Assessing the Coordination Development Level of Agricultural Economy and Ecology in China: Regional Disparities, Dynamics, and Barriers

Abstract

Achieving sustainable rural development in China requires effectively integrating agricultural growth with ecological balance. However, existing research on the coordination between agricultural economy and ecosystems has often focused on isolated aspects, such as economic growth or ecological sustainability, or has been limited to specific provinces or regions, lacking a comprehensive nationwide analysis. To address this gap, this study uses spatial data from 31 provincial-level regions in China from 2008 to 2022, developing a multidimensional framework that encompasses economic input, structure, efficiency, benefits, vitality, ecological conditions, and pressure. Using multi-factor econometric methods, we comprehensively evaluate the coordination between China’s agricultural economy and ecosystems, revealing regional disparities and spatiotemporal variations in their coupling coordination, and analyzing the barriers affecting this coordination. Our findings show that: First, coupling coordination has steadily improved, narrowing regional disparities. Second, regional differences are primarily driven by variations between the eastern, central, and western regions, with structural disparities shifting from interregional to hyper-variable density. Third, development exhibits a “club convergence” pattern, where upward transitions are difficult and downward mobility is a risk. Key barriers include farmland scale, land efficiency, afforestation area, and soil erosion control. Based on these findings, we recommend regional development strategies, dynamic monitoring mechanisms, optimized land use, and enhanced ecological protection. This study provides valuable insights for policymakers and practitioners to promote the coordinated and sustainable development of agricultural economies and ecosystems in China.

1. Introduction

At the global scale, achieving coordinated development between agriculture and ecosystems is pivotal for addressing challenges related to climate change, land degradation, and food security [1]. According to the Food and Agriculture Organization (FAO), more than 30% of global agricultural lands are undergoing degradation, and climate change further amplifies uncertainties in agricultural production. As one of the world’s major agricultural producers, China’s agricultural economy is integral to ensuring food security, driving economic growth, and maintaining social stability [2]. However, its long-standing reliance on high-input, high-output production models has intensified ecological pressures, leading to excessive land exploitation, soil erosion, and declining biodiversity [3,4,5,6]. These issues not only threaten agricultural sustainability but also exacerbate tensions between agricultural development and ecosystem integrity. Simultaneously, China’s agricultural and ecological coordination exhibits marked regional disparities. Eastern regions, supported by advanced technology and robust policies, have progressed relatively quickly toward ecological protection and agricultural modernization, while central and western regions, constrained by resource endowments and economic foundations, face more pronounced challenges in achieving coordinated development [7]. In response to these challenges, the 2024 No. 1 Central Document of the Chinese government highlights the need to advance green, low-carbon agricultural transitions, strengthen the management of non-point source pollution, and enhance ecosystem protection and restoration. These initiatives seek to narrow the regional gaps and guide agricultural-ecological coordination onto a more balanced and sustainable path. In this context, studying the coupling and coordinated development of agricultural economy and ecosystems, particularly their regional differences and spatiotemporal dynamics, helps identify key constraints and provides scientific support for resource optimization and targeted policymaking. This is crucial for advancing sustainable agriculture and ecological conservation in China. Based on this background, this study uses data from 31 provincial-level regions in China from 2008 to 2022, constructing a comprehensive evaluation framework and applying multi-factor econometric methods to systematically analyze the spatiotemporal characteristics, regional disparities, dynamic evolution, and barriers to the coordinated development of agricultural economy and ecosystems.

The anticipated contributions of this study can be summarized into three aspects: First, by utilizing the data from 31 provinces in China from 2008 to 2022, this research focuses on the critical topic of coupling coordination between agricultural economy and ecology. It addresses existing gaps in data timeliness and geographic coverage, offering the academic community a comprehensive and up-to-date empirical analysis. Second, the study innovatively applies multiple methodologies, including the entropy weight method, Dagum Gini coefficient, Markov chain model, and kernel density estimation, to systematically analyze the dynamic trends and regional differences in coupling coordination development. This provides new analytical tools and insights for research on regional disparities. Finally, the research delves into the key factors influencing the coupling coordination between agricultural economy and ecology. Based on empirical results, it proposes targeted policy recommendations, offering actionable guidance and practical pathways to achieve sustainable agricultural and ecological coordination.

Section 1 introduces the research topic and direction, setting the stage for the study. Section 2 explains the coupling mechanisms between agricultural economy and ecological systems, while reviewing the latest academic developments and research trends in the field. Section 3 details the research methodology, including data sources, analytical techniques, and the evaluation index system, providing a strong foundation for the analysis. Section 4 examines the spatiotemporal characteristics, regional disparities, evolutionary trends, and influencing factors of China’s agricultural-economic and ecological coordination, revealing its complexity and dynamic evolutionary patterns. Finally, Section 5 summarizes the key findings, proposes targeted policy recommendations, and discusses the study’s limitations while offering future research directions.

2. Literature Review

2.1. Theoretical Foundations of the Coupling-Coordination Mechanism Between the Agricultural Economy and Ecosystem

Coupling refers to the interaction between two systems through inherent mechanisms, forming a dynamic and coordinated development process [8]. He (2024) emphasized the significant value of applying system coupling theory to study the agricultural economy and ecology, proposing it as a framework to analyze their synergy and coupling [9]. In the context of China’s rural revitalization strategy, a thriving agricultural economy drives ecological protection, while a healthy ecosystem provides essential resources for agriculture [10,11]. The coupling effect between the two not only boosts agricultural productivity but also contributes to the restoration and sustainable development of the ecological environment [12]. The coupling relationship is shown in Figure 1.

Agricultural activity impacts ecosystems mainly through resource consumption and environmental pressures. Efficient agricultural practices, such as optimized production methods and sustainable inputs, can reduce resource consumption and pollution, easing the strain on ecosystems [13,14,15]. For instance, the adoption of climate-smart agricultural practices (CSAPs) offers a potential solution for enhancing crop productivity, mitigating greenhouse gas emissions, and improving climate resilience for environmental sustainability [16]. Eco-friendly agriculture—through reducing fertilizer and pesticide use, implementing crop rotation, and conserving soil and water—helps mitigate issues like soil erosion and land degradation [17]. When properly managed, agriculture supports ecosystem health and promotes sustainability. Conversely, poorly planned agricultural practices that lack green transformation can degrade ecosystems, compromising vital resources such as soil fertility and water quality, and ultimately hindering long-term agricultural development [18]. At the same time, ecosystems also play a crucial role in supporting agricultural productivity [19]. A healthy ecosystem provides critical resources, such as stable water supply and favorable climate conditions, while regulating the environment to reduce production costs and enhance product quality [20]. For instance, a healthy ecosystem ensures a stable water supply and favorable climatic conditions, which in turn support efficient agricultural production [21]. Damaged ecosystems, however, disrupt agricultural production by causing water pollution and climate anomalies, which lead to disasters, lower farmer incomes, and diminished socio-economic stability [22,23].

In conclusion, the coupling mechanism between agricultural economy and ecosystem is essential. Positive interactions between the two can achieve a win-win outcome, driving agricultural modernization and ecological civilization. This dynamic provides both theoretical support and practical pathways for the sustainable development of the agricultural economy.

2.2. Development of Evaluation Index Systems for the Agricultural Economy and Ecosystem

The development of evaluation index systems for the agricultural economy and ecosystem has become a key area of recent research. Scholars have focused on creating these systems to assess and improve the coordination between agricultural production and ecological sustainability. These index systems aim to quantify the complex interactions between agricultural activities and ecological functions, providing a scientific foundation for achieving sustainable development. Liu et al. [24] developed an evaluation index system for agricultural green production, focusing on five dimensions: supply capacity, resource utilization, environmental quality, ecosystem maintenance, and farmers’ livelihoods. Their analysis identified gaps between the current state and targets for agricultural green production in China, along with its spatial and temporal evolution. The study highlights agricultural green production as crucial for achieving a green economy and sustainable ecology. Sun et al. [25] proposed a framework for evaluating the relationship between agricultural informatization and economic development in Shandong Province. Using nine indicators, including gross agricultural output and farmers’ per capita net income, they applied the entropy method and coupling coordination models to assess their interaction from 2011 to 2019. The results showed a positive correlation and coordination between agricultural informatization and economic growth in the province. Zhang et al. [26] built a research framework to examine the impact of agricultural green total factor productivity (GTFP), using nine indicators such as labor, land, machinery, and water resources. The study applied entropy-TOPSIS and SBM-GML methods, along with panel data models, concluding that the digital economy significantly boosts GTFP by promoting agricultural innovation, with regional variations. Chen et al. [27] explored the effect of factor misallocation on high-quality agricultural development, focusing on agricultural efficiency and equity. Analyzing data from 154 major grain-producing regions (2004–2020) with spatial econometric models, the study found low factor misallocation but identified its inhibiting effect on high-quality agricultural development, with notable spatial and temporal differences. Gao [28] proposed a coupling coordination framework based on eight dimensions, including agricultural economic vitality, ecological protection, and ecological pressure. Using data from 31 provinces (2011–2021), the study applied the coupling coordination degree model and Moran index to analyze spatiotemporal trends between high-quality agricultural development and ecological construction. The results revealed a low coupling coordination level, with agricultural development lagging behind ecological progress. Qing et al. [29] examined the coupling coordination between agricultural carbon emissions efficiency and economic growth in the Yellow River Basin from 2010 to 2020. Using data from 30 cities and dimensions such as labor, land, agricultural capital, and water resources input, the study found significant spatial imbalances and a declining trend in the coupling coordination degree. Hou et al. [30] developed an evaluation framework for agricultural ecological transformation based on low-carbon innovation theory. Using 10 indicators, such as cultivated land area and fertilizer application, the study applied the Super-SBM model and dynamic panel threshold model to analyze the nonlinear impact of the digital economy on agricultural ecological transformation. The findings indicate steady improvement, though the overall level remains relatively low. Castrillon-Gomez et al. [31] developed a methodology integrating system dynamics modeling and ANP to evaluate and prioritize green projects. The approach involves three stages: community participation, model calibration to simulate agricultural and ecological impacts, and decision-making through peer review. A case study in Colombia demonstrated the methodology’s effectiveness in balancing local needs and expert evaluations, improving decision-making and reducing biases. This approach helps align green projects with agricultural development and environmental sustainability. Wang et al. [32] examined the impact of agricultural inputs and urbanization on urban-rural income disparity in China. They focused on the dimensions of agricultural input and urbanization, using a dataset from 1997 to 2015. The study found that urbanization helped reduce income disparity, and fertilizer use had a varying impact depending on urbanization levels. Luo et al. [33] investigated how farmers’ cooperatives’ education affects the adoption of green prevention and control technologies (GCTs) in rural China. They used education-related indicators and analyzed the adoption of technologies like pest control and fertilizer integration. The study showed that cooperative education significantly increased the adoption of GCTs and improved farmers’ environmental awareness. Qing et al. [34] studied the influence of farmers’ environmental awareness on rural residential environment improvement in Sichuan Province. They analyzed three dimensions of environmental awareness: problem cognition, pollution tolerance, and environmental protection attitude. The study revealed that higher awareness promoted participation in environmental improvements.

2.3. Empirical Studies on the Coupling and Coordination of the Agricultural Economy and Ecosystem

Wang et al. [35] used a coupling coordination degree model to examine the agricultural eco-economic system in Yan’an City from 2010 to 2018. The study revealed that the coordination level significantly improved, driven by land consolidation and ecological restoration projects. Jiang et al. [36] analyzed the impact of the digital economy on agricultural green development using panel data from 30 provinces in China from 2011 to 2020. The study identified regional heterogeneity, nonlinear “increasing marginal effects”, and spatial spillover effects of the digital economy. Liu et al. [37] applied the entropy weight method and a coupling coordination degree model to analyze the relationships among agriculture, economy, environment, and society in Anhui Province. The study identified excessive pesticide use and insufficient technical support as major barriers to sustainable development. Yao et al. [38] used the CRITIC entropy weight method to assess the agro-ecological-economic systems in the Ebinur Lake Basin from 2001 to 2021. The study found high coupling (0.8) but low coordination (0.36), with ecological lag replacing economic lag as the primary issue after 2010. Zhang et al. [39] examined the coupling coordination between agriculture and ecology in 16 major grain-producing counties in Jilin Province from 2004 to 2018. The study found steady improvements in coordination, with ecological development lagging behind economic growth in central regions. Wang et al. [7] analyzed the spatiotemporal coupling coordination between agricultural ecology and economy across 31 provinces in China from 2010 to 2020. The study revealed steady improvements in coordination levels but persistent regional disparities. Qing et al. [29] employed the super-efficient slacks-based measure (SBM) model to evaluate agricultural carbon emission efficiency in the Yellow River Basin from 2010 to 2020. The results showed significant spatial disparities and a declining coupling coordination trend. Xiong et al. [40] investigated the “science and technology innovation-economy-ecology” systems in the Yangtze River Basin from 2010 to 2021. The study found a “high coupling, low coordination” characteristic, with significant ecological fluctuations and regional disparities. The above studies indicate that the coupling coordination level between China’s agricultural economy and ecological systems has generally improved over time, but significant regional disparities remain. These findings provide valuable references for further exploring the spatiotemporal evolution and obstacles in the coordinated development of agricultural economy and ecology.

In summary, existing studies have analyzed the coupling relationship between agricultural economy and ecology from various methods and perspectives but are primarily focused on regions such as Anhui, Yan’an, the Yellow River Basin, Ebinur Lake Basin, the North China Plain, Jilin, and the Yangtze River Basin. Nationwide studies, however, remain limited. To address this, this study applies coupling coordination theory combined with the Dagum Gini coefficient, kernel density estimation, Markov chain, and obstacle degree models to conduct an empirical analysis of the regional differences, spatiotemporal evolution, and limiting factors in the coupling and coordination of agricultural economy and ecology across 31 provinces in China. It examines spatial distribution patterns, evolutionary trends, quantifies the sources of regional disparities, and identifies key constraints to provide scientific guidance for optimizing regional agricultural and ecological coordination.

3. Research Design

3.1. Research Object and Data Sources

The data used in this study covers the period from 2008 to 2022, spanning 15 years. Due to significant differences in political, statistical, and policy contexts between Taiwan, Hong Kong, Macau, and Mainland China, which could affect the accuracy and comparability of the results, these regions are excluded from this study. The data selection and processing methods in this study aim to ensure the reliability and practicality of the results. To maintain consistency and comparability in regional analysis, this study follows the regional classification standard set by the National Bureau of Statistics of China. According to this standard, China is divided into three regions—eastern, central, and western—based on geographical, economic, and social factors, as shown in

Table 1. Scope of the three major regions in China.

Region	Scope
Eastern	Beijing, Shanghai, Jiangsu, Zhejiang, Tianjin, Hebei, Fujian, Shandong, Guangdong, Hainan, Liaoning
Central	Shanxi, Anhui, Jiangxi, Henan, Hubei, Hunan, Jilin, Heilongjiang
Western	Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Tibet, Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang

.

The basic data required for this study mainly comes from the 2008–2022 editions of the China Statistical Yearbook, China Rural Statistical Yearbook, China Food Yearbook, China Environmental Statistical Yearbook, China Water Resources Statistical Yearbook, the statistical yearbooks of the 31 provinces, and the China Agricultural Statistical Yearbook (2008–2019).

3.2. Construction of the Evaluation Index System

3.2.1. Agricultural Economy Evaluation Indicators

The agricultural economy refers to the economic system formed around agricultural production, circulation, consumption, and related activities, serving as an integral part of the national economy [41]. With the development of modern agriculture, the agricultural economic system has become increasingly complex and diverse. A single indicator cannot comprehensively measure its multidimensional and integrated development level. Therefore, constructing a multi-level and multi-dimensional evaluation indicator system is essential for scientifically assessing the development of the agricultural economic system. This study draws on the findings of Liu et al. (2020) [24], Zhang et al. (2023) [26], Chen et al. (2023) [27], Gao (2023) [28], Guo et al. (2024) [42], Wang et al. (2024) [43], Guo et al. (2023) [44], Liu et al. (2022) [45] and Sun et al. (2022) [25]. Based on these studies, 19 indicators were selected from five dimensions: economic input, economic structure, production efficiency, economic benefits, and economic activities, to construct an evaluation indicator system for the agricultural economic system.

The data used in this study mainly come from the China Statistical Yearbook, The Statistical Yearbooks of the 31 Provinces of China, China Rural Statistical Yearbook, Rural Revitalization Statistical Database, China Grain Yearbook, China Financial Yearbook, and China Agricultural Statistics. The weights of each indicator were calculated using the entropy method, as shown in Formulas (1)–(7). More details are provided in

Table 2. Index system for evaluating the agricultural economy.

Main Index	First-Tier Indexes	Second-Tier Indexes	Indicator Interpretation	Nature of Indicators	Weight
Agricultural Economy	Economic Input	Proportion of Employment in the Primary Sector (%)	The percentage of people working in agriculture, forestry, and fishing out of total employment.	Positive	0.0409
		Cultivated Land Area (hm2)	The total area of land suitable for growing crops.	Positive	0.0980
		Agricultural Machinery Power per Unit of Sown Area (kW/hm2)	The amount of machinery power available per unit of farmland, showing the level of mechanization.	Positive	0.0782
	Economic Structure	Agricultural Industrial Structure Adjustment Index (%)	Evaluates the balance and efficiency of agricultural structure.	Positive	0.0242
		Proportion of Agricultural Total Output Value (%)	The proportion of agricultural output value in the national economy.	Positive	0.0483
		Coordination of Crop Sowing Structure (%)	Reflects the balance between different crop planting areas.	Positive	0.0351
	Production Efficiency	Contribution of Primary Industry’s Value Added (%)	The contribution of agriculture to overall economic growth.	Positive	0.0606
		Agricultural Labor Productivity (%)	Output value per agricultural worker, indicating labor efficiency.	Positive	0.0713
		Land Utilization Rate (%)	The proportion of farmland being effectively used.	Positive	0.1060
		Volatility of Agricultural Economic Development (%)	Measures the stability or fluctuation of agricultural economic growth.	Positive	0.0082
		Scale of Farmland Operation (10,000 people/hm2)	Average size of farmland operated by a single farm or household.	Positive	0.1205
	Economic Benefits	Per Capita Grain Output (kg/person)	The average amount of grain available per person.	Positive	0.0884
		Farmers’ Disposable Income (yuan)	Income that farmers can freely spend or save, reflecting living standards.	Positive	0.0717
		Per Capita Total Agricultural Output Value (yuan/person)	The average agricultural output per agricultural worker.	Positive	0.0650
		Engel’s Coefficient of Rural Residents (%)	The proportion of food expenses in total spending, showing living standard improvement.	Negative	0.0255
	Economic Vitality	Growth Rate of Agricultural GDP (%)	The growth rate of agricultural production value.	Positive	0.0105
		Urban-Rural Income Gap Index (%)	Reflects the income difference between urban and rural residents.	Negative	0.0185
		Growth Rate of Rural Residents’ Net Income (%)	The growth rate of average income for rural residents.	Positive	0.0200
		Comparison of Urban-Rural Consumption Levels (%)	Compares spending levels of urban and rural residents.	Negative	0.0092

, with the specific data sources presented in Supplementary Table S1.

3.2.2. Agricultural Ecosystem Evaluation Indicators

Agricultural ecology refers to the system formed by the interaction between agricultural production and the ecological environment, including ecological conditions, ecosystem services, and the impact of agricultural activities on the environment. It serves as a critical foundation for sustainable agricultural development and ecological balance. With the development of modern agriculture, agricultural ecosystems face increasing environmental pressures, along with growing challenges in complexity and stability. To comprehensively evaluate its development level, it is essential to construct a multi-level, multi-dimensional evaluation indicator system. This study draws on the research findings of Liu et al. (2020) [24], Hou et al. (2024) [30], Jiang et al. (2022) [36], Liu et al. (2022) [45], and Yang et al. (2019) [46]. Based on these studies, 11 indicators were selected from two dimensions: ecological conditions and ecological pressures, to construct an evaluation indicator system for agricultural ecology.

The data used in this study mainly come from The Statistical Yearbooks of the 31 Provinces of China, China Forestry and Grassland Statistical Yearbook, China Forestry Statistical Yearbook, China Environmental Statistical Yearbook, China Agricultural Statistical Data (1949–2019), China Rural Statistical Yearbook, and China Water Resources Statistical Yearbook. The weights of each indicator were calculated using the entropy method, as shown in Formulas (1)–(7). More details are provided in

Table 3. Index system for evaluating the agricultural ecology.

Main Index	First-Tier Indexes	Second-Tier Indexes	Indicator Interpretation	Nature of Indicators	Weight
Agricultural Ecological	Ecological Conditions	Forest Coverage Ratio (%)	The percentage of land covered by forests.	Positive	0.1369
		Irrigation Rate of Cultivated Land (%)	The proportion of farmland with irrigation systems.	Positive	0.1455
		Annual Precipitation (mm)	The average amount of rainfall in a year.	Positive	0.1436
		Agricultural Water Consumption (m2/person)	The total water used for agricultural production.	Negative	0.0261
	Ecological Pressure	Pesticide Use Intensity (t/hm2)	The amount of pesticides used per unit of farmland.	Negative	0.0186
		Chemical Fertilizer Use Intensity (t/hm2)	The amount of fertilizers applied per unit of farmland.	Negative	0.0346
		Agricultural Plastic Film Use Intensity (t/hm2)	The amount of plastic film used for farming purposes.	Negative	0.0198
		Agricultural Diesel Use Intensity (t/hm2)	The amount of diesel fuel used per unit of farmland or output value.	Negative	0.0193
		Crop Disaster Rate (%)	The percentage of crops affected by natural disasters.	Negative	0.0254
		Total Afforestation Area (khm2)	The total area of land turned into forest through planting.	Positive	0.2166
		Soil and Water Conservation Treatment Area (khm2)	The area of land treated to prevent soil erosion.	Positive	0.2136

Note: The soil and water conservation treatment area in Shanghai was calculated using interpolation (mean value).

, with the specific data sources presented in Supplementary Table S2.

3.3. Research Methods

3.3.1. Entropy-Weighted Comprehensive Evaluation Method

The agricultural economy and ecosystem involve multiple complex indicators. Due to differences in data magnitude, direct comparison is challenging, requiring a scientific method to assign weights and ensure data comparability. The entropy weight method, originating from physics, measures the disorder of a system [47]. Introduced into decision analysis in 1965 [48], it has been widely applied in information theory, ecology, and economics. By quantifying the uncertainty of indicators, lower entropy values indicate higher variability and greater weight. With its objectivity and reliability, the entropy weight method has become an essential tool for multi-indicator evaluation [49]. Thus, we use the entropy weight method in this study to calculate the weights for the agricultural economy and ecosystem indicators. The computational process is as follows:

(1)

Standardization of Original Data:

For positive indicators:

$$X_{ab}^{\text{'}}={\displaystyle \frac{x_a-min\left(x_b\right)}{\mathit{max}\left(x_b\right)-min\left(x_b\right)}}+ 0.0001$$

(1)

For negative indicators:

$$X_{ab}^{\text{'}}={\displaystyle \frac{\mathit{max}\left(x_b\right)-x_{ab}}{\mathit{max}\left(x_b\right)-min\left(x_b\right)}}+ 0.0001$$

(2)

(2)

Calculation of the Proportion of Indicator b in Year a:

$$P_{ab}={\displaystyle \frac{X_{ab}^{\text{'}}}{\sum _{i=a}^m{X}_{ab}^{\text{'}}}}$$

(3)

(3)

Calculation of the Entropy Value for Indicator b:

$$E_b=-{\displaystyle \frac{1}{\mathit{ln}m}}{\sum }_{b=1}^m{P}_{ab}\mathit{ln}{P}_{ab}$$

(4)

(4)

Calculation of the Divergence Coefficient for Indicator b:

$$d_b=1-E_b$$

(5)

(5)

Determination of the Weight for Indicator b:

$$W_b={\displaystyle \frac{d_b}{\sum _{b=1}^n{d}_b}}$$

(6)

(6)

Calculation of the Comprehensive Development Index:

$$U={\sum }_{b=1}^n(X_{ab}^{\prime }\times W_b)$$

(7)

In the Formulas (1)–(7), $X_{ab}^{\text{'}}$ represents the standardized value of the b-th indicator in year a, and $P_{ab}$ is the proportion of the b-th indicator in year a relative to the total of all standardized values. The entropy value, $E_b$, measures the uncertainty of the b-th indicator, and the divergence coefficient, $d_b$, reflects its variability. The weight $W_b$ of each indicator is determined based on its divergence coefficient. Finally, the comprehensive development index U is obtained by summing the weighted standardized values of all indicators.

3.3.2. Modified Coupling Coordination Degree Model

The coupling coordination model was initially developed in physics to describe the interactions and coordination levels between systems [50]. When applied to the social sciences, traditional models often face the issue of insufficient differentiation in calculation results, making it difficult for the coupling degree to accurately reflect the true coordination of systems [51]. To address this problem, Wang Shujia and other scholars proposed a modified coupling coordination model. This modified model introduces a nonlinear adjustment mechanism and dynamic weights, enhancing its adaptability to the interactions within complex systems. These improvements enable the model to better handle nonlinear relationships and dynamic changes between systems, increasing the differentiation of calculation results and significantly improving the validity of the coupling degree [51]. Today, the modified coupling coordination model is widely applied in fields such as economics, environmental science, and ecology [52,53,54]. In this study, the modified coupling coordination model is adopted to analyze the coordination level of agricultural economic and ecosystem development, aiming to enhance the accuracy and reliability of the analysis results. The computational process is as follows:

$$C=\sqrt{\left[1-{\displaystyle \frac{{\sum }_{i>j,j=1}^n\sqrt{{\left(U_i-U_j\right)}^2}}{\sum _{m=1}^{n-1}m}}\right]\times {\left(\prod _{i=1}^n{\displaystyle \frac{U_i}{max{U}_i}}\right)}^{{\displaystyle \frac{1}{n-1}}}}$$

(8)

This study focuses on the agricultural economic system and the agricultural ecological system, hence n = 2. $U_i$ and $U_j$ represent the comprehensive development indices of the agricultural economy and ecology, respectively. Assuming max($U_i$) is ${\mathrm{U}}_2$, the equation is simplified to Equation (9).

$$C=\sqrt{[1-\left(U_2-U_1\right)]\times {\displaystyle \frac{U_1}{U_2}}}$$

(9)

$$T=\mathsf{\alpha }{U}_1+\mathsf{\beta }{U}_{2 } \alpha +\beta =1$$

(10)

$$D=\sqrt{C\times T}$$

(11)

In Equations (9)–(11), C represents the coupling degree between the agricultural economy and ecology, with a range of [0, 1]. T denotes the comprehensive coordination index, and α and β represent the weights of the agricultural economy and ecology, respectively. Given the mutual interaction between the two in the development process and considering previous research findings, it is assumed that they are equally important in coordinated development, hence α = β = 0.5 [25,29,46,55,56]. D represents the coupling coordination index between the agricultural economy and ecology, with a range of [0, 1]. A higher D value indicates a better coupling coordination relationship between the two. Based on the equal-interval division method [57], the coupling coordination degree between the agricultural economy and ecology is classified into 10 levels (

Table 4. Evaluation criteria for the coupling coordination of agricultural economy and agricultural ecology.

Coupling Coordination	Coupling Effect Level	Coupling Coordination	Coupling Effect Level
[0.0~0.1)	Severe Imbalance	[0.5~0.6)	Barely Coupled Coordination
[0.1~0.2)	Significant Imbalance	[0.6~0.7)	Primary Coupled Coordination
[0.2~0.3)	Moderate Imbalance	[0.7~0.8)	Intermediate Coupled Coordination
[0.3~0.4)	Mild Imbalance	[0.8~0.9)	Good Coupled Coordination
[0.4~0.5)	On the Verge of Imbalance	[0.9~0.10]	High-Quality Coupled Coordination

).

3.3.3. Dagum Gini Coefficient

The Dagum Gini coefficient, proposed by Camilo Dagum in 1980, is an extension of the traditional Gini coefficient designed to better analyze and interpret inequality [58]. The traditional Gini coefficient has limitations in revealing regional disparities, particularly its inability to effectively distinguish between intra-regional and inter-regional inequality. To address these shortcomings, the Dagum Gini coefficient decomposes total inequality into intra-regional inequality, inter-regional inequality, and a contribution rate, thereby overcoming the limitations of the traditional model [58]. This decomposition provides a more precise understanding of regional disparities, making the Dagum Gini coefficient widely applicable in fields such as economics, regional science, and social development [59,60]. In this study, the Dagum Gini coefficient is employed to measure regional disparities in the coupling and coordinated development of agricultural economy and ecosystems. It enables a deeper analysis of intra-regional and inter-regional inequality components, providing a scientific basis for promoting coordinated regional development. The formula for calculating the overall Gini coefficient is as follows:

$$G={\displaystyle \frac{\sum _{j=1}^k\sum _{h=1}^k\sum _{i=1}^{n_j}\sum _{r=1}^{n_h}\mid y_{ji}-y_{hr}\mid }{2n^2\overline{y}}}$$

(12)

$$G_{jj}={\displaystyle \frac{\sum _{i=1}^{n_j}\sum _{r=1}^{n_h}\mid Y_{ji}-Y_{jr}\mid }{2n_j^2{\bar{y}}_j}}$$

(13)

$$G_w={\sum }_{i=1}^{n_j}{G}_{jj}{p}_j{s}_j$$

(14)

$$G_{jh}={\displaystyle \frac{\sum _{i=1}^{n_j}\sum _{r=1}^{n_h}\mid Y_{ji}-Y_{hr}\mid }{n_j{n}_h\left({\overline{y}}_j+{\overline{y}}_h\right)}}$$

(15)

$$G_{nb}={\sum }_{j=2}^k{\sum }_{h=1}^{j-1}{G}_{jh}\left(p_j{s}_h+p_h{s}_j\right)D_{jh}$$

(16)

$$G_t={\sum }_{j=2}^k{\sum }_{h=1}^{j-1}{G}_{jh}\left(p_j{s}_h+p_h{s}_j\right)\left(1-D_{jh}\right)$$

(17)

$$P_j={\displaystyle \frac{n_j}{n}}$$

(18)

$$S_j={\displaystyle \frac{n_j\overline{Y_j}}{n\overline{y}}}$$

(19)

$$D_{jh}={\displaystyle \frac{d_{jh}-p_{jh}}{d_{jh}+p_{jh}}}$$

(20)

$$d_{jh}={\int }_0^{\infty }d{F}_j\left(Y\right){\int }_0^Y\left(Y-x\right)d{F}_h\left(x\right)$$

(21)

$$p_{jh}={\int }_0^{\infty }d{F}_h\left(Y\right){\int }_0^Y\left(Y-x\right)d{F}_j\left(x\right)$$

(22)

In Equation (12), G represents the Dagum Gini coefficient. j and $h$ represent different regions, while i and r represent provinces within those regions. k is the total number of regions, and $n_j$ and $n_h$ are the number of provinces in regions j and $h$, respectively. $Y_{ji}$ and $Y_{hr}$ are the coupling coordination levels of provinces i and r in regions j and h, respectively, and $\overline{y}$ is the average coupling coordination level across all provinces. In Equations (13)–(17), the Gini coefficient is broken down into three parts: within-region disparity ($G_w$), between-region disparity ($G_{nb}$), and cross-region variation density ($G_t$). In Equations (18)–(22), several key variables are defined: $P_{\mathrm{j}}$ is the proportion of provinces in region j, $S_j$ is the average coupling coordination level for region j, and $D_{jh}$ represents the difference between regions j and h. $d_{jh}$ and $p_{jh}$ are used to calculate the differences in coupling coordination levels between regions. $F_j$ and $F_h$ are the cumulative distribution functions of the coupling coordination levels in regions j and h, respectively.

3.3.4. Kernel Density Estimation

Kernel Density Estimation (KDE), proposed by Rosenblatt in 1956, is a non-parametric method widely used for estimating probability density functions [61]. KDE generates a continuous probability density curve by applying a smoothing kernel function to each observed data point and calculating the weighted sum. This approach effectively captures the distribution characteristics of the data [62]. Compared to traditional statistical methods, KDE does not require assumptions about the data’s distribution type, offering greater flexibility. It is particularly well-suited for uncovering nonlinear structures and diversity in data. As a result, KDE has been widely applied in fields such as economics, sociology, and ecology [63,64], and it demonstrates significant advantages in multidimensional data analysis. Therefore, in this study, Kernel Density Estimation is used to analyze the distribution characteristics and trends of the coupling and coordinated development of agricultural economy and ecosystems. The calculation formula is as follows:

$$f\left(x\right)={\displaystyle \frac{1}{Nh}}{\sum }_{i=1}^{N}K\left({\displaystyle \frac{X_i-\overline{x}}{h}}\right)$$

(23)

$$k\left(x\right)={\displaystyle \frac{1}{\sqrt{2\pi }}}exp\left(-{\displaystyle \frac{x^2}{2}}\right)$$

(24)

In Equations (23) and (24), f(x) is the density function of the random variable X, where $X_i$ represents the coupling coordination level of agriculture and ecology in each province, $\overline{x}$ is the mean coupling coordination level, N is the total sample size, h is the bandwidth, and K(·) is the kernel function. K$\left(x\right)$ is the standard normal kernel function.

3.3.5. Markov Chain

Traditional Markov Chain

The traditional Markov Chain, proposed by Andrey Markov in 1906, is a model used to describe random transitions between system states [65]. It is widely applied to analyze state transition probabilities and predict future states. In this study, the Markov Chain is used to analyze changes in the coupling coordination degree between China’s agricultural economy and ecosystems from 2008 to 2022. By constructing a Markov transition matrix, the model reveals transition patterns of coupling coordination, providing data for future predictions.

The Markov Chain reflects the probability distribution of transitions between different states of coupling coordination in China over time. In this model, the state $X_t$ at time t depends solely on the state $X_{t-1}$ at the previous time step. The transition probability $P_{ij}^{t,t+n}$ refers to the probability of transitioning from state i to state j between years t and t+n, calculated as:

$$p_{ij}^{t,t+n}=P\left\{X_{t+n}=j\mid X_t=i\right\}$$

(25)

Spatial Markov Chain

The traditional Markov Chain is effective in revealing the transition patterns of coupling coordination degrees over time but fails to account for spatial dependence and regional differences. To address this limitation, the spatial Markov Chain was developed, incorporating a spatial adjacency matrix to account for the influence of neighboring regions. This approach allows for the analysis of spatial agglomeration and transmission effects, enhancing the model’s ability to capture regional evolution [66]. In this study, the spatial Markov Chain is applied to analyze the spatial transition characteristics of the coupling coordination degree between the agricultural economy and ecosystems in China from 2008 to 2022. To examine how the coupling coordination of neighboring provinces influences each other, the spatial Markov Chain is combined with Moran’s I index to test spatial correlation [67]. The formula for Moran’s I is as follows:

$$I={\displaystyle \frac{n\sum _{i=1}^n\sum _{j=1}^n{w}_{ij}\left(z_i-\bar{z}\right)\left(z_j-\bar{z}\right)}{\left(\sum _{i=1}^n\sum _{j=1}^n{w}_{ij}\right)\sum _{i=1}^n{\left(z_i-\bar{z}\right)}^2}}$$

(26)

$$p_{ij}^{\left(t,t+n\right)}={\displaystyle \frac{\sum _{k=1}^n{w}_{ik}{S}_{ik}^{\left(t,t+n\right)}}{\sum _{k=1}^n{w}_{ik}}}$$

(27)

3.3.6. Obstacle Degree Model

The obstacle degree model, rooted in systems theory and complex network theory, quantifies the impact of restrictive factors on a system’s efficiency and coordination [68]. Based on the “bottleneck theory” and “complex systems theory,” it focuses on identifying and removing obstacles to promote system development [69]. In this paper, the obstacle degree model is used to identify barriers to the coupled development of China’s agricultural economy and ecosystem, offering insights for improving their coordination. The calculation formula is as follows:

$$D_j=1-X_{ab}^{\text{'}}$$

(28)

$$h_b={\displaystyle \frac{D_b\times F_b}{\sum _{b=1}^n(F_b\times D_b)\times 100\%}}$$

(29)

$$H_b=\sum h_{ab}$$

(30)

In Equations (28)–(30): $X_{ab}^{\text{'}}$ represents the standardized value of the index. $F_b$ denotes the weight of the index. $h_b$ indicates the obstacle degree of each index to coupling coordination development. $H_b$ represents the obstacle degree at the criterion level, with larger values indicating a greater hindrance of the corresponding sub-indicator to the coupling coordination degree of the two systems.

4. Results and Analysis

4.1. Analysis of Coupling Coordination Measurement Results

4.1.1. Overall Characteristics

Using Equations (8)–(11), the coupling coordination degree between China’s agricultural economy and ecology from 2008 to 2022 was calculated. The trends of coupling degree and coordination level were plotted using Excel 2016, as shown in Figure 2.

From the perspective of coupling degree, although China’s agricultural economy and ecological coupling degree experienced a year-on-year decline in certain years during the study period, it generally showed an upward trend. The average coupling degree increased from its lowest value of 0.7270 in 2008 to 0.7844 in 2014, then slightly declined to 0.7632 in 2019, and reached its peak at 0.8267 in 2022. This indicates that the coupling relationship between the agricultural economy and the ecosystem has gradually improved, moving toward a higher level of coupling.

In terms of coupling coordination degree, it showed a steady annual increase, rising from 0.4363 in 2008 to 0.5406 in 2022, with a relative growth rate of 23.91%. However, the overall coordination level remains relatively low. Based on the classification standards for coupling coordination levels, the study period can be divided into two phases: the first phase (2008–2017), where the coupling coordination degree ranged between 0.4363 and 0.4963, indicating a “near imbalance” state; and the second phase (2018–2022), where the coupling coordination degree exceeded 0.5, reaching the “barely coordinated” stage. The improvement in coupling coordination can largely be attributed to China’s implementation of the Rural Revitalization Strategy in 2017. This strategy emphasized the transformation of traditional agriculture into modern agriculture and prioritized ecological protection as a core principle of agricultural development. On the agricultural economy side, the advancement of agricultural industrialization and technology—such as modern agricultural machinery, smart agriculture platforms, and precision farming techniques—has driven the development of specialized, high-value agriculture, improving agricultural productivity and economic benefits while supporting structural reforms on the supply side. On the ecological side, measures such as rational crop rotation, intercropping, and reduced use of chemical fertilizers and pesticides have alleviated the environmental pressures of agricultural activities, promoting the balance of rural ecosystems.

In conclusion, these efforts have significantly advanced the coupling and coordination development between China’s agricultural economy and its ecosystem. However, further efforts are needed to enhance the overall coordination level to achieve sustainable agricultural development.

4.1.2. Provincial and Regional Characteristics

Based on the coupling coordination degree calculated in Section 4.1.1 from the provincial perspective, the regions were statistically classified into eastern, central, and western areas, with the classification results shown in

Table 5. Coupling coordination degree between agricultural economy and ecology in China from a regional perspective (2008–2022).

Year	Eastern Region	Central Region	Western Region	National Average
2008	0.431	0.464	0.423	0.436
2009	0.437	0.471	0.425	0.441
2010	0.445	0.477	0.429	0.447
2011	0.458	0.476	0.436	0.454
2012	0.471	0.489	0.447	0.466
2013	0.48	0.496	0.467	0.479
2014	0.491	0.494	0.468	0.483
2015	0.497	0.503	0.475	0.49
2016	0.499	0.503	0.477	0.491
2017	0.506	0.504	0.482	0.496
2018	0.513	0.509	0.485	0.501
2019	0.523	0.512	0.49	0.507
2020	0.535	0.524	0.501	0.519
2021	0.556	0.548	0.522	0.541
2022	0.555	0.549	0.522	0.541
Average	0.493	0.501	0.47	0.486

. The temporal trends of provincial coordination development levels were visualized using ArcGIS 10.6.1, as shown in Figure 3. It is worth noting that the provinces included in the eastern, central, and western regions have been specified in Section 3.1 “Research Object and Data Sources” (Table 1).

According to Table 5, the study results can be summarized in two main points: First, the coupling coordination degree of the three regions (eastern, central, and western) showed a steady improvement over time. During the study period, except for slight fluctuations in the central and western regions in 2011 and 2014, all regions experienced a yearly increase, indicating progress toward higher coordination levels between the agricultural economy and ecology. The multi-year averages of coupling coordination degree were as follows: central region (0.501) > eastern region (0.493) > western region (0.47). Second, the coupling coordination degree in the eastern region increased from 0.431 in 2008 to 0.555 in 2022, with a growth rate of 28.77%. Since 2011, it has consistently been above the national average (0.466) and surpassed the central region in 2017, maintaining the lead thereafter. This shows that the eastern region had the most significant growth trend among the three. The central region also exhibited a stable increase, rising from 0.464 in 2008 to 0.549 in 2022. Throughout the study period, its coupling coordination degree remained above the national average, though its growth rate was slightly lower than that of the eastern region. In contrast, the western region’s coupling coordination degree increased from 0.423 in 2008 to 0.522 in 2022, with a growth rate of 23.4%. However, it consistently stayed below the national average and remained the weakest among the three regions.

Based on Table 5 and Figure 3, the analysis of regional and provincial coupling coordination levels reveals notable trends. The eastern region has shown significant growth, surpassing the central and western regions since 2017 to rank first among the three regions. By 2022, 90.91% of the provinces in the eastern region had a coupling coordination degree above 0.5, with Fujian exceeding 0.6 and entering the initial coupling stage. Provinces like Shandong, Hainan, and Liaoning also approached the 0.6 threshold. The central region holds an overall advantage, with its average coupling coordination level surpassing the eastern and western regions throughout the study period. By 2022, 62.5% of its provinces exceeded a coupling coordination degree of 0.5. However, internal disparities remain. Jilin and Heilongjiang led early on, while Jiangxi and Hunan showed latecomer advantages, reaching higher levels only after 2020. Shanxi, on the other hand, experienced stagnation and a decline in 2022, highlighting challenges in achieving coordinated development. The western region, though lagging overall, demonstrated rapid growth, with its coupling coordination degree increasing by 23.4% from 2008 to 2022. Inner Mongolia stood out, achieving a coupling coordination degree of 0.684 in 2022, ranking among the best in the country. Most other western provinces showed limited growth, averaging 0.507 by 2022, reflecting steady progress despite regional challenges.

In summary, while China has made significant progress in enhancing the coupling coordination between its agricultural economy and ecology, regional disparities remain evident. To address these differences, the eastern region should leverage its technological and economic strengths to drive green innovation. The central region should focus on optimizing resource allocation and improving productivity, while the western region should prioritize ecological restoration and the development of specialized agriculture. Establishing cross-regional collaboration mechanisms to share resources, technology, and funding is crucial to narrowing regional gaps and achieving coordinated national development.

4.2. Decomposition of Regional Differences in Coupling Coordination Degree

Using Formulas (12)–(22), the overall differences, intra-regional differences, inter-regional differences, and hyper-variable density of the coupling coordination degree between China’s agricultural economy and ecology from 2008 to 2022 were calculated. The results are shown in

Table 6. Calculation results of Dagum Gini coefficients for coupling coordination degree between China’s agricultural economy and ecology (2008–2022).

Year	Overall Coefficient	Intra-Regional Gini Coefficient			Inter-Regional Gini Coefficient			Contribution Rate (%)
		Eastern	Central	Western	East-Central	East-West	Central-West	Inter-Regional	Intra-Regional	Transvariation Density
2008	0.0495	0.0427	0.0570	0.0373	0.0583	0.0431	0.0603	38.77	29.80	31.44
2009	0.0488	0.0417	0.0559	0.0360	0.0573	0.0419	0.0608	43.15	29.47	27.38
2010	0.0510	0.0419	0.0599	0.0376	0.0582	0.0443	0.0649	43.93	29.28	26.78
2011	0.0469	0.0421	0.0470	0.0370	0.0483	0.0465	0.0561	40.67	29.77	29.56
2012	0.0489	0.0422	0.0489	0.0397	0.0492	0.0502	0.0580	39.87	29.58	30.55
2013	0.0440	0.0438	0.0381	0.0391	0.0434	0.0463	0.0473	28.97	31.48	39.55
2014	0.0422	0.0411	0.0340	0.0359	0.0394	0.0485	0.0445	29.99	30.28	39.73
2015	0.0457	0.0447	0.0404	0.0397	0.0443	0.0501	0.0479	28.21	31.15	40.64
2016	0.0436	0.0424	0.0392	0.0360	0.0421	0.0482	0.0466	28.50	30.58	40.92
2017	0.0424	0.0389	0.0388	0.0362	0.0399	0.0479	0.0453	26.60	30.39	43.01
2018	0.0432	0.0398	0.0393	0.0346	0.0408	0.0500	0.0460	29.84	29.63	40.53
2019	0.0454	0.0374	0.0399	0.0354	0.0417	0.0564	0.0478	33.68	27.92	38.40
2020	0.0438	0.0347	0.0324	0.0356	0.0367	0.0576	0.0472	35.67	27.03	37.30
2021	0.0427	0.0364	0.0232	0.0371	0.0329	0.0567	0.0474	34.68	27.29	38.03
2022	0.0439	0.0402	0.0253	0.0367	0.0362	0.0551	0.0495	33.19	27.85	38.95

.

4.2.1. Overall Differences

Figure 4, generated based on the results calculated from Table 6, shows the overall differences in the coupling coordination degree between China’s agricultural economy and ecology and their evolution. During the study period, the overall difference decreased from 0.0495 in 2008 to 0.0439 in 2022, showing a fluctuating downward trend. This indicates that the overall differences in coordination development levels gradually narrowed. However, the dynamic changes were unstable, showing periodic fluctuations. For instance, the overall difference rose from 0.0495 in 2008 to 0.051 in 2010, dropped to 0.0469 in 2011, reached a low of 0.0422 in 2014, and then fluctuated around 0.0437, with two additional rises and falls during this period.

4.2.2. Intra-Regional Differences

Figure 5 was generated using the results calculated from Table 6. Figure 5 illustrates the Gini coefficients of the coupling coordination degree for the three main regions. Overall, the Gini coefficients declined, but the magnitude and patterns of decline varied across regions. Based on average values, the regional ranking was central (0.0413) > eastern (0.0407) > western (0.0369), indicating relatively higher internal imbalances in the central region. Specifically, the central region showed the largest decline, with the Gini coefficient dropping from 0.057 in 2008 to 0.0253 in 2022, a reduction of 55.61%. The eastern region exhibited a “rise–fall–rise” pattern, peaking at 0.0447 in 2015 and hitting its lowest value of 0.0347 in 2020. The western region experienced smaller fluctuations, with the Gini coefficient remaining relatively stable around 0.037. Overall, the internal differences within the three regions gradually decreased, reflecting significant progress in promoting coordination between the agricultural economy and ecology, while partially alleviating regional development imbalances.

4.2.3. Inter-Regional Differences

Figure 6 was generated based on the results calculated from Table 6. Figure 6, based on Table 6, highlights the inter-regional differences and their evolution. During the study period, the differences between the eastern and central regions fluctuated the most, followed by those between the central and western regions, while the differences between the eastern and western regions were the smallest. In terms of trends, the eastern-central and central-western differences showed similar patterns, but the central-western differences were significantly larger than the eastern-central ones. The difference between the eastern and central regions gradually narrowed and nearly equalized by 2014, whereas the gap between the central and western regions remained substantial. Meanwhile, the eastern-western differences showed an expanding trend, primarily due to the eastern region’s economic and technological advantages. The eastern region not only adjusted its agricultural structure and increased agricultural output but also quickly adopted advanced agricultural technologies, promoting ecological and sustainable agricultural development and significantly improving the coupling coordination level. This finding is supported by Section 4.1.2.

4.2.4. Sources and Contributions of Disparities

According to Table 6, the sources and contributions of differences in the coupling coordination degree of China’s agricultural economy and ecology from 2008 to 2022 were identified. During the study period, intra-regional differences exhibited an “M” pattern, following a “rise–fall–rise–fall” trend, with contributions ranging from 27.03% to 31.48%, remaining relatively stable. Inter-regional differences followed an “N” pattern, with contributions ranging from 26.6% to 43.93%, showing greater fluctuations. Hyper-variable density followed a trend similar to inter-regional differences, with contributions ranging from 27.38% to 43.01%. Furthermore, the primary sources of differences varied across time. From 2008 to 2012, inter-regional differences were the main source, indicating significant disparities in coordination levels across regions. During this period, the contributions of intra-regional differences and hyper-variable density were roughly equal, showing a balanced impact of internal imbalances and cross-regional effects on overall differences. From 2013 to 2022, hyper-variable density became the dominant factor, reflecting an increase in inter-regional imbalances in coordination levels. Therefore, narrowing inter-regional gaps has become a critical and urgent task.

4.3. Dynamic Evolution Trends of Coupling Coordination Degree

Using the Dagum Gini coefficient to analyze differences in China’s agricultural economy and ecology, this section applies the Kernel density estimation to calculate absolute differences and trends. The coupling coordination Kernel density curves, plotted with MATLAB 2021b based on Formulas (23)–(24), illustrate the dynamic evolution of coordination levels between the two, as shown in Figure 7.

4.3.1. Kernel Density Estimation Analysis

At the national level, Figure 7a highlights three main features: First, the Kernel density curve shifts to the right overall, indicating a steady improvement in the coupling coordination levels across 31 provinces and significant progress in sustainable agricultural development. Second, since 2020, the peak height of the density curve has decreased, and the curve shows an extended “right tail”, reflecting a growing gap between provinces with high coordination levels (e.g., Inner Mongolia and Heilongjiang) and those with low coordination levels (e.g., Chongqing and Shanghai). Third, the height and width of the peak fluctuate dynamically, undergoing a pattern of “expansion–contraction–re-expansion–re-contraction”, which demonstrates the dynamic variability of absolute differences in coupling coordination levels.

At the regional level, Figure 7b–d show the dynamic evolution trends for the eastern, central, and western regions, highlighting both common trends and regional characteristics. The common features include: First, the overall coupling coordination levels in all three regions show an upward trend, with the main peaks of the Kernel density curves gradually shifting to the right. Specifically, the main peaks for the eastern, central, and western regions increased from approximately 0.43, 0.46, and 0.42 in 2008 to 0.56, 0.55, and 0.52 in 2022, respectively, transitioning from the “on the Verge of Imbalance” stage to the “barely Coupled Coordination” stage. Second, all three regions exhibit a “right tail” phenomenon, indicating significant internal differences in coupling coordination levels. For instance, provinces like Liaoning and Hebei in the eastern region, Heilongjiang and Jilin in the central region, and Inner Mongolia in the western region have relatively high or low coordination levels, leading to the extended tails. Third, the absolute internal differences in each region gradually narrowed. The Kernel density curves transitioned from “flat and wide” to “sharp and narrow,” reaching their highest peaks in 2022. This suggests that internal differences within regions initially increased but eventually converged, with a clear trend toward narrowing. Regarding regional differences, distinct characteristics are observed: First, the distribution peaks differ significantly between regions. The peaks in the eastern and central regions are concentrated around 0.56 and 0.57, respectively, while the western region’s peak is around 0.485. Additionally, the peak in the western region is more concentrated, reflecting relatively higher consistency in internal coupling coordination levels compared to the eastern and central regions. Second, there are significant disparities in the trends of coordination level differentiation. The Kernel density curves in the eastern and central regions exhibit single-peak trends, indicating increasing uniformity in coordination levels. In contrast, the western region displays multi-peak patterns, reflecting notable polarization in coordination levels. This suggests that the western region faces significant internal disparities in the agricultural economy and ecological coordination development, highlighting persistent challenges with uneven development.

4.3.2. Traditional Markov Chain Analysis

Kernel density analysis provides insights into the distribution dynamics of coupling coordination development between China’s agricultural economy and ecology, revealing trends at specific time points. However, it cannot quantify transition patterns or predict future evolution. To address this, the study adopts the Markov chain model, which analyzes transition probabilities and dynamic changes between different coupling levels.

According to Equation (25), this study uses a time span of one year and classifies the coupling coordination levels of agricultural economy and ecology in each province into four categories based on their relative levels: low coupling (I): 0–25%, moderately low coupling (II): 25–50%, moderately high coupling (III): 50–75%, and high coupling (IV): 75–100%. The transition probability matrix obtained through the Markov chain model is shown in

Table 7. Markov transition probability matrix for coupling coordination development between China’s agricultural economy and ecology (2008–2022).

Lag Type	I	II	III	IV	Observed Value
I	0.8103	0.1897	0	0	116
II	0	0.7521	0.2479	0	117
III	0	0.0099	0.802	0.1881	101
IV	0	0	0.05	0.95	100

, providing a robust quantitative basis for further analysis of the dynamic evolution patterns of coupling coordination development.

First, the distribution pattern, where diagonal data is consistently higher than off-diagonal data, shows that the coupling coordination levels of agricultural economy and ecology across provinces demonstrate strong stability. Specifically, the probabilities of provinces maintaining their current coordination level after one year are 81.03%, 75.21%, 80.20%, and 95%, respectively. This indicates the significant robustness of China’s coupling coordination development in the agricultural economy and ecology, as well as a clear trend of “club convergence”.

Second, the probabilities of low-level and high-level provinces maintaining their current coordination levels are 81.03% and 95%, respectively, which are significantly higher than the 75.21% for moderately low-level provinces and 80.20% for moderately high-level provinces. This suggests that provinces at both low and high levels exhibit stronger self-stability in their coupling coordination development, further confirming the existence and significance of the “club convergence” phenomenon.

Third, transitions in coordination levels mainly occur between adjacent levels, with rare occurrences of “leapfrog” transitions across multiple levels. This indicates that improvements in the coupling coordination of the agricultural economy and ecology progress gradually. For example, as shown in Table 7, provinces at low, moderately low, moderately high, and high levels only transition to adjacent levels, either upward or downward, with no instances of transitions skipping more than one level.

Fourth, the probabilities of provinces at low, moderately low, and moderately high levels transitioning to a higher level are 18.97%, 24.79%, and 18.81%, respectively, indicating that improvements in coupling coordination levels are somewhat volatile. Meanwhile, the probabilities of moderately high-level and high-level provinces transitioning downward are 0.99% and 5%, respectively, suggesting that as coordination levels improve, the risk of downgrades increases. Therefore, provinces should focus on consolidating their current coordination levels while actively striving for upward transitions to prevent regression in the coupling coordination development of agricultural economy and ecology.

4.3.3. Spatial Markov Chain Analysis

The traditional Markov chain captures the transition probabilities of coupling coordination development but ignores spatial correlations, limiting its ability to fully reflect regional dynamics. To address this, this study introduces a spatial Markov chain by constructing an economic geography spatial weight matrix and calculating the global Moran’s I index to assess spatial autocorrelation. Based on Formula (26), the global Moran’s I index was calculated, and the results are shown in

Table 8. Global Moran’s I index of the coupling coordination development index between China’s agricultural economy and ecology (2008–2022).

Year	Moran I	Probability	Year	Moran I	Probability
2008	0.5378 ***	0.0000	2016	0.3492 ***	0.0013
2009	0.5043 ***	0.0000	2017	0.2803 ***	0.0080
2010	0.5615 ***	0.0000	2018	0.2749 ***	0.0090
2011	0.5403 ***	0.0000	2019	0.2971 ***	0.0044
2012	0.5488 ***	0.0000	2020	0.2855 ***	0.0048
2013	0.4200 ***	0.0001	2021	0.131	0.1441
2014	0.4314 ***	0.0000	2022	0.1846 *	0.0548
2015	0.4164 ***	0.0001

Note: *** and * indicate significance at the 1% and 10% levels, respectively.

.

As shown in Table 8, the global Moran’s I index for 2008–2022 is consistently positive, indicating a significant spatial positive correlation in the coupling coordination development of China’s agricultural economy and ecology at the national level. However, the Moran’s I index for 2021 did not pass the significance test. This phenomenon may be related to the limitations of the calculation method, as the Moran’s I index was calculated using a simple binary 0–1 geographic spatial weight matrix, which cannot fully capture complex spatial relationships [70]. As scholar Zhao Lei (2016) pointed out, an insignificant Moran’s I index does not necessarily indicate the absence of spatial clustering [71]. As long as the variables exhibit overall spatial clustering characteristics, a spatial Markov chain model can still be constructed [71]. Based on Formula (27), the spatial Markov transition probability matrix was calculated, and the results are shown in

Table 9. Spatial Markov transition probability matrix for coupling coordination development between agricultural economy and ecology in China (2008–2022).

Lag Type	t/t + 1	I	II	III	IV	Observed Value
I	I	0.8514	0.1486	0	0	74
	II	0	0.8947	0.1053	0	19
	III	0	0	0.8	0.2	5
	IV	0	0	0	0	0
II	I	0.7667	0.2333	0	0	30
	II	0	0.7209	0.2791	0	43
	III	0	0	0.8889	0.1111	36
	IV	0	0	0.25	0.75	4
III	I	0.6667	0.3333	0	0	9
	II	0	0.7234	0.2766	0	47
	III	0	0.0238	0.7619	0.2143	42
	IV	0	0	0.0571	0.9429	35
IV	I	0.6667	0.3333	0	0	3
	II	0	0.75	0.25	0	8
	III	0	0	0.7222	0.2778	18
	IV	0	0	0.0328	0.9672	61

.

First, the values of the main diagonal elements in the spatial Markov state transition probability matrix are significantly higher than the off-diagonal elements, indicating that after accounting for spatial factors, China’s agricultural economy and ecological coupling coordination development still exhibits a “club convergence” phenomenon.

Second, significant differences are observed among the four transition probability matrices under different spatial lag types. This suggests that disparities in the coupling coordination levels of neighboring provinces significantly influence the state transition probabilities of a given province. The development of a province displays distinct transition characteristics depending on the conditions of neighboring regions.

Third, the spatial Markov state transition probability matrix shows that state transitions at low, medium-low, medium-high, and high levels are primarily concentrated between adjacent levels, with large leaps across multiple levels being relatively rare. This further confirms the gradual and stable nature of coupling coordination development.

Fourth, different lag types have varying effects on the same coupling coordination level. For instance, under medium-low, medium-high, and high-level lag types, the probabilities of medium-high levels transitioning downward are 0%, 2.38%, and 0%, respectively. Similarly, the probabilities of high-level states transitioning downward are 25%, 5.71%, and 3.28%, respectively. The downward transition probability for high-level states decreases from 25% to 3.28%, indicating that under higher-level lag conditions, the stability of high-level states improves, and the risk of downward transition is reduced.

Finally, the impact of the same lag type also varies significantly across different coupling coordination levels. For example, under medium-high lag conditions, the probabilities of low, medium-low, and medium-high levels transitioning upward by one level are 30.33%, 27.66%, and 21.43%, respectively, showing a progressively decreasing trend. This suggests that transition probabilities are influenced not only by lag types but also by the initial levels of agricultural economy and ecological coupling coordination development.

4.4. Obstacle Factor Analysis

Building on the previous analysis of coupling coordination levels and dynamic trends, this study introduces the obstacle degree model to identify the key factors impeding the coordinated development of agricultural economy and ecology. By calculating and ranking the obstacle degrees of each indicator using Formulas (28)–(30), this model provides a detailed understanding of the primary challenges that limit coupling coordination at both national and regional levels. The results, summarized in

Table 10. Decomposition results of obstacle factors and obstacle degrees.

	Agricultural Economic System					Agricultural Ecological System
	First Factor	Second Factor	Third Factor	Fourth Factor	Fifth Factor	First Factor	Second Factor	Third Factor
National	Scale of Farmland Operation (13.80%)	Land Utilization Rate (12.19%)	Cultivated Land Area (9.96%)	Per Capita Grain Output (9.74%)	Agricultural Machinery Power per Unit of Sown Area (8.8%)	Total Afforestation Area (26.6%)	Soil and Water Conservation Treatment Area (26.12%)	Irrigation Rate of Cultivated Land (15.05%)
Eastern Region	Scale of Farmland Operation (14.77%)	Cultivated Land Area (11.18%)	Per Capita Grain Output (10.8%)	Land Utilization Rate (10.44%)	Agricultural Machinery Power per Unit of Sown Area (8.2%)	Total Afforestation Area (30.03%)	Soil and Water Conservation Treatment Area (29.15%)	Annual Precipitation (12.58%)
Central Region	Land Utilization Rate (13.73%)	Scale of Farmland Operation (13.24%)	Agricultural Machinery Power per Unit of Sown Area (9.81%)	Per Capita Grain Output (8.55%)	Cultivated Land Area (8.37%)	Total Afforestation Area (26.98%)	Soil and Water Conservation Treatment Area (26.83%)	Irrigation Rate of Cultivated Land (15.72%)
Western Region	Scale of Farmland Operation (13.28%)	Land Utilization Rate (12.76%)	Cultivated Land Area (9.92%)	Per Capita Grain Output (9.55%)	Agricultural Machinery Power per Unit of Sown Area (8.69%)	Total Afforestation Area (23.21%)	Soil and Water Conservation Treatment Area (22.88%)	Irrigation Rate of Cultivated Land (18.7%)

, highlight the top five obstacle indicators for agricultural economy and the top three for agricultural ecology, offering valuable insights into targeted areas for improvement. The rankings of obstacle factors for all indicators are presented in Supplementary Tables S3–S10.

4.4.1. Analysis of Agricultural Economic System Obstacles

According to Table 10, the top five obstacle factors affecting China’s agricultural economic system are farmland operation scale (13.8%), land utilization rate (12.19%), cultivated land area (9.96%), per capita grain yield (9.74%), and agricultural machinery power per unit sown area (8.8%). The rankings of the main obstacle factors in the three regions align with the national results, differing only in order. Notably, farmland operation scale consistently ranks first, highlighting its significant and long-term impact on agricultural economic development. Existing studies show that inadequate farmland management delays improvements in agricultural product circulation efficiency [72]. In contrast, large-scale farmland management significantly boosts agricultural product circulation. Furthermore, the expansion of farmland operation scale has somewhat restricted gains in land productivity. According to the Third National Land Survey, China’s cultivated land area has decreased by 113 million mu over the past decade, with an accelerating annual reduction rate. Per capita cultivated land area dropped from 1.59 mu in the First Survey to 1.36 mu in the Third Survey, now below 40% of the global average. This trend underscores the severe constraints posed by cultivated land shortages on the agricultural economic system.

To address these challenges, the government should strengthen farmland protection and land compensation policies to ensure effective use of farmland resources, while promoting land transfer and large-scale operations to improve land productivity. Additionally, investment in agricultural technology should be increased, with a focus on promoting water-saving irrigation and precision fertilization technologies to boost land productivity and reduce reliance on cultivated land area. Finally, the government should support the mechanization and automation of agriculture to improve production efficiency, especially in resource-scarce areas.

4.4.2. Analysis of Agricultural Ecological System Obstacles

According to Table 10, the main obstacle factors affecting the agricultural ecological systems at the national and regional levels include total afforestation area (4 occurrences), soil erosion treatment area (4 occurrences), cultivated land irrigation rate (3 occurrences), and annual precipitation (1 occurrences). From 2008 to 2022, total afforestation area and soil erosion treatment area consistently ranked as the top two obstacle factors across the nation and three major regions, with relatively high values, indicating their significant impact on the agricultural ecosystem. Specifically, total afforestation area has shown an overall decreasing trend, though at a slower pace compared to the soil erosion treatment area, suggesting that it may have a greater negative impact on the agricultural ecosystem. Meanwhile, the obstacle degree of soil erosion treatment area is gradually decreasing, indicating that its negative impact on the agricultural ecosystem is likely to ease temporarily.

To address the potential adverse effects of total afforestation area on the agricultural ecosystem, it is essential to optimize afforestation strategies, focusing on improving the quality of afforestation and enhancing ecological benefits. This includes selecting appropriate tree species, optimizing afforestation planning, and strengthening ecological restoration functions. At the same time, although the obstacle degree of soil erosion treatment area is decreasing, efforts to control soil erosion must continue, especially in regions with severe erosion. It is crucial to ensure the long-term stability of treatment outcomes and avoid ecological degradation caused by reduced treatment intensity.

5. Conclusions and Discussion

5.1. Conclusions

This study examines the coupling coordination between the agricultural economy and ecology across 31 provinces in China, using two indicator systems: one assessing the agricultural economy (including input, structure, efficiency, benefits, and vitality), and the other evaluating the agricultural ecosystem (focusing on ecological conditions and pressures). The study combines entropy-weighted evaluation and a modified coupling coordination model to assess coordination levels, and explores regional disparities and dynamics through tools such as the Dagum Gini coefficient, Kernel density estimation, Markov chains, Moran’s I, and the obstacle-degree model. This research enhances understanding of the interactions between the agricultural economy and ecosystems, offering insights for regional coordination and policy development. The main findings are as follows:

First, during the study period, the coupling and coordination between China’s agricultural economy and ecology continued to rise, with the coordination level improving from a state of near imbalance before 2017 to a barely coordinated state after 2018. Regional comparisons show that before 2017, the central region had a higher level, but after 2017, the eastern region surpassed the central region, while the western region consistently remained below the national average.

Second, the overall disparity in the coupling and coordination between China’s agricultural economy and ecology continues to decrease, with regional imbalances being the main source of this disparity. Additionally, internal regional differences have also decreased, with the central region seeing the largest reduction, followed by the eastern region and the western region. Between 2008 and 2012, the disparity was mainly due to regional imbalances, while from 2013 to 2022, “transvariation density” became the dominant factor, indicating that significant imbalances between regions still exist.

Third, overall, the coupling and coordination levels have improved nationwide and in all three regions, with a reduction in absolute disparities. However, Markov results show a “club convergence” phenomenon, where future progress will be gradual and limited to neighboring levels, making significant leaps unlikely and presenting a risk of regression.

Fourth, the main obstacles in the agricultural economic system include farmland management scale, land utilization rate, cultivated area, per capita grain yield, and mechanization per unit of sown area, with insufficient farmland management scale and land utilization being the most prominent. In the agricultural ecosystem, the key obstacles are total afforestation area, soil erosion control area, annual rainfall, and irrigation rate of arable land, with significant shortcomings in afforestation and soil erosion control.

5.2. Discussion

This study focuses on the coupling and coordination relationship between China’s agricultural economy and ecosystem, quantitatively assessing its regional differences, dynamic evolution patterns, and key obstacles. The results show that, although the coupling and coordination level between agricultural economy and ecosystem in China has generally improved during the study period, significant imbalances still exist between regions. This finding is consistent with previous research [7,73]. The study further reveals the path dependence and hierarchical rigidity in the development of coupling and coordination. Specifically, the “club convergence” phenomenon in the coupling and coordination development between China’s agricultural economy and ecosystem is observed. The future trend indicates that the coupling and coordination level across provinces will mostly show small fluctuations within adjacent levels, rather than large leaps across levels. This finding offers important implications for policymakers, emphasizing the need for layered and region-specific policies to break path dependence and promote coordinated development across regions. Additionally, this study identifies key obstacles hindering the coupling and coordination between the agricultural economy and ecosystem, such as insufficient land management, low land-use efficiency, inadequate afforestation, and challenges in soil and water conservation. To address these issues, the study recommends policies promoting sustainable land use, eco-friendly agricultural technologies, and increased investment in ecological restoration.

It is also important to note that this study draws on successful examples from other countries in agricultural and ecological coordination. In the Netherlands, precision agriculture technologies like remote sensing, smart irrigation, and soil analysis have balanced agricultural production with ecological protection, improving resource efficiency and reducing environmental pollution [74,75]. New Zealand’s ecological agriculture model, emphasizing crop rotation, organic farming, and resource recycling, provides valuable lessons for sustainable farming practices [76]. Israel’s water management strategies, such as drip irrigation and water recycling, are particularly relevant for areas facing water scarcity and soil erosion [77,78]. These global experiences offer useful insights for improving agricultural sustainability in China, especially in regions with similar challenges.

In conclusion, this study deepens the understanding of the coupling and coordinated development of China’s agricultural economy and ecosystem. It reveals regional differences, dynamic trends, and key obstacles, providing empirical evidence for policymakers. In the future, policymakers should develop differentiated regional strategies based on the specific characteristics of each region, balancing economic growth and ecological sustainability, thus, laying a solid foundation for creating a more resilient and sustainable agricultural-ecological collaborative system.

5.3. Recommendations

Based on previous research and the current state of coupling and coordination between China’s agricultural economy and ecosystem, this study offers the following recommendations to promote balanced regional development and enhance coordination:

First, significant regional disparities remain despite overall progress in coordinated development. Differentiated strategies should be tailored for the eastern, central, and western regions. The eastern region should leverage its technological and economic advantages to promote green agricultural technologies and ecological conservation. The central region should focus on optimizing resource allocation and improving agricultural productivity. The western region should prioritize ecological protection and the development of characteristic agricultural industries. Additionally, fostering cross-regional collaboration will help reduce disparities and promote balanced development nationwide.

Second, attention should be given to the “club convergence” effect and risks of stagnation. A dynamic monitoring mechanism is needed to track changes in coordination levels, identify risks early, and adjust policies accordingly, ensuring stability and sustainability in development.

Third, overcoming key obstacles is crucial. Small-scale farmland management and low land utilization efficiency are major challenges for the agricultural economy, while the ecological system struggles with insufficient afforestation and soil erosion control. To address these, policies should support large-scale farming practices, optimize land use, and enhance ecological restoration efforts, facilitating a green and low-carbon transition in both systems.

5.4. Limitations and Prospects

It is undeniable that our study still has some limitations. In this study, we constructed an indicator system for China’s agricultural economy and ecosystems based on 30 indicators and conducted an empirical analysis using panel data from 31 provinces. However, due to the unique characteristics of municipal or county-level units within each province, empirical analysis using city or county-level data would offer higher precision than provincial-level data. Therefore, future research should consider improvements in this regard. In addition, this study includes only three regions of China, but there are significant developmental differences among provinces within each region. Thus, narrowing the regional scope is recommended.

In future research, we plan to expand the breadth and depth of the study on the coupling and coordination between China’s agricultural economy and ecosystems. At the data level, we will use higher-resolution city or county-level data to improve precision and regional applicability. In terms of regional classification, we will adopt more detailed regional divisions to narrow the regional scope, which will help capture the differences in agricultural-ecosystem coupling and coordination across regions.