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Article

Dynamic Spatio-Temporal Simulation of Land Use and Ecosystem Service Value Assessment in Agro-Pastoral Ecotone, China

by
Longlong Liu
1,
Shengwang Bao
2,*,
Maochun Han
1,
Hongmei Li
1,
Yingshuang Hu
3 and
Lixue Zhang
3
1
Forestry and Grassland Bureau Comprehensive Security Center, Xilin Gol League, Autonomous Region, Xilinhot 152500, China
2
School of Economic and Resources Management, Beijing Normal University, Zhuhai 519000, China
3
School of Economic and Management, Zhejiang Ocean University, Zhoushan 316022, China
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(14), 5922; https://doi.org/10.3390/su16145922
Submission received: 31 May 2024 / Revised: 3 July 2024 / Accepted: 5 July 2024 / Published: 11 July 2024
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Abstract

:
In the past, during development processes, major ecological and environmental problems have occurred in the agro-pastoral ecotone of China, which have had a strong impact on regional sustainable development. As such, analyzing the evolution of the regional ecosystem service value (ESV) and predicting the futural spatio-temporal evolution under different development scenarios will provide a scientific basis for further sustainable development. This research analyzed the regional land use and land cover change (LUCC) from 2000 to 2020, adopted the Mark-PLUS model to construct different scenarios (prioritizing grassland development, PDG; prioritizing cropland development, PCD; business as usual, BAU), and simulated the future LUCC. The driving factors influencing each land use type were revealed using the PLUS model. Based on the LUCC data, the spatio-temporal distribution of the regional ESV was calculated via the ESV equivalent factor method, including four primary services (supply service, adjustment service, support service, and cultural service) and eleven secondary services (water resource supply, maintaining nutrient circulation, raw material production, aesthetic landscape, food production, environmental purification, soil conservation, maintaining biodiversity, gas regulation, climate regulation, and hydrologic regulation). The results showed that the total ESV increased first and then declined from 2000 to 2020, reaching the highest value of CNY 8207.99 million in 2005. In the different future scenarios, the ESV shows a trend of PGD (CNY 8338.79 million) > BAU (CNY 8194.82 million) > PCD (CNY 8131.10 million). The global Moran index also follows this distribution. Additionally, precipitation (18%), NDVI (16%), and DEM (16%) are the most important factors in the regional LUCC. The spatial agglomeration characteristics of ESV were revealed using the global Moran’s index and local indicators of spatial auto-correlation, which show a high coordination degree between the high–high cluster areas and water areas. These results point out the key points in the next step of ecological restoration projects and help with achieving the sustainable development goals more effectively.

1. Introduction

In 2015, 17 sustainable development goals (SDGs) were introduced to call for global action to protect the planet and improve human well-being [1]. The 15th SDG (Life on Land) clearly identifies the need to improve the environment and aims to curb ecological land degradation and diversity loss [2]. In response to the 15th SDG, China has adopted a number of ecological restoration projects, such as the Grain to Green Project [3] and the “Three-North Shelterbelt” System Construction Project [4], and has established many nature reserves to maintain fragile ecosystems, such as the Three-River-Sources National Reserve [5], which has significantly improved the ecosystem’s service capacity.
Ecosystem service value (ESV) is the quantitative expression [6] and representation of ecosystem service capabilities and the human welfare that is directly or indirectly obtained from ecosystem services [7]. ESV assessment provides an important basis for formulating regional ecological protection and compensation policies [8,9] and is an important way to clarify the spatial status of regional and global ecosystem service capacities [10]. Two main methods have been adopted to calculate the ESV in the relevant research: direct calculation methods for single ecosystem services [11] and the ESV equivalent factor method [12]. The direct calculation methods, represented by the InVEST model, mainly reveal the spatial distribution of a specific ecosystem service [13], and the assessment is mainly based on material quantity [14], such as water conservation [15], soil retention [16], etc. However, this method relies heavily on field investigations to obtain key parameters and has strong regional attributes, so it is not suitable for large-scale accounting [17]. The ESV equivalent factor method converts ecosystem services into monetary prices, which not only solves the problem of the lack of comparability among different ecosystem services but can also be used for large-scale assessment [18]. Therefore, the method has been widely used in many areas and on different scales.
Under the dual influence of climate change and human activities, represented by the urbanization process, drastic land use and cover changes (LUCCs) have significantly affected the structure, function, and sustainability of ecosystems and have caused significant changes in ESV assessments [19,20]. LUCC is an important factor driving ESV changes; accordingly, ESV responds to LUCC [21]. Many studies have explored the influencing factors of and mechanisms of changes in ESV [22] by means of geographical detector models [23] and geographically weighted regression models [24]. ESV is calculated based on LUCC data, and the ESV equivalent coefficients correspond to land use types so these studies have essentially explored the factors driving LUCC [23,25]. Therefore, it is necessary to quantitatively determine the factors influencing LUCC directly to ensure regional sustainable development. On this basis, future LUCCs and ESV changes can be predicted considering the impact of the influencing factors to realize integrated ecosystem management.
The PLUS model is an important model used for simulating regional LUCCs, which is not only suitable for large-scale simulation but is also highly precise [26]. Other LUCC coupling models, such as GeoSOS-FLUS [27], FLUS [28], CLUE-s [29], etc., have a small carrying capacity and do not easily simulate large-scale LUCC. The CA-Markov model [30], SD-CA model [31], etc., cannot determine the influence of driving factors on land use types. In addition, these models struggle to control the interaction between different land use types and to construct scenarios under different future development paths, so they are unsuitable for these tasks. The PLUS model is expected to improve this situation: the PLUS model not only has a high bearing capacity and driver mining ability but also integrates the Markov model and Linear model [32]. At present, modifying the probability of the Markov model has become an important means of constructing the LUCC for scenarios being analyzed in a region [33]. Therefore, the PLUS model has high application value in understanding the interaction of land use types and constructed scenarios and is expected to further reveal the spatial–temporal evolution of ESV.
The agro-pastoral ecotone is an interactive zone between traditional husbandry and farming in China [34]. In this region, cropland and grassland are crisscrossed and superimposed with multiple implemented ecological restoration projects [35]. It has a unique economic form that is different from that of single agricultural and pastoral areas, so it is a vital and unique agricultural space. In recent years, a large amount of grassland has been reclaimed for cropland, which has not only led to the imbalance in regional agricultural and husbandry industry structure but is also seriously threatening the sustainability of the service abilities of the ecosystem [36]. Serious imbalances in the LUCC structure also lead to changes in the ESV. Therefore, the analysis of the trends in LUCC and ESV evolution in the agro-pastoral ecotone and the prediction under different development scenarios will provide a direct reflection of the current situation as well as an important basis for the regional industrial structure adjustment and ecological protection measures. To better realize regional and global sustainable development, this research can help point out the next work of ecological restoration projects and give the basis for regional spatial planning. Moreover, as one of the important ecosystem services, biodiversity maintenance can be further restored and a really suitable habitat species can be constructed on the basis of ESV evolution and spatial agglomeration analysis. Therefore, the main objectives of this research are as follows: (1) To analyze the direction and trend of LUCC transfer in the agro-pastoral ecotone from 2000 to 2020, and to calculate the spatio-temporal evolution trend of regional ESV. (2) To predict LUCC in the future development scenario with the regional characteristics of the agro-pastoral ecotone. This will require the analysis of regional ESV changes and provide path selection for regional ecological protection and sustainable development. (3) The spatial clustering degree of ESV in the agro-pastoral ecotone was analyzed at both the county scale and pixel scale to provide scientific suggestions for urban coordinated development and clarify the key points of regional ecological security.

2. Study Area and Data Sources

2.1. Study Area

The agro-pastoral ecotone is located in the semi-arid ecological transition zone (34°88′–45°25′ N, 105°19′–122°97′ E). This region serves as a crucial interface between the agricultural areas of eastern China and the grassland and pastoral regions of western China. Encompassing an area of 726,000 km2, this zone represents the periphery of agricultural productivity and is characterized by its ecological fragility (as shown in Figure 1). Under the multiple influences of transitional natural characteristics and anthropogenic disturbances, the production systems in the study area are extremely unstable with frequent disasters and a fragile ecosystem. The agro-pastoral ecotone extends across seven provinces: Shanxi, Hebei, Inner Mongolia, Liaoning, Shaanxi, Gansu, and Ningxia, incorporating a total of 144 county-level cities (former 146, with the recent annulment of two counties). This agro-pastoral ecotonal zone functions as a climate intersection zone between the semi-humid area and semi-arid area with 350–550 mm of annual precipitation. Agricultural activities in this region are predominantly focused on the cultivation of maize, while the livestock sector primarily supports the rearing of dairy cows, beef cattle, and mutton sheep.
Since the 1980s, the rapid growth of the regional population has promoted the development of reserve cropland resources to expand into grassland areas and even into desert areas. This expansion has predominantly encroached on pastures that previously benefited from superior water and grass conditions. Consequently, this has led to significant vegetation destruction, agricultural and pastoral damage, and widespread grassland degradation. In the agro-pastoral ecotone, land use is primarily dominated by cropland and grassland. The competition and restriction between cropland and grassland have become an important issue threatening regional ecological security. Therefore, a harmonious development between cropland and grassland has become imperative for ensuring regional sustainable development.

2.2. Data Sources and Processing

The data used in this research are shown in Table 1. LUCC data are categorized into six primary land classes (cropland, grassland, forest, water area, construction land, and unused land) according to the land use classification standard established by the Chinese Academy of Sciences Institute [37]. The selection of driving factors encompasses topography, natural climate, socioeconomic conditions, and other relevant aspects, as detailed in Table 1. The coordinates of each factor were standardized as Krasovsky 1940 Albers with a resolution of 30 m. To calculate the ecosystem service value (ESV) equivalents, statistical yearbooks and compilations of agricultural returns were employed.
The climate data, GDP, and population data are mainly at a resolution of 1000 m. The results show that the ANUSPLIN interpolation method and Kriging interpolation method can effectively improve the data resolution. Specifically, the ANUSPLIN interpolation method is employed for processing precipitation and temperature data [38], while the Kriging interpolation method is used for processing GDP and population data, turning points through the grid, and resampling [39]. After interpolation, the data resolution is standardized to 30 m, maintaining the Krasovsky_1940_Albers coordinate system.

3. Methodology

The spatial–temporal evolution trend of ESV (including 4 primary services: supply service, adjustment service, support service, and cultural service; 11 secondary ecosystem services: water resource supply, maintaining nutrient circulation, raw material production, aesthetic landscape, food production, environmental purification, soil conservation, maintaining biodiversity, gas regulation, climate regulation, and hydrologic regulation) have been revealed by ESV equivalent factor method from 2000 to 2020. By combining the Markov model and PLUS model, it is ensured that the time step remains consistent with the historical period. Therefore, land use data from 2005 and 2020 were chosen to simulate the future LUCC in 2035. This year is pivotal as it marks the target for carbon neutrality and the culmination of industrial adjustment in the agro-ecotone area, in accordance with the Guiding Opinion on the Adjustment of Agricultural Structure in the Northern Agro-Pastoral Ecotone (Abbreviation: Guiding). Moreover, spatial auto-correlation analysis is used to analyze the spatial–temporal evolution of the ESV value. Based on this basis, specific measures to achieve sustainable development in the agro-pastoral ecotone are proposed. The flow chart is shown in Figure 2.

3.1. The Calculation of ESV Based on ESV Equivalent Factor Method

The economic value of ESV per standard unit area in the agro-pastoral ecotone was calculated with the data on the main crop sown area and crop yield by China county statistical Yearbook and National cost and benefit compilation of agricultural products. This calculation encompasses various ecosystem services, including water resource supply, maintaining nutrient circulation, raw material production, aesthetic landscape, food production, environmental purification, soil conservation, maintaining biodiversity, gas regulation, climate regulation, and hydrologic regulation have been considered to display the regional ecosystem services ability. Considering the specific distribution of land use types and the regional social and economic development, the table of ecosystem service value coefficient for each land use type is modified [40], which can be seen in Table 2. The economic value of ESV per standard unit area was calculated according to Xie’s [19] formula as follows:
E i = P × Q 7
where E i is the ESV equivalent factor, which represents the equivalent price of ESV. P is the unit price of grain in the study area, and Q is the grain output in the study area. In order to eliminate the effect of price fluctuations on value changes, the study uses 2020 food prices as the benchmark. The “7” is the constant number proposed by Xie, which is mainly used to modify the ESV equivalent price.
Combined with the land use distribution in the agro-pastoral ecotone, the regional ESV was calculated with the formula as follows:
E S V = ∑ i = 1 m ∑ j = 1 n E i j × A i
where ESV is the total value of ecosystem service values in the agro-pastoral ecotone (CNY, Yuan), A i is the area of j land use type, with each land use area extracted to calculate the area of each land use type. E i j is the unit ecosystem service price of the Class i ecosystem functions of j land type ( C N Y / hm 2 ), which is the result of Equation (1). Each grid of ESV is assessed by calculating the ESV equivalent coefficient and the area of each land use type. The regional ESV can be assessed by summarizing the total grids of ESV.

3.2. Analysis of Spatial Auto-Correlation

The spatial correlation cluster trend of ESV in the agro-pastoral ecotone was revealed by Global Moran’s I index [41]. The local spatial auto-correlation was calculated to reflect the local spatial cluster features of ESV in the agro-pastoral ecotone by the local indicators of spatial auto-correlation (LISA Index) [42]. A LISA value greater than 0 indicates a spatial agglomeration structure characterized by high–high or low–low clusters. Conversely, a LISA less than 0 indicates a spatial cluster structure characterized by high–low or low–high clusters. The specific calculation formula is as follows:
G l o b a l   M o r a n ’ s   I   I n d e x = n ∑ i = 1 n ∑ j = 1 n w i j ( x i − x ¯ ) ( x j − x ¯ ) ∑ i = 1 n ∑ j = 1 n w i j ∑ i = 1 n ( x i − x ¯ ) 2
where x i is the observed value of unit i; w i j is a row-normalized spatial weight matrix.
L I S A = x i − x ¯ S 2 ∑ j = 1 , j ≠ i n w i j ( x i − x ¯ )
where s is the standard deviation of the observed values of each unit.

3.3. Future LUCC Simulation under Different Scenarios

3.3.1. Parameter Setting and Accuracy Validation of PLUS Model

The PLUS model, a LUCC simulation model proposed by Liang et al., has been demonstrated to achieve higher simulation accuracy compared to other models [43]. Therefore, the PLUS model was used to simulate the future LUCC in the agro-pastoral ecotone. The parameters for this model mainly include three aspects [44]: driving factors (as shown in Figure 3), land use transfer matrix, and neighborhood weight (Table 3).
Simulation accuracy validation is fundamental for future LUCC simulations and a key step to verify the effectiveness of the PLUS model [44]. The kappa index [45] was used to cross-verify the real data and simulated data. This validation aims to demonstrate the accuracy of the PLUS model. Therefore, the land use data of 2010 and 2015 were chosen to dig the expansion of each land use type. Moreover, according to the time step limit of the Markov model, the chosen time horizon from 2010 to 2015 represents the nearest and minimum time period of the simulation of LUCC in 2020.

3.3.2. Future Differentiation Scenario Settings

The future LUCC under differentiation scenarios is primarily simulated for the year 2035 based on the land use data of 2005 and 2020. Maintaining the same time step can also help reveal the evolution of change in LUCC in the past, which serves as a reference for the future LUCC projections and forms the basis for each scenario after moderate modification. According to the Guiding [46] issued by the Ministry of Agriculture and Rural Affairs of the People’s Republic of China in 2017, the focus of future work will be on the vigorous development of herbivore animal husbandry and circular agriculture. Therefore, this research sets three future development scenarios with characteristics to identify the spatio-temporal evolution of LUCC and ESV in the agro-pastoral ecotone. According to the Markov model, the adjustment interval of different land use transfer probabilities is 0~30% [33,47], which effectively shows the change between different scenarios.
(1)
Business As Usual scenario (BAU): In this scenario, the land use data of 2005 and 2020 are used to predict the LUCC of the agro-pastoral ecotone in 2035 by the Markov model.
(2)
Priority of Cropland Development scenario (PCD): Under this scenario, based on the Markov model prediction, the transfer probability of grassland and unused land to cropland increases by 30%, and the transfer probability of forest to cropland increases by 15%. The transfer probability of grassland and unused land to construction land increases by 15%. Additionally, cropland retention amount increases by 5%.
(3)
Priority of Grassland Development scenario (PGD): Under this scenario, based on the Markov model prediction, the transfer probability of cropland and unused land to grassland increases by 30%, the transfer probability of forest to grassland increases by 15%, and grassland retention amount increases by 10%.

4. Result

4.1. Evolution Trend of LUCC in Agro-Pastoral Ecotone

4.1.1. Spatial–Temporal Changes in LUCC from 2000 to 2020

  • The spatial and temporal changes in LUCC from 2000 to 2020 are depicted in Figure 4. Overall, during the past two decades, the cropland area exhibited a significant downward trend (9852.20 km2, p < 0.01). Both the forest and water areas initially decreased and then showed an increasing trend. The area of unused land decreased significantly (2942.33 km2, p < 0.05) while the grassland area changed gently. The conversion of land use in the agro-pastoral ecotone is mainly influenced by the Grain for Green Project and the implementation of the “Three-North Shelterbelt” System Construction Project. Specifically, the regional forest area increased from 82,101.66 km2 to 89,732.11 km2. The water area increased from 8757.18 km2 in 2000 to 881.68 km2 in 2015, and then decreased to 8722.24 km2. In addition, rapid economic development and intense urbanization processes resulted in a substantial increase in construction land, which showed a significant increasing trend (5753.95 km2, p < 0.01), reflecting a 65.30% increase compared to 2000.

4.1.2. Validation and Simulation of LUCC under Different Scenarios in 2035

  • Based on the land use data from 2010 and 2015, the PLUS model and Markov model were adopted to simulate the land use in 2020 and compare with the actual situation, as shown in Figure 5. The results demonstrated a kappa coefficient of 0.9326 (p < 0.01), indicating a very high simulation accuracy, validating the models’ competency for future LUCC simulations.
  • Futural LUCC under different development scenarios was simulated by the PLUS model, as shown in Figure 6. The results showed that under the PCD scenario, the regional cropland area increased by 3962.28 km2 (2.54%) compared with 2020. Conversely, the grassland area decreased by 6072.80 km2 (3.33%) and the unused land decreased by 770.53 km2 (5.78%). Notably, under the PCD scenario, construction land increased by 1548.08 km2. This increase is attributed to the expansion of cropland, which can support a larger population and further stimulate economic development and urbanization. Under the BAU scenario, LUCC followed the previous development trend. Cropland will rapidly decrease by 8486.02 km2, while grassland and construction land will increase by 5948.81 km2 and 5727.56 km2, respectively. This scenario aligns with the development of circular agriculture proposed in the Guiding. Although grassland grew rapidly in this scenario, it ultimately reduced by 2346.48 km2. In the PGD scenario, the area of grassland increased rapidly (12,524.43 km2 compared with 2020). Meanwhile, the area of cropland decreased by 4783.13 km2 and the unused land decreased by 603.49 km2.
  • According to the string diagram of land use transfer, the growth of cropland mainly comes from grassland under the PCD scenario. In addition, a large area of grassland was converted to forest, indicating that in order to protect regional ecological security, forest will further occupy grassland. Under the BAU scenario, there were notable interactions between cropland, grassland, and forest. The expansion of construction land mainly came from the encroachment of cropland. In the PDG scenario, the increase in the grassland area was mainly at the expense of the cropland and forest areas.

4.2. Evolution Trend of ESV in Agro-Pastoral Ecotone

4.2.1. Spatial–Temporal Changes in ESV from 2000 to 2020

  • A 900 m scale regional grid was established, and the land use types within each grid in the region were statistically analyzed, as shown in Figure 7. Consequently, the ESV within each grid was calculated, respectively. From 2000 to 2020, the total ESV of the agro-pastoral ecotone exhibited a trend of an initial increase followed by a decrease. In 2000, the total value of ESV was CNY 8194.74 million. The highest value was recorded in 2005, with a total ESV of CNY 8207.99 million. In 2020, the total value of ESV was CNY 8195.13 million. From 2000 to 2005, the total ESV increased by CNY 13.24 million, followed by a decline of CNY 12.86 million from 2005 to 2020. In general, the ecosystem service value of the different land use types followed the trend of grassland > water area > forest > cropland > unused land. The ESV of grassland and water area accounted for more than 68% of the total value and constituted an important part of the ESV of the agro-pastoral ecotone. From a spatial perspective, the high-ESV areas were mainly distributed in the northern part of Inner Mongolia and the western part of Liaoning Province, showing a high degree of synergy with grassland distribution.
  • The different aspects of ESV are illustrated in Figure 8. The largest single ecosystem service is hydrological regulation (CNY 327.75 billion on average), followed by climate regulation (CNY 151.42 billion on average). These results indicate that hydrological regulation and climate regulation are crucial components of ecosystem services, accounting for approximately 58.40% of the total ESV. Food production showed a significant downward trend (CNY 20.02 billion, p < 0.01), strongly correlated with the decrease in cropland. Maintaining biodiversity averaged CNY 72.17 billion, accounting for 8.8% of the total value. The aesthetic landscape is CNY 33.09 billion, also constituting an important part of the agro-pastoral ecotone.

4.2.2. ESV Evolution under Different Scenarios in 2035

  • The ESV also exhibited different development trends under each scenario, as shown in Figure 9. ESV was CNY 8338.79 million, CNY 8194.82 million, and CNY 8131.10 million in the PGD, BAU, and PCD scenarios, respectively. In general, the regional ESV was in a significant downward trend (CNY 64.03 million, p < 0.05) in PCD. In the BAU scenario, the change in ESV has a certain randomness, with an insignificant decrease of CNY 0.31 million compared with 2020. Under the PGD scenario, ESV showed a significant upward trend. Compared with 2020, the total ESV increased by CNY 143.66 million, and p < 0.05.
  • Each secondary ecosystem service value under different scenarios is illustrated in Figure 10, which reflects the same trend of the total ESV. The highest secondary ESV is hydrologic regulation, while the lowest is water resource supply. This phenomenon is primarily due to the dominance of land use types such as grassland and cropland, which require substantial water to support crop growth and animal husbandry. Overall, the secondary ESVs, regardless of the development scenario, follow the following trend: water resource supply < maintaining nutrient circulation < raw material production < aesthetic landscape < food production < environmental purification < soil conservation < maintaining biodiversity < gas regulation < climate regulation < hydrologic regulation.

5. Discussion

5.1. Environmental Factors’ Contribution to LUCC

  • The PLUS model identifies the contribution rates of different environmental factors to different land use types, as illustrated in Figure 11. The main factors affecting cropland were PRE (19%), DEM (17%), and NDVI (14%), highlighting the importance of precipitation and elevation. For forest, the primary influencing factors were slope (19%), PRE (17%), and NDVI (16%). The main factors affecting grassland were TEM (17%), slope (17%), and DEM (15%). The main influencing factors of unused land were precipitation (28%), DEM (16%), and NDVI (16%). DEM is an important factor affecting the distribution and transformation of cropland, grassland, and unused land. Overall, precipitation (18%), NDVI (16%), and DEM (16%) are important factors affecting LUCC in the agro-pastoral ecotone. Understanding the quantitative influence of these factors provides a basis for the specific protection of each land use type. This knowledge aids in realizing regional sustainable development by implementing proactive measures to maintain the regional environment.

5.2. Scale Effect of Spatial Aggregation

  • The county-scale ESV was analyzed and calculated by the ArcGIS 10.8 software to obtain the Global Moran’s I index, which explores the overall agglomeration characteristics of ESV spatial distribution at the county level. The results, shown in Figure 12, indicate that from 2000 to 2020, the spatial agglomeration and its significance increased (Z > 2.58, p < 0.01).
  • Under different development scenarios, the overall spatial aggregation degree in PGD showed an upward trend. However, the overall spatial aggregation degree in the BAU scenario and PCD scenario showed a downward trend. The degree of spatial aggregation in all the scenarios passed the significance test of 99%.
  • The LISA index was calculated by the Geoda software (version 1.22), with the result presented in Figure 13. The results reveal that the northeast region of Inner Mongolia mainly shows a high–high spatial cluster correlation. The central area (mainly in Shanxi Province) and the southern area (mainly in Gansu Province) of the agro-pastoral ecotone are mainly concentrated in low–low spatial cluster correlation. In addition, the low–high spatial cluster correlation area is mainly dominated in Xinghe County. In general, the spatial structure of spatial heterogeneity and spatial agglomeration demonstrates a certain stability, exhibiting a high clustering trend in the north and a low clustering trend in the central and southern regions.
Limited by the capacity of the Geoda model and considering the spatial integrity of the agro-pastoral ecotone, this research constructs a grid with a pixel size of 900 m to provide a reference for regional small-scale pixel analysis. The Geoda software was used to analyze and calculate the spatial auto-correlation of pixel scales in the agro-pastoral ecotone, as shown in Figure 14. The results show that the land use types such as river (water area) are predominant in the high cluster area of the agro-pastoral ecotone, mainly distributed in the northern region. In addition, the low–low cluster areas are mainly distributed in the northeast, central, and southwest regions of the agro-pastoral ecotone, which is consistent with the spatial auto-correlation results at the county level.
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Figure 12. Overall agglomeration characteristics of ESV. (a): Global Moran’s I index; (b) Z-score; (c): P-test.
Figure 12. Overall agglomeration characteristics of ESV. (a): Global Moran’s I index; (b) Z-score; (c): P-test.
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Figure 13. Spatial agglomeration distribution of ESV in the agro-pastoral ecotone.
Figure 13. Spatial agglomeration distribution of ESV in the agro-pastoral ecotone.
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Figure 14. Spatial agglomeration distribution analysis at the pixel level.
Figure 14. Spatial agglomeration distribution analysis at the pixel level.
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In previous research, the scale of spatial auto-correlation analysis has primarily focused on provinces [27,47], cities [42], and river basins [48]. This focus stems from the need to reveal spatial correlations between administrative units to identify areas with high synergy and to achieve inter-city (inter-regional) synergistic development [42]. In addition, some studies have analyzed regional spatial auto-correlation by establishing grids (25 km2 and 100 km2) [49,50], etc. However, these grid sizes mainly demonstrate the degree of spatial clustering within a region, essentially functioning as a re-division of administrative units. Therefore, pixel-scale spatial auto-correlation analysis not only provides a higher level of accuracy but also becomes an important method to reveal the characteristics of regional ESV spatial aggregation [51]. However, starting from a single scale lacks a comparative analysis of the spatial heterogeneity of the ecosystem services at different scales, limiting its guiding value for ecological protection measures and policy formulation. At present, current studies have analyzed the spatial agglomeration of the Xia-Zhangzhou-Quan metropolitan area using differentiation scales: prefecture-level, county-level, and grid [52]. Therefore, the emphasis of spatial auto-correlation analysis at different scales is different. In future research, it is suggested to adopt a multi-scale approach (both pixel-scale and administrative unit-scale) for collaborative analysis to reveal the different spatial agglomeration performances. This approach will help differentiate ecological protection measures and enhance the guiding value of policy formulation.

5.3. Suggestions for the Future Development Strategy of Agro-Pastoral Ecotone

The total ESV in the agro-pastoral ecotone exhibited an initial increase followed by a decrease. This trend is primarily attributed to the expansion of the regional construction land and the encroachment on land use types with high ecological service values, such as grassland and forest. The research results show that the regional LUCC can directly affect ESV changes [53]. In the past two decades, multiple ecological restoration projects in the agro-pastoral zone have been implemented and superimposed, such as the Grain for Green Project [54], “Three-North Shelterbelt” ecological restoration projects [55], etc., which have had a great impact on the regional land use change [56], resulting in changes in the ESV. Based on this, this research sets up a multi-scenario futural LUCC simulation in the agro-pastoral ecotone in order to compare multiple development paths and achieve regional sustainable development, and put forward the following suggestions:
(1)
Further promote the measures of “reducing crop and increasing grass” [57]. The measure of “reducing crop and increasing grass” is the core content of the future development of the agro-pastoral ecotone. Despite its importance, grassland and cropland areas have been decreasing due to the rapid growth of construction land, leading to the conversion of cropland into construction land. Therefore, in the future development process, it is necessary to accelerate the promotion of “reducing crop and increasing grass” measures, consolidate the achievements of returning cropland to forest and grassland [58], and further promote the return of sloping farmland above 25 degrees, seriously desertified cropland, and 15–25 degrees slope cropland in important water sources to grassland. In areas with groundwater over-exploitation and serious ecological degradation, cropland should be fully returned to grassland. Additionally, the growth of plants that prevent wind, fix sand, conserve water, and protect the arable layer should be encouraged [35,46].
(2)
Pay attention to regional ecological security. The agro-pastoral ecotone serves as an important ecological security barrier in China. In recent years, the imbalance in agricultural and husbandry structures, combined with increasing resource and environmental pressures, has exacerbated ecological security issues [59]. During the study period, the value of ecosystem services rose first and then declined. The reason is attributed to rapid urbanization, which has occupied a large area of ecological land, resulting in a significant decrease in land use types with high ecosystem service values. At present, regional planning under the SDGs mainly focuses on establishing a regional ecological network and determining regional priority protected areas [60,61]. Therefore, identifying regional ecological sources and corridors is essential for optimizing land resource use in line with the SDGs of the agro-pastoral ecotone.
(3)
Adjust and optimize the industrial structure and promote the integrated development of agriculture and husbandry. The agro-pastoral ecotone is an advantageous area for herbivorous animal husbandry and an important producing area for high-quality miscellaneous grains, fruits, and vegetables [62,63]. In recent years, the disconnect between planting and cultivation has become prominent. Promoting the integration of planting and cultivation is crucial for optimizing the regional industrial structure. Against the backdrop of “reducing crop and increasing grass”, developing new quality agricultural productivity and constructing water-saving, recycling, resting, and subsistence agriculture [62] are vital measures to enhance agricultural productivity and foster the integration of agriculture and animal husbandry.

5.4. Innovation, Prospect, and Deficiency

This research estimated the ESV from 2000 to 2020, filling the gap in the accounting of the ecosystem services in the agro-pastoral ecotone. By coupling the PLUS model with multi-scenario analysis, different scenarios were constructed to reveal the evolution of future LUCC and ESV from both spatial and quantitative dimensions. This approach aims to provide practical and effective scientific suggestions for achieving SDGs in the agro-pastoral zone. Therefore, from the perspective of ecological security and sustainable development, the grassland development scenario emerges as a critical path to achieve regional sustainability, aligning with the development goals outlined in the Guiding Opinions on the Adjustment of Agricultural Structure in the Northern Agro-Pastoral Ecotone.
  • However, there are still some deficiencies: First of all, in the process of accounting for the ESV, the coefficient of construction land was ignored. Considering that the expansion of construction land can be detrimental to the regional ecosystem and its specific impact remains unclear, the ecosystem service coefficient for construction land was regarded as 0 based on previous research on ESV [19,40]. Determining the impact of construction land on ecosystem services will be a focus of future research. Secondly, although the downscaling method of regional spatial data has been proven effective and reasonably accurate, it may still influence regional ecosystem services and land use change in the agro-pastoral ecotone. Therefore, developing regional high-precision data sets is crucial for realizing and supervising the sustainable development of the agro-pastoral ecotone. Thirdly, the monitoring interval of ESV in the agro-pastoral ecotone is 5 years, resulting in a small sample size that may affect the accuracy of trend fitting. This limitation is primarily due to the dependence on the monitoring of the LUCC data. Future efforts should focus on interpreting LUCC and monitoring ESV changes on an annual basis to better capture the trend with high accuracy.

6. Conclusions

The rapid urbanization process and unreasonable development modes have led to an imbalance of the regional agricultural and animal husbandry structure within the agro-pastoral ecotone. The unsustainable reduction in ecological land has become increasingly prominent, intensifying the pressure on resources and the environment. This research estimated the changes in ESV in the agro-pastoral ecotone from 2000 to 2020. The PLUS model was chosen to simulate the spatio-temporal evolution of the regional LUCC and ESV under different future development scenarios. Specific conclusions can be drawn as follows:
(1)
Due to the rapid growth of construction land caused by urbanization, the ESV in the agro-pastoral ecotone will first rise and then decline from 2000 to 2020. In 2000, the ESV was CNY 8194.74 million, peaking at CNY 8207.99 million in 2005, before decreasing to CNY 8195.13 million in 2020. The ecosystem service value of the different land use types showed the trend of grassland > water area > forest > cropland > unused land, and the spatial agglomeration showed an upward trend.
(2)
From 2000 to 2020, the land use structure remained unbalanced. In particular, the cropland area decreased rapidly (9852.20 km2) and the construction land increased significantly (5753.95 km2, p < 0.01). The forest increased from 82,101.66 km2 to 89,732.11 km2. Developing agricultural productivity and further deepening the work of returning farmland to forest and grassland will be crucial measures for achieving sustainable development in the agro-pastoral ecotone.
(3)
In different scenarios, the ESV of PGD is the highest, followed by BAU and PCD. The spatial agglomeration tended to be PGD > BAU > PCD. Under the PGD scenario, the ESV showed a significant upward trend (CNY 143.66 million, p < 0.05). The PGD scenario is the most suitable scenario for the future development of the agro-pastoral ecotone, aligning with the development concept of the agro-pastoral ecotone proposal.

Author Contributions

Conceptualization, L.L. and S.B.; methodology, S.B.; software, S.B.; validation, L.L., M.H. and H.L.; writing—original draft preparation, L.L.; writing—review and editing, L.L. and S.B.; visualization, L.L.; supervision, S.B. and L.Z. Funding: Y.H. and L.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Training Program of Innovation and Entrepreneurship for Undergraduates of China [No. 202210340065].

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Acknowledgments

We would like to express our sincere gratitude to the editors and two anonymous reviewers for their valuable comments, which have greatly improved this paper. We also would like to acknowledge Yang Fan of Zhejiang Ocean University for providing the LUCC data.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Study area.
Figure 1. Study area.
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Figure 2. Flowchart of the methodology in this study.
Figure 2. Flowchart of the methodology in this study.
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Figure 3. The selected driving factors of LUCC in agro-pastoral ecotone.
Figure 3. The selected driving factors of LUCC in agro-pastoral ecotone.
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Figure 4. Spatial and temporal evolution of LUCC from 2000 to 2020.
Figure 4. Spatial and temporal evolution of LUCC from 2000 to 2020.
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Figure 5. Simulation validation of LUCC in 2020. (a): Real LUCC Remote Sensing Map in 2020; (b): Simulated LUCC Remote Sensing Map in 2020.
Figure 5. Simulation validation of LUCC in 2020. (a): Real LUCC Remote Sensing Map in 2020; (b): Simulated LUCC Remote Sensing Map in 2020.
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Figure 6. LUCC simulation under different scenarios.
Figure 6. LUCC simulation under different scenarios.
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Figure 7. ESV evolution trend from 2000 to 2020.
Figure 7. ESV evolution trend from 2000 to 2020.
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Figure 8. Each ecosystem service value assessment in the agro-pastoral ecotone. I: water resource supply, II: maintaining nutrient circulation, III: raw material production, IV: aesthetic landscape, V: food production, VI: environmental purification, VII: soil conservation, VIII: maintaining biodiversity, XI: gas regulation, X: climate regulation, and XI: hydrologic regulation.
Figure 8. Each ecosystem service value assessment in the agro-pastoral ecotone. I: water resource supply, II: maintaining nutrient circulation, III: raw material production, IV: aesthetic landscape, V: food production, VI: environmental purification, VII: soil conservation, VIII: maintaining biodiversity, XI: gas regulation, X: climate regulation, and XI: hydrologic regulation.
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Figure 9. Spatio-temporal evolution of ESV under different scenarios in the agro-pastoral ecotone.
Figure 9. Spatio-temporal evolution of ESV under different scenarios in the agro-pastoral ecotone.
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Figure 10. Each secondary ecosystem service value under different scenarios. I: water resource supply, II: maintaining nutrient circulation, III: raw material production, IV: aesthetic landscape, V: food production, VI: environmental purification, VII: soil conservation, VIII: maintaining biodiversity, XI: gas regulation, X: climate regulation, XI: hydrologic regulation.
Figure 10. Each secondary ecosystem service value under different scenarios. I: water resource supply, II: maintaining nutrient circulation, III: raw material production, IV: aesthetic landscape, V: food production, VI: environmental purification, VII: soil conservation, VIII: maintaining biodiversity, XI: gas regulation, X: climate regulation, XI: hydrologic regulation.
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Figure 11. Simulation validation and environmental factors’ contribution.
Figure 11. Simulation validation and environmental factors’ contribution.
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Table 1. Data sources.
Table 1. Data sources.
Data NameAbbreviationSourcesResolutionTime SeriesUsage
Land Use and Land Cover ChangeLUCCResources and Environmental Science Data Platform
(https://www.resdc.cn/)
Accessed on 24 January 2024		30 m2000, 2005, 2010, 2015, and 2020PLUS model scenarios and ecosystem service assessment
Digital Elevation ModelDEMGeospatial Data Cloud
(https://www.gscloud.cn/#page1/1)
Accessed on 25 January 2024		30 m-PLUS model scenarios
SLOPE-Extraction from DEM30 m-PLUS model scenarios
ASPECT-Extraction from DEM30 m-PLUS model scenarios
Prolonged Artificial Nighttime-light DatasetNIGHTTIME LIGHTNational Tibetan Plateau Scientific Data Center
(https://data.tpdc.ac.cn/home/)
Accessed on 25 January 2024		1000 m2020PLUS model scenarios
Normalized Difference Vegetation IndexNDVINational Ecosystem Science Data Center
(http://www.nesdc.org.cn/)
Accessed on 26 January 2024		30 m2020PLUS model scenarios
Gross Domestic Product DataGDPResources and Environmental Science Data Platform
(https://www.resdc.cn/)
Accessed on 24 January 2024		1000 m2020PLUS model scenarios
Population DataPOPResources and Environmental Science Data Platform
(https://www.resdc.cn/)
Accessed on 24 January 2024		1000 m2020PLUS model scenarios
PrecipitationPREResources and Environmental Science Data Platform
(https://www.resdc.cn/)
Accessed on 24 January 2024		1000 m2020PLUS model scenarios
TemperatureTEMResources and Environmental Science Data Platform
(https://www.resdc.cn/)
Accessed on 24 January 2024		1000 m2020PLUS model scenarios
China County Statistical Yearbook--vector2020Ecosystem service assessment
National Cost and Benefit Compilation of Agricultural Products--vector2020Ecosystem service assessment
Table 2. ESV equivalent coefficient.
Table 2. ESV equivalent coefficient.
Primary Ecosystem ServiceSecondary Ecosystem ServiceCoefficient of ESV
CroplandForestGrasslandWater AreaUnused Land
Supply serviceI2201.72384.49408.211429.8712.17
II148.72576.65599.99502.6236.52
III−4243.14299.00332.401044.0024.35
Adjustment serviceIV1796.781897.762111.661741.26136.83
V922.813519.965582.054647.36121.74
VI275.161650.631842.759935.73391.97
VII4390.893536.184089.82172,129.99260.02
Support serviceVIII25.172309.662572.352107.01161.18
IX307.60176.96196.99160.1812.17
X339.962102.182337.796175.20149.00
Cultural serviceXI145.73921.421031.544291.1962.33
Summary6311.4117,374.9121,105.55204,164.411368.28
I: food production, II: raw material production, III: water resource supply, IV: gas regulation; V: climate regulation, VI: environmental purification, VII: hydrologic regulation, VIII: soil conservation, XI: maintaining nutrient circulation, X: maintaining biodiversity, and XI: aesthetic landscape.
Table 3. Land use transfer matrix.
Table 3. Land use transfer matrix.
Land Use TypeCroplandForestGrasslandWater areaConstruction LandUnused Land
Cropland111111
Forest111101
Grassland111110
Water area111100
Construction land000010
Unused land001011
Neighbor Weight0.3360.1930.3920.0190.0310.029
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Liu, L.; Bao, S.; Han, M.; Li, H.; Hu, Y.; Zhang, L. Dynamic Spatio-Temporal Simulation of Land Use and Ecosystem Service Value Assessment in Agro-Pastoral Ecotone, China. Sustainability 2024, 16, 5922. https://doi.org/10.3390/su16145922

AMA Style

Liu L, Bao S, Han M, Li H, Hu Y, Zhang L. Dynamic Spatio-Temporal Simulation of Land Use and Ecosystem Service Value Assessment in Agro-Pastoral Ecotone, China. Sustainability. 2024; 16(14):5922. https://doi.org/10.3390/su16145922

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Liu, Longlong, Shengwang Bao, Maochun Han, Hongmei Li, Yingshuang Hu, and Lixue Zhang. 2024. "Dynamic Spatio-Temporal Simulation of Land Use and Ecosystem Service Value Assessment in Agro-Pastoral Ecotone, China" Sustainability 16, no. 14: 5922. https://doi.org/10.3390/su16145922

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