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Article

Enhanced Ecosystem Services in China’s Xilingol Steppe during 2000–2015: Towards Sustainable Agropastoralism Management

by
Huashun Dou
1,
Xiaobing Li
1,2,*,
Jirui Gong
1,2,
Hong Wang
1,2,
Yuqiang Tian
1,2,
Xiaojing Xuan
1 and
Kai Wang
1
1
Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
2
State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2022, 14(3), 738; https://doi.org/10.3390/rs14030738
Submission received: 22 December 2021 / Revised: 25 January 2022 / Accepted: 1 February 2022 / Published: 4 February 2022
(This article belongs to the Special Issue Remote Sensing Based Monitoring of Terrestrial Ecosystem Service Bundles, Trade-Offs and Synergies)
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Abstract

:
Ecosystem services (ESs) provided by dryland regions are very vulnerable to environmental dynamics and management transformation, and it is still unclear how these services will be affected by the combined effects of accelerated climate variability and land-use changes at a regional macro level. In this study, the status, patterns and trends of ESs provided by the Xilingol steppe were quantitatively evaluated, and the impact of climate and land-use change on ESs were discussed. The results showed that favorable climatic conditions and the implementation of ecological policies had a substantial positive impact on regional vegetation restoration during 2000–2015. As a result of the vegetation greening, water retention and carbon sequestration increased significantly at rates of 16.01 m3 ha−1 and 1.75 g C m−2 yr−1, respectively. Soil loss caused by wind erosion, an ecosystem disservice, decreased significantly at a rate of −0.57 t ha−1 yr−1. Although several environmental factors had differing impacts on the ESs changes in the subregions, including the meadow, typical and desert steppes, the main factors that contributed to the enhancement of overall ESs benefits were the climatic humidification and the reduced grazing pressure in the entire study area. The above conclusion shows that, in the era of climate change, the implementation of sustainable farming–pastoral strategies can realize a “win-win” situation between ecological restoration and socioeconomic development in arid and semi-arid steppes.

1. Introduction

Ecosystem services (ESs) refer to benefits directly or indirectly obtained by human populations from nature, which are very important for human wellbeing [1]. ES quantification, mapping and evaluation are becoming important and reliable tools for natural-capital management and decision making [2]. A recent global ES-modeling study predicts that up to five-billion people face the threats of reduced ESs by 2050, yet these threats could be reduced by a factor of 3–10 in the context of sustainable development [3]. Climate prediction shows that by the end of this century, the degree of drought faced by global terrestrial ecosystems will generally increase, and the dryland area will expand by 11–23% [4]. These drylands are characterized by low water availability, limited and variable annual precipitation, high potential evapotranspiration (PET), nutrient-poor soils, and low vegetation productivity [5]. Thus, dryland ecosystems are considered a vulnerable part of the terrestrial environment and are sensitive to desertification.
China has 6.6 million km2 of dryland that support approximately 580 million people. More than 60% of China’s drylands are arid and semi-arid steppes, and provide a wide range of material and non-material benefits to humans, including a wide range of ES, such as food production, water supply and regulation, carbon storage and climate mitigation, and diverse cultural services [6,7]. Despite the importance of these steppes, their degradation is widespread and accelerating, which is the outcome of coupled processes primarily resulting from climate variability exacerbated by human activities in recent decades [8]. Factors including drought, reclamation, and overgrazing, have aggravated the spread of grassland desertification [9,10,11]. Expanded irrigated agriculture and mining have exacerbated local water shortages in both ecological and social systems, as evidenced by the 34.1% decline in the total water-surface area of lakes larger than 1 km2 in Inner Mongolia from 1991 to 2009 [12]. With population-growth and diet changes, large-scale disordered reclamation and an increase in the number of livestock kept on pastures have had further negative effects on the resilience of the steppes [13]. Therefore, a vicious socio-ecological cycle gradually formed in these steppes. The reduction in vegetation has led to a decline in soil nutrients and increased soil erosion, frequent sandstorm events have threatened the human habitats, and stakeholders’ livelihoods have become difficult [11]. Therefore, socio-ecological solutions are needed to combat steppe degradation and contribute to restoring its sustainability to support biodiversity, ESs, and human wellbeing.
In the last two decades, the approach of field observations combined with controlled experiments has been considered as the main way to reveal the impacts of human-induced global change factors on the structure, functioning and stability of ecosystems [14,15]. A number of studies have discussed the effects of climatic factors (e.g., warming, humidification and nitrogen deposition) and human interference (e.g., grazing, mowing and land use change) on the ecosystem functions and services of arid and semi-arid steppes in China [15,16,17]. However, their analytical scale (mostly at the community or ecosystem scale), quantitative approach (basically relying on plant functional traits and soil properties as proxies for measuring ES) and the mapping presentation of assessment results (rarely combined remote sensing observations with biophysical models) are often limited. Observations collected through remote sensing and in situ data (e.g., satellite images), and derived products obtained after data processing (e.g., vegetation indices) can effectively reflect the spatial and temporal dynamics of the biophysical structure and process covariates of an ecosystem [18]. At macroscopic scales, it is becoming increasingly popular to combine diverse remote sensing-derived ecological parameter products, biophysical models, and socioeconomic statistics for spatially explicit assessments of regional ESs [19]. For example, the leaf-area index has been incorporated into empirical mechanistic models to quantify air quality regulation services in urbanized areas [20]. In addition, the balance between steppe-forage production service and grazing pressure can be effectively revealed by linking the model-based vegetation productivity and district-based livestock statistics [21]. Thus, with the spatial and temporal dynamics of ESs already well documented, we can effectively link ecological processes and the associated socio-ecological drivers to ESs to serve trade-off decisions in ecosystem management.
On the other hand, the unforeseen consequences of global and national environmental and socioeconomic policies also exacerbate the pressure on steppes. Natural grasslands have become the target of recent carbon sequestration and climate change mitigation projects that emphasize afforestation, such as REDD+ and China’s “Grain to Green” program (GTGP), which often leads to trade-offs in different grassland services [6]. When semi-arid steppes are invaded by intensive and homogeneous woody plants with deep roots, which can consume additional deep soil water through enhanced evapotranspiration (ET) and force native grass species to struggle to survive [22]. Not only will the improved carbon sequestration and soil erosion control achieved by past ecological restoration projects be lost, but a potential water-demand conflict between the natural ecosystem and social-economic activities will also become apparent [23,24].
The factors listed above may result in the inability of steppe stakeholders to effectively integrate the framework of ESs assessment and trade-offs into environmental and economic-management decisions to balance the regional economic development goals and ecological protection pursuits in China’s dryland area. There is a lack of spatially explicit studies conducted at the regional macro scale, across a long time series to comprehensively assess and discuss patterns, trends, relationships, and multifaceted driving mechanisms of ES in China’s arid and semi-arid steppes. Hence, in this study, we selected the Xilingol as the study area, which is a representative semi-arid steppe region in northern China. Combined with remote sensing observations, biophysical models and socioeconomic statistics, we investigated the dynamics of the ESs and their response to diverse environmental driving forces in the Xilingol steppe, including climate variability, land-use change and the implementation of environmental policies. It is expected that the quantitative information provided in this study will enrich our understanding of the evolution mechanism of dryland ES, and contribute to the strategic formulation of sustainable agropastoral production methods and ecosystem-based management in global dryland regions.

2. Materials and Methods

2.1. Study Area

The Xilingol steppe, located in the central part of Inner Mongolia in China, is an important livestock-product base in China (Figure 1a). The study area contains 12 county-level administrative units with a total population of 1.05 million people and a total area of 203,000 km2 (115°13′~117°06′E, 43°02′~44°52′N). Xilingol has a mid-temperate continental arid and semi-arid climate, which is cold, arid and windy. The annual average temperature (TEM) is 0–3 °C, the annual average precipitation (PPT) is approximately 80–400 mm, and the interannual and seasonal variability in PPT is relatively large. The annual average wind speed (WINS) is 4–5 m/s; the strong wind is mainly concentrated in spring, and the maximum wind speed can reach more than 25 m/s. Before 2000, deforestation and desertification were particularly severe in the study area, where the degraded area accounts for over 60% of the entire region. The continuous expansion of the Hunshandake Sandyland in the southern part of the study area has made it a main sand source in northern China. Since 2000, several national ecological restoration programs have been implemented to protect and restore the natural ecosystems, including GTGP and the “Beijing-Tianjin Sand Source Control Project” (BTSSCP). Additionally, some grassland management strategies have been introduced to reduce grazing pressure, such as prohibiting grazing and encouraging livestock penfeeding. After more than a decade of ecological restoration, desertification has been effectively curbed, and animal husbandry production has shifted towards a sustainable pathway. As a transition zone from forest to grassland, the landscape in the Xilingol steppe has an obvious geographical gradient, which can be reflected in the differences in factors such as water and heat conditions, and biome and soil types. Therefore, to further analyze the spatial heterogeneity of ES changes within the study area, Xilingol was divided into the following three biome subregions from east to west: meadow steppe (I, 31,682 km2, 15.86%), typical steppe (II, 135,859 km2, 68.03%) and desert steppe (III, 32,162 km2, 16.11%) (Figure 1d).

2.2. Data Collection and Processing

2.2.1. Satellite Observation Data

The Moderate-resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data from 2000 to 2015 were obtained from the Earth Observing System of National Aeronautics and Space Administration (NASA) with a spatial resolution of 250 m and a 16-day interval (MOD13Q1 product). After preprocessing to reduce noise and improve data quality, we compiled the resultant 16-day MODIS NDVI data into monthly NDVI data by applying maximum value composites to the two NDVI images of each month [25]. Topographic data were derived from Advanced Spaceborne Thermal Emission and Re-flection Radiometer, Global Digital Elevation Model (ASTER GDEM), which has a horizontal resolution of 30 m. In this study, the information was used to reflect topography changes and interpolate climate data. The land use/cover maps from 2000, 2005, 2010, and 2015 were interpreted from the Landsat TM/ETM data at a spatial resolution of 100 m × 100 m, and the overall accuracy of classification was higher than 90% for the four periods in the study area [26].

2.2.2. Meteorological Station and Reanalysis Data

Diverse meteorological datasets were used to evaluate the ES and environmental factors in the study area, including observation data from meteorological stations and reanalysis data. Daily weather station data, including TEM, PPT, WINS and solar radiation, from the National Meteorological Administration of China for 2000–2015 were obtained from 102 ground stations within and near Xilingol, and interpolated onto a 250 m grid by the thin-plate spline method using ANUSPLIN software. Annual and monthly average values of climatic variables were then calculated from the daily measurements. Potential evapotranspiration, which is a principal element in the hydrological cycle and a variable that is widely used in ecosystem-service modeling, was estimated using the Penman–Monteith Evapotranspiration (FAO-56) method with daily weather data [27]. In addition, we extracted the Palmer drought severity index (PDSI) from the TerraClimate, a global high-resolution (monthly, 1/24°) reanalysis meteorological dataset [28]. Soil moisture (SMO) in the shallow layer (10–40 cm, which is influenced by rainfall infiltration and evapotranspiration), is critical for plant growth and sustaining dryland ecosystems. SMO was obtained from the NASA Global Land Data Assimilation System Version 2 (GLDAS-2) Noah Land Surface Model products (monthly, 0.25°), which can provide various continuous, high-resolution estimates of land surface states and fluxes [29].

2.2.3. Socioeconomic and Other Data

Data on population, grain and meat production and livestock stocks in the study area from 2000 to 2015 were sourced from the Inner Mongolia statistical yearbook (http://tj.nmg.gov.cn/, accessed on 23 November 2021). Additionally, all types of livestock (e.g., sheep and cattle) were converted to the sheep unit (SU) in reference to China’s current standard for calculating the carrying capacity of grassland [30]. The recently updated boundary data of administrative divisions and ecological engineering projects were from the resource and environment science data center of the Chinese Academy of Sciences. Soil data were derived from the Harmonized World Soil Database (Version 1.2), which was provided by the FAO Geonetwork (https://data.apps.fao.org/map/catalog/, accessed on 23 November 2021). This information included the soil types, particle-size distribution, soil organic matter content and soil depth at a scale of 1 km. All major datasets used in the study are detailed in Table 1. The data processing and subsequent technical process of the study are shown in Figure 2.

2.3. Quantification of Ecosystem Services (ESs)

Water retention (WR), carbon sequestration (CS) and soil erosion control (SEC) strongly interact in arid and semi-arid areas worldwide and are also the three most important ESs there [24]. In view of the increasing global demand for water and the impact of climate warming and drying, the conflicts and crises caused by water use are intensifying, especially in seasonally dry areas. Human activities also affect water yield and availability through the construction of water conservancy facilities, land use and pollutant discharge [31]. In this study, the water balance equation was used to calculate the water retention capacity of the ecosystems, which is closely related to factors that include precipitation, evapotranspiration, surface runoff and land cover type [19]. CS is an ecosystem process that regulates the concentration of greenhouse gases in the atmosphere, which generates other services, such as the supply of wood, fiber or fuel, and understanding the potential of CS and its environmental control on drylands can help reduce the uncertainty of future climate predictions [32]. The potential of CS in vegetation productivity (biomass production) can be reflected by the net primary productivity (NPP) [33]. NPP was estimated using the Carnegie-Ames-Stanford Approach (CASA) method in this study [34]. As one of the most critical environmental problems related to global dryland degradation, soil loss by wind erosion (SL) is a kind of ecosystem disservice that reflects the potential negative effects that ecosystem processes might have on the quality of human life (e.g., air quality and human health) [35]. The opposite of SL is often called sandstorm prevention or sand fixation in the SEC service category. However, increases in the fixed amount of sand are usually caused by the enhancement of the potential wind erosion modulus; therefore, we used the indicator SL to measure the benefit change in the SEC service from the opposite view. The revised wind erosion equation (RWEQ) model issued by the United States Department of Agriculture was used to predict the SL [36]. The specific quantification process and calculation formula of the three services mentioned above can be found in the Supplementary Materials of this study.

2.4. Other Methods

2.4.1. Estimating Vegetation Cover

Fractional vegetation cover (FVC) is an important parameter in many biophysical models of ESs. It is suitable for describing the condition of vegetation in arid and semi-arid regions because it addresses the issue, to some extent, of NDVI easily saturates vegetation with high coverage and makes it difficult to distinguish vegetation with saturating coverage [37]. In this study, a dimidiate pixel model (every pixel contains only two elements of vegetation and soil) was adopted to calculate the vegetation FVC [38]. We calculated both the monthly and annual average FVC to meet the input requirements of different ES models.

2.4.2. Grazing Pressure Index (GPI) Calculation

Researchers of grassland ecosystems are often troubled by the issue of estimating grazing pressure on a regional scale, and limited field monitoring data and socioeconomic statistics make it difficult to determine the regional impact of grazing management on steppe ES. A dynamic reference vegetation cover-based method was proposed in 2012, combining remote sensing-derived FVC values of pixels with a fixed-size moving window approach [39]. The method was recently simplified to a remotely sensed parameter named the grazing pressure index (GPI) and successfully applied to explore the relationship between grazing and vegetation productivity in the Xilingol grassland [40]. In this study, we used this automated and repeatable index to quantify the grazing pressure of each grassland pixel in the study area with the annual FVC image on DAY 225, which represented the overall grazing pressure during the growing season of grassland before mowing in each year. After accounting for the terrain conditions, any remaining differences in FVC among the focal pixel and the reference pixels (90–95% quantiles here) in the window could be seen as being caused by different grazing pressures. The credibility of the derived GPI in Xilingol, optimal size of the moving window, and other detailed operation details are available in the references [39,40].

2.4.3. Trend and Correlation Analysis

The Mann–Kendall (MK) test and Theil–Sen estimation were used to detect the trends of environmental and ES variables. These two nonparametric methods are not easily affected by abnormal values and have been frequently used to quantify the significance of trends in hydrometeorological time series and vegetation dynamics studies [41,42]. Sen’s slope indicates the direction and amplitude of the temporal changes in a specific variable, while the positive and negative slopes indicate increasing and decreasing trends, respectively. Spearman’s correlation analysis was also performed to detect the relationships between ES and environmental drivers, which can simultaneously return both the correlation coefficient and significance test (P-value). Sen’s slope estimator, an MK nonparametric test and a correlation analysis were all performed using R software [43].

3. Results

3.1. The Climate Showed a Trend of Humidification

From 2000 to 2015, the climate conditions in Xilingol underwent obvious changes (Figure 3). The annual average TEM decreased at a nonsignificant rate of 0.022 °C yr−1, while the PPT increased significantly at a rate of 5.206 mm yr−1. PET decreased (−2.128 mm yr−1, P > 0.05), and SMO simultaneously increased (0.432 kg m−2 yr−1, P < 0.05), which showed that the degree of drought in the study area was improving, and the climate was becoming humid. The increase in the PDSI index (0.073 yr−1, P > 0.05) also reflected the trend of climatic humidification. At the same time, the average annual WINS firstly increased and then decreased during this period, showing strong fluctuations, and generally decreased at a nonsignificant rate of −0.008 m s−1 yr−1. It is clear that the climate is becoming progressively wetter and that drought is being alleviated simultaneously.

3.2. Spatial Heterogeneity in Land Use Conversion

By comparing land-use patterns in 2000 and 2015, the largest land-use change occurred in the interconversion between the grassland and barren land, with a total of 2835.94 km2 of grassland converted to barren land and 2691.71 km2 of barren land converted to grassland. The other four land-use types in the study area had a limited increase or decrease in area during this period. Compared with 2000, the largest increase occurred in the built-up areas, reaching 123.58%, and the largest decrease occurred in the wetlands, reaching 6.62%. The land use transfer matrix of the different biome subregions during this period is shown in Table 2, and the results show that each subregion showed different land use conversion characteristics. During this period, the largest conversion in the meadow steppe area was the transformation from cultivated land to grassland, reaching 747.47 km2. The net loss of cropland was 83.34 km2, and the net increase in forestland and grassland was 5.44 and 80.16 km2, respectively, which reflect the effect of returning croplands to forests and grasslands. In the typical steppe, a total increase of 465.93 km2 in the barren land or desert areas occurred, and the net loss of grassland area reached 607.02 km2. In addition, there were different degrees of built-up urban-land expansion, wetland shrinkage and cropland expansion during the same period, with areas of 463.42, 419.08 and 62.83 km2, respectively. The characteristics of the land use conversion in the desert steppe were similar to those in the typical steppe.

3.3. GPI Showed a Decrease in Grassland Grazing Pressure

The grazing intensity of the grassland in the Xilingol showed obvious spatial heterogeneity (Figure 4a). The average GPI from 2000 to 2015 followed the following schematic: desert steppe (0.36) > meadow steppe (0.26) > typical steppe (0.24). A trend analysis showed that the annual average GPI of grassland in the study area decreased at a rate of −0.17% yr−1 (P > 0.05), and the grazing pressure of grassland weakened during the study period. The changes in the GPI at the pixel scale showed that 62.98% of the grassland in the study area experienced a declining grazing pressure without considering the significance levels, which was much larger than the proportion of grassland that showed the opposite trend. Among them, the grassland grazing pressure of 17,488 km2 (10.85%) of the area decreased significantly (P < 0.05) and was mainly distributed in the typical steppe; only 5898 km2 (3.66%) of the grassland’s grazing pressure increased significantly (P < 0.05) and was mainly distributed in the desert steppe (Figure 4b,c). The trends for the three biome subregions showed that the typical steppe was the main contributor to the reduction in the overall grazing pressure in the study area.

3.4. An Increase in Vegetation Greenness Indicated by FVC

From 2000–2015, 70.19% area of the Xilingol showed no obvious changes in vegetation coverage at the P < 0.05 significance level, exhibiting stable vegetation coverage. However, the area that showed a statistically significant (P < 0.05) change in vegetation coverage was much smaller. Of the total area of study, 29.72% was covered by significantly greening vegetation, and only 0.26% of the area was covered by browning (Figure 4e,f). In terms of space, obvious vegetation greening trends were found in the meadow and typical steppe, with rates of 0.33% and 0.29% yr−1, respectively; the vegetation showing browning trends was concentrated in the desert steppe in the southern and western portions of the study area, and the decrease in the rate of FVC in most areas was less than 0.5% yr−1. Generally, the humid climate from 2000 to 2015 tended to promote vegetation-growth enhancement in the Xilingol steppe, and an obvious resulting feature was an increase in the FVC.

3.5. Benefits of ES Improved with the Greening Trend

During 2000–2015, the amount of WR and CS in the study area exhibited an upward trend, and the SL decreased, which indicated that the overall benefits from ES in the Xilingol steppe improved (Figure 5). Driven by PPT, the trends and spatial distributions of WR and CS were similar, and the areas with the largest range of improvements were distributed in the northeastern part of the study area. The WR increased significantly at a rate of 16.01 m3 ha−1 yr−1, with a particularly significant increase by 26.12 m3 ha−1 per year in the meadow steppe. At the same time, the average NPP of the Xilingol steppe increased significantly by 1.75 g C m−2 yr−1 from 2000 to 2015, especially in the typical steppe, and the rate of increase reached as high as 2.09 g C m−2 yr−1 (P < 0.05), which was close to the increase in the desert steppe of four times the rate, suggesting that the typical steppe was the main area contributing to the increase in the vegetative carbon sink in the Xilingol steppe. The overall declining trend of SL indicates that the SEC service in the study area improved. The soil wind erosion modulus decreased significantly at a rate of −0.57 t ha−1 yr−1. The main area contributing to the SL decline was mainly distributed in the middle and southern parts of the typical steppe, and the areas of rising SL were mainly distributed in the cities of Erenhot and West Ujimqin.

3.6. Response of ES Dynamics to Environmental Drivers

To identify the main factors driving changes in ES, we compiled a correlation matrix based on the time series data between the ES and environmental variables for the entire study area and different steppe subregions (Figure 6). The factors of PPT and SMO, which reflect the main water sources required for vegetation growth, showed a significant positive correlation with FVC, WR and CS (R > 0.6, P < 0.05) and a significant negative correlation with SL (R > 0.6, P < 0.05). The GPI factor, which reflects the intensity of anthropogenic land use, had the opposite correlation with three ES, and the temperature factor was not significantly related to the FVC and all ES. In addition, we found differences in the effects driving environmental factors on the services present in different steppe subregions. First, the correlation between SMO and FVC, WR and CS was significant and found to be positive for the meadow and typical steppe, but not for the desert steppe, which indicated that vegetation growth and the service provision of steppe communities, dominated by clump grasses, were sensitive to shallow SMO, while the desert steppe with associated shrubs was not. Second, the positive correlation coefficients between WINS and SL were desert steppe > typical steppe > meadow steppe, which demonstrated that the driving effect of WINS on SL became increasingly strong as the vegetation conditions became increasingly restricted. In general, PPT and GPI showed a significant and strong correlation with FVC, WR and CS in different the subtypes of the steppe, indicating that these two factors were the main factors driving the changes of ES in all sub steppes.

4. Discussion

4.1. Win-Win Gains of ES Benefits and Livelihood Output

Since 2000, while the overall benefits of vegetation coverage and ES have significantly improved in the Xilingol steppe, the livelihood output has shown an upward trend in which grain production and meat production have increased significantly at rates of 20,400 t yr−1 and 8300 t yr−1, respectively (P < 0.001) (Figure 7a). In addition to the increase in the planting area through grassland reclamation, the increase in grain production can also be attributed in part to the increase in grain yield per hectare due to technological advances in agriculture, such as the input of high-efficiency fertilizers and the construction of water-retention irrigation facilities, which may realize the synergy between grain production and multiple regulating services. Similarly, the SEC and CS in the semi-arid Loess Plateau of China during the period of 2000–2015 improved due to extensive slope farmland rehabilitation and revegetation under the implementation of the GTGP. Although the cultivated land area decreased by 15.0%, grain production in low-yield cropland increased by 56.7% [44].
By subdividing the annual meat production data of the main livestock (Figure 7b), we found that mutton and pork production in the study area remained stable after 2000, and the increase in the total regional meat production could be mainly attributed to the significant increase in beef production. It should be made clear that the decline in the grassland-grazing rate was not contradictory to the increase in livestock meat production. In addition to increasing the grazing pressure, regional meat production can also be increased by creating a high-quality beef cattle industry and increasing the agricultural feed input for factory breeding. The Xilingol government has been promoting a structural-adjustment policy of livestock feeding called “reducing sheep and increasing cattle” since 2005, via considerable financial investment in high-quality beef cattle industries. Statistical data also showed that the number of large livestock and sheep raised in the study area increased and decreased significantly at rates of 17.49 and −27.25 × 104 SU yr−1 since 2000, respectively. Correspondingly, the trend analysis of our GPI index showed that the grazing pressure in 62.98% of Xilingol grassland has been relieved, which also affirms the influence of the above policies. Large-scale intensive breeding has restrained the growth of the grassland grazing rate and has strengthened the supply of meat production without negatively impacting grassland productivity and carbon sequestration. The decrease in the grassland grazing rate effectively reduced the negative effects of livestock grazing and trampling on soil and played a positive role in vegetation restoration. Therefore, our statistical results show that the Xilingol steppe experienced a process of synergistic gains in terms of restoring the ecological environment and promoting socioeconomic development during 2000–2015.

4.2. Impacts of Climate Change on Dryland ESs

Climate change affects ES directly or indirectly by altering hydrological processes (such as precipitation and evaporation), atmospheric CO2 concentrations and other conditions [45]. The drought caused by warming has led to an ES decrease and ecosystem disservice increase in arid and semi-arid areas, which has increased the pressure on oasis agricultural development and desert ecological protection [46]. The climatic humidification since 2000 has promoted vegetative activity in the study area, which is reflected in the increases in the NDVI, coverage and net primary production of vegetation. Conversely, although some studies have shown that thermal conditions determine the vegetation growth and photosynthetic capacity, lower surface temperatures may limit nutrient uptake, affecting vegetation’s ability to participate in the carbon cycle [47,48]. However, since 2000, the temperature in Xilingol fluctuated frequently and did not show a significant warming trend, and the correlation analysis did not show a strong relationship between temperature and all ES.
Although vegetation-growth enhancement may have caused an increase in the ET to a certain extent, considering the significant increase in precipitation and the significant decrease in PET, the overall WR of the Xilingol steppe has still improved. Notably, recent studies have pointed out that due to the significant increase in ET, vegetation recovery in the Loess Plateau seems to be near the regional threshold of the vegetation carrying capacity; furthermore, the loss of SMO and decrease in surface runoff caused by additional revegetation may threaten the water supply required for human activities. Differing from the Loess Plateau, due to the precipitation increase and the decrease in the grazing intensity since 2000, the vegetation greening in the Xilingol steppe was mainly due to the growth enhancement and natural restoration of native vegetation rather than the large-scale increase in artificial woody vegetation. In particular, the areas of water-consuming arbor forests and shrubs only increased by 1.62% during 2000–2015, so the SMO and water yield did not decrease similarly to the Loess Plateau.
Wind is a main driving force of the dryland erosion process, and the decrease in wind speed since 2000 directly reduced the potential for soil wind erosion in the study area. Generally, long-term strong wind conditions will accelerate the evaporation of soil water to increase the water deficit for plant growth. Additionally, severe dust storms can even cause plant lodging and death. The wind–sand transport processes in dryland ecosystems can aggravate the dominance of shrubs in the community, which leads to the invasion of shrubs, increasing the mortality of native grasses [49]. The decline in the wind speed hindered the negative impact of wind on the vegetation structure change, which improved the regional SEC service. In addition to alleviating drought stress on plant growth, precipitation can also directly reduce surface erodibility by increasing topsoil moisture during the growing season and snow-cover depth during the non-growing season [50].

4.3. Diverse Policies Leading to Contrasting Impacts on Agropastoralism Development

China has made unprecedented natural capital investments in curbing land degradation to achieve comprehensive national environmental governance since 1980s (Figure 8). Previous studies evaluated the effectiveness of different ERPs or steppe management strategies, and the results show that in the arid and semi-arid zones of China, ER activities have actively increased forest and grassland coverage, inhibited desertification and sandstorms, enhanced soil water-retention capacity and slowed the increase in the livestock grazing rate [10,51,52,53,54]. Increasing the grazing intensity is generally believed to reduce the soil water storage and carbon sequestration capacity of vegetation, reduce potential nutrient cycling and increase soil erosion risk in grasslands [16,55]. It is very important to use appropriate grassland management approaches to alleviate the current production pressures on grasslands, including implementing limited grazing (limiting the number of livestock and grazing time) and rested grazing (short-term prohibition of grazing in a certain period) [56]. Due to the reduction of grassland-grazing pressure and a stricter policy for protected land, the vegetation in Xilingol, especially in the meadow and typical steppe, has been greatly restored.
On the other hand, against the background of China’s declining ration consumption and rising meat and milk consumption, grass husbandry will be the key to solving the shortage of livestock products and the structural contradictions in agriculture. Due to the implementation of the “Grain to Feed” policy in recent years, the agricultural farming structure in China’s agro-pastoral transitional areas is undergoing a rapid and profound transformation. Forage farming, such as silage corn and perennial forage grass (e.g., alfalfa), is encouraged by local governments, which means that intensive artificial grassland and forage land will free a significant percentage of natural grassland from high grazing pressure [57]. In the future, by relying on the further consolidation of croplands in these areas (e.g., Duolun and Taibus in Xilingol), the establishment of a whole industry chain of forage-crop planting, processing, booking and transportation can further decouple grassland biomass production and livestock feeding in the Xilingol steppe [58].
However, due to agricultural expansion and urbanization process, cultivated land and urban built-up areas (especially industrial and mining land) are still expanding in Xilingol. Both policy-driven transformation from dry cultivated to irrigated land and mining activities motivated by economic efficiency, will lead to the loss of wetland or grassland and the decline of terrestrial water storage capacity. In this study, we found that the area of agricultural irrigation in Xilingol increased by nearly three times during 2000–2015, and the annual raw coal output increased from 0.12 × 107 t in 2000 to 14.63 × 107 t in 2012, achieving an astonishing 129-fold increase. The expansion of agriculturally irrigated areas and the increased water supply to the coal–chemical industry through water diversion projects are an important mechanism for the shrinkage of wetlands/lakes in the Xilingol steppe [12]. An evident example is the Wulagai wetland in East Ujimqin, which has dried rapidly since 2004 and has become a large-scale bare saline alkali beach, and requires management vigilance.
In addition, several strategies involving payments for ES, including steppe ecological subsidies and incentive policies, have been widely used as the basic tool for coordinating grassland ecological protection and animal husbandry development in pastoral areas of China [51]. The parallel processes of grazing control, ecological migration and diversification of nonagricultural income have reduced the direct demand for livelihood in the steppes and the need for further land development. Although these payments for ES policies have successfully protected the natural capital of steppes, recent studies have shown that this positive impact is unevenly distributed. At the economic level, including factors such as rising livestock market prices, low subsidy standards, and the short-term trend of market transfer of grassland-use rights, the ecological gains of payments for ES were offset to some extent, which threatened the sustainability of these ecological conservation and compensation strategies [59,60,61]. In the face of such a dilemma, governments must maintain the diversity and effectiveness of future policies through institutional reform and local participation, while scientists and policy makers should advocate a more sustainable planning orientation and lower cost-effectiveness to minimize the trade-off between environmental protection and economic development goals.

5. Conclusions

Based on widely used biophysical models and remote sensing data sources, combined with socioeconomic statistics, this study quantitatively evaluated the dynamics of various dryland ESs in the Xilingol steppe in China and analyzed the relationships between ESs and environmental driving forces in the context of climate and land-use changes in different biome subregions. From 2000 to 2015, precipitation and soil moisture increased significantly, the drought in the study area was alleviated and favorable climatic conditions improved vegetation greenness, especially in the meadow steppe and typical steppe subregions. Under humid climate conditions, the overall decrease in the grassland grazing pressure drove a significant increases in water retention and vegetation carbon sequestration, reducing soil loss caused by wind erosion in the study area. Specifically, the meadow steppe was the main contributor to water retention in the study area, while the typical steppe showed a faster carbon-sequestration rate and greater potential to control soil erosion in comparison. While the overall benefits of ES improved significantly, the livelihood output of the study area, including grain and meat production, showed a synchronized upward trend during the same period, resulting in a “win-win” outcome for the restoration of the ecological environment and the promotion of socioeconomic development in the Xilingol steppe.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/rs14030738/s1. The supplementary material detailed the quantification methods and calculation formulas of all ecosystem services in this study.

Author Contributions

Conceptualization, H.D. and X.L.; methodology, H.D. and X.L.; data curation, X.X. and K.W.; formal analysis, H.D., H.W. and X.X.; resources, X.L. and J.G.; writing—original draft preparation, H.D. and X.L.; writing—review and editing, X.L. and J.G.; visualization, H.D. and X.X.; supervision, X.L. and Y.T.; project administration, H.W. and Y.T.; funding acquisition, X.L. and J.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Key Science & Technology Special Program of Inner Mongolia (No. 2021ZD0011 and 2021ZD0015), the National Key Research & Development Program of China (No. 2016YFC0500502) and the Natural Science Foundation of China (No. 31570451).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Basic geographic information for the study area, including (a) the grassland ecosystem distribution in northern China and the location of Xilingol steppe, (b) the digital elevation model (DEM) and county administrative division, (c) spatial distribution pattern of land use in 2015, and (d) distribution of different biome subregions with landscape photographs (all photos were taken in July 2019).
Figure 1. Basic geographic information for the study area, including (a) the grassland ecosystem distribution in northern China and the location of Xilingol steppe, (b) the digital elevation model (DEM) and county administrative division, (c) spatial distribution pattern of land use in 2015, and (d) distribution of different biome subregions with landscape photographs (all photos were taken in July 2019).
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Figure 2. Methodology scheme of the research. Abbreviations: MODIS, Moderate-resolution Imaging Spectroradiometer; NDVI, normalized difference vegetation index; PDSI, Palmer drought severity index; GLDAS, Global Land Data Assimilation System; CASA, Carnegie-Ames-Stanford Approach; RWEQ, revised wind erosion equation.
Figure 2. Methodology scheme of the research. Abbreviations: MODIS, Moderate-resolution Imaging Spectroradiometer; NDVI, normalized difference vegetation index; PDSI, Palmer drought severity index; GLDAS, Global Land Data Assimilation System; CASA, Carnegie-Ames-Stanford Approach; RWEQ, revised wind erosion equation.
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Figure 3. Temporal variation in the annual (a) mean temperature (TEM), (b) precipitation (PPT), (c) potential evapotranspiration (PET), (d) soil moisture (SMO), (e) Palmer drought severity index (PDSI) and (f) mean wind speed (WINS) in Xilingol during 2000–2015.
Figure 3. Temporal variation in the annual (a) mean temperature (TEM), (b) precipitation (PPT), (c) potential evapotranspiration (PET), (d) soil moisture (SMO), (e) Palmer drought severity index (PDSI) and (f) mean wind speed (WINS) in Xilingol during 2000–2015.
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Figure 4. Spatial patterns of the average annual values (a,d) and overall trends (b,e) for grazing pressure index (GPI) and fractional vegetation cover (FVC) in Xilingol during 2000–2015, and the line charts (c,f) for specific annual variations of GPI and FVC in different biome subregions. Note: In the inset map at the top left corner in b and e, dark blue areas showed a statistically significant (P < 0.05) trend.
Figure 4. Spatial patterns of the average annual values (a,d) and overall trends (b,e) for grazing pressure index (GPI) and fractional vegetation cover (FVC) in Xilingol during 2000–2015, and the line charts (c,f) for specific annual variations of GPI and FVC in different biome subregions. Note: In the inset map at the top left corner in b and e, dark blue areas showed a statistically significant (P < 0.05) trend.
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Figure 5. Spatial patterns of the average annual values (a,d,g) and overall trends (b,e,h) for water retention (WR), carbon sequestration (CS) and soil loss by wind erosion (SL) in Xilingol during 2000–2015, and the line charts (c,f,i) for specific annual variations of WR, CS and SL in different biome subregions. Note: In the inset map at the top left corner in b, e and h, dark blue areas showed a statistically significant (P < 0.05) trend.
Figure 5. Spatial patterns of the average annual values (a,d,g) and overall trends (b,e,h) for water retention (WR), carbon sequestration (CS) and soil loss by wind erosion (SL) in Xilingol during 2000–2015, and the line charts (c,f,i) for specific annual variations of WR, CS and SL in different biome subregions. Note: In the inset map at the top left corner in b, e and h, dark blue areas showed a statistically significant (P < 0.05) trend.
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Figure 6. Summary of the correlations (Pearson’s R) among the ecosystem services and the environmental variables in the whole Xilingol steppe (a), meadow steppe (b), typical steppe (c) and desert steppe (d) from 2000 to 2015. Note: The orientation of the ellipse represents the positive (45° direction) and negative (−45° direction) of the correlation coefficient, and a smaller ellipse area represents a larger absolute value of the correlation coefficient. In addition, the correlation coefficients can also be distinguished from the color of the ellipse, and the gradient of the color band from dark red to dark blue represents the shift of the correlation coefficient from −1 to 1. The crosses over each ellipse showed no statistically significant correlation (P > 0.05). Abbreviations: TEM, temperature; PPT, precipitation; PET, potential evapotranspiration; SMO, soil moisture; PDSI, Palmer drought severity index (PDSI); WINS, wind speed; GPI, grazing pressure index; FVC, fractional vegetation cover (FVC); WR, water retention; CS, carbon sequestration; SL, soil loss by wind erosion.
Figure 6. Summary of the correlations (Pearson’s R) among the ecosystem services and the environmental variables in the whole Xilingol steppe (a), meadow steppe (b), typical steppe (c) and desert steppe (d) from 2000 to 2015. Note: The orientation of the ellipse represents the positive (45° direction) and negative (−45° direction) of the correlation coefficient, and a smaller ellipse area represents a larger absolute value of the correlation coefficient. In addition, the correlation coefficients can also be distinguished from the color of the ellipse, and the gradient of the color band from dark red to dark blue represents the shift of the correlation coefficient from −1 to 1. The crosses over each ellipse showed no statistically significant correlation (P > 0.05). Abbreviations: TEM, temperature; PPT, precipitation; PET, potential evapotranspiration; SMO, soil moisture; PDSI, Palmer drought severity index (PDSI); WINS, wind speed; GPI, grazing pressure index; FVC, fractional vegetation cover (FVC); WR, water retention; CS, carbon sequestration; SL, soil loss by wind erosion.
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Figure 7. The changes in different socioeconomic indicators in the study area from 2000 to 2015, including (a) grain and meat production, (b) components of livestock meat production, (c) year-end stock of large livestock and sheep, and (d) agricultural irrigation area and raw coal output.
Figure 7. The changes in different socioeconomic indicators in the study area from 2000 to 2015, including (a) grain and meat production, (b) components of livestock meat production, (c) year-end stock of large livestock and sheep, and (d) agricultural irrigation area and raw coal output.
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Figure 8. (a) The implementation timeline of ecological restoration programs and steppe management strategies in northern China since the 1980s, (b) the implementation boundary of the four main ecological restoration programs. Abbreviations: BTSSCP, Beijing-Tianjin Sand Source Control Project; GTGP, Grain to Green Program; NFCP, Natural Forest Conservation Program; TNSP, Three Norths Shelter Forest System Project.
Figure 8. (a) The implementation timeline of ecological restoration programs and steppe management strategies in northern China since the 1980s, (b) the implementation boundary of the four main ecological restoration programs. Abbreviations: BTSSCP, Beijing-Tianjin Sand Source Control Project; GTGP, Grain to Green Program; NFCP, Natural Forest Conservation Program; TNSP, Three Norths Shelter Forest System Project.
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Table
Table 1. Descriptions of the data used in this paper.
Table 1. Descriptions of the data used in this paper.
TypeDatasetData DescriptionSpatial
Resolution		Time
Scale		Data Source
Satellite dataNormalized
Difference
Vegetation Index (NDVI)		Raster data based on MOD13Q1 at a time resolution of 16 days from Moderate Resolution Imaging Spectroradiometer (MODIS)250 m2000–2015Land Processes Distributed Active Archive Center (LPDAAC) (https://lpdaac.usgs.gov/, accessed on 23 November 2021)
Digital Elevation Model (DEM)Advanced Spaceborne Thermal Emission and Reflection Radiometer, Global Digital Elevation Model (ASTER GDEM)30 m2009Geospatial Data Cloud, Chinese Academy of Sciences (http://www.gscloud.cn/, accessed on 23 November 2021)
Land use/land cover (LULC)Land use/cover maps
interpreted from Landsat TM/ETM images		30 m2000, 2005, 2010 and 2015Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC) (http://www.resdc.cn, accessed on 23 November 2021)
Meteorological dataDaily weather station dataDaily observation data from 102 meteorological stations as interpolated by ANUSPLIN: temperature, precipitation, wind speed, sunshine duration250 m2000–2015China Meteorological Sharing Service System (http://data.cma.cn/data/, accessed on 23 November 2021)
Palmer Drought Severity Index (PDSI)A standardized index that generally spans −10 (dry) to +10 (wet) to estimate relative dryness1/24°2000–2015TerraClimate dataset (http://www.climatologylab.org/terraclimate.html, accessed on 23 November 2021)
Soil moisture (SMO)Soil moisture content stored in shallow layer (10–40 cm)0.25°2000–2015Global Land Data Assimilation System (https://ldas.gsfc.nasa.gov/gldas/, accessed on 23 November 2021)
Socioeconomic dataStatistical panel dataAnnual statistical data for each county: grain production, meat production, number of livestock-2000–2015Inner Mongolia statistical yearbook
(http://tj.nmg.gov.cn/, accessed on 23 November 2021)
Basic geographic informationBoundary data of administrative regions at all levels and ecological protection projects-2015National Basic Geographic Information Center (http://ngcc.sbsm.gov.cn/, accessed on 23 November 2021)
Boundary of ecological projectsImplementation boundary data of several ecological restoration projects in China, such as GTGP-2015National Forestry and grassland administration (http://www.forestry.gov.cn/, accessed on 23 November 2021)
Other dataSoil propertiesVersion 1.2 of the Harmonized World Soil Database (HWSD): Soil texture, soil particle size, and organic carbon content of topsoil1 km2014FAO Geonetwork (https://data.apps.fao.org/map/catalog/, accessed on 23 November 2021)
Table
Table 2. Transition matrix of land use between 2000 and 2015 in different biome subregions (km2).
Table 2. Transition matrix of land use between 2000 and 2015 in different biome subregions (km2).
Biome SubregionsLand Use Type in 2015
Meadow SteppeCroplandForestlandGrasslandWetlandBuilt-up landBarren land
Land use type in 2000Cropland3742.0018.95747.4736.9344.8514.60
Forestland13.621524.41156.523.662.774.08
Grassland682.48160.3319,289.06165.5297.02474.49
Wetland41.622.88195.801822.768.3718.59
Built-up land31.511.1036.652.39170.732.10
Barren land10.232.83523.5617.056.531378.46
Typical SteppeCroplandForestlandGrasslandWetlandBuilt-up landBarren land
Land use type in 2000Cropland797.592.74269.096.793.501.67
Forestland2.61872.14133.941.720.952.64
Grassland335.31166.18114,599.29462.05448.171757.22
Wetland6.011.73507.023587.2714.72575.99
Built-up land1.670.8335.551.85155.201.52
Barren land1.024.301616.31213.9837.509015.46
Desert SteppeCroplandForestlandGrasslandWetlandBuilt-up landBarren land
Land use type in 2000Cropland2.070.000.820.000.020.00
Forestland0.0012.441.060.690.110.81
Grassland11.0216.3827,645.4545.4348.60604.23
Wetland0.530.3046.29319.940.0021.86
Built-up land0.050.164.530.0631.370.32
Barren land0.001.76551.8414.783.982495.59
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Dou, H.; Li, X.; Gong, J.; Wang, H.; Tian, Y.; Xuan, X.; Wang, K. Enhanced Ecosystem Services in China’s Xilingol Steppe during 2000–2015: Towards Sustainable Agropastoralism Management. Remote Sens. 2022, 14, 738. https://doi.org/10.3390/rs14030738

AMA Style

Dou H, Li X, Gong J, Wang H, Tian Y, Xuan X, Wang K. Enhanced Ecosystem Services in China’s Xilingol Steppe during 2000–2015: Towards Sustainable Agropastoralism Management. Remote Sensing. 2022; 14(3):738. https://doi.org/10.3390/rs14030738

Chicago/Turabian Style

Dou, Huashun, Xiaobing Li, Jirui Gong, Hong Wang, Yuqiang Tian, Xiaojing Xuan, and Kai Wang. 2022. "Enhanced Ecosystem Services in China’s Xilingol Steppe during 2000–2015: Towards Sustainable Agropastoralism Management" Remote Sensing 14, no. 3: 738. https://doi.org/10.3390/rs14030738

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