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Influence of livelihood capital on adaptation strategies: Evidence from rural households in Wushen Banner, China
Land Use Policy 89 (2019) 104228
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Land Use Policy journal homepage: www.elsevier.com/locate/landusepol
Influence of livelihood capital on adaptation strategies: Evidence from rural households in Wushen Banner, China
T
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Foyuan Kuanga,b, Jianjun Jina,b, , Rui Hea,b, Xinyu Wana,b, Jing Ninga,b a b
State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing Normal University, Beijing, 100875, China School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Farmer Livelihood capital Climate change Adaptation Boosted regression tree
Based on the sustainable livelihood framework, this paper explores the influence of each form of livelihood capital on the adoption of climate change adaptation strategies by farmers. A stratified random sampling technique was used to select 235 households in Wushen Banner, China, while the boosted regression tree model was used to analyze how different forms of livelihood capital are related to farmers’ choices regarding climate change adaptation strategies. Our results show that most farmers in the study area have adopted adaptation strategies to cope with climate change. The farmers’ livelihood capital plays an important role in their adoption of adaptation strategies. Specifically, natural capital and social capital have a positive impact on farmers’ decisions about climate change adaptation strategies. Human capital and physical capital are inclined to promote farmers’ adoption of climate change adaptation strategies. The results of this study are helpful for improving our understanding of how livelihood capital influences climate change adaptation strategies among farmers, which can provide implications for planning more effective adaptation programs.
1. Introduction Climate change is the most important underlying disaster risk factor and is related to the increase of natural disasters around the world (IPCC, 2014). The high reliance of agriculture on climate-sensitive parameters (e.g., water and temperature) makes agriculture inherently sensitive to climate change (Wheeler and Von Braun, 2013). Studies have noted that farmers in developing countries are most adversely affected by climate change (Chen et al., 2014; Pandey et al., 2017a; Khanal et al., 2018a). Given that China is a typical meteorological disaster-prone country, climate change has a substantial impact on Chinese agriculture and local farmers (Zhai et al., 2018). Adaptation is considered as one of the policy options for reducing the negative impacts of climate change (IPCC, 2014; Abid et al., 2016; Jezeer et al., 2019). Successful adaptation will provide the most effective protection for communities and individuals (Abid et al., 2016; Pandey et al., 2017b; Khanal et al., 2018b). Analyzing adaptation is therefore important for finding ways to help farmers adapt to climate change. A better understanding of the factors that affect farmers’ decisions regarding adaptive practices may provide a basis for formulating policy recommendations that would be responsive to climatic changes effectively (Piya et al., 2013; Pandey et al., 2017a). Decision making theory suggests that decision makers react ⁎
differently to risks or threats (Tversky and Kahneman, 1974; Patt and Zeckhauser, 2000). The process through which farmers engage in adaptation decision-making is complicated (Chen et al., 2014; Trinh et al., 2018). Conceptually, in the face of climate change, rational farmers adopt different adaptive practices to reduce the negative impacts of climatic change. Common adaptation strategies in agriculture include the use of new resistant crop varieties, soil conservation, crop diversification, changing planting dates and improved irrigation (Deressa et al., 2009; Abid et al., 2016). Farmers adopt various adaptation strategies based on their available and accessible assets or the resource mix of the household (Chen et al., 2014; Pandey et al., 2017b). Therefore, household livelihood capital or assets may have an influence on farmers’ adaptive practices (Trinh et al., 2018). However, there is a limited understanding of how farmers’ livelihood capital influences their climate change adaptation measures. This study is designed to fill this gap by investigating the influence of livelihood capital on households’ climate change adaptive practices in Wushen Banner, Inner Mongolia, China. The sustainable livelihood framework developed by the UK Department for International Development was employed in this study (DFID, 2000), and it is based on understanding people's access to assets, which typically include natural, human, social, physical and financial capital (Ellis, 2000). This framework has been widely used and has
Corresponding author at: State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing Normal University, Beijing, 100875, China. E-mail address: [email protected] (J. Jin).
https://doi.org/10.1016/j.landusepol.2019.104228 Received 23 January 2019; Received in revised form 21 August 2019; Accepted 14 September 2019 0264-8377/ © 2019 Elsevier Ltd. All rights reserved.
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37°38′-39°23′ N and 108°17′-109°40′ E. It has a population of over 0.13 million people, of which 77.53% engage in agriculture. Wushen Banner is in the arid and semiarid areas, which are characterized by drought and little rain, with wind and sand, a dry climate, strong solar radiation and large diurnal temperature differences. The annual average precipitation is 350˜400 mm, with a mean annual temperature of 6.4˜7.5 °C. The annual frost-free period lasts approximately 113˜156 days. With the arid geographical environment and the scarcity of resources, Wushen Banner is a highly vulnerable region in terms of natural disasters, and its agricultural production conditions are harsh. Climate change increases the vulnerability of local farmers (Pan et al., 2015). Therefore, it is of urgent practical importance to mitigate the risks of climatic change that influence farmers' livelihoods.
become a classic paradigm in family livelihood research (Ellis, 2000; Pour et al., 2018; Liu et al., 2018). As the core of the sustainable livelihood analysis framework, the capital endowment is not only the basis of household livelihood strategy but also a safeguard mechanism for families to cope with risks and vulnerabilities (Ellis, 2000; Pandey et al., 2017c; Baffoe and Matsuda, 2018). Some studies note that different forms of livelihood capital affect the decision-making behavior of agricultural production and the choice of livelihood strategy (Wu et al., 2017; Pour et al., 2018; Jezeer et al., 2019). Therefore, the key research questions of this study include how the role of each form of sustainable livelihood capital influences farmers’ adoption of climate change adaptation strategies and what practical implications can be drawn from this analysis. This study may contribute to the existing research on farmers’ adaptation to climate change in the following respects: (1) we investigate climate change adaptation strategies adopted by farmers based on local climatic, social, economic and institutional factors; (2) we evaluate the current livelihood capital situation of local farmers based on the sustainable livelihood framework; and (3) we explore how the role of each form of livelihood capital influences farmers’ climate change adaptation strategies by using the boosted regression tree model, which has higher predictive accuracies than parametric regression analysis (Müller et al., 2013; Chen et al., 2015). The results from this paper can be used by other researchers as well as policy-makers for promoting policies related to climate change adaptation in China and other developing countries.
2.2. Survey design and data collection The questionnaire design was divided into three stages. First, based on a literature review on farmers’ climate change adaptation and livelihood capital (Alam et al., 2016; Alauddin and Sarker, 2014; Khanal et al., 2018b), a draft preliminary questionnaire was designed. Second, several focus group discussions (FGDs) were organized, including six experts on climate change adaptations, three government officials, and eight local farmers. The participants were encouraged to share their opinions with others on farmers’ livelihoods and climate change adaptation behavior in a neutral and nonthreatening environment. Based on feedback from the FGDs, five types of adaptation strategies that were most commonly adopted by local farmers were identified. These strategies were adjusting the crop varieties; altering planting and harvesting dates; water and fertilizer management, such as increasing the intake of fertilizer and irrigation; agricultural finance, such as purchasing agriculture insurance; and income structure adjustment, such as leaving the agriculture business. For the third stage, the preliminary questionnaire was pretested on a sample of 30 local farmers in
2. Materials and methods 2.1. Study area Wushen Banner is located in southwestern Ordos in the Inner Mongolia Autonomous Region of China (Fig. 1). It is in the belly of the Mu Us Desert. Wushen covers an area of 11,645 km2 and lies between
Fig. 1. Location of the study area. 2
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3
0.039 0.021 0.091 0.100 0.046 0.131 0.012 0.151 0.012 0.021 0.178 0.032 0.014 0.061 0.093 0.041 0.074 0.134 0.094 0.028 0.052 0.066 0.020 0.036 0.062 0.203 0.112 0.013 0.043 0.024 H=H1×WFH1+ H2×WFH2+ H3×WFH3 N = N1×WFN1+ N2×WFN2+ N3×WFN3 P = P1×WFP1+ P2×WFP2+ P3×WFP3 F = F1×WFF1+ F2×WFF2+ F3×WFF3 S = S1×WFS1+ S2×WFS2+ S3×WFS3 Social capital/S
Financial capital/F
Physical capital/P
Labor force/H1 Health status/H2 Highest education/H3 Cultivated land/N1 Livestock/N2 Distance to the main market/N3 House structure/P1 Agricultural equipment a/P2 Vehicles b/P3 Income structure/F1 Household income/F2 Income stability/F3 Market frequency/S1 Association member c/S2 Relatives and friends/S3 Human capital/H
Natural capital/N
Index Category
Table 1 Evaluation index system of farmers’ livelihood capital.
Definition
2.3.1. Construction of the index system for livelihood capital According to the sustainable livelihood framework and existing research, an evaluation index system for livelihood capital was constructed. Each dimension is measured based on associated socioenvironment-specific indicators (Table 1). These indicators for the five forms of livelihood capital were identified based on the literature specific to the area or similar regions (Li et al., 2017; Wu et al., 2017; Pour et al., 2018; Jezeer et al., 2019). Human capital is primarily composed of peoples’ skills, knowledge, labor ability and health status, which together enable people to pursue different adaptive strategies (Baffoe and Matsuda, 2018). Human capital is evaluated from two angles, quantity and quality (Pandey et al., 2017b). The number of family labor forces (H1) and the health status of family members (H2) are commonly used as indicators. In addition, the highest educational level of family members (H3) affects farmers’ cognition of climate change and their behavioral decisions regarding adaptation strategies. Natural capital includes natural resources and services that benefit people's livelihoods (Pandey et al., 2017b). This form of capital is particularly important in rural areas where most activities (agriculture and cattle raising) are based on natural resources. In this study, except for the number of livestock raised by farmers (N2), the quantity and quality of cultivated land (N1) is an important indicator (Li et al., 2017). In China, most farmers choose the location of their buildings according to the location of their land. The distance from home to the market (N3) is included as one indicator. Physical capital refers to resources such as the infrastructure and material equipment that facilitate people’s life and production (Liu et al., 2018). The household fixed assets, agricultural instruments and household durable goods belonging to farmers are the primary measurement indicators (Wu et al., 2017; Pour et al., 2018). In China, farmers’ houses are their most important physical capital, and the
Note: a Farming tools include tractors, agricultural vehicles, grass / rice sowing (harvesting) machines, threshing / peeling machines, tillage machines. b Vehicles include trucks, cars, vans, motorcycles, electric / electric tricycles, bicycles. c Organizations include villager representative associations, women federations, village and town business organizations, professional cooperatives and agricultural industrialization enterprises.
2.3. Livelihood capital measurement
Household labor force / total population Very Poor = 1; poor = 2; general = 3; good = 4; very good = 5 Primary and below = 1; junior secondary = 2; senior secondary = 3; tertiary education = 4; undergraduate and above = 5 ≦10 = 1；10-20 = 2；20-30 = 3；30-40 = 4； > 40 = 5 (mu) Number of livestock raised ≦3 = 1；3-15 = 2； > 15 = 3(km) Shed ring / tent = 1; civil house = 2; brick house / brick house = 3; brick / concrete = 4; steel concrete = 5 Number of farming machinery and tools owned by farmers Number of vehicles owned by households Number of sources of income ≦3 = 1；3-5 = 2；5-7 = 3；7-10 = 4； > 10 = 5(Ten thousand yuan/year) Very unstable = 1; unstable = 2; general = 3; stability = 4; very stable = 5 Not more than once/month = 1; twice/month = 2; 3 times/month = 3; 4 times/month = 4; more than 4 times/month = 5 Whether a member of any organizations: no = 0; yes = 1 1-4 = 1；5-8 = 2；9-12 = 3；13-17 = 4； > 17 = 5(people)
Calculation formula
WAHP
WE
WF
April of 2018, in Wushen Banner. Some of the word order, presentation and logic problems in the questionnaire were identified. The questionnaire was then modified and clarified. The final questionnaire used in the field consisted of three primary parts. The first part primarily elicited farmers’ knowledge and views about climate change and related adaptations. The second section focused on the adaptation measures that farmers adopted to cope with climate change, such as adjusting the crop varieties, increasing irrigation, building new infrastructure, increasing the use of pesticides and chemical fertilizers, adjusting agricultural planting or harvesting dates, purchasing agricultural insurance and advanced work outside of farming. The last section collected information on the farmers’ natural, human, physical, financial and social capital, including information on the respondents’ age, educational attainment, health status, years engaged in agriculture, farm size, the total number of family members and sources of household income. The field survey was conducted in Wushen Banner in July of 2018. A multistage stratified random sampling method was used to determine the specific survey points. First, two towns (Galutu and Wudinghe) were selected from the six towns in Wushen based on the characteristics of the different townships, the area, the population, and the number of administrative villages. During the second stage, a total of five villages were randomly selected from the two towns. For the third stage, according to the population and size of the village, 30–50 households in each village were randomly selected to be interviewed. Only heads of households or household agricultural decision makers were allowed to participate in the survey. The members of the survey group were trained to obtain relevant information from the interviewees through face-to-face interviews. Finally, a total of 235 questionnaires were distributed, and 214 valid questionnaires were obtained after eliminating information loss and errors.
0.040 0.047 0.113 0.097 0.037 0.092 0.039 0.086 0.024 0.042 0.191 0.072 0.014 0.052 0.058
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multiple regression trees through random selection and self-learning. Over the course of the model operation, the influence of independent variables on dependent variables is analyzed by sampling a certain amount of data at a random number of times. The remaining data are used to test the fitting results, and the average value of the generated multiple regression is taken and output (Elith et al., 2008; Müller, et al., 2013). The BRT combines the advantages of two algorithms, the “regression tree” (which comes from the model of the classification and regression tree group) and “boosting” (which builds and combines a series of models) (Elith et al., 2008). Therefore, this method is powerful because it can address different types of predictive variables (classified variable, nominal variable, etc.) and mathematical distributions (the Poisson distribution, Bernoulli distribution, Gaussian distribution, Laplace distribution, etc.). The BRT is different from other traditional models in that it has no a priori assumption about the independence of predictive variables and can fit complex nonlinear relationships. It has strong resistance to the existence of a large number of independent predictive variables in the study. In addition, this method can also accommodate missing data and extreme values and can automatically manage possible interactions between predictive variables (Chen et al., 2015). At present, the BRT has been applied to the study of farmers’ decision-making behavior (Müller, et al., 2013) and the driving factors of ecosystem service value change (Chen et al., 2015). All of our BRT analysis was implemented in R software (R Development Core Team, 2012) using the code from the “gbm” package (Ridgeway, 2012). To ensure that the results are scientific and comparable, we set the parameters to be the same for the BRT analyses of the five types of adaptive behavior. The parameters for the BRT were set as follows: learning rate (lr) = 0.001, tree complexity (tc) = 5, and bag fraction = 0.5. In addition, we used 10-fold cross-validation to determine the number of trees.
structure of a house (P1) is an important index for evaluating its value. Durable goods such as agricultural machinery (P2) and vehicles (P3) are an investment for farmers to improve their production efficiency. The situations of farmers who own these durable goods reflect the endowment of their physical capital. Financial capital primarily reflects the cash used to buy consumption goods necessary for living and production and the availability of loans (Li et al., 2017; Liu et al., 2018; Jezeer et al., 2019). Financial capital is one of the most important assets to support any livelihood activity because it can develop and accumulate other assets (Baffoe and Matsuda, 2018). Household income (F2) and income stability (F3) are common indices for evaluating farmers’ financial capital. Furthermore, the number of income channels (F1) can reflect not only the diversity of farmers’ livelihood strategies but also the farmers’ income structure. Social capital is a network of social relations between individuals or groups, and it is a type of social resource that can include social right requirements, social relations, affiliations and associations on which people draw when exploring diversified livelihood strategies (Baffoe and Matsuda, 2018; Jezeer et al., 2019). In China, marketing (S1) is an important way for farmers to acquire agricultural production knowledge from agricultural technology stations and pesticide or fertilizer sellers. The organizational structure and association (S2), networks between relatives and friends (S3) are also the primary channel for farmers to obtain agricultural production information (Li et al., 2017; Paul et al., 2016). 2.3.2. Determination of the index weight During the evaluation of livelihood capital, the index weight is an important factor for determining the rationality associated with the quantitative results of livelihood capital (Pour et al., 2018). Therefore, the choice of index weight is particularly crucial. The methods used to determine the index weight include the subjective weighting method and the objective weighting method, and each of them has its own advantages and disadvantages. Previous studies have paid little attention to determining the index weight (Baffoe and Matsuda, 2018), which may weaken the reliability of the study results on the quantification of livelihood capital. To improve the reliability of the evaluation results, this study attempts to determine the index weight by combining subjective and objective methods. Specifically, this study first used the analytic hierarchy process (AHP) as the subjective weighting method to determine the specific index weight of five livelihood capital types (Liu et al., 2018) and denotes them as WPCA. Then, the entropy method was used as the objective weighting method to determine the index weight (WE) (Amiri et al., 2014). Finally, the objective and subjective weights were used to obtain the average value as the final weight (WF) of the evaluation index (Table 1).
3. Results 3.1. Socioeconomic characteristics of the respondents The respondents’ socioeconomic characteristics were examined and are presented in Table 2. In general, the respondents were primarily middle-aged adults. The average age of the farmers interviewed was 54 years. The results show that 64% of the farming households were maleheaded. The educational level of the respondents was generally low, and the average education level was between primary and junior high school. The descriptive results show that the farmers had generally been engaged in agriculture for a long time, for an average of 34.48 years. The mean household size of the interviewees was 3.18, which is consistent with the pattern of vast, sparsely populated land in Wushen Banner. The average yearly household income of the farmers was CNY21413 (approximately US$3124 at the time of the survey). The survey data are basically consistent with the actual situations of the farmers in Wushen Banner (WSSY, 2016).
2.4. Boosted regression tree A significant body of literature has relied on regression analysis, such as binomial (Alauddin and Sarker, 2014; Trinh et al., 2018) or multinomial logistic regressions (Alam et al., 2016), to examine the determinants of climate change adaptation strategies among farmers. These traditional regression models have scientific merit because they are easy to understand and interpret and they provide numerous options for estimating the parameters that relate the input data to the output data. However, problems with unknown and possibly nonlinear relationships between input and output variables are difficult to consider within these regression frameworks. These limitations often prevent accurate and robust results from being attained, which can compromise the model's generality (Müller et al., 2013; Chen et al., 2015). In this study, we chose the boosted regression tree (BRT) to analyze the influence of each form of livelihood capital on farmers’ adaptation strategies. The BRT is a self-learning method based on the classification regression tree algorithm (Elith et al., 2008). This method can improve the stability and prediction accuracy of the model by generating
Table 2 Primary socioeconomic characteristics of the respondents. Variable
Description
Mean
Std. Dev.
Age Gender Education
Age of the interviewee Female = 0; Male = 1 No education = 1; primary school = 2; junior middle school = 3; senior middle school = 4; college or above = 5 Number of years engaged in agriculture Number of household members Interviewee's yearly household income per capita (CNY)
54.45 0.64 2.30
12.82 0.48 1.14
34.48 3.18 21413
15.36 1.61 18052
Farming years Family size Income
4
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Table 3 Climate change adaptation strategies adopted by the farmers. Category
Adaptive strategy
Percentage
Water and fertilizer management Agricultural finance Operating time adjustment Planting variety adjustment Income structure adjustment No adaptation
Adjust fertilization or pesticide use behavior, improve irrigation method or frequency Seek agricultural and livestock credit or purchase agricultural insurance Change crop planting or harvesting dates, grazing/resting Plant new resistant seed varieties, diversify crop varieties Change the proportion of agriculture and animal husbandry, seek non-agricultural development or rent land (grass) No climate change adaptation measures were taken
89.72% 82.24% 63.55% 42.06% 20.09% 1.40%
result suggests that the farmers in the study area were relatively rich in human capital and financial capital, but relatively poor in social capital.
3.2. Respondents’ perceptions of climate change The survey results show that most farmers perceived that the climate in the region has changed over the past 10 years. Specifically, approximately 95% of the respondents suggested that the annual average temperature has risen in the past 10 years, while over 89% of the farmers believed that the number of high-temperature days and the drought frequency have increased. Furthermore, nearly 86% of the farmers interviewed believed that the annual average precipitation has decreased in the past 10 years. Approximately 91% of the respondents believed that the drought intensity has strengthened in the past 10 years.
3.5. Impact of livelihood capital on the farmers' adaptive behavior We ran R software to analyze the impact of the livelihood capital on the farmers’ adaptive behavior. The number of decision trees obtained by the five models is within the default normal range (350–1300), which indicates that the regression results are acceptable (Elith et al., 2008). Fig. 3 plots the influence of the five forms of livelihood capital on the farmers’ adaptation strategy of adjusting the crop varieties. In this figure, the abscissa represents the output value of various livelihood capital types, and the partial dependence plots show the specific influence trends of various livelihood capital types on the farmers’ adoption of adaptation strategies. The results show that social capital and natural capital had a positive impact on farmers’ adjustments in crop varieties, which indicates that better social capital or natural capital can encourage farmers to adjust their crop varieties to cope with climate change. Financial capital had a negative effect on this adaptation measure. In addition, both human capital and physical capital were inclined to promote farmers’ adoption of the strategy of adjusting their crop varieties. The influence of different livelihood capitals on farmers’ adoption of farm operating time adjustment is shown in Fig. 4. Specifically, physical capital and social capital had a positive impact on farmers’ adjustments of farm operating time. It is worth mentioning that when the farmers are rich enough in social capital, this positive impact will decline. However, improved human capital and financial capital do not encourage farmers to adjust farm operating time. In addition, only when farmers’ natural capital was rich enough did the positive impact of natural capital on the farmers’ adjustment of farm operating time become evident. According to the results of the BRT model (Fig. 5), we found the influence of different forms of livelihood capital on farmers’ adaptation strategies in terms of water and fertilizer management. The results show that improvements in farmers’ physical capital helped them to adopt water and fertilizer management approaches to respond to climate change. In general, the influence trends in natural capital, human capital, social capital and financial capital on farmers’ water and fertilizer management decisions showed a certain volatility and tended to be the same for each of these forms of capital. Specifically, when these four types of livelihood capital were poor, farmers were reluctant to adopt changes in water and fertilizer management. However, when these four types of livelihood capital were rich, farmers would be more likely to adopt water and fertilizer management means to respond to climate change. Fig. 6 shows the impact of each form of livelihood capital on farmers’ adoption of agricultural finance measures. The results suggest that social capital promoted the farmers’ adoption of agricultural finance. Compared with social capital, the results show that when farmers were poor in natural capital, this type of promoting effect was not obvious. However, when the output value of natural capital was large, the natural capital started to promote farmers' adoption of agricultural finance measures. Physical capital had a similar influence trend
3.3. Analysis of farmers’ adaptive strategies Table 3 presents the specific adaptation strategies adopted by the farmers in response to climate change. Most farmers in the region reported that they have adopted strategies to cope with climate change. Only 1.40% did not adopt any adaptation strategies. Among the major adaptation measures identified in this study, water and fertilizer management, including building new infrastructures for irrigation and increasing the use of pesticides or chemical fertilizers, was the most commonly used adaptive strategy. The second-most popular strategy was agricultural finance measures, including purchasing agricultural insurance and accessing agricultural and livestock credit. Among the surveyed farmers, the percentages of farmers who confronted climate change by adjusting the farm operating time and changing the crop varieties were 63.55% and 42.06%, respectively. Approximately 20.09% of the interviewed farmers adopted an income structure adjustment as their adaptive strategies. 3.4. Farmers’ livelihood capital measurement According to the quantification method of livelihood capital measurement, we obtain the values of five types of livelihood capital for each farmer. The means of the five livelihood capital types are used to describe the distribution of farmers’ livelihood capital in the sample area (Fig. 2). The order of the mean values for the five forms of livelihood capital from the largest to the smallest is human capital, financial capital, natural capital, physical capital and social capital. This
Fig. 2. Pentagon of the farmers’ livelihood capital. 5
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Fig. 3. Partial dependence plots of the impact of each form of livelihood capital on the farmers’ adjustments in planting varieties. The y-axes are on the logit scale and are centered to have a zero mean over the data distribution. The rug plots inside the top of the plots show the distribution of sites across that variable, in deciles.
as natural capital. Human capital and financial capital had a negative effect on farmers’ adoption of agricultural finance to address climate change. Notably, it is worth mentioning that when farmers were poor in financial capital, an increase in financial capital would encourage farmers to adopt agricultural finance measures to respond to climate change. The BRT results of the impact of livelihood capital on farmers’ adoption of income structure adjustments are shown in Fig. 7. In general, natural capital had a positive impact on farmers’ adoption of income structure adjustments to respond to climate change. This positive impact also appeared for human capital. The influence trend of social capital was similar to that of human capital, but with the increase of social capital, we can see that this promoting effect would decline. Moreover, when farmers were poor in physical capital, the physical capital had a negative influence on their adoption of income structure adjustments. However, when they were rich enough in physical capital, the physical capital would encourage the farmers to adopt adjustments in their income structures to respond to climate change. The influence trend of financial capital on farmers’ adoption of income structure
adjustments is similar to that of physical capital. The relative contribution of each form of livelihood capital to farmers' adaptation strategies is shown in Table 4. The results show that the relative contribution of various forms of livelihood capital to the adjustment of crop varieties is: social capital > natural capital > human capital > financial capital > physical capital. Approximately 31.5% of the farmers who adopted the strategy of crop varieties adjustment were affected by social capital, while the effect of physical capital was the smallest (8.8%). The results of the relative importance of different livelihood capital types on the adjustment of farm operating time are as follows: social capital > natural capital > human capital > financial capital > physical capital. The degree of determination of natural capital for water and fertilizer management measures was approximately 34.5%, and that of financial capital was 9.5%. The relative influence of each form of livelihood capital on farmers' adoption of agricultural financial measures, from the largest to the smallest, was in the order of physical capital, natural capital, human capital, social capital and financial capital. Nearly 33.8% of the farmers who adopted the agricultural financial strategy were affected by physical
Fig. 4. Partial dependence plots of the impact of each form of livelihood capital on the farmers’ adjustment of the farm operating time. The y-axes are on the logit scale and are centered to have a zero mean over the data distribution. The rug plots on the inside top of the plots show the distribution of sites across that variable, in deciles. 6
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Fig. 5. Partial dependence plots on the impact of each form of livelihood capital on the farmers’ water and fertilizer management. The y-axes are on the logit scale and are centered to have a zero mean over the data distribution. The rug plots on the inside top of the plots show the distribution of sites across that variable, in deciles.
capital, while only 11.6% were affected by financial capital. We observed that the contribution of social capital to the adjustment of income structure was relatively important, which was 25.3%. The relative importance of natural capital and human capital was 22.6% and 19.7%, respectively. On average, the influence of financial capital was relatively small (12.58%).
2016; Li et al., 2017). Our findings suggest that natural capital is the primary factor that positively affects farmers’ decisions to adopt adaptation strategies to cope with climate change. This finding is consistent with other studies. Specifically, Khanal et al. (2018b) indicated that families that cultivate a large amount of land are more likely to adopt adaptation strategies to address climate change, especially water and soil management practices. Abdulai and Huffman (2014) suggested that the farm soil conditions primarily influence farmers to adopt ridging in the field, and this strategy can significantly increase the rice yield and net income. Furthermore, this positive link might also have occurred because households with more farmland are more likely to be adversely affected by climate change, and farmers are more proactive at adopting innovative adaptation strategies (Jin et al., 2015). Our results also show that social capital is an important determinant that facilitates the adoption of climate change adaptation measures by farmers. Specifically, social capital has a positive effect on farmers’ adoption of adjustments in crop varieties, farm operating time and income structure, and this positive effect is more obvious when farmers’ social capital is abundant. This finding was expected because farmer organizations and social networks can help to increase opportunities for
4. Discussion It is widely believed that smallholder farmers are vulnerable to global climate change (Deressa et al., 2009; Fischer and Chhatre, 2016). Adaptation not only helps to mitigate the adverse impacts of climate change but also to maintain the sustainability of farmers’ livelihoods. Livelihood capital or resources, including natural, physical, financial, human and social capital, are inputs (Ellis, 2000; DFID, 2000), and farmers can transfer their livelihood capital into climate change adaptation strategies. Thus, different strategies may be constructed based on the farmers’ livelihood capital (Wu et al., 2017; Jezeer et al., 2019). Conceptually, farmers with better livelihood capital endowments have more choices for coping with changes, and a combination of capital types reduces climate risk and improves adaptive capacity (Paul et al.,
Fig. 6. Partial dependence plots of the impact of each form of livelihood capital on the farmers’ adoption of agricultural finance. The y-axes are on the logit scale and are centered to have a zero mean over the data distribution. The rug plots on the inside top of the plots show the distribution of sites across that variable, in deciles. 7
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Fig. 7. Partial dependence plots of the impact of each form of livelihood capital on farmers’ adoption of income structure adjustment. The Y-axes are on the logit scale and are centered to have a zero mean over the data distribution. The rug plots on the inside tops of the plots show the distribution of sites across that variable, in deciles.
Table 4 The relative importance of livelihood capital on the farmers’ adaptation strategies. Category
Planting variety adjustment
Operating time adjustment
Water and fertilizer management
Agricultural finance
Income structure adjustment
Average percentage
Human capital Natural capital Physical capital Financial capital Social capital Total
23.9% 25.6% 10.3% 8.8% 31.5% 100%
20.8% 21.7% 14.2% 18.2% 25.2% 100%
18.9% 34.5% 20.7% 9.5% 16.4% 100%
17.4% 22.7% 33.8% 11.6% 14.5% 100%
19.7% 22.6% 17.6% 14.8% 25.3% 100%
20.14% 25.42% 19.32% 12.58% 22.58% 100%
that farmers with more financial capital can more easily recover from extreme climatic events, and climate change has less of an impact on their lives. Moreover, it is argued that investment in the assets needed to adapt to climate change occur until farmers’ wealth reaches a certain threshold (Lemos et al., 2016). Finally, although several issues were examined and some important findings were obtained, this study has some limitations. First, we explored the role of each form of livelihood capital on the farmers’ specific adaptation strategies but ignored the interaction between the various capital types. Recent studies have shown that some forms of capital have a substitutive effect, some have a complementary effect, and others have both (Li et al., 2017). Future research can be conducted to test the possible complementary-substitution effect between livelihood capitals. Second, the findings are based on a small-scale region of China. Apparently, agricultural adaptation varies across countries or regions, and farmers adopt different adaptation strategies to cope with climate change based on local climate, social, economic and institutional factors (Deressa et al., 2009; Khanal et al., 2018b). Thus, much more research must be conducted. Despite the above limitations, the findings of our study contribute to the climate change adaptation literature through an improved understanding of how livelihood capital may be related to adaptive behavior by farmers.
farmers to obtain better information, technology and knowledge about farm management innovations and increase the possibility that farmers will adopt adaptation strategies (Abdulai and Huffman, 2014; Dinku et al., 2014). Similar results have been reported in Ghana (Abdulai and Huffman, 2014) and Africa (Dinku et al., 2014). In terms of overall impacts, human capital has a positive impact on farmers’ adoption of farm operating time and income structure adjustments. This finding is consistent with other studies (Deressa et al., 2009; Alam et al., 2016; Khanal et al., 2018). Specifically, farmers with better human capital, such as more education, are more likely to adopt adaptation strategies to minimize climate change impacts. Empirical evidence from Ethiopia (Deressa et al., 2009) and Bangladesh (Alam et al., 2016; Alauddin and Sarker, 2014), where farmers with better education were likely to adapt, supports this finding. Another finding of this study is that clear evidence of physical capital is an important factor, and it has a positive effect on encouraging farmers to adopt adaptation strategies. This positive effect is more evident when farmers’ physical capital is abundant. Pour et al. (2018) found that physical capital is the primary factor that affects farmers’ choices of livelihood strategies, especially in enhancing the propensity toward the fishery/livestock strategy. One possible reason is that durable goods, such as agricultural appliances and vehicles, can not only improve production efficiency but also support the adoption of adaptation strategies. The influence of financial capital on the farmers’ adaptation strategy is relatively small. The results show that financial capital has a negative impact on farmers’ adaptation strategies regarding agricultural finance and farm operating time adjustments, which was unexpected. Generally, richer farmers are more financially able to take measures to address climate change (Deressa et al., 2009). However, this finding of our study is consistent with the results of Zhai et al. (2018), who demonstrated that household income is negatively associated with the adoption of adaptation measures. One possible reason for this finding is
5. Conclusions In this study, we developed an integrated analytical framework to measure the five forms of livelihood capital of farmers. Then, the influence of each form of capital on farmers’ climate change adaptation strategies was explored using the boosted regression tree technique. Our results show that natural capital and social capital are the primary determinants that enable farmers to adjust crop varieties and adopt agricultural finance measures. Human capital is an important determinant that facilitates farmers’ adoption of adjustments in crop 8
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varieties, farm operating time and income structure. Physical capital has an important effect on farmers’ adoption of agricultural finance. Therefore, farmers’ livelihood capital plays a key role in adopting adaptation strategies to cope with climate change. There are several potential policy implications from the results of this study. First, in general, natural and physical capital have a positive effect on the adoption of all five adaptation strategies. This positive effect is more obvious when the farmers’ natural capital or physical capital is abundant. Therefore, farmers should be encouraged to invest in their natural and physical capital, such as using better agricultural equipment, to improve their adaptive capacity to climate change. Local governments can take measures to enhance or invest more in regional physical capital, such as building highways. Second, we find that the study area is relatively poor in social capital, which has a positive effect on farmers’ adoption of adjustments in crop varieties and farm operating time. Consequently, governments should implement some programs to improve farmers’ social capital, such as better agricultural extension services, which can enable farmers to share information through discussion and increase their adaptation knowledge and skills. Third, the improvement of human capital helps to promote farmers’ adoption of adaptation strategies such as adjusting the crop varieties, water and fertilizer management. Human capital improves the labor quality and farmer productivity, which can increase the rate of return on labor investment. Thus, policy interventions for a better education of farmers on the impacts of climate change and adaptation measures in response to climate change could be enhanced by investing in training, which will help to promote the adoption of climate change adaptation strategies.
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Influence of livelihood assets, experienced shocks and perceived risks on smallholder coffee farming practices in Peru. J. Environ. Manage. 242, 496–506. Jin, J.J., Gao, Y.W., Wang, X.M., Nam, P.K., 2015. Farmers’ risk preferences and their climate change adaptation strategies in the Yongqiao District, China. Land Use Policy 47, 365–372. Khanal, U., Wilson, C., Hoang, V.N., Lee, B., 2018a. Farmers’ adaptation to climate change, its determinants and impacts on rice yield in Nepal. Ecol. Econ. 148, 139–147. Khanal, U., Wilson, C., Lee, B.L., Hoang, V.N., 2018b. Climate change adaptation strategies and food productivity in Nepal: a counterfactual analysis. Clim. Change 148 (4), 575–590. Lemos, M.C., Lo, Y.J., Nelson, D.R., Eakin, H., Bedran-Martins, A.M., 2016. Linking development to climate adaptation: leveraging generic and specific capacities to reduce vulnerability to drought in NE Brazil. Glob. Environ. Change 39, 170–179. Li, M.P., Huo, X.X., Peng, C.H., Qiu, H.G., ShangGuan, Z.P., Chang, C., Huai, J.J., 2017. Complementary livelihood capital as a means to enhance adaptive capacity: a case of the Loess Plateau, China. Glob. Environ. Change 47, 143–152. Liu, Z.F., Chen, Q.R., Xie, H.L., 2018. Comprehensive evaluation of farm household livelihood assets in a western mountainous area of china: a case study in Zunyi city. J. Resour. Ecol. 9 (2), 154–163. Müller, D., Leitão, P.J., Sikor, T., 2013. Comparing the determinants of cropland abandonment in Albania and Romania using boosted regression trees. Agric. Syst. 117, 66–77. Pan, D.H., Jia, H.C., Yuan, Y., 2015. A GIS-Based ecological safety assessment of wushen banner, China. Hum. Ecol. Risk Assess. 21 (2), 297–306. Pandey, R., Aretano, R., Gupta, A.K., Meena, D., Kumar, B., Alatalo, J.M., 2017a. Agroecology as a climate change adaptation strategy for smallholders of TehriGarhwal in the Indian himalayan region. Small-Scale For. 16 (1), 53–63. Pandey, R., Kumar, P., Archie, K.M., Gupta, A.K., Joshi, P.K., Valente, D., Petrosillo, D., 2017b. Climate change adaptation in the western-Himalayas: household level perspectives on impacts and barriers. Ecol. Indic. 84, 27–37. Pandey, R., Shashidhar, K.J., Alatalo, J.M., Archie, K.M., Gupta, A.K., 2017c. Sustainable livelihood framework-based indicators for assessing climate change vulnerability and adaptation for Himalayan communities. Ecol. Indic. 79, 338–346. Patt, A., Zeckhauser, R., 2000. Action Bias and environmental decisions. J. Risk Uncertainty 21 (1), 45–72. Paul, C., Weinthal, E., Bellemare, M., Jeuland, M., 2016. Social capital, trust, and adaptation to climate change: evidence from rural Ethiopia. Glob. Environ. Change 36, 124–138. Pour, M.D., Barati, A.A., Azadi, H., Scheffran, J., 2018. Revealing the role of livelihood assets in livelihood strategies: towards enhancing conservation and livelihood development in the Hara biosphere Reserve, Iran. Ecol. Indic. 94, 336–347. R Development Core Team, 2012. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Ridgeway, G., 2012. R Package “gbm”: Generalized Boosted Regression Models, Version 1.6-3.2. http://cran.cnr.berkeley.edu/web/packages/gbm/gbm.pdf. Trinh Jr., T.Q., R. F. R, Camacho, L.D., Simelton, E., 2018. Determinants of farmers’ adaptation to climate change in agricultural production in the central region of Vietnam. Land Use Policy 70, 224–231. Tversky, A., Kahneman, D., 1974. Judgment under uncertainty: heuristics and biases. Science 185 (4157), 17–34. Wheeler, T., Von Braun, J., 2013. Climate change impacts on global food security. Science. 341 (6145), 508–513. WSSY, 2016. Wushen Banner Statistical Yearbook. Wushen Banner, pp. 2013–2015 (in Chinese). Wu, Z.L., Li, B., Hou, Y., 2017. Adaptive choice of livelihood patterns in rural households in a farm-pastoral zone: a case study in Jungar, Inner Mongolia. 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Acknowledgments We would like to thank the National Natural Science Foundation of China (No. 41671170, 41771192) for providing financial support to undertake this study. The authors are also grateful to anonymous referees for their very constructive comments and corrections, which have led to significant improvement of the early versions of the manuscript. References Abdulai, A., Huffman, W., 2014. The adoption and impact of soil and water conservation technology: an endogenous switching regression application. Land Econ. 90 (1), 26–43. Abid, M., Schneider, U.A., Scheffran, J., 2016. Adaptation to climate change and its impacts on food productivity and crop income: perspectives of farmers in rural Pakistan. J. Rural Stud. 47, 254–266. Alam, G.M., Alam, K., Mushtaq, S., 2016. Influence of institutional access and social capital on adaptation decision: empirical evidence from hazard-prone rural households in Bangladesh. Ecol. Econ. 130, 243–251. Alauddin, M., Sarker, M.A.R., 2014. Climate change and farm-level adaptation decisions and strategies in drought-prone and groundwater-depleted areas of Bangladesh: an empirical investigation. Ecol. Econ. 106 (1), 204–213. Amiri, V., Rezaei, M., Sohrabi, N., 2014. Groundwater quality assessment using entropy weighted water quality index (EWQI) in Lenjanat. Iran. Environ. Earth Sci. 72 (9), 3479–3490. Baffoe, G., Matsuda, H., 2018. An empirical assessment of rural livelihood assets from gender perspective: evidence from Ghana. Sustain. Sci. 13 (3), 815–828. Chen, H., Wang, J.X., Huang, J.K., 2014. Policy support, social capital, and farmers’ adaptation to drought in China. Glob. Environ. Change 24, 193–202. Chen, M.Q., Lu, Y.F., Ling, L., Wan, Y., Luo, Z.J., Huang, H.S., 2015. Drivers of changes in ecosystem service values in Ganjiang upstream watershed. Land Use Policy 47, 247–252. Deressa, T.T., Hassan, R.M., Ringler, C., Alemu, T., Yesuf, M., 2009. Determinants of farmers’ choice of adaptation methods to climate change in the Nile Basin of Ethiopia.
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Land Use Policy journal homepage: www.elsevier.com/locate/landusepol
Influence of livelihood capital on adaptation strategies: Evidence from rural households in Wushen Banner, China
T
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Foyuan Kuanga,b, Jianjun Jina,b, , Rui Hea,b, Xinyu Wana,b, Jing Ninga,b a b
State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing Normal University, Beijing, 100875, China School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Farmer Livelihood capital Climate change Adaptation Boosted regression tree
Based on the sustainable livelihood framework, this paper explores the influence of each form of livelihood capital on the adoption of climate change adaptation strategies by farmers. A stratified random sampling technique was used to select 235 households in Wushen Banner, China, while the boosted regression tree model was used to analyze how different forms of livelihood capital are related to farmers’ choices regarding climate change adaptation strategies. Our results show that most farmers in the study area have adopted adaptation strategies to cope with climate change. The farmers’ livelihood capital plays an important role in their adoption of adaptation strategies. Specifically, natural capital and social capital have a positive impact on farmers’ decisions about climate change adaptation strategies. Human capital and physical capital are inclined to promote farmers’ adoption of climate change adaptation strategies. The results of this study are helpful for improving our understanding of how livelihood capital influences climate change adaptation strategies among farmers, which can provide implications for planning more effective adaptation programs.
1. Introduction Climate change is the most important underlying disaster risk factor and is related to the increase of natural disasters around the world (IPCC, 2014). The high reliance of agriculture on climate-sensitive parameters (e.g., water and temperature) makes agriculture inherently sensitive to climate change (Wheeler and Von Braun, 2013). Studies have noted that farmers in developing countries are most adversely affected by climate change (Chen et al., 2014; Pandey et al., 2017a; Khanal et al., 2018a). Given that China is a typical meteorological disaster-prone country, climate change has a substantial impact on Chinese agriculture and local farmers (Zhai et al., 2018). Adaptation is considered as one of the policy options for reducing the negative impacts of climate change (IPCC, 2014; Abid et al., 2016; Jezeer et al., 2019). Successful adaptation will provide the most effective protection for communities and individuals (Abid et al., 2016; Pandey et al., 2017b; Khanal et al., 2018b). Analyzing adaptation is therefore important for finding ways to help farmers adapt to climate change. A better understanding of the factors that affect farmers’ decisions regarding adaptive practices may provide a basis for formulating policy recommendations that would be responsive to climatic changes effectively (Piya et al., 2013; Pandey et al., 2017a). Decision making theory suggests that decision makers react ⁎
differently to risks or threats (Tversky and Kahneman, 1974; Patt and Zeckhauser, 2000). The process through which farmers engage in adaptation decision-making is complicated (Chen et al., 2014; Trinh et al., 2018). Conceptually, in the face of climate change, rational farmers adopt different adaptive practices to reduce the negative impacts of climatic change. Common adaptation strategies in agriculture include the use of new resistant crop varieties, soil conservation, crop diversification, changing planting dates and improved irrigation (Deressa et al., 2009; Abid et al., 2016). Farmers adopt various adaptation strategies based on their available and accessible assets or the resource mix of the household (Chen et al., 2014; Pandey et al., 2017b). Therefore, household livelihood capital or assets may have an influence on farmers’ adaptive practices (Trinh et al., 2018). However, there is a limited understanding of how farmers’ livelihood capital influences their climate change adaptation measures. This study is designed to fill this gap by investigating the influence of livelihood capital on households’ climate change adaptive practices in Wushen Banner, Inner Mongolia, China. The sustainable livelihood framework developed by the UK Department for International Development was employed in this study (DFID, 2000), and it is based on understanding people's access to assets, which typically include natural, human, social, physical and financial capital (Ellis, 2000). This framework has been widely used and has
Corresponding author at: State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing Normal University, Beijing, 100875, China. E-mail address: [email protected] (J. Jin).
https://doi.org/10.1016/j.landusepol.2019.104228 Received 23 January 2019; Received in revised form 21 August 2019; Accepted 14 September 2019 0264-8377/ © 2019 Elsevier Ltd. All rights reserved.
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37°38′-39°23′ N and 108°17′-109°40′ E. It has a population of over 0.13 million people, of which 77.53% engage in agriculture. Wushen Banner is in the arid and semiarid areas, which are characterized by drought and little rain, with wind and sand, a dry climate, strong solar radiation and large diurnal temperature differences. The annual average precipitation is 350˜400 mm, with a mean annual temperature of 6.4˜7.5 °C. The annual frost-free period lasts approximately 113˜156 days. With the arid geographical environment and the scarcity of resources, Wushen Banner is a highly vulnerable region in terms of natural disasters, and its agricultural production conditions are harsh. Climate change increases the vulnerability of local farmers (Pan et al., 2015). Therefore, it is of urgent practical importance to mitigate the risks of climatic change that influence farmers' livelihoods.
become a classic paradigm in family livelihood research (Ellis, 2000; Pour et al., 2018; Liu et al., 2018). As the core of the sustainable livelihood analysis framework, the capital endowment is not only the basis of household livelihood strategy but also a safeguard mechanism for families to cope with risks and vulnerabilities (Ellis, 2000; Pandey et al., 2017c; Baffoe and Matsuda, 2018). Some studies note that different forms of livelihood capital affect the decision-making behavior of agricultural production and the choice of livelihood strategy (Wu et al., 2017; Pour et al., 2018; Jezeer et al., 2019). Therefore, the key research questions of this study include how the role of each form of sustainable livelihood capital influences farmers’ adoption of climate change adaptation strategies and what practical implications can be drawn from this analysis. This study may contribute to the existing research on farmers’ adaptation to climate change in the following respects: (1) we investigate climate change adaptation strategies adopted by farmers based on local climatic, social, economic and institutional factors; (2) we evaluate the current livelihood capital situation of local farmers based on the sustainable livelihood framework; and (3) we explore how the role of each form of livelihood capital influences farmers’ climate change adaptation strategies by using the boosted regression tree model, which has higher predictive accuracies than parametric regression analysis (Müller et al., 2013; Chen et al., 2015). The results from this paper can be used by other researchers as well as policy-makers for promoting policies related to climate change adaptation in China and other developing countries.
2.2. Survey design and data collection The questionnaire design was divided into three stages. First, based on a literature review on farmers’ climate change adaptation and livelihood capital (Alam et al., 2016; Alauddin and Sarker, 2014; Khanal et al., 2018b), a draft preliminary questionnaire was designed. Second, several focus group discussions (FGDs) were organized, including six experts on climate change adaptations, three government officials, and eight local farmers. The participants were encouraged to share their opinions with others on farmers’ livelihoods and climate change adaptation behavior in a neutral and nonthreatening environment. Based on feedback from the FGDs, five types of adaptation strategies that were most commonly adopted by local farmers were identified. These strategies were adjusting the crop varieties; altering planting and harvesting dates; water and fertilizer management, such as increasing the intake of fertilizer and irrigation; agricultural finance, such as purchasing agriculture insurance; and income structure adjustment, such as leaving the agriculture business. For the third stage, the preliminary questionnaire was pretested on a sample of 30 local farmers in
2. Materials and methods 2.1. Study area Wushen Banner is located in southwestern Ordos in the Inner Mongolia Autonomous Region of China (Fig. 1). It is in the belly of the Mu Us Desert. Wushen covers an area of 11,645 km2 and lies between
Fig. 1. Location of the study area. 2
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0.039 0.021 0.091 0.100 0.046 0.131 0.012 0.151 0.012 0.021 0.178 0.032 0.014 0.061 0.093 0.041 0.074 0.134 0.094 0.028 0.052 0.066 0.020 0.036 0.062 0.203 0.112 0.013 0.043 0.024 H=H1×WFH1+ H2×WFH2+ H3×WFH3 N = N1×WFN1+ N2×WFN2+ N3×WFN3 P = P1×WFP1+ P2×WFP2+ P3×WFP3 F = F1×WFF1+ F2×WFF2+ F3×WFF3 S = S1×WFS1+ S2×WFS2+ S3×WFS3 Social capital/S
Financial capital/F
Physical capital/P
Labor force/H1 Health status/H2 Highest education/H3 Cultivated land/N1 Livestock/N2 Distance to the main market/N3 House structure/P1 Agricultural equipment a/P2 Vehicles b/P3 Income structure/F1 Household income/F2 Income stability/F3 Market frequency/S1 Association member c/S2 Relatives and friends/S3 Human capital/H
Natural capital/N
Index Category
Table 1 Evaluation index system of farmers’ livelihood capital.
Definition
2.3.1. Construction of the index system for livelihood capital According to the sustainable livelihood framework and existing research, an evaluation index system for livelihood capital was constructed. Each dimension is measured based on associated socioenvironment-specific indicators (Table 1). These indicators for the five forms of livelihood capital were identified based on the literature specific to the area or similar regions (Li et al., 2017; Wu et al., 2017; Pour et al., 2018; Jezeer et al., 2019). Human capital is primarily composed of peoples’ skills, knowledge, labor ability and health status, which together enable people to pursue different adaptive strategies (Baffoe and Matsuda, 2018). Human capital is evaluated from two angles, quantity and quality (Pandey et al., 2017b). The number of family labor forces (H1) and the health status of family members (H2) are commonly used as indicators. In addition, the highest educational level of family members (H3) affects farmers’ cognition of climate change and their behavioral decisions regarding adaptation strategies. Natural capital includes natural resources and services that benefit people's livelihoods (Pandey et al., 2017b). This form of capital is particularly important in rural areas where most activities (agriculture and cattle raising) are based on natural resources. In this study, except for the number of livestock raised by farmers (N2), the quantity and quality of cultivated land (N1) is an important indicator (Li et al., 2017). In China, most farmers choose the location of their buildings according to the location of their land. The distance from home to the market (N3) is included as one indicator. Physical capital refers to resources such as the infrastructure and material equipment that facilitate people’s life and production (Liu et al., 2018). The household fixed assets, agricultural instruments and household durable goods belonging to farmers are the primary measurement indicators (Wu et al., 2017; Pour et al., 2018). In China, farmers’ houses are their most important physical capital, and the
Note: a Farming tools include tractors, agricultural vehicles, grass / rice sowing (harvesting) machines, threshing / peeling machines, tillage machines. b Vehicles include trucks, cars, vans, motorcycles, electric / electric tricycles, bicycles. c Organizations include villager representative associations, women federations, village and town business organizations, professional cooperatives and agricultural industrialization enterprises.
2.3. Livelihood capital measurement
Household labor force / total population Very Poor = 1; poor = 2; general = 3; good = 4; very good = 5 Primary and below = 1; junior secondary = 2; senior secondary = 3; tertiary education = 4; undergraduate and above = 5 ≦10 = 1；10-20 = 2；20-30 = 3；30-40 = 4； > 40 = 5 (mu) Number of livestock raised ≦3 = 1；3-15 = 2； > 15 = 3(km) Shed ring / tent = 1; civil house = 2; brick house / brick house = 3; brick / concrete = 4; steel concrete = 5 Number of farming machinery and tools owned by farmers Number of vehicles owned by households Number of sources of income ≦3 = 1；3-5 = 2；5-7 = 3；7-10 = 4； > 10 = 5(Ten thousand yuan/year) Very unstable = 1; unstable = 2; general = 3; stability = 4; very stable = 5 Not more than once/month = 1; twice/month = 2; 3 times/month = 3; 4 times/month = 4; more than 4 times/month = 5 Whether a member of any organizations: no = 0; yes = 1 1-4 = 1；5-8 = 2；9-12 = 3；13-17 = 4； > 17 = 5(people)
Calculation formula
WAHP
WE
WF
April of 2018, in Wushen Banner. Some of the word order, presentation and logic problems in the questionnaire were identified. The questionnaire was then modified and clarified. The final questionnaire used in the field consisted of three primary parts. The first part primarily elicited farmers’ knowledge and views about climate change and related adaptations. The second section focused on the adaptation measures that farmers adopted to cope with climate change, such as adjusting the crop varieties, increasing irrigation, building new infrastructure, increasing the use of pesticides and chemical fertilizers, adjusting agricultural planting or harvesting dates, purchasing agricultural insurance and advanced work outside of farming. The last section collected information on the farmers’ natural, human, physical, financial and social capital, including information on the respondents’ age, educational attainment, health status, years engaged in agriculture, farm size, the total number of family members and sources of household income. The field survey was conducted in Wushen Banner in July of 2018. A multistage stratified random sampling method was used to determine the specific survey points. First, two towns (Galutu and Wudinghe) were selected from the six towns in Wushen based on the characteristics of the different townships, the area, the population, and the number of administrative villages. During the second stage, a total of five villages were randomly selected from the two towns. For the third stage, according to the population and size of the village, 30–50 households in each village were randomly selected to be interviewed. Only heads of households or household agricultural decision makers were allowed to participate in the survey. The members of the survey group were trained to obtain relevant information from the interviewees through face-to-face interviews. Finally, a total of 235 questionnaires were distributed, and 214 valid questionnaires were obtained after eliminating information loss and errors.
0.040 0.047 0.113 0.097 0.037 0.092 0.039 0.086 0.024 0.042 0.191 0.072 0.014 0.052 0.058
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multiple regression trees through random selection and self-learning. Over the course of the model operation, the influence of independent variables on dependent variables is analyzed by sampling a certain amount of data at a random number of times. The remaining data are used to test the fitting results, and the average value of the generated multiple regression is taken and output (Elith et al., 2008; Müller, et al., 2013). The BRT combines the advantages of two algorithms, the “regression tree” (which comes from the model of the classification and regression tree group) and “boosting” (which builds and combines a series of models) (Elith et al., 2008). Therefore, this method is powerful because it can address different types of predictive variables (classified variable, nominal variable, etc.) and mathematical distributions (the Poisson distribution, Bernoulli distribution, Gaussian distribution, Laplace distribution, etc.). The BRT is different from other traditional models in that it has no a priori assumption about the independence of predictive variables and can fit complex nonlinear relationships. It has strong resistance to the existence of a large number of independent predictive variables in the study. In addition, this method can also accommodate missing data and extreme values and can automatically manage possible interactions between predictive variables (Chen et al., 2015). At present, the BRT has been applied to the study of farmers’ decision-making behavior (Müller, et al., 2013) and the driving factors of ecosystem service value change (Chen et al., 2015). All of our BRT analysis was implemented in R software (R Development Core Team, 2012) using the code from the “gbm” package (Ridgeway, 2012). To ensure that the results are scientific and comparable, we set the parameters to be the same for the BRT analyses of the five types of adaptive behavior. The parameters for the BRT were set as follows: learning rate (lr) = 0.001, tree complexity (tc) = 5, and bag fraction = 0.5. In addition, we used 10-fold cross-validation to determine the number of trees.
structure of a house (P1) is an important index for evaluating its value. Durable goods such as agricultural machinery (P2) and vehicles (P3) are an investment for farmers to improve their production efficiency. The situations of farmers who own these durable goods reflect the endowment of their physical capital. Financial capital primarily reflects the cash used to buy consumption goods necessary for living and production and the availability of loans (Li et al., 2017; Liu et al., 2018; Jezeer et al., 2019). Financial capital is one of the most important assets to support any livelihood activity because it can develop and accumulate other assets (Baffoe and Matsuda, 2018). Household income (F2) and income stability (F3) are common indices for evaluating farmers’ financial capital. Furthermore, the number of income channels (F1) can reflect not only the diversity of farmers’ livelihood strategies but also the farmers’ income structure. Social capital is a network of social relations between individuals or groups, and it is a type of social resource that can include social right requirements, social relations, affiliations and associations on which people draw when exploring diversified livelihood strategies (Baffoe and Matsuda, 2018; Jezeer et al., 2019). In China, marketing (S1) is an important way for farmers to acquire agricultural production knowledge from agricultural technology stations and pesticide or fertilizer sellers. The organizational structure and association (S2), networks between relatives and friends (S3) are also the primary channel for farmers to obtain agricultural production information (Li et al., 2017; Paul et al., 2016). 2.3.2. Determination of the index weight During the evaluation of livelihood capital, the index weight is an important factor for determining the rationality associated with the quantitative results of livelihood capital (Pour et al., 2018). Therefore, the choice of index weight is particularly crucial. The methods used to determine the index weight include the subjective weighting method and the objective weighting method, and each of them has its own advantages and disadvantages. Previous studies have paid little attention to determining the index weight (Baffoe and Matsuda, 2018), which may weaken the reliability of the study results on the quantification of livelihood capital. To improve the reliability of the evaluation results, this study attempts to determine the index weight by combining subjective and objective methods. Specifically, this study first used the analytic hierarchy process (AHP) as the subjective weighting method to determine the specific index weight of five livelihood capital types (Liu et al., 2018) and denotes them as WPCA. Then, the entropy method was used as the objective weighting method to determine the index weight (WE) (Amiri et al., 2014). Finally, the objective and subjective weights were used to obtain the average value as the final weight (WF) of the evaluation index (Table 1).
3. Results 3.1. Socioeconomic characteristics of the respondents The respondents’ socioeconomic characteristics were examined and are presented in Table 2. In general, the respondents were primarily middle-aged adults. The average age of the farmers interviewed was 54 years. The results show that 64% of the farming households were maleheaded. The educational level of the respondents was generally low, and the average education level was between primary and junior high school. The descriptive results show that the farmers had generally been engaged in agriculture for a long time, for an average of 34.48 years. The mean household size of the interviewees was 3.18, which is consistent with the pattern of vast, sparsely populated land in Wushen Banner. The average yearly household income of the farmers was CNY21413 (approximately US$3124 at the time of the survey). The survey data are basically consistent with the actual situations of the farmers in Wushen Banner (WSSY, 2016).
2.4. Boosted regression tree A significant body of literature has relied on regression analysis, such as binomial (Alauddin and Sarker, 2014; Trinh et al., 2018) or multinomial logistic regressions (Alam et al., 2016), to examine the determinants of climate change adaptation strategies among farmers. These traditional regression models have scientific merit because they are easy to understand and interpret and they provide numerous options for estimating the parameters that relate the input data to the output data. However, problems with unknown and possibly nonlinear relationships between input and output variables are difficult to consider within these regression frameworks. These limitations often prevent accurate and robust results from being attained, which can compromise the model's generality (Müller et al., 2013; Chen et al., 2015). In this study, we chose the boosted regression tree (BRT) to analyze the influence of each form of livelihood capital on farmers’ adaptation strategies. The BRT is a self-learning method based on the classification regression tree algorithm (Elith et al., 2008). This method can improve the stability and prediction accuracy of the model by generating
Table 2 Primary socioeconomic characteristics of the respondents. Variable
Description
Mean
Std. Dev.
Age Gender Education
Age of the interviewee Female = 0; Male = 1 No education = 1; primary school = 2; junior middle school = 3; senior middle school = 4; college or above = 5 Number of years engaged in agriculture Number of household members Interviewee's yearly household income per capita (CNY)
54.45 0.64 2.30
12.82 0.48 1.14
34.48 3.18 21413
15.36 1.61 18052
Farming years Family size Income
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Table 3 Climate change adaptation strategies adopted by the farmers. Category
Adaptive strategy
Percentage
Water and fertilizer management Agricultural finance Operating time adjustment Planting variety adjustment Income structure adjustment No adaptation
Adjust fertilization or pesticide use behavior, improve irrigation method or frequency Seek agricultural and livestock credit or purchase agricultural insurance Change crop planting or harvesting dates, grazing/resting Plant new resistant seed varieties, diversify crop varieties Change the proportion of agriculture and animal husbandry, seek non-agricultural development or rent land (grass) No climate change adaptation measures were taken
89.72% 82.24% 63.55% 42.06% 20.09% 1.40%
result suggests that the farmers in the study area were relatively rich in human capital and financial capital, but relatively poor in social capital.
3.2. Respondents’ perceptions of climate change The survey results show that most farmers perceived that the climate in the region has changed over the past 10 years. Specifically, approximately 95% of the respondents suggested that the annual average temperature has risen in the past 10 years, while over 89% of the farmers believed that the number of high-temperature days and the drought frequency have increased. Furthermore, nearly 86% of the farmers interviewed believed that the annual average precipitation has decreased in the past 10 years. Approximately 91% of the respondents believed that the drought intensity has strengthened in the past 10 years.
3.5. Impact of livelihood capital on the farmers' adaptive behavior We ran R software to analyze the impact of the livelihood capital on the farmers’ adaptive behavior. The number of decision trees obtained by the five models is within the default normal range (350–1300), which indicates that the regression results are acceptable (Elith et al., 2008). Fig. 3 plots the influence of the five forms of livelihood capital on the farmers’ adaptation strategy of adjusting the crop varieties. In this figure, the abscissa represents the output value of various livelihood capital types, and the partial dependence plots show the specific influence trends of various livelihood capital types on the farmers’ adoption of adaptation strategies. The results show that social capital and natural capital had a positive impact on farmers’ adjustments in crop varieties, which indicates that better social capital or natural capital can encourage farmers to adjust their crop varieties to cope with climate change. Financial capital had a negative effect on this adaptation measure. In addition, both human capital and physical capital were inclined to promote farmers’ adoption of the strategy of adjusting their crop varieties. The influence of different livelihood capitals on farmers’ adoption of farm operating time adjustment is shown in Fig. 4. Specifically, physical capital and social capital had a positive impact on farmers’ adjustments of farm operating time. It is worth mentioning that when the farmers are rich enough in social capital, this positive impact will decline. However, improved human capital and financial capital do not encourage farmers to adjust farm operating time. In addition, only when farmers’ natural capital was rich enough did the positive impact of natural capital on the farmers’ adjustment of farm operating time become evident. According to the results of the BRT model (Fig. 5), we found the influence of different forms of livelihood capital on farmers’ adaptation strategies in terms of water and fertilizer management. The results show that improvements in farmers’ physical capital helped them to adopt water and fertilizer management approaches to respond to climate change. In general, the influence trends in natural capital, human capital, social capital and financial capital on farmers’ water and fertilizer management decisions showed a certain volatility and tended to be the same for each of these forms of capital. Specifically, when these four types of livelihood capital were poor, farmers were reluctant to adopt changes in water and fertilizer management. However, when these four types of livelihood capital were rich, farmers would be more likely to adopt water and fertilizer management means to respond to climate change. Fig. 6 shows the impact of each form of livelihood capital on farmers’ adoption of agricultural finance measures. The results suggest that social capital promoted the farmers’ adoption of agricultural finance. Compared with social capital, the results show that when farmers were poor in natural capital, this type of promoting effect was not obvious. However, when the output value of natural capital was large, the natural capital started to promote farmers' adoption of agricultural finance measures. Physical capital had a similar influence trend
3.3. Analysis of farmers’ adaptive strategies Table 3 presents the specific adaptation strategies adopted by the farmers in response to climate change. Most farmers in the region reported that they have adopted strategies to cope with climate change. Only 1.40% did not adopt any adaptation strategies. Among the major adaptation measures identified in this study, water and fertilizer management, including building new infrastructures for irrigation and increasing the use of pesticides or chemical fertilizers, was the most commonly used adaptive strategy. The second-most popular strategy was agricultural finance measures, including purchasing agricultural insurance and accessing agricultural and livestock credit. Among the surveyed farmers, the percentages of farmers who confronted climate change by adjusting the farm operating time and changing the crop varieties were 63.55% and 42.06%, respectively. Approximately 20.09% of the interviewed farmers adopted an income structure adjustment as their adaptive strategies. 3.4. Farmers’ livelihood capital measurement According to the quantification method of livelihood capital measurement, we obtain the values of five types of livelihood capital for each farmer. The means of the five livelihood capital types are used to describe the distribution of farmers’ livelihood capital in the sample area (Fig. 2). The order of the mean values for the five forms of livelihood capital from the largest to the smallest is human capital, financial capital, natural capital, physical capital and social capital. This
Fig. 2. Pentagon of the farmers’ livelihood capital. 5
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Fig. 3. Partial dependence plots of the impact of each form of livelihood capital on the farmers’ adjustments in planting varieties. The y-axes are on the logit scale and are centered to have a zero mean over the data distribution. The rug plots inside the top of the plots show the distribution of sites across that variable, in deciles.
as natural capital. Human capital and financial capital had a negative effect on farmers’ adoption of agricultural finance to address climate change. Notably, it is worth mentioning that when farmers were poor in financial capital, an increase in financial capital would encourage farmers to adopt agricultural finance measures to respond to climate change. The BRT results of the impact of livelihood capital on farmers’ adoption of income structure adjustments are shown in Fig. 7. In general, natural capital had a positive impact on farmers’ adoption of income structure adjustments to respond to climate change. This positive impact also appeared for human capital. The influence trend of social capital was similar to that of human capital, but with the increase of social capital, we can see that this promoting effect would decline. Moreover, when farmers were poor in physical capital, the physical capital had a negative influence on their adoption of income structure adjustments. However, when they were rich enough in physical capital, the physical capital would encourage the farmers to adopt adjustments in their income structures to respond to climate change. The influence trend of financial capital on farmers’ adoption of income structure
adjustments is similar to that of physical capital. The relative contribution of each form of livelihood capital to farmers' adaptation strategies is shown in Table 4. The results show that the relative contribution of various forms of livelihood capital to the adjustment of crop varieties is: social capital > natural capital > human capital > financial capital > physical capital. Approximately 31.5% of the farmers who adopted the strategy of crop varieties adjustment were affected by social capital, while the effect of physical capital was the smallest (8.8%). The results of the relative importance of different livelihood capital types on the adjustment of farm operating time are as follows: social capital > natural capital > human capital > financial capital > physical capital. The degree of determination of natural capital for water and fertilizer management measures was approximately 34.5%, and that of financial capital was 9.5%. The relative influence of each form of livelihood capital on farmers' adoption of agricultural financial measures, from the largest to the smallest, was in the order of physical capital, natural capital, human capital, social capital and financial capital. Nearly 33.8% of the farmers who adopted the agricultural financial strategy were affected by physical
Fig. 4. Partial dependence plots of the impact of each form of livelihood capital on the farmers’ adjustment of the farm operating time. The y-axes are on the logit scale and are centered to have a zero mean over the data distribution. The rug plots on the inside top of the plots show the distribution of sites across that variable, in deciles. 6
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Fig. 5. Partial dependence plots on the impact of each form of livelihood capital on the farmers’ water and fertilizer management. The y-axes are on the logit scale and are centered to have a zero mean over the data distribution. The rug plots on the inside top of the plots show the distribution of sites across that variable, in deciles.
capital, while only 11.6% were affected by financial capital. We observed that the contribution of social capital to the adjustment of income structure was relatively important, which was 25.3%. The relative importance of natural capital and human capital was 22.6% and 19.7%, respectively. On average, the influence of financial capital was relatively small (12.58%).
2016; Li et al., 2017). Our findings suggest that natural capital is the primary factor that positively affects farmers’ decisions to adopt adaptation strategies to cope with climate change. This finding is consistent with other studies. Specifically, Khanal et al. (2018b) indicated that families that cultivate a large amount of land are more likely to adopt adaptation strategies to address climate change, especially water and soil management practices. Abdulai and Huffman (2014) suggested that the farm soil conditions primarily influence farmers to adopt ridging in the field, and this strategy can significantly increase the rice yield and net income. Furthermore, this positive link might also have occurred because households with more farmland are more likely to be adversely affected by climate change, and farmers are more proactive at adopting innovative adaptation strategies (Jin et al., 2015). Our results also show that social capital is an important determinant that facilitates the adoption of climate change adaptation measures by farmers. Specifically, social capital has a positive effect on farmers’ adoption of adjustments in crop varieties, farm operating time and income structure, and this positive effect is more obvious when farmers’ social capital is abundant. This finding was expected because farmer organizations and social networks can help to increase opportunities for
4. Discussion It is widely believed that smallholder farmers are vulnerable to global climate change (Deressa et al., 2009; Fischer and Chhatre, 2016). Adaptation not only helps to mitigate the adverse impacts of climate change but also to maintain the sustainability of farmers’ livelihoods. Livelihood capital or resources, including natural, physical, financial, human and social capital, are inputs (Ellis, 2000; DFID, 2000), and farmers can transfer their livelihood capital into climate change adaptation strategies. Thus, different strategies may be constructed based on the farmers’ livelihood capital (Wu et al., 2017; Jezeer et al., 2019). Conceptually, farmers with better livelihood capital endowments have more choices for coping with changes, and a combination of capital types reduces climate risk and improves adaptive capacity (Paul et al.,
Fig. 6. Partial dependence plots of the impact of each form of livelihood capital on the farmers’ adoption of agricultural finance. The y-axes are on the logit scale and are centered to have a zero mean over the data distribution. The rug plots on the inside top of the plots show the distribution of sites across that variable, in deciles. 7
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Fig. 7. Partial dependence plots of the impact of each form of livelihood capital on farmers’ adoption of income structure adjustment. The Y-axes are on the logit scale and are centered to have a zero mean over the data distribution. The rug plots on the inside tops of the plots show the distribution of sites across that variable, in deciles.
Table 4 The relative importance of livelihood capital on the farmers’ adaptation strategies. Category
Planting variety adjustment
Operating time adjustment
Water and fertilizer management
Agricultural finance
Income structure adjustment
Average percentage
Human capital Natural capital Physical capital Financial capital Social capital Total
23.9% 25.6% 10.3% 8.8% 31.5% 100%
20.8% 21.7% 14.2% 18.2% 25.2% 100%
18.9% 34.5% 20.7% 9.5% 16.4% 100%
17.4% 22.7% 33.8% 11.6% 14.5% 100%
19.7% 22.6% 17.6% 14.8% 25.3% 100%
20.14% 25.42% 19.32% 12.58% 22.58% 100%
that farmers with more financial capital can more easily recover from extreme climatic events, and climate change has less of an impact on their lives. Moreover, it is argued that investment in the assets needed to adapt to climate change occur until farmers’ wealth reaches a certain threshold (Lemos et al., 2016). Finally, although several issues were examined and some important findings were obtained, this study has some limitations. First, we explored the role of each form of livelihood capital on the farmers’ specific adaptation strategies but ignored the interaction between the various capital types. Recent studies have shown that some forms of capital have a substitutive effect, some have a complementary effect, and others have both (Li et al., 2017). Future research can be conducted to test the possible complementary-substitution effect between livelihood capitals. Second, the findings are based on a small-scale region of China. Apparently, agricultural adaptation varies across countries or regions, and farmers adopt different adaptation strategies to cope with climate change based on local climate, social, economic and institutional factors (Deressa et al., 2009; Khanal et al., 2018b). Thus, much more research must be conducted. Despite the above limitations, the findings of our study contribute to the climate change adaptation literature through an improved understanding of how livelihood capital may be related to adaptive behavior by farmers.
farmers to obtain better information, technology and knowledge about farm management innovations and increase the possibility that farmers will adopt adaptation strategies (Abdulai and Huffman, 2014; Dinku et al., 2014). Similar results have been reported in Ghana (Abdulai and Huffman, 2014) and Africa (Dinku et al., 2014). In terms of overall impacts, human capital has a positive impact on farmers’ adoption of farm operating time and income structure adjustments. This finding is consistent with other studies (Deressa et al., 2009; Alam et al., 2016; Khanal et al., 2018). Specifically, farmers with better human capital, such as more education, are more likely to adopt adaptation strategies to minimize climate change impacts. Empirical evidence from Ethiopia (Deressa et al., 2009) and Bangladesh (Alam et al., 2016; Alauddin and Sarker, 2014), where farmers with better education were likely to adapt, supports this finding. Another finding of this study is that clear evidence of physical capital is an important factor, and it has a positive effect on encouraging farmers to adopt adaptation strategies. This positive effect is more evident when farmers’ physical capital is abundant. Pour et al. (2018) found that physical capital is the primary factor that affects farmers’ choices of livelihood strategies, especially in enhancing the propensity toward the fishery/livestock strategy. One possible reason is that durable goods, such as agricultural appliances and vehicles, can not only improve production efficiency but also support the adoption of adaptation strategies. The influence of financial capital on the farmers’ adaptation strategy is relatively small. The results show that financial capital has a negative impact on farmers’ adaptation strategies regarding agricultural finance and farm operating time adjustments, which was unexpected. Generally, richer farmers are more financially able to take measures to address climate change (Deressa et al., 2009). However, this finding of our study is consistent with the results of Zhai et al. (2018), who demonstrated that household income is negatively associated with the adoption of adaptation measures. One possible reason for this finding is
5. Conclusions In this study, we developed an integrated analytical framework to measure the five forms of livelihood capital of farmers. Then, the influence of each form of capital on farmers’ climate change adaptation strategies was explored using the boosted regression tree technique. Our results show that natural capital and social capital are the primary determinants that enable farmers to adjust crop varieties and adopt agricultural finance measures. Human capital is an important determinant that facilitates farmers’ adoption of adjustments in crop 8
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varieties, farm operating time and income structure. Physical capital has an important effect on farmers’ adoption of agricultural finance. Therefore, farmers’ livelihood capital plays a key role in adopting adaptation strategies to cope with climate change. There are several potential policy implications from the results of this study. First, in general, natural and physical capital have a positive effect on the adoption of all five adaptation strategies. This positive effect is more obvious when the farmers’ natural capital or physical capital is abundant. Therefore, farmers should be encouraged to invest in their natural and physical capital, such as using better agricultural equipment, to improve their adaptive capacity to climate change. Local governments can take measures to enhance or invest more in regional physical capital, such as building highways. Second, we find that the study area is relatively poor in social capital, which has a positive effect on farmers’ adoption of adjustments in crop varieties and farm operating time. Consequently, governments should implement some programs to improve farmers’ social capital, such as better agricultural extension services, which can enable farmers to share information through discussion and increase their adaptation knowledge and skills. Third, the improvement of human capital helps to promote farmers’ adoption of adaptation strategies such as adjusting the crop varieties, water and fertilizer management. Human capital improves the labor quality and farmer productivity, which can increase the rate of return on labor investment. Thus, policy interventions for a better education of farmers on the impacts of climate change and adaptation measures in response to climate change could be enhanced by investing in training, which will help to promote the adoption of climate change adaptation strategies.
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Acknowledgments We would like to thank the National Natural Science Foundation of China (No. 41671170, 41771192) for providing financial support to undertake this study. The authors are also grateful to anonymous referees for their very constructive comments and corrections, which have led to significant improvement of the early versions of the manuscript. References Abdulai, A., Huffman, W., 2014. The adoption and impact of soil and water conservation technology: an endogenous switching regression application. Land Econ. 90 (1), 26–43. Abid, M., Schneider, U.A., Scheffran, J., 2016. Adaptation to climate change and its impacts on food productivity and crop income: perspectives of farmers in rural Pakistan. J. Rural Stud. 47, 254–266. Alam, G.M., Alam, K., Mushtaq, S., 2016. Influence of institutional access and social capital on adaptation decision: empirical evidence from hazard-prone rural households in Bangladesh. Ecol. Econ. 130, 243–251. Alauddin, M., Sarker, M.A.R., 2014. Climate change and farm-level adaptation decisions and strategies in drought-prone and groundwater-depleted areas of Bangladesh: an empirical investigation. Ecol. Econ. 106 (1), 204–213. Amiri, V., Rezaei, M., Sohrabi, N., 2014. Groundwater quality assessment using entropy weighted water quality index (EWQI) in Lenjanat. Iran. Environ. Earth Sci. 72 (9), 3479–3490. Baffoe, G., Matsuda, H., 2018. An empirical assessment of rural livelihood assets from gender perspective: evidence from Ghana. Sustain. Sci. 13 (3), 815–828. Chen, H., Wang, J.X., Huang, J.K., 2014. Policy support, social capital, and farmers’ adaptation to drought in China. Glob. Environ. Change 24, 193–202. Chen, M.Q., Lu, Y.F., Ling, L., Wan, Y., Luo, Z.J., Huang, H.S., 2015. Drivers of changes in ecosystem service values in Ganjiang upstream watershed. Land Use Policy 47, 247–252. Deressa, T.T., Hassan, R.M., Ringler, C., Alemu, T., Yesuf, M., 2009. Determinants of farmers’ choice of adaptation methods to climate change in the Nile Basin of Ethiopia.
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