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Farmers’ adaptability to the policy of ecological protection in China—A case study in Yanchi County, China
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Farmers’ adaptability to the policy of ecological protection in China—A case study in Yanchi County, China Caixia Hou a,c , Lihua Zhou a,b,∗ , Yan Wen d , Yong Chen a a Key Laboratory of Desert and Desertification, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China b Institutes of Science and Development, Chinese Academy of Sciences, Beijing 100190, China c University of Chinese Academy of Sciences, Beijing 100049, China d College of Resources Science and Technology, Beijing Normal University, Beijing 100875, China
a r t i c l e
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Article history: Received 21 November 2017 Received in revised form 6 June 2018 Accepted 6 June 2018 Available online xxx Keywords: PGP Adaptability Perception Farmers Yanchi County China
a b s t r a c t Due to long-term human activities, grassland ecosystems have been severely damaged. To protect grassland ecosystems, the prohibited grazing policy (PGP), a grassland ecological protection policy, was instituted for Chinese grasslands in 2002. However, it is unknown whether farmers have effectively adapted to the PGP. The adaptability of farmers to this policy has directly influenced the effective implementation of the policy and the sustainable development of the ecosystem. Previous research on adaptability has not focused on the adaptation to political change. This article uses a case study in Northwest China to investigate the adaptability of farmers to the policy. First, we study the restoration status of ecosystems. In addition, this paper studies the perceptions regarding farmers’ adaptability to the policy and explores the adaptation strategies of different types of farmers. Finally, this paper discusses the main factors that influence farmers’ choice of adaptation strategies. The implementation of the PGP has achieved remarkable ecological benefits. Farmers of different types had different adaptation perceptions, and their choices of adaptation strategies also varied. In addition to the farmers’ perception of the policy, the main factors that influenced farmers’ adaptation strategies also included livelihood capital. © 2018 Published by Elsevier Inc. on behalf of Western Social Science Association.
1. Introduction Grassland areas in China account for 393 million hectares, or approximately 41.7% of the total territory. Grasslands are the largest land ecosystem in China (Gao & Lin, 2015; Hou et al., 2011). However, the grassland ecological carrying capacity is limited. With population and socioeconomic pressures, excessive land use and the lack of
∗ Corresponding author at: Institutes of Science and Development, Chinese Academy of Sciences, West Dong gang Road, Lanzhou 730000, Gansu Province, China. E-mail addresses: [email protected] (C. Hou), [email protected] (L. Zhou).
protection in grassland areas, degradation began to various degrees in many grassland areas in the 1950s (Fan, Zhong, Chen, & Zhang, 2007; Long, Dong, & Hu, 2005). Additionally, the annual direct and indirect losses from grasslands total $8.3 billion (Cui, Li, & Li, 2011). Grassland degradation has become the most serious problem in the sustainable development of grassland animal husbandry (Yang, Min, & Li, 2007). On December 16, 2002, the Chinese government formally approved a prohibited grazing policy (PGP) in areas where grasslands were severely damaged in Northwest China, an area among the 1,100 counties in 25 provinces nationwide (Hou et al., 2011). The main purpose of the policy is to restore the grassland social-ecological system to
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a steady state system. The government provided ecological compensation to farmers in the prohibited grazing area in Northwest China. The government subsidized feed grain by 82.5 kg/ha per year since 2003 and compensated farmers for grassland fence construction funds, feed grain costs and sowing fees from 2005 to 2010. From 2011 to 2015, the government gave each household $76.90 as a production subsidy and $13.80/ha per year as a prohibited grazing subsidy, which increased to $17.30/ha per year in 2016. The PGP has been implemented for more than 14 years; in this period, most grassland ecosystems in Northwest China have been well protected and vegetation has significantly improved (Lu, Zhou, & Chen, 2015; Wang, Tao, & Song, 2012). However, farmers are the main economic actors and direct users of grasslands, and the PGP has had the most direct effect on farmers (Chen, Wang, Zhou, Liu, & Huang, 2014); therefore, they have the most direct perceptions of the changes introduced by this policy. These perceptions and adaptations to the PGP also provide important feedback for decision makers. The implementation of the PGP has moved farmers away from grasslands and changed their livelihoods. Therefore, determining whether farmers can smoothly adapt to a certain policy is a key to policymaking and policy implementation. This study of the adaptability of farmers to the PGP will ease the conflicts between farmers’ livelihoods and the PGP and will be beneficial to the smooth implementation of this policy and the sustainability of grassland ecosystem protection. Adaptability is a strategy derived from the natural sciences, particularly evolutionary ecology (Futuyama, 1978; Winterhalder, 1980), and it is primarily used in climate studies (Elum, Modise, & Marr, 2016; Gandure, Walker, & Both, 2012; Mertz, Mbow, Reenberg, & Diouf, 2009). Steward (1977) first applied the concept of adaptability to humans via the concept of “cultural adaptation”, which describes how a regional society adjusts its behavior according to the natural environment. Denevan (1983) suggested that genetic characteristics in the natural sciences are similar to cultural practices in the social sciences, and incorporated disciplines such as demography, economics, and organization into the study of human adaptability. Furthermore, Denevan also implied that the interpretation scope of adaptability has expanded to include a wider range of political, economic, social and other interpretations. O’Brien and Holland (1992) also proposed that adaptability is the result of behavioral choices informed by cultural practices in a changing environment. Berkes, Colding, and Folke (2003) argued that the study of adaptability should encompass social-ecological systems that include human societies and ecosystems. Jerry (2000) and Michel and Nicolas (2003) argued that the relationships among ecosystems, politics, and the economy are also related to the adaptability to politics, social forces, resource uses, and global economic risks. Bahinipati (2011) also proposed that the key component of adaptability research in the field of political ecology is how individuals and families adapt to social, political, and economic processes. Therefore, adaptability is defined as the response of individuals, organizations and groups to certain environmental and policy changes and is based on their perceptions and abilities to formulate measures to deal with those changes (Smithers
& Smit, 1997; Smit & Wandel, 2006). Additionally, previous research on adaptability focuses less on the adaptation to political change. This paper studied the adaptability of farmers to the PGP policy to provide a basis for future research. Seoones (1998) first proposed a livelihood analysis framework and divided livelihood capital into natural capital, financial capital, human capital and social capital. Subsequently, the Cooperative for American Relief Everywhere proposed a livelihood security framework for farmers (Frankenberger, Drinkwater, & Maxwell, 2000), which focused on the analysis of famers’ family capital and the public capital owned by households. The UK Department for International Development (DFID) (1999) established the Sustainable Livelihood Framework (SLF) model. The analytical framework divided the capital affecting sustainable livelihood into five parts: natural capital, human capital, financial capital, material capital and social capital. The SLF described how farmers use large amounts of property, rights and possible strategies to enhance their livelihoods in the context of the risk posed by markets, institutional policies, and natural factors, reflecting the interaction between livelihood capital structure, livelihood process and livelihood goals (Chambers & Conway, 1992). Since the 1980s, the SLE analysis method was widely applied in research on the livelihoods of farmers and the practices of poor governance (Amartya, 2015; Dzanku, 2015; Micheal, 2017; Prowse, 2003). This study used the SLE to analyze the factors that influence the choice of strategies of farmers to cope with the policy. To understand the adaptability of farmers to the policy, Yanchi County in Northwest China was used as an example, and the adaptability of farmers to the PGP was evaluated based on remote sensing data and household survey data. First, this paper determined the farmers’ perceptions regarding the implementation of the PGP (called adaptability perceptions in this paper). Second, the adaptation strategies used by different types of farmers in response to this policy change were identified. Finally, this study discussed the main factors that influenced the choice of adaptation strategies in Yanchi County. The purpose of this study was to identify the processes and adaptation mechanisms through which farmers adapt to new ecological policies and to provide a scientific basis for adjusting and formulating the next phase of ecological policies. 2. Study area and methods 2.1. Overview of the study area Yanchi County is located in Northwest China (37◦ 04 –38◦ 10 north latitude, 106◦ 30 –107◦ 41 east longitude). The total area of the county is approximately 6744 km2 . The area is 1295–1951 m above sea level. The northern boundary is adjacent to the Mu Us Desert, and the southeastern boundary is adjacent to the Loess Plateau. Yanchi County is part of the typical transitional zone from the loess hilly region to the Ordos platform (sand), which changes from a semi-arid to an arid area and from steppe vegetation to desert steppe vegetation. This geographical transition has resulted in a fragile ecosystem. The annual
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Fig. 1. Study area.
average temperature of Yanchi County is 8.1 ◦ C, and the annual precipitation is only 250–350 mm, decreasing from southeast to northwest. The annual evaporation is 5 times as much as the precipitation; the annual average wind speed is 2.8 m/s; the average number of annual windy days is 24.2; and the average number of sandstorm days is 20.6. All these characteristics are typical of a temperate continental climate. The vegetation types include shrubs, grasslands, meadow, sandy vegetation and desert vegetation, with grasslands and sandy vegetation representing the most common and widely distributed vegetation types. Therefore, the social-ecological system is fragile and susceptible to disturbance (Fig. 1). Grasslands cover 556,930 ha, accounting for 65.12% of the total area of Yanchi County, and are the economic pillar of Yanchi County. Based on the local climatic conditions and the pressure of human activities, the ecological footprint of human activities extended beyond the grassland ecological carrying capacity after the 1990s. In recent years, the effects of human activities have increased the ecological deficit (An, Zhou, & Yang, 2017; Ma, Wu, & Zhang, 2011), resulting in severe grassland degradation. Thus, grasslands are facing considerable pressures. In 2003, the government implemented the PGP in Yanchi County. The total population of Yanchi County is 171,000. Specifically, the agricultural population is 137,000, which represents 79.1% of the population of the county. The arable land area is 88886 ha; the average area of arable land per
capita is 0.52 ha; and the average grassland area per capita is 3.26 ha. By the end of 2015, forests encompassed an area of 20444 ha. The agricultural production value of the region was approximately $175 million in 2015, which was divided as follows: agriculture, $67 million; animal husbandry, $84 million; forestry, $14 million; and fisheries, $111 thousand. Yanchi County’s economy is dominated by animal husbandry and agriculture, and the area is known as “licorice town” and “Tan sheep town.” 2.2. Data sources Yanchi County is located in the transition zone from arid areas to the semi-arid zone, and the summer is the period of high vegetation growth; therefore, we chose MODIS normalized difference vegetation index (NDVI) products in the summer (June, July and August) to reflect the vegetation growth. MODIS-Terra (MOD13Q1; 2002–2015) NDVI products from C6 were downloaded from NASA’s Earth Observing System Data and Information System Reverb (http://reverb.echo.nasa.gov/). We used the MODIS Reprojection Tool (MRT) to map the projection transformation data and calculate the max NDVI from the original data using the maximum value composites (MVC) method within a year (Holben, 1986). Thus, we obtained the summer NDVI in Yanchi County from 2002 to 2015. The main method of data acquisition was interviews with farmers in their homes. Because the head of house-
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Table 1 Indicators of adaptability perceptions of the PGP. Indicators
Explanatory variables
Value
Mean
Standard deviation
Impact effect
What is the impact of the PGP on your family life?
2.94
1.044
Adaptation cost
Is the adaptation cost of the PGP high? Is your family affected by the PGP?
No impact = 1, small impact = 2, moderate impact = 3, large impact = 4, maximum impact = 5 Very low = 1, low = 2, medium = 3, high = 4, very high = 5 Very low = 1, low = 2, medium = 3, high = 4, very high = 5 Will become bad = 1, may become bad = 2, will not change = 3, may become good = 4, will become good = 5
3.44
0.624
3.10
0.463
3.86
0.872
Self-efficacy Adaptation prediction
If the PGP continues to be implemented, how will the living standard change in the future?
hold is the main family member involved in production and the living arrangement of the household, this survey was mainly directed at the head of the household; the other members of the household were supplementary to the investigated problems. We randomly selected 3 or 4 villages in every town as the sample area. Each household survey time was approximately 30 min to an hour. In total, 305 households were surveyed, and 300 valid questionnaires were obtained. Although the number of questionnaires was relatively small, the sample family size was 4.09 persons per household, and the average annual income was $549 per person, which was approximately the same as that of farmers reflected in the local statistical yearbook (in which the average household size was 3.97 persons per household and the average annual income was $534 per person). The survey mainly included the following information: (1) characteristics of the interviewees, including gender, age, health status, education, occupation, household income, farmland area, number of breeding livestock, housing, and durable goods, and (2) whether farmers are satisfied with the implementation of the policy and the impact perceptions, adaptation cost perceptions, and adaptation capability perceptions of the farmers.
ers’ non-agricultural endeavors and the main source of income, farmers were divided into pure farmers, semifarmers and non-farmers. Among them, pure farmers are those whose families are mainly engaged in farming, breeding and other agricultural production activities, and the main family income is derived from agriculture, forestry and animal husbandry. Semi-farmers are those who engage in farming, breeding and other agricultural activities as part of their labor; their other labor involves external work, businesses, and income from agriculture and non-agricultural activities. Most non-farmers engage in non-agricultural activities, and their income is derived from non-agricultural activities. According to this division, there were 98 pure farmers, 116 semi-farmers and 86 nonfarmers in the survey, representing 32.6%, 38.6% and 28.8% of the total responses, respectively. Based on previous research, this study delineates the farmers’ adaptability perceptions to the impact effect, self-efficacy, adaptation costs, and adaptation prediction perceptions (Grothmann & Patt, 2005; Kuruppu & Liverman, 2011). The questionnaire was used to obtain information regarding the adaptability perceptions (Table 1). To test the farmers’ perceptions of the PGP, answers were assigned different values, and the values were averaged to obtain the farmers’ perception index.
2.3. Methods 2.3.1. The measurement method of grassland ecosystem recovery In grasslands, an ecosystem change is mainly manifested as a change in grassland vegetation. The NDVI is the main index used to measure vegetation change. Therefore, a linear regression method was used to analyze the change of interannual variations in the NDVI in the summers of 2002–2015: k=
n
n
i=1
iNDVI(x, y)i −
n
n
i=1
i2
n n
−
i=1
i
i=1
n 2 i=1
NDVI(x, y)i
i
where n is the number of years in the study period (n = 14); NDVI(x, y)i is the NDVI value of a pixel in row (x) and column (y) in year i, and k is the slope of the NDVI change during the study period, reflecting the NDVI trend. 2.3.2. Farmers’ adaptability perceptions of the PGP Based on previous research results regarding household type (Zhao & Xue, 2015), according to data from the Yanchi County household survey, the degree of farm-
2.3.3. Method of calculating the adaptation strategy diversity index To clarify the adaptation strategies of farmers, based on the household survey data, the adaptation strategies were divided into three categories (Carney, Drinkwater, & Rusinow, 1999): (1) expansion strategies, in which agricultural investments are expanded via the purchasing or leasing of land, increasing the scale of production, increasing the number of livestock, and constructing fences; (2) adjustment strategies, in which adjustments are made to the structure of agriculture and animal husbandry, including improvements to irrigation methods, improvements to crops, adjustments to the livestock structure and feeding, adjustments to grazing time, and working outside; and (3) contraction strategies, in which reductions are made in the investments and scales of production and livelihoods, such as by reducing expenditures, selling or leasing land, and reducing the number of livestock. To describe the degree of diversity among farmers adapting to the PGP, this paper introduced the adaptation strategy diversity index. The adaptation strategy diversity
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index is the sum of all the adaptation strategies adopted by a farmer. For example, if a farmer adopted the strategies of reducing the livestock quantity and adjusting the grazing time, the associated diversity index value is 2. The diversity index of the farmers’ adaptation strategies was averaged to obtain the diversity index of each type of farmer. 2.3.4. Factors that influenced farmers’ adaptation strategies In this paper, a binary logistic regression model was used to analyze the factors that influence the choice of adaptation strategies, and the regression parameters were estimated by the maximum likelihood estimation method. To clearly and concisely estimate factors when designing the model, the adoption of an adaptation strategy was selected as the main dependent variable and given a value of 1, and the non-adoption of that adaptation strategy was given a value of 0. The specific model was as follows: P = e(b0 +b1 x1 +b2 x2 +...bm xm ) /(1 + e(b0 +b1 x1 +b2 x2 +...bm xm ) ) where P is the main dependent variable (i.e., the adaptation strategy) and Xi is the explanatory variable, which refers to each factor that affects the choice of adaptation strategy. The odds ratio (OR) is defined as P/(1-P), and it is used to interpret the logistic regression coefficients of independent variables. The OR is calculated using the following formula: odd (Pi ) = Exp(ˇ0 + ˇ1 xi1 + . . . + ˇm xm1 ) where bi before the explanatory variable is a regression coefficient and ln(OR) changes in bi units correspond to explanatory variable xi changes in 1 unit. If the regression coefficient bi is positive, for every increment of the explanatory variable, the OR will increase correspondingly; if bi is negative, for every increment of the explanatory variable, the OR will decrease correspondingly. The predictive power of the logistic regression model was evaluated using the significance level of the regression coefficient and the goodness of fit of the model (Kuruppu & Liverman, 2011). 3. Results 3.1. Restoration of grassland ecosystems after the PGP To study the restoration status of grassland ecosystems after the implementation of the PGP, this paper analyzes the NDVI trend in Yanchi County during the period from 2002 to 2015 and divides the vegetation change into four grades according to the linear slope: greatly improved (>0.02), improved (0.005–0.02), maintained (0–0.005) and degraded (<0). The results showed that grassland vegetation in most areas of the county were gradually restored to a great extent from 2002 to 2015 (Fig. 2). The statistics show that the area of improved vegetation was 4587.92 km2 , representing 67.21% of the county area. Moreover, the area of grassland degradation was 100 km2 , representing only 1.46% of the county area. In addition, according to the local grassland monitoring data, the grassland coverage increased from approximately 40% to approximately 70%
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from 2002 to 2015, and the grass yield increased from less than 1,500 kg/ha to 2250 kg/ha. The relationships between the NDVI change value and the change value of the average precipitation and average temperature in the same period in 2002 and 2015 exhibit low correlation coefficients, and no significant correlation is observed. Grassland ecosystems are mainly dependent on precipitation and temperature, and in recent years, precipitation and temperature have not changed considerably in Yanchi County. Thus, grassland ecosystems can be significantly restored, and the implementation of the PGP plays a crucial role in the restoration of grasslands. Based on this finding, farmers’ perceptions of the effects of the PGP and their satisfaction regarding the implementation of the PGP are investigated (Table 2). The survey results show that 87% of farmers believe that the implementation of the PGP was effective for environmental improvement, which corresponded to an environmental efficacy perception index of greater than 4. In addition, 76.33% of farmers were satisfied with the implementation of the PGP, and the perception index of satisfaction was 3.72, respectively (Table 2). Therefore, the implementation of the PGP had a significant effect on the improvement of the local ecosystems. 3.2. Farmers’ adaptability perceptions of the PGP Farmers are the main group in the social system, and their adaptability to ecological polices directly affects the stability of the social–ecological system in the entire grassland region. The study results show that 69.33% of farmers believed that the PGP had a considerable influence on their production and lifestyle (Table 3), which led to an impact effect perception of the policy of 2.94. Among them, 60% of farmers perceived a decrease in arable land and grassland acreage; 49.33% perceived a decrease in livestock; 40% perceived a decrease in income; 19.33% perceived a change in production and lifestyle; and 17% perceived an increase in production costs. Clearly, the PGP has had a notable impact on farmers. The adaptation cost and self-efficacy were the key factors that reflected the farmers’ abilities to adapt to the impact of the PGP. The results show that 48.67% of farmers considered the adaptation cost of the PGP relatively high. Additionally, 48.33% of farmers believed that they did not have the ability to adapt to the policy. The farmers’ adaptation costs and self-efficacy perception index values were 3.44 and 2.10, respectively (Table 3). Most farmers associated adapting to the policy with relatively high costs, and they did not believe that they had the ability to adapt to the policy. The predicted adaptations to the PGP can provide a suitable understanding of the farmers’ attitudes towards the sustainability of the policy. The results of the study showed that 75.33% of farmers believe that if the PGP continues to be implemented, living standards may increase in the future. The farmers’ adaptation prediction index of the PGP was 3.86 (Table 3). This result suggests that farmers have high expectations of the PGP; therefore, they believe that if the policy continues, it will improve the living standards of local farmers.
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Fig. 2. Vegetation restoration trend in Yanchi County. Table 2 Farmers’ perceptions of the ecosystem effects of the PGP. Indicator
Very small
Relatively small
Moderate
Relatively large
Very large
Perception index
Environmental efficacy Satisfaction
0.00% 2.33%
3.00% 9.33%
10.33% 12.00%
58.67% 67.00%
28.00% 9.33%
4.12 3.72
Table 3 Farmers’ adaptability perception index. Value Indicator
1
2
3
4
5
Perception index
Impact effect Adaptation cost Self-efficacy Adaptation prediction
12.00% 1.33% 6.33% 1.00%
18.67% 5.00% 42% 8.00%
36.00% 45.00% 35.33% 15.67%
30.33% 47.67% 14.33% 54.33%
3.00% 1.00% 2.00% 21.00%
2.94 3.44 2.10 3.86
Table 4 Adaptability perception index for different types of farmers. Types of farmers
Impact effect
Adaptation cost
Self-efficacy
Adaptation prediction
Pure farmers Semi-farmers Non-farmers Total index
3.02 2.92 2.82 2.92
3.49 3.43 3.36 3.43
2.07 2.09 2.18 2.11
3.85 3.86 3.88 3.86
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Table 5 Adaptation strategies and the diversity of farmers from different income classes. Adaptation strategies
Different livelihood farmers
Expansion strategies Adjustment strategies Contraction strategies Diversity index
Pure farmers
Semi-farmers
Non-farmers
17.44% 34.88% 47.67% 3.17
17.80% 46.61% 35.59% 2.93
16.22% 48.65% 35.14% 2.45
Table 6 Factors affecting the farmers’ selection of adaptation strategies. Indicator
Explanatory variable
Assigned value
Mean
Standard deviation
Human capital
Proportion of the labor force (0.44) Labor force education (0.56)
The share of the labor force in the household Average level of education of the labor force in the household
0.69 2.38
0.274 0.869
Natural capital
Per capita arable land area (0.47) Per capita grassland area (0.53)
Total arable land/total population Total grassland area/total population
13.39 34.62
17.791 36.910
Material capital
Value of other fixed assets (0.20)
40497.70
113872.769
Number of houses (0.20) Number of livestock (0.60)
The total value of means of transportation and furniture Masonry structure * 0.7 + civil structure * 0.3 The number of livestock (sheep units)
3.02 66.29
1.149 155.217
Financial capital
Household annual revenue (0.75) Credit capability (0.25)
Household annual revenue Whether the family has the ability to get a loan
37706.67 3.63
39886.384 1.638
Social capital
Number of relatives who can help (0.55) Number of family members going to town (0.20) Leadership (0.25)
1 (0 households)–5 (10 households or more) 1 (never)–4 (often)
2.70 2.57
1.032 1.346
How many village committee members in your family
0.07
0.250
In regard to farmers’ livelihoods, the impact effect and adaptation cost prediction indexes decreased sequentially from pure farmers to semi-farmers to non-farmers, and the prediction index of self-efficacy increased in the same sequence (Table 4). This result suggests that the impact of the PGP was greatest on the production and livelihood of pure farmers. Therefore, pure farmers believed that high adaptation costs were required to adapt to the PGP. In addition, pure farmers depend on arable land and grasslands, and farming is their only livelihood; therefore, their ability to adapt was lower than that of the other two groups.
3.3. Adaptation strategies and diversity indexes of farmers with different livelihoods Farmers of different livelihoods had different adaptation perceptions regarding the PGP. Therefore, the farmers chose different adaptation strategies to cope with the effects of the policy. The results showed that after the PGP was implemented, farmers mainly selected contraction and adjustment strategies, with 47.67% of pure farmers choosing contraction strategies; however, semi-farmers and non-farmers mainly chose adjustment strategies. The highest diversity index was observed for pure farmers (3.17), followed by semi-farmers (2.93) and non-farmers (2.45) (Table 5). This finding is because the livelihood of pure farmers includes planting and animal husbandry, which were significantly affected by the policy; therefore, these farmers had to adopt greater number of adaptation measures than did other farmer types. The livelihood of non-farmers includes working and doing business; there-
fore, they were the least impacted by the PGP and required the lowest number of adjustments to their livelihood. 3.4. Factors affecting the farmers’ selection of adaptation strategies The choice of adaptation strategy was a highly subjective process embedded within a certain social context. Farmers’ perceptions of the PGP were key variables that influenced the choice of adaptation strategies, and studies have shown that the amount of capital in the household can be used to determine the livelihood strategies adopted by farmers (Osman, Elhassan, & Ahmed, 2005). Therefore, this study selected the farmers’ perception of the PGP and their livelihood capital as explanatory variables to analyze the factors that influenced the farmers’ selected adaptation strategies. In accordance with the Sustainable Livelihoods Framework proposed by the UK Department for International Development (DFID), the farmers’ livelihood capital was divided into five categories: human capital, physical capital, natural capital, financial capital and social capital (Carney et al., 1999). The expert consultation method was used to determine the importance of each indicator and the value of each indicator, and values for all categories of living capital indicators were calculated using the weighted summation method (Table 6). The factors that influence the farmers’ choices of adaptation strategies were analyzed by introducing the adaptation strategy type, the farmer adaptation perception and living capital into the binary logistic regression model (Table 7). A Chi-square test yielded a value of 47.821 in model (P < 0.01), and the −2 log-likelihood value was
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Table 7 Factors that influence the farmers’ adaptation strategy types. Indicator
Coefficient
Standard deviation
Odds ratio
Constant Adaptation cost Self-efficacy Human capital Financial capital Model test
−6.210c 0.534b −0.486a −0.664a −0.320b −2 log-likelihood Chi-square test
2.238 0.308 0.437 0.398 0.260 225.138 47.821c
0.002 1.706 1.627 1.942 1.377
a b c
Significant at the 0.1 level. Significant at the 0.05 level. Significant at the 0.01 level.
225.138. These results indicated that the model results were significant. The results showed that the farmers’ adaptation cost perception, self-efficacy perception, human capital and financial capital had significant effects on the selection of adaptation strategies. For each additional unit of adaptation cost perception, the probability of selecting contraction strategies increased by a factor of 1.706. For each additional unit of human capital, financial capital and self-efficacy perception, the probability of selecting contraction strategies decreased by 1.942, 1.377 and 1.627 times, respectively. 4. Conclusions and discussion The implementation of the ecological policy has played an important role in ecosystem protection and has achieved remarkable ecological benefits. Then PGP has largely reduced grassland grazing and trampling, provided time for pasture restoration, and improved the quantity, height and density of grassland vegetation. The associated ecological benefits of grasslands are obvious (Chen & Zhou, 2016). Land desertification prevention, water storage, air purification, biological diversity conservation and other ecological functions of grasslands continue to improve (Wang et al., 2012). The implementation of the ecological policy has a great impact on the local social and economic systems (Mills, Turpie, O’Connor, & Robertson, 2007; Wang et al., 2012). Farmers’ production and lifestyle propensities were forced to change, but the ecological compensation of the policy was not sufficient for transforming livelihood and compensating for the losses caused by the policy, resulting in a decrease in some farmers’ incomes (Ma, Zhou, & Lu, 2015). However, the PGP policy has caused livestock husbandry to change from free grazing to captivity to reduce the operational risk (Yang et al., 2006). Large numbers of laborers have been liberated from the grasslands, entering into other businesses, and gradually integrating themselves into modern urban life, which has markedly increased their standard of living (Wang et al., 2012). They have brought hope to other farmers who have had a great influence on the policy. The implementation of ecological policies has promoted the continuous improvement of the local ecosystems. Coupled with vigorous support from the government for the implementation of ecological policies and the improvement of the Social Security system, the living standards of the farmers in the ecological policy imple-
mentation area are forecasted to greatly improve in the future. Farmers of different types had different adaptation perceptions, and their choices of adaptation strategies also varied (Davis and Lopez-Carr, 2014). Pure farmers had the highest degree of dependence on ecosystems and were the most affected by ecological policies; therefore, their perception of ecological policies was the strongest. Because of their sole reliance on farming for their livelihood, low economic level and low level of education, it has been difficult for them to change their livelihoods to adapt to ecological policies; therefore, farmers have reduced expenses and adopted other contraction adaptation strategies to maintain their livelihood. Compared to pure farmers, it was easier for semi-farmers to adapt to ecological policies. These farmers participate in other production activities, and their livelihoods are diverse. Thus, after ecological policies were implemented, they successfully changed their livelihoods and adjusted their livelihood strategies to adapt to ecological policies. The implementation of ecological policies had little effect on non-farmers’ livelihood or life; therefore, they exhibited a weak perception of ecological policies, and after ecological policies were implemented, non-farmers adapted by slightly adjusting their livelihoods. We observed that farmers’ own livelihood diversity and livelihood capital directly determined the adaptability of the local residents in the face of policies (De Sherbinin et al., 2008). Therefore, to ensure the smooth implementation of some policies, future policymaking processes should effectively consider the livelihood and capital of local residents (Xu, Kang, & Jiang, 2012). Governments should strengthen the production skill training of local residents to help them adjust their livelihood strategies (Rakodi, 2010) and increase the accumulation of farmers’ livelihood capital (Bhandari, 2013) to enable them to effectively deal with the impacts of some policies.
Conflict of interest The authors declare that they have no conflict of interest.
Acknowledgement This paper was financially supported by the National Key Research and Development Program of China [No. 2016YFC0500909], the National Natural Science Founda-
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Farmers’ adaptability to the policy of ecological protection in China—A case study in Yanchi County, China Caixia Hou a,c , Lihua Zhou a,b,∗ , Yan Wen d , Yong Chen a a Key Laboratory of Desert and Desertification, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China b Institutes of Science and Development, Chinese Academy of Sciences, Beijing 100190, China c University of Chinese Academy of Sciences, Beijing 100049, China d College of Resources Science and Technology, Beijing Normal University, Beijing 100875, China
a r t i c l e
i n f o
Article history: Received 21 November 2017 Received in revised form 6 June 2018 Accepted 6 June 2018 Available online xxx Keywords: PGP Adaptability Perception Farmers Yanchi County China
a b s t r a c t Due to long-term human activities, grassland ecosystems have been severely damaged. To protect grassland ecosystems, the prohibited grazing policy (PGP), a grassland ecological protection policy, was instituted for Chinese grasslands in 2002. However, it is unknown whether farmers have effectively adapted to the PGP. The adaptability of farmers to this policy has directly influenced the effective implementation of the policy and the sustainable development of the ecosystem. Previous research on adaptability has not focused on the adaptation to political change. This article uses a case study in Northwest China to investigate the adaptability of farmers to the policy. First, we study the restoration status of ecosystems. In addition, this paper studies the perceptions regarding farmers’ adaptability to the policy and explores the adaptation strategies of different types of farmers. Finally, this paper discusses the main factors that influence farmers’ choice of adaptation strategies. The implementation of the PGP has achieved remarkable ecological benefits. Farmers of different types had different adaptation perceptions, and their choices of adaptation strategies also varied. In addition to the farmers’ perception of the policy, the main factors that influenced farmers’ adaptation strategies also included livelihood capital. © 2018 Published by Elsevier Inc. on behalf of Western Social Science Association.
1. Introduction Grassland areas in China account for 393 million hectares, or approximately 41.7% of the total territory. Grasslands are the largest land ecosystem in China (Gao & Lin, 2015; Hou et al., 2011). However, the grassland ecological carrying capacity is limited. With population and socioeconomic pressures, excessive land use and the lack of
∗ Corresponding author at: Institutes of Science and Development, Chinese Academy of Sciences, West Dong gang Road, Lanzhou 730000, Gansu Province, China. E-mail addresses: [email protected] (C. Hou), [email protected] (L. Zhou).
protection in grassland areas, degradation began to various degrees in many grassland areas in the 1950s (Fan, Zhong, Chen, & Zhang, 2007; Long, Dong, & Hu, 2005). Additionally, the annual direct and indirect losses from grasslands total $8.3 billion (Cui, Li, & Li, 2011). Grassland degradation has become the most serious problem in the sustainable development of grassland animal husbandry (Yang, Min, & Li, 2007). On December 16, 2002, the Chinese government formally approved a prohibited grazing policy (PGP) in areas where grasslands were severely damaged in Northwest China, an area among the 1,100 counties in 25 provinces nationwide (Hou et al., 2011). The main purpose of the policy is to restore the grassland social-ecological system to
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a steady state system. The government provided ecological compensation to farmers in the prohibited grazing area in Northwest China. The government subsidized feed grain by 82.5 kg/ha per year since 2003 and compensated farmers for grassland fence construction funds, feed grain costs and sowing fees from 2005 to 2010. From 2011 to 2015, the government gave each household $76.90 as a production subsidy and $13.80/ha per year as a prohibited grazing subsidy, which increased to $17.30/ha per year in 2016. The PGP has been implemented for more than 14 years; in this period, most grassland ecosystems in Northwest China have been well protected and vegetation has significantly improved (Lu, Zhou, & Chen, 2015; Wang, Tao, & Song, 2012). However, farmers are the main economic actors and direct users of grasslands, and the PGP has had the most direct effect on farmers (Chen, Wang, Zhou, Liu, & Huang, 2014); therefore, they have the most direct perceptions of the changes introduced by this policy. These perceptions and adaptations to the PGP also provide important feedback for decision makers. The implementation of the PGP has moved farmers away from grasslands and changed their livelihoods. Therefore, determining whether farmers can smoothly adapt to a certain policy is a key to policymaking and policy implementation. This study of the adaptability of farmers to the PGP will ease the conflicts between farmers’ livelihoods and the PGP and will be beneficial to the smooth implementation of this policy and the sustainability of grassland ecosystem protection. Adaptability is a strategy derived from the natural sciences, particularly evolutionary ecology (Futuyama, 1978; Winterhalder, 1980), and it is primarily used in climate studies (Elum, Modise, & Marr, 2016; Gandure, Walker, & Both, 2012; Mertz, Mbow, Reenberg, & Diouf, 2009). Steward (1977) first applied the concept of adaptability to humans via the concept of “cultural adaptation”, which describes how a regional society adjusts its behavior according to the natural environment. Denevan (1983) suggested that genetic characteristics in the natural sciences are similar to cultural practices in the social sciences, and incorporated disciplines such as demography, economics, and organization into the study of human adaptability. Furthermore, Denevan also implied that the interpretation scope of adaptability has expanded to include a wider range of political, economic, social and other interpretations. O’Brien and Holland (1992) also proposed that adaptability is the result of behavioral choices informed by cultural practices in a changing environment. Berkes, Colding, and Folke (2003) argued that the study of adaptability should encompass social-ecological systems that include human societies and ecosystems. Jerry (2000) and Michel and Nicolas (2003) argued that the relationships among ecosystems, politics, and the economy are also related to the adaptability to politics, social forces, resource uses, and global economic risks. Bahinipati (2011) also proposed that the key component of adaptability research in the field of political ecology is how individuals and families adapt to social, political, and economic processes. Therefore, adaptability is defined as the response of individuals, organizations and groups to certain environmental and policy changes and is based on their perceptions and abilities to formulate measures to deal with those changes (Smithers
& Smit, 1997; Smit & Wandel, 2006). Additionally, previous research on adaptability focuses less on the adaptation to political change. This paper studied the adaptability of farmers to the PGP policy to provide a basis for future research. Seoones (1998) first proposed a livelihood analysis framework and divided livelihood capital into natural capital, financial capital, human capital and social capital. Subsequently, the Cooperative for American Relief Everywhere proposed a livelihood security framework for farmers (Frankenberger, Drinkwater, & Maxwell, 2000), which focused on the analysis of famers’ family capital and the public capital owned by households. The UK Department for International Development (DFID) (1999) established the Sustainable Livelihood Framework (SLF) model. The analytical framework divided the capital affecting sustainable livelihood into five parts: natural capital, human capital, financial capital, material capital and social capital. The SLF described how farmers use large amounts of property, rights and possible strategies to enhance their livelihoods in the context of the risk posed by markets, institutional policies, and natural factors, reflecting the interaction between livelihood capital structure, livelihood process and livelihood goals (Chambers & Conway, 1992). Since the 1980s, the SLE analysis method was widely applied in research on the livelihoods of farmers and the practices of poor governance (Amartya, 2015; Dzanku, 2015; Micheal, 2017; Prowse, 2003). This study used the SLE to analyze the factors that influence the choice of strategies of farmers to cope with the policy. To understand the adaptability of farmers to the policy, Yanchi County in Northwest China was used as an example, and the adaptability of farmers to the PGP was evaluated based on remote sensing data and household survey data. First, this paper determined the farmers’ perceptions regarding the implementation of the PGP (called adaptability perceptions in this paper). Second, the adaptation strategies used by different types of farmers in response to this policy change were identified. Finally, this study discussed the main factors that influenced the choice of adaptation strategies in Yanchi County. The purpose of this study was to identify the processes and adaptation mechanisms through which farmers adapt to new ecological policies and to provide a scientific basis for adjusting and formulating the next phase of ecological policies. 2. Study area and methods 2.1. Overview of the study area Yanchi County is located in Northwest China (37◦ 04 –38◦ 10 north latitude, 106◦ 30 –107◦ 41 east longitude). The total area of the county is approximately 6744 km2 . The area is 1295–1951 m above sea level. The northern boundary is adjacent to the Mu Us Desert, and the southeastern boundary is adjacent to the Loess Plateau. Yanchi County is part of the typical transitional zone from the loess hilly region to the Ordos platform (sand), which changes from a semi-arid to an arid area and from steppe vegetation to desert steppe vegetation. This geographical transition has resulted in a fragile ecosystem. The annual
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Fig. 1. Study area.
average temperature of Yanchi County is 8.1 ◦ C, and the annual precipitation is only 250–350 mm, decreasing from southeast to northwest. The annual evaporation is 5 times as much as the precipitation; the annual average wind speed is 2.8 m/s; the average number of annual windy days is 24.2; and the average number of sandstorm days is 20.6. All these characteristics are typical of a temperate continental climate. The vegetation types include shrubs, grasslands, meadow, sandy vegetation and desert vegetation, with grasslands and sandy vegetation representing the most common and widely distributed vegetation types. Therefore, the social-ecological system is fragile and susceptible to disturbance (Fig. 1). Grasslands cover 556,930 ha, accounting for 65.12% of the total area of Yanchi County, and are the economic pillar of Yanchi County. Based on the local climatic conditions and the pressure of human activities, the ecological footprint of human activities extended beyond the grassland ecological carrying capacity after the 1990s. In recent years, the effects of human activities have increased the ecological deficit (An, Zhou, & Yang, 2017; Ma, Wu, & Zhang, 2011), resulting in severe grassland degradation. Thus, grasslands are facing considerable pressures. In 2003, the government implemented the PGP in Yanchi County. The total population of Yanchi County is 171,000. Specifically, the agricultural population is 137,000, which represents 79.1% of the population of the county. The arable land area is 88886 ha; the average area of arable land per
capita is 0.52 ha; and the average grassland area per capita is 3.26 ha. By the end of 2015, forests encompassed an area of 20444 ha. The agricultural production value of the region was approximately $175 million in 2015, which was divided as follows: agriculture, $67 million; animal husbandry, $84 million; forestry, $14 million; and fisheries, $111 thousand. Yanchi County’s economy is dominated by animal husbandry and agriculture, and the area is known as “licorice town” and “Tan sheep town.” 2.2. Data sources Yanchi County is located in the transition zone from arid areas to the semi-arid zone, and the summer is the period of high vegetation growth; therefore, we chose MODIS normalized difference vegetation index (NDVI) products in the summer (June, July and August) to reflect the vegetation growth. MODIS-Terra (MOD13Q1; 2002–2015) NDVI products from C6 were downloaded from NASA’s Earth Observing System Data and Information System Reverb (http://reverb.echo.nasa.gov/). We used the MODIS Reprojection Tool (MRT) to map the projection transformation data and calculate the max NDVI from the original data using the maximum value composites (MVC) method within a year (Holben, 1986). Thus, we obtained the summer NDVI in Yanchi County from 2002 to 2015. The main method of data acquisition was interviews with farmers in their homes. Because the head of house-
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Table 1 Indicators of adaptability perceptions of the PGP. Indicators
Explanatory variables
Value
Mean
Standard deviation
Impact effect
What is the impact of the PGP on your family life?
2.94
1.044
Adaptation cost
Is the adaptation cost of the PGP high? Is your family affected by the PGP?
No impact = 1, small impact = 2, moderate impact = 3, large impact = 4, maximum impact = 5 Very low = 1, low = 2, medium = 3, high = 4, very high = 5 Very low = 1, low = 2, medium = 3, high = 4, very high = 5 Will become bad = 1, may become bad = 2, will not change = 3, may become good = 4, will become good = 5
3.44
0.624
3.10
0.463
3.86
0.872
Self-efficacy Adaptation prediction
If the PGP continues to be implemented, how will the living standard change in the future?
hold is the main family member involved in production and the living arrangement of the household, this survey was mainly directed at the head of the household; the other members of the household were supplementary to the investigated problems. We randomly selected 3 or 4 villages in every town as the sample area. Each household survey time was approximately 30 min to an hour. In total, 305 households were surveyed, and 300 valid questionnaires were obtained. Although the number of questionnaires was relatively small, the sample family size was 4.09 persons per household, and the average annual income was $549 per person, which was approximately the same as that of farmers reflected in the local statistical yearbook (in which the average household size was 3.97 persons per household and the average annual income was $534 per person). The survey mainly included the following information: (1) characteristics of the interviewees, including gender, age, health status, education, occupation, household income, farmland area, number of breeding livestock, housing, and durable goods, and (2) whether farmers are satisfied with the implementation of the policy and the impact perceptions, adaptation cost perceptions, and adaptation capability perceptions of the farmers.
ers’ non-agricultural endeavors and the main source of income, farmers were divided into pure farmers, semifarmers and non-farmers. Among them, pure farmers are those whose families are mainly engaged in farming, breeding and other agricultural production activities, and the main family income is derived from agriculture, forestry and animal husbandry. Semi-farmers are those who engage in farming, breeding and other agricultural activities as part of their labor; their other labor involves external work, businesses, and income from agriculture and non-agricultural activities. Most non-farmers engage in non-agricultural activities, and their income is derived from non-agricultural activities. According to this division, there were 98 pure farmers, 116 semi-farmers and 86 nonfarmers in the survey, representing 32.6%, 38.6% and 28.8% of the total responses, respectively. Based on previous research, this study delineates the farmers’ adaptability perceptions to the impact effect, self-efficacy, adaptation costs, and adaptation prediction perceptions (Grothmann & Patt, 2005; Kuruppu & Liverman, 2011). The questionnaire was used to obtain information regarding the adaptability perceptions (Table 1). To test the farmers’ perceptions of the PGP, answers were assigned different values, and the values were averaged to obtain the farmers’ perception index.
2.3. Methods 2.3.1. The measurement method of grassland ecosystem recovery In grasslands, an ecosystem change is mainly manifested as a change in grassland vegetation. The NDVI is the main index used to measure vegetation change. Therefore, a linear regression method was used to analyze the change of interannual variations in the NDVI in the summers of 2002–2015: k=
n
n
i=1
iNDVI(x, y)i −
n
n
i=1
i2
n n
−
i=1
i
i=1
n 2 i=1
NDVI(x, y)i
i
where n is the number of years in the study period (n = 14); NDVI(x, y)i is the NDVI value of a pixel in row (x) and column (y) in year i, and k is the slope of the NDVI change during the study period, reflecting the NDVI trend. 2.3.2. Farmers’ adaptability perceptions of the PGP Based on previous research results regarding household type (Zhao & Xue, 2015), according to data from the Yanchi County household survey, the degree of farm-
2.3.3. Method of calculating the adaptation strategy diversity index To clarify the adaptation strategies of farmers, based on the household survey data, the adaptation strategies were divided into three categories (Carney, Drinkwater, & Rusinow, 1999): (1) expansion strategies, in which agricultural investments are expanded via the purchasing or leasing of land, increasing the scale of production, increasing the number of livestock, and constructing fences; (2) adjustment strategies, in which adjustments are made to the structure of agriculture and animal husbandry, including improvements to irrigation methods, improvements to crops, adjustments to the livestock structure and feeding, adjustments to grazing time, and working outside; and (3) contraction strategies, in which reductions are made in the investments and scales of production and livelihoods, such as by reducing expenditures, selling or leasing land, and reducing the number of livestock. To describe the degree of diversity among farmers adapting to the PGP, this paper introduced the adaptation strategy diversity index. The adaptation strategy diversity
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index is the sum of all the adaptation strategies adopted by a farmer. For example, if a farmer adopted the strategies of reducing the livestock quantity and adjusting the grazing time, the associated diversity index value is 2. The diversity index of the farmers’ adaptation strategies was averaged to obtain the diversity index of each type of farmer. 2.3.4. Factors that influenced farmers’ adaptation strategies In this paper, a binary logistic regression model was used to analyze the factors that influence the choice of adaptation strategies, and the regression parameters were estimated by the maximum likelihood estimation method. To clearly and concisely estimate factors when designing the model, the adoption of an adaptation strategy was selected as the main dependent variable and given a value of 1, and the non-adoption of that adaptation strategy was given a value of 0. The specific model was as follows: P = e(b0 +b1 x1 +b2 x2 +...bm xm ) /(1 + e(b0 +b1 x1 +b2 x2 +...bm xm ) ) where P is the main dependent variable (i.e., the adaptation strategy) and Xi is the explanatory variable, which refers to each factor that affects the choice of adaptation strategy. The odds ratio (OR) is defined as P/(1-P), and it is used to interpret the logistic regression coefficients of independent variables. The OR is calculated using the following formula: odd (Pi ) = Exp(ˇ0 + ˇ1 xi1 + . . . + ˇm xm1 ) where bi before the explanatory variable is a regression coefficient and ln(OR) changes in bi units correspond to explanatory variable xi changes in 1 unit. If the regression coefficient bi is positive, for every increment of the explanatory variable, the OR will increase correspondingly; if bi is negative, for every increment of the explanatory variable, the OR will decrease correspondingly. The predictive power of the logistic regression model was evaluated using the significance level of the regression coefficient and the goodness of fit of the model (Kuruppu & Liverman, 2011). 3. Results 3.1. Restoration of grassland ecosystems after the PGP To study the restoration status of grassland ecosystems after the implementation of the PGP, this paper analyzes the NDVI trend in Yanchi County during the period from 2002 to 2015 and divides the vegetation change into four grades according to the linear slope: greatly improved (>0.02), improved (0.005–0.02), maintained (0–0.005) and degraded (<0). The results showed that grassland vegetation in most areas of the county were gradually restored to a great extent from 2002 to 2015 (Fig. 2). The statistics show that the area of improved vegetation was 4587.92 km2 , representing 67.21% of the county area. Moreover, the area of grassland degradation was 100 km2 , representing only 1.46% of the county area. In addition, according to the local grassland monitoring data, the grassland coverage increased from approximately 40% to approximately 70%
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from 2002 to 2015, and the grass yield increased from less than 1,500 kg/ha to 2250 kg/ha. The relationships between the NDVI change value and the change value of the average precipitation and average temperature in the same period in 2002 and 2015 exhibit low correlation coefficients, and no significant correlation is observed. Grassland ecosystems are mainly dependent on precipitation and temperature, and in recent years, precipitation and temperature have not changed considerably in Yanchi County. Thus, grassland ecosystems can be significantly restored, and the implementation of the PGP plays a crucial role in the restoration of grasslands. Based on this finding, farmers’ perceptions of the effects of the PGP and their satisfaction regarding the implementation of the PGP are investigated (Table 2). The survey results show that 87% of farmers believe that the implementation of the PGP was effective for environmental improvement, which corresponded to an environmental efficacy perception index of greater than 4. In addition, 76.33% of farmers were satisfied with the implementation of the PGP, and the perception index of satisfaction was 3.72, respectively (Table 2). Therefore, the implementation of the PGP had a significant effect on the improvement of the local ecosystems. 3.2. Farmers’ adaptability perceptions of the PGP Farmers are the main group in the social system, and their adaptability to ecological polices directly affects the stability of the social–ecological system in the entire grassland region. The study results show that 69.33% of farmers believed that the PGP had a considerable influence on their production and lifestyle (Table 3), which led to an impact effect perception of the policy of 2.94. Among them, 60% of farmers perceived a decrease in arable land and grassland acreage; 49.33% perceived a decrease in livestock; 40% perceived a decrease in income; 19.33% perceived a change in production and lifestyle; and 17% perceived an increase in production costs. Clearly, the PGP has had a notable impact on farmers. The adaptation cost and self-efficacy were the key factors that reflected the farmers’ abilities to adapt to the impact of the PGP. The results show that 48.67% of farmers considered the adaptation cost of the PGP relatively high. Additionally, 48.33% of farmers believed that they did not have the ability to adapt to the policy. The farmers’ adaptation costs and self-efficacy perception index values were 3.44 and 2.10, respectively (Table 3). Most farmers associated adapting to the policy with relatively high costs, and they did not believe that they had the ability to adapt to the policy. The predicted adaptations to the PGP can provide a suitable understanding of the farmers’ attitudes towards the sustainability of the policy. The results of the study showed that 75.33% of farmers believe that if the PGP continues to be implemented, living standards may increase in the future. The farmers’ adaptation prediction index of the PGP was 3.86 (Table 3). This result suggests that farmers have high expectations of the PGP; therefore, they believe that if the policy continues, it will improve the living standards of local farmers.
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Fig. 2. Vegetation restoration trend in Yanchi County. Table 2 Farmers’ perceptions of the ecosystem effects of the PGP. Indicator
Very small
Relatively small
Moderate
Relatively large
Very large
Perception index
Environmental efficacy Satisfaction
0.00% 2.33%
3.00% 9.33%
10.33% 12.00%
58.67% 67.00%
28.00% 9.33%
4.12 3.72
Table 3 Farmers’ adaptability perception index. Value Indicator
1
2
3
4
5
Perception index
Impact effect Adaptation cost Self-efficacy Adaptation prediction
12.00% 1.33% 6.33% 1.00%
18.67% 5.00% 42% 8.00%
36.00% 45.00% 35.33% 15.67%
30.33% 47.67% 14.33% 54.33%
3.00% 1.00% 2.00% 21.00%
2.94 3.44 2.10 3.86
Table 4 Adaptability perception index for different types of farmers. Types of farmers
Impact effect
Adaptation cost
Self-efficacy
Adaptation prediction
Pure farmers Semi-farmers Non-farmers Total index
3.02 2.92 2.82 2.92
3.49 3.43 3.36 3.43
2.07 2.09 2.18 2.11
3.85 3.86 3.88 3.86
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Table 5 Adaptation strategies and the diversity of farmers from different income classes. Adaptation strategies
Different livelihood farmers
Expansion strategies Adjustment strategies Contraction strategies Diversity index
Pure farmers
Semi-farmers
Non-farmers
17.44% 34.88% 47.67% 3.17
17.80% 46.61% 35.59% 2.93
16.22% 48.65% 35.14% 2.45
Table 6 Factors affecting the farmers’ selection of adaptation strategies. Indicator
Explanatory variable
Assigned value
Mean
Standard deviation
Human capital
Proportion of the labor force (0.44) Labor force education (0.56)
The share of the labor force in the household Average level of education of the labor force in the household
0.69 2.38
0.274 0.869
Natural capital
Per capita arable land area (0.47) Per capita grassland area (0.53)
Total arable land/total population Total grassland area/total population
13.39 34.62
17.791 36.910
Material capital
Value of other fixed assets (0.20)
40497.70
113872.769
Number of houses (0.20) Number of livestock (0.60)
The total value of means of transportation and furniture Masonry structure * 0.7 + civil structure * 0.3 The number of livestock (sheep units)
3.02 66.29
1.149 155.217
Financial capital
Household annual revenue (0.75) Credit capability (0.25)
Household annual revenue Whether the family has the ability to get a loan
37706.67 3.63
39886.384 1.638
Social capital
Number of relatives who can help (0.55) Number of family members going to town (0.20) Leadership (0.25)
1 (0 households)–5 (10 households or more) 1 (never)–4 (often)
2.70 2.57
1.032 1.346
How many village committee members in your family
0.07
0.250
In regard to farmers’ livelihoods, the impact effect and adaptation cost prediction indexes decreased sequentially from pure farmers to semi-farmers to non-farmers, and the prediction index of self-efficacy increased in the same sequence (Table 4). This result suggests that the impact of the PGP was greatest on the production and livelihood of pure farmers. Therefore, pure farmers believed that high adaptation costs were required to adapt to the PGP. In addition, pure farmers depend on arable land and grasslands, and farming is their only livelihood; therefore, their ability to adapt was lower than that of the other two groups.
3.3. Adaptation strategies and diversity indexes of farmers with different livelihoods Farmers of different livelihoods had different adaptation perceptions regarding the PGP. Therefore, the farmers chose different adaptation strategies to cope with the effects of the policy. The results showed that after the PGP was implemented, farmers mainly selected contraction and adjustment strategies, with 47.67% of pure farmers choosing contraction strategies; however, semi-farmers and non-farmers mainly chose adjustment strategies. The highest diversity index was observed for pure farmers (3.17), followed by semi-farmers (2.93) and non-farmers (2.45) (Table 5). This finding is because the livelihood of pure farmers includes planting and animal husbandry, which were significantly affected by the policy; therefore, these farmers had to adopt greater number of adaptation measures than did other farmer types. The livelihood of non-farmers includes working and doing business; there-
fore, they were the least impacted by the PGP and required the lowest number of adjustments to their livelihood. 3.4. Factors affecting the farmers’ selection of adaptation strategies The choice of adaptation strategy was a highly subjective process embedded within a certain social context. Farmers’ perceptions of the PGP were key variables that influenced the choice of adaptation strategies, and studies have shown that the amount of capital in the household can be used to determine the livelihood strategies adopted by farmers (Osman, Elhassan, & Ahmed, 2005). Therefore, this study selected the farmers’ perception of the PGP and their livelihood capital as explanatory variables to analyze the factors that influenced the farmers’ selected adaptation strategies. In accordance with the Sustainable Livelihoods Framework proposed by the UK Department for International Development (DFID), the farmers’ livelihood capital was divided into five categories: human capital, physical capital, natural capital, financial capital and social capital (Carney et al., 1999). The expert consultation method was used to determine the importance of each indicator and the value of each indicator, and values for all categories of living capital indicators were calculated using the weighted summation method (Table 6). The factors that influence the farmers’ choices of adaptation strategies were analyzed by introducing the adaptation strategy type, the farmer adaptation perception and living capital into the binary logistic regression model (Table 7). A Chi-square test yielded a value of 47.821 in model (P < 0.01), and the −2 log-likelihood value was
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Table 7 Factors that influence the farmers’ adaptation strategy types. Indicator
Coefficient
Standard deviation
Odds ratio
Constant Adaptation cost Self-efficacy Human capital Financial capital Model test
−6.210c 0.534b −0.486a −0.664a −0.320b −2 log-likelihood Chi-square test
2.238 0.308 0.437 0.398 0.260 225.138 47.821c
0.002 1.706 1.627 1.942 1.377
a b c
Significant at the 0.1 level. Significant at the 0.05 level. Significant at the 0.01 level.
225.138. These results indicated that the model results were significant. The results showed that the farmers’ adaptation cost perception, self-efficacy perception, human capital and financial capital had significant effects on the selection of adaptation strategies. For each additional unit of adaptation cost perception, the probability of selecting contraction strategies increased by a factor of 1.706. For each additional unit of human capital, financial capital and self-efficacy perception, the probability of selecting contraction strategies decreased by 1.942, 1.377 and 1.627 times, respectively. 4. Conclusions and discussion The implementation of the ecological policy has played an important role in ecosystem protection and has achieved remarkable ecological benefits. Then PGP has largely reduced grassland grazing and trampling, provided time for pasture restoration, and improved the quantity, height and density of grassland vegetation. The associated ecological benefits of grasslands are obvious (Chen & Zhou, 2016). Land desertification prevention, water storage, air purification, biological diversity conservation and other ecological functions of grasslands continue to improve (Wang et al., 2012). The implementation of the ecological policy has a great impact on the local social and economic systems (Mills, Turpie, O’Connor, & Robertson, 2007; Wang et al., 2012). Farmers’ production and lifestyle propensities were forced to change, but the ecological compensation of the policy was not sufficient for transforming livelihood and compensating for the losses caused by the policy, resulting in a decrease in some farmers’ incomes (Ma, Zhou, & Lu, 2015). However, the PGP policy has caused livestock husbandry to change from free grazing to captivity to reduce the operational risk (Yang et al., 2006). Large numbers of laborers have been liberated from the grasslands, entering into other businesses, and gradually integrating themselves into modern urban life, which has markedly increased their standard of living (Wang et al., 2012). They have brought hope to other farmers who have had a great influence on the policy. The implementation of ecological policies has promoted the continuous improvement of the local ecosystems. Coupled with vigorous support from the government for the implementation of ecological policies and the improvement of the Social Security system, the living standards of the farmers in the ecological policy imple-
mentation area are forecasted to greatly improve in the future. Farmers of different types had different adaptation perceptions, and their choices of adaptation strategies also varied (Davis and Lopez-Carr, 2014). Pure farmers had the highest degree of dependence on ecosystems and were the most affected by ecological policies; therefore, their perception of ecological policies was the strongest. Because of their sole reliance on farming for their livelihood, low economic level and low level of education, it has been difficult for them to change their livelihoods to adapt to ecological policies; therefore, farmers have reduced expenses and adopted other contraction adaptation strategies to maintain their livelihood. Compared to pure farmers, it was easier for semi-farmers to adapt to ecological policies. These farmers participate in other production activities, and their livelihoods are diverse. Thus, after ecological policies were implemented, they successfully changed their livelihoods and adjusted their livelihood strategies to adapt to ecological policies. The implementation of ecological policies had little effect on non-farmers’ livelihood or life; therefore, they exhibited a weak perception of ecological policies, and after ecological policies were implemented, non-farmers adapted by slightly adjusting their livelihoods. We observed that farmers’ own livelihood diversity and livelihood capital directly determined the adaptability of the local residents in the face of policies (De Sherbinin et al., 2008). Therefore, to ensure the smooth implementation of some policies, future policymaking processes should effectively consider the livelihood and capital of local residents (Xu, Kang, & Jiang, 2012). Governments should strengthen the production skill training of local residents to help them adjust their livelihood strategies (Rakodi, 2010) and increase the accumulation of farmers’ livelihood capital (Bhandari, 2013) to enable them to effectively deal with the impacts of some policies.
Conflict of interest The authors declare that they have no conflict of interest.
Acknowledgement This paper was financially supported by the National Key Research and Development Program of China [No. 2016YFC0500909], the National Natural Science Founda-
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