Coupling and Coordination Relationship between Livelihood Capital and Livelihood Stability of Farmers in Different Agricultural Regions

Abstract

Sustainable livelihood of farmers is the key to rural revitalization. The purpose of this study was to establish livelihood capital cultivation models suitable for farmers in different agricultural regions and to enhance the sustainability of farmers’ livelihoods by selecting four agricultural regions (suburban agriculture, intensive agriculture, plain field agriculture, and mountain agriculture) and 1025 rural households and employing Participatory Rural Appraisal (PRA) to systematically analyze the characteristics of livelihood capital and livelihood stability of farmers in different agricultural regions and the coupling coordination relationship between them based on a Sustainable Livelihood Framework (SLF). The results show that: (1) The average household livelihood capital is highest in intensive agricultural regions and lowest in mountain agricultural regions. (2) Farmers in suburban agricultural regions have a variety of sources of income; thus, the livelihood stability is the highest. while it is lowest in intensive agricultural regions. (3) The coupling coordination degree of livelihood capital and livelihood stability in suburban agricultural regions and plain field agricultural regions is primary coordination, while in intensive agricultural regions and mountain agricultural regions, livelihood capital and stability are slightly out of balance.

1. Introduction

Eradicating poverty is the greatest global challenge and an indispensable requirement for sustainable development [1]. The United Nations 2030 Agenda for Sustainable Development proposes to “End poverty in all its forms everywhere”. However, farmers are vulnerable to multiple risks such as natural disasters, policy changes and disease outbreaks, and are easily trapped in a vicious cycle of chronic poverty [2]. Currently, global climate change and geopolitical conflicts are further threatening agricultural production and the livelihoods of farmers, especially smallholder farmers in developing countries [3].

China accomplished a “complete victory” in the fight against poverty and opened a new chapter of rural revitalization in 2020. However, at the same time, there is a high incidence of returning to poverty among those who have escaped from poverty, and according to statistics, the annual return rate of farmers who have escaped from poverty in all poor areas is more than 20%, with a potential return group of about 58.25 million people [4]; thus, the sustainability of poverty eradication faces serious challenges. Rural poverty in China will enter a new phase characterized by secondary poverty and relative poverty [5,6]. In the face of the new poverty pattern, it is especially necessary to enhance the sustainability of livelihoods of farmers who have escaped from poverty, which is not only the focus of rural poverty alleviation in the new stage, but also an important foundation for the implementation of rural revitalization [7]. However, there are obvious differences in resource endowments in different regions of China [8], and farming types are complex and diverse; thus, farmers’ livelihoods are bound to present diverse characteristics. Then, what kind of differences exist in farmers’ livelihood capital in different agricultural regions? Does the accumulation of livelihood capital significantly improve the livelihood stability of farmers? How can the sustainability of farmers’ livelihood be improved? In the context of increasingly severe risks faced by farmers, scientific answers to these questions will help farmers in different regions choose rational livelihood strategies that suit their needs, build sustainable livelihoods, and improve their ability to cope with risks. At the same time, policy measures can be taken, adapted to local conditions, to push forward all-round rural vitalization, and promote sustainable regional development. This is not only important for achieving sustainable livelihoods of rural households in China, but also has reference value for the development of small farmers in developing countries. Up to now, far too little attention has been paid to them.

In fact, the Sustainable Livelihood Framework (SLF) composed of vulnerability context, livelihood assets, structures and processes, livelihood strategies, and livelihood outcomes provides a new perspective for livelihood capital measurement and livelihood sustainability assessment [9]. Firstly, according to socio-economic development, scholars are continuously improving the measuring index of farmers’ livelihood capital, which provides reference for the selection of a livelihood capital index in this study [10,11,12]. Secondly, livelihood stability is the ability of farmers to maintain or restore their livelihood assets when facing external or internal pressure, and is also a key component of farmers’ livelihood sustainability [13]. Many studies have focused on the coupling coordination relationship between farmers’ sustainable livelihood and factors such as land use pattern [14,15], choice of livelihood strategies [16,17,18], and ecosystem services [19,20,21]. These studies have put much emphasis on livelihood capital measurement and livelihood stability assessment, but there are few studies about the coupling coordination relationship between livelihood capital and livelihood stability. Additionally, previously published studies are limited to single or local areas [22,23,24]. Comprehensive studies on the coupling and coordination relationship between livelihood capital and livelihood stability of farmers in different regions in China have not been reported. This will undoubtedly make it difficult to develop locally appropriate all-round rural vitalization strategies.

China has a vast territory, and farmland use patterns vary greatly under different regional systems, forming unique agricultural regions; these may be categorized as suburban agricultural regions, intensive agricultural regions, plain field agricultural regions and mountain agricultural regions [25]. Suburban agricultural regions develop modern agriculture such as green ecological agriculture and leisure agriculture with support from cities. Intensive agricultural regions increase crop production through establishment of vegetable greenhouses and developing facility agriculture by applying advanced agricultural technology, more labor, and production materials. In plain field agricultural regions, farmland can be connected into a large block, thus achieving large-scale operation, and they are mainly planted with the main grain crops. Mountain agricultural regions have a larger proportion of sloping farmland where farmers mainly plant corn, sweet potatoes, and other crops.

In view of this, taking suburban agricultural regions, intensive agricultural regions, plain field agricultural regions and mountain agricultural regions as study areas, based on the sustainable livelihood framework, this study constructs a measurement index of livelihood capital and livelihood stability. The study systematically reveals the characteristics of livelihood capital and livelihood stability of farmers in different agricultural regions, and their coupling coordination relationship by employing the coupling coordination model to provides a scientific basis for improving the sustainable livelihood of farmers in different regions.

2. Materials and Methods

2.1. Data

This study used a combination of stratified sampling and random sampling to select survey respondents. First, typical representative areas of the four types of agricultural regions, namely Beijing City, Shandong province, Hubei province and Chongqing City, were selected according to differences of topography and farmland use patterns, which represent suburban agricultural regions, intensive agricultural regions, plain field agricultural regions and mountain agricultural regions in turn. Second, 2–3 typical districts or counties were selected from each province or city according to the economic development, agricultural structure, and location conditions, with a total of 10 districts being selected. Then, 2–4 towns were selected from each district or county according to the per capita income and farmland management characteristics, with a total of 24 towns. Finally, 3–6 villages were randomly selected in each township and 10–30 rural households were randomly selected from each village for investigation, according to the village population size. Figure 1 shows the geographical location of the study area.

The research group carried out questionnaire surveys to record the farmland management, income source, labor capacity, social security and so on per rural household in October 2018, using semi-structured interviews for participatory rural appraisal (PRA) [26], which provided data support for this study. A total of 1025 questionnaires were distributed and 905 completed questionnaires were recovered with an 88.29% effective rate. The questionnaire included information on family labor force, basic characteristics of land use, family production and operation, family dwelling and fixed assets, and total annual family expenditure and total income. The household demographic information (number of workers, education level), farmland operation (farmland area, crop type and number of plots) and income and expenditure (agricultural income, farming income and expenditure on energy, education, medical care, communications, etc.) involved in this study were included. These indicators can meet the needs of the study and provide good data support for this paper.

Table 1. Description of the survey of rural households in the study areas.

Agricultural Regions	Study Districts or Counties	Number of Selected Villages	Number of Selected Rural Household	Number of Effective Questionnaires	Effective Rate/%
Suburban agricultural regions	Daxing District, Tongzhou District and Shunyi District of Beijing	23	234	204	87.18
Intensive agricultural regions	Shouguang City, Qingzhou City and Changle County of Shandong	19	202	172	85.15
Plain field agricultural regions	Jianli County and Honghu City of Hubei	24	287	257	89.55
Mountain agricultural regions	Wulong District and Youyang County of Chongqing	18	302	272	90.07
Total		84	1025	905	88.29

presents a description of the survey of rural households in the study area.

2.2. Methods

2.2.1. Livelihood Capital Measurement

Livelihood capital refers to the available resources that can be stored, communicated or allocated to generate income streams or other benefits necessary for people to live [27]. Livelihood capital within the Sustainable Livelihood Framework which was developed by the Department for International Development (DFID) of the United Kingdom includes natural capital, physical capital, financial capital, human capital, and social capital [28]. Referring to the general situation of study areas and existing research, this study constructs the measurement index system of farmers’ livelihood capital in order to reflect the livelihood capital of farmers comprehensively and accurately in four provinces or cities [23,29].

Table 2. Measurement index system of farmers’ livelihood capital.

Livelihood Capital	`Index	Index Property	Index Meaning	Weight
Natural capital (N)	Farmland area (N1)	Direct	Area of farmland per household, including paddy fields and dry land (mu a).	0.324
	Quantity of farmland plot (N2)	Inverse	The number of farmland plots per household. Change the inverse index to the direct index by taking the reciprocal b.	0.333
	Crop variety (N3)	Direct	Variety of crops planted by farmers, including food crops and cash crops.	0.343
Physical capital (P)	House area (P1)	Direct	House area per household (m2).	0.181
	Electricity charge (P2)	Direct	Annual electricity payment per household (yuan c).	0.176
	Whether the village has open roads (P3)	Direct	Yes = 1; No = 0.	0.181
	The cleanliness level of roads in villages (P4)	Direct	Very messy = 1; Messy = 2; Moderate = 3; Clean = 4; Very clean = 5.	0.184
	Number of tools and durable goods (P5)	Direct	Number of durable goods per household, including farming machinery and travel tools.	0.176
	Livestock capital (P6d)	Direct	According to livestock production costs and market values, the livestock includes pigs (P61), cattle (P62), sheep (P63), poultry (P64), freshwater aquaculture (P65) and other (P66), with values of 0.2, 0.8, 0.3, 0.02, 0.02 and 0.02, respectively.	0.102
Financial capital (F)	Net agricultural income (F1)	Direct	Annual net agricultural income per household (yuan c).	0.200
	Wage income (F2)	Direct	Annual wage income per household (yuan c).	0.210
	Transfer income (F3)	Direct	Annual transfer income per household (yuan c).	0.196
	Property income (F4)	Direct	Annual property income per household (yuan c).	0.148
	Non-agricultural operating income (F5)	Direct	Annual non-agricultural operating income per household (yuan c).	0.129
	Loan amount (F6)	Direct	Annual loan amount per household (yuan c).	0.117
Human capital (H)	Number of labor (H1)	Direct	Total number of household labor, i.e., the healthy labor aged 14–65.	0.151
	Number of non-agricultural labor (H2)	Direct	Total number of household non-agricultural labor.	0.152
	Labor capacity (H3)	Direct	According to age and health condition of farmers, they are divided into no labor ability, semi-labor ability and perfect labor ability, with values of 0, 0.5 and 1, respectively.	0.151
	Educational level (H4e)	Direct	According to the impact of different educational levels on human capital, educational levels are divided into illiteracy (H41), primary education (H42), junior middle school education (H43), senior high school education (H44), junior college education and above (H45), with values of 0, 1, 2, 3 and 4, respectively.	0.151
	Education expenditure (H5)	Direct	Annual education expenditure per household (yuan c).	0.132
	Proportion of male (H6)	Direct	Proportion of men in family members (%).	0.153
	Medical expenditure (H7)	Inverse	Annual medical expenditure per household (yuan c). Change the inverse index to the direct index by taking the reciprocal b.	0.110
Social capital (S)	Transportation (S1)	Direct	Annual transportation expenditure per household (yuan c).	0.201
	Communication (S2)	Direct	Annual communication expenditure per household (yuan c).	0.230
	Number of people working in urban areas for more than half a year (S3)	Direct	Total number of people per household working in urban areas for more than half a year.	0.220
	Whether there are village cadres in the household (S4)	Direct	Yes = 1; No = 0.	0.165
	Whether there are communists in the household (S5)	Direct	Yes = 1; No = 0.	0.184

^a 1 mu ≈ 667 m² or 0.067 ha. ^b in order to avoid invalid formula, the original value 0 was taken as 0.1 when this study changed the inverse index to the direct index by taking the reciprocal. ^c 1 yuan ≈ 0.15 dollar or 0.14 euro. ^d P₆ = 0.2 × P₆₁ + 0.8 × P₆₂ + 0.3 × P₆₃ + 0.02 × P₆₄ + 0.02 × P₆₅ + 0.02 × P₆₆. ^e H₄ = 0 × H₄₁ + 1 × H₄₂ + 2 × H₄₃ + 3 × H₄₄ + 4 × H₄₅.

presents the measurement index system of farmers’ livelihood capital.

Considering the differences in the properties and unit of each index, in this study, the inverse index inverse normalization method and the extreme difference standardization method are used for homogenization and dimensionless processing, and the entropy weight assignment method is used to measure the weights of each index [30]; then, the per household livelihood capital of farmers in four provinces or cities (C_j) is obtained, respectively [23]. The calculation formula of livelihood capital is as follows:

$$C_j={{\displaystyle \sum }}_{i=1}^n{X}_{ij}{W}_j$$

(1)

where n represents the number of effective questionnaires in four provinces or cities, with values of 204, 172, 257 and 272, respectively; i represents the ith rural household; j represents the jth livelihood capital index; $X_{ij}$ represents standard values of each sub-item livelihood capital; $W_j$ represents the weight of the jth livelihood capital index.

According to Formula (1), the average values of natural capital $V_1$, physical capital $V_2$, financial capital $V_3$, human capital $V_4$ and social capital $V_5$ sub-values of rural households in four provinces or cities were obtained, respectively, and then the comprehensive value of livelihood capital $f\left(t\right)$ was obtained by taking the average of these five values. The formula for calculating the comprehensive value of livelihood capital is as follows:

$$f\left(t\right)={{\displaystyle \sum }}_{p=1}^5{V}_p/Z$$

(2)

where $V_p$ represents the value of the pth sub-item livelihood capital; $Z$ represents the number of livelihood capital sub-items and the value of $Z$ is 5 in this study.

2.2.2. Livelihood Stability Measurement

Livelihood stability refers to the ability of farmers to withstand risks in the face of external or family changes [23], and is one of the important indicators for farmers to maintain basic livelihood security and one of the important indicators for livelihood sustainability [31]. Stability is an important characteristic of natural resources and economic income systems as proposed by Holtz [32]. Combining the existing research results, this paper uses income diversity, agricultural livelihood diversity, nonfarm livelihood diversity, income dependence, labor dependence, and resource dependence to measure the stability of farmers’ livelihoods.

(1) Livelihood Diversity Indexes. Farmers’ livelihood diversity is calculated according to the number of livelihood activities engaged in by rural households, which can be divided into agricultural livelihood diversity and non-agricultural livelihood diversity. The higher the livelihood diversity is, the more stable the farmers’ livelihood is and the stronger is the ability to resist risks. The calculation formula of farmers’ livelihood diversity is as follows:

$$K_{act}=Y_i/Y$$

(3)

where K_act represents farmers’ livelihood diversity; Y_i represents the number of different livelihood activities the ith rural households engaged in; Y represents total number of farmers’ livelihood activities. The diversity of agricultural livelihood includes crop cultivation, forestry cultivation and livestock breeding. The diversity of non-agricultural livelihood includes transfer income (relatives and friends’ remittances, agricultural subsidies, subsidies for the Grain-for-Green project, pensions, government benefits, etc.), property income (farmland acquisition-compensation and land transfer rents, etc.), wage income, household loan amount and other non-agricultural operating income.

The Shannon–Wiener diversity index is used in this study to reflect the degree of balance of rural household income sources directly at the same time [24]. The larger the value of the index is, the more diverse and balanced the income sources of farmers are, and the higher farmers’ livelihood stability is. The calculation formula of the Shannon–Wiener diversity index is as follows:

$$K_{inc}=-{{\displaystyle \sum }}_{n=1}^s{P}_{n}ln{P}_n$$

(4)

where $K_{inc}$ represents the Shannon–Wiener diversity index; $P_n$ represents the ratio of the nth income to total rural household income; s represents the range of income sources.

(2) Livelihood Dependence Indexes. The income dependence index reflects rural household dependence on a certain source of income, that is, the proportion of a certain kind of income in the total household income [33]. The larger the value, the higher the dependence on a certain type of income, the more homogeneous the household structure is, and the poorer the ability to resist risks, which threatens the stability of the household’s livelihood. It is necessary to seek the diversification of household income channels or narrow the gap between various incomes [34]. The dependence index of labor is the proportion of farmers’ income from labor in the total household income. The calculation formulas are as follows:

$$D_{inc}={\displaystyle \sum }_{n=1}^s\frac{X_n\left(X_n-1\right)}{X\left(X-1\right)}$$

(5)

$$D_{wor}=X_w/X$$

(6)

where $D_{inc}$ represents the income dependence index; $X_n$ represents the nth income of farmers; $X$ represents total household income; s represents varieties of income sources. $D_{wor}$ represents labor dependency index, $X_w$ represents farmers’ income from labor.

In addition, the natural resource dependence index reflects farmers’ dependence on natural resources. The higher the value of the index is, the higher is the dependence of farmers on natural resources and the higher are the rural household livelihood risks. For rural households, the general application of natural resources is mainly reflected in the income from agricultural operations [35]; thus, in this study, the proportion of net agricultural income (crop cultivation income, forestry cultivation income and livestock breeding income, etc.) in the total household income is used to calculate the natural resource dependence index. The calculation formula of the natural resource dependence index is as follows:

$$D_{sou}=N/T$$

(7)

where $D_{sou}$ represents the natural resource dependence index; N represents net household agricultural income; T represents total household income.

2.2.3. Coupling Coordination Degree Model of Livelihood Capital and Livelihood Stability

Coupling reflects the degree of interaction and influence between two or more systems [36], while the coupling coordination degree reveals the synergistic effect among the interacting systems [37]. This study calculates the coupling coordination degree between livelihood capital and livelihood stability of farmers to reveal the differences in farmers’ livelihood sustainability in different agricultural regions. The higher the coupling coordination degree is, the higher farmers’ livelihood stability will be when the rural household livelihood capital is accumulated at the same speed. The calculation formulas are as follows:

$$C={\left\{\frac{f\left(t\right)\times f\left(v\right)}{({\left(f\left(t\right)+f\left(v\right)/2\right)}^2}\right\}}^{\frac{1}{2}}$$

(8)

$$D=\sqrt{C·T}=\sqrt{C\times \left({\beta }_{1}f\left(t\right)+{\beta }_{2}f\left(v\right)\right)}$$

(9)

where C represents the coupling degree; $f\left(t\right)$ represents the comprehensive value of livelihood capital; $f\left(v\right)$ represents the comprehensive value of livelihood stability; D represents the coupling coordination degree; T represents the comprehensive evaluation score of $f\left(t\right)$ and $f\left(v\right)$. In this study, livelihood capital and livelihood stability are equally important, so β₁ = β₂ = 0.5.

Referring to previous studies [38,39], this study divides the coupling degree into low-level coupling (0 ≤ C ≤ 0.3), antagonism (0.3 < C ≤ 0.5), running-in level (0.5 < C ≤ 0.8) and high-level coupling (0.8 < C ≤ 1), and divides the coupling coordination degree into four levels: serious imbalance, mild imbalance, primary coordination and high-quality coordination (

Table 3. Classification of coupling coordination degree.

Ranges of Coupling Coordination Degrees	Coupling Coordination Degrees
0.00 < D ≤ 0.35	Serious imbalance
0.35 < D ≤ 0.55	Mild imbalance
0.55 < D ≤ 0.75	Primary coordination
0.75 < D ≤ 1.00	High-quality coordination

).

3. Results

3.1. Comparison of Livelihood Capital per Rural Household in Different Agricultural Regions

Table 4. Comparison of livelihood capital per rural household in different agricultural regions.

Different Agricultural Regions	Natural Capital	Physical Capital	Financial Capital	Human Capital	Social Capital	Comprehensive Value of Livelihood Capital
Suburban agricultural regions	0.2877	0.3178	0.0561	0.2922	0.1945	0.2297
Intensive agricultural regions	0.2164	0.3973	0.0606	0.3099	0.1713	0.2311
Plain field agricultural regions	0.1790	0.3897	0.0544	0.3500	0.1701	0.2287
Mountain agricultural regions	0.1767	0.3593	0.0328	0.2835	0.1257	0.1956

shows the comprehensive value of livelihood capital per rural household in different agricultural regions. The values for intensive agricultural regions, suburban agricultural regions, plain field agricultural regions and mountain agricultural regions are 0.2311, 0.2297, 0.2287 and 0.1956, respectively; the value for intensive agricultural regions is thus 0.0355 higher than that for mountain agricultural regions. Figure 2 shows the sub-item livelihood capital per household in different agricultural regions. The natural capital and social capital per rural household in suburban agricultural regions are 1.63 times and 1.55 times higher than those in mountain agricultural regions, respectively. Relying on the development and demand of cities, the farmers in suburban agricultural regions can plant rich varieties of vegetables, fruits and flowers with high added value, and the surplus agricultural labor can also actively work in the city, which leads to more frequent population movements, and closer ties with the outside word. The physical capital and financial capital per rural household in intensive agricultural regions are higher with value of 0.3973 and 0.0606, respectively. Farmers in intensive agricultural regions can use advanced technologies and management tools to improve agricultural income, and then earn higher financial income and generally own more agricultural machinery and tools. The human capital per rural household in plain field agricultural regions is higher with a value of 0.3500. Rural households in plain field agricultural regions have enough labor with better health and higher education level.

The development of farmers’ livelihood capital is more balanced in other agricultural regions than in mountain agricultural regions. Restricted by complex topography and other factors, the degree of farmland fragmentation is high and agricultural infrastructure is outdated in mountain agricultural regions. It is difficult to use large agricultural machinery, so agricultural comparative income is declining and farmers’ livelihood capital is at a relatively low level in mountain agricultural regions.

3.2. Comparison of Livelihood Stability per Rural Household in Different Agricultural Regions

3.2.1. Analysis of Livelihood Diversity

Table 5. Comparison of livelihood stability per household in different agricultural regions.

Different Agricultural Regions	Income Diversity Index	Agricultural Livelihood Diversity	Non-Agricultural Livelihood Diversity	Income Dependence	Natural Resource Dependence	Labor Dependence	Comprehensive Value of Livelihood Stability
Suburban agricultural regions	0.6601	0.2402	0.4647	0.6179	0.2234	0.5204	0.5253
Intensive agricultural regions	0.3007	0.3333	0.2140	0.8116	0.7687	0.2016	0.3730
Plain field agricultural regions	0.4587	0.3139	0.4039	0.7363	0.2219	0.6955	0.4540
Mountain agricultural regions	0.4011	0.1907	0.3993	0.7665	0.1485	0.6158	0.4348

shows the comprehensive value of livelihood stability per rural household in different agricultural regions, The values of suburban agricultural regions, plain field agricultural regions, mountain agricultural regions and intensive agricultural regions are 0.5253, 0.4540, 0.4348 and 0.3730, respectively, and that of suburban agricultural regions is 0.0355 higher than that of intensive agricultural regions.

Figure 3 shows that values of the income diversity index and non-agricultural livelihood diversity of suburban agricultural regions are the highest, at 0.6601 and 0.4647, respectively, indicating that income sources of farmers in suburban agricultural regions are diverse and balanced, and the proportion of non-agricultural income is slightly higher, including non-agricultural operating income, wage income and government benefits. In contrast, values of the income diversity index and non-agricultural livelihood diversity of intensive agricultural regions are relatively low; this is probably because farmers have high utilization efficiency of farmland and plant rich varieties of cash crops and livestock breeding with high value, so net agricultural income accounts for a large proportion of total rural household income. The income diversity index of mountain agricultural regions is also small. Due to complex topography and farmland fragmentation, the yield of crops in the mountain agricultural regions is low, most crops are used for self-sufficiency, and labor export is also hindered, thus making farmers’ livelihood less diverse than in plain field agricultural regions.

3.2.2. Analysis of Natural Resources and Income Dependence

Figure 4 shows the characteristics of rural household income dependence in different agricultural regions. We found that, on the one hand, the natural resources dependence of farmers in intensive agricultural regions is the highest, being five times higher than that in mountain agricultural regions. In intensive agricultural regions, net agricultural income accounts for a large proportion of total rural household income and the rural household livelihood are excessively dependent on natural resources. However, drought and flood disasters are frequent in China and infrastructures can be inadequate to prevent and mitigate natural disasters. Agricultural production is less resilient to natural disasters, which greatly threatens the livelihood stability of farmers in intensive agricultural regions. On the other hand, the labor dependence of farmers in plain field agricultural regions and mountain agricultural regions are higher with values of 0.6955 and 0.6158, respectively, which are three times higher than that in intensive agricultural regions. Migrant work certainly increases household income, but if there are changes like the COVID-19 pandemic in the external environment or in families, leading to the interruption of migrant work, it will be difficult for rural households to maintain their basic livelihood.

3.3. Coupling Coordination Degree of Livelihood Capital and Livelihood Stability per Household in Different Agricultural Regions

Table 6. Coupling coordination degree of livelihood capital and livelihood stability per household in different agricultural regions.

Different Agricultural Regions	Comprehensive Value of Livelihood Capital	Livelihood Stability	Coupling Degree	Coupling Coordination Degree
Suburban agricultural regions	0.2297	0.5253	0.9201	0.5893
Intensive agricultural regions	0.2311	0.3730	0.9720	0.5419
Plain field agricultural regions	0.2287	0.4540	0.9439	0.5676
Mountain agricultural regions	0.1956	0.4348	0.9252	0.5400

shows the coupling degree and coupling coordination degree of livelihood capital and livelihood stability per household in different agricultural regions. The coupling degrees of livelihood capital and livelihood stability in suburban agricultural regions, plain field agricultural regions, mountain agricultural regions, and intensive agricultural regions are all above 0.9, showing high-level coupling, indicating that livelihood capital and livelihood stability have a very important impact on farmers’ livelihood sustainability.

The coupling coordination degree of livelihood capital and livelihood stability per household in suburban agricultural regions and plain field agricultural regions has values of 0.5893 and 0.5676, respectively,. This corresponds to primary coordination, that is, the rate of rural household livelihood capital accumulation is slightly faster than the rate of rural household livelihood stability development, but livelihood capital and livelihood stability develop in a generally coordinated way. In contrast, the coupling coordination degree of livelihood capital and livelihood stability per household in intensive agricultural regions and mountain agricultural regions has values of 0.5419 and 0.5400, respectively, which shows mild imbalance; that is, the rate of rural household livelihood stability development is significantly lower than the rate of rural household livelihood capital accumulation. Restricted by complex topography and farmland fragmentation, the farmland asset value in mountain agricultural regions has declined seriously [25], and the agricultural income is low, which leads to the excessive dependence of rural livelihood on migrant work. Meanwhile, the livelihood of farmers in intensive agricultural regions is mainly based on net agricultural income, while agricultural production is vulnerable to natural disasters and market fluctuations; when agricultural production is threatened, the livelihood stability is low due to the single livelihood structure of farm households, which makes it difficult to resist unforeseen risks. Thus, livelihood stability is not significantly improved.

4. Discussion

4.1. Characteristics of Rural Household Livelihood Sustainability in Different Agricultural Regions

Urbanization and industrialization have changed population distribution and land use patterns in different areas, resulting in differences in the livelihood sustainability of farmers. Figure 5 shows the evolution of rural livelihood differences in different agricultural regions.

First, for suburban areas, urbanization and industrialization have led to the absorption of large amounts of rural labor and increased the demand for fresh fruits and vegetables and leisure tourism in cities, which promote the transformation from traditional agriculture to modern agriculture and provide suburban farmers with a variety of non-agricultural employment ways to broaden their livelihood sources. However, there is also the problem of urban land expansion occupying suburban farmland [40], which restricts the sustainable improvement of rural household livelihood in suburban agricultural regions.

Secondly, for plain areas, the terrain is flat and farmers adopt labor-saving elements such as machinery and modern agricultural management methods to realize large-scale farming, and the profit of cash crops is significantly higher than that of grain crops [25]. Therefore, the livelihoods of farmers in intensive agricultural regions are mainly based on net agricultural income, with high dependence on natural resources. When encountering natural disasters such as drought and flood, rural livelihoods will face severe challenges. For example, in 2018, the catastrophic flood in Shouguang City, Shandong Province, caused 106,000 greenhouses and 35,000 hectares of crops to be damaged. Many farmers lost half their life savings. In contrast, in plain field agricultural regions, farmland rents continue to rise, causing the cost of land to increase and making food production less profitable [41]. As a result, farmers reduce the planting of grain crops and increase the planting of cash crops. The “non-grain” use of farmland will seriously threaten national food security [42].

Thirdly, for mountainous areas, the fragmentation of farmland seriously limits the use of labor-saving elements such as machinery [43], resulting in high agricultural labor costs and low agricultural benefits. In addition, attracted by more employment opportunities and public services in cities, farmers give up farming and go to cities for work, resulting in rent-free farmland transfer or farmland abandonment in mountainous areas; even terraces and other high-quality farmland resources are gradually abandoned [44]. Farmland marginalization in mountainous areas will become more serious, the asset value of farmland in such areas has experienced a great decline and the high dependence on migrant work will threaten the livelihood sustainability of farmers in mountainous regions over time.

4.2. Policies on Increasing Rural Household Livelihood Sustainability Adapted to Local Conditions

First, to address the problem that “non-agricultural” use of farmland restricts the sustainable improvement of rural household livelihood in suburban agricultural regions, the government should promote the flow of urban capital, technology, and information into suburban agricultural regions to support the transformation and upgrading of traditional agriculture and increase the net agricultural income of farmers. At the same time, the government should organize the orderly transfer of agricultural surplus labor to non-agricultural markets, and improve the well-being of migrant farmers. In addition, farmland resources should be managed with attention to detail, and policies on the balance of farmland requisition and compensation in terms of quality, production capacity, and ecology should be improved [45], helping to ensure the stability of farmers’ agricultural income.

Second, for plain areas, on the one hand, the government should focus on reducing farmers’ dependence on natural resources by deeply developing local tourism resources, increasing employment posts and service skills training in the tertiary industry, and encouraging farmers to become part-time workers. Improvement in the disaster prevention and mitigation system is needed, ensuring that basic agricultural catastrophe insurance covers all farmers, and comprehensively strengthening the ability to resist natural disasters. On the other hand, the government should pay attention to the “non-grain” use of farmland, and curb the irrational increase of farmland rent by promoting agricultural operations on a moderate scale [46]. Additionally, the government should refine grain subsidy policies to effectively subsidize farmers who plant grains [47], and broaden sales channels of grain to improve farmers’ motivation to grow grain.

Finally, as for the mountainous areas, the key is to reduce farmers’ dependence on migrant work and broaden farmers’ income streams. Firstly, the government should promote and popularize advanced technology in crop planting and livestock breeding, and cultivate green mountain agricultural products with high economic value. Secondly, it should promote the upgrading of agricultural product marketing models through the improvement of transportation, information, and communication infrastructure. We should explore advanced marketing models such as “leading enterprises + bases + farmers” [48], and “Internet + agriculture” actively. In addition, the protection and restoration of the ecological environment in mountainous areas should be strengthened, practicing the concept of “lucid waters and lush mountains are invaluable assets”. For example, the conversion of farmland to forests and eco-tourism development should be carried out in ecologically fragile areas.

4.3. Research Shortage and Envisage

From the perspective of research objects, the suburban agricultural regions, intensive agricultural regions, plain field agricultural regions, and mountain agricultural regions selected in this study cover most of agricultural regions in China, but some special agricultural regions need supplementary investigation to reveal the livelihood sustainability of farmers more comprehensively. From the perspective of data sources, the research for this paper was conducted in 2018; it is necessary to update the relevant livelihood data of farmers in order to dynamically reflect the livelihood of farmers in different agricultural regions.

5. Conclusions

In terms of livelihood capital, the comprehensive value of livelihood capital per rural household in intensive agricultural regions is the highest, at 0.2311, while that in mountain agricultural regions is the lowest, only 0.1956. In terms of sub-item livelihood capital per rural household, the natural capital and social capital of farmers in suburban agricultural regions are higher, the physical capital and financial capital of farmers in intensive agricultural regions are higher, and the human capital of farmers in plain field agricultural regions is higher. However, the various types of livelihood capital of farmers in mountainous regions are at a relatively low level.

In terms of livelihood stability, the value of livelihood stability per rural household in suburban agricultural regions is the highest, at 0.5253, indicating that income sources of farmers in suburban agricultural regions are diverse and balanced. The value of livelihood stability per rural household in intensive agricultural regions is the lowest, only 0.3730. Net agricultural income accounts for a large proportion of total rural household income and the rural household livelihood is excessively dependent on natural resources in intensive agricultural regions. However, farmers in plain field agricultural regions and mountain agricultural regions have higher dependence on migrant work.

In terms of the coupling coordination between livelihood capital and livelihood stability, suburban agricultural regions and plain field agricultural regions are in primary coordination, with values of 0.5893 and 0.5676, respectively. Intensive agricultural regions and mountain agricultural regions are slightly out of balance, with values of 0.5419 and 0.5400, respectively, that is, the rate of livelihood stability of rural households is obviously lower than the rate of livelihood capital accumulation.

In view of differences in livelihood sustainability of farmers in different agricultural regions, this study puts forward differentiated suggestions to improve farmers’ livelihoods. In intensive agricultural regions, the income streams of farmers should be broadened, farmers should be encouraged to become part-time workers, basic agricultural insurance should cover all rural households, and disaster prevention and mitigation facilities should be built. In mountainous areas, it is necessary to curb the decline in the asset value of farmland and cultivate characteristic agricultural products with high economic benefits, improve the infrastructure of transportation and communication, and develop modern marketing models. Suburban agricultural regions and plain field agricultural regions should pay attention to the phenomenon of “non-agricultural” and “non-grain” use of farmland to ensure the livelihood stability of farmers.

As the smallest livelihood unit in rural society, farmers bear multiple livelihood risks such as nature, market and policy. The stability of farmers’ livelihood is the core of realizing the sustainable livelihood of farmers, which is not only related to the livelihood security of farms, but also concerns the sustainable development of local areas. and even affects the development and direction of the laws of rural human-land relationship. Based on the sustainable livelihood framework, this study reveals the characteristics and coupling coordination relationship between livelihood capital and livelihood stability in different agricultural regions with the help of a coupling coordination degree model. The purpose is to establish targeted livelihood capital cultivation models for different livelihood types of rural households according to the characteristics of different agricultural regions, improve farmers’ livelihoods, enhance the stability of farmers’ livelihoods, and effectively link the consolidation of poverty eradication achievements with rural revitalization. At the same time, this study provides an empirical reference for different agricultural regions around the world to achieve sustainable livelihoods of rural households and promote sustainable regional development.