Linking smallholders’ livelihood resilience with their adaptation strategies to climate impacts: insights from the Tibetan Plateau (2024)

Research

INTRODUCTION

The climate change we are currently experiencing is unprecedented (IPCC 2023). Faster rates of warming, higher intensities, longer durations, and more frequent disasters will continue to affect the lives, health, and livelihoods of billions of people for the foreseeable future (Currie-Alder et al. 2021). How to improve the resilience of different groups and promote effective adaptation to climate change has become an important issue. In this study we aim to explore the relationships between climate adaptation strategies and livelihood resilience with focus on smallholder farmers.

Smallholder farmers are rural agricultural producers who farm less than 2 hectares (FAO. 2014, Lowder et al. 2021). With more than 475 million smallholder farmers worldwide, they farm 12% of the land but produce nearly 35% of the world’s food (Lowder et al. 2021), playing an important role in reducing poverty and ensuring sustainability issues such as food security. Climate change may be one of the greatest challenges facing smallholders, with dangerous climate change disproportionately affecting their livelihood systems as they are often resource poor and have inadequate access to technology/finance (Morton 2007), while at the same time being highly dependent on climate elements (temperature, precipitation, climatic disasters, etc.) for their livelihood activities. Based on this understanding, how to promote smallholder adaptation to climate change has received increasing attention from governments (Asare-Nuamah et al. 2019, He et al. 2022a), NGOs (Beyuo 2020), and academics (Cohn et al. 2017, Bukchin-Peles and Fishman 2021). From the lens of social-ecological systems theory (Adger 2000), increasing the resilience of smallholder livelihoods to external shocks such as climate change and extreme events is an effective way to facilitate their adaptation.

In general, the resilience of smallholder livelihoods refers to the ability of their livelihoods to recover from stresses or disturbances while maintaining or improving basic characteristics and functions (Speranza et al. 2014). Quantitative measures of smallholder livelihood resilience can help improve the understanding of livelihood resilience, identify the most vulnerable groups, and in turn take action to improve the ability of smallholder livelihoods to cope with external stresses or disturbances, maintain the stability of smallholder upgrading, and improve livelihood outcomes. A number of studies have focused on quantitative measures of smallholder livelihood resilience (Speranza et al. 2014, Mutabazi et al. 2015, Fang et al. 2018, Quandt 2018), which have contributed significantly to the understanding of livelihood resilience. However, resilience can be influenced by human actions (Walker et al. 2004, Yang et al. 2021), and the core of livelihood resilience is the adaptive strategies adopted by individuals or households in times of stress and shocks (Liu et al. 2020). In the face of climate change stress, smallholders have adopted a range of adaptation practices, and different adaptation strategies will lead to different livelihood resilience (Nelson 2011, Pagnani et al. 2021). Therefore, studies on livelihood resilience need to take into account the adaptation strategies of smallholders.

Smallholders’ climate change adaptation refers to their behavior of changing their livelihoods to cope with the impacts of climate change. A number of studies have reported adaptation practices undertaken by smallholders in response to climate change (Harvey et al. 2018, Shaffril et al. 2018, Menghistu et al. 2020). Following the classification of Dorward et al. (2009), we classify the adaptation strategies adopted by smallholders as “stepping up” (SU) and “stepping out” (SO). This classification has been generally accepted by scholars (DFID 2015, Hansen et al. 2019, Wang et al. 2019, Islam et al. 2021). In a broad sense, SU can be defined as adapting or improving agriculture through new investments and activities, whereas SO can be defined as reducing or exiting agriculture for nonfarm or nonagricultural activities. SU and SO denote two different adaptation options for smallholders in response to climate change, and smallholders may also choose not to adapt to climate change. We attempt to link such a classification of adaptation strategies to the resilience of smallholder livelihoods in order to help guide better adaptation to climate change and facilitate effective government interventions to improve the resilience of smallholder livelihoods.

Therefore, using data from 1552 questionnaires collected in four regions of the Tibetan Plateau (TP), we attempt to (1) establish an indicator system to measure the resilience of smallholder farmers’ livelihoods to climate change; (2) assess the difference in livelihood resilience of smallholder farmers who adopt different strategies; and (3) explore the factors that influence smallholder farmers to adopt different adaptation strategies. Based on our results, we make targeted policy recommendations to promote the resilience of smallholder farmers’ livelihoods.

Literature review

Definition of livelihood resilience

After decades of development, the concept of resilience has been widely applied by scholars in the field of climate change adaptation (Nelson et al. 2007, Adger et al. 2011, Ayeb-Karlsson et al. 2016, Nath et al. 2020). We often refer to climate change resilience as the ability of a social-ecological system to absorb or withstand climate-related perturbations and stresses and still maintain the same structure and function, while retaining options for development (Holling 1973, Gunderson and Holling 2002, Nelson 2011). As mentioned by Tanner et al. (2015), the concept of resilience needs to focus more on human livelihoods if it is to address the development needs of the poorest and most vulnerable people on the planet. Livelihoods are the capabilities, assets (physical and social), and activities that people need to make a living (Ellis 2000). Incorporating the concept of resilience into a livelihoods perspective not only enhances the understanding of dynamic livelihoods and the resilience of livelihood systems to shocks (Speranza et al. 2014), but also overcomes the challenge of using resilience thinking to make resilience work better for human development (Tanner et al. 2015). In fact, the concept of resilient livelihoods was originally introduced by Chambers and Conway (1992) as part of the concept of sustainable livelihoods, and is defined as the ability of livelihoods to respond to and recover from stresses and shocks. However, the broader concept of livelihood resilience should include not only the response and recovery of systems to shocks and stresses, but also the self-organization and learning of livelihood systems, shifting livelihood models to adapt to challenges, maintaining stability in the functioning of livelihood systems, and increasing assets (Sina et al. 2019). On this basis, and following the definition of Speranza et al. (2014), we define smallholder livelihood resilience to climate change as the ability of livelihoods to absorb or withstand stresses and disturbances, while self-organizing and learning to maintain or improve essential characteristics and functions. Livelihood resilience is characterized by actors’ assets and strategies to maintain and increase assets, self-organize, and learn. A livelihood is resilient if it can maintain its essential functions (food, income, insurance, poverty alleviation, etc.) and absorb the effects of disturbances without causing a significant decline in production and well-being.

Adaptation strategies to climate impacts of smallholders

Smallholder farmers have long engaged in a variety of actions to cope with the impacts of climate change (Burnham and Ma 2016). Scholars have categorized smallholders’ climate change adaptation actions in different ways (Shaffril et al. 2018, Aryal et al. 2020), but the internal logic suggests that smallholders’ adaptation actions revolve around their livelihood patterns and can be divided into two categories: (1) agricultural improvements, such as changing planting time (Kgosikoma et al. 2018), planting improved varieties (Pandey et al. 2018), increasing agricultural inputs (Keshavarz et al. 2014), reclaiming new cropland (Wu et al. 2021), and increasing livestock (Silvestri et al. 2012); (2) non-agricultural transitions, that is, reducing or exiting agriculture, such as abandoning cropland (Paudel et al. 2020), reducing livestock size (Ojo et al. 2021), and migrating or transforming the livelihood of the whole family to a nonagricultural livelihood (Alam et al. 2017). The first category is the stepping up (SU) strategy, while the second category is the stepping out (SO) strategy.

There has been debate as to which of the SU or SO strategies adopted by smallholders may yield better development outcomes. Some arguments suggest that it is more appropriate for smallholders to move out of agriculture than to stay in agriculture (Hansen et al. 2019, Stringer et al. 2020), whereas other arguments suggest that improving agriculture would be a better option (Taylor 2014, Agarwal and Agrawal 2017). Introducing the concept of resilience can bring a new perspective to this long-standing debate: strategies that lead to more resilient smallholder livelihoods may be more adaptable to current and future climate change conditions.

Linking smallholder livelihood resilience to adaptation strategies is not a new formulation. Scholars have noted that climate resilience and adaptation have an inherent resonance: both involve processes of change and are complementary (Nelson 2011). Good adaptation helps build resilience (Prado et al. 2015, Mohammed et al. 2021), and similarly, maladaptation tends to undermine resilience (Liu et al. 2020). For the most vulnerable groups of smallholders, although there is a growing body of research on differences in livelihood resilience across smallholder adaptation strategies (Twine 2013, Prado et al. 2015, Oulahen et al. 2019, Li and Zander 2020, Mohammed et al. 2021, Pagnani et al. 2021, Luo et al. 2022), research that directly addresses the relationship between smallholder livelihood resilience and adaptation strategies is still lacking. At the same time, because of the place-based impacts of climate change (Butts and Adams 2020) and the diversity of smallholder adaptation strategies (Berrang-Ford et al. 2021), relevant research in typical areas is essential to advance the understanding of smallholder climate change adaptation and resilience, and to guide corresponding policy practices.

Measurement of livelihood resilience

Although the concept of livelihood resilience is generally accepted by scholars, the quantitative measurement of livelihood resilience remains a challenging task (Ingrisch and Bahn 2018). Scholars have developed a variety of methods to measure smallholder livelihood resilience, including individual livelihood history interviews (Ayeb-Karlsson et al. 2016), focus group discussions and key informant interviews (Hirons et al. 2018), structural dynamics (Fang et al. 2018), and indicator system assessment methods (Speranza et al. 2014, Mutabazi et al. 2015, Quandt 2018). Currently, the quantifiable indicator proxy remains the most preferred method by scholars to quantitatively measure smallholders’ livelihood resilience (Mutabazi et al. 2015, Béné et al. 2016, Liu et al. 2020, Ahmad and Afzal 2021, Awazi and Quandt 2021, Nasrnia and Ashktorab 2021). The quantifiable indicator proxy approach is an objective measure of resilience that selects standardized indicators based on the definition of resilience and can be applied to compare different populations, which has been proven in many cases (Weldegebriel and Amphune 2017, Sina et al. 2019, Zhao et al. 2022). However, objective resilience assessment methods have the following shortcomings: first, agreeing on a common set of resilience indicators is a major challenge; second, some characteristics of livelihood resilience are difficult to measure (e.g., social capital, etc.), coupled with difficulties in obtaining objective data; and, most importantly, they do not take into account people’s perceptions of resilience (Jones and d'Errico 2019). These shortcomings have led scholars in recent years to explore subjective measures of livelihood resilience, based on a definition of livelihood resilience that complements subjective measures of livelihood resilience based on people's judgments of what constitutes resilience and their assessments of their ability to cope with risk (Jones 2019a). Despite its potential, however, the subjective approach has drawbacks: little research has been conducted on subjective measures of resilience, and little is known about the strengths and limitations of different measures (Jones 2019b). The validity and reliability of subjective measures of resilience remain to be tested (Jones et al. 2021). Existing case studies also suggest that people’s knowledge of their own resilience is inaccurate (Jones et al. 2018). Both approaches have their strengths and weaknesses, and some scholars have proposed combining subjective and objective methods of measuring resilience (Quandt and Paderes 2023) in an attempt to complement each other and gain a better understanding of resilience, but more case studies are needed.

We believe that subjective and combined subjective and objective measures of livelihood resilience will play an important role in future livelihood resilience measurement, but with this paper we focus on comparing the livelihood resilience of populations using different measures, and relatively mature objective measures were chosen as the quantitative measure of livelihood resilience. Based on the definition of livelihood resilience and existing research (Walker et al. 2004, Speranza et al. 2014, Liu et al. 2020, Zhao et al. 2022), we construct an indicator system to measure smallholder livelihood resilience to climate change in three dimensions: buffering capacity, self-organizing capacity, and learning capacity.

Conceptual framework

Scholars have developed different conceptual frameworks to measure smallholder livelihood resilience. The resilience framework, which originated from sustainable livelihoods, uses five categories of livelihood assets, namely human, physical, financial, social, and natural assets, to measure the livelihood resilience of smallholders (Nasrnia and Ashktorab 2021, Pagnani et al. 2021, Aguilar et al. 2022). Livelihood assets are the basis of smallholders’ resistance to adverse conditions and are also a necessary buffer against stress and shocks (Daniel et al. 2019). Quandt further developed this framework by incorporating subjective measures of resilience, proposing the household livelihood resilience approach (HLRA) and applying it to case studies in different regions (Quandt et al. 2017, Quandt 2019, Awazi and Quandt 2021). However, the core of the HLRA framework remains the five categories of livelihood assets. Such a framework can be criticized for ignoring the self-organization and learning capacity of livelihood systems. Carr (2020) points out that such livelihood resilience studies are simply sustainable livelihood studies in new clothes.

Speranza et al. (2014) combine sustainable livelihoods with resilience thinking to propose a livelihood resilience framework. This framework measures livelihoods resilience along three dimensions: buffer capacity, self-organization capacity, and learning capacity. The five livelihood assets and the ability to access them are represented as components of buffer capacity. Such a resilience assessment framework includes measures of smallholders’ livelihood self-organization and learning capacity, in addition to their own capital endowments, and emphasizes the influence of the local institutional context, which also provides scope for resilience interventions by policy makers. This framework is also supported by case studies from different regions (Chen et al. 2018, Liu et al. 2020, Zhao et al. 2022).

Our study aims to link smallholders’ adaptation strategies to their livelihood resilience. Therefore, we propose the conceptual framework of this paper based on Speranza’s livelihood resilience framework (Fig. 1). We establish a system of indicators to quantitatively measure livelihood resilience at three levels: buffering capacity, self-organizing capacity, and learning capacity, which is the basis for understanding livelihood resilience. Furthermore, our framework adds two parts: (1) the interaction between livelihood resilience and adaptation strategies. Under the impacts of climate change, smallholder farmers will adopt different adaptation strategies based on their own endowments, leading to different livelihood outcomes and thus differences in their livelihood resilience. (2) Our framework emphasizes the interaction between government interventions and smallholder livelihood resilience (government interventions often directly affect smallholder livelihood resilience [Li and Zander 2020, Liu et al. 2020]). We place the resilience of smallholder livelihoods in the context of government and institutions because smallholder responses to climate change are inevitably influenced by local institutional policies (Agrawal et al. 2010). For example, smallholders in the TP affected by drought may adopt strategies such as reducing cropland and shifting labor to non-agricultural activities (SO) to avoid drought risk, but they may also respond to drought by changing planting dates and diversifying cropping strategies (SU). The two different types of strategies will lead to different livelihood outcomes, and livelihood resilience to climate change impacts will differ among smallholders adopting different types of strategies. In addition, some local government interventions such as training, cooperatives, credit, and infrastructure may also play a role in this process, leading to differences in smallholder livelihood resilience.

METHODS

Study area

The TP is known as the Earth’s “third pole” and the “water tower of Asia” (Yao et al. 2012), with an average elevation of more than 4000 meters. The extremely high altitude makes it highly sensitive to climate change (Qiu 2008). The TP region has been warming rapidly in recent decades (Fang et al. 2019), and Wang et al. (2017) reported that the annual temperature in the TP increased by 0.42 °C per decade between 1979 and 2012, much higher than the global warming rate during the same period. In addition, the frequency of climatic disasters has increased, threatening the safety of local people and property (Yao et al. 2019). More than 10 million people live in the TP, and their livelihoods are based on agriculture and livestock, which are highly vulnerable to the impacts of climate change. Assessing the resilience of smallholder livelihoods on the TP to promote smallholder adaptation to climate change is important not only to ensure the stability of smallholder livelihoods, but also to prevent smallholders from falling into poverty traps due to climate shocks, and to help smallholders achieve sustainable development.

Therefore, we collected data in four agricultural and pastoral areas in the TP with different orientations and altitudes (Fig. 2). The Yarlung Zangbo, Nyangqu, and Lhasa River (YNL) area is the main grain-producing area and the political, economic, and cultural center of the Tibet Autonomous Region (Wang et al. 2019), with elevations ranging from 2700 to 4200 m. The main livelihood activity of smallholders in this area is agriculture, and some smallholders in higher elevations also raise livestock. The Yellow River and Huangshui River Valley (YHV) area is the main area of crop production in Qinghai Province and is a typical agro-pastoral zone on the TP, with an altitude between 1689 and 5218 m (Zhang et al. 2022). Zamtang County (ZTC) is located in the northwestern part of Sichuan Province, with an altitude between 2528 and 5178 m, and agriculture and animal husbandry are the main livelihoods of smallholders in the region (Wu et al. 2021). The Pumqu River Basin (PRB) is located on the southwestern border of Tibet, bordering Nepal. The average elevation is over 4500 m, and smallholder farmers are mainly engaged in agriculture and livestock production. Because it is an important part of the Qomolangma National Nature Reserve in China and the border area between China and Nepal, the area has smallholder farmers who are subject to greater government intervention (He et al. 2021).

Smallholders in the study area depend on their own livelihoods for income from three main sources: agriculture, livestock, and off-farm employment. In the lower altitude valley areas, such as YHV and YNL, which have good water and thermal conditions, smallholder livelihoods are dominated by agriculture, with barley, wheat, rapeseed, and other crops grown, and livestock is only a complementary activity to their livelihoods, which generally consists of small-scale livestock rearing. In the relatively high-altitude ZTC and PRB areas, the share of livestock in farmers’ livelihoods has increased, and the combination of agriculture and livestock is a popular livelihood option for local smallholders. Local smallholders mainly grow barley and rapeseed, and some farmers grow wheat and pasture. It is worth noting that very few farmers have constructed irrigation facilities, relying mostly on rain-fed irrigation, but more than half of the farmers report that the government has constructed some irrigation facilities (irrigation ponds, ditches) for them. Non-farm employment also varies across the four regions. YHV and YNL are close to the economic centers of TP and have a high level of accessible off-farm employment opportunities, with smallholders having the option of either going out or staying in the area to work. However, because of a lack of skills, men are mostly engaged in manual labor such as construction, while women are mostly engaged in construction or services. The ZTC and PRB are far from the economic centers and have fewer off-farm employment opportunities, so local smallholders generally have to leave their place of residence in search of work opportunities, possibly in their own county or city, as well as in neighboring counties and cities. Construction is the dominant form of non-agricultural employment.

We focus on climate impacts on smallholder livelihoods, but the impacts of climate change are many and varied. To make the study more focused, we asked each household surveyed about their perception of climate change impacts in the last 10 years and found that the majority of smallholder farmers in the study area perceived the increase in temperature and drought conditions. Therefore, we focus on the impact of increased temperature and drought on smallholder farmers’ livelihoods. Please refer to Appendix 1 for the smallholder farmers’ perceptions of the impacts of climate change.

Household survey and questionnaire data

The data for our study were obtained from four rounds of household surveys conducted in the TP from 2015 to 2018. The questionnaire survey mainly consisted of the following steps: first, we designed the household questionnaire based on previous experience and with reference to existing research; then, we conducted a pre-survey of about seven days and revised and improved the questionnaire based on feedback. The formal survey was conducted using participatory rural appraisal tools, which were divided into two main phases. First, interviews were conducted with local government staff to identify sample townships based on local socioeconomic, natural environment, and agricultural and pastoral conditions. The selected townships must be representative of the region, including both well-developed towns and poor towns. Second, a stratified random sampling technique was used to select the sample villages and smallholders. The socioeconomic development status (production value of primary, secondary, and tertiary industries, per capita disposable income, etc.) of the sample townships and villages was divided into three levels; a number of sample villages were selected for each level, and smallholders in each village were randomly surveyed.^[1] Most respondents were household heads, and other family members could provide more information.^[2] Each respondent was interviewed for 1–2 hours, and local college students (real-time translation) who had received standard training were hired as interpreters for the survey. Each regional survey included the above steps. The survey was conducted in July and August of each year. We received a total of 1552 questionnaires from smallholders in 83 villages in the TP. The main contents of the questionnaire included the basic household status of smallholders, their livelihood assets, their perceptions of climate change impacts, their adaptation strategies, and local government interventions, etc. Please refer to Table 1 for the basic overview of the questionnaire, Table 2 for the specific description of the indicators, and Appendix 2 for the corresponding questionnaire questions.

Quantitative evaluation of livelihood resilience

Indicator system

According to the conceptual framework, the indicator system for measuring smallholder livelihood resilience consists of three dimensions: buffer capacity, self-organization, and learning capacity. Therefore, based on their definition, the existing relevant literature and the actual situation in the TP, 17 indicators selected from the questionnaire data were used to measure the resilience of smallholder livelihoods (Table 2). The literature sources for each indicator and its expected impact on livelihood resilience are presented in Table 2.

(1) Buffer capacity

Buffer capacity refers to the amount of change (disturbance) that a system can withstand or absorb while maintaining its normal function and structure (Carpenter et al. 2001). From the perspective of smallholder livelihoods, buffer capacity refers to the ability of smallholders to buffer against external disturbances in order to achieve better livelihood outcomes (Speranza 2013). The core of buffer capacity lies in asset acquisition, and the assets at the household level mainly include human capital, physical capital, natural capital, financial capital, and social capital. Specifically. Human capital includes the labor capacity, skill knowledge, and health status of smallholders. Therefore, the four indicators of household size (Pagnani et al. 2021), dependency ratio (Speranza et al. 2014), age of householder (Patnaik et al. 2019), and health condition (Quandt 2018) are classified as indicators of human capital. The dependency ratio is classified as a negative indicator because the higher its value, the higher the dependency pressure on the household. Natural capital includes the cropland area (Awazi and Quandt 2021, Zhao et al. 2022) and the number of cropland plots (Wu et al. 2021). In addition, because of the high altitude of the TP, we included the elevation of the smallholders’ residence as one of the indicators of natural capital indicators (He et al. 2022b). The number of cropland plots and elevation are classified as negative indicators: more plots mean more fragmented cropland, which is less conducive to agricultural production (He et al. 2021); the higher the elevation of the smallholders’ residence, the more significantly their livelihoods are affected by climate change. Financial capital is represented by the nonagricultural income of smallholders (Wang et al. 2019), and physical capital is represented by the number of livestock (Zhao et al. 2022) as well as agricultural equipment (Kuang et al. 2019). Social capital reflects the connection between households and society, and the distance to the market is an important indicator of the social capital of smallholders on the TP (Zhang et al. 2022). Smallholders who are far from the market have weak ties with the society. At the same time, some government policies can increase the buffering capacity of smallholders, such as government subsidies (Macours et al. 2022), credit (Pagnani et al. 2021), which we call government safety nets.

(2) Self-organization

The capacity of self-organization primarily reflects the impact of human institutions, power, and social networks on resilience (Obrist et al. 2010). More narrowly, it refers to the occurrence, form, process, and outcome of processes determined and designed by individuals influenced by emerging structures under conditions of crisis and instability, and is an important attribute of livelihood resilience (Speranza et al. 2014). Self-organizational capacity includes leadership, community organization, and social network capacity, and we used the indicator of village leaders (Chen et al. 2018), borrow money (Hua et al. 2017), and cooperatives (Alam et al. 2018) to represent each of these three capacities.

(3) Learning capacity

Learning capacity implies adaptive management, including the ability to learn, transform, share, and provide feedback (Speranza et al. 2014). This suggests that the resilience system is a learning system that incorporates past experiences into current actions; that is, it is not only about acquiring knowledge and skills, but also about communicating among members of society, thus improving the actual capacity of making a living. The learning capacity of smallholder livelihoods can be reflected in two indicators: educational attainment (Crittenden 2003) and skills training (Chen et al. 2018). In the case of education, for example, higher education increases the possibility of employment and creates favorable conditions for livelihood diversification (Crittenden 2003). As shown by Khasalamwa (2009), adequate vocational knowledge and skills not only increase the employability of vulnerable households, but also predict their adaptive capacity in the face of shocks.

Quantitative measurement

The measurement of livelihood resilience included data standardization, weight determination, and weighted summation. The standardization of data mainly adopts the maximizing deviations method to eliminate the variability of indicators among different scales, while the determination of weights adopts the entropy weighting method. The principle of the entropy weighting method is to determine the weight according to the degree of data variation, which can not only overcome the randomness and assumption of the subjective weight assignment method, but also effectively avoid the overlapping problem of information among multi-index variables (Amiri et al. 2014, Kuang et al. 2019).This method has now been applied to the process of assigning weights to indicators in the assessment of livelihood resilience (Zhao et al. 2022). The specific steps and results of the data standardization, entropy weighting method, and indicator weights are described in Appendix 3. After determining the weights of each livelihood resilience indicator using the entropy weighting method, additive aggregation was used to derive the value of smallholder livelihood resilience.

Statistics and econometric analysis

One-way analysis of variance

According to the type of adaptation strategy adopted, we divided the surveyed smallholders into four categories: SO smallholders who adopted SO strategies, SU smallholders, NA smallholders who did not adopt any adaptation strategies, and Both smallholders who adopted both SU and SO strategies. To compare the differences in the components of livelihood resilience among smallholders who adopted different adaptation strategies, a one-way analysis of variance was conducted to compare the differences in the means of each component of livelihood resilience among different types of smallholders, and the Bonferroni method was used to determine the differences between groups.

Multinomial logit regression model

Because the dependent variables in this paper are discrete and multiple choice variables (i.e., 1,2,3,4) that are mutually exclusive, a multinomial logit model (mlogit model) was used to explore which components of livelihood resilience influence smallholders to adopt different adaptation strategies. The mlogit model has been widely used in studies of smallholder livelihoods in different sectors (Hua et al. 2017, Mohammed et al. 2021, Salgueiro-Otero et al. 2022).

Establishing the mlogit model required a reference: because we need to explore the factors influencing the adoption of different adaptation strategies, smallholder farmers who did not adopt any adaptation strategies were selected as the reference. The mlogit model of household types was expressed as follows:

(1)

In this formula, m denotes the household type; x_n denotes the factors influencing the household type; α_m is the constant; and β_mn denotes the estimated coefficient value corresponding to the n influencing factor of the m family.

Because the data used in this study are cross-sectional data from four regions over four years, a dummy variable was generated to control for both regional and time-related model bias. The variables included in the mlogit model were those that are not normalized. Because of the large values of government subsidies and nonagricultural income, they were logged to reduce the absolute differences between the data and to avoid the influence of extreme values.

STATA 17.0 was used for measurement and statistical analysis. To ensure the reliability and robustness of the model results, Robust was employed to correct the estimated results. In addition, the Pearson correlation coefficient (PCC), tolerance and variance inflation factor (VIF) were adopted to test the results. The results showed that the PCC between Proportion of agricultural equipment and Number of cropland plots was the highest, reaching 0.468 (0.468 < 0.8); the tolerance of elevation was the lowest at 0.511 (0.310 > 0.1); while its VIF was the highest at 1.96 (1.96 < 10). Based on the above results, there was no multicollinearity among the independent variables. In other words, the analysis results of the model would not be affected.

Because the assumption of independence of irrelevant alternatives (IIA) is the premise of the mlogit model, before running the mlogit model, the Hausman test was used to test whether the data satisfy the IIA assumption, and the chi² values of the obtained results were all positive, indicating that the data satisfy the IIA assumption and can be estimated by the mlogit model. The Wald chi² value of the mlogit estimation was 457.64, and the corresponding Prob > chi² value was 0.000, indicating that the model fits well and that the results are stable and credible. In addition, the multinomial probit (mprobit) model results were used to verify the robustness of the mlogit model results.

RESULTS

Different adaptation strategies of smallholders

The field survey found that smallholders in four regions of the TP mainly adopted four types of adaptation measures: crop management (changing planting dates, changing planting patterns, increasing pesticide and fertilizer inputs, and diversifying crop), 761 smallholders (49.03%); cropland management (reclaiming new cropland), 251 smallholders (16.17%); livestock management (raising more livestock), 271 smallholders (17.46%); and off-home activities (off-farm work away from home), 676 smallholders (43.56%).^[3] According to our definitions of SU and SO, the first three activities were classified as SU strategies, and off-home activities were classified as SO strategy. Therefore, we divided the 1552 smallholders surveyed into four categories: 212 smallholders who adopted the SO strategies (SO smallholders, 13.66%), 573 who adopted the SU strategy (SU smallholders, 36. 92%), 303 who did not adopt any adaptation measures (NA smallholders, 19.52%), and 464 who adopted both SU and SO strategies (Both smallholders, 29.90%; Table 3). Because of the differences in production conditions and geographical location in the different regions, the proportion of smallholders adopting different adaptation strategies varied across the four regions, resulting in significant differences in the resilience of smallholder livelihoods in each region,^[4] but in general, there were more smallholders adopting the SU strategy and fewer adopting the SO strategy in each region.

Livelihood resilience of smallholders with different adaptation strategies

Based on the above methods and data, the livelihood resilience of smallholder farmers in the study area was calculated. A higher value of the buffer capacity index indicates a higher capital endowment or a better safety net for the smallholder. A higher value of the self-organization capacity index indicates that the smallholder receives stronger support from top-down or bottom-up organizations. A larger value of the learning capacity index indicates that the smallholder has a higher capacity for learning feedback, and a higher value of livelihood resilience index indicates that the household has a higher adaptive capacity in the face of climate change shocks and disturbances. The value of the livelihood resilience index is not an absolute measure of resilience, but a relative measure of the livelihood resilience of the sample smallholders.

The values of each indicator and livelihood resilience were calculated (Fig. 3). According to Figure 3, the livelihood resilience of SO smallholders was 16% higher than that of SU smallholders, while the livelihood resilience of SU smallholders was 10% higher than that of NA smallholders. The Both smallholders had the highest livelihood resilience. In terms of the three components of livelihood resilience, the SO smallholders had the highest buffering capacity and self-organization ability, while the Both smallholders had the highest learning capacity. Not surprisingly, the NA smallholders had the lowest scores for all three types of capacity, as well as for livelihood resilience.

We compared the differences in the means of the livelihood resilience indicators among the different categories of smallholders (Table 4). It can be seen that the means of most of the indicators show significant differences, suggesting that our classification effectively represents the different characteristics of smallholders. There are several notable findings: (1) smallholders that have adopted adaptation strategies, especially the SO strategies, tend to have a better health condition; (2) the SO smallholders have a significantly higher proportion of access to credit than other smallholders; (3) the rate of participation in cooperatives among SO and Both smallholders is much higher than that of other smallholders; (4) the rate of participation in training among smallholders who have adopted adaptation strategies is much higher than that of NA smallholders.

Linkage between smallholders’ livelihood resilience and their adaptation strategies

The mlogit model estimates the factors associated with the adoption of adaptation strategies by smallholder farmers (Table 5). The mprobit model was used to validate the results of the mlogit model (Table 6), and the significance indices of the two models are consistent, indicating that the regression results are robust.

According to the results, compared to NA smallholders, the effect of human capital on the adoption of SO strategies is positive: the household size and health condition imposed significant positive impacts on the adoption of SO strategies, the possible reason may lie in that a healthy and sufficient labor force is an important prerequisite for the adoption of off-home activities. The number of cropland plots also imposed a significant positive impact on the adoption of SO strategies. The possible reason is that the large number of cropland plots of smallholders indicates that their cropland is scattered, which is not conducive to agricultural production, resulting in their preference for the SO strategy. It is worth noting the results of some indicators related to local government. Government subsidies had a significant negative impact on smallholders’ choice of SO strategies, while credit, cooperatives, and training had a significant positive impact on smallholders’ choice of SO strategies. The relative risk ratios showed that compared to NA smallholders, smallholders who participated in credit were 147.9% more likely to choose the SO strategy, and smallholders who participated in credit, cooperatives, and training were 147.9%, 67.9%, and 139% more likely to choose the SO strategy, respectively.

Compared to NA smallholders, household size had a significant negative effect on the adoption of SU strategies. Combined with the previous analysis (Table 4), we suggest that the possible reason for this is that off-home activities are considered when the household size is larger (the effect of household size is positive for the other two types of smallholders). Agricultural equipment had a positive effect on the adoption of SU strategies, and smallholders with more agricultural equipment whose livelihoods depend primarily on agriculture were more likely to adopt SU strategies. Similarly, government interventions, credit, and skills training had positive significant effects on the adoption of SU strategies. Compared to NA smallholders, smallholders who received credit and training were 92.2% and 45.1% more likely, respectively, to adopt SU strategies.

DISCUSSION

Differences in smallholder livelihood resilience across different adaptation strategies

Our aim was to explore the relationship between smallholder livelihood strategies and resilience. The results of the study showed that smallholders who adopted different types of adaptation strategies (SO, SU) had different livelihood resilience, with their livelihood resilience ranked as SO > SU > NA. This suggests a positive interaction between smallholders’ climate change adaptation and their livelihood resilience on the TP. This finding is consistent with some case studies in other regions: Pagnani et al. (2021) found that adaptation actions can reduce the negative impacts of climate change and increase the livelihood resilience of smallholders in India; and Mohammed et al. (2021) also showed that farmers with livelihood diversification strategies tended to have higher livelihood resilience in Ghana. Several studies focusing on smallholder development have also found that for smallholders, strengthening agricultural production pathways (SU) or shifting to the non-agricultural sector (SO) are appropriate ways to escape the poverty trap (Hansen et al. 2019), and SO strategies may lead to better welfare for poor smallholders (Stringer et al. 2020). However, it is important to note that adaptation does not always interact positively with resilience (Nelson 2011), and inappropriate adaptation strategies may increase exposure and sensitivity to climate change (Schipper 2020) and reduce the resilience of smallholder livelihoods; for example, Quandt (2021) found that low-wage nonfarm labor reduces resilience. Therefore, more insight is needed into the linkages between smallholder adoption of SU and SO strategies, livelihood resilience, and climate change impacts.

Smallholder livelihoods are vulnerable to climate change because agriculture-based livelihood activities are more sensitive to climate change impacts (droughts, floods, etc. often disrupt agriculture; Morton 2007, Cohn et al. 2017). Nonagricultural jobs are less affected by climate change compared to agricultural production (e.g., masons, carpenters), so smallholder livelihoods are less vulnerable to climate when they adopt SO strategies (Cohn et al. 2017, Bukchin-Peles and Fishman 2021), which may explain our finding that smallholders adopting SO strategies have the highest buffering capacity. At the same time, smallholders who adopt the SO strategies tend to be more active in accessing information (SO smallholders participate in cooperatives at much higher rates than NA and SU smallholders), have better social networks than other types of smallholders, and thus have the highest self-organization capacity. SU smallholders adapt to climate change by diversifying crops, changing cropping patterns, etc. These behaviors can also reduce the vulnerability of agricultural production to climate (Islam et al. 2021), thus increasing the buffering capacity of SU smallholders compared to NA smallholders. In addition, active participation in cooperatives and training are important factors for SO and SU smallholders to have higher self-organization and learning capacity.

Factors influencing smallholders’ different adaptation strategies

Once the interaction between adaptation strategies and livelihood resilience is understood, further consideration of the factors influencing different adaptation strategies adopted by smallholders is needed to inform policy. By comparing smallholders who adopted SU or SO strategies with NA smallholders, we sought to understand the factors associated with smallholders’ adoption of different types of adaptation strategies. Consistent with some existing studies, our results showed that household size (Deressa et al. 2009), health condition (Sina et al. 2019), number of cropland plots (Wu et al. 2021), nonagricultural income (He et al. 2021), number of livestock (Kuang et al. 2019), and agricultural equipment (Wang et al. 2019) play an important role in the adoption of adaptation strategies by smallholders, which are all part of their livelihood assets endowment.

Beyond household-level factors, we would like to highlight some government intervention factors. Scholars have noted the important impact of local government or institutional actions on smallholder climate change adaptation (Wang et al. 2013, Burnham et al. 2018) because smallholder adaptation does not occur in an institutional vacuum (Kiragu 2010), and some studies have also included the role of government/institutions as an important factor in livelihood resilience (Speranza et al. 2014). Our study finds that some key government policies are not only important components of smallholder livelihood resilience, but also play an important role in the smallholder adaptation process.

First, we found that credit participation has a significant positive effect on smallholder adoption of SO or SU strategies; smallholders who participated in credit were significantly more likely to adopt SU or SO strategies than those who did not participate in credit (Table 5). Low-interest loans provided to smallholders by the government and local banks alleviated the financial constraints they faced, allowing them to improve their livelihoods and invest in agriculture and other adaptation strategies (Ojo et al. 2021). Second, we found a significant positive relationship between participation in cooperatives organized by local governments and smallholders’ adoption of SO strategies. Rapid organizational action plays an important role when shocks and disasters occur (Tewari et al. 2015), and smallholders who participate in cooperatives tend to be more active in adapting to climate change. Third, government-provided skills training also shows a significant positive relationship with smallholder adoption of SO and SU strategies, because learning relevant skills encourages smallholders to take adaptation actions to improve their livelihoods (Ndamani and Watanabe 2016). At the same time, these organizations and activities (training, cooperatives) facilitate smallholders’ access to and exchange of information (Wang et al. 2021) and encourage the adoption of new technologies or strategies (Genius et al. 2014, He et al. 2022b), thereby promoting smallholder development. Finally, our results showed that government subsidies discourage the adoption of SO strategies by smallholders. Table 4 shows that SO smallholders received significantly higher government subsidies than SU and NA smallholders. Although government subsidies directly improve smallholders’ financial situation, high government subsidies may hinder smallholders’ adaptation to climate change.

Policy implications

As climate change intensifies, how to promote the resilience of smallholder livelihoods is becoming a major concern for policy makers. Linking adaptation and livelihood resilience from a household perspective not only contributes to a better understanding of livelihood resilience, but also provides more effective tools for planning and responding to current or future changes (Nelson 2011). Our findings can contribute to policy formulation in terms of promoting smallholder adaptation to climate change and enhancing livelihood resilience: First, governments can enhance the resilience of smallholder livelihoods by expanding the reach of credit, cooperatives, and skills training. These measures will help increase smallholders’ buffering capacity, self-organization, and learning ability under climate change impacts, and encourage smallholders to proactively adopt strategies to cope with climate change and improve smallholder livelihoods. Second, governments can promote smallholders’ adoption of SO or SU strategies by improving primary health care services to improve smallholders’ health conditions, promoting agricultural equipment, and improving agricultural production conditions. Third, the government can improve smallholders’ skills and promote communication by diversifying the forms of cooperatives, enriching the content of training, and increasing the frequency of training, thereby improving the resilience of smallholders’ livelihoods to climate change. Fourth, the government needs to be cautious when planning subsidies and should appropriately control the amount of subsidies to promote smallholders’ climate adaptation by converting government subsidies into animal feed, improving seed distribution, and subsidizing the purchase of agricultural machinery.

Future research

Our findings contribute to the existing literature in at least two ways: (1) revealing the relationship between SO and SU strategies and livelihood resilience, and enriching empirical research linking smallholders’ livelihood resilience to their adaptation strategies; (2) highlighting the role of government interventions in building smallholders’ livelihood resilience, and suggesting policy optimization. Of course, there are shortcomings in this study: the objective measure of resilience ignores the impact of smallholders’ own perspectives on livelihood resilience; the survey focuses on household heads and ignores the perspectives and experiences of their family members; and the survey design process does not sufficiently consider gender differences. Despite these shortcomings, our study remains instructive: (1) research is needed to focus on how to achieve positive interactions between smallholder livelihood resilience and climate change adaptation; (2) the relationship between climate change adaptation and smallholder livelihood resilience can be extended to other sectors affected by climate change, such as fisheries (smallholder fishers also face the option of maintaining, diversifying, or abandoning small-scale fisheries [Salgueiro-Otero et al. 2022]) or agroforestry (Quandt et al. 2023). Therefore, more efforts are needed to better link smallholders’ adaptation with their livelihood resilience. This should include, but not be limited to, exploring the impact mechanisms and theoretical analyses of the two concepts, and also including more case studies across different groups and sectors in different regions and hazards to explore feasible pathways for achieving a positive interaction between adaptation and resilience. In addition, further research exploring the mechanisms of how different adaptive strategies enhance or impair resilience is equally important. We look forward to further studies that optimize our methodology and data to further explore how the synergistic relationship between adaptation and livelihood resilience can be leveraged to improve smallholder farmers’ livelihoods and promote their development and well-being.

CONCLUSION

Achieving the UN’s Sustainable Development Goals requires embedding the resilience paradigm into the fabric of the most vulnerable smallholder livelihoods. Our research aims to link smallholder climate change adaptation to their livelihood resilience. To achieve this goal, (1) we developed a framework for integrating smallholder climate change adaptation with livelihood resilience and constructed an indicator system for assessing smallholder livelihood resilience; (2) we classified smallholder climate change adaptation actions into two categories: stepping out, stepping up; the cross-sectional data from four regions of the Tibetan Plateau were used to assess the livelihood resilience of different types of smallholders and the differences; and (3) we used the multinomial logit model to explore the factors influencing the adoption of different adaptation strategies by smallholders.

Our results show the synergistic effect of adaptation strategies and livelihood resilience: smallholders who adopted adaptation strategies had higher livelihood resilience than those who did not, and smallholders who adopted stepping out strategies had higher livelihood resilience than those who adopted stepping up strategies. The results of the regression model showed that the indicators of household size, health condition, number of cropland plots, agricultural equipment, number of livestock, and nonagricultural income play different roles in the adoption of different adaptation strategies by smallholders. Of greater interest are government interventions (credit, cooperatives, training) that not only increase the resilience of smallholder livelihoods, but also promote the adoption of adaptation strategies by smallholders.

The findings provide recommendations for planning and optimizing policy interventions aimed at amplifying the positive impact of government interventions on smallholder livelihoods, both in terms of livelihood resilience and adaptation. Governments can promote adaptation and livelihood resilience by expanding the coverage of credit, training and cooperatives, diversifying the forms of cooperatives, enriching the content of training, and improving the primary health care system.

__________

^[1] The fieldwork was conducted in July–August, when some of the smallholders were either busy farming or working outside the village, or on their way to high altitude summer pastures to graze their livestock, while the low population density of the TP limited the number of smallholders available for interviews, and our randomized surveys within the villages were conducted on a house-to-house basis to try to interview all smallholders who were still at home and able to communicate normally.
^[2] Of the 1552 smallholders surveyed, 1338 were male-headed smallholders, and the gender ratio was seriously imbalanced. Two approaches were used to control for the influence of gender differences on the results: (1) the questions in the questionnaire mainly describe the objective situation of the household and include less subjective variables; (2) during the survey, other members can add and correct the answers. These two methods avoid to some extent the influence of households with different gender heads on climate change resilience. In addition, we compared the differences in livelihood resilience among smallholders with different gender heads, and the results show that although the livelihood resilience of smallholders with male heads (0.20) is higher than that of smallholders with female heads (0.19), there is no significant difference. Thus, gender differences do not have a significant effect on the results of this study. Gender differences are still important issues that cannot be ignored in subjective resilience measurement studies.
^[3] The sum of these numbers will be greater than the number of smallholders surveyed because some smallholders adopt multiple adaptation strategies simultaneously.
^[4] Comparing the mean values of smallholder livelihood resilience in the four districts using one-way analysis of variance (ANOVA), the livelihood resilience in four regions were significantly different from each other at the 0.1 level with an F-value of 39.22 and a p-value of 0.0000.

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