Grassland Ecosystem

Soil Respiration Dynamics and Influencing Factors in Typical Steppe of Inner Mongolia under Long-term Nitrogen Addition

  • DU Wei 1 ,
  • WU Shanmei 1 ,
  • NIE Cheng 1 ,
  • LI Yue 1 ,
  • SHAO Rui 2 ,
  • LIU Yinghui , 1, * ,
  • SUN Nan , 3, *
  • 1. State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
  • 2. Department of Geography, Ghent University, Ghent 9000, Belgium
  • 3. Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences/National Engineering Laboratory for Improving Quality of Arable Land, Beijing 100081, China
*Corresponding author: LIU Yinghui, E-mail: ; SUN Nan, E-mail:

Received date: 2018-10-17

  Accepted date: 2018-12-29

  Online published: 2019-03-30

Supported by

National Key Research and Development Program of China (2017YFC0503805)

National Natural Science Foundation of China (31770519).


All rights reserved


We investigated soil respiration (Rs) dynamics and influencing factors under different nitrogen (N) addition levels (0, 2, 4, 8, 16, 32 g m-2 yr-1) on typical grassland plots in Inner Mongolia. We measured soil respiration, temperature, moisture and nutrients. We found that N addition did not change dynamic characteristics of Rs; daily and seasonal dynamics followed a single peak curve. N addition reduced Rs during the growing season. Rs under N2, N4, N8, N16 and N32 treatments decreased by 24.00%, 21.93%, 23.49%, 30.78% and 28.20% in the growing season, respectively, compared to the N0 treatment. However, Rs in the non-growing season was not different across treatments. Rs was significantly positively correlated with soil temperature and moisture and these two factors accounted for 72%-97% and 74%-82% of variation in Rs, respectively. The soil respiration temperature sensitivity (Q10) was between 2.27 and 4.16 and N addition reduced Q10 except in the N8 treatment.

Cite this article

DU Wei , WU Shanmei , NIE Cheng , LI Yue , SHAO Rui , LIU Yinghui , SUN Nan . Soil Respiration Dynamics and Influencing Factors in Typical Steppe of Inner Mongolia under Long-term Nitrogen Addition[J]. Journal of Resources and Ecology, 2019 , 10(2) : 155 -162 . DOI: 10.5814/j.issn.1674-764X.2019.02.006

1 Introduction

The IPCC (Intergovernmental Panel on Climate Change) Fifth Assessment Report points out that the global average temperature increased by 0.85 °C from 1880 to 2012, and increasing CO2 emissions will continue this warming trend (Stocker et al., 2013). The global soil carbon stock is 1300-2000 Pg C, and small changes in soil carbon pools have a significant impact on atmospheric CO2 concentrations (Liu and Fang, 1997), thus playing an important role in the global carbon balance. Rs is the main way of carbon emissions from the soil to the atmosphere. Global soil respiration emissions are 68-100 Pg C per year, accounting for about 10% of the atmospheric CO2 cycle (Peng et al., 2011). China has become one of the three regions with the highest N deposition globally, and a large amount of N input in terrestrial ecosystems affects Rs (Yu et al., 2013). Research on the effects of N addition on Rs has been carried out in forests, farmland, wetlands, grasslands and other ecosystems (Micks et al., 2004; Wang et al., 2013; Al-Kaisi et al., 2008.). However, there remains a lack of research on temperate arid and semi-arid grasslands (Peng et al., 2011).
Grassland accounts for 40% of the China’s land area. Rs of grassland is 20% higher than that of forests under the same environmental conditions and plays an important role in the carbon cycle's response to global change (Raich et al., 2000). N is the main limiting factor for grassland ecosystem productivity and 78% of grasslands are located in temperate arid and semi-arid regions in the north. These areas are in a state of N deficiency (Hooper and Johnson, 1999) and sensitive to changes in exogenous N input. The critical N load of natural typical temperate steppe was 5 g m-2 yr-1 (Liu et al., 2011). The trend of N deposition is intensifying (Galloway and Cowling, 2002) and is bound to have a dramatic impact on Rs in grassland.
In previous studies there was no consistent conclusion on the effect of N deposition on Rs in grassland. Luo et al. (2016) conducted N addition experiments in temperate grassland of Inner Mongolia, and the results showed that high N addition reduced soil N limitation by increasing soil nitrate N content, thereby promoting plant growth and significantly increasing Rs. The results of He et al. (2018) indicated that N addition had no significant influence on soil microbial biomass carbon and Rs. Studies have also found that N addition has different effects on autotrophic respiration and heterotrophic respiration, so N addition increases Rs in wet years and inhibits Rs in dry years (Yan et al., 2010). Research on the effect of N deposition on Rs in grassland remains controversial because different studies have different N addition levels, application forms, durations and ecological environments, making the response mechanism of Rs to N addition more complicated. Studying the response of Rs to N addition in grassland ecosystems is important for the accurate assessment of grassland carbon budgets.
In order to investigate Rs dynamics, factors affecting Rs for typical steppe under different N addition levels, and support when estimating typical steppe carbon budgets we selected a typical grassland plot in Inner Mongolia and conducted long-term N addition experiments. We measured Rs in the growing and non-growing seasons in the field, and examined soil physical (soil temperature and moisture), chemistry (soil dissolved organic carbon, total organic N, NH4+-N, NO3--N and pH), and biological (microbial biomass carbon) properties.

2 Materials and methods

2.1 Study area

The study area is located at the Duolun Restoration Ecology Experimental Demonstration Research Station of the Institute of Botany, Chinese Academy of Sciences, Duolun County, Xilin Gol League, Inner Mongolia Autonomous Region(115°50°-116°55°E, 41°46°-42°39°N). Duolun County in the experimental area is a semi-annular basin with a high circumference and a low middle, with an altitude of 1150-1800 m. The climate of the study area is a typical continental climate with a transition from a temperate semi-arid climate to a semi-humid climate. The annual average temperature is 2.1 °C (Yu, 2016). The average annual precipitation is 385.5 mm, the precipitation from June to August accounts for 67% of the whole year, and the annual average evaporation is 1748 mm (Yang, 2014). The grass community is dominated by Stipa krylovii, Leymus chinensis, Agropyron cristatum, and Artemisia frigida (Yang et al., 2004). The most widely distributed soil type is chestnut soil, which accounts for 70% of the total land area.

2.2 Experimental design

The experimental plot contains a total of 64 sample plots, each of which has an area of 15 m × 10 m and a buffer zone of 4 m between each plot. Eight N addition treatments and 8 replicates per treatment were set by the Latin square design method: N0: 0 g m-2 yr-1, N1: 1 g m-2 yr-1, N2: 2 g m-2 yr-1, N4: 4 g m-2 yr-1, N8: 8 g m-2 yr-1, N16: 16 g m-2 yr-1, N32: 32 g m-2 yr-1and N64: 64 g m-2 yr-1. Urea was added once in the middle of the growing season every year from 2003, and applied on 17 July in 2016. Our experiment selected three replicate plots of six N addition treatments: N0, N2, N4, N8, N16, and N32 in the north of the plot. So there are 18 plots totally (six N addition treatments × three replicates).

2.3 Field observation

One PVC collar (20 cm in diameter and 14 cm tall) was randomly inserted to a depth of 5-6 cm below the ground surface in each plot. The vegetation on the surface of the collar was cut off 24 h before the experiment. The LI-COR 8150 multi-channel soil carbon flux automatic measurement system was used to measure daily Rs dynamics for each plot in June-September and December 2016, and February-May 2017. The daily dynamics of soil temperature and moisture at a depth of 10 cm were measured using the temperature and moisture probe of a LI-COR 8150; if there was rain during the observation period this was conducted at intervals of half a day to one day.

2.4 Soil sample collection and index determination

In August and December 2016, 4 random soil samples were collected in each plot at a depth of 20 cm using a 3 cm diameter soil drill. These 4 soil samples were mixed evenly, passed through a 2 mm sieve, and stored at -20 °C for cryopreservation.
Soil dissolved organic carbon, microbial biomass carbon, ammonium N, nitrate N, total organic N and pH of soil samples were measured. The soil was leached with 40 ml of 0.5 mol/L K2SO4 solution and the leach liquor was shaken, centrifuged and filtered. TOC instrument (TOC-V CPH/ CPN/WP, Shimadzu) was used to measure soil dissolved organic carbon (DOC). Soil microbial biomass carbon content (MBC) was determined by the chloroform fumigation extraction-TOC instrument method. Ammonium N (NH4+-N) and nitrate N (NO3--N) were determined using a continuous flow analyzer. The soil was soaked with hydrochloric acid, washed with distilled water and dried in the oven, after which total organic N (TON) was measured using a PerkinElmer 2400 series II CHNS/O elemental analyzer. The pH was measured using a pH meter.

2.5 Data analysis

One-way ANOVA was used to analyze differences between Rs in different months and different N addition treatments. Pearson correlation was used to analyze the relationship between soil temperature, moisture, soil nutrients and Rs. The relationship between soil temperature and Rs was analyzed using exponential regression, and Rs temperature sensitivity (Q10) was calculated. The relationship between soil moisture and Rs was analyzed using power function regression.
The relationship between temperature and Rs rate:
Rs = a×ebT, Q10 = e10b
Where Rs is the Rs rate; T is the temperature; a is the Rs rate at 0 °C; and b is the temperature response coefficient (Wei and Liu, 2014).
The relationship between moisture and Rs rate: Rs=aMb
Where Rs is the Rs rate, M is the soil moisture, and a and b are the undetermined parameters.

3 Results

3.1 Dynamic of soil temperature and soil moisture

Daily variation in soil temperature at a depth of 10 cm follows a single peak curve, with the highest value between 13:00-16:00 and the lowest value between 4:00-7:00 am. Daily variation in soil moisture at 10 cm shows no significant change (Fig. 1).
Fig. 1 Daily dynamic curve of soil temperature and moisture in different months (mean ± SE)
The average soil temperature peaked in August 2016 at 20.39-0.36 °C, the lowest appeared in December 2016 at 7.52+0.43 °C. The highest average soil moisture was 28.32% in July 2016, and the lowest value was 1.94% in December 2016 (Fig. 2).
Fig. 2 Seasonal dynamic curve of soil temperature and moisture (mean ± SE)

3.2 Rs dynamics

In June, August and December 2016, and February 2017, daily Rs showed a single peak curve. N addition reduced the value of Rs without changing the overall trend. In different months, the daily maximum value of Rs was between 12:00-15:00 and the minimum value was between 4:00- 7:00 am, similar to the daily change in soil temperature at 10 cm (Fig. 3).
Fig. 3 Daily soil respiration rate in different months under different nitrogen additions (mean ± SE)
The seasonal variation curve of Rs is shown in Fig. 4. The N addition treatment did not change the seasonal dynamic trend in Rs. Across the whole observation period, seasonal dynamics of Rs under different N addition treatments showed a single peak curve. There was a significant difference in Rs between different months (P<0.001), and the Rs in the growing season was higher than in other periods. The highest value of the average Rs was 3.24±0.21 μmol m-2 s-1 in July 2016 and the lowest value was 0.10± 0.01 μmol m-2 s-1 in February 2017 (Fig. 4).
Fig. 4 Seasonal dynamic curve of soil respiration rate (mean ± SE)
Note: * indicates a significant difference between at least two nitrogen addition treatments during the month.

3.3 Effect of N addition on Rs

During the growing season (June to September 2016), the change in Rs under different N addition treatments accounted for the majority of year-round change (Fig. 5). Compared with the N0 treatment, the Rs under the N2, N4, N8, N16 and N32 treatments declined by 24.00%, 21.93%, 23.49%, 30.78%, and 28.20%, respectively. In August and September 2016, Rs was significantly different between N addition treatments (Fig. 4). There was a significant negative correlation between Rs and the N gradient in August 2016 (P<0.05). There was a significant difference between N addition treatments (P<0.01), and Rs under the N2, N4, N8, N16 and N32 treatments was 53.23%, 25.06%, 26.21%, 60.24%, and 59.22% lower than for the N0 treatment. N addition showed significant inhibition on Rs.
Fig. 5 Dynamic soil respiration rate during the growing and non- growing seasons under different N addition levels (mean ± SE)
Note: Different uppercase letters indicate significant differences in different nitrogen addition treatments during the growing season and lowercase letters for the non-growing season.
Rs in the non-growing season (December 2016, February to May 2017) showed a slight downward trend with increasing N; Rs under the N2, N4, N8, N16 and N32 treatments decreased by 14.14%, 4.66%, 8.74%, 16.53% and 10.28%, respectively, compared with the control N0. There was no significant difference in Rs under different N addition treatments in each month of the non-growing season.

3.4 Response of Rs to soil temperature and soil moisture under different N addition levels

Soil temperature and moisture are important environmental factors affecting Rs. Pearson correlation analysis of soil temperature, soil moisture and Rs showed that soil temperature and soil moisture were positively correlated with Rs (P<0.001); the correlation coefficients were 0.829 and 0.751, respectively.
Regression analysis of Rs and soil temperature under different N addition treatments showed that there was a significant exponential relationship between Rs and soil temperature (P<0.01). Equations under the six N addition treatments simulated the effect of soil temperature on Rs well; soil temperature explains 72%-97% of variation in the rate of Rs. Rs temperature sensitivity Q10 was between 2.27 and 4.16, specifically N8>N0>N4>N2>N32>N16. Q10 under the N8 treatment was 4.16, slightly higher than under the N0 treatment, and Q10 under other N treatments was lower than under the N0 treatment (Table 1).
Table 1 Function fitting of soil respiration (Rs) and soil temperature and moisture
N addition
(g m-2 yr-1)
Rs and soil temperature Rs and soil moisture
Formula R2 P Q10 Formula R2 P
0 Rs = 0.2245e0.1417T 0.97 <0.01 4.13 Rs = 0.014M1.646 0.76 <0.01
2 Rs = 0.3851e0.0945 T 0.75 <0.05 2.57 Rs = 0.017M1.499 0.80 <0.01
4 Rs = 0.2385e0.1250 T 0.96 <0.01 3.49 Rs = 0.013M1.600 0.75 <0.01
8 Rs = 0.1736e0.1425 T 0.95 <0.01 4.16 Rs = 0.015M1.535 0.75 <0.01
16 Rs = 0.4146e0.0819 T 0.72 <0.05 2.27 Rs = 0.013M1.560 0.82 <0.01
32 Rs = 0.3644e0.0927 T 0.77 <0.05 2.53 Rs = 0.014M1.530 0.74 <0.01
The Rs and soil moisture at 10 cm under different N addition levels were fitted with power functions in the form of y=axb, and the fitting degree R2 was 0.74-0.82. This indicates that the change in soil moisture had significant positive effects on Rs. The coefficient b represented the Rs moisture sensitivity, which varies from 1.499 to 1.646, specifically N0>N4>N16>N8>N32>N2. In general, N addition reduced the moisture sensitivity of Rs (Table 1).

3.5 Effects of soil nutrients on Rs

Pearson correlation analysis of soil nutrients and Rs showed that Rs was significantly negatively correlated with NH4+-N and NO3--N content (P<0.05), and positively correlated with soil pH and MBC content (P<0.01) in August 2016. There was no significant correlation between Rs and soil nutrient indicators in December 2016 (P>0.05) (Table 2).
Table 2 Pearson correlation between soil nutrients and soil respiration (Rs)
Rs (August 2016) -0.079 0.604** -0.582* -0.483* 0.088 0.615**
Rs (December 2016) 0.378 -0.076 0.390 0.149 -0.226 -0.154

Note: * P<0.05,** P<0.01

Pearson correlation analysis of N addition and soil nutrients showed that N addition was positively correlated with NH4+-N and NO3--N content (P<0.01), and negatively correlated with soil pH and MBC content (P<0.01) in August and December 2016. In December 2016, there was a significant positive correlation between N addition and soil DOC content (P<0.01) (Table 3).
Table 3 Pearson correlation between nitrogen (N) addition and soil nutrients
N addition (August 2016) N addition (December 2016)
DOC 0.207 0.613**
MBC -0.676** -0.841***
NH4+-N 0.700** 0.753***
NO3--N 0.743*** 0.656**
TON -0.011 -0.335
pH -0.962*** -0.951***

Note: ** P<0.01, *** P<0.001

4 Discussion

4.1 The effect of N addition on Rs seasonal dynamics

There was a significant difference in Rs between different months and the Rs in the growing season was higher than other periods. The highest and lowest average Rs values appeared in July and February, respectively, in line with the seasonal dynamic characteristics of grassland Rs (high in summer, low in autumn and winter). This dynamic is mainly caused by differences in meteorological conditions and above-ground biomass (Jia and Zhou, 2009; Qi et al., 2005).
The effects of N addition on the Rs of grassland include promotion, inhibition and non-significant change. In this study, N addition inhibited Rs during the growing season, which is consistent with many previous studies. Li et al. (2015) studied the effects of water and N addition on Rs of Stipa breviflora grassland and found that N addition in the growing season inhibited the increase in Rs. Zhang (2016) looked at soil microbial respiration in alpine meadow of the Qinghai-Tibet Plateau and showed that Rs decreased significantly when N application increased. Rs in the non-growing season was not significantly different under different N gradients. The same result was found in a study of Stipa krylovii grassland, that is, N addition inhibited Rs in July and September, but there is no significant impact in November (Haszulah, 2015). Studies have also shown that N addition promotes Rs (Jia et al., 2012; Lin et al., 2015) or has no significant effect (Shan et al., 2015; Zhang, 2014), possibly due to differences in climate, soil and vegetation in different regions, as well as different N addition treatments and observation methods.

4.2 Response of Rs to soil temperature and moisture to N addition

The effects of soil temperature on microbial respiration are mainly the result of changes to the microbial activity and soil enzyme activity. Soil temperature affects root respiration by affecting root metabolism, changing root respiratory enzyme activity, influencing N diffusion and changing the N content of root tissues (Jarvi and Burton, 2017). The effects of soil moisture on Rs include regulating the decomposition of organic matter and changing the gas content in soil (Zhou et al., 2009). Oxygen content that is too low will inhibit the activities of plant roots and oxygenated microorganisms, thus reducing Rs. When soil moisture decreases, plants allocate more carbon to the roots to obtain more water and nutrients, which affects root respiration. Studies have shown that elevated temperatures promote Rs (Scheffer et al., 2006; Andreas et al., 2010) and that precipitation also increase Rs (Liu et al., 2010); interactions exist between these parameters (Bontti et al., 2010) and both are affected by grassland type and observation time.
The daily dynamics of the Rs rate showed a peak curve at 12:00-15:00, similar to the daily dynamic change in soil temperature. This shows that the daily change in the Rs rate was mainly affected by soil temperature, consistent with other research (Cao et al., 2004; Jia and Zhou, 2009). The function fitting of Rs and soil temperature and moisture in different months showed that Rs had a significant exponential correlation with soil temperature and a significant power correlation with soil moisture. The effect of temperature on Rs was greater than moisture.
Q10 refers to the multiple of the increase in Rs when the temperature rises by 10 °C, reflecting the response intensity of Rs to temperature (Yang et al., 2011). The Rs Q10 ​​in this study ranged from 2.27 to 4.16 with a mean of 3.19, slightly higher than the median of 2.4 reported in the literature (Raich and Schlesinger, 1992). In this study, medium-level N addition (N8) promoted the Q10 of Rs, and low N (N2 and N4) and high N (N16 and N32) decreased Q10, consistent with Wang (2013) and Zhou et al. (2015). Probably because the Q10 of root respiration is greater than that of microbial respiration in Rs components (Boone et al., 1998; Schindlbacher et al., 2008), high root biomass leads to a high Q10 of Rs. Studies have shown that the root biomass decreases first, then increases and decreases with increasing N (Zi et al., 2018), consistent with variation in Q10 under different N levels in this study.
Some studies have concluded that high N addition reduces the Q10 of Rs (Luo et al., 2016; Zhang et al., 2014), probably because N addition increases litter and plant carbon distribution to underground roots, increasing soil labile carbon (Liang et al., 2017) and reducing the temperature sensitivity of Rs (Craine et al., 2007). It may also be that high N addition significantly increases the available N content in soil (Jin et al., 2010). The Q10 of Rs is controlled by soil organisms, substrate quality and substrate supply (Yang et al., 2011). The mechanism of N addition on Rs Q10 needs further investigation.
In this study, N addition reduced the moisture sensitivity of Rs compared to CK treatment. N addition can significantly increase the N content of plant roots and change the above-ground distribution pattern of plant carbon, which affects root activity and evaporation of water, and changes the relationship between Rs and soil moisture (Coleman et al., 2004). Soil microbial respiration and root respiration have different moisture sensitivities (Lin et al., 2016), and N addition may affect moisture sensitivities by altering the proportion of microbial respiration and root respiration.

4.3 Possible mechanisms of N addition affecting Rs

N addition does not directly affect Rs, but affects soil microbial respiration and plant root respiration by changing soil nutrient status, underground biomass, soil microbial community structure and function, and soil microbial activity (Zhu et al., 2016). In this study, N addition inhibited Rs in the growing season and showed significance in August. One possible reason is that N addition significantly increased the NH4+-N and NO3--N content of the soil in August (Table 2). Thus soil nitrification is enhanced (Wang et al., 2011) and the ammonia oxidation process is promoted in soil (Chen et al., 2013), which reduces soil pH (Wang et al., 2014; Zhang et al., 2009). On the one hand, soil acidification inhibits plant root growth, leading to a decrease in root exudates which inhibit plant root respiration; on the other hand, it reduces soil microbial activity and quantity (Liu et al., 2007). Reduced soil enzyme activity hinders nutrient release and inhibits Rs. In the non-growing season, N addition also had a significant effect on soil nutrients, but only slightly reduced Rs. There was no significant difference in Rs between different N addition treatments in each month, probably because temperature was the main factor affecting Rs in cold months (Yang and Wang, 2006); the difference caused by N addition is much smaller than the effect of temperature on Rs.

5 Conclusions

N addition reduced the daily emission intensity of soil respiration, but did not change daily and seasonal dynamics. Dynamics in soil respiration were still mainly affected by soil temperature and moisture. N addition inhibited soil respiration during the growing season and was significant in August 2016 due to soil acidification leading to a decrease in microbial activity. However, N addition in the non-growing season only had a weak negative effect on soil respiration, probably due to the limitation of soil hydrothermal conditions, so changes in soil nutrients via N addition cannot affect soil respiration. N additions alter the temperature sensitivity of soil respiration. Under a background of global warming and intensified atmospheric N deposition, it will be more difficult to estimate the carbon emissions of typical grassland in Inner Mongolia.


We thank the Duolun Restoration Ecology Research Station (part of the Institute of Botany, Chinese Academy of Sciences) for providing access to the study site.

The authors have declared that no competing interests exist.

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