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The increasing frequency of global drought events poses a significant threat to the stability of grassland ecosystems' functionality. The Inner Mongolian grasslands stand out as one of the world's most drought-prone regions, facing elevated drought risks compared to other biomes. An in-depth comprehension of the impact of drought on grassland ecosystems is paramount for their long-term sustainability. Using the Standardized Precipitation Evapotranspiration Index (SPEI) from 1982 to 2018, this study identified various drought events within the Inner Mongolian grasslands, encompassing moderate drought, severe drought, and extreme drought. The resistance of the vegetation to the different drought conditions, assessed through net primary productivity (NPP) as a metric (reflecting its capacity to maintain its original level during drought periods), was examined. The research findings indicated that the period from 2001 to 2018 witnessed a substantial increase in both the frequency and the extent of drought events compared to the period from 1982 to 2000, particularly concerning severe and extreme droughts. The areas most severely impacted by extreme drought were the Xilingol League and the Alxa League. From 1982–2000 to 2001–2018, under moderate drought conditions, vegetation resistance exhibited a minor decrease in the central and eastern regions but experienced a slight increase in the western region. In contrast, under severe drought conditions, the western region saw a significant decrease in vegetation resistance. Remarkably, under extreme drought conditions, the western region showed a substantial increase in vegetation resistance, while the central and eastern regions experienced a slight decrease. Across all three drought conditions, as precipitation levels declined, the resistance of the meadow–steppe–desert ecosystems demonstrated a high–low–high distribution pattern. The temperate desert steppe exhibited a minimal vulnerability to drought, boasting resistance levels exceeding 0.9. Notably, extreme drought had the most pronounced impact on the temperate meadow steppe, temperate steppe, and temperate desert steppe, particularly within the temperate meadow steppe category. Given these findings, the authorities responsible for grassland management should prioritize regions characterized by frequent drought occurrences and low drought resistance, such as Ulanqab City, the Xilingol League, and the western part of Hulun Buir City. Safeguarding steppe ecosystems is of paramount importance for stabilizing vegetation productivity and land carbon sinks, especially under the anticipated exacerbation of climate conditions in the future. [ABSTRACT FROM AUTHOR]

Grassland vegetation growth directly reflects plant growth conditions, and growth processes are an important component of ecological status assessments of grasslands and can provide timely guidance for agricultural production. In this study, the Xilingol Grassland in Inner Mongolia was used as the study area, and a monitoring indicator system for the remote sensing of grassland vegetation growth was established based on 16 days of Moderate Resolution Imaging Spectroradiometer (MODIS) data and ground sampling data, which were used to assess the suitability of the indicator system. A monitoring indicator system for the remote sensing of grassland vegetation that included the modified growth index (MGI)-normalized difference vegetation index (NDVI), MGI-enhanced vegetation index (EVI), growth index (GI)-NDVI, and GI-EVI was established by using the year 2000 as the base year, two vegetation indices and difference and normalized difference methods. A model for estimating the ground growth (g) was then constructed by using expert opinion scoring and the Analytic Hierarchy Process (AHP) to determine the weights of vegetation coverage (c), height (h), and yield (y) in the ground plots, with the model calculated as follows:g = 0.2543c + 0.1848 h + 0.5609y. Additionally, the ground growth value was calculated according to the ground growth model, and the values obtained from the remote-sensing indicators in the corresponding region were subjected to a correlation analysis based on this partition. The remote-sensing growth indices suitable for temperate steppe, meadow steppe, and desert steppe regions were GI-EVI, MGI-NDVI, and GI-NDVI, respectively. Finally, vegetation growth in the Xilingol Grassland was evaluated using the optimal remote-sensing GI for each area, and the results indicated that the areas with the greatest growth improvement occurred in the temperate steppe region followed by the meadow steppe region, whereas vegetation growth improvement was insignificant in the desert steppe region. The results of this study have important significance for the economic development and ecological environmental improvement of pastoral and semi-pastoral regions and can be used to determine the optimal use and scientific management of grasslands. [ABSTRACT FROM PUBLISHER]

Grassland is not only an important landscape of the Beijing–Tianjin sandstorm source control project area, but also a significant object of the Beijing–Tianjin sandstorm source control project. By taking the situation in 2000 as the base of comparison and using the established grassland vegetation growth model, the monitoring and evaluation of grassland vegetation dynamic variation in the project area from 2000 to 2010 was conducted based on MODIS 16 days NDVI data. The conclusions are as follows: (1) The comparative result of average growth between each year from 2001 to 2010 and the base year was on the good side in general; the grassland growth was good both in the early and later periods of grassland growth peak season than in the first years of the project implementation, indicating that the implementation of the Beijing–Tianjin sandstorm source project has significantly improved the growth conditions of grassland vegetation; (2) With regard to the annual dynamic variation of grassland growth, the area proportions of the grasslands, of which the average grassland growth was on the good side, fluctuated and increased slightly with the time changes in general. The area proportions of the grasslands, of which the average grassland growth was on the bad side, fluctuated and decreased in general. The area proportions of the grasslands with normal growth showed an increasing overall trend; (3) From the regional perceptive on four zones, including the northern arid grassland desertification control zone, Hunshandake sandy land control zone, the farming-pastoral area of desertified land control zone, and the water conservation zone of Yanshan hills and mountains, except that the grassland growth in the farming-pastoral area of desertified land control zone was bad, the average growth of other three zones was good each year from 2001 to 2010 compared with the base year. (4) In respect of space, the regions with big grassland growth variation in the project area were concentrated in the western and eastern sections of the northern arid grassland desertification control zone and the western section of Hunshandake sandy land control zone. The grassland growth variation in the water conservation zone of Yanshan hills and mountains and the farming-pastoral area of desertified land control zone were relatively stable. On one hand, the conclusions of this paper can evaluate the effectiveness of the project control, on the other hand, it can also provide scientific basics to grassland management departments, facilitate the rational utilization of grassland, and preserve the regional ecological balance. [ABSTRACT FROM AUTHOR]

Temperature and precipitation are considered to be the most important indicators affecting the green-up date. Sensitivity of the green-up date to temperature and precipitation is considered to be one of the key indicators to characterize the response of terrestrial ecosystems to climate change. We selected the main grassland types for analysis, including temperate steppe, temperate meadow steppe, upland meadow, and lowland meadow. This study investigates the variation in key meteorological indicators (daily maximum temperature (Tmax), daily minimum temperature (Tmin), and precipitation) between 2001 and 2018. We then examined the partial correlation and sensitivity of green-up date (GUD) to Tmax, Tmin, and precipitation. Our analysis indicated that the average GUD across the whole area was DOY 113. The mean GUD trend was −3.1 days/decade and the 25% region advanced significantly. Tmax and Tmin mainly showed a decreasing trend in winter (p > 0.05). In spring, Tmax mainly showed an increasing trend (p > 0.05) and Tmin a decreasing trend (p > 0.05). Precipitation showed no significant (p > 0.05) change trend and the trend range was ±10 mm/decade. For temperate steppe, the increase in Tmin in March promotes green-up (27.3%, the proportion of significant pixels), with a sensitivity of −0.17 days/°C. In addition, precipitation in April also promotes green-up (21.7%), with a sensitivity of −0.32 days/mm. The GUDs of temperate meadow steppe (73.9%), lowland meadow (65.9%), and upland meadow (22.1%) were mainly affected by Tmin in March, with sensitivities of −0.15 days/°C, −0.13 days/°C, and −0.14 days/°C, respectively. The results of this study reveal the response of vegetation to climate warming and contribute to improving the prediction of ecological changes as temperatures increase in the future. [ABSTRACT FROM AUTHOR]

The accumulation of heat and moderate precipitation are the primary factors that are used by grasslands to trigger a green-up date. The accumulated growing degree-days (AGDD) requirement over the preseason is an important indicator of the response of grassland spring phenology to climate change. This study adopted the Normalized Difference Phenology Index (NDPI), which derived from the Moderate Resolution Imaging Spectroradiometer (MODIS), to extract annual green-up dates in the Hulun Buir grassland in China between 2001–2015. Our analysis indicated that the range (standard deviation) and trend for the green-up date were DOY (day of year) 104 to DOY 144 (10.6 days) and −2.0 days per decade. Nine point two percent of the study area had significant (p < 0.05) changes in AGDD requirements. The partial correlations between the AGDD requirements and chilling days (67.04%, pixels proportion) were negative and significant (p < 0.05). The partial correlations between the AGDD requirement and precipitation (28.87%) were positive and significant (p < 0.05). Finally, the partial correlation between the AGDD requirement and insolation (97.65%) were positive and significant (p < 0.05). The results of this study could reveal the response of vegetation to climate warming and contribute to improving the phenological mechanism model of different grassland types in future research. [ABSTRACT FROM AUTHOR]

[Display omitted] Structural information of grassland changes on the Tibetan Plateau is essential for understanding alterations in critical ecosystem functioning and their underlying drivers that may reflect environmental changes. However, such information at the regional scale is still lacking due to methodological limitations. Beyond remote sensing indicators only recognizing vegetation productivity, we utilized multivariate data fusion and deep learning to characterize formation-based plant community structure in alpine grasslands at the regional scale of the Tibetan Plateau for the first time and compared it with the earlier version of Vegetation Map of China for historical changes. Over the past 40 years, we revealed that (1) the proportion of alpine meadows in alpine grasslands increased from 50% to 69%, well-reflecting the warming and wetting trend; (2) dominances of Kobresia pygmaea and Stipa purpurea formations in alpine meadows and steppes were strengthened to 76% and 92%, respectively; (3) the climate factor mainly drove the distribution of Stipa purpurea formation, but not the recent distribution of Kobresia pygmaea formation that was likely shaped by human activities. Therefore, the underlying mechanisms of grassland changes over the past 40 years were considered to be formation dependent. Overall, the first exploration for structural information of plant community changes in this study not only provides a new perspective to understand drivers of grassland changes and their spatial heterogeneity at the regional scale of the Tibetan Plateau, but also innovates large-scale vegetation study paradigm. [ABSTRACT FROM AUTHOR]

Knowledge about grassland biomass and its dynamics is critical for studying regional carbon cycles and for the sustainable use of grassland resources. In this study, we investigated the spatio-temporal variation of biomass in the Xilingol grasslands of northern China. Field-based biomass samples and MODIS time series data sets were used to establish two empirical models based on the relationship of the normalized difference vegetation index (NDVI) with above-ground biomass (AGB) as well as that of AGB with below-ground biomass (BGB). We further explored the climatic controls of these variations. Our results showed that the biomass averaged 99.01 Tg (1 Tg=1012 g) over a total area of 19.6×104 km2 and fluctuated with no significant trend from 2001 to 2012. The mean biomass density was 505.4 g/m2, with 62.6 g/m2 in AGB and 442.8 g/m2 in BGB, which generally decreased from northeast to southwest and exhibited a large spatial heterogeneity. The year-to-year AGB pattern was generally consistent with the inter-annual variation in the growing season precipitation (GSP), showing a robust positive correlation (R2=0.82, P<0.001), but an opposite coupled pattern was observed with the growing season temperature (GST) (R2=0.61, P=0.003). Climatic factors also affected the spatial distribution of AGB, which increased progressively with the GSP gradient (R2=0.76, P<0.0001) but decreased with an increasing GST (R2=0.70, P<0.0001). An improved moisture index that combined the effects of GST and GSP explained more variation in AGB than did precipitation alone (R2=0.81, P<0.0001). The relationship between AGB and GSP could be fit by a power function. This increasing slope of the GSP–AGB relationships along the GSP gradient may be partly explained by the GST–GSP spatial pattern in Xilingol. Our findings suggest that the relationships between climatic factors and AGB may be scale-dependent and that multi-scale studies and sufficient long-term field data are needed to examine the relationships between AGB and climatic factors. [ABSTRACT FROM AUTHOR]

Grassland aboveground biomass (AGB) is an important indicator for studying the change in grassland ecological quality and carbon cycle. The rapid development of high-resolution remote sensing and unmanned aerial vehicles (UAV) provides a new opportunity for accurate estimation of grassland AGB on the plot scale. In this study, the mountain grassland was taken as the research object. Using UAV Light Detection and Ranging (LiDAR) data and multispectral satellite images, the influence of topographic correction methods on AGB estimation was compared and a series of LiDAR metrics and vegetation indices were extracted. On this basis, a comprehensive indicator, the vegetation index-height-intensity model (VHI), was proposed to estimate AGB quickly. The results show that: (1) Among the four topographic correction methods, the Teillet regression has the best effect, and can effectively improve the accuracy of AGB estimation in mountain grassland. The correlation between corrected ratio vegetation index and AGB was the highest (correlation coefficient: 0.682). (2) Among the height and intensity metrics, median height and max intensity yielded the higher accuracy in estimating AGB, with Root Mean Square Error (RMSE) of 322 g/m2 and 333 g/m2, respectively. (3) The VHI integrated spectrum and LiDAR information, and its accuracy for AGB estimation for mountain grassland, was obviously better than other indicators, with an RMSE of 272 g/m2. We also found that the accuracy of VHI in univariate models was comparable to that of complex multivariate models such as stepwise regression, support vector machine, and random forest. This study provides a new approach for estimating grassland AGB with multi-source data. As a simple and effective indicator, VHI has shown strong application potential for grassland AGB estimating in mountainous areas, and can be further applied to grassland carbon cycle research and fine management. [ABSTRACT FROM AUTHOR]

Desertification is treated as an important and critical environmental hazard. In the face of increasingly serious grassland desertification, China has made great efforts to combat desertification and a series of key national ecological projects has been launched. However, accurate, timely, and effective monitoring and assessment are required to determine whether these projects work well. The Horqin sandy land represents the largest area of sandy land in China. In this article, the Naiman and Ongniud Banners were studied because these contain the main desertified grassland in Horqin. Next, a desertification classification and grading system was designed for the Horqin sandy land after conducting fieldwork. Based on spectral mixture analysis (SMA) and decision-tree methods, we interpreted Landsat Thematic Mapper/Enhanced Thematic Mapper Plus/Operational Land Imager (TM/ETM+/OLI) images of the study area from four years: 1985, 1992, 2001, and 2013. Overall, the following results were obtained. The total area of desertified grassland in the Naiman and Ongniud Banners increased from 5979 km2in 1985 to 9195 km2in 1992 (an increase of 53.79%) and then decreased to 7828 km2in 2001 and to 6023 km2in 2013. The changes in the areas of desertified grassland with various degrees of desertification displayed the same trends as that of the total desertified grassland area. The severely desertified grassland expanded from 1872 km2in 1985 to 3723 km2in 1992 before shrinking to 2189 km2in 2013. The annual rates of expansion of desertified grassland during the periods 1985–1992, 1992–2001, and 2001–2013 were 7.68%, −1.65%, and −1.92%, respectively, and the corresponding expansion rates of severely desertified grassland were 14.12%, −3.48%, and −1.19%, respectively. Both the desertified grasslands and the areas with various degrees of desertification displayed significant expansion during the period 1985–1992. Since 1992, this trend has reduced. During the study period, the changes in temperature and precipitation did not benefit the reversal of grassland sandy desertification. Furthermore, the growing population and expansion of livestock production and farming inhibited such reversal. However, the results presented in this article suggest that a reversal in grassland sandy desertification has been occurring since 1992. The results indicate that ecological engineering measures have helped reverse desertification and have promoted the restoration of grassland vegetation. [ABSTRACT FROM PUBLISHER]

Estimating the Start of Growing Season (SOS) of grassland on the global scale is an important scientific issue since it can reflect the response of the terrestrial ecosystem to environmental changes and determine the start time of grazing. However, most remote sensing data has coarse- temporal and spatial resolution, resulting in low accuracy of SOS retrieval based on remote sensing methods. In recent years, much research has focused on multi-source data fusion technology to improve the spatio-temporal resolution of remote sensing information, and to provide a feasible path for high-accuracy remote sensing inversion of SOS. Nevertheless, there is still a lack of quantitative evaluation for the accuracy of these data fusion methods in SOS estimation. Therefore, in this study, the SOS estimation accuracy is quantitatively evaluated based on the spatio-temporal fusion daily datasets through the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and other models in Xilinhot City, Inner Mongolia, China. The results show that: (1) the accuracy of SOS estimation based on spatio-temporal fusion daily datasets has been slightly improved, the average Root Mean Square Error (RMSE) of SOS based on 8d composite datasets is 11.1d, and the best is 9.7d ( f s t a r f m 8 ); (2) the estimation accuracy based on 8d composite datasets ( R M S E ¯ = 11.1d) is better than daily fusion datasets ( R M S E ¯ = 18.2d); (3) the lack of the Landsat data during the SOS would decrease the quality of the fusion datasets, which ultimately reduces the accuracy of the SOS estimation. The R M S E ¯ of SOS based on all three models increases by 11.1d, and the STARFM is least affected, just increases 2.7d. The results highlight the potential of the spatio-temporal data fusion method in high-accuracy grassland SOS estimation. It also shows that the dataset fused by the STARFM algorithm and composed for 8 days is better for SOS estimation. [ABSTRACT FROM AUTHOR]

Shrub encroachment, characterized by the proliferation of shrubs into grasslands, is a challenge faced by grasslands worldwide that significantly impacts livestock production and ecosystem functions. Rapid and accurate estimation of shrub dominance is important for understanding changes in plant community structures and formulating grassland management policies. However, the limited spatial resolution of commonly used satellite imagery poses a challenge when estimating shrub dominance at the landscape scale. The rapid development of Unoccupied Aerial Vehicles (UAVs) has opened up new opportunities for cross-scale observations of shrub encroachment in grasslands by effectively bridging the scale gap between ground sampling and satellite image pixels while reducing the required groundwork. This study utilized ground reference data, UAV data (RGB, hyperspectral, and LiDAR), and satellite data (Sentinel-1 and Sentinel-2) to estimate shrub and total above-ground biomass (AGB) in temperate grasslands to map the shrub dominance. First, UAV data were applied at the plot scale for the classification of shrub and herbaceous vegetation using the maximum entropy model (MaxEnt), estimation of shrub AGB by employing the vegetation index weighted canopy volume model (CVM VI), and estimation of herbaceous AGB based on the partial least squares regression (PLSR). Second, UAV AGB mapping results were upscaled as samples at the landscape scale and integrated with satellite imagery to establish the shrub and total AGB models using the extreme gradient boosting (XGBoost). Finally, shrub dominance, represented as shrub AGB/total AGB, was mapped across the study area. We found that at the plot scale, the MaxEnt model achieved an overall accuracy of 0.990 for object-based classification. The CVM VI combined with canopy height model and narrow-band vegetation index achieved the highest accuracy for estimating shrub AGB (R2 = 0.821, RMSE = 30.1 g). The PLSR combined with features derived from all UAV data achieved the highest accuracy for estimating herbaceous AGB (R2 = 0.856, RMSE = 9.1 g/m2). At the landscape scale, the XGBoost achieved high accuracy for estimating both the shrub AGB (R2 = 0.719, RMSE = 4.2 g/m2) and total AGB (R2 = 0.961, RMSE = 5.0 g/m2). The high-precision mapping results further facilitate the generation of shrub dominance maps at a landscape scale. This study presents a more accurate and efficient framework for mapping shrub AGB, total AGB, and shrub dominance using multi-scale remote sensing data, which offers new approaches for large-scale grassland AGB mapping and monitoring of shrub encroachment in grasslands. • Multisensory UAV data were evaluated for shrub and herbaceous AGB estimation at the plot scale. • SAR and multispectral satellite data were used to map AGB and shrub dominance at the landscape scale. • Our multi-scale framework outperformed the traditional AGB estimation method. • Assessing shrub dominance is important for understanding shrub encroachment. [ABSTRACT FROM AUTHOR]

Aboveground biomass (AGB) in grasslands directly reflects the net primary productivity, making it a sensitive indicator of grassland resource quality and ecological degradation. Accurately estimating AGB over large regions to reveal long-term AGB evolution trends remains a formidable challenge. In this study, we divided Inner Mongolia Autonomous Region (IMAR) grasslands into three study regions based on their spatial distribution of grassland types. We combined remote sensing data with ground-based sample data collected over the past 19 years from 6114 field plots using the Google Earth Engine platform. We constructed random forest (RF) and traditional regression AGB inversion models for each region and selected the best-performing model through accuracy assessment to estimate IMAR grassland AGB for the period 2000–2022. We also examined the trends in AGB changes and identified the driving forces affecting IMAR grasslands through the application of Theil-Sen estimation, Mann-Kendall trend analysis, and the Geodetector model. The main findings are as follows: (1) Compared with the univariate parametric traditional regression model, the AGB monitoring accuracy of the multivariate non-parametric RF model in the three study regions increased by 5.94%, 5.08% and 19.14%, respectively. (2) The average AGB per unit area of IMAR grasslands from 2000 to 2022 was 731.41 kg/hm2, with alpine meadow having the highest average AGB (1271.70 kg/hm2) and temperate grassland desertification having the lowest (469.06 kg/hm2). IMAR grasslands exhibited an overall increasing trend in AGB over the past 23 years (6.01 kg/hm2•yr), with the increasing trend covering 83.52% of the grassland area and the decreasing trend covering 16.48%. (3) Spatially, IMAR grassland AGB showed a gradual decline from northeast to southwest and exhibited an increasing trend with increasing longitude (45.423 kg/hm2 per degree) and latitude (71.9 kg/hm2 per degree). (4) Meteorological factors were the most significant factors affecting IMAR grassland AGB, with precipitation (five-year average q value of 0.61) being the most prominent. In the western part of IMAR, where precipitation is consistently limited throughout the year, the primary drivers of influence were human activities, with particular emphasis on the number of livestock (with a five-year average q value of 0.44). It is evident that reducing human activity disturbance and pressure in fragile grassland areas or implementing near-natural restoration measures will be beneficial for the sustainable development of grassland ecosystems. The results of this research hold substantial reference importance for the protection and restoration of grasslands, the supervision and administration of grassland resources, as well as the development of policies related to grassland management. [Display omitted] • The aboveground biomass (AGB) of grasslands in IMAR increased over the past 23 years. • IMAR grassland AGB shows an upward trend with increasing longitude and latitude. • Precipitation is the main factor affecting grassland AGB in IMAR. • Human activities have a greater impact on the grassland AGB in western IMAR. [ABSTRACT FROM AUTHOR]

[Display omitted] • A selection strategy for base-period images of spatio-temporal fusion was proposed. • The spatio-temporal inconsistency between field samples and images was reduced. • The estimation accuracy of grassland aboveground biomass was improved. The accurate estimation of the aboveground biomass (AGB) is crucial for the sustainable utilization and management of grassland resources. Spatio-temporal inconsistencies between field samples and satellite images are major source of error in the estimation of grassland AGB. To solve this problem, this study selected the Three Rivers Headwater Region as the study area and proposed a selection strategy for base-period images of spatio-temporal fusion that was suitable for use at a large spatial scale in which cloud-free images are difficult to obtain. The spatial and temporal adaptive reflectance fusion model (STARFM) based on the selection strategy was used to generate a synthetic Normalized Difference Vegetation Index (NDVI) dataset with high spatial-temporal resolution by using the maximum value composite of GF-1 NDVI and MODIS NDVI to enhance the spatial-temporal quality of the images for field-scale application. Three estimation models for grassland AGB were then constructed by the random forest algorithm using synthetic NDVI, MODIS NDVI, GF-1 NDVI respectively, together with ancillary data. Following this, the estimation model with the highest accuracy was used to generate a 16-m eight-day time-series AGB in the growing season. The results showed: (1) The synthetic NDVI was correlated closely with the observed GF-1 NDVI, with an average R of 0.825 and a RMSE of 0.087. The temporal trend of the synthetic NDVI for each grassland type was highly consistent with that of the MODIS NDVI in the growing season with a correlation higher than 0.9. (2) The synthetic NDVI reduced the spatial difference between field samples and images to 16-fold, and the temporal difference was controlled to within four days under ideal conditions. (3) The synthetic NDVI improved the estimation accuracy of grassland AGB by about 15.9% and 19.7% (R2), and 13.7% and 17.5% (RMSE) relative to MODIS NDVI and GF-1 NDVI, respectively. (4) The time-series AGB revealed accurately the spatial distribution of and seasonal temporal variations in the grassland biomass. The results of this study may serve as scientific guidance for timely monitoring of grassland conditions and precise management of grassland resources in China. [ABSTRACT FROM AUTHOR]

• N deposition effects on soil C:N:P stoichiometry of ungrazed and grazed grassland varied. • Livestock grazing may weaken N deposition effects on soil C:N:P stoichiometry. • Climate variability and other environmental factors can also mediate N deposition effects. Increased nitrogen (N) deposition is expected to impact the grassland ecosystem by changing soil C:N:P stoichiometry. However, N deposition effects on soil C:N:P stoichiometry is still in debate. In addition, we still know less about how N deposition affects soil C:N:P stoichiometry in grazed grassland as most studies focus on natural grassland. Hence, it is necessary to examine N deposition effects on both ungrazed and grazed grassland and clarify the disparity. We conducted a simulated N deposition experiment in ungrazed and grazed grassland of two grassland types including alpine meadow (AM) and alpine steppe (AS) to test such effects. Results showed that N deposition effects on ungrazed and grazed grassland varied. Soil C, soil N, soil P, soil C:N, soil N:P and soil C:P were significantly affected by N deposition in ungrazed grassland while only soil N, soil P, soil C:N, soil N:P were significantly affected by N deposition in grazed grassland. Soil C and soil N:P were not sensitive to N deposition in grazed grassland. N deposition negatively affects soil P in ungrazed grassland yet positively affected soil P in grazed grassland. N deposition effects accounted for 30% in ungrazed grassland, while significantly decreased to 12% when grazing effects were considered suggesting that livestock grazing might weaken N deposition effects on soil C:N:P stoichiometry. In addition, other climate and environmental factors such as MAT, MAP, and grassland type can also mediate N deposition effects on soil C:N:P stoichiometry in both ungrazed and grazed grasslands. On the whole, our findings suggested that there existed a disparity of N deposition effects on soil C:N:P stoichiometry between ungrazed and grazed grassland, and livestock grazing may weaken N deposition effects on soil C:N:P stoichiometry in alpine grassland of the Qinghai-Tibetan Plateau. [ABSTRACT FROM AUTHOR]