Variation in and Regulation of Carbon Use Efficiency of Grassland Ecosystem in Northern China

Abstract

Ecosystem carbon use efficiency (CUE) is a key parameter in the carbon cycling of terrestrial ecosystems. The magnitude of CUE reflects the ecosystem’s potential for CO₂ sequestration. China’s grasslands play an important role in the carbon cycle. Here, we aimed to investigate the comparation of CUE and its environmental regulation among different grassland in Northern China based on eddy covariance carbon fluxes measurements of 31 grassland sites. The results showed that the average CUE of grassland in Northern China was 0.05 ± 0.22, with a range from −0.42 to 0.66. It was demonstrated that there were significant differences in CUE among different grassland types, and CUE values were ranked by type as follows: alpine grassland > temperate meadow steppe > temperate typical steppe > temperate desert steppe, driven by a combination of climatic, soil, and biological factors, with net ecosystem productivity (NEP) having the greatest impact on them. Except for meadow steppes, moisture had a greater impact on grassland CUE in Northern China than temperature. While temperate desert grassland CUE decreased with increasing soil water content (SWC), the CUE of other grassland types increased with higher precipitation and SWC. These findings will advance our ability to predict future grassland ecosystem carbon cycle scenarios.

1. Introduction

The carbon sequestration capacity of terrestrial ecosystems is largely determined by the efficiency of ecosystems in converting the gross primary productivity (GPP) into plant and soil storage [1,2], i.e., the ratio of the net ecosystem productivity (NEP) to GPP, known as ecosystem carbon use efficiency (CUE) [3,4]. CUE reflects the ability of ecosystems to store atmospheric carbon dioxide and is a fundamental parameter of carbon cycling in terrestrial ecosystems [5,6]. A high CUE indicates that the ecosystem releases less carbon to the atmosphere, which can promote carbon stability and long-term sequestration, as opposed to low CUE ecosystems where more of the carbon fixed by photosynthesis is released in the form of CO₂ or other forms and where the carbon cycle is more open [6,7,8]. Understanding the variation in CUE, particularly across different environmental conditions, is crucial for analyzing patterns of carbon fluxes and allocation [9] and key to gaining insights into the potential for carbon sequestration in terrestrial ecosystem and its feedback to climate change [8,10,11,12,13,14].

Some previous studies have suggested that CUE values remain constant [9,15]. Recently, more and more studies have shown significant spatial differences in CUE across various ecosystems and hydrothermal conditions [12,13,14,15,16,17,18]. Studies have compared CUE across different ecosystem types, with some suggesting higher CUE in grassland and farmland compared to forest and shrub [6,17], while others indicate similar or even lower CUE in grassland compared to forest, cropland, and wetland [2,18]. Variations in CUE are also strongly influenced by climatic factors, especially temperature and precipitation. On global and regional scales, CUE tends to decrease with increasing temperature [6,19,20], but CUE in eastern U.S. forests increases with increasing temperature [21]. The results of some studies indicate that CUE is positively correlated with precipitation [6,20,21], while others suggest that CUE is negatively correlated with precipitation [13]. In addition, CUE is also influenced by biological factors such as leaf area index, leaf habit, NEP, and GPP [22,23,24]. The effects of ecosystem type, temperature, precipitation, and biological factors such as leaf area index on CUE are likely to be interactive rather than individual, but the combined effects of these driver factors on CUE have been discussed less.

Grassland ecosystems are important components of terrestrial ecosystems, which have huge carbon stocks and play an important role in regulating the carbon balance of terrestrial ecosystems [7,25,26]. China’s grassland area covers approximately 42 percent of the national territorial area and is primarily located in the northern regions, encompassing the Northeast Plain, the Inner Mongolian Plateau, the Xinjiang Mountains, and the Qinghai–Tibet Plateau. Grassland types mainly include temperate meadow steppe, temperate typical steppe, temperate desert steppe and alpine grassland [27]. With its extensive longitudinal range, different types of grassland ecosystems in China exhibit diverse climatic and geographic patterns in carbon fluxes [5,28]. However, few studies have systematically compared CUE and its interactions with multiple environmental factors among different types of grassland ecosystems in China.

In the current research, eddy covariance carbon fluxes measurements of 31 grassland sites from Northern China were used to (1) explore the spatial pattern of CUE; (2) investigate the combined effects of abiotic environmental factors (temperature, precipitation, soil moisture, and soil temperature) and biological factors (GPP, NEP, and vegetation index) on CUE; and (3) compare the controlled mechanisms of CUE among different types of grassland ecosystems in Northern China. The findings of this study will enhance knowledge of the carbon sink capacity of grasslands in China and advance our ability to predict future grassland ecosystem carbon cycle scenarios.

2. Materials and Methods

2.1. Study Area

The study primarily focuses on the grasslands of Northern China (Figure 1), located on the eastern flank of the Eurasian grassland. It is mainly composed of temperate grasslands, which are continuously distributed across the Inner Mongolian Plateau, with a total area of approximately 3 × 10⁸ hm², accounting for over 85% of the total area of natural grasslands in China, making it the main body of natural grasslands in China. The region includes various types such as temperate meadow steppe, temperate typical steppe, temperate desert steppe, and alpine grassland [27].

2.2. Data

The carbon flux (NEP and GPP) data were collected from peer-reviewed papers from ISI Web of Science (http://apps.webofknowledge.com (accessed on 19 May 2024)), the China National Knowledge Infrastructure (http://www.cnki.net (accessed on 19 May 2024)), and shared datasets from the National Tibetan Plateau Data Center (https://www.tpdc.ac.cn (accessed on 20 May 2024)), the Science Data Bank (https://www.scidb.cn (accessed on 19 May 2024)), and the Scientific Data Center of CAS (https://www.casdc.cn (accessed on 23 March 2024)), using “Eddy covariance”, “Carbon flux”, “productivity”, “gross primary productivity”, “net ecosystem productivity”, “net ecosystem exchange (NEE)”, “Grassland”, and “China” as keywords. To avoid bias in the publication selection, we only selected the literature and datasets that satisfied the following three criteria: (1) the NEP and GPP values were measured using the eddy covariance technique; (2) the data were continuously observed for at least one year; (3) coordinate rotation, WPL correction, night flux calculation, and other adjustments were applied to calibrate the data [13].

Carbon flux data were obtained for a total of 163 site-years from 31 grassland sites in Northern China, covering four types of grassland ecosystems, alpine grassland, desert steppe, typical steppe, and meadow steppe, and most observations were conducted during 2002–2022 (

Table 1. Site information in this study.

Site	Observation Year	Longitude (° E)	Latitude (° N)	Altitude (m)	Grassland Type	MAP (mm)	MAT (°C)	Reference
Shenzha	2019–2022	88.70	30.95	4750	Alpine Grassland	283.43	0.99	[29]
Damxung	2004–2011	91.08	30.85	4333	Alpine Grassland	438.26.	2.73	[30,31]
Naqu1	2008 2013–2014	91.90	31.37	4509	Alpine Grassland	528.25	0.38	[32,33]
Naqu2	2017–2021	92.01	31.64	4598	Alpine Grassland	475.07	−0.90	[34,35]
Naqu3 *	2014 2017–2021	92.01	31.64	4598	Alpine Grassland	467.08	−1.04	[34,36,37]
Shule	2009–2012	98.32	38.42	3885	Alpine Grassland	364.83	−3.81	[38]
Yakou	2015–2016	100.24	38.01	4148	Alpine Grassland	479.90	−4.20	[39]
Arou	2013–2018	100.46	38.05	3033	Alpine Grassland	440.17	−0.15	[40,41]
Guoluo1	2007–2008	100.55	34.35	3958	Alpine Grassland	561.70	−0.40	[42,43]
Guoluo2	2006–2008	100.55	34.35	3980	Alpine Grassland	544.95	−1.27	[44,45]
Sanjiangyuan	2012–2016	100.70	35.25	3960	Alpine Grassland	384.68	2.19	[46]
Haiyan	2010–2011	100.85	36.95	3140	Alpine Grassland	354.20	1.00	[47]
Haibei1	2003–2020	101.32	37.62	3200	Alpine Grassland	464.08	−1.20	[48,49]
Haibei2	2002–2004 2015–2020	101.33	37.62	3250	Alpine Grassland	465.77	−0.65	[50,51]
Maqu	2010–2011	102.14	33.89	3424	Alpine Grassland	600.00	2.95	[52,53]
Zoige	2016–2020	102.60	32.80	3500	Alpine Grassland	730.93	2.92	[54]
SACOL	2007–2012	104.13	35.95	1966	Desert Steppe	376.32	8.07	[55]
Yanchi	2012–2016	107.23	37.71	1530	Desert Steppe	320.62	9.41	[56]
Damao	2011–2018	110.33	41.64	1407	Desert Steppe	237.81	5.12	[57]
Siziwangqi	2010–2011	111.89	41.79	112	Desert Steppe	271.30	3.75	[58]
Sunitezuoqi	2008–2010	113.57	44.08	970	Desert Steppe	154.67	2.50	[59]
Naiman	2015–2018	120.70	42.92	345	Desert Steppe	288.18	7.66	[60]
Ansai	2012–2014	109.32	36.86	1260	Typical Steppe	579.53	9.35	[61]
Duolun	2006–2008	116.28	42.05	1350	Typical Steppe	367.33	3.26	[62]
Xilinhot1	2010–2021	116.31	44.14	1160	Typical Steppe	304.90	2.31	[57]
Xilinhot2	2004–2006	116.33	44.13	1030	Typical Steppe	228.33	1.90	[63]
Inner Mongolia	2003–2010	116.40	43.33	1200	Typical Steppe	260.56	1.83	[64]
Maodeng	2013–2017	116.49	44.16	1200	Typical Steppe	305.16	3.15	[65]
Keerqin	2011–2012	122.28	43.29	203	Meadow Steppe	365.43	6.79	[66]
Tongyu	2003–2017	122.92	44.34	184	Meadow Steppe	345.16	6.34	[65,67]
Changling	2007–2013	123.51	44.59	136	Meadow Steppe	401.01	5.96	[68]

Note: “*” represents grazing at this site.

). These sites are distributed from 30 to 45° N in latitude, 88 to 124° E in longitude, and 135 to 4750 m in elevation (Figure 1). For each site, we also recorded supporting information, including mean annual temperature (MAT), annual precipitation (MAP), soil temperature (Ts), and soil water content (SWC). Additionally, we obtained the MODIS enhanced vegetation index (EVI) dataset (MOD13A1, 16-day, 500 m) from https://lpdaac.usgs.gov/products/mod13a1v061 (accessed on 19 May 2024).

Missing meteorological and soil data were interpolated using reliable datasets: MAT and MAP data were obtained from the annual values of temperature and precipitation from 824 standard meteorological stations provided by the National Meteorological Science Data Center (https://data.cma.cn/dataService/cdcindex/datacode/A.0053.0002.S005 (accessed on 23 March 2024) and https://data.cma.cn/dataService/cdcindex/datacode/A.0053.0002.S007 (accessed on 23 March 2024)). Data from meteorological stations within 1° proximity of their observation sites were used for interpolation. The monthly Ts and SWC data (0.1°) were acquired from the fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis dataset ERA5-Land (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land-monthly-means (accessed on 23 March 2024)). To ensure uniformity in the analysis, all datasets were ultimately transformed into site-year data.

2.3. Methods

2.3.1. CUE Calculation

Ecosystem CUE is defined as the ratio of NEP to GPP [8,69,70]:

$$\mathrm{CUE}=\frac{\mathrm{NEP}}{\mathrm{GPP}},$$

(1)

where NEP is the annual net ecosystem productivity (g C m⁻² yr⁻¹) and GPP is the annual gross primary productivity (g C m⁻² yr⁻¹).

2.3.2. Statistical Analyses

Linear regression analysis was employed to examine the relationship between NEP and GPP across different grassland types, while Pearson correlation tests were conducted to explore the relationships between various factors and CUE. Both analyses were performed using R software (version 4.3.1, R Development Core Team, Vienna, Austria). The one-way analysis of variance (ANOVA) was used to test the differences in carbon use efficiency among different grassland types in China (SPSS for Windows, Chicago, IL, USA).

In order to investigate the direct or indirect effects of climate, vegetation, and soil factors on CUE, a structural equation model (SEM) was established with the data including mean annual temperature (MAT), annual precipitation (MAP), soil temperature (Ts), soil water content (SWC), enhanced vegetation index (EVI), net ecosystem productivity (NEP), and gross primary productivity (GPP). In the model evaluation, we employed the comparative fit index (CFI) and the root mean square error of approximation (RMSEA). Parameter estimations utilized the unbiased maximum likelihood method, while model identification was carried out through the chi-square (χ²) test. The model construction and data analysis were executed utilizing Amos 26.0. (Amos Development Corporation, Chicago, IL, USA).

3. Results

3.1. The Relationship between NEP and GPP in Grassland Ecosystem in Northern China

Based on the site average, the estimated average NEP of grasslands in Northern China is 43.83 ± 86.08 g C m⁻² yr⁻¹, indicating notable spatial variability with a range of NEP variation between −86.28 and 303.09 g C m⁻² yr⁻¹. The typical steppe of the Ansai site (303.09 g C m⁻² yr⁻¹) exhibited the highest annual net ecosystem carbon uptake, with the alpine grassland of the Maqu site (220.50 g C m⁻² yr⁻¹) and the Arou site (210.31 g C m⁻² yr⁻¹) following behind. The typical steppe of the Inner Mongolia site exhibited the lowest net carbon uptake, with the desert steppe ecosystem site Naiman (−52.64 g C m⁻² yr⁻¹) and the typical steppe of the Xinlinhot2 site (−51.64 g C m⁻² yr⁻¹) identified as significant carbon sources thereafter. The average GPP across all sites was 456.79 ± 268.95 g C m⁻² yr⁻¹, with an annual variation ranging from 107.00 to 1103.90 g C m⁻² yr⁻¹, showing a wide range of variation. Among these sites, the meadow steppe of the Keerqin site had the highest GPP, while the desert steppe of the Siziwangqi site had the lowest GPP.

The relationship between NEP and GPP is significantly positive in Northern Chinese grassland ecosystems (Figure 2, p < 0.05). The slope of this relationship (NEP = 0.19 × GPP − 53.28) represents a general estimate of CUE for grassland ecosystems in Northern China. However, there are substantial differences among different grassland types, and meadow steppe exhibits the strongest correlation, while the typical steppe shows the weakest correlation. The NEP-GPP relationship is more stable in meadow steppe ecosystems, whereas it exhibits greater variation in typical steppe ecosystems.

3.2. Variation in and Spatial Patterns of CUE in Northern Chinese Grassland Ecosystems

During this study, the average site CUE in Northern Chinese grasslands was 0.05 ± 0.22, with a range from −0.42 to 0.66. The typical steppe of the Ansai site exhibited the highest CUE and strongest carbon sequestration capacity, while the desert steppe of the Siziwangqi site had the lowest CUE and weakest carbon sequestration capacity. CUE significantly decreased with increasing longitude and showed an increase followed by a decrease with latitude in Figure 3.

The average CUE values for alpine grasslands, desert steppes, typical steppes, and meadow steppes were 0.14, −0.14, 0.03, and 0.04, respectively (Figure 4). The CUE of alpine grassland is significantly higher than that of desert steppes (p < 0.001), indicating a higher carbon sequestration capacity in alpine grasslands and a lower carbon sequestration capacity in desert steppes. The average value of CUE in typical steppes and meadow steppes is not significantly different from that in other ecosystems. The CUE in typical steppes varies from −0.40 to 0.66, showing the greatest variation. Most of the typical steppe CUE values fall between −0.40 and 0.10, with the CUE of the Ansai typical steppe being exceptionally higher than that of other typical steppe sites, while the CUE in meadow steppes ranges from −0.16 to 0.16, indicating the most stable ecosystem. The range of CUE in alpine grasslands was from −0.02 to 0.46. The majority of sites exhibited a concentration of CUE values between 0 and 0.30, with a relatively small range of fluctuations. In desert steppes, CUE varies between −0.42 and 0.07, with a relatively wide range of variation. The spatial variations in CUE and differences among the four grassland types in Northern China may be the consequence of environmental changes different ecosystem types.

3.3. Regulation Mechanism of CUE in Different Types of Grassland Ecosystems

The Pearson correlation analysis between CUE and influencing factors for different grassland types is shown in Figure 5. Biological factors exert a positive impact on grassland CUE, and NEP emerges as the most significant factor influencing CUE in Northern China. Environmental factors exhibit varying effects on CUE across different grassland types. Temperature positively influences CUE in desert steppes, typical steppes, and meadow steppes but negatively impacts CUE in alpine grassland. Compared to temperature and MAP, SWC emerges as a major factor affecting CUE in grasslands, positively impacting CUE in alpine grassland, typical steppes, and meadow steppes while negatively impacting CUE in desert steppes.

Figure 6 illustrates the adjusted SEM for CUE across various grassland types. All models exhibit a close fit to the data. The variables explain 79%, 90%, 84%, and 96% of the variation in CUE for alpine grassland, desert steppe, typical steppe, and meadow steppe ecosystems, respectively. NEP emerges as the most influential factor affecting CUE across all four grassland types, exerting a significant direct positive impact. Additionally, GPP not only directly influences CUE but also indirectly affects CUE through NEP. The regulatory mechanisms of other factors on CUE vary among different grassland types.

In alpine grassland ecosystems, the biological factor EVI has a weak indirect positive effect (0.12) on CUE through GPP. Moisture (MAP and SWC) positively impacts CUE, with MAP showing a significant direct positive effect on CUE (0.29) and indirectly influencing CUE through the EVI (0.03), resulting in a total coefficient of 0.32. SWC indirectly affects CUE via NEP and it also affects GPP through the EVI and then indirectly affects CUE. The overall impact coefficient of SWC on CUE is 0.33. Ts has a significant direct negative effect (−0.35) on CUE and indirectly affects CUE through NEP (−0.07), resulting in an overall negative impact of soil temperature (−0.42) on alpine grassland CUE (

Table 2. Standardized direct, indirect, and total effects of environmental factors on CUE. * represent p < 0.05.

Predictor	Path to CUE	Effect
		Alpine Grassland	Desert Steppe	Typical Steppe	Meadow Steppe
NEP	Direct effect	0.78 *	1.14 *	0.99 *	1.00 *
	Indirect effect	0	0	0	0
	Total effect	0.78	1.14	0.99	1.00
GPP	Direct effect	−0.15	−0.27 *	0.20 *	−0.09
	Indirect effect	0.30	0.86	0	0.47
	Total effect	0.15	0.59	0.20	0.38
EVI	Direct effect	0	0.34 *	-	0
	Indirect effect	0.12	−0.44	-	0.18
	Total effect	0.12	−0.10	-	0.18
MAP	Direct effect	0.29 *	0	−0.20	0
	Indirect effect	0.03	0.29	0.64	0.17
	Total effect	0.32	0.29	0.44	0.17
SWC	Direct effect	0	0	-	−0.16 *
	Indirect effect	0.33	−0.06	-	0.53
	Total effect	0.33	−0.06	-	0.37
MAT	Direct effect	-	-	0	-
	Indirect effect	-	-	0.33	-
	Total effect	-	-	0.33	-
Ts	Direct effect	−0.35 *	0.14	-	0.17 *
	Indirect effect	−0.07	0.18	-	0.56
	Total effect	−0.42	0.32	-	0.73

Note: Given are direct, indirect, and total effects derived from the SEM shown in Figure 6. “-” represents a missing pathway.

).

In desert steppe ecosystems, the EVI exerts a significant direct positive effect (0.34) on CUE but exerts a larger indirect negative effect through NEP (−0.44), resulting in an overall negative impact (−0.10) on CUE. Ts has a direct positive effect on CUE and indirectly affects CUE through GPP (Figure 6b), with a total impact coefficient of 0.32. Moisture has opposite effects on desert steppe CUE: MAP has an indirect positive effect on CUE through GPP (0.29), while SWC has negative effects on CUE through GPP and the EVI (−0.06) (Table 2).

In typical steppe ecosystems, MAT has a greater indirect positive effect through NEP than the indirect negative effect through GPP, resulting in an overall positive impact (0.33) on CUE. MAP directly affects CUE negatively (−0.20) and indirectly influences CUE through NEP and GPP, respectively, resulting in a total impact coefficient of 0.44 (Table 2).

In meadow steppe ecosystems, the EVI indirectly affects CUE positively through GPP, with a total effect coefficient of 0.18. Ts ranks as the second largest influencing factor for meadow steppe CUE, with a direct positive effect (0.17) on CUE and indirect positive effects through NEP (0.47) and indirectly through the EVI and GPP (0.09), resulting in an overall positive impact (0.73) on CUE. Both moisture factors positively impact CUE, with SWC exerting a greater influence than MAP. SWC not only has a direct negative effect (−0.16) on CUE but also has positive effects on CUE through NEP and GPP, resulting in an overall positive impact (0.37) on CUE. MAP indirectly enhances CUE through the EVI, GPP (0.04), and NEP (0.13), resulting in a total impact of 0.17 on CUE.

4. Discussion

4.1. Variations in CUE in Northern Chinese Grasslands

According to the site average values, we offered insights into the fundamental condition of CUE in Northern Chinese grasslands, suggesting that the average CUE stands at 0.05 ± 0.22, which indicated that the ecosystem sequestered an average of 5% photosynthetic productivity. This efficiency is lower than the CUE values in grassland ecosystems in Western Europe, Northern Finland, and the United States [16,71,72], indicating a lower carbon sequestration capacity in the Northern Chinese grasslands. In contrast, the grassland ecosystems in those regions benefit from favorable hydrothermal conditions and are often intensively managed or artificially planted, contributing to their higher carbon sink capacity [73,74,75,76]. Additionally, the average CUE value in our study is lower than that reported for other ecosystem types in China, such as forests and farmlands [13,15,77]. Our study primarily concentrates on the grasslands situated in Northern China, where vegetation primarily thrives in cold or arid regions like the Qinghai–Tibet Plateau, northeastern regions, the Loess Plateau, and the Inner Mongolia Plateau. In this region, characterized by the prevalence of low biomass and short growth cycles in vegetation, the GPP tends to be lower compared to other grassland areas or even other ecosystem types [33,78]. However, ecosystem respiration rates are not lower than those of other ecosystems [79]. Consequently, the CUE is relatively diminished.

In this study, the range of CUE values in the Northern Chinese grasslands varied from −0.42 to 0.66, and the spatial variation was greater than in other studies [15,80]. The increased variation in CUE may be attributed to a notable departure from heterotrophic respiration (Rh) relative to the ratio of net primary productivity (NPP) [81,82]. We found that geographical changes lead to significant spatial variations, characterized by a decreasing trend in CUE with increasing longitude, consistent with previous studies on vegetation CUE [24,83]. As the latitude increased, CUE initially exhibited an upward trend, followed by a subsequent decline. The highest CUE values were observed in mid-latitude areas, consistent with previously reported latitudinal patterns of CUE and carbon sequestration [10,18], as well as significant losses. The parabolic pattern observed is likely due to increased respiratory costs in warm conditions at low latitudes during the dormant season, along with restricted productivity input caused by low temperatures in high-latitude regions [84,85]. The average CUE values for alpine grasslands, desert steppes, typical steppes, and meadow steppes in Northern China are 0.14 ± 0.12, −0.14 ± 0.17, 0.03 ± 0.36, and 0.04 ± 0.17, respectively. CUE in alpine grasslands is significantly higher than that in desert steppes. The highest carbon sequestration capacity is observed in alpine grasslands, while CUE in desert steppes is the lowest, suggesting that the respiratory consumption of the desert steppe ecosystem is greater than the accumulation of organic matter, and the desert steppe ecosystem is the carbon source. The carbon sequestration capacities of typical steppes and meadow steppes are close to the average values of the Northern Chinese grasslands. The differences in CUE values among different grassland types may be related to their geographical distribution. Alpine grasslands are primarily distributed in the Qinghai–Tibet Plateau, characterized by perennial cold climates with lower temperatures. Plants in cold environments consume less energy to maintain their biomass, resulting in higher carbon storage efficiency [79,86,87]. In contrast, desert steppe ecosystems are mainly distributed in arid or semi-arid regions where severe drought suppresses plant photosynthesis, leading to a reduction in ecosystem CUE [88,89]. The CUE values of typical grasslands varied from −0.40 to 0.66, with the Ansai site having the largest CUE value (0.66). This was mainly due to the higher precipitation at the Ansai site (480–730 mm compared to 180–300 mm for the other typical grassland sites in this study), which resulted in higher GPP and lower Re values [16,40], leading to a higher CUE value at this site. Excluding the Ansai site, the CUE of typical steppes in this study varied in the range of −0.40 to 0.10, which is close to the CUE variation range of the American prairies [16]. For meadow steppes in this study, their CUE values show smaller fluctuations, ranging from −0.16 to 0.16, suggesting more stable variations. The lower variation in meadow steppe CUE is attributed to the limited latitudinal span and minimal variation in water and thermal conditions within meadow steppe ecosystems.

4.2. The Regulation Mechanism of CUE in Different Grassland Ecosystems in Northern China

Many studies have demonstrated significant impacts of climate variability on NEP and GPP at both global and regional scales [78,79,89,90,91]. As CUE represents the ratio of NEP to GPP, it undergoes substantial variations in response to environmental factors. Temperature as the dominant factor influencing carbon flux dynamics [10,22]. Temperature affects carbon fluxes by modulating photosynthesis and respiration rates [92,93]. Previous studies have shown that temperature has a negative effect on grassland CUE [4,20], as the negative response of GPP to temperature rise outweighs that of ecosystem respiration (Re). Our findings reveal that temperature has different effects on the CUE of different types of grasslands in Northern China. Specifically, temperature has a positive impact on the CUE of desert steppes, typical steppes, and meadow steppes; the higher the temperature, the higher the CUE in these grasslands. Conversely, temperature has a negative impact on the CUE of alpine grasslands. This difference may be related to the hydrothermal differences in different grassland types. Desert steppes, typical steppes, and meadow steppes are predominantly located in arid and semi-arid regions where high temperatures and low moisture levels render soil respiration sensitive to temperature fluctuations, leading to a rapid decline in vegetation respiration rates with increasing temperature and consequent positive effects on CUE [94]. In contrast, alpine grasslands are mainly located on the Qinghai−Tibet Plateau, characterized by low temperatures throughout the year. Although diurnal temperature variations are conducive to carbon accumulation, they also act as limiting factors for carbon fixation [95].

Moisture is considered another crucial factor influencing carbon fluxes [18,24,28,96]. As the primary water source, precipitation not only affects plant photosynthesis but also influences autotrophic respiration (Ra) and Rh processes through soil moisture changes, thereby impacting carbon fluxes [96,97]. Generally, precipitation has a positive impact on grassland CUE [20,79]. Our study indicates a positive influence of precipitation on grassland CUE in Northern China, as increased precipitation leads to higher rates of photosynthesis and transpiration, resulting in elevated GPP [98,99,100]. Moreover, enhanced precipitation weakens respiratory processes, thereby increasing grassland CUE [4,5]. Our research suggests that SWC exhibits a consistent positive influence on grassland CUE in most Northern Chinese grasslands, except for desert steppes (Table 2). Desert steppes receive limited MAP, resulting in high Ts and lower SWC. In water-limited grassland ecosystems, plant physiological activities are highly sensitive to variations in available water. When subjected to water stress, plants reduce water or nutrient supply to seedlings, decrease leaf growth rates, and subsequently alter photosynthetic rates [101]. Additionally, in arid and semi-arid regions, water exerts a strong stimulating effect on soil respiration [102,103]; thus, an increase in soil moisture enhances ecosystem respiration [103,104].

Biological factors exert the most significant influence on grassland ecosystem carbon sequestration capacity. The EVI exhibits a positive correlation with carbon flux [79]. Our research indicates a positive effect of the EVI on CUE in alpine grasslands and meadow steppes. The EVI reflects the vegetation growth status, with a higher vegetation growth status corresponding to higher ecosystem GPP and Re. However, GPP is more sensitive to EVI changes than Re [81]; thus, the EVI has a positive effect on CUE. However, the EVI has a negative effect on desert steppe CUE, possibly because high temperatures and arid conditions can maintain high Re levels, leading to reduced NEP and negative effects on CUE. NEP and GPP are the primary direct influences on CUE. GPP not only directly impacts CUE but also indirectly influences NEP, subsequently affecting CUE. Both NEP and GPP have a positive impact on grassland CUE, with NEP exerting a greater influence, thus dominating the positive effect on grassland CUE variation.

5. Conclusions

This study found that there were significant differences in CUE among different grassland types in Northern China, with the highest CUE in alpine grassland and the lowest CUE in temperate desert grassland. The SEM analysis revealed that the variation in grassland CUE in Northern China is influenced by a combination of climatic, soil, and biological factors, with NEP having the greatest impact on grassland CUE. Climatic and soil conditions mainly affect CUE indirectly through the changes in NEP and GPP, while biological factors had a more direct effect on CUE. Except for meadow steppes, moisture had a greater impact on grassland CUE in Northern China than temperature. While temperate desert grassland CUE decreased with increasing SWC, the CUE of other grassland types increased with higher MAP and SWC.