Does rural land transfer promote high-quality agriculture development? Empirical evidence from 30 provinces in China

1 Introduction

The comprehensive implementation of the Rural Revitalization Strategy is a pivotal policy decision supporting “Agriculture, Rural Areas, and Farmers,” within which high-quality agricultural development (HQAD) serves as the primary task and cornerstone (Liu et al., 2025). Promoting high-quality development centered on agricultural industries is an essential path toward achieving agricultural and rural modernization and a core requirement for building global agricultural power. Recent scholarship has advanced multidimensional frameworks for HQAD evaluation, encompassing material output, social security, ecological environment, and cultural development (Xiang et al., 2024), reflecting the growing recognition that agricultural quality encompasses far more than production efficiency alone. To fully stimulate the productive vitality of rural resources, the Central Committee of the Communist Party of China has consistently deepened rural land system reforms. The Report to the 20th National Congress emphasized that resolving the “three rural issues” requires “developing new agricultural business entities (NABE) and socialized services, and promoting moderate-scale agricultural operations.” Given that moderate-scale operations are pivotal to agricultural modernization (Guo et al., 2026), Rural land transfer (RLT)—as the fundamental pathway to achieving such scales—provides expanded horizons for HQAD. Yet the urgency of this institutional innovation is underscored by recent evidence that approximately 12% of cropland-dominant areas in Asia may face net primary productivity (NPP) stagnation or decline (Li et al., 2025), highlighting the vulnerability of agricultural productivity and the critical need for institutional mechanisms that can reverse these trends through more efficient land resource allocation.

Since the Reform and Opening-up, RLT and consolidation have been regarded as essential tools for addressing land fragmentation and achieving moderate-scale operations (Xu and Zhang, 2026). Existing literature has extensively explored the impact of RLT on agricultural industrial development. Currently, China’s agricultural sector faces numerous challenges, including simplistic industrial structures, severe product homogenization, low operational efficiency, and difficulties in increasing farmers’ incomes (Li et al., 2026). In this context, RLT, as a fundamental method for optimizing land resource allocation, has become an endogenous driver for enhancing agricultural productivity (Huo and Chen, 2024). First, RLT facilitates rural development by increasing household income, optimizing land management benefits, and improving production efficiency. It helps bridge the income gap among rural households, optimizes income distribution structures (Huo and Chen, 2024), and reduces the vulnerability of households to relative poverty (Li et al., 2021). Furthermore, RLT encourages the concentration of fragmented land-use rights, enabling large-scale, intensive, and industrialized operations that enhance economic returns (Li et al., 2024). From a farmer-oriented perspective, RLT also corrects land misallocation, thereby boosting overall production efficiency (Fei et al., 2021). Second, RLT contributes significantly to the reduction of chemical fertilizer use and the advancement of mechanized operations. Post-transfer land typically exhibits “contiguous” characteristics, where the expansion of management scale coincides with the enlargement of plot sizes. The resulting economies of scale promote a reduction in fertilizer intensity (Zhu et al., 2018), an increase in large-scale mechanization (Ma et al., 2025a), and the adoption of green production technologies (Peng et al., 2025a), ultimately driving the agricultural industry toward “quality enhancement,” “efficiency gains,” and “green transformation.” To maximize these benefits, scholars have further investigated the transmission mechanisms, identifying “factor allocation” (Jiang et al., 2022), “land rights security” (Wei et al., 2025), and “scale operation” [14] as significant intermediary factors through which RLT propels industrial development.

In summary, while research on RLT and agricultural development has yielded valuable insights, several areas require deeper exploration. Specifically, there is a need to clarify the theoretical logic connecting RLT to high-quality development, understand the specific role of NABE in this process, and provide robust empirical evidence. Under the backdrop of the Rural Revitalization Strategy, where various resources are flowing into rural areas, it is crucial to leverage land—the most fundamental production factor. There remains significant research potential regarding how to align RLT more closely with high-quality development goals. Accordingly, this paper treats the implementation of RLT as a quasi-natural experiment. Using panel data from 30 provincial-level administrative regions in China from 2005 to 2020, we employ a continuous DID model and a mediating effect model to empirically analyze the impact and mechanisms of RLT. This study aims to assess the effectiveness of the nexus between RLT policies and HQAD goals, providing a decision-making basis for the comprehensive promotion of rural revitalization and the realization of agricultural modernization.

2 Theoretical analysis and research hypotheses

2.1 Definition of HQAD

The agricultural industry plays a foundational role in promoting the development and integration of other sectors (Chen et al., 2024), serving as a critical variable in advancing rural industrial revitalization. Broadly defined, agriculture encompasses five sectors: crop farming, forestry, animal husbandry, fisheries, and side-line production. The agricultural industry involves multiple stages, including the production, processing, circulation, and trade of agricultural products, as well as the flow of commodities, factors, and information alongside resource allocation. While research on HQAD should ideally consider every aspect of the agricultural value chain to ensure coordination and integrity, this paper focuses its core analysis on the agricultural production stage due to space constraints. As the foundation and prerequisite of the entire agricultural value chain, the production stage holds a decisive position; thus, focusing on it allows for a deeper dissection of the connotations and objective requirements of HQAD.

The Report to the 20th National Congress of the CPC points out that Chinese-style modernization is characterized by common prosperity for all, and increasing farmers’ income is a crucial link in this pursuit. Addressing the shortfall in rural income depends heavily on the development of the agricultural sector and rural areas; therefore, ensuring agricultural production efficiency and expanding farmers’ income are the primary tasks of HQAD. As reforms progress, accelerating the transition from “quantitative expansion” to “quality enhancement” is both a necessary condition and a core tenet of this development. Furthermore, China’s agricultural sector faces a series of ecological challenges, such as the declining utilization of water and soil resources, severe non-point source pollution, and food safety risks (Sun and Chen, 2023), all of which run counter to the construction of ecological civilization. Resolving these issues requires integrating the transformation of agricultural development models and the improvement of green development levels into the goal of high-quality growth.

Based on the synthesis above, this paper defines the connotation of HQAD as follows: with agricultural production as the foundation and the comprehensive realization of rural revitalization as the ultimate goal, its core essence lies in enhancing economic efficiency, emphasizing industrial quality, and elevating green development levels, all aimed at achieving large-scale, market-oriented, and modernized agricultural development.

2.2 The mechanism of RLT in promoting HQAD

Integrating the aforementioned definition of HQAD, this paper constructs a theo-retical analytical framework for how RLT facilitates this process (Figure 1). Specifically, the promotional role of RLT is discussed through the triple dimensions of “production efficiency, industrial quality, and green development.”

First, increasing agricultural production efficiency. The large-scale implementation of RLT nationwide has propelled agricultural production toward an intensive and large-scale development path, fostering the initial formation of a modern agricultural production and management system. The execution of RLT enables contiguous land management, improving the current fragmented and decentralized allocation pattern (Lu et al., 2022). This has exerted a positive impact on agricultural production costs, returns, and labor productivity, thereby enhancing overall production efficiency (Wu and Zhang, 2024). The economies of scale formed by the expansion of land production space through transfer constitute the foundation of agricultural scale economies (Liu et al., 2019). Achieving these economies helps attract modern production factors such as capital, knowledge, and technology, and promotes the transfer of credit funds and information technology to rural areas, which is conducive to extending the agricultural value chain and unleashing agricultural multifunctionality. The phenomenon of large-scale land management triggered by transfer is linked not only to production efficiency but also to other factors, such as capital injection, optimization of cooperative management models, and the synergy of agricultural socialized services. As RLT deepens, scale management of land will gradually evolve into scale management of services, and economies of scale will transition toward economies of specialized division of labor, ultimately promoting the overall enhancement of agricultural production efficiency.

Second, improving the quality of the agricultural industry. To begin with, RLT facilitates improvements in the technical quality of agricultural production. It alters the fragmented land ownership pattern inherent in the household contract responsibility system, overcomes the inefficiencies of decentralized smallholder management, and assists in the popularization of mechanized farming and the upgrading of agricultural technologies (Rogers et al., 2021). Through the coordinated advancement of land titling, the “three-rights separation” reform, and RLT policies, agricultural business entities form stable expectations regarding property rights. This encourages increased capital investment in production, such as the procurement of modern agricultural machinery and the adoption of advanced cultivation techniques (e.g., automated irrigation and mechanical cultivation) to improve production conditions (Cheng and Cai, 2026). Furthermore, RLT contributes to the enhancement of human capital cultivation among agricultural business entities. As transfer drives the continuous expansion of operational scales and the deepening of specialized division of labor, business models continue to innovate. To pursue higher economic returns, operators constantly improve their professional skills and quality. Compared to smallholders, those who transfer land in possess stronger learning motivation, actively participating in vocational farmer training to master advanced production techniques and management methods, gradually transforming into “new professional farmers.” Finally, RLT is conducive to improving the quality of agricultural products. The introduction of policies such as “extending the second round of land contracts for another 30 years” has improved expectations for land investment for both parties involved in transfers, stimulating long-term investment in production factors. Given the substantial cost outlays, large-scale operators are more rational in seed selection, enhancing product traits such as disease resistance and shelf-life through scientific variety selection and structural optimization. Additionally, large-scale operations facilitate the broad application of digital information technologies, including agricultural big data and remote monitoring, which enable real-time monitoring of farmland conditions and pest outbreaks, thereby improving the quality and safety of agricultural products.

Third, enhancing the level of green agricultural development. By merging “small plots into large ones,” RLT promotes the intensification of production materials (Wei, 2026), breaking away from the previous extensive application of fertilizers and pesticides. This facilitates the reduction of chemical inputs and provides the necessary conditions for the adoption of green technologies. Long-term and stable RLT contracts help reduce the application rate of agricultural inputs like fertilizers, thereby improving the environmental efficiency of production (Ma et al., 2025b; Yang et al., 2025). Stable contractual relationships further stimulate a collective sense of responsibility for land quality and the ecological environment among farmers and business entities. Large-scale operators are more inclined to adopt production methods such as conservation tillage and crop rotation to maintain soil structure and fertility, enhancing the natural recovery capacity of farmland and maintaining the health and stability of the agricultural ecosystem. Consequently, large-scale farmers emerging from RLT possess more proactive market thinking, development logic, and green concepts (Peng et al., 2025b). They are more likely to adopt production technologies conducive to sustainable agriculture, thus elevating the level of green agricultural development.

Based on the above, this paper proposes research hypothesis H1: RLT plays a positive role in promoting HQAD.

2.3 The transmission mechanism of RLT promoting HQAD: NABE

Market-oriented operation is a vital goal for agricultural development, and the prerequisites for achieving marketization are the expansion of production scale and the transformation of agricultural economic organizations. RLT lays the foundation for large-scale production and market-oriented operations, facilitating the identity shift of traditional smallholders into “NABE.” Currently, these entities primarily include farmers’ cooperatives, leading agricultural enterprises, and family farms. Among them, farmers’ cooperatives are mutual-aid economic organizations voluntarily formed and democratically managed by farmers to safeguard their collective interests. Compared to other entities, cooperatives possess stronger cohesion and appeal to the farming community due to their spontaneous formation and widespread distribution. RLT promotes the concentration and efficient allocation of agricultural production factors, providing a basis for farmers’ cooperatives to adopt specialized and differentiated management models. A significant form of current RLT is the transfer of land contract management rights by farmers through shareholding cooperation. By transferring land to cooperatives, unified management and efficient planning of land resources are achieved, thereby enhancing the quality and market competitiveness of agricultural products. Through cooperatives, farmers establish interest linkages that form a collective countervailing power and trading advantage; this move substantially strengthens farmers’ market negotiation positions and enables them to better mitigate market risks (Jin et al., 2021). The role of cultivating NABE in promoting HQAD is primarily reflected in the “consolidation effect” (integrating fragments into a whole) of farmer cooperation. This effect manifests in three key areas: Production Integration: Integrating production processes during the production stage to achieve scale in both production and services, overcoming the drawbacks of decentralized and fragmented smallholder production, and significantly improving agricultural production efficiency (Luan and Zhang, 2023). Resource Integration: Consolidating resources during the marketing stage to build brand advantages, which enhances the market competitiveness of agricultural products and strengthens farmers’ risk-resilience (Lin et al., 2026). Value Chain Integration: Integrating the entire chain—including production, processing, and sales—to maximize the commercialization rate of agricultural products and realize the development of “order-based agriculture.”

Based on the above, this paper proposes research hypothesis H2: NABE play a crucial role as a transmission mechanism in the process of RLT promoting HQAD.

3 Materials and methods

3.1 Materials

This study selects the panel data of 30 provincial-level administrative regions in China from 2005 to 2020 as the research sample for analysis. Due to the severe lack of data in regions such as Hong Kong, Macau, Taiwan, and Tibet, they are excluded and not included in the sample analysis system. Considering that the impact effects of RLT implementation generally exhibit a lag, it is necessary to retain samples for a longer period after implementation; therefore, the sample data from 2005 to 2020 are selected as the timeframe for evaluating the impact effects of RLT implementation. Data on provincial RLT and the number of farmers’ cooperatives are sourced from the China Rural Management Statistical Annual Report and the National Rural Economic Situation Statistical Data. Data related to the measurement of the level of HQAD are derived from the National Bureau of Statistics website, the China Agricultural Statistical Yearbook, the China Population and Employment Statistical Yearbook, and the EPS database, etc. Individual missing values are filled using linear interpolation and moving average methods, and outliers are removed. Due to multicollinearity in the control variable “economic development level,” first-order differencing was applied to this variable beforehand.

3.2 Variable selection

3.2.1 Explained variable

HQAD level (Y it). Based on the preceding theoretical analysis, an evaluation index system for HQAD is constructed from three dimensions—“production efficiency, industrial quality, and green development”—comprising 13 specific indicators. To avoid the limitations of single weighting approaches, this study adopts a combined subjective and objective weighting method to determine the indicator weights.

The objective weights are derived using the entropy weight method. The underlying principle is that a smaller information entropy of an indicator reflects greater data dispersion and thus more information content, warranting a higher weight. The calculation procedure is as follows: first, the raw data are normalized using the min-max standardization method, with positive and negative indicators standardized separately; second, the proportion matrix for each indicator is computed; third, the information entropy is calculated; finally, the redundancy degree is derived, and the objective weight for each indicator is determined accordingly.

For the subjective weighting procedure, expert judgment was incorporated using the Analytic Hierarchy Process (AHP). The relative importance of the indicators was evaluated using Saaty’s 1–9 scale, and the resulting judgment matrix was subjected to a consistency test. The maximum eigenvalue of the judgment matrix was 13.000, yielding a consistency index (CI) of 0.000, which indicates that the judgment matrix satisfies the required consistency condition (Table 1). To reduce the influence of a single weighting approach, the AHP-derived subjective weights were subsequently combined with the entropy weights, and the final composite weights were obtained by averaging the two sets of weights.

Table 1. Summary of analytic hierarchy process consistency test results.

The final composite weights are obtained by taking the arithmetic mean of the entropy weights and AHP weights, as reported in Table 2.

Table 2. Evaluation index system for the high-quality development level of agriculture.

3.2.2 Core explanatory variable

Implementation intensity of RLT ( RL T it ). This study measures RLT implementation intensity using the interaction between the provincial RLT proportion and the post-policy indicator. Specifically, L A it denotes the proportion of rural land transferred in province (i) in year (t), calculated as the annual transferred rural land area divided by the corresponding contracted cultivated land area of rural households. Pos t t equals 1 for years from 2010 onward and 0 otherwise. Accordingly, the continuous DID treatment-intensity variable is defined as ( RL T it= L A it × Pos t t). As an alternative measure, the interaction between RLT scale and the post-policy indicator is used in the robustness analysis.

3.2.3 Mediating variable

NABE (M it). Given that farmers’ cooperatives are a vital form of cultivating NABE at present, and compared with other types of NABE, they possess spontaneity in formation and wide distribution, which can represent the cultivation and development of regional NABE to a certain extent. Therefore, this study adopts the number of farmers’ cooperatives to characterize the cultivation degree of NABE, testing the transmission role of NABE in the process of RLT promoting HQAD.

3.2.4 Control variables

To control for exogenous interference, this study selects economic development level (characterized by per capita GDP), road accessibility, urbanization rate, fiscal support for agriculture, gross agricultural output value, agricultural insurance premium level, foreign trade dependence, and grain planting scale as control variables. To ensure data stationarity, logarithmic transformation is applied to all control variables except for industrial structure, road accessibility, urbanization rate, and fiscal support for agriculture. Due to multicollinearity among some variables, the first-order differencing method was used to process these variables. The description and descriptive statistics of each variable are shown in Table 3.

Table 3. Variable meanings and descriptive statistics.

3.3 Model specification

3.3.1 Baseline regression model

Rural land transfer (RLT) in China has undergone a gradual process of institutional development. The 2010 No. 1 Central Document further strengthened the regulation and standardization of rural land transfer by emphasizing improvements in the transfer management and service system, standardized transfer contracts, and the development of appropriate-scale agricultural operations. Therefore, this study regards 2010 as an important national-level milestone marking intensified regulatory guidance and policy implementation within an evolving institutional framework, rather than as the starting point of RLT institutionalization. Given the gradual nature of institutional implementation and agricultural adjustment, the effects associated with this regulatory intensification may not emerge immediately.

Given China’s vast territory and the significant differences in natural and socioeconomic conditions across regions, the intensity of RLT implementation varies substantially across provinces. Following the continuous DID approach adopted in related studies (Chen and Wang, 2022), this study uses the 2010 national regulatory-intensification milestone to define the post-policy period and exploits cross-provincial variation in RLT intensity to examine its association with HQAD. Unlike a conventional binary DID design, the continuous DID specification retains the variation in the intensity of RLT across provinces and years. The constructed baseline regression model is as follows:

Y it = α + βL A it × Pos t t + δcontro l it + μ i + γ t + ε it (1)

In the above formula, Y it is the explained variable, representing the provincial level of HQAD, where the subscripts i and t represent the province and year, respectively; L A it × Pos t t is the core explanatory variable, namely the interaction term between RLT and the implementation year; L A it denotes the RLT intensity of province (i) in year (t), and Pos t t is the policy time dummy variable equals when t ≥ 2010, Pos t t*=* 1; when t < 2010, Pos t t= 0; β and δ are the coefficients to be estimated, and contro l it is a series of control variables; μ i and γ t represent the province and time fixed effects, respectively; and ε it is the error term.

3.3.2 Parallel trend test model

A parallel trend test is selected to analyze the dynamic effects of policy implementation. With reference to relevant research (Wen et al., 2019), the event study method is adopted to estimate the dynamic effects of RLT implementation, and the following model is constructed for testing:

Y it = α + ∑ 2005 2020 β t L A it × δcontro l it × D t + μ i + γ t + ε it (2)

In the above formula, the interaction term between RLT and the implementation year is replaced with the interaction term between the year dummy variable and the treatment group variable, and a joint hypothesis test is performed on the estimated coefficients. D t is the year dummy variable, and the symbols and meanings of other variables are consistent with Equation 1.

3.3.3 Mediating effect model

To identify the transmission role of NABE in RLT promoting HQAD, this study adopts the stepwise regression method (Deng et al., 2022) to construct a mediating effect model for testing, with the model specified as follows:

M it = α + ηL A it × Pos t t + δcontro l it + μ i + γ t + ε it (3)

Y it = α + θL A it × Pos t t + φ M it + δcontro l it + μ i + γ t + ε it (4)

In Equations 3 and 4, M it represents the cultivation and development status of NABE in different provinces and years. η is the impact coefficient of RLT implementation on the cultivation and development of NABE, θ represents the direct effect, and φ represents the indirect effect. The remaining variables are consistent with Equation 1 mentioned above.

4 Results

4.1 Measurement results of HQAD level

Figure 2 depicts the evolution of HQAD and RLT areas in China and its three major regions from 2005 to 2020. Collectively, both indicators show a consistent upward trend. Regionally, the East remained the frontrunner, with its HQAD index climbing from 0.41 to 0.53. Leveraging its strong economic and technological base, the East has maintained steady growth despite its high initial level. The Central and Western regions shared similar HQAD levels; the former rose steadily from 0.32 to 0.37, while the latter, starting from a lower base, exhibited an undulating upward trajectory. The West’s resilience and “late-mover advantage” are largely driven by sustained infrastructure investment via national initiatives like the “Western Development Strategy” and “Poverty Alleviation” campaigns. Although regional gaps remain, all areas have entered an upward channel, reflecting the concurrent improvement of agricultural development conditions and the increasing scale of rural land transfer during the study period.

4.2 Estimation results of the baseline mode

Prior to the baseline regression, a Variance Inflation Factor (VIF) test was conducted on all variables. The mean VIF was 2.95, and the VIF values for all variables were less than 5 (as shown in Table 3), thus excluding the issue of multicollinearity. This study employs the OLS pooled regression model, the two-way fixed effects (FE) model, and the Bootstrap resampling method (1,000 times) for estimation, respectively. The baseline regression results of the impact of RLT on the level of HQAD are presented in Table 4. In the table, models (1), (3), and (5) report the estimation results without control variables, while models (2), (4), and (6) report the estimation results with all control variables included. The results indicate that the estimated coefficients of RLT are significantly positive across the different model specifications. After the inclusion of control variables, the coefficient remains 0.118 and is statistically significant at the 1% level. This result indicates that higher RLT intensity is significantly associated with higher HQAD after the 2010 national regulatory-intensification period, supporting the proposed positive relationship between RLT and HQAD. The reason for this is that RLT, as a major achievement of rural land system reform, plays an important role in promoting HQAD. First, it promotes the growth of agricultural production efficiency, achieving more efficient resource utilization; second, it drives the improvement of agricultural industrial quality by introducing modern means to increase the added value of agricultural products; third, it facilitates the green transformation of agricultural production, laying a foundation for sustainable agricultural development, thereby exerting a positive and far-reaching impact on HQAD.

Table 4. Estimation results of benchmark regression.

*, , and * indicate significance at the 1, 5, and 10% levels, respectively. t-statistics are reported in parentheses.

4.3 Parallel trend test

The premise for the validity of the continuous difference-in-differences (DID) method is passing the parallel trend assumption; that is, the growth rate of the dependent variable for the treatment and control groups should remain consistent before receiving the treatment. Testing is conducted using the model in Equation 2, and the trend of the estimated coefficients after adding control variables is shown in Figure 3. Before 2010, the estimated coefficients do not exhibit statistically significant differential effects, providing support for the absence of systematic pre-policy differences associated with RLT intensity. After 2010, the estimated coefficients gradually increase and become more pronounced in the subsequent years. This pattern indicates that the relationship between RLT intensity and HQAD emerged gradually rather than immediately following the national regulatory-intensification milestone. Such a delayed pattern is consistent with the time required for institutional implementation, land-market adjustment, infrastructure investment, and changes in agricultural production organization.

Table 5 reports the dynamic estimates in detail. The estimated coefficients before 2010 are statistically insignificant, suggesting no systematic pre-policy differential trend associated with RLT intensity. The positive association becomes more apparent from 2014 onward. Rather than indicating an instantaneous policy shock, this pattern is consistent with the cumulative effects of gradual institutional implementation and agricultural adjustment.

Table 5. Estimation results of the dynamic effect of RLT on high-quality agricultural development.

4.4 Robustness check

To further test the stability of the baseline regression results, this section employs five methods to conduct robustness checks on the estimation model, and the results are shown in Table 5.

• Replacing the core explanatory variable. To a certain extent, the scale of RLT carries a similar meaning to the proportion of RLT area, as both can reflect the implementation intensity of RLT in a specific region. Therefore, the interaction term between RLT scale and the policy timing is used to replace the interaction term between the original core explanatory variable and the policy timing. Regression analysis is performed using the two-way fixed effects model, and the results are shown in Model (1) of Table 6. The test results are consistent with the baseline regression, confirming the previous research findings.
• Recalculating the explained variable. Considering that the level of HQAD measured by the combined weighting method may have a certain degree of subjectivity, leading to measurement bias, this study recalculates the level of HQAD using the entropy weight method based on the original evaluation indicators. As shown in Model (2) of Table 6, the regression coefficient exhibits a significant positive impact at the 1% significance level, and the regression results remain robust. This confirms that the promotion effect of RLT on HQAD is robust and sustainable.
• Lagging the explained variable. Considering the continuity of the socio-economic environment and the periodicity of agricultural production, the explained variable lagged by one period is used for empirical testing. The estimation results in Model (3) of Table 6 show that the regression coefficient remains significantly positive at the 1% significance level, with an impact coefficient of 0.164, confirming the robustness of the previous research findings.
• Excluding other policy interference. In 2013, the State Council released the document Several Opinions on Accelerating the Development of Modern Agriculture and Enhancing the Vitality of Rural Development, which aimed to promote the large-scale, professional, and standardized development of agriculture. It encouraged farmers to adopt modern technologies, transform production modes, and supported the concentration of land towards production entities such as professional large households, family farms, and agricultural cooperatives. With the issuance of these opinions, rural economic development and RLT efficiency were further enhanced. Therefore, after excluding the sample data from the following year (2014), regression tests were conducted on the sub-sample. The results in Model (4) of Table 6 show that the regression coefficient did not change significantly compared to before the exclusion and still shows a significant positive impact, confirming the robustness of the previous research findings.
• Fictional policy timing. 2007 is randomly selected as the time point for the RLT policy shock, and data from 2005–2010 are retained for a placebo test. The results in Model (5) of Table 6 show that the impact coefficient is not significant, indicating that no impact effect of RLT existed in or before 2007. The reliability of the empirical results is further verified.

Table 6. Robustness test results.

*, , and * indicate significance at the 1, 5, and 10% levels, respectively. t-statistics are reported in parentheses.

4.5 Heterogeneity analysis

Theoretically, the promotional effect of RLT on HQAD is not uniform but is moderated by regional factor endowments, institutional environments, and developmental stages, thereby exhibiting differentiated patterns. From a regional perspective, the eastern region¹—characterized by advanced urbanization and industrialization—has experienced a declining agricultural share and near-saturation of land transfer, with marginal improvements in agricultural production substantially narrowing; the development trajectory is shifting from “land scale-driven” toward “capital and technology-driven.” In contrast, the central region benefits from both favorable resource endowments and institutional dividends, with active transfer markets and emerging agricultural business entities beginning to take shape, creating strong synergies between factor reallocation and institutional innovation; thus, the policy effect is expected to be most pronounced. The western region, however, faces constraints including harsh natural conditions, inadequate infrastructure, and limited market size, resulting in insufficient supply–demand elasticity for land transfer and a relatively slow release of policy efficacy. Regarding grain production functional zones², major grain-producing areas operate under rigid national food security mandates that constrain planting structure adjustments, limiting RLT-induced changes primarily to grain-oriented scale operations rather than diversified, high-value industrial layouts. Non-major grain-producing areas, free from such constraints, exhibit more diversified agricultural structures, allowing land transfer to more flexibly support specialty agriculture, brand development, and industrial integration, thereby theoretically generating stronger driving effects on HQAD. Based on these theoretical expectations, we employ subgroup regression analysis for empirical testing (Table 7).

• Geographical location heterogeneity. This study divides the total sample into three sub-samples—Eastern, Central, and Western—based on geographical location differences to examine the regional differences in the impact of RLT on HQAD. The test results, shown in Models (1), (2), and (3) of Table 7, indicate that the implementation of RLT has the greatest impact on HQAD in the Central region, with an impact coefficient of 0.434, which is significantly positive at the 1% significance level. The impact on HQAD in the Western region is relatively small but significantly positive at the 10% significance level, with an impact coefficient of 0.131, while the impact for the Eastern region is not significant and failed to pass the corresponding statistical tests. It can be concluded that there is obvious regional heterogeneity in the promotion effect of RLT on HQAD. A possible explanation is that the Eastern region has a relatively high level of economic development, comprehensive development advantages, and a massive economic scale, making the driving role of RLT in HQAD relatively weak. The policy effect is most prominent in the Central region because provinces such as Henan and Anhui have served as pilot areas for RLT policies, featuring well-developed transfer markets, active NABE, and accelerated transformation and upgrading of agricultural production. Consequently, the driving role of RLT implementation is more obvious, and the alignment between RLT and the goals of HQAD is more effective. In the Western region, the economic development level is relatively lagging, and the inherent advantages of natural resource endowments are insufficient; thus, the policy potential of RLT needs to be further stimulated.
• Grain production functional zone heterogeneity. This section further analyzes the differences in the impact of RLT on HQAD at the level of grain production functional zones. Models (4) and (5) in Table 6 report the evaluation results of the impact effects of RLT implementation for the major grain-producing areas and non-major grain-producing areas samples. In the samples of major grain-producing areas and non-major grain-producing areas, the estimated impact coefficients of RLT implementation on HQAD are 0.137 and 0.155, respectively, passing the tests at the 10 and 1% significance levels. This indicates that the driving effect of RLT policies on HQAD is stronger in non-major grain-producing areas than in major grain-producing areas. A possible reason is that, compared to major grain-producing areas, non-major grain-producing areas usually possess a more diversified agricultural industrial structure, including not only grain production but also the production of various agricultural products such as vegetables, fruits, and livestock. Secondly, non-major grain-producing areas may be more flexible in policy execution and resource allocation, responding more actively to national RLT initiatives by using RLT as a leverage point to promote agricultural structural adjustment and the improvement of agricultural industrial quality. Relatively speaking, major grain-producing areas may be influenced by strategic tasks such as ensuring grain yield and security, making the implementation of RLT more cautious, thus resulting in a comparatively moderate impact effect.

Table 7. Heterogeneity test of geographical location and grain production functional zones.

*, , and * indicate significance at the 1, 5, and 10% levels, respectively. t-statistics are reported in parentheses.

4.6 Mediating effect analysis

The results of Model (1) show that the estimated coefficient of RLT on HQAD is 0.118 and statistically significant at the 1% level. Model (2) indicates that RLT is positively associated with the development of NABE, with an estimated coefficient of 0.209. After NABE is introduced into Model (3), the coefficient of RLT decreases from 0.118 to 0.113 and remains statistically significant, while the coefficient of NABE is 0.177 and significant at the 1% level. These results provide evidence consistent with a partial transmission pathway through NABE. The estimated indirect contribution is approximately 31.4% (0.177 × 0.209/0.118).

5 Discussion

Although province and year fixed effects and multiple robustness checks are incorporated, potential bias arising from time-varying unobserved factors cannot be completely excluded. In particular, province-specific climatic fluctuations and unobserved changes in agricultural institutions or labor allocation may affect both RLT and HQAD. Therefore, the empirical findings should be interpreted within the identification scope of the continuous DID framework Table 8.

Table 8. Estimation results of mediating effect.

*, , and * indicate significance at the 1, 5, and 10% levels, respectively. t-statistics are reported in parentheses.

This study demonstrates that RLT exerts a significant, robust, and sustained promotional effect on HQAD. Departing from prior research that primarily focused on the unilateral impact of RLT on grain yields or household income (Liu et al., 2026), we expand the policy evaluation framework to a multidimensional high-quality development paradigm encompassing production efficiency gains, industrial structure upgrading, and green transformation. This systematically confirms the comprehensive dividends of market-based land factor allocation in the process of agricultural modernization. The underlying mechanism lies in the fact that RLT effectively mitigates the long-standing contradictions of farmland fragmentation and labor part-time farming. By clarifying property rights and reducing transaction costs, it facilitates the optimal allocation of land factors across regions and entities (Liu et al., 2024; Wen et al., 2024; Zhang et al., 2025). The formation of large-scale operations not only leverages economies of scale in mechanized operations, irrigation infrastructure, and technology adoption but also optimizes the input structure, providing the necessary physical foundation for the reduction of chemical inputs, the penetration of green technologies, and the vertical extension of the industrial chain (Sheng et al., 2024). The continuous DID estimates indicate a significant positive association between RLT intensity and HQAD following the 2010 national regulatory-intensification milestone. This relationship remains stable across the alternative specifications and robustness checks reported in the study. However, given the limitations of the empirical identification strategy, the estimated relationship should not be interpreted as definitive causal evidence.

The mediation analysis provides evidence consistent with NABE constituting a potential transmission pathwayl between RLT and HQAD. This finding addresses the academic debate over whether “land scale expansion inevitably leads to improved production quality” (Fei et al., 2021; Yu et al., 2024; Lu et al., 2025). Our results confirm that land concentration alone does not automatically translate into higher development quality; RLT merely achieves the “spatial agglomeration” of factors, whereas NABE—such as farmer specialized cooperatives and family farms—act as the core hubs for “efficiency conversion and structural upgrading.” Leveraging modern management philosophies, capital integration capabilities, and technological adaptability, these entities transform transferred land into standardized production bases or platforms for the integration of primary, secondary, and tertiary industries(Li and Shi, 2026; Wang and Huang, 2026). Simultaneously, they generate significant positive externalities through socialized services, effectively integrating smallholders into the modern agricultural system (Cheng et al., 2021).

The dynamic pattern revealed by the parallel trend test—whereby positive effects only emerged after 2014 despite the gradual implementation of the 2010 regulatory-intensification framework—merits further theoretical explanation. This four-year lag reflects the inherent inertia of agricultural production cycles and the gradual nature of institutional adaptation. Unlike manufacturing sectors where policy impacts can materialize rapidly, agricultural transformation requires time for contract renegotiation, infrastructure adjustment, technology adoption, and the organizational learning of new business entities. The gradual increase in impact coefficients from 2014 to 2019 (Table 5) suggests a cumulative process of institutional maturation, where early movers demonstrate the viability of RLT-driven modernization, generating demonstration effects that encourage broader participation and more sophisticated operational models over time. This temporal pattern underscores that RLT policies should be evaluated with patient, long-term perspectives rather than expecting immediate outcomes.

However, the interpretation of this mediating effect warrants caution. Due to data availability constraints, this study employs the number of farmers’ cooperatives as a proxy for NABE development. While cooperatives are the most widely distributed and spontaneously formed type of NABE in China, this measurement approach has notable limitations. First, it does not distinguish between “active cooperatives” with substantive operations and “shell cooperatives” that exist only in name, potentially overstating the actual level of NABE cultivation in certain regions. Second, it neglects other important forms of NABE, including family farms and leading agricultural enterprises, which may play distinct and complementary roles in the transmission from RLT to HQAD. These measurement limitations may lead to either overestimation or underestimation of the true mediating effect, depending on the regional composition of cooperative quality and the relative importance of non-cooperative entities. Therefore, the reported mediating proportion (approximately 31.4%) should be interpreted as an indicative rather than precise estimate. Future research should construct a more comprehensive NABE development index incorporating multiple indicators—such as cooperative membership, operating income, number of family farms, and output value of leading enterprises—to more accurately capture the organizational transformation underlying the RLT-HQAD nexus.

Heterogeneity analysis reveals that the promotional effect of RLT is more prominent in Central China and non-major grain-producing areas. This result should be interpreted within an interactive framework of regional development stages and agricultural functional positioning. Regarding regional differences, Central China achieves a high degree of synergy between resource endowments, institutional environments, and policy execution. Compared to Eastern China, the Central region has a lower HQAD baseline but faster urbanization and labor transfer, leading to higher supply elasticity for RLT (Huang et al., 2023). Compared to Western China, it possesses superior infrastructure and stronger contract enforcement, which effectively lowers transaction costs and default risks (Huang et al., 2023). Regarding functional zones, non-major grain-producing areas face fewer rigid constraints from food security mandates. Consequently, business entities can flexibly adjust cropping structures based on comparative advantages, responding rapidly to market demands and green standards. This allows RLT to integrate deeply with high-quality goals such as brand building and the realization of ecological product value. In contrast, major grain-producing areas bear the strategic responsibility of national food security; here, RLT is primarily oriented toward ensuring stable yields, which to some extent constrains the exploratory space for high-value-added crop layouts and radical green ecological transitions (Xu et al., 2024).

Beyond the aforementioned measurement and identification concerns, several additional limitations should be acknowledged. First, the study relies primarily on statistical yearbook data to construct the provincial-level panel dataset. Although these data provide relatively consistent and comparable socioeconomic information over time, they are largely aggregated, static, and subject to reporting and publication lags. More importantly, yearbook statistics provide limited information on the actual spatial configuration, fragmentation, conversion, and temporal dynamics of cultivated land. This limitation may constrain our ability to accurately capture the fine-scale changes in agricultural land use associated with rural land transfer and may introduce measurement errors when evaluating the relationship between land transfer and high-quality agricultural development. Future research should therefore integrate multi-source remote sensing data with conventional statistical information to establish a more dynamic and spatially explicit monitoring framework for cultivated land and agricultural development. Recent advances in remote sensing and deep learning provide promising methodological foundations for this improvement. For example, Causal-YOLO introduces causal feature alignment to improve the robustness of object detection in remote sensing imagery under environmental and sensor-related variations, while quantitative regularization has been shown to enhance the robustness of Vision Transformers to domain discrepancies in remote sensing image classification (Song et al., 2024; Song and Liang, 2026). Similarly, the hybrid feature consistency alignment framework improves the consistency of remote sensing feature representations under variations in imaging conditions and enhances object detection performance (Song et al., 2026). These advances suggest that future studies could combine satellite remote sensing imagery, deep-learning-based land-cover classification and object detection, and panel econometric models to identify cultivated land boundaries, monitor land-use changes, and quantify the spatial and temporal effects of RLT with greater precision. Such an integrated framework would allow researchers to move beyond the static and aggregated information provided by yearbooks toward more continuous, fine-grained, and spatially explicit measurements of cultivated land dynamics, thereby strengthening the empirical identification of the RLT-HQAD relationship. Second, the reliance on provincial-level panel data also limits our ability to capture micro-heterogeneity at the county, village, or household level. Future research could combine remote sensing observations with micro-survey and administrative data to construct multi-level analytical frameworks that examine how household characteristics, village institutions, local market conditions, and actual land-use changes moderate the RLT-HQAD relationship. Third, due to data accessibility constraints, this study does not fully address the potential endogeneity of RLT intensity. Although we have implemented multiple robustness checks, including placebo tests, alternative specifications, and lagged variable regressions, the absence of instrumental variables or quasi-experimental designs (e.g., PSM-DID) limits the strength of causal inference. Future research could employ historical land endowments, geographic characteristics, or policy pilot variations as instrumental variables to further strengthen identification. Fourth, the impact of RLT is moderated by factors such as farmers’ willingness, the rural financial environment, and market structures, which are not fully captured in the current analysis. Future research should examine these boundary conditions and dynamic evolutionary paths to provide more targeted theoretical support for the construction of a strong agricultural sector. Fifth, the spatial spillover effects across provinces are not examined in this study; given the interconnected nature of agricultural markets and factor mobility, RLT in one province may influence neighboring regions through demonstration effects, competition for investment, or supply chain linkages. Future research employing spatial econometric models (e.g., SDM, SEM) could capture such interdependencies and provide a more complete picture of RLT’s regional impacts. Finally, this study focuses exclusively on the agricultural production stage due to space constraints, yet HQAD encompasses the entire value chain from production to processing, circulation, and consumption. Future research should extend the analytical framework to cover post-production stages, examining how RLT influences value addition, market integration, and rural industrial convergence.

Despite these limitations, this study contributes to the literature by systematically evaluating the RLT-HQAD nexus within a unified analytical framework and identifying the mediating role of NABE. The spatial visualization (Figure 1) reinforces our finding of a clear East-Central-West gradient, providing intuitive evidence for the regional heterogeneity that underpins our policy recommendations. The findings offer valuable policy implications for deepening rural land system reforms and promoting agricultural modernization in the Chinese context, particularly by highlighting the need for regionally differentiated approaches that account for heterogeneous resource endowments, institutional environments, and development stages.

6 Conclusions and recommendations

6.1 Conclusion

Based on panel data from 30 provincial-level administrative regions in China from 2005 to 2020, this study scientifically measures and analyzes the regional levels and evolutionary characteristics of HQAD. Using a continuous Difference-in-Differences (DID) model and a mediating effect model, we empirically analyze the promotional effects and mechanisms of RLT. The findings are as follows:

Spatio-temporal disparities: The overall level of HQAD in China exhibits a steady upward trend but displays significant spatio-temporal variations. Development levels in the eastern region are notably higher than those in the central and western regions. Although the western region currently has the lowest development level, it demonstrates substantial growth potential.

Robust promotional effects: RLT exerts a positive, robust, and sustainable promotional effect on HQAD. NABE serve as a vital transmission mechanism in this process.

Regional and functional heterogeneity: The impact of RLT varies by region and grain production function. The promotion effect is most significant in the central region and non-major grain-producing areas, suggesting a more effective alignment between RLT policies and high-quality development goals in these regions.

6.2 Recommendations

Based on the above conclusions, the following recommendations are proposed:

First, continue deepening the reform of the rural land system. Stabilize land contract relationships, implement the “three-right separation” reform, improve laws, regulations and management systems for land transfer, standardize transfer contracts, and establish review and risk prevention mechanisms for social capital participation, thereby providing institutional guarantees for high-quality agricultural development.

Second, implement differentiated regional transfer policies. In light of the significant regional heterogeneity in policy effects across the eastern, central and western regions, adopt tailored strategies. In the eastern region, focus on improving transfer quality and promoting integration with digital technologies and smart agriculture, shifting from “scale-driven” to “technology-driven” development. In the central region, where the policy effect is strongest, expand transfer scale, cultivate and strengthen new agricultural business entities, and build demonstration zones for RLT-HQAD synergy. In the western region, prioritize improving agricultural infrastructure, guide voluntary farmer participation, advance comprehensive land consolidation, and gradually unlock the potential of transfer policies.

Third, adopt function-oriented strategies based on grain production zones. In non-major grain-producing areas, leverage the flexibility of industrial structure and policy implementation to encourage transfers toward high-value-added specialty agriculture and industrial integration. In major grain-producing areas, under the premise of ensuring food security, guide contiguous transfers to develop high-quality specialized grain production, promote large-scale and standardized operations, and achieve both stable output and quality improvement.

Fourth, vigorously cultivate new agricultural business entities. Focus on supporting substantively operating farmers’ cooperatives, improving their internal governance and service functions. Enhance their productivity and market competitiveness through technical training, fiscal subsidies, and credit support. Establish close benefit linkage mechanisms, encourage cross-regional and cross-industry integration among various entities, and ensure that farmers share in the value-added benefits along the industrial chain.

Fifth, establish a dynamic monitoring and evaluation mechanism. Given the lagged policy effects, establish a provincial-county two-level tracking system for transfer outcomes, conduct regular policy performance evaluations, and dynamically adjust implementation strategies based on evaluation results, ensuring that transfer policies remain aligned with the goals of high-quality development.

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Footnotes

Edited by: Mengying Feng, University of Roehampton London, United Kingdom

Reviewed by: Huaxiang Song, Hunan University of Arts and Science, China

Fengjuan Wang, Liaocheng University, China

Footnotes

The eastern region of China is primarily located along the coast and in economically developed areas, comprising the following provinces and municipalities: Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan. The central region, situated inland and bridging the east and west, includes Shanxi, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei, and Hunan. The western region, which covers a vast territory and boasts abundant resources but has a relatively lower level of economic development, includes Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Tibet, Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang.

Major grain-producing areas include Hebei, Inner Mongolia, Liaoning, Jilin, Heilongjiang, Jiangsu, Anhui, Jiangxi, Shandong, Henan, Hubei, Hunan, and Sichuan; non-major grain-producing areas are the provinces other than the major grain-producing areas.