Increase in Grain Production Potential of China Under 2030 Well-Facilitated Farmland Construction Goal

Abstract

To promote high-quality agricultural development and implement the “storing grain in the land” strategy, the construction of Well-Facilitated Farmland (WFF) plays a critical role in enhancing grain production capacity and optimizing the spatial distribution of food supply, thereby contributing to national food security. However, accurately assessing the potential impact of WFF construction on China’s grain production and regional self-sufficiency by 2030 remains a significant challenge. Existing studies predominantly focus on the provincial level, while fine-grained analyses at the city level are still lacking. This study quantifies the potential increase in grain production in China under the 2030 WFF construction target by employing effect size analysis, multi-weight prediction, and Monte Carlo simulation across multiple spatial scales (national, provincial, and city levels), thereby addressing the research gap at finer spatial resolutions. By integrating 2030 population projections and applying a grain self-sufficiency calculation formula, it further evaluates the contribution of WFF to regional grain self-sufficiency: (1) WFF could generate an additional 31–48 million tons of grain, representing a 5.26–8.25% increase; (2) grain supply in major crop-producing regions would expand, while the supply–demand gap in balanced regions would narrow; and (3) the number of cities with grain self-sufficiency ratios below 50% would decrease by 11.1%, while those exceeding 200% would increase by 25.5%. These findings indicate that WFF construction not only enhances overall grain production potential but also facilitates a transition from “overall supply-demand balance” to “structural security” within China’s food system. This study provides critical data support and policy insights for building a more resilient and regionally adaptive agricultural system.

1. Introduction

On a global scale, the frequent occurrence of extreme weather events caused by climate change, the reduction in arable land due to urbanization, and the degradation of agricultural ecosystems are collectively posing severe challenges to the grain production system and threatening global food security. These pressures make it an urgent common issue for countries to address how to increase grain production capacity under limited resource conditions [1]. As the country with the largest population in the world, China’s food supply issue has always been a concern of the international community [2,3]. This is attributed to the fact that China bears the survival needs of approximately 18% of the global population (around 1.4 billion) [4,5], while concurrently confronting the triple pressures arising from rapid urbanization [6], increasingly severe climate change [7], and the degradation of agricultural ecosystems [8]. Among these external factors, climate change brings uncertainties such as extreme weather and changes in precipitation patterns, threatening crop yields and soil health [9,10,11]. Urbanization has led to a decrease in arable land and a shrinkage in the agricultural labor force [12,13,14]. Constructing Well-Facilitated Farmland with advanced irrigation and drainage systems can better withstand climate-induced water-related pressures and help offset productivity losses caused by the reduction in arable land due to urbanization [15,16,17]. In addition, Well-Facilitated Farmland can attract and retain surplus agricultural labor through more efficient and mechanized operations, thereby reducing the impact of external pressures on agricultural production. This provides a unique research sample for solving the global challenge of “how to produce more food with fewer resources”. For China, driven by the upgrading of household consumption, the demand for grain is extending from a single quantity guarantee to a direction of quality and diversification [18]. However, the rigid constraints of arable land resources [19], water scarcity [20], loss of agricultural labor [21], and tightening of ecological protection red lines [22] make it difficult to sustain the traditional extensive agricultural production model. How to tap into the potential of grain production under limited resource conditions and build a more resilient supply system has become a core issue that urgently needs to be addressed in the national strategic layout.

The modernization transformation of agricultural infrastructure has always been an important pillar of China’s food security system [23,24]. Since the introduction of the farmland protection system in the early 21st century [25], the evolution of policies from the delineation of basic farmland to the special protection of permanent basic farmland, from land consolidation projects to the promotion of agricultural technology, has clearly reflected the continuous attention of the country to the construction of farmland quality [26]. In particular, in 2022, the No. 1 central document of the Central Government clearly proposed to “implement a new round of Well-Facilitated Farmland construction plan”, which means that about 8004 thousand hectares of Well-Facilitated Farmland will be built by 2030 [27,28]. This strategic deployment marks a new stage in which China’s farmland construction has shifted from scale expansion to quality improvement.

Specifically, Well-Facilitated Farmland (WFF) is cropland with flat terrain, complete infrastructure, efficient water-saving systems, and concentrated, connected plots. It has fertile soil, is friendly to the environment, and can resist disasters well. It fits modern farming methods, ensuring steady and high yields whether there is drought or flood, and includes supporting agricultural facilities [29]. In simple terms, WFF is high-quality farmland. It is a key part of keeping the country’s food supply safe on a large scale and helping to better deal with natural disasters. Currently, in the stage of improving land across regions, the focus is on protecting farmland in three ways: keeping enough of it, making sure it is good quality, and protecting its ecology. This all-around way of working shows how important WFF is in balancing farm output, sustainable use of resources, and stable ecological conditions [30,31].

The deep value of Well-Facilitated Farmland construction lies in its restructuring and efficiency release of agricultural production factors [32]. Compared with traditional farmland, Well-Facilitated Farmland emphasizes the comprehensive support of “field, soil, water, road, forest, electricity, technology, and management”. Its essence is to eliminate structural defects such as farmland fragmentation [33], land fertility degradation, and weak disaster resistance through engineering measures such as land leveling [34], irrigation and drainage facility upgrading [35], and soil improvement [36,37]. More importantly, this process is not simply a hardware transformation, but is combined with institutional innovations such as smart agriculture technology, moderate scale operation, and ecological compensation mechanisms [38,39]. For example, the integration of IoT monitoring systems with water-saving irrigation facilities not only improves water resource utilization efficiency, but also provides data support for precision fertilization and pesticide application [40]. The consolidation of fields and the construction of mechanized farming roads have created physical space for the popularization of agricultural mechanization, thereby promoting the dual improvement of labor productivity and land output rate [41]. The collaborative empowerment of Well-Facilitated Farmland infrastructure and technology is continuously improving the efficiency of China’s grain production.

In addition, international experience has shown that the improvement in arable land quality has a significant multiplier effect on the contribution of grain production capacity. The United States has achieved sustained yield growth while reducing the total cultivated land area through large-scale land fallow and soil fertility cultivation programs [42]; under the constraint of extremely scarce arable land resources, Japan has maintained its self-sufficiency ratio in rice through refined management and technology intensive investment [43]. Although China’s Well-Facilitated Farmland construction path has significant national characteristics, its internal logic is deeply in line with the global trend of sustainable agricultural development—that is, seeking a dynamic balance between production efficiency, resource conservation, and ecological balance while ensuring food security [44]. This exploration has an important reference value for developing countries that are also facing the contradiction between population growth and resource constraints.

Currently, the construction of Well-Facilitated Farmland in China has entered a critical stage. With the gradual reduction in high-quality arable land resources suitable for centralized and contiguous rectification, the subsequent project promotion will involve more areas that are more difficult to implement, such as the transformation of medium- and low-yield fields and the management of arable land in hilly and mountainous areas [45]. This has increasingly highlighted the problem of increasing marginal costs and prolonged benefit release cycles in the construction of Well-Facilitated Farmland. At the same time, there are deep-seated issues such as the contradiction between regional differences in construction standards and the long-term effectiveness of management and protection mechanisms, the conflict between single financial investment and insufficient participation of multiple entities, and the obstacles to the connection between improving farmland quality and increasing farmers’ income demands [46]. All of these may pose a real test of the effectiveness of policy implementation. The existence of these challenges precisely highlights the necessity of systematically evaluating the capacity gain mechanism of Well-Facilitated Farmland construction—only by clarifying the role path and ultimate effectiveness of Well-Facilitated Farmland construction can a scientific basis be provided for policy optimization.

The remaining part of this article is divided into the following key areas of content and the framework diagram of this article is shown in Figure 1. Section 2 provides a literature review of existing models and conclusions on the construction of Well-Facilitated Farmland and the evaluation of grain yield increase. Section 3 introduces the data sources of WFF, population, and elasticity coefficient data used in this study. Section 4 provides a detailed introduction to the calculation methods used in this study, including Monte Carlo simulation and the grain self-sufficiency ratio. Section 5 introduces the results of these analyses, such as the increase in grain production and changes in self-sufficiency ratio by 2030 under the construction of Well-Facilitated Farmlands and provides relevant policy recommendations. Section 6 discusses the impact of climate change and policies on yield estimates, as well as the shortcomings and prospects of research. Finally, Section 7 summarizes this study by summarizing key insights based on the research findings.

The analyses in this study were conducted in four distinct stages (Figure 1). First, a descriptive statistical analysis was performed to identify the structure and temporal as well as regional trends in the Well-Facilitated Farmland (WFF) area across China. Second, this study clarified the effect size (elasticity coefficient) and explained how it was used to quantify the impact and potential of WFF on grain production. Multiple weighting schemes were also applied using provincial-level WFF data—based on the 2020 WFF area, 2020 cropland area, and 2020 grain production to project WFF distribution at the city level. Third, Monte Carlo simulation based on a triangular distribution was employed to simulate uncertainties associated with the multi-weight projections, in order to enhance the robustness of the estimates. Finally, this study evaluated how WFF development could improve the gap and ratio of grain self-sufficiency (GSSG, RSSG) across three major regional types: major production areas, balanced areas, and main consumption areas.

2. Literature Review

Based on the strategic necessity of constructing good and convenient farmland established in the introduction [47], this literature review comprehensively summarizes the academic consensus on its operational approach. Empirical evidence consistently identifies three interrelated dimensions of how WFF improves agricultural productivity [48]: upgrading basic elements, improving production capacity, and enhancing risk resilience, to contextualize WFF’s role in optimizing grain output under spatially heterogeneous resource endowments and policy implementations, where the provincial classification of grain functional zones in China is provided in Table S3.

The theoretical basis of Well-Facilitated Farmland construction (WFF) in this study originates from the analytical framework of “institutional change property rights allocation institutional incentives”, and its core logic is rooted in the theories of institutional change and property rights [49]. From the perspective of institutional change, the institutional change in Well-Facilitated Farmland construction is a marginal improvement process around the food security strategy, with both inducement and compulsion characteristics [50]. Through a continuously optimized institutional system, it promotes adaptive adjustment of the farmland property rights allocation market; from the perspective of property rights allocation, based on the “separation of three rights”, following the core logic of property rights clarification and transaction costs in Coase’s theorem [51], promoting the allocation of management rights to entities with comparative advantages through detailed ownership and clear responsibility, and achieving optimal utilization of land elements; in the dimension of institutional incentives, emphasis is placed on incentive compatibility under multi-center governance, which requires the integration of formal and informal rules, and the formation of a collaborative mechanism based on the flexible governance function of grassroot entities. This theoretical framework provides systematic support for analyzing the institutional logic of Well-Facilitated Farmland construction [52,53,54].

2.1. Upgrade of Basic Elements

The current research mostly focuses on the direct impact of specific measures for Well-Facilitated Farmland construction on grain production capacity as illustrated in Figure 2, emphasizing the consolidation of the basic conditions for agricultural production through measures such as eliminating farmland fragmentation, improving irrigation and drainage facilities, and enhancing soil fertility [55,56], promoting an increase in grain production. For example, land leveling and land consolidation not only create physical space for mechanized operations, but also reduce production costs by optimizing field management [57]. The modular design of irrigation and drainage systems enhances the ability of farmland to resist drought and flood disasters, reducing the risk of reduced production due to disasters [58]. However, such analyses often remain at the static evaluation of a single technological path or local areas [59,60] failing to fully reveal the complex impact of different regional resource endowments and policy execution on the overall yield increase effect. Regional studies have shown that due to the potential for large-scale production, the yield increasing effects of major production areas rely more on land consolidation and mechanization popularization [61]. The main consumption areas is limited by the scarcity of arable land resources, so it is even more necessary to improve unit area output through facility upgrades and precise management [62]. The equilibrium zone needs to seek a dynamic balance between increasing production and ecological protection [63]. This regional differentiation feature requires policy design to break through the “one size fits all” model [64].

2.2. Improvement of Production Capacity

In terms of data usage and methodology, the granularity of data for Well-Facilitated Farmland construction evaluation has mostly remained at the provincial level [70]. That is to say, traditional methods overly rely on provincial statistical data, which makes it difficult to reflect the differences in the construction status and policy priorities of city units in Well-Facilitated Farmland [71]. For example, plain areas within the same province may quickly complete land consolidation due to terrain advantages, while hilly areas may progress slowly due to high engineering costs [72,73], leading to differences in the ratio of increased production at the city level. The provincial internal impact mechanism of uneven economic development between major grain production areas and other regions can indirectly lead to imbalances in grain production [74]. However, most of the existing literature using the elasticity coefficient method is based on linear relationship results. For example, some scholars have used the farmland coefficient method to calculate the quantitative and qualitative potential for farmland consolidation, where the standard farmland coefficient for each farmland plot is determined by the average farmland coefficient of the same slope to which the plot belongs [75]. Some scholars have also used spatial econometric models (SARAR) to study the linear impact of Well-Facilitated Farmland construction on grain TFP [76], or analyzed the promotional relationship between farmland use and grain production using the field grain coefficient (FGEC) [77].

2.3. Enhancement of Risk Resilience

At the quantitative level of policy effects, existing methods often rely on statistical correlation analysis of historical data, and evaluate the effectiveness of construction by constructing a marginal benefit relationship between policy intervention and food output [78]. For example, economically developed areas may achieve full coverage of technological equipment faster due to sufficient funds, while remote areas are constrained by lagging infrastructure, resulting in spatial and temporal differences in the release of technological dividends [79,80], leading to differences in grain production increases in the short to medium term. In addition, traditional research often uses extrapolation methods to predict grain production capacity [81], with less integration of multidimensional linkage effects of changes in factors such as Well-Facilitated Farmland construction [82,83,84]. Especially in the context of population aging and slowing population growth [85], the issue of agricultural labor shortages that may restrict the full release of grain production benefits from Well-Facilitated Farmland is rarely considered as a whole [86].

In addition, the existing framework for analyzing food supply and demand is mostly based on static assumptions rather than reflecting the effectiveness of policy interventions from a dynamic perspective. Research has shown that without sustained promotion of Well-Facilitated Farmland construction, the future gap between food supply and demand may further widen due to population growth and consumption upgrading, and regional imbalances may intensify [87]. The main production areas can maintain high production capacity through scale and technology integration, but balanced areas with strong ecological constraints may face dual pressures of increasing production and environmental protection [88]. The main consumption areas needs to upgrade facilities and enhance disaster resistance capabilities to tap into the potential production capacity on limited arable land [89].

2.4. Limitation of Existing Reference and Innovation Points

Overall, although the existing literature has extensively studied the static framework of various specific measures and policy effects for the construction of Well-Facilitated Farmland, there are still the following limitations: (1) Current studies primarily focus on historical and static evaluations, lacking forward-looking assessments and quantitative analysis of the potential grain yield increase by 2030, which limits their ability to inform mid- to long-term food security strategies; (2) Most existing analyses are conducted at the provincial or broader scale, with insufficient fine-grained investigation at the city level. The spatial patterns and productivity effects of WFF construction at the city scale have not been systematically revealed; (3) Previous studies have largely focused on production outcomes alone, with limited evaluation of how WFF contributes to grain self-sufficiency and national food security. Systematic and quantitative assessments of policy impacts in these areas are still underdeveloped.

To address the above limitations, this study makes the following contributions:

① This study assesses the potential impact of Well-Facilitated Farmland construction on grain yield increases by 2030 by integrating an effect size model and fitting the marginal benefits of policy interventions using historical data.

② This study refines data granularity by incorporating data on production, cultivated land area, and Well-Facilitated Farmland, and uses different weights to make precise predictions of construction effects at the city level. An innovative “provincial-level” dynamic allocation model and regional differentiation evaluation framework are constructed, providing theoretical support for the refined design and dynamic adjustment of agricultural policies, while also enhancing consideration of regional policy execution differences.

③ This study combines multi-scenario forecasting with Monte Carlo simulation to systematically analyze the yield improvement pathways of Well-Facilitated Farmland construction and quantitatively evaluate its contribution to grain self-sufficiency in major production, balanced, and consumption regions. The findings reveal the far-reaching impact of policy adjustments on national food security.

3. Data

The data of Well-Facilitated Farmland (WFF) construction used in this study are derived from the dataset-based research by [90], including provincial-level WFF data for 2020 and 2030, as well as city-level WFF, cropland, and grain production data for 2020. The effect size (elasticity coefficient) used to estimate the potential increase in grain production resulting from WFF construction is obtained from the literature [91]. Population projections for the year 2030 are sourced from the dataset-based research by [4]. Here, the effect (elasticity coefficient) is derived from a model that incorporates both province and time fixed effects and therefore can be broadly applied across all regions of China and remains relatively constant over time [91]. Among them, the visualization of WFF data for 2020 and 2030 is shown in Figure S1, Figure 3 and Figure 4:

According to the provincial data in 2020, shown in Figure S1, the proportion of high-standard farmland in the main production areas is 50–75%, and the color is dark, reflecting the advantages of early construction. The proportion of ecologically fragile areas and megacities is mostly 0–25%, and the color is light, reflecting the differences in resource constraints and policy priorities. By 2030, the main production areas will be further concentrated to a high proportion, and the color of some provinces will be deepened, indicating the continuous progress of large-scale renovation. At the city level shown in Figure 3, in 2020, the high coverage areas are mainly distributed in the North China Plain, the Northeast Plain, and the river valley cities in Xinjiang. The ecological fragile areas and the northwest arid areas have the smallest urban coverage. The construction scale of some areas is considerable, but the southeast coastal cities are relatively underdeveloped. In 2030, the high coverage area will expand, the growth ratio of cities in the main production areas will be obvious, and some low coverage hilly cities will achieve breakthroughs through technological transformation, and the absolute gap between regions will still exist. In contrast, the provincial and city data reflect the significant progress of construction, and the main production areas have always been the focus. The provincial data highlight the differences in regional policy orientation, and the city data reveal the spatial heterogeneity in the same province in more detail.

The 2020 city WFF construction area shown in Figure 4 presents a current situation: in 2020, the areas with the highest coverage of high-standard farmland (with a value of 256 or more) were mostly distributed in cities in the vast river valleys of the North China Plain, Northeast China Plain, and Xinjiang. However, in China’s ecologically fragile Tibet region and cities in the northwest arid regions represented by Qinghai and western Inner Mongolia, the area of high-standard farmland was the smallest (with a value below 29.1). In addition, there are also cities in the middle and lower reaches of the Yangtze River where the construction area of high-standard farmland is considerable. Compared to other cities, the construction area of high-standard farmland in southeastern coastal cities of China is still relatively small.

Overall, from 2020 to 2030, the proportion of Well-Facilitated Farmland construction (WFF) in China, as shown in Figure 5, has shown a significant increase, with distinct regional differences. The average coverage ratio of both the main production and main consumption areas has increased from about 50% to nearly 70%, with an increase of nearly 20%, reflecting the continuous deepening of large-scale rectification. The average coverage ratio of the balanced zone increased from about 35% to about 50%, reflecting the coordinated promotion of marginal land development and ecological adaptation. Overall, the main production and sales areas contribute the most incremental growth, while the catching up effect in the balanced areas is significant. The regional imbalance is gradually converging, but the absolute gap in coverage still exists. Further optimization of resource allocation is needed to support the national food security pattern.

4. Methodology

4.1. Grain Yield Assessment Based on Elastic Coefficient

According to the elasticity coefficient provided by Hu [91], it represents the impact of changes in the proportion of Well-Facilitated Farmland on changes in grain yield. That is, for every 1 percentage point increase in the coverage of Well-Facilitated Farmland construction, the average increase in grain yield is 0.14%. This reflects the marginal effect between policy intervention (changes in Well-Facilitated Farmland coverage) and grain yield. We predict the potential for increasing grain production through Well-Facilitated Farmland construction by 2030 using this elasticity coefficient. The calculation process is as follows:

Firstly, calculate the logarithmic variation in the proportion of Well-Facilitated Farmland:

Δ ln(share) = ln(share₁) − ln(share₀)

(1)

According to the definition of elasticity coefficient, calculate the logarithmic variation in grain yield:

Δ ln Y = β × Δ ln(share)

(2)

Next, calculate the target grain yield based on logarithmic changes:

Y₁ = Y₀ × exp (Δ ln Y) = Y₀ × exp [β × (ln(share₁) − ln(share₀))]

(3)

where share₀ refers to the initial proportion of Well-Facilitated Farmland; share₁ refers to the proportion of target Well-Facilitated Farmland; Y₀ refers to the initial grain yield; Y₁ refers to the target grain yield; and β refers to the elastic coefficient.

Afterwards, we can calculate the relative and absolute values of the increased production:

$$∆Y=Y_1-Y_0=Y_0[\mathit{exp}\left(\beta ∆ln share\right)-1]$$

(4)

$$Relative increase in grain production=∆Y/Y_0$$

(5)

4.2. Grain Self-Sufficiency

Grain self-sufficiency can be assessed using various indicators. To evaluate the balance between grain production and consumption, the most straightforward and direct approach is to compare actual grain production with the regional per capita grain consumption standards and the population it supports. By calculating the difference or ratio between production and consumption, the grain supply situation in the region can be effectively assessed, and potential risks to grain self-sufficiency can be identified [92]. This method provides a practical framework for analyzing the dynamic changes in regional grain security and helps to identify areas that require targeted interventions to address imbalances in grain supply and demand. This study estimates the contribution of high-standard farmland construction to improving China’s grain self-sufficiency ratio in 2030, based on the predicted grain production for 2030 and population forecast data. The formula for calculating the ratio of self-sufficiency and gap of self-sufficiency is as follows:

$${RSSG}_{i,y}={\displaystyle \frac{Y_{i,y}}{S\times P_{i,y}}}$$

(6)

$${GSSG}_{i,y}=Y_{i,y}-(S\times P_{i,y})$$

(7)

where ${RSSG}_{i,y}$ (unit: %) indicates ratio of self sufficiency of grain in region i of year y; $Y_{i,y}$ (unit: kg/ha) is grain production in region i of year y; $S$ (unit: kg grain/person*year) is standard of individual grain consumption per year; and $P_{i,y}$ (unit: person) is the population in region i of year y. ${GSSG}_{i,y}$ (unit: %) indicates the gap of self sufficiency of grain in region i of year y. If ${GSSG}_{i,y}$ is positive, it indicates that the grain production in the region exceeds the local consumption needs, ensuring self-sufficiency. Conversely, a negative ${GSSG}_{i,y}$ implies a grain deficit, requiring imports from other regions.

This section makes assumptions regarding population size and grain consumption demand. Population projections are based on the dataset provided by Zhang [4], which models and forecasts city-level population changes in China under multiple global scenarios. However, the unique circumstances of China—characterized by comprehensive poverty reduction, compulsory education policies, and carbon neutrality goals—make global scenarios like SSP4 and SSP5 (Shared Socioeconomic Pathways) less directly applicable. Therefore, this dataset has been further refined to incorporate adjusted fertility and migration scenarios better suited to China’s context (for a detailed explanation of the parameters, please refer to [4]). To minimize the influence of extreme scenarios, we adopt the median value across all scenarios as the estimated population size for 2030 in this study. Regarding dietary patterns, considering that Well-Facilitated Farmland (WFF) is designated as permanent basic farmland primarily intended for grain production, we assume that the per capita grain consumption structure remains unchanged throughout the study period. The calculation of actual grain production and consumption is influenced by various factors, including grain loss during sowing, harvesting, and storage processes. In addition to direct consumption as food, grains are also used for indirect consumption such as feed and food processing, which create additional constraints that are usually estimated using conversion coefficients, such as grain loss accounting for about 12% of total production [93,94]. An average of 400 kg per person per year is set as the food security threshold. This method framework provides a structured approach for quantifying food self-sufficiency ratios and identifying regional differences in food supply, which are crucial for ensuring food security [95].

4.3. Scenario Simulation of City-Level WFF in 2030

In addition to studying the grain production situation at the provincial level in China, we should also narrow the research scale and delve into the total grain production at the city level. This detailed analysis is significant, as it not only enables a more accurate assessment of the differences in grain production capacity across regions but also provides more targeted data support for regional grain security and agricultural policy formulation. By analyzing data at the city level, we can gain a clearer understanding of the grain production potential and challenges faced by each city, thereby providing scientific evidence for optimizing resource allocation, improving grain production efficiency, and ensuring grain security.

Due to the unavailability of city-level WFF data for 2030, we conducted scenario simulations based on the ① WFF, ② cropland, and ③ grain data at the city and provincial levels for 2020, as well as provincial-level WFF data for 2030. Through these simulations, we constructed three scenarios, named projection by 2020 WFF area; projection by 2020 cropland area; projection by 2020 grain production, and applied different weights to calculate the 2030 city-level WFF. Subsequently, we estimated the total grain production at the city level for 2030. Therefore, the calculation formula for the 2030 city-level WFF is as follows:

$$WF{F}_{20301-city}=WF{F}_{2020-city}÷WF{F}_{2020-province}\times WF{F}_{2030-province}$$

(8)

$$WF{F}_{20302-city}={Cropland}_{2020-city}÷{Cropland}_{2020-province}\times WF{F}_{2030-province}$$

(9)

$$WF{F}_{20303-city}={Grain}_{2020-city}÷{Grain}_{2020-province}\times WF{F}_{2030-province}$$

(10)

where in $WF{F}_{a-b}$, a refers to the scenario, b refers to the WFF at the scale (city/provincial); in ${Cropland}_{a-b}$, a refers to the scenario, b refers to the scale (city/provincial) of cropland; in ${Grain}_{a-b}$, a refers to the scenario, b refers to the grain scale (city/provincial), and province corresponds to the city, where province is the province to which the city belongs.

4.4. Monte Carlo Simulation

This study analyzes the grain production and consumption situation across different regions in China, classifying them into three major categories: major production areas, main consumption areas, and balanced areas. Based on the previously mentioned grain production predictions at the city level, this section further utilizes the Monte Carlo simulation [96] method to conduct scenario simulations for each region and at the national level, in order to assess the changes and uncertainties in grain production under different scenarios. The Monte Carlo simulation uses a triangular distribution for random sampling of the data [97]. The triangular distribution [97,98,99,100] is a commonly used probability distribution that describes uncertainty by setting a minimum value, a maximum value, and the most likely value. In this study, the minimum, maximum, and most likely values represent the lower limit, upper limit, and most likely value of grain production under different scenarios. These values are determined by the three key weights mentioned in Section 4.3: projection by 2020 WFF area; projection by 2020 cropland area; projection by 2020 grain production. These three weights collectively influence the uncertainty in grain production and serve as the basis for the minimum, maximum, and mode values in the simulation (see Table S2). The purpose of applying the Monte Carlo simulation is to reduce uncertainty by integrating multiple data sources and generating a range of possible outcomes. By taking the median value of the simulation results, we aim to minimize the influence of data variability and enhance the robustness of the final estimates. To ensure the stability and reliability of the simulation results, the simulation was run for 10,000 iterations. Using this method, we can obtain the distribution of grain production for each region and nationally, allowing further analysis of the uncertainties in grain production under different scenarios and their potential impacts on grain security.

5. Result

5.1. National Grain Production Increase Under Three Weight Indicators

We constructed a yield estimation function based on the elasticity coefficient in the benchmark regression model, and solved for the 2030 grain yield, absolute and relative values of yield increase under three different weights. The results are shown in

Table 1. 2030 grain production under three weights.

Weight	2030 Grain Production (Unit: Million Tons)	Absolute Increase in Production (Unit: Million Tons)	Relative Increase in Production (Unit: %)
Projection by 2020 WFF area	610	3051	5.26
Projection by 2020 Cropland area	625	4500	7.76
Projection by 2020 Grain production	628	4787	8.25

.

The prediction results based on different weight settings show that there are significant differences in the improvement effect of Well-Facilitated Farmland construction on grain production in 2030. Based on the weight calculation of the projection by 2020 WFF area, the grain output in 2030 is 6.1 million tons, with an increase of 30.51 million tons (a relative increase of 5.26%), reflecting the optimization potential of existing facilities. However, its marginal benefit decline is significant—mature agricultural areas have limited production space due to high coverage, while ecologically fragile areas are limited by facility aging and resource constraints, resulting in limited incremental contributions. When weighted by the projection by 2020 Cropland area, the output increased to 62.5 million tons, an increase of 45 million tons (an increase of 7.76%), highlighting the fundamental role of cultivated land scale in production capacity, but may conceal the risk of ecological red line constraints and rising marginal land transformation costs. Based on the weight of the projection by 2020 Grain production, it is predicted that the output will reach 62.8 million tons, with an increase of 47.87 million tons (an increase of 8.25%), reflecting the efficiency logic of technology diffusion and policy coordination in high-yield areas. However, it may also exacerbate regional imbalances, leading to weak contributions from low-yield areas due to insufficient technological penetration.

5.2. The Impact of Well-Facilitated Farmland Construction on Provincial Grain Production Increase

Based on the elasticity coefficient and existing data, we can calculate the grain production, grain growth ratio, and grain self-sufficiency ratio of each province before and after 2020 and 2030, as shown in Table S1.

As the core guarantee area of national food security, the main grain producing areas undertake the strategic task of ensuring the total grain supply and providing commodity grain. Relying on the advantages of large-scale planting and resource endowment, they strengthen production capacity through high-standard farmland construction and other measures. Affected by urbanization and resource constraints, the main consumption areas aims to ensure the basic balance between regional grain supply and demand, focuses on improving unit capacity through technological upgrading, and relies on external transportation to supplement demand. The balance zone needs to seek a dynamic balance between grain production and ecological protection, which not only ensures a certain self-sufficiency, but also takes into account ecological security. It is the key area to coordinate food security and sustainable development. The three together constitute the structural support system of national food security.

From the perspective of changes in grain production, the performance of various provinces between 2020 and 2030 shows significant regional differentiation characteristics, reflecting the interaction between different regional resource endowments and the priority of Well-Facilitated Farmland construction policies. The grain production in major production areas such as Henan Province and Heilongjiang Province increased from 68.26 million tons and 75.41 million tons in 2020 to 69.48 million tons and 80.30 million tons in 2030, with growth ratios of 1.23 million tons and 4.89 million tons, respectively. The reason behind this significant growth is not only due to their inherent advantages in terms of farmland scale and soil fertility (such as the black soil area in Heilongjiang benefiting from systematic soil protection policies to improve fertility), but also due to the synergistic effect of high-standard farmland construction—local governments provide large financial subsidies for land leveling, irrigation system upgrades, and the introduction of advanced agricultural machinery, and encourage the use of organic fertilizers and the implementation of no-till agriculture technology, which promotes the large-scale operation of farmland and creates economies of scale. In contrast, major sales regions such as Beijing and Shanghai are facing the strict impact of rapid urbanization, limited by rigid constraints on arable land resources, with grain production growth ratios of only 1.78 million tons and 1.62 million tons, respectively. Although these cities have improved their production capacity per unit area through facility upgrades, such as building modern greenhouse facilities, applying precision irrigation systems, and providing subsidies for smart greenhouse construction, proving the marginal role of technology intensive roads in resource scarce areas, the yield increase effect is still minimal due to the limited land foundation.

The regional differences in grain growth ratio are highly correlated with resource endowment, policy adaptability, and technological penetration capacity. Among the main production areas, Heilongjiang Province led the way with an increase of 4.89 million tons, thanks to the synergistic effect of land consolidation projects and mechanization popularization, as well as the implementation of black soil protection policies. Yunnan Province, as a representative of the balanced zone, saw an increase of 1.73 million tons, mainly due to the rapid increase in Well-Facilitated Farmland coverage from 26.40% to 49.34%. Combined with the construction of small-scale water conservancy facilities in karst landform areas, it effectively alleviated the constraints of seasonal drought on yield. At the same time, Yunnan has also introduced policies to encourage the development of characteristic agriculture and promote the cultivation of high-yield and high-quality crop varieties suitable for local conditions. In the main consumption areas, Tianjin’s growth ratio was only 9.57 million tons, indicating that its high-quality arable land resources are highly scarce (with a Well-Facilitated Farmland area of only about 206 hectares by 2030), which poses a growth bottleneck. In order to make up for the shortcomings of traditional planting, Tianjin is exploring the development of urban agriculture. The local government has introduced policies to support the construction of rooftop farms and plant factories, providing technical guidance and financial support for relevant enterprises. However, due to the high cost and relatively small scale of urban agricultural development, its impact on the growth of total grain output is limited. In addition, regional differences also need to be noted. For example, the grain production in Liaoning Province increased from 38.02 million tons to 60.53 million tons (an increase of 53.11%), while in Gansu Province it only increased from 12.02 million tons to 14.01 million tons (an increase of 16.47%). The differences between the two may be due to Liaoning having a flat terrain and abundant water resources, which enables the comprehensive implementation of large-scale well sited construction projects. The local government also actively introduces agricultural technology enterprises and promotes the popularization of advanced planting and breeding technologies. In contrast, Gansu is mainly mountainous and arid areas with poor natural conditions for agriculture. Although the local government has made efforts to facilitate farmland construction, such as implementing water-saving irrigation projects, the yield increase effect is not as significant as in Liaoning due to limited financial resources and the harsh natural environment.

The change in self-sufficiency ratio of grain further highlights the differences in regional functional positioning and resource carrying capacity. Among the main production areas, the self-sufficiency ratio of Heilongjiang Province has jumped from 594.51% in 2020 to 741.40% in 2030, far exceeding local consumer demand, confirming its strategic position as the core production area of commodity grain in the country. However, excessive reliance on a single production area may lead to systemic risks, such as production cuts or market disruptions caused by extreme weather conditions. As another major production area, Henan Province’s self-sufficiency ratio has slightly increased from 171.66% to 174.39%, indicating a dynamic balance between its production capacity improvement and population growth (from 99.41 million to 99.69 million). In the main consumption areas, Beijing’s self-sufficiency ratio has increased from 3.48% to 5.69%. Although the absolute value is relatively low, its self-sufficiency ratio has significantly increased through the application of facility agriculture and vertical farm technology, indicating the improvement effect of technological upgrading on the local supply and demand balance. The self-sufficiency ratio of Shanghai has increased from 9.18% to 9.49%, reflecting the significant pressure of increasing grain production under the dual pressure of rapid population growth. In the balance area, the Xizang Autonomous Region has increased its self-sufficiency ratio from 70.29% to 79.61% through the promotion of plateau adaptive crop varieties through the coverage ratio of Well-Facilitated Farmland from 62.99% to 89.12%, highlighting the potential of ecologically fragile areas to achieve food security through technological adaptation. However, its absolute output (from 10.29 million tons to 10.60 million tons) has been limited, suggesting the need to further optimize the input–output efficiency, such as by exploring more suitable high-yield crop varieties and improving the utilization rate of agricultural resources.

5.3. Prediction of Total Grain Production Based on Monte Carlo Simulation

5.3.1. National Grain Production

As shown in Figure S2, the simulation results indicate that the median predicted national grain production is about 620 million tons (corresponding to a peak density range of 615–625 million tons), with a slight right-skewed distribution. This right-skewed state implies the possibility of upward fluctuations in production. The overall production is concentrated between 610 and 625 million tons, and the predicted results are relatively concentrated, which strongly reflects the robustness of high-standard farmland construction in improving the national grain production capacity. From a spatial trend perspective, this concentrated distribution reflects the comprehensive effectiveness of high-standard farmland construction at the national level, ensuring stable grain production within a certain range. In terms of regional impact, this indicates that most areas have effectively improved and stabilized their production capacity through the construction of high-standard farmland. However, the occurrence of extreme values exceeding 625 million tons suggests that we need to pay attention to the possibility of unexpected production increases caused by climate change or technological breakthroughs. Climate change may make certain regions more suitable for grain production, and technological breakthroughs such as new seed cultivation or advanced irrigation technologies may lead to higher than expected yields.

5.3.2. Main Production Areas

The median output of the main production areas is about 488 million tons (with peak density concentrated between 484 and 492 million tons), and the distribution shows a narrow peak symmetry feature, indicating that the predicted results are highly consistent. The core production range (484–492 million tons) has an absolute advantage, fully reflecting the deterministic benefits brought by large-scale planting and high-standard farmland construction. From a spatial perspective, the production conditions in various regions of the main production areas tend to be consistent under the promotion of high-standard farmland construction, resulting in a concentrated distribution of yield. At the regional level, this means that the main production areas have become a key force in ensuring stable grain production through large-scale and high-standard construction. But the narrow production distribution range (480–492 million tons) also suggests that the marginal effects of current policies or technologies are stabilizing. The current policies and technological applications have already tapped into most of the potential for increased production. To break through existing bottlenecks, technological innovation is needed, such as introducing intelligent agricultural management systems or developing new and efficient fertilizers.

5.3.3. Balance Areas

The median production in the equilibrium zone is about 104 million tons (corresponding to a peak density of 103–105 million tons), with a highly concentrated and left-skewed distribution, indicating that the predicted results are highly reliable, but the growth potential is limited. Its production is concentrated between 102 and 105 million tons, reflecting the rigid constraints of ecological and arable land resources on production capacity. From the perspective of spatial trend analysis, the equilibrium zone is limited by its own ecological conditions and cultivated land scale, and there is not much room for yield increase. In terms of regional impact, the equilibrium zone is difficult to increase yield through large-scale expansion of arable land or changes in ecological conditions. In the future, it is necessary to explore marginal benefits through eco-friendly technologies such as water-saving agriculture and soil carbon sequestration. For example, water-saving agricultural technology can improve water use efficiency in situations where water resources are limited, while soil carbon sequestration technology can help improve soil quality and explore potential areas for increased production without damaging the ecology.

5.3.4. Main Consumption Areas

The median output of the main consumption areas is about 105 million tons (assuming a distribution similar to the equilibrium zone), with a narrow density distribution range (102–106 million tons) and a significant peak (density 0.6), reflecting its limited production capacity due to high urbanization and reduced arable land area. The predicted results have a high degree of certainty, and slight fluctuations in production (such as 106 million tons) may be due to local breakthroughs in agricultural technology. However, from the perspective of spatial trends, the urbanization process in the main consumption areas has led to a significant occupation of arable land, resulting in a continuous reduction in land available for grain production. At the regional level, the main consumption areas find it difficult to achieve significant production increases on their own. Overall, the growth potential of the main consumption areas is limited. Although local technological breakthroughs can bring about a small increase in production, it is difficult to change the situation of limited overall production capacity.

5.4. Spatiotemporal Heterogeneity Analysis of Changes in Self-Sufficiency Ratio of Grain

By calculating the grain self-sufficiency gap (gap of self-sufficiency of grain) for 2020 and 2030, this study also includes the 2030* scenario, simulating the situation in 2030 without the implementation of high-standard farmland construction. To provide a more intuitive visualization of the grain supply, demand, and supply–demand gap across different regions, a bar chart is presented, as shown in Figure 5. This chart allows for a clear observation of the grain supply and demand conditions in each region under different scenarios and further reveals the trends in the grain self-sufficiency gap under each scenario.

The analysis indicates that for the major production areas, after ten years of high-standard farmland construction, grain production in 2030 increases significantly, by approximately 30 million tons, while grain consumption decreases by about 8 million tons. As a result, the grain surplus in the major production areas is around 20 million tons. In contrast, in the 2030* scenario, where no high-standard farmland construction is implemented, the grain surplus is significantly lower than that in the constructed scenario. High-standard farmland construction has markedly enhanced the grain production capacity in the major production areas, while also reducing the demand for grain consumption, thereby effectively increasing the grain surplus. For the balanced areas, although grain self-sufficiency has never been achieved, with the grain self-sufficiency gap remaining negative, after high-standard farmland construction, the gap is significantly reduced, improving by approximately 7 million tons. Interestingly, in the 2030* scenario, where no high-standard farmland construction is implemented, the grain self-sufficiency gap in the balanced areas is smaller than it was in 2020. In the main consumption area, although the increase in grain production is relatively small, a slight rise in grain consumption results in a reduction in the grain gap. Notably, in the 2030* scenario, the grain gap in the main consumption area is the smallest.

Overall, the analysis across all regions shows that in the 2030* scenario, the grain self-sufficiency gap is generally lower than in 2030, suggesting that high-standard farmland construction contributes to reducing the grain supply–demand gap and improving grain self-sufficiency. High-standard farmland construction not only enhances grain production capacity but also optimizes the grain supply–demand balance across regions, thereby fostering a more stable and sustainable grain production and consumption system for the future.

To provide a clearer understanding of the changes in grain self-sufficiency ratios across Chinese cities under the four scenarios of 2020, projection by 2020 WFF area; projection by 2020 cropland area; projection by 2020 grain production, this study classifies cities into four groups based on their grain self-sufficiency ratios: <50%, 50–100%, 100–200%, and >200%. A bar chart is used for data visualization. This approach effectively highlights the changing trends in grain self-sufficiency ratios under different scenarios and clearly illustrates the differences in the number of cities within each self-sufficiency category, as shown in Figure 6.

The year 2020 serves as the baseline scenario, representing the situation before the implementation of the 10-year high-standard farmland construction. In this scenario, the number of cities with a grain self-sufficiency ratio below 50% is as high as 72, accounting for a significant proportion. Meanwhile, the number of cities with a self-sufficiency ratio in the range of 50–100% is the largest, reaching 98; whereas only 47 cities have a self-sufficiency ratio above 200%. In this baseline scenario, many cities exhibit relatively weak grain self-sufficiency, with considerable regional disparities. In contrast, the other three scenarios (projection by 2020 WFF area; projection by 2020 Cropland area; projection by 2020 Grain Production) are based on different weightings to estimate the grain self-sufficiency ratio in 2030 after the completion of high-standard farmland construction. These scenarios reflect the improvement effects of the 10-year high-standard farmland construction, indicating that such construction has a significant impact on enhancing urban grain self-sufficiency. Specifically, in these scenarios, the number of cities with a grain self-sufficiency ratio below 50% is reduced to fewer than 70 in each case, with only 64 cities in the projection by 2020 grain production scenario exhibiting a self-sufficiency ratio below 50%. This shows that high-standard farmland construction significantly reduced the number of cities with extremely low self-sufficiency. Furthermore, the number of cities with self-sufficiency ratios in the 50–100% range also decreased significantly, from 98 to about 85, further suggesting that more cities have improved their grain self-sufficiency. Notably, in the projection by 2020 WFF area scenario, the number of cities with self-sufficiency ratios in the 100–200% range significantly increased to 109. This indicates that high-standard farmland construction has significantly enhanced the grain self-sufficiency of cities, with more cities achieving a higher level of self-sufficiency.

More prominently, in the projection by 2020 cropland area and projection by 2020 grain production, the number of cities with a self-sufficiency ratio above 200% grows substantially, reaching 59, demonstrating the strong impact of high-standard farmland construction in boosting grain self-sufficiency. This improvement can be attributed to the enhancement of agricultural production conditions, particularly in areas such as land quality, irrigation facilities, farming techniques, and agricultural mechanization. These factors combine to greatly improve grain production efficiency, thereby strengthening the cities’ grain self-sufficiency. Through high-standard farmland construction, these cities not only achieved the goal of grain self-sufficiency but also laid a solid foundation for future grain security. Therefore, the 10 years of high-standard farmland construction not only significantly boosted grain self-sufficiency in the short term but also created favorable conditions for long-term grain security and sustainable agricultural development.

5.5. Policy Suggestions

As a key factor in ensuring national food security and achieving agricultural modernization transformation, the construction of high-standard farmland needs to be further optimized within the existing policy framework, and a multi-level and multi-dimensional collaborative promotion system needs to be constructed. Based on the regional differences revealed by research and the existing literature, future policies should focus on the following directions to promote the deep integration of grain production capacity and sustainable resource utilization:

① Strengthen regional planning and coordination between regions

The Ministry of Agriculture and Rural Affairs mentioned in the “Implementation Plan for Gradually Building Permanent. Basic Farmland into High Standard Farmland” that the goal of high-standard farmland construction is to strive to build a total of about 9005 thousand hectares of high-standard farmland by 2030, upgrade about 1868 thousand hectares of high-standard farmland, coordinate planning and synchronously implement efficient water-saving irrigation, and increase the area of efficient water-saving irrigation by about 53 thousand hectares. By 2035, China will strive to build all permanent basic farmland that meets the conditions into high-standard farmland, with a total renovation and improvement of about 303 thousand hectares, and an additional about 87 thousand hectares of high-efficiency water-saving irrigation area [66,101]. This macro goal is quite challenging and needs to be refined at the grassroots level to steadily promote implementation [102]. Therefore, the construction of Well-Facilitated Farmland should be included in the long-term development plans of the localities, with clear construction goals, tasks, and time nodes [103,104]. By constructing differentiated regional development strategies, we aim to enhance the adaptability of resource endowments [105]. Simultaneously, differentiated technological paths and policy support plans should be developed to address the resource endowment differences between major grain producing areas, balanced areas, and major sales areas [106]. Moreover, a cross-departmental coordination mechanism should be established to ensure the orderly progress of Well-Facilitated Farmland construction [107], avoid redundant construction and resource waste, and lay a solid foundation for increasing grain production and ensuring food security.

② Increase capital investment, policy incentives, and guarantee mechanisms

The Ministry of Agriculture and Rural Affairs has issued a document requiring the implementation of high-quality agricultural development requirements [65], deepening the strategy of storing grain in the land and technology, with the primary goal of increasing grain production capacity, deepening the mechanism of government bank cooperation, actively exploring innovative investment and financing models, guiding policy financial institutions to increase credit capital investment, strengthen the construction of high-standard farmland, improve the quality of arable land, improve agricultural production conditions, gradually build all permanent basic farmland into high-standard farmland, and comprehensively consolidate the foundation of food security. As the main body of high-standard farmland construction, farmers should receive appropriate material incentives. To motivate local governments and agricultural business entities, agricultural subsidy policies can be improved [108], with subsidy funds tilted towards high-standard farmland construction. A diversified policy support system including technology, services, and finance should be established to provide various information services such as agricultural guidance throughout the production cycle [109], market information, and disaster prevention and mitigation for small farmers and other business entities [110,111]. Encouraging farmers to adopt high-quality seeds and advanced farming techniques can increase grain output and quality. Meanwhile, strengthening the implementation of farmland protection policies can help prevent farmland from being used for non-agricultural purposes or non-grain production [112], ensuring that high-standard farmland is used for grain production and safeguarding the self-sufficiency ratio of grain and national food security.

③ Smart agriculture empowers grain production increase

Smart agriculture is an important focus for the development of modern agriculture and a strategic high ground for building a strong agricultural country, and the use of agricultural technology has always been a key factor in increasing grain production [113]. In the guidance on vigorously developing smart agriculture, the Ministry of Agriculture and Rural Affairs points out that we should base ourselves on China’s basic national conditions and agricultural conditions, take promoting the all-round and full chain popularization and application of information technology in the agricultural and rural fields as the main work line, and comprehensively improve the total factor productivity of agriculture and the efficiency of agricultural and rural management services as the main goal [114]. We should strengthen top-level design, increase policy support, and enhance application orientation, focus on solving bottleneck problems in information perception, intelligent decision-making, and precise operation, coordinate the research and development, integrated application, demonstration and promotion of technical equipment, significantly improve the level of agricultural intelligence, and provide new impetus for accelerating the modernization of agriculture and rural areas. Therefore, it is necessary to increase investment in agricultural technology research and development, promote advanced agricultural planting techniques, irrigation techniques, soil improvement techniques, and other measures. Especially in the construction of Well-Facilitated Farmland, the widespread application of intelligent irrigation equipment, precision fertilization systems, and agricultural Internet of Things technology [115,116] can significantly improve agricultural production efficiency and resource utilization, reduce human input, achieve precision sowing, fertilization, harvesting, and other operations, and thereby increase grain yield. In addition, it is possible to strengthen cooperation with research institutions and agricultural cooperatives [117], establish long-term stable cooperation mechanisms, cultivate high-quality agricultural technology talent teams [118], provide technical support and intellectual guarantee for the construction of Well-Facilitated Farmland, and promote grain production increase and sustainable agricultural development.

④ Strengthening the construction of monitoring system for grain production

Agricultural ecological environment monitoring is a long-term and fundamental work. Conducting agricultural ecological environment monitoring is of great significance for accurately judging the current situation of the agricultural ecological environment in China, implementing the action plan for agricultural and rural pollution control, continuously improving the quality of the agricultural ecological environment, and ensuring the quality and safety of agricultural products. The Chinese government website and the Ministry of Agriculture and Rural Affairs emphasized this and issued targeted methods for investigating, monitoring [119,120] and evaluating the quality of cultivated land. Specifically, establishing a sound monitoring and evaluation system for Well-Facilitated Farmland construction is conducive to tracking and dynamically monitoring the construction process and effectiveness [121], providing data support for ensuring food security [122]. By regularly evaluating the soil pollution [29], irrigation facilities, and field roads of Well-Facilitated Farmland during the growth period of crops, problems can be identified and rectified in a timely manner. This can continuously provide reference for the subsequent construction of Well-Facilitated Farmland [123], and form more scientific and reasonable evaluation indicators to ensure the quality of Well-Facilitated Farmland construction [124], enhance the ability to increase grain production, and improve the level of national food security.

6. Discussion

6.1. Sensitivity Analysis

To better capture the impact of regional differences in natural conditions and policy implementation on grain yield projections, and to enhance the robustness of the model, this section introduces a sensitivity analysis. Specifically, climate change introduces uncertainties like extreme weather events and altered precipitation patterns, which pose threats to crop yields and soil health [9,10,11]. The construction of Well-Facilitated Farmland, equipped with advanced irrigation and drainage systems, enables better resistance to climate-induced water-related pressures and helps compensate for productivity losses arising from the urbanization-driven reduction in arable land [15,16,17]. Furthermore, the Well-Facilitated Farmland can attract and retain surplus agricultural labor through more efficient and mechanized operations, thus mitigating the impact of external pressures on agricultural production. In addition to the general coefficient [91], two alternative coefficients are incorporated under different scenarios. Climate coefficient (0.077) accounts for climate and disaster factors, simulating the potential yield increase from Well-Facilitated Farmland under climate stress conditions. Endowment (0.092) incorporates factors such as the development of farmland transfer markets and agricultural service organizations based on the climate scenario, reflecting the influence of institutional and structural conditions on yield improvement. The results of these sensitivity analysis are presented in Figure 7.

The data show that under the general scenario, the total grain output is 622 million tons, the total consumption is 516 million tons, and the supply–demand gap is in surplus, 105 million tons. In the endowment scenario, the total output fell to 606 million tons, the total consumption remained unchanged at 516 million tons, and the surplus of supply and demand narrowed to 90 million tons. The total output of the climate scenario was further reduced to 601 million tons, the total consumption was still 516 million tons, and the surplus of supply and demand was 85 million tons.

From the key findings of scenario comparison, the general scenario as the benchmark predicted the highest yield and surplus, which reflected the maximum yield potential under the condition of ignoring regional constraints. However, this scenario overestimates the actual production capacity of ecologically fragile or climate sensitive regions (such as Gansu and Qinghai), which are significantly limited by high natural risks and low institutional efficiency. Under the endowment scenario, compared with the general scenario, the output was reduced by 16 million tons, and the surplus was reduced by 16 million tons. This result shows that natural endowment and institutional factors (such as the fragmentation of farmland in mountain areas, the lagging development of service organizations in rural areas, etc.) have significant constraints on the increase in output. Taking the balanced regions (such as Yunnan and Guizhou) as an example, due to the limited circulation of agricultural land and the low popularization rate of mechanization, the construction effect of high-standard farmland is affected, resulting in a relatively gentle increase in output. The output and surplus of the climate scenario are the lowest in the three scenarios, highlighting the key impact of climate risk. For regions vulnerable to extreme weather (such as the Yangtze River Basin suffering from floods and the Yellow River Basin facing drought), compared with the endowment scenario, the surplus is further reduced by 4 million tons, which is consistent with the empirical research results, that is, even after the construction of high-standard farmland, the climate impact will still have a great impact on regions with a weak disaster prevention infrastructure [30,125].

Sensitivity analysis confirmed that the use of a single elastic coefficient has limitations. Specifically, the general scenario can provide a reference for areas with superior natural conditions (such as the North China Plain). Endowment scenarios suggest that for less developed regions, targeted policies need to be formulated, such as promoting farmland integration, supporting agricultural service organizations, etc., to enhance the support capacity at the institutional level. The climate scenario emphasizes that in areas with a fragile climate, climate resilience should be included in the construction standards of high-standard farmland (such as improving drought-resistant irrigation systems, drainage facilities, etc.). These adjustments not only improve the flexibility of the model, make the prediction results more in line with the actual situation of the region, but also help to enhance the scientificity and credibility of policy recommendations.

The analysis shows that adjusting the elasticity coefficient significantly affects the projected yield increase. This highlights the importance of considering regional heterogeneity and external uncertainties in the policy-making process to improve the scientific basis and practical applicability of agricultural policies.

6.2. The Potential of Well-Facilitated Farmland Construction to Increase Grain Production in China

The implementation of the food security strategy requires quantifying the actual driving effect of policy tools on production capacity. This study systematically verified the core role of high-standard farmland construction in the “storing grain in the land” strategy through a combination analysis of elasticity coefficient method and Monte Carlo simulation. The effect size (elasticity coefficient) fitted based on provincial panel data from 2006 to 2017 shows that every 1% increase in high-standard farmland coverage can drive a 0.14% increase in grain yield [91]. This marginal effect is highly consistent with the 0.0015 degree of impact of farmland quality improvement on production capacity in the literature [76]. Applying this effect size to the calculation of the 2030 construction target, it was found that the potential for national grain production increase reached 31–48 million tons, with a growth ratio of 5.26–8.25% (Table 1). This result is not significantly different from Wei et al.’s (2025) estimation of a grain production increase rate of about 13% from 2026 to 2030 based on water resource endowment, which confirms the robustness of the calculation framework [126]. The median result of the Monte Carlo simulation (620 million tons) further reveals that even with regional endowment differences and climate risks, policies can still steadily release capacity gains (Figure S2). This stability stems from the systematic optimization of agricultural production factors through the construction of high-standard farmland: reducing land occupation through land leveling, upgrading irrigation facilities to improve water resource utilization efficiency, and increasing organic matter content through soil improvement [49]. Sensitivity analysis shows that under climate stress scenarios, the potential for increased yield decreases by about 2.5% compared to the baseline scenario, which is consistent with the conclusion found by Fu et al. (2023) that “extreme precipitation can cause a 1/12 loss of rice production capacity in China” [127], suggesting the need to strengthen water-saving projects in arid areas and improve drainage systems in flood-prone areas to enhance resilience [128].

The core contradiction of food security has shifted from overall shortage to regional imbalance. This study reveals for the first time the reshaping effect of high-standard farmland construction on regional supply and demand patterns at the provincial and municipal levels by constructing a “ratio and gap of self-sufficiency of grain” dual indicator system (RSSG and GSSG). In the main production areas, the grain surplus of Heilongjiang Province increased from 37 million tons in 2020 to 42 million tons in 2030, and the self-sufficiency ratio increased from 594.51% to 741.40% (Table S1), which is consistent with the conclusion of Qian et al. (2024) that “the synergistic effect of large-scale operation and high-standard farmland in main production areas can increase commodity ratios by 15–20%”, consolidating its strategic position as the national “granary” [129]. The breakthrough in the equilibrium zone has more policy implications. Taking Yunnan as an example, through the support of small-scale water conservancy facilities in karst landform areas, the self-sufficiency ratio of grain has increased from 68.3% to 79.6%, and the supply-demand gap has narrowed by 7 million tons (Figure 5b), which confirms the effectiveness of the “marginal land transformation + ecological adaptation technology” path [130]. The main consumption areas presents the characteristic of “limited increment but quality improvement”: Beijing has achieved a self-sufficiency ratio of 5.69% from 3.48% through intelligent greenhouse and vertical farm technology, which is highly consistent with the viewpoint proposed by Du et al. (2025) that “resource constrained areas should take a technology intensive path” [131]. The change in urban scale is most convincing: the number of cities with a self-sufficiency ratio of less than 50% has decreased from 72 to 64, while the number of cities with a self-sufficiency ratio of more than 200% has increased from 47 to 59 (Figure 6). This phenomenon of “polarization at both ends” reveals the optimization effect of policies on spatial efficiency: cities in the North China Plain rely on contiguous construction to achieve the scale effect (increase in unit output by 10–15%), while cities in the southeast coastal areas tap potential through facility upgrading. This discovery responds to the question in the introduction about “how to achieve structural safety through spatial reconstruction” and provides a micro basis for precise policy implementation.

The existing research mostly focuses on analyzing the total amount at the provincial level, which makes it difficult to reflect the differentiated performance of policies at the grassroots level. This study achieved the dynamic simulation of urban high-standard farmland area for the first time through a three-level weight allocation model (formulas 8–10). The spatial pattern in 2020 shows that high-coverage cities are concentrated in the North China, Northeast China Plain, and Xinjiang River Valley (Figure 3a), which is consistent with the resource endowment characteristics of “high contiguous arable land and low difficulty in transformation” in these regions [132]. By 2030, hilly cities will achieve breakthroughs in coverage through the “consolidation of small and large blocks”, but the absolute gap with plain areas will still reach 30–40 percentage points (Figure 3b), confirming the transmission mechanism of “terrain constraints cost differences progress differentiation” [72]. The prediction differences under different weights are more profound: Weight 1 based on the existing high-standard farmland area (with an increase of 5.26%) reflects path dependence, while Weight 3 based on grain yield (with an increase of 8.25%) highlights efficiency orientation (Table 1). This difference suggests that policies should balance “stock optimization” and “incremental quality improvement”: using a weight of 3 for the main production areas to strengthen their advantages and using a weight of 2 (cultivated land area) for the balanced areas to balance fairness [60]. The 10,000 iterations of Monte Carlo simulation further quantified the uncertainty range (±3%), providing a quantitative basis for risk prevention and control [96].

6.3. The Limitations of the Study and Future Research Directions

Although this study has made several contributions, there are still three limitations to this study. Firstly, the analysis of climate factors is not sufficiently detailed. Although climate and disaster-related variables were incorporated into the sensitivity analysis, along with corresponding elasticity coefficients for comparison, this study did not delve into the specific impacts of particular extreme weather events—such as droughts and heatwaves—on grain production across different regions. This limitation restricts a comprehensive understanding of the mechanisms by which climate risks affect agricultural output. Secondly, data limitations have constrained the refinement of scenario construction. At present, there is still a lack of research on yield growth coefficients across different regions and construction scenarios. We were only able to obtain region-level average effect values. Additionally, inconsistencies may arise during the integration of datasets from various sources, as they differ in terms of focus and statistical standards, potentially affecting the accuracy of simulation results. Finally, this study is primarily based on projections from 2020 to 2030 and does not cover longer-term periods. As a result, it does not fully capture the cumulative effects and potential ecological feedback mechanisms of Well-Facilitated Farmland construction over extended time horizons.

In response to the limitations identified in the current study, future research can be further advanced in several directions. Future research could incorporate more refined meteorological data and climate simulation models to analyze the specific impacts of typical extreme weather events (such as droughts and heatwaves) on grain yields across different regions and crop types. Also, future studies should enhance the collection and integration of diversified and differentiated data, with greater attention to regional heterogeneity, in order to build model input systems that are more locally adaptive and context-specific. Lastly, future research could also utilize longer historical time series and extended projection periods to systematically assess the long-term effects of Well-Facilitated Farmland construction on yield trends, ecological changes, and overall sustainability. In addition, future research could further integrate methods for exploring low-carbon pathways with data-driven approaches and policy simulation tools to investigate how to achieve the coordination between increased grain production potential and carbon reduction targets under the future Well-Facilitated Farmland Construction goals.

7. Conclusions

This study systematically evaluated the impact of Well-Facilitated Farmland construction on China’s grain production potential by 2030 using the elasticity coefficient method, multi-scenario simulation, and Monte Carlo analysis method. The calculation results based on the elasticity coefficient show that under the complex background of resource constraints, regional differences, and policy dynamic adjustments, the construction of Well-Facilitated Farmland can significantly improve grain production capacity and self-sufficiency ratio, but its effectiveness exhibits obvious spatial heterogeneity and marginal diminishing characteristics. According to the prediction based on the elasticity coefficient, if the Well-Facilitated Farmland area in 2020 is used as the weight, the national grain production is expected to increase by 5.26% to 610 million tons in 2030. When weighted by cultivated land area and grain production, the growth rates reached 7.76% (625 million tons) and 8.25% (628 million tons), respectively. This result confirms the core role of Well-Facilitated Farmland construction in optimizing production factor allocation and unleashing land potential, but also reveals the differentiation of regional contributions under different weights: the main grain producing areas (such as Heilongjiang and Henan) have achieved significant increases in production through large-scale land consolidation and mechanization popularization (Heilongjiang’s output has increased by 4.889 million tons, and self-sufficiency ratio has jumped from 594.51% to 741.40%), while the main consumption areas (such as Beijing and Shanghai) are limited by rigid constraints on arable land. Although the unit production capacity has been improved through facility agriculture technology (Beijing’s self-sufficiency ratio has increased from 3.48% to 5.69%), the absolute increase is limited (Shanghai only increased by 16,200 tons).

In addition, Monte Carlo simulation further revealed the dynamic characteristics of regional production capacity: the median national grain production is about 620 million tons, and the production in the main production areas is concentrated between 484 and 492 million tons, showing a stable scale benefit but a decreasing trend in technological margin. The balance zone and the main sales zone are constrained by ecology and resources, with production concentrated at 103–105 million tons and 102–106 million tons, respectively, and limited growth space. It is worth noting that the construction of Well-Facilitated Farmland has significantly improved the imbalance of regional self-sufficiency ratios: by 2030, the number of cities with grain self-sufficiency ratios below 50% will decrease from 72 to 64, while the number of cities with self-sufficiency ratios above 200% will increase from 47 to 59, promoting the transformation of the national food security pattern from “overall balance” to “structural optimization”.

There are still three further limitations to this study. First, while climate-related variables were included in the sensitivity analysis, the impacts of specific extreme weather events (e.g., droughts, heatwaves) were not examined in detail, limiting insights into regional climate risk mechanisms. Second, due to data constraints, region-specific yield coefficients and finer scenario variations were unavailable, and potential inconsistencies in multi-source data integration may affect simulation accuracy. Third, this study focuses on 2020–2030 and does not capture long-term cumulative effects or ecological feedbacks of WFF construction, which future studies could address with extended time series.

And this study also exposed the limitations of administrative policies: the marginal cost of the transformation of hilly and mountainous areas and medium and low-yield fields is rising (for example, the yield increase in Gansu is only 16.47%, far lower than that in Liaoning’s 53.11%), the technology penetration in ecologically fragile areas is lagging behind (the absolute output of Xizang is only increasing by 51,200 tons), and the mechanism of multi-subject participation is not yet sound. Future research can be further advanced in several directions. First, integrating high-resolution meteorological data and climate simulation models could help assess the specific impacts of extreme weather events—such as droughts and heatwaves—on grain yields across different regions and crop types. Second, improving the availability and integration of regionally differentiated data, including labor, investment, and land quality indicators, would enhance model adaptability to local conditions. Third, extending the time horizon through long-term panel data and remote sensing monitoring could reveal the cumulative effects, ecological feedback, and policy lags associated with Well-Facilitated Farmland construction. These efforts will contribute to more precise, resilient, and sustainable farmland policy design, particularly for resource-constrained regions.