Substituting ecological intensification of agriculture for conventional agricultural practices increased yield and decreased nitrogen losses in North China

Applied Soil Ecology 147 (2020) 103395

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Substituting ecological intensification of agriculture for conventional agricultural practices increased yield and decreased nitrogen losses in North China

T

Sami Ullah, Chao Ai, Shaohui Huang, Dali Song, Tanveer Abbas, Jiajia Zhang, Wei Zhou, Ping He* Ministry of Agriculture Key Laboratory of Plant Nutrition and Fertilizer, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100081, PR China

A R T I C LE I N FO

A B S T R A C T

Keywords: Ecological intensification Potential nitrification activity Potential denitrification activity N cycling genes N2O emissions GHG emissions

There is global concern about the adverse impacts of conventional agricultural practices on the environment. Recent evidence has shown that ecological intensification (EI) of agriculture can safeguard the environment from negative impacts of agricultural practices and simultaneously produce substantially higher crop productivity. Here, we employed the concept of EI and compared it with conventional agriculture or farmer’s practice (FP). We explored the effects of EI and FP treatments on maize yield, N losses via potential nitrification activity (PNA), potential denitrification activity (PDA), N2O emissions, greenhouse gas (GHG) emissions, and nitrogen (N) cycling microbial populations associated with nitrification and denitrification in fluvo-aquic soil and black soil of North China. There were four treatments, i.e., EI N-, FP N-, EI N+, FP N + at each site, - and + indicate no N addition and N addition, respectively. The results revealed that across the two soils, N addition increased PNA and PDA; however, compared with the FP N + treatment, lower PNA and PDA were observed in the EI N + treatment. Similarly, the abundance of N cycling genes, including AOA amoA and AOB amoA, for nitrification and nirS, nirK, and nosZ for denitrification were significantly increased under N addition, and compared with the FP N + treatment, reduced abundance was noted in the EI N + treatment. N2O and GHG emissions were quantified, and it was observed that, in comparison to the FP treatment, reduced N2O and GHG emissions occurred in EI treatments in the two locations. EI with best management practices also increased crop yield relative to FP. Owing to higher N rates in FP treatments, substantial soil acidification was noted in FP plots but not in EI plots. In addition, PNA and PDA were significantly positively linked with soil nitrifying and denitrifying communities, particularly in the black soil. Moreover, the N availability pathway rather than soil acidification mainly regulated N cycling microbial communities. Our results suggest that EI could be a sustainable and environmentally friendly approach due to higher crop productivity and lower N losses via PNA, PDA, N2O, and GHG emissions, thus preventing the negative impact of agricultural practices, especially N fertilization, on the environment.

1. Introduction The human population has hit the highest numbers ever recorded, and in upcoming decades, we will observe a tremendous increase in food demand that will definitely create huge pressure to produce more food from the same amount of arable land (Godfray et al., 2010). In fact, agricultural land has only increased by 9 % while human population doubled since 1961 (Pretty, 2007; UN, 2019). To address this issue, scientists have proposed the Green Revolution, which is characterized by the addition of inorganic fertilizers, pesticides,

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conventional tillage, and crop breeding (Tilman et al., 2001). Although conventional agriculture or agricultural intensification has successfully solved the issue of food security by increasing crop productivity, substantial negative impacts on biodiversity and the environment are becoming more evident (Moss, 2007; Potts et al., 2010). It is well accepted that crop production is not possible without fertilization, particularly nitrogen (N) fertilization, which has a critical role in crop production (Gruber and Galloway, 2008). There is no doubt that N fertilization has improved crop productivity but has also resulted in serious environmental issues, such as elevated greenhouse gas emissions, e.g., nitric

Corresponding author. E-mail address: [email protected] (P. He).

https://doi.org/10.1016/j.apsoil.2019.103395 Received 8 August 2019; Received in revised form 19 October 2019; Accepted 20 October 2019 Available online 31 October 2019 0929-1393/ © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).

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Likewise, Northeast China is dominated by ‘black soil’ (Mollisol). This region accounts for 14 % grain production in China (Xu et al., 2010). Over the past several decades with the aim of obtaining higher yields, excessive fertilization has been practiced in these two regions of China, which has profoundly degraded soil quality and negatively impacted the environment, including elevated greenhouse gas (GHG) emissions (Cui et al., 2008, 2013; Xu et al., 2016). It is noteworthy that previous studies examining the response of soil N cycling microbial populations under long-term fertilization were mainly based on a single location; furthermore, these studies were conducted with different sets of treatments or fertilization approaches (Wang et al., 2018; Yin et al., 2015), which cannot provide detailed information about how these microbial communities respond to fertilization under different soil types. This study focuses on the response of soil N transformation-related microbial populations and N2O and GHG emissions under an EI system in multiple locations with the same set of treatments, which will help us to understand the dynamics of N transformation processes under different soil types. Thus, this study was conducted with the following objectives: (1) to check the response of PNA, PDA, and abundance of N cycling microbial populations under EI and FP, (2) to quantify N2O emissions and GHG emissions under EI and FP and to explore key soil properties regulating soil N cycling microbial populations. We hypothesized that EI with best agricultural management practices would have higher maize yield relative to FP; and at the same time low PNA, PDA, and gene copies of N cycling microbial communities would be observed under EI treatment over FP. Moreover, compared with FP, reduced N2O and GHG emissions would be observed in EI.

oxide (NO) and nitrous oxide (N2O), with the latter being the most notorious gas causing ozone depletion and having 296 times higher warming capability than CO2 (Conrad, 1996). If this condition continues to prevail in coming decades, then it could severely impact human well-being as well as crop production. To fulfill future economic, social, and climatic challenges, agriculture needs to be more efficient, stable, resilient, and productive with minimal environmental effects (Foley et al., 2005). To combat the damaging effects of conventional agriculture or farmer practice (FP), researchers proposed the concept of ecological intensification (EI) of agriculture, which aimed to yield higher crop productivity with low resource input and at the same time prevent negative impacts of conventional agriculture on the environment (Cassman, 1999; Doré et al., 2011). It is well recognized that N cycling has a marked role in terrestrial ecosystems, and any alteration in N cycling can hamper plant growth and productivity (Nannipieri and Eldor, 2009). The two important steps in N cycling include nitrification and denitrification, and these processes are exclusively carried out with the help of microorganisms (Vitousek et al., 1997). Nitrification is defined as the conversion of ammonia (NH3) to nitrate (NO3− N), is often considered a rate-limiting step of aerobic respiration and is performed by ammonia-oxidizing bacteria (AOB) and archaea (AOA) (Purkhold et al., 2000). It is generally assumed that AOB are the primary ammonia oxidizers in terrestrial and marine ecosystems (Purkhold et al., 2000). With the recent identification of AOA and their widespread presence in various ecosystems, the question of the relative importance of AOA and AOB has arisen (Brochier-Armanet et al., 2008; Francis et al., 2005). It has been well demonstrated that both AOB and AOA have a central role in ammonia oxidation, but AOB have a more pronounced role in ammonia oxidation than AOA in arable lands (Jia and Conrad, 2009). Earlier, the extensive distribution of AOB and observation of nitrification activity in acidic soils led to the assumption that AOB can thrive best under low pH environments (Kowalchuk and Stephen, 2001). In contrast, with the discovery of AOA, under acidic conditions, AOA are more dominant than AOB in agricultural soils (He et al., 2007). Denitrification is a series of biological processes during which NO3− N or nitrite (NO2− N) is reduced stepwise, assisted by denitrifying microbes, through NO, N2O, and finally dinitrogen (N2) (Hofstra and Bouwman, 2005; Yin et al., 2014). Although there are numerous ways in which N is lost from the environment of arable land, N loss as a result of denitrification is of prime importance because this N is not available to crops, thus decreasing N use efficiency and crop productivity (Hofstra and Bouwman, 2005). The process of denitrification is estimated by the most widely used functional markers, including nitrite reductase (nir) and nitrous oxide reductase (nos) genes (Wang et al., 2018; Yin et al., 2015). Nitrite reductases (nirK and nirS) play a role in the conversion of nitrate to nitrite, while nitrous oxide reductase (nosZ) converts N2O to N2 (Yin et al., 2015; Zhang et al., 2019). It is globally accepted that elevated N input in cropland has significantly increased the potential nitrification activity (PNA) and potential denitrification activity (PDA) (Wang et al., 2018; Yin et al., 2015; Zhang et al., 2019). Changes in soil chemical properties such as soil acidification or enhanced nutrient availability as a result of N addition primarily regulate the denitrification process and N cycling microbial population (Wang et al., 2018; Zhang et al., 2019). To date, numerous researchers have explored the impacts of both inorganic and organic fertilization on soil N transformation related processes such as denitrification, nitrification, and N cycling microbial populations (Nannipieri and Eldor, 2009; Yin et al., 2015; Zhang et al., 2019), yet little or no evidence is available regarding soil N transformation under the EI approach. Therefore, there is an urgent need to investigate the response of soil N transformation under the EI approach. North Central China is predominantly occupied by ‘fluvo-aquic soil’. The region is characterized by summer-maize and winter-wheat rotation farming systems. Sustainable utilization of agricultural land in this region is very important for China’s food security (Gong et al., 2009).

2. Material and methods 2.1. Experimental sites The long-term field trial was established in 2009 at the Dahe Experimental Station located in Shijiazhuang City, Hebei Province (38°07ʹ N and 114°29ʹ E), North Central China. This region has fluvoaquic soil. The study area has a typical warm temperate and subhumid continental monsoon climate. The average annual temperature is 14.3 °C, and precipitation is 400 mm. Another field trial was established in 2009 in Liufangzi County, Gongzhuling City, Jilin Province (43°34.86ʹ N and 124°53.92ʹ E) in northeastern China. This study area has black soils called Haplic Phaeozems (FAO) and Mollisols in the USDA classification. This site has a temperate and semihumid continental monsoon climate. The average annual temperature is 4–5 °C. The mean annual precipitation is 500–900 mm. 2.2. Experimental design In this study we referred conventional agriculture as FP. The key differences between EI and FP are summarized in Table 1. In both study locations, the experiment was set up in a split-plot design with four replicates with the management system (M) as the entire plot consisting of EI and FP and N fertilization (N) as a subplot with two levels (N- and N+), and each subplot measured 45 m2 (5 m × 9 m) and 40 m2 (10 m × 4 m) in fluvo-aquic soil and black soil sites, respectively. Thus, there were four treatments in the fluvo-aquic soil: EI N0, FP N0, EI N182, FP N225 kg ha−1. Similarly, there were four treatments in the black soil: EI N0, FP N0, EI N200, FP N251 kg ha−1. In total, there were 32 experimental plots (4 treatments × 4 replicates × 2 sites). We used Nutrient Expert® (NE) to develop fertilizer recommendations for hybrid maize in the EI treatments (Xu et al., 2016). NE provides fertilizer recommendations based on the right source, right place, right time, and right rate (4R). The fertilizer rate in the EI treatments was consistent with the concept of the ecological intensification system and describes a new approach to fertilizer application. Furthermore, in the EI treatments, one-quarter of the N and all P2O5 and K2O were provided in a basal application, with the remaining N applied at critical growth stages 2

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251-145-100 All NPK fertilizer applied as basal

200-75-90 ¼ N and total P and K was applied as basal Rest of N was applied as topdressing at critical planting stages like heading stage and tasseling stage Nutrient Expert ® 225-120-50 All NPK fertilizer applied as basal

65,000 ha−1 Pioneer 335 Yes

Soil samples were collected in September 2018 from both locations. Generally, five soil cores (2 cm in diameter) were collected from each experimental plot to a depth of 0–20 cm and were pooled to create a composite sample. One part of the subsample was stored at −80 °C for further molecular analysis. Nitrate-nitrogen (NO3− N) and ammoniumnitrogen (NH4+ N) were extracted from a 12 g fresh soil sample using a 1:10 ratio of soil to 0.01 mol L-1 CaCl2 solution and then analyzed using continuous flow analysis (Foss FlAstar 5000, Sweden). The soil water content was determined by oven drying soil at 105 °C. The total N (TN) and soil organic carbon (SOC) were analyzed by Kjeldahl digestion and dichromate oxidation, respectively. The soil pH was measured with a compound electrode (PE-10, Sartorius, Germany) using a soil:water ratio of 1:2.5. Dissolved organic carbon (DOC) and dissolved organic N (DON) was extracted with 0.5 M K2SO4 and determined by a total organic C/N analyzer (Multi N/C 3100/HT1300, Analytik Jena AG, Germany). 2.4. Potential nitrification activity and potential denitrification activity PNA was determined via the shaken slurry method described by Hart et al. (1994), which evaluates the maximum nitrate production rate of a soil sample. Briefly, fresh soil samples (15 g) were placed in Erlenmeyer flasks with 100 ml of a 1.5 mM NH4+ and 1 mM PO43− mixture with the pH adjusted to 7.2. The slurry was then shaken on an orbital shaker at 180 rpm for 24 h at 25 °C to maintain aeration in the dark. Aliquots of 5 ml were subsequently removed using a wide-mouth pipette at 2, 6, 12, 22 and 24 h after the start of the incubation. The aliquots were then centrifuged, and the supernatant was filtered and stored at −20 °C until analysis. The NO3- N concentrations were measured using a flow injection auto analyzer (FLA star 5000 Analyzer, Foss, Denmark), after which PNA was calculated from the rate of a linear regression of nitrate concentrations over time (μg NO3- N g-1 h-1). PDA was determined by the acetylene inhibition technique, as described by Philippot et al. (2011), with some modifications. Briefly, moist soil (10 g) and 20 mL of DEA solution (1 mM KNO3 and 1 mM glucose) were added to the microcosms (120 ml serum bottles). The serum bottles were subsequently sealed and purged three times by pumping out the ambient air and filling them with He gas. Gas samples were taken at 0.5, 1.5, 3.5 and 5.5 h after the start of the incubation, and N2O was measured by a gas chromatograph (Agilent GC-7890A) equipped with an electron capture detector (ECD) and a Poropak Q column. The temperature of the detector and oven was set to 300 °C and 70 °C, respectively.

Note: 4R = right source, right, time, right place, right rate.

78,000 ha−1 Pioneer 335 Yes Maize seed density Maize variety 4R nutrient stewardship used

• •

Fertilizer recommendation method

Average fertilizer rate applied in Hebei (North Central China) 60,000 ha−1 Zhengdan 958 No

• •

2.3. Sampling and determination of soil chemical properties

182-73 -70 ¼ N and total P and K was applied as basal Rest of N was applied as topdressing at critical planting stages like heading stage and tasseling stage Nutrient Expert ® NPK fertilizer rate (kg ha ) Fertilizer application method

of the crop, such as heading and tasseling. In contrast, the FP treatments represented the average fertilizer rate applied by farmers in northcentral (fluvo-aquic soil) and northeastern (black soil) China, with all of the N, P2O5, and K2O provided in a basal application. Another improvement adopted in the EI treatments was the high plant population density, with 78,000 and 65,000 maize plants per hectare at the fluvoaquic soil and black soil sites, respectively, whereas in the FP treatments, the plant population density was 60,000 and 50,000 at the fluvo-aquic soil and black soil sites, respectively. Furthermore, in the EI system, high-yielding maize hybrids, such as Pioneer 335, were used at both sites. In the FP system, the maize variety Zhengdan 958 was used at the fluvo-aquic soil site, and Lvyu 4117 was used at the black soil site.

Average fertilizer rate applied in Jilin (Northeast China) 50,000 ha−1 Lvyu 4117 No

FP EI FP

−1

EI

Black soil (Jilin) Fluvo-aquic soil (Hebei) Management practices

Table 1 Main management features of the treatments of ecological intensification (EI) and the average farmer practice (FP) in the experiments carried-out in fluvo-aquic soil and black soil of North China.

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2.5. DNA extraction and quantitative real -time PCR (qPCR) Genomic DNA was extracted from 0.5 g soil samples using the E.Z.N.A. ® Bacterial DNA Kit (Omega Biotek, Norcross, GA, USA) according to the manufacturer’s instructions. DNA quality was tested by 1 % agarose gel electrophoresis and spectrophotometry (optical density 3

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Table 2 Soil physicochemical properties and maize yield under EI and FP. NO3− N (mg kg−1)

NH4+ N (mg kg−1)

TN (g kg−1)

SOC (g kg−1)

pH

Fluvo-aquic soil EI N0 FP N0 EI N182 FP N225

3.07 b 2.14 b 14.65 a 14.88 a

1.54 1.11 1.79 1.29

1.15 1.12 1.24 1.27

b b a a

18.90 18.54 20.28 20.73

bc c ab a

7.88 7.96 7.80 7.78

b a c c

16.44 16.55 16.39 16.34

Black soil EI N0 FP N0 EI N200 FP N251

3.02 b 2.94 b 3.85 b 19.55 a

1.97 b 0.98 b 2.52 b 11.59 a

1.24 1.24 1.27 1.30

a a a a

24.47 24.46 26.66 25.58

a a a a

6.01 6.05 5.87 5.19

a a a b

19.76 19.67 20.94 19.77

a a a a

DON (mg kg−1)

DOC (mg kg−1)

Yield (kg ha−1)

a a a a

18.22 14.57 40.65 49.10

b b a a

83.06 83.47 95.77 98.21

5309 5118 6574 5933

a a a a

18.53 16.41 22.64 29.61

a a a a

98.69 a 98.88 a 107.57 a 111.76 a

C:N

a a a a

b b a ab

1904 c 1963 c 12040 a 10738 b

Data are the means, n = 4. Different letters indicate significant differences among treatments at P ≤ 0.05 as determined by LSD. NO3− N = nitrate N, NH4+ N = ammonium N, TN = total N, C:N = soil organic carbon: nitrogen ratio, DON = dissolved organic N, DOC = dissolved organic C.

microbial populations using partial least squares path modeling (PLSPM). A statistical tool was used to show the cause and affect relationships among the observed variables and latent variables (Tenenhaus et al., 2005). Path coefficients (direct effect) indicated the direction and strength of the linear relationship between variables, whereas indirect effects indicated multiplied path coefficients between a predictor and a response variable, adding the product of all possible paths except the direct effect (Barberán et al., 2014). The estimates of the coefficients of determination (R2) and the estimate of the path coefficient in our model were validated using the “plspm” package (1000 bootstraps) in R software version 3.4.4. To separate the impact of management system and soil types on soil physicochemical properties and N cycling microbial populations, we conducted permutational multivariate analysis of variance (PERMANOVA) using Primer software version 6.0 (Plymouth, UK).

at 260 nm/280 nm ratio). All qPCR assays were carried out in an iCycler system (BioRad, USA) using SYBR Green I chemistry, and the data were analyzed by Bio-Rad iQ5 v2.0, as described previously (Fan et al., 2011). Each 20 μl reaction solution contained 10 μl of 2 × SuperMix (Bio-Rad, USA), 1.6 μl of 10-fold diluted DNA, 0.4 μl each primer (10 μM) and 7.6 μl sterilized water. Standard curves were obtained using a 10-fold serial dilution series (108-102 copies) of known copy numbers of plasmid DNA containing AOA, AOB, nirK, nirS, and nosZ gene fragments. Real-time PCR was performed in triplicate. A detailed illustration of the primers used in this study is presented in Table S1. 2.6. N2O and GHG emission estimation Total N2O emissions expressed as kg N ha−1 included direct and indirect N2O emissions related to the N fertilizer rate. The calculation method of direct N2O emissions (Cui et al., 2013) and indirect N2O emissions, including ammonia (NH3) volatilization and nitrate (NO3) leaching for maize, is provided below (Wu et al., 2014). Direct N2O emission = 0.576 × e

3. Results

(0.0049 × N rate)

3.1. Soil physicochemical properties and maize yield

NH3 volatilization = 0.24 × N rate + 1.30 N leaching = 4.46 × e

The effect of EI and FP treatments on soil physicochemical properties and maize yield in 2018 is shown in Tables 2 and S2. In the fluvoaquic soil, N addition significantly increased NO3− N, TN, and SOC levels compared to plots without N amendment. The NO3− N, TN, and SOC levels under FP N225 were higher compared with EI N182, but these effects were not significant. It was further observed that N enrichment enhanced the soil DOC and DON contents compared to treatments without N addition. Slightly higher contents of DOC and DON were observed in FP N225 relative to EI N182. No significant effect of N addition was observed regarding NH4+ N levels and the C:N ratio. Notably, maize yield was significantly increased under N addition, and the EI N182 treatment produced a significantly higher yield over FP N225. The soil pH decreased following N fertilization, and the lowest pH was observed in the FP N225 treatment as opposed to the EI N182 treatment. In the black soil, inorganic N input significantly increased soil NO3− N and NH4+ N concentrations compared with those in plots that received no N input, and in comparison to the EI N200 treatment, higher values were recorded in the FP N251 treatment, but the effect was not significant. No significant effect of N fertilization was noted regarding TN, SOC, DON, DOC, and the C:N ratio. N enrichment significantly increased maize yield compared with plots without N amendments. Furthermore, compared with the FP N251 treatment, a higher yield was obtained from the EI N200 treatment. It was further observed that soil pH declined in plots treated with N fertilizer, and compared with EI N200, a significant reduction was noted in the FP N251 treatment. The PCA results showed a clear distinction in soil physicochemical properties among EI and FP treatments (Fig. 1A). PCA also revealed a marked separation in soil physicochemical properties between the fluvo-aquic soil and black soil. According to PERMANOVA,

(0.0094 × N rate)

Indirect N2O emission was estimated as 1 % and 0.75 % of NH3 volatilization and N leaching, respectively. Total GHG emissions during the entire life cycle of maize production, including CO2, CH4, and N2O (CH4 emission can be ignored in agroecosystem), consisted of three components; a detailed equation is provided below (Forster et al., 2007; Zhang et al., 2013). GHG = (GHGm + GHGt) × N rate + total N2O × 44/28 × 298 + GHGothers where GHG represents the total GHG emission calculated as CO2 eq, GHGm is the GHG emission factor of N product manufacturing, and GHGt is the GHG emission factor of N fertilizer transportation, GHGothers represents GHG emission of P and K fertilizer production and transportation (GHGm and GHGt were 8.21 and 0.09 kg CO2 eq ha−1; GHGothers for P and K were 0.79 and 0.55 kg CO2 eq ha−1, respectively). GHG emission intensity was expressed as kg CO2 eq Mg−1 grain. 2.7. Data analyses For each variable measured (e.g., soil chemical properties or N cycling microbial populations), the data were analyzed by one-way analysis of variance (ANOVA) using Fisher’s least significant difference (LSD) at P ≤ 0.05 to compare the treatment means. A two-way ANOVA was used to examine the significance of management system (EI and FP), N fertilization (N- and N+) and their interaction at P < 0.05 using SPSS software version 24 (SPSS, Inc., Chicago, IL, USA). We further explored the relationship between soil properties and N cycling 4

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Fig. 1. Principle component analysis (PCA) of the soil physicochemical (A) and N cycling microbial population (C) data for the EI N-, FP N-, EI N+, and FP N + treatments for two different soil types. Permutational multivariate analysis of variance (PERMANOVA) comparing the main and interactive effects of management system and soil type on the soil physicochemical properties (B) and N cycling microbial population (D) at (999 permutations). The letters S and M indicate soil type and management system, respectively. Asterisks indicate significant differences at P < 0.01 and P < 0.001 probability levels (* and ** respectively). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).

increased under N fertilization in both soils, and the highest abundance was recorded in FP N225 and FP N251 treatments as opposed to EI N182 and EI N225 treatments (Fig. 3). N2O emissions, GHG emissions, and GHG emission intensity between EI and FP were quantified. The observed results showed that compared with the FP treatment, a substantial reduction in N2O emissions, GHG emissions, and GHG emission intensity was noted in the EI treatment (Fig. 4). The PCA results showed a clear distinction in N cycling gene abundance among EI and FP treatments. PCA also revealed a marked separation in N cycling gene abundance between the fluvo-aquic soil and black soil (Fig. 1C). According to PERMANOVA, the effect of soil type on N cycling gene abundance explained the majority of the variation compared with the management system (Fig. 1D).

the effect of soil type on the soil physicochemical traits explained the majority of the variation compared with the management system (Fig. 1B).

3.2. N transformation, N2O emissions, and GHG emissions The results showed that across the two soils, N fertilization significantly increased PNA and PDA compared with treatments without N fertilization, and the highest values were affiliated with FP N225 and FP N251 treatments in fluvo-aquic and black soil, respectively (Fig. 2A–D; Table S3). The abundance of AOA increased following N fertilization across the two soils (Fig. 2E–F). In the fluvo-aquic soil, the highest abundance was noted in the EI N182 treatment, while in the black soil, the highest abundance was found in the FP N251 treatment. Similarly, in both soils, the abundance of AOB significantly increased under N fertilization, and the highest abundance was found in FP N225 and FP N251 treatments in the fluvo-aquic soil and black soil, respectively (Fig. 2G–H). Furthermore, when we compared the abundance of AOA and AOB between the fluvo-aquic soil and black soil, we found that a higher abundance of AOA was observed in the black soil than in the fluvo-aquic soil, while a higher abundance of AOB was found in the fluvo-aquic soil than in the black soil. The abundance of the N cycling genes involved in denitrification, including nirS, nirK, and nosZ,

3.3. Partial least square path modeling Path modeling was employed to explore the relationship between soil chemical properties and N cycling genes (Fig. 5). The results showed that in the fluvo-aquic soil, N addition had a significant direct negative relationship with soil pH (-0.80), while N addition had a significant direct positive relationship with NO3− N (0.97). Both PNA and PDA were positively linked with the abundance of nitrifier genes and denitrifier genes, but no significant relationship was noted. Soil NO3− N 5

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Fig. 2. Potential nitrification activity (PNA) and potential denitrification activity (PDA) under EI and FP (A–D) in the two soils. Gene copy number of AOA-amoA and AOB-amoA under EI and FP in the two soils (E–H).

Fig. 3. Gene copy number of nirK, nirS, and nosZ under EI and FP in the two soils.

6

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Fig. 4. N2O emissions, greenhouse gas (GHG) emissions, and GHG emissions intensity under EI and FP in the two soils.

relationship with PNA (-0.81) and PDA (-0.62). PNA had an important direct positive link with nitrifier genes (0.59); similarly, PDA had a significant direct positive effect on denitrifier genes (0.50). Interestingly, NH4+ N alone regulated the abundance of nitrifier genes (0.95).

alone mediated the abundance of nitrifier genes (0.94) and denitrifier genes (0.96). In the black soil, the observed results indicated that N addition had a significant direct negative relationship with soil pH (-0.64), while N addition had a significant direct positive relationship with NH4+ N (0.61). Soil pH had a significant direct negative

Fig. 5. Directed graph of the partial least squares path model (PLSPM) revealing the relationship between soil properties and N cycling microbial populations. Each box represents an observed variable (i.e., measured) or latent variable (i.e., constructs). The loading for nitrifying genes and denitrifying genes that create the latent variables are shown in the dashed rectangle. Path coefficients are calculated after 1000 bootstraps and reflected in the width of the arrow, with blue and red indicating positive and negative effects, respectively. Dashed arrows show that coefficients did not differ significantly from 0 (P ≥ 0.05). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article). 7

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4. Discussion

increased in the EI and FP plots that received N fertilizer; however, higher levels of DOC and DON were observed in FP relative to EI. Harter et al. (2014) speculated that elevated DOC levels are associated with higher N2O emissions because labile C has a key role as an electron acceptor for microbes that carry out denitrification. A study by Nishisaka et al. (2019) further showed that higher N2O emissions were closely linked with DOC levels and nirK, and nosZ genes. Huang et al. (2019) measured higher N2O emissions when soil had a higher concentration of DON, implying that heterotrophic ammonia oxidation supplied by DON could be the key driver in N2O formation. It is worth mentioning that in our study, in comparison to FP treatments, EI had substantially lower N2O and GHG emissions. It is well established that N fertilization has improved crop productivity but has also resulted in serious environmental issues, such as elevated greenhouse gas emissions, e.g., NO and N2O, with the latter being the most notorious gas causing ozone depletion and having 296 times higher warming capability than CO2 (Conrad, 1996). N2O emissions are caused by both anthropogenic and natural sources, and arable lands subjected to inorganic or organic N fertilizers are the main source of anthropogenic N2O emissions, accounting for approximately 75 % of anthropogenic emissions in China (NDRC, 2014). Quantifying the influences of elevated N2O emissions from agricultural soils to the ecosphere and human beings is thus critically important (Butterbach-Bahl et al., 2013). In this regard, the Chinese government is placing much effort on reducing the adverse effects of over- and irrational fertilization on the environment and initiated a grand project in 2015, namely, “Zero Growth of Chemical Fertilizer by 2020”. Our results suggested that EI management with minimal fertilizer input and best management practices not only increased crop yield but also reduced N2O and GHG emissions. Thus, we can say that EI is an environmentally friendly agricultural approach, and our study can contribute well to the task of “Zero Growth of Chemical Fertilizer by 2020”. In agro-ecosystems, we cannot completely eliminate all N loss and GHG emitted from arable lands; we can only reduce emissions and negative impacts by employing good agricultural practices. Our results indicated that N addition to EI and FP treatments increased PNA and PDA; a similar result was found in previous studies (Wang et al., 2018; Yang et al., 2017). The EI treatment had lower PNA and PDA than the FP treatment, and the higher nitrification and denitrification contributed to an increase in N loss and N2O emissions (Wang et al., 2018). Moreover, a significant relationship was found between PNA and nitrifier gene abundance, particularly in the black soil. Although both AOA and AOB had a significant relationship with PNA, studies have shown that the contribution of AOB to ammonia oxidation is higher than that of AOA (Di et al., 2009; Jia and Conrad, 2009). AOA and AOB have contrasting pathways of ammonia oxidation (Kozlowski et al., 2016). Studies have also shown that compared with AOA, AOB have larger cell sizes (Lehtovirta-Morley et al., 2016). These factors likely impact their responses to the availability of ammonium (Martens-Habbena et al., 2009). In addition, a significant relationship was found between PDA and the denitrifier gene abundance, particularly in the black soil. This result is consistent with Yang et al. (2017), who showed that denitrifier gene abundance had a significant correlation with denitrification activity. Given that the nirK, nirS, and nosZ genes are considered key indicators of dentrification potential (Philippot et al., 2011; Wang et al., 2018; Yang et al., 2017), their ratios mainly drive N2O emissions (ŠImek and Cooper, 2002). Alternatively, other researchers claim that nirK denitrifying communities play a vital role in denitrification in comparison to nirS denitrifying communities (Attard et al., 2011). The not significant relationship between PDA and denitrifier gene copy numbers in the fluvo-aquic soil can be ascribed to the fact that, other than the denitrifying community abundance, the denitrifying community structure also plays an important role in regulating PDA (Philippot et al., 2011; Yin et al., 2014). Another reason could be different soil environmental conditions because different soil environmental conditions play a key role in driving potential

Our results indicated that across the two soils, the abundance of both AOB and AOA increased as a result of N addition in the EI and FP treatments. These results are supported by a recent meta-analysis showing that N addition increased the abundance of soil nitrifiers (Ouyang et al., 2018). Moreover, it was noted that the abundance of AOB was more responsive to N addition than AOA, particularly in black soils. Erguder et al. (2009) demonstrated that addition of agricultural mineral fertilizer resulted in higher gene copies of AOB over AOA. It is possible that some AOB are capable of producing urease and are therefore able to utilize urea for chemolithotrophic growth (Koper et al., 2004). Interestingly, in the fluvo-aquic soil, the EI N182 treatment increased the AOA abundance, while the abundance of AOA declined in the highest fertilizer N-treated plot (FP N225). The decline in AOA gene copy numbers under elevated N rates could likely be ascribed to improved soil fertility levels because AOA thrive best under oligotrophic or low-fertility conditions (Erguder et al., 2009). A study by Wang et al. (2018) in fluvo-aquic soil showed that lower N rates enhanced AOA abundance while elevated N rates decreased AOA abundance. Our findings further demonstrated that AOA gene copies outnumbered AOB gene copies in the black soil (acidic soil), but in the fluvo-aquic soil (alkaline soil), AOB gene copies outnumbered AOA gene copies. Studies have shown that the AOA population is more prevalent in acidic soil (He et al., 2007), implying the ability of AOA ecotypes to adapt to low pH soils. Nevertheless, compared with AOB, higher AOA abundance was noted in various ecosystems (Leininger et al., 2006; Wessén et al., 2010). However, this dominance does not indicate that AOA have a greater contribution to ammonia oxidation than AOB, as some studies indicated that in addition to the higher abundance of AOA, they have a lower contribution to ammonia oxidation relative to AOB (Di et al., 2009; Jia and Conrad, 2009). A study by Xia et al. (2011) in fluvo-aquic soil of the North China plain demonstrated a greater autotrophic AOB microbial population compared with AOA. Similar to the abundance of soil nitrifiers, the abundance of denitrifiers, including nirK, nirS, and nosZ, also increased with N addition in the EI and FP treatments, and these results are consistent with a recent meta-analysis of soil nitrifier and denitrifier abundances (Ouyang et al., 2018). However, some studies found no relationship between the abundance of nitrifier and denitrifier gene copies, which can be ascribed to the fact that N cycling microbial groups have very distinct life behaviors (for example, aerobic versus anaerobic and autotrophic versus heterotrophic), and thus different conditions are responsible for shifts in their abundances (Jin et al., 2014; Kastl et al., 2015). For instance, in submerged conditions or wetlands, the abundance of soil nitrifiers generally decreased while the abundance of denitrifiers increased (Ligi et al., 2014). We found that the abundance of soil denitrifiers was higher in FP compared with EI treatments. It is worth mentioning that a higher abundance of denitrifiers is not good for ecosystem stability because this leads to a higher rate of denitrification, and higher denitrification means higher losses of N due to volatilization, N2O emissions, and the addition of reactive N2 in the atmosphere (Yang et al., 2017). The higher gene abundance of both nitrifiers and denitrifiers in FP relative to EI also indicates relatively higher rates of N loss and inefficient N use by crops (Robertson and Vitousek, 2009). In contrast, reduced gene abundance of soil nitrifiers and denitrifiers indicates relatively lower rates of N loss and efficient N use by crops in the EI treatments, possibly due to the adoption of the best management practices employed in the EI treatments, such as improved hybrid selection, optimum planting density, and 4R nutrient stewardship (right source, right rate, right time, right place). A previous study showed that the EI approach successfully increased N use efficiency (Zhao et al., 2016), and it is well accepted that N2O emissions as a result of denitrification are low if there is high N use efficiency (IPNI, 2018). The findings of this study further showed that DOC and DON contents 8

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Acknowledgements

denitrification (Attard et al., 2011). Additionally, the experimental data showed that acidic (black soil) and alkaline (fluvo-aquic soil) environments were detected in the present study. Compared with the alkaline soil, soil pH had a significantly stronger relationship with PDA in the acidic soil. A higher contribution of heterotrophic ammonia oxidation to N2O emissions was previously detected in acidic soils (Zhang et al., 2011). The non significant relationship of soil pH with PDA and PNA in the fluvo-aquic soil may have occurred because N fertilizer did not drastically impact soil pH, i.e., soil pH decreased by < 0.15 units, owing to strong carbonate buffering in the soil (Glaser et al., 2010). The results further indicated that alteration in N cycling communities after N fertilization primarily resulted from the enhanced N availability and not from the soil acidification. This finding is in line with Geisseler and Scow (2014) and Zhang et al. (2019), who showed that resource availability mainly regulates N cycling gene abundance and microbial community composition rather than soil acidification.

This research was supported by the National Key Research and Development Program of China (No. 2016YFD0200101), the International Plant Nutrition Institute (IPNI) and CAAS-IPNI Joint Lab for Plant Nutrition Innovation Research supported by Fundamental Research Funds for Central Non-profit Scientific Institution (No.1610132019047). We are grateful to two anonymous reviewers for their constructive comments and suggestions on this manuscript. Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.apsoil.2019.103395. References Attard, E., Recous, S., Chabbi, A., De Berranger, C., Guillaumaud, N., Labreuche, J., Philippot, L., Schmid, B., Le Roux, X., 2011. Soil environmental conditions rather than denitrifier abundance and diversity drive potential denitrification after changes in land uses. Glob. Change Biol. 17, 1975–1989. Barberán, A., Ramirez, K.S., Leff, J.W., Bradford, M.A., Wall, D.H., Fierer, N., 2014. Why are some microbes more ubiquitous than others? Predicting the habitat breadth of soil bacteria. Ecol. Lett. 17, 794–802. Bender, S.F., Wagg, C., van der Heijden, M.G., 2016. An underground revolution: biodiversity and soil ecological engineering for agricultural sustainability. Trends Ecol. Evol. 31, 440–452. Brochier-Armanet, C., Boussau, B., Gribaldo, S., Forterre, P., 2008. Mesophilic Crenarchaeota: proposal for a third archaeal phylum, the Thaumarchaeota. Nat. Rev. Microbiol. 6, 245. Butterbach-Bahl, K., Baggs, E.M., Dannenmann, M., Kiese, R., Zechmeister-Boltenstern, S., 2013. Nitrous oxide emissions from soils: how well do we understand the processes and their controls? Philos. Trans. R. Soc. B: Biol. Sci. 368 20130122. Cassman, K.G., 1999. Ecological intensification of cereal production systems: yield potential, soil quality, and precision agriculture. Proc. Natl. Acad. Sci. U. S. A. 96, 5952–5959. Caviglia, O., Rizzalli, R.H., Monzon, J.P., García, F., Melchiori, R., Martinez, J.J., Cerrudo, A., Irigoyen, A., Barbieri, P., Van Opstal, N.V., 2019. Improving resource productivity at a crop sequence level. Field Crops Res. 235, 129–141. Conrad, R., 1996. Soil microorganisms as controllers of atmospheric trace gases (H2, CO, CH4, OCS, N2O, and NO). Microbiol. Mol. Biol. Rev. 60, 609–640. Cui, Z., Chen, X., Miao, Y., Fei, L., Zhang, F., Li, J., Ye, Y., Yang, Z., Qiang, Z., Liu, C., 2008. On-farm evaluation of winter wheat yield response to residual soil nitrate-N in North China Plain. Agron. J. 100, 1527–1534. Cui, Z., Yue, S., Wang, G., Meng, Q., Wu, L., Yang, Z., Zhang, Q., Li, S., Zhang, F., Chen, X., 2013. Closing the yield gap could reduce projected greenhouse gas emissions: a case study of maize production in C hina. Glob. Change Biol. 19, 2467–2477. Di, H., Cameron, K., Shen, J.P., Winefield, C., O’callaghan, M., Bowatte, S., He, J., 2009. Nitrification driven by bacteria and not archaea in nitrogen-rich grassland soils. Nat. Geosci. 2, 621. Doré, T., Makowski, D., Malézieux, E., Munier-Jolain, N., Tchamitchian, M., Tittonell, P., 2011. Facing up to the paradigm of ecological intensification in agronomy: revisiting methods, concepts and knowledge. Eur. J. Agron. 34, 197–210. Erguder, T.H., Boon, N., Wittebolle, L., Marzorati, M., Verstraete, W., 2009. Environmental factors shaping the ecological niches of ammonia-oxidizing archaea. FEMS Microbiol. Rev. 33, 855–869. Foley, J.A., DeFries, R., Asner, G.P., Barford, C., Bonan, G., Carpenter, S.R., Chapin, F.S., Coe, M.T., Daily, G.C., Gibbs, H.K., 2005. Global consequences of land use. Science 309, 570–574. Forster, P., Ramaswamy, V., Artaxo, P., Berntsen, T., Betts, R., Fahey, D.W., Haywood, J., Lean, J., Lowe, D.C., Myhre, G., 2007. Changes in atmospheric constituents and in radiative forcing. Chapter 2. Climate Change 2007. The Physical Science Basis. Francis, C.A., Roberts, K.J., Beman, J.M., Santoro, A.E., Oakley, B.B., 2005. Ubiquity and diversity of ammonia-oxidizing archaea in water columns and sediments of the ocean. Proc. Natl. Acad. Sci. U. S. A. 102, 14683–14688. Geisseler, D., Scow, K.M., 2014. Long-term effects of mineral fertilizers on soil microorganisms–a review. Soil Biol. Biochem. 75, 54–63. Glaser, K., Hackl, E., Inselsbacher, E., Strauss, J., Wanek, W., Zechmeister-Boltenstern, S., Sessitsch, A., 2010. Dynamics of ammonia-oxidizing communities in barley-planted bulk soil and rhizosphere following nitrate and ammonium fertilizer amendment. FEMS Microbiol. Ecol. 74, 575–591. Godfray, H.C.J., Beddington, J.R., Crute, I.R., Haddad, L., Lawrence, D., Muir, J.F., Pretty, J., Robinson, S., Thomas, S.M., Toulmin, C., 2010. Food security: the challenge of feeding 9 billion people. Science 327, 812–818. Gong, W., Yan, X., Wang, J., Hu, T., Gong, Y., 2009. Long-term manure and fertilizer effects on soil organic matter fractions and microbes under a wheat–maize cropping system in northern China. Geoderma 149, 318–324. Gruber, N., Galloway, J.N., 2008. An earth-system perspective of the global nitrogen cycle. Nature 451, 293. Harter, J., Krause, H.-M., Schuettler, S., Ruser, R., Fromme, M., Scholten, T., Kappler, A.,

5. Study limitations Although the present study comprehensively shed light on the N transformation processes and GHG emissions under EI and FP, it has some limitations. For instance, in this study, qPCR was employed to characterize the nitrifier and denitrifier microbial abundance as indicators of PNA and PDA. However, in addition to microbial abundance, studies have emphasized the significance of microbial community composition in determining PDA in response to environmental stress (Philippot et al., 2011; Yin et al., 2014). In this regard, nextgeneration sequencing and meta-transcriptomics approaches are needed in future studies, which will provide a clearer picture of the N cycling process. Furthermore, we quantified N2O and GHG emissions of EI and FP treatments mainly through calculations. To test the validity of the EI approach with respect to N2O and GHG emissions, farm or field GHG measurements are needed. Regarding the concept of EI employed in this study, different researchers have proposed different concepts of EI. For instance, Bender et al. (2016) showed that EI means increasing crop productivity and reducing resource input by ecological approaches or ecological engineering, such as increasing biodiversity through rotation, intercropping, cover crops, use of inoculum of beneficial bacteria or fungi, etc. In our study, there was no rotation, intercropping, cover crop, use of inoculum of beneficial bacteria or fungi; however, the concept of EI applied in our study is consistent with that in other studies (Caviglia et al., 2019; Zhao et al., 2016).

6. Conclusions In this era, producing sufficient food to feed the ever-rising human population and to protect the ecosphere from adverse effects of agricultural practices are the two main challenges. Our study demonstrated that the EI approach substantially increased crop yield and simultaneously prevented the adverse effects of excessive N fertilization, such as soil acidification. Moreover, reduced N losses via denitrification were observed in the EI approach compared to FP. It was also observed that the EI approach resulted in lower N2O and GHG emissions relative to FP. Furthermore this study demonstrated that the N availability pathway rather than soil acidification primarily mediated the N cycling microbial populations.

Declaration of Competing Interest We have conflicts of interest with Prof. Marcel G.A. van der Heijden Plant-Soil Interactions, Agroscope Institute for Sustainability Sciences, 8046 Zurich, Switzerland, regarding his and our concept about the Ecological intensification of agriculture. 9

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345–353. Pretty, J., 2007. Agricultural sustainability: concepts, principles and evidence. Philos. Trans. R. Soc. B: Biol. Sci. 363, 447–465. Purkhold, U., Pommerening-Röser, A., Juretschko, S., Schmid, M.C., Koops, H.-P., Wagner, M., 2000. Phylogeny of all recognized species of ammonia oxidizers based on comparative 16S rRNA and amoA sequence analysis: implications for molecular diversity surveys. Appl. Environ. Microbiol. 66, 5368–5382. Robertson, G.P., Vitousek, P.M., 2009. Nitrogen in agriculture: balancing the cost of an essential resource. Annu. Rev. Environ. Resour. 34, 97–125. ŠImek, M., Cooper, J., 2002. The influence of soil pH on denitrification: progress towards the understanding of this interaction over the last 50 years. Eur. J. Soil Sci. 53, 345–354. Tenenhaus, M., Vinzi, V.E., Chatelin, Y.-M., Lauro, C., 2005. PLS path modeling. Comput. Stat. Data Anal. 48, 159–205. Tilman, D., Fargione, J., Wolff, B., D’antonio, C., Dobson, A., Howarth, R., Schindler, D., Schlesinger, W.H., Simberloff, D., Swackhamer, D., 2001. Forecasting agriculturally driven global environmental change. Science 292, 281–284. United Nations, 2019. World Population Prospects. (Accessed 18 October 2019). http:// population.un,org/wpp/. Vitousek, P.M., Aber, J.D., Howarth, R.W., Likens, G.E., Matson, P.A., Schindler, D.W., Schlesinger, W.H., Tilman, D.G., 1997. Human alteration of the global nitrogen cycle: sources and consequences. Ecol. Appl. 7, 737–750. Wang, F., Chen, S., Wang, Y., Zhang, Y., Hu, C., Liu, B., 2018. Long-term nitrogen fertilization elevates the activity and abundance of nitrifying and denitrifying microbial communities in an upland soil: implications for nitrogen loss from intensive agricultural systems. Front. Microbiol. 9, 2424. Wessén, E., Hallin, S., Philippot, L., 2010. Differential responses of bacterial and archaeal groups at high taxonomical ranks to soil management. Soil Biol. Biochem. 42, 1759–1765. Wu, L., Chen, X., Cui, Z., Zhang, W., Zhang, F., 2014. Establishing a regional nitrogen management approach to mitigate greenhouse gas emission intensity from intensive smallholder maize production. PLoS One 9, e98481. Xia, W., Zhang, C., Zeng, X., Feng, Y., Weng, J., Lin, X., Zhu, J., Xiong, Z., Xu, J., Cai, Z., 2011. Autotrophic growth of nitrifying community in an agricultural soil. ISME J. 5, 1226. Xu, X., He, P., Pampolino, M.F., Li, Y., Liu, S., Xie, J., Hou, Y., Zhou, W., 2016. Narrowing yield gaps and increasing nutrient use efficiencies using the nutrient expert system for maize in Northeast China. Field Crops Res. 194, 75–82. Xu, X., Xu, Y., Chen, S., Xu, S., Zhang, H., 2010. Soil loss and conservation in the black soil region of Northeast China: a retrospective study. Environ. Sci. Policy 13, 793–800. Yang, L., Zhang, X., Ju, X., 2017. Linkage between N 2 O emission and functional gene abundance in an intensively managed calcareous fluvo-aquic soil. Sci. Rep. 7, 43283. Yin, C., Fan, F., Song, A., Cui, P., Li, T., Liang, Y., 2015. Denitrification potential under different fertilization regimes is closely coupled with changes in the denitrifying community in a black soil. Appl. Microbiol. Biotechnol. 99, 5719–5729. Yin, C., Fan, F., Song, A., Li, Z., Yu, W., Liang, Y., 2014. Different denitrification potential of aquic brown soil in Northeast China under inorganic and organic fertilization accompanied by distinct changes of nirS-and nirK-denitrifying bacterial community. Eur. J. Soil Biol. 65, 47–56. Zhang, C., Song, Z., Zhuang, D., Wang, J., Xie, S., Liu, G., 2019. Urea fertilization decreases soil bacterial diversity, but improves microbial biomass, respiration, and Ncycling potential in a semiarid grassland. Biol. Fertil. Soils 1–14. Zhang, J., Cai, Z., Zhu, T., 2011. N2O production pathways in the subtropical acid forest soils in China. Environ. Res. 111, 643–649. Zhang, W.-f., Dou, Z.-x., He, P., Ju, X.-T., Powlson, D., Chadwick, D., Norse, D., Lu, Y.-L., Zhang, Y., Wu, L., 2013. New technologies reduce greenhouse gas emissions from nitrogenous fertilizer in China. Proc. Natl. Acad. Sci. U. S. A. 110, 8375–8380. Zhao, R., He, P., Xie, J., Johnston, A.M., Xu, X., Qiu, S., Zhao, S., 2016. Ecological intensification management of maize in northeast China: agronomic and environmental response. Agric. Ecosyst. Environ. 224, 123–130.

Behrens, S., 2014. Linking N 2 O emissions from biochar-amended soil to the structure and function of the N-cycling microbial community. ISME J. 8, 660. He, J.z., Shen, J.p., Zhang, L.m., Zhu, Y.g., Zheng, Y.m., Xu, M.g., Di, H., 2007. Quantitative analyses of the abundance and composition of ammonia‐oxidizing bacteria and ammonia‐oxidizing archaea of a Chinese upland red soil under long‐term fertilization practices. Environ. Microbiol. 9, 2364–2374. Hofstra, N., Bouwman, A., 2005. Denitrification in agricultural soils: summarizing published data and estimating global annual rates. Nutr. Cycl. Agroecosyst. 72, 267–278. Huang, R., Wang, Y., Liu, J., Li, J., Xu, G., Luo, M., Xu, C., Ci, E., Gao, M., 2019. Variation in N2O emission and N2O related microbial functional genes in straw-and biocharamended and non-amended soils. Appl. Soil Ecol. 137, 57–68. I.P.N.I, 2018. Nitrogen notes. (Accessed on 05 May 2019). http://www.ipni.net/ publication/nitrogen-en.nsf/0/668099AE825517CB85257DD600054B8C/$FILE/ NitrogenNotes-EN-5.pdf. Jia, Z., Conrad, R., 2009. Bacteria rather than Archaea dominate microbial ammonia oxidation in an agricultural soil. Environ. Microbiol. 11, 1658–1671. Jin, Z.-j., Li, L.-q., Liu, X.-y., Pan, G.-x., Qaiser, H., Liu, Y.-z., 2014. Impact of long-term fertilization on community structure of ammonia oxidizing and denitrifying bacteria based on amoA and nirK genes in a rice paddy from Tai Lake Region, China. J. Integr. Agric. 13, 2286–2298. Kastl, E.-M., Schloter-Hai, B., Buegger, F., Schloter, M., 2015. Impact of fertilization on the abundance of nitrifiers and denitrifiers at the root–soil interface of plants with different uptake strategies for nitrogen. Biol. Fertil. Soils 51, 57–64. Koper, T.E., El-Sheikh, A.F., Norton, J.M., Klotz, M.G., 2004. Urease-encoding genes in ammonia-oxidizing bacteria. Appl. Environ. Microbiol. 70, 2342–2348. Kowalchuk, G.A., Stephen, J.R., 2001. Ammonia-oxidizing bacteria: a model for molecular microbial ecology. Annu. Rev. Microbiol. 55, 485–529. Kozlowski, J.A., Stieglmeier, M., Schleper, C., Klotz, M.G., Stein, L.Y., 2016. Pathways and key intermediates required for obligate aerobic ammonia-dependent chemolithotrophy in bacteria and Thaumarchaeota. ISME J. 10, 1836. Lehtovirta-Morley, L.E., Ross, J., Hink, L., Weber, E.B., Gubry-Rangin, C., Thion, C., Prosser, J.I., Nicol, G.W., 2016. Isolation of ‘Candidatus Nitrosocosmicus franklandus’, a novel ureolytic soil archaeal ammonia oxidiser with tolerance to high ammonia concentration. FEMS Microbiol. Ecol. 92. Leininger, S., Urich, T., Schloter, M., Schwark, L., Qi, J., Nicol, G.W., Prosser, J.I., Schuster, S., Schleper, C., 2006. Archaea predominate among ammonia-oxidizing prokaryotes in soils. Nature 442, 806. Ligi, T., Truu, M., Truu, J., Nõlvak, H., Kaasik, A., Mitsch, W.J., Mander, Ü., 2014. Effects of soil chemical characteristics and water regime on denitrification genes (nirS, nirK, and nosZ) abundances in a created riverine wetland complex. Ecol. Eng. 72, 47–55. Martens-Habbena, W., Berube, P.M., Urakawa, H., José, R., Stahl, D.A., 2009. Ammonia oxidation kinetics determine niche separation of nitrifying Archaea and Bacteria. Nature 461, 976. NDRC, 2014. Te People’s Republic of China National Greenhouse Gas Inventory. China Environmental Science Press, Beijing. Moss, B., 2007. Water pollution by agriculture. Philos. Trans. R. Soc. B: Biol. Sci. 363, 659–666. Nannipieri, P., Eldor, P., 2009. The chemical and functional characterization of soil N and its biotic components. Soil Biol. Biochem. 41, 2357–2369. Nishisaka, C.S., Youngerman, C., Meredith, L.K., do Carmo, J.B., Navarrete, A.A., 2019. Differences in N2O fluxes and denitrification gene abundance in the wet and dry seasons through soil and plant residue characteristics of tropical tree crops. Front. Environ. Sci. 7, 11. Ouyang, Y., Evans, S.E., Friesen, M.L., Tiemann, L.K., 2018. Effect of nitrogen fertilization on the abundance of nitrogen cycling genes in agricultural soils: a meta-analysis of field studies. Soil Biol. Biochem. 127, 71–78. Philippot, L., Andert, J., Jones, C.M., Bru, D., Hallin, S., 2011. Importance of denitrifiers lacking the genes encoding the nitrous oxide reductase for N2O emissions from soil. Glob. Change Biol. 17, 1497–1504. Potts, S.G., Biesmeijer, J.C., Kremen, C., Neumann, P., Schweiger, O., Kunin, W.E., 2010. Global pollinator declines: trends, impacts and drivers. Trends Ecol. Evol. 25,

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