Mitigating climate disaster vulnerability in grain production through high-standard farmland construction: impact mechanisms and spatial spillover effects

Abstract

High-standard farmland construction (HSFC) is a critical initiative for mitigating climate disaster risks of grain production and ensuring national food security. Using panel data for 30 Chinese provinces from 2003 to 2020, this study measures the climate disaster vulnerability of grain production (CDVG) with an improved vulnerability assessment framework, and systematically examines the impact mechanisms and spatial spillover effects of HSFC on CDVG. The results reveal that: (1) Temporally, the evolution of CDVG is characterized by an initial rapid decline followed by slow, fluctuating decreases. Spatially, it exhibits pronounced regional disparities and spatial clustering, with the eastern coastal areas, central major grain-producing regions, and Xinjiang displaying lower vulnerability than other regions. (2) Baseline regression results confirm that HSFC significantly reduces CDVG, and this conclusion remains robust after a series of robustness tests. (3) Mechanism tests demonstrate that HSFC reduces CDVG through ecological regulation effects, infrastructure guarantee effects, and technological support effects. (4) Spatial econometric analysis reveals that HSFC exerts a significant spatial spillover effect on CDVG. It not only mitigates local vulnerability but also reduces vulnerability in neighboring regions. (5) Heterogeneity analysis reveals that vulnerability-reducing effect of HSFC is more pronounced in provinces with medium-to-low vulnerability quantiles, in major grain-producing areas, and in regions with relatively flat terrain. Based on these findings, this study recommends optimizing the spatial layout of HSFC, improving supporting systems, and tailoring strategies to local conditions to better mitigate climate disaster risks and ensure national food security.

1 Introduction

Against the backdrop of increasing frequency and intensity of global extreme climate events, the climate vulnerability of grain production has become increasingly prominent. Climate anomalies have led to increasingly frequent extreme weather events such as heatwaves, floods, droughts, typhoons, and cold snaps, which severely disrupt ecosystem balance (Li et al., 2025) and threaten the stability and high-quality development of grain production (Lu et al., 2019; Xiang et al., 2024). In its 2023 report, the Food and Agriculture Organization (FAO) noted that over the past two decades, the annual number of disaster events worldwide has risen from roughly 100 in the 1970s to around 400 today. These disasters have inflicted crop and livestock losses totaling approximately USD 3.8 trillion, with losses in the agricultural sectors of low- and middle-income countries alone accounting for about 15 percent of their total agricultural output value (FAO, 2023). Consequently, scientifically assessing the vulnerability of grain systems to climate disasters, and exploring and formulating effective strategies to mitigate climate disaster risks, have become urgent priorities in the fields of climate change economics and agricultural risk management.

The concept of vulnerability originated in the 1970s within geographic studies on natural disasters and was subsequently extended to various fields, including ecosystem vulnerability (Rao et al., 2019), livelihood vulnerability (Mamun et al., 2023), and farmland system vulnerability (Niu et al., 2022), gradually evolving into a cross-disciplinary, systematic, and independent research framework. Climate disaster vulnerability in grain production (CDVG) is a vital branch of vulnerability research, aiming to reflect the extent to which grain production systems are susceptible to climate disasters or lack adequate coping capacity. It comprises three dimensions, including exposure, sensitivity, and adaptive capacity. The Chinese government has consistently placed high priority on mitigating climate risks in grain production. The 2021 No.1 Central Document explicitly called for accelerating the implementation of the “storing grain in the land and storing grain in technology” strategy, enhancing agricultural disaster prevention and mitigation capabilities to reduce output volatility and vulnerability. As a key initiative of the “storing grain in the land” strategy, the high-standard farmland construction (HSFC) policy aims to build a modern farmland system characterized by “drought- and flood-resilient, high-efficiency output, and ecological sustainability” through engineering measures such as land consolidation, water conservancy facilities, soil improvement, and farmland shelterbelts. The advancement of this policy is therefore crucial for mitigating CDVG and safeguarding national food security. As of the end of 2024, China has cumulatively developed 1 billion mu of high-standard farmland, accounting for about 50% of the country’s total cultivated land area. By 2030, an additional 1.2 billion mu of high-standard farmland is planned to be established. Against the backdrop of vigorously promoting HSFC, exploring its impact on CDVG and its underlying mechanisms will not only help clarify policy implementation effectiveness but also provide references for standardizing HSFC and mitigating grain climate disaster risks.

Two strands of research are pertinent to this study: one on CDVG and the other on HSFC. Research directly focusing on CDVG remains relatively limited. Current studies primarily address broader agricultural climate vulnerability, with scholars conducting research mainly from perspectives such as comprehensive assessment of agricultural climate vulnerability and its influencing factors. For the comprehensive assessment of agricultural climate vulnerability, studies primarily employ three approaches: first, by using crop growth models to simulate yield responses to climate change, thus characterizing agricultural climate vulnerability from a biophysical standpoint (Li et al., 2015; Wang et al., 2020); second, by constructing a multidimensional evaluation framework based on both natural and socio-economic dimensions (Ahmadalipour and Moradkhani, 2018); Third, by constructing a vulnerability index for measurement (Li et al., 2022). Regarding the influencing factors of agricultural climate vulnerability, research indicates that urbanization, and the loss and aging of agricultural labor significantly exacerbate the agricultural climate vulnerability. In contrast, agricultural inputs such as fertilizers, pesticides, and agricultural machinery can effectively mitigate agricultural climate vulnerability (Wang et al., 2024). Some studies also indirectly suggest that agricultural irrigation infrastructure (Wang et al., 2024), agricultural insurance (Zeng et al., 2025), and innovative management strategies (Zhang et al., 2023) play significant roles in mitigating agricultural climate risks. Overall, due to the difficulties in establishing evaluation criteria, there is no consensus in academia on assessment methods for agricultural climate vulnerability. Furthermore, most existing research remains at the stage of identifying influencing factors, lacking a systematic theoretical analysis of their underlying mechanisms and transmission pathways.

Research on the relationship between HSFC and CDVG remains at an early stage, and scholars have mainly assessed the policy effects of HSFC from the perspectives of grain yield increase (Hu and Dai, 2022), enhancement of agricultural productivity (Sun et al., 2024), reduction of agricultural carbon emissions (Chen and Wang, 2023), transformation of crop structure (Song et al., 2023), and farmland transfer (Qian et al., 2023). Peng et al. (2024a, 2024b) examined the impact and mechanisms of HSFC on farmland disaster reduction, while Gao and Qin (2024) explored its role in mitigating grain production risks. However, the above studies either focus solely on the extent of agricultural losses or merely on fluctuations in grain yield. They fail to systematically integrate the exposure, internal stability, and adaptive capacity of the grain system to climate disasters, making it difficult to comprehensively assess the role of HSFC in mitigating the climatic disasters risk to grain production.

Existing studies provide important theoretical basis for this paper, but there remains room for further exploration: First, studies on the measurement and spatiotemporal evolution of of China’s grain systems’ vulnerability to climate disasters remain limited. Current research primarily focuses on specific regions or provinces (Li et al., 2023), and predominantly employs composite indicator methods for measurement (Ahmadalipour and Moradkhani, 2018). While this approach effectively captures the richness and multidimensionality of vulnerability, it has limitations, such as subjectivity in indicator selection, high data requirements, and challenges in making cross-regional comparisons; Second, existing research has extensively examined the negative impact of extreme climate on grain production, but there has been relatively little discussion of measures to mitigate extreme climate risks. In particular, research on the role and mechanisms of HSFC, a major agricultural policy, in mitigating CDVG remains limited. Ignoring this not only hinders a comprehensive assessment of the policy effects of HSFC, but also limits further research and practical innovation in climate risk mitigation measures; Moreover, due to factors such as the spatial proximity of farmland and the mobility of production factors, the HSFC in this region may have a spatial spillover effect on grain production in neighboring regions, and this potential effect has not yet been fully examined.

Considering the preceding discussion, this study makes several potential contributions: First, by employing an improved vulnerability measurement framework, this study assesses CDVG across 30 provinces in China, and conduct an in-depth analysis of its spatiotemporal evolution and regional differences. This framework not only effectively avoids the subjectivity in indicator selection but also comprehensively captures both the impacts of climate disasters on grain production and the system’s inherent recovery capacity, thereby providing a more objective and comparable assessment. Second, this study incorporates HSFC and CDVG into a unified theoretical framework, empirically tests their relationship, and further confirms that HSFC primarily reduces CDVG through ecological regulation, infrastructure guarantee, and technical support effects; Third, by introducing spatial factors, this study constructs a spatial econometric model to empirically examine the spatial spillover effects of HSFC on the CDVG, further enriching the existing body of research. This study therefore aims to systematically reveal the spatiotemporal evolution and regional disparities of CDVG across China, to empirically assess the mitigating effect of HSFC on CDVG and its underlying mechanisms, and further identify the possible spatial spillover effects and regional variations in the policy’s impact. Research on the above objectives can assist policymakers in various countries in exploring feasible and effective land consolidation measures to mitigate climate disaster risks in grain production and ensure national food security.

2 Theoretical anlysis framework

This section first theoretically defines the concept of CDVG from three dimensions—exposure, sensitivity, and adaptive capacity—and elaborates on the interactive relationships among these three components. Subsequently, based on the conceptual framework of CDVG and HSFC measures, we conduct a theoretical analysis of the mechanisms by which HSFC influences CDVG. Specifically, HSFC involves seven key measures: “farmland, soil, water, roads, forests, electricity, and technology.” Building upon engineering projects such as field consolidation, irrigation and drainage systems, field roads, and farmland power grids, it strengthens soil improvement, farmland ecological conservation, and technology dissemination. This study posits that HSFC mitigates CDVG through ecological regulation effects, infrastructure guarantee effects, and technological support effects. Furthermore, given the spatial proximity of farmland and the mobility of production factors, HSFC can also reduce CDVG in neighboring areas through spatial spillover effects. The theoretical framework is illustrated in Figure 1.

Figure 1

2.1 The concept of CDVG

As research on vulnerability deepens, vulnerability has evolved from a broad concept into a systematic and independent conceptual framework, achieving broad consensus across various research fields. Drawing from multiple disciplinary definitions, vulnerability, in a broad sense, refers to “a state in which a system is prone to damage and may evolve in an unsustainable direction due to its sensitivity to internal and external disturbances and lack of coping capacity, emphasizing the interaction between the system’s internal characteristics and external disturbances” (Dong et al., 2018). In the context of grain production, CDVG can be defined as “when grain production systems are exposed to climate disasters, the tendency for systems to be vulnerable to damage or threats due to the sensitivity of structure and functions and lack of adaptive capacity”. The IPCC’s Fifth Assessment Report quantifies vulnerability as a function of exposure, sensitivity, and adaptive capacity (Klein et al., 2014). Exposure reflects the extent to which grain production system is subjected to climate disaster impacts, which is related to both the system itself and external climate shocks. The higher the exposure, the higher the vulnerability; Sensitivity is the attribute of grain system to quickly perceive climate shocks, depending on the stability of the system’s inherent characteristics and structural attributes. The higher the sensitivity, the more easily the system’s stability is to change, and the higher the vulnerability (Fang et al., 2024). Adaptive capacity refers to the ability and potential of grain systems to take effective measures to respond to risks and recover from shocks, with particular emphasis on external interventions (Li et al., 2023; Klein et al., 2014). The weaker the adaptive capacity, the higher the vulnerability.

Exposure, sensitivity, and adaptive capacity are not independent, but rather exist in a complex interdependent relationship (Fang et al., 2024). First, exposure affects both sensitivity and adaptive capacity. When exposure exceeds the system’s threshold of tolerance, it can damage the internal structure and functions of the grain system, undermining its defence and recovery mechanisms; Second, the sensitivity of grain systems reflects the internal natural attributes and structural stability of the system. Once the internal structure of the system becomes unstable, it will amplify the intensity of external shocks and exacerbate the system’s exposure. Under the dual effects of high exposure and high sensitivity, the system’s adaptive capacity will be severely constrained, limiting its ability to regulate effectively. Adaptive capacity emphasizes the ability or potential of a system to intervene, adjust, and transform to recover from damage caused by external shocks. By implementing effective adaptation measures, it is possible to repair the inherent natural attributes of a system and optimise its structural functions, thereby enhancing its stability.

2.2 The mechanism through which HSFC affects the CDVG

2.2.1 Ecological regulation effects

Natural factors such as extreme climate shocks and imbalanced surface runoff, combined with human factors like long-term unsustainable farming practices, and the excessive use of fertilizers and pesticides, have jointly contributed to a series of agricultural environmental issues, including water and soil pollution, soil degradation, and biodiversity loss (Li et al., 2024). These problems undermine the internal stability of grain systems, increasing their sensitivity to climate change. HSFC aims to improve the ecological conditions of farmland and enhance its capacity for ecological regulation through a series of measures. Specifically, it effectively addresses soil degradation issues such as sandy, excessively clayey, and acidified soils through engineering, biological, and chemical interventions. Meanwhile, HSFC expands plot sizes through field consolidation and land leveling, thereby easing constraints on green mechanised production. This facilitates the adoption of environmentally friendly farming practices by farmers and improves the soil ecological conditions. High-quality soil can increase vegetation coverage, maintain the dynamic balance of soil moisture and nutrients under extreme climatic conditions, and reduce the risk of pest outbreaks (Das et al., 2022), thus reducing the climate exposure and sensitivity of grain production. In addition, HSFC improves the ecological regulation functions of farmland through measures such as shelterbelt construction and slope protection, which help improve microclimate, prevent wind erosion and sand fixation, and conserve soil and water (He et al., 2020). These measures effectively reduce the direct damage caused by extreme weather events such as high temperatures, wind erosion and sandstorms to crops, while also reducing the severity of droughts, floods, and their associated secondary disasters, thereby reducing the CDVG (Ji et al., 2023).

2.2.2 Infrastructure guarantee effects

Well-developed farmland infrastructure is fundamental to ensuring the stable and sustainable development of grain production systems. However, China’s agricultural water conservancy infrastructure faces several notable deficiencies, including low irrigation efficiency, aging equipment, and regional imbalances. These issues not only hinder the optimal allocation and efficient use of water resources, but also intensify climate disasters such as droughts and floods. As a result, grain production becomes increasingly dependent on climatic conditions, exhibiting a pronounced “weather-dependent” pattern and heightened climate sensitivity. HSFC establishes a comprehensive irrigation and drainage system—from water sources to farmland—through the construction of field-level water conservancy facilities. This facilitates the precise management and efficient use of farmland water resources, effectively regulating water balance of farmland under extreme climatic conditions (Wang et al., 2024; Xie and Xue, 2024), thereby reducing the CDVG. In addition, HSFC also improves farmland accessibility and the conditions for mechanical operations through land levelling, improving farm production roads, and upgrading farmland power systems. These measures can effectively reduce the time and economic costs of agricultural machinery operations and agricultural input transportation, providing a fundamental guarantee for grain system to respond promptly to extreme climate change. Specifically, they enable the rapid allocation and deployment of materials and protective measures during the pre-disaster prevention stage, ensure the efficient operation of mechanised harvesting, emergency drainage, and other disaster mitigation efforts during the disaster response stage, and facilitate the smooth progress of farmland restoration and reproduction during the post-disaster recovery stage (Watanabe et al., 2018), thereby enhancing the climate adaptability of grain system and reducing the CDVG.

2.2.3 Technical support effects

The climate adaptation capacity of grain production primarily refers to the human ability or potential to take effective measures in response to climate shocks. The innovation and application of agricultural production technologies serve as the core driving force behind the modernisation of agricultural production, providing important support for mitigating the CDVG. HSFC facilitates the simultaneous expansion of plot size and land scale, thereby deepening the division of labour and advancing technological progress in grain production. On the one hand, by consolidating scattered and fragmented plots into contiguous and large-scale farmland, HSFC encourages farmers to shift from decentralised, diversified planting models to specialized, uniform planting models, forming a multi-regional and multi-centered specialized production pattern (Zhang et al., 2023), thereby promoting the deepening of horizontal division of labour in agriculture. Horizontal specialization in agricultural production facilitates the diffusion of technologies and the integration and sharing of production resources, thereby generating external economies of scale (Yin et al., 2024). This process helps to lower the threshold and costs of technology adoption, creating favorable conditions for the widespread application of mechanised operations as well as climate-adaptive technologies such as water-saving irrigation and integrated pest management. On the other hand, land levelling and expansion of plot scale help alleviate constraints on large-scale mechanised operations, reduce agricultural machinery service costs, and promote the scaling-up of grain production services, thereby deepening vertical division of labour (Sun et al., 2024). In the traditional smallholder farming model, farmers are dispersed and resource-constrained, which limit their ability to effectively respond to climate risks and result in low adaptive capacity. Agricultural service organizations are typically equipped with a large number of specialized and environmentally friendly machinery, characterized by their technology-intensive nature, and serve as both carriers and transmitters of advanced production technologies (Zhang et al., 2023). By outsourcing part or all of the production processes, service organizations provide farmers with with technical services such as machinery leasing, standardized crop protection, integrated pest management, and intelligent monitoring, thereby enhancing the level of agricultural production technology (Han et al., 2024). The improvement in technological capabilities can significantly enhance the climate adaptation capacity of grain production and reduce CDVG.

2.3 Spatial spillover effects of HSFC on CDVG

Grain production exhibits significant spatial interdependence (Zhou and Wen, 2024), implying that the impact of HSFC on the CDVG may not be limited to the implementation area itself, but may also generate spillover effects on surrounding regions through multiple channels. First, HSFC enhances the ecological regulation capacity of farmland through ecological protection projects. Given the inherently connected and fluid nature of ecological environments, improvements in one region may positively influence neighboring areas via groundwater flow, carbon cycling, and microclimate regulation, thereby helping to mitigate climate risks in adjacent farmland (Peng et al., 2024a). Second, HSFC promotes the technological progress of agricultural production through measures such as enlarging plot size, optimizing layout, and upgrading infrastructure. These advanced agricultural technologies may diffuse to neighboring regions via information sharing and technology extension, resulting in technological spillover effects (Wang et al., 2025) that help reduce CDVG in surrounding areas. Third, there is a policy imitation effect between governments (Wang et al., 2025). As a “green box” agricultural subsidy policy, HSFC has become a priority in government agendas at all levels (Sun et al., 2024). Regions that implement HSFC early often develop successful models, which provide replicable experiences for neighboring areas. This reduces the trial-and-error costs of policy experimentation in neighboring regions, thereby enhancing their construction efficiency and quality and ultimately contributing to lower CDVG.

This study proposes the following hypotheses:

H1: HSFC significantly mitigates CDVG.

H2: HSFC mitigates CDVG through ecological regulation effects, infrastructure guarantee effects, and technological support effects.

H3: HSFC not only reduces CDVG in the local region but also mitigates CDVG in neighboring provinces through spatial spillover effects.

3 Research design

3.1 Variable description

3.1.1 Measurement framework for CDVG

According to the IPCC’s Fifth Assessment Report, vulnerability is defined as a positive function of exposure and sensitivity, and a negative function of adaptive capacity. We use the following formula to measure the CDVG:

Exposure reflects the intensity of climate disasters faced by the grain system. We use the ratio of affected area to cultivated area as a proxy variable for exposure. The larger the ratio, the higher the exposure. The calculation is given by Equation 2:

Following Li et al. (2022) and Dong et al. (2018), we decompose actual grain yield into trend yield and climate yield, which serve as proxy variables for adaptive capacity and sensitivity, respectively.

It is generally accepted that factors affecting grain yield can be categorized into socioeconomic factors and natural factors. The evolution of grain yield over time can be decomposed into two main components (Nguyen-Huy et al., 2018; Li et al., 2025):

• (1) Long-term trend. This component reflects the long-term, stable increase in yield driven by socioeconomic factors such as technological progress, policy interventions, and agricultural management practices. The influence of these factors typically shows a relatively smooth long-term evolutionary trend, rather than abrupt short-term fluctuations, and is defined as “trend yield.”

• (2) Short-term fluctuations. Agricultural production is an economic activity with prominent natural attributes. Among natural elements, soil, topography, and hydrology are relatively stable, whereas meteorological disasters exhibit significant interannual variability and frequently trigger sharp short-term fluctuations in grain yield, causing deviations from its long-term trend. Therefore, short-term fluctuations in grain yield are widely regarded in existing literature and practice as being primarily driven by meteorological conditions and related disasters, referred to as “climate yield.” In view of this, actual grain yield can be decomposed as follows:

In Equation 3, represents the actual grain yield in year i; represents the climate yield; represents the trend yield. represents the impact of other random factors on yield. In general, due to their high degree of randomness and the absence of a deterministic functional form for reliable quantification, this component is typically treated as negligible.

Adaptive capacity refers to the ability and potential of grain systems to take effective measures to respond to risks and recover from shocks, which is related to the level of grain production technology and management practices. This study employs trend yield as a proxy variable for adaptive capacity. Trend yield is positively correlated with adaptive capacity. The calculation is given by Equation 4:

Sensitivity is the attribute of the grain system to rapidly perceive climatic disaster shock, which reflects the stability of the system’s structure. Climate yield reflects the impact of climate change on grain production, including both favorable and unfavourable effects, and is the primary factor contributing to yield fluctuations. This study uses climate yield as a proxy variable for sensitivity. Sensitivity is negatively correlated with climate yield. The calculation is given by Equation 5:

It is crucial to use scientific methods to determine the trend yield from actual grain yield and then extract the climate yield. Common methods include the linear moving average, HP filter, and Logistic function fitting method. We use a five-year centre moving average model (CMA) to separate climate yield. The CMA model effectively eliminates short-term fluctuations and captures long-term trends through symmetric smoothing. It is widely used in studies on climate yield separation due to its wide applicability and strong robustness (Yuan and Yamagata, 2015; Li et al., 2020). Given the potential discrepancies resulting from different methods, we further employ the HP filter (Hodrick et al., 1997) method to separate climate yield in subsequent robustness tests, re-measuring the CDVG to enhance the reliability of the results. The formula for the CMA model is shown in Equation 6:

In Equation 6, is the moving average of the yield for year i, which represents the trend yield for that year. Subsequently, the climate yield for year i is derived using Equation 7.

Considering that exposure, sensitivity, and adaptive capacity have different orders of magnitude, all three indicators are first standardised, and then calculate the CDVG based on Equation 1.

3.1.2 Core explanatory variable

Drawing on existing literature (Chen et al., 2023; Liang et al., 2024; Qian et al., 2023), this study employs the ratio of land consolidation area to cultivated land area as the core explanatory variable for HSFC. The land consolidation area is the sum of medium- and low-yield farmland improvement area and HSFC area. Furthermore, land consolidation projects serve as key initiatives in comprehensive agricultural development, primarily focused on medium- and low-yield farmland improvement and integrated ecological management. The progress and intensity of HSFC are directly determined by investments in land consolidation under comprehensive agricultural development, which reasonably reflect the scale and quality of HSFC. Therefore, in robustness checks, this study uses investment per unit area in comprehensive agricultural development as a substitute variable for the core explanatory variable.

3.1.3 Control variables

To minimise the omission of variables, considering data availability and referencing relevant literature (Hu and Dai, 2022; Sun et al., 2024; Xie and Xue, 2024), the following control variables were selected from aspects such as agricultural production conditions, macroeconomic development, climate change: grain sown area, rural population aging, urbanization rate, urban–rural income gap, proportion of primary industry, industrial sructure upgrading, average annual temperature, average annual precipitation.

3.1.4 Mechanism variables

To better understand how HSFC mitigates CDVG, we introduce three mechanism variables from the perspectives of ecological regulation, infrastructure guarantee, and technical support, measured by vegetation coverage, effective irrigated area, and agricultural mechanization level, respectively. Although each mechanism may involve multiple factors, we select a single representative variable for each mechanism based on data availability and theoretical relevance. Ecological regulation refers to the ability of farmland ecosystems to buffer and regulate external disturbances through their inherent structures and functions. As a vital component of ecosystems, vegetation plays a central role in soil and water conservation, thermal regulation, and microclimate improvement. Thus, vegetation coverage is a key indicator for assessing the ecological regulation capacity of farmland (Ji et al., 2023). HSFC enhances vegetation coverage through shelterbelt construction and ecological improvement projects, thereby reducing the system’s exposure to extreme climate shocks; Infrastructure guarantee reflects the basic engineering conditions of farmland to resist climate risks, particularly in managing water resources during extreme events. Irrigation systems are among the most critical infrastructure components, directly affecting climate adaptability of grain production; Agricultural mechanization level reflects the extent to which technological inputs are embedded in agricultural production and represents the fundamental productive capacity. HSFC improves the mechanical operation environment and enhanced mechanical operation efficiency, thereby significantly reduces the CDVG. Table 1 reports the descriptive statistics of the variables.

3.2 Study area and data sources

Considering data availability, this study employs panel data for 30 provincial-level administrative regions in mainland China from 2003 to 2020 (excluding Tibet, Hong Kong, Macao, and Taiwan). In the subsequent analysis, these 30 provinces are classified into major grain-producing and non-major grain-producing regions (see Figure 2).

Figure 2

The data on land consolidation area and agricultural comprehensive development investment are sourced from the China Fiscal Statistical Yearbook; data on grain planting area, rural aging population, effective irrigation area, cultivated land area, and total agricultural machinery power are from the China Rural Statistical Yearbook; data on GDP, urban and rural disposable income, value added of the primary, secondary, and tertiary industries, employment in the primary, secondary, and tertiary industries, disaster-affected area are from the China Statistical Yearbook. Annual average temperature and precipitation data are from the China Meteorological Data Sharing Service Network.¹ Vegetation coverage data comes from the dataset published by Gao et al. (2022) on the National Tibetan Plateau Data Center.² Furthermore, considering that the nationally published land consolidation area data are available only until 2017, and following the approach of relevant literature (Peng et al., 2024b), we apply the proportional method to impute the missing values for 2018–2020 to address the limitation of data timeliness.

3.3 Model setting

3.3.1 Baseline regression model

Since the establishment of the Land Development and Construction Fund by the State Council in 1998, China has undertaken the continuous exploration and practice of transforming medium- and low-yield farmland into high-standard farmland. As HSFC is a consistently advancing policy rather than a sudden policy shock, this study does not adopt the traditional DID model. Instead, following the approach of Qian et al. (2023), a two-way fixed effects model is employed to examine the impact of HSFC on the CDVG, as detailed below:

In Equation 8, represents the CDVG; denotes the level of HSFC; is a set of control variables; and represent regional and time fixed effects, respectively; and is the random error term; ,, and are the parameters to be estimated, among which is the estimated coefficient of primary interest, indicating the impact of HSFC on CDVG.

3.3.2 Spatial econometric models

This study constructs the Spatial Durbin Model (SDM), Spatial Lagged Model (SLM), and Spatial Error Model (SEM) to investigate the spatial spillover effects of the independent variables. The specifications of the models are given by Equations 9, 10:

Among them, is the spatial weight matrix; is the constant term; , are regression coefficients; , , are spatial autocorrelation coefficients; , are error terms. if , model (9) becomes the SDM; If , model (9) becomes the SLM; if , model (9) becomes the SEM. In the empirical results, this study focuses on the coefficients and statistical significance of , , and .

4 Results analysis

4.1 Spatiotemporal evolution analysis of CDVG

4.1.1 Temporal trends of CDVG

Figure 3 shows the temporal trends of CDVG at the national level and across different functional regions. Nationally, CDVG has steadily declined from 0.733 in 2003 to 0.060 in 2020, with an average annual reduction rate of 14.3%. The evolution of CDVG in China exhibits distinct phase characteristics: a rapid decline from 2003 to 2012 (average annual decrease of 22.1%) and a slower decline from 2013 to 2020 (average annual decrease of 11.9%). Regionally, the CDVG of major grain-producing areas has consistently been lower than that of non-major grain-producing areas, but the regional gap has gradually narrowed over time. A possible reason is that major grain-producing regions possess substantial advantages in resource endowments and policy support. In particular, during the initial stages of HSFC, these regions were designated as priority development zones. However, with the gradual advancement of HSFC in non-major grain-producing regions, coupled with the rapid development of internet technologies and the digital economy, the barriers to development in terms of information, resources, and technology in non-grain-producing regions have been broken, leading to a gradual narrowing of the vulnerability gap between regions.

Figure 3

4.1.2 Spatial patterns of CDVG

This study employs ArcGIS 10.8 to visualize the spatial distribution patterns of CDVG. Four time points were selected for analysis: 2003, 2008, 2014, and 2020. The results are shown in Figure 4. As shown in Figure 4, the CDVG in China exhibits significant regional disparities and notable spatial clustering characteristics. Overall, regions with lower vulnerability are primarily concentrated in the eastern coastal areas, the central grain-producing regions, and the far western region of Xinjiang. Over time, the regional differences in CDVG have gradually narrowed, yet the overall spatial distribution pattern has not changed significantly. One possible explanation is that the eastern coastal regions, with well-developed economic foundations and agricultural infrastructure, demonstrate lower CDVG levels due to their high capacity for agricultural innovation and disaster response. Inland major grain-producing provinces benefit from favorable natural resources, organized production systems, and sustained food policy support, enhancing their climate resilience. In contrast, the central and western production-consumption balanced regions often face frequent natural disasters, weak infrastructure, and limited policy support, resulting in higher CDVG levels.

Figure 4

4.2 Empirical results analysis

4.2.1 Baseline regression analysis

Table 2 presents the estimation results of the baseline regression model. Columns (1) to (4) report the results based on conventional standard errors, robust standard errors, clustered robust standard errors at the provincial level, and bootstrap standard errors for 1,000 replicate samples, respectively. It is evident that the estimated effect of HSFC on the CDVG is statistically significant under all types of standard errors, suggesting that the results are highly robust. Moreover, the coefficient of HSFC is negative, indicating that the implementation of HSFC policies can significantly mitigate the CDVG.

4.2.2 Robustness tests

4.2.2.1 Addressing endogeneity

HSFC, as a government-led measure, is characterized by strong policy exogeneity (Qian et al., 2023), thereby suggesting that endogeneity of the econometric model is not severe. However, to minimise potential endogeneity bias and omitted variable issues, we employ the one-period lagged independent variable as an instrumental variable and utilize the system generalized method of moments (SGMM) model for robustness testing. The results are reported in Column (1) of Table 3. The test results show that the AR (1) p-value is below 0.1, the AR (2) p-value exceeds 0.1, and the p-value of the Hansen J test is greater than 0.1, all of which indicate the validity of SGMM. Moreover, the coefficient of HSFC remains significantly negative, consistent with the baseline regression results, thereby confirming the robustness of the findings.

4.2.2.2 Replacing the independent variable

Referring to relevant studies (Zhang et al., 2023; Qian et al., 2023; Liang et al., 2024), we employ the investment in comprehensive agricultural development per unit area as a proxy variable for HSFC, and re-estimate the model accordingly. The results reported in Column (2) of Table 3 show that, even after replacing the core explanatory variable, the impact of HSFC on the CDVG remains significantly negative.

4.2.2.3 Replacing the dependent variable

In the preceding section, the CMA model was used to separate grain climate yield. As a robustness test, we further employ the HP filter method to separate grain yield and re-measure the vulnerability index. The results in Column (3) show that the findings remain robust across different measurement methods of the dependent variable, with the estimated coefficient continuing to be significantly negative.

4.2.2.4 Considering policy lag effects

Given the cyclical nature of agricultural production and the time required for policy implementation to take effect, we further examine the impact of HSFC in the current year on CDVG in the following year. The results reported in Column (4) indicate that the estimated coefficients remain significantly negative.

4.2.2.5 Excluding other policy interference

In April 2018, the Ministry of Agriculture and Rural Affairs and the Ministry of Finance launched a key fiscal policy to support agriculture and farmers, significantly increasing subsidies for land fertility protection. This concurrent policy intervention could potentially confound the accurate estimation of the HSFC’s impact. Therefore, we exclude samples from 2018 onward to eliminate the influence of the aforementioned policy. As shown in column (5) of Table 3, the effect of HSFC on CDVG remains significantly negative, providing further evidence for the robustness of the baseline regression results.

4.2.3 Mechanism analysis

While the preceding analysis has demonstrated that HSFC has a significant effect on reducing CDVG. However, the underlying mechanisms through which HSFC influences CDVG remain unclear. Table 4 reports the results of the mechanism tests. Columns (1) and (2) show that HSFC significantly increases FVC, and that FVC has a significant negative effect on CDVG, indicating that HSFC can reduce CDVG through ecological regulation mechanism. Columns (3) and (4) indicate that HSFC significantly improves the EIA, which in turn significantly reduces CDVG, confirming the validity of the infrastructure guarantee mechanism in mitigating CDVG. Columns (5) and (6) show that HSFC significantly enhances ML, the increase in ML level then significantly reduces CDVG, confirming the role of the technical support mechanism in mitigating CDVG.

4.2.4 Spatial spillover effects

4.2.4.1 Suitability test of spatial econometric model

To examine the suitability of constructing spatial econometric models, this study conducts a series of tests. Figure 5 presents the local Moran’s I scatter plot of China’s CDVG. From the distribution of the scatter plot, it can be observed that most provinces are concentrated in the first and third quadrants, showing characteristics of “high–high” and “low–low” clustering. This indicates a significant positive spatial autocorrelation in CDVG, demonstrating the necessity of using spatial econometric models.

Figure 5

The results of the spatial autocorrelation test based on the residuals from the Least Squares Dummy Variable (LSDV) regression are shown in Table 5. The p-value of Moran’s I is significantly positive at the 1% level, and both the LM-error and LM-lag statistics are statistically significant. These results indicate a strong positive spatial autocorrelation in the residuals of the conventional panel model, suggesting that the use of spatial econometric models is appropriate (Table 5).

4.2.4.2 Results of spatial spillover effects

Columns (1), (2), and (3) of Table 6 report the estimated spatial spillover effects of HSFC based on the SDM, SLM, and SEM models, respectively. To mitigate potential estimation bias, this study employs the quasi-maximum likelihood estimation (QMLE) method proposed by Lee and Yu (2010). The results show that the spatial autocorrelation coefficient ρ is significantly positive in both the SDM and SLM models, and the spatial error coefficient λ in the SEM model is also significantly positive. This indicates that unobserved spatial effects have a significant impact on the results, and the use of spatial econometric models is reasonable.

Since the regression coefficients of explanatory variables in spatial econometric models do not directly represent their marginal effects, we further decompose the spatial effects of HSFC on the CDVG. Table 7 presents the decomposition results based on the SDM model. The results indicate that both the direct and indirect effects of HSFC are significantly negative, suggesting that HSFC not only significantly reduces CDVG in the local region but also indirectly reduces CDVG in neighboring areas through spatial spillover effects.

4.2.5 Heterogeneity analysis

4.2.5.1 Heterogeneity by CDVG quantiles

Given that traditional OLS models may produce estimation biases when assessing the impact of HSFC on CDVG due to data distribution asymmetry, and that they only capture overall mean effects, potentially masking differences in marginal effects of HSFC across varying vulnerability levels. Therefore, we further employ a quantile regression model to identify the heterogeneous effects of HSFC under different vulnerability distributions, with results shown in Panel A in Figure 6. Overall, HSFC exhibits a significant negative effect at medium-to-low vulnerability quantiles (such as P30, P50, P70), and this effect strengthens as the quantile increases. However, at the 0.9 quantile, the impact of HSFC on CDVG is no longer significant. A possible reason is that regions with higher vulnerability often have weaker agricultural foundations or are exposed to more extreme climate risks. The effectiveness of HSFC in improving grain production conditions often requires reaching a certain scale, making it difficult to surpass the threshold required for disaster resistance in the short term.

Figure 6

4.2.5.2 Heterogeneity by grain-producing functional zones

Given the significant differences in resource endowments and policy support among different grain production functional zones, we further divide the sample into two groups: major grain-producing areas and non-major grain-producing areas, to examine the heterogeneous effects of HSFC. The results are shown in Panel B in Figure 6. The results indicate that HSFC significantly reduces CDVG in both types of regions, with a more pronounced effect observed in major grain-producing areas. One possible explanation is that major grain-producing regions generally possess more favorable natural endowments and more advanced agricultural technologies, making it easier for these areas to mitigate climate risks by improving production conditions. Moreover, based on the national strategy for food security, these regions have been prioritized for HSFC implementation, receiving greater policy resources and technical support, which has substantially enhanced the effectiveness of HSFC.

4.2.5.3 Heterogeneity by terrain characteristics

Both grain production and HSFC are significantly constrained by terrain characteristics. Drawing on the classification of terrain relief across Chinese regions proposed by You et al. (2018), we divide the full sample into two groups: regions with low terrain relief and those with high terrain relief. The results, presented in Panel C in Figure 6, indicate that HSFC has a significant mitigating effect on the CDVG only in regions with low terrain relief, while its impact is statistically insignificant in areas with high terrain relief. This result is consistent with expectations. One possible explanation is that regions with high terrain relief are often characterized by rugged landscapes, steep slopes, and fragmented farmland, which undoubtedly increase the difficulty and cost of HSFC and constrain its construction quality. Moreover, complex terrain also constrains the development of agricultural production service markets, further hindering the realization of scale economies brought about by HSFC and thereby weakening its effectiveness in these regions.

5 Discussion

Extreme climate disasters are increasingly threatening grain production due to their growing frequency, intensity, and spatial extent (Lu et al., 2019). In this context, scientifically assessing the responses of grain production systems to climate disasters and exploring effective mitigation measures have become urgent tasks for addressing global food-security challenges. While prior research has examined the impacts of climate change and the roles of production behaviors, agricultural insurance, and mechanization in mitigating climate risks in agriculture (Wang et al., 2024; Zeng et al., 2025; Elahi et al., 2022), this study shifts attention to climate-disaster vulnerability in grain production (CDVG) and, from a land-consolidation perspective, theoretically analyses and empirically tests the effects of China’s high-standard farmland construction (HSFC) on CDVG.

Using an improved vulnerability assessment framework and climate-yield separation methods, we measure CDVG across 30 provinces in China from 200 to 2020. Adaptive capacity is proxied by trend yield, which captures contributions from technology, institutions, and changes in management practices to crop yields (Dong et al., 2018; Li et al., 2022); sensitivity is reflected by climate yield, and exposure is measured by the disaster-affected area ratio. This integrated framework captures both external climate-disaster shocks and the system’s internal stability and recovery capacity. Results show that CDVG follows a trajectory of “rapid initial decline followed by a slower, fluctuating decline” over time. Spatially, CDVG exhibits pronounced regional disparities and spatial clustering, with lower levels observed in eastern coastal areas, major grain-producing regions in central China, and Xinjiang. This pattern aligns with global trends and trends in China in agricultural climate disaster vulnerability as reported by Cheng et al. (2024) and Li et al. (2025), and it also accords with regional realities. These findings suggest that policymakers should prioritize land consolidation, farmland infrastructure investment, and targeted climate disaster adaptation measures in regions with higher levels of CDVG.

Baseline regressions confirm that HSFC significantly reduces CDVG. This finding extends and deepens the conclusions of Peng et al. (2024a, 2024b) and Gao and Qin (2024). The former finds that HSFC significantly reduces the disaster-affected rate and crop failure rate, with a stronger mitigating effect on droughts than on floods. The latter shows that HSFC reduces the variance of grain output, thereby lowering climate risks in grain production. Building on these studies, we incorporate climate-disaster exposure, system stability, and disaster-response capacity into a unified evaluation framework and assess the effect of HSFC on this composite indicator, thereby providing a useful complement to existing research. Mechanism analyses indicate that HSFC mainly reduces CDVG through three channels: ecological regulation, infrastructure guarantees, and technological support. Existing studies have confirmed that these channels can effectively enhance the agricultural system’s capacity for disaster prevention and mitigation and reduce climate disaster risks (Wang et al., 2024; Elahi et al., 2022; Das et al., 2022), thereby supporting our findings. Furthermore, this study confirms that HSFC exhibits significant spatial spillover effects on CDVG. This aspect has been largely overlooked in prior research. Our evidence provides empirical support for building cross-regional, collaborative disaster-mitigation systems and for the coordinated planning and joint implementation of disaster-prevention projects within the HSFC program.

Despite the valuable insights provided by this study, several limitations remain, which could be addressed in future research. First, different climate-yield separation methods may yield different vulnerability assessment results. How to more accurately isolate the climate component, and how to choose appropriate decomposition methods for specific research purposes, remain important directions for future work. Second, provincial-level data may overlook regional differences within provinces and micro-level factors; future research could use finer-grained data to improve precision. Third, the effectiveness of various engineering projects in HSFC may vary. How to scientifically plan and construct HSFC projects that effectively reduce CDVG based on the actual conditions of farmland and local climate is an area that still requires further exploration in the future.

6 Conclusions and policy implications

This study provides an in-depth exploration of the impact of high-standard farmland construction (HSFC) on the climate disaster vulnerability of grain production (CDVG). First, based on the improved vulnerability measurement framework, this study measures CDVG from three dimensions of exposure, sensitivity and adaptive capacity, and systematically analyzing the spatiotemporal evolution characteristics and regional differences of CDVG. Building on this, the study further examines the impact mechanism of HSFC on CDVG and its spatial spillover effects, drawing the following main conclusions: (1) Temporally, CDVG in China showed a trend of “rapid initial decline followed by slow and fluctuating decreases” during the study period. Spatially, vulnerability shows significant regional differences and spatial clustering, with vulnerability levels in the eastern coastal regions and major grain-producing areas generally lower than in other provinces, and the regional disparity showing a gradual convergence trend. (2) Empirical analysis indicates that HSFC can significantly reduce CDVG, and this conclusion remains valid after a series of robustness tests. (3) HSFC not only effectively reduces CDVG in the local region but also indirectly reduces CDVG in neighboring provinces through spatial spillover effects. (4) Mechanism analysis shows that the vulnerability-reducing effect of HSFC is mainly achieved through ecological regulation, infrastructure guarantee, and technological support mechanisms. (5) Heterogeneity analysis reveals that, in terms of vulnerability distribution, HSFC has a more significant mitigating effect in provinces at medium-to-low vulnerability quantiles. In terms of grain production functional zones, HSFC has a more pronounced effect in reducing vulnerability in major grain-producing areas. In terms of terrain characteristics, HSFC significantly reduces vulnerability only in areas with relatively flat terrain.

Based on the above findings, this study proposes the following policy implications:

• (1) HSFC should continue to be strengthened nationwide, with the aim of gradually transforming all permanent basic farmland into high-standard farmland. Areas with high disaster risk, complex terrain, and limited financial resources require special attention. Innovative PPP models can be introduced to attract private investment, and new types of agricultural business entities should be encouraged to participate in project development.

• (2) The focus of HSFC should be adjusted in accordance with the climatic characteristics and soil conditions of each region. For example, in drought-prone areas, emphasis should be on adopting water-saving irrigation techniques to ensure reliable irrigation coverage; in flood-prone areas, priority should be given to constructing efficient drainage facilities; and in areas susceptible to low-temperature and chilling damage, emphasis should be placed on promoting improved cultivation techniques and introducing or breeding high-quality, cold-tolerant crop varieties.

• (3) Construction should be prioritized in high-vulnerability regions. The mitigation effect of HSFC is weaker in regions where the disaster vulnerability is high, which are mainly located in non-major grain-producing areas in central and western China, and construction resources should be further tilted toward these areas in the future.

• (4) Supporting systems for HSFC should be strengthened. This includes improving the agricultural land transfer market, encouraging the development of agricultural service organizations, and effectively implementing soil and water conservation as well as integrated prevention and control measures. Furthermore, engineering measures should be integrated with the promotion of advanced technologies to ensure the sustained effectiveness of HSFC.

References

• 1

  AhmadalipourA.MoradkhaniH. (2018). Multi-dimensional assessment of drought vulnerability in Africa: 1960-2100. Sci. Total Environ.644, 520–535. doi: 10.1016/j.scitotenv.2018.07.023,

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 2

  ChenY. B.WangS. (2023). Evaluation of agricultural carbon reduction effects of comprehensive agricultural development investment: An event analysis based on the high-standard farmland construction policy. Agric. Technol. Econ.6, 67–80. doi: 10.13246/j.cnki.jae.20220301.001

  • CrossRef
  • Google Scholar

• 3

  ChengW.LiY.ZuoW.DuG.StannyM. (2024). Spatio-temporal detection of agricultural disaster vulnerability in the world and implications for develo climate-resilient agriculture. Sci. Total Environ.928:172412. doi: 10.1016/j.scitotenv.2024.172412,

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 4

  DasB. S.WaniS. P.BenbiD. K.MudduS.BhattacharyyaT.MandalB.et al. (2022). Soil health and its relationship with food security and human health to meet the sustainable development goals in India. Soil Security.8:100071. doi: 10.1016/j.soisec.2022.100071

  • CrossRef
  • Google Scholar

• 5

  DongZ.PanZ.HeQ.WangJ.HuangL.PanY.et al. (2018). Vulnerability assessment of spring wheat production to climate change in the Inner Mongolia region of China. Ecol. Indic.85, 67–78. doi: 10.1016/j.ecolind.2017.10.008

  • CrossRef
  • Google Scholar

• 6

  ElahiE.KhalidZ.TauniM. Z.ZhangH.LirongX. (2022). Extreme weather events risk to crop-production and the adaptation of innovative management strategies to mitigate the risk: a retrospective survey of rural Punjab, Pakistan. Technovation117:102255. doi: 10.1016/j.technovation.2021.102255

  • CrossRef
  • Google Scholar

• 7

  FangB.LiX. Y.ZhouX. Y.ZhangZ. C. (2024). Diagnosis of cultivated land use system vulnerability and identification of influencing factors: a case study of northern Jiangsu. China Land Sci.38, 79–90.

  • Google Scholar

• 8

  FAO (2023). The Impact of Disasters on Agriculture and Food Security: Avoiding and Reducing Losses through Investment in Resilience. Roma: FAO, 1–30.

  • Google Scholar

• 9

  GaoX.QinS. (2024). Meteorological disasters, downside risk of grain yield and mitigation effect of high-standard farmland constrGuction policy in China. Clim. Risk Manag.45:100633. doi: 10.1016/j.crm.2024.100633

  • CrossRef
  • Google Scholar

• 10

  GaoJ.ShiY.ZhangH.ChenX.ZhangW.ShenW.et al. (2022). China regional 250m fractional vegetation cover data set (2000–2024). National Tibetan Plateau/Third Pole Environment Data Center.

  • Google Scholar

• 11

  HanH.ZouK.YuanZ. (2024). Impact of specialized agricultural services on climate-smart agricultural practices: evidence from biopesticide application in Jiangsu Province, China. Environ. Impact Assess. Rev.105:107430. doi: 10.1016/j.eiar.2024.107430

  • CrossRef
  • Google Scholar

• 12

  HeM.WangY.TongY.ZhaoY.QiangX.SongY.et al. (2020). Evaluation of the environmental effects of intensive land consolidation: a field-based case study of the Chinese loess plateau. Land Use Policy94:104523. doi: 10.1016/j.landusepol.2020.104523

  • CrossRef
  • Google Scholar

• 13

  HodrickR. J.PrescottE. C.PostwarU. S. (1997). Business cycles:an empirical investigation. Money Credit Bank.29, 1–16. doi: 10.2307/2953682

  • CrossRef
  • Google Scholar

• 14

  HuX. Y.DaiM. H. (2022). The grain yield increase effect of high-standard farmland construction policy. J. South China Agric. Univ. (Soc. Sci. Ed.)21, 71–85.

  • Google Scholar

• 15

  JiM.FengT.XiaoH.XinZ.LiJ.WangD. (2023). Effects of Different Farmland Shelterbelts on Soil Water and Nutrient Storage in the Hetao Irrigation District. Arid Zone Research.

  • Google Scholar

• 16

  KleinR. J.MidglevG. F.PrestonB. L.AlamM.BerkhoutF. G. H.DowK.et al. (2014). Climate change 2014: Impacts, Adaptation, and Vulnerability. Stockholm, Sweden: IPCC fifth assessment report, 1–1150.

  • Google Scholar

• 17

  LeeL. F.YuJ. H. (2010). Estimation of spatial autoregressive panel data models with fixed effects. J. Econ.154, 165–185. doi: 10.1016/j.jeconom.2009.08.001

  • CrossRef
  • Google Scholar

• 18

  LiY. H.ChengW. J.ZuoW. J.DuG. M. (2023). Study on agricultural drought vulnerability and response strategies in the northeast black soil region: a case study of Heilongjiang province. J. Agric. Resour. Environ.40, 953–964. doi: 10.13254/j.jare.2022.0682

  • CrossRef
  • Google Scholar

• 19

  LiY.GaoG.WenJ.ZhaoN.DuG.StannyM. (2025). The measurement of agricultural disaster vulnerability in China and implications for land-supported agricultural resilience building. Land Use Policy148:107400. doi: 10.1016/j.landusepol.2024.107400

  • CrossRef
  • Google Scholar

• 20

  LiW.GuoJ.TangY.ZhangP. (2024). Resilience of agricultural development in China’s major grain-producing areas under the double security goals of “grain ecology”. Environ. Sci. Pollut. Res.31, 5881–5895.

  • Google Scholar

• 21

  LiY.HuangH.JuH.LinE.XiongW.HanX.et al. (2015). Assessing vulnerability and adaptive capacity to potential drought for winter-wheat under the RCP 8.5 scenario in the Huang-Huai-Hai plain. Agric. Ecosyst. Environ.209, 125–131. doi: 10.1016/j.agee.2015.03.033

  • CrossRef
  • Google Scholar

• 22

  LiM.LiangL.HuangZ.SongH.WangS.WangQ.et al. (2025). Spatiotemporal dynamics and driving factors of net primary productivity in Asian terrestrial ecosystems. Ecol. Model.510:111322. doi: 10.1016/j.ecolmodel.2025.111322

  • CrossRef
  • Google Scholar

• 23

  LiS.WangB.FengP.LiD.LiL.ShiL.et al. (2022). Assessing climate vulnerability of historical wheat yield in South-Eastern Australia's wheat belt. Agric. Syst.196:103340. doi: 10.1016/j.agsy.2021.103340

  • CrossRef
  • Google Scholar

• 24

  LiX. Y.YiZ.YanX. Z.YangS. B. (2020). Comparative study on main crop yield separation methods. J. Appl. Meteorol. Sci.31, 74–82.

  • Google Scholar

• 25

  LiangZ. H.ZhangL.ZhangJ. B. (2024). Land consolidation and fertilizer reduction: quasi-natural experimental evidence from China’s well-facilitated capital farmland construction. China Rural Economy.12, 23–43.

  • Google Scholar

• 26

  LuS.BaiX.LiW.WangN. (2019). Impacts of climate change on water resources and grain production. Technol. Forecast. Soc. Chang.143, 76–84. doi: 10.1016/j.techfore.2019.01.015

  • CrossRef
  • Google Scholar

• 27

  MamunA. A.IslamA. R. M. T.AlamG. M.SarkerM. N. I.Erdiaw-KwasieM. O.BhandariH.et al. (2023). Livelihood vulnerability of char land communities to climate change and natural hazards in Bangladesh: an application of livelihood vulnerability index. Nat. Hazards115, 1411–1437. doi: 10.1007/s11069-022-05599-y

  • CrossRef
  • Google Scholar

• 28

  Nguyen-HuyT.DeoR. C.MushtaqS.An-VoD. A.KhanS. (2018). Modeling the joint influence of multiple synoptic-scale, climate mode indices on Australian wheat yield using a vine copula-based approach. Eur. J. Agron.98, 65–81. doi: 10.1016/j.eja.2018.05.006

  • CrossRef
  • Google Scholar

• 29

  NiuP.JiangY.YangY.WangL. (2022). The characteristics and influencing factors of change in farmland system vulnerability: a case study of Sanmenxia City, China. Front. Environ. Sci.10:887570. doi: 10.3389/fenvs.2022.887570

  • CrossRef
  • Google Scholar

• 30

  PengW. B.KuangC. E.LiW. Y.DiY. X. (2024a). Urban renewal and ecological resilience spatial association network and its impacts in the urban agglomeration of the middle reaches of the Yangtze River. J. Geogr. Sci.44, 1936–1945.

  • Google Scholar

• 31

  PengJ. Q.LiuY.ChenL. (2024b). The impact and mechanisms of high-standard farmland construction on agricultural disaster mitigation. Acta Ecol. Sin.44, 7589–7603.

  • Google Scholar

• 32

  QianL.LiuC.ZhengL. Y. (2023). How high-standard farmland construction affects farmland transfer. China Land Sci.37, 62–70.

  • Google Scholar

• 33

  RaoC. S.KareemullaK.KrishnanP.MurthyG. R. K.RameshP.AnanthanP. S.et al. (2019). Agro-ecosystem based sustainability indicators for climate resilient agriculture in India: a conceptual framework. Ecol. Indic.105, 621–633. doi: 10.1016/j.ecolind.2018.06.038

  • CrossRef
  • Google Scholar

• 34

  SongY.LiuW. P.XieZ. (2023). Model evaluation of the impact of high-standard farmland construction policy on crop planting structure. Trans. Chin. Soc. Agric. Eng.39, 227–235.

  • Google Scholar

• 35

  SunX. T.YuZ.ZhangG. S. (2024). The impact of improving element quality on the comprehensive production capacity of grain: evidence from high-standard farmland construction. China Agric. Econ. Rev.

  • Google Scholar

• 36

  WangZ. N.ChenX.ShiL. Z. (2025). Grain production incentive policies and county-level grain production in China: An analysis based on the spatial difference-in-differences model. Trans. Agric. Technol. Econ.2, 1–29. doi: 10.13246/j.cnki.jae.20240618.002

  • CrossRef
  • Google Scholar

• 37

  WangB.FengP.LiuD.WatersC. (2020). Modelling biophysical vulnerability of wheat to future climate change: a case study in the eastern Australian wheat belt. Ecol. Indic.114:106290. doi: 10.1016/j.ecolind.2020.106290

  • CrossRef
  • Google Scholar

• 38

  WangD.ZhangP.ChenS.ZhangN. (2024). Adaptation to temperature extremes in Chinese agriculture, 1981 to 2010. J. Dev. Econ.166:103196. doi: 10.1016/j.jdeveco.2023.103196

  • CrossRef
  • Google Scholar

• 39

  WatanabeT.CullmannJ.PathakC. S.TurunenM.EmamiK.GhinassiG.et al. (2018). Management of climatic extremes with focus on floods and droughts in agriculture. Irrig. Drain.67, 29–42. doi: 10.1002/ird.2204

  • CrossRef
  • Google Scholar

• 40

  XiangH.ZhouC.SongH. (2024). High-quality agricultural development in the Central China: empirical analysis based on the Dongting Lake area. Geomatica.76:100010. doi: 10.1016/j.geomat.2024.100010

  • CrossRef
  • Google Scholar

• 41

  XieY. G.XueY. J. (2024). Study on the disaster reduction and increment effects of farmland water conservancy construction on grain production and its mechanisms in China. Disaster Science.39, 54–61.

  • Google Scholar

• 42

  YinG.XuX.PiaoH.LyuJ. (2024). The synergy effect of agricultural dual-scale management on farmers’ income: evidence from rural China. China Agric. Econ. Rev.16, 591–607. doi: 10.1108/CAER-01-2023-0005

  • CrossRef
  • Google Scholar

• 43

  YouZ.FengZ. M.YangY. Z. (2018). A 1km terrain relief dataset for China. J. Glob. Chang. Data Sci.2:151-155 + 274-278.

  • Google Scholar

• 44

  YuanC.YamagataT. (2015). Impacts of IOD, ENSO and ENSO Modoki on the Australian winter wheat yields in recent decades. Sci. Rep.5:17252. doi: 10.1038/srep17252,

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 45

  ZengX.LuH.QiH.JiL. (2025). Does extreme weather affect the resilience of agricultural economies? Analysis based on agricultural insurance. Front. Environ. Sci.13:1551030. doi: 10.3389/fenvs.2025.1551030

  • CrossRef
  • Google Scholar

• 46

  ZhangM. L.WengZ. L.GaoX. P. (2023). The impact of high-standard farmland construction and agricultural socialized services on pesticide use: based on survey data from 605 rice farmers in Jiangxi province. Chin. Land Sci.37, 106–116.

  • Google Scholar

• 47

  ZhouF.WenC. (2024). Research on the evolution of the spatial association network structure and driving factors of China’s agricultural green development. Agriculture14:683. doi: 10.3390/agriculture14050683

  • CrossRef
  • Google Scholar