Development of an Evaluation Indicator System for Medium–Low Yield Farmlands on the Basis of the Synergistic Improvement of Soil Carbon Sequestration and Production Capacity: A Theoretical Framework

Abstract

This study aims to systematically examine the concept and characteristics of medium–low yield farmland (MLYF), to identify the key factors influencing the coordination between soil carbon sequestration (SCS) and production capacity (PC) in MLYF, and develop an evaluation indicator system (EIS) to provide innovative approaches for transforming MLYF to enhance food security and emission reduction capabilities. Focusing on the synergistic improvement of SCS and PC in MLYF, this research employs theoretical analysis, systematic inference, and inductive deduction to analyze the relationships between these factors and construct the EIS. The findings reveal that (1) MLYF is characterized by inherent limitations and suboptimal management practices, resulting in low grain PC but significant potential for yield improvement. (2) A positive correlation exists between the soil organic carbon (SOC) content and crop yield, with MLYF demonstrating substantially greater potential for synergistic improvement than high-yield fields. (3) On the basis of soil science principles, the key factors affecting the synergistic enhancement of carbon sequestration and productivity in MLYF include climatic conditions, soil properties, and biological factors. (4) A comprehensive “Demand-Function-Dimension-Element-Indicator” framework was established, incorporating an EIS designed for national, provincial, and city/county levels to address the management requirements of MLYF across various scales, thereby facilitating comprehensive quality improvement. This research contributes to the theoretical understanding of MLYF transformation, offering valuable insights for ensuring national food security and achieving carbon emission reduction goals.

1. Introduction

Cultivated land is the most valuable agricultural resource and essential production component [1]. High-quality cultivated land resources are crucial for promoting the harmonious development of the national economy, stabilizing national food security, and ensuring the country’s long-term stability [2,3,4]. However, according to the United Nations’ Food and Agriculture Organization, 33% of the world’s land is affected by erosion, acidification, pollution, desertification, and compaction, leading to a continuous decline in the quality of degraded cultivated land [5]. Additionally, 28.9% of the global population experiences moderate or severe food insecurity [6]. The situation is even more severe in developing countries. As the world’s largest developing country, China faces immense pressure to ensure national food security, feeding 20% of the world’s population with only 9% of the world’s cultivated land. Furthermore, owing to the unreasonable and excessive use of chemical fertilizers and pesticides, the quality of cultivated land has significantly declined. According to the 2019 National Cultivated Land Quality Grade Bulletin issued by the Ministry of Agriculture and Rural Affairs of the People’s Republic of China, the average quality grade of cultivated land in China is 4.76, with medium–low yield farmland (MLYF) accounting for 68.76% of the total cultivated land area. Sun et al. [7] developed a rice-wheat–oil crop planting system tailored for coastal saline–alkali MLYF in eastern China. These findings demonstrated that the yield of MLYF could be increased by 22% to 34%. Using data from the China County (City) Socioeconomic Statistical Yearbook, Liu et al. [8] reported that the average grain yield in high-yield areas was 9.583 × 10⁵ tons, whereas it was only 5.168 × 10⁵ tons in MLYF areas—less than half of the high-yield counties. Given its large proportion, poor quality, and low yield, MLYF has gradually become a potential threat to sustainable grain production in China. The above situation indicates that China faces significant instability in grain production, particularly due to the high proportion of MLYF, which has a substantially negative impact on grain production. However, these findings also reveal that China’s grain production capacity has significant potential for improvement. The utilization and enhancement of MLYF are inevitable paths for China to achieve sustainable development goals and eliminate hunger in the future.

Soil organic carbon (SOC) is a critical component of soil and influences the soil’s physical and chemical properties as well as soil fertility through various processes and mechanisms [9]. Increasing the soil carbon sequestration capacity (SCS) of MLYF can improve soil nutrient cycling, optimize the soil structure, and stimulate microbial activity, thereby increasing overall soil health and quality [10]. Furthermore, the soil carbon pool represents the largest active carbon pool in terrestrial ecosystems, with reserves approximately 2–3 times greater than the atmospheric carbon reserves and 2–4 times larger than the terrestrial vegetation carbon pools [11]. Even minor changes in the SOC pool can significantly impact global greenhouse gas concentrations, contributing to global climate change. The SOC levels in MLYF are generally low and well below the SOC saturation level, indicating substantial carbon sequestration potential. Appropriate improvements in these areas can enhance the SCS capacity. The utilization of the carbon sequestration potential of MLYF is therefore highly important for mitigating global climate change. Additionally, increasing the SOC content improved the quality of MLYF, resulting in a synergistic increase in both SCS and productivity. Consequently, improving the SCS capacity of MLYF is essential for ensuring national food security, reducing atmospheric greenhouse gas concentrations, and promoting sustainable agricultural development.

Currently, scholars have extensively researched the evaluation of MLYF, its improvement, the enhancement of production capacity (PC), and the exploration of soil carbon sequestration (SCS) mechanisms. Bai et al. [12] systematically reviewed the evaluation process, methods, and indicators for MLYF. The evaluation and development of MLYF have evolved through stages, including quantity determination, targeted transformation, potential assessment for quality improvement, and gradual transition toward green agricultural development. The evaluation indicators primarily encompass three aspects: yield indicators, obstacle factor indicators, and cultivated land quality grade indicators. The evaluation methods are mainly based on the yield method, obstacle factor method, and cultivated land quality grade method. Zhuang et al. [13] proposed that the PC of cultivated land, which reflects its comprehensive production capacity, serves as an integrated indicator encompassing both the natural attributes and socioeconomic qualities of these resources. Additionally, some scholars have integrated agricultural productivity with the ecological environment quality of cultivated land, emphasizing that agricultural productivity is jointly influenced by natural conditions, socioeconomic development levels, and the ecological environment quality [14,15,16]. Furthermore, the SCS is widely acknowledged in the academic community as an effective approach for reducing atmospheric CO₂ concentrations and mitigating the greenhouse effect [17]. The key factors influencing the SCS include water and heat conditions, soil properties, fertilization practices, and tillage methods [18,19]. The aforementioned research demonstrates that the academic community has achieved significant progress in related fields, providing a solid theoretical and practical foundation for this study.

However, most existing research has focused on cultivated land in general, with relatively limited attention given to an evaluation indicator system (EIS) for the MLYF. In 1996, the Ministry of Agriculture issued the National Technical Specification for Classification and Improvement of Medium and Low Yield Fields (NTSCIMLYFT) (NY/T 310–1996), which defines the concept, types, and classification standards of MLYF. However, this framework has long emphasized the improvement of MLYF without establishing a systematic evaluation system, making it inadequate for meeting current evaluation needs. The absence of unified evaluation criteria has led to unclear assessments of MLYF quality. This not only hinders the improvement of MLYF and the sustainable enhancement of production capacity but also poses a significant potential threat to national food security. In recent years, both the state and academia have made efforts to evaluate MLYF. The national standard Cultivated Land Quality Grade (GLQG) (GB/T33469-2016) [20] divides China into nine regions and specifies appropriate evaluation indicators for each region. Additionally, some scholars have conducted evaluations on MLYF areas. For instance, Liang et al. [21] selected the Horqin Zuoyihou Banner, a typical arid, decertified MLYF area in China, as a case study. They constructed an EIS and explored approaches to improve the quality of the MLYF from four dimensions: ecology, quantity, spatial combination, and scale. However, most of these studies directly treat the entire study area as the evaluation object, with a limited focus specifically on MLYF. Furthermore, the potential for synergistic improvement between the SCS and productivity in MLYF has been insufficiently explored, and the development of an EIS based on this synergy remains notably absent in the literature. Additionally, existing methods often suffer from issues such as an overly complex EIS, extensive sampling requirements, and high costs. The construction of a persuasive EIS for an MLYF that is simple, easy to implement, cost-effective, and capable of accurately reflecting the quality of the MLYF with a minimal number of indicators remains a critical challenge and a key focus for future research.

Building on this foundation, this study addresses China’s dual challenges of ensuring national food security and achieving carbon neutrality—challenges that are representative of those faced by many nations globally. Rooted in the theoretical frameworks of soil science, systems science, and agricultural engineering, we define the concept of MLYF and analyze its critical potential for the synergistic enhancement of the SCS and grain PC. We systematically explore the key factors influencing this dual optimization process. Additionally, on the basis of the practical needs for MLYF management at different scales and the availability of evaluation data, we identify the most important indicators that may affect the synergistic improvement of carbon sequestration and PC at the national, provincial, and city/county scales. From these three scales, we develop a comprehensive EIS that spans macro- to microlevels, progresses from simple to complex, and is designed to be persuasive and practical. This system accurately reflects the true quality of the MLYF and meets the management needs of different scales. We propose that this EIS is applicable for evaluating MLYF within organic agricultural systems. It aims to improve soil quality, optimize land use efficiency, and reduce excessive reliance on chemical fertilizers and pesticides, thereby protecting the vulnerable soil and water resources characteristic of such lands. Furthermore, this framework could provide a theoretical reference for other countries facing similar challenges.

2. Concept and Key Characteristics of MLYF

2.1. Definition of MLYF

Since the 1980s, China has implemented numerous improvement and evaluation initiatives for MLYF to ensure national food security and increase food production, leading to a continuously evolving understanding of MLYF. However, a unified definition of MLYF has yet to be established, with the academic community proposing various concepts from different perspectives. Initially, the classification of MLYF was primarily based on yield. For example, Fischer [22] emphasized that low crop yield is a defining characteristic of MLYF. The NTSCIMLYFT defines MLYF as cultivated land with various soil obstacle factors that restrict agricultural production, characterized by low and unstable yields. This framework focused mainly on improving MLYF and mitigating these obstacles. Since the start of the 21st century, the concept of MLYF has been further enriched through ongoing improvement and evaluation efforts, becoming closely associated with cultivated land fertility, quality, and productivity [23,24]. Zhang et al. [25] highlighted that low basic soil fertility is a key feature of MLYF. Zeng et al. [26] distinguished between medium-yield and low-yield fields and noted that the factors influencing soil fertility and crop yield in medium-yield fields are related primarily to tillage, fertilization, and management practices, whereas low-yield fields are predominantly affected by cultivation-hindering factors. Yan et al. [27] emphasized that MLYF represents cultivated land with significant production potential under natural and socioeconomic conditions, where current output levels fall far below achievable targets.

In summary, the concept of MLYF can be characterized by two key aspects. First, it pertains to the PC of cultivated land, where MLYF results in poor land quality, insufficient soil fertility, and low crop yield but possesses significant potential for increased production. Second, the low productivity of MLYF is attributed to the presence of obstacles, which hinder soil fertility and prevent it from meeting the requirements for optimal crop production. Additionally, inadequate management practices and insufficient infrastructure further exacerbate this issue, making it difficult to achieve normal soil fertility levels, resulting in poor disaster resistance. Therefore, the formation of MLYF is the result of the combined effects of natural conditions, management levels, and input factors, making it a comprehensive concept. Low productivity serves as the external manifestation and primary criterion for identifying MLYF. Poor management and inadequate infrastructure are significant contributors to reduced crop productivity. Crucially, the poor soil fertility in MLYF, caused by obstacles, fails to meet the needs of crop growth, representing the fundamental factors affecting the PC and crop yield of MLYF. Consequently, in this study, MLYF is defined as cultivated land characterized by low crop productivity due to inherent obstacle factors and insufficient postnatal management and input, yet with considerable potential for increased production (Figure 1).

2.2. Key Characteristics of MLYF

On the basis of the analysis of the concept of MLYF, the primary reason for low crop yields in MLYF is the presence of inherent obstacle factors that result in poor soil fertility. This limits the ability of the soil to provide sufficient water, nutrients, air, heat, and other essential conditions for crop growth, making it difficult to sustain normal crop development. SOC plays a critical role in determining soil fertility levels. An increase in SCS capacity can improve a soil’s physical, chemical, and biological properties; increase soil nutrient availability; increase water and nutrient retention; regulate soil pH; increase soil biological activity; and ultimately improve soil fertility. These improvements create a more suitable environment for crop growth and promote higher crop yields. However, the relationship between the SOC content and crop yield is not infinitely linear but rather exhibits a threshold effect. Lal’s research demonstrated that in any agricultural system, crop yield initially increases with increasing soil organic matter levels but then stabilizes after reaching a certain threshold (Figure 2a) [28]. The relationships between the SOC content and crop yield, as well as yield stability, can be linear or nonlinear exponential. Zhang et al. [29] conducted long-term experiments across 17 typical farmlands in China and identified a clear threshold for the contribution of SOC to crop yield via linear platform fitting (Figure 2b). Above this threshold, SOC no longer positively influences crop yield. At this stage, factors such as pests, diseases, crop varieties, and climate become more significant determinants of crop yield than SOC levels.

Research by Lal and Zhang indicated that when the SOC level reaches saturation, it ceases to be the primary limiting factor affecting crop yield. Consequently, in high-yield fields, where SOC levels are near saturation, further yield increases are challenging to achieve at current levels. In contrast, the MLYF treatment resulted in low SOC and crop yields, with a clear synergistic relationship between the SOC content and crop yield. Building on this, we further deduce that point d in Figure 2c represents the current SOC and crop yield levels in MLYF. The impact of improving SOC at point d on crop yield is significantly greater than that when SOC levels approach the threshold of soil organic content. Therefore, enhancing the SCS capacity of MLYF is crucial for promoting its PC, as the two demonstrate a synergistic improvement. This synergistic relationship represents a key distinguishing feature of the MLYF field compared with high-yield fields.

3. Mechanisms and Key Factors for Synergistic Improvement

3.1. Mechanisms of Productivity Enhancement

Cultivated land PC refers to the production capacity formed through the comprehensive input of various agricultural resource elements under specific regional, temporal, and socioeconomic conditions. PC can be categorized into three levels: theoretical PC, achievable PC, and actual PC [30]. The theoretical PC represents the maximum productivity that cultivated land can attain under optimal socioeconomic and technological conditions and is influenced by factors such as light, temperature, and precipitation. The achievable PC refers to the productivity that cultivated land can reach given the existing socioeconomic and technological levels, whereas the actual PC denotes the current productivity of crops achieved on cultivated land. MLYF is characterized by a low actual PC but a high theoretical and achievable PC, indicating significant potential for improvement. The key factors influencing the enhancement of the PC of MLYF can be summarized via the three elements of “favorable timing”, “geographical advantage”, and “human harmony”, which are concepts derived from the strategic successes of Mencius.

First, “favorable timing” refers to the most direct climatic conditions for crop growth, including light, temperature, and precipitation, which influence crop species and quality. These needs can be met by cultivating drought-resistant varieties, implementing timely irrigation, and adopting suitable intercropping systems. Second, “geographical advantage” emphasizes the soil fertility conditions, such as soil organic matter, nitrogen, and phosphorus, which directly affect crop yield and quality. Soil fertility can be enhanced through rational fertilization systems and management practices. Finally, “human harmony” involves improving cultivated land productivity through human inputs and management practices, including seed selection, fertilization, and mechanization. By collaborating with agricultural research institutions to develop scientific planting management systems, the limitations of “favorable timing” and “geographical advantage” can be addressed, effectively promoting increased crop yields.

For agricultural production, favorable timing serves as the foundation for optimal soil fertility. Human intervention and technological advancements enhance production conditions through scientific and technological investments, ultimately improving soil fertility. Addressing the low productivity of MLYF requires a focus on soil fertility as a central pathway. SOC is a key factor influencing soil fertility, and its increase directly or indirectly enhances soil fertility and promotes grain production by improving soil structure, strengthening microbial activity, increasing the soil buffering capacity, and reducing soil pollution. However, current SOC levels in Chinese farmlands are generally low, with MLYF exhibiting even lower SOC contents. Therefore, increasing the SOC content in MLYF represents a critical endogenous driver for promoting the sustainable improvement of the PC in MLYF. It is also essential for transforming production potential into actual P in MLYF.

3.2. Mechanisms of the SCS

SCS is a highly complex ecosystem process influenced by climate, plants, soil, microorganisms, and other factors [31]. First, plants fix CO₂ through photosynthesis, converting it into biomass, which then enters the soil as litter and root exudates. A portion of this carbon is directly converted into a stable carbon pool, whereas another portion is assimilated by microorganisms. After microbial apoptosis, it is incorporated into the stable soil carbon pool as microbial residue. Second, some organic carbon is released into the atmosphere as CO₂ or CH₄ through root respiration, microbial respiration, and soil animal respiration. However, the mechanisms underlying SCS remain incompletely understood. On the basis of the input and output processes of SOC in MLYF, this study summarizes the mechanisms and key factors affecting SCS in MLYF from three perspectives: climatic conditions, soil properties, and biological factors. This analysis aims to provide insights for enhancing the SCS capacity in MLYF (Figure 3).

3.2.1. Climatic Conditions

Climatic conditions influence the decomposition and mineralization rates of SOC through soil moisture and temperature, thereby affecting the SOC content [32]. Soil moisture conditions impact the decomposition rate of SOC by altering soil aeration. When soil moisture is sufficient, soil permeability is reduced, making native SOC less prone to decomposition, whereas exogenous organic carbon inputs are more easily degraded by water [33]. Small-molecule organic matter is retained in the soil, contributing to increased SOC content [34]. Conversely, insufficient water and strong soil permeability accelerate organic carbon decomposition, hindering its accumulation in the soil [35]. In general, increasing temperatures accelerate SOC decomposition by increasing microbial activity and increasing soil respiration rates, which negatively impacts soil carbon fixation. Additionally, topographic conditions such as altitude, slope, and aspect influence the redistribution of surface water and heat, altering local climatic conditions and subsequently affecting SOC fixation.

3.2.2. Soil Properties

Soil physical and chemical properties are critical factors influencing SOC sequestration. First, soil provides the essential living environment for crops, animals, and microorganisms, affecting their type, quantity, and diversity, which in turn influences the decomposition and mineralization of organic carbon. Second, the physical and chemical properties of soil determine its maximum carbon sequestration potential, reflecting the degree of change in SOC caused by exogenous organic carbon input. Soil texture affects soil structure, aeration, and water permeability, thereby influencing SOC mineralization [36]. Owing to their large specific surface area, clay particles strongly adsorb organic carbon, limiting contact with organisms and reducing bioavailability [37]. Consequently, soils with higher clay contents tend to have higher organic carbon contents. Similarly, soil iron and aluminum ions, as well as iron oxides, can form strong bonds with SOC, protecting organic carbon and promoting SOC fixation [38]. Soil aggregates reduce the contact between organic carbon and microorganisms, enzymes, and oxygen through “compartmentation”, acting as a barrier to protect organic carbon and facilitate SOC sequestration [39]. Generally, large aggregates contain more organic carbon but are more prone to mineralization, whereas small aggregates consist mostly of highly humified inert carbon with a long renewal cycle, making them relatively stable. Additionally, the SOC content influences the degree of increase in organic carbon content resulting from exogenous carbon input, and the soil phosphorus content also plays a role in organic carbon fixation.

3.2.3. Biological Factors

Soil animals and microorganisms play crucial roles in linking soil carbon input and output, participating in multiple processes of the soil carbon cycle and driving organic carbon fixation [40]. Soil microorganisms contribute to organic carbon fixation through their metabolic activities, and after undergoing apoptosis, their residues enter the stable carbon pool [41]. Previous studies have indicated that soil microbial residue carbon can account for more than 50% of the total SOC, making microbial carbon fixation a key factor in SCS [42,43]. Soil microbial carbon sequestration is significantly influenced by soil pH. Extremely high (>8.5) or low (<5.5) pH values inhibit microbial activity, reduce the decomposition rate of organic carbon, and hinder its accumulation [44]. Additionally, the soil C/N ratio affects the diversity and abundance of soil microorganisms [45]. An optimal C/N ratio ensures sufficient carbon sources to promote the decomposition of organic materials while preventing rapid carbon mineralization into CO₂ [46]. Furthermore, certain large soil animals, particularly earthworms, play a significant role in SCS. Through activities such as foraging and burrowing, earthworms improve soils’ physical and chemical properties, accelerate litter decomposition, promote crop growth, and enhance SOC fixation [47,48].

3.3. Synergistic Effects and Key Influencing Factors

The ongoing impacts of global climate change, including rising temperatures, uncertain precipitation patterns, increasing atmospheric CO₂ concentrations, and frequent extreme climate events, have increased the degree of risk to global food security and severely disrupted the stability of food production. Addressing these challenges requires urgent efforts to stabilize food production and mitigate climate change. China, in particular, faces the dual imperative of ensuring national food security and reducing carbon emissions to achieve “carbon neutrality” as soon as possible. As the primary component of China’s cultivated land (accounting for approximately two-thirds or more), MLYF plays a critical role in both SCS and grain production, with significant potential for synergistic improvement. This synergy is key to addressing the instability of China’s global grain production patterns and navigating international political uncertainties, thereby ensuring the safety of domestic grain production and enhancing China’s capacity for carbon sequestration and emission reduction to mitigate global climate change.

Therefore, on the basis of the functions of SCS and grain production in MLYF, this study identifies the mechanism of synergistic improvement between SCS and grain PC in MLYF and highlights the key factors influencing this synergy. The core of the synergistic relationship between the SCS capacity and grain PC in MLYF lies in the potential for increased carbon sequestration. As the carbon sequestration capacity improved, the physical and chemical properties of the soil increased, further increasing the soil fertility and grain PC in MLYF. Building on these findings, the synergistic mechanism of SCS and productivity in MLYF is further explored. The key factors affecting the synergistic improvement of SCS and productivity in MLYF are summarized into three categories: (1) climatic factors, including temperature, precipitation, altitude, slope, and aspect; (2) soil properties, such as soil texture, iron and aluminum oxide content, soil aggregate structure, SOC content, and soil phosphorus content; and (3) biological factors, including the C/N ratio, soil pH, and soil earthworms.

4. Development of an EIS

Food security and the mitigation of greenhouse gas emissions are critical strategic priorities for China’s sustainable development, both now and in the future. MLYF holds significant potential for SCS and grain production, making it vital for achieving the dual goals of “storing grain in land and technology” and realizing “double carbon” targets early. These objectives are closely tied to China’s national economy and people’s livelihoods. However, the spatial distribution of the MLYF is complex, influenced by a combination of natural, social, economic, and management factors, and exhibits significant regional variations. Therefore, clarifying key indicators and constructing an appropriate EIS based on the mechanism of synergistic improvement between SCS and PC in MLYF are essential for the utilization of its SCS and production potential. This approach is crucial for strengthening China’s national food security and carbon-emission-reduction efforts. To address this, this study developed an evaluation framework for the synergistic improvement of the SCS and PC in MLYF, which is structured around the five aspects of the “demand-function-factor-dimension indicator” (Figure 4). Furthermore, considering the differences in management needs and data availability at various scales, a differentiated EIS was constructed, ranging from simple to subdivided levels, across three dimensions: macro (national), meso (provincial), and micro (city and county) (Figure 5). This framework aims to promote differentiated and refined improvement strategies for MLYF at different scales and propose targeted transformation plans to enhance the overall SCS and grain PC of MLYF.

4.1. Framework for the EIS

Currently, the demand for MLYF focuses on two main aspects: (1) improving grain PC, as the poor, actual grain PC of MLYF is a significant factor contributing to instability in current grain production. Enhancing its PC is a top priority to further strengthen national food security. (2) Improving its SCS capacity, as the SOC content in MLYF is low, resulting in poor SCS capacity. Increasing its carbon sequestration capacity is a critical lever for effectively advancing carbon neutrality and mitigating global climate change. Furthermore, SCS and grain production are fundamental functions of MLYF. Owing to its underperformance in these two areas and its significant potential for synergistic improvement, even a modest increase in SCS capacity and grain PC in MLYF could serve as a powerful catalyst for addressing China’s pressing challenges in terms of food security and carbon emissions. Therefore, identifying the factors influencing the synergistic improvement of SCS and productivity in MLYF and evaluating the degree of this synergy are of particular importance.

The SCS capacity and grain PC of MLYF are influenced by multiple factors, including soil type, climatic conditions, human management, and biological effects. The impact of the same factor varies significantly across different scales. For example, temperature and precipitation play crucial roles in determining grain yield and SCS capacity at a large scale, whereas in small- and medium-sized regions, where climatic conditions are relatively stable, soil factors and human management become more significant. Given the relative uniformity of natural conditions at smaller scales, designing a targeted EIS that addresses the specific obstacle factors of MLYF and aligns with actual production needs is challenging. Especially at the small scale, obstacle factors may be the primary constraints limiting the SCS capacity and grain PC of MLYF. According to the NTSCIMLYFT, China is categorized by eight obstacle factors: barrenness, acidification, salinization, sandstorms, structural obstacles, waterlogging, erosion, and drought. There are significant regional differences in the types of obstacle factors, making it difficult for a completely unified EIS to meet the diverse requirements for assessing MLYF. To address this, the concept of “dimension” was introduced to refine influencing factors across three levels: large scale, medium scale, and small scale. At the small scale, the “n + X” model was applied based on the distinct characteristics of different obstacle factors. This hierarchical approach to constructing the EIS better serves the evaluation of MLYF.

Additionally, the development of the EIS must consider data availability and existing evaluation criteria, which may necessitate the elimination or substitution of certain indicators. For example, slope aspect refers to the orientation of slope land, such as shady slopes or sunny slopes, which influences the redistribution of surface water and heat, thereby affecting the water and temperature conditions of the land. However, temperature and precipitation data can directly reflect regional differences in temperature and water availability, and slope aspect has rarely been selected as an evaluation indicator in previous studies. Therefore, this indicator appears to overlap with other indicators, and such conflicts need to be resolved. Secondly, although the content of iron and aluminum oxides is also an important factor affecting the SCS, this indicator has seldom been used in previous evaluations of cultivated land and MLYF, and obtaining these data is costly. To simplify the implementation of this EIS, this indicator should be excluded. Similarly, soil aggregates play a significant role in soil aeration, water retention, and nutrient retention, but obtaining such data is often difficult and expensive. The soil bulk density, which is closely related to the soil aggregate structure, can also represent the degree of soil aeration and water permeability. Since the experimental method for measuring soil bulk density is relatively simple and less costly, it is used as a substitute for soil aggregate structure. In summary, the two key factors of slope aspect and iron–aluminum oxide were excluded, and the more straightforward soil bulk density was adopted as a replacement for the soil aggregate structure. These adjustments aim to improve the EIS, reduce the difficulty of data acquisition, and enhance the feasibility of the EIS.

4.2. Hierarchical Construction of the EIS

4.2.1. National-Level Evaluation Indicators

The evaluation of MLYF at the macro level typically involves a comprehensive consideration of national interests from a long-term and higher-level perspective [49]. It serves as the overarching guideline for the transformation, utilization, and renovation of MLYF across the country, characterized by its policy-oriented, strategic, and holistic nature [50,51]. The core objective of national-scale evaluation is to assess the overall status of MLYF, identify the dominant factors contributing to MLYF in different regions, and propose macrocontrol strategies. These strategies are then delegated to lower-level governments to ensure that the quality and PC of MLYF nationwide do not decline. Additionally, obtaining high-precision soil data at the national scale is challenging, necessitating careful consideration of data availability. In summary, this study selected six indicators at the national scale: (1) accumulated temperature ≥ 10 °C; (2) precipitation to characterize climatic conditions; (3) elevation; (4) slope to reflect terrain conditions; (5) soil pH; and (6) soil organic matter to represent soil properties. The accumulated temperature and rainfall are closely related to crop quality and yield, and on a large scale, they are also key factors contributing to regional differences in SCS levels. Elevation reflects the distribution of surface water and heat influenced by topographic relief. Generally, high-elevation areas are sparsely distributed and often lack sufficient hydrothermal conditions to support crop growth, whereas low-elevation areas are more suitable for agricultural activities. Slope is another critical factor for crop growth, as steep slopes are more prone to soil erosion, which negatively impacts crop growth, reduces topsoil thickness, and diminishes SCS capacity. Soil pH is not only a crucial factor for crop growth but also a key determinant of soil microbial activity, playing a significant role in SCS. Soil organic matter serves as the primary nutrient source for crop growth and is a key factor determining the upper limit of regional carbon sequestration potential. Therefore, these six indicators were selected. These indicators are readily accessible and play a pivotal role in the synergistic improvement of SCS and PC in MLYF on a large scale, providing valuable guidance for provincial governments to further transform and renovate MLYF.

4.2.2. Provincial-Level Evaluation Indicators

The provincial scale serves as a critical link between the national scale and the city/county scales, playing a connecting and coordinating role [52,53,54]. It is responsible for implementing the overarching strategic needs of the country within the provincial context and transmitting these directives to cities and counties. While ensuring the quality and PC of MLYF at the national level, the provincial scale aims to gradually enhance the SCS and grain PC of MLYF, identify key areas with synergistic potential, and coordinate lower-level cities and counties to implement MLYF transformation, reduce obstacles, and provide feedback on national strategic objectives. Additionally, the reduced geographic scope at the provincial scale makes soil sampling and data collection more feasible. At this scale, the reduced regional area leads to smaller spatial variation gradients in accumulated temperature and precipitation, along with coarser data accuracy. Consequently, these factors fail to adequately capture the heterogeneity in the quality changes of MLYF. Additionally, the collinearity between accumulated temperature, precipitation, and altitude may increase, potentially causing redundancy in these indices. In contrast, lower sampling costs allow soil properties to serve as more intuitive proxies for hydrothermal condition variations. For instance, soil bulk density not only reflects differences in hydrothermal conditions but also maintains high accuracy through spatial interpolation. Similarly, altitude could be replaced by slope, which is more directly relevant to agricultural production, thereby avoiding potential correlations with climatic indicators. Considering these aspects, the three indicators of accumulated temperature, precipitation and altitude were excluded. On the basis of these factors, utilization objectives and strategies for MLYF in cities and counties can be proposed, and key areas for provincial transformation can be identified, laying the groundwork for subsequent fine and differentiated management.

In summary, eight indicators were selected at the provincial scale: slope; soil organic matter content; soil pH; soil bulk density; effective soil layer thickness; soil texture; the C/N ratio; and available phosphorus, which reflect the key factors influencing the synergistic improvement of SCS and productivity in MLYF. Slope, soil organic matter, and soil pH, as critical indicators of crop yield and SCS, play a significant role in enhancing the synergy between SCS and productivity at the provincial scale. Therefore, these three indicators were retained. Soil bulk density influences soil aeration and permeability, affects the carbon sequestration capacity of SOC, and impacts the ability of soil to supply the nutrients essential for crop growth. The thickness of the effective soil layer determines the depth and availability of soil nutrients that crops can absorb, with a greater thickness providing more space for SCS. Soil texture not only directly affects SOC sequestration but also serves as the foundation for crop growth and development, playing a significant role in both processes. The C/N ratio of nitrogen is not only a fundamental element required for crop growth but also a key driver of microbial carbon sequestration. An appropriate C/N ratio is crucial for both crop yield and SCS. Similarly, available phosphorus is essential for crop growth. Additionally, studies have shown that the phosphorus content is a critical factor in the SCS. Therefore, these five indicators were added at the provincial scale. These indicators are readily accessible at the provincial level and provide essential support for identifying key areas and control zones for the MLYF within a province.

4.2.3. City- and County-Level Evaluation Indicators

The city and county scales refine the evaluation of MLYF on the basis of provincial-level assessments, adopting targeted obstacle reduction and transformation technologies tailored to the specific characteristics of MLYF in the region [55]. This approach effectively promotes improvements in the SCS capacity and grain PC of MLYF. At this scale, the EIS for MLYF emphasizes greater specificity and operability to identify key factors driving SCS and PC improvement and implement precise transformation technologies [56]. However, the obstacle factors affecting the MLYF are highly complex. The NTSCIMLYFT categorizes MLYF into eight types of obstacle factors: barrenness, acidification, salinization, wind and sand, structural obstacles, waterlogging, erosion, and drought (Figure 6). These factors represent the most significant obstacles in real-world farmland management. The distribution of these obstacle factors in specific fields or plots exhibits significant spatial heterogeneity, making it challenging to apply a uniform EIS. Therefore, at this scale, the EIS for MLYF should adopt a “commonality + individuality” model, referred to as the “n + X” EIS. Here, “n” represents the common characteristics of MLYF, whereas “X” represents the unique characteristics of different obstacle types in MLYF.

The selection of individual indicators at various levels is based on eight indicators from the provincial-level evaluation system: slope, soil organic matter, soil pH, soil bulk density, effective soil layer thickness, soil texture, the C/N ratio, and available phosphorus. These indicators meet the basic requirements for improving the SCS and productivity in MLYF. These indicators remain highly significant in the evaluation of MLYF at the city and county scale and are therefore retained. Additionally, the reduced regional area decreases the difficulty in obtaining the soil’s biological data. The impact of biological factors on carbon sequestration and grain PC in MLYF can be assessed by measuring soil earthworm populations and microbial activity via soil biological sampling methods. Among them, soil earthworms are important carbon-neutral animals that not only enhance soils’ physical and chemical properties through their activities but also promote litter decomposition, increase SOC content, and improve crop productivity. Microorganisms are the primary drivers of SCS, and their activity is crucial for this process. The carbon fixed by microorganisms is ultimately transformed into essential elements required for crop growth, which also significantly contributes to crop productivity. Therefore, these two indicators were added.

Furthermore, based on the eight major obstacle factors classified by NTSCIMLYFT, the individuality indicators are further refined and constructed. However, in the development of the EIS, some obstacle factors overlap with the common indicators already established, or the common indicators can sufficiently reflect the characteristics of these obstacle factors. Additionally, there is conceptual overlap between some obstacle factors. Specifically, barrenness refers to low soil fertility in MLYF, while acidification refers to low soil pH. Since the common indicators already include measures that characterize soil fertility and soil pH, the distinctiveness of the barrenness and acidification obstacle types is diminished. Wind-blown sand, a form of soil desertification caused by wind erosion, falls under the broader concept of erosion, which includes hydraulic erosion, wind erosion, freeze–thaw erosion, and gravitational erosion. As a result, the concept of erosion as an obstacle factor already encompasses wind-blown sand. Therefore, to simplify and streamline the EIS, the two obstacle factors of barrenness and acidification are excluded, and the two obstacle factors of wind-blown sand and erosion are merged into a single obstacle factor type: erosion. Finally, after screening, individual indicators were established for the remaining five obstacle types: saline-alkali, structural obstacles, waterlogging, erosion, and drought.

First, salinization primarily results from the high concentration of salt ions, leading to the unreasonable movement of soil water and salt [57]. Electrical conductivity is an indicator that measures the ionic conductivity in a soil solution, and directly reflects the concentration of soluble salt ions in the soil. Therefore, it serves as a critical measure for assessing the severity of soil salinization. The soluble salt content refers to the total amount of salt that can dissolve in water within the soil, representing the primary cause of soil salinization. These two indicators are essential for evaluating soil salinization and are considered specific indicators for the MLYF affected by saline–alkali obstacles. Second, structural obstacles arise from natural factors and inappropriate farming practices, causing soil compaction and hardening, which negatively impact soil permeability [58]. The surface refers to the compressive strength of the soil surface, which directly reflects the compactness of the soil. Higher surface hardness indicates soil compaction, which may reduce soil permeability and water infiltration, thereby affecting crop root growth and the absorption of water and nutrients. A higher content of water-stable aggregates suggests greater soil structural stability and improved permeability, whereas a lower content indicates poor soil structure, potentially adversely affecting crop growth. Therefore, surface hardness and water-stable aggregates are selected as specific indicators for MLYF with structural obstacles. Third, waterlogging typically occurs in low-lying areas with poor drainage and is often exacerbated by continuous precipitation or heavy rainstorms [59]. Drainage conditions refer to the ability of farmland to remove surface water and groundwater, which directly influences the soil moisture status. Poor drainage conditions increase the likelihood of waterlogging, which can significantly negatively impact crop growth. The flood control standard of farmland refers to its capacity to withstand floods. Areas of MLYF of the waterlogging type are often located in floodplain areas or regions prone to flooding. Insufficient flood control capacity exacerbates waterlogging issues. The quality of farmland flood control standards directly determines the extent of damage during heavy rainfall or floods. Therefore, drainage conditions and farmland flood control standards are selected as specific indicators for waterlogging-affected MLYF.

Fourth, erosion involves the loss of surface soil from farmland due to natural forces or improper tillage practices [60,61]. Soil erosion can result in the loss of fertile topsoil, reducing soil fertility and subsequently affecting crop growth and farmland productivity. Soil erosion intensity directly reflects the extent of topsoil loss on farmland. Higher erosion intensity indicates more severe soil loss, leading to greater negative impacts on farmland productivity. The farmland forest network rate refers to the area ratio of forest networks and other vegetation coverage within farmland, effectively reflecting the level of vegetation coverage. Vegetation coverage plays a crucial role in mitigating soil erosion and enhancing soil biodiversity and fertility. Therefore, soil erosion intensity and farmland forest network rate are selected as specific indicators for eroded MLYF. For the drought-type, the primary cause is insufficient soil water due to low precipitation, uneven distribution, or inadequate irrigation [62]. Soil moisture content refers to the amount of water present in the soil, directly reflecting the soil’s water supply capacity and the overall water status of the farmland. This information is essential for developing scientific irrigation plans to meet the water requirements of crop growth. The irrigation guarantee rate refers to the probability that farmland irrigation facilities can satisfy crop water demand within a specific period, directly influencing the efficiency of water resource utilization in farmland. A higher guarantee rate indicates better irrigation conditions and stronger drought resistance, making it a critical measure of the farmland’s ability to withstand drought. Therefore, soil moisture content and irrigation guarantee rate are selected as specific indicators for arid MLYF.

5. Discussion

5.1. From Theory to Implementation: A Future Work Plan for the EIS

Owing to the large administrative area at the national scale, obtaining high-precision soil sample data is challenging. Therefore, data sources at this scale rely primarily on public datasets. The ≥10 °C accumulated temperature, precipitation, and elevation data were obtained from the China Resources and Environment Science and Data Platform (http://www.resdc.cn (accessed on 1 January 2024)). Slope data are derived from the Geospatial Data Cloud (https://www.gscloud.cn (accessed on 1 January 2024)), which can meet the needs of slope data at the national, provincial, and city/county scales. The soil organic matter and pH data were downloaded from the National Qinghai-Tibet Plateau Data Center (http://data.tpdc.ac.cn (accessed on 1 January 2024)). These datasets are strictly controlled, and the data accuracy and quality can meet the needs of the evaluation of MLYF at the national scale. At the provincial scale, the difficulty of soil sample collection is reduced because of the relatively small administrative area, but the cost of implementing large-scale sampling remains high. Therefore, representative soil samples are determined by integrating climate, topography, and parent material data from MLYF at the provincial scale on the basis of the principles of geographical similarity and the soil landscape model, which greatly reduces costs. At the city/county scale, the smaller administrative boundaries reduce the logistical difficulty and cost of soil sampling, whereas the variability in natural conditions across the region remains limited. Given these factors, we propose employing a systematic random sampling approach for MLYF. Sampling grids of varying sizes (e.g., 3 km × 3 km, 4 km × 4 km, and 5 km × 5 km) will be established, with approximately 100 randomly distributed sampling points allocated for data collection (Figure 7).

Specifically, the implementation of the evaluation indicator system under different-scale situations can follow the steps below. First, data collection is carried out. At the national scale, the data are downloaded from the public dataset via the website mentioned above. At the provincial scale and the city/county scale, samples collected through soil sampling and field investigations are sent to the laboratory for analysis. After obtaining the data results, preprocessing is performed to initially construct the MLYF indicator database. On this basis, following the national cultivated land quality classification standard and existing research, the membership function is used to classify the indicators into four types: upper limit, lower limit, peak, and conceptual. Thereafter, the indicator data are standardized to form a standardized MLYF database for indicator weight processing and score calculation.

Next, the weight of the indicator is determined. An analysis of previous studies revealed that the determination of indicator weights relies primarily on subjective and objective weight determination methods. Among these methods, the subjective weight determination method is predominantly implemented through the expert scoring method. This involves inviting experts familiar with the region to score each indicator and then comprehensively determining the indicator weight on the basis of the expert scores. This method is simple, easy to use, and widely applicable, but it is prone to being significantly influenced by the subjective preferences of experts. On the other hand, the objective weight determination method is mainly based on the entropy weight method, which calculates the indicator weight through information entropy. This approach is more objective and capable of calculating weights for multiple indices, but it is susceptible to data quality issues. Although these two methods have their own limitations, combining them can offset their respective shortcomings. Therefore, this study employs a combined subjective and objective weighting method to determine the weights. Specifically, local experts in agriculture and soil science, familiar with the research scale, were repeatedly invited to score the indicators. The average of their scores was used as the subjective weight. Concurrently, based on actual data, the entropy weight method was applied to calculate the objective weight. The final weight for each index was derived from the arithmetic mean of the subjective and objective weights. Specifically, the evaluation results of MLYF are obtained by multiplying the average weight of the two methods by the membership degree of each indicator. The formula is as follows:

$$W_i=\sum \left(C_i\times F_i\right)$$

(1)

where W_i represents the evaluation score of MLYF, C_i denotes the membership degree of the i-th indicator, and F_i indicates the comprehensive weight of the i-th indicator.

Finally, the evaluation results and evaluation indicator data were spatially interpolated via geostatistical methods to determine the distribution range of the evaluation indicators and evaluation scores of the MLYF in the study area. On the basis of the results from the MLYF, the key factors affecting SCS capacity and grain PC were further identified from the National Key R&D Program Project 2023, and the key areas of the MLYF were delineated. According to the identified key factors and key areas, policy recommendations and technical support are provided to local governments for implementing targeted improvement measures, aiming to effectively increase the contribution of SCS and PC in MLYF while also offering references for other countries.

5.2. Research Importance and Potential Contribution

China’s MLYF is widely distributed and has significant potential for improving grain PC and carbon sequestration capacity. The utilization of this potential is crucial for ensuring national food security, advancing carbon neutrality strategies, and accelerating progress toward the UN’s sustainable development goals. However, to evaluate and transform MLYF effectively, it is essential to clarify its grade, area, and distribution and to implement targeted improvement strategies on the basis of a comprehensive EIS. Currently, research on the construction of an EIS for MLYF remains limited. Therefore, this study systematically reviewed the development process and classification criteria for MLYF evaluation, integrating relevant theories such as soil science. The concept and characteristics of MLYF are analyzed, and the factors influencing the synergistic improvement of the SCS and PC in the MLYF are explored from a theoretical perspective. Additionally, considering the management needs of MLYF at different scales, common indicators were established at the national, provincial, and city/county levels on the basis of the shared characteristics of MLYF. The “n + X” model was applied to address the unique characteristics of obstacle factors in MLYF at smaller scales. The findings of this study can, to a certain extent, meet China’s needs for evaluating and managing MLYF across different scales. Through this multiscale EIS for MLYF, policymakers can gain a comprehensive understanding of the overall status of MLYF in the region, identify priority areas for their transformation, and develop targeted improvement measures based on the characteristics of obstacle factors. This EIS provides policy recommendations for governments to enhance the utilization efficiency of MLYF and promote the synergistic improvement of SCS and PC. Additionally, it offers a valuable theoretical reference for other countries facing similar challenges, providing practical insights and methodologies for addressing analogous agricultural and environmental issues.

5.3. Limitations and Future Research Directions

However, in this study, a hierarchical and multiscale EIS was constructed on the basis of the relationship between SCS and productivity improvement in MLYF, which can meet the management needs of different scales. Nevertheless, the analysis in this study is limited to theoretical-level exploration of the factors influencing the synergy between SCS and grain PC in MLYF. Owing to data limitations, this study solely proposes an index system and work plan for evaluating MLYF. The feasibility of the EIS still requires further verification. Different geographical regions may face challenges of insufficient data for conducting the evaluation of MLYF. Only the most critical factors affecting the coordination of SCS and PC in MLYF were selected, while some indicators that may influence the evaluation results were omitted. Although an EIS combining “commonness” and “individuality” was proposed, its applicability to specific types of obstacle factors in MLYF still requires further validation. These limitations need to be addressed in future research. To address the data challenges, we recommend establishing long-term monitoring sites at the city and county scale based on obstacle factors in different regions, enabling regular data collection on the status of MLYF. In addition, remote sensing (RS), geographic information system (GIS), and artificial intelligence (AI) technologies can be utilized to facilitate the development of a shared big data platform for MLYF across various countries, thereby resolving the issue of insufficient data in the evaluation of MLYF in different geographic regions. Additionally, the integration of existing methods should be strengthened to eliminate internal correlations within the EIS and more accurately identify the key factors affecting the synergy between SCS and PC in MLYF, providing robust support for theoretical analysis. Finally, addressing the complex and interdisciplinary nature of MLYF requires collaborative efforts from soil scientists, economists, engineering scientists, land remediation experts, and administrative managers. Such collaboration will help develop a reasonable approach, identify the most significant factors influencing the synergistic improvement of SCS and PC, and propose practical, comprehensive transformation plans tailored to the characteristics of specific obstacle factors.

6. Conclusions

MLYF constitutes the main body of cultivated land in China. The utilization of its synergistic improvement potential for SCS and PC represents a powerful approach to consolidating national food security and advancing “carbon neutrality” in the future. However, few studies have systematically clarified the concept and characteristics of MLYF, identified the key factors influencing the synergistic improvement of SCS and PC in MLYF, or constructed a comprehensive EIS for MLYF. Therefore, on the basis of the theories of soil science, systems science, and agricultural engineering, this study defines the concept and characteristics of MLYF, identifies the factors influencing the synergistic improvement of SCS and PC in MLYF, and integrates management methods across different scales to develop a simple and practical EIS. The main findings are as follows: (1) MLYF is a comprehensive concept, referring to cultivated land with low crop productivity due to inherent obstacles and insufficient management and investment, but significant yield potential. The core issue is that poor soil fertility cannot meet the normal growth requirements of crops. (2) Currently, China faces the dual challenges of ensuring national food security and mitigating greenhouse gas emissions. The substantial synergistic potential of SCS and PC in MLYF provides a strategic opportunity to address these issues. Improving the quality of MLYF is crucial for the country’s sustainable development. The key factors affecting the synergistic improvement of carbon sequestration and productivity in MLYF are summarized into three categories: climatic conditions, soil properties, and biological factors. (3) Building on this analysis, an EIS framework of “demand-function-factor-dimension indicator” is proposed. This framework constructs a hierarchical EIS for MLYF across three dimensions—macro (national), meso (provincial), and micro (city and county)—to meet the management needs of MLYF at different scales. This study aims to promote the transformation and improvement of MLYF, enhance the overall quality of cultivated land, and provide theoretical support for the coordinated improvement of national SCS capacity and grain PC.