2016 International Conference on Modern Economic Development and Environment Protection (ICMED 2016) ISBN:

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1 2016 International Conference on Modern Economic Development and Environment Protection (ICMED 2016) ISBN: Difference in FVC Changes between Desert Grasslands with Different Stocking Rates in Response to Air Temperature and Precipitation Sarenna WANG 1,2,a, Guo-Dong HAN 2,b,, Yu-hai BAO 1,c 1 Key Laboratory of Remote Sensing and Geographic Information System, Inner Mongolia Normal University, Hohhot, China ; 2 College of Ecology and Environmental Science, Inner Mongolia Agricultural University, Hohhot, China ; a sarenna_2001@126.com, b nmghanguodong@163.com, c baoyuhai@imnu.edu.cn Keywords: Fraction of Vegetation Cover (FVC), Desert grassland, Precipitation, Air temperature, Stocking rate. Abstract. The relation between climate and vegetation has always been an important research subject in the field of sustainable development of regional environment. Annual dynamic FVC (Fraction of Vegetation Cover) s in desert grasslands with different stocking rates in response to meteorological factors were investigated based on FVC and meteorological data of the grasslands, which was obtained through RS (remote sensing) monitoring during The results show that: 1) inter-annual dynamic FVC s in desert grasslands were correlated significantly with precipitation in summer and autumn as well as air temperature in July and August; 2) precipitation and air temperature had greater influence on dynamic FVC rates in grazed zones than those in control zones; 3) dynamic FVC rates in MG (Moderately Grazed) zones and HG (Heavily Grazed) zones were greater than those in LG (Lightly Grazed) zones and CK (Control) zones. Therefore, it can be concluded that MG and HG zones are more sensitive to meteorological factors than LG and CK zones, and that conservative grazing in desert grasslands can help restore their FVC and reduce the range of FVC s. Introduction Fraction of Vegetation Cover (FVC) is an important index used to measure ecological environment s. With the development of remote sensing (RS) technology, FVC, which is estimated based on vegetation index, has become an important part in monitoring ecological environment s[1-3]. Desert grasslands are located in transition belts between grasslands and deserts, where the ecological environment is arid and vegetation is vulnerable to climate s and human activities [4]. Therefore, long-time-series studies on FVC of desert grasslands are of great significance in understanding the changing trend of regional vegetation. However, many RS dynamic monitoring studies only focused on grasslands with uniform stocking rates, there are few long-time-series RS monitoring studies on vegetation with different stocking rates. For this reason, FVC data, obtained through RS monitoring, of a Stipa breviflora desert grassland, which has been divided into zones with different stocking rates for a long time, was studied in this paper, and the influence of meteorological factors on different grazed zones was explored through a correlation analysis between dynamic FVC rate and precipitation and air temperature. This study could provide a scientific basis for managing sustainable development of desert grasslands. Methods Overview of Research Area The research area is located in a Stipa breviflora desert grassland in Siziwang Banner, Ulanqab, Inner Mongolia. The area has a typical continental climate in mid-temperate zone, with less precipitation and coincident rainy season and hot season. The average annual precipitation is 250

2 mm and average annual evaporation is 2343mm. The rainy season is from May to September, accounting for more than 70% of the total precipitation. The average annual air temperature is 3.4 C. The hot season with the highest mean monthly temperature includes June, July, and August. The annual accumulated temperature ( 10 C) is 2200~2500 C. The research area covers an area of 51.9hm 2, which was divided into 12 sample enclosures with each enclosure covering an average area of 4.33hm 2. 4 grades (Moderately grazed (MG), lightly grazed (LG), heavily grazed (HG), and Control (CK)) were applied to the 12 enclosures, with 3 enclosures at a same grade. The stocking rates of LG, MG, and HG zones were respectively 0.91 sheep/ hm 2 every half year, 1.82 sheep/ hm 2 every half year, and 2.71 sheep/ hm 2 every half year, and the CK zones were not grazed. The pattern of grazing in summer and autumn and no grazing in winter and spring was used, and the grazing period in a year was 6 months in total (from June to November). The grazing mode, characterized by different stocking rates, started in 2004, and had lasted for 11 years by Data Source and Data Processing Data source (1) RS image data: multispectral data obtained by Landsat during For there are large stripes in the RS images of the research area for 2003, 2005, and 2012, only the data on the following 10 dates was analyzed in this study: August 15, 2002, August 4, 2004, August 18, 2006, August 12, 2007, August 31, 2008, August 17, 2009, July 28, 2010, August 7, 2011, August 21, 2013, and July 30, The data is from Geospatial Data Cloud of Computer Network Information Center, Chinese Academy of Science ( (2) Meteorological data: monthly air temperature and precipitation data during , which was provided by Inner Mongolia Bureau of Meteorology and recorded by Siziwang Banner Weather Station. (3) Data of quadrats actually measured on the site In July 2010, 120 1m 1m plant quadrats, 10 quadrats from each enclosure, were collected in the research area. Total vegetation coverages of these quadrats were estimated by visual observation, and their latitude and longitude coordinates were recorded. Data Processing (1) RS data processing: geometric correction, radiometric calibration, atmospheric correction, unified projection transformation, and other pretreatments were made for the RS data, and ENVI software was used to calculate NDVI, which was then used to calculate FVC. The equation for calculating FVC is as follows: FC = NDVI NDVI NDVI NDVI (1) ( ) ( ) soil Where, FC represents the Fraction of Vegetation Cover; NDVI represents the Normalized Difference Vegetation Index; NDVI soil represents the digital number of a bare soil pixel; NDVI veg represents the maximum digital number of a pixel fully covered by vegetation. (2) Classification of FVC: According to the results of on-site investigation of the plant quadrats, the calculated FVCs were classified into 5 grades: low vegetation cover (FVC 15%), middle-low vegetation cover (15% FVC<25%), middle vegetation cover (25% FVC<35%), middle-high vegetation cover (35% FVC<45%), and high vegetation cover (FVC 45%). According to the above classifications, density separation was carried out for FVCs estimated by RS were in ENVI software and FVC classification data for each year was then obtained. (3) Meteorological data processing Classification of year type by precipitation: dynamic FVC rate is affected by precipitation, which would lead to difference in inter-annual dynamic s of FVC. Therefore, the years were classified into different year types according to its annual precipitation or precipitation in spring and summer (May to July) [5-7], which would influence vegetation growth in August [8]. The classification standard is as follows: High precipitation year: Pi>P+0.33δ (2) veg soil 251

3 Low precipitation year: Pi<P-0.33δ (3) Where, Pi is the annual precipitation in the year or precipitation in the spring and summer (mm); P is the average annual precipitation or average precipitation in spring and summer throughout the years (mm); δ is the mean-square deviation of precipitation in years or precipitation in springs and summers (mm). Given that using annual precipitation or precipitation in spring and summer would lead to different results, classification of year type for a year is subject to either of the results. Classification of year type by air temperature: the RS data used in this study was mainly generated from the end of July to the beginning of August. Therefore, the years were classified into different year types according to air temperature in summer (July and August). The classification standard is as follows: Cool: T -б; Slightly cool: -б< T -0.5б; Normal: -0.5б< T<0.5б; Slightly hot: 0.5б T<б; Hot: б T. Where, T is the temperature departure, namely the difference between average temperature in the summer (July and August) and average temperature in summer throughout the years (July and August); б is the standard deviation of average temperature in the summer (July and August) [9]. (4) Precision verification: the precision of FVC values in 2010, estimated according to LANDSAT data, was verified. To eliminate discrepancy in scale and improve the accuracy of the verification, the average of FVCs of quadrats (no less than 2) within a circle whose radius was 15m and center was at a quadrat point was taken as an actually measured value. There were 7 actually measured values in all (see Table. 1). Then, the correlation between the actually measured values and the estimated values corresponding to the center point coordinates was tested, and the correlation coefficient was The values were correlated significantly with each other at the level of The results indicate the RS estimates can reflect the reality of the grassland precisely. Analytical Methods Table 1. Field investigation values contrasted with its estimates values (%). Sample the average of FVCs of Estimates Error No. quadrats values Dynamic FVC rate Dynamic FVC rate refers to the inter-annual s of vegetation, and dynamic FVC s can be obtained by using detection methods. Based on FVC classifications, inter-annual differences in FVC of corresponding pixels were extracted, and the areas where s occurred were identified. Then, the ratio of changing area to the area of the enclosures was calculated, and the dynamic FVC rate was determined. FVC d in two directions. The first was forward changing direction, namely changing from low FVC to high FVC; the second was backward changing direction, namely changing from high FVC to low FVC (see Table. 2 for details). 252

4 Table 2. Classification of dynamic rate of vegetation coverage. Classification Change Level Content Classification Change Level Content forward Level-1 Level-2 Level-3 low FVC turn into middle-low FVC ; middle-low FVC turn into middle FVC; middle FVC turn into middle-high FVC; middle-high FVC turn into high FVC low FVC turn into middle FVC; middle-low FVC turn into middle-high FVC; iddle FVC turn into high FVC low FVC turn into middle-high FVC or high FVC;middle-low FVC turn into high FVC backward Level-1 Level-2 Level-3 high FVC turn into middle-high FVC ; middle-high FVC turn into middle FVC; middle FVC turn into middle-low FVC; middle-low FVC turn into low FVC high FVC turn into middle FVC; middle-high FVC turn into middle-low FVC; middle FVC turn into low VC; high FVC turn into middle-low FVC or low FVC; Middle high VC turn into low FVC Statistics methods Correlation analysis between dynamic FVC rates of the zones with different stocking rates and factors influencing vegetation growth (precipitation and air temperature) during was carried out. The difference of precipitation and air temperature in influencing dynamic FVC rate was studied. The effect of precipitation year types and air temperature year types on dynamic FVC rate was analyzed. Meanwhile, the characteristics of FVC s in the zones with different stocking rates in response to climate factors were studied. Results and Analysis Spatial and Temporal Changing Characteristics of Total FVCs of Desert Grasslands with Different Stocking Rates during As shown in Fig. 1, the research area had been degenerative in 2002; total FVCs of all the four types of research zones were less than 25%, and there was no significant difference between the four types of research zones. After 2004, CK zones and LG zones had higher total FVCs than MG zones and HG zones during the monitoring periods, and the FVCs were also higher than the values in Except 2004, 2008, and 2013 in which LG zones had higher FVCs than CK zones due to abundant precipitation, total FVCs of the four types of research zones showed the overall trend of CK>LG>MG>HG. In 2014, the total FVCs of CK zones and LG zones had been significantly higher than those of MG zones and HG zones (P<0.05); however, there was no significant difference in total FVC between CK zones and LG zones, as well as between MG zones and HG zones. Figure 1. Mean value comparison of vegetation coverage under different stocking rates from 2002 to2014. Different lowercases in same year indicate significant differences at 0.05 level in different treatment. 253

5 Influence of Precipitation on FVC Changes in Desert Grasslands with Different Stocking Rates Relation between precipitation and inter-annual dynamic FVC rate Antecedent moisture conditions are a key environmental factor influencing seasonal growth of pasture [10]. The month (August), in which FVC values were obtained, and the hysteretic nature of precipitation were considered in the correlation analysis between precipitation and vegetation, and three precipitation factors, annual precipitation (from August of the last year to July of the current year), precipitation in spring and summer (May to July of the current year), and precipitation in summer (June to July), which have influence on vegetation growth, were proposed. As the correlation analysis reveals (see Table. 3), dynamic FVC rates in zones with different stocking rates were correlated significantly with precipitation (p < 0.01). The three precipitation factors were positively correlated with forward of FVC, and negatively correlated with backward of FVC. The influence of the precipitation factors on forward dynamic FVC rates of the grazed zones (LG, MG, and HG) was in the order of precipitation in spring and summer > annual precipitation > precipitation in summer. Precipitation was more correlated with the rates of the grazed zones than those of CK zones. Dynamic FVC rates of the grazed zones were in the order of MG>LG>HG>CK. Precipitation Annual precipitation Precipitation in spring and summer Precipitation in summer Table 3. Correlation of precipitation and dynamic rates of vegetation coverage. Forward Dynamic rates of vegetation coverage (%) CK LG MG HG Backward Forward Backward Forward Backward Forward Backward 0.739** * 0.776** ** 0.802** ** 0.752** ** 0.730** ** 0.852** ** 0.890** ** 0.847** ** 0.541** * 0.697** ** 0.730** ** 0.691** ** Sign * indicate there are significant relation between precipitation and s of vegetation coverage at 0.05 level(double side), Sign ** indicate there are significant relation between precipitation and s of vegetation coverage at 0.01 level(double side). Relation between precipitation year type and dynamic FVC rate High precipitation year: in high precipitation years, the FVCs of all the four types of research zones mainly presented forward rates, and backward rates were low. There was no significant difference in dynamic FVC rate at the same level between the zones. When the previous year was a low precipitation year, there was no significant difference in dynamic FVC rate between the four types of research zones, except that the level-3 forward dynamic rates of MG zones were significantly higher than those of CK zones. The level-3 forward rates were in the order of MG>HG>LG>CK, and the level-2 forward rates were in the order of LG>MG>HG>CK. Leve-2 forward s were mainly observed in MG and HG zones, and some level-3 forward s were also observed. Leve-2 forward s were mainly observed in LG and CK zones, and some level-1 forward s were also observed. When the previous year was a middle precipitation year, level-3 forward dynamic s were mainly observed in all the four types of research zones, and some level-2 forward s were also observed. There was no significant difference in dynamic FVC rate at various levels between the zones. However, level-3 forward rates were in the order of MG>HG>LG>CK, and level-2 forward rates were in the order of HG> CK>LG>MG. Middle precipitation year: in middle precipitation years, there were both forward and backward dynamic FVC s in the four types of research zones. Forward FVC rates were in the order of CK>HG>LG>MG, and backward FVC rates were in the order of MG>LG>CK>HG. There was no significant difference between the zones. Low precipitation year: in low precipitation years, backward FVC s were mainly observed in all the four types of research zones. However, backward rates varied from each other 254

6 depending on the precipitation in the last year. When the previous year was a high precipitation year and the current year was a low precipitation year, level-1 backward s were mainly observed in CK zones; level-2 backward s were mainly observed in LG zones; level-3 backward s were mainly observed in MG and HG zones; there was no significance difference between the zones. When the previous year was a low precipitation year and the current year was a low precipitation year as well, there were both forward and backward FVC s. However, the main rates were level-1 backward rates in the order of MG>HG>LG>CK, and level-2 backward rates were in the order of HG>MG>LG>CK. With the increase of stocking rate, backward s tended to become greater. Figure 2. Dynamic of vegetation coverage under different stocking rates between different precipitation type years. Different lowercases in same level indicate significant differences at 0.05 level in different stocking rates. Influence of Air Temperature on FVC Changes in Desert Grasslands with Different Stocking Rates Influence of air temperature on dynamic FVC rate Air temperature has indirect influence on vegetation growth through influencing soil moisture evaporation[7]. Rainy season and hot season are coincident in the desert grasslands, so temperatures 255

7 respectively in May-August, June-August, July-August, and August were taken as independent variables in the correlation analysis (see Table. 4). As the data reveals, air temperature was positively correlated with backward FVC rate and negatively correlated with forward FVC rate. However, only air temperature in July-August was significantly correlated with forward dynamic rates in CK, MG, and HG zones at the level of Air temperatures respectively in May-August, June-August, and August had no significant influence on FVCs of all the four types of research zones. Air temperature was more correlated with dynamic FVC rates in MG and HG zones than those in LG and CK zones. Table 4. The correlation of temperature and dynamic. Dynamic rates of vegetation coverage (%) CK LG MG HG Temperature Forward Backward Forward Backward Forward Backward Forward Backward May-August June-August July-August -.684* * * August Sign * indicate there are significant relation between precipitation and s of vegetation coverage at 0.05 level (Double side). Relation between air temperature year type and dynamic FVC rate Slightly cool year: in slightly cool years, level-3 forward dynamic FVC rates were mainly observed in all the four types of research zones, and some level-2 forward rates were also observed. However, there was no significant difference between the zones. In addition, level-3 forward rates were in the order of LG>MG>CK>HG. Normal year: when the previous year was a slightly cool year, backward dynamic FVC rates were mainly observed in all the four types of research zones. Of them, level-3 backward rates were mainly observed in MG and HG zones; level-3 backward rates in HG zones were significantly higher than those in LG and CK zones, and level-3 backward rates in MG zones were significantly higher than those in CK zones. When the previous year was a hot year, level-3 forward rates were mainly observed in HG and MG zones, and level-2 forward rates were mainly observed in LG and CK zones. Level-3 forward dynamic rates were in the order of MG>HG>LG>CK, and level-2 forward dynamic rates were in the order of LG>HG>CK>MG. When the previous year was a normal year, there were both forward and backward rates in all the four types of research zones, and backward rates were mainly observed. Level-3 backward rates were in the order of HG>MG>LG>CK; level-2 backward rates were in the order of LG>MG>HG>CK; level-1 backward rates were in the order of LG>MG>HG>CK. Hot year: when the previous year was a normal year, level-1 forward dynamic FVC rates and level-1 backward dynamic FVC rates were mainly observed in all the four types of research zones, but the area experiencing backward s was greater than that experiencing forward s. There was no significant difference in dynamic FVC rate between zones with different stocking rates. However, level-1 backward dynamic rates were in the order of LG>MG>CK>HG, and level-2 backward dynamic rates were in the order of HG>LG>CK>MG. 256

8 Discussions Figure 3. Dynamic of vegetation coverage under different stocking rates between different temperature type years. Different lowercases in same level indicate significant differences at 0.05 level in different stocking rates. Inter-annual FVC s are closely related to meteorological factors. Research has shown that precipitation has greater influence on FVC s in desert grasslands than air temperature [7,11], which is consistent with the results obtained in this study. Precipitation was significantly correlated with dynamic FVC rate at the level of Although air temperature was also correlated with dynamic FVC rate, only air temperature in July-August was significantly correlated with dynamic FVC rates in MG and HG zones at the level of The results indicate that precipitation has greater influence on vegetation growth in desert grasslands. Influence of Precipitation on FVC of Desert Grasslands with Different Stocking Rates As far as desert grasslands, precipitation is the only way for vegetation to get water [11]. Wei Zhijun [4] and Chen Xiaoqiu [9] indicated that antecedent precipitation in desert grasslands is positively correlated with FVC, and precipitation in spring and early summer is particularly essential and determines the above-ground biomass. This study reaches a similar conclusion that precipitation in spring and summer is significantly correlated with dynamic FVC s at the level of 0.01 and has great influence on FVC s. Grazed zones were more responsive to precipitation in spring and summer than CK zones, and this is partly because the grazed zones had lower FVC than CK zones. Due to grazing, the grazed zones had lower FVC and tended to grow annual and biennial 257

9 plants [13] when precipitation was abundant, and thus larger forward dynamic rates were observed in the zones. When precipitation was short, CK zones had relatively higher FVC and the evapotranspiration in the zones was lower than the grazed zones that had smaller FVC. For this reason, the plants in CK zones grew better than those in the grazed zones. Due to foraging of livestock, bare surface, poor water-holding capacity of soil, and other reasons, vegetation growth in MG and HG zones was restricted, and inter-annual backward FVC rates in these zones were higher than those in LG zones and CK zones. In high precipitation years, level-3 forward dynamic rates were in the order of MG>HG>LG>CK. In low precipitation years, both level-2 and level-3 backward dynamic rates were in the order of HG>MG>LG>CK. The results also indicate that vegetation in grasslands with high stocking rate is more likely to be affected by precipitation. With the increase of stocking rate, dynamic FVC rates tend to become greater. Influence of Air Temperature on FVC of Desert Grasslands with Different Stocking Rates High temperature would promote transpiration [12]. In arid and semiarid region, soil moisture is easily evapotranspired by high temperatures in summer due to sparse vegetation, and growth of plants would be influenced. According to relevant data, there was no significant difference in dynamic FVC s between grasslands with different stocking rates in slightly cool years and hot years, while there was significant difference in dynamic FVC s between the grasslands in normal years. When the previous year was a hot year and the current year was a normal year, inter-annual forward dynamic FVC rates were mainly observed, and FVC rates in MG zones, where level-3 forward rates were mainly observed, were significantly higher than those in CK zones (p<0.05), presenting the order of MG>HG>LG>CK. When the previous year was a slightly cool year, FVC rates in HG zones, where level-3 backward rates were mainly observed, were significantly higher than those in LG and CK zones (p<0.05), presenting the order of HG>MG>LG>CK. The significant difference in FVC rate between MG and CK zones in a normal year, which was next to a slightly cool year or a hot year, has something to do with precipitation. Because a slightly cool year always corresponds to a high precipitation year, plants grew well in the year. However, the next year was a normal year, during which the precipitation was less, and thus vegetation in all the research zones grew worse than the previous year. However, backward dynamic rates in MG and HG zones were significantly higher for vegetation growth in the zones was restricted due to foraging of high-density livestock and poor water-holding capacity of soil. Likewise, when the previous year was a hot year with normal or slightly lower precipitation and the current year was a normal year with more precipitation, bare surface in MG and HG zones would be covered by annual and biennial plants. In this case, FVC of MG and HG zones increased faster compared to CK zones, and dynamic FVC rates in the zones experienced a significant growth. Conclusions Precipitation and air temperature are two important factors influencing FVC s in desert grasslands, and precipitation has greater influence on vegetation in desert grasslands. Forward FVC rates in grazed zones has the highest correlation with precipitation in spring and summer (p < 0.01), followed by annual precipitation and precipitation in summer. Meanwhile, precipitation is more correlated with FVCs of MG and HG zones than those of LG and CK zones. Air temperature is negatively correlated with dynamic FVC rate, and only air temperature in July-August is significantly correlated with dynamic FVC rates in MG and HG zones (p<0.05), indicating MG and HG zones with low FVC are more sensitive to air temperature than LG and CK zones. 258

10 Acknowledgment This study was financially supported by the Natural Science Foundation of Inner Mongolia(No.2016MS0412), the Natural Science Foundation of IMNU (No.2015YBXM014) and National science and technology support project (No.2013BAK05B01). Refenrences [1] Zhu Wen-quan, Pan Yao-zhong, Liu Xin, Wang Ai-ling. Spatio-temporal distribution of net primary productivity along the northeast China transect and its response to climatic [J].Journal of Forestry Research, 2006, Vol.17(2): 93~98. [2] Mu Shao-jie, Li Jian-long, Yang Hong-fei, et al. Spatio-temporal variation analysis of grassland net primary productivity and its relationshio with climate over the past 10 years in Inner Mongolia[J].Acta Prataculture Sinica, 2013, Vol.22(3):6~15. [3] Nemani R R, Keeling C D, Hashimoto H, et al Climate-driven increases in global terrestrial net primary production from 1982 to Science, 2003, 300: 1560~1563. [4] Wei Zhi-jun, Han Guo-dong, Zhao Gang, Li De-xin. Research on Chinese Desert Grassland Ecosystem[M].Beijing: Science Press, [5] Lao Busheng Daoerji, Sun Chang-zai, Chen Zuo-zhong, et al. The Dynamic of Biomass and a Relationship Between Biomassand Precipitation of Desert Steppe in Inner Mongolia [J].Arid Land Geography, 1990,Vol. 13(1):10~16. [6] Chen Yan-li. Study on the Grassland Change Monitoring in Xilingol Based on NDVI and Climate Data [D].China Agricultural University, Master's degree thesis, [7] Li Xiao-bing, Chen Yun-hao, Zhang Yun-xia, et al. Impact of Climate Change on Desert Steppe in Northern China [J]. Advances in Earth Sciences, 2002, 17(2):254~261. [8] Zhang Bei-ying, Xu Xue-xuan, Liu Wen-zhao, et al. Dynamic s of soil moisture in loess hilly and gully region under effects of different yearly precipitation patterns[j]. Chinese Journal of Applied Ecology, 2008, Vol. 19(6):1234~1240. [9] Hao Yan-na, Ding Yu-guo. Spatial temporal distribution characteristics of temperature variability in winter and summer in the Yangtze-Huaihe river basin in recent 30 years [C]. Proceedings of Short-term Climate Prediction, the 29 th annual meeting of Chinese Meteorological Society, [10] Chen Xiaoqiu, Wang Heng. Spatial and Temporal Variations of Vegetation Belts and Vegetation Cover Degrees in Inner Mongolia from 1982 to 2003 [J]. Acta Geographica Sinica, 2009, Vol. 64(1):84~94. [11] Bao Gang, Bao Yu-hai, Qin Zhi-hao, et al. Vegetation Cover Changes in Mongolian Plateau and Its Response to Seasonal Climate Changes in Recent 10 Years[J]. Scientia Geographica Sinica, 2013, Vol. 33(5):613~621. [12] Jiang Han-qiao, Duan Chang-qun, Yang Shu-hua, et al. Plant Ecology [M]. Beijing: Higher Education Press, [13] Ding Hai-jun, Han Guo-dong, Wang Zhong-wu, et al. Effect of different stocking rate on plant community characteristics in Stipa breviflora desert steppe[j].chinese Journal of Grassland, 2014, Vol. 36(2):55~