Livelihood Resilience and Influential Factors of Nongchuangke: A Case Study of Zhejiang Province,China

Abstract

Nongchuangke refers to a new group of professional farmers that promotes agricultural innovation and sustainable development. Here, a comprehensive evaluation system for the livelihood-resilience index of the Nongchuangke in Zhejiang Province was constructed using three parameters: the buffer, self-organization, and learning capacities of the Nongchuangke. The entropy method was employed to comprehensively evaluate the livelihood resilience level of the Nongchuanke in Zhejiang Province, and a decision-making trail and evaluation laboratory with interpretive structural modeling was employed to clarify the hierarchical relationships among the limiting factors of the livelihood resilience of the Nongchuangke in Zhejiang Province and identify the key limiting factors. The results revealed that the average livelihood resilience level of the Nongchuangke in Zhejiang Province was low. Furthermore, the policy support, attraction of entrepreneurial investment, information-acquisition ability, and the number of Nongchuangke per 10,000 farmers were identified as the key factors influencing the livelihood resilience level of Nongchuangke in Zhejiang Province. Thus, new approaches that improve the identified influencing factors must be considered and implemented to improve the livelihood resilience level of the Nongchuangke.

Keywords

nongchuangke livelihood resilience influencing factors DEMATEL–ISM Zhejiang Province

Introduction

Traditional agriculture is presently unsustainable, requiring reform (Pretty, 1995). Since 2010, scholars from various countries have been investigating agricultural innovation as a strategy for promoting the transformation and sustainable development of agriculture (Chen & Yada, 2011; Morgan et al., 2020; Thanh et al., 2021), and several achievements have been recorded. “Nongchuangke” a group of agricultural entrepreneurs and innovators, was introduced by the Chinese government in 2015. These agricultural entrepreneurs can be considered a part of the new type of professional farmers. Through agricultural innovation, Nongchuangke has significantly promoted China’s agricultural transformation and contributed to the sustainable development of agriculture. The eradication of extreme poverty from rural China was achieved in 2020 (Shi et al., 2022), becoming the first country to do so. Nongchuangke played an indispensable role in the poverty-eradication process, considerably promoting the multidimensional development of the rural economy and society via technological innovation (S. Fan & Cho, 2021), industrial integration (Yang et al., 2022), e-commerce (Peng et al., 2021), etc. Therefore, elucidating Nongchuangke is key to understanding and disseminating China’s poverty-eradication experience. However, the cultivation system of this group is not yet mature in China even though Western countries have already developed this group. Additionally, some scholars believe that education plays a positive role in agricultural innovation by enhancing farmers’ mastery of agricultural skills, as well as their adaptation to industrial evolution (Fielke & Bardsley, 2014). In most sectors of Irish agriculture, the implementation of agricultural education for professional farmers has resulted in increased production (O’Donoghue & Heanue, 2018). This indicates that nurturing new agricultural entities has certain significance for future agricultural development. However, the essence of Nongchuangke is still farmers who are affected by the natural shortcomings of agriculture, particularly climate change, increased disasters, and soil degradation (Sorgho et al., 2020; Tao et al., 2023). These factors affect the sustainable development of Nongchuangke, further affecting the smooth, sustainable development of agriculture. Thus, focusing on the resilience of Nongchuangke and improving the livelihood resilience level must be a key component in the research on sustainable agricultural development.

Strengthening resilience is a crucial strategy for promoting sustainable development (Walker et al., 2004), offering a new perspective on the sustainable development of Nongchuangke. The concept of resilience, first proposed by Holling (1973), refers to the degree of disturbance a system can buffer or absorb before undergoing institutional transformation, which involves its restructuring based on different structures, processes, and functions. “Livelihood” was first proposed by Chambers and Conway (1992) as a survival tool comprising abilities; assets, including material and social assets; and overall activities (Chambers & Conway, 1992). Both concepts subsequently laid the foundation for the “livelihood resilience” concept, which further advanced the research on livelihoods and resilience. Generally, livelihood resilience is the ability of a livelihood system to absorb and adapt to various types of interferences, including impending and predictable multiple interferences, and still maintain the normal operation of all its components (Kates et al., 2012; Speranza et al., 2014). Currently, livelihood resilience represents a new concept for promoting sustainable livelihood development and exhibits application potential in various research fields, including geography, climate change, and sustainable development (Birch-Thomsen et al., 2009; Sneddon, 2000; Thompson-Hall et al., 2016).

Recent years witnessed increasing studies on livelihood resilience in rural contexts, with scholars investigating household livelihood resilience amid natural disasters, such as droughts (Nasrnia & Ashktorab, 2021; Savari et al., 2023), hurricanes (Forster et al., 2014), and climate change (Gil et al., 2017; Poelma et al., 2021). Additionally, most recent extant studies focused on the measurement and evaluation of livelihood resilience. For example, Speranza et al. (2014) constructed a livelihood-resilience index (LRI) evaluation system based on the following three dimensions: buffering, self-organization, and learning capacities (Speranza et al., 2014). The indicator system was constructed from the perspectives of natural, social, material, financial, and human capitals, considering that the acquisition and accumulation of these capitals are key to strengthening livelihood resilience (Jurjonas & Seekamp, 2018). The Household Livelihood Resilience Approach, which draws from sustainable livelihood methods, proposes five main indicators for measuring livelihood resilience levels (Quandt, 2018). Sina et al.’s (2019) indicators for measuring resilient livelihoods are divided into four categories: individual livelihood-coping ability, individual well-being, access to livelihood resources, and socio-physical robustness of the local community (Sina et al., 2019). Another group of scholars researched livelihood resilience in post-disaster reconstruction (Liu et al., 2020; Sina et al., 2018), providing some theoretical guidance on the improvement and enhancement of the livelihood capabilities of post-disaster settlement areas.

However, some theoretical gaps still exist in the study of livelihood resilience. For example, regarding the research objects, although individual farmers, such as Nongchuangke, have been studied (Zhao et al., 2022), they were not the primary players in agricultural innovation. Most discussions on livelihood resilience still revolve around communities or large regions, predominantly focusing on rural communities impacted by natural environmental disruptions, such as natural disasters and climate change, particularly in semi-arid and landless territories (Shiferaw et al., 2014). Considering these research gaps, this study was conducted to (a) establish an applicable LRI-evaluation system for Nongchuangke based on a three-dimensional framework (Speranza et al., 2014) and select alternative, easily quantifiable indicators to scientifically estimate the group’s livelihood resilience and (b) identify the key factors affecting the livelihood resilience of Nongchuangke and propose targeted strategies and recommendations to better resist and respond to livelihood challenges, as well as promote the sustainable development of this group. Additionally, this study contributes to the existing literature by expanding the concept of livelihood resilience to include Nongchuangke, as well as investigating the sustainable development of this type of agricultural innovation and its influencing factors. The research object and perspective are innovative and unique. Concurrently, an evaluation system for Nongchuangke’s LRI was innovatively constructed based on the three-dimensional livelihood analysis framework, providing some references for future related research. Moreover, the research presented here offers a theoretical foundation for addressing the predicaments encountered by Nongchuangke in its pursuit of sustainable development. It also provides scientific backing for formulating appropriate policies that can enhance the resilience of Nongchuangke’s livelihood. Finally, Nongchuangke fits the era of agricultural modernization and represents the main force of China’s rural revitalization, promoting the completion of the cause for common prosperity; thus, this study provides a Chinese perspective for alleviating the global poverty issue.

Materials and Methods

Setting of the Case Study

As shown in Figure 1, we selected Zhejiang Province as our case study area. Following the national policy of “focusing on the rural area and precisely supporting them,” Zhejiang Province first proposed the “Nongchuangke” concept in 2015 and actively responded to the call for “mass entrepreneurship and innovation.” As a new type of agricultural business entity, Nongchuangke has been growing rapidly in all the prefecture-level cities in the Province, causing a wave of new agricultural boom. By the end of 2022, the numerical strength of the Nongchuangke group in Zhejiang Province had exceeded 40,000 and expanding, and this makes Zhejiang Province a a good practical basis and feasibility for the study.

Figure 1.

Map of the study area.

Methods

Research Design

To measure resilience, we first constructed an LRI evaluation system for Nongchuangke, after which we standardized the raw data to eliminate the magnitude and magnitude order of the differences between the indicators. Next, we employed the entropy method to determine the weighting coefficients and used the composite index method to measure LRI. Finally, decision-making trial and evaluation laboratory with interpretive structural modeling (DEMATEL–ISM) was used to identify Nongchuangke’s livelihood resilience and their influencing factors. Figure 2 shows the empirical research framework.

Figure 2.

Schematic of the empirical research framework.

Connotation and Evaluation System for Nongchuangke’s Livelihood-resilience Index

Speranza et al (2014) proposed an analytical framework for livelihood resilience, which considered three livelihood resilience dimensions: the buffer, self-organization, and learning capacities. The buffer capacity refers to the degree of change or disturbance that a system can withstand while maintaining its original structure, function, and feedback. The self-organization capacity emphasizes the impact of human subjectivity, adaptability, power, and social interaction on resilience. The learning capacity is the degree to which a person has mastered the processes of absorbing, understanding, applying, and accumulating knowledge. This approach ensures a clear differentiation of dimensions from specific indicators. Presently, various scholars are using this livelihood analysis framework to examine the livelihood resilience levels of farming households (T. Li et al., 2022; Tohidimoghadam et al., 2023; Zhao et al., 2023). Therefore, in this study, we adopted Speranza et al.’s (2014) livelihood resilience analysis framework and selected appropriate assessment indicators from the three dimensions (buffering, self-organization, and learning capacities) to study Nongchuangke’s livelihood resilience.

In this study, the buffer capacity refers to the scale of Nongchuangke’s tolerance after encountering event shocks, mainly from the number of Nongchuangke per 10,000 farmers (Hu et al., 2018), the proportion of available project-reserve funds to the total assets (Speranza et al., 2014), the number of agriculture-related colleges/higher education institutions (Minh, 2019), the proportion of agricultural “research & development (R&D)” expenditures to regional R&D investments (Cho, 2022), capability of attracting entrepreneurial investments (Quandt et al., 2017), and the financing ability (Quandt et al., 2017). Cho (2022) argued that the R&D and inputs in digital agriculture directly impact agricultural exports. Additionally, in the case of Nongchuangke, farm-related R&D cost inputs also exert direct impacts on agricultural production. Additionally, the knowledge infrastructure is a necessary condition for the innovation subject, and agricultural universities are knowledge reserves for the development of regional agricultural innovators (Minh, 2019). Therefore, the number of agriculture-related colleges/higher education institutions is a crucial factor that reflects the buffer capacity. The proportion of agricultural R&D expenditures to regional R&D investment (Cho, 2022) reflects the agriculture-related investment in the region hosting the Nongchuangke, which can indirectly reflect Nongchuangke’s buffer capacity. Each household must balance its capital assets to maintain its adaptive capacity and well-being (Quandt et al., 2017), and this is also true for the Nongchuangke group. In the group, the proportion of available project-reserve funds to the total assets (Speranza et al., 2014), the proportion of agricultural R&D expenses to regional R&D investment (Cho, 2022), and the financing ability (Quandt et al., 2017) reflect the livelihood capital of the group.

The self-organization capacity manifests Nongchuangke’s self-organization for resolving social needs or temporary issues, as well as for achieving purposes. The self-organization capacity can be specifically described using four indicators: policy support (Speranza et al., 2014), road-network density (Sina et al., 2014), ability to employ local villagers (Lecegui et al., 2022), and ability to contact villagers’ organizations, such as cooperatives (Speranza et al., 2014). The policy support (Speranza et al., 2014) reflects the policy support for the Nongchuangke group in the host area and is measured by the policy index. Transportation accessibility plays a key role in evaluating competitive advantage of a local area (Vulevic & Ana, 2016), and the road network density is used to indicate the ease of accessing a particular location. Scholars, such as Lecegui et al. (2022), often measure the number of nonhousehold laborers, where the rural labor force can be considered nonhousehold labor. The ability to create employment opportunities for local villagers (Lecegui et al., 2022) reflects Nongchuangke’s driving effect on the rural labor force, which only provides manpower for Nongchuangke’s entrepreneurship. However, it can solve the issues of a surplus rural labor force, particularly expressed in terms of the number of employed people.

Speranza et al. (2014) adapted the innovation by drawing on the existing learning-capacity structures, as well as argued that it could be identified at the level of shared social vision, knowledge sharing, and transfer ability. Regarding Nongchuangke, we adapted and innovated Speranza’s framework for assessing the learning aspects of livelihood resilience to include the research capacity of an agricultural patent as an extension of experimental indicators and knowledge-transfer capacity. Therefore, the learning capacity includes the patent-research ability of the agricultural item (Speranza et al., 2014), education level (Speranza et al., 2014), the average index level of the vocational-skill training (Speranza et al., 2014), the duration of the agricultural entrepreneurship (Speranza et al., 2014), and the information-access ability (Shah et al., 2013). Among them, the patent-research ability of the agricultural item (Speranza et al., 2014) represents the number of patent applications by Nongchuangke, whereas the average vocational-skill training-index level (Speranza et al., 2014) and information-access ability (Shah et al., 2013) are expressed by the level of training received and the number of channels for obtaining information, respectively. If the education level (Speranza et al., 2014) and information-access ability (Shah et al., 2013) are considered the absorption and understanding of knowledge, the agricultural-item patent-research ability (Speranza et al., 2014) and average index level of the vocational skill training (Speranza et al., 2014) refer to the application of knowledge. Further, the agricultural entrepreneurship-duration level (Speranza et al., 2014) refers to the accumulation of knowledge. The information access reflects Nongchaungke’s information- and knowledge-acquisition abilities (Shah et al., 2013): the more a person absorbs and understands, the better the application and the richer the accumulation of the person. This would also be reflected by the person’s strengthened learning capacity.

Entropy Method for Measuring the Livelihood-resilience-evaluation Indicators

In the information theory, entropy represents an uncertainty measure. The greater the amount of information, the smaller the uncertainty and the smaller the entropy. Conversely, the smaller the amount of information, the greater the uncertainty and the larger the entropy (T. Wang et al., 2020). The entropy method is an objective assignment-evaluation method, which can overcome the overlapping of information between indicators and the subjectivity of artificial assignment. We employed the entropy method to assign weights to the evaluation system comprising multiple indicators. As the study establishes a comprehensive evaluation system comprising multiple indicators and the data outlines of different indicators vary, it would be challenging to make direct comparisons. Therefore, the data must first be normalized. Thereafter, the entropy method can be used to determine the weight of each indicator, wherein the buffer, self-organization, and learning capacities are weighted 53.108%, 21.278%, and 25.614%, respectively. Finally, the weighted-sum method is used to calculate Nongchuangke’s livelihood-resilience level, as follows:

LRI = 53.108 % B_{i} + 21.278 % S_{i} + 25.614 % L_{i}

(1)

where LRI represents Nongchuangke’s livelihood-resilience index, $B_{i}$ , $S_{i}$ , and $L_{i}$ represent the buffer, self-organization, and learning capacities of the research object. The evaluation results of the composite index ranged from 0 to 1. Thus, the larger(lower) the composite-evaluation index, the higher(lower) Nongchuangke’s LRI was (Alam et al., 2018). Based on the existing research results, Nongchuangke’s livelihood resilience was classified into five levels according to the magnitude of the composite evaluation index (Table 1).

Table 1.

Classification of the Livelihood-resilience Levels of Nongchuangke in Zhejiang Province.

Livelihood-resilience level of Nongchuangke in Zhejiang Province	Types	Meanings
LRI ≤ 0.2	Low-resilience level	Nongchuangke’s livelihood-resilience level is very poor state, requiring urgent, overall strengthening
0.2 < LRI ≤ 0.4	Lower-resilience level	Nongchuangke’s livelihood-resilience level is poor
0.4 < LRI ≤ 0.6	Medium-resilience level	Nongchuangke’s livelihood-resilience level is average, requiring overall improvement
0.6 < LRI ≤ 0.8	Higher resilience level	Nongchuangke’s livelihood-resilience level is but requires further improvement
0.8 < LRI ≤ 1	High resilience level	Nongchuangke’s livelihood-resilience level is excellent

Analysis of the Factors Affecting Nongchuangke’s Livelihood Resilience

We employed DEMATEL–ISM to analyze the factors influencing Nongchuangke’s livelihood resilience. As DEMATEL and ISM are two analytical identification models with different advantages and disadvantages, we integrated them into DEMATEL–ISM, which can identify the key influencing factors and clarify the influence degree of each constraint on the complex system, as well as clarify the relationship between the hierarchical structure of each constraint to further strengthen the explanatory power (Huo et al., 2023).

In this study, we determined the constraints based on LRI evaluation and finally constructed a multilevel recursive-structure diagram using the cause and center degrees of each factor to clarify the relationship between the influencing factors. Based on this method, the influence degree of each factor on Nongchuangke’s livelihood degree and several key constraints could be determined to allow us to focus on and implement these key constraints. Figure 3 shows the specific model.

Figure 3.

Flow chart for implementing DEMATEL–ISM.

Additionally, the specific steps for constructing the model are detailed in a previous paper, ‘‘What is the driving mechanism for the carbon emissions in the building sector? An integrated DEMATEL-ISM model”(Huo et al., 2023).

Data Sources

A part of the data was obtained from the statistical yearbook of each municipality (2020) and the annual departmental accounts of each municipal agricultural and rural bureau (2020), whereas the missing data were derived from the growth rate of the adjacent years.

The other part of the data was obtained from the 2022 questionnaire survey conducted by the group on Nongchuangke in Zhejiang Province. Stratified random sampling was performed based on the administrative areas to increase and reduce the sample representativeness and sampling error, respectively. The relevant data and information on Nongchuangke were obtained through questionnaires and interviews. After excluding the questionnaires with incomplete or inconsistent responses, 190 questionnaires were collected, corresponding to a 100% effective-recovery rate. The gender composition of the sample was 124 males (65.26%) and 66 females (34.74%). The age composition included 51 people (22–33), accounting for 26.84%, and 139 people (34–45), accounting for 73.16%. The cultural composition of the sample included 93 college students (48.95%), 88 undergraduates (46.32%), and 9 masters (4.74%). Regarding the duration of agricultural entrepreneurship, there were 2 (1.05%), 57 (30.00%), 67 (35.26%), and 64 (33.68%) people with ≤1, 2 to 5, 6 to 10, and >10 years of entrepreneurship, respectively, indicating that many people engaged in agricultural entrepreneurship after the implementation of the ‘‘Nongchuangke’’ policy in 2015 and confirming that the policy contributed greatly to the development of agricultural entrepreneurship. Among the samples, 159 (83.68%) and 31 (16.32%) were local and non-household members, respectively, as most members of the Nongchuangke group returned to their hometowns to establish their businesses or become ‘‘second-generation farmers.’’ In the sample, 33.16% of them incorporated tourism, whereas 28.95% integrated leisure and catering commerce and industry into their ventures. These findings indicate that the scope of Nongchuangke is expanding beyond the primary sector.

Results

Measurement Results of Nongchuangke’s Livelihood Resilience

As presented in Table 2, the indicators with higher comprehensive indicator-level weights represent the proportion of agricultural R&D expenses to the regional R&lD expenses (16.183%) and the number of agricultural colleges/universities and vocational schools (11.737%). This indicates that the scale of scientific and technological education, as well as financial investment, exert a certain impact on Nongchuangke s livelihood resilience. From the perspective of the comprehensive evaluation results (Table 3), we observed that Nongchuangke s livelihoodresilience level presented a ladder-shaped pattern with significant differences. Among them, Huzhou City obtained the highest comprehensive evaluation result (0.6785), which was considerably higher than the provincial average (0.3801), leading second-placed Jinhua City (0.4809) by a large margin. Its score was approximately 5.5 times that of the last-ranked Zhoushan City (0.1165).

Table 2.

Comprehensive Evaluation System for Nongchuangke’s Livelihood-resilience Index.

Target layer	Dimension layer	Indicator layer	Serial number	Description	Evaluation	Source	Weighting (%)
Livelihood-resilience-integrated evaluation indicator	Buffer capacity	The number of Nongchuangke members per 10,000 farmers (people)	A₁	Reflects the size of the Nongchuangke community	From the equation, $\frac{Number of local Nongchuangke}{\begin{matrix} Number of people employed \\ in the local primary sector \end{matrix}}$ , calculate the number of Nongchuangke members per 10,000 farmers, which is the data for this indicator	Hu et al. (2018)	7.983
		Proportion of available project-reserve funds to the total assets (%)	A₂	Reflects the economic strength of the Nongchuangke group	0%–20% = 1, 21%–40% = 2, 41%–60% = 3, 61%–80% = 4, 81%–100% = 5	Speranza et al. (2014)	3.146
		Number of agriculture-related colleges/higher education institutions (place)	A₃	Reflects the agricultural strength of the region hosting the Nongchuangke group	Statistics of the number of colleges/higher education institutions offering agriculture-related majors per city and region, that is, the data of this indicator	Minh (2019)	11.737
		Proportion of the agricultural R&D expenses to the regional R&D investment (%)	A₄	Reflects the economic importance of the region hosting the Nongchuangke group	From the equation, $\frac{\begin{matrix} Agriculture - related science \\ and technology expenditure \end{matrix}}{R & D expenditure}$ , calculate the value of the ratio, which is the data of this indicator	Cho (2022)	16.183
		Entrepreneurial-investment-attraction capability	A₅	Reflects Nongchuangke’s attraction to investment	Under 100 thousand = 1, 110–500 thousand = 2, 510 thousand–1 million = 3, >1 million = 4	Quandt et al. (2017)	9.700
		Financing ability	A₆	Reflects the level of financing feasible for Nongchuangke, as well as the ability to sustain access to long-term quality capital	<1 million = 1, 1–3 million = 2, 3–6 million = 3, 6–10 million = 4, >10 million = 5	Quandt et al. (2017)	4.359
	Self-organization capacity	Policy support	B₁	Reflects the importance of the policy in the region hosting Nongchuangke	Search the government website using the keywords, “Nongchuangke” and “rural talents,” as well as 2015 as the time range, to obtain the search data. Define the total number of searches in Hangzhou as 1 and assign it to the other 10 cities.	Speranza et al. (2014)	7.634
		Road-network density (km/km²)	B₂	Reflects the accessibility of Nongchuangke’s host area	From the equation, $\frac{Total length of the road}{Total area of the region}$ , calculate the road network density per municipality, that is, the data of this indicator	Sina et al. (2014)	6.550
		Ability to create employment for local villagers	B₃	Reflects Nongchuangke’s driving effect of on the rural labor force	Number of local farmers employed: <10 people = 1, 10–40 people = 2, 41–80 people = 3, 81–150 people = 4, >150 people = 5	Lecegui et al. (2022)	3.668
		Ability to contact villagers’ organizations, such as cooperatives	B₄	Reflects Nongchuangke’s ability to organize regional social ties	Number of rural organizations, such as cooperatives: Did not participate = 1, 1 group = 2, 2–4 groups = 3, >4 groups = 4	Speranza et al. (2014)	3.426
	Learning capacity	Agricultural-item patent-research ability	C₁	Reflects Nongchuangke’s agricultural research level	Number of agricultural patents owned: 0 = 1, 1–3 = 2, 4–8 = 3, 9–15 = 4, ≥15 = 5	Speranza et al. (2014)	6.888
		Education level	C₂	Reflects Nongchuangke members’ education level	College = 1, Bachelor = 2, Master = 3, Doctor = 4	Speranza et al. (2014)	5.493
		Average index level of vocational-skill training	C₃	Reflects Nongchuangke members’ agricultural-skill level	Number of training received per year: 0 = 1, 1–2 = 2, 3–5 = 3, >5 times = 4	Speranza et al. (2014)	2.866
		agricultural entrepreneurship duration level	C₄	Reflects the degree of experience accumulated by Nongchuangke	Duration of business: <1 year = 1, 1–5 years = 2, 6–10 years = 3, >10 years = 4	Speranza et al. (2014)	3.683
		Information-access ability	C₅	Reflects Nongchuangke’s means of gathering information from different sources	Statistics of the number of channels through which Nongchuangke obtains relevant policy information, that is, the data of this indicator	Shah et al. (2013)	6.685

Table 3.

Comprehensive Calculation Results of Nongchuangke’s Livelihood Resilience.

City	Buffer capacity		Self-organization capacity		Learning capacity		Comprehensive evaluation
City	Score	Rank	Score	Rank	Score	Rank	Score	Rank
Huzhou	0.3841	1	0.0832	7	0.2112	1	0.6785	1
Jinhua	0.2424	3	0.1114	4	0.1353	4	0.4809	2
Hangzhou	0.2444	2	0.0723	8	0.1212	5	0.4379	3
Shaoxing	0.2308	4	0.0837	6	0.1179	6	0.4324	4
Wenzhou	0.1986	5	0.1319	2	0.0816	9	0.4121	5
Ningbo	0.1769	6	0.1354	1	0.0983	8	0.4107	6
Jiaxing	0.1268	7	0.0945	5	0.1558	3	0.3772	7
Quzhou	0.0769	10	0.0704	9	0.1611	2	0.3084	8
Taizhou	0.0804	9	0.1144	3	0.1109	7	0.3058	9
Lishui	0.1084	8	0.0446	10	0.0679	10	0.2210	10
Zhoushan	0.0422	11	0.0347	11	0.0395	11	0.1165	11
Average	0.1738	/	0.0888	/	0.1182	/	0.3801	/

Based on the classification criteria (Table 1), low (0, 0.2], lower (0.2, 0.4], medium (0.4, 0.6], higher (0.6, 0.8], and high (0.8, 1] levels, the overall livelihood-resilience level of the research area was relatively low, with the average level being only 0.3801. Nongchuangke’s livelihood-resilience level is concentrated in the “lower” and “medium” categories. Presently, no prefecture-level city has attained the “high-resilience level.”

The classification results of the spatial distribution of Nongchuangke’s livelihood resilience were visualized through ArcGIS (Figure 4). The visualization results revealed that the spatial distribution exhibited a strong clustering trend.

Figure 4.

Schematic of the spatial distribution of Nongchuangke’s livelihood resilience 9a case study of Zhejiang Province.

(1) Higher-resilience level: Huzhou City (0.6785), located in the Northern region of Zhejiang Province, obtained a comprehensive livelihood-resilience score that was much higher than the provincial average (0.3801), corresponding to the development of Nongchuangke across the province. At the criterion level, the buffer and learning capacities were ranked first in the province, whereas the self-organization capacity (0.0832) was ranked seventh, slightly lower than the provincial average (0.0888). Overall, Huzhou was in a good state of being at the forefront of all-round development.

(2) Medium-resilience level: Jinhua (0.4809), Hangzhou (0.4379), Shaoxing (0.4324), Wenzhou (0.4121), and Ningbo (0.4107) Cities are bordering cities, except Wenzhou, and Nongchuangke’s livelihood resilience in these four cities were higher than the provincial average. Jinhua, Hangzhou, and Shaoxing Cities exhibited the top buffer capacities at the criterion level in the province, although the self-organization and learning capacities require strengthening to further improve the Nongchuangke’s livelihood resilience in these areas. Although Wenzhou and Ningbo dominated the criterion-level self-organization capacity, the learning capacity required improvement.

(3) Lower-resilience level: Jiaxing (0.3772), Quzhou (0.3084), Taizhou (0.3058), and Lishui (0.2210) Cities are mainly in the Southwestern and Eastern coastal regions of Zhejiang. Jiaxing and Quzhou were among the top regarding their learning capacity, although their other criterion levels were relatively weak, requiring considerable improvement. Taizhou was among the top in terms of self-organization, although its scores in the other two criterion levels were below the provincial average. Its buffer and learning capacities must be considered using increased investments. Lishui is currently at the bottom in all criterion levels, with poor livelihood resilience for Nongchuangke. It required urgent, overall improvement.

(4) Low-resilience level: Zhoushan City (0.1165), located in the Northeast of Zhejiang, is the only island city in the province. The livelihood resilience of Zhoushan’s Nongchuangke is considerably lower than the provincial average, and the margin is large compared with the scores of the other 11 prefecture-level cities. All aspects were relatively weak, exhibiting great development potential.

Analysis of the Factors Affecting Livelihood Resilience

We introduced DEMATEL–ISM to identify the main influencing factors of Nongchuangke’s livelihood resilience, as well as the hierarchical relationships between them.

DEMATEL Model

The influence degree, affected degree, centrality, and causality of each constraining factor were calculated and presented in Table 4.

Table 4.

DEMATEL Analysis Results.

Serial number	Influence degree	Affected degree	Centrality	Centrality ranking	Causality	Factor attribute
A₁	1.730	2.275	4.005	5	−0.546	Resultant factor
A₂	1.498	1.617	3.114	15	−0.119	Resultant factor
A₃	1.818	1.727	3.545	11	0.090	Causal factor
A₄	1.740	2.112	3.852	7	−0.372	Resultant factor
A₅	1.637	2.561	4.198	2	−0.924	Resultant factor
A₆	1.662	2.227	3.888	6	−0.565	Resultant factor
B₁	2.007	2.708	4.715	1	−0.701	Resultant factor
B₂	1.832	2.204	4.037	4	−0.372	Resultant factor
B₃	1.959	1.424	3.383	13	0.534	Causal factor
B₄	1.839	1.488	3.327	14	0.350	Causal factor
C₁	1.772	1.849	3.621	10	−0.077	Resultant factor
C₂	1.944	1.787	3.731	8	0.158	Causal factor
C₃	2.022	1.488	3.510	12	0.534	Causal factor
C₄	2.051	1.623	3.674	9	0.427	Causal factor
C₅	2.830	1.249	4.079	3	1.581	Causal factor

The factors with a causal degree of >0 can influence other factors within the system; thus, they are known as causal factors. Table 4 reveals that the information-access ability (C5) exhibited the highest causal degree, indicating that this factor significantly impacted the others within the system. Additionally, the ability to create employment for the local villagers (B3); average vocational-skill-training index level (C3); agricultural entrepreneurship duration level (C4); ability to contact villagers’ organizations, such as cooperatives (B4); the education level (C2); and number of agriculture-related colleges/higher education institutions (A3) impacted the other factors. Therefore, these factors must be explored to improve Nongchuangke’s livelihood resilience.

The factors with a causal degree of <0 are known as the resultant factors. Table 4 reveals that the number of Nongchuangke members per 10,000 farmers (A1), proportion of available project-reserve funds to the total assets (A2), proportion of agricultural R&D expenses to the regional R&D investment (A4), entrepreneurial-investment-attraction capability (A5), financing ability (A6), policy support (B1), road-network density (B2), and the agricultural-patent research capacity (C1) were resultant factors. Among them, the top three resultant factors in absolute value were A5, B1, and A6. These factors were easily affected by other factors, further impacting Nongchuangke’s livelihood resilience. Therefore, focusing on these factors would improve the group’s resilience level.

Centrality mainly represents the overall impact of the constraint factor on the complex system. The top three factors regarding the centrality ranking were B1, A5, and C5, indicating that these three factors exerted the most prominent improvement effect on Nongchuangke’s livelihood resilience. Among them, B1 and A5 were the first- and second-ranked regarded the affected factors, respectively, and can be considered critical constraint factors. C5 exhibited high centrality and causal degree and could be considered a core influencing factor.

ISM

Based on the DEMATEL analysis results, the centrality of the constraints on the livelihood resilience of Nongchuangke in Zhejiang Province was incorporated into ISM (using ISM as the foundation) to construct DEMATEL–ISM, which can provide a more comprehensive analysis and evaluation of the key factors and their interactions in the system (Figure 5).

Figure 5.

DEMATEL–ISM for the constraining factors of livelihood resilience of Nongchuangke in Zhejiang Province.

(1) Direct influencing factors: The top-level constraint factors, namely A1 and A5, were direct influencing factors, as well as the key elements that must be focused on to improve the livelihood resilience of Nongchuangke in Zhejiang Province. The former reflects the local agricultural entrepreneurship atmosphere, whereas the latter reflects Nongchuangke’s financial strength. According to Table 4, their centralities are ranked fifth and second, and their influence degrees are ranked third and second, respectively. This indicates that both factors were crucial and exerted a direct driving effect on the improvement of Nongchuangke’s livelihood resilience, making them key constraint factors. However, addressing these direct influencing factors requires starting with indirect and root causes.

(2) Indirect influencing factors: This is situated between the direct and root influencing factors, including seven levels. The first level is A6, a resultant factor with a centrality ranking of 6 and an affectedness ranking of 4, indicating that this factor is easily influenced by other factors and exerts a certain impact on the whole system. This makes it worthy of attention to improve Nongchuangke’s livelihood resilience. The second level comprises A4 and B1, both of which are resultant factors with centralities rankings of 7 and 1 and affectedness rankings of 6 and 1, respectively. This demonstrates that B1 is the most crucial constraining factor, as well as the most easily influenced. Thus, it is a key constraining factor that requires urgent, prioritized attention. A3 and B2 are causal and resultant factors with centrality rankings of 11 and 4, respectively. The fourth level includes A2, B3, and C1, with the latter two being resultant factors, with centrality rankings of 15, 13, and 10, respectively. The fifth level includes B4 and C2, both of which are causal factors with centrality rankings of 14 and 8, respectively. The sixth and seventh levels include C3 and C4, which are causal factors.

(3) Influencing factors: C5 is ranked the least and is a causal factor with centrality, affectedness, and causality rankings of 3, 1, and 1, respectively. This indicates that this factor significantly impacts other constraining factors, making it a key constraining factor in improving the livelihood resilience of Nongchuangke in Zhejiang Province.

In summary, based on DEMATEL–ISM, the key constraining factors of Nongchuangke’s livelihood resilience include A1, A5, B1, and C5. These four factors are very crucial to improving Nongchuangke’s livelihood resilience. Thus, effectively implementing these four aspects will help formulate effective policies and measures for solving the development constraints encountered by Nongchuangke in Zhejiang Province, as well as considerably enhance their resilience.

Discussion

Based on the three-dimensional livelihood-resilience-analysis framework (Speranza et al., 2014), we quantitatively measured the livelihood-resilience level of Nongchuangke and identified the key influencing factors to propose targeted policy recommendations for the sustainable development of Nongchuangke.

Dissimilar to the extant studies, which generally focused on the impact of natural factors on livelihood resilience (Campbell, 2021; Y. Fan et al., 2022), this study revealed that the key factors affecting the Nongchuangke’s livelihood resilience included the number of Nongchuangke members per 10,000 farmers, attraction of entrepreneurial investment, the policy support, and access to information. This result indicates that the highest constraint on Nongchuangke, dissimilar to those on traditional farmers, does not correspond to natural environmental conditions. Additionally, our findings are consistent with those of recent studies that indicated that the establishment of social networks is key to improving farmers’ livelihood resilience (Y. Wang et al., 2021). We also identified the direct influence of the number of Nongchuangke members per 10,000 farmers on Nongchuangke’s livelihood resilience and recommended that identifying and developing more Nongchuangke members is closely linked to improving their social networks. Therefore, this factor must be prioritized to enhance Nongchuangke’s social network. This indicator reflects the size of the Nongchuangke group in the region. First, an inadequate group would affect the group’s scale effect (Laughlin et al., 2003). Conversely, a sufficiently large Nongchuangke group would spontaneously form interconnected networks (the more network nodes there are, the stronger the resilience of the Nongchuangke network would be), enabling each Nongchuangke group to receive improved support, thereby improving their survival capacities and development statuses. Second, when Nongchuangke exhibited a certain scale and quantity, it naturally exerted agglomeration effects, sharing infrastructure and technical services, etc. (Merrell et al., 2022), and effectively playing the role of “passing on” and promoting local agricultural innovation and entrepreneurship and attracting increased investment to the Nongchuangke group.

However, resolving issues still requires addressing their indirect and root causes. The entrepreneurial investment-attraction capabilities, policy support, and information-access ability are also key factors in improving Nongchuangke’s livelihood resilience. For entrepreneurial groups, having sufficient funds is crucial. In our research phase, we observed that many innovative projects for Nongchuangke with development potential often fail because of the lack of funds. Additionally, attributed to the natural attributes of agriculture, its financing limitation has always been higher than those of other industries (Villalba et al., 2023), and this greatly limits Nongchuangke’s investment-attraction ability. Therefore, this issue must be addressed in the process of cultivating Nongchuangke. Resolving the funding issue will greatly improve Nongchuangke’s living and development environments and further optimize this group.

The research field in the case-study area demonstrated that Nongchuangke played a supportive role in modernizing agriculture. Thus, policy support must be provided for this group. Generally, as a new group, it is typical for Nongchuangke to undergo a small, micro, and scattered development stage that is almost inevitable. This puts the group in a disadvantaged position in terms of market competition. A lack of effective policy protection means that the group may be squeezed out and eliminated by the market in the initial competition stages, and this would result in frequent survival crises and complicate the feasibility of achieving sustainable development. Therefore, the government must provide appropriate support to the group (Lukiyanto & Wijayaningtyas, 2020). Additionally, the ability to access information is a crucial factor that must be emphasized. The extant studies revealed that knowledge and information sharing positively impact innovation (Allameh, 2018). As agricultural innovators, members of the Nongchuangke must engage in agricultural innovation. Therefore, an information-sharing platform must be created, and a Nongchuangke’s alliance must be established for them. Further, school–enterprise cooperation can promote information sharing, toward achieving this goal (Leischnig & Geigenmueller, 2020; X. Li et al., 2014).

The extant studies rarely measured new farmers’ (Nongchuangke) livelihood resilience quantitatively. Therefore, we innovatively established a relatively scientific indicator system within the framework of the buffer, self-organization, and learning capacities to obtain measurement results of livelihood resilience in the research area, thus providing certain theoretical references for future related studies. However, the research on Nongchuangke’s livelihood resilience is still in its infancy; thus, the setting of research indicators must be improved and revised. Additionally, a concern about the sample area of these findings was limited to Zhejiang Province, which is located in the economically developed coastal region of eastern China with better economic conditions and fewer natural and geological disasters. Therefore, the sample lacks nationwide universality. Despite these limitations, the results of this study can still serve as a reference for promoting the sustainable development of Nongchuangke in regions, such as the Yangtze River Delta and Pearl River Delta.

Conclusions

Based on field research and questionnaire surveys, we constructed a comprehensive evaluation system for the livelihood resilience of Nongchuangke in Zhejiang Province. The evaluation system was used to quantitatively measure and classify Nongcuangke’s LRI. Furthermore, we employed DEMATEL–ISM to reveal the key influencing factors of the livelihood-resilience level of Nongchuangke in Zhejiang Province. These findings can significantly promote the sustainable development of agricultural innovation entities, such as Nongchuangke, within China and globally.

The results of this study revealed the following: (a) The overall livelihood-resilience level of Nongchuangke in Zhejiang Province was only 0.3801, indicating that various aspects must be urgently improved through corresponding measures. (b) Each city’s livelihood-resilience level exhibited a stepped distribution, with the following general trend: higher and lower levels in the north and south, respectively. Among them, Huzhou and Zhoushan Cities accounted for the highest (0.6785) and worst (0.4809) levels, respectively. (c) Through factor identification, we observed that the policy support, the attraction of entrepreneurial investments, the information-acquisition ability, and the number of Nongchuangke members per 10,000 farmers were key to constraining the livelihood-resilience level of Nongchuangke in Zhejiang Province and crucial to promoting its improvement. Therefore, to strengthen the livelihood-resilience level of Nongchuangke in Zhejiang Province, talent cultivation and introduction mechanism for Nongchuangke must be improved, and policy support must be increased by setting up special funds and attracting venture capitals, as well as encouraging more people to become Nongchuangke. Additionally, promoting university–enterprise cooperation and building information-exchange platforms must be considered valuable strategies for improving Nongchuangke’s livelihood-resilience level.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by Humanities and Social Sciences Fund Project of the Ministry of Education [grant number 21YJCZH005]; the National Natural Science Foundation of China [grant number 42071159]; and National Natural Science Foundation Youth Program [grant number 51908498].

ORCID iD

Xinyi Ge

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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