How Does the Growth of Digital Technology Influence Farmland Abandonment? Evidence from Rural China

Abstract

Farmland abandonment has become a significant challenge in global agriculture, with the issue being particularly severe in China. This phenomenon not only threatens food security but also contributes to various environmental problems. The rise of digital technology offers new opportunities to address this pressing issue. This study systematically analyzes the impact of digital technology on farmland abandonment from a micro-level perspective, using a nationwide survey of 3409 households. A multi-dimensional indicator framework is developed, incorporating digital general technology, digital information exchange, and digital functionality. Empirical models, including IV-Probit and 2SLS, were employed to analyze the data. The results show that digital technology plays a significant role in reducing farmland abandonment by increasing farmers’ income levels and encouraging the adoption of agricultural production services. Specifically, the use of digital tools enhances farmers’ income, which in turn strengthens their willingness to continue farming. Moreover, it facilitates access to agricultural production services, lowering production costs and improving land-use efficiency. The study also finds that the impact of digital technology on farmland abandonment varies depending on factors such as terrain, urban-rural divides, and farmer types. The suppressive effect of digital technology on farmland abandonment is more pronounced in non-plain areas, non-suburban regions, and among full-time or part-time farmers. Based on these findings, the study recommends expanding digital infrastructure, streamlining land transfers, implementing region-specific support, and enhancing policy incentives to integrate digital technologies with agriculture, reducing farmland abandonment. These measures are intended to effectively curb farmland abandonment and foster sustainable agricultural development.

1. Introduction

In the context of modern global agricultural development, farmland abandonment has emerged as a pressing challenge that demands attention [1,2]. Since the second half of the 20th century, developed countries have witnessed a significant rise in farmland abandonment, a trend that has gradually transformed into a global pattern of land use change. Data published by the Food and Agriculture Organization of the United Nations (FAO) in 2021 indicate that over the past 50 years, approximately 200 million hectares of arable land have fallen into disuse due to various factors [3]. This phenomenon has not only reduced the availability of land resources but also poses a serious threat to global food security. Amid rapid population growth and accelerated urbanization, the limited supply of arable land is becoming increasingly crucial in meeting the growing demand for food.

In China, the issue of farmland abandonment is particularly pronounced. As one of the most populous nations globally, China faces an immense demand for food [4]. However, the rapid progress of industrialization and urbanization has drawn a significant portion of the rural labor force into urban areas, leaving large swathes of farmland unused. Recent statistics indicate that farmland abandonment in China is intensifying, with abandonment rates exceeding 20% in some provinces. This trend has placed considerable pressure on China’s grain self-sufficiency [5]. Furthermore, farmland abandonment can lead to environmental problems such as soil erosion and biodiversity loss, undermining ecosystem stability and sustainability. Addressing this issue has become a priority for the government and society at large. To counter this challenge, Chinese authorities have introduced a series of policy measures aimed at motivating farmers to maintain their agricultural activities [6]. While these measures have yielded some positive outcomes, their long-term effectiveness remains to be further observed and evaluated.

Research on farmland abandonment highlights a variety of contributing factors. Globally, natural conditions such as poor soil quality and water scarcity indeed hinder agricultural production [7]. However, in the specific context of China, the impact of these factors is often compounded by region-specific challenges. In China, the massive migration of labor to urban areas and the decline in agricultural profitability are important drivers of farmland abandonment [8]. For instance, the rapid development of non-agricultural industries, the diversification of farmers’ income sources, and the relative decline in agricultural returns [9] have led many farmers to abandon agricultural production in favor of other industries, resulting in large-scale idle farmland. Furthermore, although the effective operation of the land transfer mechanism can promote large-scale farming and enhance agricultural productivity, many regions still face issues such as inefficient land transfer channels and high transaction costs, which hinder the optimal allocation of land resources. These factors are particularly prominent in China, especially in the context of rural labor outflow, where some regions still have underdeveloped land transfer systems, leading to the wastage of agricultural land resources [10]. According to statistics from the Ministry of Agriculture of China, the abandonment rate of arable land in some provincial regions exceeded 20% in 2021. The root causes of this issue are intertwined factors such as labor mobility, declining agricultural profitability, and the lag in land transfer systems. Additionally, when farmland abandonment reaches a certain scale, it can trigger a vicious cycle [11,12]. Abandoned land, lacking effective management, can lead to reduced vegetation cover, intensified soil erosion, and diminished soil fertility, further weakening the land’s future productivity.

This phenomenon is particularly pronounced in China, where farmland abandonment is driven not only by natural factors but also by socio-economic changes and farmers’ psychological expectations. For example, in rural areas, a common psychological effect occurs where farmers tend to abandon farming if others do, further accelerating the trend of farmland abandonment [13]. Therefore, the occurrence of farmland abandonment is closely tied to China’s unique socio-cultural and economic context. While global studies on the trend of farmland abandonment provide valuable insights, China faces specific challenges, such as large-scale labor migration, declining agricultural profitability, and an imperfect land transfer system, which warrant more in-depth exploration in research [14]. Additionally, the impact of population growth and urbanization on farmland supply, particularly in China, should be further integrated into research perspectives. Relevant studies suggest that population growth and urbanization have intensified pressure on farmland resources, especially in areas surrounding large cities [15,16]. As urbanization progresses, farmland is converted into land for urban construction, leading to a reduction in the available area for cultivation.

The rapid development of information technology is gradually transforming traditional agricultural practices in China’s rural areas [17,18,19]. In recent years, the government has actively encouraged farmers to adopt digital tools such as e-commerce platforms, smart agricultural equipment, and online education to enhance productivity and service delivery. The use of smartphones, the Internet, and other digital technologies is becoming increasingly common among farmers, modernizing agricultural practices and improving access to information [20]. According to the Ministry of Agriculture and Rural Affairs, by 2023, over 70% of administrative villages in China had broadband access, and smartphone ownership among rural households had reached nearly 90%, providing a robust infrastructure for the application of digital technologies [21]. Studies suggest that digital technology can enhance agricultural efficiency, reduce production costs, optimize resource allocation, facilitate the circulation of agricultural products, and support the advancement of precision agriculture [22,23,24].

Despite the growing adoption of digital technology in rural areas, significant disparities remain in its practical application. The use of digital technology is primarily concentrated in economically developed regions along the eastern coast, while the digital divide persists in the remote mountainous areas of the central and western regions [25]. Additionally, limited digital skills, weak digital awareness, and the unequal distribution of digital resources further constrain the contribution of digital technology to agricultural production [26]. Against the backdrop of a complex and evolving global food security landscape, traditional agricultural production models face considerable challenges. Promoting the application of digital technology in agricultural production and farmland utilization has, therefore, become an essential course of action [27].

Although existing studies have made some progress in exploring the relationship between rural households’ use of digital technologies and farmland abandonment [17,28,29,30], several gaps remain. First, most research relies primarily on descriptive analysis or macro-level case studies, lacking in-depth exploration of individual farmer behaviors, particularly the differences in farmers’ acceptance and application of digital technologies. This makes it more difficult to accurately grasp the real situation in rural areas. Second, despite widespread theoretical consensus, the mechanisms through which digital technologies specifically influence farmland abandonment have yet to be comprehensively revealed. As a result, current research struggles to explain how digital technologies affect farmers’ agricultural decisions or assess the actual effectiveness of digital technology interventions in reducing farmland abandonment.

This study therefore fills these gaps by examining the relationship between farmers’ use of digital technologies and farmland abandonment from a micro-level perspective and exploring the practical role of digital technologies in farmland management. Using data from the 2020 China Rural Revitalization Comprehensive Survey, this study employs empirical models such as IV-Probit and 2SLS, aiming to provide new insights and empirical support for this issue. The academic contributions of this study are reflected in the following aspects:

• Pioneering a micro-level examination of farmers’ decision-making dynamics, complementing traditional macro-level analysis: Unlike previous macro-level studies, this research focuses on individual farmers’ decision-making behavior, investigating the differences in farmers’ use of digital technologies and their impact on farmland abandonment. This fills the gap in existing literature regarding the micro-level mechanisms of farmers’ behavior.
• Utilizing multidimensional indicators to assess digital technology adoption patterns: This study develops a framework that includes multidimensional indicators such as digital general technologies, digital information communication, and digital functionality use. It offers a comprehensive evaluation of how farmers apply digital technologies in agricultural production, addressing the limitations of prior studies that relied on single-dimensional or overly simplistic indicators.
• Applying instrumental variable methods to address endogeneity issues: To overcome potential endogeneity problems in traditional regression models, this study adopts instrumental variable methods, improving the accuracy and reliability of the model’s estimation results.

In conclusion, this study not only provides a new analytical perspective for the academic community but also offers empirical evidence for policymakers. It enhances understanding of the impact of digital technology use on farmland abandonment behavior and provides new approaches to tackling farmland abandonment, both in China and globally.

2. Theoretical Analysis

Compared with traditional agriculture, the application of digital technology has brought transformative changes to agricultural production [31]. Advancements in modern information technology have gradually penetrated every stage of agricultural production [28]. These technological innovations not only enhance the efficiency and precision of agricultural activities but also reduce labor intensity and production costs, making it more economically viable to maintain the regular operation of farmland. Moreover, through smartphone applications, farmers can access weather forecasts, market price information, and government-issued policies and regulations. This enables farmers to plan crop structure more effectively and organize agricultural activities more rationally, thus avoiding decision-making errors or blind actions caused by information asymmetry.

Traditional agricultural production, however, is often subject to uncertainties such as natural disasters and outbreaks of pests and diseases, which can destabilize farmers’ incomes and increase the risk of farmland abandonment. Digital platforms offering disaster warning services and insurance product recommendations enable farmers to take preventive measures in advance, mitigate risks in a timely manner, and minimize losses. Additionally, online consultation services allow farmers to quickly access professional guidance and support when problems arise, ensuring that challenges are effectively addressed. In the long term, the application of digital technology contributes to creating a more stable and reliable agricultural production environment, thereby directly mitigating the occurrence and progression of farmland abandonment [32,33].

Beyond technical factors, the psychosocial changes brought about by digital technology should not be overlooked. As digital technology becomes more widespread, a growing number of young people are returning to rural areas to start businesses, bringing with them innovative ideas and modern technological approaches that help accelerate the transformation of traditional agriculture into more modernized systems [34]. This demographic is generally more open to new concepts, eager to experiment with innovative farming methods, and adept at leveraging online resources for marketing. Their presence has reshaped local social dynamics and fostered a collective enthusiasm for exploring new agricultural development pathways.

In summary, the widespread application of digital technology strengthens farmers’ production capacity on multiple levels, enhances land resource utilization, reduces farming costs, and makes the continuous operation of farmland more feasible. Consequently, it serves as a significant deterrent to farmland abandonment. Based on this understanding, the following hypothesis is proposed:

H1: 

The use of digital technology by farmers inhibits their farmland abandonment behavior.

The inhibitory effect of digital technologies in reducing farmland abandonment is primarily reflected in the following key pathways:

First, digital technologies increase farmers’ income by expanding marketing channels for agricultural products. Specifically, with the help of e-commerce platforms and online marketing tools, farmers can directly connect with consumers, reducing intermediary steps, which in turn improves sales efficiency and profitability. This leads to a significant increase in farmers’ household income, strengthening their economic motivation to continue agricultural production. The increase in income directly enhances farmers’ ability to sustain agricultural activities, providing them with more resources to maintain land cultivation, thus reducing the tendency to abandon farmland.

Second, digital technologies promote the adoption of agricultural production services by raising awareness about farming practices, lowering the barriers to mechanization, and offering online consulting services. These services help farmers use agricultural tools more efficiently, optimize planting decisions, and streamline production processes. As agricultural production technologies and services improve, farmers are better able to control production costs and increase land utilization, thereby reducing farmland abandonment caused by inefficiency in agricultural production [35,36].

Additionally, the widespread application of digital technologies provides farmers with diversified income sources. By engaging in non-agricultural activities such as rural tourism and e-commerce logistics distribution, farmers diversify their economic income streams, thereby reducing reliance on single agricultural production. The diversification of income sources lowers farmers’ inclination to abandon farming due to market fluctuations during agricultural production [29].

Finally, the use of digital technologies broadens farmers’ horizons, enabling them to access more information and new ideas. Through social media and short video platforms, farmers can learn about the latest trends and technologies from the outside world, thereby stimulating their motivation to improve their living conditions and production methods. This information not only boosts farmers’ sense of self-efficacy but also encourages some young farmers to return to rural areas to develop niche agricultural projects, thus promoting local economic growth and reducing farmland abandonment [37,38].

In conclusion, digital technologies enhance farmers’ income, agricultural productivity, and survival capacity through multiple mechanisms, thereby effectively curbing farmland abandonment. Through the pathways outlined above, digital technologies play a crucial role in the prevention and reduction of farmland abandonment, forming a complete causal chain: adoption of digital technologies → expansion of agricultural product marketing channels → increase in farm income → improvement of agricultural survival capacity → reduction in abandonment tendency. Based on this, H2 is proposed:

H2: 

The use of digital technology by farmers broadens income sources and increases household income levels, thereby indirectly influencing farmland abandonment behavior.

Digital technologies have played a crucial role in promoting the adoption of agricultural production services. Specifically, digital technologies simplify the pathways to access agricultural services, reducing the barriers to production for farmers, and thereby effectively improving the efficiency and sustainability of agricultural production.

First, digital technologies have transformed traditional farming practices by lowering the barriers to the use of agricultural machinery [39,40]. Traditional agricultural models heavily rely on manual labor and have low levels of mechanization, resulting in high labor costs and difficulties in scaling production [41]. However, with the application of digital technologies, farmers can easily access professional agricultural consultations through online platforms, addressing issues such as pest control or soil improvement in a timely manner. In the past, farmers had to travel to cities or county towns for assistance, a process that was both time-consuming and economically inefficient. Now, through online platforms, farmers can simply upload images or brief descriptions of their problems and receive expert guidance from agricultural technicians across the country, saving considerable time and resources.

Moreover, the application of digital technologies has encouraged agricultural service providers to expand their businesses through Internet platforms, offering farmers more flexible and diversified options [42]. Farmers can now book services as needed, without having to invest in expensive fixed assets. This is particularly important in remote mountainous areas where transportation and logistics challenges, along with the limitations of traditional dispersed farming models, make it difficult to meet the demands of modern large-scale production. Therefore, a socialized agricultural service system supported by digital technologies becomes especially crucial. Such systems not only fill the gaps in local public service provision but also support small-scale farmers in integrating into the modern agricultural value chain.

In conclusion, the widespread application of digital technologies has improved the pathways for accessing agricultural production services, reducing operational costs for farmers, enhancing agricultural competitiveness, and effectively decreasing the likelihood of farmland abandonment. The application of digital technologies not only increases farmers’ production capacity but also strengthens their motivation to continue farming by creating more flexible and efficient production systems. Thus, digital technologies play a pivotal role in reducing farmland abandonment, and their causal chain can be expressed as follows: digital technology deployment → improved access to agricultural production services → reduced operational costs → enhanced agricultural competitiveness → reduced abandonment probability. Based on this, H3 is proposed (Figure 1):

H3: 

Farmers’ use of digital technology transforms their planting practices and promotes the adoption of agricultural production services, thereby indirectly influencing farmland abandonment behavior.

3. Materials and Methods

3.1. Data Sources

The data used in this study comes from the China Rural Revitalization Survey (CRRS), which is a systematic and comprehensive rural tracking dataset aimed at gaining an in-depth understanding of the basic conditions of rural China, thereby providing a solid data foundation for rural revitalization efforts.

The CRRS survey was conducted using a multi-stage stratified random sampling method. First, ten representative provinces were randomly selected, taking into account factors such as economic development level, regional location, and agricultural development status. These provinces included Zhejiang, Shandong, and others. Next, within each province, counties were chosen using an equidistant random sampling method based on GDP, and further random samples of towns and villages were selected within these counties. Finally, based on the village resident lists, a random sample of farming households was selected for the survey. This sampling method ensured the breadth and representativeness of the sample, allowing the CRRS data to accurately reflect the overall conditions of rural areas across the country [43].

In this large-scale survey, data was collected from 50 counties and 156 towns nationwide, with more than 300 village surveys and over 3800 valid household surveys completed, gathering family member information from more than 15,000 individuals. During the subsequent data cleaning process, strict quality control measures were implemented. The survey data underwent three rounds of manual checks and was recorded and tracked using computer-assisted technologies to further enhance data quality. Additionally, the quality control team conducted real-time reviews of the questionnaires to ensure the authenticity and reliability of the data [39]. After cleaning out blank, invalid, and incomplete questionnaires, a final total of 3409 valid household surveys were compiled.

3.2. Variable Selection

The dependent variable in this study is farmers’ farmland abandonment behavior. Drawing on relevant studies [2,44,45,46], two indicators are used to measure farmland abandonment: the abandonment decision and the abandonment proportion. The abandonment decision captures whether a farmer engages in abandonment behavior (1 = Yes, 0 = No), while the abandonment proportion reflects the ratio of abandoned land area to the total farmland area, offering a measure of the relative extent of abandonment.

The explanatory variable in this study is farmers’ use of digital technology [31,39]. Although digital technology is a broad concept, for farmers, it primarily involves accessing the internet through digital devices such as computers and smartphones. These devices form the foundation of most digital technology applications in daily life and agricultural production. Therefore, examining farmers’ use of digital technology through the lens of internet devices and program usage better reflects practical realities [46].

The measurement of farmers’ digital technology use involves two key steps. First, an index system is constructed to assess digital technology use, along with the quantification method for each indicator. Second, a weighting method is applied to calculate the weights of each dimension, and the data are standardized across the three measurement dimensions. The entropy weight method is then used to integrate the indicators into a comprehensive measure of digital technology use. Particular attention is given to how digital technology directly supports farmers’ production and daily life, such as smartphone usage, participation in agricultural e-commerce platforms, and the role of digital tools in improving agricultural product sales and knowledge acquisition [28,34]. Considering the current state of digital infrastructure in rural China and national policy directions, this measurement approach aligns with the broader trend of rural digitalization and technological development. It provides a more realistic and representative assessment of farmers’ digital engagement, ensuring that the findings are both empirically robust and practically relevant. The specific indicators and their definitions are presented in

Table 1. Digital technology uses an index system.

Variable	Primary Indicator	Secondary Indicator	Mean	Standard Deviation
Digital Technology Usage	Digital General Technology	Home internet access (1 = yes; 0 = no)	0.94	0.23
		Number of smart devices at home (e.g., smartphone, computer, tablet)	1.44	0.87
		Frequency of daily use of smart devices or technology (1~5: Rarely ~ Frequently)	2.94	1.23
	Digital Information Exchange	Whether the network information is obtained in a timely manner (1~3: Not timely enough ~ Very timely)	2.52	0.58
		Use of smart devices for public communication (1 = yes; 0 = no)	0.82	0.38
	Digital Function Usage	Online product sales (1 = yes; 0 = no)	0.03	0.16
		Frequency of transactions using the network (1~5: Rarely ~ Frequently)	2.80	1.23

.

In this study, the mediating variables consist of income level and agricultural production services. The income level is quantified by the logarithmic value of the household’s annual per capita income, which captures the potential impact of digital technology on improving the household’s economic well-being. Agricultural production services are represented by a binary variable, with a value of 1 indicating that the farmer has purchased such services, and 0 indicating that they have not.

The village-level digital technology adoption rate, defined as the proportion of other farmers in the same village who use digital technology (excluding the subject of study), is used as an instrumental variable. This selection is based on the following considerations: omitted variable bias and bidirectional causality may exist between digital technology use and farmland abandonment, creating endogeneity in the core explanatory variable [47,48]. Furthermore, farmers’ decisions to adopt digital technology may be influenced by the adoption rate among other farmers in their villages. In villages with a high adoption rate, a demonstration effect may encourage farmers to adopt digital technology. However, the digital technology adoption decisions of other farmers do not directly affect the farmland abandonment behavior of the studied farmers. Therefore, the instrumental variable is highly correlated with the endogenous variable while remaining independent of the dependent variable, theoretically satisfying the relevance and exogeneity assumptions.

Building on previous studies, this research incorporates three sets of control variables, which are categorized at the individual, household, and village levels [5,30,44,46]. Individual-level controls include gender, age, and education level. Household-level controls cover the presence of a village leader in the household, the number of family members, agricultural income, farm willingness, and the proportion of elderly family members. Village-level controls include the proportion of irrigable land, village location, and the distance between the village and the nearest town. Regional dummy variables are also included to account for regional differences. These controls aim to reduce potential bias in model estimation caused by omitted variables.

Table 2. Descriptive statistical analysis of variables.

	Variable	Definitions	Mean	Standard Deviation
Dependent Variable	Decision	Whether or not farmers have farmland abandonment (1 = yes; 0 = no)	0.08	0.27
	Proportion	Proportion of farmland abandoned by farmers to total operating area	0.03	0.15
Explanatory Variable	Digital Technology	Measurements are shown in Table 1	0.97	0.25
Control Variable	Sex	Sex of household head (1 = male; 0 = female)	0.52	0.50
	Age	Age of head of household (years)	43.97	21.73
	Edu	The average years of education	8.00	4.30
	Leader	Whether there is a village leader among the family members (1 = yes; 0 = no)	0.03	0.17
	Members	Number of family members	4.22	1.85
	Non-farm	Share of agricultural income in total income in the household	0.35	0.48
	Older	Proportion of older household members	0.18	0.30
	Willing	Future family plans (0 = continue farming; 1 = work outside in non-agricultural jobs)	0.15	0.36
	Location	Whether the village is an urban suburb (1 = yes; 0 = no)	0.19	0.39
	Village-town	Logarithm of village councils’ kilometers from town governments	1.69	0.88
	Irrigate	Village irrigable cropland to total cropland area	0.67	0.39
Intermediation Variable	Income	Logarithm of annual per capita income of farm households	9.14	1.87
	Services	Whether farmers purchase agricultural production services (1 = yes; 0 = no)	0.38	0.49

provides the definitions and descriptive statistics for the variables. Approximately 8% of the sampled farmers reported abandoning farmland, with the average proportion of abandoned land representing 3% of the total cultivated area. The mean and standard deviation for digital technology usage among farmers were 0.97 and 0.25, respectively, reflecting a generally high adoption rate, although individual variation is evident. Regarding the control variables, the average age of household heads was 43.97 years, with an average education level of 8 years. Male household heads constituted 52% of the sample, and the average household size was 4.22 members. On average, agricultural income contributed 35% of household income, 18% of household members were elderly, 15% of farmers were unwilling to continue farming, and 3% of households had a village cadre member. At the village level, 19% of villages were located in urban suburbs, the average distance to the town government was 1.69, and 67% of arable land was irrigable, reflecting relatively well-developed agricultural infrastructure but with notable regional disparities. These statistical features provide important background information and a research basis for the subsequent empirical analysis.

3.3. Model Setup

Since farmland abandonment proportion is a continuous variable, while the decision to abandon farmland is binary, this study employs the OLS (Ordinary Least Squares) and Probit models to estimate the impact of farmers’ digital technology usage on farmland abandonment. To address potential endogeneity issues, the village-level digital technology adoption rate is selected as an instrumental variable. Subsequently, the 2SLS (Two-Stage Least Squares) model and the IV-Probit (Instrumental Variables Probit) model are used for further estimation. The estimation equation is as follows:

$$Y_i={\alpha }_0+{\alpha }_1{Dig}_i+{{\alpha }_2\Sigma Con}_i+{\epsilon }_i$$

In the equation, $Y_i$ represents the farmland abandonment proportion and decision for the farmer; ${Dig}_i$ represents the farmer’s digital technology adoption; ${\Sigma Con}_i$ represents the control variables at the household head, household, and village levels; ${\alpha }_0$ is the constant term, and ${\epsilon }_i$ is the random disturbance term; ${\alpha }_1$ and ${\alpha }_2$ are the regression coefficients.

4. Results

4.1. Analysis of Regression Results

Prior to conducting the regression analysis, the model was examined for multicollinearity. The variance inflation factors (VIF) for each variable range from 1.03 to 2.28, all of which are below 5, suggesting that multicollinearity is not a significant issue in the model. As presented in

Table 3. Regression results.

	Farmland Abandonment
	Decision	Proportion	Decision	Proportion
Digital Technology	−0.738 ***	−0.054 ***	−0.685 ***	−0.051 ***
	(0.135)	(0.014)	(0.149)	(0.014)
Sex			0.088	0.006
			(0.070)	(0.005)
Age			−0.003 *	−0.000
			(0.002)	(0.000)
Edu			−0.003	−0.001
			(0.009)	(0.001)
Leader			−0.167	−0.003
			(0.232)	(0.013)
Members			−0.040 **	−0.002
			(0.020)	(0.002)
Non-farm			−0.202 **	−0.011 **
			(0.080)	(0.005)
Older			-0.088	-0.012
			(0.146)	(0.010)
Willing			0.136	0.003
			(0.097)	(0.007)
Location			−0.031	0.004
			(0.085)	(0.008)
Village-town			0.091 **	0.001
			(0.044)	(0.002)
Irrigate			−0.757 ***	−0.043 ***
			(0.099)	(0.008)
_cons	−0.726 ***	0.087 ***	−4.981 ***	0.084 ***
	(0.128)	(0.014)	(0.238)	(0.018)
Regional Variables	Yes	Yes	Yes	Yes
N	3409	3409	3409	3409

Note: *, ** and *** refer to p < 0.1, p < 0.05, and p < 0.01, respectively.

, the baseline regression results indicate that farmers’ use of digital technology has a notable negative effect on both the decision to abandon farmland and the abandonment ratio. Even after incorporating control variables, the coefficient for digital technology remains significantly negative. This suggests that the use of digital technology can effectively reduce the extent of farmland abandonment behavior among farmers. H1 is verified.

4.2. Endogeneity Treatment

Building upon the baseline regression results, this study employs an instrumental variable approach to tackle the endogeneity issue within the model. As shown in

Table 4. Endogeneity results.

	First Stage	Second Stage
	Digital Technology	Farmland Abandonment
		Decision	Proportion
Digital Technology		−2.623 ***	-0.147 **
		(0.521)	(0.064)
Instrumental Variable	0.009 ***
	(0.001)
Control Variables	Yes	Yes	Yes
Regional Variables	Yes	Yes	Yes
Wu-Hausman tests		0.004	0.082
Cragg-Donald		137.651 ***	295.784 ***
chi2		263.761	160.107
N	3409	3409	3409

Note: ** and *** refer to p < 0.05 and p < 0.01, respectively.

, the regression outcomes reveal that after applying the instrumental variable method, the coefficient for digital technology remains statistically significant. Additionally, the instrumental variable successfully passes the weak instrument test, further reinforcing the model’s robustness. A thorough analysis of both the baseline regression results and the instrumental variable test provides support for H1, indicating that the use of digital technology can significantly decrease farmland abandonment behavior.

4.3. Robustness Test

To assess the robustness of the regression results, this study employed two distinct testing methods. First, the independent variable was substituted with “digital technology usage”, which was specifically measured by farmers’ use of digital devices like smartphones and tablets. A new regression analysis was then conducted to examine the validity and consistency of the original indicator, ensuring that its influence on farmland abandonment remained unaffected by changes in the variable’s definition. Second, the dependent variables “farmland abandonment proportion” and “farmland abandonment decision” were replaced with “farmland abandonment area”, and a separate regression analysis was performed to further validate the results.

The results of the robustness test, presented in

Table 5. Robustness tests.

	Farmland Abandonment
	Decision	Proportion	Area
Usage Utilization	−0.594 ***	−0.057 ***
	(0.144)	(0.018)
Digital Technology			−4.402 ***
			(1.512)
Control Variables	Yes	Yes	Yes
Regional Variables	Yes	Yes	Yes
chi2	6264.977		58.133
N	3409	3409	3409

Note: *** refer to p < 0.01.

, demonstrate that even after substituting the two variables, the findings remain statistically significant. This indicates that the original analysis results are stable, further confirming that farmers’ use of digital technology has a positive impact in reducing farmland abandonment.

4.4. Mechanism Analysis

This study explores the mechanisms by which the use of digital technology influences farmland abandonment. The findings, presented in

Table 6. Mediating effects.

	Income	Farmland Abandonment		Services	Farmland Abandonment
		Decision	Proportion		Decision	Proportion
Digital Technology	1.267 ***	−0.630 ***	−0.047 ***	0.703 ***	−0.650 ***	−0.045 ***
	(0.134)	(0.152)	(0.014)	(0.106)	(0.150)	(0.014)
Income		−0.043 ***	−0.003 *
		(0.016)	(0.001)
Services					−0.246 ***	−0.027 ***
					(0.090)	(0.004)
Control Variables	Yes	Yes	Yes	Yes	Yes	Yes
Regional Variables	Yes	Yes	Yes	Yes	Yes	Yes
N	3409	3409	3409	3409	3409	3409

Note: * and *** refer to p < 0.1 and p < 0.01, respectively.

, reveal that digital technology usage reduces farmland abandonment through two primary channels. First, the widespread adoption of digital technology leads to a significant increase in farmers’ income levels. As their income grows, farmers become more motivated and capable of continuing land cultivation, thereby effectively reducing the likelihood of farmland abandonment. Second, digital technology facilitates the adoption of agricultural production services. By leveraging digital tools, farmers gain easier access to a variety of services essential for agricultural production, which enhances the efficiency and productivity of their operations, ultimately leading to a decrease in farmland abandonment. H2 and H3 are thus validated.

4.5. Further Analysis

To deeply explore how digital technology can have a differentiated impact on farmland abandonment in different situations, this study combined with the actual rural situation, and conducted a detailed classification of research samples from two dimensions of geographical location and farmer type. This approach aims to identify differences in the effects of farmers’ adoption of digital technologies across different agricultural market contexts and to provide a scientific basis for rural digital transformation. In terms of geographical location, the region is divided into suburban and non-suburban, plain and non-plain, etc., to analyze the difference in the role of digital technology in different regional conditions [30]. In terms of farmer types, farmers were divided into full-time farmers, part-time farmers, and non-agricultural farmers to explore the heterogeneity of their response to farmland abandonment behavior [49]. It reveals how farmers’ production priorities affect their acceptance and utilization of digital technology, and provides support for the formulation of differentiated policies.

The analysis of terrain differences, as shown in

Table 7. Differences in terrain.

	Farmland Abandonment
	Plain		Non-Plain
	Decision	Proportion	Decision	Proportion
Digital Technology	3.043 ***	0.120	−3.044 ***	−0.358 ***
	(0.552)	(0.084)	(0.443)	(0.110)
Control Variables	Yes	Yes	Yes	Yes
Regional Variables	Yes	Yes	Yes	Yes
Coefficient Differences	Farmland Abandonment Decision: 6.087 *** Farmland Abandonment Proportion: 0.478 ***
chi2	104.529	25.448	91.612	91.294
N	1335	1335	2074	2074

Note: *** refer to p < 0.01.

, indicates that in non-plain areas, the use of digital technology by farmers significantly suppresses farmland abandonment. This may be because non-plain areas have more complex terrain, and traditional farming practices face more challenges. The introduction of digital technology provides farmers with more precise and efficient agricultural production methods, thus improving agricultural productivity and enhancing farmers’ willingness and ability to continue cultivating the land. However, in some plain areas, the use of digital technology has, to some extent, exacerbated farmers’ decisions to abandon farming. This may be related to the agricultural production characteristics of plain areas. In plain regions, there are more diverse agricultural management options, and the use of digital technology may encourage some farmers to shift to more profitable agricultural sectors, thus leading to farmland abandonment. Therefore, when promoting digital technology, it is essential to fully consider regional differences and the actual needs of farmers.

The analysis of village location differences, presented in

Table 8. Differences in village location.

	Farmland Abandonment
	Non-Suburban		Suburban
	Decision	Proportion	Decision	Proportion
Digital Technology	−2.682 ***	−0.276 ***	0.227	0.305 *
	(0.548)	(0.067)	(1.038)	(0.176)
Control Variables	Yes	Yes	Yes	Yes
Regional Variables	Yes	Yes	Yes	Yes
Coefficient Differences	Farmland Abandonment Decision: 2.909 * Farmland Abandonment Proportion: 0.581 ***
chi2	81.010	110.727	21.109	28.214
N	2499	2499	636	636

Note: * and *** refer to p < 0.1 and p < 0.01, respectively.

, reveals that digital technology has a more pronounced effect in reducing farmland abandonment in non-suburban areas. This can be explained by the fact that digital technology enhances agricultural productivity, allowing farmers to manage land resources more efficiently, thereby lowering the likelihood of farmland abandonment. Technologies such as precise fertilization have facilitated more refined management practices, improving crop yields and agricultural income, which in turn strengthens farmers’ motivation to continue farming. However, it is important to recognize that suburban areas often experience faster urbanization and industrialization, leading to higher land values and an increased opportunity cost for agricultural land. The introduction of digital technology may make farmers more aware of the potential non-agricultural value of their land, encouraging them to abandon farming and shift to more profitable land uses. Therefore, the impact of digital technology on farmland abandonment shows distinct patterns in non-suburban and suburban areas, reflecting the varied economic, social, and environmental conditions across different regions.

As demonstrated in

Table 9. Differences in types of farm households.

	Farmland Abandonment
	Full-Time Farmers		Part-Time Farmers		Non-Agricultural Farmers
	Decision	Proportion	Decision	Proportion	Decision	Proportion
Digital Technology	−5.162 ***	−0.117	−3.836 ***	−0.177 ***	0.223	0.070
	(0.715)	(0.141)	(0.384)	(0.059)	(0.810)	(0.107)
Control Variables	Yes	Yes	Yes	Yes	Yes	Yes
Regional Variables	Yes	Yes	Yes	Yes	Yes	Yes
chi2	59.289	4.763	169.022	21.515	32.705	24.068
N	121	121	1863	1863	1425	1425

Note: *** refer to p < 0.01.

, the analysis of differences among various types of farmers shows that digital technology usage has a significantly positive impact on reducing farmland abandonment, particularly among full-time and part-time farmers. This result implies that when farmers increasingly depend on digital technology for agricultural production and management, they are more effective in utilizing land resources, thereby minimizing the chances of land being left idle or abandoned.

For full-time farmers, digital technology likely provides more precise planting information and market forecasts, helping them make more informed planting decisions. Part-time farmers, on the other hand, may use digital technology to better balance the time allocation between agricultural production and other occupations, thereby improving land use efficiency. However, non-farmers, who are less dependent on agricultural production, are more likely to use their land for other purposes or leave it idle, and the introduction of digital technology has not been able to change this situation.

5. Discussion and Policy Suggestion

5.1. Discussion

Due to the continued global population growth and accelerated urbanization, the protection and rational use of arable land have become urgent challenges worldwide. The frequent occurrence of farmland abandonment not only wastes valuable land resources but also severely threatens global food security. This issue is particularly prominent in China, the most populous country in the world, where farmland abandonment is especially severe [33]. Large-scale rural labor migration to urban areas has led to significant amounts of uncultivated land in some regions, and the rate of farmland abandonment has been increasing year by year. This trend not only affects food production but also exacerbates environmental issues such as soil erosion and biodiversity loss. As the key participants in agricultural production, farmers’ behavior and decision-making largely determine how arable land is utilized. With the rapid development of digital technologies, more and more agricultural practices are adopting digital tools, providing innovative solutions to address farmland abandonment [40]. Therefore, exploring how farmers utilize digital technologies to tackle farmland abandonment and investigating the mechanisms behind their impact is of significant practical importance. In particular, the application of digital technologies can play a crucial role in formulating effective agricultural policies, improving land use efficiency, ensuring food security, and promoting sustainable agricultural development [50,51,52].

Based on the 2020 National Farmers Survey data, this study systematically analyzes the impact of farmers’ use of digital technologies on farmland abandonment behavior. Building on existing literature, this paper adopts a micro-level perspective of farmers to construct a comprehensive theoretical framework and further explores the innovation in research perspectives and variable selection. By utilizing multidimensional variable selection and instrumental variable methods, the study reveals the specific paths and mechanisms through which digital technologies influence farmland abandonment behavior and provides empirical evidence for formulating more differentiated policies. From the research perspective, this study focuses on the micro level of farmers, exploring the impact of household digital technology use on farmland abandonment behavior. This complements the research by Corbelle-Rico et al. (2022), which examines the economic effects and the popularization of e-commerce [1]. This study not only focuses on the impact of digital technology on farmers’ economic returns but also delves deeper into its multiple mechanisms in influencing the adoption of agricultural production services and farming decisions. Although Wang et al. (2023) analyzed the impact of e-commerce popularization on farmland abandonment, their research adopts a more macro perspective and does not focus on the farmer level [2]. In contrast, this study takes a micro perspective, thoroughly analyzing the specific pathways through which digital technology affects farmland abandonment behavior, marking a significant distinction from previous research [8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28].

Furthermore, in terms of variable selection, this study constructs a multidimensional indicator system for digital technology use, including digital general technology, digital information exchange, and digital function usage. Compared to single-dimensional or macro-level variable selections (such as only considering internet access or e-commerce platform usage), this approach provides a more comprehensive reflection of farmers’ actual use of digital technology [34,39,46]. The study finds that farmers’ use of digital technology significantly suppresses farmland abandonment. This conclusion also reveals the underlying social psychological mechanisms and economic drivers: first, broadening income channels and increasing household income levels; second, changing planting awareness and promoting the purchase of agricultural production services.

The innovation of this study is that it constructs a theoretical analysis framework from the micro-perspective of farmers, and reveals the specific mechanism of digital technology in reducing farmland abandonment through empirical research [5,42]. This micro-perspective enables the research to more accurately reveal the actual role of digital technology in agricultural production and provides a more specific basis for policy formulation. At the same time, this study not only verified the inhibitory effect of digital technology on farmland abandonment but also revealed its differences in different regions and types of farmers. For example, the inhibitory effect of digital technology is found to be more significant in non-plain areas, non-suburban areas, and full-time or part-time farmers, which provides a strong basis for the development of differentiated policies. In contrast, previous studies mostly focused on descriptive analysis or case studies at the macro level, lacking in-depth analysis and differentiation discussion at the micro level [19,40].

This study offers a new perspective on the issue of farmland abandonment by examining the application of digital technologies. It explores how digital technologies, by increasing farmers’ income and promoting the adoption of agricultural services, can indirectly reduce farmland abandonment. This approach not only enhances the sustainable use efficiency of land but also contributes to the sustainable modernization of agriculture, sustained ecological protection, and long-term food security, thereby supporting the sustainable development of agricultural production. By delving into the mechanisms through which digital technologies influence farmland abandonment, this research provides valuable theoretical foundations and policy recommendations for achieving global agricultural sustainability goals and ensuring the long-term stability of rural societies.

It is important to note that the assumption of a unidirectional causal relationship between income growth and reduced abandonment rates in existing studies has certain limitations [33]. While most analytical frameworks suggest that income growth can reduce farmland abandonment, they do not fully consider the possibility that income growth might stimulate the migration of agricultural labor to the non-agricultural sector, which could, in turn, exacerbate farmland abandonment. If income increases lead more farmers to leave agriculture and shift to other industries, the net inhibitory effect of digital technologies on farmland abandonment could be smaller than initially estimated. Therefore, future research should be cautious about the potential for reverse causality.

5.2. Policy Suggestion

Bridging the Digital Divide and Expanding Rural Digital Infrastructure. To address the low adoption rates of digital technologies in central and western regions, as well as in non-plain areas, the government should strengthen rural digital infrastructure, particularly in network coverage and the promotion of smart devices. These measures would lay a solid foundation for the widespread adoption of digital technologies in rural areas. Additionally, the government should enhance farmers’ digital skills through specialized training programs and demonstration projects, improving their awareness and acceptance of digital technologies. A Digital Agriculture Development Fund could be established to subsidize farmers’ purchase of smart agricultural equipment, thereby accelerating the modernization of agriculture.

Optimizing Land Transfer Mechanisms to Improve Land Utilization. To promote more efficient land use, local governments should refine land transfer mechanisms by simplifying procedures and reducing transaction costs. This would help optimize land resource allocation and encourage active farmland management, preventing abandonment. Additionally, policy incentives should support digital platforms that facilitate land transactions and agricultural service exchanges, making it easier for farmers to engage in collaborative or contract-based farming models.

Enhancing Digital Agricultural Services and Supporting Farmer Cooperatives. Governments should encourage the development of digital agricultural service platforms that provide farmers with affordable and accessible production services, such as precision farming tools, mechanized farming assistance, and online technical support. Financial support and subsidies should be provided to local farmer cooperatives and agricultural service providers, particularly in remote and economically disadvantaged areas. These initiatives would lower production costs and improve overall land-use efficiency.

Developing Targeted Support Policies Based on Regional and Farmer Characteristics. In regions with high agricultural dependency, particularly in non-plain and non-suburban areas, digital technology adoption should be prioritized through targeted incentives. Specific support measures could include subsidies for smallholder farmers to acquire smart farming equipment, as well as policies promoting direct-to-consumer e-commerce models to enhance farm profitability. Pilot programs in major agricultural provinces should test and refine these policies before nationwide implementation.

Strengthening Policy Support for the Deep Integration of Digital Technology in Agriculture. To maximize the benefits of digital agriculture, the government should establish long-term policy frameworks that encourage innovation and investment in smart farming solutions, digital financial services, and supply chain integration. Given current fiscal constraints, initial efforts should focus on pilot projects in key agricultural regions, with gradual expansion based on observed outcomes. Financial incentives such as tax reductions and subsidies should be introduced to encourage farmers’ participation in digital agriculture, ensuring the sustainable use of farmland.

Given fiscal constraints, the government should initially prioritize pilot projects in major agricultural provinces. This approach would allow for the accumulation of successful experiences to inform nationwide expansion. Financial support could be secured through the establishment of dedicated funds or grant channels, particularly considering the pressure on fiscal budgets and the actual needs of rural areas. Moreover, to ensure the sustainability and effectiveness of policy execution, the government should encourage the involvement of private capital and social investments, thereby diversifying the funding sources for policy implementation.

6. Conclusions

This study uses large sample survey data to systematically assess the impact of digital technology on farmland abandonment behavior, and the following main conclusions are drawn:

In this study, the results show that approximately 8% of the sampled farmers engage in farmland abandonment, with abandoned land accounting for 3% of the total farmland area. Although the usage rate of digital technologies is high, there is still room for improvement in the adoption of agricultural e-commerce and proficiency in using smart devices. These findings validate Hypothesis H1, which posits that farmers’ use of digital technologies effectively curbs farmland abandonment.

Furthermore, digital technologies significantly reduce the extent of farmland abandonment by increasing farmers’ income levels and promoting the adoption of agricultural production services. As income rises, farmers’ motivation to continue farming has been enhanced, while the application of agricultural services has effectively reduced production costs and improved land utilization efficiency. This result supports Hypothesis H2.

Finally, the impact of digital technologies varies based on geographic conditions, urban-rural differences, and the type of farmer. Specifically, in non-plain areas, non-suburban areas, and among full-time or part-time farmers, the inhibitory effect of digital technologies on farmland abandonment is more pronounced, demonstrating strong regional adaptability and targeting of specific farmer groups. This result validates Hypothesis H3.

Although this study provides valuable insights into the relationship between rural households’ use of digital technology and farmland abandonment, some limitations remain. First, the data in this study come from the 2020 China Rural Revitalization Comprehensive Survey (CRRS), which may not fully reflect the latest trends in the relationship between rural households’ use of digital technology and farmland abandonment. Due to the time limit of data collection, this deficiency cannot be corrected at present. Future research will continue to track and timely update data to more accurately reflect the dynamic impact of the development of digital technology. Secondly, there may be certain limitations in the selection of variables in this study, which fails to fully cover all factors affecting farmland abandonment, such as policy-oriented market demand and climate change This is mainly due to the complexity of research design and the difficulty of data acquisition. In light of this limitation, future research should broaden the range of variables considered, develop a more comprehensive analytical framework, and foster interdisciplinary collaboration with fields such as economics, sociology, and other relevant disciplines. This approach will allow for a deeper exploration of the profound impact of digital technology on agricultural production and the social economy, ultimately providing robust support for the development of more scientifically grounded and effective agricultural policies.