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Using meteorological and grassland fire data for Hulun Buir, China, from 1990 to 2019, we explored variation in meteorological factors, assessed the frequencies and areas of grassland fires, and used an autoregressive distributed lag (ARDL) model to investigate correlations between burned areas and meteorological factors. Fires in Hulun Buir grassland mainly occurred between 1990 and 1999; from 1990 to 2009, most fires occurred in Prairie Chenbarhu Banner, Xin Barag Right Banner and Xin Barag Left Banner. Grassland fires in Hulun Buir decreased significantly from 2010 to 2019. From 1990 to 2019, grassland fires mostly occurred in spring and autumn. Among the seven meteorological factors, wind speed, relative humidity and sunshine hours were most strongly correlated with burned areas. Regarding long-term and short-term effects, relative humidity had the biggest influence on burned area. The results demonstrated that ARDL models can reveal the long-term and short-term sensitivity of meteorological factors related to fire, and the ARDL econometrics method can be used effectively to study trends in grassland fire burning area. [ABSTRACT FROM AUTHOR]

Purpose: As the main component of terrestrial carbon pool, soil organic carbon (SOC) is vital to soil fertility and biogeochemical cycle. Quantifying the spatial distribution of regional SOC stocks (SOCS) provides critical support for climate change and food security decisions. Our aim was to explore the optimal interpolation method to improve the accuracy of spatial prediction of SOCS in temperate grasslands. Materials and methods: To support such research, we performed soil sampling to depths of 0 to 20 and 20 to 30 cm throughout the Hulun Buir grassland of Inner Mongolia. We compared prediction of the spatial patterns of SOCS using regression kriging (RK) and ordinary kriging (OK). We used topographic factors, climate variables, satellite data (the normalized-difference vegetation index (NDVI)), and soil texture as predictors in the RK method. Results and discussion: SOCS was significantly positively correlated with precipitation, NDVI, topographic variables, and clay content, but negatively correlated with temperature and sand content. NDVI explained more than 40% of the SOCS spatial variation and was the dominant factor. Geostatistical analysis showed strong and moderate spatial dependence of SOCS in the 0–20- and 20–30-cm soil layers, respectively. The RK and OK soil pools to a depth of 30 cm were 607.28 and 559.46 Tg, respectively. Conclusion: Compared with OK, the RK method improved the SOCS prediction accuracy by 20.4, 30.1, and 23.9% for soil depths of 0–20, 20–30, and 0–30 cm, respectively. Our findings suggest that OK may be acceptable where the environmental conditions are homogeneous, but that RK performs better in heterogeneous areas. [ABSTRACT FROM AUTHOR]

Constantly increasing vegetation changes pose serious challenges to the sustainable use of global ecosystems. Thus, facing the increasingly serious climate and ecological environment problems and improving vegetation coverage is crucial to the sustainable development of the region. Along these lines, in this work, a monitoring model of vegetation cover change was proposed and developed by using Landsat TM (1989, 1999, and 2011) and Landsat OLI-TIRS (2021) data. More specifically, it was used to assess vegetation change. Based on this model, the vegetation change in the core area of Hulun Buir Grassland was systematically analyzed., From the acquired results, the existence of spatial differences in the vegetation coverage changes in the study area were demonstrated. The total area of vegetation coverage changes was 758.95 km2, and the area from low vegetation coverage to high vegetation coverage was 456.41 km2, accounting for 60.14% of the total change area. The area from high vegetation coverage to low vegetation coverage was 302.57 km2, accounting for 39.86% of the total change area, whereas the area of the area without vegetation coverage was 1963.92 km2, accounting for 72.13% of the study area, and the overall vegetation coverage is improving. Vegetation cover change monitoring models can also be used to reveal and describe large-scale vegetation landscape changes and obtain clear vegetation change results through easy-to-obtain data; our work suggests that in the process of pursuing regional economic development and accelerating urbanization, industrialization, and agricultural modernization, human beings should assume more responsibilities and pursue the sustainable development of the natural environment. The results of this work are of great importance to further study the potential driving mechanism of the vegetation coverage changes and provide theoretical guidance for relevant managers to formulate vegetation restoration measures. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Applied Ecology / Yingyong Shengtai Xuebao is the property of Chinese Journal of Applied Ecology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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.

Studies on effects of nitrogen deposition were mainly focused on temperate grasslands in Inner Mongolia of China. In addition, there are substantial differences between the present simulation methods and the natural nitrogen deposition. A three-year experiment was carried out to compare the effects of simulation methods (common urea and slow-released urea) and nitrogen deposition rates (0, 25, 50, 75, 100, 150, 200 and 300 kg N µ hm-2 µ a-1) on soil nutrients and biological characteristics in Hulun Buir Grassland. We found that simulated nitrogen deposition had significant influences on soil chemical properties, biological properties and enzyme activities. With the increases of nitrogen deposition, soil pH declined with the greatest extent of 0.2 units, while the highest concentrations of total dissolved nitrogen (TDN) and dissolved organic carbon (DOC) increased by 5-7 times and 12% - 36%, respectively. There was a decline trend for soil total phosphorus (TP) and organic phosphorus (TOP). Microbial biomass and metabolic activity increased firstly and then decreased. Moderate simulated nitrogen deposition rates significantly increased soil carbon, nitrogen and phosphorus related enzyme activities. Compared to common urea, using slow- released urea to simulate nitrogen deposition decelerate the decline of soil pH and the increase of dissolved nutrients, and smoothed the change of microbial biomass, metabolic activity, and nitrogen hydrolyzed enzyme activities. Overall, the results confirmed that continuous nitrogen input caused the decline of soil pH and the increase of bioavailable carbon and nitrogen, and then changed microbial biomass and activity. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ecology & Rural Environment is the property of Journal of Ecology & Rural Environment Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Climate change has resulted in instability of semi-arid ecosystems under global warming, especially during the last decades. Hulun Buir Grassland is suffering from prominent warming and drying. Therefore, it is vital to research the change of climate factors such as precipitation, temperature and carbon dioxide concentrations [ C O 2 ] on vegetation dynamics. A vegetation–climate dynamic model is applied in a typical semi-arid area of Hulun Buir Grassland in China to research the effects on vegetation distribution due to climate change. Various climate scenarios with representative concentration pathways (Current, RCP2.6, RCP4.5, RCP8.5) are compared. Temperature, precipitation and C O 2 fertilization effects play critical roles on vegetation growth. The vegetation pattern shows the synergies of temperature, precipitation and [ C O 2 ]. From our research, the vegetation pattern here may change from dense spot state to bare state. The ecosystem shows the fastest degradation under Current scenario, suggesting that Current scenario is the worst climate condition for vegetation growth there. However, the RCP2.6 scenario is an ideal climate state for vegetation growth in which the radiative forcing is low, precipitation increases slightly, and vegetation can grow well. • Our climate–vegetation model can simulate climate change driven vegetation pattern well. • Current scenario is the worst condition for vegetation growth among all scenarios here. • Increase in concentration of C O 2 will benefit vegetation growth here. • Vegetation pattern will not format after tipping-point due to climate factors. [ABSTRACT FROM AUTHOR]

The semi-arid grasslands in Inner Mongolia, China have been degraded by long-term grazing. A series of ecological restoration strategies have been implemented to improve grassland service. However, little is known about the effect of these ecological restoration practices on soil carbon and nitrogen storage. In this study, characteristics of vegetation and soil properties under continued grazing and exclusion of livestock for six years due to a nationwide conservation program-'Returning Grazing Lands to Grasslands (RGLG)' were examined in semi-arid Hulun Buir grassland in Inner Mongolia, China. The results show that removal of grazing for six years resulted in a significant recovery in vegetation with higher above and below-ground biomass, but a lower soil bulk density and pH value. After six years of grazing exclusion, soil organic C and total N storage increased by 13.9% and 17.1%, respectively, which could be partly explained by decreased loss and increased input of C and N to soil. The effects of grazing exclusion on soil C and N concentration and storage primarily occurred in the upper soil depths. The results indicate that removal of grazing pressure within the RGLG program was an effective restoration approach to control grassland degradation in this region. However, more comprehensive studies are needed to evaluate the effectiveness of the RGLG program and to improve the management strategies for grassland restoration in this area. [ABSTRACT FROM AUTHOR]

In order to select a best mowing regime for natural grassland in Hulun Buir with highly efficient hay-making and sustainable utilization, we conducted an experiment to examine the effects of mowing time (July 31, August 10, August 20, August 30, and September 9), stubble height (3, 9, and 15 cm), and their interaction on grassland community characteristics and forage yield and quality in a grassland of Hulun Buir from 2019 to 2021. The results showed that late mowing and low stubble-height mowing significantly reduced community-level height and coverage in the next year. Low stubble-height mowing reduced community aboveground biomass in the following year, but the effect of mowing time was not significant. The relative biomass of grasses and sedges first increased and then decreased with the delay of mowing time, forbs showed an opposite pattern, and legumes showed no signi-ficant change. Low stubble-height mowing could enable higher hay yield in the same year, but resulted in substantial reduction in the next year. When mowing on August 30 with 3 cm high stubble left, the hay yield reached maximum value of 469 g·m -2 , which was 361.5% of the minimum value. The crude protein content of forage reached the maximum at the mowing time of August 30, which was higher than the minimum value by 6.5%, and the content was higher at high stubble-height mowing. The contents of acid and neutral detergent fiber increased gradually over the growing season, with those from mowing on September 9 being 8.0% and 5.9% higher than those from mowing on July 31 respectively, and were decreased with the increases of stubble height. We found that the reasonable mowing regime of Hulun Buir grassland as the mowing implemented from August 20 to 30 with about 9 cm high stubble left. Long-term mowing experiments are needed to draw a more reliable and objective conclusion.

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. [ABSTRACT FROM AUTHOR]

Studies on effects of nitrogen deposition were mainly focused on temperate grasslands in Inner Mongolia of China. In addition, there are substantial differences between the present simulation methods and the natural nitrogen deposition. A three-year experiment was carried out to compare the effects of simulation methods (common urea and slow-released urea) and nitrogen deposition rates (0, 25, 50, 75, 100, 150, 200 and 300 kg N·hm -2 ·a -1 ) on soil nutrients and biological characteristics in Hulun Buir Grassland. We found that simulated nitrogen deposition had significant influences on soil chemical properties, biological properties and enzyme activities. With the increases of nitrogen deposition, soil pH declined with the greatest extent of 0.2 units, while the highest concentrations of total dissolved nitrogen (TDN) and dissolved organic carbon (DOC) increased by 5-7 times and 12%-36%, respectively. There was a decline trend for soil total phosphorus (TP) and organic phosphorus (TOP). Microbial biomass and metabolic activity increased firstly and then decreased. Moderate simulated nitrogen deposition rates significantly increased soil carbon, nitrogen and phosphorus related enzyme activities. Compared to common urea, using slow-released urea to simulate nitrogen deposition decelerate the decline of soil pH and the increase of dissolved nutrients, and smoothed the change of microbial biomass, metabolic activity, and nitrogen hydrolyzed enzyme activities. Overall, the results confirmed that continuous nitrogen input caused the decline of soil pH and the increase of bioavailable carbon and nitrogen, and then changed microbial biomass and activity.