The Impact of and Mechanism behind High-Standard Farmland Construction in Farmland Abandonment: A Moderated Mediating Analysis

Abstract

At present, farmland abandonment (FA) is a serious problem in China, severely restricting agricultural production. In this context, it is of great significance to explore the logical relationship between high-standard farmland construction (HSFC) and FA to optimize land resource allocation and guarantee national food security. Based on a sample of 838 farmers in the main rice production area of the Yangtze River Basin in China, this study employed the Tobit model, the mediating effect model, and the moderated mediating effect model to analyze the impact of HSFC on FA at the micro level. The results show the following: (1) HSFC inhibits FA and the FA proportion decreases by 1.15% for every 1% increase in the HSFC proportion; the robustness test and endogeneity treatment also yield consistent conclusions. (2) The inhibitory effect of HSFC on FA varies greatly among different farmers and is more significant for part-time farmers and those with a higher degree of land fragmentation. (3) Agricultural socialization services (ASS) play a positive mediating role in the influence path. HSFC promotes the farmers’ purchase of ASS, which in turn inhibits FA. (4) The agricultural labor transfer distance (ALTD) plays a positive moderating role in the relationship between HSFC and FA. The farther the distance, the more likely it is that HSFC can promote the farmers’ purchase of ASS and inhibit FA. The results provide insights regarding how to precisely implement the HSFC policy, i.e., to inhibit FA by improving the construction of high-standard farmland and the post-construction management and protection system. Building targeted construction programs and operational systems that consider the differences in the target groups, improving the standard and capacity of ASS to ensure sustainable benefits for farmers, and promoting the non-agricultural transfer of surplus agricultural labor can create conditions for the modern transformation of the traditional rural economy.

1. Introduction

Farmland abandonment (FA) is a transformation resulting from changing land use patterns with changes in economic and social development. Since the 20th century, the acceleration of industrialization and urbanization worldwide has led to FA in most countries, evolving into a global socioeconomic phenomenon [1,2]. In China, the transition in land use began in the 1990s [3]. Economic development has created more non-agricultural employment opportunities, accelerating labor transfer to secondary and tertiary industries. Coupled with the low comparative efficiency and poor production conditions in agriculture, farmland has gradually been excluded from agricultural production, leading to widespread FA. Research indicates that the proportion of FA in China has reached 20% [4], while the outward domestic labor transfer has already surpassed the Lewis turning point [5], showing a trend of rural labor depletion. Furthermore, the persistent small-scale, fragmented farming model of “one mu and three fields per capita” complicates efforts to improve the production efficiency and achieve economies of scale [6], indicating the intensification of FA. Long-term FA causes a serious waste of land resources, threatening the adequate supply of agricultural products and national food security [7]. Therefore, ensuring the sustainable use of farmland resources is a critical challenge in achieving comprehensive and sustainable social, economic, and ecological development.

FA has attracted widespread attention from policymakers worldwide. For example, the Japanese government established the Direct Payment Scheme for Farmers in Hilly Mountainous Areas to counter FA by increasing direct payments to unfavorable regions [8]. Similarly, the European Union (EU) introduced the Areas of Natural Constraints (ANC) program to stimulate farmers in designated areas facing major problems due to factors such as remoteness, a complex topography, the climate, and the soil conditions to continue farming by providing financial support. However, the support level and policy effectiveness have varied significantly across the EU. In contrast, the Chinese government focuses on the coordinated utilization of land and tries to inhibit FA through HSFC. HSFC mainly refers to a series of comprehensive measures, mainly funded by the government, aiming to compensate for the shortcomings of agricultural production through a series of measures such as land leveling, soil improvement, water-saving irrigation, and field road repair [9]. The Ministry of Agriculture and Rural Affairs (MARA)’s “Guiding Opinions on the Coordinated Utilization of Abandoned Land to Promote the Development of Agricultural Production”, published in 2021, highlighted the need to improve the conditions of sloping land in hilly and mountainous areas, promote the cultivation of fragmented plots, increase investments, improve the infrastructure, and enhance the suitable machine operation level. The conditioned abandoned land can be included within the scope of HSFC. Therefore, it is of great practical significance to explore the impact of the HSFC policy on FA.

Driven by natural endowment limitations [10], climate change [11], population out-migration [12], regime change [13,14], and other natural or social factors, FA has become a widespread challenge that adversely affects agricultural production. Existing studies have proposed governance solutions such as promoting agricultural mechanization [15], land transfer [16], and population migration [17]. Other scholars have categorized the drivers of FA into two main groups: natural environmental factors and socioeconomic factors. Regarding natural factors, soil quality, land fragmentation, and other constraints on agricultural development, as well as the inhibition of market competitiveness among agricultural producers are factors that cannot be ignored. As a result, land consolidation is frequently cited as a solution [18]. Most existing studies suggest that land consolidation can overcome the comparative disadvantage of agricultural production by enhancing the essential elements for agricultural production and consolidating the infrastructure, thereby stimulating farmers’ enthusiasm for agriculture and reducing FA [19,20,21]. However, some scholars suggest that although land consolidation barely maintains the area of usable farmland, it cannot avoid a slight reduction in farmland [22]. There is no consistent conclusion on whether land leveling can inhibit FA. In terms of economic factors, the New Economics of Migration (NELM) explains the causes of FA. The theory considers a large quantity of agricultural labor force precipitation as a key driver of FA [23], suggesting that ASS can alleviate the constraints of farmers’ human resources [24], serving as an alternative to the loss of rural young and adult laborers. For small-scale farmers lacking comparative advantages in production, outsourcing specific underperforming production activities to professional service organizations significantly enhances the efficiency of labor resource allocation, effectively inhibiting FA [25,26]. Other studies have explored the relationship between ASS, agricultural labor force non-farm transfer, and FA, but have not reached a consistent conclusion. Chen Jingshuai and Han Qing argue that the impact of ASS on FA is related to the number of service links purchased by farmers and the proportion of non-farm employment in labor, and they suggest that multi-link ASS inhibits FA more significantly than single-link ASS [27]. However, there exist different research findings. For instance, Zhang X et al. found that, for areas with a low non-agricultural employment proportion, ASS could not inhibit FA [28].

The existing literature serves as a valuable foundation for this study, yet several gaps remain. First, the existing studies mainly explore the relationship between land consolidation and FA. At the same time, HSFC, an important policy implemented by the Chinese government to improve agricultural productivity [29], is rarely considered in the relevant literature. It remains unclear whether there is a practical effect of FA inhibition after the completion of HSFC. Second, although the influence of ASS on FA has been examined, the role of HSFC in facilitating the development of ASS has not been thoroughly explored within a unified analytical framework that encompasses all three elements. Third, the existing studies on the relationship between ASS and FA lack a detailed examination of the extent of agricultural labor departure. Therefore, this research, adopting 838 micro-research data from the main rice-producing areas in China’s Yangtze River Basin, explores the relationship between HSFC, ASS, and FA with a Tobit model, a mediating effect model, and a moderated mediating effect model. Furthermore, it probes the new path of the sustainable development of agriculture under the trend of non-agriculture labor transfer, providing a reference for the improvement of relevant policies.

2. Theoretical Framework

2.1. High-Standard Farmland Construction and Farmland Abandonment

Rising operational costs have led to a decline in agricultural incomes and weakened the economic productive capacity of farmland. According to the rational peasant theory, farmers, as rational economic agents, aim to maximize their interests by recombining production factors. When the non-agricultural employment opportunities increase, and the income disparity between the agricultural and non-agricultural sectors gradually widens [30], it becomes economically sensible for the able-bodied labor force within families to transition away from agriculture. This shift results in a shortage of agricultural laborers, an aging workforce, and underutilized farmland. Additionally, the slow development of the farmland transfer market, institutional rigidity, land fragmentation, and other real-world factors increase the transaction costs associated with farmland transfer, making farmland abandonment (FA) an inevitable outcome. HSFC can improve farmland’s comprehensive production capacity, ease agricultural labor constraints, and facilitate farmland transfer, thus inhibiting FA.

Firstly, HSFC has improved the comprehensive production capacity of farmland, mitigating FA that arises from the low comparative returns and high risks of the uncertainty of agricultural production. On one hand, HSFC has improved the quality and basic soil productivity conditions through soil improvement and fertilization, laying the groundwork for stable and increased yields. Furthermore, HSFC is mainly funded by the government’s public investment, which, under the same conditions, offsets a portion of the farmers’ capital investment, thereby improving the input–output ratio for farmers. On the other hand, HSFC improves the field water irrigation and drainage infrastructure, enhancing the farmland’s ability to prevent disasters and thus ensure crop outputs.

Secondly, HSFC has mitigated the FA caused by the agricultural labor shortage and the rising labor costs amid continuous labor migration. Initially, by constructing field roads and adapting land for machinery use, high-standard farmland facilitates the use of advanced agricultural technology and equipment. This development makes it feasible for machinery to replace manual labor and enhances the agricultural production efficiency, narrowing the income gap between agriculture and the secondary and tertiary industries. According to the migration theory [31], laborers tend to return to rural areas when agriculture offers higher earnings, which helps to alleviate the issues of labor loss and aging. Furthermore, HSFC improves the land concentration and contiguity through measures such as field amalgamation, supporting the transition towards larger-scale, specialized agricultural operations. This shift encourages new agricultural management entities to invest in rural areas, providing alternatives for part-time farmers.

Finally, HSFC encourages farmland transfer, thereby inhibiting FA. For the farmers who are considering the transfer of their land, this decision follows the cost–benefit principle as they are inclined to transfer their land when the benefits outweigh the costs; otherwise, they may abandon it. HSFC improves farming conditions, potentially increasing the transfer prices to raise the farmers’ incomes from land transfer and stimulating transfer activities. For the farmers who are considering the transfer of their land, high-standard farmland addresses the limitations of traditional agricultural production by reducing the need for labor and other inputs, enhancing the output of agricultural products, and increasing the productive income. Therefore, HSFC makes farmland more appealing for farmers by providing comparative production advantages, encouraging them to acquire and cultivate additional land. Thus, the following hypothesis is proposed.

H1: 

HSFC inhibits FA.

2.2. High-Standard Farmland Construction, Agricultural Socialization Services, and Farmland Abandonment

The transformation of traditional agriculture relies on the introduction of modern production factors, with agricultural machinery being a pivotal new factor that can alter the input structure and operational mode of traditional farming. However, the high technological threshold asset specificity and small farmers’ limited direct purchasing capabilities present challenges. ASS organizations can serve as conduits for capital and technology, reducing the constraints on agricultural production at a minimal cost, thereby facilitating their seamless integration with modern agricultural practices and subsequently mitigating FA. Specifically, ASS curtails FA through the following three mechanisms.

First, the labor substitution effect, stemming directly from the transfer of labor to non-agricultural sectors, intensifies the constraints on the agricultural labor supply. To maintain agricultural operations, farmers may resort to hiring labor or engaging ASS to compensate for the labor shortages. Given the high costs associated with hiring labor, ASS emerges as a more cost-effective alternative. Second, the technology substitution effect sees ASS organizations intervening to replace farmers as the main providers of investment, technology, and management. They act as both the carriers and facilitators of new technology, integrating high-value-added production technology into the agricultural process at lower costs, thus enhancing the efficiency of farming operations. Third, the technology spillover effect amplifies the role of ASS organizations in empowering adjacent traditional agricultural producers and farmers. By facilitating the adoption of advanced production technologies, management experience, and organizational systems, ASS help optimize the set of input factors and bolster business returns. However, in order for ASS to fully leverage these advantages, certain conditions must be met, including the availability of smooth agricultural roads, flat land plots, and areas conducive to mechanized operations. HSFC addresses these needs by developing inter-field roads, consolidating land plots, and adapting the land for machinery, thus providing ASS organizations with large-scale, mechanically compatible land equipped with comprehensive infrastructure. This enables the effective use of ASS in preventing FA. Thus, the following hypothesis is proposed.

H2: 

HSFC promotes the farmers’ purchase of ASS, thereby inhibiting FA.

2.3. Moderating Role of Agricultural Labor Transfer Distance

The transfer of agricultural labor to non-agricultural sectors is not a uniform process without differences; instead, the transfer distance reflects the degree to which laborers have departed from agriculture. The varying degrees of departure influence the family’s allocation of production factors and agricultural land disposal. This study posits that the mitigating effect of HSFC and ASS on FA is affected by the ALTD through the following three primary pathways.

First, the moderating effect of the ALTD on the relationship between HSFC and FA is significant. The farmers who transfer locally may not fully leave agricultural production due to HSFC’s effectiveness in improving the farming conditions and reducing the intensity of agricultural labor. This enables part-time farmers to engage in basic agricultural production activities without reducing the operational scale. However, this also leads to a situation in which, despite operating on high-standard farmland, these farmers are unable to shift away from the traditional extensive farming practices, failing to achieve agricultural transformation and upgrading. The comparative income disadvantage of agricultural operations cannot be mitigated, and there is still the potential risk of FA. The opportunity cost of farming is higher for long-distance transfer farmers, who are more inclined to leave agricultural production. HSFC enhances the quality of farmland and increases the transfer price, encouraging some farmers to lease out their farmland for a high rent price, thereby inhibiting FA.

Second, the moderating effect of the ALTD on the relationship between HSFC and ASS is noteworthy. After the off-farm transfer of labor, the family’s income structure changes, reducing their reliance on farmland for their livelihoods. However, for many farmers, farmland remains an irreplaceable asset of personal significance, often associated with higher value and even serving a social security role. For the farmers engaging in long-distance transfer, the opportunity cost of farming is elevated, making it challenging to balance non-agricultural employment with agricultural production. Consequently, those with lucrative non-agricultural jobs at a distance are more inclined to maintain the value of their farmland by purchasing ASS to compensate for the lost labor elements, thereby reducing the opportunity cost associated with agricultural production. In contrast, the close-distance transfer farmers leave their land but not their native country, and their non-agricultural employment time is more flexible, so they can adapt to the seasonal and geographical characteristics of agricultural production activities and therefore have a smaller demand for ASS than the long-distance transfer farmers.

Third, the moderating effect of the ALTD on the relationship between ASS and FA is crucial. The involvement of ASS organizations has significantly altered the traditional agricultural production methods. However, the key factor in boosting agricultural outputs and mitigating FA through adopting new and modernized production techniques relies on the extent to which advanced technologies are applied and adopted. The farmers who have transferred over long distances have engaged heavily with ASS, transforming these providers into key decisionmakers in the production process. By introducing efficient management methods and modern organizational systems, ASS enhances the efficiency of agricultural operations, thereby inhibiting FA. Conversely, the total demand for ASS among the farmers who transfer over short distances is limited, making it challenging for the intervention of a single ASS to effect a transformation in agricultural production methods. As a result, the inhibitory effect of ASS on FA is less pronounced among these farmers. Thus, the following hypotheses are proposed.

H3a: 

The ALTD positively moderates the relationship between HSFC and FA. Specifically, the greater the distance, the stronger the inhibitory effect of HSFC on FA.

H3b: 

The ALTD positively moderates the relationship between HSFC and ASS. Specifically, the greater the distance, the more HSFC encourages farmers to purchase ASS, which, in turn, inhibits FA.

H3c: 

The ALTD positively moderates the relationship between ASS and FA. Specifically, the greater the distance, the more pronounced the inhibitory effect of ASS on FA.

In summary, ASS may play a mediating role between HSFC and FA, with this role being moderated by the ALTD. Thus, we have constructed a hypothesis model for testing, as depicted in Figure 1. The impact of HSFC on FA through ASS is termed the mediating effect, while the impact of HSFC on FA without any intermediaries is referred to as the direct effect. The total effect of HSFC on FA is the sum of both the mediating and direct effects. The ALTD may moderate the mediating model via three pathways: path d represents the moderating effect of the ALTD on the direct effect, corresponding to Hypothesis H3a; path e represents the moderating effect of the ALTD on the relationship between HSFC and ASS, corresponding to Hypothesis H3b; and path f represents the moderating effect of the ALTD on the relationship between ASS and FA, corresponding to Hypothesis H3c.

3. Data and Experimental Methods

3.1. Data Collection

The microdata used in this study come from the “One Hundred Villages, One Thousand Households” field research conducted in January–February 2022 by Peking University and Jiangxi Agricultural University. The reasons for the selection of Jiangxi Province in China as the sample area are as follows: First, Jiangxi Province is the main rice-producing province in China’s Yangtze River Basin, with the third-highest rice production in the country, and it has the important responsibility of guaranteeing national food security. Second, the province is a region in which FA represents a serious problem [32]. Third, the province has completed HSFC for 64.5 percent of the total area of farmland, and the policy’s implementation has been effective. The research group used a combination of stratified and random sampling methods. Firstly, based on the per capita GDP, the 100 counties (cities and districts) in Jiangxi Province were divided into three levels, and four sample counties were randomly selected at each level, namely Xinjian District, Jinxian County, Pengze County, Fuliang County, Yushan County, Zixi County, Fenghsin County, Luxi County, Wan’an County, Yongxin County, Dayu County, and Ruijin City. ArcGIS 10.8 was used to select the sample counties. The county distribution map is shown in Figure 2. Subsequently, based on their level of economic development and geographic location, three sample townships (towns) were selected in each sample county, three administrative villages were selected in each sample township (town), and ten farmers were randomly selected in each administrative village. A total of 1080 questionnaires were distributed to the farmers, and 1071 valid questionnaires were retrieved, with a validity rate of 99.17%. Agricultural management is usually based on collective decision making by families, so this study begins at the family level and limits the sample to those with contracted management rights for farmland, eliminating the samples with incorrect information and missing data on the core variables, finally obtaining 838 valid samples. We use Stata 17.0 for empirical analysis.

3.2. Description of Variables

Explained variable: FA proportion. Most of the existing literature measures FA according to “whether it is abandoned or not”. However, using only a dummy binary variable, it is not possible to reflect the degree of abandonment, and it can easily lead to the omission of information. Therefore, this study refers to the work of Xu D et al. to measure the ratio of the area of FA to the total area of family-contracted farmland [30]. First, abandoned farmland is defined as land that has not been cultivated in the current year, and the actual area is measured using a question in the questionnaire, referring to “the area of abandoned farmland this year”. Then, the ratio of its area to the area of family-contracted land is calculated, which serves as an indicator of FA. At the same time, a dummy variable named “whether it is abandoned or not” is applied in the robustness test to contrast the baseline regression.

Core explanatory variable: HSFC. HSFC is a systematic project that is highly time-consuming and has a large capital demand that ordinary farmers cannot afford alone. It is usually planned and constructed in a unified manner by the jurisdictional government or village collectives through public financial expenditure. Therefore, with reference to the existing studies [33,34], the level of HSFC is measured as the proportion of the area of high-standard farmland to the area of farmland in the village in which the farmer is located.

Mediating variable: ASS. Referring to the studies of Cheng C et al. and Zhang M et al. [32,35], six production segments, namely land leveling, seedling cultivation, transplanting, fertilizing, pesticides, and harvesting, were selected to measure the total purchased number of ASS.

Moderating variable: Agricultural ALTD. Referring to the study by Liao W et al. [36], the destination of the householder’s non-agricultural work is used as a measure of the agricultural ALTD.

Control variables: Referring to the current literature [3,37], the household, land, and village characteristics are introduced to reduce the estimation bias. The household characteristics include the education level, household size, agricultural income proportion, and non-agricultural employment proportion. The land characteristics include the area of operated farmland, land transfer, farmland fragmentation degree, and farmland rights. The village characteristics include the area of farmland in the village, unirrigated farmland proportion, and aging proportion in the village. The variables and descriptive statistics included in the model are shown in

Table 1. Main explanatory variables.

Variable Name	Meaning and Assignment
Explained variable
FA proportion	The proportion of abandoned farmland to the total area of family-contracted farmland
Whether it is abandoned or not	Is the family’s farmland abandoned? 0 = No; 1 = Yes
Core explanatory variable
HSFC proportion	The proportion of the HSFC area to the total farmland area of the village
Availability of HSFC	Has the village implemented HSFC? 0 = No; 1 = Yes
Mediating variable
ASS	The total number of agricultural socialization services purchased for land leveling, seedling cultivation, transplanting, fertilizing, pesticides, and harvesting (number)
Moderating variable
ALTD	The destination of the non-agricultural work of householder, 0 = no non-agricultural employment; 1 = within the village; 2 = within a commune outside the village; 3 = within a county outside the commune; 4 = within a province outside the county; 5 = outside the province
Farmer characteristics
Educational level	The educational level of householder, 1 = no schooling, 2 = elementary/private schooling, 3 = junior high school, 4 = high school/secondary school, 5 = college and above
Family size	The number of family members (persons)
Agricultural income proportion	The proportion of income from agricultural operations
Non-agricultural employment proportion	The proportion of non-agricultural employment to the total family size
Land characteristics
Area of operating farmland	The area of farmland cultivated by farmers (hectares)
Land transfer	Are you involved in land transfer? 0 = No, 1 = Yes
Farmland fragmentation degree	Farmland area under household contract/number of plots (ha/plot)
Confirmation of farmland rights	Yes or no certificate of entitlement? 0 = No, 1 = Yes
Village characteristics
Farmland area of the village	According to actual survey data (hectares)
Unirrigated farmland proportion	Farmland proportion in the village without access to surface and groundwater irrigation
Village aging proportion	The proportion of the number of people aged 65 and over in the village to that of the registered population
Observations	838

.

3.3. Sample Description

From the statistical results in

Table 2. Benchmark regression results of HSFC on FA.

Model (1)	Explained Variable: FA Proportion
	Coefficient	Marginal Effect
HSFC proportion	−0.626 ** (0.267)	−0.115 ** (0.049)
Farmer characteristics
Educational level	−0.086 (0.056)	−0.016 (0.010)
Family size	−0.077 ** (0.031)	−0.014 ** (0.006)
Agricultural income proportion	−0.380 * (0.235)	−0.070 ** (0.041)
Non-agricultural employment proportion	0.401 * (0.222)	0.074 * (0.041)
Land characteristics
The area of operating farmland	−0.172 (0.184)	−0.032 (0.034)
Land transfer	−0.322 *** (0.121)	−0.059 *** (0.022)
Farmland fragmentation degree	−2.531 *** (1.309)	−0.465 *** (0.239)
The confirmation of farmland rights	−0.265 * (0.149)	−0.049 * (0.027)
Village characteristics
Farmland area of the village	−0.002 * (0.001)	−0.0003 * (0.0002)
Unirrigated farmland proportion	1.628 *** (0.374)	0.299 *** (0.068)
Village aging proportion	1.098 (0.772)	0. 202 (0.142)
Prob > chi2	0.000
Observation	838	838

Note: *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively, with robust standard errors in parentheses.

, the average FA proportion is 8.8%, of which 13.6% of the farmers exhibit FA behavior, indicating that the FA in the sample area is serious. The mean value of HSFC is 0.499, and the mean value of the HSFC ratio is 19.2%, indicating that nearly half of the villages have implemented HSFC, but the construction efficiency is still low, which is in line with the findings of the existing studies [38]. We then plotted the relationship between the core variables in a scatter plot, as shown in Figure 3, which shows that as the value of the ratio of HSFC on the horizontal axis grows, the distribution of the scatter points becomes more sparse. The value of the corresponding vertical axis, denoting the FA proportion, decreases, indicating that, in general, HSFC can inhibit FA, and this result is more consistent with the hypothesis put forward in the framework. The mean value of ASS is 0.943, which indicates that, in the six production segments, only 0.943 instances of ASS were purchased by each farmer on average. This suggests that the demand for ASS in the sample area is not strong; it is possibly constrained by the farmers’ lack of understanding of ASS and the fact that the market for ASS is not yet mature [39]. The mean value of the ALTD is 1.060, indicating that most of the farmers are predominantly engaged in local part-time work. This is mainly due to the development of rural industries in China, which provide more opportunities for farmers to engage in non-agricultural economic activities in close proximity to their land.

Regarding the farmers’ characteristics, the average educational level of the sampled farmers is 2.271, reflecting the actual situation characterized by a low human capital level in rural China. The average family size is 4.412 persons, and the average agricultural income proportion is only 21.3%, indicating that most of the income comes from non-agricultural work. However, the average non-agricultural employment proportion is 34.2%, which is in line with the fact that the young laborers in rural areas travel away to work and the older adults farm at home and take care of their grandchildren [40]. In terms of the land characteristics, the average area of farmland operated by the farmers is 0.439 hectares, while the average area of each piece of farmland is only 0.078 hectares, indicating that the small-scale management of farmland on a farmer basis is the main operational mode of agricultural production in the research area. This confirms the basic situation across China as being “a large country with small-scale farmers”, as well as reflecting the situation of the agricultural industry. Within the sample, 56.6% of the farmers have participated in land transfer, but only 27.3% of the farmland has the correct certification. In terms of the village characteristics, the average farmland area of the villages is 133.517 hectares, 95% of which exhibits good irrigation conditions. The average aging proportion of the villages is 15.5%, higher than the national average of 14.9% in the same year, showing that the aging problem of the rural population in the research area is serious. This is mainly because the increase in wages in the non-agricultural sector promotes the transfer of young laborers from the countryside, which poses a challenge to agricultural production.

3.4. Empirical Methodology

3.4.1. Benchmark Regression

First, the relationship between HSFC and FA is analyzed. It should be noted that, considering that 86.4% of the farmers did not exhibit FA behavior, the FA proportion can be described as a restricted dependent variable with obvious left-end subsumption characteristics, and the use of the traditional linear regression model will lead to an obvious bias in the results. Therefore, this study refers to the work of Xu D et al. and Ji D et al. to construct a Tobit model based on the maximum likelihood method [30,41]. The specific settings are as follows:

$${\mathit{AR}}_i{=\beta }_0{+\beta }_1{\mathit{HSF}}_{i }+\sum {\beta }_2{\mathit{controls}}_i{+\epsilon }_i{, \mathit{AR}}_{i }=\left\{\begin{array}{c}{\mathit{AR}}_i^{*}{, \mathit{AR}}_i^{*} > 0\\ {0, \mathit{AR}}_i^{*} \le 0\end{array}\right.$$

(1)

In Equation (1), AR_i is the explained variable, denoting the FA proportion of the i farmer, and HSF_i is the core explanatory variable, denoting the HSFC proportion in the village of the i farmer. controls_i are a series of control variables reflecting the characteristics of the householder, family, land, and village. β_k denotes the coefficients of each variable, where k = 0, 1, 2, and ε_i is a randomized disturbance term.

3.4.2. Mediating Effect Test

Second, the mediating effect of ASS is tested. In this study, we refer to the research of Baron R M and Kenny D A and Zhou C et al. to construct the mediating effect model [42,43]. This has the advantage of reducing the probability of errors in the product coefficient model, and it is set up as follows:

$${\mathit{AR}}_i{=a}_0{+a}_1{\mathit{HSF}}_i+\sum a_2{\mathit{controls}}_i{+\epsilon }_i$$

(2)

$${\mathit{ASS}}_i{=b}_0{+b}_1{\mathit{HSF}}_i+\sum b_2{\mathit{controls}}_i{+\epsilon }_i$$

(3)

$${\mathit{AR}}_i{=c}_0{+c}_1{\mathit{ASS}}_i{+c}_2{\mathit{HSF}}_i+\sum c_3{\mathit{controls}}_i{+\epsilon }_i$$

(4)

In Equations (2)–(4), ASS_i is the mediating variable, denoting the number of ASS purchased by farmer i; a_i, b_i, and c_i are the coefficients of each variable. The remaining variables are consistent with Equation (1) and will not be repeated here.

3.4.3. Moderated Mediating Effect Test: Moderating Effect of Agricultural Labor Transfer Distance

The mediating effect of ASS was tested in the previous section, so, when testing the moderating effect of the ALTD, a moderated mediating effect model was constructed. Drawing on the method of BarNir A et al. [44], the regression equation was constructed as follows, corresponding to the direct path (d), the first half of the path (e), and the second half of the path (f), which are shown in Figure 1.

$${\mathit{AR}}_{i }{=d}_0{+d}_1{\mathit{HSF}}_{i }{+d}_2{\mathit{NF}}_i{+d}_3{\mathit{HSF}}_i{ \times \mathit{NF}}_i+\sum d_4{\mathit{controls}}_i{+\epsilon }_i$$

(5)

$${\mathit{ASS}}_i{=e}_0{+e}_1{\mathit{HSF}}_i{+e}_2{\mathit{NF}}_i{+e}_3{\mathit{HSF}}_i{ \times \mathit{NF}}_i+\sum e_4{\mathit{controls}}_i{+\epsilon }_i$$

(6)

$${\mathit{AR}}_i{=f}_0{+f}_1{\mathit{HSF}}_i{+f}_2{\mathit{ASS}}_{i }{+f}_3{\mathit{NF}}_i{+f}_4{\mathit{HSF}}_i{ \times \mathit{NF}}_i{+f}_5{\mathit{ASS}}_i{ \times \mathit{NF}}_i+\sum F_6{\mathit{controls}}_{i }{+\epsilon }_i$$

(7)

In Equations (5)–(7), NF_i denotes the moderating variables to be tested. The coefficients of the interaction terms HSF_i × NF_i and ASS_i × NF_i are the moderating effects of the moderating variables in each path, and the remaining terms are consistent with those in Equations (2)–(4).

4. Results

4.1. Impact of High-Standard Farmland Construction on Farmland Abandonment

4.1.1. Benchmark Regression

Using the stata16 software, the Tobit model was used to analyze the impact of HSFC on FA, and the estimated coefficients and marginal effects are shown in Table 2. The P value of Model (1) is significant at the 1% level, indicating that the overall fitting effect is good. The estimated coefficient of the HSFC proportion is negative and significant at the 5% level, indicating that HSFC can significantly inhibit FA and Hypothesis (1) is verified; the result matches the findings of Zhang Y et al. [45]. The results of the marginal effect test show that, with other influencing factors remaining unchanged, for every 1% increase in the HSFC proportion, the FA proportion decreases by 1.15%.

The results for the control variables show that the household size, the proportion of agricultural income, land transfer, agricultural land rights, the degree of land fragmentation, and the village farmland area significantly and negatively affect FA. The ratio of non-agricultural employment and the proportion of unirrigated areas significantly and positively affect FA. Specifically, the larger the household size, the more sufficient the number of laborers and the less likely it is for FA to occur. Households with a high proportion of agricultural income, who rely on agricultural operations, will preferentially devote laborers to agricultural production, reducing the likelihood of FA. This verifies the conclusions of the study by Yan J et al., indicating that an increase in non-farm income promotes the migration of laborers out of agricultural production, thus increasing FA [46]. Land transfer causes part-time farmers to relinquish idle farmland to obtain income, thus reducing the possibility of FA, which is consistent with the findings of Shao J et al. [47]. The confirmation of the right to farmland strengthens farmers’ knowledge of the residual claim right of the farmland; FA will cause a decline in land strength and the depreciation of the value of the property rights of the farmland, which prevents the realization of the residual claim right. Thus, farmers are more willing to activate their abandoned farmland and make the expected benefit-maximizing decisions [48], which is also verified in Zheng L and Qian W’s study [49]. The higher the degree of land fragmentation, i.e., the smaller the average area of farmland contracted by each household, the higher the production cost per unit area, further reducing the comparative efficiency of agriculture and thus inducing FA, which is in line with Liu G et al.’s findings [50]. Villages with large farmland areas pay more attention to agricultural infrastructure construction, making the farmland more attractive in the transfer market. In contrast, as the ratio of non-farm employment increases, the more obvious the lack of effective labor input in agricultural production, and the higher the possibility of FA. A higher proportion of unirrigated areas indicates poorer irrigation conditions of the farmland, and the risk of agricultural operations increases, causing the farmer to reduce the area of agricultural operation due to risk avoidance when making planting decisions. The effects of the farmland area and village aging rate on FA are not statistically significant and need to be further examined in future research.

4.1.2. Robustness Test

In the robustness test, the independent variable “HSFC proportion” is replaced by “whether there is HSFC” for the robustness test, and the results are shown in Model (2) in

Table 3. Robustness test results.

	Model (2) Explained Variable: FA Proportion		Model (3) Explanatory Variable: Whether FA or Not
	Coefficient	Marginal Effect	Coefficient
The availability of HSFC	−0.255 ** (0.127)	−0.047 ** (0.023)
HSFC proportion			−0.532 ** (0.247)
Farmer characteristics
Educational level	−0.080 (0.056)	−0.015(0.010)	−0.097 *(0.058)
Family size	−0.079 ** (0.031)	−0.014 ** (0.006)	−0.068 ** (0.031)
Agricultural income proportion	−0.392 * (0.226)	−0.072 * (0.042)	−0.249 (0.217)
Non-agricultural employment proportion	0.399 * (0.222)	0.073 * (0.041)	0.489 ** (0.231)
Land characteristics
The area of operating farmland	−0.173 (0.187)	−0.032 (0.034)	−0.109 (0.085)
Land transfer	−0.312 *** (0.121)	−0.057 *** (0.022)	−0.355 *** (0.119)
Farmland fragmentation degree	−2.506 * (1.306)	−00461 * (0.027)	−2.413 (1.499)
The confirmation of farmland rights	−0.251 * (0.149)	−0.046 * (0.027)	−0.299 ** (0.150)
Village characteristics
Farmland area of the village	−0.002 ** (0.001)	−0.0003 ** (0.0002)	−0.002 ** (0.001)
Unirrigated farmland proportion	1.624 *** (0.375)	0.298 *** (0.069)	1.819 *** (0.367)
Village aging proportion	0.992 (0.780)	0.182 (0.143)	1.249 * (0.685)
Prob > chi2	0.000		0.000
Observation	838		838

Note: *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively, with robust standard errors in parentheses.

. The determination of whether HSFC has a significant and negative effect on the FA proportion and the marginal effect test show that the results for the control variables have not changed significantly, indicating that the research conclusions are robust.

Then, the dependent variable is replaced with the binary variable “whether it is abandoned or not”, and the Probit model is used for estimation. The results are shown in Model (3) in Table 3. The coefficient of the impact of HSFC on FA is negative and significant at the 5% level, and the direction and significance of the other variables are the same as those of the baseline regression, which once again verifies the robustness of the research findings.

4.1.3. Endogeneity Discussion

The endogeneity in this study comes from two aspects, namely the two-way causality and omitted variable bias. First, two-way causality is adopted when the village’s FA proportion is too high or when agriculture is no longer the main industry; the village collective will reduce its investment in HSFC. Second, omitted variable bias refers to the inevitable bias wherein, for the set of control variables regarding the characteristics of the farmers, land, and village, the possibility of omitted variables cannot be completely excluded. Therefore, referring to the method of Li B and Shen Y [51], “whether there is a large-scale agricultural industry in the village” is selected as an instrumental variable for HSFC. The reasons for the inclusion of this instrumental variable are as follows: first, the existence of a large-scale agricultural industry in a village has a strong exogenous relationship with FA; second, the existence of a large-scale agricultural industry in a village has a strong correlation with HSFC, which is conducive to the realization of the large-scale operation of agriculture.

The IV-Tobit method is used to test the possible endogeneity of the model, and the results are shown in

Table 4. Estimation results of the instrumental variable approach to the endogeneity treatment.

Models (4)	Explained Variable: FA Proportion
	Phase I	Phase II
	(OLS)	(IV-Tobit)
IV: The availability of large-scale agroindustry	0.526 *** (0.013)
HSFC		−1.793 *** (0.443)
Control variable	controlled	controlled
F	144.190
The Wald test of exogeneity		16.98
Prob > chi2		0.000
Observation	838

Note: *** indicates significance at the 1% level and robust standard errors are given in parentheses.

. The results of the weak instrumental variable test show that the value of the F-statistic is 144.190, which is much larger than the critical value of 10, indicating that the original hypothesis of the existence of weak instrumental variables is rejected. The results of the Wald test of exogeneity are significant at the 1% level, indicating that HSFC can be considered an endogenous variable at the 1% level, which verifies the necessity of the instrumental variable method. The first stage of the IV-Tobit model is an ordinary least squares regression, and the results indicate that HSFC promotes the formation of a large-scale agricultural industry in villages. The coefficients of the second stage indicate that HSFC has a significant and negative effect on FA, and the absolute value is larger than the corresponding estimated coefficients of Model (1), which suggests that the inhibitory effect on FA may be underestimated if the endogeneity of HSFC is not taken into account. In addition, the coefficients of the variables are significant, and the direction does not change, further verifying Hypothesis H1.

4.2. Heterogeneity Analysis

In order to further analyze the differences in the impact of HSFC on FA under different types of part-time work and different degrees of farmland expertise, this study divides the sample into different sub-samples and conducts group regressions.

• Different types of part-time work. Referring to Weng Zhenlin et al.’s criteria for the division of farmers into different types of part-time farmers [52], the sample is divided into three levels according to the share of agricultural income into pure farmers (agricultural income is greater than 90%), part-time farmers (agricultural income accounts for 10% to 90%), and non-farming farmers (agricultural income is less than 10%). The share of agricultural income is excluded from the control variables and is estimated through the Tobit model and the models in

  Table 5. Results of heterogeneity analysis.

  Variable	Different Part-Time Farmers			Different Degrees of Land Fragmentation
  	(5) Non-Farming Farmers	(6) Part-Time Farmers	(7) Pure Farmers	(8) High Fragmentation	(9) Low Fragmentation
  HSFC proportion	−0.096 (0.073)	−0.122 * (0.065)	−0.063 (0.359)	−0.148 ** (0.061)	−0.008 (0.078)
  Control variable	Controlled	Controlled	Controlled	Controlled	Controlled
  Prob > chi2	0.000	0.002	0.016	0.000	0.013
  Observation	471	274	93	602	236

  Note: The table reports the marginal effects of the Tobit model; * and ** denote significance at the 10% and 5% levels, respectively, with robust standard errors in parentheses.

  . Models (5)–(7) report the regression coefficients for the different types of part-time farmers. The coefficient of HSFC on FA for the part-time farmers is negative and significant at the 10% level, and the coefficients of HSFC on FA for the pure and non-farming farmers are not significant. This indicates that the inhibitory effect of HSFC on FA is more obvious for part-time farmers. The possible reasons are that part-time farmers rely predominantly on non-farm income, their agricultural production activities are mainly aimed at retaining the right to operate the land only, and the young labor force is preferentially directed towards the non-farm sector. This leads to a shortage in the number of laborers invested in agricultural production and can lead to the phenomenon of marginal farmland abandonment. High-standard farmland creates the conditions for ASS, which can effectively replace the missing labor force, thus inhibiting FA. The income of pure farmers relies on a single mode of agricultural production, and land is the most basic production factor in agriculture, so there is no serious FA phenomenon. For non-farming farmers, the choice to transfer the right to operate in exchange for rental income is made to maximize their interests, and FA is less likely.
• Different levels of land fragmentation. Taking the mean value of the degree of land fragmentation as the dividing criterion, the sample was divided into two levels of high fragmentation (below 65 years of age) and low fragmentation (65 years of age and above). Then, the degree of land fragmentation was excluded from the control variables, which were estimated by the Tobit model, and the regression coefficients of the different degrees of land fragmentation are reported in Table 5; see Model (8) and Model (9).

In Model (8), the coefficient of the impact of HSFC on FA for high-fragmentation farmers is −0.148, being negative and significant at the 5% level. In Model (9), the coefficient of the impact of HSFC on FA for low-fragmentation farmers is not significant. This indicates that the inhibitory effect of HSFC on FA for high-fragmentation farmers is more obvious. A possible reason is that, compared with the farmers with concentrated and continuous land, the farmers with fragmented and dispersed contracted land experience high levels of difficulty in farming and high business risks, and they are more likely to choose to abandon farming. Meanwhile, HSFC improves the quality of marginal farmland and the infrastructure conditions, so as to guarantee the income from grain cultivation, and it strengthens the enthusiasm of this type of farmer so that the inhibition of FA is more obvious.

4.3. Mediating Effect Test of Agricultural Socialization Services

The mediating effect test was carried out with ASS as the mediating variable to reveal the role path by which HSFC inhibits FA, and the results of the Sobel method are shown in

Table 6. Results of Sobel’s mediating effect test for ASS.

	Model (10)	Model (11)	Model (12)
	FA Proportion	ASS	FA Proportion
HSFC	−0.084 *** (0.033)	0.776 *** (0.150)	−0.068 ** (0.033)
ASS			−0.021 *** (0.008)
Control variable	controlled	controlled	controlled
Sobel	−0.017 ** (Z = −2.490)
Goodman−1	−0.017 ** (Z = −2.454)
Goodman−2	−0.017 ** (Z = −2.526)
The proportion of mediating effects	19.66%

Note: ** and *** indicate significance at the 5% and 1% levels, respectively, and robust standard errors are given in parentheses.

. Model (10) verifies the effect of HSFC on the FA proportion, and the regression coefficient of HSFC is −0.084, which is significant at the 1% level, indicating that HSFC inhibits FA. Model (11) examines the effect of HSFC on ASS, and the coefficient of HSFC is 0.776, which is significant at the 1% level, indicating that HSFC promotes the farmers’ purchase of ASS. Model (12) tests the effects of HSFC, ASS, and the FA proportion; the coefficient of ASS is negative and significant at the 1% level, and the coefficient of HSFC is negative and significant at the 5% level. The absolute value is smaller than that of Model (10), indicating that the mediating effect holds. ASS has a partial mediating effect in the influence path of HSFC on FA. The results for the mediating effect proportion show that approximately 19.66% of the inhibitory effect of HSFC on FA is achieved through ASS. In addition, three significance tests, namely the Sobel, Goodman1, and Goodman2 tests, were performed through the Sgmediation command test, and the results met the requirements. The bootstrap method was utilized to test the robustness of the mediating effect, so as to reduce the possible bias of the coefficient product, and the results are shown in

Table 7. Results of the bootstrap mediating effect test for ASS.

Model (13)	Effect	SE	Z	P	Bias Corrected (95%)
					LLCI	ULCI
Direct effect	−0.017	0.006	−2.61	0.009	−0.029	−0.004
Indirect effect	−0.068	0.024	−2.84	0.005	−0.115	−0.021

Note: A total of 1000 replicate sampling times using the bias-corrected nonparametric percentile bootstrap method.

. The mediating effect is −0.068, the confidence interval is [−0.115, −0.021], and the interval does not contain 0, indicating that the mediating effect is significant. In summary, HSFC promotes the farmers’ purchase of ASS, which in turn inhibits FA, verifying Hypothesis H2.

4.4. Further Analysis: Moderated Mediating Effect Test

The test results regarding the moderating effect of the ALTD are shown in

Table 8. Moderated mediating effect test results.

Variable	Moderating Variable: ALTD
	(14) FA Proportion	(15) ASS	(16) FA Proportion
ASS			−0.015 (0.013)
HSFC proportion	−0.108 * (0.056)	0.248 * (0.128)	−0.112 ** (0.056)
ALTD	0.021 *** (0.008)	−0.055 ** (0.025)	0.024 *** (0.008)
HSFC proportion * ALTD	−0.002 (0.024)	0.116 * (0.060)	0.011 (0.025)
ASS * ALTD			−0.006 (0.006)
Control variable	Controlled	Controlled	Controlled
Observation	838	838	838

Note: The table reports the marginal effects of the Tobit model; *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively, with robust standard errors in parentheses.

. In Model (14), the interaction term between the HSFC proportion and the agricultural labor force transfer distance is not significant, indicating that there is no moderating effect of the ALTD on the direct effect, and Hypothesis H3a is rejected. A possible reason is that after HSFC, in order to pursue the absolute fairness of the contracted land distribution, some regions divide a huge field into small fields belonging to different contracted farmers, and there is again a fragmentation phenomenon. The farmers employed nearby cannot improve their production efficiency to complete the agricultural production activities, and they cannot incentivize the farmland transfer behavior of the farmers employed far away, and the farmland is similarly underutilized. In Model (15), the main effect coefficients are positive and significant, and the interaction term coefficients are positive and significant, indicating that the ALTD increases the explanatory role of the main effect. The greater the ALTD, the more that HSFC promotes the purchase of ASS by farmers, which inhibits FA, and this verifies Hypothesis H3b. The coefficients of both ASS and the interaction term in Model (16) are not significant, indicating that there is no moderating effect of the ALTD on the second half of the mediating process, and Hypothesis H3c is rejected. A possible reason is that the development of China’s ASS market is in the early stage, and there are still many individual or private service subjects, mainly villagers in their villages or in the neighboring villages, whose operation and management styles lag behind those of the professional organizations, or may even be no different from those of ordinary farmers. When farmers purchase many such “non-professional” ASS, the promotion of changes in agricultural production methods may be relatively limited.

5. Conclusions

Based on a research sample of 838 farmers in Jiangxi Province, a principal rice-producing region within the Yangtze River Basin of China, this study employs the Tobit model, the mediating effect model, and the moderated mediating effect model to assess the impact of HSFC on FA. Additionally, it investigates the intermediary role of ASS and the moderating role of ALTD within this context. The findings are as follows: First, HSFC significantly reduces FA, with a 1.15% decrease in FA for every 1% increase in HSFC, a result that remains robust following the robustness checks and endogeneity treatment using the IV-Tobit method. Second, the inhibitory effect of HSFC on FA varies greatly among different farmers, and the inhibitory effect on part-time farmers is more significant, while the inhibitory effect on farmers with a higher degree of land fragmentation is also more significant. Third, ASS has a partial mediating effect on the influence path of HSFC regarding FA. HSFC enhances farmers’ engagement with ASS, which in turn inhibits FA. Fourth, the influence path by which HSFC affects FA via ASS is positively moderated by the ALTD; in particular, the greater the ALTD, the more that HSFC promotes the farmers’ purchase of ASS, thus inhibiting FA.

Based on the above research conclusions, this study puts forward the following policy recommendations: First, HSFC should continue to be promoted. While HSFC has been effective in mitigating FA, the current construction standards are not yet optimal. The government should increase its financial and social capital investment in HSFC to ensure the quality of the construction project. Additionally, establishing a diverse set of stakeholders to participate in the post-construction management system will guarantee the long-term benefits of these projects. Secondly, targeted construction programs and operational systems should be constructed considering the differences in endowment and the preferences of different target groups. First of all, priority should be given to transforming marginal land with low quality, serious fragmentation, and poor transportation conditions, so as to increase farmers’ enthusiasm for production and reduce the possibility of FA. In addition, village collectives should provide non-farming farmers with unified land management services and promote the centralized and continuous transfer of land through village collectives, shareholding by farmers, entrusting farmland to village collectives on behalf of farmers, and confirming the rights but not the land, so as to reduce the possibility of abandonment due to information asymmetry. Third, it is necessary to improve the standard and capacity of ASS to ensure that farmers continue to benefit, thus inhibiting FA. On the one hand, local governments should explore the establishment of socialized service group standards, socialized service subject directories, and management mechanisms to drive the overall upgrading of ASS. At the same time, they should actively unite with leading upstream and downstream industrial enterprises, scientific research institutes, etc., in order to enhance the service capacity of socialized service organizations. On the other hand, through order purchase, guaranteed dividends, share cooperation, employment, and other forms, it is necessary to build a multi-win benefit linkage mechanism with village collectives and economic organizations, new agricultural management subjects, and farmers. Fourth, the non-agricultural transfer of the surplus agricultural labor force is a necessary strategy to promote the modern transformation of the traditional rural economy. On the one hand, the government should strengthen the knowledge and skill training provided to the agricultural transfer labor force to improve their cultural literacy and knowledge and skills so that they can obtain suitable positions. On the other hand, it should optimize the institutional security system after the transfer of the agricultural labor force; this would not only provide more public services, so that they can be integrated into the city as soon as possible, but also promote the integration of the social security system between urban and rural areas.

Compared with the existing studies, this work mainly has the following theoretical and practical implications: First, this study integrates the HSFC policy with the new economics of labor migration theory and agricultural labor division theory and reveals the internal mechanism by which the HSFC policy affects FA. Second, this study utilizes first-hand microdata from Jiangxi Province, the main rice-producing province in China’s Yangtze River Basin, to conduct an econometric analysis to identify the impacts of the HSFC policy on FA and to provide real-world data support for the extrapolation of theoretical logic. Previous experience generally suggests that, in the context of the predominance of smallholder agriculture in China, the loss of agricultural labor is an important factor causing FA, and the greater the ALTD, the greater the likelihood of complete FA. However, after adding the ALTD to the mediation path of “ HAFC–ASS–FA”, we find that the mediating effect is positively regulated by the long-distance transfer of agricultural labor. This reflects that, against the current background of farmers’ unwillingness to farm and relinquish their land contracting rights, it is necessary to actively develop production methods and promote land trusteeship and ASS in order to improve the utilization efficiency of high-standard farmland and guarantee the sustainable development of agriculture. Thus, the conclusions of this study have important application value and provide unique insights.

6. Limitations and Future Studies

This study also has the following limitations: First, the research area is only a representative province within the main rice-producing area in the Yangtze River Basin, and the conclusions of the study may not be universal due to the differences in the degree of FA and the progress of the construction of high-standard farmland among different regions. Future research could expand the scope of the sample area to make the research conclusions more scientific and objective. Second, there is often a delayed effect in the actual implementation of policies and regulations, and this study lacks an analysis of the temporal effect of the HSFC policy in inhibiting FA. As HSFC is a continuous policy, we will conduct a follow-up survey on the sample farmers in a subsequent study to use panel data to understand the trends in the inhibition of FA as HSFC continues to advance. Third, with the Chinese government’s vigorous promotion of comprehensive land management throughout the whole region, numerous policies for farmland management have been put forward. However, this study lacks comparative analyses regarding which policy most strongly targets the inhibition of FA. Therefore, the effectiveness of other land consolidation policies in China could be explored to identify new paths to inhibit FA.