Research Articles

Adaptive evolution of the rural human-environment system in farming and pastoral areas of northern China from 1952-2017

  • LI Wenlong , 1, 2 ,
  • KUANG Wenhui 1 ,
  • LYU Jun 2 ,
  • ZHAO Zhonghua 3 ,
  • ZHANG Boyuan , 2, *
  • 1. Key Laboratory of Land Surface Pattern and Simulation, Beijing 100101, China
  • 2. School of Resources and Environmental Economics, Inner Mongolia University of Finance and Economics, Inner Mongolia Key Laboratory of Economic Data Analysis and Mining, Hohhot 010070, China
  • 3. Tourism School, Shanghai Normal University, Shanghai 200234, China
* Zhang Boyuan, Lecturer, E-mail:

Li Wenlong, PhD and Associate Professor, specialized in social and ecological sustainable development in farming and pastoral areas. E-mail:

Received date: 2021-01-20

  Accepted date: 2021-03-28

  Online published: 2021-08-25

Supported by

National Social Science Foundation of China(18AZD021)

National Social Science Foundation of China(17CGL024)

Major Project of the Ministry of Education of China(19JZD014)

Major Science and Technology Projects in Inner Mongolia(ZDZX2018058)


The theory on the cyclic adaptation between society and ecosystems sheds new light on the evolution and internal structure of human-environment systems. This paper introduces the risk index (RI) and adaptation capacity index (ACI) to evaluate the rural human-environment system. An evaluation index system for the adaptability of rural human-environment systems is configured in the context of climate change and policy implementation. On this basis, the stages, features, dominant control factors, and evolution mechanism were examined vis-à-vis the adaptability of the rural human-environment system in Darhan Muminggan Joint Banner from 1952 to 2017. The main results are as follows: (1) The evolution of the rural human-environment system can be divided into three stages, namely, the reorganization and rapid development stage (1952-2002) with population, cultivated land, livestock and degraded grassland increasing by 260%, 13%, 134% and 16.33%, respectively. The rapid to stable development stage (2003-2010) with population increasing by 2.8%; cultivated land, livestock and degraded grassland decreasing by 2.3%, 13.6% and 10.7%, respectively. The stable to release stage (2011-2017) with population, cultivated land, livestock and degraded grassland decreasing by 2.6%, 0.2%, 10.6% and 3.8%, respectively. (2) With the passage of time, the ACI of the rural human-environment system first increased slightly (-0.016-0.031), followed by a slight decline (0.031-0.003), and culminating in a rapid increase (0.003-0.088). In terms of spatial patterns, adaptability is high in the middle, moderate in the north, and low in the south. (3) The evolution of adaptability in the rural human-environment system was mainly controlled by the per capita effective irrigation area (22.31%) and the per capita number of livestock (23.47%) from 1990 to 2000, the desertified area (25.06%) and the land use intensity (21.27%) from 2000 to 2005, and the per capita income of farmers and herdsmen (20.08%) and the per capita number of livestock (18.52%) from 2010 to 2007. (4) Under the effects of climate change and policy interventions, the cyclic adaptation of the rural human-environment system was propelled by the interactions between two kinds of subjects: farmers and herdsmen on the one hand and rural communities on the other hand. The interaction affects the adaptive behavior of the two kinds of subjects, which in turn drives the cyclic evolution of the system. As a result, the system structure and functions developed alternatively between coordinated and uncoordinated states. Small-scale adaptive behaviors of farmers and herdsmen have a profound impact on the evolution of the rural human-environment system.

Cite this article

LI Wenlong , KUANG Wenhui , LYU Jun , ZHAO Zhonghua , ZHANG Boyuan . Adaptive evolution of the rural human-environment system in farming and pastoral areas of northern China from 1952-2017[J]. Journal of Geographical Sciences, 2021 , 31(6) : 859 -877 . DOI: 10.1007/s11442-021-1875-3

1 Introduction

In socio-economic geography, there is a need for a better understanding of the effects of resources and environment on the patterns and progress of human-environment interactions. Meanwhile, the impacts of interactions among the socio-economic factors on the formation of patterns in the system should not be ignored (Lu and Guo, 1998; Fan, 2018). As there are many factors involved during its evolution process, there is a pressing need to configure a new perspective and method pursuant of a holistic and comprehensive understanding (Mao, 2018). Theories related to socio-ecological systems (SESs) address coupled human-environment dynamics (Walker et al., 2004; Walker and Salt, 2006; Sun et al., 2007), and provide a novel framework for the comprehensive study of human-environment interactions (Holling, 2001; Gunderson and Holling, 2002). Extant research has tended to focus more on the adaptation instead of the effect (Adger et al., 2007; Kiem and Austin, 2013), as well as characteristics of the trans-scale and multi-factor system. Prof. Chuanjun Wu, a famous Chinese geographer, also emphasized the complexity of the territorial system of human-environment interactions. Adaptive research plays an important role in comprehensive understanding of the interactions between humans and the environment, indeed this has become a frontier in sustainability studies (Kates et al., 2001).
The adaptive cycle proposed by Holling is a key theory for understanding the internal structural evolution of SESs, their responses to external disturbances, and how they evolve in different stages (Angeler et al., 2011; Burkhard et al., 2011; Randle et al., 2015; Sundstrom and Allen, 2019; Fu, 2020). The adaptive cycle theory includes four phases, namely, reorganization (a), exploitation (r), conservation (k), and release (Ω) (Figure 1). Phase I (a) refers to the formation of new system structures and functions through reorganization with multiple possibilities of development. After that, the system experiences rapid growth in phase II (r). The system becomes stable afterwards, known as phase III (k). In this stage, the internal elements are more closely connected with each other, with a decreasing development rate. The structure changes from single to complicated, and its functions also strengthen. Meanwhile, the system tends to have a higher dependency upon the structure and function. Its resilience thus weakens, which means it is easier for the system to transform under external disturbance. In phase IV (Ω), the intersystem structure brakes once the interaction of external interference and internal system contradiction is beyond the tolerance of the system resilience. The system thus becomes stuck, which leads to the capital outflow of society, ecology, economy, and so on. The next reorganization (a) then begins (Luet al., 2020). However, the adaptive cycle is not always in a fixed period, and its evolution is also full of variations. After rapid growth, the system usually reaches a stable phase (k), but there is still the possibility to enter the release phase. In general, the system experiences the conservation phase (k), followed by the release (r). However, it may go back to the reorganization phase under disturbance (Luers et al., 2003). Using this information, managers can regulate the evolution of the system, lead the development of the system, and avoid collapse at the end of the conservation phase to promote the sustainable development of the system (Cote and Nightingale, 2012).
Figure 1 Adaptive cycle of the system (modified after Holling, 2001; Gunderson and Holling, 2002) (α. reorganization; r. exploitation; k. conservation; Ω. release)
Studies of human-environment interactions in typical areas play an important role in revealing the evolution process, characteristics and mechanisms of human-environment relationships (Fan, 2018). In recent years, with the acceleration of China’s urbanization, rural human-environment relationships in farming and pastoral areas in northern China are experiencing a dramatic change. The areas, with a combination of frequent droughts, a large poverty-stricken population, multi- nationalities, and fragile ecosystems, have profound influences on the rural human-environment system. Farming and pastoral areas are characterized by the complexity of the geographical environment and cultural diversity (Zhao, 1991; Chen et al., 2010; Li et al., 2019). Rural farming and animal husbandry, as well as traditional and modern land use concepts coexist here. The coupled human-environment dynamics with a multiple cross-scale system is another manifestation of these areas. That is why researchers tend to take these areas as cases to explore the adaptive cycle of human-environment interactions (Tanner et al., 2014; Ekrami et al., 2016; Leslie et al., 2017; Liu, 2018). Previous studies have tended to focus on single factors of macro-scale systems (Zhao et al., 2002), which presents limitations in terms of understanding the status of human-environment systems because such single approaches have insufficient explanatory power to resolve realistic problems (Xie and Li, 2008). This paper adopts adaptive cycle theory of SESs to explore the human-environment system evolution of farming and pastoral areas and establishes an evaluation indicator system based on drivers of climate change and policies. Analyses of evolution stages and characteristics illustrate salient spatio-temporalities and dominant factors to further reveal the influencing mechanism of the adaptive cycle of the rural human-environment system and provide theoretical references for the promotion of sustainable development of the ecotone.

2 Overview of the study area and methods

2.1 Overview of the study area

Darhan Muminggan Joint Banner (hereafter DMJB) is located in the central part of the farming and pastoral areas of northern China (41°20°N‒42°40°, 109°16°E‒111°25°E). It is a district under Baotou city of Inner Mongolia, with an area of 1.8177 km2. It borders Mongolia to the south, with a boundary line of 88.6 km (Li and Kuang, 2019). The administrative divisions of DMJB have experienced several changes since 1952. Between 1952 and 2005 there were 17 townships and 82 villages; which changed to 8 and 85, respectively between 2006 and 2010; and finally 12 and 107, respectively from 2011 onwards. Based on the administrative divisions in the 2006-2010 period, we processed the data of the other two periods so as to guarantee the consistency and reliability of the data. At the end of 2017, DMJB had a registered population of 111,586, which was 260 less than the previous year. DMJB is characterized by a spatial pattern of farming areas in the south, pastoral areas in the north, and farming and pastoral areas in between (Figure 2). To better adapt to environmental changes, residents in this region make a living through different livelihoods, i.e., there are farmer households, herdsman households, farmer and herdsman households, and people employed in tourism, as well as migrant workers. Bailingmiao town, where the administrative center of DMJB is located, is the political, economic, and cultural center, and a compound farming-pastoral type of rural area. Xar Moron town is famous for grassland tourism and whole people living in this town are naturally engaging in tourism. In 2017, tourism revenue accounted for over 70% of the per capita net income of farmer and herdsman households. Wukehudong and Shibao are counties dominated by farming, with farming population accounting for 65% of the total population in DMJB. Darhan, Ming’an, Bayinhua, and Mandula are pastoral counties, with a population in herdsman households accounting for 93.6% of the DMJB’s total (Liet al., 2019). A comprehensive town refers to as the town that is evenly developed in the primary, secondary and tertiary industries (Figure 2).
Figure 2 Administrative divisions of the study area (Darhan Muminggan Joint Banner)

2.2 Data sources

Research data used in this paper mainly consist of natural basic data and socio-economic development data, of which natural basic data include land use/cover change (LUCC), the normalized difference vegetation index (DNVI), and meteorological data for the years 1990, 1995, 2000, 2005, 2010, and 2017. Among them, LUCC data were derived from the National Land Use Database of the Chinese Academy of Sciences (CAS), with a comprehensive precision of 91.2%. NDVI data of GIMMS3g and MODIS-NDVI from 1980-2018 were collected from NASA ( Annual and monthly vegetation coverage was obtained via a dimidiate pixel model. Meteorological data were derived from a publicly available source administered by the China Meteorological Administration ( The ANUSPLIN package was used for interpolation of data that passed the quality check. We thus acquired annual mean temperature and annual precipitation for all the townships from 1990-2017. The main sources of data regarding socio-economic development were the farming and animal husbandry bureaus, hydrographic bureaus, government offices, and statistic stations of all the towns and townships, the detailed local chronicles of DMJB for 1993 and 2005, DMJB’s statistical yearbooks and bulletins from 1990-2018, reports by township governments, the Annual Report on Farming and Animal Husbandry, and the Development Plans for Farming and Animal Husbandry. We also conducted some interviews and surveys with local farmers and herdsmen to obtain supplementary information.
With respect to socio-economic statistics of DMJB, we collected data from local chronicles in 1993 and 1995, statistical yearbooks and bulletins from 1990 to 2018, government work reports, farming and animal husbandry annual reports and development plans of all townships (sumu), etc. Questionnaires regarding family status were issued to 1000 farmers and farmer households, among which 959 were valid, with an effective rate of 95.90%. According to the analysis of the valid questionnaires, the number of herdsman households, farmer households, a combination of both herdsman and farmer households, migrant workers, tourism-participated herdsman and farmer households were 363, 214, 155, 131, and 96, accounting for 37.9%, 22.3%, 16.2%, 13.7% and 10.0%, respectively. Survey questions mainly include six aspects, namely, 1) family basic information, including but not limited to population, level of education and status of health; 2) adaptive capacity, which could be evaluated from natural, physical, financial, social and human capitals; 3) adaptive behaviors to natural disasters and policy adjustment; 4) degree of life satisfaction; 5) factors that restrict the adaptive capacity, such as climate and income; and 6) cognition to strategies and policies, such as rural vitalization strategy, and comprehensive grazing prohibition policy.

2.3 Methods

Empirical analysis follows the procedure from phenomenon to influencing factors and mechanism (Li and Tang, 2018; Yang et al., 2019; Liu et al., 2020). Through analysis of the characteristics of the key factors of the rural human-environment system, we are able to understand the stages of the evolution of the adaptive cycle and describe the status and routine of the evolution of the rural human-environment system. We take the adaptive index as a major indicator and quantitative expression of the cyclic evolution of system adaptability. Recognition of the index largely determines whether adaptability can be useful in practice (Li et al., 2015; Chen et al., 2016). This paper first analyzes the changing characteristics and progress of indicative elements in the rural human-environment system in DMJB from 1952-2017, and then divides the adaptive cycle evolution into several stages. Next, an indicator system is established for key influencing factors based on a statistical method.

2.3.1 Connotation of the adaptive evolution of rural human-environment system

Adaptive cycle evolution of SESs refers to the continuous evolution progress of the human-environment system and human beings ability to adapt to the natural environment and environmental changes. It stresses the strategies and measurements for adaptation, and the comprehensive influencing mechanism of the self-organizing capability (adaptive capacity) of internal elements and external environmental disturbance of the system (risk) (Metzger et al., 2005). Adaptive behaviors include both adjustment measures of government administrations, and farmer households’ livelihood responses (Volpato and King, 2019). The adaptive cycle represents the four-stage development trend (reorganization (α), exploitation (r), conservation (k), and release (Ω)) of SESs. Humans are able to affect the evolution progress by adjusting the system adaptability, so that the system may evolve in a sustainable way (Yinet al., 2020). The ecotone of farming and pastoral areas is an SES wherein farming and animal husbandry coexist. Complex components and adaptive cycle evolution lead to the diversity in the ecotone (Chen et al., 2010). Its system structure and function keep evolving under the drivers of climate change and policies to adapt to the external environmental change. The evolution also brings about changes in the external environment, which becomes an internal factor driving the system evolution. Internal and external driving forces facilitate the evolution progress. As important components of internal drivers, rural community development pattern adjustment and the adaptive behaviors of farmer and herdsman households have a regulating effect on the evolution trend, which allows administrators to achieve sustainable development of the rural human-environment system.

2.3.2 Status and paths of different stages of rural human-environment system evolution

Driving forces of system evolution reflect the intensity of human-environment interactions whilst the coordination of structure and function depends on stability and ability. They are regarded as significant variables to describe the process and characteristics of adaptive evolution of the system (Cui et al., 2020; Fan et al., 2019; Fang et al., 2020; Liu Haimeng et al., 2020). Driving forces of human-environment system evolution mainly consist of external driving force, internal disturbing force, and the interaction in between. The intensity of human-environment relationships determines the magnitude of the driving force (Mao, 2018). For instance, the development and utilization of rural land resources in the farming and pastoral areas of northern China mainly rely on grazing and farming. Thus, the intensity of rural human-environment interactions largely depends on that of grazing and farming. Coordination between rural human-environment structure and function mainly manifests as that among the capacity of socio-economic development and the eco-environmental resilience. This paper takes rural per capita livestock and per capita arable land since the founding of DMJB (1952) to represent the driving force of rural human-environment system evolution. Population, per capita output value, and vegetation coverage are taken to represent social, economic, and ecological functions. A larger rural population and a higher output value bring about larger vegetation coverage, which means better coordination between structure and function of the system.

2.3.3 Identification of dominant factors of adaptive evolution of the rural human- environment system

(1) Establishment of adaptive model
The adaptability of the human-environment system depends on the intensity of external risk interference and the adaptability to risks (Tanner et al., 2018). Based on the conceptual framework of adaptability analysis, this study modified the method put forward by Vishnu et al. (2011) and a function model after Luers et al. (2003) and Metzger et al. (2005). We use the adaptive capacity index (ACI) and risk index (RI) to evaluate the adaptability (AD) of the rural human-environment system adaptability (AD) as expressed in Eq. 1.
ACI and RI are obtained by the weighted sum method (Zhao et al., 2002).
$ACI=\underset{i=1}{\overset{6}{\mathop \sum }}\,{{W}_{ej}}*{{Y}_{eij}}\ RI=\underset{i=1}{\overset{4}{\mathop \sum }}\,{{W}_{sj}}*{{Y}_{sij}}$
where Wej and Wsj represent weights of the adaptive capacity index and risk disturbance index, respectively; Yeij and Ysij denote standardized values of the above two indexes, respectively.
(2) Establishment of adaptability evaluation index system
Based on the concept of human-environment system adaptability and logical associations for constitutional dimensions in an evaluation framework, we configured an adaptability evaluation index system with two dimensions of risk disturbance and adaptive capacity and 10 indexes (Table 1). To guarantee its accuracy, we employed reliability analysis and multicollinearity testing by the reliability coefficient method. SPSS v. 19.0 and Stata v. 15.0 were used for the analyses. The results show that the reliability coefficient (alpha) of the system is 0.901 and the variance inflation factor is 8.7, which proves its high reliability without substantive multicollinearity. The composition of indexes, conceptualization, and computation mode of all dimensions are shown below.
Risk disturbance to the system, including external and internal risks, indicates the extent to which the system is affected by climate change and policy implementation. It could, for example, be measured and summarized by indicators of climate, land use intensity, and the degree of land desertification (Li et al., 2018). In this paper, we choose annual precipitation, annual mean temperature, land use intensity, and land desertification to express the degree to which the system is influenced by these indicators, among which annual precipitation and annual mean temperature represent the effects of climate change on human-environment interactions, land use intensity mainly reflects the disturbance from policies, and land desertification indicates the degree of internal stress generated by the evolution of internal structure and function of the system under external disturbance.
System adaptability refers to adaptability and resilience of the rural human-environment system to climate change and policy implementation. To cope with disturbance of the two factors, the rural human-environment system adjusts its structure and function, improves its resilience, and finally promotes its sustainable development, by changing the pattern of social and economic development. During this process, the enhancement of socio-economic system adaptability is deemed to be the principal driver of rural human-environment adaptive capacity, which plays an important role in the overall development (Li et al., 2018). This paper selects A1-A3 to represent farming and animal husbandry production capacity of rural human-environment system, A5-A6 to represent ecological subsystem resilience, and A4 to represent economic development capacity (Table 1).
Table 1 Evaluation index system for the adaptability of the rural human-environment system in Darhan Muminggan Joint Banner
(3) Data processing and determination of weights
1) Standardization of raw data. We process the data by range method (Li et al., 2018).
Standardized equation of raw data with index property of “+”:
C ij =( b ij –b( i, min))/( b( i, max )–x( i, min))
Standardized equation of raw data with index property of “-”:
C ij =( b( i, max) –b ij )/( b( i, max) –b( i, min))
where Cij is the value of undimensionalized raw data of the indexes, and Bij is the value of origin data of the indexes. Meanwhile, b(i, max) andb(i, min) represent the maximum and minimum values of selected raw data, respectively.
2) Calculation of the weight
The fuzzy analytical hierarchy process (FAHP) is a combination of the analytical hierarchy process (AHP) and fuzzy set theory, which may achieve both quantitative and inclusive properties. This means it reduces the effect of subjective factors. Thus, it has been used extensively in calculating weights in research on human-environment systems (Lu et al., 2020). The detailed procedure is as follows:
First, build a quantization index system. We sought suggestions from 25 experts (including professors in universities, researchers in institutes, and government officials) by email or interviews. Of the 25 questionnaires, 21 were valid. We selected indicators according to the feedbacks of the experts (Table 1).
Second, establish a fuzzy evaluation matrix. A total of 21 experts mentioned above were invited to rate the indexes on a scale of 1-9 in a subjective way. According to the results, we built a fuzzy judgment matrix as expressed in Eq. 5.
K=(r ij ) n × n , r ij=( l ij, m ij, μ ij), l ij+ μ ij= 2 m ij
where rij uses triangular fuzzy number (TFN) to represent the relative importance of two indexes, while lij, mij, and μij are respectively the possible minimum, medium (average), and maximum relative importance of the two indexes.
Third, calculate fuzzy weights. We calculated matrix K by the normalization method and achieved the fuzzy weight ${{F}_{i}}$ of each index as per Eq. 6.
${{F}_{i}}=\underset{j=1}{\overset{n}{\mathop \sum }}\,{{r}_{ij}}\times {{\left( \underset{i}{\overset{n}{\mathop \sum }}\,\underset{j}{\overset{n}{\mathop \sum }}\,{{r}_{ij}} \right)}^{-1}},\ \left( i=1,\ 2,\ \ldots,\ n \right)$
Fourth, calculate the weights of the standard layer after defuzzification. Different equations were used to acquire the exact values from fuzzy weights. If ${{M}_{1}}\left( {{l}_{1}},{{m}_{1}},{{u}_{1}} \right)$ and ${{M}_{2}}\left( {{l}_{2}},{{m}_{2}},{{u}_{2}} \right)$ are TFNs, the possibility of ${{M}_{1}}\ge {{M}_{2}}$ can be calculated using Eq. 7.
$v\left( {{M}_{1}}\ge {{M}_{2}} \right)=\left\{ \begin{matrix} 1 \\ \frac{{{l}_{2}}-{{u}_{1}}}{\left( {{m}_{1}}-{{u}_{1}} \right)-\left( {{m}_{2}}-{{l}_{2}} \right)} \\ 0 \\ \end{matrix},\ {{m}_{1}}\le {{m}_{2}},{{u}_{1}}\ge {{l}_{2}} \right\}$
$~{{A}_{i}}=min~v\left( M\ge {{M}_{i}} \right),i=1,\ 2,\ \ldots,k$
According to Eq. 8, we obtained ordering vectorAi, and after normalization we obtained weight Bi of index i.
Fifth, defuzzify the weight of the index layer. We repeated the above process in the index layer, and obtained the normalized weight Cj of this layer. With Eq. 9, we achieved the final weight Dj of the index layer (Table 3).
D j= B i×C j, ( i= 1, 2, 3; j= 1, 2, …, 10)
(4) Identification of adaptive dominant factors (Yin et al., 2020)
We introduced degree of contribution (pj), deviation of the index (vj), and limiting degree (wj) to identify the obstacle factors of the adaptive evolution of the human-environment system, and to achieve the changing characteristics of the leading factors of adaptive cycle evolution of human-environment system in DMJB. As such, this paper lays a foundation for further study on the influencing mechanism of adaptive evolution of the rural human-environment system.
The limiting degree model is expressed as follows.
${{w}_{j}}=\left( {{p}_{j}}*{{v}_{j}} \right)/\underset{1}{\overset{10}{\mathop \sum }}\,({{p}_{j}}*{{v}_{j}})$
where pj stands for the degree of effects that the evaluation system index has on the objectives of the research, that is weight of index (sj); vjis the difference from standardized value of single index (cij) to 100%. wj represents the limiting degree of the index. The larger the wj, the greater the resistance to the uplifting of system adaptability. This is the leading factor during the evolution of adaptability in the rural human-environment system.

3 Results and analyses

3.1 Characteristics of different stages of the adaptive cycle evolution of the rural
human-environment system

The rural human-environment system is multidimensional, consisting of social, economic, and ecological subsystems. There are some relative balanced statuses identified by different thresholds during the adaptive evolution of the system. Taking the four stages of adaptive cycle development of socio-ecological system (α, r, k, and Ω) as bases, we analyzed and summed up the evolution of different stages in DMJB. Seen from changes in climate, society, economy and ecology, rural areas of DMJB have experienced three stages of construction and exploration (1952-1978), rapid development (1979-2002), and comprehensive development (2003-2017). From the perspective of the social system and policy implementation, substantive changes in all elements speeded up after the reform and opening-up in 1978, and obvious differentiation can be observed in the status of the rural human-environment system. The implementation of a series of policies and projects, such as the Grain to Green Program, the Beijing-Tianjin sandstorm source control project, and the comprehensive grazing prohibition policy, also have a significant impact on the development of the human-environment system of DMJB. DMJB has experienced rapid development and huge system changes, characterized by obvious adaptive cycle evolution.
According to theoretical bases and research achievements concerning the adaptive cycle of SESs (Luers et al., 2003; Chen et al., 2016; Wang et al., 2016), this paper examines the stages of evolution in DMJB from the perspective of subsystems and changes in population, cultivated land, livestock, production value, and so on. The results show that, during the reorganization-exploitation period (α-r), the rural population, area of cultivated land, and the number of livestock increased by 260%, 13%, and 134%, respectively. This indicates a continuously growing intensity of rural human-environment interactions. At the end of 2002, the area of grassland in DMJB was 16,426.40 km2, among which the available area was 13,743.96 km2 (16.33% less than that in 1952); the area suffering from water loss and soil erosion was 16,176.3 km2, accounting for 89% of the total land area. Due to the increasing socio-economic function and the decreasing ecological function in the rural areas, the coordination degree of the human-environment system also reduced, making the system vulnerable to being transferred to the next stage. The period 2003-2010 is known as the exploitation-conservation (r-k) stage. Affected by a series of environmental management projects (e.g., comprehensive grazing prohibition), the average height of native grasses was 25.6 cm and the grassland coverage reached 25.7%. Compared with the year 2000, the grassland degraded area decreased by 10.68%, and the desertified and salinized grassland reduced by 3.68% and 33.21%, respectively. Ecological projects reduced the intensity of human-environment interactions and improved the ecological functions of the human-environment system, which promotes the harmonious development of social, economic, and ecological functions. In the conservation-release stage (k-Ω) of 2011-2017, rural areas had been seeking changes in the socio-economic development patterns, driven by measures of water-saving in farming, enclosed livestock breeding, pastoral cooperative management. To adapt to the environmental changes, the majority of the farmer and herdsman households expanded their sources of income generation, from relying only on one measure (farming or animal husbandry) to multi-measures (both farming and animal husbandry, migrant working, and tourism). Benefiting from such diversity, their risk-resistance capacities were enhanced and the intensity of rural human-environment interactions continuously decreased. The population, cultivated land, and number of livestock reduced by 2.6%, 0.2%, and 10.6%, respectively. The feedback effect of farmers and herdsmen on the evolution scale of the rural human-environment system decreased, and the socio-economic development abilities clearly declined. The rural human-environment system showed a new trend of adaptive evolution during this period. The evolution of the human-environment system in DMJB exhibits clear temporalities, with interactions and feedbacks at different scales. Adaptive behaviors of two scales of rural community and farmers and herdsmen form the collaborative driving system of the adaptive cycle evolution of the rural human-environment system (Figure 3).
Figure 3 Adaptive evolution path of the rural human-environment system of Darhan Muminggan Joint Banner from 1952-2017

3.2 Status and path of adaptive evolution of rural human-environment systems

3.2.1 Different types of rural human-environment system

(1) The rural human-environment system of farming experienced a rapid increase in population, area of cultivated lands, and economic level, which led to further deterioration of the eco-environment. Affected by the “Grain to Green” policy initiated in 2002, cultivated lands reduced, the number of farmer households decreased, the quality of the eco-environment therefore improved. In this process, rural industries transformed from original single farming to a combination of farming and animal husbandry. The income of rural residents from farming allows them to develop animal husbandry which in turn eliminated poverty in rural areas. Local people focus more on ecological function instead of only on productive function (Figure 4a).
Figure 4 Adaptive evolution paths of rural human-environment system driven by different leading industries
(2) Ecological governance policies such as “comprehensive grazing prohibition” are an important driving force in the adaptive evolution of the rural human-environment system of animal husbandry. Driven by the structural reform in pastoral areas, rural areas suffered from accelerated grassland degradation caused by the dramatic increase in population and the number of livestock. Ecological engineering policies since 2002, “comprehensive grazing prohibition” for example, brought about a sharp decline in the rural population and the number of livestock. Grassland degradation had effectively been curbed. As a result of abrupt rural transformation as well as drastic decreases in social and economic functions, there were many challenges faced by farmers and herdsmen in livelihood transformation. How to achieve socio-economic development in rural areas while maintaining the ecological function of the human-environment system is of significance, but complex ( Figure 4b).
(3) Rural tourism industry was developed from animal husbandry with obvious social and cultural changes. It heralded improvements in economic development, but at the expense of the eco-environment to varying extents. The simplified trend of system tourism function has been strengthened, which reduced people’s ability to resist the risk. The livelihood transition is characterized by obvious assimilation. According to our surveys, single-farmer households tend to be migrant working-oriented, then tourism-oriented, and finally tourism-specialized. As the specialized tourism has not developed well, a homogeneous tourism service is currently a big problem and becomes more serious (Figure 4c).
(4) In the comprehensive evolution process of the human-environment systems, the function advantage of the central place is prominent. The industries in the system transfer from a single emphasis on farming and animal husbandry to a more diverse array of industries including farming and animal husbandry, manufacturing, and services focusing more on the benefits of the eco-environment. The structure and function of the system change as urbanization advances. Rural production, lifestyle, and the eco-environment have been well improved, and people’s livelihoods here are diverse, which significantly enhances system adaptability (Figure 4d).

3.2.2 Rural human-environment system in the study area

The rural human-environment system in DMJB experienced stages of rapid development of farming and animal husbandry, deterioration of the eco-environment and ecological governance, socio-economic transformation and development, and coordinated development of eco-environment (Figure 5). The transition of rural socio-economic development and the differentiation of adaptive behavior of farmers and herdsmen greatly affected the endogenous power of system evolution, which also had an effect on the evolution path.
Figure 5 Adaptive evolution path of the rural human-environment system in Darhan Muminggan Joint Banner

3.3 Spatiotemporal evolution characteristics of rural human-environment system adaptability

The adaptive index model is adopted to calculate the indexes of all townships in DMJB in 1990, 1995, 2000, 2005, 2010, and 2017 (Table 2).
Table 2 ACI values of each township in Darhan Muminggan Joint Banner from 1990 to 2017
Township 1990 1995 2000 2005 2010 2017
Bailingmiao 0.012 0.065 0.079 0.049 0.079 0.092
Wukehudong -0.055 -0.042 -0.027 -0.040 0.058 0.086
Shibao -0.064 -0.039 -0.034 -0.036 0.041 0.078
Xar Moron 0.088 0.049 0.060 0.017 0.065 0.094
Darhansumu -0.006 0.054 0.051 0.035 0.049 0.114
Ming’an -0.014 0.046 0.049 0.014 0.038 0.084
Bayinhua -0.068 0.037 0.037 -0.028 0.030 0.070
Mandula -0.019 0.031 0.035 -0.009 0.053 0.088
Average -0.016 0.025 0.031 0.003 0.052 0.088
The adaptive index in the rural human-environment system from 1990 to 2017 experienced a slight increase, a slow decrease, and a rapid rise (Table 2). The index increases from -0.016 in 1990 to 0.031 in 2000, during which cultivated areas expanded and livestock number increased dramatically, and system adaptive ability also improved. During 2000-2005, the index reduced from 0.031 to 0.003. In this stage, land use intensity and desertified area reached a peak due to the increase of per capita cultivated lands and livestock. This has a significant negative effect on the eco-environment, with the highest risks of interference. The index increases from 0.003 in 2005 to 0.088 in 2017, benefiting from rapid development of socio-economic development and effective ecological policies.
According to the natural-break results from ArcGIS10.2, we divided the examined adaptive indexes into five types: high adaptability, higher adaptability, medium adaptability, lower adaptability, and low adaptability, to represent the spatial variation of each township (Figure 6). In 1990, townships with low adaptability are Wukehudong and Shibao, accounting for 13.3% of the total area; the township with relatively low adaptability is Mandula, accounting for 14.6% of the total area; the township with medium adaptivity is Ming’an, accounting for 13.7% of the total; townships with relatively high adaptability are Darhansumu and Bayinhua, accounting for 37.6% of the total area; townships with high adaptability are Bailingmiao and Xar Moron, accounting for 20.5% of the total area. Spatially, high, medium, and low adaptability townships are concentrated in the central, northern, and southern parts of the study area, respectively. The spatial pattern was stable prior to 2000 while after 2005 obvious changes took place, with increasing heterogeneity and fragmentation.
Figure 6 Spatial patterns of adaptability in Darhan Muminggan Joint Banner from 1990 to 2017

3.4 Influencing mechanism of adaptive cycle evolution of the rural human-environment system

3.4.1 Climate warming/drying and policy interventions as two important driving forces of the adaptive cycle evolution of the system

The main driving forces of the adaptive cycle evolution of the rural human-environment system are climate warming/drying and policy interventions. Climate warming/drying enhances the system’s resilience to risks, and accelerates the reduction of coordination between structure and function of the rural human-environment system, which restricts the improvement of system adaptability, and alters the evolution paths of the rural system. Guided by UN sustainable development goals (SDGs), governmental stakeholders formulated a series of policies, such as ecological projects and tourism development, to adjust the land use so as to benefit the human-environment system. For instance, comprehensive grazing prohibition decreases the land use intensity. Tourism development also changes the use and function of grasslands.

3.4.2 Changes in driving factors during the adaptive evolution of the rural human-environment system

The limiting degree model is used to evaluate the obstacle degrees of 10 restrictive indicators. Obstacle degree indexes for 6 years are listed in Table 3. Overall, effective irrigated area per capita (A1), number of livestock per capita (A3), desertified area (E4), and per capita income of farmers and herdsmen (A5) are regarded as main control factors.
Table 3 The obstacle degrees and ranking of evaluation restrictive indicators for the adaptability of the rural human-environment system from 1990 to 2017
Year The obstacle degrees and ranking of evaluation restrictive indicators
1 2 3 4
1990 Effective irrigated area per capita (22.31%) Mechanical power per capita (17.64%) Annual precipitation (10.01%) Number of livestock per capita (9.83%)
1995 Mechanical power per capita (21.55%) Number of livestock per capita (19.62%) Per capita income of farmers and herdsmen (14.58%) Annual precipitation (8.36%)
2000 Number of livestock per capita (23.47%) Effective irrigated area per capita (20.03%) Annual precipitation (11.67%) Vegetation coverage (10.39%)
2005 Desertified area (25.06%) Land use intensity (21.27%) Vegetation coverage (14.69%) Ecological service value (10.22%)
2010 Number of livestock per capita (20.87%) Per capita income of farmers and herdsmen (19.66%) Effective irrigated area per capita (13.89%) Annual precipitation (10.03%)
2017 Per capita income of farmers and herdsmen (20.08%) Number of livestock per capita (18.52%) Vegetation coverage (12.74%) Annual precipitation (9.64%)
Before 2000, due to the low production efficiency of rural farms and animal products, the effective irrigated area per capita and number of livestock per capita were the leading factors; in 2005, the dominant factors changed to desertified area and land use intensity, which shows that the deterioration of eco-environment greatly restricts the improvement of adaptive capacity of the system. During 2010-2017, affected by ecological policies, the socio-economic functions reduced. As a result, leading factors shifted to per capita income of farmers and herdsmen and per capita number of livestock. In sum, the main factors in the system evolution of farming and pastoral areas are complicated, characterized by coefficient effects of climate, farming, and animal husbandry factors.

3.4.3 Changes in coordination degree of structure and function of the rural human-environment system

In the reorganization-exploitation phase (r-k), the system experienced rapid socio-economic development by seizing the opportunity of the reform of the system in rural pastoral areas, and current land resources. However, the eco-environment suffers from significant damage, which continuously reduces the coordination between structure and function of the system. During the exploitation-conservation period (k-Ω), policy intervention plays an important role in the decreasing intensity of human-environment interactions. Both ecological function and the coordination of structure and function improved. Over the conservation-release stage (Ω-α), socio-economic functions and their coordination reduced with the decreasing human-environment interactions. Overall, the system structure and function evolution fluctuate between accordance and discordance. Meanwhile, variations of system evolution can be observed in different types of rural human-environment systems.

3.4.4 Livelihood development of farmers and their mutual feedback with rural community transformation

Farmers and herdsmen correlate with the rural communities to constitute the hierarchical structure of the adaptive cycle of rural human-environment system evolution. Their connection also affects the adaptive behaviors of subjects in different scales. Driven by policies, the rural community continuously adjusts its socio-economic development paths, to adapt to sustainable development goals. However, restricted by the difference between natural and human environment, the adaptive evolution is characterized by obvious heterogeneity. Farm communities transitioned to comprehensive communities of farming and animal husbandry, while animal husbandry communities tended to be tourism-oriented. The diversity of transition became the premise and driving force for the behavioral differentiation. Farmer and herdsman households, as an important component unit, change their behavior, under the influences of climate change, policy implementation, and rural transformation. The behavioral differentiation also promotes the transition of the rural community from the bottom up (Figure 7).
Figure 7 The cyclic evolution mechanism of the rural human-environment system in farming and pastoral areas

4 Conclusions and discussion

4.1 Conclusions

(1) DMJB’s rural human-environment system has experienced three stages: reorganization-exploitation (α-r, 1952-2002), exploitation-conservation (r-k, 2003-2010), and conservation-release (k-Ω, 2011-2017). In the first stage, the population and the number of livestock soar by 260% and 134%, respectively. Meanwhile, the areas of cultivated land and degenerated grassland increase by 13% and 16.33%, respectively. During the second stage, the population and the area of cultivated land are relatively stable, with an increase of 2.8% and a decrease of 2.3%, respectively. The number of livestock and the area of degenerated grassland, however, change dramatically with reductions of 13.6% and 10.7%, respectively. In the last stage, the population, area of cultivated land, and area of degenerated grassland, all experience slight decreases, with rates of 2.6%, 0.2%, and 3.8%, respectively. The number of livestock decreases by 10.6%.
(2) DMJB’s rural human-environment adaptive index experiences a slight increase (-0.016-0.031), followed by a slow decrease (0.031-0.003), and rapid growth (0.003-0.088). Spatially, high, medium, and low adaptability townships are concentrated in the central, northern, and southern parts of the study area, respectively.
(3) The driving factors during 1990-2000, are effective irrigated area per capita (22.31%), and number of livestock per capita (23.47%). In 2005, the driving factors change to desertified area (25.06%), and land use intensity (21.27%). From 2010-2017, the dominated ones are per capita income of farmers and herdsmen (20.08%), and number of livestock per capita (18.52%).
(4) Farmers and herdsmen, and the rural communities constitute the hierarchical structure of the adaptive cycle evolution of the rural human-environment system. The correlation between the two scales also affects the adaptive behaviors of the constituents, and thus influences the adaptive cycle evolution of the system. The system structure and function evolution fluctuate between coordination and uncoordination. The adaptive evolution process shows significant variation in different types of rural human-environment systems. Livelihood adaptive behaviors of farmers and herdsmen have a substantive effect on the system evolution trend.

4.2 Discussion

Based on SESs adaptive cycle theory, we seek to establish a framework to study the evolution of the human-environment system in farming and pastoral areas, and to illustrate its path, and to get a better understanding of the evolution process, characteristics, and leading factors. On the basis of the different behaviors of farmers and herdsmen and their mutual feedback with rural community transformation, we are able to have a comprehensive understanding of the mechanism of adaptive cycle of the system. It can be regarded as the first trial in farming and pastoral areas.
The study reveals that the adaptive evolution of the rural human-environment system in farming and pastoral areas is significantly affected by policies. Sustainable development of the system in the study area includes social, economic, and ecological coordination, and a combination of different types of industries. The livelihood of farmers and herdsmen changes with the function evolution of the rural community. The previously mentioned mutual feedback also has a great effect on the evolution of the system. This is a new perspective for simulation and control within the field of rural human-environment system evolution, which provides a novel path for rural revitalization. Based on the above conclusions, this paper proposed to focus more on different types of rural communities during the revitalization, as well as on the adaptive changes in terms of livelihood.
Most of the previous studies at home and abroad divide evolution stages of the human-environment system adaptive cycle according to qualitative determination of evolution characteristics. Herein, combined with a qualitative determination of important elements of the system, the focus is more on thresholds in evolution, so as to better reveal the evolution process and mechanism, and provide a scientific theoretical basis for future studies on farming and pastoral areas.
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