Large-scale agricultural operations has emerged as a significant trend in modern agricultural development, drawing considerable academic attention regarding its economic consequences. Existing research presents three primary perspectives: positive effects, negative effects, and non-uniform effects. Studies highlighting the positive effects of large-scale agricultural operations indicate that it contributes to the optimal allocation of resources and enhanced production efficiency (Chu-zhi and Huang, 2007; Gai et al., 2023), improves environmental efficiency, and facilitates technological advancement (Li and Zi, 2020; Yi et al., 2022). Additionally, large-scale agricultural operations have been linked to increased farm income, reduced production costs, and progress in agricultural modernization, including adjustments in farming structures (Huang et al., 2016; Shi and Wang, 2021; Li et al., 2020; Yang and Ji, 2021). Conversely, some studies suggest that large-scale agricultural operations may have negative effects, particularly due to the increased use of machinery and fertilizers, which may contribute to environmental degradation (Ma X. et al., 2019; Baojing et al., 2021). Under certain conditions, large-scale agricultural operations have been found to limit per capita farm income and grain production efficiency, increasing the risks faced by smallholder farmers (Das and Ganesh-Kumar, 2018; Ali and Deininger, 2015; Zhang, 2023; Shunchen et al., 2021). In addition to these conflicting viewpoints, research suggests that the economic consequences of large-scale agricultural operations are context-dependent, with both positive and negative effects on different aspects of agricultural development. For instance, Liao and Ma (2023) and Shi and Wang (2021) found that while large-scale agricultural operations may improve resource efficiency and, in some cases, reduce per-unit carbon emissions through technological advancements, they can also lead to lower grain yield per unit of cultivated land. This decline in yield may result from changes in cropping patterns, soil fertility challenges, or a shift toward mechanization-intensive practices that prioritize efficiency over yield maximization. Additionally, while large-scale agricultural operations may increase output for certain crops, it can negatively impact the unit yield and total production costs of others, depending on land use strategies and input allocation (Shi and Wang, 2021; Liao and Ma, 2023). Similarly, Noack and Larsen (2019) reported that while large-scale agricultural operations increases farmers’ income, it simultaneously reduces grain yields (Noack and Larsen, 2019). Zhang et al. (2019) observed that with the expansion of large-scale agricultural operations, allocation inefficiency costs decreased, whereas technological inefficiency costs increased (Zhang et al., 2019). Furthermore, some studies suggest that the impact of large-scale agricultural operations may be nonlinear. Yuan et al. (2023) and Luan and Han (2020) found that the effect of large-scale agricultural operations on farmers’ income and grain yield follows an “inverted U-shaped” pattern, initially promoting growth but later inhibiting it. Similarly, studies by Chen and Zhou (2016) and Rada and Fuglie (2019) indicate that the technology-driven impact of large-scale agricultural operations varies by region and country, exhibiting a complex mix of both positive and negative effects.

Land transfer policies, widely implemented in modern agriculture to optimize land resource allocation and enhance agricultural efficiency, enable the orderly transfer of land through subcontracting, exchange, transfer, shareholding, and cooperative arrangements to facilitate agricultural transformation. Existing literature has revealed the multifaceted impacts of these policies on agriculture. On one hand, studies emphasize the strong linkage between land transfer policies and large-scale agricultural operations, with their synergistic effects recognized as a critical mechanism driving agricultural modernization. For instance, Liu and He (2024) argue that land transfer policies reshape land allocation logic through the clarification of property rights, establishing an institutional foundation for large-scale agricultural operations. Ye (2015) demonstrates that land transfer reforms enhance agricultural modernization by elevating the level of large-scale farming, while Huo and Chen (2024) further reveal that transfer-driven scale expansion significantly increases operational land area and promotes sustainable agricultural development. On the other hand, scholars have cautioned against potential adverse effects. Chen et al. (2024) warn that land transfer policies may trigger non-grain conversion of farmland and diminishing returns to scale, necessitating safeguards against compounded risks from management costs and market volatility. Haizhen et al. (2016), using an Interpretative Structural Modeling (ISM) approach, identify social risks such as unstable agricultural incomes, regional economic disparities, and ecological degradation linked to land transfers. Simultaneously, research has explored contextual factors influencing policy outcomes, including farmers’ land attachment (Liu G. et al., 2022), social trust (Chen Y. et al., 2023), terrain characteristics, irrigation infrastructure, and land rental costs (Liu K. et al., 2022). Furthermore, studies highlight broader impacts beyond scale expansion, such as effects on rural incomes (Wang and Wang, 2017), farmland quality preservation (Sheng et al., 2025), and land-use efficiency (Ma and Chen, 2022) underscoring the policy’s systemic ramifications.

The concept of resilience originates from physics, referring to an object’s ability to absorb energy and withstand external forces. In the field other than physics, the word resilience was first applied to the study of ecology, and Holling (1973) defined it as the self-regulation and repair ability of ecosystem after external shocks. Aura et al. (2002) pioneered its application to economic systems, conceptualizing economic resilience as the ability to rebound swiftly post-shock. In recent years, scholarly attention has expanded to resilience within food systems. Tendall et al. (2015) define food system resilience as the capacity to maintain stability amid natural or anthropogenic shocks, while Béné et al. (2016) emphasize its multidimensional nature, spanning adaptive and transformative capabilities at individual, local, national, and global supply chain levels. Building on these foundations, this study conceptualizes “grain production resilience” as a composite of three interdependent capacities: resistance (withstanding shocks), recovery (post-shock restoration), and reconstruction (system reconfiguration for long-term sustainability). This tripartite framework acknowledges the inherent complexity of resilience in agricultural production systems, where external shocks—from climate extremes to market volatility—trigger cascading impacts. Consequently, the influencing factors of grain production resilience are inherently multifaceted. The existing literature has studied the influencing factors of grain production resilience from many angles. Jiang et al. (2023) used standard deviation ellipse and geographical detector model to study and found that urbanization rate, scientific and technological factors and price changes of agricultural means of production are the main factors affecting grain production resilience. Zhou et al. (2024) used two-way fixed effect model, IV-2SLS, moderating effect model and other empirical research results to show that agricultural insurance is an important factor affecting grain production resilience. Zeng and Cai (2024) took the panel data of China province as the sample, which showed that the labor transfer would significantly affect grain production resilience. Liu and Qin (2024) paid attention to the policy of high-standard farmland construction, and made an empirical test by using the multi-stage DID method, and finally came to the conclusion that farmland construction has an important impact on grain production resilience. Charatsari et al. (2022) paid attention to the factors of technological innovation in agricultural production, and the research showed that agricultural technological innovation also had an important impact on grain production resilience. Ma et al. (2024) and Zhu and Zhang (2024) respectively paid attention to technical training and agricultural productive services in agricultural activities from the perspective of farmers, and proved their important influence on grain production resilience. Recent research has also explored the role of digital empowerment (Hao and Tan, 2022; Zhang and Yong, 2025; Li et al., 2024a; Zuo et al., 2024) and climatic factors (Fan et al., 2024; Chen et al., 2024; Ma Y. et al., 2025), both of which have been found to significantly influence grain production resilience.

Existing literature provides evidence supporting both positive and negative economic consequences of large-scale agricultural operations. Meanwhile, the influencing factors of grain production resilience are indeed multifaceted. The literature on the economic implications of large-scale agricultural operations exhibits three salient limitations. Firstly, scholarly attention remains disproportionately skewed toward “economic efficiency,” with resilience outcomes largely marginalized, while studies on large-scale agricultural operations predominantly focus on direct operational impacts rather than policy mechanisms or evaluative frameworks. Secondly, although the influencing factors of grain production resilience have been explored across economic, technological, and financial domains, the role of large-scale agricultural operations—particularly its systemic linkages to grain production resilience—remains under-theorized. Land transfer policies exert “systemic impacts” on grain production resilience through large-scale agricultural operations, spanning economic equity, social stability, and ecological sustainability, whereas singular factors like technology adoption or crop insurance yield context-bound effects confined to production or risk management. Crucially, the directional ambiguity—whether large-scale agricultural operations enhances or undermines grain production resilience—persists due to insufficient causal evidence, reflecting a critical knowledge gap. Thirdly, existing policy analyses, while addressing efficiency gains, social trade-offs, and environmental externalities of land transfers, overlook resilience as a latent systemic property. Methodologically, we find that most studies rely on linear regression or descriptive case analyses, lacking robust causal identification. Although quasi-natural experimental designs are pivotal for disentangling causality, their application to large-scale agricultural operations and the influencing factors of grain production resilience remains scarce. To address the existing research gaps, this study leverages China’s land transfer policy as a quasi-natural experiment to rigorously investigate how large-scale agricultural operations shape grain production resilience. The innovations of this work are threefold. Theoretically, it pioneers a systemic analysis framework to capture the multidimensional impacts of agricultural large-scale operations under land transfer policies—spanning economic, social, and ecological interactions—contrasting sharply with reductionist single-factor approaches prevalent in the literature. Methodologically, it employs a staggered difference-in-differences (DID) design combined with mechanism analysis to overcome the causal identification limitations of prior studies reliant on linear models or descriptive case methods. Empirically, focusing on “resilience,” it provides evidence for the persistent scholarly debate on whether large-scale agricultural operations enhances or undermines grain production resilience.

As a critical pathway for modern agricultural transformation, the impact of large-scale agricultural operations on grain production resilience not only finds robust support in the theory of economies of scale and the theory of agricultural technological progress but also receives profound explanation from the theory of increasing returns to land scale. Firstly, analyzed through the lens of “the theory of economies of scale,” large-scale agricultural operations contribute to a substantial reduction in agricultural production costs by expanding the scale of production. This cost reduction is driven by the resource concentration effect and scale effect, which allow agricultural producers to acquire essential inputs—such as land, fertilizers, and pesticides—at lower costs (Sun et al., 2019; Zhang et al., 2024). Additionally, large-scale agricultural operations facilitates specialization and labor division in agricultural production, leading to significant improvements in production efficiency (Wang and Zhu, 2017; Song et al., 2020). The combined effects of cost reduction and efficiency gains strengthen the economic foundation of grain production, enhancing its resilience against external shocks such as natural disasters and market fluctuations. Secondly, analyzed through the lens of “the theory of increasing returns to land scale,” in agricultural production, when the scale of land management is expanded to a certain extent, the efficiency of land output will increase, because agricultural producers can allocate production factors more rationally and realize the optimal combination of land, labor force, capital and other factors. For example, large-scale agricultural machinery can fully play its role in vast land to improve operating efficiency, and large-scale agricultural operations is also convenient to adopt advanced irrigation technology and management mode. They reduce the waste of resources per unit area, increase the output per unit land area and reduce the cost relatively, and further enhance the stability of grain production and the ability to cope with external shocks (Xiaoying et al., 2023). Thirdly analyzed through the lens of “the theory of agricultural technological progress,” large-scale agricultural operations provides a favorable environment for the adoption and diffusion of advanced agricultural technologies. Large-scale production necessitates more efficient and technologically advanced equipment, driving innovation and research in agricultural science and technology (Shen et al., 2017; Xue et al., 2021). The integration of these advancements enhances the modernization and technological capacity of grain production systems, improving their ability to recover from disruptions and increasing overall resilience. Furthermore, technological progress has facilitated the transformation of grain production methods, promoting sustainable, intelligent, and environmentally friendly agricultural practices. These advancements contribute to the long-term stability and adaptability of grain production, ensuring a more resilient and sustainable agricultural sector.

Based on the above analysis, this paper puts forward the research hypothesis H1: Influence of large-scale agricultural operations on grain production resilience is promotion.

Income distribution theory posits that equitable income allocation mechanisms are pivotal for enhancing household consumption capacity and productive investments, serving as a cornerstone for rational socioeconomic resource distribution and profoundly shaping economic behaviors and welfare. Within the context of land transfer policies driving large-scale agricultural operations, scaled farming entities leverage economies of scale and operational efficiency to generate higher economic returns, thereby elevating rural household incomes. This income redistribution operates through dual pathways. On the one hand, guided by production factor theory, scaled operations optimize the allocation and utilization of land, capital, and labor, reducing production costs and amplifying efficiency. The resultant profit expansion enables income streams—such as land rents and wages—to flow back to farmers, augmenting their property and wage incomes, which epitomizes the rationalized mobility and remuneration of production factors (Ao et al., 2021). On the other hand, scaled farming catalyzes agricultural value chain diversification and emerging agribusiness models, creating off-farm employment opportunities and broadening income channels. This reflects the spillover effects of income redistribution across broader economic sectors (Li and Guo, 2022), ultimately fostering inclusive rural development through structural economic transformation.

Rooted in human capital theory, income growth empowers farmers to enhance living standards and invest in education and skill development, fostering human capital accumulation that incentivizes proactive engagement in production management (Wang and Ding, 2011). This human capital upgrading not only elevates production efficiency but also bolsters risk-coping capacities against market volatility and operational uncertainties. Concurrently, rising incomes enable farmers to adopt advanced equipment, high-quality inputs, and improved irrigation infrastructure. Aligned with risk resilience theory, such investments optimize factor allocation, strengthening systemic capacity to withstand climatic shocks and price fluctuations, thereby safeguarding production stability (Girão et al., 1974). Moreover, higher income levels mitigate agricultural labor outmigration by enhancing sectoral attractiveness, ensuring sustained labor inputs to reinforce resilience foundations. Obviously, the improvement of rural residents’ income level can promote grain production resilience.

Based on the above analysis, this paper puts forward the research hypothesis H2: Large-scale agricultural operations exert a positive mediating effect on grain production resilience by enhancing rural residents’ income level.

Social network and organizational theories emphasize the critical role of interactions and cooperation among individuals and organizations within social networks in facilitating resource acquisition, information dissemination, and problem-solving. In the context of large-scale agricultural operations driven by land transfer policies, farmer professional cooperatives—functioning as key nodes within social networks and vital organizational entities—play a pivotal role. While the consolidation of agricultural production factors and market resources under scaled operations enhances efficiency, individual farmers often remain ill-equipped to cope with complex market dynamics and production risks. By organizing scattered individual farmers into cohesive social networks, these cooperatives enable members to share information, techniques, and resources, thereby fostering synergistic collaboration through the mutual reinforcement of strengths. This collective approach not only mitigates systemic vulnerabilities but also amplifies adaptive capacities in response to socioeconomic and environmental uncertainties.

Specifically farmer cooperatives provide members with comprehensive technical training and guidance, disseminating advanced cultivation techniques and management practices to enhance farmers’ technical literacy and production competencies. This institutional support elevates the technology adoption rate and stabilizes crop yields through knowledge-driven agricultural intensification (Dong et al., 2019). In response to abrupt disruptions such as natural disasters, cooperatives mobilize collective disaster response mechanisms, enabling rapid resource coordination for post-disaster recovery and significantly mitigating livelihood vulnerabilities (Zhou and Liu, 2021). Furthermore, cooperatives exhibit comparative advantages in aggregating, analyzing, and operationalizing market intelligence. By establishing real-time information-sharing platforms, they empower members to navigate market dynamics strategically, optimize production portfolios, and hedge against price volatility—effectively decoupling smallholders from systemic market risks that threaten grain production sustainability. Obviously, the improvement of farmers’ professional cooperation level can promote grain production resilience.

Based on the above analysis, this paper puts forward the research hypothesis H3: Large-scale agricultural operations exert a positive mediating effect on grain production resilience by enhancing farmers’ professional cooperation level.

The revised Land Contract Law, enacted in early 2019 in China, strengthened farmers’ land contract management rights by extending contract durations, ensuring the legal right to transfer land, and further refining the regulatory framework for land contract management. Under this revised law, land transfer mechanisms have become more diversified, transfer periods more flexible, and regulatory oversight more standardized. As a result, the policy reform represents a significant measure aimed at promoting the large-scale agricultural operations of rural land in China.

Given this context, this study examines the impact of large-scale agricultural operations on grain production resilience, using the 2019 revision of the Land Contract Law as an exogenous policy event in a quasi-natural experimental design. To assess changes in grain production resilience before and after the policy implementation, the study utilizes panel data from 30 provincial-level administrative regions in China, covering the period from 2007 to 2022. The data sources include: the CSMAR database from Taian, the China Agricultural Management Annual Report, the China Rural Statistical Yearbook, the China Population and Employment Statistical Yearbook, and the Chinese government network.

The descriptive statistics for the key variables in the model are presented in Table 3. The mean value of grain production resilience is approximately 0.429, with a standard deviation of 0.081. The difference between the maximum (0.625) and minimum (0.229) values is 0.396, indicating substantial variation in grain production resilience levels across the sample.

The mean value of large-scale agricultural operations, as measured through policy exposure under the land transfer policy, is 0.117, suggesting that 11.7% of the sample regions were significantly affected by the policy intervention.

Furthermore, the control variables, mechanism variables, and moderator variables exhibit considerable variation, reflecting heterogeneous economic, financial, and labor conditions across different regions. These variations provide a robust data foundation for the subsequent empirical analysis.

The baseline regression results examining the impact of land transfer policy on grain production resilience are presented in Table 4.

The declining coefficients from 0.058 to 0.015 demonstrate the effectiveness of omitted variable correction in model refinement. Year fixed effects absorbed approximately 37% of the interference from macroeconomic fluctuations and climatic variability, indicating substantial systemic uncertainties embedded in economic conditions and climate patterns. Provincial fixed effects further accounted for 63% of the heterogeneity attributable to geographical endowments, aligning with China’s pronounced regional disparities in agroecological characteristics. Across Models (2)–(4), both fiscal support and industrial development exhibited statistically significant impacts on the dependent variable (p < 0.05). While economic development level showed significance prior to controlling provincial and year fixed effects, its influence became statistically indistinguishable after their inclusion. Notably, agricultural producer price levels at the micro-scale emerged as significant predictors post-adjustment, confirming the appropriateness of control variable selection. Throughout Models (1)–(4), although the economic effect magnitudes attenuated compared to initial estimates, they remained statistically significant at the 1% level (β = 0.122–0.187). It can be seen that large-scale agricultural operations may indeed have resource concentration effect and scale effect, allocate production factors more reasonably, or support the grain production resilience through mechanization and technology diffusion. This result confirms hypothesis H1.

The parallel trend assumption is a fundamental prerequisite for the DID model, ensuring that, prior to the implementation of the land transfer policy, the trends in grain production resilience were similar between the treated group and the comparison group. This validates the comparison group as an appropriate counterfactual for estimating the causal effect of large-scale agricultural operations.

To test this assumption, an event study approach is employed. Specifically, the policy implementation variable (Treat_i,t × Time_t) is replaced with year-specific dummy variables for the 5 years preceding the policy implementation, the year of implementation, and the 3 years following the policy.

The results, presented in Figure 1, indicate that:

To ensure that the observed effects of the land transfer policy on grain production resilience are not driven by random factors or unobserved shocks, a placebo test was conducted. This test assesses whether the estimated policy effect is due to chance rather than a true causal relationship.

The placebo test was implemented by randomly resampling the data 500 times to generate a pseudo-policy dummy variable, which serves as a fictitious treatment assignment unrelated to actual policy implementation. The regression model was then re-estimated using this pseudo-policy variable, and the distribution of the estimated coefficients and p-values was analyzed.

The results, presented in Figure 2, show that:

These findings confirm that the observed impact of large-scale agricultural operations on grain production resilience is not driven by random factors. Therefore, the results of the study remain robust and reliable.

To address potential endogeneity issues in the model—such as reverse causality, measurement errors, and omitted variable bias, a series of robustness tests were conducted based on established methodologies.

In addition to the parallel trend test, placebo test, and endogeneity test, additional robustness checks are conducted to ensure the reliability of the research findings. The following methods are applied.

To further explore the mechanisms through which large-scale agricultural operations affects grain production resilience, this analysis focuses on two key factors: farmers’ professional cooperation level and rural residents’ income level. The mediation effect test method proposed by Chen J. et al. (2023) is applied to assess these relationships.

The results presented in Table 8 (columns 1 & 3) confirm this mediation effect. The relationship between large-scale agricultural operations and farmers’ professional cooperation, as well as the relationship between professional cooperation and grain production resilience, is statistically significant. The findings indicate that large-scale agricultural operations strengthens resilience by increasing professional cooperation among farmers. Obviously, the enhanced level of farmer professional cooperation facilitates intensive resource sharing and technology exchange, which accelerates organizational integration in grain production systems. This institutional consolidation generates dual mechanisms of economies of scale and synergistic effects, leading to a progressive reduction in production costs and enhanced market competitiveness—both critical drivers of production resilience. Concurrently, elevated cooperation provides structural support through market intelligence platforms and financial service networks, empowering producers to mitigate compound risks arising from price volatility and climatic shocks, grain production resilience is improved. The hypothesis H2 is verified here.

The results, as shown in Table 8 (columns 2 & 4), confirm that large-scale agricultural operations positively influences rural residents’ income, which in turn contributes to greater grain production resilience. Obviously, the increase of income will enhance consumers’ consumption ability and willingness. For agricultural producers, this means that they have more funds to improve production conditions, introduce new technologies and improve food quality, which not only helps to improve the stability and output of food production, but also enhances agricultural producers’ resistance to natural disasters and market fluctuations, thus enhancing the resilience of food production. The hypothesis H3 is verified here.

To further examine whether certain key factors influence the relationship between large-scale agricultural operations and grain production resilience, this study considers the effects of labor outflow and the number of rural financial institutions as moderating variables.

Labor outflow affects grain production resilience by reducing the availability of agricultural labor, which may limit the full advantages of large-scale agricultural operations. As rural workers migrate to urban areas in pursuit of higher incomes, their reduced engagement in agricultural activities can diminish the incentives for farmers to invest in agricultural technology and capital improvements. This shift constrains the ability of large-scale agricultural operations to enhance grain production resilience. Moreover, although migrating farmers may transfer their land to other farmers or agricultural enterprises, this process is often uneven and inefficient, leading to issues related to land quality variability and suboptimal resource allocation. These challenges can further weaken the resilience of grain production systems. These challenges are structurally exacerbated within China’s unique institutional and socioeconomic contexts. Firstly, the household registration system (hukou) and entrenched urban–rural dualism have shaped a distinct pattern of labor migration—characterized as “physically detached from rural livelihoods while retaining land entitlements.” This institutional hybridity creates path dependencies where migrant workers maintain contracted farmland rights despite urban employment, substantially constraining the effectiveness of land transfer policies and the realization of scale economies in agricultural operations. Secondly, the unbalanced development of economic space in China leads to more migrant workers being young workers. The outflow of labor accelerates the aging of agricultural groups, and the adoption rate of smart devices by elderly farmers is low, which weakens the technical efficiency potential of large-scale operations.

The results presented in Table 9, Column (1), indicate that labor outflow has a significantly negative effect on grain production resilience. Additionally, the interaction term between labor outflow and large-scale agricultural operations is also negative and statistically significant, suggesting that labor migration not only directly weakens resilience but also reduces the effectiveness of large-scale agricultural operations in enhancing grain production resilience.

While increased access to rural financial institutions is expected to strengthen grain production resilience by providing farmers with easier access to credit and financial support, its interaction with large-scale agricultural operations introduces complexities that may lead to unintended negative consequences. The expansion of rural financial institutions improves the accessibility and convenience of financial services, thereby supporting investment in agricultural activities. However, this benefit may not be evenly distributed across all farmers. Larger farms or agricultural enterprises often find it easier to secure financial support, while small-scale farmers may continue to face difficulties in obtaining loans due to high borrowing costs or collateral requirements. As a result, the potential benefits of financial access are unevenly distributed, weakening the overall improvement in grain production resilience. At present, there is still structural exclusion in the rural financial system in China: the average interest rate of loans from leading agricultural enterprises is significantly lower than that of small farmers, and large-scale entities obtain low-cost funds by mortgage of land management rights, while it is normal for small farmers to face financing constraints. Furthermore, financial resources from rural institutions are not always used effectively to enhance productivity or resilience. In some cases, funds may be diverted to non-agricultural investments or used to expand production scale rather than improve efficiency, which fails to enhance grain production resilience. At present, due to the high risk of agricultural production, the limited profitability of agricultural real economy and the relative lag of financial supervision, rural financial institutions and policy financial institutions in China have a tendency of “deviating from reality to emptiness” at the same time, showing a trend that financial resources flow more to non-agricultural fields. Additionally, as large-scale agricultural operations develops, the cost of production may rise, and the cost of financial services may increase, further exacerbating financial pressures on agricultural producers. These rising costs can counteract some of the benefits of financial access and lead to a net negative moderating effect. At present, the high coverage of digital finance in China and the weak use ability of elderly farmers aggravate the dilemma of the cost of financial services and agricultural production costs.

The results shown in Table 9, Column (2), suggest that while an increase in rural financial institutions positively influences grain production resilience, the interaction term between financial institutions and large-scale agricultural operations is negative and statistically significant. This finding indicates that although financial institutions independently contribute to resilience, their presence weakens the positive impact of large-scale agricultural operations on grain production resilience.

Empirical evidence confirms that large-scale agricultural operations significantly enhance grain production resilience. The government can deepen the reform of land transfer policy according to the actual situation and build an efficient and intensive agricultural management system. The specific measures are: focusing on land transfer, giving priority to the establishment of a trinity reform framework of “clear property rights-active market-institutional guarantee.” Focus on improving three aspects: Firstly, strengthen the market mechanism, develop flexible modes such as cross-season circulation and contract management, establish a national land transfer trading platform, realize the digitalization of ownership registration, price evaluation and contract management through blockchain technology, and reduce transaction costs and default risks; Secondly, improve legal protection, clarify the property right of land management right, set up a special dispute arbitration institution, explore the mortgage financing system of management right, and enhance long-term investment confidence; The third is to optimize policy incentives, give targeted support such as tax relief and infrastructure facilities to the main body of continuous circulation for many years, and establish a linkage mechanism between transfer scale and ecological compensation to promote sustainable intensification. This kind of reform is especially suitable for areas with serious land fragmentation, such as Southeast Asia and Africa, and can release land integration dividends on a large scale.

Empirical evidence confirms that farmers’ cooperatives play an important mediating role in the process of large-scale agricultural operations promoting grain production resilience. The government can formulate relevant policies to strengthen the construction of farmers’ cooperative organizations and activate the socialized service network. The specific measures are: promoting the transformation of cooperatives from “quantity growth” to “function upgrading,” focusing on cultivating three types of capabilities: production and service capabilities (providing agricultural machinery sharing and popularizing smart agricultural technology), market docking capabilities (building a direct power supplier platform and developing order agriculture) and risk resistance capabilities (jointly insured with climate index insurance). Establish a grading certification system for cooperatives, and give priority to opening up resources such as government procurement and scientific research projects to high-level organizations. At the same time, cooperatives and scientific research institutions are encouraged to build a “technology diffusion center” to promote resilient technologies such as water-saving irrigation and stress-resistant varieties.

Empirical evidence confirms that farmers’ income play an important mediating role in the process of large-scale agricultural operations promoting grain production resilience. The government can design diversified income-increasing paths to enhance farmers’ resilience against risks. The specific measures are: implementing the two-wheel drive strategy of “increasing efficiency in the main business and generating income by sideline business.” In the agricultural field, we should promote the cooperative mode of “scale management subject+small farmers” and share intensive benefits through guaranteed purchase and profit sharing; in the non-agricultural field, develop county labor-intensive industries and establish an integrated employment support system of “skills training-job docking-social security connection.” In addition, explore new income-increasing channels such as “agricultural carbon sink trading” and “realization of ecological product value,” and include the improvement of grain production resilience in the scope of green financial support.

Spatially differentiated policies are essential due to heterogeneous impacts across agroecological zones. The government should formulate the policy of spatial adaptation and break the regional resource constraints. Specific measures should be based on agricultural ecological zoning and agricultural conditions to carry out differentiated support: In production-marketing balance areas, increase subsidies and the construction of farmland water conservancy facilities; In main grain-producing areas, guide the circulation of cultivated land and promote green agriculture, stimulate stable production through horizontal financial transfer payment; in main grain-selling areas, optimize the industrial structure, strengthen land management, and establish a “cultivated land protection compensation fund”; In the plain areas with contiguous land (such as North America and Eastern Europe), we should focus on developing fully mechanized super-large-scale farms; In hilly and mountainous areas with broken terrain (such as South Asia and Latin America), “service scale” (such as UAV plant protection and hosting services) should be promoted. By implementing region-specific policies, agricultural development can be better aligned with local environmental, economic, and structural conditions, ensuring targeted and effective resilience-enhancing interventions.

Based on the empirical results that the number of rural financial institutions and the outflow of labor force play a moderating role in the process of large-scale agricultural operation affecting the resilience of grain production, governments should optimize the factor flow system and ease the labor and financial constraints. It is a feasible scheme to implement the combination policy of “labor force return+financial innovation”: on the one hand, pilot the “new professional farmers’ housing project” in the main grain producing areas, provide targeted benefits such as housing subsidies and children’s education, and attract young people to return home; On the other hand, we will develop financial instruments such as “special loans for scale operation” and “climate resilience insurance,” and use satellite remote sensing and big data to build a credit evaluation model for business entities to lower the financing threshold. In international cooperation, we can learn from the rural development fund model of EU Common Agricultural Policy (CAP), and bind financial support with the adoption of green agricultural technology.