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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]

The grassland ecological compensation policy (GECP) is currently the largest grassland ecosystem payment program in the world, trying to manage and restore degraded grasslands to achieve a virtuous cycle of grassland ecosystems. However, responses to the policy may vary across different regions. Based on panel data from 395 counties in six provinces in China's pastoral areas from 2001 to 2021, this paper uses the difference-in-difference (DID) and moderation effect models to test the relationship between GECP and grassland quality from the perspective of grassland ecosystem vulnerability. The study found the following: (1) The spatial differentiation pattern of grassland ecosystem vulnerability in the six provinces of China's pastoral areas is obvious, and the vulnerability is mainly slight and moderate (2) The GECP generally has a significant positive impact on grassland quality. (3) Grassland ecosystem vulnerability has a negative regulatory effect on the impact of the GECP on grassland quality. The positive impacts of policies become more pronounced as the vulnerability of grassland ecosystems decreases. [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]

Land-use–cover change (LUCC)/vegetation cover plays a critical role in Earth system science and is a reflection of human activities and environmental changes. LUCC will affect the structure and function of ecosystems and a series of other terrestrial surface processes, such as energy exchange, water circulation, biogeochemical circulation, and vegetation productivity. Therefore, accurate LUCC mapping and vegetation cover monitoring are the bases for simulating the global carbon and hydrological cycles, studying the interactions of the land surface and climate, and assessing land degradation. Based on field GPS surveys and UAV data, with cloud-free and snow/glacier algorithms and the SVM classifier to train and model alpine grassland, the alpine grassland and LUCC were extracted by using Landsat-8 OLI satellite images in Sanjiangyuan National Park in this paper. The latest datasets of vegetation types with 30 m × 30 m spatial resolution in the three parks were prepared and formed. The classification results show that the SVM classifier could better distinguish the major land-use types, and the overall classification accuracy was very high. However, in the alpine grassland subcategories, the classification accuracies of the four typical grasslands were relatively low, especially between desert steppes and alpine meadows, and desert steppes and alpine steppes. It manifests the limitations of Landsat-8 multispectral remote sensing imageries in finer-resolution grassland classifications of high-altitude alpine mountains. The method can be utilized for other multispectral satellite imageries with the same band matching, such as Landsat 7, Landsat 9, Sentinel-2, etc. The method described in this paper can rapidly and efficiently process annual alpine grassland maps of the source areas of the Yellow River, the Yangtze River, and the Lancang River. It can provide timely and high-spatial-resolution datasets for supporting scientific decisions for the sustainable management of Sanjiangyuan National Park. [ABSTRACT FROM AUTHOR]

The restoration of grassland ecosystems and the development of the livestock industry in China are constrained by a gap in livestock feed supply and demand. The importation of forage products, as an important means to bridge this feed gap, deserves attention for its relationship with grassland quality and livestock production. This paper empirically examines the relationship between forage products trade and grassland quality, as well as livestock production. The paper uses a cointegration test and error correction model based on data on forage product trade, grassland quality, and livestock production in seven livestock-type regions from 2006 to 2020. The results show that forage product trade has a long-term stable equilibrium relationship with grassland quality in China. The relationships between forage product trade and livestock production in different livestock-type regions are heterogeneous. Among these heterogeneous results, livestock production in the Qinghai-Tibet Plateau region, Loess Plateau Region, Southwest Mountain Region, and Northeast Region are closely related to forage product trade. It appears that importation of feed reduces pressure on natural grasslands, as well as providing high-quality feed that improves livestock production. The above findings can help explain the characteristics of livestock production in different livestock-type regions in China, as well as provide empirical evidence and new ideas for restoring grassland environments and developing herbivorous animal husbandry. [ABSTRACT FROM AUTHOR]

The mining area in the Muli region, Qinghai Province, China, is an important source of water and an ecological security barrier in the Qilian Mountains region and has a very important ecological status. A series of ecological problems such as vegetation degradation and loss of biodiversity caused by mining have attracted widespread attention. In this paper, we used Landsat secondary data from 2000 to 2021 from the Muli region to obtain the spatial and temporal distribution characteristics of the vegetation in the Muli region by inversion of the fractional vegetation cover, above-ground biomass and the land surface phenology to comprehensively analyze the ecological changes in the vegetation in the Muli region. The results showed the following: (1) the above-ground biomass and cover of grassland in the Muli region showed a decreasing trend between 2000 and 2021, with a particularly pronounced decrease in grassland cover between 2009 and 2016; (2) the start of the vegetation growth cycle, i.e., the beginning of the vegetation growing season (SOG) became more advanced, the end of the vegetation growing season (EOG) was delayed, and the length of the growing cycle (LOG) became longer for most of the vegetation in the Muli region; (3) the results of this comprehensive analysis showed that the grassland in the Muli region showed dynamic changes with complex characteristics from 2000 to 2021, and anthropogenic disturbances had some influence on ecological indicators such as fractional vegetation cover and biomass. The extension of the vegetation growing season might be related to climate change. Based on the results of this paper, it is recommended to utilize biomass and fractional vegetation cover as indicators to assess the grass growth status of mining sites. This study analyzed the spatial and temporal characteristics of grasslands in the Muli area with several indicators, which will help relevant departments continue to improve and optimize ecological restoration measures. In addition, this study provides a reference for achieving comprehensive restoration of the ecological environment and ecological functions in mining areas. [ABSTRACT FROM AUTHOR]

Land use and land cover (LULC) change has greatly altered ecosystem carbon storage and exerted an enormous impact on terrestrial carbon cycling. Characterizing its impact on ecosystem carbon storage is critical to balance regional carbon budgets and make land use decisions. However, due to the availability of LULC data and the strong variability in LULC change, uncertainty remains high in quantifying the effect of LULC change on the historical and future carbon stock. Based on four historical LULC maps and one future LULC projection, this study combined the Land Use and Carbon Scenario Simulator (LUCAS) with a process-based CENTURY model to evaluate the historical and future LULC change and its impact on grassland carbon storage from 1991 to 2050 in northern China. Results showed that grassland experienced a drastic decrease of 16.10 × 103 km2 before 2005, while agriculture and barren land increased by 16.91 × 103 km2 and 3.73 × 103 km2, respectively. After that, grassland was projected to increase, agriculture kept steady, and barren land decreased. LULC change has resulted in enormous total ecosystem carbon loss, mainly in agro-pasture areas; the maximum 8.54% of carbon loss happened in 2000, which was primarily attributed to agriculture to grassland, forest to grassland, grassland to agriculture, and grassland to barren. Before 2000, the grassland net biome productivity was projected to be −15.54 Tg C/yr and −2.69 Tg C/yr with and without LULC change. After 2001, the LULC change showed a positive impact on the grassland carbon balance, and the region was projected to be a carbon sink. Ecological projects have made a significant contribution to grassland carbon storage. The paper provides a framework to account for the effects of LULC change on ecosystem carbon and highlights the importance of improving grassland management in balancing the grassland carbon budget, which is helpful to understand the regional carbon budget and better inform local land use strategies. [ABSTRACT FROM AUTHOR]

C, N and P are the key biogenic elements of terrestrial ecosystems, and their biogeochemical processes regulate nutrient cycling and play a key role in restoring degraded ecosystems. In this paper, the grassland ecosystem under artificial restoration measures (Dactylis glomerata (DG), Lolium perenne (LP), Lolium perenne + Trifolium repens (LT)), and the natural restoration measures (NG) in a typical karst plateau desertification control area of southwest China, were taken as the research object. The C, N, and P concentrations and the ecological stoichiometry of the plant–soil–microorganism system in grasslands under different restoration measures in the karst desertification area were explored. We established the following findings: (1) Compared with NG, the C, N and P concentrations of plants and soil in DG, LP and LT were higher, and LT was the highest. (2) The microbial biomass carbon (MBC), microbial biomass nitrogen (MBN) and microbial biomass phosphorus (MBP) concentrations in LT was also the highest. (3) The C:N ratio of plant and soil indicated that the N mineralization and nutrient release rate of DG, LP and LT were stronger than that of NG, and the plant growth of NG was most seriously limited by nitrogen. The N:P ratio in plant and soil indicated that the grassland was limited by P in the four treatments. (4) The result of correlation analysis showed that the cycling process of C, N and P in the plant–soil–microorganism system was coupled, and that the elements closely affected each other. In general, the effect of artificial restoration on a degraded ecosystem was relatively better than the natural restoration for increasing plant, soil and microbial nutrient concentrations, especially in the mixed-seed ecosystem of leguminous and gramineous forage. However, in the future, grassland management, appropriate N fertilizer or N-fixing plants and P fertilizer should be considered to improve the limitation of plant N and P, so as to realize the sustainable development of grasslands and the restoration of degraded ecosystems in the karst desertification control area. [ABSTRACT FROM AUTHOR]

Synergistically enhancing and realizing the value of grassland ecosystem services (ES) for economic activity is an important but challenging task for achieving sustainability in the karst desertification control (KDC). However, how to use grassland ES value characteristics in the KDC to make decisions on ES improvement, human well-being enhancement, and sustainable development remains unclear. In this paper, we took the contiguous region of karst desertification in Yunnan-Guangxi-Guizhou, China, a global hotspot, as the study area. Based on the valuation of the gross ecosystem product (GEP) and county economic intensity, we analyzed the structural and spatial characteristics of grassland GEP in the KDC using spatial analysis methods. We found that: (1) the grassland GEP in the KDC is mainly distributed in counties with low economic intensity (86.05% of the total number of counties) and vulnerable to losses caused by the livelihood of farmers; (2) the grassland GEP in the KDC is spatially small and scattered (the geographic concentration lies between 0.015 and 0.237), which makes it difficult to form industrial scale advantages; (3) the public product index (66.22–96.77%) and industry scale concentration (97.87–99.86%) of grassland GEP in the KDC are high, and most of the GEP is difficult to transform on the private market. Based on our findings, we proposed three corresponding recommendations for economic decision-making. The results of this study can provide a reference for economic decision-making regarding the management of grassland ES in karst areas with similar conditions and beyond. [ABSTRACT FROM AUTHOR]

Grassland ecosystems are among the largest terrestrial ecosystems in China, and grazing, as an important grassland management method, has direct and indirect impacts on grassland ecosystems. Meta-analyses can be used to systematically evaluate and summarize multiple findings from existing studies, but there have been few comparisons of meta-analysis methods. In this review, we summarize the effects of grazing on grassland plants and soil in the existing meta-analysis studies in China from 38 meta-analysis papers. The results show that they have consistent conclusions, such as grazing reduces the aboveground biomass by approximately half, increases the soil pH, decreases the C:N:P ratio, and reduces the number of topsoil microorganisms, but the conclusions of light and moderate grazing index changes vary greatly from study to study. The belowground biomass was generally found to increase, but it slightly decreased in some cases, and the total biomass generally decreased, but it slightly increased in other cases. Vegetation coverage increased during moderate grazing; the soil moisture content was highest for light grazing, and microbial diversity increased at light to moderate levels of grazing. There are also very inconsistent conclusions due to the different datasets and quantities of samples used in meta-analysis studies, as well as variations in the types and scales of grassland areas. The ranges of changes in other indicators were large, especially for the root-shoot ratio and soil carbon. However, changes in the aboveground biomass were generally stable. We suggest subsequent meta-analyses of grazing should further clarify the classification of grassland types and compare conclusions at different scales. Additionally, standardized network analyses are recommended for field manipulation experiments to further improve the accuracy of meta-analysis and reduce the temporal and spatial limitations of existing data. [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]

Leymus chinensis is a primary plant in the meadow steppe and typical steppe of China. With global warming and increasing grazing intensity, grassland degradation is being exacerbated. To better protect the L. chinensis grassland in this area and provide a theoretical basis for restoring it, this paper compared the germinable seed bank in the soil and the germination characteristics of L. chinensis (including initial germination time, duration of germination, germination termination time, germination dynamics, and germination index) in free-grazing and enclosed areas. At the same time, combining information about the density of L. chinensis on the ground and previous research results, a comprehensive analysis was conducted. The major results were: (1) there was no significant difference in the number of germinable seeds in the soil between the free-grazing area and the enclosed area, and these seeds were mainly concentrated in the 0–2 cm soil layer. (2) The free-grazing area resulted in a significant increase in the number of germinable L. chinensis seeds and advanced the initial germination time. (3) The number of soil germinated seeds and the number of L. chinensis germinated seeds decreased with the increase in soil depth. (4) Livestock grazing behaviours increased seed burial, thus improving the L. chinensis germination rate. At the same time, the L. chinensis seed bearing percentage and seed quality and the number of germinable L. chinensis seeds were significantly higher in the free-grazing area than in the enclosed area. However, this result still needs to be further explored. [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]

The gross primary productivity (GPP) of vegetation stores atmospheric carbon dioxide as organic compounds through photosynthesis. Its spatial heterogeneity is primarily influenced by the carbon uptake period (CUP) and maximum photosynthetic productivity (GPPmax). Grassland, cropland, and forest are crucial components of China's terrestrial ecosystems and are strongly influenced by the seasonal climate. However, it remains unclear whether the evolutionary characteristics of GPP are attributable to physiology or phenology. In this study, terrestrial ecosystem models and remote sensing observations of multi-source GPP data were utilized to quantitatively analyze the spatio-temporal dynamics from 1982 to 2018. We found that GPP exhibited a significant upward trend in most areas of China's terrestrial ecosystems over the past four decades. Over 60% of Chinese grassland and over 50% of its cropland and forest exhibited a positive growth trend. The average annual GPP growth rates were 0.23 to 3.16 g C m−2 year−1 for grassland, 0.40 to 7.32 g C m−2 year−1 for cropland, and 0.67 to 7.81 g C m−2 year−1 for forest. GPPmax also indicated that the overall growth rate was above 1 g C m−2 year−1 in most regions of China. The spatial trend pattern of GPPmax closely mirrored that of GPP, although local vegetation dynamics remain uncertain. The partial correlation analysis results indicated that GPPmax controlled the interannual GPP changes in most of the terrestrial ecosystems in China. This is particularly evident in grassland, where more than 99% of the interannual variation in GPP is controlled by GPPmax. In the context of rapid global change, our study provides an accurate assessment of the long-term dynamics of GPP and the factors that regulate interannual variability across China's terrestrial ecosystems. This is helpful for estimating and predicting the carbon budget of China's terrestrial ecosystems. [ABSTRACT FROM AUTHOR]

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

The Songnen grassland is an important resource for livestock production in China. Due to the intensification of anthropogenic activities in recent years, vegetation degradation has worsened, and the salinization of grassland has become increasingly serious, which severely affects the sustainable development of grassland animal husbandry. In this study, organic fertilizer addition was carried out at saline-and-alkaline-degraded Songnen grassland sites with photovoltaic panels, and we investigated the effects of organic fertilizer treatments on the vegetation and soil bacteria in these areas. The results showed that both organic fertilizer treatments increased the community composition and diversity indices of plants (p < 0.05); they also had significant effects on soil electrical conductivity and rapidly available potassium (p < 0.05). In the dominant phylum of bacteria, the relative abundance of Firmicutes increased without adding organic fertilizer under the photovoltaic panel; the addition of organic fertilizer had a significant effect on the relative abundance of Firmicutes and Desulfobacterota (p < 0.05), reducing their relative abundance, respectively. There were differences in the number of bacteria at the genus level under different treatments compared to the control, with the highest enrichment of bacteria occurring at the OFE position, and a significant difference (p < 0.05) being found between the control and the other four groups at the genus level of g_norank_f_norank_o_Actinomarinales. Organic fertilizer had a significant effect on the bacterial Simpson diversity index, with the most significant increasing trend found in OFE (the front eaves of the photovoltaic panel in fertilization area). The results of a correlation analysis showed that pH, electrical conductivity, and total nitrogen were the main factors affecting the soil bacterial community. [ABSTRACT FROM AUTHOR]

Forests and grasslands play an important role in carbon cycling. They not only absorb CO2 from the air through vegetation biomass and soil carbon sinks, but also reduce and control the horizontal transport of soil carbon (i.e., reinforcing soil carbon storage via soil conservation), thus avoiding erosion-induced CO2 emissions. In this study, vegetation biomass and soil carbon sinks, soil carbon reinforcement and reduced carbon emissions via soil conservation by forests and grasslands were quantified on the scale of the whole of China. The analysis was based on the distribution of biomass and the soil carbon pool and soil erosion rates derived from national surveys, as well as carbon density values from field surveys and literature. In 2021, forests and grasslands in China generated 394.18 Mt C/year (y) of steady-state carbon sinks through vertical biomass and soil absorption. The biomass carbon sinks of grasslands, and those of leaves, twigs, flowers and fruits of the forests, were not taken into account when quantifying the stable biomass sink, because they can become net producers of CO2 due to seasonal withering and carryover, or they can form soil organic carbon as potential soil carbon sinks. The amount of horizontal soil carbon reinforcement in China's forests and grasslands in 2021 was 20.31 Mt C/y, which was positively correlated with the reduction in the water erosion area; consequently, vertical emissions of approximately 14.89–29.78 Mt of CO2 into the atmosphere were avoided. Overall, in 2021, China's forests and grasslands absorbed atmospheric CO2 and reduced emissions by 1.46–1.47 Gt CO2/y, equivalent to approximately 13% of China's annual fossil CO2 emissions. This study demonstrates the fact that the adoption of forest and grassland measures sequesters carbon in soil and biota and reduces the risks of CO2 emissions by both vertical and horizontal paths, which is important for achieving carbon neutrality and mitigating climate change. [ABSTRACT FROM AUTHOR]

Maximal light use efficiency (LUE) is an important ecological index of a vegetation essential attribute, and a key parameter of the LUE-based model for estimating large-scale vegetation productivity by remote sensing technology. However, although currently used in different models there still exists extensive controversy. This paper takes the Zoige Plateau in China as a case area to develop a new approach for estimating the maximal LUEs for different vegetation. Based on an existing land cover map and MODIS NDVI product, the linear unmixing method with a moving window was adopted to estimate the time-series NDVI for different end members in a MODIS NDVI pixel; then Particle Swarm Optimizer (PSO) was applied to search for the optimization of LUE retrievals through the CASA (Carnegie-Ames-Stanford Approach) model combined with time-series NDVI and ground measurements. The derived maximal LUEs present significant differences among various vegetation types. These are 0.669 gC·MJ-1, 0.450 gC·MJ-1 and 0.126 gC·MJ-1 for the xerophilous grasslands with high, moderate and low vegetation fraction respectively, 0.192 gC·MJ-1 for the hygrophilous grasslands, and 0.125 gC·MJ-1 for the helobious grasslands. The field validation shows that the estimated net primary productivity (NPP) by the derived maximal LUE is closely related to the ground references, with R2 of 0.8698 and root-mean-square error (RMSE) of 59.37 gC·m-2·a-1. This indicates that the default set in the CASA model is not suitable for NPP estimation for the regional mountain area. The derived maximal LUEs can significantly improve the capability of NPP mapping, and open up the perspective for long-term monitoring of vegetation ecological health and ecosystem productivity by combining the LUE-based model with remote sensing observations. [ABSTRACT FROM AUTHOR]

Grasslands are one of the most important terrestrial ecosystems on the planet and have significant economic and ecological value. Accurate and rapid discrimination of grassland communities is critical to the conservation and utilization of grassland resources. Previous studies that explored grassland communities were mainly based on field surveys or airborne hyperspectral and high-resolution imagery. Limited by workload and cost, these methods are typically suitable for small areas. Spaceborne mid-resolution RS images (e.g., Sentinel, Landsat) have been widely used for large-scale vegetation observations owing to their large swath width. However, there still keep challenges in accurately distinguishing between different grassland communities using these images because of the strong spectral similarity of different communities and the suboptimal performance of models used for classification. To address this issue, this paper proposed a superpixel-based grassland community classification method using Genetic Programming (GP)-optimized classification model with Sentinel-2 multispectral bands, their derived vegetation indices (VIs) and textural features, and Sentinel-1 Synthetic Aperture Radar (SAR) bands and the derived textural features. The proposed method was evaluated in the Siziwang grassland of China. Our results showed that the addition of VIs and textures, as well as the use of GP-optimized classification models, can significantly contribute to distinguishing grassland communities, and the proposed approach classified the seven communities in Siziwang grassland with an overall accuracy of 84.21% and a kappa coefficient of 0.81. We concluded that the classification method proposed in this paper is capable of distinguishing grassland communities with high accuracy at a regional scale. [ABSTRACT FROM AUTHOR]

The nitrogen deposition process, as an important phenomenon of global climate change and an important link in the nitrogen cycle, has had serious and far-reaching impacts on grassland ecosystems. This study aimed to investigate the survival adaptation strategies of plants of different functional groups under nitrogen deposition, and the study identified the following outcomes of differences in biomass changes by conducting in situ simulated nitrogen deposition experiments while integrating plant nutrient contents and soil physicochemical properties: (1) nitrogen addition enhanced the aboveground biomass of grassland communities, in which Poaceae were significantly affected by nitrogen addition. Additionally, nitrogen addition significantly influenced plant total nitrogen and total phosphorus; (2) nitrogen addition improved the plant growth environment, alleviated plant nitrogen limitation, and promoted plant phosphorus uptake; and (3) there was variability in the biomass responses of different functional groups to nitrogen addition. The level of nitrogen addition was the primary factor affecting differences in biomass changes, while nitrogen addition frequency was an important factor affecting changes in plant community structure. [ABSTRACT FROM AUTHOR]

Grasslands play a vital role in the global ecosystem. Efficient and reproducible methods for estimating the grassland aboveground biomass (AGB) are crucial for understanding grassland growth, promoting sustainable development, and assessing the carbon cycle. Currently, the available methods are limited by their computational inefficiency, model transfer, and sampling scale. Therefore, in this study, the estimation of grassland AGB over a large area was achieved by coupling the PROSAIL model with the support vector machine regression (SVR) method. The ill-posed inverse problem of the PROSAIL model was mitigated through kernel-based regularization using the SVR model. The Zoigê Plateau was used as the case study area, and the results demonstrated that the estimated biomass accurately reproduced the reference AGB map generated by zooming in on on-site measurements (R2 = 0.64, RMSE = 43.52 g/m2, RRMSE = 15.13%). The estimated AGB map also maintained a high fitting accuracy with field sampling data (R2 = 0.69, RMSE = 44.07 g/m2, RRMSE = 14.21%). Further, the generated time-series profiles of grass AGB for 2022 were consistent with the trends in local grass growth dynamics. The proposed method combines the advantages of the PROSAIL model and the regression algorithm, reduces the dependence on field sampling data, improves the universality and repeatability of grassland AGB estimation, and provides an efficient approach for grassland ecosystem construction and planning. [ABSTRACT FROM AUTHOR]

Ecosystem carbon use efficiency (CUE), water use efficiency (WUE), and light use efficiency (LUE) are critical parameters for estimating CO2 uptake, water circulation, and ecosystem balance. Research on the change trends of individual use efficiency has matured; however, studies on the spatiotemporal heterogeneity and driving mechanisms of divergence patterns for multi-use efficiencies capability are limited. Therefore, taking southwest China as an example, this study constructed a compound use efficiency (COM) through CUE, WUE, and LUE. Based on the spatiotemporal patterns and divergence characteristics analysis of water–carbon–light use efficiencies, the scale effects and driving mechanism of its divergence characteristics for COM at the optimal scale were clarified. The results revealed that the average value of CUE, LUE, WUE, and COM were 0.49, 0.7 gC m−2 MJ−1, 2.31 gC kg−1 H2O, and 0.87, respectively. Apart from CUE, the LUE, WUE, and COM parameters exhibited a fluctuating upward trend. Statistically, there was a high COM in karst and ecological restoration regions, reflecting the strong adaptability of karst vegetation and the effectiveness of ecological restoration; as the elevation rose, COM increased and then decreased, with the highest value at the elevation of 3000 m; the lowest COM was found in grassland, refuting the inference that it can be used as an optimal vegetation type for China's Grain to Green program from the perspective of use efficiency. Sub-basin was the most optimal divergence scale, and although temperature and elevation were the dominant single force causing COM divergence, the couplings of precipitation and population density and elevation and population density had more controlling impacts than a single force. These findings enrich the understanding of ecosystem use efficiency and are beneficial for the improvement in ecological restoration strategies in karst landscapes. [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]

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]

Grassland desertification stands as an ecological concern globally. It is crucial for desertification prevention and control to comprehend the variation in area and severity of desertified grassland (DGL), clarify the intensities of conversion among DGLs of different desertification levels, and explore the spatial and temporal driving factors of desertification. In this study, a Desertification Difference Index (DDI) model was constructed based on albedo-EVI to extract desertification information. Subsequently, intensity analysis, the Geo-detector model, and correlation analysis were applied to analyze the dynamics and driving factors of desertification. The results showed the following: (1) Spatially, the DGL in Xilingol exhibited a zonal distribution. Temporally, the degree of DGL decreased, with the proportion of severely and moderately desertified areas decreasing from 51.77% in 2000 to 37.23% in 2020, while the proportion of nondesertified and healthy areas increased from 17.85% in 2000 to 37.40% in 2020; (2) Transition intensities among different desertification levels were more intense during 2000–2012, stabilizing during 2012–2020; (3) Meteorological factors and soil conditions primarily drive the spatial distribution of DDI, with evapotranspiration exhibiting the most significant influence (q-value of 0.83), while human activities dominate interannual DDI variations. This study provides insights into the conversion patterns among different desertification levels and the divergent driving forces shaping desertification in both spatial and temporal dimensions in Xilingol. [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]

Due to the effects of global climate change and altered human land-use patterns, typical shrub encroachment in grasslands has become one of the most prominent ecological problems in grassland ecosystems. Shrub coverage can quantitatively indicate the degree of shrub encroachment in grasslands; therefore, real-time and accurate monitoring of shrub coverage in large areas has important scientific significance for the protection and restoration of grassland ecosystems. As shrub-encroached grasslands (SEGs) are a type of grassland with continuous and alternating growth of shrubs and grasses, estimating shrub coverage is different from estimating vegetation coverage. It is not only necessary to consider the differences in the characteristics of vegetation and non-vegetation variables but also the differences in characteristics of shrubs and herbs, which can be a challenging estimation. There is a scientific need to estimate shrub coverage in SEGs to improve our understanding of the process of shrub encroachment in grasslands. This article discusses the spectral differences between herbs and shrubs and further points out the possibility of distinguishing between herbs and shrubs. We use Sentinel-2 and Gao Fen-6 (GF-6) Wide Field of View (WFV) as data sources to build a linear spectral mixture model and a random forest (RF) model via space–air–ground collaboration and investigate the effectiveness of different data sources, features and methods in estimating shrub coverage in SEGs, which provide promising ways to monitor the dynamics of SEGs. The results showed that (1) the linear spectral mixture model can hardly distinguish between shrubs and herbs from medium-resolution images in the SEG. (2) The RF model showed high estimation accuracy for shrub coverage in the SEG; the estimation accuracy (R2) of the Sentinel-2 image was 0.81, and the root-mean-square error (RMSE) was 0.03. The R2 of the GF6-WFV image was 0.72, and the RMSE was 0.03. (3) Texture feature introduced in RF models are helpful to estimate shrub coverage in SEGs. (4) Regardless of the linear spectral mixture model or the RF model being employed, the Sentinel-2 image presented a better estimation than the GF6-WFV image; thus, this data has great potential to monitor shrub encroachment in grasslands. This research aims to provide a scientific basis and reference for remote sensing-based monitoring of SEGs. [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]

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]

Arid and semiarid wetlands, the core geographical unit of desert oases, significantly benefit and improve the ecological environment. In this study, we systematically compared the spatiotemporal dynamics and driving forces of natural and constructed wetlands in arid and semiarid regions of northern China from 1995 to 2019. For these comparisons, we utilized a land-use transition matrix, partial least-squares–structural equation model (PLS-SEM), and geographically weighted regression (GWR) model. The results showed that (1) the area of wetlands as a whole showed an upward trend, with natural and constructed wetlands increasing by 4.16% and 11.86%, respectively. The increases mainly resulted from conversions of grassland and other lands (shrub, sparse vegetation, and bare land). (2) The direct dominant factors that drove natural wetland changes were soil and terrain, while those that drove constructed wetlands were human disturbances. Human disturbance, by affecting soil, had a higher significant indirect effect on natural wetlands. Heat, by affecting moisture, had the greatest indirect effect on constructed wetlands. (3) The sensitivity of natural and constructed wetlands to the responses of different drivers showed significant spatial heterogeneity. This study explores the interaction and driving mechanisms of human and natural attributes on natural and constructed wetlands and provides a scientific foundation for the restoration and sustainable development of wetlands in arid and semiarid areas. [ABSTRACT FROM AUTHOR]

Simple Summary: We proposed a pragmatic method for quantifying the grazing density (GD) and herding proximities (HP) based on unmanned aerial vehicles. We further tested its feasibility at three typical household pastures on the Qinghai-Tibetan Plateau, China. The proposed method is ideal for studying animal behavior and determining the correlation between the distribution of pastoral livestock and resource usability. Grazing management is one of the most widely practiced land uses globally. Quantifying the spatiotemporal distribution of livestock is critical for effective management of livestock-grassland grazing ecosystem. However, to date, there are few convincing solutions for livestock dynamic monitor and key parameters quantification under actual grazing situations. In this study, we proposed a pragmatic method for quantifying the grazing density (GD) and herding proximities (HP) based on unmanned aerial vehicles (UAVs). We further tested its feasibility at three typical household pastures on the Qinghai-Tibetan Plateau, China. We found that: (1) yak herds grazing followed a rotational grazing pattern spontaneously within the pastures, (2) Dispersion Index of yak herds varied as an M-shaped curve within one day, and it was the lowest in July and August, and (3) the average distance between the yak herd and the campsites in the cold season was significantly shorter than that in the warm season. In this study, we developed a method to characterize the dynamic GD and HP of yak herds precisely and effectively. This method is ideal for studying animal behavior and determining the correlation between the distribution of pastoral livestock and resource usability, delivering critical information for the development of grassland ecosystem and the implementation of sustainable grassland management. [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]

Rapidly predicting and revealing the spatiotemporal characteristics and driving factors of land-use changes in carbon storage within megacities under different scenarios is crucial to achieving sustainable development. In this study, Jinan City (JNC) is taken as the study area, and the Markov-FLUS-InVEST model is utilized to predict and analyze the spatiotemporal variation in carbon storage in 2030 under three scenarios, namely, the natural development scenario (S1), the ecological conservation scenario (S2), and the economic development scenario (S3). The drivers of carbon storage changes were identified using an optimal parameter-based geographic detection (OPGD) model. The findings indicate that (1) land use from 2010 to 2018 shows a trend of continuous expansion of construction land and reduction in arable land. (2) The main types of carbon pools were cropland, forest, and grassland, accounting for more than 96% of the total amount. Carbon storage showed a decreasing trend from 2010 to 2018, and the main type of carbon pool that decreased was cropland. The center of gravity of carbon storage increases and decreases was located in the southern Lixia District, and the center of gravity of increase and decrease moved to the southwest by 3057.48 m and 1478.57 m, respectively. (3) From 2018 to 2030, the reductions in carbon stocks were 3.20 × 106 t (S1), 2.60 × 106 t (S2), and 4.26 × 106 t (S3), and the carbon release was about 9 times (S1), 4 times (S2), and 10 times (S3) that of the carbon sink. (4) The contribution of slope (A2) ∩ nighttime light index (B6) and elevation (A1) ∩ nighttime light index (B6) to the regional heterogeneity of carbon stocks was the largest among the interaction drivers. To sum up, this study deepens the simulation of spatial and temporal dynamics of carbon storage under land-use changes in megacities and the related driving mechanism, which can provide the basis for scientific decision-making for cities to conduct territorial spatial planning and ecological protection and restoration. [ABSTRACT FROM AUTHOR]

In grassland ecosystems, the occurrence and transmission of foliar fungal diseases are largely dependent on grazing by large herbivores. However, whether herbivores that have different body sizes differentially impact foliar fungal diseases remains largely unexplored. Thus, we conducted an 8-year grazing experiment in an alpine grassland on the Qinghai–Tibet Plateau in China and tested how different types of livestock (sheep (Ovis aries), yak (Bos grunniens), or both)) affected foliar fungal diseases at the levels of both plant population and community. At the population level, grazing by a single species (yak or sheep) or mixed species (sheep and yak) significantly decreased the severity of eight leaf spot diseases. Similarly, at the community level, both single species (yak or sheep) and mixed grazing by both sheep and yak significantly decreased the community pathogen load. However, we did not find a significant difference in the community pathogen load among different types of livestock. These results suggest that grazing by large herbivores, independently of livestock type, consistently decreased the prevalence of foliar fungal diseases at both the plant population and community levels. We suggest that moderate grazing by sheep or yak is effective to control the occurrence of foliar fungal diseases in alpine grasslands. This study advances our knowledge of the interface between disease ecology, large herbivores, and grassland science. [ABSTRACT FROM AUTHOR]

Grassland ecosystems are an important component of global terrestrial ecosystems and play a crucial role in the global carbon cycle. Therefore, it is important to study the carbon dioxide (CO2) process in the Middle Tien Shan grassland ecosystem, which can be regarded as a typical representative of the mountain grasslands in Xinjiang. Eddy covariance (EC) and the global carbon fluxes dataset (GCFD) were utilized to continuously monitor the Middle Tien Shan grassland ecosystem in Xinjiang throughout the 2018 growing season. The findings revealed notable daily and monthly fluctuations in net ecosystem exchange (NEE), gross primary productivity (GPP), and ecosystem respiration (Reco). On a daily basis, there was net absorption of CO2 during the day and net emission during the night. The grassland acted as a carbon sink from 6:00 to 18:00 and as a carbon source for the remaining hours of the day. On a monthly scale, June and July served as carbon sinks, whereas the other months acted as carbon sources. The accumulated NEE, GPP, and Reco during the growing season were −329.49 g C m−2, 779.04 g C m−2, and 449.55 g C m−2, respectively. On the half-hourly and daily scales, soil temperature (Ts) was the main contributor to CO2 fluxes and had the greatest influence on the variations in CO2 fluxes. Additionally, air temperature (Ta) showed a strong correlation with CO2 fluxes. The grassland ecosystems exhibited the strongest CO2 uptake, reaching its peak at soil temperatures of 25 °C. Moreover, as the air temperatures rose above 15 °C, there was a gradual decrease in NEE, while CO2 uptake increased. The applicability of GCFD data is good in the grassland ecosystem of the Middle Tien Shan Mountains, with correlations of 0.59, 0.81, and 0.73 for NEE, GPP, and Reco, respectively, compared to field observations. In terms of remote sensing spatial distribution, the Middle Tien Shan grassland ecosystem exhibits a carbon sink phenomenon. [ABSTRACT FROM AUTHOR]

Optimizing the connectivity-carbon sequestration coupling coordination of forest and grassland ecological spaces (F&GES) is a crucial measure to enhance carbon sequestration effectively in mining areas. However, the prevailing strategies for optimizing F&GES often overlook the connectivity-carbon sequestration coupling coordination of the network. Therefore, this study aimed to propose a novel restoration plan to improve the connectivity-carbon sequestration coupling coordination of existing networks. Taking a typical mining area in northwestern China (Eyu County) as an example, we extracted the existing F&GES based on remote sensing ecological indicators and ecological risk assessments. Subsequently, we optimized the network using the connectivity-carbon sequestration coupling coordination degree (CSCCD) model from the perspective of connectivity-carbon sequestration coupling coordination, proposed potential alternative optimization schemes, and evaluated the optimization effects. The results showed that the range of Eyu County's F&GES structure had been determined. Ecological source sites with better carbon sequestration effects were primarily distributed in the central and northeastern parts of Eyu County. After optimization, the network added 26 ecological patches, and the added area reached 641.57 km2. Furthermore, the connectivity robustness, edge restoration robustness, and node restoration robustness of the optimized network were significantly improved, and the carbon sequestration effect of the forest and grassland ecological space was increased by 6.78%. The contribution rate of ecological source sites was 97.66%, and that of ecological corridors was 2.34%. The CSCCD model proposed in this study can effectively improve the carbon sequestration effect in mining areas, promote carbon neutrality, and save network optimization time while improving efficiency. This restoration strategy is also applicable to forest and grassland ecosystem management and optimization of ecological spaces in other mining areas, which has positive implications for promoting ecological civilization construction and sustainable development. [ABSTRACT FROM AUTHOR]

To enable grasslands to serve a larger ecological and service role against the backdrop of climate change and human activity, management is essential following long-term fencing. Using desert grassland that had been fenced for 20 years, we conducted experiments on different mowing frequencies (once, twice, and three times a year) and different amounts of vegetation coverage (mowing removal, mowing, and in situ mulch; and mowing, in situ mulch, and additional coverage) and control (no mowing, no removal, and no cover). In 2021 and mid-August 2022, the aboveground biomass, species diversity, and vegetation cover of each species were assessed, and the impacts of species complementarity and competition on the productivity of the fenced grassland were examined. Our findings revealed several significant outcomes: (1) Two and three times of mowing per year promoted growth and development of annual or biennial plants and increased species diversity of the community. (2) The interaction impact of mowing and covering two and three times a year was the most important, as it encouraged compensatory development of plants, particularly the dominant species, and increased aboveground biomass of the fenced grassland (p < 0.05). (3) The number of complementary species groups was c. 11% lower for mowing and covering than for mowing removal, indicating that the interspecific relationship for the mowing and covering interaction was dominated by competition, which was more beneficial to the increase in community productivity. (4) Lespedeza potaninii Vass. (which dominated the semi-shrubs) and Agropyron mongolicum Keng (which dominated the tufted grasses) exhibited compensatory growth, which alleviated the change of mowing disturbance and precipitation fluctuation on the productivity of this grassland ecosystem. (5) Adding vegetation cover after mowing positively influenced community production but did not significantly impact litter decomposition. Our research provides data support for the sustainable development of long-term fenced desert grasslands. [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]

Whether or not large-scale vegetation restoration will lead to a decrease in regional terrestrial water storage is a controversial topic. This study employed the Geodetector model, in conjunction with observed and satellite hydro-meteorological data, to detect the changes in terrestrial water storage anomaly (TWSA) and to identify the contributions of climate change and vegetation greening across China during the years 1982–2019. The results revealed that: (1) during the period of 1982–2019, TWSA showed a downward trend in about two thirds of the country, with significant declines in North China, southeast Tibet, and northwest Xinjiang, and an upward trend in the remaining third of the country, with significant increases mainly in the Qaidam Basin, the Yangtze River, and the Songhua River; (2) the positive correlation between normalized vegetation index (NDVI) and TWSA accounts for 48.64% of the total vegetation area across China. In addition, the response of vegetation greenness lags behind the TWSA and precipitation, and the lag time was shorter in arid and semi-arid regions dominated by grasslands, and longer in relatively humid regions dominated by forests and savannas; (3) furthermore, TWSAs decreased with the increase in NDVI and evapotranspiration (ET) in arid and semi-arid areas, and increased with the rise in NDVI and ET in the humid regions. The Geodetector model was used to detect the effects of climate, vegetation, and human factors on TWSA. It is worth mentioning that NDVI, precipitation, and ET were some of the main factors affecting TWSA. Therefore, it is essential to implement rational ecological engineering to mitigate climate change's negative effects and maintain water resources' sustainability in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

Food security is critical for socioeconomic development. In grassland areas, inappropriate food consumption patterns can cause irreversible damage to vulnerable local ecosystems. This study aims to examine the household dietary diversity status and development trend over the past 20 years in Chinese herder communities. We draw on a cross-sectional dataset of 230 households involving 652 family members from the Xilin Gol Grassland areas in North China. Household dietary diversity was assessed using the household dietary diversity score (HDDS), which was calculated based on 12 food groups. Results show that HDDS increased from 3.74 in 1999 to 5.92 in 2019, with an annual average growth rate of 2.45% during the past 20 years. The increase in plant-based food scores made a major contribution to the HDDS improvement. The variations in household dietary diversity status between pastoral areas and agro-pastoral areas showed differences among different types of grassland in arid and semiarid transitional zones. It is worth paying more attention to monitoring the main impact factors that affect HDDS and how these changes might impact the local ecosystem, which will benefit regional sustainable development. [ABSTRACT FROM AUTHOR]

Land use is a process that turns a piece of land's natural ecosystem into an artificial one. The mix of plant and man-made covers on the Earth's surface is known as land cover. Land use is the primary external force behind change in land cover, and land cover has an impact on how land use is carried out, resulting in a synergistic interaction between the two at the Earth's surface. In China's Shandong Peninsula city cluster, Dongying is a significant coastal port city. It serves as the administrative hub for the Yellow River Delta and is situated in Shandong Province, China's northeast. The changes in its urban land use and land cover in the future are crucial to understanding. This research suggests a prediction approach that combines a patch-generation land use simulation (PLUS) model and long-term short-term memory (LSTM) deep learning algorithm to increase the accuracy of predictions of future land use and land cover. The effectiveness of the new method is demonstrated by the fact that the average inaccuracy of simulating any sort of land use in 2020 is around 5.34%. From 2020 to 2030, 361.41 km2 of construction land is converted to cropland, and 424.11 km2 of cropland is converted to water. The conversion areas between water and unused land and cropland are 211.47 km2 and 148.42 km2, respectively. The area of construction land and cropland will decrease by 8.38% and 3.64%, respectively, while the area of unused land, water, and grassland will increase by 5.53%, 2.44%, and 0.78%, respectively. [ABSTRACT FROM AUTHOR]

Wetland vegetation aboveground biomass (AGB) directly indicates wetland ecosystem health and is critical for water purification, carbon cycle, and biodiversity conservation. Accurate AGB estimation is essential for the monitoring and supervision of ecosystems, especially in seasonal floodplain wetlands. This paper explored the capability of spectral and texture features from the Sentinel-2 Multispectral Instrument (MSI) for modeling grassland AGB using random forest (RF) and extreme gradient boosting (XGBoost) algorithms in Shengjin Lake wetland (a Ramsar site). We use five-fold cross-validation to verify the model effectiveness. The results indicated that the RF and XGBoost models had a robust and efficient performance (with root mean square error (RMSE) of 126.571 g·m−2 and R2 of 0.844 for RF, RMSE of 112.425 g·m−2 and R2 of 0.869 for XGBoost), and the XGBoost models, by contrast, performed better. Both traditional and red-edge vegetation indices (VIs) obtained satisfactory results of AGB estimation (RMSE = 127.936 g·m−2, RMSE = 125.879 g·m−2 in XGBoost models, respectively), with the red-edge VIs contributed more to the AGB models. Moreover, we selected eight gray-level co-occurrence matrix (GLCM) textures calculated by four processing window sizes using the mean value of four offsets, and further analyzed the results of three analysis sets. Textures derived from traditional and red-edge bands using a 7 × 7 window size performed better in biomass estimation. This finding suggested that textures derived from the traditional bands were as important as the red-edge bands. The introduction of textures moderately improved the accuracy of modeling AGB, whereas the use of textures alo ne was not satisfactory. This research demonstrated that using the Sentinel-2 MSI and the two ensemble algorithms is an effective method for long-term dynamic monitoring and assessment of grass AGB in seasonal floodplain wetlands, which can support sustainable management and carbon accounting of wetland ecosystems. [ABSTRACT FROM AUTHOR]

The desert-oasis ecotone, as a crucial natural barrier, maintains the stability of oasis agricultural production and protects oasis habitat security. This paper investigates the dynamic evolution of the desert-oasis ecotone in the Tarim River Basin and predicts the near-future land-use change in the desert-oasis ecotone using the cellular automata–Markov (CA-Markov) model. Results indicate that the overall area of the desert-oasis ecotone shows a shrinking trend (from 67,642 km2 in 1990 to 46,613 km2 in 2015) and the land-use change within the desert-oasis ecotone is mainly manifested by the conversion of a large amount of forest and grass area into arable land. The increasing demand for arable land for groundwater has led to a decline in the groundwater level, which is an important reason for the habitat deterioration in the desert-oasis ecotone. The rising temperature and drought have further exacerbated this trend. Assuming the current trend in development without intervention, the CA-Markov model predicts that by 2030, there will be an additional 1566 km2 of arable land and a reduction of 1151 km2 in forested area and grassland within the desert-oasis ecotone, which will inevitably further weaken the ecological barrier role of the desert-oasis ecotone and trigger a growing ecological crisis. [ABSTRACT FROM AUTHOR]

Monitoring grassland growth in large areas usually needs multiple images from different sensors or on different dates to cover the study area completely. Images from different sensors or on different dates need consistency correction to eliminate the sharp differences between images. The main contribution of this study is to promote a method for consistency correction of images on different days by constructing a linear regression equation of land cover types and the classification pixel mean. Taking a prefecture-level area in China as a test area, the consistency corrected images were applied for monitoring grassland growth. The results showed the following. First, compared with the normal correction equation constructed for two images, taking all features into account, the coefficient of determination of the equation corrected by the land cover types was improved, and the root mean square error was also significantly reduced. Secondly, the areas of consistency in the corrected image were improved compared with the original image, with an improvement rate of 21% for images from the same sensor and 25% for images from different sensors. The pixel average was much closer to the benchmark images, indicating that the corrected image was more consistent than the original image. Thirdly, when applied for monitoring grassland growth, consistency correction can solve the problem of misjudging grassland degradation. Grassland that was judged to be degraded using direct imagery, in fact, showed stable growth after consistency correction, and this type accounted for 7.33% of the regional grassland area. The seasonal characteristics of grass growth in the region were also obtained by monitoring the growth of grass in the region throughout the year. The application test showed that an effective image consistency correction method can improve the accuracy of grassland growth monitoring across a large area. [ABSTRACT FROM AUTHOR]

The responses of grassland net primary productivity (NPP) to climate change (CC) and human activities (HA) have received much attention and are inconsistent on different spatial scales. The accurate and quantitative evaluation of the impacts of CC and HA on grassland NPP at a county scale is very important to reveal the external driving factors on grassland NPP and guide the protection of the grassland ecosystem in the arid sandy area of China. In this study, the improved CASA model was adopted to quantify the grassland NPP in Otog Banner, China from 2001 to 2020. The spatiotemporal dynamics of grassland NPP and the relationships between grassland NPP and climate factors in space were analyzed using the methods of simple linear regression and relative sensitivity coefficient. Furthermore, the relative contributions to grassland NPP dynamics caused by CC and HA were explored using the quantitative method based on partial derivative. The results revealed that the mean value of grassland NPP was 175.17 g C m−2 yr−1, and exhibited a significant decrease trend periodically at a rate of 2.14 g C m−2 yr−1 from 2001 to 2020. The spatial distribution of grassland NPP increased from west to east gradually and ranged in 17.48–498.09 g C m−2 yr−1. Grassland NPP exhibited significant linear patterns along the gradients of climate factors, and was the most sensitive to sunshine duration (SSD). Approximately 86.83% of the grassland showed a degradation trend and 39.71% showed a serious degradation trend. The CC contribution to grassland NPP dynamics was 0.593 g C m−2 yr−1, and precipitation was the key driving climate factor, while the contribution of HA was −2.733 g C m−2 yr−1, which was the primary factor leading to large-scale degradation of grassland in Otog banner. This study indicates that the status of the grassland ecosystem in Otog Banner is not optimistic, and measures for grassland ecosystem restoration and improvement need to be further strengthened. [ABSTRACT FROM AUTHOR]

Scientific selection of appropriate herbage planting management mode is an important guarantee to promote artificial grassland development and grassland productivity. In this study, three-year-old alfalfa (Medicago sativa L.) and bromus inermis were applied to analyze the effects of planting patterns (bromus inermis and alfalfa mixed-sowing D1, bromus inermis mono-sowing D2), nitrogen application (pure nitrogen) level (N1: 60 kg·ha−1, N2: 120 kg·ha−1), and water regulation (upper and lower limits of irrigation are calculated as a percentage of field capacity θf, W1: slight water deficit 65~85% θf, W2: moderate water deficit 55~85% θf, W3: serious water deficit 45~85% θf) on herbage growth and water-nitrogen use efficiency. This research applied the principal component analysis, the TOPSIS model, and the combination evaluation to evaluate each treatment. Results demonstrated that (1) the plant height, leaf area index, and yield of mixed-sowing herbage were 81.63%, 119.52%, and 111.51%, higher than the mono-sowing herbage. Increasing the amount of irrigation and nitrogen application could enhance herbage yield. The herbage yield with the W1N2 treatment was the highest. In this treatment, the mixed-sowing herbage yield was 26,050.73 kg·ha−1, and the mono-sowing herbage yield was 12,186.10 kg·ha−1. (2) The crude protein content of mixed-sowing herbage increased by 41.44%, higher than mono-sowing herbage, and the relative feeding value decreased by 16.34%. Increasing irrigation and nitrogen application could improve the quality of herbage. Meanwhile, the quality of herbage treated with W1N2 was the best. (3) The water use efficiency (WUE), irrigation water use efficiency (IWUE), partial factor productivity of nitrogen (PFPN), and crude protein water use efficiency (CPWUE) of mixed-sowing herbage were significantly higher than mono-sowing herbage. The PFPN and the CPWUE of herbage improved with increasing irrigation amount. Meanwhile, the WUE, the IWUE, and the CPWUE of herbage also improved with increasing nitrogen application amount. The results showed that mixed-sowing of alfalfa and bromus inermis with slight water deficit (upper and lower limit of irrigation was 65~85% θf) and nitrogen application (120 kg·ha−1) could have the best comprehensive production effect. At the same time, it was a planting and management mode of high yield, high quality, and high efficiency of artificial herbage in the oasis-desert interlacing area of Hexi, Gansu Province, China, and areas with similar climates. [ABSTRACT FROM AUTHOR]

The semi-arid region of northern China is highly sensitive to environmental changes, especially the Hulunbuir Grassland, which has an essential ecological status and a fragile environment. This study focused on the NDVI characteristics of three different ecosystems and their dominant influencing factors. It proposed a method to show the immediate effects of factors influencing NDVI on a statistical level. The results showed that: (1) NDVI of floodplain wetland > NDVI of meadow > NDVI of sand ribbon. There were obvious differences among the three ecosystems, and the spatial distribution of NDVI was consistent with altitude. (2) The main explanatory factors were the phenological period, humidity, temperature, accumulated precipitation, runoff, and evaporation, which accounted for 68.8% of the total explanation. (3) Phenological period, humidity, and precipitation were positively correlated with NDVI. Temperature and evaporation had a positive effect on NDVI within a certain range. This study revealed the differences in environmental factors in different ecosystems, enriched the theory of NDVI influencing factors, and provided a scientific basis for future NDVI research and regional ecological conservation. [ABSTRACT FROM AUTHOR]

Enclosure is one of the useful measures to protect and restore degraded grasslands, and it is widely used around the world. The vegetation characteristics of grasslands directly reflect the recovery status of degraded grasslands; however, conflicting results of plant traits were continually achieved in the numerous on-site studies of enclosure in the last two decades. It is necessary to conduct a systematic assessment to find a general conclusion for the effects of enclosure on different grasslands. Studies on the enclosure grasslands in China were taken as the objects to refine the relationships between grassland vegetation characteristics and enclosure measures using meta-analysis. Enclosure had positive effects on the restoration of vegetation coverage, aboveground and belowground biomass, and diversity of degraded grasslands. Different vegetation characteristics and grassland types showed different responses to enclosure duration. The vegetation productivity reached a maximum in the 11–15 years of enclosure for alpine grasslands and typical steppe grasslands, 6–10 years for desert grasslands, and more than 15 years of enclosure for meadow grasslands. Plant species diversity reached the peak values when alpine grasslands and typical steppe grasslands were enclosed approximately 10 years, desert grasslands approximately 11–15 years, and meadow grasslands approximately 5 years. These results indicated that the management strategies of enclosed grasslands should be adjusted reasonably according to the types and the management objectives of grasslands in order to maintain or even improve the condition and services of grassland ecosystems. [ABSTRACT FROM AUTHOR]

Grassland farms form the basis of grassland resource management in China. Farm sizes in China are generally small, which obviously increases the risk of grassland ecosystems. It is necessary to analyze the impact of farm size on grasslands from the perspective of livestock production in order to improve grassland management. This study combines field investigations and statistical analysis from 2004 to 2020, using a total of 126 farms from the Xilinguole League of Inner Mongolia in China as samples. These sample farms are divided into large farms and small farms. Different production scale and management behaviors are explored, along with their different impacts on grassland resources use. The results show that the expansion of farm size is constrained by the government management policies. Different behaviors are adopted by large and small farms in terms of finance, grassland circulation, and overgrazing management. The differentiation mechanisms of different farm size and the utilization of grassland resources are clarified in this study. This work suggests that managers promote sustainable use based on farm size and build appropriate policies to avoid future risks. The results of this study can provide a framework for solving similar problems. [ABSTRACT FROM AUTHOR]

Aboveground biomass (AGB) in managed grasslands can vary across a suite of environmental and management conditions; however, there lacks a quantitative assessment at the national scale of China. Although the potential effects of individual drivers (e.g., species, nutrient fertilization, and water management) have been examined in China's managed grasslands, no attempts have been made to comprehensively assess the effects of multiple variables on AGB. Using a meta-data analysis approach, we created a database composed of AGB and associated attributes of managed grasslands in China. The database was used to assess the responses of AGB to anthropogenic factors, in addition to a suite of natural variables including climate, soil, and topography. The average AGB in managed grasslands of China is approximately 630 g m−2 of dry matter, ranging from 55 to 2172 g m−2 (95% confidence interval). Medicago sativa is the most widely planted species in China's managed grasslands, followed by Elymus dahuricus and Bromus japonicus. The national average AGB of these three species was around 692, 530, and 856 g m−2, respectively. For each species, AGB shows a large discrepancy across different places. In general, grassland AGB depends substantially on species, environments, and management practices. The dependence can be well described by a linear mixed-effects regression in which a series of biotic and abiotic factors are used as predictors. We highlight that establishing managed grassland can potentially contribute to not only AGB enhancement, but also grassland restoration on degraded natural grasslands. [ABSTRACT FROM AUTHOR]