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Purpose Grassland in Qinghai as the main type of ecosystem in this region is located in arid and semi-arid areas. The ecosystem is fragile and sensitive to climate change. Grassland ecosystem not only provides essential ecological and life functions for human society but also plays a vital role in mitigating and adapting to climate change. The empirical literature on grassland ecosystem services value (ESV) does not consider the impact of climate change and regional economic development level factors, which prevents policymakers from making appropriate decisions. This paper aims to analyze the influencing factors of grassland ESV assessment, and, based on the meta-prediction model, account the grassland ESV in Qinghai province. Design/methodology/approach To understand the value of grassland ecosystem services in China under climate change, this paper used 61 research literature on the evaluation of grassland ESV in China, including a total of 564 value observations to establish a value transfer database. Based on the meta-analysis method, this study has constructed a value transfer model, to evaluate the grassland ESV in Qinghai province, and an interpretation model, which can analyze if the independent variables affect the grassland ESV significantly. Findings The study finds that the evaluation methods, types of ecosystem service functions, climate change and grassland types can affect the grassland ESV significantly. Based on the meta-regression prediction model to evaluate the grassland ESV in Qinghai is US$1,542.67/ha/year. It indicates several targeted approaches to increase the grassland ESV, and climate change also has a specific impact on the value of grassland ecosystem services. Research limitations/implications This study provides a scientific basis for grassland management related to the development of grasslands and ecological compensation, as well as promote the sustainable development of grassland ecosystems. Originality/value This paper contributes to the field of grassland ESV assessment in at least three aspects; first, it innovatively introduces the meta-analysis to carry out an integrated analysis of previous research results; second, it includes a broader set of influence variables in the analysis, including meteorological and economic factors; and third, it establishes a methodological basis for the field of grassland ESV accounting.

Global climate change is altering the amounts of ice and snow in winter, and this could be a major driver of soil microbial processes. However, it is not known how bacterial and fungal communities will respond to changes in the snow cover. We conducted a snow manipulation experiment to study the effects of snow removal on the diversity and composition of soil bacterial and fungal communities. A snow manipulation experiment was carried out on the meadow steppe in Hulunbuir, Inner Mongolia, China, during the winter period October 2019–March 2020. Soil samples were collected from the topsoil (0–10 cm) in mid-March 2020 (spring snowmelt period). Snow removal significantly reduced soil moisture and soil ammonium concentration. Lower snow cover also significantly changed the fungal community structure and beta diversity. Snow removal did not affect the bacterial community, indicating that fungal communities are more sensitive to snow exclusion than bacterial communities. The relative importance analysis (using the Lindeman–Merenda–Gold method) showed that available nitrogen (AN), soil water content (SWC), total organic carbon (TOC), microbial biomass carbon (MBC), and microbial biomass nitrogen (MBN) together explained 94.59% of the variation in soil fungal beta diversity, where AN was identified as the most important predictor. These finding provide insights into potential impacts of climate warming and associated reduced snow cover on soil microbial communities and processes.

Common grassland management practices affect plant and soil element stoichiometry, but the primary environmental factors driving variation in plant C/N ratios for different species in different types of grassland management remain poorly understood. We examined the three dominant C/N stoichiometric responses of plants to different land uses (moderate grazing and mowing) in the temperate meadow steppe of northern China. Our results showed that the responses of the C/N ratio of dominant plants differed according to the management practice. The relative abundance of N in plant tissues increased due to increased soil NO3−, with a consequent decrease in plant C: N in the shoots of Leymus chinensis, but the C/N ratio and nitrogen concentration in the shoots of Bromus inermis and Potentilla bifurca were relatively stable under short-term moderate grazing management. Mowing reduced the concentration of soil NH4+, thus reducing the nitrogen concentration of the roots, resulting in a decrease in the root C/N ratio of Potentilla bifurca. Structural equation model (SEM) showed that the root C/N ratio was affected by both root N and soil inorganic N, while shoot C/N ratio was only affected by the soil inorganic N. Our findings provide a mechanistic understanding of the responses of plant C/N ratio to land use change. The species-level responses of plant stoichiometry to human-managed grasslands deserve more attention.

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