and its aftereffect on the succeeding wheat (Triticum aestivum L.) crop was tested in the North China Plain. First, the model was calibrated and corroborated to simulate crop yield and nitrogen (N) uptake based on a field experiment with a typical double cropping system. With a wheat crop in winter, the experiment included five treatments in summer: maize monoculture, soybean monoculture, intercropping of maize and soybean with no N topdressing to maize (N0), intercropping of maize and soybean with 75 kg N ha–1 topdressing to maize (N75), and intercropping of maize and soybean with 180 kg N ha–1 topdressing to maize (N180). All treatments had 45 kg N ha–1 as basal fertilizer. After calibration and corroboration, DNDC was used to simulate long-term (1955 to 2012) treatment effects on yield. Results showed that DNDC could stringently capture the yield and N uptake of the intercropping system under all N management scenarios, though it tended to underestimate wheat yield and N uptake under N0 and N75. Long-term simulation results showed that N75 led to the highest maize and soybean yields per unit planting area among all treatments, increasing maize yield by 59% and soybean yield by 24%, resulting in a land utilization rate 42% higher than monoculture. The results suggest a high potential to promote soybean production by intercropping soybean with maize in the North China Plain, which will help to meet the large national demand for soybean.

Received 17 November, 2017 Accepted 13 March, 2018 ZHANG Yi-tao, E-mail: [email protected]; LIU Jian, E-mail: [email protected]; Correspondence ZHANG Ji-zong, E-mail: [email protected] * These authors contributed equally to this study. © 2018 CAAS. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/) doi: 10.1016/S2095-3119(18)61945-8

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Keywords: maize intercropping with soybean, DNDC, topdressing N, yield, N uptake

1. Introduction The North China Plain is very important to China’s food security, producing nearly 20% of the national grain crops (NBSC 2016). Dominated by rotation of maize (Zea mays L.)-wheat (Triticum aestivum L.) crops, crop production in the North China Plain is greatly intensified with large plant densities and high inputs of irrigation water, chemical fertilizers, and pesticides (Fang et al. 2006, Chen et al. 2014; Ju et al. 2016). However, excessive applications of chemicals have caused severe environmental problems (Zhang et al. 2004) including nitrate pollution of groundwater (Ju et al. 2006), greenhouse gas emissions (Zhang et al. 2012), and soil acidification (Blumenberg et al. 2013). At present, nitrogen (N) loss is one of the main concerns in this region. Large amounts of nitrate leaching are observed during maize growing seasons, as a result of heavy summer rains combined with excessive N application, a common phenomenon in the North China Plain (Ju et al. 2009). Therefore, best management practices are necessary to ensure both agronomic productivity and environmental quality. Potential best management practices lie in sustainable cropping systems that can efficiently utilize solar radiation and land resources with minimal anthropogenic inputs (Zhang et al. 2015a). For example, an intercropping system is demonstrated to have advantages of yield increase and improved light and heat utilization over crop monocultures (Zhang and Li 2003; Liu et al. 2017). An intercropping system usually contains two or more crops grown simultaneously for a certain period of time (Zhang et al. 2007a), so at least two crops can be harvested in one growing season while maintaining the yield of the main crop. Often, intercropping leads to high productivity, effective control of pests and diseases, efficient resource utilization, good ecological services, and better economic benefits (Thierfelder et al. 2012; Xia et al. 2013; Midega et al. 2014; Wu and Wu 2014). As involvement of more crops can result in higher labor costs, in practice only two crops are used in most intercropping systems (Caviglia et al. 2011). Moreover, challenges remain for sowing and harvesting crops as well as weed control in intercropping systems (Feike et al. 2010). Therefore, an optimal intercropping pattern, which is suitable for mechanical operation, is required to compensate for the complexity of field management and labor costs. Among different intercropping patterns, strip intercropping, i.e.,

one crop strip intercropped with another crop strip, is most convenient for mechanical operations (Lesoing and Francis 1999). Cereal intercropping with legumes is one of the most popular options. In a previous study in the North China Plain, Zhang et al. (2015a) successfully intercropped soybean with maize in a strip intercropping system and determined that the optimum ratio of rows of maize and soybean was 4:6, because it allowed for machine operations. Intercropping soybean with cereal crops has very important agronomic and environmental implications. In China, the yield of soybean is commonly low but the price is very high, and the imported, genetically modified soybean is not well accepted by consumers (Zheng and Wang 2013; Wang and Zhu 2016). The main region growing soybean is located in Northeast China, however, the growing area has decreased due to low yield during past years (Iizumi and Ramankutty 2016). To promote soybean production, China’s government aims to increase the soybean growing area to 9.3 million hectares by 2020, an increase of 2.7 million hectares from 2015 according to the Guidelines for Promoting the Development of Soybean Production issued by the Ministry of Agriculture of China. Intercropping soybean during the maize growing season is one of the most promising practices to improve soybean planting area without reducing maize yield. Moreover, maize intercropping with soybean could reduce N use in per unit area compared to maize monoculture, because soybean can fix N in the atmosphere and thus requires little additional fertilizer N inputs, if any (Moyer-Henry et al. 2006; Yang et al. 2015; Zhang et al. 2015a). Nitrogen fixed by legumes could be transferred to maize, which could further increase maize yield and reduce N application to maize strips (Fan et al. 2006). Higher yield can result in more N uptake from soil. Additional N uptake advantages in intercropping systems can be derived from enhanced light use efficiency aboveground and enhanced nutrient (e.g., N) use efficiency belowground (Lv et al. 2014). Moreover, inclusion of legumes in intercropping systems was demonstrated to have positive aftereffect, i.e., benefiting yield production of subsequent crops (Olasantan 1998; Bergkvist et al. 2011; Zhang et al. 2015a). Use of crop models is an important approach to analyzing yield potentials, yield gaps, or N utilization. One of the greatest strengths of model simulations is that verified models can accurately predict yield variabilities and examine long-term effects of different planting patterns using available weather data (Chen et al. 2015; Zhang et al. 2017). Indeed, some models can be used to simulate

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intercropping systems. These models often include competition for light, water, and N, such as ALMANAC (Agricultural Land Management Alternatives with Numerical Assessment Criteria) for weed relay intercropping with wheat (Debaeke et al. 1997), STICS (Simulateur mulTIdisciplinaire pour les Cultures Standard) for pea– barley intercrops (Corre-Hellou et al. 2009), RUE (radiation use efficiency) for wheat-maize relay strip intercropping (Gou et al. 2017), and FASSET (Farm Assessment Tool) for pea intercropped with spring barley (Berntsen et al. 2004). However, the existing models are generally established for full mixtures of intercropping crops and are less suitable for strip intercropping (Gou et al. 2017). In contrast, the most well-known crop models used for research such as DNDC (DeNitrification-DeComposition) and DSSAT (Decision Support System for Agro-technology Transfer) mainly focus on crop monocultures (Song et al. 2009; Min et al. 2011; Zhang et al. 2015b). Whether such common models could be used to simulate intercropping systems has not been reported. Because these models require relatively fewer parameters, they can be potentially widely used if proven to be suitable for simulating intercropping systems. In this study, we focused on the DNDC model, because it is a biogeochemical model that is process-based and it has been verified by field data worldwide (Tonitto et al. 2007; Deng et al. 2011). The model combines biogeochemical processes with hydrological dynamics, and it can be used to simulate physiological processes such as N uptake, N stress, and water stress during growth of various plants (Zhang et al. 2002). A large number of studies have used DNDC to identify the best management practices to achieve yield or environmental goals (Gopalakrishnan et al. 2012; Werner et al. 2012). However, the model is usually used to simulate carbon and N cycles of monoculture systems (Li et al. 2014; Zhang et al. 2015b), and it has not been tested for intercropping systems. Based on its applicability to monoculture simulations, we simulated two crops planted simultaneously to form an intercropping system, and we assumed that different intercropping patterns were reflected via the maximum biomass of two crops. Results of competition for resources aboveground (e.g., light) and belowground (e.g., water and nutrients) could be shown by crop yield. In this study, we calibrated and corroborated the DNDC model based on field experiments that examined crop yield and N uptake as affected by different N application rates for maize and soybean intercropping as well as maize monoculture and soybean monoculture and used the model to simulate the long-term effects of intercropping and N management. The objectives were to: (1) evaluate the applicability of the DNDC model to simulate yield and N uptake for intercropping systems under different N application rates, and (2) identify advantages of

intercropping compared with monoculture based on longterm DNDC simulations.

2. Materials and methods 2.1. Study area The field experiment was conducted at Liucun, Xushui (38°09´–39°09´N, 115°19´–115°46´E) of Hebei Province in the North China Plain from 2011 to 2012. This region has a temperate continental monsoon climate with four distinct seasons. The annual mean temperature is 11.9°C, annual precipitation is 567 mm, and annual evaporation is 1 200 mm. Annual sunshine duration is 2 745 h and the frost-free period is 184 days. Maize rotated with wheat is a common cropping system in this region and this system had been used from 1990 to 2010 on the experimental site. The field was tilled with a disk plough before sowing of maize and wheat. The experimental site had a Haplic Luvisol soil according to FAO Taxonomy. The soil had organic carbon content of 18.56 g kg–1 and total N content of 1.09 g kg–1 in the top 20-cm soil layer. Basic soil information needed for model setup was either determined or obtained from Li (2007).

2.2. Field experiments Experimental design This study was based on the traditional rotation of summer maize and winter wheat (maize-wheat), for which soybean was introduced to be intercropped with maize. The experiment was conducted with a randomized complete block design from June 24th, 2011 to June 17th, 2012. It included five treatments with different crop and N management practices in the summer growing season but identical management for winter wheat. The different summer treatments were: monoculture of maize, monoculture of soybean, and intercropping of maize and soybean with three different N treatments, where maize always received 45 kg N ha–1 basal urea fertilizer (46% N) but with 0 N ha–1 (N0), 75 N ha–1 (N75), or 180 kg N ha–1 (N180) topdressing urea fertilizer at the jointing stage (i.e., 40 days after sowing). Monoculture of maize, monoculture of soybean, and winter wheat were supplied with 225, 45 and 225 kg N ha–1 as urea, respectively. For soybean, all N fertilizers were applied basally, while for monoculture of maize and winter wheat, half of the N was incorporated into the top 20 cm soil as a base fertilizer at sowing and the remaining half was topdressed 40 days after planting. All crops were supplied with 33 kg P ha–1 in calcium superphosphate (12% P) and 62 kg K ha–1 as potassium sulfate (52% K) as basal fertilizers. Topdressing was implemented by broadcasting urea on the soil surface between maize rows. Each treatment was replicated three

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times in plots that have an area of 70 m2 for monoculture of maize or soybean and 110 m2 for intercropping of maize and soybean (Table 1). The detailed planting pattern of the experimental cropping systems is shown in Table 1. For intercropping of maize and soybean, four rows of maize were intercropped with six rows of soybean as recommended by Zhang et al. (2015a). Both maize (variety Zhengdan 958, Henan Academy of Agricultural Sciences) and soybean (variety Zhonghuang 30, Chinese Academy of Agricultural Sciences) were sown on June 24th, 2011 and harvested on October 6th, 2011. After harvest of summer crops, a subsequent winter wheat (variety Tangmai 6, Tangshan Academy of Agricultural Sciences) crop was sown to all plots (at a 15-cm inter-row distance) on October 7th, 2011 and harvested on June 17th, 2012. Throughout the yearly crop rotation, six flooding irrigations were applied: 40, 60, 60, 70, 70, and 70 mm of water at maize growth stages of sowing, and wheat growth stages of sowing, overwintering, erecting, booting and filling, respectively. Sampling and analysis of soil and plants Before plants were sown on June 24th, 2011, soils were sampled randomly across the experimental field using a 5-cm diameter soil auger to determine basic physical and chemical properties. Soil moisture was measured with the oven-drying method: a total of 20 g fresh soil was dried at 105°C for 12 h to a constant weight. Soil organic matter and total N concentrations were determined by the potassium dichromate method and the automatic Kjeldahl method, respectively, after wet digestion (Perez et al. 2001). At harvest, samples of maize, soybean, and wheat were collected from each plot to determine yield and N uptake. Ten plants of maize or soybean were sampled from every row in summer, and wheat plant samples within an area of 2 m2 were taken separately for the previously established maize or soybean strips. Stalks and grains were harvested separately. Plant samples were oven-dried first at 105°C for 30 min and then at 85°C to a constant weight. After grinding, the samples were wet-digested with H2SO4 and H2O2 to determine total N content by the automatic Kjeldahl method (Lithourgidis et al. 2011).

2.3. Model simulations with DNDC Model description Originally, the DNDC model was developed to simulate nitrous oxide emissions from agricultural lands in the United States (Li et al. 1992a, b). During the past 20+ years, the model was expanded to assess C and N turnover in agro-ecosystems, involving the processes of methane emissions, ammonia volatilization, changes in soil organic carbon and soil climate, crop production, and nitrate leaching, etc (Li et al. 1997, 2005, 2006; Li 2000; Deng et al. 2011; Zhang et al. 2015b). Using the inputs of ecological drivers such as meteorological data, soil properties, and crop and nutrient management practices, DNDC simulates soil environments such as temperature, moisture content, oxidation-reduction potential, pH, and substrate concentration gradients. Furthermore, DNDC simulates crop growth and turnover of nutrients including microbial related processes of nitrification, de-nitrification and fermentation. In the DNDC model, there is a component to simulate crop growth, for which photosynthesis, respiration, C allocation, water and N uptake by crops are calculated on a daily basis during simulation. Both water and N uptake rely on several factors such as soil N distribution, soil moisture content, and root length, etc. Water utilization depends on potential transpiration linked with leaf area index and climatic conditions. Water stress is simulated when potential transpiration is relatively higher than normal or actual water supply. Nitrogen need by crop could be calculated according to the optimal crop growth and plant C/N ratio on a daily basis, while plant growth would be inhibited by N stress when plant N uptake is below a critical value. Model setup, calibration and corroboration In this study, we used the newest model version (DNDC95) downloaded from the internet (UNH 2013). The DNDC model was set up with relevant inputs including daily meteorological data (the maximum and minimum temperature, precipitation, wind speed and humidity), atmospheric N deposition and NH3 concentration, soil information (texture, pH, bulk density and SOC content), and field management practices of crop, tillage, fertilization and irrigation. During the

Table 1 Detailed planting pattern of the experimental cropping systems Season

Crop

Summer

Maize monoculture

Winter

Soybean monoculture Intercropped maize Intercropped soybean Wheat

Row spacing (cm) Wide space, 80 cm; narrow space, 50 cm 35 50 30 15

Plant spacing (cm) 25

20 25 20 –

Width of strip Spacing between crop Plot area (cm) strips (cm) (m2) – – 70

– 150 150 –

–

70

30

110

–

70 or 110

Crop density (plant ha–1) 60 984

283 216 44 000 165 200 3 000 000

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Adjustment value 0.25 1.53 0.49 0.0250 7.8 0.22 0.451 0.01077 12.95 1.24

Data source Measurement Measurement Default Default Li (2007) Default Default Measurement Measurement Measurement

Stem 80 65 45 25 95 50 Leaf 80 55 45 20 95 30 Grain 50 40 10 8 40 25 Stem 0.22 0.22 0.22 0.22 0.21 0.20 Leaf 0.22 0.22 0.22 0.22 0.21 0.20 Wheat

Default value 0.19 – 0.49 0.0250 – 0.22 0.451 – – –

Intercropped soybean

Soil parameter Clay fraction Bulk density (g cm–3) Field capacity Hydro-conductivity (S m–1) Soil pH Wilting point Porosity SOC (kg C kg–1) Nitrate (mg N kg–1) Ammonium (mg N kg–1)

Parameter

Table 2 Parameter adjustments for soil

Table 3 Parameter adjustments for crops

Where, Yim (kg ha–1) and Yis (kg ha–1) are respective yields of intercropped maize and soybean per ha intercropping area, and Ysm (kg ha–1) and Yss (kg ha–1) are yields of maize and soybean in monoculture. An intercropping system presents yield advantage over crop monoculture if LER is greater than 1.00. In the field experiment, N uptake by a crop (Nup, kg ha–1) was calculated according to: (2) Nup=M×Ncon Where, M is the crop dry matter (kg ha–1) at harvest and Ncon is the N concentration in the plant (%). Nitrogen uptake by grain and stalk was calculated individually and summed up to obtain total N uptake.

Max. biomass production (kg C ha–1 yr–1) Grain Leaf Stem Default value 4 124 2 268 2 268 Adjustment value 3 500 1 540 1 540 Default value 1 229 773 773 Adjustment value 1 180 519 519 Default value 3 120 1 598 1 598 Adjustment value 3 690 1 476 1 476

Biomass fraction

Land equivalent ratio (LER), which is usually considered as an indicator of intercropping benefit (Tariah and Wahua 1985), was calculated according to: Y Y LER= im + is (1) Ysm Yss

Intercropped maize

2.4. Data analysis

Grain 0.40 0.50 0.35 0.50 0.41 0.50

Biomass C/N ratio

Thermal Water demand N fixation index degree days (g water g–1 dry (Crop N/Soil N) matter) for maturity 2 550 150 1 2 500 220 0 1 500 350 2.5 2 500 120 3 1 300 200 1 2 200 190 0

Optimum temperature (°C) 30 30 25 25 22 16

simulation process, the 50-cm soil profile was characterized as most of the crop roots were concentrated from 0 to 50 cm. In order to demonstrate the applicability of DNDC to simulating intercropping, the model was first calibrated with the N180 treatment of intercropping of maize and soybean then rotation with wheat. Simulated crop yield and N uptake were compared with the measured values. Comparisons of default and adjusted values (based on measurements or previous studies (Li 2007)) for soil and crop parameters are shown in Tables 2 and 3, respectively. After calibration, the DNDC simulated crop yield and N uptake were corroborated with the observed values in the treatments of N0 and N75 in the cropping system of maize intercropped with soybean then rotation with wheat. Finally, the verified model was used to simulate long-term yield production in different cropping systems with climatic data from 1955 to 2012. Taking the first five years from 1955 to 1959 as the spin-up time, the objective of these simulations was to examine if summer intercropping had a yield advantage compared to traditional maize-wheat rotation over the long-term. In the simulation process, parameters of max biomass production of intercropped maize and soybean were set up individually. The DNDC model could not separate N application to the intercropped crops, so maize and soybean were simulated with the same fertilization regime during their simultaneous growth period. This was different from the conventional farming practice that intercropped maize received N topdressing and intercropped soybean was only supplied with basal N. However, as soybean can fix atmospheric N and N is not a limiting factor, the N applied to soybean in the model would not affect its production and N uptake.

Crops

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3. Results 3.1. Model calibration of maize intercropping with soybean and its aftereffect on winter wheat The DNDC simulated crop yields were strongly correlated with the observed yields of the intercropped summer maize and soybean and the succeeding winter wheat under the treatment of N180, in which maize received 45 kg ha–1 basal N before sowing and was topdressed with 180 kg N ha–1 at the jointing stage (Fig. 1-E). Specifically, the observed yields of the intercropped maize and soybean were (7 605±488) and

(1 915±40) kg ha–1, respectively, and the yield of winter wheat was (7 408±330) kg ha–1. The simulated yields of these crops (7 345 kg ha–1 for intercropped maize, 1 915 kg ha–1 for intercropped soybean and 7 843 kg ha–1 for wheat) were stringent (“very good”) for capturing the magnitudes of field observations. Similar to yields, DNDC also performed very well in capturing the magnitudes of plant N uptake (Fig. 1-F). Simulated N uptake of intercropped maize and soybean were 122 and 130 kg ha–1, respectively, in comparison with the measured N uptake of (129±9) kg ha–1 for intercropped maize and (127±3) kg ha –1 for intercropped soybean.

Simulated yield A 8 000

Crop N uptake (kg ha–1)

Crop yield (kg ha–1)

7 000 6 000 5 000 4 000 3 000 2 000 1 000 0 C

Maize

Soybean

D Crop N uptake (kg ha–1)

Crop yield (kg ha–1)

7 000 6 000 5 000 4 000 3 000 2 000 1 000

E

9 000

Maize

Soybean

F

N180

Crop N uptake (kg ha–1)

Crop yield (kg ha–1)

8 000 7 000 6 000 5 000 4 000 3 000 2 000 1 000 0

Maize

Soybean

Wheat

150 100 50

250

Maize

Soybean

Wheat

N75

200 150 100 50 0

Wheat

N0

200

0

Wheat

N75

8 000

0

Measured yield B 250

N0

2795

250

Maize

Soybean

Wheat

N180

200 150 100 50 0

Maize

Soybean

Wheat

Fig. 1 Comparisons of measured and simulated yields and N uptake of maize, soybean and wheat under different treatments in the cropping system of maize intercropped with soybean then rotation with wheat. N0, 45 kg ha–1 basal N without topdressing; N75, 45 kg ha–1 basal N and 75 kg ha–1 topdressing N to maize; N180, 45 kg ha–1 basal N and 180 kg ha–1 topdressing N to maize.

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Simulated and measured wheat N uptake were 203 kg ha–1 and (217±16) kg ha–1, respectively.

3.2. Model corroboration of maize intercropping with soybean and its aftereffect on winter wheat Comparisons between simulated and observed results demonstrated that the DNDC model stringently (“very well”) captured the magnitudes and patterns of yield and N uptake of intercropped maize and soybean for both treatments of N0 and N75 (Fig. 1-A–D). However, despite the accurate simulation of wheat yield by the model for N75, it underestimated the wheat yield of N0 and N uptake of both N0 and N75. Along with the comparisons for simulating N180, the results indicated that the model could predict yield production of all N management scenarios in the experimental of maize intercropped with soybean then rotation with wheat. The model could also accurately simulate N uptake of the intercropped crops but tended to underestimate N uptake by the succeeding crop, especially when a relatively low rate or no N was applied to the previous maize crop.

3.3. Simulation of monoculture of summer maize or soybean and its aftereffect on winter wheat Notably, some of the parameter values obtained from simulating intercropping had to be recalibrated to simulate monoculture of summer maize or soybean. The maximum grain production of maize was adjusted to 4 500 for maize monoculture and even more parameters were adjusted for soybean monoculture to achieve yield and N uptake that were comparable to field observations. Specifically, the adjusted parameters for soybean monoculture included maximum biomass production, biomass fraction and biomass C/N ratio of grain, leaf, and stem, respectively (Table 4). After calibration, crop yields in both maize-wheat and soybean-wheat rotations were accurately simulated by the model (Fig. 2). For maize-wheat rotation, the observed yields were (9 630±115) and (7 398±743) kg ha–1, respectively, compared to the simulated yields of 9 728 and 7 428 kg ha–1. For soybean-wheat rotation, the yields

were (3 775±100) and (7 188±650) kg ha–1 compared to the simulated yields of 3 903 and 8 058 kg ha–1. Similarly, N uptake of all crops except wheat in maize-wheat rotation was all accurately simulated (Fig. 2). Specifically, maize-wheat rotation took up (160±3) kg N ha–1 in maize and (229±11) kg N ha–1 in wheat in the field, while uptake of N by wheat was underestimated by 37 kg ha–1 in the model simulation. The observed N uptake of soybean-wheat was (237±2) and (210±12) kg ha–1 compared to 237 and 205 kg ha–1 simulated by the model.

3.4. Long-term impacts of intercropping on yield and advantages of intercropping For the simulations from 1960 to 2012, compared with monoculture of maize or soybean, intercropping patterns increased yields of both maize and soybean per unit area. Over the three N management scenarios, intercropping increased maize yield by 48–59% per hectare of maize growing area and increased soybean yield by 19–24% per hectare of soybean growing area. While the yield of intercropped soybean varied very slightly among the three N topdressing treatments ((2 630±625) kg ha–1 yr–1 in N0, (2 607±650) kg ha–1 yr–1 in N75, and (2 490±779) kg ha–1 yr–1 in N180), the yield of intercropped maize was significantly affected by different N topdressing rates (Fig. 3). Given no N topdressing (N0), maize yield was as low as (3 268±179) kg ha–1, which was smaller than half of that with N topdressing of 75 kg ha–1 ((7 386±812) kg ha–1) and N topdressing of 180 kg ha–1 ((6 888±1 120) kg ha–1). Notably, N75 had even higher and more stable maize yield than N180. Therefore, a rate of 75 kg N ha–1 could be considered as the optimal N topdressing rate for maize in the maize and soybean intercropping system. With regard to the aftereffect, yield of winter wheat was affected by the previous crops and N management scenarios (Fig. 3). The highest wheat yield of (8 630±542) kg ha–1 was achieved in the treatment with soybean monoculture compared to 7 373–8 418 kg ha–1 in other treatments. The second highest wheat yield was found in the N180 treatment. The N0 and N180 treatments resulted in similar wheat yields, which were lower than that in maize monoculture. Driven by precipitation, crop yields changed dramatically

Table 4 Parameter adjustments for management of crop monoculture1) Crop Maize Soybean

1)

Parameter Max. biomass production (kg C ha–1 yr–1) Max. biomass production (kg C ha–1 yr–1) Biomass fraction Biomass C/N ratio

Only those differing from Table 3 are listed.

Default value Grain Leaf Stem 4 124 2 268 2 268 1 229 773 773 0.35 0.22 0.22 10 45 45

Adjustment value Grain Leaf Stem 4 500 1 540 1 540 1 900 844 844 0.45 0.20 0.20 10 25 25

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Measured yield

Simulated yield B Crop N uptake (kg ha–1)

10 000 8 000 6 000 4 000 2 000 0

Crop yield (kg ha–1)

C

Maize

300 250 200 150 100 50 0

Wheat

9 000

D 300

8 000

250

Crop N uptake (kg ha–1)

Crop yield (kg ha–1)

A 12 000

7 000 6 000 5 000 4 000 3 000 2 000 1 000 0

Soybean

Wheat

2797

Maize

Wheat

Soybean

Wheat

200 150 100 50 0

Fig. 2 Comparisons of measured and simulated crop yields and N uptake in the cropping systems of maize-wheat (A and B) and soybean-wheat (C and D).

during the 53 years of simulation. For example, extremely low yields of maize and soybean were simulated in 1965, 1968, 1975, 1984, 1997 and 2000, where annual precipitations were only 307, 393, 207, 290, 301, and 242 mm, respectively. The results indicated that suitable water management needs to be adopted in addition to crop and N management to achieve high crop yields. Evaluation on the land utilization rate (LER) showed that when appropriate N management was provided, intercropping of maize and soybean had a yield advantage over maize or soybean monoculture. The LER values of N75 (average 1.42 with range of 1.04–1.61) and N180 (average 1.32 with range of 0.96–1.46) were both greater than 1, and the land utilization rates in these systems were 32–42% higher than the rates in the two monocultures. In contrast, the LER value of N0 (average 0.99 with range of 0.88–1.27) was lower than 1, indicating a lack of intercropping advantage.

4. Discussion Generally, well managed intercropping could produce more yield than crop monoculture, which is an important measure to enhance food security. Historically, intercropping was widely used by farmers in China (Li et al. 2001; Zhang

et al. 2007b), but its usage has declined in recent years with the increasing labor price and demand of suitable machineries (Feike et al. 2012). Optimally, intercropping should be designed to accommodate existing machineries so that farmers are willing to adopt this practice. In a previous field study, Zhang et al. (2015a) confirmed that strip intercropping, which could both reduce labor inputs and be operated by plant seeders and harvesters, had a high potential for use in the North China Plain. However, uncertainties remain with regard to N management and weather effects on the efficiency of strip intercropping. While extensive evaluations are almost impossible to accomplish using field experiments due to limitations of time and costs, model applications can provide more efficient evaluations. This study confirmed that the DNDC model could accurately simulate yield and N uptake of intercropped maize and soybean under different N management strategies. In the past, DNDC was mainly used to simulate C and N biogeochemistry in agro-ecosystems as well as yield production of crop monocultures (Zhang et al. 2017). The confirmation of DNDC’s ability to simulate intercropping systems in this study provides important insights to expand the application of this model. Use of DNDC could also potentially compensate for the weakness of many current models that are used to assess yield potentials or

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10 000 8 000 6 000 4 000 2 000 0

Intercropped maize under N0 Intercropped maize under N180

Intercropped maize under N75 Monoculture of maize

1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012

Maize yield (kg ha–1)

12 000

6 000 5 000

Intercropped soybean under N0 Intercropped soybean under N180

Intercropped soybean under N75 Monoculture of soybean

4 000 3 000 2 000 1 000 0

1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012

Soybean yield (kg ha–1)

7 000

10 000 Wheat yield (kg ha–1)

9 000 8 000 7 000 6 000 5 000 4 000 3 000 2 000 1 000

Wheat after intercropping of maize and soybean under N0 Wheat after intercropping of maize and soybean under N75 Wheat after intercropping of maize and soybean under N180 Wheat after monoculture of maize Wheat after monoculture of soybean 1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012

0

Fig. 3 Comparisons of long-term crop yields in different crop and N treatments simulated with the DeNitrification-DeComposition (DNDC) model. N0, 45 kg ha–1 basal N without topdressing; N75, 45 kg ha–1 basal N and 75 kg ha–1 topdressing N to maize; N180, 45 kg ha–1 basal N and 180 kg ha–1 topdressing N to maize.

yield gaps in crop monocultures only (Liang et al. 2011; van Ittersum et al. 2013; Zhang et al. 2015b). Notably, however, different values were needed for some crop parameters to best simulate intercropping and maize or soybean monoculture. This is most likely because crop growth patterns in intercropping are different from that in a monoculture due to interactions between the crops (Zhang and Li 2003; Zuo and Zhang 2008; Betencourt et al. 2012). The model also accurately predicted yield and N uptake of

wheat, which was grown following the intercropping, in the high N topdressing treatment (N180). However, the model tended to underestimate yield and N uptake of wheat when a low rate of N (N75) or no N (N0) were topdressed to the previous maize crop. This may be due to an underestimation of the aftereffect of intercropping on wheat growth by the model or an underestimation of atmospheric N deposition during the wheat growing season. In the future, the DNDC model should be further developed in the aspect of modeling

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the aftereffect of intercropping. Despite fluctuation of yields with years due to weather conditions, long-term simulations showed constant yield advantage of intercropping over monoculture. The differences in crop yields between intercropping and monoculture may be partly due to different plant densities in the two systems. Notably, however, the plant densities implemented in this study were consistent with farmers’ conventional practices. More importantly, the yield advantage of intercropping results from a balance between interspecific facilitation and competition (Zhang and Li 2003; Zuo and Zhang 2008; Betencourt et al. 2012). Facilitative interactions, such as efficient utilization of photosynthetically active radiation or higher radiation use efficiency (Liu et al. 2017), can improve crop growth and nutrient utilization, while two crops completing for water and nutrients in intercropping systems could drive atmospheric N2 fixation in soybean by Rhizobium (Corre-Hellou et al. 2006). The simulated results revealed that the higher rate of N topdressing (N180) did not achieve higher maize yield compared to that of the low N rate (N75). This may be an indication that there were fewer facilitative interactions in N180 due to sufficient supply of N to maize. Obviously, however, N topdressing to maize is needed to produce high grain yield in intercropping because no N topdressing led to low yields of the intercropped maize and the succeeding wheat crop. In the long-term simulation, intercropped maize and soybean with topdressing N of 75 kg ha–1 to the system increased crop yield by 59 and 24% compared with monoculture of maize and soybean, respectively. The LER value of N75 was 1.42, greater than 1, which indicated that the land utilization rate was 42% higher than the rates of two monocultures. Moreover, the results suggest that yield of soybean could be increased through intercropping with maize in the North China Plain, which will help to meet the high consumer demand for soybean in China and alleviate reliance on importing soybean from other countries. It should be noted that crop yields in the low precipitation years are very likely to be underestimated in long-term simulations. In practice, farmers would adopt some additional measures such as irrigation or soil mulching to combat drought conditions. However, the simulation results seem to be valid because the same patterns were simulated under all weather conditions.

5. Conclusion This study demonstrated that the DNDC model could be used to simulate yield production and N uptake in intercropping systems. The model stringently captured the yield and N uptake of intercropping maize and soybean under different N management scenarios in the summer.

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The model tended to underestimate yield and N uptake of the wheat crop grown after intercropping when intercropped maize received no or a low rate of topdressing N. This indicated a need to improve the ability of the DNDC model to simulate the aftereffect of intercropping. Long-term model simulations suggested that intercropping had greater agronomic efficiency than crop monoculture. Topdressing of 75 kg N ha–1 to the intercropped maize produced higher crop yield than topdressing of 180 kg ha–1 or no topdressing, providing application of the same rate of N (i.e., 45 kg N ha–1) as a basal fertilizer. Over the long term, intercropping maize and soybean could increase their yields by up to 59 and 24%, respectively, in per hectare growing area, compared to monoculture. Intercropping with N75 resulted in a LER value of 1.42. Furthermore, the results suggest a large potential to increase China’s soybean production by intercropping soybean with maize in the North China Plain.

Acknowledgements This research was supported by the National Natural Science Foundation of China (31701995 and 31572208), the National Key Research & Development Program of China (2016YFD0800101), the Newton Fund of UK-China (BB/N013484/1). This paper was also supported by China Scholarship Council (2015-7169).

References Bergkvist G, Stenberg M, Wetterlind J, Bath B, Elfstrand S. 2011. Clover cover crops under-sown in winter wheat increase yield of subsequent spring barley - Effect of N dose and companion grass. Field Crops Research, 120, 292–298. Berntsen J, Hauggard-Nielsen H, Olesen J E, Petersen B M, Jensen E S, Thomsen A. 2004. Modelling dry matter production and resource use in intercrops of pea and barley. Field Crops Research, 88, 69–83. Betencourt E, Duputel M, Colomb B, Desclaux D, Hinsinger P. 2012. Intercropping promotes the ability of durum wheat and chickpea to increase rhizosphere phosphorus availability in a low P soil. Soil Biology & Biochemistry, 46, 181–190. Blumenberg M, Berndmeyer C, Moros M, Muschalla M, Schmale O, Thiel V. 2013. Bacteriohopanepolyols record stratification, nitrogen fixation and other biogeochemical perturbations in Holocene sediments of the central Baltic Sea. Biogeosciences, 10, 2725–2735. Caviglia O P, Sadras V O, Andrade F H. 2011. Yield and quality of wheat and soybean in sole- and double-cropping. Agronomy Journal, 103, 1081–1089. Chen H X, Zhao Y, Feng H, Li H J, Sun B H. 2015. Assessment of climate change impacts on soil organic carbon and crop yield based on long-term fertilization applications in Loess Plateau, China. Plant and Soil, 390, 401–417. Chen X P, Cui Z L, Fan M S, Vitousek P, Zhao M, Ma W Q,

2800

ZHANG Yi-tao et al. Journal of Integrative Agriculture 2018, 17(12): 2790–2801

Wang Z L, Zhang W J, Yan X Y, Yang J C, Deng X P, Gao Q, Zhang Q, Guo S W, Ren J, Li S Q, Ye Y L, Wang Z H, Huang J L, Tang Q Y, et al. 2014. Producing more grain with lower environmental costs. Nature, 514, 486–489. Corre-Hellou G, Faure M, Launay M, Brisson N, Crozat Y. 2009. Adaptation of the STICS intercrop model to simulate crop growth and N accumulation in pea-barley intercrops. Field Crops Research, 113, 72–81. Corre-Hellou G, Fustec J, Crozat Y. 2006. Interspecific competition for soil N and its interaction with N2 fixation, leaf expansion and crop growth in pea-barley intercrops. Plant and Soil, 282, 195–208. Debaeke P, Caussanel J P, Kiniry J R, Kafiz B, Mondragon G. 1997. Modelling crop: Weed interactions in wheat with ALMANAC. Weed Research, 37, 325–341. Deng J, Zhou Z, Zhu B, Zheng X, Li C, Wang X, Jian Z. 2011. Modeling nitrogen loading in a small watershed in southwest China using a DNDC model with hydrological enhancements. Biogeosciences, 8, 2999–3009. Fan F, Zhang F, Song Y, Sun J, Bao X, Guo T, Li L. 2006. Nitrogen fixation of faba bean (Vicia faba L.) interacting with a non-legume in two contrasting intercropping systems. Plant and Soil, 283, 275–286. Fang Q X, Yu Q, Wang E L, Chen Y H, Zhang G L, Wang J, Li L H. 2006. Soil nitrate accumulation, leaching and crop nitrogen use as influenced by fertilization and irrigation in an intensive wheat-maize double cropping system in the North China Plain. Plant and Soil, 284, 335–350. Feike T, Chen Q, Graeff-Honninger S, Pfenning J, Claupein W. 2010. Farmer-developed vegetable intercropping systems in southern Hebei, China. Renewable Agriculture and Food Systems, 25, 272–280. Feike T, Doluschitz R, Chen Q, Graeff-Honninger S, Claupein W. 2012. How to overcome the slow death of intercropping in the North China Plain. Sustainability, 4, 2550–2565. Gopalakrishnan G, Negri M C, Salas W. 2012. Modeling biogeochemical impacts of bioenergy buffers with perennial grasses for a row-crop field in Illinois. Global Change Biology Bioenergy, 4, 739–750. Gou F, van Ittersum M K, van der Werf W. 2017. Simulating potential growth in a relay-strip intercropping system: Model description, calibration and testing. Field Crops Research, 200, 122–142. Iizumi T, Ramankutty N. 2016. Changes in yield variability of major crops for 1981–2010 explained by climate change. Environmental Research Letters, 11, 034003. van Ittersum M K, Cassman K G, Grassini P, Wolf J, Tittonell P, Hochman Z. 2013. Yield gap analysis with local to global relevance - A review. Field Crops Research, 143, 4–17. Ju X T, Gu B J, Wu Y Y, Galloway J N. 2016. Reducing China’s fertilizer use by increasing farm size. Global Environmental Change (Human and Policy Dimensions), 41, 26–32. Ju X T, Kou C L, Zhang F S, Christie P. 2006. Nitrogen balance and groundwater nitrate contamination: Comparison among three intensive cropping systems on the North China Plain. Environmental Pollution, 143, 117–125. Ju X T, Xing G X, Chen X P, Zhang S L, Zhang L J, Liu X J, Cui Z L, Yin B, Christie P, Zhu Z L, Zhang F S. 2009.

Reducing environmental risk by improving N management in intensive Chinese agricultural systems. Proceedings of the National Academy of Sciences of the United States of America, 106, 3041–3046. Lesoing G W, Francis C A. 1999. Strip intercropping effects on yield and yield components of corn, grain sorghum, and soybean. Agronomy Journal, 91, 807–813. Li C S. 2000. Modeling trace gas emissions from agricultural ecosystems. Nutrient Cycling in Agroecosystems, 58, 259–276. Li C S, Farahbakhshazad N, Jaynes D B, Dinnes D L, Salas W, McLaughlin D. 2006. Modeling nitrate leaching with a biogeochemical model modified based on observations in a row-crop field in Iowa. Ecological Modelling, 196, 116–130. Li C S, Frolking S, Crocker G J, Grace P R, Klir J, Korchens M, Poulton P R. 1997. Simulating trends in soil organic carbon in long-term experiments using the DNDC model. Geoderma, 81, 45–60. Li C S, Frolking S, Frolking T A. 1992a. A model of nitrousoxide evolution from soil driven by rainfall events .1. Model structure and sensitivity. Journal of Geophysical Research (Atmospheres), 97, 9759–9776. Li C S, Frolking S, Frolking T A. 1992b. A model of nitrousoxide evolution from soil driven by rainfall events. 2. Model applications. Journal of Geophysical Research (Atmospheres), 97, 9777–9783. Li C S, Frolking S, Xiao X M, Moore B, Boles S, Qiu J J, Huang Y, Salas W, Sass R. 2005. Modeling impacts of farming management alternatives on CO2, CH4, and N2O emissions: A case study for water management of rice agriculture of China. Global Biogeochemical Cycles, 19, GB3010. Li H, Wang L G, Qiu J J, Li C S, Gao M F, Gao C Y. 2014. Calibration of DNDC model for nitrate leaching from an intensively cultivated region of Northern China. Geoderma, 223, 108–118. Li L, Sun J H, Zhang F S, Li X L, Yang S C, Rengel Z. 2001. Wheat/maize or wheat/soybean strip intercropping I. Yield advantage and interspecific interactions on nutrients. Field Crops Research, 71, 123–137. Li X. 2007. Fate of fertilizer nitrogen and gaseous n loss in winter wheat-summer maize rotation system in North China Plain. MSc thesis, Agricultural University of Hebei, China. (in Chinese) Liang W L, Peter C, Wang G Y, Lu R H, Lu H Z, Xia A P. 2011. Quantifying the yield gap in wheat-maize cropping systems of the Hebei Plain, China. Field Crops Research, 124, 180–185. Lithourgidis A S, Vlachostergios D N, Dordas C A, Damalas C A. 2011. Dry matter yield, nitrogen content, and competition in pea-cereal intercropping systems. European Journal of Agronomy, 34, 287–294. Liu X, Rahman T, Yang F, Song C, Yong T W, Liu J, Zhang C Y, Yang W Y. 2017. PAR Interception and utilization in different maize and soybean intercropping patterns. PLoS ONE, 12, e0169218. Lv Y, Francis C, Wu P T, Chen X L, Zhao X N. 2014. Maizesoybean intercropping interactions above and below ground. Crop Science, 54, 914–922.

ZHANG Yi-tao et al. Journal of Integrative Agriculture 2018, 17(12): 2790–2801

Midega C A O, Salifu D, Bruce T J, Pittchar J, Pickett J A, Khan Z R. 2014. Cumulative effects and economic benefits of intercropping maize with food legumes on Striga hermonthica infestation. Field Crops Research, 155, 144–152. Min J, Zhao X, Shi W M, Xing G X, Zhu Z L. 2011. Nitrogen balance and loss in a greenhouse vegetable system in southeastern China. Pedosphere, 21, 464–472. Moyer-Henry K A, Burton J W, Israel D, Rufty T. 2006. Nitrogen transfer between plants: A 15 N natural abundance study with crop and weed species. Plant and Soil, 282, 7–20. NBSC (National Bureau of Statistics of China). 2016. China Statistical Yearbook. China Statistics Press, Beijing. (in Chinese) Olasantan F O. 1998. Effects of preceding maize (Zea mays) and cowpea (Vigna unguiculata) in sole cropping and intercropping on growth, yield and nitrogen requirement of okra (Abelmoschus esculentus). Journal of Agricultural Science, 131, 293–298. Perez D V, de Alcantara S, Arruda R J, Meneghelli N D A. 2001. Comparing two methods for soil carbon and nitrogen determination using selected Brazilian soils. Communications in Soil Science and Plant Analysis, 32, 295–309. Song X Z, Zhao C X, Wang X L, Li J. 2009. Study of nitrate leaching and nitrogen fate under intensive vegetable production pattern in northern China. Comptes Rendus Biologies, 332, 385–392. Tariah N, Wahua T. 1985. Effects of component populations on yields and land equivalent ratios of intercropped maize and cowpea. Field Crops Research, 12, 81–89. Thierfelder C, Cheesman S, Rusinamhodzi L. 2012. A comparative analysis of conservation agriculture systems: Benefits and challenges of rotations and intercropping in Zimbabwe. Field Crops Research, 137, 237–250. Tonitto C, David M B, Drinkwater L E, Li C S. 2007. Application of the DNDC model to tile-drained Illinois agroecosystems: Model calibration, validation, and uncertainty analysis. Nutrient Cycling in Agroecosystems, 78, 51–63. UNH (University of New Hampshire). 2013. The DNDC model. [2013-10-18]. http://www.dndc.sr.unh.edu Wang C F, Zhu Y Y. 2016. Investigation of transgenic soybean components in soybean from an area of China. Journal of the Science of Food and Agriculture, 96, 3169–3172. Werner C, Haas E, Grote R, Gauder M, Graeff-Honninger S, Claupein W, Butterbach-Bahl K. 2012. Biomass production potential from Populus short rotation systems in Romania. Global Change Biology Bioenergy, 4, 642–653. Wu K X, Wu B Z. 2014. Potential environmental benefits of intercropping annual with leguminous perennial crops in Chinese agriculture. Agriculture Ecosystems & Environment, 188, 147–149.

2801

Xia H Y, Wang Z G, Zhao J H, Sun J H, Bao X G, Christie P, Zhang F S, Li L. 2013. Contribution of interspecific interactions and phosphorus application to sustainable and productive intercropping systems. Field Crops Research, 154, 53–64. Yang F, Wang X C, Liao D P, Lu F Z, Gao R C, Liu W G, Yong T W, Wu X L, Du J B, Liu J, Yang W Y. 2015. Yield response to different planting geometries in maize soybean relay strip intercropping systems. Agronomy Journal, 107, 296–304. Zhang F, Shen J, Li L, Liu X. 2004. An overview of rhizosphere processes related with plant nutrition in major cropping systems in China. Plant and Soil, 260, 89–99. Zhang F S, Li L. 2003. Using competitive and facilitative interactions in intercropping systems enhances crop productivity and nutrient-use efficiency. Plant and Soil, 248, 305–312. Zhang J, Hu K L, Li K J, Zheng C L, Li B G. 2017. Simulating the effects of long-term discontinuous and continuous fertilization with straw return on crop yields and soil organic carbon dynamics using the DNDC model. Soil & Tillage Research, 165, 302–314. Zhang L, Spiertz J H J, Zhang S, Li B, Werf W. 2007a. Nitrogen economy in relay intercropping systems of wheat and cotton. Plant and Soil, 303, 55–68. Zhang L, van der Werf W, Zhang S, Li B, Spiertz J H J. 2007b. Growth, yield and quality of wheat and cotton in relay strip intercropping systems. Field Crops Research, 103, 178–188. Zhang Y, Li C S, Zhou X J, Moore B. 2002. A simulation model linking crop growth and soil biogeochemistry for sustainable agriculture. Ecological Modelling, 151, 75–108. Zhang Y T, Liu J, Zhang J Z, Liu H B, Liu S, Zhai L M, Wang H Y, Lei Q L, Ren T Z, Yin C B. 2015a. Row ratios of intercropping maize and soybean can affect agronomic efficiency of the system and subsequent wheat. PLoS ONE, 10, e0129245. Zhang Y T, Wang H Y, Liu S, Lei Q L, Liu J, He J Q, Zhai L M, Ren T Z, Liu H B. 2015b. 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, 388–398. Zhang Y Y, Liu J F, Mu Y J, Xu Z, Pei S W, Lun X X, Zhang Y. 2012. Nitrous oxide emissions from a maize field during two consecutive growing seasons in the North China Plain. Journal of Environmental Sciences, 24, 160–168. Zheng S, Wang Z G. 2013. Pricing efficiency in the Chinese NGM and GM soybean futures market. China: An International Journal, 11, 48–67. Zuo Y M, Zhang F S. 2008. Effect of peanut mixed cropping with gramineous species on micronutrient concentrations and iron chlorosis of peanut plants grown in a calcareous soil. Plant and Soil, 306, 23–36.

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