Complementary livelihood capital as a means to enhance adaptive capacity: A case of the Loess Plateau, China
Introduction
Following the publication of the sustainable livelihoods framework (SLF) (Chambers and Conway, 1991), scholars have recently begun to focus on the relationship between livelihood capital and adaptive capacity. Livelihood comprises the capacity, assets, and activities required to make a living, and assets include one’s human, natural, physical, financial, and social capital (Carney et al., 1999). Different indicators are used to measure each capital type according to its definition. For example, human capital refers to personal resources, such as knowledge, health, skills, and ability, and is generally measured by educational attainment, health status, etc. Adaptive capacity, in the context of the SLF, is the capacity of a system to cope with the impacts of climate change.
The interactions between the five types of livelihood capital are complex, and many scholars simply assume they are complementary. Based on the premise of capital complementarity, it is assumed that increasing livelihood capital improves adaptive capacity. Thulstrup (2015) argues that, in the context of rural Vietnam, households with greater livelihood capital have higher adaptive capacity, while those with less endowment and a single livelihood activity have low adaptive capacity. In a study of farmers in the Heihe River basin in China, Su and Shang (2012) found that financial capital and human capital improved farmers’ adaptive capacity and reduced vulnerability to risk; further, a combination of livelihood capitals enhanced farmers’ adaptability to the adverse effects of drought. Farmers with a higher livelihood capital index have more options for dealing with shocks (Su et al., 2009), and a portfolio of assets reduces climate risk and increases adaptive capacity (Moser and Satterthwaite, 2008, Zhao, 2014, Paul et al., 2016). Given that increasing livelihood capital improves adaptive capacity, some scholars have argued that the interaction of the five types of livelihood capital can be used to measure farmers’ adaptive capacity. Frusher et al. (2015) defined adaptive capacity as the sum of these five livelihood capitals, while Bryan et al. (2015) and Huai (2016a) quantified adaptive capacity as the availability of livelihood capital, which, in turn, depends on farmers’ capital endowments (Wang et al., 2016). Farmers’ overall adaptive capacity is the weighted average of the five overarching types of capital (Singh and Nair, 2014).
However, the SLF assumes that all forms of capital are complementary and that a greater amount of overall capital leads to greater adaptability, ignoring interactions and transformations between the five asset types under certain conditions (Su et al., 2009). For example, human capital affects the ownership of other types of capital, financial capital transforms itself into other capitals, and social capital promotes the development of human capital, broadening financing channels for farmers and expanding rural livelihood capital portfolios (Xu et al., 2015, Wang and Xie, 2014). Such interactions cast doubt on the assumption that increasing capital improves adaptive capacity. Paul et al. (2016) found that, in the Ethiopian context, social capital seldom moderated the adverse effects of climate change. Further, research conducted among Australian wheat farmers (Bryan et al., 2015) found that financial capital had no significant influence on adaptive capacity. While the cost of fertilizer and soil water retention capacity reduced adaptive capacity among these wheat growers, an increase in area under cultivation and an increase in annual rainfall increased overall adaptive capacity. Tian and Chen (2014) found that financial savings increased farmers’ adaptive capacity to climate change, while the consumption of financial assets reduced the funds available to farmers; for example, when the consumption of gold and silver jewelry and household appliances increased, rural households lacked of financial assets to cope with climate change.
The substitutive relationships between these five capitals challenge the SLF assumption, which is essential to understanding adaptive capacity and resilience. On the one hand, substitutions between livelihood capitals are reflected as transformations of capital investments. For example, farmers substitute financial capital for physical capital when livestock breeders use income and savings to invest in residential housing or modern poultry production units. In this instance, financial capital is reduced, but physical capital increases through investment in more lucrative activities that are less vulnerable to climate change (Alary et al., 2014). On the other hand, it is argued that investment in the assets needed to adapt to climate change cannot occur until a farmer’s wealth reaches a certain threshold (Lemos et al., 2016). Other research has found that increasing investment in complementary capitals can improve adaptability. In Uganda, for example, fishermen who invest in agricultural inputs for crop cultivation and livestock farming achieve greater income and food security as well as higher adaptive capacity than fishermen who invest in gear, boats, and other fishing assets alone (Musinguzi et al., 1995). McGrath et al. (2007) found a similar increase in adaptive capacity in the Lower Amazon through complementary fishing and agricultural practices. Substitutive and complementary relationships, therefore, influence adaptive capacity when a farmer’s capital portfolio reaches a level that will reduce vulnerability to climate change (Feng and Huai, 2015, Li et al., 2007).
Therefore, to accurately measure adaptive capacity and confirm the assumption that increasing capital improves adaptive capacity, we tested the hypothesis that complementarity-substitution exists between these five livelihood capitals, and that only increasing complementary capital can improve adaptive capacity. Based on a survey of 334 households in the Loess Plateau in northwest China, we used regression analysis to evaluate substitution and complementarity between capital indicators. We then used factor analysis to integrate these complementary indicators into adaptive capacity and finally identified the effects of complementary livelihood capital on adaptability. This paper contributes to existing knowledge and understanding of the selection of complementary livelihood capital to measure farmers’ adaptability, thus providing a theoretical basis for climate change vulnerability assessment.
Section snippets
Study area
The Loess Plateau, China (34–41°N, 98–114°E), lies in a semiarid continental monsoon climate zone, ranging from arid/semiarid to subhumid (Burnham and Zhao, 2016). The annual average temperature is 9 °C, and approximately 70% of annual precipitation is concentrated in July to September (Zhang et al., 2016). The Loess Plateau was chosen as the study site for a number of reasons. First, it is China’s largest apple-producing region due to its superior natural environment for apple cultivation
Identifying complementarity and substitution between livelihood capital indicators
Positive or negative standardized regression coefficients show complementarity or substitution between capital indictors (Table 2). The results show that there are no influences on land quality, and it, in turn, has no effect on other forms of capital, indicating that land quality is neither a complementary nor substitution capital. Cultivated land area, distance to markets, agricultural income, number of relatives, and village cadres are five complementary forms of capital. For example,
Conclusion
Based on testing the complementary-substitution effect between livelihood capitals, this paper has presented the quantification and interpretation of adaptive capacity at the household level, in support of assessments of vulnerability to climate change. Complementary capital indicators were integrated into adaptive capacity to overcome the shortcomings of simply quantifying adaptive capacity by the five types of capital. Graph theory and the decision-tree method provided favorable directions
Competing financial interests
The author declare that the authors have no competing interests as defined by EVISE Publishing Group, or other interests that might be perceived to influence the results and/or discussion reported in this paper.
Acknowledgments
We are grateful for the comments of several anonymous reviewers and the editors of LetPub, which have greatly improved this manuscript. This work was supported by the Program of the NSFC [grant number 71473196], Project Sponsored by the SRF for ROCS and SEM, and the Program of the National Apple Industrial System of China [grant number CARS-28], and the Chinese Scholarship Council, 2015 International Seed Fund of Science and Technology Cooperation Project of Northwest A&F University titled by
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