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Understanding Sustainable Livelihoods with a Framework Linking Livelihood Vulnerability and Resilience in the Semiarid Loess Plateau of China

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Regional climate is complicated and unpredictable in the context of global climate change. Farmers on the Loess Plateau, who rely on agriculture and natural resources for subsistence, are one of the groups feeling the early effects of climate change. Their vulnerability is determined by their degree of connection with the natural environment. Frequent droughts on the Loess Plateau have severely challenged farmers’ livelihoods, although some actions have been taken to adapt to these changes. To enable farmers to find sustainable livelihood strategies in challenging natural conditions, we established a research framework to link livelihood vulnerability and resilience and applied it to Jiaxian County, a specific research area in the Loess Plateau of China. To validate previous research, we studied the fluctuation trends of farmers’ livelihood vulnerability and livelihood resilience in the past 30 years and the interrelationships between these two trends and their influencing factors. The results are as follows: since 1990, livelihood vulnerability has been polarized; however, moderate vulnerability has always been dominant. Livelihood resilience shows a trend of continuous enhancement. The relationship between livelihood vulnerability and resilience is complex, and the direction of change between the two can be both similar and different. The topography, arable land conditions, soil quality, and irrigation conditions in different areas impact vulnerability and resilience, and the degree of impact is different in different periods. Farmers’ livelihood strategies depend on their cognitive decision making and livelihood assets, which are critical vulnerability and resilience factors. Most farmers in the study area have undergone significant livelihood strategy changes, while some maintain their original livelihood strategies. These findings provide policy implications for reducing vulnerability, enhancing resilience, and helping smallholder farmers find sustainable livelihood strategies to avoid poverty traps.
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Citation: Ye, W.; Wang, Y.; Yang, X.;
Wu, K. Understanding Sustainable
Livelihoods with a Framework
Linking Livelihood Vulnerability and
Resilience in the Semiarid Loess
Plateau of China. Land 2022,11, 1500.
https://doi.org/10.3390/
land11091500
Academic Editor: Hossein Azadi
Received: 13 August 2022
Accepted: 2 September 2022
Published: 7 September 2022
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land
Article
Understanding Sustainable Livelihoods with a Framework
Linking Livelihood Vulnerability and Resilience in the
Semiarid Loess Plateau of China
Wenli Ye 1,2, Yin Wang 2, Xinjun Yang 1,2 ,* and Kongsen Wu 2
1Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Northwest
University, Xi’an 710127, China
2College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China
*Correspondence: yangxj@nwu.edu.cn
Abstract:
Regional climate is complicated and unpredictable in the context of global climate change.
Farmers on the Loess Plateau, who rely on agriculture and natural resources for subsistence, are one
of the groups feeling the early effects of climate change. Their vulnerability is determined by their
degree of connection with the natural environment. Frequent droughts on the Loess Plateau have
severely challenged farmers’ livelihoods, although some actions have been taken to adapt to these
changes. To enable farmers to find sustainable livelihood strategies in challenging natural conditions,
we established a research framework to link livelihood vulnerability and resilience and applied it to
Jiaxian County, a specific research area in the Loess Plateau of China. To validate previous research,
we studied the fluctuation trends of farmers’ livelihood vulnerability and livelihood resilience
in the past 30 years and the interrelationships between these two trends and their influencing
factors. The results are as follows: since 1990, livelihood vulnerability has been polarized; however,
moderate vulnerability has always been dominant. Livelihood resilience shows a trend of continuous
enhancement. The relationship between livelihood vulnerability and resilience is complex, and the
direction of change between the two can be both similar and different. The topography, arable land
conditions, soil quality, and irrigation conditions in different areas impact vulnerability and resilience,
and the degree of impact is different in different periods. Farmers’ livelihood strategies depend on
their cognitive decision making and livelihood assets, which are critical vulnerability and resilience
factors. Most farmers in the study area have undergone significant livelihood strategy changes,
while some maintain their original livelihood strategies. These findings provide policy implications
for reducing vulnerability, enhancing resilience, and helping smallholder farmers find sustainable
livelihood strategies to avoid poverty traps.
Keywords: farmers’ livelihood; vulnerability; resilience; the Loess Plateau; China
1. Introduction
In its Fifth Assessment Report, the Intergovernmental Panel on Climate Change (IPCC)
concluded that climate change would significantly impact many regions, especially low-
income developing countries. It may accelerate urbanization, exposing farmers who relocate
to cities and towns to severe risks of infectious diseases and rising food prices [
1
] (Serdeczny
et al., 2016). The most noticeable effect is that all countries face significant risks of rising
temperatures and drought under climate change [
2
], and droughts will likely become more
common in presently dry regions, as will the problems associated with water availability, food
security, and farmer’s livelihoods in rural areas by the end of the 21st century.
Drought is one of the main factors among climatic events affecting the livelihood of
more than two billion people in arid regions, covering 41% of the world’s land area [
3
].
Since the second half of the 20th century, increased droughts due to global warming have
caused unprecedented hardships and challenges for countless farming populations that
Land 2022,11, 1500. https://doi.org/10.3390/land11091500 https://www.mdpi.com/journal/land
Land 2022,11, 1500 2 of 14
depend on soil and water for their agriculture-related activities. In other words, increased
drought poses a significant threat to the sustainability of rural livelihoods; other studies
have shown that drought hinders national GDP growth [
4
]. Relatively fragile ecology
and long-term high-intensity exploitation of the Loess Plateau in China have led to the
degradation of the ecosystem, which was once one of the most devastated areas of soil
erosion in the world [
5
]. In addition, evaporation is often greater than precipitation, and
drought severely restricts agricultural development; a farmers’ livelihood is more difficult
in the Loess Plateau than in other regions. The scarcity of natural resources, ecological
degradation, and drought worsened by climate change make it difficult for farmers to meet
their basic needs through agriculture. To achieve the goal of a sustainable livelihood on the
Loess Plateau, farmers need to respond to and recover from stresses and shocks, maintain
or enhance their capabilities and assets, and provide sustainable livelihood opportunities
for the next generation, resulting in net benefits for other livelihoods at the local and
global levels and in the short and long term [
6
]. Therefore, the following issues need to
be clarified: What is farmers’ livelihood vulnerability? What is the primary manifestation
of this vulnerability? What is resilience? What is the primary manifestation? What is the
relationship between vulnerability and resilience?
The concepts of vulnerability and resilience are increasingly crucial for understand-
ing the relationship between human activities and natural environments. The concept of
vulnerability stems from research on natural disasters and poverty, resulting in different
definitions from different perspectives, but a common attribute is the ability to respond to
disturbances. Existing research has largely focused on case studies of natural disasters and
climate change [
7
]. An actor-oriented approach is often preferred, seeking the root causes,
scale and relevant actors of vulnerability to identify pathways to respond [
8
]. Resilience
originated from the study of ecosystems, flora and fauna by ecologists. It is a measure of
the ability of the systems to absorb disturbances and continue to maintain their functions,
which then determines the persistence of relationships within the system [
9
]. Existing
research focuses on the theoretical model in ecology and mathematics, taking a systematic
approach to emphasize interactions across time and scales [
10
]. Vulnerability and resilience
are two interconnected concepts. Vulnerability is concerned with the degree of exposure,
susceptibility and adaptability of a system to external shocks, and resilience considers the
dynamics of families or communities and how they respond to and recover from external
shocks. Much research has focused on qualitative and quantitative vulnerability assess-
ments [
11
17
], and few studies have simultaneously focused on resilience [
18
], which often
relies on aggregating static vulnerability. Resilience facilitates a comprehensive analysis
of vulnerability, and its forward-looking nature contributes to exploring and dealing with
uncertainty [
19
]. The separate concepts and approaches of vulnerability and resilience are
disconnected from reality and do not meet the needs of sustainable development. Thus,
this initiative brings together a concern for vulnerability and resilience [
8
]. We use quan-
titative analysis to link vulnerability to resilience through adaptive capacity and apply it
to livelihood systems to explore the relationship between them, and then we analyze the
influencing factors that support sustainable livelihoods for farmers on the Loess Plateau.
This study’s objectives are twofold. The first is to develop a framework for estimating
farmers’ livelihood vulnerability and resilience in the ecologically fragile region of the
Loess Plateau. The second is to investigate the connection between livelihood vulnerability
and resilience. Studying farmers’ livelihoods on a microscale is essential for improving
their ability to cope with, respond to, and recover from the impact of perturbations, as
well as to capitalize on opportunities. This objective is particularly conducive to policy
formulation and implementation. This paper is structured in the following way. Section 2
provides a brief introduction to the data. Section 3develops the analytical framework,
Section 4presents the findings, and the final section concludes with a discussion. The
above research takes Jiaxian County, northern Shaanxi Province as the research area, and
the temporal scope is from 1990 to 2020.
Land 2022,11, 1500 3 of 14
2. Materials and Methods
2.1. Study Area and Data Collection
This study was undertaken in Jiaxian County, northern Shaanxi Province (Figure 1).
The region has a continental dry monsoon climate, with 386~451.1 mm of annual precipita-
tion. Due to severe soil erosion in the study area, the Mu Us Desert has gradually invaded
southward, forming three distinct geomorphological subregions: the hilly and sandy area
in the north, the hilly and gully area in the southwest, and the hilly and rocky area along
the Yellow River in the southeast. The desert covers the ground in flaky shapes with
varying thickness, high terrain, and lighter water erosion in the sandy area. The landform
is rounded, with a long beam-shaped landform, endless sand dunes, large hills with gentle
slopes, and alternating ditch beams. The gully area is fragmented, and the terrain slopes
northwest to southeast; the deeper the gully is, the steeper the slope. The low-lying rocky
area has high mountains, deep gullies, and steep cliffs. The administrative region has a
total area of 2029.82 km
2
, with the sandy area accounting for 30.4% of the total area, the
gully area accounting for 52.2%, and the rocky area accounting for 17.4%. The area along
the Yellow River is the most suitable for growing dates in Northern China and has a long
planting history. The date, industry has become an important economic pillar of the county,
with a planting area of 51,880 hm2and an annual output of 271,500 tons.
Land 2022, 11, x FOR PEER REVIEW 3 of 16
provides a brief introduction to the data. Section 3 develops the analytical framework,
Section 4 presents the findings, and the final section concludes with a discussion. The
above research takes Jiaxian County, northern Shaanxi Province as the research area, and
the temporal scope is from 1990 to 2020.
2. Materials and Methods
2.1. Study Area and Data Collection
This study was undertaken in Jiaxian County, northern Shaanxi Province (Figure 1).
The region has a continental dry monsoon climate, with 386~451.1 mm of annual precipi-
tation. Due to severe soil erosion in the study area, the Mu Us Desert has gradually in-
vaded southward, forming three distinct geomorphological subregions: the hilly and
sandy area in the north, the hilly and gully area in the southwest, and the hilly and rocky
area along the Yellow River in the southeast. The desert covers the ground in flaky shapes
with varying thickness, high terrain, and lighter water erosion in the sandy area. The land-
form is rounded, with a long beam-shaped landform, endless sand dunes, large hills with
gentle slopes, and alternating ditch beams. The gully area is fragmented, and the terrain
slopes northwest to southeast; the deeper the gully is, the steeper the slope. The low-lying
rocky area has high mountains, deep gullies, and steep cliffs. The administrative region
has a total area of 2029.82 km2, with the sandy area accounting for 30.4% of the total area,
the gully area accounting for 52.2%, and the rocky area accounting for 17.4%. The area
along the Yellow River is the most suitable for growing dates in Northern China and has
a long planting history. The date, industry has become an important economic pillar of
the county, with a planting area of 51,880 hm2 and an annual output of 271,500 tons.
Figure 1. The location of the sample county and villages.
This study used microlevel survey data collected from rural households in Jiaxian
County from 2017 to 2021. In October 2017, we conducted a preliminary investigation and
visited the statistics bureau and county annals office, bureau of natural resources, weather
department, date industry management office, and other government departments to ob-
tain data and information. We extracted three towns using stratified random sampling
per the three geomorphological areas. We randomly selected three villages from each
town, and we interviewed two households from each village for approximately 30 min. A
total of 52 effective recordings of key figures’ interviews (village cadres, household heads,
etc.) were obtained. A total of 61 villages were chosen, and each of these villages had 5–7
Figure 1. The location of the sample county and villages.
This study used microlevel survey data collected from rural households in Jiaxian
County from 2017 to 2021. In October 2017, we conducted a preliminary investigation
and visited the statistics bureau and county annals office, bureau of natural resources,
weather department, date industry management office, and other government departments
to obtain data and information. We extracted three towns using stratified random sampling
per the three geomorphological areas. We randomly selected three villages from each
town, and we interviewed two households from each village for approximately 30 min. A
total of 52 effective recordings of key figures’ interviews (village cadres, household heads,
etc.) were obtained. A total of 61 villages were chosen, and each of these villages had
5–7 households chosen randomly. We eventually collected 381 effective questionnaires
from these 61 villages, with 84 in the sandy area, 223 in the gully area, and 74 in the rocky
area; the rate of efficiency was 99.5%. The subjects of the questionnaire ranged in age from
40 to 70 years old, and each questionnaire took 40–60 min to complete. Interviews were
conducted, and questionnaires were distributed to village cadres and other key figures,
from whom 42 questionnaires were obtained. The questionnaire includes six parts: basic
Land 2022,11, 1500 4 of 14
household information, natural capital, material capital, financial capital, social capital, and
household perceptions since 1990.
2.2. A Framework Linking Livelihood Vulnerability and Resilience
The IPCC incorporated vulnerability as an essential concept into the sustainable liveli-
hood (SL) framework (2001), defining it as the degree to which a system is susceptible to, or
unable to cope with, adverse effects such as climate change variability and extremes. The
IPCC also stated that the systems’ vulnerability is determined by three factors: exposure,
sensitivity, and adaptive capacity. Exposure is the degree of nature and significant climate
variations; sensitivity is the degree affected, either adversely or beneficially; adaptive
capacity is defined as the ability to adjust to the potential threat, take advantage of the
opportunity, or deal with the consequences [
20
]. Livelihood vulnerability can be under-
stood as an outcome of biophysical and social factors, and biophysical vulnerability refers
to the degree of exposure from the physical impacts of climate change, such as drought
intensification [
21
]. In this paper, livelihood vulnerability means that rural households’
livelihoods are vulnerable to drought and the barren geographical environment on the
Loess Plateau.
Incorporating the concept of resilience into livelihood research helps frame livelihood
in terms of sustainability [
22
]; furthermore, using resilience as a method can help clarify
the dynamics of how people make a living and the complexity of the adaptive system [
23
].
Connecting livelihood approaches to resilience thinking can help us better understand
livelihood dynamics and how households maintain and improve their livelihoods in the
face of stress and shocks. In this paper, we chose to use Chamber and Conway’s widely
accepted definition of livelihood vulnerability and resilience [
6
]. They believed a resilient
livelihood would have little stress on primary productivity and could create new economic
and social development opportunities. Livelihood resilience is characterized by actors’
assets and strategies for maintaining and increasing assets, self-organizing, and learning.
Thus, livelihood resilience is determined by the effectiveness of livelihood, the capacity
and agency of actors, and the social, institutional, and natural conditions.
Only a few researchers have attempted to build a connection between vulnerability
and resilience [
24
]. As we will analyze below, almost none of these has been carried out
in China’s context of the Loess Plateau. Examining the trade-offs between vulnerability
and resilience perspectives in livelihood theory can help supplement academic research in
this field. Resilience approaches typically emphasize the interaction of long-term, slowly
changing and short-term, rapidly changing variables, as well as how they affect scales
in time and space [
25
]. Vulnerability, on the other hand, focuses on human agency and
hazards in much shorter time frames [
26
]. A framework that combines resilience and
vulnerability can provide a perspective that considers both long-term and short-term time
and space. Thus, we employ Mura’s framework and incorporate it into the livelihood
system [27] (Figure 2).
We consider livelihood vulnerability and resilience as overlapping concepts connected
by adaptive capacity. In this framework, vulnerability and resilience become feedback loops,
and the strength of two feedback loops when facing different perturbations at different times
is different. In general, multiple stresses may cause the vulnerability loop to temporarily
dominate, while the resilience loop may be latent. The fragile ecological environment in
the Loess Plateau has negative and positive impacts on farmers’ adaptive capacity, thereby
determining vulnerability and resilience. The adverse effects are mainly reflected in the
drought worsened by climate change and its natural conditions (such as natural resources,
agricultural production conditions, and infrastructure), weakening the adaptive capacity
of the vulnerability loop and thereby leading to increased vulnerability. At the same time,
this vulnerability is most visible in short-term feedback and undesirable long-term forms of
resilience, such as poverty traps. The positive effects are mainly reflected in the unfavorable
living environment, which may enable farmers to be more adaptable than farmers in other
regions and reinforce desirable resilience sequentially in the resilience loop.
Land 2022,11, 1500 5 of 14
Land 2022, 11, x FOR PEER REVIEW 5 of 16
Figure 2. The framework of linking vulnerability and resilience.
We consider livelihood vulnerability and resilience as overlapping concepts con-
nected by adaptive capacity. In this framework, vulnerability and resilience become feed-
back loops, and the strength of two feedback loops when facing different perturbations at
different times is different. In general, multiple stresses may cause the vulnerability loop
to temporarily dominate, while the resilience loop may be latent. The fragile ecological
environment in the Loess Plateau has negative and positive impacts on farmers’ adaptive
capacity, thereby determining vulnerability and resilience. The adverse effects are mainly
reflected in the drought worsened by climate change and its natural conditions (such as
natural resources, agricultural production conditions, and infrastructure), weakening the
adaptive capacity of the vulnerability loop and thereby leading to increased vulnerability.
At the same time, this vulnerability is most visible in short-term feedback and undesirable
long-term forms of resilience, such as poverty traps. The positive effects are mainly re-
flected in the unfavorable living environment, which may enable farmers to be more
adaptable than farmers in other regions and reinforce desirable resilience sequentially in
the resilience loop.
2.3. Approaches to Measuring Livelihood Vulnerability
The vulnerability analysis framework has matured and has been widely used over
time [28–31], scholars typically assess it for three key dimensions of vulnerability: (1) ex-
posure; (2) sensitivity; and (3) adaptive capacity [29,3236]. Based on the preceding dis-
cussions, we identified the following livelihood vulnerability index (LVI) measures as
proxies for livelihood vulnerability (Table 1). The ecological environment and the arid
climate have a significant impact on the livelihood of farmers on the Loess Plateau, and
the impact of these two factors is continuous and cannot be defined at a specific time as
Kumar did [18], so we used drought perceptions among farmers in this paper.
Figure 2. The framework of linking vulnerability and resilience.
2.3. Approaches to Measuring Livelihood Vulnerability
The vulnerability analysis framework has matured and has been widely used over
time [
28
31
], scholars typically assess it for three key dimensions of vulnerability: (1) expo-
sure; (2) sensitivity; and (3) adaptive capacity [
29
,
32
36
]. Based on the preceding discus-
sions, we identified the following livelihood vulnerability index (LVI) measures as proxies
for livelihood vulnerability (Table 1). The ecological environment and the arid climate have
a significant impact on the livelihood of farmers on the Loess Plateau, and the impact of
these two factors is continuous and cannot be defined at a specific time as Kumar did [
18
],
so we used drought perceptions among farmers in this paper.
Table 1. An assessment framework for evaluating the livelihood vulnerability.
Dimension Indicators Description Weight *
exposure-sensitivity
the ratio of a family bringing up the proportion of the population without the ability to work
(such as children, the disabled, the elderly, etc.) 0.12
health the proportion of family medical expenditure in total
expenditure 0.11
quality of arable land land the proportion of slope arable land in total household arable
land 0.07
proportion of reduced production
caused by natural disasters
the proportion of agricultural output lost due to natural
disasters (1: 0–20%; 2: 20–40%; 3: 40–60%; 4: 60–80%; 5:
80–100%)
0.06
agricultural income dependence the proportion of agricultural income in total income 0.08
drinking condition and quality
drinking water sources and water quality. the water source
(1: rainwater; 2: river or lake; 3: storage water; 4: well water;
5: tap water), quality rated on a 5-point Likert Scale
0.06
adaptive capacity
the proportion of non-farm income the proportion of nonagricultural income in total income 0.13
loan opportunities
the opportunity to obtain a loan from the bank (yes: 1; no: 0)
0.03
amount of participation in skill
training participation in technical training such as date planting 0.03
neighbors’ communication the degree of communication between neighbors rated on a
5-point Likert scale 0.06
Information-capturing ability diversity of access to information 0.08
policy awareness the degree of policy known is rated on a 5-point Likert scale 0.05
average education years of laborers the ratio of the number of schooling years of laborers to the
number of laborers in a household 0.12
* The weight is calculated by Analytic Hierarchy Process (AHP).
The type and magnitude determine vulnerability, and rate of climate change and
variation to which a system is subjected, as well as the system’s sensitivity and adaptive
Land 2022,11, 1500 6 of 14
capacity. Hahn provided a reference for calculating LVI [
37
], which considered LVI to be a
function of exposure sensitivity to adaptive capacity and calculated it using Equation (1):
LVI =exposure sensitivity/ada ptive capacity (1)
where
ex posure sensitivity =n
i=1wixi
,
ada ptive capacity =n
i=1wixi
,
wi
represents the
weight of the indicator, and xirepresents the standardization value of the indicator.
Data standardization was performed using the following equations:
xi=xxmin
xmax xmin (xis a positive indicator)(2)
xi=xmax x
xmax xmin (xis a negative indicator)(3)
where
x
is an observed value in an array of observed values for a given variable;
xmax
is
the highest value in the same array; and xmin is the lowest value in the same array.
2.4. Approaches to Measuring Livelihood Resilience
Linking livelihood approaches to resilience is beneficial for understanding livelihood
dynamics and improving their ability to deal with various stresses and shocks [
22
]. In
essence, livelihood resilience can be defined as the capacity of livelihoods to protect against
stresses and disturbances while maintaining or improving their essential properties and
functions. Livelihood resilience is characterized by actors’ assets and strategies for main-
taining and increasing their assets, self-organizing, and learning [
38
]. The following authors
established various research frameworks and indicators for various research backgrounds.
Quandt measured resilience using the SL approach and its five capital assets [
39
,
40
]; Sallu
used a livelihood trajectory approach to investigate the shocks and stresses that affect liveli-
hoods and build resilience [
41
]. Individual livelihood coping ability, individual well-being,
access to livelihood resources, and the sociophysical robustness of the local community
are the four indicators used to assess livelihood resilience by Sina [
42
]; Saker measured
livelihood resilience based on riverine island dwellers’ adaptive capacity, absorptive ca-
pacity, and transformative capacity in the face of natural disasters [
43
]. We used three
major attributes from the definition of resilience in this paper; these are buffer capacity,
self-organization, and learning capacity, which can be further deconstructed into various
indicators based on the literature, expert consultation, and field experience [
38
] (Table 2).
Buffer capacity has been defined as the amount of change (disturbance) a system can
absorb while retaining the same structure, function, identity, and feedback on function
and structure [
44
]. Self-organization emphasizes how human agency, adaptive capacities,
power, and social interactions shape social resilience [
45
]. Adaptive capacity refers to the
ability to learn, which is essential for developing resilience in an individual’s livelihood.
We used the composite index model as follows:
LRI =18
i=1wixi(4)
where LRI represents livelihood resilience index,
wi
represents the weight of the
i
indicator,
and
xi
represents the standardization value of the
i
indicator. The data standardization
method is the same as Equations (2) and (3).
Land 2022,11, 1500 7 of 14
Table 2. An assessment framework for evaluating the livelihood resilience.
Dimension Indicators Description Weight *
buffer capacity
number of labors the number of people able to work 0.10
household incomes per capita the ratio of total income to population 0.09
income diversity index types of household income sources 0.05
housing conditions
housing type and per capita housing area (1:
earth kiln; 2: civil house; 3: stone kiln; 4: concrete
house; 5: storied building)
0.02
per arable land area the ratio of total arable land area to population 0.02
household fixed assets total household fixed assets 0.02
livestock capital the sum of large livestock such as cattle, horses,
pigs, etc. 0.02
self-organization capacity
community support types of community support the family receives 0.07
neighborhood trust proportion of trustworthy neighbors (1: 0–20%;
2: 20–40%; 3: 40–60%; 4: 60–80%; 5: 80–100%) 0.03
organization and
management ability
organization and management ability of village
leaders rated on a 5-point Likert scale 0.16
social network
the number of people who have access to unpaid
loans 0.07
adaptive capacity
share of non-farm income nonagricultural income as a proportion of total
income 0.08
loan opportunities the opportunity to obtain a loan from the bank
(yes: 1; no: 0) 0.02
amount of participation in
skill training
participation in technical training such as date
planting 0.01
neighbors’ communication
the degree of communication between neighbors
rated on a 5-point Likert scale 0.04
information-capturing ability diversity of access to information 0.04
policy awareness the degree of policy known is rated on a 5-point
Likert Scale 0.04
the average education years of
family labors
the ratio of the number of schooling years of
labors to the number of labors in a household 0.10
* The weight is calculated by AHP.
3. Results
3.1. LVI
We divided the LVI results into three categories: low vulnerability, moderate vul-
nerability, and high vulnerability. Table 3depicts the detailed distribution for the three
categories. According to the table, the proportion of low- and high-vulnerability households
has gradually increased, while moderate-vulnerability households have decreased since
1990. In particular, the number of low-vulnerability households increased from 9% to 14%,
the number of high-vulnerability households increased from 2% to 18%, and the number of
moderate-vulnerability households decreased from 87% to 66%. This demonstrates that the
LVI of Jiaxian County has gradually become polarized; however, moderate vulnerability
has always prevailed. According to Table 4, the proportion of households with weakened
vulnerability is 12.3%, the proportion with the same category of vulnerability is 65.1%, and
the strengthened vulnerability is 22.6%. The majority of them are weakened from moderate
to low vulnerability, the vast majority of the remaining households are vulnerable from
moderate to high vulnerability, and moderate to high vulnerability dominates among the
strengthened households.
Land 2022,11, 1500 8 of 14
Table 3. Distribution of households’ LVI percentage from 1990 to 2020.
Categories 1990 2000 2010 2020
L10.09 (38 4)0.10 (41) 0.12 (49) 0.14 (57)
M20.87 (334) 0.83 (319) 0.75 (287) 0.66 (254)
H30.02 (9) 0.05 (21) 0.11 (45) 0.18 (70)
1L—Low, 2M—Moderate, 3H—High, 4Values in the parentheses indicates the number of households.
Table 4. LVI categories in quantity changes of households from 1990 to 2020.
Type and Quantity Weaken Maintain Strengthen
M-L H-M H-L L-L M-M H-H L-M L-H M-H
43 0 4 14 229 5 21 3 62
total 47 (12.3%) 248 (65.1%) 86 (22.6%)
3.2. LRI
The LRI results were divided into three categories: low resilience, moderate resilience,
and high resilience. Table 5depicts the detailed distribution for the three categories. As
shown in the table, the proportion of households with low resilience decreased steadily
from 34% to 0.3%, the proportion of households with moderate resilience increased and then
decreased, the overall trend decreased from 64% to 49%, and the proportion of households
with high resilience increased steadily to 47%. As a result, the LRI in Jiaxian County is
improving steadily. In terms of quantity fluctuations in Table 6categories, there were only
three households with weakened resilience, 32.5% of households with changeless resilience,
and 66.7% of households with enhanced resilience. Among them, the weakened households
all decreased from moderate to low. The weakened households all dropped from moderate
to low. Moderate resilience predominated among households that remained unchanged,
while moderate to high resilience predominated among strengthened households.
Table 5. Distribution of households LRI percentage from 1990 to 2020.
Categories 1990 2000 2010 2020
L10.34 (130 4)0.20 (77) 0.07 (29) 0.03 (12)
M20.64 (246) 0.73 (281) 0.72 (275) 0.49 (188)
H30.01 (5) 0.06 (23) 0.20 (77) 0.47 (181)
1L—Low, 2M—Moderate, 3H—High, 4Values in the parenthesis indicates the number of households.
Table 6. LRI categories in quantity changes of households from 1990 to 2020.
Type and Quantity Weaken Maintain Strengthen
M-L H-M H-L L-L M-M H-H L-M L-H M-H
3 0 0 9 110 5 78 43 133
total 3 (0.8%) 124 (32.5%) 254 (66.7%)
3.3. The Relationship between Livelihood Vulnerability and Resilience
We analyzed the vulnerability and resilience calculation results to summarize the vul-
nerability and resilience relationship, as well as the level changes between the two, to reveal
their relationship. After 1990, resilience among low-vulnerability households gradually
increased from low to medium to high. The proportion of low resilience decreased from
34% to 4%, while moderate resilience increased from 63% to 83% and then decreased to
56%. The proportion of people with high resilience increased steadily from 2% to 40%.
Among moderately vulnerable households, resilience gradually increased from low to
Land 2022,11, 1500 9 of 14
moderate to high, with the proportion of low resilience dropping from 34% to 3%, the
proportion of moderate resilience increasing from 64% to 72% and then decreasing to 49%,
and the proportion of high resilience increasing from 1% to 48%. Among high-vulnerability
households, moderate resilience gradually shifted to medium-high resilience, low resilience
decreased from 11% to 3%, moderate resilience decreased from 88% to 46%, and the propor-
tion of high resilience increased from 0 to 51%. This shows that low, moderate, and high
vulnerability dominance correspond to the resilience grades, all of which are moderate and
high. The relationship between vulnerability and resilience is complex and dynamic, and
there is no linear relationship.
In terms of changes in vulnerability and resilience (Table 7), among the 47 house-
holds with weakened vulnerability, the resilience of 16 households was maintained at its
original level, the resilience of 29 households increased by one level, and the resilience
of 2 households increased by two levels. The vulnerability of 248 households remained
unchanged. Only two households saw their resilience level reduced, while 80 remained at
their original level, 138 increased by one level, and 28 increased by two levels. Only 1 of
the 83 households whose vulnerability increased by one level had resilience weakened by
one level, 28 maintained their original level, 42 increased by one level, and 12 increased
by two levels. Three households’ vulnerability increased by two levels, two by one level,
and one by two levels. We conclude that the change direction between vulnerability and
resilience varies over time. Not only does vulnerability sometimes weaken while resilience
grows, but vulnerability also can remain constant while resilience grows, and both can
even increase at the same time.
Table 7. LVI and LVR categories in quantity changes of households from 1990 to 2020.
LVI * (47) 0 (248) + (83) ++ (3)
LRI
0 (16) 2(1) + (2)
+ (29) 0 (180) 0 (28) ++ (1)
++ (2) + (138) + (42)
++ (28) ++ (12)
*
indicates the level is weakened, “0” indicates the level remains unchanged, “+” indicates the level increased
by one, “++” indicates the level increased by two, values in the parenthesis indicate the number of households.
4. Discussion
4.1. The Impact of Topography on Livelihood Vulnerability
From 1990 to 2020, the study area generally accounted for the majority of moderately
vulnerable households, and moderately vulnerable households evolved into low or high
vulnerability, and most of them evolved into high vulnerability. According to the three
major geomorphological zones, the proportion of high vulnerability households in the
sandy area increased and then decreased, while those in the gully and rocky areas increased.
The proportions were higher in the sandy and gully areas than in the rocky area along the
Yellow River (Table 8).
Table 8. Distribution of households’ LVI percentage in different terrain areas from 1990 to 2020.
1990 2000 2010 2020
L M H L M H L M H L M H
The sand area 0.12
(10 *)
0.86
(72)
0.02
(2)
0.12
(10)
0.83
(70)
0.05
(4)
0.15
(13)
0.60
(50)
0.25
(21)
0.17
(14)
0.63
(53)
0.20
(17)
The gully area 0.08
(18)
0.89
(198)
0.03
(7)
0.11
(25)
0.83
(184)
0.06
(14)
0.14
(31)
0.74
(165)
0.12
(27)
0.14
(32)
0.67
(149)
0.19
(42)
The rocky area 0.14
(10)
0.86
(64) 00.08
(6)
0.88
(65)
0.04
(3)
0.07
(5)
0.82
(61)
0.11
(8)
0.15
(11)
0.70
(52)
0.15
(11)
* Values in the parenthesis indicates the number of households.
Land 2022,11, 1500 10 of 14
Households with high vulnerability were mainly concentrated in the northern sandy
area and the southwestern gully area. Most of these households were traditional agricul-
tural households that primarily planted corn and had only one source of income since
1990. The northern sandy area is a grain-grazing area with a large woodland area and
some breeding grounds, a large arable land area, and plentiful water resources, but the soil
is desertified and low in fertility. Farmers used extensive management techniques, such
as extensive planting and thin harvesting. The southwest gully area is a grain area with
good soil texture where farmers grow primarily traditional grains, but the area has sparse
vegetation, severe soil erosion, and a lack of water resources. The rocky area along the
Yellow River’s southeastern bank is part of the date–grain intercropping area, which is
suitable for planting various fruit trees. Farmers have planted a large area of red dates with
high yields and quality since the 1950s.
The vulnerability of livelihoods depends on the ratio of household exposure sensitivity
to adaptive capacity. Many households have cultivated land that is primarily sloping and
unsuitable for large-scale planting and is greatly affected by natural disasters such as drought.
Drought is the most serious natural disaster in Jiaxian County, and the average annual
precipitation over many years can meet only half of the crop’s water demand. Precipitation is
concentrated in July, August, and September, with a very low utilization rate. Furthermore,
hail and frost that occur during crop growth and maturity cause significant damage to
agricultural production. For a long time, since the reform and opening up, rural households’
income has been primarily based on agricultural income, and climatic conditions and natural
capital status primarily determine the livelihood status of these families. Furthermore, if
family members suffer from chronic diseases or the core labor force suffers from emergencies
such as illness or accidents that necessitate significant household economic expenditures, the
impact on livelihood vulnerability can be profound. Persistence is one of the spectacular
characteristics of drought in the Loess Plateau; however, farmers may be more vulnerable to
impending drought before fully recovering from a previous drought.
4.2. The Impact of Topography on Livelihood Resilience
The LRI of Jiaxian County households increased from 1990 to 2020, with the pro-
portion of low- and moderate-resilience households decreasing and the proportion of
high-resilience households increasing. In terms of the three major geomorphological di-
visions, households with low and moderate resilience continued to decline in the sandy
area, while those with high resilience increased and occupied the majority and the highest
proportion of the three types of landforms. In the gully area, households with low and
moderate resilience originally accounted for the majority but have predominantly evolved
into moderate and high resilience, and the differences between these proportions are slight.
In the rocky area, the proportion of households with low resilience has always been the
lowest and eventually dropped to 0, while the proportion of households with moderate
resilience has always been the highest, although it has dropped from 70% to 51%. The
proportion of those with high resilience rose from 0 to 49% (Table 9).
Table 9. Distribution of households’ LRI percentage in different terrain areas from 1990 to 2020.
1990 2000 2010 2020
L M H L M H L M H L M H
The sand areas 0.33
(28 *)
0.65
(55)
0.01
(1)
0.20
(17)
0.71
(60)
0.08
(7)
0.06
(5)
0.69
(58)
0.25
(21)
0.04
(3)
0.43
(36)
0.54
(45)
The gully areas 0.36
(80)
0.62
(139)
0.02
(4)
0.21
(46)
0.73
(163)
0.06
(14)
0.08
(18)
0.73
(163)
0.25
(42)
0.04
(9)
0.51
(114)
0.45
(100)
The rocky areas 0.14
(22)
0.70
(52) 00.19
(14)
0.78
(58)
0.03
(2)
0.08
(6)
54
(0.73)
0.19
(14) 00.51
(38)
0.49
(36)
* Values in the parenthesis indicate the number of households.
Land 2022,11, 1500 11 of 14
Farmers made a living primarily through stock breeding and traditional grain culti-
vation in the 1990s, but the arid climate, severe land desertification, mostly sloping land,
and lack of irrigation water sources seriously hampered their livelihood. With the advance-
ment of China’s urbanization process and changes in the agricultural structure, livelihoods
have undergone tremendous changes, and household income has increased significantly.
Following the relocation of a large number of rural laborers to cities and towns, the num-
ber of laborers in most families engaged in nonagricultural activities increased, as did
nonagricultural income. When family members choose to engage in nonagricultural work
in urbans, they will obtain more opportunities to diversify their income sources; family
material capital, such as housing conditions and the number of household daily necessities,
has greatly improved. Furthermore, implementing the targeted poverty alleviation policy
has allowed farmers to obtain more community support, such as free loans, and learn more
skills through training opportunities, allowing those with lower levels of education to learn
livelihood skills in addition to farming. At the institutional level, government and social
institutions help smallholder farmers further build adaptive capacity through publicity and
education; specific measures include providing job skills training, more job opportunities,
and lending opportunities. In addition, social activities should be organized to broaden
their social trust and organizational management skills.
4.3. Determinants of Vulnerability and Resilience
The external conditions in which a farmer lives and the family’s internal decision
making collectively determine a family’s mode of livelihood. The mode of livelihood
determines the household’s livelihood status, vulnerability, and resilience. The external
conditions where farmers live include the geographic location of the place of residence,
climatic conditions, and other objective facts that are difficult to change. These external
conditions have a notable impact on farmers’ livelihoods. Landforms, for example, deter-
mine farmland quality, and climate conditions determine whether there is a superior and
appropriate agricultural foundation. The Loess Plateau’s delicate ecological environment
is difficult to alter in a short period. For a long time, farmers engaged in agricultural
production have been exposed to the fragile ecological environment due to desertification,
frequent droughts, soil erosion, and other issues. Furthermore, farmers’ adaptability is
generally low, which is the primary reason for their livelihoods’ vulnerability.
Farmers’ livelihoods in Jiaxian County have changed dramatically since the 1990s.
Approximately 10% of the surveyed households’ livelihoods have always been traditional
grain cultivation, while approximately 20% have switched from grain cultivation to date
cultivation. As a result of market fluctuations, many family members moved to cities and
towns to engage in nonagricultural activities, and the proportion of households engaged
in nonagricultural activities increased from 30% to 88%. Their livelihoods shifted from
traditional agriculture to nonagricultural modes to meet ever-increasing living require-
ments. Their livelihoods gradually diversified under the nonagricultural mode, and their
reliance on agriculture diminished or even vanished. Without taking into account other
external factors, the livelihood status of rural households is determined by their means
of living. According to the traditional agricultural livelihood model, livelihood status is
primarily determined by natural conditions and the quantity and quality of natural capital,
with natural capital determining physical and financial capital. Market fluctuations are an
essential factor in the new agricultural model, in addition to the effects of natural condi-
tions and natural capital. Compared with the traditional agricultural model, household
livelihoods have gradually improved, but market prices seriously affect farmers’ income
and are extremely unstable. Farmers have more employment opportunities and sources
of income in the nonagricultural model, and their livelihoods are no longer influenced
by multiple external factors, such as geographic location, natural conditions, and market
fluctuations. The primary constraints are one’s educational level and vocational skills.
Land 2022,11, 1500 12 of 14
4.4. The Relationship between Vulnerability and Resilience
In this paper, we concluded that the relationship between vulnerability and resilience
is complicated and dynamic, and there is no linear relationship. In particular, there is no
clear pattern in their direction of fluctuation, and there are scenarios where vulnerability
decreases and resilience increases, and the consistent finding is that resilience is the flip
side of vulnerability [
19
]. Additionally, Folke argues that vulnerability is the antonym of
resilience [
46
], and a vulnerable system has lost resilience [
47
]. However, there are situations
in which vulnerabilities remain the same while resilience grows, or both at the same time;
similar findings have been observed by Turner et al. Their research shows resilience is not
considered the flipside of vulnerability; they do not have a simple linear relationship [
48
].
Therefore, we can merely draw the conclusion that the relationship between vulnerability
and resilience is complex and difficult to generalize with simple laws in livelihood systems,
and the specific situation of different regions needs to be analyzed in detail. Additionally,
we need to further explore the sequence of farm household adaptive-capacity enhancement
for resilience enhancement and vulnerability reduction when farmers suffer from external
disturbances such as natural disasters. Finally, this study is the result of our research on the
framework of the vulnerability and resilience of farmers’ livelihoods applied to ecologically
fragile areas of the Loess Plateau. It remains to be seen whether these findings can be
applied to other areas. Furthermore, in-depth investigations and further studies on more
and different typical cases are required to arrive at a generalizable framework suitable for
most regions.
5. Conclusions
Farmers are the most basic unit of production and consumption in rural areas, and
their livelihoods are extremely vulnerable to external factors, especially in ecologically
fragile areas in developing countries. Understanding the changing trends in vulnerability
and resilience and the relationship between them in terms of sustainable livelihood is
critical. In this study, we aimed to determine how certain rules have changed and hope
to supplement the research on the relationship between vulnerability and resilience and
provide practical enlightenment for improving the livelihoods of farmers in ecologically
fragile areas. We established a research framework that linked vulnerability and resilience
through adaptive capacity; utilized Jiaxian County, a typical Loess Plateau area, as a case
study; and established an index system to quantify and analyze changing trends and the
dynamic relationship between the two over 30 years.
The results show that most households’ LVI was in the moderate category over the
30 years
, presenting the trend of the development for the two levels of differentiation. The
proportion of low- and high-vulnerability households increased, and the LRI continued
to increase, so we conclude that in the complex nonlinear dynamic relationship between
vulnerability and resilience, high vulnerability and high resilience coexist. According to
the findings of the study, the livelihood system of rural households in Jiaxian County has
entered a long-term resilience loop. Following the emergence of the short-term vulnerability
loop caused by climatic conditions and the ecological environment, various adaptive
strategies were used to enhance resilience. The fragile ecological environment of the
Loess Plateau poses risks and obstacles to the livelihoods of farmers who have lived in
the area for a long time and rely heavily on the natural environment. The livelihood
vulnerability caused by this difficulty cannot be easily or quickly reduced and may even
worsen in the future. As the findings show, the LVI of some households has continued
to rise. However, because they have been living in this environment for a long time,
they may be more adaptable to the vulnerable environment than farmers in other regions,
and this adaptability will gradually increase. Adaptation can reduce the risks of climate
change impacts, but it has limitations, particularly as the magnitudes and rates of climate
change increase. Longer-term, in the context of sustainable development, there is a greater
likelihood that more immediate adaptation actions will also improve future options and
preparedness. Due to the limitations of this study area, the results of this paper still need
Land 2022,11, 1500 13 of 14
to be further verified; in addition, the understanding of the causes of vulnerability and
resilience, and the direction and possibility of the livelihood transformation of farmers
under the sustainable development goals should be strengthened, so as to provide guidance
for formulating effective policies.
Author Contributions:
Investigation, K.W., Y.W. and X.Y.; writing—original draft preparation, W.Y.;
writing—review and editing, W.Y.; visualization, W.Y.; supervision, X.Y.; funding acquisition, X.Y. All
authors have read and agreed to the published version of the manuscript.
Funding:
This research was funded by the National Natural Science Foundation of China, grant
number 41771574.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
The data presented in this study are available on request from the
corresponding author.
Acknowledgments:
We would like to thank Wu Kongsen, Wang Yin, Min Dian, and Tang Honglin for
contributing to the household survey and data collection. Furthermore, the authors would like to express
their appreciation to anonymous reviewers for the insightful comments that improved this manuscript.
Conflicts of Interest: The authors declare no conflict of interest.
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... Land is one of the important factors of production [33]. The quality of the land will lead to the emergence of poverty by reducing the income from agricultural production [34]. In recent years, some scholars have found that the coupling relationship between rocky desertification and poverty has changed in the new era, and the ecological environment is no longer the main factor leading to poverty in Karst areas of China [28]. ...
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