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Article

Impact of Climate, Phenology, and Soil Factors on Net Ecosystem Productivity in Zoigê Alpine Grassland

by
Rui Qu
1,2,3,
Zhengwei He
1,2,*,
Li He
1,2,
Joseph Awange
4,
Yongze Song
5,*,
Bing Wang
1,2,5,
Bo Wen
6 and
Jiao Hu
5,6
1
State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China
2
College of Geography and Planning, Chengdu University of Technology, Chengdu 610059, China
3
School of Earth and Planetary Sciences, Discipline of Spatial Sciences, Curtin University, P.O. Box U1987, Perth, WA 6845, Australia
4
Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong
5
School of Design and the Built Environment, Curtin University, P.O. Box U1987, Perth, WA 6845, Australia
6
College of Earth and Planet Science, Chengdu University of Technology, Chengdu 610059, China
*
Authors to whom correspondence should be addressed.
Agronomy 2025, 15(3), 685; https://doi.org/10.3390/agronomy15030685
Submission received: 28 February 2025 / Revised: 9 March 2025 / Accepted: 10 March 2025 / Published: 12 March 2025
(This article belongs to the Special Issue Advances in Grassland Productivity and Sustainability — 2nd Edition)
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Versions Notes

Abstract

:
Net ecosystem productivity (NEP) is a crucial metric for quantifying carbon storage, exchange, and cycling across global atmospheric and terrestrial ecosystems. This study examines the spatiotemporal patterns of NEP in China’s Zoigê alpine grassland and its response to climate variability, phenological changes, and soil conditions from 2000 to 2020. The results show a statistically significant increase in the annual NEP of the Zoigê Plateau, with an average rate of 3.18 g C/m2/year. Spatially, NEP displays strong heterogeneity, with higher values in the southwestern and northeastern marginal areas (>80 g C/m2) and lower values in the central region (<0 g C/m2). In alpine meadows (standardized total effect coefficient [STEC] = 0.52) and alpine steppes (STEC = 0.43), NEP is primarily regulated by soil moisture modulation, influenced by both water and temperature factors. This study accurately assesses NEP by incorporating regional soil characteristics, providing a more precise evaluation of changes in vegetation carbon sink sources in high-altitude areas.

1. Introduction

Net ecosystem productivity (NEP) is an essential metric for evaluating carbon balance in terrestrial ecosystems. It is a key indicator of plant activity, ecosystem nutrient storage, and carbon fluxes between the atmosphere and terrestrial ecosystems. NEP reflects the impact of climate change and human activities on vegetation in terrestrial ecosystems [1,2,3]. For example, NEP can demonstrate the consequences of extreme droughts, potentially modifying community structures during vegetation succession and increasing vegetation mortality, thus significantly reducing carbon uptake. Additionally, NEP can capture the effects of extreme temperatures that negatively impact ecosystems. By regulating energy, water, and carbon cycles, vegetation plays a key role in land–atmosphere interactions [4].
Terrestrial ecosystems exhibit distinct carbon sink characteristics, which are evident in their varying spatial distribution patterns [1,5] and susceptibility to external conditions [6,7]. However, substantial uncertainty remains regarding the dynamics and driving mechanisms of carbon sources and sinks at regional scales. This uncertainty makes the estimation and analysis of spatiotemporal variations and attributions of vegetation NEP focal points in current research on global carbon cycle changes [8,9,10]. Understanding NEP is crucial for determining whether these ecosystems function as carbon sinks or sources, which is vital for modeling the global carbon cycle and climate change impacts.
The importance of NEP in grassland ecosystems has been well documented [11]. However, the alpine grassland ecosystem, a distinct ecological environment associated with high-altitude regions and unique geographical and climatic conditions [12], has not been extensively studied. This is particularly true for the Zoigê alpine grassland, located on the northeastern edge of the Qinghai-Tibet Plateau. This region, characterized by carbon-sequestering vegetation, represents a typical high-altitude grassland ecosystem [13,14]. Its vegetation primarily consists of cold-resistant and drought-tolerant herbaceous plants, which exhibit short and low-growing forms to adapt to low temperatures and strong winds [15].
Generally, climate change affects the carbon balance and photosynthetic carbon efficiency of terrestrial ecosystems by altering phenology (length of the growing season, LOS) and soil factors [16,17]. Global warming significantly impacts vegetation phenology, altering the timing of global vegetation cycles and resulting in reduced biodiversity and NEP [18,19]. Soil factors, such as soil moisture (SM) and soil total nitrogen content (soil N), influence NEP by affecting plant growth, photosynthesis, and microbial activity [20]. Therefore, gaining a thorough understanding of the correlation between climate, soil variables, and NEP can enhance our understanding of the carbon balance in the Zoigê alpine grassland ecosystem.
While some research has investigated the spatiotemporal variation of NEP in terrestrial ecosystems [21] and explored the mechanisms of NEP’s response under climate change conditions [2,22,23], and explored the potential influencing factors, such as global warming and precipitation [24,25], few have investigated the spatiotemporal distribution of NEP and its driving factors in Zoigê. This region is highly sensitive to climate variability and faces threats to its carbon balance efficiency and biodiversity related to NEP. For instance, Hou et al. (2022) indicated that climate variability and human activities exacerbate the degradation and shrinkage of the Zoigê grassland [26]. However, the influence of climate variability on NEP in this region remains unexplored.
This study focuses on the Zoigê Plateau as the study area, examining the spatiotemporal variations in NEP within alpine grassland ecosystems and their responses to climate change, phenology, and soil factors. The main aims of the research include: (i) to examine the spatiotemporal variations in NEP, Aridity Index (AI), and LOS in the Zoigê alpine grassland from 2000 to 2020; (ii) to quantify the links between plant phenology, climatic variables, soil components, and NEP; and (iii) to study the effects of climate variability, plant phenology, and soil variables on NEP changes in the Zoigê alpine grassland.

2. Data and Methods

2.1. Zoigê Plateau

The Zoigê Plateau (31°48′4″–34°48′28″ N and 100°47′35″–103°39′37″ E) is situated at the northeastern edge of the Qinghai-Tibet Plateau, bordering Sichuan Province and Gansu Province (Figure 1). It consists of Zoigê County, Hongyuan County, Aba County in Sichuan Province, and Maqu County, Luqu County in Gansu Province. The Zoigê Plateau covers an area of approximately 42,797 km2, with elevations ranging from 2392 to 5057 m above sea level. It is a plateau basin characterized by primarily carbon-sequestering grass vegetation. It represents a typical alpine meadow and grassland ecosystem of the Qinghai–Tibet Plateau, predominantly composed of cold-resistant and drought-tolerant herbaceous species. The climate in this area is classified as a plateau sub-frigid semi-humid continental monsoon climate, with long sunshine duration and an average annual temperature of 2.0 °C. Annual precipitation ranges from 600 to 800 mm, mainly concentrated from April to October, with more rainfall in summer and dry conditions in winter.

2.2. Data

Monthly average precipitation and temperature data were sourced from the China Meteorological Administration. Soil factors include soil organic matter (SOM) (g/100 g), total nitrogen (TN) (g/100 g), and quick-acting phosphorus (AP) (mg/kg), and the data were obtained from the China Land Surface Modeling Soil Database (CLSMD), which belongs to the Research Group on Land–Atmosphere Interactions of Sun Yat-sen University. Plant phenology observations for the Tibetan Plateau (2000–2015) were obtained from the National Tibetan Plateau Science Data Center (NTPSDC) (Table 1). Soil temperature and moisture data were extracted from ERA5-Land’s monthly averaged data. Additionally, different vegetation types found in the Zoigê Plateau were sourced from the 10 m resolution vegetation map of the Tibetan Plateau.

2.3. Methods

2.3.1. Estimation of NEP Products

In this study, the coupled ecological remote sensing model BEPS is used to estimate NEP for the Zoigê area. The BEPS model integrates the ecosystem process model and the light energy utilization model using the leaf area index, which combines the advantages of both models to reflect the spatial distribution of NEP accurately at regional and global scales. This integration enhances the accuracy and operability of the estimated NEP. Additionally, the BEPS model combines remote sensing data with mechanistic ecological processes, facilitates the spatial and temporal transformation of model parameter variables, and merges carbon and water cycle processes. This approach efficiently monitors the dynamics of vegetation NEP at large scales and reduces the number of parameters in the process model. The model calculates daily photosynthesis by simulating instantaneous photosynthesis at the leaf scale and then expands to different time scales through daily integration to obtain the total primary productivity (GPP) of vegetation. Finally, it calculates vegetation NEP by subtracting vegetation respiration. The equations are as follows [27,28]:
G P P = A c a n o p y L d a y F G P P ,
R a = R m + R g = ∑ R m , i + R g , i ,
where L d a y represents the day length, F G P P represents the scaling factor of photosynthesis G P P , R a represents autotrophic respiration, R m represents maintenance respiration, R g represents growth respiration, and i represents the different parts of the vegetation (1, 2, and 3 represent leaves, stems, and roots, respectively).
R h = ∑ i = 1 9 C i k i f T s f θ f T e ,
N E P = G P P − R a − R h ,
where C i is the size of 1,2 , 3 , ⋯ , 9 9 apoplastic and soil carbon pools, k i is the reference respiration rate of each pool; T s denotes temperature; θ denotes soil moisture; and T e denotes soil texture.

2.3.2. Aridity Index

The Aridity Index (AI) is calculated using annual mean precipitation and temperature data for the period 2000 to 2020, with these variables being spatially interpolated via Anusplin at a 1 km resolution. De Martonne’s climatic classification [29] relies on aridity index values represented by A I = P / (   T + 10 ) , where P represents the mean annual precipitation (mm), and T denotes the mean annual temperature (°C). Recently, it has seen widespread application in many regional studies [30]. The calculation formula is as follows:
A I = A M P A M T + 10 ,
where A I represents the aridity index; A M P denotes the annual mean precipitation, and A M T stands for the annual mean temperature. In this study, we classify the A I values into humid climate ( A I > 30 ), semi-humid climate ( 15 < A I < 30 ), and arid climate ( A I < 15 ).

2.3.3. Structural Equation Model (SEM)

To illustrate the impact of climate change, plant phenology, and soil variables on the NEP of the Zoigê Plateau grassland ecosystems, SEM is used to construct a statistical framework for assessing multivariate associations. SEM examines the connections among variables by applying their covariance matrices, with the benefit of incorporating latent variables and measurement errors [31]. Recently, SEM has become extensively applied to study issues in economics, management, and ecology [32,33]. In SEM, the measurement model outlines the connections between observed and latent variables. Observed variables are influenced by latent variables, which represent abstract concepts that cannot be directly measured. The basic expression for the measurement model is as follows:
Y = Λ y η + ϵ ,
where Y represents the vector of observed variables, Λ y is the loading matrix between the observed and latent variables, η is the vector of latent variables, and epsilon is the error term. The measurement model allows for the quantification of the association between latent and observed variables, thus providing the basis for the following structural model.
The SEM combines the measurement model with the structural model into an overall model structure. The overall equation illustrates the connections between observed and latent variables, as well as the causal paths between latent variables. The equation for the overall model is expressed as:
y = B y + Γ x + ζ ,
where y and x represent the column vectors representing endogenous and exogenous variables, respectively. B , Γ , and ζ denote the interrelationships among endogenous variables, the effect of exogenous variables on endogenous variables, along with the residuals in the structural equations, respectively. All statistical procedures were carried out with AMOS 26 software (IBM SPSS Inc., Chicago, IL, USA), and the choice of model run method was based on the asymptotic free distribution method.

3. Results

3.1. Evaluation Results of NEP Datasets

The correlation values for 2005, 2010, 2015, and 2020 were 0.86, 0.84, 0.81, and 0.88, respectively. Moreover, the mean error values for these years were ±3.17, ±3.72, ±0.66, and ±2.91, whereas the root mean square errors were 7.57, 7.98, 7.93, and 9.07, respectively (Figure 2).

3.2. Spatio-Temporal Patterns of NEP, AI, and LOS in the Zoigê Plateau

The total annual NEP of the Zoigê Plateau increased by 3.18 g C/m2/year, rising from 15.502 g C/m2 in 2000 to 82.368 g C/m2 in 2020 (R2 = 0.11, p < 0.05; Figure 3a). Similarly, the mean annual aridity index (AI) and mean annual length of the growing season (LOS) exhibited a significant increase at a rate of 0.48 days per year (R2 = 0.24; Figure 3b) and 1.61 days per year (R2 = 0.34; Figure 3c), respectively. Figure 3d shows a distribution of mean annual LOS values ranging from 140 to 193 days. From 2000 to 2020, NEP, AI, and LOS exhibited a gradual upward trend. The overall annual NEP in alpine meadows was notably higher than that in alpine grasslands and showed a gradually increasing trend (Figure 3e). In contrast, mean annual AI values and mean annual LOS in alpine meadows were smaller than in alpine grasslands, with alpine meadows having the lowest mean annual AI (Figure 3f,g).
The spatial pattern of the AI values on the Zoigê Plateau from 2000 to 2020 were heterogeneous. The multi-year average AI (AI > 80) was predominantly located in the northwest and southeast, gradually decreasing toward the plateau’s center (AI < 45; Figure 4a). High standard deviation values of AI (>15) were primarily observed in the northwest, while lower values (<50) were found in the northeast and central regions (Figure 4b). Additionally, AI exhibited a declining trend in most areas, particularly in the southwest and northeast, with significant concentrations in the northwest and southeast (Figure 4c). The highest NEP values (>80 g C/m2) were primarily found in the southwestern and northeastern marginal areas, while the lowest values (<0 g C/m2) were identified in the central region (Figure 4d). The standard deviation of NEP exhibited an opposite pattern to NEP itself (Figure 4e). The most prominent upward trend was mainly observed at the northeastern edge of the Zoigê Plateau, while the most notable downward trend was predominantly observed in the northwestern, central, and southeastern areas (Figure 4f). The spatial distribution of multi-year average LOS ranged between 140 and 193 days, with LOS decreasing from over 180 days in the southeast to less than 140 days in the northwest. The longest growing seasons were primarily located at the plateau’s edge. In contrast, shorter growing seasons were concentrated in the central alpine grassland ecosystem (Figure 4g). Moreover, the variability in LOS diminished from the edges toward the center (Figure 4h). LOS exhibited a significant lengthening trend, uniformly distributed throughout the Zoigê Plateau (Figure 4i).

3.3. Relationships Among Climate Factors, Phenology, Soil Factors, and NEP in Various Grassland Ecosystems of the Zoigê Plateau

In alpine meadow ecosystems, areas with higher NEP values exhibited a modest but statistically significant increasing trend with rising mean precipitation (R2 = 0.01, p < 0.05), mean temperature (R2 = 0.009), AI (R2 = 0.006), and LOS (R2 = 0.003; Figure 5a–d). Additionally, NEP showed a weak but significant positive association with soil moisture (SM, R2 = 0.01) and a weak negative association with soil temperature (ST, R2 = 0.05) and available phosphorus (AP, R2 = 0.01). NEP also exhibited statistically significant but weak associations with total nitrogen (TN, R2 = 0.002) as well as soil organic matter (SOM, R2 = 0.02). These findings suggest complex and variable relationships between NEP and climatic and soil factors, with total nitrogen showing the weakest association among the variables analyzed (Figure 5e–i).
In the alpine steppe ecosystem, NEP exhibited a weak but statistically significant increase with rising precipitation (R2 = 0.006, p < 0.05; Figure 6a) and a weak but statistically significant decrease with rising temperature (R2 = 0.01; Figure 6b). A statistically significant but weak correlation of NEP with AI (R2 = 0.01) and LOS (R2 = 0.004) was observed (Figure 6c,d). Additionally, NEP exhibited statistically significant but generally weak correlations with soil factors, including soil moisture (SM, R2 = 0.02), soil temperature (ST, R2 = 0.03), available phosphorus (AP, R2 = 0.01), total nitrogen (TN, R2 = 0.004), as well as soil organic matter (SOM, R2 = 0.002) (Figure 6e–i). These findings suggest that in alpine steppe ecosystems, climatic factors like precipitation and temperature, as well as soil factors such as moisture and organic matter, play a role in shaping NEP dynamics, albeit with generally weak explanatory power.
Analysis of the relationship between NEP and LOS, as well as AI, revealed ecosystem-specific differences (Figure 7a,d). In alpine meadows, an AI of approximately 100 and an LOS of around 225 days were identified as optimal conditions for NEP (Figure 7a). LOS in alpine meadows exhibited a notable negative correlation with AI (R2 = 0.24; Figure 7b), suggesting that prolonged growing seasons may reduce aridity indices due to increased water demand. Similarly, a strong negative correlation between LOS and AI was found in alpine grassland ecosystems (R2 = 0.22; Figure 7e), emphasizing consistent relationships between growing season length and aridity across ecosystems. Negative correlations between annual mean temperature (AMT) and annual mean precipitation (AMP) were observed in both alpine meadows (R2 = 0.11; Figure 7c) and alpine grasslands (R2 = 0.03; Figure 7f), indicating that higher temperatures are associated with reduced precipitation levels, which may limit NEP development. These findings suggest that arid climatic conditions, characterized by reduced precipitation and high temperatures, constrain the development of NEP in the Zoigê Plateau, with distinct responses observed across alpine meadow and grassland ecosystems.
Pathway analysis identifies soil moisture as a key determinant of NEP in both alpine meadows and grasslands, exerting a significant positive influence on NEP in both alpine meadows (STEC = 0.52) and alpine steppes (STEC = 0.43) (Figure 8a,b). In alpine meadow ecosystems, other climatic and soil variables exhibit comparatively weaker effects on NEP, as indicated by lower standardized total effect coefficients (STEC): AI (0.11), ST (0.16), AP (0.09), SOM (0.36), TN (0.05), and LOS (0.10). Meanwhile, AI (0.04), SOM (0.19), and AP (0.13) have significant effects on NEP in alpine grassland ecosystems (p < 0.001), with SOM playing a relatively larger role compared to AI and AP. These results underscore the essential role of soil moisture in influencing NEP and stress the importance of tailored management strategies to boost carbon sequestration in these ecosystems amidst shifting climatic conditions.

4. Discussion

4.1. Uncertainty Analysis

The primary vegetation type in the Zoigê Plateau region is alpine meadow, characterized by unique topography and complex ecosystems. Numerous uncertainties affect the carbon cycle in this area [30,34]. Terrestrial carbon cycle models are susceptible to various biases, including uncertainties in model structure and parameters, remote sensing data, and meteorological data [35]. Consequently, NEP flux estimates derived from multiple models or remote sensing exhibit considerable variation, and current studies on grassland NEP often overlook data uncertainty. In this study, the coupled ecological remote sensing model BEPS was used to estimate NEP on the Zoigê Plateau from 2000 to 2020. The BEPS model integrates the ecosystem dynamics model and the light energy utilization framework through remote sensing data of the leaf area index, reducing uncertainty caused by interpolation and error and depicting the spatial distribution of NEP at a regional level, thereby enhancing the terrestrial accuracy of NEP estimation. However, it can be observed that regional NEP estimation is still underestimated in this study. This may result from variations in model structure, parameterization, and input data, which continue to exhibit some uncertainties, especially in the colder areas of the plateau, where the standard deviation is considerable [36]. Additionally, uncertainty remains in the study outcomes due to errors in meteorological datasets, including temperature and precipitation [37]. Finally, remotely sensed data are affected by atmospheric conditions and sensor limitations, leading to inherent errors. The LOS data in this study have certain inaccuracies, and there is significant uncertainty in the estimated phenological data due to relative errors in the data and methodological limitations [38]. Different vegetation ecosystems have their characteristics, and the optimal parameter values for the study area and vegetation types vary due to different vegetation growth trajectories. Improper parameter values result in significant uncertainty in smoothing results, leading to inaccurate phenological extraction results [39].

4.2. Spatial and Temporal Patterns of NEP

From 2000 to 2020, the total NEP in the Zoigê Plateau demonstrated an increasing trend (slope = 1.19, p < 0.05). This trend was linked to increasing precipitation, temperature, LOS, and soil temperature, emphasizing the role of hydrothermal conditions in supporting vegetation growth. The Zoigê grassland is a highly sensitive area to vegetation response to climate change in China and a vulnerable area for terrestrial ecosystems. The favorable hydrothermal environment in the region provides important growth conditions for grassland plants [40]. Soil moisture plays a pivotal role in regulating the hydrothermal balance of terrestrial ecosystems. Stable soil moisture ensures normal plant photosynthesis under sufficient light and CO2 concentration [41] while enhancing the mineralization of SOM [42,43].
Vegetation development and phenology are influenced by climate change, with temperature being a crucial factor for plant growth. Suitable temperatures favor grassland vegetation development [44]. Additionally, temperature affects photosynthesis efficiency, promoting the activity of photosynthetic enzymes and significantly enhancing the carbon cycle efficiency of plants [45]. The multi-year average NEP of the Zoigê Plateau exhibited spatial heterogeneity, with higher values in the southwest and northeast marginal areas (>80 g C/m2) and lower values in the central region (<0 g C/m2) (Figure 4d). Our findings indicate that the physiological, biochemical, and morphological plant responses to environmental conditions in the Zoigê alpine grassland explain the NEP distribution pattern (Figure 1 and Figure 4). Increased precipitation affects soil crust (BSC) and nitrogen-fixing enzyme activities at high altitudes, therefore modifying microbial community activities and soil fertility [46]. As a result, modifying soil moisture retention in alpine grassland ecosystems can directly or indirectly affect net ecosystem productivity.
As the altitude of the Zoigê Plateau increases from the center to the periphery, soil moisture and fertility gradually decrease, affecting the uptake of organic matter by plants [47,48]. In addition to external environmental factors, the carbon uptake efficiency of alpine vegetation is primarily influenced by its physiological and ecological characteristics. Relevant studies have shown that vegetation at high altitudes is more sensitive to temperature, with interannual sensitivity surpassing seasonal sensitivity above 4700 m [49]. The physiological functions related to photosynthesis weaken with increasing altitude, reducing photosynthesis intensity and inhibiting carbon uptake by plants [50]. Additionally, nitrogen plays a key role in the carbon cycle, and the lower carbon sink capacity of plants around the Zoigê Plateau may be related to the low nitrogen content at high altitudes [51].
The nitrogen concentration in plant leaves determines the maximum photosynthetic capacity of leaves and is a crucial indicator for controlling the rate of photosynthesis in the vegetative canopy. Nitrogen is an essential element for vegetation, playing a significant role in the biosynthesis of proteins, nucleic acids, chlorophyll, and enzymes, which are vital for photosynthesis [52,53]. Therefore, changes in nitrogen content, soil temperature, and humidity in alpine regions weaken grassland plants’ ability to uptake essential nutrients and perform photosynthesis, lowering the carbon sequestration capacity of grassland plants. In summary, the unique hydrothermal pattern of the Zoigê Plateau and the physiological and environmental characteristics of grassland vegetation leads to a gradual decrease in NEP from the center to the periphery.

4.3. NEP Response to Vegetation Climate, Phenology, and Soil

Climate factors primarily influence carbon fluxes through the regulation of vegetation, soil, and phenology. These factors play different synergistic roles in the NEP of grassland vegetation ecosystems in alpine regions [20]. Changes in regional soil characteristics and climatic conditions lead to variations in plant NEP across different ecosystems (Figure 5, Figure 6 and Figure 8). As regional temperatures rise, snow and glacier melting increase soil water content and accelerate the decomposition of soil organic matter, creating favorable hydrothermal and nutrient conditions for plant growth [54]. The results of this study indicate that alpine meadows (0.52) and alpine steppe (0.43) ecosystems are primarily influenced by soil moisture (Figure 8). Soil moisture regulates the efficiency of carbon cycling during the physiological processes of grassland plants [55]. Increasing nitrogen input significantly increases NEP when soil volumetric moisture content exceeds 15%, but the effect is inconsistent when soil moisture is below this threshold [56]. Low levels of SOM, TN, and fast-acting potassium, along with relatively low temperatures and moisture deficits, impede nutrient transport through the soil. However, soil moisture exerts a lesser negative impact on soil respiration, indicating that soil organic matter and moisture are vital in regulating carbon balance in grasslands [57]. Additionally, changes in soil communities affect leaf phenology and plant growth patterns across populations and elevation gradients. Favorable hydrothermal conditions prolong the growing season by enhancing the activity of photosynthetic enzymes in collaboration with soil microbes [58]. In summary, the synergistic effects of climate and soil factors notably enhance the NEP of vegetation, explaining why various factors affect ecosystem productivity differently.

5. Conclusions

Accurately assessing the combined effects of climate change, soil factors, and vegetation phenology on NEP in alpine grassland ecosystems is essential for understanding ecosystem carbon dynamics and improving carbon management strategies; consequently, given the identified sensitivity of the Zoigê Plateau to climate variability, regional authorities should promote sustainable grazing practices to prevent overgrazing and soil degradation. In this study, we analyzed the spatiotemporal patterns of NEP in the Zoigê Plateau using the BEPS model and explored its interactions and responses to climate, phenology, and soil factors. The main findings included the following: (i) The spatial patterns of NEP, AI, and LOS in the Zoigê Plateau exhibited significant spatial heterogeneity, primarily shaped by variations in the plateau’s climatic gradients and altitudinal differences. (ii) High soil nutrient levels, coupled with adequate water and temperature conditions, significantly enhanced carbon sink efficiency in alpine grassland ecosystems. (iii) The integrated response of NEP to climate change, phenology, and soil factors showed considerable variation across different grassland vegetation types, reflecting ecosystem-specific impacts. Therefore, prioritizing research on the functionality of carbon sinks in high-altitude cold region vegetation ecosystems is vital for advancing global carbon cycle understanding and informing sustainable resource management. This research provides important insights into the mechanisms governing the global carbon cycle and contributes to the development of strategies for the sustainable management of natural resources.

Author Contributions

Conceptualization, R.Q. and Z.H.; methodology, R.Q.; software, R.Q.; validation, R.Q., Z.H. and L.H.; formal analysis, R.Q., Z.H. and L.H.; investigation, R.Q., J.H., B.W. (Bing Wang) and B.W. (Bo Wen); data curation, R.Q., B.W. (Bo Wen) and J.H.; writing—original draft preparation, R.Q.; writing—review and editing, Z.H., J.A. and Y.S.; visualization, R.Q.; supervision, Z.H., J.A. and Y.S.; funding acquisition, R.Q. and L.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the National Natural Science Foundation of China (Grant No. 42301456); the Natural Science Foundation of Sichuan Province (Grant No. 2025ZNSFSC0321); the Independent Research Project of the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project (Grant No. SKLGP2021Z003), and the China Scholarship Council (Grant No. 202308510301).

Data Availability Statement

The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Acknowledgments

The author appreciates all the data provided by each open database. The author thanks anonymous reviewers and academic editors for their comments.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Location of the study area: (a) the location of the Gansu and Sichuan Provinces in China; (b) the location of the Zoigê Plateau in Sichuan and Gansu; and (c) the Zoigê Plateau.
Figure 1. Location of the study area: (a) the location of the Gansu and Sichuan Provinces in China; (b) the location of the Zoigê Plateau in Sichuan and Gansu; and (c) the Zoigê Plateau.
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Figure 2. Scatterplot of net ecosystem productivity (NEP) generated by the 2005, 2010, 2015, and 2020 models versus the validation product before NEP.
Figure 2. Scatterplot of net ecosystem productivity (NEP) generated by the 2005, 2010, 2015, and 2020 models versus the validation product before NEP.
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Figure 3. Characteristics of interannual variations in the mean values of (a) net ecosystem productivity (NEP), (b) aridity index (AI), and (c) length Of the growing season (LOS). Note: The blue, yellow, and green lines in the figure represent the interannual variation of the predicted values, and the gray boundary represents the range of fluctuation of the predicted values; (d) Time–frequency distribution of LOS. Temporal variation of NEP (e), AI (f), and LOS (g) in (A) Alpine Meadow and (B) Alpine Steppe. Note: The left y-axis represents the mean values of NEP, AI, and LOS, respectively; the right y-axis represents the temporal trends of NEP, AI, and LOS, respectively.
Figure 3. Characteristics of interannual variations in the mean values of (a) net ecosystem productivity (NEP), (b) aridity index (AI), and (c) length Of the growing season (LOS). Note: The blue, yellow, and green lines in the figure represent the interannual variation of the predicted values, and the gray boundary represents the range of fluctuation of the predicted values; (d) Time–frequency distribution of LOS. Temporal variation of NEP (e), AI (f), and LOS (g) in (A) Alpine Meadow and (B) Alpine Steppe. Note: The left y-axis represents the mean values of NEP, AI, and LOS, respectively; the right y-axis represents the temporal trends of NEP, AI, and LOS, respectively.
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Figure 4. (a,d,g) Spatial distribution of aridity index (AI), net ecosystem productivity (NEP g C/m2), and LOS (day); (b,e,h) standard deviation; (c,f,i) temporal changes.
Figure 4. (a,d,g) Spatial distribution of aridity index (AI), net ecosystem productivity (NEP g C/m2), and LOS (day); (b,e,h) standard deviation; (c,f,i) temporal changes.
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Figure 5. Net ecosystem productivity (NEP) of alpine meadow systems in relation to climate, soil and phenological factors. (a) Precipitation (mm), (b) Temperature (°C), (c) AI, (d) LOS (day), (e) SM (m3/m3), (f) ST (°C), (g) AP (mg/kg), (h) TN (g/100 g), (i) SOM (g/100 g).
Figure 5. Net ecosystem productivity (NEP) of alpine meadow systems in relation to climate, soil and phenological factors. (a) Precipitation (mm), (b) Temperature (°C), (c) AI, (d) LOS (day), (e) SM (m3/m3), (f) ST (°C), (g) AP (mg/kg), (h) TN (g/100 g), (i) SOM (g/100 g).
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Figure 6. Net ecosystem productivity (NEP) of alpine steppe systems in relation to climate, soil and phenological factors. (a) Precipitation (mm), (b) Temperature (°C), (c) AI, (d) LOS (day), (e) SM (m3/m3), (f) ST (°C), (g) AP (mg/kg), (h) TN (g/100 g), (i) SOM (g/100 g).
Figure 6. Net ecosystem productivity (NEP) of alpine steppe systems in relation to climate, soil and phenological factors. (a) Precipitation (mm), (b) Temperature (°C), (c) AI, (d) LOS (day), (e) SM (m3/m3), (f) ST (°C), (g) AP (mg/kg), (h) TN (g/100 g), (i) SOM (g/100 g).
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Figure 7. Relationships among NEP, AI, and LOS for (a) alpine meadows, (d) alpine steppe, (b) alpine meadows, (e) alpine steppe, and the aridity index (AI); (c) alpine meadows, (f) alpine steppe, and the relationship between annual mean precipitation (AMP) and annual mean temperature (AMT).
Figure 7. Relationships among NEP, AI, and LOS for (a) alpine meadows, (d) alpine steppe, (b) alpine meadows, (e) alpine steppe, and the aridity index (AI); (c) alpine meadows, (f) alpine steppe, and the relationship between annual mean precipitation (AMP) and annual mean temperature (AMT).
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Figure 8. Mechanisms affecting NEP distribution in (a) alpine meadows and (b) alpine steppe were analyzed using SEM to evaluate the overall impacts of the variables. Red and black solid lines represent negative and positive standardized SEM coefficients, respectively, with line thickness representing the magnitude of these coefficients for different vegetation types. SM = soil moisture, ST = soil temperature, AP = available phosphorus, SOM = soil organic matter, TN = total nitrogen. Note: standardized total effect coefficients < 0.20 are not indicated in the figure.
Figure 8. Mechanisms affecting NEP distribution in (a) alpine meadows and (b) alpine steppe were analyzed using SEM to evaluate the overall impacts of the variables. Red and black solid lines represent negative and positive standardized SEM coefficients, respectively, with line thickness representing the magnitude of these coefficients for different vegetation types. SM = soil moisture, ST = soil temperature, AP = available phosphorus, SOM = soil organic matter, TN = total nitrogen. Note: standardized total effect coefficients < 0.20 are not indicated in the figure.
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Table 1. Summary of data sources.
Table 1. Summary of data sources.
No.UsageDurationResolutionSourceLinks
1Monthly precipitation and temperature2000–2020/China Meteorological Administrationhttp://data.cma.cn/ (accessed on 10 December 2024)
2Soil organic matter (SOM)
Total nitrogen (TN)
Quick-acting phosphorus (AP)		/1 kmThe Soil Database of China for Land Surface Modelinghttp://globalchange.bnu.edu.cn/research/soil2 (accessed on 10 December 2024)
3Plant phenology observation data for the Tibetan Plateau2000–201516 daysNational Tibetan Plateau Science Data Centerhttps://data.tpdc.ac.cn/zh-hans/data/6466bf35-06ed-4c4d-ad21-) 0f64aedbdec0 (accessed on 10 December 2024)
4Soil temperature (0–10 cm),
soil moisture (0–10 cm)		2000–20201 kmERA5-Landhttps://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land-monthly-means?tab=form (accessed on 15 December 2024)
5Qinghai-Tibetan Plateau vegetation type/1 monthNational Tibetan Plateau Science Data Centerhttps://data.tpdc.ac.cn/zh-hans/data/8c12e483-bd59-402d-a6b5-fbc72da9f771 (accessed on 20 December 2024)
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Qu, R.; He, Z.; He, L.; Awange, J.; Song, Y.; Wang, B.; Wen, B.; Hu, J. Impact of Climate, Phenology, and Soil Factors on Net Ecosystem Productivity in Zoigê Alpine Grassland. Agronomy 2025, 15, 685. https://doi.org/10.3390/agronomy15030685

AMA Style

Qu R, He Z, He L, Awange J, Song Y, Wang B, Wen B, Hu J. Impact of Climate, Phenology, and Soil Factors on Net Ecosystem Productivity in Zoigê Alpine Grassland. Agronomy. 2025; 15(3):685. https://doi.org/10.3390/agronomy15030685

Chicago/Turabian Style

Qu, Rui, Zhengwei He, Li He, Joseph Awange, Yongze Song, Bing Wang, Bo Wen, and Jiao Hu. 2025. "Impact of Climate, Phenology, and Soil Factors on Net Ecosystem Productivity in Zoigê Alpine Grassland" Agronomy 15, no. 3: 685. https://doi.org/10.3390/agronomy15030685

APA Style

Qu, R., He, Z., He, L., Awange, J., Song, Y., Wang, B., Wen, B., & Hu, J. (2025). Impact of Climate, Phenology, and Soil Factors on Net Ecosystem Productivity in Zoigê Alpine Grassland. Agronomy, 15(3), 685. https://doi.org/10.3390/agronomy15030685

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