A
Can Rural-Urban Migration Benefit Sustainable Agriculture with Large-Scale Farming?Abstract
Achieving sustainable agricultural development is essential for ensuring stable global food supplies. Rural-urban migration, as a widespread socioeconomic phenomenon, introduces complexities and uncertainties to sustainable agricultural practices. This study constructs an analytical framework linking migration, large-scale farming, and sustainable agriculture, leveraging empirical evidence from 37,648 village-level samples in China. The findings demonstrate that rural-urban migration significantly enhances sustainable agriculture, as evidenced by increased mechanization and reduced fertilizer use, facilitated through large-scale farming. We further highlight the heterogeneity in large-scale farming decisions by agricultural entities under rural labor outmigration, shaped by economic rationality. Favorable production, distribution, and living conditions significantly amplify the positive impact of migration on large-scale farming, thereby contributing to enhanced sustainable agricultural outcomes. This study advances the understanding of the land-labor relationship under rural-urban migration and offers actionable insights for formulating policies to promote sustainable agricultural development.
Keywords:
Rural-urban migration
Sustainable agriculture
Large-scale farming
China
A
1. Introduction
Ensuring food security is a challenge faced by both developed and developing countries. The need to enhance sustainable agricultural production to meet growing food demand has thus become a shared global objective (Branca et al., 2013). The Food and Agriculture Organization (FAO) defines sustainable agriculture through four key pillars: better production, better nutrition, a better environment, and a better life (FAO, 2021). However, from a global perspective, conventional, extensive agricultural practices have long posed significant challenges to achieving sustainability. These practices contribute to environmental degradation, soil depletion, water contamination, and rising production costs (Ha, 2023). In developing regions such as Latin America, South Asia, East Asia, and Sub-Saharan Africa, these challenges are further compounded by large populations, land fragmentation, and limited farmer knowledge and skills (Guo et al., 2024). In these contexts, advancing sustainable agriculture remains a formidable task.
One of the factors influencing sustainable agricultural development is rural-to-urban migration, a global phenomenon that has profound implications for agricultural practices. Between the 1990s and 2020s, there has been a large-scale rural-urban migration in all countries (De et al., 2014; Steinführer et al., 2024; Luzi, 2025), driven by factors such as agricultural income volatility, diversified job opportunities, and climate change (Sugden, et al., 2022; Thiede et al., 2016). While migration has contributed to the growth of urban development, its impact on agricultural production remains unclear. Some studies suggest that migration reduces agricultural productivity due to labor shortages (Shi, 2018), while others argue that migration can benefit agricultural production by improving labor allocation and efficiency (Ge, et al., 2018; Ye, 2018). Notably, Addai et al. (2025) criticize previous studies for overemphasizing the challenges associated with rural-urban migration, without sufficiently addressing how migration might drive sustainable agricultural practices. This research gap underscores the importance of further investigation into how rural labor migration affects sustainable agriculture.
Land, as a critical factor of agricultural development, provides a valuable lens for understanding the impact of rural-urban migration on sustainable agriculture. Migration alters the land-labor relationship in rural areas, leading to various land use changes (Chen, et al., 2014). Scholars have offered contrasting interpretations of the "migration—land abandonment—land use change" phenomenon. On one hand, some argue that rural-urban migration results in land abandonment, particularly in areas where non-agricultural employment opportunities are growing (Xu et al., 2018; Xu et al., 2019). On the other hand, some studies posit that migration facilitates land transfers, enabling the expansion of large-scale farming, which can enhance agricultural productivity (Vranken & Swinnen, 2006; Ji et al., 2018). These differing perspectives on land use underscore the broader debate on the relationship between migration and sustainable agriculture. However, much of the existing literature relies on micro-level analyses using household data, often generalizing individual behaviors to the entire rural community. This assumption overlooks potential variations at the community level, which can significantly influence agricultural dynamics. To address this gap, research at the village or regional level is crucial, as it allows for a more comprehensive understanding of land use dynamics and agricultural practices.
A
A
China offers a unique context for studying the relationship between rural-urban migration and sustainable agricultural development. Since the 1980s, the expanding urban-rural income gap and reforms to the household registration system have spurred significant rural-urban migration (Zhang &Song, 2003; Hu & Jia, 2025). Official figures released by China’s National Bureau of Statistics show that the rural migrant workforce expanded from 140.4 million in 2008 to 176.6 million by 2023. As one of the most populous and agricultural countries globally, China—despite occupying only 9% of the world's arable land—must feed nearly 20% of the global population (Lam et al., 2013). Moreover, the shift in dietary demand from plant-based to animal-based foods is intensifying competition for agricultural resources, particularly land (Fukase & Martin, 2016). Despite the importance of sustainable agriculture, challenges remain, including the overuse of fertilizers and pesticides, as well as land leasing policies that, while addressing fragmentation, complicate mechanization and exacerbate pollution (Li et al., 2017; Qi et al., 2023; Ye et al., 2024). Understanding the impact of rural labor migration on sustainable agriculture in China is crucial not only for the country itself but also for other developing nations facing similar challenges.In this study, we examine the effects of rural-urban migration on sustainable agriculture, focusing on large-scale farming, using data from 37,684 villages in Sichuan Province, China. Our study makes three key contributions to the literature. First, we integrate rural-urban migration, land consolidation, and sustainable agriculture into a unified theoretical framework, emphasizing the central role of land-labor dynamics in promoting sustainable agricultural practices. Second, by utilizing a large sample of village data, we move beyond the prevailing focus on micro-level individual behaviors and shift attention to village-level dynamics. Our findings contribute to the ongoing debate on the impact of migration on sustainable agriculture. Third, we propose a systematic framework in which production, circulation, and living conditions mediate the impact of migration on large-scale farming. This approach enriches the understanding of how migration exerts heterogeneous effects on sustainable agriculture across different villages, offering a more comprehensive perspective on the factors that influence the land-labor relationship in rural-urban migration contexts. Through these contributions, the study not only advances the theoretical framework of sustainable agricultural development but also provides empirical insights and policy recommendations for addressing rural-urban migration challenges.
2 Theoretical analysis and research hypothesis
2.1 Rural-urban migration, large-scale farming and sustainable agriculture
This study focuses on the impact of rural-urban migration on agricultural sustainability, specifically examining the roles of mechanization and green production. Sustainable agriculture aims to minimize environmental degradation while maintaining productivity. Migration-induced labor loss directly affects agricultural productivity, and thus, agriculture assumes a dual role in environmental protection—both positive and negative (Yang et al., 2016; Ge et al., 2020; Mergoni et al., 2024). Balancing agricultural production with environmental protection in the context of labor-out migration becomes a critical challenge (Lykogianni et al., 2021). Green production practices reduce pollution and ensure the sustainable use of natural resources (Adnan et al., 2019), while mechanization addresses labor shortages and boosts production efficiency (Hamilton et al., 2022; Reckling et al., 2023; Schanz et al., 2023). Thus, this study examines agricultural sustainability through two key dimensions—mechanized and green production—both of which enhance yields and improve production efficiency (Chi, et al., 2021).
Rural-urban migration significantly influences sustainable agriculture, particularly through its effects on land resources—a cornerstone of agricultural production. In many developing countries, the persistent challenge of "too many people and too little land" presents a structural constraint, limiting the potential for sustainable agricultural practices (Van et al., 2007). This imbalance, marked by dense rural populations and limited arable land, exacerbates land scarcity, creating significant obstacles to agricultural efficiency and environmental sustainability. One key manifestation of this imbalance is the fragmentation of agricultural plots, where farmland is divided into small and scattered parcels. This fragmentation undermines economies of scale by limiting the centralization of operations and restricting the effective use of agricultural machinery, which often requires larger, contiguous areas (Li et al., 2017). As a result, farmers are constrained in transitioning to more efficient, technology-driven practices, reducing overall productivity. Additionally, fragmented plots often lead to increased reliance on chemical inputs, including fertilizers and pesticides, to preserve harvest levels (Cao et al., 2022). This overuse contributes to soil degradation, water pollution, and biodiversity loss, further threatening sustainable agriculture and underscoring the necessity of land consolidation to overcome these challenges.
Rural-urban migration has the potential to alleviate these constraints by promoting large-scale farming. Labor migration reduces dependency on human inputs, shifting the dynamic from "too many people and too little land" to "fewer people and more land". Between 1960 and 2016, rural populations in major emerging economies such as Brazil, Russia, India, and China decreased by 73%, 44%, 18%, and 47%, respectively (Liu & Li, 2017). While concerns over potential land abandonment due to labor shortages exist, some studies suggest that rural-urban migration facilitates land consolidation through land transfer mechanisms (Min et al., 2017). This consolidation supports economies of scale, mitigates labor shortages (Xie & Jiang, 2016), and enhances agricultural productivity by optimizing resource use and enabling more efficient farming practices (Wang et al., 2021).
Large-scale farming serves as a catalyst for mechanization in agriculture. Expanded agricultural operations enable task-specific expertise and workforce allocation throughout various phases of cultivation, such as soil preparation, planting, nutrient application, irrigation, integrated pest management, and crop collection. These processes reduce production costs and improve efficiency (Wang et al., 2017). In addition, labor shortages have incentivized larger farms to adopt advanced agricultural machinery (Hu et al., 2022), addressing labor constraints while achieving economies of scale. China's Agricultural Machinery Purchase Subsidy, launched in 2004, exemplifies government efforts to promote mechanization. By 2020, cumulative fiscal investments of 239.2 billion yuan enabled over 38 million farming entities to purchase more than 48 million units of agricultural machinery.
In addition to driving mechanization, large-scale farming contributes to green production. Profit-driven operators are more responsive to evolving consumer preferences and are better positioned to adopt environmentally friendly practices due to their access to capital and diversified supply chains (Lapka et al., 2011; Mponela et al., 2016; Zhang et al., 2024). Furthermore, large farms tend to use fertilizers more efficiently, reducing the average input per unit of land (Ju et al., 2016). Enhanced operational efficiency optimizes resource use, lowers production costs, and mitigates environmental impacts, fostering sustainable agricultural practices.
2.2 Village Heterogeneity Influences Migration-Driven Farming Strategies
Rural-urban migration is a livelihood strategy adopted by rational smallholder farmers in response to economic opportunities during the processes of industrialization and urbanization. Simultaneously, land consolidation and large-scale farming represent a production model characterized by the allocation of substantial capital, technology, and other inputs based on market demand and operational risk assessment. This approach reflects the rational economic behavior of actors seeking to optimize resource allocation under a market-oriented economy (Willock et al., 1999).
The economies of scale in agricultural production consist of two key components: internal economies of scale, which arise from changes in the input-output ratio within a single production unit, and external economies of scale, which emerge from market and industrial agglomeration effects (Xu et al., 2011). In neoclassical economics, the assumption underpinning economies of scale is that transaction costs are negligible. However, in practice, the formation of large-scale farming is influenced by a variety of transaction costs, including those related to information asymmetry, land leasing, and contractual risks (Schoneveld, 2017). The decision to adopt large-scale farming is thus not universal but contingent on the heterogeneity of farming entities. These entities vary in terms of factor endowments, comparative advantages, and behavioral capacities, leading to differentiated strategies. Within this framework, land consolidation reflects a livelihood strategy grounded in market logic, as heterogeneous economic agents navigate transaction costs and resource constraints to determine the optimal allocation of agricultural land (Zhang et al., 2021).
The output value of land directly determines the surplus value for agricultural operators. Key characteristics of land—such as its fixed spatial location, limited scale, varying quality, and inherent scarcity—are critical factors influencing the investment decisions of large-scale agricultural operators across different regions and villages (Schalkwyk & Groenewald, 2008; Jia et al., 2012). On one hand, production conditions such as land fertility, sunlight availability, water resources, and topography lead to variations in crop productivity, making these natural endowments central to decision-making (Caswell & Zilberman, 1986). These attributes directly impact the output and quality of agricultural production, thereby determining the profitability of large-scale farming. On the other hand, within a market economy framework, the market access of agricultural products is equally critical to profitability. Minimizing transaction and transportation costs emerges as a key operational strategy (Key et al., 2000). Large-scale farming operations located in areas with well-developed road infrastructure or efficient logistics networks benefit from enhanced market accessibility and reduced distribution costs (Jansen et al., 2006). Thus, agricultural production and logistic conditions jointly influence the surplus value of large-scale farming, shaping the geographic patterns of resource investments.
Besides, Maslow's hierarchy of needs theory emphasizes that human motivations progress from basic physiological requirements to higher-order needs such as safety, social belonging, esteem, and self-actualization. Unlike smallholder farmers, whose decisions primarily revolve around meeting basic subsistence needs, large-scale farming entities exhibit a stronger sense of agency and seek to fulfill these higher-order needs (Knoop & Theuvsen, 2020; Hu, 2018). Consequently, living conditions—including the quality of the residential environment, public services, and social and cultural development in the villages where land is located—become critical determinants of investment decisions (Agarwal, B. & Agrawal, A., 2017; Huang, 2024). These factors not only enhance the personal well-being of large-scale farming entities but also indirectly affect the sustainability and success of their agricultural production by shaping the broader operational environment. Integrating considerations of both personal well-being and operational efficiency ensures that investment strategies align with long-term goals for sustainable agriculture and economic viability.
In summary, both production conditions (natural endowments and transportation infrastructure) and living conditions (residential and community factors) significantly influence the decisions to undertake large-scale farming. These factors shape not only the operational efficiency and profitability of agricultural production but also the overall attractiveness of rural areas for large-scale agricultural investments. As rural-urban migration leads to labor outflow, it creates opportunities for land consolidation and the transition to large-scale farming, which can enhance agricultural productivity and resource efficiency. However, the success and sustainability of this process depend on the interplay between production and living conditions, which collectively moderate the impact of rural-urban migration on the feasibility and outcomes of large-scale farming (as illustrated in Fig. 1). This highlights the pivotal role of rural infrastructure, natural resources, and social development in ensuring that migration contributes positively to sustainable agriculture.
Fig. 1
The mechanisms of rural-urban migration affecting sustainable agriculture
Based on the above analysis, this study proposes the following hypotheses:
H1: Rural-urban migration promotes sustainable agriculture.
H1a: Rural-urban migration promotes sustainable agriculture by fostering green agricultural practices through large-scale farming.
H1b: Rural-urban migration promotes sustainable agriculture by enhancing agricultural machinery utilization through large-scale farming.
H2: Villages with favorable production, logistics, and living conditions are more likely to promote sustainable agriculture through large-scale farming.
3 Data and methods
3.1 Data source
The data used in this study were sourced from the Rural Revitalization Strategy Statistical Data (RRSSD) provided by the Sichuan Provincial Bureau of Statistics, China. The dataset encompasses 18 cities and 183 counties, with detailed information on rural land management, socioeconomic conditions at the county and township levels, and the socioeconomic status of villages in Sichuan Province. Sichuan, as a major labor-exporting region and a key grain production area in China, offers a representative context for examining rural-urban migration and sustainable agriculture. This study utilizes cross-sectional data from 2018. After rigorous and systematic data-cleaning processes, the final dataset includes 37,684 valid observations.
3.2 Variable description
3.2.1 Dependent variable
The dependent variable in this study is sustainable agriculture, which encompasses green agricultural practices and agricultural machinery utilization. First, green agricultural practices are measured by the volume of chemical fertilizer utilized per unit of cultivated land, following the approach of Nin-Pratt and McBride (2014). This metric is derived from dividing the aggregate volume of fertilizer used in the village (converted to pure nutrients) by the year-end aggregated farmland acreage. Second, agricultural machinery utilization is assessed using the proportion of cultivated land area operated with machinery. Farmers typically engage in mechanized production through two primary approaches: acquiring agricultural machinery assets or contracting third-party mechanization services (Ji et al., 2012). In this study, the focus is on whether mechanized operations are used in production, rather than the method by which machinery is obtained. Following the methodology of Ma et al. (2018), the level of mechanization is calculated as the ratio of the area of key crops sown and harvested using machinery to the total cultivated land area in the village at the end of the year.
3.2.2 Independent variable
The independent variable is the proportion of the migrant population at the village level. Rural-urban migration typically involves individuals moving to urban areas to participate in non-farming industries. According to the standards of the National Bureau of Statistics of China, only those who have been outside their home village for a sustained duration exceeding six months are classified as migrant population. Therefore, this study utilizes the proportion of individuals who have migrated for over six months, as recorded in the survey, to measure rural-urban migration.
3.3.3 Mediating Variables
The mediating is the proportion of large-scale farming. Large-scale farming refers to the consolidation of fragmented and small-scale landholdings into contiguous tracts through the transfer of land use rights, enabling more efficient and concentrated agricultural operations. The realization of large-scale farming depends on the gradual reduction in the number of smallholders and the redistribution of land to larger farming entities (Xie & Lu, 2017). To measure this, we calculate the proportion of cultivated land under large-scale farming as a percentage of the total cultivated land area by year-end.
3.2.3 Control variable
This study, drawing on existing literature (Berchoux & Hutton, 2019; Wang et al., 2021; Liu et al., 2024), incorporates control variables selected from both village and township levels. At the village level, the variables include villager income, total village area, road connectivity, internet access, collective village revenue, agricultural cooperatives, agricultural enterprises, plain terrain, hilly terrain, and mountainous terrain. At the township level, the variables include the number of agricultural service agencies and financial service branches. By accounting for internal capital endowments and external socioeconomic development factors, this study aims to minimize estimation bias caused by omitted variables. Table 1 provides detailed definitions and descriptive statistics for all variables included in the analysis.
Variable | Description | Mean | SD |
|---|---|---|---|
Green agricultural practices | Fertilizer usage per unit area (kg/mu) | 33.191 | 34.661 |
Agricultural machinery utilization | Proportion of mechanized sowing (or harvesting) area (%) | 21.919 | 28.207 |
Proportion of migrant population | Proportion of population migrating for over six months (%) | 23.868 | 19.032 |
Proportion of large-scale farming | Proportion of cultivated land under large-scale farming (%) | 10.906 | 20.754 |
Villager income | Per capita disposable income of rural residents (1 = below 3,000 RMB; 2 = 3,000–5,000 RMB; 3 = 5,000–10,000 RMB; 4 = 10,000–15,000 RMB; 5 = 15,000–20,000 RMB; 6 = above 20,000 RMB.) | 4.146 | 0.920 |
Total village area | Administrative area (hectares) | 454.765 | 706.730 |
Road connectivity | Proportion of villager groups connected by roads (%) | 97.322 | 10.897 |
Internet access | Proportion of villager groups connected to the internet (%) | 91.622 | 22.559 |
Collective revenue of villages | Income of village collective economic organizations (10,000 RMB) | 17.904 | 324.350 |
Agricultural cooperatives | Number of agricultural cooperatives in the village | 1.436 | 1.892 |
Agricultural enterprises | Number of agricultural enterprises in the village | 0.391 | 1.200 |
Plain terrain | Plain terrain (1 = yes; 0 = no) | 0.051 | 0.219 |
Hilly terrain | Hilly terrain (1 = yes; 0 = no) | 0.727 | 0.446 |
Mountainous terrain | Mountainous terrain (1 = yes; 0 = no) | 0.222 | 0.416 |
Agricultural service agencies | Number of agricultural service agencies in the township | 1.895 | 3.942 |
Financial service branches | Number of financial service branches in the township | 3.226 | 4.597 |
3.3 Economic model
This study seeks to address the primary question of whether rural-urban migration influences sustainable agriculture. The dependent variables—green agricultural practices and agricultural machinery utilization—are both limited dependent variables. Ordinary Least Squares estimation for such limited dependent variables may produce biased results due to the violation of linearity assumptions. In contrast, the Tobit model effectively accounts for the truncated nature of the data, reducing estimation bias and improving regression accuracy. Following prior studies (Li & Wang, 2014; Paltasingh, 2022), this study employs the Tobit model to analyze the impact of rural-urban migration on green production and mechanized production. The specific model specification is as follows:
1
Where
is the sustainable agriculture of the village, which refers to the situation of green agricultural practices and agricultural machinery utilization.
is the proportion of migrant population in the village.
is a set of control variables,
is the random error term.
is the constant term, and
is the coefficient of interest in this study, reflecting the impact of the increase in the proportion of migrant population on agricultural production. If it is significantly negative, it indicates that the increase in the proportion of migrant population in the village significantly promotes green agricultural practices. If it is significantly positive, it indicates that the increase in the proportion of migrant population in the village significantly promotes agricultural machinery utilization.
4 Results
4.1 Baseline result
The baseline regression results are presented in Table 2. Columns (1) and (3) include village-level control variables, while Columns (2) and (4) incorporate additional township-level controls. The results in Column (1) show a significantly negative coefficient for fertilizer use per unit of land at the 1% level, which remains robust in Column (2) after including township-level controls. For agricultural machinery utilization, the coefficient for the proportion of mechanized farming area is significantly positive at the 1% level in Column (3), and this relationship persists in Column (4) with the addition of township-level controls. These findings provide preliminary support for H1, suggesting that rural-urban migration contributes to sustainable agriculture by promoting green production and mechanized production.
Green agricultural practices | Agricultural machinery utilization | |||
|---|---|---|---|---|
(1) | (2) | (3) | (4) | |
Proportion of migrant population | -0.074*** | -0.076*** | 0.122*** | 0.131*** |
(0.011) | (0.011) | (0.012) | (0.012) | |
Villager income | -0.966*** | -0.896*** | 8.441*** | 8.043*** |
(0.227) | (0.228) | (0.276) | (0.275) | |
Total village area | -0.000 | -0.000 | -0.002*** | -0.002*** |
(0.000) | (0.000) | (0.001) | (0.001) | |
Road connectivity | -0.019 | -0.018 | 0.265*** | 0.260*** |
(0.017) | (0.017) | (0.022) | (0.022) | |
Internet access | 0.040*** | 0.040*** | 0.082*** | 0.077*** |
(0.009) | (0.009) | (0.011) | (0.011) | |
Collective revenue of villages | -0.001** | -0.001** | 0.000 | 0.000 |
(0.000) | (0.000) | (0.001) | (0.001) | |
Agricultural cooperatives | 0.102 | 0.114 | 0.254** | 0.185 |
(0.105) | (0.105) | (0.126) | (0.125) | |
Agricultural enterprises | -0.247 | -0.222 | 1.091*** | 0.964*** |
(0.151) | (0.151) | (0.185) | (0.183) | |
Plain terrain | 13.580*** | 13.883*** | 56.964*** | 55.505*** |
(1.178) | (1.185) | (1.246) | (1.241) | |
Mountain terrain | -3.796*** | -3.832*** | -18.956*** | -18.727*** |
(0.488) | (0.490) | (0.755) | (0.747) | |
Agricultural service agencies | -0.188*** | 0.907*** | ||
(0.042) | (0.093) | |||
Financial institution branches | -0.018 | 0.079 | ||
(0.041) | (0.055) | |||
Constant | 37.505*** | 37.474*** | -58.168*** | -57.486*** |
(2.037) | (2.040) | (2.609) | (2.595) | |
Observations | 37,684 | 37,684 | 37,684 | 37,684 |
| Note: The values in parentheses represent standard deviations. *p < 0.1, **p < 0.05, ***p < 0.01. | ||||
4.2 Robustness test
To verify the robustness of the baseline regression results, this study employs two methods, with the regression results presented in Table 3. First, the core explanatory variable, the proportion of migrant population, is replaced with the proportion of migrant households. The movement from rural to urban regions is not merely a personal choice but frequently a collective family approach., particularly in regions with unstable agricultural incomes (Luo, 2012). Thus, the proportion of migrant households can serve as a proxy for the proportion of migrant population. The results in Columns (1) and (3) indicate that the coefficient for green agricultural practices is significantly negative at the 1% level, while the coefficient for the agricultural machinery utilization is significantly positive at the 1% level. Second, given the potential for county-level policy differences to influence the implementation of green and mechanized production strategies, this study includes county-level fixed effects to account for such heterogeneity. The regression results, shown in Columns (2) and (4), indicate that the coefficient for green agricultural practices remains significantly negative, while the coefficient for agricultural machinery utilization remains significantly positive. These results are consistent with the baseline findings, reinforcing the validity of the inferences.
Green agricultural practices | Agricultural machinery utilization | |||
|---|---|---|---|---|
(1) | (2) | (3) | (4) | |
Proportion of migrant households | -0.068*** | 0.141*** | ||
(0.011) | (0.013) | |||
Proportion of migrant population | -0.107*** | 0.082*** | ||
(0.011) | (0.012) | |||
County-level fixed effect | No | Yes | No | Yes |
Village-level controls | Yes | Yes | Yes | Yes |
Township-level controls | Yes | Yes | Yes | Yes |
Observations | 37,684 | 37,684 | 37,684 | 37,684 |
| Note: The values in parentheses represent standard deviations. ***p < 0.01. | ||||
Endogeneity in this study arises primarily from two potential sources. First, there is a risk of reverse causality, as improved agricultural productivity and mechanization may influence rural-urban migration decisions, creating a bidirectional relationship between migration and sustainable agriculture. Second, unobserved heterogeneity at the village or township level—such as variations in land quality, access to markets, or local economic conditions—may simultaneously affect both migration patterns and agricultural practices. These unobservable factors, if not adequately accounted for, can introduce bias and inconsistency in the regression estimates.
To address these endogeneity concerns and accurately evaluate the impact of rural-urban migration on sustainable agriculture, this study employs the IV-Tobit method. The proportion of migrant population at the township level is used as an instrumental variable for the village-level proportion of migrant population. Townships, as the basic administrative units in rural areas, provide a broader regional perspective on migration patterns. The township-level migration proportion effectively captures regional migration trends while remaining exogenous to individual migration decisions within villages. Importantly, village-level migration decisions are unlikely to substantially influence the overall migration behavior at the township level, thereby satisfying the exclusion restriction criterion.
Table 4 reports the results of the IV-Tobit estimation. In the first stage, the F-statistic exceeds 10 and is significant at the 1% level, rejecting the null hypothesis of weak instruments. This confirms that the township-level migration proportion is a valid and strong instrument for the village-level migration proportion. In the second stage, the empirical results reveal that the proportion of migrant population exerts a statistically inverse correlation with green agricultural practices while showing a direct positive association with agricultural machinery utilization. These findings are consistent with the baseline regression results, providing robust support for the study's conclusion that rural-urban migration influences sustainable agriculture by reducing reliance on chemical inputs while promoting mechanization.
Proportion of migrant population | Green agricultural practices | Agricultural machinery utilization | |
|---|---|---|---|
Proportion of the migrant population in the township | 96.472*** | ||
(0.532) | |||
Proportion of migrant population | -0.111*** | 0.248*** | |
(0.015) | (0.018) | ||
Village-level controls | Yes | Yes | Yes |
Township-level controls | Yes | Yes | Yes |
Wald Chi2 | —— | 368.45*** | 6581.23*** |
Exogeneity test (Chi2) | —— | 10.24*** | 87.50*** |
Observations | 37,684 | 37,684 | 37,684 |
| Note: The values in parentheses represent standard deviations. *p < 0.1, ***p < 0.01. | |||
4.3 Mechanism test
To clarify how rural-urban migration influences sustainable agriculture, this study employs mediation effect analysis. Rural-urban migration is driven by income disparities between non-agricultural employment and farming. The diversification of household labor into off-farm sectors corresponds to enhanced institutional propensity for farmland leasing, with market-driven mechanisms accelerating agricultural land reallocation processes (Cheng et al., 2022), enabling migrants to earn from both wages and land leasing. Rational economic agents seeking to maximize income naturally opt to lease their land (Huo & Chen, 2021), leading to increased land transfer and the expansion of large-scale farming. Large-scale farming acts as a key intermediary, linking migration to improvements in agricultural production. This study uses the proportion of land under large-scale farming as the mediating variable to test H1a and H1b. Regression results in Table 5 confirm these hypotheses, highlighting the essential role of large-scale farming in translating migration effects into sustainable agricultural practices.
The results in Column (1) indicate that the proportion of migrant population exhibits a statistically robust positive effect on the proportion of large-scale farming at the 1% level, suggesting that rural-urban migration facilitates the consolidation of fragmented farmland into larger farming operations. Columns (2) and (3) further report the effect of large-scale farming on green agricultural practices and agricultural machinery utilization. The findings demonstrate that the proportion of large-scale farming has a statistically significant negative impact on green agricultural practices at the 5% level and a statistically significant positive effect on agricultural machinery utilization at the 1% level. These findings collectively validate the hypothesized mechanisms (H1a and H1b), demonstrating that rural-urban migration promotes both green production and mechanized production indirectly through its role in fostering large-scale farming.
Proportion of large-scale farming | Green agricultural practices | Agricultural machinery utilization | |
|---|---|---|---|
(1) | (2) | (3) | |
Proportion of migrant population | 0.052*** | ||
(0.012) | |||
Proportion of large-scale farming | -0.026** | 0.262*** | |
(0.011) | (0.012) | ||
Village-level controls | Yes | Yes | Yes |
Township-level controls | Yes | Yes | Yes |
Observations | 37,684 | 37,684 | 37,684 |
| Note: The values in parentheses represent standard deviations. **p < 0.5, ***p < 0.01. | |||
4.4 Further analysis
Large-scale farming involves increased resource inputs and shifts agricultural producers from subsistence-oriented smallholders to market-driven economic entities, transforming agriculture into a professionalized activity. However, large-scale farming strategies vary depending on village characteristics, even under similar rural-urban migration conditions. Profit maximization remains a core objective, influenced by factors like land productivity and market accessibility. Additionally, living conditions and cultural development in villages impact the quality of life for farming entities, shaping their decisions. This study further explores how village conditions influence large-scale farming decisions, identifying key factors that drive these strategies.
4.4.1 Production conditions as moderators of migration's impact on large-scale farming
A
Irrigation conditions and topography are pivotal factors influencing agricultural production. To measure irrigation conditions, we construct the variable "Irrigation conditions", assigning a value of 1 to villages with drainage and irrigation stations and 0 otherwise. These facilities play a crucial role in regulating water availability by ensuring adequate supply during dry seasons and effectively draining excess water during rainy periods. Topography, specifically mountainous terrain, is used to evaluate cultivation conditions. Villages classified as "mountainous" are coded as 1, while others are coded as 0, based on survey responses regarding terrain type. Mountainous areas pose significant constraints to large-scale farming due to steep slopes that hinder mechanized operations and increase labor requirements. Poor soil quality, limited water retention, and restricted accessibility in mountainous regions further challenge the scalability and profitability of agricultural operations.The regression results in Table 6 highlight the regulating effects of production conditions on the relationship between rural-urban migration and large-scale farming. Column (1) indicates that the interaction between the proportion of migrant population and irrigation conditions is positive and significant at the 1% level, suggesting that rural-urban migration more effectively facilitates large-scale farming in villages equipped with drainage and irrigation infrastructure. These facilities enhance land productivity and reduce operational risks, making such villages more attractive for scale expansion. In contrast, Column (2) reveals that the interaction between the proportion of migrant population and mountainous terrain is negative and significant at the 1% level, indicating that steep slopes hinder the expansion of large-scale farming in migration contexts. These findings emphasize the pivotal role of production conditions in shaping the evolution of large-scale farming and the need for targeted investments in rural infrastructure to address regional constraints.
(1) | (2) | |
|---|---|---|
Proportion of migrant population | 0.040*** | 0.079*** |
(0.014) | (0.013) | |
Proportion of migrant population × Irrigation conditions | 0.073*** | |
(0.025) | ||
Irrigation conditions | 6.425*** | |
(0.758) | ||
Proportion of migrant population × Mountain terrain | -0.114*** | |
(0.028) | ||
Mountainous terrain | -0.142 | |
(0.927) | ||
Village-level controls | Yes | Yes |
Township-level controls | Yes | Yes |
Observations | 37,684 | 37,684 |
| Note: The values in parentheses represent standard deviations. ***p < 0.01. | ||
4.4.2 Logistics conditions as moderators of migration's impact on large-scale farming
The quality of transportation and logistics infrastructure is a critical determinant of the market competitiveness of agricultural products. To measure transportation infrastructure, this study considers the presence of asphalt roads connecting villages to township centers. Asphalt roads, characterized by their smooth surfaces, durability, and resistance to water damage, significantly enhance transportation efficiency and reliability. This is particularly crucial in rural settings, where road quality directly impacts the timeliness and safety of transporting agricultural products to markets. Logistics infrastructure is assessed based on the availability of e-commerce delivery stations within villages. These stations play an essential role in integrating production sites into broader supply chains, improving the marketability of agricultural products, and enhancing their potential for online sales.
The regression results in Table 7 demonstrate the moderating effects of logistics conditions on the relationship between rural-urban migration and large-scale farming. Column (1) shows that the cross-product term among the proportion of migrant population and transportation condition is positive and significant at the 1% level, revealing that migration significantly promotes large-scale farming in villages with well-developed road networks, such as asphalt roads. Improved road infrastructure enhances market accessibility for agricultural products, providing a strong logistical foundation and market environment that facilitates the proliferation of large-scale agricultural operations. Column (2) reveals that the interaction term between the proportion of migrant population and the E-commerce activities is also positive and significant at the 1% level. This finding suggests that rural-urban migration has a stronger effect on promoting large-scale farming in villages equipped with e-commerce delivery facilities. As noted by Xiao et al. (2023), e-commerce platforms offer farmers broader market access and dynamic pricing strategies, which increase sales profitability. Our findings further illustrate that superior logistics conditions amplify the positive effects of migration on large-scale farming, accelerating the shift from traditional small-scale farming to modern, large-scale agricultural production.
(1) | (2) | |
|---|---|---|
Proportion of migrant population | 0.043*** | 0.032** |
(0.012) | (0.014) | |
Proportion of migrant population × Transportation condition | 0.152*** | |
(0.044) | ||
Transportation condition | 2.686** | |
(1.093) | ||
Proportion of migrant population × E-Commerce activities | 0.063*** | |
(0.024) | ||
E-Commerce activities | 2.814*** | |
(0.744) | ||
Village-level controls | Yes | Yes |
Township-level controls | Yes | Yes |
Observations | 37,684 | 37,684 |
| Note: The values in parentheses represent standard deviations. **p < 0.5, ***p < 0.01. | ||
4.4.3 Living conditions as moderators of migration's impact on large-scale farming
Factors such as job satisfaction and subjective well-being play a critical role in shaping investment decisions in large-scale farming (Agarwal, B. & Agrawal, A., 2017). Beyond agricultural income, this study investigates how village living conditions influence large-scale agricultural production, using two dimensions: rural livability and rural civilization. Rural livability is evaluated through the government's "livable village" standard, based on survey responses indicating whether a village meets the classification criteria (coded as 1 for "yes" and 0 for "no"). This standard encompasses various dimensions, including enhancements in living environments, village infrastructure, and ecological development, serving as a comprehensive indicator of the quality of life for residents. Rural civilization is measured by whether a village is classified as a "county-level civilized village," with responses similarly coded. This classification reflects multiple dimensions, such as effective local leadership, continuous economic development, and strong social and moral values, offering a robust representation of both the material and cultural progress of the village.
The regression results presented in Table 8 highlight the moderating effects of rural living conditions on the relationship between rural-urban migration and large-scale farming. Column (1) shows that the interaction term between the proportion of migrant population and rural livability is positive and significant at the 1% level. This indicates that in villages with favorable livability conditions, migration more effectively promotes large-scale farming. Such villages attract farming entities by offering better living infrastructure, improved ecological environments, and other advantages that encourage increased investment in agricultural operations. Column (2) reveals that the interaction term between the proportion of migrant population and rural civilization is also positive and significant at the 1% level. Villages with greater levels of civilization often feature better education, stronger social cohesion, and advanced management practices. These factors not only enhance the profitability of agricultural operations but also provide a comfortable living environment, motivating farming entities to scale up their operations. This conclusion concurs with the findings of Paltasingh et al. (2022). These findings confirm the validity of H2.
(1) | (2) | |
|---|---|---|
Proportion of migrant population | 0.050*** | 0.042*** |
(0.012) | (0.015) | |
Proportion of migrant population × Rural livability | 0.110* | |
(0.059) | ||
Rural livability | 6.629*** | |
(1.540) | ||
Proportion of migrant population × Rural civilization | 0.045** | |
(0.023) | ||
Rural civilization | 3.859*** | |
(0.718) | ||
Village-level controls | Yes | Yes |
Township-level controls | Yes | Yes |
Observations | 37,684 | 37,684 |
| Note: The values in parentheses represent standard deviations. *p < 0.1, **p < 0.5, ***p < 0.01. | ||
5. Discussion
This study offers a novel perspective on the relationship between rural-urban migration and sustainable agriculture. While rural-urban migration is often criticized for reducing agricultural productivity and causing land abandonment (Mullan et al., 2011), our findings suggest that rural-urban migration can positively contribute to sustainable agriculture by promoting green agricultural practices and agricultural machinery utilization. Rural-urban migration induced labor shortages encourage farmers to adopt mechanized production, compensating for reduced workforce availability. Mechanized farming not only enhances grain production, which typically requires fewer chemical inputs than cash crops but also reduces the environmental impact of conventional agricultural practices (Ji et al., 2011; Zhong et al., 2016). Furthermore, the infusion of affordable labor into non-agricultural sectors stabilizes input costs, incentivizing remaining farmers to invest in sustainable practices such as eco-friendly pesticides and reduced fertilizer use. That is, by encouraging the shift towards mechanization and green production, rural-urban migration transforms the relationship between labor inputs and resource use in agriculture. This shift highlights the dual role of migration: while it reduces direct agricultural labor availability, it simultaneously acts as a catalyst for sustainable agricultural practices. The observed adjustments in input structures and cropping patterns underscore the capacity of rural communities to adapt to demographic changes, fostering resilience and sustainability in agricultural systems.
Our study confirms that rural-urban migration facilitates green and mechanized production primarily through the expansion of large-scale farming. Rural-urban migration reduces both labor supply and the willingness to farm, prompting a shift from fragmented smallholder operations to consolidated systems. Large-scale farming achieves economies of scale by lowering per-unit production costs, optimizing input efficiency, and reducing inefficiencies associated with fragmented investments. Mechanization is further incentivized by the indivisibility of agricultural machinery, enabling labor substitution and enhancing productivity. Contiguous farming also allows for more efficient fertilizer application, reducing per-unit chemical use. In comparison with small-scale farmers, scale-intensive agricultural entities are more likely to adopt green technologies, reducing reliance on traditional fertilizers and promoting sustainable practices (Mao et al., 2021). These results emphasize the transformative potential of migration-driven large-scale farming in addressing sustainability challenges. By integrating mechanization and green technologies, large-scale farming not only compensates for labor shortages but also drives structural shifts in input use and production processes.
Lastly, our study adopts an analytical framework that integrates production, market accessibility, and living conditions to examine their combined influence on the relationship between rural-urban migration and large-scale farming. While production conditions and market accessibility factors have long been recognized as key determinants of large-scale farming, our research introduces living conditions as an innovative and critical dimension. Living conditions, encompassing residential infrastructure and social cohesion, provide an essential yet often overlooked aspect of the decision-making process for large-scale farming entities. Villages with better living environments not only offer comfort and quality of life but also foster stability and long-term commitment from farming entities. By emphasizing the role of living standards alongside traditional economic factors, this study highlights the importance of holistic rural development strategies. Ensuring sustainable agricultural growth in migration-affected areas requires balancing economic incentives with enhanced living conditions to create environments conducive to both productivity and well-being.
This study has several limitations that warrant consideration. First, this study relies on cross-sectional data, which limits the ability to fully address potential endogeneity issues and to capture dynamic changes over time. Second, this study primarily focuses on village-level variables and their changes, without delving deeply into the complex interactions between farming entities and village development. Future research could incorporate micro-level data to provide a more nuanced understanding of these interactions and to better explore the underlying mechanisms. Lastly, the findings require validation across broader regions, as Sichuan's agricultural practices and rural dynamics may differ from other areas. Future research should incorporate data from multiple provinces to better capture the multidimensional impacts of rural-urban migration on sustainable agriculture.
6. Conclusion
This study empirically investigates the impact of village-level migration on sustainable agriculture, with three key findings. First, rural-urban migration significantly promotes sustainable agriculture by advancing green agricultural practices and agricultural machinery utilization, addressing labor shortages while fostering efficient agricultural practices. Second, this effect is primarily mediated through large-scale farming, which consolidates fragmented landholdings, enhances economies of scale, and links rural-urban migration to improved sustainable agriculture. Third, the impact of rural-urban migration is more pronounced in villages with favorable conditions, including conducive production environments, robust distribution infrastructure, and improved living standards. These results underline the importance of leveraging rural-urban migration as a catalyst for sustainable agricultural transformation through targeted policies that enhance village conditions.
The findings of this study allow us to draw several policy implications. First, targeted support should be provided to villages with high rural-urban migration rates, including financial incentives and policy measures to facilitate land consolidation and reduce transaction costs associated with large-scale farming. These efforts would create favorable conditions for scaling up agricultural operations. Second, public investment in improving agricultural production and distribution infrastructure should be prioritized. This includes large-scale irrigation projects such as reservoirs, drainage systems, and water channels to enhance farmland resilience, as well as rural road construction and the establishment of logistics and distribution centers. Developing cold-chain logistics systems is particularly critical to maintaining agricultural product quality and ensuring stable supply chains. Third, improving rural living conditions should be a key focus to attract and retain large-scale farming entities. Beyond agricultural subsidies, investments in rural environmental protection, waste management, sewage treatment, and public service facilities are essential. These initiatives would enhance the overall sustainability and attractiveness of rural areas for large-scale farming.
A
Funding Statement
This study is supported by the National Social Science Fund of China (No. 24FGLB050).
Conflict of interest statement
There is no conflict of interest with any of the authors.
A
Data Availability
The data used in this study were sourced from the Rural Revitalization Strategy Statistical Data (RRSSD) provided by the Sichuan Provincial Bureau of Statistics, China.
A
Author Contribution
Each author made substantial contributions to the research and manuscript preparation. Chang Xu was responsible for the conceptualization of the study, the development of the methodological framework, and the formal analysis. He also took the lead in drafting the original manuscript and revising it throughout the writing process. Xuefeng Li contributed to the conceptualization and co-drafted the manuscript, providing critical input during the writing and revision stages. Ankang Cai participated in the conceptual design and contributed to reviewing and editing the manuscript to enhance its clarity and coherence. Jie Zhu provided overall supervision of the research process and offered valuable feedback during the manuscript revision. The combined efforts and expertise of all authors were essential to the successful completion of this study.
Reference
Addai G, Guodaar L, Dinye RD, Boafo J, Purohit S, Felix K, Abi VA (2025) Advancing agricultural diversification within rural regions: The dynamic role of interregional rural-rural migration. Habitat Int 156:103273. https://doi.org/10.1016/j.habitatint.2024.103273
Adnan N, Nordin SM, Bahruddin MA, Tareq AH (2019) A state-of-the-art review on facilitating sustainable agriculture through green fertilizer technology adoption: Assessing farmers behavior. Trends Food Sci Technol 86:439–452. https://doi.org/10.1016/j.tifs.2019.02.040
Agarwal B, Agrawal A (2017) Do farmers really like farming? Indian farmers in transition. Oxf Dev Stud 45(4):460–478. https://doi.org/10.1080/13600818.2017.1283010
Berchoux T, Hutton CW (2019) Spatial associations between household and community livelihood capitals in rural territories: An example from the Mahanadi Delta, India. Appl Geogr 103:98–111. https://doi.org/10.1016/j.apgeog.2019.01.002
Branca G, Lipper L, McCarthy N, Jolejole MC (2013) Food security, climate change, and sustainable land management. A review. Agron Sustain Dev 33:635–650. https://doi.org/10.1007/s13593-013-0133-1
Cao A, Guo L, Li H (2022) How does land renting-in affect chemical fertilizer use? The mediating role of land scale and land fragmentation. J Clean Prod 379:134791. https://doi.org/10.1016/j.jclepro.2022.134791
Caswell MF, Zilberman D (1986) The effects of well depth and land quality on the choice of irrigation technology. Am J Agric Econ 68(4):798–811. https://doi.org/10.2307/1242126
Chen R, Ye C, Cai Y, Xing X, Chen Q (2014) The impact of rural out-migration on land use transition in China: Past, present and trend. Land Use Policy 40:101–110. https://doi.org/10.1016/j.landusepol.2013.10.003
Cheng Y, Hu Y, Zeng W, Liu Z (2022) Farmer heterogeneity and land transfer decisions based on the dual perspectives of economic endowment and land endowment. Land 11(3):353. https://doi.org/10.3390/land11030353
Chi Y, Zhou W, Wang Z, Hu Y, Han X (2021) The influence paths of agricultural mechanization on green agricultural development. Sustainability 13(23):12984. https://doi.org/10.3390/su132312984
De Brauw A, Mueller V, Lee HL (2014) The role of rural-urban migration in the structural transformation of Sub-Saharan Africa. World Dev 63:33–42. https://doi.org/10.1016/j.worlddev.2013.10.013
Fukase E, Martin W (2016) Who will feed China in the 21st century? Income growth and food demand and supply in China. J Agric Econ 67(1):3–23. https://doi.org/10.1111/1477-9552.12117
Ge D, Long H, Zhang Y, Tu S (2018) Analysis of the coupled relationship between grain yields and agricultural labor changes in China. J Geog Sci 28:93–108. https://doi.org/10.1007/s11442-018-1461-5
Ge D, Long H, Qiao W, Wang Z, Sun D, Yang R (2020) Effects of rural-urban migration on agricultural transformation: A case of Yucheng City, China. J Rural Stud 76:85–95. https://doi.org/10.1016/j.jrurstud.2020.04.010
Guo X, Zhu AL, Zhu X, Liang Z, Zhao X, Cui C, Zhang F (2024) Contributing to sustainable smallholder agriculture through optimizing key agricultural inputs in China. J Clean Prod 471:143429. https://doi.org/10.1016/j.jclepro.2024.143429
Ha LT (2023) An investigation of digital integration's importance on smart and sustainable agriculture in the European region. Resour Policy 86:104158. https://doi.org/10.1016/j.resourpol.2023.104158
Hamilton SF, Richards TJ, Shafran AP, Vasilaky KN (2022) Farm labor productivity and the impact of mechanization. Am J Agric Econ 104(4):1435–1459. https://doi.org/10.1111/ajae.12273
Hu S, Jia N (2025) The impact of the Full Liberalization of Household Registration policy on the free migration of rural labor. China Econ Rev 89:102330. https://doi.org/10.1016/j.chieco.2024.102330
Hu Y, Li B, Zhang Z, Wang J (2022) Farm size and agricultural technology progress: Evidence from China. J Rural Stud 93:417–429. https://doi.org/10.1016/j.jrurstud.2019.01.009
A
Huang M, Zhao X, Zhuang J (2024) Welfare enhancement or environment improvement: How does China's rural revitalization assistance policy work? Evidence from China. Land Degrad Dev 35(14):4173–4188. https://doi.org/10.1002/ldr.5213Huo C, Chen L (2021) Research on the impact of land circulation on the income gap of rural households: Evidence from CHIP. Land 10(8):781. https://doi.org/10.3390/land10080781
Jansen HG, Pender J, Damon A, Wielemaker W, Schipper R (2006) Policies for sustainable development in the hillside areas of Honduras: A quantitative livelihoods approach. Agric Econ 34(2):141–153. https://doi.org/10.1111/j.1574-0864.2006.00114.x
Ji Y, Yu X, Zhong F (2012) Machinery investment decision and off-farm employment in rural China. China Econ Rev 23(1):71–80. https://doi.org/10.1016/j.chieco.2011.08.001
Ji X, Qian Z, Zhang L, Zhang T (2018) Rural labor migration and Households' land rental behavior: Evidence from China. China World Econ 26(1):66–85. https://doi.org/10.1111/cwe.12229
Jia XP, Huang JK, Xu ZG (2012) Marketing of farmer professional cooperatives in the wave of transformed Agrofood market in China. China Econ Rev 23(3):665–674. https://doi.org/10.1016/j.chieco.2010.07.001
Ju X, Gu B, Wu Y, Galloway JN (2016) Reducing China’s fertilizer use by increasing farm size. Glob Environ Change 41:26–32. https://doi.org/10.1016/j.gloenvcha.2016.08.005
Key N, Sadoulet E, Janvry AD (2000) Transactions costs and agricultural household supply response. Am J Agric Econ 82(2):245–259. https://doi.org/10.1111/0002-9092.00022
Knoop M, Theuvsen L (2020) Perception and Classification of Stress Factors of Young Farmers. German J Agricultural Econ 69(4):311–324. https://doi.org/10.30430/69.2020.4.311-324
Lam HM, Remais J, Fung MC, Xu L, Sun SSM (2013) Food supply and food safety issues in China. Lancet 381(9882):2044–2053. https://doi.org/10.1016/S0140-6736(13)60776-X
Lapka M, Cudlinova E, Rikoon JS, Pělucha M, Kvetoň V (2011) Rural development in the context of agricultural Green subsidies: Czech farmers’ responses. Agric Econ 57(6):259–271. https://doi.org/10.17221/4/2011-AGRICECON
Li J, Rodriguez D, Tang X (2017) Effects of land lease policy on changes in land use, mechanization and agricultural pollution. Land Use Policy 64:405–413. https://doi.org/10.1016/j.landusepol.2017.03.008
Li M, Wang Q (2014) International environmental efficiency differences and their determinants. Energy 78:411–420. https://doi.org/10.1016/j.energy.2014.10.026
Liu C, Wang Y, Yang B, Zheng L (2024) How do socioeconomic differentiation and rural governance affect households' land transfer decisions? Evidence from China. Land Degrad Dev 35(2):884–897. https://doi.org/10.1002/ldr.4959
Liu Y, Li Y (2017) Revitalize the world’s countryside. Nature 548(7667):275–277. https://doi.org/10.1038/548275a
Luo G (2012) China’s family support system: Impact of rural-urban female labor migration. Aging in China: Implications to social policy of a changing economic State, 10, 99–126. https://doi.org/10.1007/978-1-4419-8351-0_7
Luzi C (2025) The gap between administration and migrants: Terminologies and experiences of urban-rural migration in Japan. J Rural Stud 113:103500. https://doi.org/10.1016/j.jrurstud.2024.103500
Lykogianni M, Bempelou E, Karamaouna F, Aliferis KA (2021) Do pesticides promote or hinder sustainability in agriculture? The challenge of sustainable use of pesticides in modern agriculture. Sci Total Environ 795:148625. https://doi.org/10.1016/j.scitotenv.2021.148625
Ma W, Renwick A, Grafton Q (2018) Farm machinery use, off-farm employment and farm performance in China. Australian J Agricultural Resource Econ 62(2):279–298. https://doi.org/10.1111/1467-8489.12249
Mao H, Zhou L, Ying R, Pan D (2021) Time Preferences and green agricultural technology adoption: Field evidence from rice farmers in China. Land Use Policy 109:105627. https://doi.org/10.1016/j.landusepol.2021.105627
Mergoni A, Dipierro AR, Colamartino C (2024) European agricultural sector: The tortuous path across efficiency, sustainability and environmental risk. Socio-Economic Plann Sci 92:101848. https://doi.org/10.1016/j.seps.2024.101848
Min S, Waibel H, Huang J (2017) Smallholder participation in the land rental market in a mountainous region of Southern China: Impact of population aging, land tenure security and ethnicity. Land Use Policy 68:625–637. https://doi.org/10.1016/j.landusepol.2017.08.033
Mponela P, Tamene L, Ndengu G, Magreta R, Kihara J, Mango N (2016) Determinants of integrated soil fertility management technologies adoption by smallholder farmers in the Chinyanja Triangle of Southern Africa. Land Use Policy 59:38–48. https://doi.org/10.1016/j.landusepol.2016.08.029
Mullan K, Grosjean P, Kontoleon A (2011) Land tenure arrangements and rural-urban migration in China. World Dev 39(1):123–133. https://doi.org/10.1016/j.worlddev.2010.08.009
National Bureau of Statistics of China (2008) and 2023). Migrant workers monitoring survey report
Nin-Pratt A, McBride L (2014) Agricultural intensification in Ghana: Evaluating the optimist’s case for a Green Revolution. Food Policy 48:153–167. https://doi.org/10.1016/j.foodpol.2014.05.004
Paltasingh KR, Basantaray AK, Jena PK (2022) Land tenure security and farm efficiency in Indian agriculture: Revisiting an old debate. Land Use Policy 114:105955. https://doi.org/10.1016/j.landusepol.2021.105955
Qi H, Dong Z, You X, Li Y, Zhao Y, Sun X (2023) Extended exergy accounting for assessing the sustainability of agriculture: A case study of Hebei Province, China. Ecol Ind 150:110240. https://doi.org/10.1016/j.ecolind.2023.110240
Reckling M, Watson CA, Whitbread A, Helming K (2023) Diversification for sustainable and resilient agricultural landscape systems. Agron Sustain Dev 43(4):44–69. https://doi.org/10.1007/s13593-023-00898-5
Schanz L, Oehen B, Benoit M, Bernes G, Magne MA, Martin G, Winckler C (2023) High work satisfaction despite high workload among European organic mixed livestock farmers: a mixed-method approach. Agron Sustain Dev 43(1):1–15. https://doi.org/10.1007/s13593-022-00852-x
Schoneveld GC (2017) Host country governance and the African land rush: 7 reasons why large-scale farmland investments fail to contribute to sustainable development. Geoforum 83:119–132. https://doi.org/10.1016/j.geoforum.2016.12.007
Van Schalkwyk HD, Groenewald JA (2008) Agricultural land price and quality. Dev South Afr 10(3):401–410. https://doi.org/10.1080/03768359308439702
Steinführer A, Osterhage F, Tippel C, Kreis J, Moldovan A (2024) Urban-rural migration in Germany: A decision in favour of ‘the rural’ or against ‘the urban’? J Rural Stud 111:103431. https://doi.org/10.1016/j.jrurstud.2024.103431
Sugden F, Nigussie L, Debevec L, Nijbroek R (2022) Migration, environmental change and agrarian transition in upland regions: Learning from Ethiopia, Kenya and Nepal. J Peasant Stud 49(5):1101–1131. https://doi.org/10.1080/03066150.2021.1894552
Shi X (2018) Heterogeneous effects of rural-urban migration on agricultural productivity: Evidence from China. China Agricultural Economic Rev 10(3):482–497. https://doi.org/10.1108/CAER-10-2017-0193
The Food and Agriculture Organization (2021) FAO Strategic Framework 2022-31
Thiede B, Gray C, Mueller V (2016) Climate variability and inter-provincial migration in South America, 1970–2011. Glob Environ Change 41:228–240. https://doi.org/10.1016/j.gloenvcha.2016.10.005
Vranken L, Swinnen J (2006) Land rental markets in transition: Theory and evidence from Hungary. World Dev 34(3):481–500. https://doi.org/10.1016/j.worlddev.2005.07.017
Van Hung P, MacAulay TG, Marsh SP (2007) The economics of land fragmentation in the north of Vietnam. Australian J agricultural resource Econ 51(2):195–211. https://doi.org/10.1111/j.1467-8489.2007.00378.x
Wang S, Bai X, Zhang X, Reis S, Chen D, Xu J, Gu B (2021) Urbanization can benefit agricultural production with large-scale farming in China. Nat Food 2(3):183–191. https://doi.org/10.1038/s43016-021-00228-6
Wang X, Huang J, Rozelle S (2017) Off-farm employment and agricultural specialization in China. China Econ Rev 42:155–165. https://doi.org/10.1016/j.chieco.2016.09.004
Willock J, Deary IJ, Edwards-Jones G, Gibson GJ, McGregor MJ, Sutherland A, Grieve R (1999) The role of attitudes and objectives in farmer decision making: business and environmentally-oriented behavior in Scotland. J Agric Econ 50(2):286–303. https://doi.org/10.1111/j.1477-9552.1999.tb00814
Xiao L, Ni Y, Wang C (2023) Research on the pricing strategy of e-commerce agricultural products: should presale be adopted? Electron Commer Res 7:1–28. https://doi.org/10.1007/s10660-023-09731-3
Xie Y, Jiang Q (2016) Land arrangements for rural-urban migrant workers in China: Findings from Jiangsu Province. Land Use Policy 50:262–267. https://doi.org/10.1016/j.landusepol.2015.10.010
Xie H, Lu H (2017) Impact of land fragmentation and non-agricultural labor supply on circulation of agricultural land management rights. Land Use Policy 68:355–364. https://doi.org/10.1016/j.landusepol.2017.07.053
A
Xu D, Deng X, Guo S, Liu S (2019) Labor migration and farmland abandonment in rural China: Empirical results and policy implications. J Environ Manage 232:738–750. https://doi.org/10.1016/j.jenvman.2018.11.136Xu D, Deng X, Huang K, Liu Y, Yong Z, Liu S (2019) Relationships between labor migration and cropland abandonment in rural China from the perspective of village types. Land Use Policy 88:104164. https://doi.org/10.1016/j.landusepol.2019.104164
Xu Q, Yin RL, Zhang H (2011) Economies of scale, returns to scale and the problem of optimum-scale farm management: An empirical study based on grain production in China. Econ Res J 3:59–71 (In Chinese)
Yang J, Wang H, Jin S, Chen K, Riedinger J, Peng C (2016) Migration, local off-farm employment, and agricultural production efficiency: evidence from China. J Prod Anal 45:247–259. https://doi.org/10.1007/s11123-015-0464-9
Ye J (2018) Stayers in China's hollowed-out villages: a counter narrative on massive rural-urban migration. Popul Space Place 24(4):e2128. https://doi.org/10.1002/psp.2128
Ye S, Wang J, Jiang J, Gao P, Song C (2024) Coupling input and output intensity to explore the sustainable agriculture intensification path in mainland China. J Clean Prod 442:140827. https://doi.org/10.1016/j.jclepro.2024.140827
Zhang J, Mishra AK, Hirsch S (2021) Market-oriented agriculture and farm performance: Evidence from rural China. Food Policy 100:102023. https://doi.org/10.1016/j.foodpol.2021.102023
Zhang K, Song S (2003) Rural-urban migration and urbanization in China: Evidence from time-series and cross-section analyses. China Econ Rev 14(4):386–400. https://doi.org/10.1016/j.chieco.2003.09.018
Zhang R, Feng Y, Li Y, Zheng K (2024) Can different information channels promote farmers’ adoption of Agricultural Green Production Technologies? Empirical insights from Sichuan Province. PLoS ONE 19(8):e0308398. https://doi.org/10.1371/journal.pone.0308398
Zhong F, Lu W, Xu Z (2016) Does rural off-farm employment go against grain production? - analysis of the substitution of household elements and the adjustment of grain-crop structure and constraint conditions Chinese Rural Economy, (07), 36–47. 10.20077/j.cnki.11-1262/f.2016.07.004 (In Chinese)