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Lifespan of fine roots plays important roles in regulating carbon (C) cycling in terrestrial ecosystems. Determination of root lifespan and elucidation of its regulatory mechanism in different plant communities are essential for accurate prediction of C cycling from ecosystem to regional scales. Temperate steppes in Inner Mongolia grasslands have three major types, i.e., Stipa krylovii, Stipa grandis and Stipa breviflora grasslands. There have been no studies to compare the root dynamics among the three types of grasslands. In the present study, we determined root lifespan of the three grasslands using the rhizotron. We found that root lifespan differed substantially among the three types of grasslands within the temperate steppes of Inner Mongolia, such that root lifespan of Stipa breviflora > Stipa grandis > Stipa krylovii grasslands. Root lifespan across the three types of grasslands in the Inner Mongolian temperate steppes displayed a similar temporal pattern, i.e. lifespan of the roots produced in spring and autumn was shortest and longest, respectively, whereas lifespan of summer-produced roots was between that of roots produced in spring and autumn. The spatial and temporal differences in root lifespan across the three types of grasslands were mainly determined by contents of soluble sugars in roots of the dominant species. The differences in root lifespan across the major types of grasslands and different seasons highlight the necessity to take into account these differences in the prediction of C cycling within grassland ecosystem by the simulating model. [ABSTRACT FROM AUTHOR]

In order to study the correlation between the vegetation coverage of typical grassland and the thickness of chestnut calcium soil layer, three typical geomorphic types of Wuzhumuqin typical grassland in Inner Mongolia were selected as experimental plots. It uses 3S technology and the methods of landscape ecology and geostatistics to test the image data and field quadrat in the past three years. It closely combines vegetation ecology with soil environment, the relationship between soil characteristics and vegetation landscape, and makes a correlation analysis on the synergistic evolution of chestnut soil thickness. The relationship between chestnut soil thickness, soil water content, and vegetation coverage under three different geomorphic conditions was obtained. Based on the quantitative analysis of the temporal and spatial differences between the vegetation coverage of typical grassland and the thickness of chestnut soil, the spatial distribution patterns of different soil types are revealed. Based on Landsat 8 TM image data, this paper makes statistics of vegetation index (NDVI) at three test sites. The spatial overlap method and the actual observation data are abandoned for spatial interpolation, and the similarity conclusion is obtained. The study shows that among the three sample plots in 2021, except for the original wavy high flat sample plot whose natural environment has been greatly damaged, the correlation coefficient between chestnut calcium soil layer thickness and vegetation coverage in the other two sample plots is also significant in the significance of 0.01 or 0.05 (two-sided test). It shows that there is a very significant positive correlation between the thickness of chestnut calcium soil layer and the thickness of chestnut calcium soil layer. The purpose of this paper is to use hyperspectral images to classify different kinds of plants, so as to realize the monitoring of chestnut soil. It will provide rapid and dynamic technical support for desertification control in the future. [ABSTRACT FROM AUTHOR]

Enhanced deposition of atmospheric nitrogen (N) resulting from anthropogenic activities has negative impacts on plant diversity in grassland ecosystems globally. Several mechanisms have been proposed to explain the species loss. Ion toxicity due to N deposition-induced soil acidification has been suggested to be responsible for species loss in acidic grasslands, while few studies have evaluated the role of soilmediated homeostasis of ions in species loss under elevated N deposition in alkaline grasslands. To determine whether soil-mediated processes are involved in changes in species composition by N deposition, the effects of 9 yrN addition on soil properties, aboveground biomass (AGB) and species composition were investigated in an Inner Mongolia steppe. Low to moderate N addition rate (2, 4, 8 g N m-2 yr-1) significantly enhanced AGB of grasses, while high N addition rate (> 16 g N m-2 yr-1) reduced AGB of forbs, leading to an overall increase in AGB of the community under low to moderate N addition rates. Forb richness was significantly reduced by N addition at rates greater than 8 gNm-2 yr-1, while no effect of N addition on grass richness was observed, resulting in decline in total species richness. N addition depleted base cations (Ca2+, Mg2+ and K+) in soils, reduced soil pH and mobilized Mn2+, Fe3+ and Cu2+ ions in soils. Soil inorganic-N concentration was negatively correlated with forb richness, explaining 27.2% variation of forb richness. The concentrations of base cations (Ca2+ and Mg2+) and metal ions (Mn2+ and Cu2+) showed positively and negatively linear correlation with forb richness, accounting for 25.9 and 41.4% variation of forb richness, respectively. These results reveal that disruption of metal ion homeostasis in soils by N addition, particularly enhanced release of soil Mn2+ and Cu2+ may be associated with reduction in forb richness in temperate steppe of Inner Mongolia. [ABSTRACT FROM AUTHOR]

The general requirements of China's rural revitalization strategy are industrial prosperity, ecological livability and rich life. However, the traditional livestock breeding model has struggled to balance the dual requirements of production development and ecological protection, and it is urgent to inject new impetus and explore new development paths. At present, e-commerce has become a bridge between pastoral areas and cities, herdsmen and consumers. E-commerce participation is not only an important starting point for herdsmen to increase the added value and profit space of livestock products, but also an effective way to change the original breeding behavior based on the premise of destroying grassland. Therefore, this paper presents an in-depth study on the issue of e-commerce enabling grassland ecological restoration, aiming to provide more scientific and effective guidance for e-commerce to be used to achieve a win–win situation in economy and ecology. Therefore, based on the data of 271 herdsmen in pastoral areas of Inner Mongolia, we used the OLS model and the two-stage least squares (2SLS) method to identify the direct impact of herdsmen's e-commerce participation on grassland utilization pressure. The empirical results show that e-commerce participation can significantly decrease the grassland utilization pressure. The conclusion was still valid after alleviating endogeneity and conducting a robustness test. The results of a mechanism analysis suggest that the reduction effect of e-commerce participation on grassland utilization pressure is mainly due to price incentive, reputation incentive and place identity. Subjective norms can strengthen the inhibitory effect of e-commerce participation on grassland utilization pressure. Furthermore, a heterogeneity analysis demonstrates that e-commerce participation has a better, decreased impact on the grassland utilization pressure on the banners of China's rural e-commerce demonstration county program. Under a counterfactual assumption, if herdsmen who can participate in e-commerce choose not to do this, their grassland utilization pressure will increase. [ABSTRACT FROM AUTHOR]

Among the methods used to describe soil-available water in terms of yield, field water capacity and permanent wilting coefficient were considered as the upper and lower boundaries, respectively, for normal plant growth. This study investigated the soil properties and groundwater resources of the Hailiutu pasture in Inner Mongolia, China. Soil water characteristics and hydraulic conductivity function curves of the pasture soil were measured based on filter paper method. Experimental results showed that air-entry and residual value were determined to be 34.6% and 13.1%, corresponding to the field water capacity and permanent wilting coefficient, respectively, and the available water capacity was 218.0 mm. The downward velocity of the soil wetting front in the silt is 3.0 mm/h. Site investigation revealed that the pasture was a natural capillary barrier, and its critical suction could optimize the upper boundary of irrigation. Considering the limited underground water resources, shallow water level, and the transpiration of Caragana Korshinskii in the pasture, an available drip irrigation method was designed. The spacing between drip irrigation pipes is 60 cm, the spacing between drip holes is 60 cm, the drip irrigation volume per hole is 475.2~838.8 mL/d, the drip irrigation time is 110~200 min at each night. Each hectare pasture exhausts 13.5~22.5 m3 water each day. Field tests showed that the proposed method is feasible and can be used for irrigation in dryland pastures. [ABSTRACT FROM AUTHOR]

Polarimetric target decomposition algorithms have played an important role in extracting the scattering characteristics of buildings, crops, and other fields. However, there is limited research on the scattering characteristics of grasslands and a lack of volume scattering models established for grasslands. To improve the accuracy of the polarimetric target decomposition algorithm applicable to grassland environments, this paper proposes an adaptive polarimetric target decomposition algorithm (APD) based on the anisotropy degree (A). The adaptive volume scattering model is used in APD to model volume scattering in forest and grassland regions separately by adjusting the value of A. When A > 1, the particle shape becomes a disk, and the grassland canopy is approximated as a cloud layer composed of randomly oriented disk particles; when A < 1, the particle shape is a needle, simulating the scattering mechanism of forests. APD is applied to an L-band AirSAR dataset from San Francisco, a C-band AirSAR dataset from Hunshandak grassland in Inner Mongolia Autonomous Region, and an X-band COSMO-SkyMed dataset from Xiwuqi grassland in Inner Mongolia Autonomous Region to verify the effectiveness of this method. Comparison studies are carried out to test the performance of APD over several target decomposition algorithms. The experimental results show that APD outperforms the algorithms tested in terms of this study in decomposition accuracy for grasslands and forests on different bands of data. [ABSTRACT FROM AUTHOR]

Estimating the grass yield of a grassland area is of vital theoretical and practical significance for determining grazing capacity and maintaining ecological balance. Due to the spatial inconsistency between sampling and remote sensing data, improving the accuracy of fresh grass yield (FGY) estimation based on remote sensing is difficult. Using vegetation coverage at different spatial scales, this paper proposes a spatial scale transformation (SST)-based estimation model for FGY adopting normalized difference vegetation index (NDVI) as its estimation factor, using the grassland in Xilingol League, Inner Mongolia, as the study area. Results showed that the SST-based FGY estimation model was able to greatly improve estimation precision; the relative estimation error (REE) of the estimation models constructed using linear with intercept zero (linear-0) and power functions were 18.16% and 18.35%, respectively. The estimation models constructed using linear-0 and power functions were employed to estimate the grass yield of the grassland in Xilingol League, and the total FGYs estimated were 8.777 × 1010 kg and 8.583 × 1010 kg, respectively. The two models obtained roughly the same estimates, but there were significant differences between them in the spatial distributions of FGY per unit. Taking net primary productivity (NPP) as an example, the effectiveness of other remote sensing data as estimation factors was further verified, and the results showed that SST-based estimation for FGY also effectively improved the estimation accuracy of grass yield. [ABSTRACT FROM AUTHOR]

Unregulated livestock breeding and grazing can degrade grasslands and damage the ecological environment. The combination of remote sensing and artificial intelligence techniques is a more convenient and powerful means to acquire livestock information in a large area than traditional manual ground investigation. As a mainstream remote sensing platform, unmanned aerial vehicles (UAVs) can obtain high-resolution optical images to detect grazing livestock in grassland. However, grazing livestock objects in UAV images usually occupy very few pixels and tend to gather together, which makes them difficult to detect and count automatically. This paper proposes the GLDM (grazing livestock detection model), a lightweight and high-accuracy deep-learning model, for detecting grazing livestock in UAV images. The enhanced CSPDarknet (ECSP) and weighted aggregate feature re-extraction pyramid modules (WAFR) are constructed to improve the performance based on the YOLOX-nano network scheme. The dataset of different grazing livestock (12,901 instances) for deep learning was made from UAV images in the Hadatu Pasture of Hulunbuir, Inner Mongolia, China. The results show that the proposed method achieves a higher comprehensive detection precision than mainstream object detection models and has an advantage in model size. The m A P of the proposed method is 86.47%, with the model parameter 5.7 M. The average recall and average precision can be above 85% at the same time. The counting accuracy of grazing livestock in the testing dataset, when converted to a unified sheep unit, reached 99%. The scale applicability of the model is also discussed, and the GLDM could perform well with the image resolution varying from 2.5 to 10 cm. The proposed method, the GLDM, was better for detecting grassland grazing livestock in UAV images, combining remote sensing, AI, and grassland ecological applications with broad application prospects. [ABSTRACT FROM AUTHOR]

In order to study the change law of the fatigue degree of grassland expressway drivers over time, this paper takes the semidesert grassland landscape of Xilinhot city as the experimental environment and takes the provincial highway S101 (K278–K424) as an example to design an actual driving test. Taking Urumqi, Inner Mongolia Autonomous Region, as the experimental section, combined with the Biopac MP150 multichannel physiological instrument and its auxiliary knowledge software and mathematical statistics methods, the relationship between EEG and time was studied. The test results show that the primary fatigue factor F1 and the secondary fatigue factor F2 can summarize the fatigue law characterized by 96.42% of EEG information. During 130 minutes of driving on the prairie highway, the periods of high fatigue were 105 minutes and 125 minutes, respectively. Driving fatigue can be divided into three stages over time: 5–65 min fatigue-free stage, 70–85 min fatigue transition stage, and 90–130 min fatigue stage. Fatigue changes over time. The law follows the Gaussian function and the sine function. [ABSTRACT FROM AUTHOR]

In this paper, field spectroradiometer and aboveground biomass (AGB) data were acquired at the harvest stage at two sites in semiarid grasslands in Inner Mongolia, China. Four forms of commonly used vegetation indices (VIs) using all possible combinations of narrow-band first derivative (FDR) and raw reflectance (RR) were calculated, and the best FDR-VIs and RR-VIs were chosen by a linear regression analysis against AGB. The stepwise multiple linear regression (SMLR) models using the optimal FDR-VIs, RR-VIs, and both FDR-VIs and RR-VIs as input variables were developed for estimating the AGB. Results demonstrated that the estimation performance using the best FDR-VIs were comparable with the best RR-VIs, while the accuracy has been further improved by combining the best FDR-VIs and RR-VIs (maximum decrease in RMSE of 44% and minimum RMAE of 4.7%). The approach was found to be an important step for more accurate and effective grassland AGB estimation. [ABSTRACT FROM AUTHOR]

The services provided by grassland ecosystems are important and irreplaceable in maintaining the balance and stability of ecosystems. The spatiotemporal variations of grassland ecosystem service value (ESV) and its influencing factors in Inner Mongolia from 2000 to 2019 were studied in this paper. Based on the socio-economic data, remote sensing data, geographic data, and meteorological data, a dynamic ESV assessment method based on the equivalent factors was used to calculate the grassland ESV for each year. The spatiotemporal dynamic variation and future trend of grassland ESV were studied by coefficient of variation index (CV), Theil–Sen median trend analysis, Mann–Kendall test, and Hurst index, and the Geodetector was used to determine the main factors affecting the distribution of ESV. The results indicated that (1) the annual average grassland ESV of Inner Mongolia was higher in the northeast than in southwest, the average ESV was 2.0794 million CNY/km2, and the pixels were concentrated from 1 to 3 million CNY/km2, accounting for 75.46% of the study area; (2) during the study period, the average grassland ESV increased slowly with time at an annual growth rate of 0.2, and the total ESV decreased first and then increased with the change in grassland area; (3) the average volatility was 0.16, and pixels with CV values between 0.1 and 0.2 accounted for 69.2% of the study area, indicating the fluctuation of ESV was relatively stable during the study period; (4) 37.16% of the grassland ESV in Inner Mongolia decreased slightly and 41.77% increased slightly during these years, and the two parts showed opposite trends in the future; and (5) the single factor influencing the spatial distribution of grassland ESV was mainly normalized vegetation index (NDVI) and precipitation, and the multi-factor interactions were NDVI∩slope and NDVI∩precipitation. All influencing factors exhibited a stronger impact through the two-factor interaction. This study can provide reference values for the policymaking of natural resource conservation or restoration. [ABSTRACT FROM AUTHOR]

The 16 years of normalized difference vegetation index (NDVI) and precipitation data are used to analyze the time‐lag effects of the growing‐season NDVI response to precipitation at regional scales. This study focuses on the arid and semi‐arid Hulunbuir grassland dominated by perennials in northeast China. The multi‐month time‐lag effects are examined using simple statistical approaches, which can detect the two distinct time‐lags for four subregions with four major land‐cover types. A "positive" time‐lag effect of the growing‐season NDVI response to precipitation is observed at 1‐month (May in the current year) time‐lag and 13‐month (May in the previous year) time‐lag while a "negative" time‐lag effect is observed at 9‐month (September in the previous year) time‐lag. In addition, the prediction results of NDVI based on precipitation indicate that the NDVI prediction model considered the lagged monthly precipitation has good performance. Therefore, revealing the time‐lag effects is very important for accurately predicting the growing‐season NDVI and evaluating the vegetation dynamics. Recommendations for Resource Managers: The multi‐month time‐lag effects of the growing‐season NDVI to precipitation in arid and semi‐arid grasslands in northeastern China is analyzed in this paper. We can detect the two distinct time‐lags for four subregions with four major land‐cover types. The following implications could be realized based on the observations: The "positive" 1‐month (May in the current year) and 13‐month (May in the previous year) time‐lag memory of the growing‐season NDVI response to precipitation can be found.The "negative" 9‐month time‐lag memory of the growing‐season NDVI response to precipitation associated with September (late growing‐season) meteorological condition can be detected. This "negative" effect does not support the green up of the growing‐season in the next year. [ABSTRACT FROM AUTHOR]

In this paper, we selected Xilingol League in Inner Mongolia, China, as the research area, based on the land-use data of five Landsat remote sensing images taken between 1980 and 2015. Then, we calculated the complex network eigenvalues, such as the average shortest path, betweenness centrality, and degree, to identify the key land-use types, stability, and ecological environment change regularity from the perspective of the land-use system. Finally, we used the Canonical Correspondence Analysis (CCA) method to explore the main driving forces behind changes in the land-use system, to provide scientific support for the study of changing trends in land-use, and regional grassland ecological management. The findings are shown below. First, in the last 35 years, grasslands have always been the major type of change in land-use transfer matrices. Grasslands play a controlling role in the whole land-use system. Second, grassland and cultivated land are the major "transfer out" type of land in the Xilingol area, while construction land and water area belong to the major "transfer in" type of land. Third, the average shortest path values of four transition matrix networks were all less than 1.5, indicating that the land-use system becomes less stable, but the average shortest path values of these four networks present an increasing trend, leading to a more stable development of the land-use system. However, on the whole, it shows an upward trend, and the land-use system is moving in a stable direction. Fourth, the average annual rainfall, population, topographic factors, GDP, and distance from settlements play a prominent role in determining the spatial distribution of change in land-use in Xilingol. [ABSTRACT FROM AUTHOR]

• This paper analyzes whether the financial support for grassland ecological protection and restoration has improved the vegetation coverage of grassland. • There is a nonlinear relationship between financial support for ecological restoration and grassland vegetation coverage. • Different fiscal expenditure structure has a heterogeneous response. • The degree of fiscal decentralization has a significant positive regulatory effect on the ecological benefits. Grassland ecological restoration is an important cornerstone to maintain the sustainable development of grassland ecosystem. One of the goals of financial support for grassland ecological protection and restoration is whether the ecological benefits of grassland are fully realized, and grassland vegetation coverage is the main index to measure the ecological benefits. This paper aims to analyze whether the financial support for grassland ecological protection and restoration has improved the vegetation coverage of grassland. and then verify whether there is room for improvement in the fiscal effect of grassland ecological protection and restoration? Based on the panel data of 54 animal husbandry and semi-agricultural and semi-animal husbandry counties in Inner Mongolia Autonomous Region of China, this paper adopts a bidirectional fixed effect model and cross-fixed effect model to introduce natural factors affecting grassland vegetation coverage into the model and systematically examines the financial effects of ecological benefits of financial support for grassland ecological protection and restoration. The main findings are as follows: (1) there is a nonlinear relationship between financial support for grassland ecological protection and restoration and grassland vegetation coverage when natural factors are taken into account in the model. (2) different fiscal expenditure structure has a heterogeneous response to the impact of grassland ecological protection and restoration on ecological benefits. (3) the degree of fiscal decentralization and the relative performance of the government have a significant positive moderating effect on the ecological benefits of fiscal support for grassland ecological protection and restoration. [ABSTRACT FROM AUTHOR]

A method for predicting the dynamic spatio-temporal variations of the normalized difference vegetation index (NDVI) based on precipitation is proposed using combined nonlinear autoregressive with exogenous input (NARX) networks and artificial neural networks (ANNs). The proposed method is validated by applying to predict the spatio-temporal NDVI for the Hulunbuir grassland located in Inner Mongolia, China. The results show the good predictive ability for the spatio-temporal variations of NDVI with the mean absolute percentage error of 11.59%, mean absolute error of 7.11 × 10−2 and root mean square error of 8.06 × 10−2, respectively. The approach presented in the paper can be further used as the guidance to reduce the occurrence of overgrazing in the arid and semi-arid grasslands. [ABSTRACT FROM AUTHOR]

Biomass is an important indicator for monitoring vegetation degradation and productivity. This study tests the applicability of Hyperspectral Remote-Sensing in situ measurements for high-precision estimation aboveground biomass (AGB) on regional scales of Khorchin grassland in Inner Mongolia, China. In order to improve prediction accuracy of AGB which is frequently used as an indicator of aboveground net primary productivity (ANPP), this paper combined ground measurement with remote sensing inversion to build the spectral model. The ground normalized difference vegetation index (SOC_NDVI) calculated from ground spectral of grassland vegetation which was measured by a portable visible/NIR hyperspectral spectrometer (SOC 710). Meanwhile, the remote normalized difference vegetation index (TM_NDVI) calculated from remote spectral of grassland vegetation which was measured by Thematic Mapper (TM) from Landsat 8 which launched by National Aeronautics and Space Administration (NASA). According to regression analysis for the relationship between AGB and SOC_NDVI, SOC_NDVI and TM_NDVI, the evaluation model for aboveground biomass was developed (AGB = 12.523×e3.370×(0.462×TM_NDVI+0.413), standard error = 24.74 g m-2, R2 = 0.636, p < 0.001). The model accuracy verification results show that the correlation between the measured value and the predicted value of biomass was better with low model standard error. The model could make up for the lack of timeliness and comprehensiveness of conventional ground biomass survey, and provide technical support for high-precision large-area productivity estimation and ecological degradation diagnosis of regional scale grassland. [ABSTRACT FROM AUTHOR]

Fault identification in vegetated area (e.g., grassland) is a major challenge compared to that in outcrop areas. To identify concealed faults in a grassland covered area, a hybrid method combining the temperature vegetation dryness index (TVDI) and the singularity index was proposed in this paper to extract TVDI anomalies associated with concealed fractures in the Sonid Left Banner Grassland, Inner Mongolia, North China. In the triangle space of LST/NDVI (land surface temperature/normalized difference vegetation index), scattered points were concentrated in the areas of partial and full cover, while few data points were in the areas of bare soil with low values of NDVI and high values of LST; this result is consistent with the semiarid grassland landscape of the study area. Although TVDI imaging shows an obvious linear pattern with an NE-NNE trend, which indicates the existence of concealed faults, the surrounding background weakened and reduced the significance of the anomalies. To better delineate the concealed faults, the singularity index was employed to remove the influence of the background and enhance the TVDI anomalies associated with the concealed faults. The TVDI imaging and singularity index mapping showed NNE and NE orientations; this finding is consistent with the regional tectonic framework. Geological mapping footprints showed that the hybrid method is useful to identify concealed faults in covered areas of grassland. [ABSTRACT FROM AUTHOR]

Aims Remote sensing technology has been proved useful in mapping grassland vegetation properties. Spectral features of vegetation cover can be recorded by optical sensors on board of different platforms. With increasing popularity of applying unmanned aerial vehicle (UAV) to mapping plant cover, the study aims to investigate the possible applications and potential issues related to mapping leaf area index (LAI) through integration of remote sensing imagery collected by multiple sensors. Methods This paper applied the collected spectral data through field-based (FLD) and UAV-borne spectroradiometer to map LAI in a Sino–German experiment pasture located in the Xilingol grassland, Inner Mongolia, China. Spectroradiometers on FLD and UAV platforms were taken to measure spectral reflectance related to the targeted vegetation properties. Based on eight vegetation indices (VIs) computed from the collected hyperspectral data, regression models were used to inverse LAI. The spectral responses between FLD and UAV platforms were compared, and the regression models relating LAI with VIs from FLD and UAV were established. The modeled LAIs by UAV and FLD platforms were analyzed in order to evaluate the feasibility of potential integration of spectra data for mapping vegetation from the two platforms. Important Findings Results indicated that the spectral reflectance between FLD and UAV showed critical gaps in the green and near-infrared regions of the spectrum over densely vegetated areas, while the gaps were small over sparsely vegetated areas. The VI values from FLD spectra were greater than their UAV-based counterparts. Out of all the VIs, broadband generalized soil-adjusted vegetation index (GESAVI) and narrow-band nNDVI2 were found to achieve the best results in terms of the accuracy of the inversed LAIs for both FLD and UAV platforms. We conclude that GESAVI and nNDVI2 are the two promising VIs for both platforms and thus preferred for LAI inversion to carry spectra integration of the two platforms. We suggest that accuracy on the LAI inversion could be improved by applying more advanced functions (e.g. non-linear) considering the observed bias for the difference between the UAV- and FLD-inversed LAIs, especially when LAI was low. [ABSTRACT FROM AUTHOR]

The forage-livestock balance is an important component of natural grassland management, and realizing a balance between the nutrient energy demand of domestic animals and the energy supply of grasslands is the core challenge in forage-livestock management. This study was performed at the Xieertala Ranch in Hulunbuir City, Inner Mongolia. Using the GRAZPLAN and GrazFeed models, we examined the forage-livestock energy balance during different grazing periods and physiological stages of livestock growth under natural grazing conditions. Data on pasture conditions, climatic factors, supplemental feeding, and livestock characteristics, were used to analyze the metabolizable energy (ME), metabolizable energy for maintenance (MEm), and total metabolizable energy intake (MEItotal) of grazing livestock. The results showed that the energy balance between forage and animals differed for adult cows at different physiological stages. In the early lactation period, although the MEItotal was greater than MEm, it did not meet the requirement for ME. MEItotal was greater than ME during midlactation, but there was still an energy imbalance in the early and late lactation periods. In the late lactation period, MEItotal could meet ME requirements from April-September. Adult gestational lactating cows with or without calves were unable to meet their ME requirement, especially in the dry period, even though MEItotal was greater than MEm. Adult cows at different physiological stages exhibited differences in daily forage intake and rumen microbial crude protein (MCP) metabolism, and the forage intake by nonpregnant cows decreased as follows: early lactation > mid-lactation > late lactation, pregnant cows' lactation > dry period. For the degradation, digestion and synthesis of rumen MCP, earlylactation cows were similar to those in the mid-lactation group, but both were higher than those in the late-lactation group, while pregnant cows had greater degradation, digestion, and synthesis of MCP in the lactation period relative to the dry period. For lactating cows, especially those with calves, grazing energy requirements, methane emission metabolism and heat production were highest in August, with increased energy expenditure in winter. Overall, grazing energy, methane emissions and heat production by dry cows were low. In the context of global climate change and grassland degradation, managers must adopt different strategies according to the physiological stages of livestock to ensure a foragelivestock balance and the sustainable utilization and development of grasslands. [ABSTRACT FROM AUTHOR]

Driven by continuously evolving precipitation shifts and temperature increases, the frequency and intensity of droughts have increased. There is an obvious need to accurately monitor drought. With the popularity of machine learning, many studies have attempted to use machine learning combined with multiple indicators to construct comprehensive drought monitoring models. This study tests four machine learning model frameworks, including random forest (RF), convolutional neural network (CNN), support vector regression (SVR), and BP neural network (BP), which were used to construct four comprehensive drought monitoring models. The accuracy and drought monitoring ability of the four models when simulating a well-documented Inner Mongolian grassland site were compared. The results show that the random forest model is the best among the four models. The R2 range of the test set is 0.44–0.79, the RMSE range is 0.44–0.72, and the fitting accuracy relationship could be described as RF > CNN > SVR ≈ BP. Correlation analysis between the fitting results of the four models and SPEI found that the correlation coefficient of RF from June to September was higher than that of the other three models, though we noted the correlation coefficient of CNN in May was slightly higher than that of RF (CNN = 0.79; RF = 0.78). Our results demonstrate that comprehensive drought monitoring indices developed from RF models are accurate, have high drought monitoring ability, and can achieve the same monitoring effect as SPEI. This study can provide new technical support for comprehensive regional drought monitoring. [ABSTRACT FROM AUTHOR]

Grasshopper disasters threaten grassland animal husbandry, and overgrazing is widely recognized as one of the main causes of locust infestation in grassland regions. However, the impact of overgrazing on grasshopper disasters remains unclear. To address this knowledge gap, this study interviewed 541 households living in locust-prone areas in Inner Mongolia, China. The generalized Poisson model and OLS regression examined the relationship between herders' production behavior and locust disasters. The results showed that 42% of the herders had suffered from locusts more than three times over the past 15 years, with an average of 49 ha of grassland damaged per household. In addition, with the increase in grazing rates, the scale of locust disasters decreased before it increased. The results also showed that operating grassland areas and feeding forage reduced locust disasters significantly, while renting grassland areas and grazing rates worked oppositely. These results suggest that grazing intensity can make a significant difference in the occurrence of locust disasters. [ABSTRACT FROM AUTHOR]

Rodents are essential to the balance of the grassland ecosystem, but their population outbreak can cause major economic and ecological damage. Rodent monitoring is crucial for its scientific management, but traditional methods heavily depend on manual labor and are difficult to be carried out on a large scale. In this study, we used UAS to collect high–resolution RGB images of steppes in Inner Mongolia, China in the spring, and used various object detection algorithms to identify the holes of Brandt’s vole (Lasiopodomys brandtii). Optimizing the model by adjusting evaluation metrics, specifically, replacing classification strategy metrics such as precision, recall, and F1 score with regression strategy-related metrics FPPI, MR, and MAPE to determine the optimal threshold parameters for IOU and confidence. Then, we mapped the distribution of vole holes in the study area using position data derived from the optimized model. Results showed that the best resolution of UAS acquisition was 0.4 cm pixel-1, and the improved labeling method improved the detection accuracy of the model. The FCOS model had the highest comprehensive evaluation, and an R² of 0.9106, RMSE of 5.5909, and MAPE of 8.27%. The final accuracy of vole hole counting in the stitched orthophoto was 90.20%. Our work has demonstrated that UAS was able to accurately estimate the population of grassland rodents at an appropriate resolution. Given that the population distribution we focus on is important for a wide variety of species, our work illustrates a general remote sensing approach for mapping and monitoring rodent damage across broad landscapes for studies of grassland ecological balance, vegetation conservation, and land management. [ABSTRACT FROM AUTHOR]

Plant spring phenology in grasslands distributed in the Northern Hemisphere is highly responsive to climate warming. The growth of plants is intricately influenced by not only air temperature but also precipitation and soil factors, both of which exhibit spatial variation. Given the critical impact of the plant growth season on the livelihood of husbandry communities in grasslands, it becomes imperative to comprehend regional-scale spatial variation in the response of plant spring phenology to climate warming and the effects of precipitation and soil factors on such variation. This understanding is beneficial for region-specific phenology predictions in husbandry communities. In this study, we analyzed the spatial pattern of the correlation coefficient between the start date of the plant growth season (SOS) and the average winter–spring air temperature (WST) of Inner Mongolia grassland from 2003 to 2019. Subsequently, we analyzed the importance of 13 precipitation and soil factors for the correlation between SOS and average WST using a random forest model and analyzed the interactive effect of the important factors on the SOS using linear mixing models (LMMs). Based on these, we established SOS models using data from pastoral areas within different types of grassland. The percentage of areas with a negative correlation between SOS and average WST in meadow and typical grasslands was higher than that in desert grasslands. Results from the random forest model highlighted the significance of snow cover days (SCD), soil organic carbon (SOC), and soil nitrogen content (SNC) as influential factors affecting the correlation between SOS and average WST. Meadow grasslands exhibited significantly higher levels of SCD, SOC, and SNC compared to typical and desert grasslands. The LMMs indicated that the interaction of grassland type and the average WST and SCD can effectively explain the variation in SOS. The multiple linear models that incorporated both average WST and SCD proved to be better than models utilizing WST or SCD alone in predicting SOS. These findings indicate that the spatial patterns of precipitation and soil factors are closely associated with the spatial variation in the response of SOS to climate warming in Inner Mongolia grassland. Moreover, the average WST and SCD, when considered jointly, can be used to predict plant spring phenology in husbandry communities. [ABSTRACT FROM AUTHOR]

Ecological stoichiometry (ES) is an important index that reflects the balance of various elements in ecological processes. Therefore, it is of great significance to understand the soil nutrient cycle to clarify the environmental control of soil carbon (C), nitrogen (N), phosphorus (P), and potassium (K). In this study, we analyzed the spatial distribution of soil C, N, P, and K contents and the C:N:P:K stoichiometric characteristics of 0–20 cm and 20–40 cm of farmland and grassland in four agro-pastoral areas in Inner Mongolia. Spearman correlation was used to analyze the effects of environmental factors on the soil C:N:P:K stoichiometric relationship. The results showed that there was no fixed Redfield ratio for the soil stoichiometric relationship of farmland and grassland in Inner Mongolia, and the values were 15:2:1:9 to 145:10:1:26 and 25:1:1:29 to 228:15:1:65, respectively. The stoichiometric relationships between farmland and grassland were consistent with the law of geographical and spatial heterogeneity. The ratios of C:N, C:P, C:K, N:P, and N:K showed an N distribution from west to east, while the ratio of P:K showed a V distribution. The stoichiometric relationships in grassland soil were mainly affected by soil organic carbon and total nitrogen content, while those in farmland were mainly affected by total nitrogen and total phosphorus content. The annual mean precipitation has a significant effect on stoichiometric relationships in farmland, while the annual mean temperature has a more significant effect on grassland. In conclusion, the spatial distribution difference in the soil stoichiometric relationship in the agro-pastoral ecotone of Inner Mongolia was more significant than the difference in the land use pattern. The influences of annual mean temperature and annual mean precipitation on soil ecological stoichiometry were in accordance with the geographical spatial similarity law. Compared with grassland, the stoichiometric relationship of farmland soil was greatly affected by fertilization, and farmland in this region was mainly limited by carbon and nitrogen. Thus, field management should be carried out according to local conditions. This study is of great significance as it promotes the rational utilization of land resources and the sustainable development of agriculture. [ABSTRACT FROM AUTHOR]

The Payments for Ecosystem Services (PES) program is an innovative approach that provides economic incentives directly to natural resource exploiters in order to shape their behavior. Although the implementation of PES programs and the context in which these programs are implemented are often different across space, the spatial heterogeneities in the impacts of PES programs are often neglected in studies. In this study, we demonstrate the spatial and temporal dynamics of the Normalized Differential Vegetation Index (NDVI) in the grassland where China's Grassland Ecological Compensation Program (GECP) has been implemented, and we evaluate the impacts of the GECP on the NDVI. We found that most of the grassland in the GECP area showed small changes in NDVI between 2000 and 2010. On average, the GECP only had a marginal positive effect on the NDVI of grassland. Although the magnitude of impacts of the GECP was relatively small in most places where the program is implemented, we detected substantial heterogeneities in the impacts of the GECP on the NDVI. The impact of the GECP on the NDVI differed substantially, particularly between Inner Mongolia, Sichuan, and other provinces. Our findings suggest that there can be substantial heterogeneities in the impacts of PES programs across space, which can be leveraged to promote the efficacy of the GECP and many other PES programs around the world. [ABSTRACT FROM AUTHOR]

Nitrogen (N) deposition exhibits significant impacts on ecosystem functions and processes. Previous studies have indicated that N addition has an impact on the stoichiometry of plant leaf C:N:P ratios. However, few studies have focused on effects of N addition on belowground systems. This study aims to examine the impact of 7 years of N addition on above- and belowground C:N:P stoichiometry at plant community level in a temperate grassland located in Inner Mongolia. A 7-year field N addition experiment was conducted, which included six treatments: Cont: control; N1: 0.4 mol·m−2 N; N2: 0.8 mol·m−2 N; N3: 1.6 mol·m−2 N; N4: 2.8 mol·m−2 N; N5: 4 mol·m−2 N with six replicates. Above- and belowground plant biomass and C:N:P stoichiometry were measured and analyzed. Our results showed that N addition resulted in a reduction of aboveground C concentration, but an increase in aboveground N and P concentrations, with a decrease in C:N and C:P ratios and an increase in N:P ratio. Furthermore, the aboveground C, N, and P pools all exhibited an increase as a result of N addition. However, N addition did not have any significant effect on belowground C, N, P concentrations, ratios, pools, or stoichiometric characteristics in the soil layers of 0–10, 10–30, 30–50, and 50–100 cm. These results suggest that increasing levels of N deposition significantly alter the aboveground C:N:P stoichiometry at the plant community level, which may affect functions and processes in the grassland ecosystem, but have little effect on belowground C:N:P stoichiometry. [ABSTRACT FROM AUTHOR]

Grassland has great potential for carbon sequestration; however, the relationship between carbon storage (CS) and greenhouse gas (GHG) budget and their influencing factors in the natural restoration process in grassland mining areas are rarely studied. In this study, taking Zhalainuoer mining area in Inner Mongolia as an example, the subsidence soil for 1-, 2-, 5-, 10-, and 15-year and non-subsidence soil were selected as the research objects to explore the relationship between CS and the GHG budget and their influencing factors. The results show that there is a significant negative correlation between CS and the GHG budget. Soil organic carbon storage accounts for 99% of CS. CS is positively correlated with SOM and AP, and with the bacteria Entotheonellaeota. The GHG budget is mainly affected by CO2 emission, which is positively correlated with subsidence time, plant biomass, and coverage, negatively correlated with the bacteria Actinobacteriota and Deinococcota, and positively correlated with Cyanobacteria. In summary, soil plays a major role in storing carbon. Carbon sequestration is a physiological process produced by plants and organisms. Subsidence affects soil CS by changing soil properties and thus affecting its aboveground vegetation and soil microorganisms. This study investigates the changes in soil carbon storage following subsidence caused by mining activities. The findings contribute to our understanding of the impact of mining subsidence on soil CS and can inform the development of low-carbon remediation technologies. [ABSTRACT FROM AUTHOR]

Context: Forest–grassland ecotones, the transitional zones between forests and grasslands, often harbor high levels of biodiversity. According to the portfolio theory, communities with such high biodiversity should exhibit high stability in ecosystem functioning. On the contrary, the theory of 'bi-stability' or 'multistability' predicts that ecosystems exhibiting multiple states or regimes (e.g. forest and grassland) are less stable. Understanding the stability of forest–grassland ecotone has broad implications for forest management and restoration. Objectives: In this study, by quantifying landscape heterogeneity and temporal invariability of regional climatic and vegetational metrics, we aimed to reveal how landscape diversity influences the stability of ecosystem functioning in the forest–grassland ecotone. Methods: We focused on the forest–grassland ecotone in northern China (i.e. from the forest belt in the Greater Khingan Mountains to the steppe grassland in Inner Mongolia) and used remote sensing data of land cover dynamics from 1992 to 2015 to analyze the landscape diversity and ecosystem dynamics at different grain sizes. To measure the temporal stability of climatic variables and vegetational dynamics, we calculated the interannual variation of temperature (T), wind speed (WS), precipitation (P), and the net primary productivity (NPP) on the grided sampling cells at different spatial scale over the same period. Results: Our results showed that landscape diversity across this forest–grassland ecotone was positively related to the stability of T and NPP, but negatively related to that of WS and P. These opposite patterns may be explained by the differential nature of ecosystem variables, namely whether they involve transporting substance across space (WS and P) or not (T and NPP). We also found that as the spatial scale increases, the diversity-stability relationships strengthened for all three climatic variables (i.e., T, P, WS). However, the stabilizing effects of landscape diversity on NPP first increased but then decreased with increasing grain size, peaking at ~ 30 km. Conclusions: Our empirical analyses demonstrate strong, but context-dependent diversity-stability relationships, shedding light on the paradox in the stability of ecosystem functioning in the ecotone. These findings also have implications for the management of landscape resources to maintain the sustainability of forest–grassland ecotones. [ABSTRACT FROM AUTHOR]

Wildfires spread rapidly and cause considerable ecological and socioeconomic losses. Inner Mongolia is among the regions in China that suffer the most from wildfires. A simple, effective model that uses fewer parameters to simulate wildfire spread is crucial for rapid decision-making. This study presents a region-specific technological process that requires a few meteorological parameters and limited grassland vegetation data to predict fire spreading dynamics in Inner Mongolia, based on cellular automata that emphasize the numeric evaluation of both heat sinks and sources. The proposed method considers a case that occurred in 2021 near the East Ujimqin Banner border between China and Mongolia. Three hypothetical grassland wildfires were developed using GIS technology to test and demonstrate the proposed model. The simulation results suggest that the model agrees well with real-world experience and can facilitate real-time decision-making to enhance the effectiveness of firefighting, fire control, and simulation-based training for firefighters. [ABSTRACT FROM AUTHOR]

Biodiversity is sensitive to climate change and human activity. Grazing management practices have a profound impact on plant species–genetic diversity in grassland and woodland communities. In this study, we explored the responses of species and genetic diversity to grazing in Ulmus pumila L. communities in the Hunshandak Sandy Land, analyzed the relationship between species and genetic diversity, and revealed the effects of climate factors on them. We found that the dominant species were Spiraea trilobata, Caragana microphylla and Artemisia intramongolica in U. pumila communities. Plant species richness in the banned grazing (BG) and seasonal grazing (SG) communities was significantly higher than that in the delayed grazing (DG) community. Plant Simpson's diversity index showed a downward trend with increasing grazing duration. There was no difference in allelic richness in nuclear DNA (nrDNA) of U (U. pumila) and chloroplast DNA (cpDNA) of NU (other dominant species besides U. pumila) among grazing management types. The expected heterozygosity of U in nrDNA and cpDNA was significantly affected by grazing management, and the trend was BG > SG > DG. The genetic diversity of U was lower than that of NU. The genetic diversity characteristics of U in cpDNA were lower than those in nrDNA. The analysis of molecular variance (AMOVA) showed that 98.08% of the variation in U and 95.25% of the variation in NU was attributed within populations and the differences within grazing management types were 13.35% in U and 24.08% in NU (p < 0.001). The species richness of communities was positively correlated with the genetic diversity of U, NU and all dominant species (U + NU) in communities. The nineteen climatic variables together explained 94.24% and 79.08% of the total variation in U and NU genetic and species diversity. The mean temperature of the warmest quarter and temperature seasonality were the main factors affecting genetic diversity (p = 0.046; 0.01), while the maximum temperature of the warmest month was the main factor affecting species diversity (p = 0.05). [ABSTRACT FROM AUTHOR]

Quantifying the ecological carrying capacity has emerged as a crucial factor for maintaining ecosystem stability for sustainable development in vulnerable eco-regions. Here, we propose a new framework for ecological carrying capacity quantification suitable for vulnerable eco-regions. We applied this framework to calculate the ecological carrying capacity of Inner Mongolia from 1987–2015 and used a geographical detector to identify the driving factors behind spatial heterogeneity. Our results revealed the following. (1) The above-ground net primary production (ANPP) required to support the ecosystem service of soil conservation (ANPPSC) decreased from northeast to southwest, whereas the distribution pattern of ANPP required to support the ecosystem service of sand fixation (ANPPSF) exhibited a contrary trend. The average annual ANPP required to support the ecosystem service of natural regeneration (ANPPNR) in Inner Mongolia from 1987 to 2015 was 101.27 gCm-2year-1, revealing a similar spatial distribution with ANPP. (2) The total ecological carrying capacity of Inner Mongolian grassland was 78.52 million sheep unit hm-2. The regions with insufficient provisioning service capability accounted for 4.18% of the total area, primarily concentrated in the east and northwest. (3) The average optimal livestock number for grasslands in Inner Mongolia was 1.59 sheep unit hm-2 from 1987–2015, ranging from 0.77 to 1.69 sheep unit hm-2 across different zones. The average ecological carrying capacity of the cold temperate humid, medium-temperate arid, and warm temperate semi-humid regions was less than 1.08 sheep unit m-2, suggesting a need to prohibit grazing in these areas. (4) The primary influencing factors affecting ecological carrying capacity distribution were normalized difference vegetation index (NDVI), precipitation, and soil type. The framework developed herein can help identify sustainable development potential from the ecosystem service perspective and effectively contribute to decision-making in grassland ecosystem management. [ABSTRACT FROM AUTHOR]

The agro-pastoral ecotone serves as an important ecological transition zone between grassland and cropland. It plays a crucial role in providing forage and maintaining the ecological security of Northern China. China has initiated many ecological restoration projects in the agro-pastoral ecotone. Separating the affecting factors of anthropogenic activities and natural factors change on grassland may help guide future ecological restoration projects. This study investigated the grassland change over the agro-pastoral ecotone in Inner Mongolia (APEI) using land use data, vegetation index, and monthly climate data, social and economic data from 2010 to 2020. Two indicators were introduced to evaluate the grassland change in quality and quantity. The average annual maximum NDVI of grassland within each sample was used as a proxy for grassland quality, and the proportion of grassland within each sample was used as a proxy for grassland quantity. The driving mechanisms of grassland quality were examined on different types of grassland. The results showed that (1) from 2010 to 2020, the quantity of grassland in APEI slightly decreased while the quality of the grassland significantly improved; (2) the change in grassland quality was jointly caused by natural factors and human activities, with 66.77% being attributed to natural factors and 33.23% to human activities. In some areas, the alteration in grassland quality was solely determined by either the impacts of natural factors or human-induced disturbances; and (3) temperature and precipitation played different roles in affecting grassland quality among different types of grassland. In some areas, anthropogenic activities, such as farmland and population, would affect grassland quality. The findings of this study would contribute to the understanding of the mechanism of ecological environmental changes in the agro-pastoral ecotone and guide the planning and implementation of future ecological restoration projects in agro-pastoral areas. [ABSTRACT FROM AUTHOR]

Techniques for mapping and monitoring vegetation status under human disturbance (e.g., grazing livestock) can contribute to sustainable development of grasslands. The main objective of this paper is to investigate whether the effects of differences in grazing intensity (measured by stocking rate (SR)) could be discriminated using in situ spectral data. The experiment was conducted with controlled SRs on the Sino-German experimental farm in the Xilingol grassland, Inner Mongolia, P.R. China. Canopy spectral measurements were made on experimental paddocks that varied by SRs, terrain and management type. Eight vegetation indices (VIs) were calculated from ASD field spectrometer data for each paddock. We modelled the variability within and between paddocks using a linear mixed model to isolate the effect of grazing intensity on VI response. All VIs showed significant negative relationships to grazing intensity that were ameliorated on paddocks with haying compared to traditional direct grazing. The best VIs for detecting differences in paddocks due to the levels of SR were photochemical reflectance index (PRI), broadband normalized difference vegetation index (bNDVI) and narrowband NDVI (nNDVI1). The responses of VIs to vegetation conditions as affected by different SRs offer promise for the use of airborne and satellite remote sensing to map vegetation status and to evaluate the effects of grazing intensities at regional scales. [ABSTRACT FROM AUTHOR]

Validation of Moderate-Resolution Imaging Spectroradiometer (MODIS) land surface reflectance products is important to effective utilization of such products for earth systems science. Ground-based measurements are normally utilized for such validation. However, the major scale mismatch between the ground ‘point’ measurement and MODIS resolution (500 m and 1 km) makes direct comparison infeasible over many land surface types. In this paper, an indirect comparison between ground ‘point’ measurements and MODIS land surface products via high-resolution remotely sensed imagery (Landsat Thematic Mapper/TM) was utilized in semi-arid grassland of Inner Mongolia in summer 2005, where ground measurements are relatively sparse in comparison with other locations around the world. Within the validation, the TM reflectance imagery was first calibrated by the ground ‘point’ measurements, and then aggregated to MODIS data resolution for determination of their accuracy. Besides common direct spectral band comparison of reflectance between TM and MODIS, empirical/indirect comparison between TM and MODIS was also implemented. Both types of validation showed that the absolute error of bidirectional reflectance from atmospheric correction (MOD09) is less than 9.4%, and for nadir bidirectional reflectance distribution function (BRDF)-adjusted reflectance (MOD43B4) it is less than 3.1%, in which the error of visible bands of two data sets is less than 1.35% and 0.95%, respectively. This validation will help improve the accuracy of MODIS products used in this area. [ABSTRACT FROM AUTHOR]

Assessment of the nitrogen (N) balance and its long-term trend is necessary for management practices because of the negative environmental effects caused by an imbalance of reactive N in grassland ecosystems. In this study, we designed a module for the IAP-N (Improving Anthropogenic Practices of managing reactive Nitrogen) model to enable it to assess the N budget of regional grasslands. The module was developed to quantify the individual components of the N inputs and outputs for grassland ecosystems using livestock and human populations, grassland area, and fossil-energy consumption data as the model inputs. In this paper, the estimation approaches for individual components of N budget, data acquisition, and parameter selection are described in detail. The model was applied to assess the N budget of Inner Mongolia in 2006 at the county scale. The simulation results show that the most important pathway of N outputs from the grassland was livestock intake. The N output from livestock intake was especially large in the middle of Inner Mongolia. Biological fixation, atmospheric deposition, and livestock excreta deposition were comparably important for the N inputs into the grassland. The N budget for Inner Mongolia grassland in 2006 was −1.7×10±0.6×10 kg. The case study for Inner Mongolia shows that the new grassland module for the IAP-N model can capture the characteristics of the N budget in a semiarid grassland. [ABSTRACT FROM AUTHOR]

Estimating the carbon (C), nitrogen (N), and phosphorus (P) contents of a large-span grassland transect is essential for evaluating ecosystem functioning and monitoring biogeochemical cycles. However, the field measurements are scattered, such that they cannot indicate the continuous gradient change in the grassland transect. Although remote sensing methods have been applied for the estimation of nutrient elements at the local scale in recent years, few studies have considered the effective estimation of C, N, and P contents over large-span grassland transects with complex environment including a variety of grassland types (i.e., meadow, typical grassland, and desert grassland). In this paper, an information enhancement algorithm (involving spectral enhancement, regional enhancement, and feature enhancement) is used to extract the weak information related to C, N, and P. First, the spectral simulation algorithm is used to enhance the spectral information of Sentinel-2 imagery. Then, the enhanced spectra and meteorological data are fused to express regional characteristics and the fractional differential (FD) algorithm is used to extract sensitive spectral features related to C, N, and P, in order to construct a partial least-squares regression (PLSR) model. Finally, the C, N, and P contents are estimated over a West–East grassland transect in Inner Mongolia, China. The results demonstrate that: (i) the contents of C, N, and P in large-span transects can be effectively estimated through use of the information enhancement method involving spectral enhancement, regional feature enhancement, and information enhancement, for which the estimation accuracies (R2) were 0.88, 0.78, and 0.85, respectively. Compared with the estimation results of raw Sentinel-2 imagery, the RMSE was reduced by 3.42 g/m2, 0.14 g/m2, and 13.73 mg/m2, respectively; and (ii) the continuous change trend and spatial distribution characteristics of C, N, and P contents in the west–east transect of the Inner Mongolia Plateau were obtained, which showed decreasing trends in C, N, and P contents from east to west and the characteristics of meadow > typical grassland > desert grassland. Thus, the information enhancement algorithm can help to improve estimates of C, N, and P contents when considering large-span grassland transects. [ABSTRACT FROM AUTHOR]

Community ecologists and vegetation scientists in grassland research have a strong interest in quantifying biotic communities in detail. However, a satisfactory classification with fine biotic details has been challenged by the coarse resolutions of Landsat images, although they are easily accessible. In this paper, a hybrid fuzzy classifier (HFC) for vegetation classification with Landsat ETM+ imagery on the typical grassland in Xilinhe River Basin, Inner Mongolia, China has been developed. Three vegetation classification systems were created from different aspects: the botanical system (Bio-classes, also as the final mapping units for vegetation cover), the combined botanical and spectral system (Bio-S classes), and the spectral system (Spec-classes). The HFC designed a fuzzy logic to measure the similarity between Spec-classes, extracted by the unsupervised classification, and Bio-S classes, built from the field samples, when considering the spectral variations of samples within the same Bio-class. Then, Bio-S classes, which served as a bridge for assigning Spec-classes to the target Bio-classes, were merged to restore Bio-classes for the final mapping. To assess the classification accuracy, the HFC was compared with a conventional supervised classification (CSC). The overall result of the HFC was much better than that of the CSC, with an accuracy percentage of 80.2% as compared to 69.0% for the CSC. [ABSTRACT FROM AUTHOR]

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 is one of the largest terrestrial ecosystems and contains approximately 20 percent of the world's soil organic carbon (SOC) stock. A relatively small SOC change can cause large impacts on the global climate. However, the contributions from climatic factors to SOC changes, relative to other natural and anthropogenic factors, remains controversial. Here, we evaluate the relative contributions of climate, landscape, and management factors to SOC variabilities using variance decomposition coupled with generalized additive models and resampled soil data from the original Second National Soil Survey profile locations across the temperate grasslands in northern Inner Mongolia in 2022. Our results indicate that climate contributions increased from 13.7% in the 1980s to 65.5% in 2022, compared to decreased contributions from landscape and management factors. The relative contributions from landscape and management factors decreased from 37.5% and 48.8% in the 1980s, respectively, to 19.2% and 15.4% in 2022. This shows that the climate has shifted from being a minor contributor to a primary controller of grassland SOC variability over the 40 years since the 1980s. We, therefore, argue that future grassland management and policy regimes should become climate-centric, while the current institutional momentum for grassland conservation and restoration should be maintained. [ABSTRACT FROM AUTHOR]

Background and aims: Grasslands hold one of the most important soil carbon stocks in the world, which is vulnerable to climate change (i.e. precipitation) and human disturbance (i.e. land-use). This study aimed to investigate responses and mechanisms of soil organic carbon (SOC) decomposition and accumulation to precipitation and land-use in an Inner Mongolian grassland. Methods: Using a randomized complete block design with a split plot, an experiment with land-use regimes (fencing, grazing, and mowing, since 2011) and altered precipitation amount (wet, + 50% precipitation; CT, ambient precipitation; dry, −50% precipitation; since 2016) was conducted to explore their impacts on SOC decomposition (represented by soil heterotrophic respiration and extracellular enzyme activities) and accumulation (represented by SOC and its physical fractions) from samples collected in 2019. Results: SOC decomposition significantly increased under wet treatment, but decreased under dry treatment. Wet treatment increased SOC accumulation via the increment of mineral-associated organic carbon (MAOC), and vice versa for dry treatment. Precipitation amount may affect soil microbial biomass and activities via alterations of water supply, plant-derived carbon input, and other soil properties, leading to changes of SOC dynamics. Nevertheless, land-use regimes had little influences on SOC dynamics. Conclusions: Compared to land-use regimes, precipitation treatments can significantly change SOC dynamics. Overall, SOC increased under higher precipitation amount, but decreased with less precipitation. We emphasize that the SOC stock in Inner Mongolia temperate grassland may have an unexpectable fast response to precipitation alteration, but more investigation is still needed in longer terms. [ABSTRACT FROM AUTHOR]

Aims: The anthropogenic nitrogen (N) input has considerable consequences on soil microbial biomass, which is critical for biogeochemical cycling. As prevalent grassland management, mowing may reduce soil N storage, enhance plant biodiversity and soil microbial carbon (C) availability, all of which are important regulators of soil microbial biomass. However, convincing data is still scarce about how mowing affects soil microbial biomass under N enrichment. Methods: The interactive effects of N addition (0 – 50 g N m−2 yr−1) and mowing (unmown vs. mown) on soil microbial biomass C (MBC) were measured by manipulating 6 years' N addition experiment in Inner Mongolia grassland of China. Results: Mowing increased soil inorganic N concentration, available Cu2+ concentration, plant aboveground net primary production (ANPP), species richness, Shannon-Wiener biodiversity and ratio of fungal to bacterial biomass, and decreased soil available Mn2+ concentration under N enrichment. Mowing also significantly reduced the dominance of Leymus chinensis. While mowing did not affect the soil MBC compared with that in only N added plots. The soil MBC was positively regulated by plant species richness and biodiversity, while was negatively regulated by ANPP, soil inorganic N, available Cu2+ and Mn2+ concentration in mown plots. Conclusions: The results highlight that mowing cannot mitigate the negative effects of N enrichment on soil MBC. The soil, plant and microbial properties play important roles in regulating the response of soil MBC to mowing in N-enriched soil. This work improves the mechanistic understanding of the linkages between plant community and soil microbial C cycling. [ABSTRACT FROM AUTHOR]

Purpose: Mowing is an important management practice in the Inner Mongolia grassland and can significantly regulate plant and soil microbial communities. However, impact of long-term mowing on soil microbiota has been rarely evaluated in grassland ecosystems, and the effects of plant community on belowground microbiota under mowing were mainly focused on plant α-diversity, while the role of plant β-diversity is largely unknown. Methods: We examined the soil bacterial, fungal and protistan communities under the influence of a 17-year mowing treatment in a natural grassland of Inner Mongolia, and studied the relationship between the soil microbial communities and plant/soil parameters, and the correlations between β-diversity of the soil microbial and plant communities. We also constructed co-occurrence networks to investigate the legacy of mowing on the interactions between plant community and the three microbial groups (bacteria, fungi, protists). Results: We found that long-term mowing had significant effects on the community structures of plants and the three microbial groups. The microbial community structures mainly correlated to the plant parameters including aboveground biomass, plant litter, root carbon-to-nitrogen ratio, and plant Shannon index rather than to soil properties. Moreover, β-diversity of the soil microbial communities was significantly regulated by plant community β-diversity. Furthermore, mowing enhanced plant-microbe interactions in the networks under long-term mowing. Conclusions: Long-term mowing significantly alters plant and soil microbial community compositions and can reinforce their connections. The induced shifts of plant community attributes by long-term mowing rather than soil properties may be more effective factors in shaping soil microbial communities in grasslands. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) is a key indicator of vegetation structure and function, and its products have a wide range of applications in vegetation condition assessment and usually act as important input parameters for ecosystem modeling. Grassland plays an important role in regional climate change and the global carbon cycle and numerous studies have focused on the product-based analysis of grassland vegetation changes. However, the performance of various LAI products and their discrepancies across different grassland types in drylands remain unclear. Therefore, it is critical to assess these products prior to application. We evaluated the accuracy of four commonly used LAI products (GEOV2, GLASS, GLOBMAP, and MODIS) using LAI reference maps based on both bridging and cross-validation approaches. Under different grassland types, the GLASS LAI performed better in meadow steppe (R2 = 0.26, RMSE = 0.41 m2/m2) and typical steppe (R2 = 0.32, RMSE = 0.38 m2/m2); the GEOV2 LAI performed better in desert steppe (R2 = 0.39, RMSE = 0.30 m2/m2). When we assessed their spatial and temporal discrepancies during the period from 2010 to 2019, the four LAI products overall showed a high spatial and temporal consistency across the region. Compared with GLASS LAI, the most consistent to least consistent correlations can be ordered by GEOV2 LAI (R2 = 0.94), MODIS LAI (R2 = 0.92), and GLOBMAP LAI (R2 = 0.87). The largest differences in LAI throughout the year occurred in July for all grassland types. Limited by the location and number of sample plots, we mainly focused on spatial and temporal variations. The spatial heterogeneity of land surface is pervasive, especially in vast grassland areas with rich grassland types, and the results of this study can provide a basis for the application of the product in different grassland types. Furthermore, it is essential to develop highly accurate and reliable satellite-based LAI products focused on grassland from the regional to the global scale according to these popular approaches, which is the next step in our work plan. [ABSTRACT FROM AUTHOR]

As a common grassland management practice in many high‐latitude regions worldwide, mowing has great impacts on grassland functioning and stability. Species richness, species asynchrony and species stability have been suggested as central in responses to environmental change. Mowing can evoke plant defence system due to physical damages to plants. However, no studies have comprehensively evaluated the role of plant defence functional traits, species richness, species asynchrony and stability in ecosystem functioning under mowing regimes across timescales.In this study, we set up short‐term (4 years) and long‐term (16 years) mowing experiments with three stubble heights (control, 10 cm, 2 cm) in a temperate steppe of Inner Mongolia. We investigated the effects of mowing‐induced changes in distribution metrics associated with plant defence traits, that is mean, variance, skewness and kurtosis of trait distribution, on ecosystem stability of grassland communities using structural equation modelling.We found that grassland ecosystem stability was enhanced by increasing mowing duration and decreasing stubble height. Mowing‐induced increase in abundance and diversity of plant defence traits contributed to greater ecosystem stability by enhancing species asynchrony and population stability. Moreover, we found that mowing enhanced the abundance and diversity of plant defence traits of dominant species and contributed to population stability and species asynchrony, thus enhancing temporal stability of grassland ecosystems.These results demonstrate the important roles of plant defence traits in maintaining stability of grasslands under mowing, and highlight that, in addition to species richness, asynchrony and population stability, plant functional defence trait acts in stabilizing ecosystem functions under human‐induced environmental changes. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

Collaborative governance is a promising pattern for grassland governance because the failure of single-subject governance has gradually increased in recent years. To conduct better collaborative governance, it is necessary to specify the roles of the factors that influence grassland collaborative governance. However, there are still few studies that focus on the influencing mechanisms for grassland collaborative governance. To systematically explore the influencing mechanisms, a case study was conducted in 4 banners and counties of Inner Mongolia based on questionnaire surveys and the structural equation model (SEM). The results show that the following three factors all have a positive influence on the collaboration degree (CD): the participation degree of collaborative participants (PDCP), the effectiveness of collaborative leadership (ECL) and the trust degree (TD), and their effects vary. Additionally, TD has a positive mediation effect on the influential paths of PDCP to CD and ECL to CD. Moreover, multigroup analysis shows that both gender and income have significant moderating effects. These findings not only offer a theoretical foundation for the further exploration of the mechanism of collaborative governance but also provide a practical reference for the implementation of collaborative governance in grassland governance and other fields in China and other regions of the world. [ABSTRACT FROM AUTHOR]

Dalinor Lake, the second-largest endorheic salt lake in Inner Mongolia, has shown a shrinking trend given the lack of a significant decrease in precipitation (PRE). Based on high-spatial-resolution datasets, we employed a linear regression model, Theil–Sen median trend analysis, the Mann–Kendall test, and a land use transfer matrix to identify the spatio-temporal distribution and trends of PRE and actual evapotranspiration (AET) at the watershed scale during 2001–2019; then, the water deficit (WD) caused by land use changes in different surface lithology zones was analyzed. The results showed that the annual PRE and WD of the Dalinor Lake watershed showed insignificant upward trends, while the annual AET showed a significant upward trend. Spatially, about 89% of the watershed showed a significant upward trend for AET, while 12% showed a weak significant upward trend for PRE. The WDs of the aeolian sand zone and the sand, gravel, and silt accumulation zone were most heavily affected by the new increased land use from 2001 to 2019, accounting for 43.14% and 25.56% of the total WD of the watershed, respectively. Specifically, the WD of the aeolian sand zone caused by the new increased grassland and farmland in 2019 accounted for 41.92% and 18.52% of the total WD of the zone, respectively. The WD of the sand, gravel, and silt accumulation zone caused by the new increased grassland and farmland in 2019 accounted for 37.07% and 35.59% of the total WD of the zone, respectively. The WD caused by the new increased land use was increased by 7.78 million m3 in 2019 compared with the corresponding land use type in 2001, which would decrease the water yield. It is necessary to strengthen the protection of regional forest ecosystems in the granite and terrigenous clastic rock zone; standardize pasture management and reduce farmland reclamation in the sand, gravel, and silt accumulation zone, the aeolian sand zone, and the basalt platform zone; and reduce unnecessary impervious land construction in the aeolian sand zone. [ABSTRACT FROM AUTHOR]

Background: Nitrogen- and water-use efficiency (NUE and WUE) reflect the capacity of plants to take up and utilize resources in the environments. Although N deposition and drought are known to affect plant growth and persistence, it remains elusive how plants adjust NUE, WUE and their relationship to adapt to the concurrent N deposition and drought under the context of global change. We conducted a field experiment in a temperate grassland in Inner Mongolia, in which N addition (10 g m–2 yr−1), drought (reduced 66% precipitation during growing season) and their combination were manipulated, and we explored the responses of both instantaneous (NUEINST and WUEINST) and long-term (NUELT and WUELT) WUE and NUE for six common plant species (Leymus chinensis, Stipa baicalensis, Thermopsis lanceolata, Potentilla bifurca, Thalictrum squarrosum, Klasea centauroide). Results: Across all the six species, NUEINST decreased and WUEINST increased with both N addition and drought; NUELT decreased with both N addition and drought, while WUELT decreased with N addition and increased with drought. A significant interactive effect between N addition and drought was detected, in that, drought weakened the negative effect of N addition on WUELT and NUELT, and N addition weakened the negative effect of drought on NUELT, but enhanced the positive effect of drought on WUELT. As a consequence, drought caused a trade-off between NUE and WUE, and N addition brought forth positive correlation between NUE and WUE. Meanwhile, the relationship between NUE and WUE varied among the species of different functional groups. Conclusions: Our study demonstrated that NUE, WUE and their relationship play an important role in determining their responses and adaptability to changes in soil nitrogen and water availability induced by N deposition and drought. Our results shed light on the antagonistic, synergetic and neutral effects between nitrogen deposition and drought on resource use efficiency, and further clarified the different adaptability among species of different functional groups, which will conduce to a comprehensive understanding on the role of NUE and WUE in plant persistence and coexistence in grassland ecosystems. [ABSTRACT FROM AUTHOR]

Ecological protection and high-quality development of the Yellow River Basin (YRB), China, aroused remarkable concerns from China's Central Government, and has been a major national strategy. The Inner Mongolia reach of the Yellow River Basin (IM-YRB) is a typical dryland with pervasive vegetation restoration through the actions of the ecological projects that have been conducted in recent years. However, how climate changes and human activities, such as land use and land cover (LULC) changes, jointly impact vegetation variations in this region remains poorly understood. Here, using an explainable machine learning technique, we evaluated linkages between the kernel normalized difference vegetation index (kNDVI) and air temperature, precipitation, soil moisture, and LULC changes, and relevant marginal contributions of these four drivers to the observed vegetation changes. The grassland fraction on a pixel level was selected as the quantitative LULC variable due to its key role in regional LULC. We found that interannual kNDVI changes in most areas of this study region were negatively sensitive to temperature, but positively sensitive to precipitation and soil moisture, with area fractions of 71.74%, 96.93%, and 89.33%, respectively. The area fraction of negative kNDVI sensitivity to LULC was roughly equivalent to that of positive kNDVI sensitivity. The contributions of air temperature, precipitation, soil moisture, and LULC to overall kNDVI changes were 21.54%, 33.32%, 32.19%, and 12.95%, respectively. Moisture conditions also play a critical role in vegetation changes, which was reflected by the fluctuating growth of kNDVI as interannual changes in precipitation. Nonetheless, kNDVI changes are also affected by LULC, and LULC became the dominant factor behind the kNDVI anomalies over the grassland restoration regions from barren over the IM-YRB. This research provides theoretical support for dryland vegetation restoration under the influence of climate change. [ABSTRACT FROM AUTHOR]

Based on the NDVI data of vegetation in Inner Mongolia from 1982 to 2015, the variational mode decomposition (VMD) method, which has been well applied in the field of signal decomposition, is introduced to study the periodicity of vegetation index in Inner Mongolia. The VMD method is used to extract the monthly and annual NDVI and the long time series cycle characteristics of temperature and precipitation in the same period from April 1982 to October 2015 in Inner Mongolia. The results show that temperature and precipitation are important factors affecting the growth of vegetation, and there are 6.99 and 3.49 months of the same oscillation cycle for monthly NDVI and temperature and precipitation time series; when the central frequency is the same, the amplitude of the monthly temperature and precipitation time series increases with the increase of the lag period. The annual scale NDVI has the same period of 16.95, 6.8a, and 4.85a with precipitation, and the same period of 6.8a and 4.85a with temperature. The Residue component shows that the overall NDVI and temperature in Inner Mongolia have shown a significant slow growth trend in the past 30 years. Although the precipitation has shown a significant slow decline trend in the same time period (p = 0.000), the grassland is still in the process of continuous improvement. [ABSTRACT FROM AUTHOR]

The quantification of grassland above-ground biomass (AGB) and grazing intensity (GI) and their distribution in space is of great significance to grassland management and eco-conservation. Remote-sensing technology is widely applied, but it is difficult to measure accurately when monitoring GI. In this study, the neural network, random forest and statistical function models of the relationship between Landsat NDVI and AGB were constructed by field survey and literature data collection in Inner Mongolia grassland, China. By comparing the accuracy among the three models, we constructed a remote-sensing retrieving model of grass AGB. We also estimated the grassland AGB during the peak growing season (August) for Inner Mongolia. Frequency histograms were then made to identify AGB thresholds under four GI levels (light or ungrazed, moderate grazing, overgrazing and extreme grazing) for each of three grassland types (meadow steppe, typical steppe and desert steppe). This study shows that the random forest model simulates grass AGB more accurately than other models. The spatial distribution of AGB in Inner Mongolia grasslands showed a tendency of decreasing from southeast to northwest, with an increasing trend in the last 10 years. The four GI levels in 2021 accounted for 18%, 25%, 36% and 21% of the grasslands in Inner Mongolia, respectively, and over the last 10 years the GI first improved and then deteriorated. This study provides a guideline to remote monitoring for grassland AGB and GI, and supplies scientific support for sustainable management and grassland restoration of large-scale grasslands. [ABSTRACT FROM AUTHOR]