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As the most widely distributed vegetation type on earth, grassland plays an important role in the terrestrial carbon cycle. Accurate estimation of grassland carbon stock and elucidation of the temporal and spatial variations and driving factors are crucial for understanding the global carbon cycle and projecting future climate. In this paper, we estimated both aboveground biomass (AGB) and belowground biomass (BGB) of the eastern Eurasian steppe and assessing its spatiotemporal patterns and driving factors over the period 2000–2018. In-situ measurements, vegetation indices from the Moderate Resolution Imaging Spectroradiometer (MODIS), digital elevation model (DEM) and climate variables were used to establish predictive models for AGB and BGB with random forest (RF). The total AGB and BGB of the eastern Eurasian steppe were 35.26 × 106 MgC and 897.78 × 106 MgC, respectively, and the mean AGB and BGB density was 62.16 gC m−2 and 1594.04 gC m−2, respectively. Across the region, 16.23% and 14.61% of the grasslands showed increasing trend in AGB and BGB, respectively. On average, AGB increased by 1.37 gC m−2 per year and BGB increased by 14.64 gC m−2 per year. MAP and MAT accounted for 36.99% and 3.97% of the variation of AGB, indicating that shortage of water was the main limiting factor of AGB in the peak growth season. The contribution of MAT to the variation of BGB was 66.41%, which was much higher than that of MAP (4.62%), indicating that high temperature was the main limiting factor of BGB in the peak growth season. The climatic factors were much more important than human activity factors. The relationships between environmental factors and carbon stock also varied across the region. Our results can improve our understanding of the magnitude, spatial patterns, temporal changes, and driving factors of carbon stock in the eastern Eurasian steppe.
Lei Ding; Zhenwang Li; Beibei Shen; Xu Wang; Dawei Xu; Ruirui Yan; Yuchun Yan; Xiaoping Xin; Jingfeng Xiao. Estimating aboveground and belowground carbon stock of the eastern Eurasian steppe and assessing its spatiotemporal patterns and driving factors. Science of The Total Environment 2021, 149700 .
AMA StyleLei Ding, Zhenwang Li, Beibei Shen, Xu Wang, Dawei Xu, Ruirui Yan, Yuchun Yan, Xiaoping Xin, Jingfeng Xiao. Estimating aboveground and belowground carbon stock of the eastern Eurasian steppe and assessing its spatiotemporal patterns and driving factors. Science of The Total Environment. 2021; ():149700.
Chicago/Turabian StyleLei Ding; Zhenwang Li; Beibei Shen; Xu Wang; Dawei Xu; Ruirui Yan; Yuchun Yan; Xiaoping Xin; Jingfeng Xiao. 2021. "Estimating aboveground and belowground carbon stock of the eastern Eurasian steppe and assessing its spatiotemporal patterns and driving factors." Science of The Total Environment , no. : 149700.
Understanding the effects of livestock grazing on ecosystem respiration (Re) of grassland ecosystems is critical for accurately assessing the feedback of grazing management to climate change. We examined ecosystem respiration in response to varying cattle grazing intensities during growing seasons from 2009 to 2018 in a meadow steppe ecosystem of eastern Inner Mongolia. We found that ungrazed swards had the highest mean annual Re rate, with seasonal CVs in Re ranging from 37.53% to 46.04% for all treatments. When all treatments were analysed as a whole, we identified a significant positive relationship between the annual Re rate and annual peak value of standing plant aboveground biomass. Our findings showed that controlling factors on the mean annual Re differed substantially with grazing intensity. In ungrazed and lightly-grazed plots (G0.00 and G0.23), the mean annual Re rate was controlled mainly by canopy height and/or rainfall, while it was controlled more predominantly by contents of NH4+-N and available phosphorus in moderately and heavily grazed plots. We detected significant positive relationships of the annual Re rate with rainfall, soil moisture, ammonium nitrogen, and soil available phosphorus during the entire study period, whereas significant negative relationships were detected between the annual ecosystem respiration rate and the mean growing season temperature, irrespective of grazing intensity. Our findings revealed that grazing could substantially simplify the relationship between the mean annual Re rate and biotic and abiotic parameters. It may be concluded that the relationship between the annual Re rate and the standing crop aboveground biomass was a principal mechanism underlying the effects of gradient grazing on the Re of Chinese meadow steppe ecosystems.
Ruirui Yan; Yu Zhang; Miao Wang; Ruiqiang Li; Dongyan Jin; Xiaoping Xin; Linghao Li. Interannual variation in ecosystem respiration in an Inner Mongolian meadow steppe in response to livestock grazing. Ecological Indicators 2021, 131, 108121 .
AMA StyleRuirui Yan, Yu Zhang, Miao Wang, Ruiqiang Li, Dongyan Jin, Xiaoping Xin, Linghao Li. Interannual variation in ecosystem respiration in an Inner Mongolian meadow steppe in response to livestock grazing. Ecological Indicators. 2021; 131 ():108121.
Chicago/Turabian StyleRuirui Yan; Yu Zhang; Miao Wang; Ruiqiang Li; Dongyan Jin; Xiaoping Xin; Linghao Li. 2021. "Interannual variation in ecosystem respiration in an Inner Mongolian meadow steppe in response to livestock grazing." Ecological Indicators 131, no. : 108121.
Grazing is a key driver of plant communities and soil functions in grassland ecosystems. Soil nematodes play a vital role in soil ecological functions. The aim of this study was to explore how grazing shapes soil nematode community in different soil layers. We investigated the composition, abundance, diversity, metabolic footprint, and food web metrics of soil nematodes over a gradient of grazing in the 0–10 cm and 10–20 cm soil layers in a meadow steppe. The relationships between nematode community structure and biotic and abiotic factors were analyzed by principal component analysis and structural equation model analysis. Light grazing increased the abundance of total soil nematodes by 18.5%. Intensive grazing decreased the carbon used in production and metabolic footprints of plant parasites, fungivores, and total soil nematodes in 0–10 cm soils. There was no difference in the carbon used in production and metabolic footprints of soil nematodes among different grazing intensities in the 10–20 cm soil layer. Soil moisture, aboveground biomass, belowground biomass and Shannon diversity of grass contributed more to changes in soil nematode composition in both soil layers. In the 0–10 cm soil layer, grazing directly and indirectly affected soil nematode diversity via soil moisture and aboveground biomass, while grazing directly affected soil nematode diversity in 10–20 cm soil layer. Our results indicate that increasing soil depth can weaken the effect of grazing intensities on soil nematode fauna. Grazing affected the soil nematode community structure via different paths in different soil layers.
Fengjuan Pan; Ruirui Yan; Jinling Zhao; Linghao Li; Yanfeng Hu; Ye Jiang; Jie Shen; Neil B. McLaughlin; Dan Zhao; Xiaoping Xin. Effects of grazing intensity on soil nematode community structure and function in different soil layers in a meadow steppe. Plant and Soil 2021, 1 -14.
AMA StyleFengjuan Pan, Ruirui Yan, Jinling Zhao, Linghao Li, Yanfeng Hu, Ye Jiang, Jie Shen, Neil B. McLaughlin, Dan Zhao, Xiaoping Xin. Effects of grazing intensity on soil nematode community structure and function in different soil layers in a meadow steppe. Plant and Soil. 2021; ():1-14.
Chicago/Turabian StyleFengjuan Pan; Ruirui Yan; Jinling Zhao; Linghao Li; Yanfeng Hu; Ye Jiang; Jie Shen; Neil B. McLaughlin; Dan Zhao; Xiaoping Xin. 2021. "Effects of grazing intensity on soil nematode community structure and function in different soil layers in a meadow steppe." Plant and Soil , no. : 1-14.
Accurate monitoring of grassland aboveground fresh biomass (called AGB in the study) and its spatial-temporal dynamics is indispensable for sustainable grassland management. The most common method used in estimating AGB with remotely sensed data is based on the relationship between field AGB measurements and vegetation indices (VIs); however, the existing VIs do not deliver adequate results due to the soil background and spatial, temporal and sampling size variability. In this study, the AGB estimation model with the normalized difference phenology index (NDPI) was evaluated in terms of model robustness and spatial and temporal scalability based on comparisons with the widely used ratio vegetation index (RVI), difference vegetation index (DVI), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), soil-adjusted vegetation index (SAVI), modified soil-adjusted vegetation index (MSAVI), and optimized soil-adjusted vegetation index (OSAVI). The field measurements of AGB of the natural grassland in Inner Mongolia, China, collected in 2013, 2016, and 2017 and Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance products were used for analysis. The results based on training and independent validation data showed the following: (1) The R2 value between AGB and the NDPI was the highest (0.73) among all VIs, followed by soil-line-adjusted VIs, while the R2 values of the RVI and DVI were the lowest; (2) The NDPI-based model had the best robustness for different sampling sizes; (3) The NDPI-based model also had superior spatial and temporal scalability. The results from simulation experiments using the PROSAIL model also support the superiority of the NDPI in estimating AGB. The simulation analysis further reveals that the overall superiority of the NDPI originates from the fact that the NDPI overcomes the adverse impacts of the heterogeneity of the soil background and accounts for changes in the leaf water content that contribute substantially to AGB in grassland. These findings suggest that the NDPI-based AGB estimation model is advantageous for monitoring AGB in large grasslands with significant spatial-temporal heterogeneity.
Dawei Xu; Cong Wang; Jin Chen; Miaogen Shen; Beibei Shen; Ruirui Yan; Zhenwang Li; Arnon Karnieli; Jiquan Chen; Yuchun Yan; Xu Wang; Baorui Chen; Dameng Yin; Xiaoping Xin. The superiority of the normalized difference phenology index (NDPI) for estimating grassland aboveground fresh biomass. Remote Sensing of Environment 2021, 264, 112578 .
AMA StyleDawei Xu, Cong Wang, Jin Chen, Miaogen Shen, Beibei Shen, Ruirui Yan, Zhenwang Li, Arnon Karnieli, Jiquan Chen, Yuchun Yan, Xu Wang, Baorui Chen, Dameng Yin, Xiaoping Xin. The superiority of the normalized difference phenology index (NDPI) for estimating grassland aboveground fresh biomass. Remote Sensing of Environment. 2021; 264 ():112578.
Chicago/Turabian StyleDawei Xu; Cong Wang; Jin Chen; Miaogen Shen; Beibei Shen; Ruirui Yan; Zhenwang Li; Arnon Karnieli; Jiquan Chen; Yuchun Yan; Xu Wang; Baorui Chen; Dameng Yin; Xiaoping Xin. 2021. "The superiority of the normalized difference phenology index (NDPI) for estimating grassland aboveground fresh biomass." Remote Sensing of Environment 264, no. : 112578.
Livestock grazing is one of the major land uses, causing changes in the plant community's structure and grasslands composition. We assessed the effect of grazing intensity on aboveground biomass, species richness, and plant functional group (PFG) diversity in a temperature meadow steppe in Hulunbuir in northern China, involving 78 plant species from eight functional groups. Four grazing intensity classes were characterized, including light, moderate, heavy, and no grazing, based on stocking rates of 0.23, 0.46, 0.92, and 0.00 animal units per hectare. Our results show that the richness of short species, including perennial short grass, perennial short grass, and legume increased under light to moderate grazing, while no effect of grazing was observed on the richness of shrubs. With increasing grazing intensity, the aboveground biomass of perennial tall grasses and perennial tall forbs decreased significantly, while that of annual/biennial plant functional groups increased. The community diversity and evenness of annual/biennial plants increased significantly with grazing intensity. We concluded that heavy grazing has negative impacts on plant functional group richness and aboveground biomass.
Yousif Mohamed Zainelabdeen; Ahmed Ibrahim Ahmed; Ruirui Yan; Xiaoping Xin; Cao Juan; Jimoh Saheed Olaide. The Impact Of Long-term Grazing Intensity On Functional Groups Richness, Biomass, And Species Diversity In an Inner Mongolian Steppe Grassland. 2021, 1 .
AMA StyleYousif Mohamed Zainelabdeen, Ahmed Ibrahim Ahmed, Ruirui Yan, Xiaoping Xin, Cao Juan, Jimoh Saheed Olaide. The Impact Of Long-term Grazing Intensity On Functional Groups Richness, Biomass, And Species Diversity In an Inner Mongolian Steppe Grassland. . 2021; ():1.
Chicago/Turabian StyleYousif Mohamed Zainelabdeen; Ahmed Ibrahim Ahmed; Ruirui Yan; Xiaoping Xin; Cao Juan; Jimoh Saheed Olaide. 2021. "The Impact Of Long-term Grazing Intensity On Functional Groups Richness, Biomass, And Species Diversity In an Inner Mongolian Steppe Grassland." , no. : 1.
The accurate estimation of grassland vegetation parameters at a high spatial resolution is important for the sustainable management of grassland areas. Unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) sensors with a single laser beam emission capability can rapidly detect grassland vegetation parameters, such as canopy height, fractional vegetation coverage (FVC) and aboveground biomass (AGB). However, there have been few reports on the ability to detect grassland vegetation parameters based on RIEGL VUX-1 UAV LiDAR (Riegl VUX-1) systems. In this paper, we investigated the ability of Riegl VUX-1 to model the AGB at a 0.1 m pixel resolution in the Hulun Buir grazing platform under different grazing intensities. The LiDAR-derived minimum, mean, and maximum canopy heights and FVC were used to estimate the AGB across the entire grazing platform. The flight height of the LiDAR-derived vegetation parameters was also analyzed. The following results were determined: (1) The Riegl VUX-1-derived AGB was predicted to range from 29 g/m2 to 563 g/m2 under different grazing conditions. (2) The LiDAR-derived maximum canopy height and FVC were the best predictors of grassland AGB (R2 = 0.54, root-mean-square error (RMSE) = 64.76 g/m2). (3) For different UAV flight altitudes from 40 m to 110 m, different flight heights showed no major effect on the derived canopy height. The LiDAR-derived canopy height decreased from 9.19 cm to 8.17 cm, and the standard deviation of the LiDAR-derived canopy height decreased from 3.31 cm to 2.35 cm with increasing UAV flight altitudes. These conclusions could be useful for estimating grasslands in smaller areas and serving as references for other remote sensing datasets for estimating grasslands in larger areas.
Xiang Zhang; Yuhai Bao; Dongliang Wang; Xiaoping Xin; Lei Ding; Dawei Xu; Lulu Hou; Jie Shen. Using UAV LiDAR to Extract Vegetation Parameters of Inner Mongolian Grassland. Remote Sensing 2021, 13, 656 .
AMA StyleXiang Zhang, Yuhai Bao, Dongliang Wang, Xiaoping Xin, Lei Ding, Dawei Xu, Lulu Hou, Jie Shen. Using UAV LiDAR to Extract Vegetation Parameters of Inner Mongolian Grassland. Remote Sensing. 2021; 13 (4):656.
Chicago/Turabian StyleXiang Zhang; Yuhai Bao; Dongliang Wang; Xiaoping Xin; Lei Ding; Dawei Xu; Lulu Hou; Jie Shen. 2021. "Using UAV LiDAR to Extract Vegetation Parameters of Inner Mongolian Grassland." Remote Sensing 13, no. 4: 656.
Soil nematodes, as key bioindicators, play crucial roles in soil ecological process. Management of grasslands, such as meadow steppes in northeast China, is often done by mowing, which has an impact on soil nematode communities. However, few studies have explored effects of mowing frequency on the community structure and biomass of soil nematodes. Routine field and laboratory methods concerning plant community, soil properties, and soil nematodes were applied in this study. Soil nematode community structure was analyzed by using nonmetric multidimensional scaling (NMDS) and principal component analysis (PCA). The relationships between nematode genus and biotic and abiotic factors were analyzed by redundancy analysis (RDA). High mowing frequency significantly reduced abundance, biomass, and functional or metabolic regimes of soil nematodes in this ecosystem, whereas moderate mowing frequency enhanced those indices and regimes. Our findings showed that changing patterns in nematode indices across the mowing frequency conformed with the intermediate disturbance theory. Variations in soil nematode community were related to changes in belowground biomass, aboveground litter, soil available nitrogen and acidity, and the effects of edaphic and vegetal traits appeared to be trophic or genus-specific. This study has potential benefits for grassland restoration in northeast China.
F. J. Pan; L. Y. Yang; C. L. Wang; R. R. Yan; C. J. Li; Y. F. Hu; Y. Jiang; J. Cao; H. Y. Tan; X. P. Xin. Effects of mowing frequency on abundance, genus diversity and community traits of soil nematodes in a meadow steppe in northeast China. Plant and Soil 2020, 1 -19.
AMA StyleF. J. Pan, L. Y. Yang, C. L. Wang, R. R. Yan, C. J. Li, Y. F. Hu, Y. Jiang, J. Cao, H. Y. Tan, X. P. Xin. Effects of mowing frequency on abundance, genus diversity and community traits of soil nematodes in a meadow steppe in northeast China. Plant and Soil. 2020; ():1-19.
Chicago/Turabian StyleF. J. Pan; L. Y. Yang; C. L. Wang; R. R. Yan; C. J. Li; Y. F. Hu; Y. Jiang; J. Cao; H. Y. Tan; X. P. Xin. 2020. "Effects of mowing frequency on abundance, genus diversity and community traits of soil nematodes in a meadow steppe in northeast China." Plant and Soil , no. : 1-19.
Soil organic carbon (SOC) is the most critical component of global carbon cycle in grassland ecosystems. There has been growing interest in understanding SOC dynamics and driving forces of grassland biomes at various temporal and spatial scales. Up to now, estimates of long‐term and large‐scale changes in SOC of grassland biomes have been mostly based on modelling approaches and manipulative experiments, rather than direct measurements. During 2007–2011, we repeated 141 soil profiles of the sampling in 1963–1964 (up to one meter depth) to quantify the long‐term changes of SOC storage in the major grassland types of Inner Mongolia in order to tease apart the relative contributions of climate change and grazing. We found that SOC decreased in all soil types, except in the Aeolian sandy soils, from 1963 to 2007, with an average reduction rate of 1.8 kg C m–2 (~22.9% or 0.52% yr–1) in the grassland biome of Inner Mongolia. We quantitatively clustered the soils into four groups using principal component analysis (PCA), and detected clear spatial dependency of the changes on climate and grazing. The climate change was responsible for 15.3–34.9% of the total SOC variations, whereas grazing intensity accounted for <9.5% of the changes. Our findings indicated that climate change, rather than grazing, was the primary forcing for the changes in SOC of Inner Mongolia grasslands. We presume that other driving forces, such as changes in non‐grazing‐resultant wind erosion and atmospheric nitrogen deposition, might have played a role albeit their effects need to be further examined.
Xiaoping Xin; Dongyan Jin; Yong Ge; Jianghao Wang; Jiquan Chen; Jiaguo Qi; Housen Chu; Changliang Shao; Philip J. Murray; Ruixue Zhao; Qi Qin; Huajun Tang. Climate Change Dominated Long‐Term Soil Carbon Losses of Inner Mongolian Grasslands. Global Biogeochemical Cycles 2020, 34, 1 .
AMA StyleXiaoping Xin, Dongyan Jin, Yong Ge, Jianghao Wang, Jiquan Chen, Jiaguo Qi, Housen Chu, Changliang Shao, Philip J. Murray, Ruixue Zhao, Qi Qin, Huajun Tang. Climate Change Dominated Long‐Term Soil Carbon Losses of Inner Mongolian Grasslands. Global Biogeochemical Cycles. 2020; 34 (10):1.
Chicago/Turabian StyleXiaoping Xin; Dongyan Jin; Yong Ge; Jianghao Wang; Jiquan Chen; Jiaguo Qi; Housen Chu; Changliang Shao; Philip J. Murray; Ruixue Zhao; Qi Qin; Huajun Tang. 2020. "Climate Change Dominated Long‐Term Soil Carbon Losses of Inner Mongolian Grasslands." Global Biogeochemical Cycles 34, no. 10: 1.
Grazing is one of the predominant human activities taking place today inside protected areas, with both direct and indirect effects on the vegetation community. We analyzed the effects of grazing intensity on grass composition during four grazing seasons containing 78 plant species belonging to eight plant functional groups, which include perennial tall grass (6 species), perennial short grass (6 species), shrubs (3 species), legumes (9 species), Liliaceae herb (8 species), annual/biennial plants (11 species), perennial short forbs (16 species) and perennial tall forbs (18 species). We estimated grazing intensity at four levels, control, light, moderate and heavy grazing intensity corresponding to 0.00, 0.23, 0.46 and 0.92 animal units ha−1, respectively. We found that each plant functional group showed a different response to grazing intensity. Perennial tall grasses that were dominated by high palatable mesophyte and mesoxerophyte grass showed a significant decrease with grazing intensity, while the medium palatable xerophyte and widespread grasses that were the predominant short perennial increases with grazing intensity. The perennial tall forbs that were dominated by the mesophyte grass also decreased, but the decrease was statistically insignificant. The influence of grazing density on species is also related to soil factors (soil nutrient, soil moisture and soil temperature and soil bulk density). Some functional groups such as tall fescue and Liliaceae herbs, remained stable—which may be related to the changes in the soil environment caused by grazing activities. The findings of this study could provide a standpoint for assessing the current grazing management scenarios and conducting timely adaptive practices to maintain the long-term ability of grassland systems to perform their ecological functions.
Yousif Mohamed Zainelabdeen; Ruirui Yan; Xiaoping Xin; Yuchun Yan; Ahmed Ibrahim Ahmed; Lulu Hou; Yu Zhang. The Impact of Grazing on the Grass Composition in Temperate Grassland. Agronomy 2020, 10, 1230 .
AMA StyleYousif Mohamed Zainelabdeen, Ruirui Yan, Xiaoping Xin, Yuchun Yan, Ahmed Ibrahim Ahmed, Lulu Hou, Yu Zhang. The Impact of Grazing on the Grass Composition in Temperate Grassland. Agronomy. 2020; 10 (9):1230.
Chicago/Turabian StyleYousif Mohamed Zainelabdeen; Ruirui Yan; Xiaoping Xin; Yuchun Yan; Ahmed Ibrahim Ahmed; Lulu Hou; Yu Zhang. 2020. "The Impact of Grazing on the Grass Composition in Temperate Grassland." Agronomy 10, no. 9: 1230.
The management practices required for grazing management will continue to increase, as necessitated by the reported rate of reduction in productivity, coupled with the degradation of Inner Mongolian steppe ecosystems. The current study was conducted to (i) examine the responses of aboveground net primary production (ANPP) to different grazing intensities and its relationship with soil factors and (ii) study the effects of grazing intensity on herbage growth and dry matter intake in Hulunber grasslands, Northeastern China. Six grazing rate treatments (G0.00, G0.23, G0.34, G0.46, G0.69, and G0.92 animal unit (AU ha−1) for zero, two, three, four, six, and eight young cows with ranging weight of 250–300 kg/plot), with three replications, were established during two consecutive growing seasons in 2017 and 2018. Our study concentrated on the grazing-induced degradation processes by different intensities of grazing. The highest decrease in aboveground biomass (AGB) was 64.1% and 59.3%, in 2017 and 2018, respectively, by the G0.92 treatment as compared with the G0.00 treatment. There was a positive relationship between yearly precipitation and ANPP. The grazing tolerance and growth rate of forage were higher in the wet year than in the dry year. Understanding the ecological consequences of grazing intensity provides useful information for assessing current grazing management scenarios and taking timely adaptation measures to maintain grassland capacity in a short and long-term system.
Ahmed Ahmed; Lulu Hou; Ruirui Yan; Xiaoping Xin; Yousif Zainelabdeen. The Joint Effect of Grazing Intensity and Soil Factors on Aboveground Net Primary Production in Hulunber Grasslands Meadow Steppe. Agriculture 2020, 10, 263 .
AMA StyleAhmed Ahmed, Lulu Hou, Ruirui Yan, Xiaoping Xin, Yousif Zainelabdeen. The Joint Effect of Grazing Intensity and Soil Factors on Aboveground Net Primary Production in Hulunber Grasslands Meadow Steppe. Agriculture. 2020; 10 (7):263.
Chicago/Turabian StyleAhmed Ahmed; Lulu Hou; Ruirui Yan; Xiaoping Xin; Yousif Zainelabdeen. 2020. "The Joint Effect of Grazing Intensity and Soil Factors on Aboveground Net Primary Production in Hulunber Grasslands Meadow Steppe." Agriculture 10, no. 7: 263.
Species composition and biomass are two important indicators in assessing the effects of restoration measures of degraded grasslands. In this paper, we present a field study on the temporal changes in plant community characteristics, species diversity and biomass production in a degraded temperate meadow steppe in response to an enclosure measure in Hulunbuir in Northern China. Our results showed that the plant community responded positively to the fence enclosure in terms of vegetation coverage, height, above- and belowground biomass. A year-to-year increase in aboveground biomass was observed, and this increase plateaued at the ninth year of the enclosure. Our results also showed that the existing dominant and foundation species gained predominance against other species. The sum of the biomass of these two species was more than doubled after the ninth year of the enclosure. However, belowground biomass only briefly increased until the fifth year of the enclosure and then decreased until the end of the experimental period. Plant diversity, evenness, and richness indices showed similar trends to that of belowground biomass. Overall, we found that the degraded temperate meadow steppe responded significantly positively to the enclosure treatment, but an optimal condition was only reached after approximately 5–7 years of continuous protection, providing a solid use case for grassland conservation and management at regional scales.
Lijun Xu; Yingying Nie; Baorui Chen; Xiaoping Xin; Guixia Yang; Dawei Xu; Liming Ye. Effects of Fence Enclosure on Vegetation Community Characteristics and Productivity of a Degraded Temperate Meadow Steppe in Northern China. Applied Sciences 2020, 10, 2952 .
AMA StyleLijun Xu, Yingying Nie, Baorui Chen, Xiaoping Xin, Guixia Yang, Dawei Xu, Liming Ye. Effects of Fence Enclosure on Vegetation Community Characteristics and Productivity of a Degraded Temperate Meadow Steppe in Northern China. Applied Sciences. 2020; 10 (8):2952.
Chicago/Turabian StyleLijun Xu; Yingying Nie; Baorui Chen; Xiaoping Xin; Guixia Yang; Dawei Xu; Liming Ye. 2020. "Effects of Fence Enclosure on Vegetation Community Characteristics and Productivity of a Degraded Temperate Meadow Steppe in Northern China." Applied Sciences 10, no. 8: 2952.
While a number of machine learning (ML) models have been used to estimate RE, systematic evaluation and comparison of these models are still limited. In this study, we developed three traditional ML models and a deep learning (DL) model, stacked autoencoders (SAE), to estimate RE in northern China’s grasslands. The four models were trained with two strategies: training for all of northern China’s grasslands and separate training for the alpine and temperate grasslands. Our results showed that all four ML models estimated RE in northern China’s grasslands fairly well, while the SAE model performed best (R2 = 0.858, RMSE = 0.472 gC m−2 d−1, MAE = 0.304 gC m−2 d−1). Models trained with the two strategies had almost identical performances. The enhanced vegetation index and soil organic carbon density (SOCD) were the two most important environmental variables for estimating RE in the grasslands of northern China. Air temperature (Ta) was more important than the growing season land surface water index (LSWI) in the alpine grasslands, while the LSWI was more important than Ta in the temperate grasslands. These findings may promote the application of DL models and the inclusion of SOCD for RE estimates with increased accuracy.
Xiaobo Zhu; Honglin He; Mingguo Ma; Xiaoli Ren; Li Zhang; Fawei Zhang; Yingnian Li; Peili Shi; Shiping Chen; Yanfen Wang; Xiaoping Xin; Yaoming Ma; Yu Zhang; Mingyuan Du; Rong Ge; Na Zeng; Pan Li; Zhongen Niu; Liyun Zhang; Yan Lv; Zengjing Song; Qing Gu. Estimating Ecosystem Respiration in the Grasslands of Northern China Using Machine Learning: Model Evaluation and Comparison. Sustainability 2020, 12, 2099 .
AMA StyleXiaobo Zhu, Honglin He, Mingguo Ma, Xiaoli Ren, Li Zhang, Fawei Zhang, Yingnian Li, Peili Shi, Shiping Chen, Yanfen Wang, Xiaoping Xin, Yaoming Ma, Yu Zhang, Mingyuan Du, Rong Ge, Na Zeng, Pan Li, Zhongen Niu, Liyun Zhang, Yan Lv, Zengjing Song, Qing Gu. Estimating Ecosystem Respiration in the Grasslands of Northern China Using Machine Learning: Model Evaluation and Comparison. Sustainability. 2020; 12 (5):2099.
Chicago/Turabian StyleXiaobo Zhu; Honglin He; Mingguo Ma; Xiaoli Ren; Li Zhang; Fawei Zhang; Yingnian Li; Peili Shi; Shiping Chen; Yanfen Wang; Xiaoping Xin; Yaoming Ma; Yu Zhang; Mingyuan Du; Rong Ge; Na Zeng; Pan Li; Zhongen Niu; Liyun Zhang; Yan Lv; Zengjing Song; Qing Gu. 2020. "Estimating Ecosystem Respiration in the Grasslands of Northern China Using Machine Learning: Model Evaluation and Comparison." Sustainability 12, no. 5: 2099.
Accurately estimating grassland carbon stocks is important in assessing grassland productivity and the global carbon balance. This study used the regression kriging (RK) method to estimate grassland carbon stocks in Northeast China based on Landsat8 operational land imager (OLI) images and five remote sensing variables. The normalized difference vegetation index (NDVI), the wide dynamic range vegetation index (WDRVI), the chlorophyll index (CI), Band6 and Band7 were used to build the RK models separately and to explore their capabilities for modeling spatial distributions of grassland carbon stocks. To explore the different model performances for typical grassland and meadow grassland, the models were validated separately using the typical steppe, meadow steppe or all-steppe ground measurements based on leave-one-out crossvalidation (LOOCV). When the results were validated against typical steppe samples, the Band6 model showed the best performance (coefficient of determination (R2) = 0.46, mean average error (MAE) = 8.47%, and root mean square error (RMSE) = 10.34 gC/m2) via the linear regression (LR) method, while for the RK method, the NDVI model showed the best performance (R2 = 0.63, MAE = 7.04 gC/m2, and RMSE = 8.51 gC/m2), which were much higher than the values of the best LR model. When the results were validated against the meadow steppe samples, the CI model achieved the best estimation accuracy, and the accuracy of the RK method (R2 = 0.72, MAE = 8.09 gC/m2, and RMSE = 9.89 gC/m2) was higher than that of the LR method (R2 = 0.70, MAE = 8.99 gC/m2, and RMSE = 10.69 gC/m2). Upon combining the results of the most accurate models of the typical steppe and meadow steppe, the RK method reaches the highest model accuracy of R2 = 0.69, MAE = 7.40 gC/m2, and RMSE = 9.01 gC/m2, while the LR method reaches the highest model accuracy of R2 = 0.53, MAE = 9.20 gC/m2, and RMSE = 11.10 gC/m2. The results showed an improved performance of the RK method compared to the LR method, and the improvement in the accuracy of the model is mainly attributed to the enhancement of the estimation accuracy of the typical steppe. In the study region, the carbon stocks showed an increasing trend from west to east, the total amount of grassland carbon stock was 79.77 × 104 Mg C, and the mean carbon stock density was 47.44 gC/m2. The density decreased in the order of temperate meadow steppe, lowland meadow steppe, temperate typical steppe, and sandy steppe. The methodology proposed in this study is particularly beneficial for carbon stock estimates at the regional scale, especially for countries such as China with many grassland types.
Lei Ding; Zhenwang Li; Xu Wang; Ruirui Yan; Beibei Shen; Baorui Chen; Xiaoping Xin. Estimating Grassland Carbon Stocks in Hulunber China, Using Landsat8 Oli Imagery and Regression Kriging †. Sensors 2019, 19, 5374 .
AMA StyleLei Ding, Zhenwang Li, Xu Wang, Ruirui Yan, Beibei Shen, Baorui Chen, Xiaoping Xin. Estimating Grassland Carbon Stocks in Hulunber China, Using Landsat8 Oli Imagery and Regression Kriging †. Sensors. 2019; 19 (24):5374.
Chicago/Turabian StyleLei Ding; Zhenwang Li; Xu Wang; Ruirui Yan; Beibei Shen; Baorui Chen; Xiaoping Xin. 2019. "Estimating Grassland Carbon Stocks in Hulunber China, Using Landsat8 Oli Imagery and Regression Kriging †." Sensors 19, no. 24: 5374.
By altering plant and soil properties and microclimate environments, grazing has a profound influence on the structure and function of grassland ecosystems. However, few studies have addressed the potential grazing effects on snow accumulation and subsequent spring soil water after snow melting and soil thawing. In this study, vegetation properties, snow accumulation and soil water were measured in experimental plots subjected to 8 years of cattle grazing comprising six different grazing intensity treatments in a typical temperate grassland in eastern Eurasia. The results indicated that heavy grazing reduced the snow depth by 51% and the snow mass by 40%. Snow accumulation first rapidly increased but then remained relatively stable with increases in both the aboveground biomass and canopy height. An obvious inflection point occurred at approximately 200 g m−2 aboveground biomass and at a 12.5 cm canopy height. The obvious difference in soil water content between the heavily grazed and ungrazed treatments occurred mainly in the spring after snow melting and soil thawing. The spring soil water content (0–30 cm) reached 31.5% in the ungrazed treatment (G0), which was 1.7 times that in the heavily grazed treatment (G0.92). The soil water content increased exponentially with increasing vegetation properties (aboveground biomass, canopy height and canopy cover), and a similar trend occurred with increasing snow mass and snow depth. Our structural equation modeling showed that both vegetation properties and snow accumulation had significant positive effects on spring soil water. By removing vegetation, grazing at increased intensities had significant, indirect suppressive effects on snow accumulation and spring soil water. Therefore, to obtain increased amounts of snow accumulation and spring soil water, land managers should consider reducing the grazing intensity or leaving some plots ungrazed.
Yuchun Yan; Ruirui Yan; Xu Wang; Xingliang Xu; Dawei Xu; Dongyan Jin; Jinqiang Chen; Xiaoping Xin. Grazing affects snow accumulation and subsequent spring soil water by removing vegetation in a temperate grassland. Science of The Total Environment 2019, 697, 134189 .
AMA StyleYuchun Yan, Ruirui Yan, Xu Wang, Xingliang Xu, Dawei Xu, Dongyan Jin, Jinqiang Chen, Xiaoping Xin. Grazing affects snow accumulation and subsequent spring soil water by removing vegetation in a temperate grassland. Science of The Total Environment. 2019; 697 ():134189.
Chicago/Turabian StyleYuchun Yan; Ruirui Yan; Xu Wang; Xingliang Xu; Dawei Xu; Dongyan Jin; Jinqiang Chen; Xiaoping Xin. 2019. "Grazing affects snow accumulation and subsequent spring soil water by removing vegetation in a temperate grassland." Science of The Total Environment 697, no. : 134189.
The development of fertile patches within an infertile matrix is a common phenomenon in drylands. Shrub-centered expansion of fertile islands is generally attributed to processes of sediment erosion and deposition, but there have been fewer studies of how litter might contribute to the development of fertile islands in semiarid shrub grassland. We quantified the capture of two tumble plant species (Cleistogenes squarrosa, Salsola collina; also known as tumble weeds) by shrubs across ten sites across 38,000 km2 of a semiarid grassland encroached by Caragana microphylla. Tumble plants are plants that blow across the grassland propelled by strong winds. Both tumble plant species were found over extensive areas of semiarid grassland, and their distribution coincides with the distribution of Caragana microphylla. Biomass production of both tumble plants averaged 12.2 g m−2 (range: 1.0 to 25.0 g m−2) and litter accumulation (amount accruing from wind-blown plants) of both tumble plants was significantly greater beneath shrubs (94.5 ± 28.9 g m−2 mean ± SE) than in the interspaces (3.3 ± 1.4 g m−2). Most of the material collecting under Caragana microphylla comprised tumble plants. Increases in the area of Caragana microphylla patches did not correspond to greater tumble plant capture. However, the supply of tumble plants was the strongest predictor of capture within shrub hummocks, suggesting that tumble plant capture is source limited rather than sink limited. Our structural equation model indicates that increases in grass cover and height were indirectly and negatively associated with tumble plant capture by reducing the tumble plant supply. Contrary to prediction, shrub height and shrub patch area had no overall effect on the tumble plant capture. Overall, we maintain that the capture of tumble plants by shrubs is an important self-maintaining mechanism of shrub-encroached grasslands. Tumble plant abundance is predicted to increase with increasing surface human disturbance and aridity. Therefore, the “shrub-litter island” effect is likely to be an important mechanism for maintaining and promoting the encroachment of shrubs into semiarid grasslands.
Yuchun Yan; Dawei Xu; Xingliang Xu; Deli Wang; Xu Wang; Yurong Cai; Jinqiang Chen; Xiaoping Xin; David J Eldridge. Shrub patches capture tumble plants: potential evidence for a self-reinforcing pattern in a semiarid shrub encroached grassland. Plant and Soil 2019, 442, 311 -321.
AMA StyleYuchun Yan, Dawei Xu, Xingliang Xu, Deli Wang, Xu Wang, Yurong Cai, Jinqiang Chen, Xiaoping Xin, David J Eldridge. Shrub patches capture tumble plants: potential evidence for a self-reinforcing pattern in a semiarid shrub encroached grassland. Plant and Soil. 2019; 442 (1-2):311-321.
Chicago/Turabian StyleYuchun Yan; Dawei Xu; Xingliang Xu; Deli Wang; Xu Wang; Yurong Cai; Jinqiang Chen; Xiaoping Xin; David J Eldridge. 2019. "Shrub patches capture tumble plants: potential evidence for a self-reinforcing pattern in a semiarid shrub encroached grassland." Plant and Soil 442, no. 1-2: 311-321.
Lijun Xu; Xuejuan Tang; Bo Wang; Xiaoping Xin; Qizhong Sun; Yalu Li; Jinqiang Chen; Gele Qing; Mingying Guo. Comparative transcriptome analysis of five Medicago varieties reveals the genetic signals underlying freezing tolerance. Crop and Pasture Science 2019, 70, 1 .
AMA StyleLijun Xu, Xuejuan Tang, Bo Wang, Xiaoping Xin, Qizhong Sun, Yalu Li, Jinqiang Chen, Gele Qing, Mingying Guo. Comparative transcriptome analysis of five Medicago varieties reveals the genetic signals underlying freezing tolerance. Crop and Pasture Science. 2019; 70 (3):1.
Chicago/Turabian StyleLijun Xu; Xuejuan Tang; Bo Wang; Xiaoping Xin; Qizhong Sun; Yalu Li; Jinqiang Chen; Gele Qing; Mingying Guo. 2019. "Comparative transcriptome analysis of five Medicago varieties reveals the genetic signals underlying freezing tolerance." Crop and Pasture Science 70, no. 3: 1.
The spatial distribution of different grassland types is important for effectively analyzing spatial patterns, obtaining key vegetation parameters using remote sensing (e.g., biomass, leaf area index, net primary production), and using and protecting grasslands. Existing classifications of grasslands by remote sensing are mostly divided according to the fractional vegetation cover or biomass, but classifications according to grassland types are scarce. In this study, we focused on the classification of different grassland types using remote sensing based on object-based image analysis (OBIA) with multitemporal images in combination with a 30-m digital elevation model (DEM) and the normalized difference vegetation index (NDVI). The grasslands were located in Hulunber, Inner Mongolia, and an autonomous region of China. The support vector machine (SVM) and random forest (RF) machine learning classifiers were selected for the classification. The results revealed the following: 1) It is feasible to generally extract different grassland types on the basis of OBIA with multisource data; the overall classification accuracy and Kappa value exceeded 90% and 0.9, respectively, using the SVM and RF machine learning classifiers, and the classification accuracy of the different grassland types ranged from 61.64% to 98.71%; 2) Multitemporal images and auxiliary data (DEM and NDVI) improved the separability of different grassland types. The information in the growing season was conducive for distinguishing temperate meadow steppe from temperate steppe and was favorable for extracting lowland meadow and swamp in the nongrowing season. The DEM and NDVI also effectively reduced the number of image segmentation objects and improved the segmentation effects; 3) Spectral and textural features were more important than geometric features in this study. A few main variables played a major role in the classification, while a large number of variables had either no significant effect or a negative effect on the classification results when the optimal feature subset was determined. This study provides a scientific basis and reference for the classification of various grassland types by remote sensing, including the data selection, image segmentation, feature selection, classifier selection, and parameter settings.
Dawei Xu; Baorui Chen; Beibei Shen; Xu Wang; Yuchun Yan; Lijun Xu; Xiaoping Xin. The Classification of Grassland Types Based on Object-Based Image Analysis with Multisource Data. Rangeland Ecology & Management 2018, 72, 318 -326.
AMA StyleDawei Xu, Baorui Chen, Beibei Shen, Xu Wang, Yuchun Yan, Lijun Xu, Xiaoping Xin. The Classification of Grassland Types Based on Object-Based Image Analysis with Multisource Data. Rangeland Ecology & Management. 2018; 72 (2):318-326.
Chicago/Turabian StyleDawei Xu; Baorui Chen; Beibei Shen; Xu Wang; Yuchun Yan; Lijun Xu; Xiaoping Xin. 2018. "The Classification of Grassland Types Based on Object-Based Image Analysis with Multisource Data." Rangeland Ecology & Management 72, no. 2: 318-326.
Grazing is a major modulator of biodiversity and productivity in grasslands. However, our understanding of grazing-induced changes in below-ground communities, processes, and soil productivity is limited. Here, using a long-term enclosed grazing meadow steppe, we investigated the impacts of grazing on the soil organic carbon (SOC) turnover, the microbial community composition, resistance and activity under seasonal changes, and the microbial contributions to soil productivity. The results demonstrated that grazing had significant impacts on soil microbial communities and ecosystem functions in meadow steppe. The highest microbial α-diversity was observed under light grazing intensity, while the highest β-diversity was observed under moderate grazing intensity. Grazing shifted the microbial composition from fungi dominated to bacteria dominated and from slow growing to fast growing, thereby resulting in a shift from fungi-dominated food webs primarily utilizing recalcitrant SOC to bacteria-dominated food webs mainly utilizing labile SOC. Moreover, the higher fungal recalcitrant-SOC-decomposing activities and bacterial labile-SOC-decomposing activities were observed in fungi- and bacteria-dominated communities, respectively. Notably, the robustness of bacterial community and the stability of bacterial activity were associated with α-diversity, while this was not the case for the robustness of fungal community and its associated activities. Finally, we observed that microbial α-diversity rather than SOC turnover rate can predict soil productivity. Our findings indicate the strong influence of grazing on soil microbial community, SOC turnover, and soil productivity and the important positive role of soil microbial α-diversity in steering the functions of meadow steppe ecosystems.
Weibing Xun; Ruirui Yan; Yi Ren; Dongyan Jin; Wu Xiong; Guishan Zhang; Zhongli Cui; Xiaoping Xin; Ruifu Zhang. Grazing-induced microbiome alterations drive soil organic carbon turnover and productivity in meadow steppe. Microbiome 2018, 6, 1 -13.
AMA StyleWeibing Xun, Ruirui Yan, Yi Ren, Dongyan Jin, Wu Xiong, Guishan Zhang, Zhongli Cui, Xiaoping Xin, Ruifu Zhang. Grazing-induced microbiome alterations drive soil organic carbon turnover and productivity in meadow steppe. Microbiome. 2018; 6 (1):1-13.
Chicago/Turabian StyleWeibing Xun; Ruirui Yan; Yi Ren; Dongyan Jin; Wu Xiong; Guishan Zhang; Zhongli Cui; Xiaoping Xin; Ruifu Zhang. 2018. "Grazing-induced microbiome alterations drive soil organic carbon turnover and productivity in meadow steppe." Microbiome 6, no. 1: 1-13.
Dawei Xu; Baorui Chen; Yuchun Yan; Xinbo Sun; Xiaoping Xin. Spatial-temporal dynamic monitoring of Mongolian pine (Pinus sylvestris var. mongolica) based on remote sensing data. Remote Sensing Letters 2018, 9, 1079 -1088.
AMA StyleDawei Xu, Baorui Chen, Yuchun Yan, Xinbo Sun, Xiaoping Xin. Spatial-temporal dynamic monitoring of Mongolian pine (Pinus sylvestris var. mongolica) based on remote sensing data. Remote Sensing Letters. 2018; 9 (11):1079-1088.
Chicago/Turabian StyleDawei Xu; Baorui Chen; Yuchun Yan; Xinbo Sun; Xiaoping Xin. 2018. "Spatial-temporal dynamic monitoring of Mongolian pine (Pinus sylvestris var. mongolica) based on remote sensing data." Remote Sensing Letters 9, no. 11: 1079-1088.
Few studies have addressed the potential grazing effects on microclimate, such as surface temperature and moisture, and their feedback effects on grassland function. A continuous, approximately three-year long study was conducted in experimental plots of various grazing intensities, and in situ soil temperature and moisture were measured. The results indicated that grazing significantly altered soil temperature and moisture. Soil temperature increased exponentially with increasing grazing intensity in the warm season due to the removal of aboveground biomass (AGB) and decreased linearly with increasing grazing intensity in the cold season due to decreases in both AGB and wind-blown snow accumulation. Heavy grazing increased soil temperature (10 cm depth) by an average of 2.6 °C from April to October (the largest hourly temperature increase was 8.8 °C), representing a soil warming effect 3.7 times that of global warming. Our findings showed that, compared with ungrazed plots, grazed plots had decreased soil water storage due to less winter snow accumulation, especially in the early growing season (EGS) because of the smaller amount of winter snow accumulation than in ungrazed plots. In the EGS, the average water storage in the 0–100 cm layer of the ungrazed plots was 23.3%, which was 1.3–1.8 times that of the grazed plots. Our results showed that grazing also produced warming and drying effects on grassland soil. The long-term feedback effects of grazing-induced soil warming and drying on the ecosystem might be an important mechanism accelerating the degradation and desertification of these grasslands.
Yuchun Yan; Ruirui Yan; Jiquan Chen; Xiaoping Xin; David J. Eldridge; Changliang Shao; Xu Wang; Shijie Lv; Dongyan Jin; Jinqaing Chen; Zhenjie Guo; Baorui Chen; Lijun Xu. Grazing modulates soil temperature and moisture in a Eurasian steppe. Agricultural and Forest Meteorology 2018, 262, 157 -165.
AMA StyleYuchun Yan, Ruirui Yan, Jiquan Chen, Xiaoping Xin, David J. Eldridge, Changliang Shao, Xu Wang, Shijie Lv, Dongyan Jin, Jinqaing Chen, Zhenjie Guo, Baorui Chen, Lijun Xu. Grazing modulates soil temperature and moisture in a Eurasian steppe. Agricultural and Forest Meteorology. 2018; 262 ():157-165.
Chicago/Turabian StyleYuchun Yan; Ruirui Yan; Jiquan Chen; Xiaoping Xin; David J. Eldridge; Changliang Shao; Xu Wang; Shijie Lv; Dongyan Jin; Jinqaing Chen; Zhenjie Guo; Baorui Chen; Lijun Xu. 2018. "Grazing modulates soil temperature and moisture in a Eurasian steppe." Agricultural and Forest Meteorology 262, no. : 157-165.