Land Use Change and Land Multifunction Tradeoffs

Factors Influencing Farmland Abandonment at the Village Scale: Qualitative Comparative Analysis (QCA)

  • LI Fengqin 1, 2 ,
  • XIE Hualin , 1, * ,
  • ZHOU Zaohong 2
  • 1. Institute of Ecological Civilization, Jiangxi University of Finance and Economics, Nanchang 330013, China
  • 2. School of Tourism and Urban Management, Jiangxi University of Finance and Economics, Nanchang 330032, China
*: LIU Liming, E-mail:

Received date: 2020-09-24

  Accepted date: 2020-12-10

  Online published: 2021-03-30

Supported by

The National Natural Science Foundation of China(41971243)

The National Natural Science Foundation of China(41930757)

The Key Project of Natural Science Foundation of Jiangxi Province(20202ACB203004)

The Humanities and Social Science Research Project in Jiangxi Province(GL19111)

The Academic and Technical Leaders Funding Program for Major Disciplines in Jiangxi Province(20172BCB22011)

The Fok Ying-Tung Fund(141084)

The National Social Science Fund of China(20BJY144)


As a global issue, farmland abandonment is considered to be one of the most crucial fields in the study of land use change. The clarification of its driving factors plays a vital role in improving the efficiency of rural cultivated land use and ensuring national food security. This paper aims to study the factors influencing farmland ab-andonment in 49 villages of Ganzhou City by adopting the Qualitative Comparative Analysis (QCA). The results show that: (1) Farmland abandonment is the outcome of synergism among many factors, among which the low–level of agricultural mechanization is definitely a necessary condition in Ganzhou, and it contributes a material effect to the abandonment. (2) The path leading to farmland abandonment is not unique to the study area, and can be attributed to five different combinations. These combinations can be enumerated as: A1 (a combination of convenient transportation, complete agricultural facilities, low-level agricultural mechanization, low-level land circulation, and no industrial policy support), A2 (a combination of complete agricultural facilities, low-level agricultural mechanization, low-level land circulation, sufficient agricultural labor, and no industrial policy support), A3 (a combination of convenient transportation, complete agricultural facilities, low-level agricultural mechanization, sufficient agricultural labor, and no industrial policy support), A4 (a combination of convenient transportation, low-level agricultural mechanization, low-level land circulation, sufficient agricultural labor, and industrial policy support), and A5 (a combination of inconvenient transportation, complete agricultural facilities, low-level agricultural mechanization, high-level land circulation, sufficient agricultural labor, and industrial policy support). (3) In the above-mentioned combinations, the core conditions and peripheral conditions conjointly impact on farmland abandonment. Finally, corresponding policy implications are proposed in order to further reveal the mechanism of farmland abandonment. These recommendations provide new ideas and methods for policy makers to use in making decisions and will promote the effective use of farmland.

Cite this article

LI Fengqin , XIE Hualin , ZHOU Zaohong . Factors Influencing Farmland Abandonment at the Village Scale: Qualitative Comparative Analysis (QCA)[J]. Journal of Resources and Ecology, 2021 , 12(2) : 241 -253 . DOI: 10.5814/j.issn.1674-764x.2021.02.010

1 Introduction

Land use/land cover change is the main element of sustainable development and global environmental change. Farmland is the largest land use type in the human landscape (Deng et al., 2018), and it not only helps maintain biodiversity, but also ensures global food security. Farmland marginalization refers to the decline of profits or rentals for cultivated land use, and farmland abandonment is an extreme outcome of marginalization (Li and Li, 2017). Farmland abandonment refers to cultivated land that has been idle for more than one year, cannot create agricultural value, and has no signs of expected planting in the following years (Shi and Li, 2013; Ito et al., 2016). In recent years, due to the acceleration of urbanization and industrialization, many rural people have migrated to urban areas. Population loss and low agricultural comparative benefits often result in the marginalization of farmland (Li and Zhao, 2011). The resulting problem of farmland abandonment has gradually attracted the attention of scholars all over the world, who generally believe that farmland abandonment is a universal socio-economic phenomenon in many countries in the world (Xu et al., 2019).
At present, scholars mainly conduct research on the factors influencing farmland abandonment from the scales of plots, farmers, counties, cities, and states. The reasons for the abandonment of farmland are complex, and the influencing factors and their degrees of impact which are obtained by scholars vary due to the different regions and backgrounds. Research has found that the abandonment of farmland is common in areas with high altitude, steep slope, poor soil conditions, poor infrastructure, inconvenient transportation, large distances from residential areas, and scattered distributions (Song and Zhang, 2019). In addition, agricultural mechanization (Lasanta et al., 2017), the structure and quantity of agricultural labor (Zhang et al., 2014a), land circulation (Shao et al., 2016), agricultural income (Xie et al., 2014), agricultural policies (Morera et al., 2006), and social security systems (Zhou et al., 2014) are also regarded as important factors driving farmland abandonment. The existing literature reveals that few scholars analyze the factors influencing farmland abandonment at the village scale, and usually only explore whether a single factor affects farmland abandonment and its degree of influence. However, the reasons which motivate farmland abandonment are complex, and there is a “multi-concurrent causality” relationship among the factors. The permutations and combinations of different factors will produce different effects.
Current research on farmland abandonment focuses on the use of quantitative analysis methods such as statistical modeling (Chaudhary et al., 2018), data mining techniques (Zaragozi et al., 2012) and other ways to identify the potential causes and their degrees of influence on the abandonment. However, traditional quantitative analysis methods that focus on the “net effect” analysis of individual variables (Rihoux and Ragin, 2009) are inadequate for analyzing “combined” problems, and more and more problems with their use have been exposed. Therefore, the Qualitative Comparative Analysis (QCA for short) method applicable to complex social problems with “multi-concurrent causality” has attracted wide attention from scholars (Marx et al., 2014). QCA is a method produced in sociological research which targets small and medium sample cases. At present, QCA is mainly applied in sociology, political science, economics and other fields (Ragin, 2000), and it has gradually penetrated into various sub-fields of management research (Fang et al., 2016; Tan et al., 2016). For example, Bell et al. (2014) used QCA to show how different combinations of supervision and incentive-based corporate governance mechanisms can lead to the same level of corporate investor valuation. Huang et al. (2019) used QCA to reveal the determinants of China's actual land expropriation compensation level. However, in the current research on farmland abandonment, the introduction and applications of QCA are still relatively few.
Farmland abandonment in China began in the 1980s. Since 2000, the abandonment of farmland in Chongqing, Ningxia, Jiangxi and other places has become more and more pronounced, and most of it has occurred in hilly and mountainous areas (Xie et al., 2014; Shao et al., 2016). China is currently in a period of rapid economic development. With the economic development and the shrinkage of agricultural labor forces, the phenomenon of farmland abandonment under this new situation has appeared in China, and it is showing increasingly fierce and more complex characteristics (Duan et al., 2018). The abandoned land is no longer limited to inferior quality land, and some of the cultivated land with good quality is also abandoned (Jin et al., 2013). China is a country with very limited arable land per capita. Because of the externalities of cultivated land use, continuous abandonment of farmland will affect the use of cultivated land in other areas. As a result, the contradiction between man and land is further aggravated; and China’s food security, ecological security, and the stability of rural society are endangered. Even the urban-rural coordinated development and the process of agricultural modernization in China will be hindered. However, with the rapid rise of labor costs in China and the massive migration of rural labor in recent years, the abandonment of cultivated land in the hilly and mountainous areas of southern Jiangxi has become increasingly serious. Further study on the factors influencing farmland abandonment is of great significance for regulating the rational distribution of labor resources, alleviating the abandonment of cultivated land, and realizing sustainable land use and sustainable social development.
In view of these issues, this paper takes the hilly and mountainous areas of southern Jiangxi as the research area. Based on a questionnaire survey of farmland abandonment in Ganzhou City and the statistical yearbook, this paper uses Qualitative Comparative Analysis (QCA) to explore the multi-concurrent causality and configuration relationships among various influencing factors, and to explain the factors influencing farmland abandonment in the hilly and mountainous areas. On this basis, putting forth the policy implications of farmland abandonment has important practical significance for the sustainable use of farmland and the promotion of ecological civilization.

2 Research area and data sources

The core element of QCA is the case. The case of this paper comes from a field study of 58 administrative villages in Xingguo, Chongyi, Longnan, Shicheng, and Huichang Counties in Ganzhou City, Jiangxi Province, from July to August 2019 (Fig. 1). Jiangxi is one of the 13 major grain producing areas, and one of the two provinces that have continuously provided marketable grain to the country since the founding of New China. Ganzhou is located in the southern part of Jiangxi Province, and with good agricultural production conditions and comparative advantages in grain production, it has made important contributions to ensuring national food security. A “hilly mountainous area” refers to an area with the elevation of more than 200 m and obviously undulating terrain, where the farmland is characterized by steep and fragmented slopes (He et al., 2020). The terrain of Ganzhou is dominated by mountains, hills and basins, of which the hilly mountainous area spans 32673 km2, accounting for 82.89% of the total land area of Ganzhou.
Fig. 1 Schematic diagram of the study area
The five counties in the study area have varying socio-economic development conditions and levels, and their degrees of abandonment of the farmland are quite different. On the basis of field investigation in the study area, 58 typical villages with similar natural environments were selected. The principle behind selecting typical villages is that they are located in hilly mountainous areas, where many laborers go out to work, and the degree of abandonment of the farmland varies. However, this article follows the QCA case selection principle of ensuring maximum heterogeneity within the small to moderate number of samples (Rihoux and Ragin, 2009). The sample villages have large differences in regional location, agricultural production conditions, and socio-economic conditions.
This paper used random sampling and direct interview methods. Without informing the interviewees in advance, each of the 58 sample villages was issued a questionnaire, which was filled out by the village committee cadres. Forty-nine valid questionnaires were recovered and the effective rate was 84.48%. After reliability and validity tests, the Cronbach’s Alpha coefficient was 0.819>0.80, the KMO was 0.610>0.6, and the significance level of the Bartlett’s test was 0, indicating that the questionnaire design was reasonable, and suitable for further analysis.

3 Material and methods

3.1 Qualitative Comparative Analysis (QCA)

Qualitative Comparative Analysis (QCA) is a case-oriented method that lies somewhere between statistical analysis and single-case analysis. QCA combines case-based research with Boolean algebra and set theory, and observes the relationship between conditions and results from the perspective of set theory. By using Boolean algebra algorithms to formally analyze the logical process of a problem, QCA conducts systematic and formal cross-case comparisons (Ragin, 2008). QCA can excavate multiple paths with equivalent results, which is particularly suitable for studying complex causality and multiple interactions (Fiss, 2011). The QCA method denies any form of constant causality and believes that causality is dependent on specific situations and configurations. The “multiple concurrent causality” developed by QCA focuses on how multiple different condition variables can affect the outcome variable in the form of a combination. While the traditional empirical research can only deal with the symmetric relationship between A→B, then ~A→~B( The relationship between variables is represented by formal mathematical operation symbols (“→” means “cause or derived”; and “~” means “not”, which means that the condition does not appear).), QCA solves the problem of asymmetric causality. Whether there is a sufficient and necessary relationship between conditional variables and outcome variables is determined by consistency and coverage (Ragin, 2006a). The QCA formulas are shown below. Equation (1) and Equation (2) are used to calculate the consistency and coverage of X as a necessary condition for Y. Equation (3) and Equation (4) are used to calculate the consistency and coverage of X as a sufficient condition for Y.
where, “i” represents the cases or samples, i.e., each village in this study; and “I” represents the number of cases or samples. “Xi” refers to the membership score of sample i in the condition combination X. “Yi” refers to the membership score of sample i in the outcome Y. The membership score is the degree to which a given case belongs to a set. “min(Xi, Yi)” refers to the minimum values across the membership scores in Xi and Yi. “XiYi ” means that for a condition to be necessary for outcome Y, the membership score of each case in the condition X must be equal to or greater than its membership in score Y; while “XiYi” means that for a condition to be sufficient for outcome Y, the membership score of each case in the condition X must be equal to or smaller than its membership score in Y.
Equation (1) indicates that the consistency of set Y as a subset of set X is their intersection expressed as a proportion of set Y. Similarly, other formulas can be obtained, and the consistency measures the degree to which the attribution of each solution is a subset of the result set. The range of values in the consistency index is 0-1. If the consistency is greater than or equal to 0.9, then this condition variable (combinations) X is considered a necessary condition for the outcome variable Y (Ragin, 2006b). If the consistency index is met, the explanatory power of the condition (combination) X to the result Y can be described by the coverage. That is, the coverage can measure the extent to which each solution and the whole solution explain the results. The coverage index has a value range of 0-1, and it can be considered as similar to the goodness of fit in regression analysis. If the coverage value is closer to 1, then condition (combination) X will have greater explanatory power for result Y (Huang et al., 2019).
QCA generally requires 10 to 60 samples (Bennett and Elman, 2006), which is suitable for the case analysis of small and medium sample sizes. At present, four derivative analytical techniques have been developed in academic circles, namely, csQCA (crisp-set Qualitative Comparative Analysis), fsQCA (fuzzy-set Qualitative Comparative Analysis), mvQCA (multi-value Qualitative Comparative Analysis) and tsQCA (time-series Qualitative Comparative Analysis) (Caren and Panofsky, 2005; Rihoux and Ragin, 2009). This paper uses the first developed and most widely used technique of csQCA. The binary-coded data were used in the assignment of the cause condition and the result condition, where 0 indicates full non-membership, and 1 indicates full membership. The formed set has a clear correspondence relationship. The choice of csQCA in this paper is mainly based on two considerations. The first is whether abandonment is a binary problem. The second is that our interviewees are village cadres, who may conceal or understate the amount of abandoned farmland. The adoption of csQCA can reduce the errors caused by the concealing or under-reporting to some extent.

3.2 Variable selection and design

Based on the existing research and questionnaire data, the factors influencing farmland abandonment are selected from the two aspects of agricultural production conditions and socio-economic factors. The list of factors mainly includes traffic conditions, agricultural facilities, agricultural mechanization, land circulation, agricultural labor force, and industrial policy support, which are determined as condition variables. QCA is based on the set relationship. Therefore, it is necessary to assign a set membership degree to the case, and calibrate the variables to a set based on theoretical knowledge and the actual situation (Schneider and Wagemann, 2012). According to the above variable assignment criteria, combined with the survey questionnaire, the outcome variables and condition variables of the cases are assigned (Table 1).
Table 1 Variable design and assignment
Variable type Variable Assignment Reference
Traffic Conditions (TC) Good traffic, the distance between the village and the market town is lower than the sample mean is 1; otherwise, 0 Chaudhary et al., 2018
Agricultural Facilities (AF) Good irrigation and water conservancy facilities and infrastructure
is 1; otherwise, 0
Li and Li, 2017;
Song and Zhang, 2019
Agricultural Mechanization (AM) The value greater than or equal to the sample mean is 1; otherwise, 0 Lasanta et al., 2017
Land Circulation (LC) Higher than Ganzhou land circulation rate is 1; otherwise, 0 Zhang et al., 2014b;
Shao et al., 2016
Agricultural Labor Force (AL) Higher than the proportion of agricultural labor in Jiangxi is 1;
otherwise, 0
Li and Zhao, 2011; Xie et al., 2014
Industrial Policy Support (PS) The village has industry support policies or other favorable policies is 1,
otherwise, 0
Morera et al., 2006;
Han and Song, 2019
Whether farmland is
abandoned (Outcome)
Abandonment of farmland is assigned 1, and no abandonment of
farmland is 0
Zhang et al., 2014a
3.2.1 Condition variables
Traffic Conditions (TC): Road traffic conditions are one of the vital factors affecting agricultural production, and the distance from the county will also affect the agricultural production behavior of the households. In the questionnaire, if the respondent selects good traffic, and the distance between the village and the market town is lower than the sample mean, the value is “1”, otherwise it is assigned “0”.
Agricultural Facilities (AF): Agricultural infrastructure is an important foundation for the development of rural productivity, and lagging construction of agricultural infrastructure is the main factor restricting agricultural production. In the questionnaire, if the respondent selects poor farmland water conservancy facilities or poor agricultural infrastructure, the value is “0”, otherwise it is assigned “1”.
Agricultural Mechanization (AM): The level of agricultural mechanical equipment is a significant indicator of the developmental level of agricultural production and the comprehensive agricultural production capacity. In this paper, the ratio of small agricultural machinery to the number of permanent households (i.e., small farm machinery ownership rate) in a village is used to represent the mechanization degree of the village (Yang et al., 2019). With the average value as the boundary, a value greater than or equal to the average value of the small agricultural machinery ownership rate is assigned 1, otherwise it is 0.
Land Circulation (LC): The establishment and improvement of the land circulation market will promote the circulation and reuse of abandoned farmland to a certain extent, and reduce or even avoid the occurrence of abandonment. In this paper, the circulation rate of land management rights in Ganzhou( The data come from the Ganzhou Statistical Yearbook and Department of Agriculture and Rural Affairs of Jiangxi Province. The circulation rate of land management rights in Ganzhou is equal to the circulation area of land management rights divided by the total area of farmland in Ganzhou in 2018.) is used as the standard for measuring the convenience of farmland circulation. If the circulation rate of cultivated land in the case is higher than that of Ganzhou, the value is 1, otherwise, the value is 0.
Agricultural Labor Force (AL): Labor migration is one of the main factors affecting the abandonment of farmland. Based on the availability of the data, this paper takes the proportion of people engaged in agriculture in Jiangxi( The data come from the 2018 Statistical Yearbook of Jiangxi Province. The ratio of agricultural labor in Jiangxi Province is equal to the number of agricultural laborers divided by the total labor force in Jiangxi in 2018.) as the standard for measuring the degree of rural labor abundance. In the cases where the ratio of the people engaged in agriculture is higher than that of Jiangxi, the value is “1”, otherwise, the value is “0”.
Industrial Policy Support (PS): The optimization and adjustment of agricultural policies can effectively stimulate the enthusiasm of farmers for production and planting, and promote the rational use of rural land resources. According to the questionnaire survey data, if the village has industry support policies or other favorable policies, the value is “1”, otherwise, the value is “0”.
3.2.2 Outcome variables
Since the abandonment of farmland is a sensitive topic, it is difficult to obtain comprehensive and true data on the amount of farmland that is abandoned, whether it is from official statistics or household surveys. Therefore, this paper focuses on the binary classification problem of whether farmland is abandoned (Zhang et al., 2014a). In the outcome variable, the abandonment of farmland is assigned a value of 1, and no abandonment is assigned a value of 0.

3.3 Truth table construction and verification

3.3.1 Truth table construction
The truth table contains information about outcome variables, condition variables and cases, and is one of the core elements of QCA. The truth table can reflect how the combination relationships between the appearance or non-appearance of various conditions causes a certain phenomenon to either occur or not occur. The QCA usually summarizes the data for each indicator in the unit of a case, and presents the obtained condition variables combination in the form of a matrix to produce the truth table. This paper inputs the assigned variable data into fs/QCA3.0 software to generate the truth table (Table 2).
Table 2 Truth table of factors influencing farmland abandonment
Case number Research case
(Village name)
Case1 Chengshui 0 0 0 0 1 1 0
Case2 Fenglin 1 1 0 1 1 0 1
Case3 Xixia 1 1 0 0 1 1 1
Case4 Jiaotian 0 0 0 0 1 1 0
Case5 Tongxi 1 1 0 0 1 0 0
Case6 Haoxi 1 0 1 0 0 0 1
Case7 Chashi 1 1 1 0 1 1 0
Case8 Maliang 1 1 1 0 1 0 0
Case9 Zengtian 1 0 0 0 0 0 0
Case10 Lifeng 0 0 0 0 0 0 0
Case11 Gujing 1 1 0 0 0 1 1
Case12 Heping 0 1 0 0 1 0 1
Case13 Hengjiang 0 1 0 1 1 1 1
Case14 Gaoduo 0 1 0 1 1 1 1
Case15 Dunqiu 1 0 1 0 1 0 0
Case16 Mengshan 1 1 0 0 1 0 1
Case17 Gaoxing 1 0 1 1 0 0 0
Case18 Wenxi 1 1 0 0 1 0 0
Case19 Xinxu 1 1 0 0 1 1 1
Case20 laowei 1 1 0 0 1 1 1
Case21 Gaohu 0 1 1 0 1 0 0
Case22 Xiaobai 1 1 0 0 1 0 1
Case23 Zhengfeng 0 0 0 0 1 0 1
Case24 Tangbei 1 1 0 0 0 0 1
Case25 Shenbu 1 1 1 0 1 0 0
Case26 Nantian 1 1 0 0 1 0 1
Case27 Changlong 1 1 0 0 1 0 1
Case28 Zhuzi 0 1 0 1 1 0 1
Case29 Xiangtang 1 1 0 0 1 0 0
Case30 Xinbu 1 1 0 1 1 0 1
Case31 Liantang 1 1 0 0 1 1 1
Case32 Lingxia 1 1 0 1 1 1 0
Case33 Xinda 1 1 0 1 1 0 1
Case34 Guolong 1 1 0 0 1 1 1
Case35 Shixia 1 0 0 1 0 1 1
Case36 Henggang 1 1 1 0 1 1 1
Case37 Daba 1 0 0 0 1 1 1
Case38 Xinfang 1 1 0 0 1 0 1
Case39 Shanxia 1 1 0 0 1 1 1
Case40 Xinfu 1 1 0 1 0 0 0
Case41 Luopi 0 1 0 0 1 0 1
Case42 Changxi 1 1 0 1 0 0 0
Case43 Wansheng 1 1 0 0 0 0 1
Case44 Pingshan 1 1 0 0 1 0 1
Case45 Hengtian 1 1 0 1 0 0 1
Case46 Donghong 1 0 0 0 1 1 1
Case47 Xiangjiang 1 0 0 0 1 0 1
Case48 Aobei 1 1 0 1 1 0 1
Case49 Fengchunwo 1 1 0 0 0 0 1
3.3.2 The verification and correction of contradictory configurations
The truth table shows that there are combinations which yield factual contradiction cases (as shown in Table 3), that is, two completely contradictory results which appear in the same combination of conditions. According to the contradictory configuration resolution strategies of either “Orient the result according to the frequency standard” or “Recode the result value of all contradictory configurations to 0” (Rihoux and Ragin, 2009), this paper eliminates or reduces the contradictory configurations as much as possible. That is to say, the result value is assigned according to the standard of high frequency. For example, the codes of nine groups of combinations are all (1, 1, 0, 0, 1, 0). Among them, six sets of results are displayed as “1” and three sets are displayed as “0”, so then the higher frequency result of “1” is regarded as the revised common result of all nine sets. In the same way, the result of Case 45 is amended to “0”. If the number of result values of “1” and “0” in a given case each account for half, then the result will be uniformly revised to “0”, which means “low degree, poor result” or “unclear”. After these corrections, the revised truth table is obtained (Table 4).
Table 3 Contradictory configuration situations and corrections
TC AF AM LC AL PS Outcome Case (s) Revised result
1 1 0 0 1 0 1 Case16, Case22, Case26, Case27, Case38, Case44 1
1 1 0 0 1 0 0 Case5, Case18, Case29 1
1 1 0 1 0 0 0 Case40, Case42 0
1 1 0 1 0 0 1 Case45 0
1 1 1 0 1 1 0 Case7 0
1 1 1 0 1 1 1 Case36 0
Table 4 Revised truth table of factors influencing farmland abandonment
TC AF AM LC AL PS Outcome Case (s)
1 1 0 0 1 0 1 Case16, Case22, Case26, Case27, Case38, Case44,Case5, Case18, Case29
1 1 0 0 1 1 1 Case3, Case19, Case20, Case31, Case34, Case39
1 1 0 1 1 0 1 Case2, Case30, Case33, Case48
1 1 0 0 0 0 1 Case24, Case43, Case49
1 1 0 1 0 0 0 Case40, Case42, Case45
0 1 0 0 1 0 1 Case12, Case41
1 1 1 0 1 0 0 Case8, Case25
0 0 0 0 1 1 0 Case1, Case4
1 0 0 0 1 1 1 Case37, Case46
1 1 1 0 1 1 0 Case7, Case36
0 1 0 1 1 1 1 Case13, Case14
0 0 0 0 0 0 0 Case10
1 0 0 0 0 0 0 Case9
1 0 1 0 0 0 1 Case6
1 0 1 1 0 0 0 Case17
0 0 0 0 1 0 1 Case23
1 0 0 0 1 0 1 Case47
1 0 1 0 1 0 0 Case15
0 1 1 0 1 0 0 Case21
0 1 0 1 1 0 1 Case28
1 1 0 0 0 1 1 Case11
1 0 0 1 0 1 1 Case35
1 1 0 1 1 1 0 Case32

4 Results and analysis

4.1 Analysis of necessary conditions

Necessity analysis refers to exploring the extent to which the result set constitutes a subset of the condition set (Rihoux and Ragin, 2009). Consistency is an important indicator for measuring necessary conditions, that is, to what extent a certain result requires the variable to exist (Crilly et al., 2012). This article sets the threshold of the necessary condition to 0.9.
Table 5 shows that the consistency of low-level agricultural mechanization (~AM) exceeds 0.9, so it can be regarded as a necessary condition for the abandonment of farmland. The low-level agricultural mechanization (~AM) has a coverage rate of 0.8, indicating that in the process of farmland abandonment in Ganzhou, 80% of the cases of farmland abandonment are affected by the agricultural mechanization level. Since the explanatory power is strong, it can be explained independently. This is because Ganzhou is located in hilly and mountainous areas, and the farmland is generally distributed among scattered areas with steep slopes. Thus, these farmers had to give up farming because of the hindrance of limited mechanization development and the high labor intensity of farming. Among other variables, the consistency of convenient traffic conditions (TC), perfect agricultural facilities (AF), and sufficient agricultural labor force (AL) are all higher than 0.8, and the consistency of low-level land circulation (~LC) is close to 0.8, so it can be considered to have a greater impact on the farmland abandonment. The table shows that the individual factors include only a low-level of agricultural mechanization (~AM), which is a necessary condition for farmland abandonment. Moreover, the coverage of each single antecedent condition is low, which indicates that the explanatory power of each one to farmland abandonment is weak. In other words, whether farmland is abandoned or not is the result of multiple factors, rather than any single reason. Therefore, it is necessary to carry out configuration analysis on these condition variables. These antecedent conditions were incorporated into fsQCA3.0 Software to further explore the configuration that leads to the farmland abandonment.
Table 5 Analysis on the necessary conditions of farmland abandonment
Condition variable Consistency Coverage
TC 0.82 0.72
~TC 0.18 0.60
AF 0.82 0.76
~AF 0.18 0.50
AM 0.03 0.13
~AM 0.97 0.80
LC 0.24 0.62
~LC 0.76 0.72
AL 0.82 0.76
~AL 0.18 0.50
PS 0.35 0.71
~PS 0.65 0.69

Note: “~” means “not”, which means that the condition does not appear.

4.2 Configuration analysis

Using the fs/QCA3.0 software, a Boolean minimization algorithm is applied to simplify the truth table, the revised truth table is imported into the software, and the consistency threshold is set to 0.8 (Fiss, 2011). Meanwhile, in order to avoid the impact of low-quality data on the results and cover at least 75% of the samples (Ragin et al., 2008), this paper sets the case frequency threshold to 2. After selecting the standard analysis, the results will have three kinds of solutions of varying complexity: parsimonious solution, intermediate solution and complex solution. The three solutions differ in how many logical remainders they contain, that is, the condition combination of counterfactuals which is logically possible but does not appear in the case studied. Among them, the complex solution excludes all counterfactual combinations, but the result is too complicated and has poor universality. While the parsimonious solution contains a large number of counterfactual combinations, the result is often too simple, and the conclusion may be quite different from the actual situation, so its revelation is the worst. The intermediate solution lies between these two and contains some counterfactual combinations, but there are fewer than in the parsimonious solutions. Previous research has shown it to be close to the theoretical reality and moderate in complexity, and it does not permit the elimination of the necessary conditions. Therefore, the intermediate solution has become the first choice for reporting and interpretation by most researchers who use QCA (Rihoux and Ragin, 2009; Crilly et al., 2012; Ragin et al., 2014), and, the intermediate solution is used in this paper.
In order to better explore the causal process, Fiss (2011) divided the causal conditions into core conditions and peripheral conditions. There is a strong causal relationship between the core conditions and the result. The core conditions appear in both the parsimonious solution and the intermediate solution, and they have an important impact on the result. The causal relationship between the peripheral conditions and the result is weak, and while it appears in the intermediate solution, it is excluded by the parsimonious solution, playing only an auxiliary role in the result. According to the operational results, the analysis of the intermediate solution shows that there are five condition combinations, and the results are shown in Table 6.
Table 6 The configuration of farmland abandonment
Condition variable Configuration solution
A1 A2 A3 A4 A5
Consistency 1 1 1 1 1
Raw coverage 0.35 0.32 0.38 0.24 0.06
Unique coverage 0.09 0.06 0.12 0.24 0.06
Overall solution coverage 0.82
Overall solution

Note: Black circles indicate the presence of a condition, and circles with an “×” indicate its absence. Large black circles and large circles with an“×” indicate core conditions and small circles indicate peripheral conditions. “Blank” means that the condition can either exist or not exist in the combination, but its existence does not matter.

Generally, the closer the overall solution coverage is to 1, the higher the degree to which the combination of condition variables can explain the result variable. When the overall solution consistency is close to 1 and not less than 0.75, this indicates a better relationship between the calculated condition variables combination and the condition variables combination presented by the case data itself.
Table 6 shows that the overall solution consistency in this case is 1, and the overall solution coverage is 0.82, indicating that the calculated condition variables combination has passed the test, and that it has certain convincing power for the outcome variable, which can be used to explain whether farmland is abandoned. The consistency of all path combinations in this paper is 1, which proves that these combinations have a good subset relationship with farmland abandonment. That is, antecedent conditions have a good explanatory power for the outcome variable. According to the parsimonious solution consistency logic, the combinations with the same core conditions are merged to form the following two scenarios to explain the path combination.
Scenario 1: Including two combinations of A1 and A4, their core conditions are convenient traffic, low-level agricultural mechanization, and low-level land circulation. Combination A1 (TC * AF * ~AM * ~LC * ~PS)( “*” means “and”, which indicates a parallel relationship; “~” means “not”, which means that the condition does not appear.) shows that even for villages with convenient traffic and complete production facilities, the conditions of low-level agricultural mechanization, low land circulation rate, and insufficient industrial policy support can also cause abandonment. The possibility of such a combination is 1, and this path can explain 35% of the farmland abandonment. This outcome may be due to the difficulty of large-scale farming in some villages in the mountainous and hilly areas of Ganzhou. In addition, the adjustment of agricultural-related policies is lagging and the land transfer system is not perfect. Therefore, the scale of farmland cannot reach the required level, and the operation of scale and mechanization are difficult, so farmland abandonment occurs. The raw coverage of combination A4 (TC * ~AM * ~LC * AL * PS) is 0.24, and its unique coverage is the largest, indicating that A4 aligns with the situation of farmland abandonment in many villages. There are conditions of convenient external transportation, sufficient agricultural labor and industry support policies in these villages. However, due to the difficulty of agricultural mechanization and the high cost of hired labor, it was difficult to attract farmers, enterprises, cooperatives and other agricultural organizations to transfer the land. The single-family farming income was low, so farmland was abandoned.
Scenario 2: There are three combinations of A2, A3 and A5 in scenario 2. The core conditions are sound agricultural facilities, low-level agricultural mechanization, and a sufficient labor force engaged in agriculture. Combination A2 (AF * ~AM * ~LC * AL * ~PS) shows that when the rural production facilities are perfect and the agriculture labor is sufficient, regardless of whether the external transportation is convenient or not, the village is without industrial policy support, and where the level of agricultural mechanization and the land circulation are low, the land would also be abandoned. The possibility of such a combination is 1, and this path can explain 32% of the farmland abandonment phenomenon. Although such villages have sufficient labor for agriculture, because Ganzhou is close to the economically developed areas of Fujian and Guangzhou, there are many opportunities for migrant workers, so many farmers have part-time jobs. Research showed that the average area of farmland abandoned by rural families increases by 5% for every 10% increase in part-time employment (Xu et al., 2019). Coupled with the imperfect circulation policy and industrial policy support, the influence of the endowment effect and the “land complex”, farmers are reluctant to transfer and contract the land. Therefore, large-scale mechanized operations cannot be carried out, and the possibility of abandonment is increased. Combination A3 (TC * AF * ~AM * AL * ~PS) shows that under the conditions of convenient external transportation, complete production facilities, and sufficient agriculture labor, regardless of the level of land circulation, where the level of agricultural mechanization is low and without policy support, the village would also be affected by the abandonment phenomenon. The possibility of this combination is 1, which can explain 38% of cultivated land abandonment. The prices of agricultural production materials such as seeds, pesticides, and fertilizers have remained high for a long time, which not only pushes up the cost of agricultural production, but also weakens the effect of subsidies that benefit farmers. Moreover, the current land circulation policy is not perfect, and there is no additional authoritative national or local governance policy to alleviate the abandonment of farmland. The scattered land is difficult to operate on a large scale, resulting in the abandonment of cultivated land. Combination A5 (~TC * AF * ~ AM * LC * AL * PS) shows that even with complete agricultural production facilities, high-level land circulation, sufficient agricultural labor and policy support, where the agricultural mechanization rate of the villages is not high and traffic is inconvenient, the farmland would be abandoned. This is because the land circulation system can slow down the abandonment of farmland to a certain extent, but it only has a certain effect on the farmland that is forced to be abandoned due to lack of labor. There is no weakening effect on the abandonment of low-quality farmland that cannot be worked using agricultural machinery and has an extremely low efficiency of input-output. Combined with the inconvenience of transportation, the increased cost of commuting has discouraged the farmers from farming in this combination.
In addition, as shown in Table 6, the conditions of external traffic conditions, land circulation degree and industrial policy support are contradictory in different combinations, indicating they have no significant influence on the outcome variable. They can work together with other factors in the form of auxiliary conditions in different combinations to produce different results because these external factors will gradually improve with the development of favorable agricultural policies such as precise poverty alleviation, land circulation, and social-economic development. The low-level agricultural mechanization appears in the form of a core condition in different combinations, which again indicates that it has a significant impact on the farmland abandonment. However, the high rate of agricultural labor appears in the combination of multiple variables at the same time and is not contradictory. Although it has a great influence on the abandonment of cultivated land, it is not a limiting factor because the use of agricultural machinery can (to a certain extent) alleviate the impact of the loss of rural labor on farming. In the context of a rapid increase in farming opportunity costs, mechanical replacement is the most effective way to increase labor productivity. He et al. (2020) also pointed out that improving the efficiency of mechanical farming can indeed make up for a shortage of labor, thus achieving the purpose of reducing abandonment. By adopting agricultural machinery to save farming time, farmers can increase their time for outside work.

5 Discussion and implications

5.1 Discussion

This paper abandoned the traditional statistical quantitative analysis perspective. Instead, it used the configuration perspective to discuss the farmland abandonment, and studied the sufficiency and necessity of individual antecedent conditions and antecedent condition combinations, which provided a new research perspective for the study of farmland abandonment in terms of research methods. Farmland abandonment is a dynamic process, which is driven by a combination of socio-economic, political, and environmental factors (Ustaoglu and Collier, 2018). Traditional statistics assume that the functions of individual factors are independent of each other, focusing instead on analyzing the “net effects” of various factors. However, this oversimplified assumption often fails to fully reflect the complexity of causality in the real world, and the "net effect" of a single factor may be masked or offset by other related factors. Although some scholars have tried to address the configuration effect, cases with more than three interacting variables have been difficult to explain (Du and Jia, 2017). For example, Müller et al. (2013) used the Boosted Regression Trees to explain the nonlinearities and interactions between the factors influencing farmland abandonment, but this approach only allowed for two-variable interaction effects.
Unlike the traditional regression method that emphasizes causal symmetry, the factors that cause the outcomes in the real world are often different. The asymmetry of causality and conditional effects emphasized by QCA precisely explains the opposite conclusions of the same factors in reality. For example, Shao et al. (2016) found that the amount of farmland abandoned is not entirely determined by the quality of the farming conditions, while Lin and Zhu (2014) reached the contrary conclusion, which also confirms the asymmetric nature of the abandonment of farmland.
In addition, the research cases in this paper come from different villages in different counties, so they consider the potential impacts of different sets of conditions in different counties and villages. Parcels within the same homogeneous geographic area tend to have more similarities in the degree of abandonment and time trends (Han and Song, 2019). There have been few studies of farmland abandonment on the village scale. Although Yang et al. (2019) have conducted research at the village scale, all their samples were concentrated in one county, so the potential impact of differences in county conditions were not considered.
There are still some problems that need to be further improved.
(1) The framework system of factors influencing farmland abandonment needs to be improved. Taking into account the availability and operability of the data, this paper mainly analyzes the agricultural production conditions and socio-economic factors that affect the abandoned farmland, but the analysis of natural factors such as farming distance and slope is insufficient. In addition, this paper uses csQCA, which can only be used to analyze dichotomous variables, so the division of variables is not detailed enough. In the future, the fuzzy-set Qualitative Comparative Analysis can be used to assign more detailed values to the variables to improve the accuracy of the results.
(2) It is necessary to accurately judge whether the farmland is abandoned and the amount of the abandoned land to reduce the errors. This paper directly judges whether the farmland is abandoned based on the area of abandoned farmland in the survey questionnaire. However, as the The Rural Land Contract Law and other relevant laws and regulations clearly stipulate that the abandonment of farmland is strictly prohibited, farmland abandonment has become a sensitive issue, so the data obtained through questionnaires on abandoned farmland may not be true. Therefore, combining high-resolution remote sensing images, deep learning and other methods to extract more accurate data on abandoned farmland and determine whether there is abandonment of farmland is the next crucial step in future research work.
(3) The research area and sample size need to be further expanded to make the research conclusions more broadly applicable. Although this paper has considered the potential impact of the differences in conditions between the counties and villages, all the samples are still concentrated in Ganzhou. The potential impact of the differences in the conditions of different cities and provinces has not been considered. Therefore, the explanatory power of this conclusion for other regions is still uncertain, and further research is needed.

5.2 Policy implications

Based on the above conclusions, this paper puts forward the following corresponding policy implications.
(1) Give full play to the role of agricultural machinery. A low-level of agricultural mechanization is not only a necessary factor but also a sufficient and core factor for the abandonment of farmland. Therefore, the key issue of mechanization must be handled well. The survey found that most farmers are affected by economic conditions, lengthy idle time of agricultural machinery, a low survival rate due to mechanical planting, and the variety of agricultural machinery needed, so their willingness to purchase agricultural machinery is not strong. Therefore, the promotion of small machinery usage should be combined with technical services. First, the R&D Department must accurately grasp the actual demand of farmers to ensure that agricultural machinery meets the needs of local industries. The boundaries between villages need to be broken, and large agricultural machinery households and specialized agricultural machinery service organizations should be jointly cultivated and developed to reduce the costs and realize large-scale and mechanized operations. Furthermore, for areas that cannot be concentrated into contiguous land, subsidies need to be increased and implemented to allow ordinary farmers with weak economic power to purchase agricultural machinery or technical services.
(2) Pay attention to the interactions between measures, and implement multiple measures to alleviate the abandonment of farmland. In the process of farmland abandonment, a single factor does not work alone. It must work with other factors for the abandonment of farmland to occur. This paper shows that there are five condition combinations which can result in the abandonment of farmland in the hilly and mountainous areas of Ganzhou, which provide a variety of perspectives for government managers to target in order to prevent the abandonment of farmland. Moreover, when farmland is abandoned, the measures to resolve the abandoned land should be formulated according to local conditions.
(3) Manage the abandonment of farmland systematically, and explore and promote the management strategies of the farmland abandonment in depth. The reasons affecting the abandonment of farmland are asymmetric. The reasons why the abandonment of farmland does not occur cannot be deduced backwards based on the reasons for the abandonment of farmland. This fact inspires us to treat the abandonment of cultivated land rationally, and it is not necessarily true that the phenomenon of farmland abandonment will not occur if the factors leading to farmland abandonment are changed completely. Therefore, it is necessary to treat the abandonment of farmland as a long-term systematic project. The government should effectively integrate various forces such as agricultural scientific research units, universities, agricultural enterprises, and cooperatives to conduct research on the management strategies for farmland abandonment.

6 Conclusions

In order to overcome the shortcomings of considering only single influencing factors in previous studies, this paper uses the csQCA to explore the multi-concurrent causality relationships and configurations among the influencing factors based on the configuration perspective. The main conclusions are as follows:
(1) Farmland abandonment conforms to the collective nature of social phenomena. Analysis of necessary conditions found that low-level agricultural mechanization is a necessary condition for the farmland abandonment in Ganzhou. However, the consistency of most individual antecedent conditions does not exceed 0.9, which indicates that the factors leading to the abandonment of farmland are various, rather than any single cause acting alone. The lack of unilateral conditions is not enough to cause the abandonment of farmland, and the existence of unilateral conditions cannot avoid the abandonment of farmland.
(2) Different combinations of conditions can have the same effect on the farmland abandonment. The abandonment of farmland has complexity but also a certain regularity. The configuration analysis found that the path leading to the abandonment of farmland is not unique, and further discovered five different combinations that lead to the abandonment of farmland. These five separate combinations of factors are: (I) a combination of convenient transportation, complete agricultural facilities, low-level agricultural mechanization, low-level land circulation and no industrial policy support; (II) a combination of complete agricultural facilities, low-level agricultural mechanization, low-level land circulation, sufficient agricultural labor and no industrial policy support; (III) a combination of convenient transportation, complete agricultural facilities, low-level agricultural mechanization, sufficient agricultural labor and no industrial policy support; (IV) a combination of convenient transportation, low-level agricultural mechanization, low-level land circulation, sufficient agricultural labor, and industrial policy support; and (V) a combination of inconvenient transportation, complete agricultural facilities, low-level agricultural mechanization, high-level land circulation, sufficient agricultural labor and industrial policy support.
(3) There are one or more core conditions in the condition combinations which lead to farmland abandonment. The combination of core conditions and multiple peripheral conditions have an impact on farmland abandonment. Different regions can adopt different policies according to local conditions to avoid the abandonment of farmland. But it should be noted that no matter what path is chosen, if the core factors are not adequately addressed, the phenomenon of farmland abandonment will still occur.


The authors would like to thank Zhang Yanwei and Huang Yingqian whose suggestions greatly improved the manuscript.
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