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Article

Response of Soil Respiration to Altered Snow Cover in a Typical Temperate Grassland in China

by
Yanqi Liu
and
Qin Peng
*
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
*
Author to whom correspondence should be addressed.
Agriculture 2023, 13(11), 2081; https://doi.org/10.3390/agriculture13112081
Submission received: 26 September 2023 / Revised: 23 October 2023 / Accepted: 30 October 2023 / Published: 31 October 2023
(This article belongs to the Special Issue Strategies to Mitigate Greenhouse Gas Emissions in Agricultural Production Systems)
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Abstract

:
The snow cover in temperate areas is undergoing significant changes, which may affect soil respiration (Rs), the second largest carbon flux in global carbon cycling. However, currently, there are relatively few in situ field studies on the effects of altered snow cover on Rs in temperate areas during the non-growing season compared to the research on Rs during the growing season. Therefore, it limited the accurate prediction of the characteristics and magnitude of changes in soil carbon emissions in temperate areas under global change scenarios. Here, an in situ field experiment was conducted in a typical grassland in Inner Mongolia in China to explore the characteristics of Rs under three different snow cover treatments, i.e., increasing snow (IS), decreasing snow (DS), and ambient snow that was regarded as the control check treatment (CK). The results showed that the range of Rs flux and cumulative emission flux in all treatments in the non-growing season in the study area ranged from 5.87 ± 0.20 to 55.11 ± 6.42 mg CO2 m−2 h−1 and from 22.81 ± 0.68 to 26.36 ± 0.41 g C m−2, respectively. During the observation period, the depth of the largest snow cover for each treatment did not exceed 18 cm, and none of the snow treatments caused significant variations in Rs flux (p > 0.05). However, the cumulative flux of Rs in the whole non-growing season was only stimulated significantly by 15.6% by the IS treatment compared with that of CK. The relatively high Rs flux in the non-growing season was observed to mainly occur in the soil deeply frozen period (DFP) and the soil melting period (SMP). Further analysis revealed that Rs flux under different snow treatments were mainly positively correlated with soil temperature during SMP. The main factors controlling Rs varied with different sampling periods. Our findings suggest that the non-growing season is also an important period of non-negligible carbon emissions from typical grassland soils in temperate zones.

1. Introduction

Excessive burning of fossil fuels in the production sector is a significant contributor to carbon emissions [1,2], leading to climate change [3,4]. In recent years, the concentration of CO2 in the atmosphere has continued to increase, leading to the current global average surface temperature being approximately 1 °C higher than that before industrialization [5]. At the same time, the precipitation amount and its seasonal distribution have also changed in some areas, especially in temperate areas. The dramatic rise in temperature and the changes in precipitation may have a significant impact on winter snowfall, thereby altering the depth of snow cover in temperate soil [6,7]. Changes in soil snow cover depth may alter the ability of snow to thermally insulate soil from cold air temperature, thereby changing soil temperature, water content and nutrient availability [8], and ultimately significantly affecting soil respiration (Rs), which is the most important process of CO2 emitting from soil to the atmosphere. Rs has been regarded as the second largest carbon exchange flux in terrestrial ecosystems, only following the process of photosynthetic carbon sequestration from atmosphere by plants [9]. Through the process of Rs, about 10 times more CO2 was emitted to the atmosphere than during the combustion of fossil fuel in each year, and even small fluctuations in Rs can greatly change the CO2 concentration in the atmosphere [10]. Therefore, Rs response to altered snow cover is particularly important in determining the carbon sink of a terrestrial ecosystem [11].
Currently, research on the impacts of snow cover changes on Rs has mainly concentrated on forests, tundra, farmland, and wetlands in high-latitude and high-altitude regions [12,13]. These studies have shown that non-growing-season Rs may be an important source of annual carbon emission for terrestrial ecosystems [14,15,16], as the contribution rate of non-growing-season Rs to annual soil carbon emissions in these regions could reach 3–50%, or even higher. In addition, previous studies have also shown that the results of the impacts of snow cover changes on non-growing-season Rs are inconsistent, depending on different areas and ecosystem types [17]. For example, the snow manipulation experiments conducted in deciduous forests in the United States showed that snow addition could maintain soil temperatures above 0 °C in winter and significantly increase Rs rate [18]. It was also found that snow addition could increase the Rs rate by 21.75% in plantations with transitional climate between Northern subtropical and Warm temperate climates [19]. In spruce forests in western Sichuan, China, it was found that the average Rs rate under snow removal treatment was 0.52 μmol·m−2·s−1, which reduced the average winter Rs rate by 21.02% [17]. Similarly, snow removal in wetlands and tundra reduced the average Rs rate in winter [20,21,22]. But some other studies have found completely contrary results that snow removal increased the degree of soil freezing, resulting in a 27.6% increase in Rs, and the reason could be that fine root mortality increased linearly with soil freezing severity induced by colder temperature under the treatment of reduction of snow cover and it provided a substrate for heterotrophic respiration [23]. Still, some studies have found that snow cover was not the primary factor affecting Rs in winter, and snow removal had no significant effect on Rs rates [14]. Whether altered snow cover induced a significant change in Rs or not may have been related to a threshold of snow cover thickness. Above a certain threshold, Rs may change significantly, while conversely, Rs remains unchanged [24,25,26]. The threshold of snow cover thickness may vary with region and ecosystem, and further research is needed to elucidate it.
The characteristics of snowfall and snow cover in temperate regions differ significantly from those at high latitudes or high altitudes, with seasonal snowfall events and short snow cover periods. Currently, little attention has been paid to the impact of snow cover variations on non-growing-season Rs in temperate regions, and most experiments have focused on the effects of rainfall changes on growing-season Rs [27]. In addition, most studies on the effects of snow cover changes on Rs in the non-growing season have been conducted in laboratory due to the relatively harsh climate in that period [8], while there is a relative lack of in situ field observations, especially in temperate grassland ecosystems [28,29]. However, temperate grassland ecosystems are more sensitive to climate changes due to their fragile ecology. Moreover, temperate grasslands account for 16.7% of the total carbon storage in terrestrial ecosystems, most of which (approximately 90%) is stored in soil [30]. Thus, small changes in Rs in temperate grasslands in response to perturbations significantly affect changes in atmospheric CO2 concentrations and play an important role in terrestrial carbon dynamics. In addition, soil carbon dynamic directly affects soil fertility and grassland productivity, which are important indicators of soil quality and grassland health. Therefore, it is necessary to strengthen the field study of non-growing-season Rs in typical temperate grasslands and explore the response of non-growing-season Rs to snow cover changes so as to provide a preliminary basis for further understanding of the response of Rs to annual, inter-annual, and multi-year snow cover changes on longer time scales and multiple locations, and ultimately to reduce the uncertainty in the estimation of the soil carbon budgets of terrestrial ecosystems under the scenarios of climate change.
To this end, we selected a typical temperate grassland in Inner Mongolia, China to set up a snow net to simulate the changes in snow cover in the non-growing season and to explore the effects of altered snow cover on Rs. Meanwhile, the main meteorological and soil environmental factors that may control Rs under different snow cover conditions were also preliminarily explored. The following hypotheses were tested: (i) altered snow cover significantly impacts Rs flux, with an increase in snow cover promoting Rs and a decrease in snow cover inhibiting Rs; (ii) the main environmental factors controlling the changes in Rs are soil moisture or temperature conditions.

2. Materials and Methods

2.1. Site Description

The study area is located in the Xilin River Basin in Xilin Gol, Inner Mongolia, China, within the Northeast China Land Transect (NECT) in the International Geosphere-Biosphere Program (IGBP) Global Change Study. It is a representative and typical temperate grassland in China and even in the entire Eurasian. Selecting the Leymus chinensis steppe which is the most widely distributed grassland type in the Xilin River Basin as the research object, we established a 10 m × 10 m experimental area with uniform vegetation cover and soil properties there for manipulating altered snow cover. The manipulation experimental site (43°34′ N, 116°41′ E; 1225 m a.s.l.) is approximately 20 km from the Chinese Academy of Sciences’ Inner Mongolia Grassland Ecological Positioning Research Station. The climate of this region is a temperate, semi-arid continental climate. The annual average temperature is −0.3–1 °C, with the coldest temperature of about −21.6 °C occurring in January. The average annual precipitation is 350–450 mm. With winter precipitation accounting for approximately 10% of the annual precipitation [31,32], snowfall events often occur from October to late March of the following year [33]. According to meteorological observation data from 1971 to 2015, the snow accumulation period in most areas of Xilingol is concentrated from November to March of the following year. In addition, based on 256 samples of long-term snow depth data from 15 meteorological observation stations in the region, it was found that most of the observed snow depth values fall within the range of 1 to 20 cm, accounting for 86% of the total sample size [34]. In our experimental site, the constructive species is the rhizome grass Leymus chinensis, and the dominant species are Stipa grandis and Agropyron cristatum. The soil type is dark chestnut soil (equivalent to calcareous or calcareous soils in the USDA Soil Taxonomy Classification) according to the Chinese Soil Taxonomy classification. The soil composition is 21.2% clay, 18.9% silt, and 59.9% sand. The soil profile depth was 100–150 cm, and the thickness of the soil organic layer could reach 30 cm.

2.2. Experimental Design and Snow Cover Manipulation

At the end of the 2018 growing season, a 1.1 m high snow net perpendicular to the dominant wind direction (northwest) was fixed using several 1.5 m high stainless steel columns with a 1 m distance away from each other in the central area of the experimental plot. Three types of snow cover treatments were simulated using snow net fence: increasing snow (IS), decreasing snow (DS), and ambient snow that was regarded as the control check treatment (CK). Due to the installation of the snow net fence, the snow cover on the leeward side of the net fence roughly doubled after the snowfall event. Correspondingly, the snow cover on the other side was greatly reduced, and the remaining small amount of snow on this side was also carefully transferred to the side with increasing snow cover, further forming a decreasing snow cover treatment with almost no snow accumulation. During the snow transfer process, great care was taken to minimize the disturbance. In addition, on both sides of the extension line of the snow net fence, an undisturbed ambient snow treatment (CK) was formed without the snow net fence. During sampling, three replicates were set for each snow cover treatment.

2.3. Gas Sampling and Measurement

Considering that the snow accumulation period in our study site was concentrated from November to March of the following year, the gas sampling date started from October 29th and was ended in middle of May, the ending time of the non-growing season. Sampling frequency was determined based on the condition of soil freezing and thawing as well as the snowfall frequency. Thus, during the period from October to November 2019, the sampling frequency was 3 times a week, as there were frequent snowfall events and soil freezing and thawing. During the period from December to March, due to the complete freezing of the soil, gas samples were taken only once a week. During the period from April to May, due to frequent soil freezing and thawing and several snowfall events, gas samples were conducted 2~3 times a week. The gas was collected using the static opaque chamber technique to determine soil respiration. The device for gas collection consisted of a sampling box (50 cm × 40 cm × 30 cm) and a stainless steel frame with grooves (2 cm × 2 cm × 2 cm). Three static opaque chambers were randomly arranged as the three gas sampling replications for each snow treatment (IS, DS, CK). Each repetition was at least 1 m away from each other, as it should be, to reduce disturbance. During sampling, we inserted the sampling chamber into the groove on the edge of the previously installed base (stainless steel) and sealed it with deionized water. The gas collection time was between 9:00 and 11:00 and lasted for a total of 30 min. The gas was pumped at 0, 10, 20, and 30 min and collected each time using a polyethylene needle with a three-way valve. Approximately 500 mL of gas was sealed in a sealed polyethylene bag (Dalian Platt Gas Co., Ltd., Dalian, China), and the collected gas samples were transported back to the laboratory. The CO2 gas was collected, the air temperature was measured with a mechanical ventilation thermometer, and the soil temperatures at depths of 0 cm (T0) and 5 cm (T5) were measured with a handheld XGN-1000T digital thermal sensor (Yezhiheng Science and Technology Corporation, Beijing, China). The CO2 concentration was immediately measured using an Agilent 7890A gas chromatograph. Rs was calculated using the formula described by Qi et al. [35], and Rs cumulative flux in the non-growing season was calculated using the method described by Bremer et al. [36]. The Rs flux represents the change in gas mass in the observation box per unit time and unit observation area. Generally, a positive value of Rs flux means that CO2 gas is emitted from the soil to the atmosphere, and a negative value means that the gas flows from the atmosphere to the soil or the soil absorbs and consumes the gas in the atmosphere.

2.4. Soil Sampling and Chemical Analysis

Soil samples (0–10 cm) were collected synchronously with the gas sampling using a handheld auger. In each snow treatment plot, 6 soil cores were collected randomly along the “S” route, and then they were mixed in a sealed plastic bag as one sample. Three replicates of soil samples were set for each snow treated plot. We stored soil samples quickly in a cooled incubator and brought them to a laboratory to analyze their physical and chemical properties, such as soil water content (SWC), soil microbial carbon (MBC), dissolved organic carbon (DOC), soil nitrate (NO3−−N), and soil ammonium (NH4+−N) contents. The SWC was determined by drying the samples at 105 °C until a constant weight was achieved. The soil MBC content was extracted by chloroform fumigation and determined using a total organic carbon analyzer (Elementar Vario TOC cube, Frankfurt, Germany). The DOC content was extracted with ultrapure water, and the organic carbon concentration in the filtrate was measured using a total organic carbon analyzer (Elementar Vario TOC cube, Frankfurt, Germany). For NO3−−N and NH4+−N, fresh soil (10 g) was extracted with a 2 M KCl solution (50 mL) and then analyzed with an AutoAnalyzer 3 (Bran and Luebbe AA3, Hamburg, Germany).

2.5. Statistical Analysis

All data processing and statistical analyses were completed using Microsoft Excel 2020, Origin Pro 2020, and SPSS 21.0. Differences in environmental factors and Rs under different snow treatments, sampling periods, and their interactions were analyzed using variance analysis. Multiple comparisons were performed using the least significant difference (LSD) when significant effects were detected. Pearson correlation analysis was used to determine the relationship between Rs and the environmental factors, and fitting analysis was used to explore the main environmental factors affecting Rs.

3. Results

3.1. Environmental Factors under Different Snow Cover Treatments

During the entire observation period in the non-growing season, the atmospheric temperature ranged from −16.9 °C (the lowest value appeared on 28 December) to 25.5 °C (the highest value occurred on May 10) (Figure 1a). The variation trend of 0 cm or 5 cm soil temperature was basically consistent with that of atmospheric temperature. Soil temperatures at depths of 0 cm and 5 cm ranged from −17.85 to 29.62 ± 1.02 °C and from −19.55 to 24.12 ± 4.05 °C, respectively (Figure 1c). Based on the changes in soil temperature and soil freezing and thawing conditions, the entire observation period (31 October 2018–16 May 2019) was divided into three sampling periods: (i) the initial freezing period (IFP, 31 October 2018–30 November 2018). During this period, the soil transitioned from a completely thawed to an approximately completely frozen state, undergoing frequent freeze–thaw cycles; (ii) the deeply frozen period (DFP, 1 December 2018–7 March 2019). During this period, the soil was completely frozen with no freeze–thaw alternating state; (iii) the soil melting period (SMP, 8 March 2019–16 May 2019). During this period, soil transitioned from a completely frozen state to a completely melted state, undergoing multiple freeze–thaw cycles again.
Snow cover thickness in the ambient snow cover treatment (denoted as CK) varied between 2 cm and 9 cm throughout the whole observation period (Figure 1a). In contrast, in the IS treatment, thickness fluctuated between approximately 5 cm and 18 cm, about twice as much as that in the CK treatment. However, in the DS treatment, snow cover thickness was negligible and recorded as 0 cm, which primarily resulted from the combined effects of the snow net fence’s snow-reducing capability and manual snow removal efforts. It should be noted that several heavy snowfall events occurred during the initial freezing period (IFP), leading to relatively thick snow cover in early November. The maximum snow cover thickness (about 9 cm) in CK treatment occurred during this period. However, during the deeply frozen period (DFP), snow cover thickness in the CK treatment remained at a relatively thin level, hovering between 2.3 cm and 2.8 cm. During this period, owing to strong winds, the blocking effect of the snow net fence yielded a thicker snow cover on one side (IS treatment) and a thinner snow cover on the other side (DS treatment). Then, after meticulous manual snow transfer, snow cover in the DS treatment was non-existent, registering close to 0 cm. Meanwhile, in the IS treatment, snow cover thickness varied between 5.8 and 9.3 cm, which was around 2–3 times that observed in the CK treatment (2.3 cm–2.8 cm). To summarize, over the whole observation period, average snow cover thickness was about 4.4 cm for the CK treatment, 9.8 cm for the IS treatment, and virtually 0 cm for the DS treatment. Throughout this period, no snow cover exceeded 20 cm in thickness in all the treatments.
Statistical analysis showed that sampling period rather than snow treatment (S) had a significant effect on soil temperature or soil moisture (SWC) (p < 0.05; Table 1). The interactive effect of snow treatment and sampling period (S × P) on SWC was also significant (p < 0.05; Table 1) despite the fact that it was insignificant on soil temperature. For the three different sampling periods, the average SWC values were 16.17 ± 0.53% (IFP), 17.93 ± 0.68% (DFP), and 12.98 ± 0.86% (SMP), respectively (Figure 1b), with significantly higher SWC in the IFP and DFP than that in the SMP according to multiple LSD comparisons.
Soil MBC and DOC across all the snow treatments during the entire non-growing season were 332.46 ± 45.74–441.63 ± 21.77 mg·kg−1 and 17.94 ± 0.93–24.1 ± 2.94 mg·kg−1 (Figure 2a,b), respectively. And soil NH4+-N content in the non-growing season was 0.03 ± 0.02–22.67 ± 0.49 mg·kg−1 (Figure 2d). Snow treatment (S), sampling period (P), or their interactions (S × P) did not affect soil NH4+-N content significantly (p > 0.05, Table 1). In comparison, soil NO3−-N content varied between 0.59 ± 0.03 and 25.44 ± 0.49 mg·kg−1 across all the snow treatments during the entire non-growing season. Despite the fact that snow treatment (S) did not have a significant effect on soil NO3−-N content, sampling period had a significant effect on it (p < 0.05; Table 1). And the mean NO3−-N content values in the DFP were 24.26% and 26.44% higher than those in the IFP and SMP, respectively (p < 0.05; Figure 2c).

3.2. Soil Respiration under Different Snow Cover Treatments

In the entire non-growing season, the Rs flux values in the three snow cover treatments were 5.87 ± 0.20–55.11 ± 6.42 mg CO2 m−2 h−1 (IS), 7.18 ± 0.29–53.87 ± 11.58 mg CO2 m−2 h−1 (DS), and 6.27 ± 0.52 to 32.97 ± 13.37 mg CO2 m−2 h−1 (CK), respectively. Only positive but no negative flux values of Rs were observed under all different snow treatments; that is, the soil emitted CO2 to the atmosphere but had no CO2 absorption.
Meanwhile, IS or DS treatment did not change the amount of CO2 emitted to the atmosphere from the soil significantly (p > 0.05; Table 1). However, the response of Rs flux to the three snow cover treatments varied significantly in each different soil freeze–thaw period. During the IFP, the Rs flux in the IS and CK treatments changed more sensitively to a snowfall event than that in the DS treatment. The peak soil CO2 flux (21.67 ± 7.65 mg CO2 m−2 h−1) appeared rapidly in the CK treatment after the first snowfall on 31 October. In contrast, in the IS treatment, the flux peaked (20.31 ± 1.78 mg CO2 m−2 h−1) on November 11 after multiple snowfall events. In the later time of this period, no obvious fluctuations in Rs flux were caused by more snowfall events in the IS or CK treatments. In the DS treatment, the response of the Rs flux to snowfall events was weak, and no significant peak was observed during the IFP. During the DFP, there was no significant difference in Rs flux among the three treatments caused by snowfall events. Among them, the largest peak (16 February) and smallest trough (1 December) of the Rs flux appeared in the IS treatment. During the SMP, changes in snow cover were only recorded in the early stage of the period as no snow events occurred in the later stage. During this period, no significant difference in Rs flux was induced by different treatments. Nevertheless, due to temperature fluctuation in the early stage and the continued to rise in the later stage, the Rs flux in each treatment fluctuated significantly and ultimately reached its peak on 10 May. The peaks under IS and DS treatments were 55.11 ± 6.42 mg CO2 m−2 h−1 and 53.87 ± 11.58 mg CO2 m−2 h−1, respectively, almost double those under CK treatment (32.97 ± 13.37 mg CO2 m−2 h−1) (Figure 3a).
During the entire non-growing season, the cumulative flux values of Rs under three different treatments were 96.67 ± 1.51 g CO2 m−2 (IS), 90.43 ± 4.50 g CO2 m−2 (DS), and 83.63 ± 2.49 g CO2 m−2 (CK), respectively. Compared with CK, the cumulative flux of Rs in the IS treatment was significantly increased by 15.60% (p < 0.05), and the cumulative flux of Rs in the DS treatment was increased by 8.14%. However, it did not reach a statistically significant difference (p = 0.171). Moreover, the proportion of cumulative flux values of Rs under three different treatments were 7.67% (IS), 8.32% (DS), and 10.24% (CK) during the IFP. During the DFP, the proportions of them were 45.30% (IS), 42.06% (DS), and 47.68% (CK), respectively. Comparing the differences in the cumulative flux of Rs among different treatments during IFP and DFP, it was found that the cumulative flux values of Rs for the three different snow covers were not significant. In the SMP, the proportion were 47.03% (IS), 49.62% (DS), and 42.08% (CK), respectively. The cumulative flux values of the Rs under IS and DS were 29.19% and 27.52% higher than those of CK, respectively (p < 0.05; Figure 3b). However, no obvious difference between IS and DS treatments was observed (p > 0.05; Figure 3b). Meanwhile, it can be found that the cumulative flux of Rs was mainly concentrated in DFP and SMP. Further calculation showed that the daily average Rs flux values in different sampling periods were 0.25 ± 0.02 g·m−2·d−1 (IFP) < 0.42 ± 0.03 g·m−2·d−1 (DFP) < 0.61 ± 0.08 g·m−2·d−1 (SMP) (Figure 3b).

3.3. The Relationship between Soil Respiration and Environmental Factors

Our results indicated that the main control factors affecting Rs were different during the three periods of the non-growing season. During IFP and DFP, no significant correlation was found between environmental factors and Rs flux, while during SMP, soil temperatures at the depth of 0 cm and 5 cm, as well as NH4+-N, were the main factors controlling Rs under the IS treatment, with correlation coefficients of 0.936 (p < 0.01), 0.809 (p < 0.01), and −0.609 (p < 0.05), respectively. The main controlling factors for Rs flux under the DS treatment were the same as those under the IS treatment, with correlation coefficients of 0.791 (p < 0.01), 0.645 (p < 0.05), and −0.664 (p < 0.05), respectively. The Rs flux under the CK treatment was only significantly correlated with T5 (p < 0.01; Table 2).
Further analysis showed that the relationship between Rs flux and soil temperature during SMP can be fitted with linear regression equations (Figure 4). The main factor controlling Rs flux in the IS and DS treatments was T0, and the regression equations were Y = 11.595 + 1.258XT0 (IS: R2 = 0.903; p < 0.01) and Y = 13.129 + 1.147XT0 (DS: R2 = 0.627; p < 0.01), respectively. The main factor controlling Rs flux in the CK treatment was T5, and the regression equation was Y = 15.595 + 0.659XT5 (R2 = 0.448; p < 0.05).

4. Discussion

4.1. Soil Respiration under Different Snow Cover Treatments

In our study, the effect of snow cover changes on Rs flux was insignificant (Figure 3a), which is inconsistent with our first hypothesis, but similar to some previous studies [15,37,38,39,40,41]. The insignificant impact of altered snow cover on Rs flux may be related to relatively shallow snow cover during our observation period. According to previous studies, when snow cover exceeded a certain thickness, it could produce significant thermal insulation effects, reduce temperature exchange between soil and atmosphere, and allow for relatively high temperatures to be maintained in deeper snow cover treatments, making it more prone to higher Rs fluxes than other snow cover treatments. However, when snow cover was shallow, the thermal insulation effect could not be achieved [42,43,44], resulting in a significant change in Rs flux. The threshold of snow thickness that may induce significant thermal insulation effect or significant changes in Rs flux varied in different studies. Many studies have found that when snow thickness reached 30 cm, an effective thermal insulation layer was formed, and soil temperature could be maintained at approximately 0 °C to prevent plant roots and microbes from freezing and dying [14,24,25]. When snow cover was thin (<20 cm), the thermal insulation effect caused by snow cover was relatively weak, and soil freezing intensity was high, which exerted an inhibitory effect on Rs [26]. However, some studies found that even if the snow cover reached approximately 30 cm, it still did not have a significant effect on Rs flux. However, overall, as the depth of snow cover continued to increase, Rs would also be stimulated to some extent [19]. In our study, throughout the non-growing observation period, the ambient snow cover thickness was about 5–9 cm, and in the IS treatment, the maximum observed snow depth did not exceed 18 cm. The snow cover thickness was still shallower than the threshold thickness found in previous studies that caused significant thermal insulation effect or promoted a significant increase in Rs flux [14,19,26,41,42,43,44]. Although in our study, due to the relatively thin snow cover, the thermal insulation effect may be insignificant, resulting in an increase in snow cover that did not lead to a sustained increase in Rs, in fact, at some specific periods, the transient promoting effect of snow cover changes on Rs still could be observed, which may contribute to the increase in cumulative Rs flux during the non-growing season.
Not only did the increase in snow cover not cause a significant stimulation in Rs, but the decrease in snow cover also did not significantly affect Rs, which may also be due to the fact that the decrease in snow cover only led to minor changes in temperature. According to the fitting analysis results in this study, the main control factor affecting Rs was soil temperature (Figure 4), which further indicated that the insignificant changes in soil temperature induced by relatively shallow snow cover could be one of the important reasons for the insignificant effect of snow cover changes on Rs. In addition, some previous studies have shown that the adaptation of soil microorganisms to temperature changes in different snow cover treatments may be another reason for the insignificant impact of altered snow cover on Rs flux [38].
Furthermore, our study found that the daily average Rs flux during IFP was relatively low compared to those in DFP and SMP, indicating that the non-growing-season soil CO2 emissions via Rs were mainly concentrated in DFP and SMP. We also observed a significant increase in Rs after soil thawing, which was in line with previous findings [44,45,46]. The pattern of increased Rs after soil thawing during SMP may reflect the emergence of suitable abiotic or biotic conditions favorable for microbial and root respiration at the end of winter, such as appropriate soil temperature and moisture, restoration of the growth of plant roots [44,47], as well as increased soil microbial activity [44,46]. In addition, the surge in Rs from thawed soil at the end of the non-growing season may also be caused by the rapid release of carbon dioxide stored in soil during the freezing period after the melting of the ice layer [45,48].
In our observation, the average Rs rate in the non-growing season was 0.12 μmol m−2 s−1, lower than that in many reports from previous researches, such as that in the Austrian Alpine grasslands (>1 μmol m−2 s−1) [29] and subalpine meadows in the Qilian Mountains (1.19–1.76 μmol m−2 s−1) [30]. In addition, further calculation indicated that the cumulative Rs flux in the whole non-growing season ranged from 22.81 ± 0.68 to 26.36 ± 0.41 g C m−2 (Figure 3b), which was approximately half of the value (51 ± 8 g C m−2) observed by Yang [49] in grasslands prohibited from grazing in North China. The higher cumulative Rs flux values than those in our study may be related to the following reasons: (1) the snow cover depth (approximately 24 cm) was deeper than that at our study site (the maximum depth of snow cover was about 18 cm even in the IS treatment); (2) due to years of grazing prohibition, more grassland vegetation may have been preserved than that in our study. Relatively less vegetation made it easier for snow to be blown away by the wind, thus maintaining relatively shallow snow cover, which could result in a relatively small cumulative Rs flux in our observation period. In addition, reduced vegetation may provide less carbon substrate in soil, which is not conducive to production of more CO2 [50,51].
As previous studies have shown, changing the snow cover could affect the intensity and frequency of soil freezing and thawing conditions by regulating soil temperature [40]. Some studies have found that Rs responded more sensitively to freezing and thawing (such as intensity and frequency). Compared with in situ soil freezing–thawing in field experiments, the simulated freezing–thawing intensity and frequency in most indoor experiments were relatively higher, which made it easier for aggregate soil to release more nutrients and substrates that contributed to CO2 production and emission, thereby increasing Rs flux [52,53]. Therefore, most previous indoor simulation experiments on freezing–thawing changes caused by altered snow cover might have overestimated the Rs flux during the non-growing season. More in situ studies are needed to explore the response of Rs to climate change, providing data support for accurate estimation of soil CO2 emission in winter under the scenario of global change [40]. Our in situ field observation was helpful to accurately reflect the actual effect of soil freeze–thaw on Rs flux caused by altered snow cover. However, our study was only based on observation data from a non-growing season, which cannot reflect the interannual variations in Rs fluxes induced by seasonal snow cover over different years. Therefore, it was necessary to continue many longer-term observation studies to explore the effects of altered snow cover on Rs.

4.2. Soil Respiration under Different Snow Cover Treatments

According to previous studies, snow cover regulated Rs flux mainly through soil temperature, soil free water content, and nutrient substrate content in the soil [16,18,54,55]. However, in our study, during IFP and DFP, the analysis found no significant correlation between Rs and the environmental factors such as T0, T5, SWC, MBC, DOC, NH4+-N, and NO3−-N, which is inconsistent with our second hypothesis. It indicated that during these observation periods, the variations in Rs under different snow cover treatments might not be controlled by one or two certain dominate factors, but rather be the result of the combined regulations of multiple environmental factors. Meanwhile, environmental factors, such as hydrothermal and nutrient factors, might indirectly modulate Rs via influencing some biological factors, such as microorganisms or plants [56], making the relationship between Rs and these environmental factors more complex, resulting in no significant correlation between them. In the future, we should explore the effect of snow cover on Rs in grassland ecosystems by coupling multiple indicators of soil water and heat conditions, soil nutrient substrates, and soil microorganisms.
In contrast, during SMP, soil temperature was the main controlling factor for variations in Rs, as Rs under CK treatment showed a positive linear correlation with T5, and Rs under IS and DS treatments presented a positive linear correlation with T0 and T5 (Table 2, Figure 4). This result is in line with the second hypothesis. Soil temperature was a key factor in controlling the seasonal trend of Rs, and Rs was positively correlated with soil temperature, as reported by some previous studies [40]. Soil temperature may drive variations in Rs through affecting soil microbial and enzymatic activities [57]. An increase in temperature might stimulate the number and activity of microorganisms, which in turn promotes microorganism respiration and organic matter decomposition, thus driving soil material circulation and energy flow [58]. Meanwhile, during the SMP, Rs was also found to be negatively correlated with NH4+-N under IS and DS treatments (Table 2), which meant that during SMP, soil nutrient was also an important factor affecting Rs. Since soil nutrients may be limiting factors for the activities of microorganisms and plants, during SMP, a release of NH4+-N from the melting of long-term frozen soil aggregates may have some influence on the activities of soil microorganisms and plant roots, as well as some other soil properties [23,28], ultimately resulting in significant changes in Rs indirectly. Previous studies have already found that an increase in NH4+-N content might stimulate or inhibit soil microbial community structure, decrease soil microbial biomass [59], reduce soil pH [60], and change soil C/N [61], thereby possibly causing a decrease in microbial respiration or root respiration, which are the two main components of Rs.
This study explored the impacts of snow cover changes on Rs at different stages of the non-growing season through simulation of altered snow cover in situ field experiment, filling the gaps in relevant scientific data. However, considering that only a single non-growing season and only one typical temperate grassland were observed in this study, in order to more accurately explore the characteristics of Rs under snow cover and their relationship with related environmental factors, more years of non-growing season and multi-site observations are still needed in the future. Furthermore, the potential mechanism of Rs in the non-growing season should also be further explored from the perspective of multiple factors of soil microbial and soil enzyme activities, as well as carbon and nitrogen substrates.

5. Conclusions

In our study site, soil respiration flux was about 5.87 ± 0.20–55.11 ± 6.42 mg CO2 m−2 h−1 across all the three different snow cover treatments in the non-growing season observation period. Altered snow cover treatments did not significantly change soil respiration flux overall since depth of snow cover was relatively shallow (the maximum snow cover was less than 18 cm in the whole observation period). During the entire observation period in the non-growing season, the cumulative Rs flux of all snow cover treatments ranged from 22.81 ± 0.68 to 26.36 ± 0.41 g C m−2. IS treatment significantly stimulated the cumulative Rs flux by 15.6% in the whole observation period when compared with CK, while DS treatment did not cause significant changes. When comparing the cumulative Rs flux of the three different periods (IFP, DFP and SMP), the cumulative Rs fluxes during DFP and SMP were relatively high, especially during SMP, while during IFP, the cumulative Rs flux was relatively low. Therefore, this indicated that more attention should be paid to the changes in Rs under different snow treatments during SMP in the future. In addition, soil temperature and NH4+-N rather than other factors such as soil moisture, MBC, and DOC were the key factors that controlling Rs flux during SMP. Overall, our findings provided important basic data and theoretical support for the possible mitigation of temperate typical grassland CO2 emissions in the non-growing season under the scenarios of climate change. In the future, it is necessary to further strengthen the research on the potential impact of even deeper snow thickness changes on soil respiration, the possible snow thickness threshold that may induce the significant changes in soil respiration, and its potential microbial control mechanisms.

Author Contributions

Conceptualization, Q.P.; writing—original draft preparation, Y.L.; writing—review and editing, Q.P.; visualization, Y.L.; funding acquisition, Q.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China [grant numbers 4227071657, 42177224, 41673086].

Institutional Review Board Statement

Not applicable.

Data Availability Statement

Data will be made available on request.

Acknowledgments

The authors thank Xueli Wang, Yu Guo and Xiujin Yuan for their assistance in field sampling, laboratory analysis and figure modification. We also thank the Inner Mongolia Grassland Ecosystem Research Station, Chinese Academy of Sciences, for assistance with accommodation and laboratories.

Conflicts of Interest

The authors declare no conflict of interest.

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Agriculture 13 02081 g001
Figure 1. (a) Air temperature and ambient snow cover thickness; (b) soil water content; (c) soil temperature at depths of 0 cm (T0) and 5 cm (T5) under three different observation periods (IFP: the initial freezing period from 31 October 2018 to 30 November 2018; DFP: deeply frozen period from 1 December 2018 to 7 March 2019; SMP: soil melting period from 8 March 2019 to 16 May 2019) and snow cover treatments (IS: increasing snow; DS: decreasing snow; CK: control check, i.e., ambient snow). Error bars represent means ± SE. The hatched area is used to separate three spaces for three different observation periods (IFP, DFP and SMP). Red lines in different lengths represent different snowfall events and the resulting ambient snow cover at different depths ranging from 2 cm to 9 cm.
Figure 1. (a) Air temperature and ambient snow cover thickness; (b) soil water content; (c) soil temperature at depths of 0 cm (T0) and 5 cm (T5) under three different observation periods (IFP: the initial freezing period from 31 October 2018 to 30 November 2018; DFP: deeply frozen period from 1 December 2018 to 7 March 2019; SMP: soil melting period from 8 March 2019 to 16 May 2019) and snow cover treatments (IS: increasing snow; DS: decreasing snow; CK: control check, i.e., ambient snow). Error bars represent means ± SE. The hatched area is used to separate three spaces for three different observation periods (IFP, DFP and SMP). Red lines in different lengths represent different snowfall events and the resulting ambient snow cover at different depths ranging from 2 cm to 9 cm.
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Figure 2. Response of (a) microbial biomass carbon (MBC), (b) dissolved organic carbon (DOC), (c) soil ammonia nitrogen (NH4+−N), and (d) soil nitrate nitrogen (NO3−−N) to the three different observation periods (IFP: the initial freezing period from 31 October 2018 to 30 November 2018; DFP: deeply frozen period from 1 December 2018 to 7 March 2019; SMP: soil melting period from 8 March 2019 to 16 May 2019).
Figure 2. Response of (a) microbial biomass carbon (MBC), (b) dissolved organic carbon (DOC), (c) soil ammonia nitrogen (NH4+−N), and (d) soil nitrate nitrogen (NO3−−N) to the three different observation periods (IFP: the initial freezing period from 31 October 2018 to 30 November 2018; DFP: deeply frozen period from 1 December 2018 to 7 March 2019; SMP: soil melting period from 8 March 2019 to 16 May 2019).
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Figure 3. The variations in (a) soil respiration and ambient snow cover thickness; (b) cumulative and average daily flux under different observation periods (IFP: the initial freezing period from 31 October 2018 to 30 November 2018; DFP: deeply frozen period from 1 December 2018 to 7 March 2019; SMP: soil melting period from 8 March 2019 to16 May 2019) and snow cover treatments (IS: increasing snow; DS: decreasing snow; CK: control check, i.e., ambient snow). Bars represent means ± SE. The different letters above the error bars represent significant differences among the same periods and different treatments based on post hoc testing at p < 0.05. The hatched area is used to separate three spaces for three different observation periods (IFP, DFP and SMP). Red lines in different lengths represent different snowfall events and the resulting ambient snow cover at different depths ranging from 2 cm to 9 cm.
Figure 3. The variations in (a) soil respiration and ambient snow cover thickness; (b) cumulative and average daily flux under different observation periods (IFP: the initial freezing period from 31 October 2018 to 30 November 2018; DFP: deeply frozen period from 1 December 2018 to 7 March 2019; SMP: soil melting period from 8 March 2019 to16 May 2019) and snow cover treatments (IS: increasing snow; DS: decreasing snow; CK: control check, i.e., ambient snow). Bars represent means ± SE. The different letters above the error bars represent significant differences among the same periods and different treatments based on post hoc testing at p < 0.05. The hatched area is used to separate three spaces for three different observation periods (IFP, DFP and SMP). Red lines in different lengths represent different snowfall events and the resulting ambient snow cover at different depths ranging from 2 cm to 9 cm.
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Figure 4. Rs fitted to environmental factors of (a) increasing snow cover (IS), (b) decreasing snow cover (DS), and (c) control check, i.e., ambient snow (CK) treatments during SMP (soil melting period from 8 March 2019 to 16 May 2019).
Figure 4. Rs fitted to environmental factors of (a) increasing snow cover (IS), (b) decreasing snow cover (DS), and (c) control check, i.e., ambient snow (CK) treatments during SMP (soil melting period from 8 March 2019 to 16 May 2019).
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Table 1. ANOVA results (p-values) of the effects of snow (S), period (P) and their interaction (S × P) on Rs, soil temperature at depths of 0 cm (T0) and 5 cm (T5), soil water content (SWC), microbial biomass carbon (MBC), dissolved organic carbon (DOC), soil ammonia nitrogen (NH4+-N) and soil nitrate nitrogen (NO3−-N).
Table 1. ANOVA results (p-values) of the effects of snow (S), period (P) and their interaction (S × P) on Rs, soil temperature at depths of 0 cm (T0) and 5 cm (T5), soil water content (SWC), microbial biomass carbon (MBC), dissolved organic carbon (DOC), soil ammonia nitrogen (NH4+-N) and soil nitrate nitrogen (NO3−-N).
T0T5SWCMBCDOCNH4+-NNO3−-NT0
S0.5910.1490.8820.4870.3410.4560.7380.831
Pp < 0.001 **p < 0.001 *p < 0.001 **p < 0.001 *0.9520.3610.239p < 0.05 *
S × P0.4160.9720.999p < 0.05 *0.8620.9560.5510.618
Note: The × sign suggests interaction or combined effect. * Indicates significance at p < 0.05, and ** indicates significance at p < 0.01.
Table 2. Correlation analysis between Rs and soil temperature at soil depths of 0 cm (T0) and 5 cm (T5), soil water content (SWC), microbial biomass carbon (MBC), dissolved organic carbon (DOC), soil ammonia nitrogen (NH4+-N) and soil nitrate nitrogen (NO3−-N) in different periods and treatments.
Table 2. Correlation analysis between Rs and soil temperature at soil depths of 0 cm (T0) and 5 cm (T5), soil water content (SWC), microbial biomass carbon (MBC), dissolved organic carbon (DOC), soil ammonia nitrogen (NH4+-N) and soil nitrate nitrogen (NO3−-N) in different periods and treatments.
PeriodRsT0T5SWCMBCDOCNH4+-NNO3−-N
IFPIS-Rs0.2000.0910.068−0.2480.1880.527−0.564
DS-Rs0.4180.455−0.2850.0550.018−0.0300.055
CK-Rs0.2610.1150.204−0.103−0.0060.0670.127
DFPIS-Rs0.0240.095−0.049−0.143−0.6430.095−0.024
DS-Rs0.1190.1670.2330.190−0.5710.0240.690
CK-Rs−0.0480.0710.3470.2860.167−0.095−0.333
SMPIS-Rs0.936 **0.809 **−0.4030.455−0.455−0.609 *−0.405
DS-Rs0.791 **0.645 *−0.4580.600−0.045−0.664 *−0.264
CK-Rs0.4690.752 **−0.5450.1550.545−0.209−0.045
Note: * indicates significance at p < 0.05, and ** indicates significance at p < 0.01. IS-Rs: Rs in IS (increasing snow) treatment; DS-Rs: Rs in DS (decreasing snow) treatment; CK-Rs: Rs in CK (control check, i.e., ambient snow) treatment.
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Liu, Y.; Peng, Q. Response of Soil Respiration to Altered Snow Cover in a Typical Temperate Grassland in China. Agriculture 2023, 13, 2081. https://doi.org/10.3390/agriculture13112081

AMA Style

Liu Y, Peng Q. Response of Soil Respiration to Altered Snow Cover in a Typical Temperate Grassland in China. Agriculture. 2023; 13(11):2081. https://doi.org/10.3390/agriculture13112081

Chicago/Turabian Style

Liu, Yanqi, and Qin Peng. 2023. "Response of Soil Respiration to Altered Snow Cover in a Typical Temperate Grassland in China" Agriculture 13, no. 11: 2081. https://doi.org/10.3390/agriculture13112081

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