Effect of grazing exclusion on nitrous oxide emissions during freeze-thaw cycles in a typical steppe of Inner Mongolia

Agriculture, Ecosystems and Environment 307 (2021) 107217

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Effect of grazing exclusion on nitrous oxide emissions during freeze-thaw cycles in a typical steppe of Inner Mongolia Jinbo Li a, b, c, 1, Ying Zhao a, b, *, 1, Afeng Zhang b, Bing Song a, Robert Lee Hill d a

College of Resources and Environmental Engineering, Ludong University, Yantai, Shandong 264025, China Key Laboratory of Plant Nutrition and the Agri-environment in Northwest China, Ministry of Agriculture, Northwest A&F University, Yangling, Shaanxi 712100, China c Faculty of Agriculture and Life Science, Lincoln University, Lincoln, Canterbury, 7647, New Zealand d Department of Environmental Science and Technology, University of Maryland, College Park, MD, 20742, USA b

A R T I C L E I N F O

A B S T R A C T

Keywords: Greenhouse gas emission Soil water Temperature Snow depth Grassland

Grazing exclusion of grasslands has traditionally been considered to reduce nitrogen inputs and decrease nitrous oxide (N2O) emissions. However, recent studies have shown that grazing exclusion may increase N2O emissions because of freeze-thaw cycles. In this study, year-round N2O fluxes (including high-frequency measurements during the freeze-thaw cycles) were measured in three sites that included long-term grazing exclusion (ungrazed since 1979, UG79), short-term grazing exclusion (ungrazed since 1999, UG99), and continuously grazed (CG), in Inner Mongolia grassland. The results showed that: 1) across the entire period of observations, mean annual N2O fluxes were the highest at UG99 (12.2 μg m− 2 h-1), followed by CG (8.6 μg m− 2 h-1) and UG79 (7.5 μg m− 2 h-1); 2) during spring freeze-thaw cycles, cumulative N2O emissions were significantly higher at UG99 than UG79 and CG (P < 0.05); 3) freeze-thaw cycles had significant effects on N2O emissions with strong pulse emissions exhibited at the beginning of the spring thaw period; and 4) structural equation model analysis indicated that soil water content was a predominant environmental factor that caused increased N2O emissions, while other factors tended to affect N2O emissions indirectly. The results confirmed that short-term grazing exclusion could increase N2O emissions owing to the high N2O concentration during freeze-thaw cycles, associated with high quantities of snowmelt water. However, the effects of long-term grazing exclusion on N2O emissions showed no differences compared with grazing management. This suggests that grazing exclusion does not always result in an increase in N2O emissions during freeze-thaw cycles as it is more controlled by soil liquid water content. Thus the new finding of the event emissions of N2O should come up with more reliable estimates of soil-atmosphere trace fluxes.

1. Introduction Nitrous oxide (N2O) is a trace gas that exacerbates global warming and stratospheric ozone destruction (Liebig et al., 2010). Unfortunately, the atmospheric concentration of N2O has been increasing during the last century, partially attributable to human disturbance of the global nitrogen cycle (Wolf et al., 2010). Grasslands are widely used as pasture, cover about 40% of the terrestrial surface (except for Antarctica and Greenland), and have the potential to become a significant contributor to atmospheric N2O (Kemp et al., 2013). It is estimated that N2O emis­ sions from grazing animal excreta are responsible for 1.5 Tg of the total 6.7 Tg of anthropogenic N2O emissions (Oenema et al., 2005; Menon

et al., 2007). Therefore, even small changes in N2O emissions in this ecosystem may significantly affect the global atmospheric budgets (Holst et al., 2008). Grazing (especially over-grazing) is considered to increase N2O emissions by accelerating the rate of nitrogen cycling (Liebig et al., 2010). For this reason, reduced grazing is a potential management practice to reduce N2O emissions and mitigate grassland degradation. Grazing exclusion (GE) has been identified as a beneficial management ´ option for grassland restoration (Alvarez-Martínez et al., 2013; Tarhouni et al., 2015). However, no consistent conclusions on the effects of GE on N2O emissions have been determined, especially in the areas experi­ encing seasonal freezing and thawing effects. For example, in a typical

* Corresponding author at: College of Resources and Environmental Engineering, Ludong University, Yantai, Shandong 264025, China. E-mail address: [email protected] (Y. Zhao). 1 Those authors contribute equally. https://doi.org/10.1016/j.agee.2020.107217 Received 25 August 2020; Received in revised form 13 October 2020; Accepted 15 October 2020 Available online 6 November 2020 0167-8809/© 2020 Elsevier B.V. All rights reserved.

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steppe environment of Inner Mongolia, Wolf et al. (2010) found that grazing intensity has a considerable negative correlation with spring thaw N2O emissions, which dominate the annual N2O emissions. Compared with grazing sites, GE promoted N2O emissions during soil thawing (Wolf et al., 2010). However, in another alpine grassland in Xinjiang province, China, which has a similar latitude but the long duration of the frozen soil, Li et al. (2012) found less significant differ­ ences between GE and grazing sites with regards to N2O emissions. These differences may result from different freeze-thaw processes in these two places. Soil freeze-thaw cycles (FTCs) are caused by changes in seasonal and daily air temperatures that often occur in locations subjected to cold weather, which are known to induce N2O emissions. It tends to have remarkable effects on N2O emissions through the following mechanisms: 1) the physical release of N2O, thought to have been produced over the winter, and emitted after snow covers or ice layers melt during thawing times (Risk et al., 2013); 2) anaerobic soil conditions causing N2O pro­ duction by denitrification due to snow and ice melting which typically leads to high soil water and low soil O2 availability (Congreves et al., 2018); and 3) increased substrate availability and denitrifying enzymes caused by soil FTCs predominantly controls the immediacy and magnitude of soil N2O fluxes (Congreves et al., 2017; Hu et al., 2015; N´emeth et al., 2014). Many previous studies have revealed that the FTCs promote N2O emissions (Wagner-Riddle et al., 2017; Wolf et al., 2010). In some cases, as high as 50–70% of the annual emissions from grassland environments occurred during soil thawing (Kammann et al., 1998; Wolf et al., 2010). In the agriculture ecosystem, the global N2O emissions may be underestimated by 17–28% by neglecting the effect of soil FTCs

(Wagner-Riddle et al., 2017). Therefore, accurate assessment of N2O emissions during FTCs have received increased attention (Wagner-Rid­ dle et al., 2017), highlighting the importance of considering non-growing season N2O fluxes to estimate global N2O budgets. Grazing exclusion alters aboveground plant communities (e.g., vegetation coverage, vegetation height, and plant biomass) (Medi­ ´n et al., 2012; Jing et al., 2014; Steffens et al., 2008) and soil na-Rolda properties (e.g., soil water content, soil bulk density, soil organic carbon, and soil nitrogen content) (Zhao et al., 2011a; Qiu et al., 2013; Jing et al., 2014). These differences between grazing sites and GE sites may lead to variations in N2O emissions. However, there is limited knowl­ edge concerning the quantities of N2O release or absorption for grass­ lands implementing grazing exclusion during FTCs, especially in GE history. Most previous studies have conducted indoor controlled ex­ periments, and these measurements may not reflect the “real world” conditions (Henry, 2007). Consequently, systematic research addressing the mechanism of field freeze-thaw cycles on N2O emissions, associated with the grazing exclusion, is warranted. Here year-round N2O emis­ sions were monitored with high-frequency measurements during soil freezing and thawing periods at different grazing sites in Inner Mongolia grassland. The objectives of this study were: (1) to investigate the effect of GE on N2O emissions, and further evaluate the pulse N2O emissions during FTCs, and (2) to better understand the driving mechanisms of N2O emissions during FTCs under GE.

Fig. 1. The experiment sites and sampling design in this study. a) distribution of grassland types across China, cited from Kang et al. (2007); b) the location of three sampling sites; c) the field condition as well as the sampling instruments (UG99 as an example); and d) a conceptual diagram of field measurements. UG79: Long-term GE (grazing exclusion); UG99: Short-term GE (grazing exclusion); CG: Continuously grazed. 1: Closed opaque chambers were used for N2O flux measurement; 2: Soil gas tubes were used for collecting soil gas samples in winter; 3: Soil gas tubes were used for collecting soil gas samples in summer. 2

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2. Materials and methods

selected in relatively flat areas to avoid spatial variability. Four indi­ vidual gas samples were taken from inside the chamber at 0, 10, 20, and 30 min using a 50 mL plastic syringe after the chambers were placed into the base groove filled with water. Then these samples were transferred immediately into a pre-evacuated 100 mL air bag. The sampling was carried out between 11:00 am to 2:00 pm from July 2017 to July 2018, and were conducted at the same time for different sites. Meanwhile, soil gas samples were collected using soil gas tubes in situ from different soil depths (5, 10, 20, 35 and 50 cm) (Fig. 1) to determine the N2O concentrations in the soil (Xu et al., 2016). There were three replicates for both measurements; thus 15 gas samples from the soil profile were collected for each site every measurement. The soil gas collection system consisted of five independent tubes (the height was 5.0 cm and the inside diameter was 4.0 cm). There were eight evenly distributed holes in each tube to allow the soil gas collection (Fig. 1). Three-way stopcocks were connected along the lengths of the glass tubes to collect soil gas samples. The valves were usually kept closed with the sampling tubes remaining in-situ during the entire experiment. The N2O fluxes and concentrations were analyzed using a gas chromatograph (Agilent 7890 B, USA) equipped with an electron capture detector (ECD) within one week. The oven temperature was controlled at 55 ◦ C, and the temperatures of the ECD was set at 330 ◦ C. As the research aims more on FTCs, the sampling interval during growing season and winter was slightly lower but with high frequency during FTCs. The N2O fluxes and concentrations were manually measured twice per month during the growing season (July 10 to November 6, 2017; and April 28 to July 9, 2018) and once per month during winter (November 7, 2017 to March 20, 2018). The whole freezethaw periods were divided into winter freeze-thaw cycles (W-FTCs, i.e., November 7–24, 2017) and spring freeze-thaw cycles (S-FTCs, i.e., from March 12 to April 27, 2018) (Wang et al., 2017). During these two pe­ riods, flux and concentration measurements of N2O were taken 2–3 days in the first week, every 3–4 days in the second week (14 measurements in total).

2.1. Site description This study was conducted at a long-term experimental site in the Xilin river catchment, which belongs to the Inner Mongolia Grassland Ecosystem Research Station (IMGERS, 43◦ 37′ 50′′ N, 116◦ 42′ 18′′ E, about 1270 m) (Fig. 1). The region is characterized by the continental semiarid grasslands of the Eurasian steppe ecosystems, with a dry and cold climate. The mean annual air temperature is 0.7 ◦ C and the mean annual precipitation is around 350 mm, most of which occurred in summer from June to August (Zhao et al., 2010). Three sites that included long-term grazing exclusion (ungrazed since 1979, UG79), short-term grazing exclusion (ungrazed since 1999, UG99), and continuously grazed (CG) were selected (Fig. 1). Note that these sites do not have strict replicates as the research aim was to compare long-term effects (>15 years) of grazing exclusion on N2O emissions, and more sites were not able to be involved in the study area. Before fencing in 1979, all sites had been slightly grazed (Zhao et al., 2010). After 1979, moderate grazing (2 sheep units hm− 2 yr-1) was used until the area was fenced in 1999 for UG99. The control site (CG) was continuously grazed with approximate 2 sheep units hm− 2 yr-1 before 2010 and 2–4 sheep units hm− 2 yr-1 in the growing season after 2010. These grazing densities were estimated by local farmers and referenced to previous studies (Gao et al., 2008; Zhao et al., 2011a). Besides, these three sites did not receive any additional fertilizers (e.g., N fertilizer, manure, etc.). Vegetation at all sites is dominated by Leymus Chinensis and Stipa Grandis, which are naturally growing species in this study area and grow under natural conditions (Zhao et al., 2010). The soil is clas­ sified as calcic chernozem (Zhao et al., 2011b). The soil type is the same in these three sites. Major soil and site information are provided in Table 1. 2.2. Gas sampling and analysis

2.3. Soil samples and environmental factors

N2O fluxes were monitored using static closed opaque chambers (50 cm × 40 cm × 25 cm) covered with reflecting tin foil to prevent rapid increases in temperature (Qi et al., 2007; Zou et al., 2004). The cham­ bers were equipped with circulating fans to ensure complete gas mixing, and was placed on a collar (50 cm × 40 cm × 10 cm) with a groove to prevent leakage during gas sampling. Each site had three replicate chambers that were not related. These sampling sites were randomly

Precipitation and air temperatures were recorded using a standard micrometeorological station, located 50 m from the monitoring plots at UG79. The temperatures inside the chambers were measured using mercury thermometers that coincided with the gas sampling. Soil tem­ peratures and volumetric soil water contents were also recorded using an automatic monitoring instrument (EM50 data recorder connected with ECH2O 5TE sensor, DECAGON, USA) at 30-min intervals. For each treatment, three monitoring instruments were installed and the data were recorded at soil depths of 10, 30, 50 cm, respectively (Fig. 1). The sensor reading refers to the volumetric unfrozen water content in cases when the soil was frozen. Snow depths were measured with a ruler following snowfall events. Vegetation coverage was analyzed using a non-destructive method based on Braun-Blanquet (1964). Aboveground biomass was sampled randomly on three 1 m2 quadrats by cutting the plant at 10 mm height, including the standing dead material. Above­ ground biomass was collected at the end of the growing seasons. The harvested plant materials were oven-dried at 65 ℃ for 48 h and weighed to determine biomass. During FTCs, three composited soil samples for each treatment were collected simultaneously with gas sampling at a depth of 0− 10 cm. These samples were stored at 4 ℃ and were analyzed within one week (Gao et al., 2019). Gravimetric soil water content was determined by − mass content after drying at 105 ℃ for 12 h. Soil NH+ 4 -N and NO3 -N -1 were extracted with 2 mol L KCl as described by Maynard et al. (1993), and the extracts were analyzed using an automated flow injection analyzer AA3 (Braun and Lübbe, Norderstedt, Germany). Soil microbial biomass carbon (MBC) and microbial biomass nitrogen (MBN) were analyzed using the chloroform fumigation extraction method (Vance et al., 1987). Specifically, fresh soil samples (12.5 g) with chloroform

Table 1 Basic characteristics of the sampling plots (mean ± standard error). Sampling plots

UG79

UG99

CG

Area (ha) Altitude (m) Location

24 1259 43◦ 33.0′ N, 116◦ 39.9′ E 2.7− 3.0 31.0 ± 5.5a

35 1274 43◦ 33.0′ N, 116◦ 40.0′ E 2.6− 3.0 25.5 ± 6.3b

40 1273 43◦ 32.9′ N, 116◦ 39.7′ E 2.7− 2.9 22.2 ± 3.7c

1.06 ± 0.04b

1.13 ± 0.07b

1.36 ± 0.06a

80.26 ± 10.49b

103.90 ± 12.70a

67 ± 0.02a 48.6 ± 3.2b 35.2 ± 2.5b 16.3 ± 1.0b

68 ± 0.02a 46.1 ± 4.9b 37.4 ± 3.4c 16.5 ± 1.8b

36.69 ± 10.25c 47 ± 0.06b 44.6 ± 5.6a 36.6 ± 3.5c 18.7 ± 2.2c

Slope (◦ ) SOC (g kg− 1, 0− 10 cm)B BD (g cm− 3, 0− 5 cm)B AB (g m− 2)B VC (%)B Sand (%, 0− 4 cm)A Silt (%, 0− 4 cm)A Clay (%, 0− 4 cm)A

SOC: soil organic carbon; BD: bulk density; AB: aboveground biomass; VC: vegetation coverage. UG79: Long-term GE (grazing exclusion); UG99: Shortterm GE; CG: Continuously grazed. A Mean values followed by the standard deviation with n = 98 (UG79), n = 99 (UG99), and n = 88 (CG); B Mean values followed by the standard deviation with n = 3. Different lower case letters indicate significant differences among treatments (P < 0.05, LSD). Note: The soil texture data were quoted from Zhao et al., 2011b. 3

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and un-fumigated soil samples were extracted by 0.5 M K2SO4, and the extracts were measured using a Phoenix 8000 TOC analyzer (Teledyne Tekmar Mason, USA) and an automated flow injection analyzer AA3 (Braun and Lübbe, Norderstedt, Germany) to determine MBC and MBN, respectively. They were calculated using conversion factors 0.45 for microbial C and 0.54 for microbial N (Jenkinson and Ladd, 1981). Be­ sides, air-dried and mixed soil samples (0− 10 cm) were passed through a 2 mm sieve to analyze soil organic carbon (SOC). SOC was determined by wet digestion using sulfuric acid, potassium dichromate (H2SO4–K2CrO7) (Nelson and Sommers, 1996). Undisturbed samples were taken at 0− 5 cm depth using a stainless steel cylinder (100 cm3) to determine soil bulk density. It was calculated with the mass of the oven-dry soil (105 ◦ C) divided by the core volume.

values represented microbial activities; only the MBC variable was used in the current analysis as it considered a sensitive index (Liu et al., 2012). Also, NH+ 4 -N concentration was not accounting in the analysis due to its low content (Table 2) and its weak relationship with other factors (Table 3). The quality of the final model was evaluated by using the χ2 tests, P-values (>0.05 represents significant model fit) and root mean square error of approximation (RMSEA) (between 0 and 0.1 de­ scribes the model fits in with a good quality) (Yang et al., 2018). The SEM was carried out using the Amos 17.0 software package (Small­ waters Corporation, Chicago, IL, USA). Besides, the software Surfer 13.0 was used to create interpolated contour plots of date as the x-axis and soil depth as the y-axis, and soil temperature, soil water, and soil N2O concentration as the z variable. The interpolation method was Kriging. 3. Results

2.4. Data analysis

3.1. Soil basic properties and aboveground biomass

The N2O flux was calculated flowing closed-chamber equation (Rolston, 1986): V ΔC 273 F=ρ× × A Δt (273 + T)

Table 1 shows that significant differences in soil and vegetative properties existed between the GE sites and the CG site. Specifically, the soil organic carbon (SOC) exhibited the largest value (31.0 g kg− 1) at the UG79 site and the lowest value (22.2 g kg− 1) at the CG site (Table 1). The aboveground plant biomass and vegetation coverage were also greater at the GE sites as compared with CG. Noted that the aboveground plant biomass values were larger at UG99.

(1)

where F is the N2O flux (μg m− 2 h-1), ρ is the N2O gas density (μg m-3), V is the chamber volume (m3), A is the chamber area (m2), ΔC/Δt is the change in N2O concentration inside the chamber during the sampling period (linear regression of correlation coefficient (r2) was used), and T is the average chamber temperature (◦ C) during the sampling period. The cumulative emissions of N2O were sequentially calculated from the emissions averaged on every two adjacent intervals of the mea­ surements following equation (Singh et al., 1999): Cumulative N2 O emission =

n ∑ (Ri × Di )

3.2. N2O emissions and soil N2O concentrations during freeze-thaw cycles Overall, the steppe ecosystem was an important source of atmo­ spheric N2O, and FTCs-induced N2O fluxes dominated annual N2O emissions in the study area (Fig. 2b). The FTCs began on November 7, 2017 when the surface soil layer started to freeze; however, no signifi­ cant N2O emissions were observed at any of the sites from November 7–24 (W-FTCs) (Fig. 2b). Similarly, the N2O concentration in the soil profiles was low (close to 323 μL m− 3) during this period (Fig. 4a), and with fewer differences among the different soil layers (Fig. 4b). How­ ever, large pulses of N2O fluxes were observed during the period starting from freezing to thawing cycles (S-FTCs) (Fig. 2b). The N2O fluxes were 26.9, 60.6, and 50.1 μg m-2 h-1 at UG79, UG99, and CG, respectively. After that, N2O fluxes dramatically decreased to -0.6 and 3.4 μg m-2 h-1 in about three days post-thawing at UG79 and CG, respectively, while it remained high (25.4 μg m-2 h-1) even after two weeks at the UG99 (Fig. 2b). Soil N2O concentrations also simultaneously increased to about 1500 μL m–3 at the UG99 and CG sites (Fig. 4a) at the beginning of S-FTCs, but there was no apparent increase at the UG79 site (Fig. 4a). Also, it is worth note that another peak of N2O fluxes was surprisingly found on October 1, 2017 when the air temperature was close to 0 ℃ and FTCs might exist (Fig. 2a).

(2)

i

where Ri is the rate of N2O flux (g ha− 1 day− 1) in the ith sampling in­ terval, Di is the number of days in the ith sampling interval and n is the number of sampling intervals. The data were analyzed following a similar method described by Yang et al. (2018). First, mean values and standard deviations were calculated for all of the parameters. The frequency distributions of the dependent and in­ dependent variables were log-transformed and tested for normality using the Kolmogorov-Smirnov test (P > 0.05). The effect of GE on analyzed parameters was tested with a one-way analysis of variance (ANOVA) using SPSS 18.0 (IBM SPSS, Chicago, IL). LSD (least significant difference) method was used as a post hoc test to identify significant differences (P < 0.05). Second, Pearson correlation analysis was performed to explore the relationships of the N2O fluxes with soil parameters (soil mineral ni­ trogen contents, soil microbial biomass, soil water, soil temperature, and soil N2O concentration) among soil thawing stage (S-WFTCs). When P < 0.05, correlations and differences were considered statistically signifi­ cant and P < 0.01 was defined as highly significant. The Pearson cor­ relation analysis was conducted using SPSS 18.0 statistical software (IBM SPSS, Chicago, IL). Third, structural equation modeling (SEM), which combines pathway analyses and multiple regression analyses, was performed to determine the direct and indirect controlling pathways regulating the N2O fluxes (Yang et al., 2018). A priori model was established depen­ dent on the current knowledge of factors affecting N2O emissions. In this model, we hypothesized that: 1) microbial activities (expressed by MBC) have direct effects on soil mineral nitrogen and hence affected profiled soil N2O concentration and therefore N2O fluxes, 2) soil water affects N2O emissions directly by influencing soil gas transport, and 3) soil water indirectly affects N2O emissions by regulating microbial biomass and the availabilities of soil N compounds. Note that both MBN and MBC

3.3. Contribution of spring thaw to annual N2O fluxes The mean annual N2O fluxes were 7.5 (UG79), 12.2 (UG99), and 8.6

μg m− 2 h-1 (CG) across the entire period of observations. Due to the high

pulse emissions of N2O at the beginning of S-FTCs, the average N2O fluxes during S-FTCs were about 67% (UG79), 211% (UG99), and 124% (CG) higher than the mean annual N2O fluxes. As a result, the contri­ bution of spring thaw to annual N2O cumulative emissions was less significant at UG79 (4.4%) and CG (7.4%). However, it accounted for almost one-fourth of the annual budget at UG99, indicating the high contribution of spring thaw to the yearly N2O emissions (Fig. 3). 3.4. Effects of grazing exclusion on soil parameters during freeze-thaw cycles Compared to CG, GE significantly reduced soil mineral N concen­ tration during FTCs but increased soil microbial biomass and soil water

4

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Table 2 Difference of soil parameters during freeze-thaw cycles (mean ± standard error). Time W-FTCs (n = 15) S-FTCs (n = 21)

Sites

NO−3 -N (mg kg− 1 )

UG79 UG99 CG UG79 UG99 CG

13.28 12.68 44.85 15.70 11.72 26.90

± 1.93b ± 1.07b ± 8.23a ± 1.61b ± 1.70b ± 6.49a

NH+ 4 -N (mg kg− 1)

MBN (mg kg− 1)

MBC (mg kg− 1)

SWC (g g− 1)

3.00 ± 0.69b 2.48 ± 0.01b 4.46 ± 2.05a 3.79 ± 0.41a 4.33 ± 0.38a 3.09 ± 0.61b

37.39 ± 3.80a 42.85 ± 1.80a 6.31 ± 2.08b 25.17 ± 0.45b 40.65 ± 2.68a 11.70 ± 4.49c

504.74 ± 17.76a 541.30 ± 32.44a 250.36 ± 27.06b 158.31 ± 8.02b 241.87 ± 8.73a 92.17 ± 20.47c

0.12 ± 0.13 ± 0.07 ± 0.14 ± 0.21 ± 0.10 ±

ST (℃)

0.01a 0.01a 0.01b 0.01b 0.01a 0.02c

− 4.76 ± 0.23a − 3.92 ± 0.03a − 4.51 ± 0.06a 2.52 ± 0.07a 2.32 ± 0.10a 2.83 ± 0.02a

W-FTCs: Winter freeze-thaw cycles. S-FTCs: Spring freeze-thaw cycles. MBN: Microbial biomass nitrogen; MBC: Microbial biomass carbon; SMC: Soil water content; ST: Soil temperature. UG79: Long-term GE (grazing exclusion); UG99: Short-term GE; CG: Continuously grazed. Different letters indicate significant differences among treatments (P < 0.05, LSD). Table 3 Pearson’s correlation coefficients between the N2O fluxes and other variables. − 1

MBN (mg kg ) MBC (mg kg− 1) NO−3 (mg kg-1) − 1 NH+ 4 (mg kg ) ST (℃) SWC (g g− 1) NC (μL m− 3) NF (μg m− 2 h-1)

MBC

MBN

NO−3

NH+ 4

ST

SMC

NC

NF

1 0.78** − 0.80* − 0.20 − 0.26 0.82** − 0.19 0.43*

1 − 0.75** 0.072 − 0.64** 0.88** − 0.10 0.52**

1 0.19 0.30 − 0.75** 0.25 − 0.36

1 − 0.09 − 0.03 0.03 0.13

1 − 0.42* − 0.25 − 0.37

1 − 0.09 0.61**

1 0.76**

1

MBC: Microbial biomass carbon; MBN: Microbial biomass nitrogen; ST: Soil temperature; SWC: Soil water content; NC: N2O concentration (0− 10 cm); NF: N2O flux. *and ** indicate the Pearson correlation coefficients were significant at the P < 0.05 and 0.01 levels, respectively. UG79: Long-term GE (grazing exclusion); UG99: Short-term GE; CG: Continuously grazed.

Fig. 2. Daily rainfall and air temperature (a) and dynamics of N2O fluxes in different treatments from July 2017 to July 2018 (b) (mean ± SD, n = 3). W-FTCs: Winter freeze-thaw cycles. S-FTCs: Spring freeze-thaw cycles. UG79: Long-term GE (grazing exclusion); UG99: Short-term GE; CG: Continu­ ously grazed.

Fig. 3. Cumulative N2O emissions at different grazing exclusion sites during spring freeze-thaw cycles (S-FTCs) and other periods. UG79: Long-term GE (grazing exclusion); UG99: Short-term GE (grazing exclusion); CG: Continuously grazed. Different letters indicate significant differences among treatments (one-way ANOVA, P < 0.05, LSD).

content obviously in winter (P < 0.05) (Table 2). During winter freezethaw cycles (W-FTCs), the average soil NO3-N and soil NH+ 4 -N concen­ trations were about 13.0 and 3.0 mg kg− 1 at GE sites, respectively (Table 2). However, these two values were 44.9 and 4.5 mg kg− 1 at CG site, significantly higher than GE sites. In contrast, microbial biomass (MBC and MBN) concentrations were significantly higher at GE sites than CG (P < 0.05). For example, the MBN concentration was 42.9 mg kg− 1 at UG99, while it was only 6.3 mg kg− 1 at CG (Table 2). Higher values of MBN, MBC, and NH4-N concentration at GE sites were also found within short-term spring freeze-thaw cycles (S-FTCs). Besides, during S-FTCs, long-term GE decreased microbial biomass compared with short-term GE. The soil water content was higher for the GE treatments within both periods. At the beginning of W-FTCs, with the decreased air

temperatures (Fig. 2a), available soil water contents gradually reduced and remained at low levels (0.1 m3 m− 3) attributed to soil freezing (Fig. 4 d). However, great differences were still found between the GE sites and the control, with the highest value at UG99 site (Table 2). After that, soil water content gradually decreased and reached the lowest value (<0.01 m3 m− 3) (Fig. 4d) until the frozen process terminated and the air temperature was above 0 ℃ (Fig. 4c). Once the air temperature became positive (above 0 ℃), soil water content increased rapidly due to snow melting and soil thawing (Fig. 4d). As the air temperature increased, the soil water mainly redistributed into the 30-cm layer for the CG site and the 0− 10 cm layer for the GE sites (Fig. 4d). In addition, 5

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Fig. 4. Field monitoring of N2O emissions and their environmental variables. (a) characteristics of profiled soil N2O concentration; (b) the statistical difference of N2O concentration in the soil profile during W-FTCs and S-FTCs; (c) soil temperature; and (d) soil water content. UG79: Long-term GE (grazing exclusion); UG99: Short-term GE; CG: Continuously grazed. W-FTCs: Winter freeze-thaw cycles. S-FTCs: Spring freeze-thaw cycles. Different letters indicate significant differences among treatments at the same depth of soil profile (one-way ANOVA, P < 0.05, LSD).

with the topsoil N2O concentration (r2 = 0.76, P < 0.01) (Table 3); however, weak correlations with either soil temperature or mineral N concentration were observed. SEM analysis showed that this model fitted well in describing variable data distributions (χ2 = 9.309, df = 9, P = 0.409, RMSEA = 0.036) (Fig. 5). The profiled N2O concentration, soil water content, and MBC (not presented) explained 66% of the total variance in N2O emissions. However, the N2O concentration in the soil profile and soil water content with standardized coefficients are 62% and 63%, respectively. Therefore, it is concluded that these two vari­ ables were the major factors controlling N2O emissions during soil thawing. Fig. 5 also indicates that the effects of soil temperature on N2O fluxes were small, and soil water content was the most dominant

due to snow melting, the mean soil water content was higher during SFTCs period compared with W-FTCs (Table 2). For different sites, soil water content was much higher for the GE sites during the W-FTCs than the CG. Noticeably, during S-FTCs, the mean soil water content increased to 21% at the UG99 site and was progressively higher than the values observed at the UG79 and CG sites. Concerning soil temperature, the differences among treatments were not significant (Table 2, Fig. 4c). 3.5. Factors controlling N2O emissions during spring freeze-thaw cycles The correlation analysis showed that N2O fluxes exhibited strong positive correlations with soil water content (r2 = 0.61, P < 0.01) and

Fig. 5. Structural equation model (SEM) anal­ ysis examining the effects of soil parameters on N2O flux. Note: In the model, square boxes indicate vari­ ables. The arrow directions connecting the boxes indicate the effects of causation. The width of the arrows (also with the adjacent numbers) indicates the strength of path co­ efficients. The red arrows indicate a positive relationship, while the green arrows indicate a negative relationship. MBC is soil microbial biomass carbon. R2 indicates the proportion of variance explained. Significance levels are as follows: *P < 0.05, **P < 0.01, and ***P < 0.001. Non-significant relationships are not presented.

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environmental factor. It should be noted that soil temperature may in­ fluence N2O emissions indirectly by modifying the microbial biomass (Fig. 5). Similarly, NO−3 -N may indirectly influence N2O emissions by changing profiled N2O concentration via N addition (Fig. 5).

the higher soil water content in short-term grazing exclusion might lead to cellular rupture and increase C and N substrates. In addition, high soil water content could provide suitable anaerobic conditions for the microorganism-based denitrification process and N2O production. The significant correlations between soil N2O fluxes and microbial biomass also indicate that microorganisms significantly affect N2O productions (Table 3). N2O emissions have been associated with microbial biomass levels in various ecosystems (Liu et al., 2009; Reed and Martiny, 2013). For example, Balser and Firestone (2005) demon­ strated that soil bacterial biomass was firmly related to the N2O fluxes in grassland ecosystems. Besides, the results of structural equation modeling analysis imply that the changes in microbial biomass were mainly caused by differences in soil water content (Fig. 5). Note that high soil organic C in the grazing exclusion site is another potential factor increasing the N2O emissions for short-term grazing exclusion. A previous study showed that the availability of the C substrate played an essential role in freeze-thaw-related N2O emissions (Sehy et al., 2004). The higher soil water content in the short-term grazing exclusion could be attributed to the differences in snow covers (Fig. 6) that were usually associated with aboveground standing biomass. For example, Geddes et al. (2005) reported that upright vegetation was the deter­ mining factor in snow-holding capacity. Grazing exclusion increased plant height and aboveground standing biomass; thus, more snow was captured and melted into the soil during the spring thawing periods. Noticeably, high aboveground biomass was observed in the short-term grazing exclusion site, compared with the long-term grazing exclusion site (Table 1), which is supported by previous studies (Zhao et al., 2011b; Jing et al., 2014; Liu et al., 2020), i.e., the effectiveness of grazing exclusion on aboveground biomass and species richness decreased with the duration of grazing exclusion due to competitive dominance. Accordingly, this study found higher aboveground standing biomass corresponded well to the higher soil water content in the short-term grazing exclusion compared with the long-term grazing exclusion (21% vs. 14%). These results are different from alpine grass­ land where soil water content (<20%) at all sites was lower than the threshold value and thus did not induce significant N2O fluxes during freeze-thaw cycles (Li et al., 2012). Previous studies suggested that soil temperature is an essential factor influencing N2O emissions (Dobbie and Smith, 2001; Congreves et al., 2018). However, in this study, there was no significant relationship between N2O fluxes and soil temperature (Table 3). One of the reasons is that the N2O was released suddenly at the start of spring freeze-thaw cycles (Fig. 2b), but the changes in soil temperature were small

4. Discussion 4.1. Effects of grazing exclusion on N2O emissions during freeze-thaw cycles In this study, it is demonstrated that long-term grazing exclusion (about 38 years) had no significant impact on N2O emissions when compared with grazing site during freeze-thaw cycles. In the same study area, Wolf et al. (2010) found that grazing exclusion (short-term in that case) increased the N2O emissions during freeze-thaw cycles, raising one cautious comment that this conclusion may only be limited to the defined grazing history. The N2O emission should be less at a long-term grazing exclusion site (>20 years), but their study did not involve it. Besides, the research also indicates that N2O fluxes were small during winter freeze-thaw cycles (Fig. 1b), but there were spasmodic emissions at the beginning of the spring freeze-thaw cycles (Fig. 2b). This result is consistent with most previous studies, which showed that the N2O fluxes in spring were higher than the winter periods (Dusenbury et al., 2008; Norman et al., 2008; Wolf et al., 2010; Wang et al., 2017). For example, Wolf et al. (2010) revealed that spring thawing-induced N2O emissions could contribute more than 70% of the annual emissions in semi-arid grasslands. Meanwhile, the results also differed from previous studies of Li et al. (2012). They found no pulse N2O emissions during either winter or spring period, and no apparent differences existed between grazing exclusion and grazing sites in alpine grassland, Northwest China. Therefore, the trend of grazing effects on N2O emission is subject to not only the grazing intensity (e.g., grazing history) but also the locations associated with the local environment (e.g., temperature, soil water, and the period of soil thawing). The air temperature was extremely low in winter in the Xinjiang alpine grasslands, especially in January, usually below -30 ℃ (Li et al., 2012). Most microbial communities cannot sur­ vive at such a low temperature. In comparison, the mean air temperature in the typical steppe of Inner Mongolia was about -20 ℃ in January and may have permitted the survival of some microorganisms related to N2O production. In addition, the lower soil water content in the alpine grasslands has the potential to restrict denitrification and hence result in low N2O emissions because this study found soil water content is the key to determine the N2O flux. This will be discussed in the following. 4.2. Factors controlling N2O emissions during spring freeze-thaw cycles The observations indicated that soil water was the determining fac­ tor for N2O emissions during the spring freezing-thawing periods. This is consistent with the previous studies (Liu et al., 2007; Wolf et al., 2011; Wu et al., 2013). Wu et al. (2013) reported that the largest N2O emis­ sions were found at water-filled pore space values around 50%, and a moisture threshold value might exist that stimulates N2O emissions. Soil water not only serves as a medium for C and N transformation, but also affects O2 availability within the soil, and thereby determines soil N transformation (e.g., nitrification and denitrification) (Congreves et al., 2018). In this study, topsoil (0− 10 cm) water content was higher in short-term grazing exclusion than other sites during spring thawing periods (Table 2). On the one hand, the high soil water content could increase the availability of C and N compounds and hence promote N2O fluxes. Previous studies showed that upon rapid soil freezing-thawing processes, water influx into microbial cells could cause a cellular rupture, thereby providing substrates for N2O production (Congreves et al., 2018). In this research, the weak relationship between N2O fluxes and soil mineral N (Table 3) was probably due to low levels of available substrates in the grazing site caused by low soil water content. However,

Fig. 6. Snow depth in different treatments. UG79: Long-term GE (grazing exclusion); UG99: Short-term GE; CG: Continu­ ously grazed. Different letters indicate significant differences among treatments (one-way ANOVA, P < 0.05, LSD). 7

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(Fig. 4c). The results also showed that soil N2O concentration was higher in the deep soil layer at the grazing site compared to grazing exclusion sites. The movement of mineral N likely causes this. The animal wastes are probably regarded as the primary source of these soil N as there was no other fertilization management in this study. The high N2O concen­ tration was associated with increased microbial actives, like structural equation modeling analysis revealed that soil microbial biomass carbon had a significant effect on N2O concentration in the soil profile (Fig. 5). Soil water content and N2O concentration explained most of the N2O production. The rest of this production may be related to the physical release of an accumulation of N2O emissions as N2O fluxes increased dramatically at the start of soil melting (Fig. 2a). However, this process may not be well revealed by Wolf et al. (2010). They explained the low N2O emissions in winter were related to low N2O concentration in the soil profile (Wolf et al., 2010). To our knowledge, current technics cannot accurately measure N2O concentration for frozen soils because N2O may be sealed within the ice crystals.

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5. Conclusion This study demonstrated that: (1) high pulse N2O emissions were found during spring freeze-thaw cycles in a typical grassland of Inner Mongolia; (2) compared with the grazing site, the short-term grazing exclusion increased N2O emission during the spring freeze-thaw cycle, but the long-term grazing exclusion had no significant effect; and (3) soil water content and N2O concentration in the soil profile dominate the patterns of N2O emission, which explained about 66% of the total variance in N2O emissions during spring freeze-thaw cycles. These findings have two important implications. Firstly, it highlights the importance of considering the grazing exclusion history and freeze-thaw cycles when evaluating and predicting N2O emissions. Secondly, soil water content, associated with snow depth, may be the predominant environmental factor in regulating N2O emissions during the spring freeze-thaw cycles. Besides, captured snow was correlated with grazinginduced vegetation variation. This suggests that controlling vegetation coverage and snow depth before spring thawing periods may be critical to reducing thaw-induced N2O emissions in locations subjected to cold weather. Declaration of Competing Interest There is no conflict of this study. Acknowledgments This work was supported by the Natural Science Foundation of China (41977009, 41371234) and the Taishan Scholars Program, China (201812096). We are very grateful to the Inner Mongolia Grassland Ecosystem Research Station for their assistance. References ´ Alvarez-Martínez, J., G´ omez-Villar, A., Lasanta, T., 2013. The use of goats grazing to restore pastures invaded by shrubs and avoid desertification: a preliminary case study in the Spanish Cantabrian mountains. Land Degrad Dev. 27, 3–13. Balser, T.C., Firestone, M.K., 2005. Linking microbial community composition and soil processes in a California annual grassland and mixed-conifer forest. Biogeochemistry 73, 395–415. Braun-Blanquet, J., 1964. Pflanzensoziologie: Grundzüge Der Vegetationskunde. Springer-Verlag. Congreves, K.A., Brown, S.E., N´emeth, D.D., Dunfield, K.E., Wagner-Riddle, C., 2017. Differences in field-scale N2O flux linked to crop residue removal under two tillage systems in cold climates. Gcb Bioenergy 9, 666–680. Congreves, K.A., Wagner-Riddle, C., Si, B.C., Clough, T.J., 2018. Nitrous oxide emissions and biogeochemical responses to soil freezing-thawing and drying-wetting. Soil Biol. Biochem. 117, 5–15. Dobbie, K.E., Smith, K.A., 2001. The effects of temperature, water-filled pore space and land use on N2O emissions from an imperfectly drained gleysol. Eur. J. Soil Sci. 52, 667–673.

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