Impact of human activities and climate change on the grassland dynamics under different regime policies in the Mongolian Plateau

Science of the Total Environment 698 (2020) 134304

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Impact of human activities and climate change on the grassland dynamics under different regime policies in the Mongolian Plateau☆,☆☆ Yanzhen Zhang a, Qian Wang a, Zhaoqi Wang a,b, Yue Yang a,c, Jianlong Li a,⁎ a b c

Department of Ecology, School of Life Science, Nanjing University, Nanjing 210023, China Department of Ecology, School of Urban and Environment, Peking University, Beijing 100871, China Nanjing Institutes of Environmental Sciences, Ministry of Environmental Protection of the People's Republic of China, Nanjing 210042, China

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Land use and cover change (LUCC) as one of driving factors was quantified. • Inner Mongolia had a higher increase of land conversion NPP than Mongolia. • Grasslands in most areas of the Mongolian Plateau show a recovery trend. • Human activities are still the dominant factor in promoting degradation in the MP.

a r t i c l e

i n f o

Article history: Received 5 June 2019 Received in revised form 3 September 2019 Accepted 3 September 2019 Available online 04 September 2019 Editor: Ouyang Wei Keywords: The Mongolia Plateau Land use and cover change Grassland degradation Major ecological projects Sustainable development of grassland

a b s t r a c t To mitigate the grassland degradation in the Mongolian Plateau (MP), both China and Mongolia governments have carried out a series of new policies and ecological projects. However, the effect of such restoration measures on the productivity of grassland in the MP under different political systems remains unclear. Here we study the effects of land use and land cover change, human activities and climate change on the net primary productivity (NPP) of grassland in Mongolia (MG) and Inner Mongolia (IM) from 2001 to 2014. Results showed that the area of grassland increased in both MG and IM, accounted for 4.45 × 104 and 10.31 × 104 km2, respectively. The extended grassland contributed 4.34 × 108 Gg C (Gg = 109 g) to the total NPP, while the loss of grassland led to a decrease of 0.19 × 108 Gg C. The total NPP of grasslands in 2014 increased about 17.88% and 30.49% respectively in MG and IM since 2001. Specifically, IM exhibited a higher increase in land converted NPP than MG. The area of grassland restoration in IM and MG accounted for 90.21% and 81.45%, respectively, indicating that the grassland of the MP was restored. Although human activity was the dominant factor on grassland degradation, which was accounted for 9.79% and 18.55% in IM and MG, it has a positive effect on most of the grassland NPP in the MP. Overall, policy measures and ecological projects in IM brought a more positive effect compared with that in MG. © 2019 Elsevier B.V. All rights reserved.

☆ Declaration: Images/drawings/photographs were created by authors of the paper. ☆☆ English language support: The language proofreading was supported by American Journal Experts. ⁎ Corresponding author at: Department of Ecology, School of Life Science, Nanjing University, Xianlin Road 163, Nanjing 210046, China. E-mail address: [email protected] (J. Li).

https://doi.org/10.1016/j.scitotenv.2019.134304 0048-9697/© 2019 Elsevier B.V. All rights reserved.

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Y. Zhang et al. / Science of the Total Environment 698 (2020) 134304

1. Introduction

2.2. Data and processing

Land degradation is one of the most critical global issues due to its adverse impact on terrestrial ecosystem productivity, the environment, and its threats on livelihood and food security (Bai et al., 2010; Batunacun et al., 2018; Le et al., 2014). Grassland covers N40% of Earth surface and plays an important role in converted regional ecosystem productivities and biogeochemical processes (Horion et al., 2013; Kemp et al., 2013). Approximately 49.3% of grassland in the world faces degradation problems (Gang et al., 2014), for which land use and cover change (LUCC) is one of the most important causations (Foley et al., 2005; Zhou et al., 2013). It is essential to quantitatively assess grassland degradation and its driving factors, which has recently become a research hot spot. Net primary productivity (NPP) is one important indicator to characterize vegetation growth status and the material basis for ecosystem developments (Gang et al., 2015). NPP has been widely used to indicate the change of ecosystem function and vegetation dynamics. Previous studies demonstrated the roles of climate change and human contributions to the change of NPP (Yang et al., 2016; Zhou et al., 2015). As reported by (Gang et al., 2018), grassland NPP is also affected by land conversion and management measures. The Mongolia Plateau (MP) is covered by natural grasslands, which is a typical pastoral region in the world (Gang et al., 2018). The MP is rich in grassland resources, with animal husbandry as the main economic production mode (Goenster-Jordan et al., 2018; John et al., 2013). The main part of the MP include the Mongolian (MG) and Inner Mongolia Autonomous Region (IM) of China, which are under different institution and management systems (Chen et al., 2018). Regional grassland ecosystems are especially vulnerable to the change of driving factors (Gang et al., 2018; Qi et al., 2012). During the recent decades, China and Mongolia have experienced increasingly severe land degradation and desert expansion (Sternberg et al., 2011; Zhao et al., 2002). Additionally, human activities and climate change cause a series of environmental problems in the MP. However, there has been few quantitative researches on grassland degradation in the whole MP. Taking the grassland in the MP as the study objective, this study investigates the land use and cover change during 2001–2014 and quantitatively calculated corresponding NPP to evaluate the status of vegetation degradation and restoration under the variety grassland restoration policies carried by the two governments. The results will reveal the efficiency of land covered type changes and management measures on grassland ecosystem restoration in ecological restoration projects in both governments but also will offer an effective theoretical basis for the determination of a sustainable development strategy.

2.2.1. Land cover data The yearly global land cover maps of 2001 and 2014 were obtained from the MODIS MCD12Q1 data products with the International Geosphere-biosphere Programme (IGBP) classification system. The MCD12Q1 Version 6 data product is derived from MODIS reflectance data obtained by both Terra and Aqua using supervised classifications. We used satellite data of two different years taken for the same season. The IGBP classification system defines 17 classes of primary land cover types. In this study, according to our need, we reclassified land cover types into 7 main classes (Table 1). Particularly, Class 6–11 were combined as a single land cover type for grassland.

2. Materials and methods 2.1. Study area The MP (37°22′~53°20′ N, 87°43′~126°04′ E) is located in central Asia with a territory of 2.74 × 106 km2, including MG (57%) and IM (43%) in China (Zhou and Yamaguchi, 2018) (Fig. 1). The average altitude is approximately 1580 m. It is characterized as arid and semiarid continental climate, and the annual average temperature and rainfall are 1.9 °C and 233 mm, respectively (Bai et al., 2008). As the most important vegetation type in the MP, grassland takes up 66.29% of the area of MG based on the MODIS land cover product (MCD12Q1, 2014). Land cover types in the MP mainly include grassland (1.82 × 10 6 km 2), desert (7.78 × 10 5 km2 ), forest (7.10 × 104 km 2 ), farmland (6.69 × 104 km 2 ), urban (4.35 × 103 km 2 ) and farmland/natural vegetation (3.30 × 10 3 km 2 ) in descending order.

2.2.2. Meteorological data Air temperature, precipitation and downward shortwave radiation at a spatial resolution of 0.5° and on a monthly basis during 2001–2014 were obtained from the gridded Climatic Research Unit (CRU) version 4.02 (http://www.cru.uea.ac.uk/) data set. 2.2.3. Statistical data The statistical data from 2001 to 2014 used in this study were obtained from different agencies due to different sources and administrations for Mongolia and Inner Mongolia. The data of populations, GDP and livestock for Mongolia were downloaded from Food and Agriculture Organization of the United Nations. The similar data for Inner Mongolia were collected from Inner Mongolia Statistical Yearbook. To spatially match different data sets, all abovementioned data were resampled to a 0.08° resolution with the Albers equal-area conic projection and the World Geodetic Systerm1984 (WGS1984) coordinate system. 2.3. Methods 2.3.1. Estimation of actual NPP Actual NPP was extracted from the MOD17A3 data set at a spatial resolution of 1 km, which is calculated based on the BIOME-BGC model (White et al., 2000). The specific formula is as follows: NPP ¼

365 X

  PSNet− Rm þ Rg

ð1Þ

t

PSNet ¼ GPP−Rlr

ð2Þ

where NPP refers the actual NPP (gCm−2 year−1); PSNet is the net photosynthesis; Rm indicates annual maintenance respiration of live cells in woody tissue; Rg delineates annual growth respiration and Rlr represents the daily leaf and fine root maintenance respiration. 2.3.2. Estimation of potential NPP Potential NPP is an index that can reflect the effect of climate change on grassland NPP. The present study estimated potential NPP by using the Thornth Waite Memorial model, which is developed based on the Miami model (Lieth, 1975). It expresses as follows: L ¼ 3000 þ 25t þ 0:05t 3

ð3Þ

1:05r V ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 þ ð1 þ 1:05r=LÞ2

ð4Þ

h i NPP ¼ 3000 1−e−0:0009695ðv−20Þ

ð5Þ

where L represents for the annual average evapotranspiration (mm), t is the annual average temperature (°C); V refers the annual actual total

Y. Zhang et al. / Science of the Total Environment 698 (2020) 134304

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Fig. 1. Location of the Mongolia Plateau in the world and land use types derived from MODIS land cover product (MCD12Q1) by the year 2014.

evapotranspiration (mm); r delineates the annual total precipitation (mm) and NPP represents for the annual total NPP (g C/(m2·yr)). 2.3.3. Sub-division of grassland NPP In general, climate change and human activities are considered as the main factors controlled the changes of grassland NPP (Chen et al., 2014). The total NPP of grassland in 2001 (NPP2001) and 2014 (NPP2014) are mainly expressed as follows:

2014; NPPunchanged2014 represent for total NPP of unchanged grassland and NPPturnin is the NPP of converted-in grassland. Human activities dominated NPP change mainly contains land use change caused NPP (NPPlucc) and human management measures caused NPP (NPPmanage). The afore-mentioned index can be calculated from actual NPP and potential NPP that expressed as follows: NPPhuman ¼ NPP lucc þ NPPmanage

ð8Þ ð9Þ

NPP2001 ¼ NPP unchange2001 þ NPPturnout

ð6Þ

NPPlucc ¼ NPPturnin −NPP turnout −NPP climate−new

NPP2014 ¼ NPPunchange2014 þ NPP turnin

ð7Þ

NPPmanage ¼ NPP unchange2014 −NPPunchange2001 −NPP climate−unchange

where NPP2001 is the total grassland in 2001; NPPunchanged2001 represents the total NPP of unchanged grassland; NPPturnout refers the NPP of converted-out grassland; NPP2014 delineates the total grassland in

Table 1 The reclassification of land use type according to the IGBP classification system. Serial number

Land use type after reclassification

1

Forest

2

Grassland

3 4 5 6

Farmland Urban and built-up Farmland/natural vegetation Desert

7

Water

Original land use type 1 Evergreen Needleleaf Forest 2 Evergreen Broadleaf Forest 3 Deciduous Needleleaf Forest 4 Deciduous Broadleaf Forest 5 Mixed Forest 6 Closed Shrublands 7 Open Shrublands 8 Woody Savannas 9 Savannas 10 Grasslands 11 Permanent Wetlands 12 Croplands 13 Urban and Built-up 14 Cropland/Natural Vegetation Mosaic 16 Barren or Sparsely Vegetated 15 Snow and Ice 17 Water Bodies

Note: Class 1–5 were selected as a single land cover type for forest; Class 6–11 were selected as a single land cover type for grassland; Class 15 and 17 were selected as a single land cover type for water.

ð10Þ

where NPPlucc represents for newly added grassland NPP variation caused by LUCC; NPPturnin is NPP of grassland converted from other land cover types; NPPturnout refers NPP of other land cover types converted into grassland; NPPclimatenew is climate NPP in the newly added grassland. Meanwhile, NPPmanage represents for NPP generated by human management measures; NPPunchange2014 delineates NPP of steady grassland in 2014; NPPunchange2001 refers NPP of steady grassland in 2001; NPPclimate-unchange is climate variation caused NPP. 2.3.4. Grassland dynamic analysis The present study selected a variety of NPP to evaluate grassland productivity and its driving factors. We utilized the least squares regression to analyze the NPP dynamics of grassland. The significance of the variation tendency is determined by the slope calculated as follows and uses the F-test to represent the confidence level of variation. (Wang et al., 2016). The formulas are:    n n n n  ∑i¼1 i  NPP i − ∑i¼1 i ∑i¼1 NPPi Slope ¼  2 n n n  ∑i¼1 i2 − ∑i¼1 i

ð11Þ

F ¼ U  ðn−2Þ=Q

ð12Þ

n



Q ¼ ∑i¼1 yi −ybi



2 n  U ¼ ∑i¼1 ybi −y

ð13Þ ð14Þ

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Y. Zhang et al. / Science of the Total Environment 698 (2020) 134304

where i is the number from 1 to 14, while 1 for the year 2001, 2 for the year 2002, and so on; n is 14 as the study period of this study. NPPi represents for the value of annual NPP in time of i year. Meanwhile, U represents for regression sum of squares, n is 14 years, while Q is the sum of square error; yi refers the actual NPP in the year i, ybi is the regression value, and y represents for the mean NPP during the 14 years. F test (P b 0.01 or P b 0.05) have access to indicate the significance of NPP change. 2.3.5. Quantitative assessment method The health condition of grassland was quantitatively evaluated by a combination analysis of the slope of different NPP variables. The grassland degradation and restoration can be distinguished by the slope of actual NPP using Eq. (1). Positive and negative SA values represents the restoration and degradation of grassland, respectively. The different combination of the slope of potential NPP (SP) and human NPP (SH) could reflect the effects of climate change and human activities. Therefore, there are totally 6 scenarios to assess the role of factors in health condition of grassland, which is shown in Table 2.

types and other land use types transferred to grassland were 5.34 × 104 and 9.78 × 104 km2, respectively. The total loss of NPP in grassland were 3.88 × 104 Gg C by the year of 2014. The majority of the NPP loss was a conversion from grassland to forest, and desert, accounting for 2.12 × 104 and 1.65 × 104 Gg C (54.69% vs 42.40%), respectively. The total amount of NPP brought by the newly added grassland in 2014 were 2.77 × 105 Gg C, of which the total amount of NPP converted from the forest were accounted for 36.40%, followed by farmland, accounted for 30.33%. For Inner Mongolia, the unchanged area of grassland without change was 5.92 × 105 km2. The total grassland turn-out and turn-in were 5.23 × 104 and 1.55 × 105 km2, respectively. The total loss of NPP in grassland were 8.29 × 104 Gg C by the year of 2014. The majority of the NPP loss was the conversion from grassland to farmland and forest, accounting for 2.16 × 104 and 5.77 × 104 Gg C (69.61% vs 26.05%), respectively. The total amount of NPP brought by the newly added grassland in 2014 were 5.32 × 105 Gg C, of which the total amount of NPP converted from the forest were 2.85 × 105 Gg C. It's the largest proportion of the total amount of NPP in the newly added grassland (53.52%).

3. Results 3.3. Characteristic analysis of different NPP variables

3.1. LUCC change in MG and IM Fig. 2 shows the spatial pattern of grassland related to land conversion. 63.04% and 52.41% of grassland areas remained stable during the study period in MG and IM, respectively. Geographically, grassland expansion was more obvious than loss in both MG and IM. Grassland expansion of MG is shown in Fig. 2A-1. The spatial distribution of the transition to the desert (2.46%) was mainly distributed in the southern and southwestern desert and grassland interlaced zone. Similarly, the conversion from forest to grassland is 1.23%, mainly concentrated in the southeast of Sayan Mountains. The conversions from forest to grassland (5.20%), farmland to grassland (3.47%) and from desert to grassland (3.31%) were relatively evidently distribution in IM (Fig. 2B-1). Forest conversion to grassland was mainly distributed among Daxinganling regions in northeast IM, while farmland conversion to grassland spread over Tongliao, Chifeng and Xingan League. Desert conversion to grassland also distributed over desert and grassland interlaced zone in southwestern IM. Desert (2.82%) and forest (0.44%) are the main turn out land cover types in MG (Fig. 2A-2), while farmland (2.54%) and forest (1.49%) are the main kinds in IM. In addition, forest and farmland declined by 37.40% and 36.77%, respectively (Fig. 2B-2). The spatial distribution of grassland losses was similar to grassland expansion amount. In general, the decline rate of the desert is about 10.72%, whereas the area of grassland increased by 8.57% in the MP. 3.2. Impacts of LUCC on grassland area and NPP The changes of area, mean and total NPP during the study period are shown in Table 3. For Mongolia, the unchanged area of grassland was 1.02 × 106 km2. The areas for grassland transferred to other land use

Table 2 Scenarios to assess the role of factors about climate and human activities in grassland healthy condition. Grassland condition

SP

SH

Scenario

Roles of factors about climate and human activities

Restoration (SA N 0)

N0 b0 N0 b0 N0 b0

N0 b0 b0 b0 N0 N0

S1 S2 S3 S4 S5 S6

Climate-caused (CDR) Human activities-caused (HDR) Both two factors caused (BDR) Climate-caused (CDD) Human activities-caused (HDD) Both two factors caused (BDD)

Degradation (SA b 0)

The spatial distribution and changing trend of potential NPP (PNPP), actual NPP (ANPP), land use-caused NPP (LNPP) and management measures-caused NPP (MNPP) in the Mongolia Plateau from 2001 to 2014 are shown in Fig. 3. Fig. 3A, and B shows the spatial distribution of four kinds of NPP in 2001 and 2014, respectively, while Fig. 3C illustrates the corresponding spatial distribution of different significance levels. The average value of PNPP, ANPP, and LNPP in 2014 was greater than that in 2001; and their productivity growth rate was 29.90%, 56.22%, and 101.59%, respectively. However, MNPP exhibited a decreasing trend with −12.98%. Spatially, both PNPP and ANPP showed a decreasing trend from northeast to southwest in IM, and north to south in MG. The LNPP increased in east Hulunburr, south Tongliao and northwest Xilin Gol in IM, as well as north Kent and Selenge in MG. The decreased regions of MNPP were mainly located in east MG and central IM. PNPP with clear increases and extremely evident increases distributed among the northeast MP, mainly including Kent, Sukhbaatar and Dornod province in MG, as well as Hulunbuir and Xing’ an league in IM. ANPP with clear increase and extremely evident increase distributed sporadically, mainly gathered in northern Khuvsgul, northern Arkhangai, and Dornod province and its surrounding areas in MG and western Hulunbeir, eastern Xing’ an league and south Ordos in IM. The regions of MNPP with an extremely significant increase (p b 0.01) were mainly distributed in the junction of Selenge, Central and Kent province, northern Sukhbaatar province in MG and Hinggan league in IM. The relative contributions of different driving factors to NPP in MG and IM grassland are shown in Fig. 4. The grassland NPP in 2014 was greater than that in 2001 in both MG and IM. Differences of grassland coverage area between MG and IM may have influenced the grassland total NPP. Furthermore, the growth amount caused by LUCC and human management measures is higher in IM. The total NPP dominated by management measures in the unchanged grassland regions are 4.76 × 10 5 Gg C/yr and 6.98 × 105 Gg C/yr in MG and IM, respectively. LUCC led to a loss of NPP in the turn out grassland regions are about 1.41% and 0.49% in IM and MG, respectively. Additionally, the amount of grassland NPP increased by LUCC was 4.48% and 1.51% in IM and MG, respectively. In general, NPP dominated by human activities showed an increasing trend from 2001 to 2014, with the value of 4.42 × 105 Gg C/yr and 6.04 × 105 Gg C/ yr in MG and IM, respectively. Climate change also contributed to the increase in total NPP in both regions over this period. In general, the grassland related land conversion had a positive effect on the increase of NPP, and it is greater in IM.

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Fig. 2. The spatial distribution of transformation between grassland with other land cover types of MG and IM from 2001 to 2014. A-1 and A-2 show that other land cover types transformed into grassland and grassland transformed into other land cover types in MG, and similarly, B-1 and B-2 show the spatial distribution for IM.

3.4. Analysis of driving factors for NPP change The spatial distribution of driving factors of grassland degradation and restoration in MG and IM is shown in Fig. 5. The distribution of degraded grassland area was obviously smaller than that of restored grassland area. Degradation area mainly centered in northwest MG (Fig. 5A1) and south-central IM (Fig. 5B-1). Degradation with a climate dominated distributed among northern Khuvsgul, middle Uvs province in MG and Ulaan Chab and Baotou city in IM. Degradation areas with human-dominated were mainly scattered in Zavkhan, Bayan-Ulgii, southern Khuvsgul and eastern Dundgovi in MG and western Xilin Gol in IM. Degradation areas with both climate and human-dominated were chiefly concentrated in northern Khuvsgul, east Uvs province in MG and the north Ordos in IM. The vast majority of grasslands in TMP had a recovery trend. Restoration with human-dominated was mainly located in southern Zavkhan, northeastern Gobi- Altai and northern Selenge in MG (Fig. 5A-2) and the western Ordos, eastern Bayannur,

Baotou, and Ulaan Chab in IM (Fig. 5B-2). The areas of grassland restoration of IM and MG accounted for 90.21% and 81.45%, respectively. We counted the different pixel values of the contribution of driving factors to grassland health contribution (Fig. 6). For grassland degradation in the MP, human activities, climate change, and the combination accounted for 47.63%, 23.27% and 29.10%, respectively (Fig. 6A). Human activities are the primary factor accounting for 41.57% and 49.99% in IM and MG, respectively. For grassland restoration of MP (Fig. 6B), climate change is the dominated factor (52.19%), which is greater than human activities (11.00%) and combination of these two factors (36.82%). Climate change is the major factor of restoration, accounting for 49.00% and 54.80% in IM and MG, respectively. The restoration of grassland led to an increase in NPP, while reduction of NPP due to grassland degradation. From 2001 to 2014, the NPP decreasing caused by grassland degradation in the Mongolian plateau were − 393.37 Gg C (Fig. 7A). Human activities and combination of human and climate were the most important factors, accounting for

Table 3 Statistics of grassland area and NPP associated with LUCC between 2001 and 2014 in Mongolia and Inner Mongolia. Area (km2)

Change type

Unchanged grassland

Turn out grassland

Turn in grassland

Grassland to grassland Grassland to forest Grassland to farmland Grassland to farmland/vegetation Grassland to city Grassland to desert Sum of turn out grassland Forest to grassland Farmland to grassland Farmland/vegetation to grassland City to grassland Desert to grassland Sum of turn in grassland

Mean NPP (g C/(m2·yr))

Total NPP (Gg C)

MG

IM

MG

IM

MG

IM

1,021,982.6 7085.08 108.17 0.00 540.85 45,647.40 53,381.5 20,119.47 22,769.62 14,440.59 594.93 39,914.44 97,839.04

591,847.78 16,766.23 28,556.67 216.34 1189.86 5570.71 52,299.81 58,627.71 39,211.34 18,010.17 1784.79 37,372.46 155,006.46

313.48 261.87 113.13 0 109.96 101.79

386.91 190.64 170.33 182.31 94.43 112.37

313.47 326.19 326.19 188.99 110.76

299.82 294.44 373.61 253.88 127.19

3,067,420 21,242.4 224.5 0 905.32 16,471.32 38,843.54 100,806.5 83,989.08 68,905.72 1634.45 21,613.67 276,949.42

2,673,660 21,590.51 57,697.26 322.65 1012.7 2265.86 82,888.98 284,949.6 127,331.6 73,302.66 3840.73 43,009.95 532,434.5

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Fig. 3. Spatial distribution of grassland PNPP, ANPP, LNPP, and MNPP in the TMP in 2001 (A-1, A-2, and A-3) and 2014 (B-1, B-2 and B-3) and the corresponding significance analysis during 14 years (C-1, C-2 and C-3). PNPP, ANPP, LNPP and MNPP denote potential NPP, actual NPP, LUCC influenced NPP, and management measures influenced NPP, respectively.

−31.41 Gg C and − 416.57 Gg C in IM and MG, respectively. For NPP changes of grassland caused by grassland restoration (Fig. 7B), climate and combination of human and climate were the dominated factors, accounting for 56.62% and 42.29% in the MP, respectively. Climate change was the most important factor of MG (1.09 × 104 Gg C), while the combination of human activities and climate change was the dominating factor in IM (9.68 × 103 Gg C).

4. Discussion 4.1. Effects of LUCC on grassland dynamics This study indicated that the MP has undergone rehabilitation of degraded grasslands during the study period. The grasslands are mainly transferred from deserts, farmlands, and forests in both MG and IM.

Fig. 4. The total grassland NPP of MG (A) and IM (B) in 2001 and 2014, and relative contributions of factors to grassland NPP change.

Y. Zhang et al. / Science of the Total Environment 698 (2020) 134304

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Fig. 5. The spatial distribution of grassland degradation in MG (A-1) and IM (B-1) and restoration in MG (A-2) and IM (B-2) induced by climate and human factors.

This transform in IM is benefited from the implementation of the Green Great Wall project, which is aimed to control desertification sandstorms (Shao et al., 2017). Particularly, because of the implementation of returning farmland to forest and grassland, the grasslands expanded from farmlands. Coincidently, the largest grassland area was converted from forests in both Mongolia and Inner Mongolia, which is consistent with recent studies (Chen et al., 2015; Mu et al., 2013), as well as the description in the Statistical Yearbook of IM, which was also supported our results. In general, these results demonstrated that the policies of environment protection have effectively contributed to the sustainable development of the grassland, especially for regions in the intersection of deserts and grasslands. We found that IM has a higher increase in land conversion induced NPP than MG, due to obvious land use changing of China's large-scale ecological project (Yang et al., 2016). The governments of IM and MG have different institutional and socioeconomic trajectories, which has been widened in the past century (Chen et al., 2018). The statistical number of livestock (LSK), human population (POP), and gross domestic product (GDP) was collected from FAO and Statistical Yearbook of Inner Mongolia (Fig. 8). LSK

increased in the MP (Fig. 8A). POP decreased in MG, except in Ulaanbaatar and Bulgan, while POP increased in IM, except in Xing'an, Chifeng, Ulaan Chab and Bayannnur (Fig. 8B). GDP increased in the MP, except in Darhan Uul, Orhon and Tuv in MG, and Ordos, Hohhot and Baotou in IM (Fig. 8C). The conversion from grassland to farmland was almost entirely concentrated in IM, while the conversion from grassland to desert was mostly distributed in MG. The number of livestock showed an increasing trend in the corresponding regions in the northwest MG and southwest IM. 4.2. Effects of climate variation on grassland dynamics We found that the dynamic change of grassland was affected by temperature and precipitation. Temperature decreased over 95.70% of the MP (Fig. 9A-1). The areas where the temperature declined were mainly located in the northern and eastern regions. The regions with increased precipitation (40.03%), as well as radiation (39.76%), were mainly distributed among the northeast. (Fig. 9B-1, C-1). From the temporal perspective, temperature exhibited an overall decreasing trend,

Fig. 6. Contribution of driving factors to grassland health contribution. A, B, HDD, CDD, BDD, HDR, BDR, and CDR represent degradation regions, restoration regions, human-dominated degradation, climate-dominated degradation, combined-dominated degradation, human-dominated restoration, climate-dominated restoration, and combined-dominated restoration, respectively.

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Y. Zhang et al. / Science of the Total Environment 698 (2020) 134304

Fig. 7. Analysis of grassland NPP because of health contribution to different study areas. A: degradation regions; B: restoration regions; HDD: human-dominated degradation; CDD: climate-dominated degradation; BDD: combined-dominated degradation; HDR: human-dominated restoration; CDR: climate-dominated restoration; BDR: combined-dominated restoration (A, B, HDD, CDD, BDD, HDR, BDR, and CDR represent degradation regions, restoration regions, human-dominated degradation, climate-dominated degradation, combineddominated degradation, human-dominated restoration, climate-dominated restoration, and combined-dominated restoration, respectively.)

while precipitation and radiation had experienced a rising trend during the study period (Fig. 9A-2, B-2, and C-2). Generally, the precipitation and radiation fluctuated upward, whereas the temperature showed a steady fluctuated downward trend. In this study, climate variation dominated the grassland restoration of both IM and MG. The result was consistent with previous studies, which pointed out that climate variation was beneficial to vegetation growth and the contribution of climate variation was greater than that of human activities. (Xin et al., 2008; Zhu et al., 2016b) The temperature exhibited an overall decreasing trend, while precipitation and radiation experienced a rising trend during the study period (Fig. 8). These variations agreed with previous results, which reported that the climate of IM trends to be cooler and wetter (Mu et al., 2013). Zhang et al. (2019) also concluded that the carbon sequestration capacity was enhanced by increased precipitation in the desert-grassland ecosystem, which also supports our findings.

Similarly, China has been implementing a series of vegetation restoration programs, including the Three-North Shelter Forest Program, the Natural Forest Protection Project, the Grain for Green Program, the Beijing–Tianjin Sand Source Control Project and the Returning Grazing Land to Grassland Project. (Batunacun et al., 2018). The Grain for Green Program policies mainly include reduced-livestock, forbiddengrazing, and returning farmland to grassland. The Fencing Grassland and Moving Users project was another strategy enforced in 2002 carried out by the local government in Inner Mongolia, which was aimed to move herdsman away from degraded grassland regions to lighten the burden of damaged grasslands (Wen et al., 2007; Yu and Farrell, 2013). They also implemented an ecological protection subsidy and reward mechanism to alleviate the contradiction between increasing the income of herdsmen and grassland ecological protection. These new institutional arrangements all have improved grassland restoration (Meng et al., 2018; Min et al., 2018; Schaffrath et al., 2011). 5. Conclusions

4.3. Effects of management measures on the sustainable development of grasslands The sustainable use of grassland resources is a huge challenge for Mongolia and China (Zhu et al., 2016a). To address the problem of land degradation and desertification, both Mongolia and China have launched a series of effective environmental laws and policies to balance economic development and environmental protection (Fig. 10). (Gao et al., 2016; Yang et al., 2014). Mongolia implemented the Greenbelt Project beginning in 2005 that aimed to plant trees covering 3000 km from east to west in southern Mongolia (Lee and Ahn, 2016). The government had promulgated the Pastureland Law in 2007. They also established a National Desertification Control Committee and implemented the National Desertification Control Plan to supervise the implementation at the same time.

To estimate the impact of restoration measures on grassland productivity under different political systems, we quantitatively evaluated the effects of land use on grassland NPP. We also assessed the relative effect of climate change and human activities on grassland health condition in MG and IM. The results showed a net increase in grassland area in both IM and MG during the study period. This was mainly attributed to the conversion from deserts and forests for both MG and IM, while farmland was also important in augmenting grasslands in IM. IM has a significantly higher increase of land conversion NPP and a net increase in total NPP than MG; the restoration area of grassland in IM and MG accounted for 90.21% and 81.45%, respectively. Furthermore, human activities were the dominant factor of degradation regions in the MP, while climate was the dominant factor in restoration regions. Although there are likely uncertainties in the remote sensing data and

Fig. 8. The Number of livestock (A), population (B) and Gross domestic product (C) changes in the main provinces in MG and cities in IM between 2001 and 2014.

Y. Zhang et al. / Science of the Total Environment 698 (2020) 134304

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Fig. 9. The spatial distribution and temporal change trends of climate factors in the MP from 2001 to 2014.

interpolated climate variables due to the different regimes in the MP; this study provides valuable insights in the grassland restoration process of MG and IM. We demonstrated that different ecological restoration projects conducted by two major parties of the MP seemingly have similarly improved the areas and grassland NPP during the study period. The grassland degradation affected by both climate change and human activities. Because the present research only evaluated the

impacts of human activities and climate variation on grassland dynamics, future studies should investigate the effects of extreme climatic events on grassland health condition. Declaration of competing interest The authors have declared no conflict of interest

Fig. 10. The legal and political measures in MG and IM on grassland restoration and management.

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Acknowledgments This research was funded by the National key Research and Development project (2018YFD0800201), the “APN Global Change Fund Project (No. ARCP2015-03CMY-Li & CAF2015-RR14-NMY-Odeh)”, the Jiangsu Province Agricultural Three Renovations Project of China (No. SXGC [2014]287), the National Natural Science Foundation of China (No. 41271361), the Key Project of Chinese National Programs for Fundamental Research and Development (973 Program, No. 2010CB950702), the National High Technology Project (863 Plan, No. 2007AA10Z231), The project of National Ethnic Affairs Commission of the People's Republic of China (2019-GMD-034), the National Natural Science Foundation (41501575), and the Public Sector Linkages Program by the Australian Agency for International Development (PSLP: No. 64828). We are grateful to the editor and anonymous reviewers. We also appreciate the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC) for sharing a series of original remote sensing dataset and Climatic Research Unit in University of East Anglia for sharing climate dataset. We also thank Prof. Jeffrey Robens from Nature Editorial department for his guidance of language on this work. References Bai, Y., Wu, J., Xing, Q., Pan, Q., Huang, J., Yang, D., Han, X., 2008. Primary production and rain use efficiency across a precipitation gradient on the Mongolia Plateau. Ecology 89, 2140–2153. Bai, Z.G., Dent, D., Olsson, L., Schaepman, M., 2010. Proxy global assessment of land degradation. Soil Use Manag. 24, 223–234. Batunacun, Nendel, C., Hu, Y., Lakes, T., 2018. Land-use change and land degradation on the Mongolian Plateau from 1975 to 2015-a case study from Xilingol, China. Land Degrad. Dev. 29, 1595–1606. Chen, B., Zhang, X., Tao, J., Wu, J., Wang, J., Shi, P., Zhang, Y., Yu, C., 2014. The impact of climate change and anthropogenic activities on alpine grassland over the Qinghai-Tibet Plateau. Agricultural & Forest Meteorology 189-190, 11–18. Chen, Y., Wang, K., Lin, Y., Shi, W., Yi, S., He, X., 2015. Balancing green and grain trade. Nat. Geosci. 8, 739–741. Chen, J., John, R., Sun, G., Fan, P., Henebry, G.M., Fernández-Giménez, M.E., Zhang, Y., Park, H., Tian, L., Groisman, P., Ouyang, Z., Allington, G., Wu, J., Shao, C., Amarjargal, A., Dong, G., Gutman, G., Huettmann, F., Lafortezza, R., Crank, C., Qi, J., 2018. Prospects for the sustainability of social-ecological systems (SES) on the Mongolian plateau: five critical issues. Environ. Res. Lett. 13, 1–16. Foley, J.A., Ruth, D., Asner, G.P., Carol, B., Gordon, B., Carpenter, S.R., F Stuart, C., Coe, M.T., Daily, G.C., Gibbs, H.K., 2005. Global consequences of land use. Science 309, 570–574. Gang, C., Wei, Z., Chen, Y., Wang, Z., Sun, Z., Li, J., Qi, J., Odeh, I., 2014. Quantitative assessment of the contributions of climate change and human activities on global grassland degradation. Environ. Earth Sci. 72, 4273–4282. Gang, C., Zhou, W., Wang, Z., Chen, Y., Li, J., Chen, J., Qi, J., Odeh, I., Groisman, P.Y., 2015. Comparative assessment of grassland NPP dynamics in response to climate change in China, North America, Europe and Australia from 1981 to 2010. Journal of Agronomy & Crop Science 201, 57–68. Gang, C., Zhao, W., Zhao, T., Zhang, Y., Gao, X., Wen, Z., 2018. The impacts of land conversion and management measures on the grassland net primary productivity over the Loess Plateau, Northern China. Sci. Total Environ. 645, 827–836. Gao, L., Kinnucan, H.W., Zhang, Y., Qiao, G., 2016. The effects of a subsidy for grassland protection on livestock numbers, grazing intensity, and herders' income in inner Mongolia. Land Use Policy 54, 302–312. Goenster-Jordan, S., Jannoura, R., Jordan, G., Buerkert, A., Joergensen, R.G., 2018. Spatial variability of soil properties in the floodplain of a river oasis in the Mongolian Altay Mountains. Geoderma 330, 99–106. Horion, S., Cornet, Y., Erpicum, M., Tychon, B., 2013. Studying interactions between climate variability and vegetation dynamic using a phenology based approach. Int. J. Appl. Earth Obs. Geoinf. 20, 20–32. John, R., Chen, J., Ou-Yang, Z.-T., Xiao, J., Becker, R., Samanta, A., Ganguly, S., Yuan, W., Batkhishig, O., 2013. Vegetation response to extreme climate events on the Mongolian Plateau from 2000 to 2010. Environ. Res. Lett. 8, 1–12. Kemp, D., Han, G., Hou, X., Michalk, D., Hou, F., Wu, J., Zhang, Y., 2013. Innovative grassland management systems for environmental and livelihood benefits. Pnas 110, 8369–8374.

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