The Digital Economy and Agricultural Modernization in China: Measurement, Mechanisms, and Implications

Abstract

Promoting the progression of agricultural modernization and digital economy integration has introduced new opportunities and challenges for the advancement of China’s agriculture towards high-quality development. Based on panel data from 31 provinces in China from 2011 to 2020, we use the entropy method, coupling coordination model, spatial auto-correlation model, and obstacle model to explore the degree of coupling coordination and influencing factors of China’s digital economy and agricultural modernization. The results show the following. (1) China’s digital economy and agricultural modernization are underdeveloped on the whole, but they show a positive upward trajectory. The digital economy is outpacing the development of agricultural modernization, but the gap between the two is gradually narrowing. (2) The degree of coupling coordination between China’s digital economy and agricultural modernization shows an upward trend. Additionally, there is regional heterogeneity and uneven development of the coupling coordination, i.e., it is high in the eastern region and low in the western region, accompanied by strong spatial agglomeration and correlation. (3) The main obstacle to the progression of the digital economy is digital infrastructure, while the main obstacle to the progression of agricultural modernization is agricultural production capacity. Based on China’s national conditions, we should fully promote the role of the digital economy, explore plans and strategies for promoting the digital economy in differentiated areas, mitigate any imbalanced development, promote the coordinated development of the digital economy and agricultural modernization, and provide decision-making references for the implementation of a digitized China, as well as rural revitalization strategies.

1. Introduction

China is a country with very limited arable land area, but it needs to feed the largest population in the world. Under the natural constraints of high population density, China can only develop a sustainable agricultural model with intensive farming and sustainable resource maintenance. Today, agriculture provides employment for nearly 200 million people, and rural areas are home to nearly 500 million farmers. Agricultural and rural development play an important role in maintaining long-term social stability. Therefore, the Chinese government attaches great importance to agricultural development. Under the traditional agricultural development mode, pesticide, fertilizer, and animal husbandry waste emission pollution is serious. Amidst the pressures of high input costs, pollution, and consumption, agriculture confronts increasingly acute issues such as ecological degradation, resource mismanagement, and concerns over agricultural product quality and safety. These challenges have compelled a shift in the development trajectory of ecological agriculture construction in our country. Entering the new era, our country has given extremely high importance to the “three agriculture” (agriculture, countryside, and farmers) framework. However, due to the poor agricultural foundation and insufficient per capita resources, the “agriculture” and “countryside” components are still weak links. Compared with the new industrialization, information technology, and urbanization, agricultural modernization lags behind. In agricultural production, efficiency is relatively low, agricultural labor productivity is only 25.3% of non-agricultural production, agricultural efficiency is low, the international competitiveness of agricultural products is obviously insufficient, and the prices of domestic grain and other agricultural products generally exceed the international market. Rural infrastructure and public services lag behind urban areas, which is an important reason for China’s emphasis on accelerating its efforts to become an agricultural powerhouse.

The digital economy represents the third economic paradigm following the agricultural and industrial economies. It takes data as the core element, technological innovation as the key support, communication network as an important medium, and focuses on the deep integration of traditional economy and modern information technology. With the continuous penetration of the digital economy into agriculture and rural areas, digital economy and the “three rural” issues have also attracted the attention of academic circles. Since the 18th National Congress of the Communist Party of China, China has emphasized the modernization of agriculture and rural areas, implemented a series of important deployments such as digital China, digital agriculture, and rural revitalization, and promoted the integration of digital technology and agricultural and rural development. To this end, a series of policy documents to guide the development of digital agriculture have been promulgated, including “Outline of Digital Village Development Strategy” and “Digital Agriculture and Rural Development Plan (2019–2025)”. The “No. 1 Central Document” for 2023 stipulates the general requirements and specific arrangements for accelerating the construction of an agricultural power, and also provides strategic guidance for China’s agricultural modernization. The digital economy has become a new engine to alleviate the problem of imbalanced and insufficient agricultural and rural development. Moreover, accelerating the construction of an agricultural power and promoting the deep integration between the digital economy and agriculture have become important trends in agricultural and rural development. The United States and Germany, which have the world’s highest levels of agricultural digitization, had agricultural digitization rates of 48.9% and 38.7%, respectively, in 2020 [1]. In contrast, the agricultural digitization rate in China is only 8.2%. In economically developed countries, the integration of the digital economy and agriculture has been prolific. For example, the United States has reduced the cost of agricultural production through precision agriculture and consolidated the international competitive advantage of its agricultural products. Likewise, France relies on digital technology to upgrade its agricultural information service system and increase the commercialization rate of agricultural products in the process of integrating farmers into digital agriculture. Also, smart agriculture in countries such as Japan, the Netherlands, and Israel uses digital technology to promote precision farming models and intensify agricultural production to alleviate shortages of arable land resources. Additionally, the agricultural industry chain network in the European Union and the information-based village in South Korea have not only improved agricultural efficiency, competitiveness, and resource utilization rates but also have improved labor productivity and management efficiency [2,3]. It can be seen that the digital economy is having an important impact on the field of agriculture and has become a new engine of agricultural modernization.

Enhancing the holistic progress of the digital economy and agricultural modernization is one of the important ways to realize Chinese-style agricultural modernization under the background of “small farmers in large countries”. Given the fundamental national and agricultural circumstances of “small farmers in a big country”, China’s agriculture–rural modernization path faces a large number of small farmers and long-term rigid constraints [4]. The development of agricultural and rural modernization in China is still faced with problems such as prominent conflicting interests between humans and the health of arable land, scattered management patterns, increasing ecological pressure, and poor internal and external circulation. Existing studies have discussed the related influence of the digital economy and agricultural and rural modernization construction at different levels, but there is a lack of research on in-depth depth correlations and influence mechanisms of the digital economy and agricultural and rural modernization. Whether there is a spatial effect between the digital economy and the construction of agricultural and rural modernization is also rarely explained. In our empirical study, the goals are to: (1) measure the degree of coordinated development between the digital economy and agricultural modernization, (2) analyze the characteristics of the coupling coordination relationship, and (3) analyze the degree of influence of each factor on the two systems of the digital economy and agricultural modernization. Based on the above considerations, this paper expounds the internal mechanism of the coupling and coordinated development of the digital economy and agricultural modernization from the theory and practice of China’s digital economy and agricultural modernization, and conducts a space-time analysis and obstacle factor analysis of the coupling relationship between the digital economy and agricultural modernization in 31 provinces of China, striving to offer guidance for the advancement of the digital economy and the construction of agricultural modernization in China.

2. Literature Review and Coupling Mechanism Analysis

2.1. Literature Review

Some scholars have conducted research pertaining to China’s digital economy. First of all, the research on the digital economy mainly focuses on the method of measuring the development level of the digital economy and the impact of macroeconomic research. On the one hand, in relation to assessing the development level of the digital economy, Xu, X.C. and Zhang, M.H. formulated an accounting framework for the scale of China’s digital economy from an international comparative perspective. Their measurements revealed that China’s digital economy has exhibited a significantly higher annual growth rate in terms of value-added contributions compared to the United States and Australia in recent years, further improving the system of the digital economy in our country accounting [5]. Based on the input–output and growth accounting method, Zhou, L. and Gong, Z.M. measured the scale of China’s digital economy and studied the impact of digital factors on China’s economic productivity. They found that the actual growth rate of China’s digital economy increased year by year in recent years. This has emerged as a significant catalyst for economic expansion [6]. Leng, J. and Zhong, M.C. employed the entropy weight method to quantify the development status of China’s digital economy, further examining its spatiotemporal evolution patterns through the utilization of the Moran index, a modified gravity model, and a standard deviation ellipse. Their research revealed a sluggish overall growth trajectory in China’s digital economy, accompanied by notable regional disparities, with a decreasing trend from east to west on the whole [7]. On the other hand, the impact of the digital economy on macroeconomic factors has also been a subject of research [8]. For example, Ren, B.P. and He, H.C. believe that the theoretical logic of the digital economy to promote high-quality economic development is mainly realized by improving the quality of the entire supply system and improving total factor productivity [9]. Deng, R.R. et al. used the degree of coupling coordination model to study the coupling relationship between the digital economy and economic growth quality. It is pointed out that the degree of coupling of the digital economy and economic growth is mainly affected by multiple factors, such as per capita GDP and technological level [10]. Wei, P. and Gu, Y. used the intermediary model to discuss the mechanism of the digital economy to promote industrial structure optimization. The research culminated in a finding that the digital economy fosters the optimization and restructuring of industrial composition through the enhancement of innovation output levels [11]. Utilizing data from listed manufacturing companies, Liang, X.T. and Wen, Z.Y. proposed insights into digital industrialization, the stage of industrial digitization, and the extent of their mutual integration. The research shows that digital industrialization, industrial digitization, or the degree of coupling of the two are conducive to promoting the high-quality development of manufacturing industry [12].

The majority of the research that has been written about the evolution of agricultural modernization comes from studies on how to quantify it and how to best promote it. On the one hand, regarding agricultural modernization, Yang, T.P. and Zhong, G.Z. used the TOPSIS method to measure the level of China’s agricultural and rural modernization and found that the level of China’s agricultural and rural modernization was on the rise as a whole, the regional differences were gradually narrowing, and the agricultural and rural modernization index was on the decline from east to west—a spatial agglomeration effect [13]. Based on the DEA model and Malmquist model, Li, M. and Gu, S.G. measured the agricultural production efficiency of each province in China. The results show that domestic agricultural production lacks regional balance and there is a large waste of agricultural production input [14]. Based on the most recent phase of agricultural modernization in our nation, Xin, L. and Hao, H. employed the multi-index comprehensive measure approach to gauge and assess the degree of agricultural modernization [15]. On the other hand, regarding the development path of agricultural modernization [16], Cheng, Y.S., Zhang, D.Y. and Wang, X. measured the development level of green agriculture from the perspective of input and output [17]. Luo Haoxuan and Chen Heqiang proposed that in order to follow a fruitful path of Chinese-style agricultural modernization, we should actively introduce the results of the fourth industrial revolution to promote agricultural development, combine artificial intelligence technologies such as big data, blockchain, cloud computing, the Internet of Things, and the metauniverse with modern agricultural development, and build a smart agricultural system and model so as to promote the modernization and transformation of traditional Chinese farming culture [18].

Academics have talked about the relationship between digitization and Chinese agriculture in light of historical background, practical issues brought up by agricultural modernization, and China’s national circumstances and agricultural conditions [19,20,21]. The modernization of Chinese agriculture and rural areas has witnessed significant developments against the backdrop of the digital economy [22,23]. According to Tang W. et al., by enhancing rural governance and increasing agricultural production efficiency, the digital economy may play a major role in advancing agricultural modernization. The digital economy has played a significant role in promoting the resilience of the rural grain production system, the integrated development of rural industries, high-quality rural development, and rural revitalization [24]. Chen Y.M. believes that the integration of the digital economy and agriculture has multiplier and spillover effects. While achieving high-quality output, it also helps promote the replacement of old and new driving forces. Using a semi-parametric panel data model, Wang S.G. proposed that there is an inverted U-shaped relationship between the digital economy and high-quality agricultural development [25]. Wang W.C. stated that the development of the digital economy is conducive to driving the modernization of the agricultural industry chain [26]. Zhang Y. pointed out that through strengthening regional innovation ability and encouraging rural industrial integration, the agricultural and rural digital economies can subtly advance urban and rural common wealth [27]. The progression of agricultural informatization is fundamental to unleashing the potential of the agricultural digital economy. Agricultural modernization has brought about an overall improvement in standards of living and new market demand, providing a solid material foundation and development environment for the digital economy. Moreover, better education for rural workers and technological investment in agricultural production due to agricultural modernization have provided a steady stream of talent input and scientific and technological assistance for the growth of the digital economy. As such, the digital economy has become a new driving force for agricultural modernization, and agricultural modernization is providing a broad enabling space for the digital economy. The two are inherently intertwined and mutually empowering [28,29].

Based on an examination and synthesis of previous studies, currently, the majority of pertinent literature has a singular focus on how the digital economy affects modern agriculture. Quantitative analysis of the cooperative relationship between the digital economy and agricultural and rural modernization is lacking, and there are few studies that examine the relationship between the development of the digital economy and agricultural modernization construction from the perspective of coupling coordination. Furthermore, in China, imbalanced growth has become the norm. This is primarily evident in the uneven development that occurs within areas as well as between the north and the south. Analyzing the distinctions between the cooperative relationship between the digital economy and agricultural modernization is very important from a practical standpoint. In order to shed light on the mechanisms underlying the mutual promotion of the digital economy and agricultural modernization, this paper examines the degree of coupling coordination between the two based on provincial panel data. It also makes policy recommendations to encourage the coordinated development of the two from the perspective of spatiotemporal coupling. The following are this paper’s marginal contributions in comparison to earlier research. First, from the perspective of coupling and coordination, the interactions and incentives between the digital economy and agricultural modernization development are clarified. Secondly, on the basis of measuring the development level of the digital economy and agricultural modernization, the spatiotemporal differentiation characteristics of the digital economy and agricultural modernization are analyzed by constructing a degree of coupling coordination model.

2.2. Analysis of Coupling Mechanism between the Digital Economy and Agricultural Modernization

The dynamic interaction between two or more subsystems that cooperate, rely on, and sometimes even resist one another is referred to as coupling. Positive coupling describes the benign positive connection between the two, whilst negative coupling describes the relationship between mutual limitations. Modern agriculture and the digital economy are coupled in a way that describes how two subsystems coordinate their development and encourage each other: the digital economy and agricultural modernization. This relationship drives the interaction, coordination and matching of agricultural development elements, optimizes the allocation of agricultural resource factors, adjusts for the industrial structure, and promotes the sustainable development of agricultural economic development (Figure 1).

2.2.1. The Digital Economy Promotes the Development of Agricultural Modernization

The externalizations of the digital economy have the potential to lower information acquisition costs, facilitate the flow of technology and knowledge between regions, and allow interregional information sharing. The creation and sharing of knowledge, information, and ideas can be aided by the digital economy. Additionally, this can promote information sharing and resource sharing among agricultural operators and give birth to new models of agricultural production and operation as well as new mechanisms of mutual assistance and cooperation. Moreover, the digital economy can facilitate organic connections between the decentralized management of traditional smallholder farms and the large-scale operations of modern agriculture, while improving agricultural operations and management [30].

Farmers can now leverage the power of the digital economy to quickly grasp the latest developments in agricultural markets and flexibly adjust the distribution of key agricultural production factors such as labor, land, and technology. They can also use it to alleviate agricultural shortages, minimize agricultural losses, and promote the use of green agricultural resources. Finally, with the innovation of digital technology and the strong support of financial power, the agricultural industry is building a comprehensive organizational structure covering the whole chain of planting, production and trading. This integrated architecture not only realizes the deep integration of the agricultural industry but also greatly promotes the optimization and upgrading of the entire agricultural industry structure through collaborative operation and specialized production modes within the industry [31].

Moreover, synergy between the digital economy and agricultural science and technology has enabled the digital economy to play a positive role in agricultural modernization. The combination of digital technology and agricultural production can promote the mechanization, automation, and intelligence of agricultural production, as well as the modernization of agricultural production methods [32,33,34]. In addition, digital intelligent equipment and technology are prevalent in the agricultural field, which is conducive to real-time monitoring and precise control of the agricultural production process. This promotes both the refined operations of all facets of agricultural production and water and energy conservation in agricultural production. It also leads to the reduction in and sensible application of petrochemical elements such as pesticides and fertilizers. This improves agricultural production efficiency and the quality of agricultural products while protecting the rural ecological environment. Finally, the widespread use of cutting-edge information technologies in agricultural and rural regions, like big data, cloud computing, and artificial intelligence, has increased farmer autonomy, leading to significant improvements in rural government and enhancing the effectiveness of public services [35,36].

Digitization of agricultural processes also has industrial and technological effects. Firstly, the expansion of the digital economy into rural and agricultural areas has the potential to create new agricultural development models, foster the birth and growth of new agricultural formats like leisure agriculture and rural tourism, and increase the multifunctionality of agriculture [37,38]. This can facilitate the linkage between diversified agricultural economic forms and promote the integration of primary, secondary, and tertiary industries in rural areas. Furthermore, by allowing innovation, coordination, and correlation to flourish, the widespread use of digital technology can reduce information asymmetry between market participants, quicken the spread and transmission of information, and improve the industrial structure [16,39].

Finally, the inclusive effect of the digital economy reduces both capital information asymmetry and market incompleteness. This leads to the optimal allocation of funds, breaks the financing constraints of the agricultural sector, and provides the possibility for vulnerable groups to improve their lives. In this way, it improves the economy in underdeveloped areas and narrows the gap between urban and rural areas. Additionally, the multiplier effect and cumulative effect of digital technology have enabled the digital dividend to continue to expand, bringing more development opportunities and equalizing opportunities for people in all regions and all walks of life [40,41].

In sum, the digital economy’s externalities, inclusive effects, integration effects, and synergistic effects in conjunction with agricultural science and technology will support the modernization and high-quality development of agriculture by lowering costs, increasing productivity, streamlining the industrial structure, and enhancing product quality.

2.2.2. Agricultural Modernization Contributes to Digital Economy Transformation

To achieve agricultural modernization, it is necessary to modernize agricultural production methods in order to boost crop yields and reduce production costs [42,43,44]. According to the cost–benefit theory, investment in technology can improve efficiency and reduce costs so as to maximum benefits. In the digital transformation of agriculture, the application of digital technology is the bridge between the costs and benefits of technological input. This determines whether the initial technology investment can produce the maximum benefit. Digital technology in agricultural production can help agricultural production departments cut costs, improve production efficiency, optimize industrial structure, and improve agricultural production efficiency. This plays an important role in promoting the transformation of agricultural modernization, which boosts the demand for the digital economy in agricultural production [35,45]. Secondly, the modernization of agriculture has gradually consolidated agricultural infrastructure, which is crucial to the digital economy. The construction of water conservation infrastructure, highways, electric power infrastructure, cold chain logistics and other infrastructure has created conditions for the realization of smart water conservation, smart transportation, smart grids, smart agriculture and smart logistics. Another important manifestation of agricultural modernization is the modernization of agricultural labor. Unlike the traditional agricultural production environment, modern agricultural production requires high-quality rural talent, and workers must have the ability to effectively screen information and master advanced production technologies and production methods. In this way, their education levels and scientific and technological expertise have greatly exceeded those of the traditional agricultural period. This has provided a sufficient talent pool for promoting digital transformation in agriculture and rural areas [46,47,48]. In addition, with agricultural modernization, farmers’ ideological and material lives have improved, rural residents’ incomes have increased, and consumption patterns have shifted towards urbanization, with increased consumption and more attention to brands and functionality in daily consumption. The pursuit of leisure, such as in books and movies, has also been on the rise [49]. This has provided a strong demand pull for extending the digital economy’s development space and expanding the economic benefits of the digital economy. Some studies have shown that regional macroeconomic conditions are an important factor behind the digital transformation of industries [16,50]. Economically developed counties have a high degree of digital technology maturity, superior information infrastructure, and can better adapt digital technology to local agriculture. In addition, the digital transformation of agriculture not only relies on the financial support of the government but also social capital as a factor driving the transformation process. For example, some technology companies (e.g., Alibaba, Pinduoduo, JD.com) have amassed extensive experience in digital production and agricultural sales. This has accelerated the process of agricultural digitization in terms of reducing the transaction costs of agricultural products and reshaping the supply capacity of county markets.

Therefore, combined with theoretical analysis, this paper puts forward the following research hypotheses.

Hypothesis H1.

The digital economy and agricultural modernization influence each other, and there is a coupling and coordination relationship between them.

Hypothesis H2.

There is regional heterogeneity in the degree of coupling coordination between the digital economy and agricultural modernization.

2.2.3. Analysis of Spatial Spillover Mechanism of the Digital Economy and Agricultural Modernization

In the era of the digital economy, the iterative updating of the digital economy represented by blockchain, the Internet of Things and artificial intelligence has broken the limitations of traditional economic development in space and time and created economies of scale and scope. From the perspective of spatial interaction, the intersection of the digital economy with traditional industries has a stronger “dividend diffusion effect”. The “penetration effect” of digital infrastructure access on industrial integration development, the “cumulative effect” formed by digital technology iteration, the “multiplier effect” brought by digital technology use, and the “synergy effect” brought by data element sharing show that the development dividend of the digital economy can break through the restrictions of industries and regions, realize the sharing of digital dividends between regions and industries, and thus play a role in reducing the gap between areas of agricultural modernization. The development of the digital economy can not only have a positive impact on local agricultural modernization construction but also have a positive impact on the agricultural modernization construction of the surrounding area.

However, on the contrary, as an emerging industry, due to the differences in natural endowments and location factors, the impact of the digital economy on local agricultural modernization construction and the impact of local agricultural modernization construction on the development of the digital economy will have different degrees of difference. These may also form a “siphon effect”. On the one hand, regions with a good economic foundation and a high level of agricultural construction may preempt the competition for resources, leading to the seizure of economic development factors in neighboring regions and aggravating the imbalance of agricultural development among regions. On the other hand, the unreasonable competition between regions caused by the local government’s pursuit of digital economy development leads to a blind increase in fiscal expenditure in some regions that are not suitable for the development of the digital economy, resulting in repeated and ineffective construction, aggravating the economic pressure on local government, and then affecting the construction of agricultural modernization. Based on the above analysis, this study proposes the following hypothesis regarding the spatial interaction effect between the digital economy development and agricultural modernization construction.

Hypothesis H3.

There is a spatial spillover effect on the degree of coupling coordination between the digital economy and agricultural modernization.

3. Research Design

3.1. Indicator System Construction

3.1.1. Data Source

We utilized panel data spanning from 2011 to 2020, encompassing 31 provinces, municipalities, and autonomous regions (hereinafter referred to as “provinces”) in mainland China, excluding Hong Kong, Macao, and Taiwan, as our sample dataset. The data were obtained from various sources including the China Statistical Yearbook, the China Rural Statistical Yearbook, the China Urban Statistical Yearbook, and the China Stock Market Accounting Research Database (CSMAR).

Because of complex factors, outliers in the data will impact how accurate the statistical findings are, so this paper uses the Grubbs test method to test the sample data for outliers and uses the method of scatter trend fitting to fill the outliers and missing values, and then eliminates the interference of special circumstances to a greater extent to obtain relatively realistic results.

3.1.2. Evaluation Indicator System

Drawing on the literature [51,52], this paper focuses on the integration of digital infrastructure, agricultural industry development, and digital economy industry, and measures the development of the agricultural digital economy in three domains: digital infrastructure, agricultural industry digitization, and the agricultural digital industry. Drawing on existing studies [53,54], the development level of China’s agricultural modernization, and considering the availability of data, we selected four domains—comprehensive agricultural production capacity, the modernization of rural infrastructure and public services, the modernization of rural residents’ ideology and quality of life, and the modernization of rural governance systems and governance capacity—to measure and evaluate the level of agricultural modernization. We then constructed two subsystem index evaluation systems, as shown in

Table 1. Index systems and weights.

Coupling Subsystem	Target Layer	Indicator Layer	Metric Attributes	Weights
Digital economy	Digital infrastructure (0.6206)	Domain names	+	0.1596
		Webpages (in 10,000 s)	+	0.2499
		Internet broadband access ports (in 10,000 s)	+	0.0710
		Broadband internet users (in 10,000 s)	+	0.0719
		Long-distance optical cable length (in 10,000 km)	+	0.0391
		Mobile phone penetration (%),	+	0.0290
	Digitization of agriculture (0.1282)	average service population of rural postal branches (10,000)	+	0.0990
		Fixed assets invested in agriculture, forestry, animal husbandry and fishery (CNY 100 million)	+	0.0292
	Digital industrialization of agriculture (0.2513)	Peking University Digital Financial Inclusion Index (PKU-DFIIC)	+	0.0299
		Online retail sales (CNY 100 million)	+	0.2213
Modernization of agriculture	Comprehensive production capacity (0.5164)	Grain yield per unit of cultivated land (kg/hectare)	+	0.0197
		Meat product output (10,000 tons)	+	0.0738
		Total mechanical power per unit of cultivated land area (10,000 kW/1000 hectares)	+	0.0639
		Proportion of effective irrigated area/cultivated land area (%)	+	0.0550
		Agricultural electricity consumption (100 million kWh)	+	0.1780
		Personnel engaged in scientific and technological activities (person/year)	+	0.1260
	Modernization of infrastructure and public services (0.2649)	Household waste disposal rate (%)	+	0.0086
		Penetration rate of rural access to sanitary latrines (%)	+	0.0244
		Medical personnel per 1000 people among the rural population (person/thousand)	+	0.1087
		Number of rural cultural stations	+	0.0680
		Local government expenditure on education (CNY 100 million)	+	0.0552
	Modernization of residents’ ideology and quality of life (0.0959)	Rural residents’ disposable income (CNY)	+	0.0455
		Engel coefficient of rural households (%)	-	0.0134
		Rural residents’ per capita consumption expenditure (CNY)	+	0.0369
	Modernization of rural governance systems and capacity (0.1228)	Rural residents’ guaranteed minimum livelihood (per person)	-	0.0138
		Urban–rural income gap (CNY)	-	0.0138
		Number of villagers’ committees	+	0.0953

.

3.2. Data Processing

3.2.1. Entropy Weight Method

There are 17 indicators for the agricultural modernization embodiment and 10 indicators for the digital development level complete evaluation index system. In order to eliminate the influence of variation in variable units and data size, we first standardized the data [55]. For positive indicators, the calculation formula is:

$$R_{ij}=\frac{x_{ij}-\min (x_{ij})}{\max (x_{ij})-\min (x_{ij})}$$

(1)

For negative indicators, the formula is:

$$R_{ij}=\frac{\max (x_{ij})-x_{ij}}{\max (x_{ij})-\min (x_{ij})}$$

(2)

In Equation (1), i represents the province, j represents the year, X_ij represents the initial indicator value, and R_ij represents the normalized indicator value.

Because the data interval of the difference method is [0, 1], the entropy method cannot be used directly used, and it is translated as:

$$r_{ij}=R_{ij}+0.001$$

(3)

The weight is determined according to the degree of difference of each index datum, which objectively reflects the importance of each index in the index evaluation system, so as to overcome the problems of information overlap between multiple index variables and subjective weight determination artificially. The specific process is as follows.

Under the same indicator, the proportion of the value taken in each year of the total value is calculated as:

$$p_{ij}=\frac{r_{ij}}{{\displaystyle \sum _{i=1}^n{r}_{ij}}}\left(i=1,2,\dots ,n;j=1,2,\dots ,m\right)$$

(4)

The information entropy of each indicator is calculated as follows:

$$E_{\mathrm{j}}=-\ln {(n)}^{-1}{\displaystyle \sum _{i=1}^n{p}_{ij}\ln p_{ij}}$$

(5)

Then, the weighting of each indicator is determined:

$$w_j=\frac{1-E_j}{k-{\displaystyle \sum E_j}}(j=1,2,\dots ,m)$$

(6)

where k refers to the number of indicators, i.e., k = m. The composite score for each scenario is then calculated:

$$s_i={\displaystyle \sum _{j=1}^m{w}_j}{x}_{ij}$$

(7)

The higher the value of S_i, the better the development of the province’s digital economy and agricultural modernization.

3.2.2. Degree of Coupling Coordination Model

The term “coupling”, which describes the results of system interactions, has its roots in physics. Referring to the research method of Han Z.A. [40], the degree of coupling is calculated as follows:

$$T=2\frac{\sqrt{s_1{s}_2}}{s_1+s_2}$$

(8)

The development stages of the digital economy and agricultural modernization are denoted by S1 and S2, respectively. The degree of coupling T represents the degree of interaction between the digital economy and agricultural modernization, but it is difficult to reflect the synergistic effect between the systems. Thus, to assess the degree of coordinated development between systems, the degree of coupling coordination is presented. Therefore, the coupling coordination model for the two is constructed:

$$M=\alpha s_1+\beta s_2$$

(9)

$$y=\sqrt{T\times M}$$

(10)

In Equations (9) and (10), y is the degree of coupling coordination, reflecting the level of coordinated development of the two: the value range is 0–1, and the greater the y value, the better the coordinated development trend of the two. The stronger the correlation between the digital economy and agricultural modernization, the closer the value of T is to 1. T stands for the degree of coupling of the two. The synergistic effect of the two systems is reflected by the comprehensive coordination index (M). The weight coefficients are α and β, and α + β = 1. In this paper, the digital economy and agricultural modernization are considered to be of equal importance, and α = β = 0.5 is calculated. The value range of the degree of coupling coordination is 0–1, and the degree of coupling coordination is divided according to the different y values.

Based on existing research [41], we divided the coupling coordination relationship into four categories: low, moderate, high, and extreme. On this basis, we divided it into 10 subcategories (see

Table 2. Evaluation levels for coupling coordination.

Coupling Coordination	Basic Type	Coupling Coordination Level	Coupling Coordination	Basic Type	Coupling Coordination Level
[0, 0.1)	Low level	Extreme dysregulation	[0.5, 0.6)	High level	Little coordination
[0.1, 0.2)		Severe dysregulation	[0.6, 0.7)		Primary coordination
[0.2, 0.3)		Moderate disorder	[0.7, 0.8)		Intermediate coordination
[0.3, 0.4)	Moderate level	Mild disorder	[0.8, 0.9)	Extreme level	Good coordination
[0.4, 0.5)		On the verge of disorder	[0.9, 1]		High-quality coordination

).

3.2.3. The Kernel Density Function

In order to investigate the features of the degree of coupling and coordination between the digital economy and agricultural modernization throughout time, referring to the research method of Xie, H.Q. [55], we used kernel density estimation to explore the distribution and dynamic evolution characteristics of the digital economy and digital economy and agricultural modernization in the entire country and seven specific geographical areas. The kernel density formula is:

$$f(\mathrm{y})=\frac{1}{nh}{\displaystyle \sum _{i=0}^{n}K\left(\frac{y_i-Y}{h}\right)}$$

(11)

In Equation (11), y_i and Y represent the observed value and mean value of the coupling and coordination of the digital economy and agricultural modernization, respectively, and n, h and K are the sample number, function bandwidth, and kernel density functions, respectively.

3.2.4. The Moran Index

In order to investigate the features of the spatial evolution of the degree of coupling and coordination between the digital economy and contemporary agriculture, referring to the research method of Xie H.Q. [56], this paper uses the global Moran index and the local Moran index to test the spatial autocorrelation and spatial agglomeration, respectively.

The global Moran index formula is:

$$I=\frac{n\times {\displaystyle {\sum }_{i=1}^n{\displaystyle {\sum }_{j=1}^n{w}_{ij}({\mathrm{y}}_i-\overline{x})(y_j-\overline{y})}}}{{\displaystyle {\sum }_{i=1}^n{(y_i-\overline{y})}^2\times ({\displaystyle {\sum }_{i=1}^n{\displaystyle {\sum }_{j=1}^n{w}_{ij}}})}}$$

(12)

The local Moran index formula is:

$$I_i=x_i{\displaystyle {\sum }_{j\ne 1}^n{w}_{ij}{y}_{ij}}$$

(13)

where n is the number of provinces, y_i and y_j are the coupling coordination values of regions i and j, W_ij is the spatial weight matrix. If region i is adjacent to region j, W_ij = 1; otherwise W_ij = 0. The Moran index has a value range of [−1, 1], and a value greater than 0 indicates positive relativity between geographical units; a value less than 0 indicates negative relatively between geographical units. If the value is equal to 0, there is no correlation between geographical units.

3.2.5. Obstacle Degree Model

A rational, reasonable, orderly, and complicated evolution process can only be developed for the coupling and coordination system for the digital economy and agricultural modernity through interaction and mutual promotion between the two subsystems. In order to further analyze the inherent correlation of the digital economy and agricultural modernization endeavors and subsystems, we must rate the obstacles affecting the system. In this paper, we use a model of degree of obstacles to analyze the obstacle factors affecting coupling coordination. Referring to the research method of Liu, L. [56], the specific formula is:

$$E_{\mathrm{ij}}=1-M_{ij}$$

(14)

$$f_j=\frac{G_j{E}_{ij}}{{\displaystyle \sum _{j=1}^n{G}_j{E}_{ij}}}\times 100\%$$

(15)

$$F_j={\displaystyle \sum f_j}$$

(16)

In Equation (14), M_ij is the index’s standard value, equal to r_ij in the entropy weight method; E_ij is the deviation of the index, that is, the gap between the single index and the coupled coordination goal; G_j is the factor contribution, that is, the weight of the individual index in the coupled coordination goal; and f_j and F_j are the obstacles of the index layer and the element layer, respectively.

4. Empirical Analysis

4.1. Characteristics of the Digital Economy–Agricultural Modernization Development Index

Under the same index system, the development level index of the quantum system of the digital economy and agricultural modernization is calculated according to Equations (1)–(7). The development scores for the digital economy and agricultural modernization elucidate the characteristics and disparities of these two subsystems in the development process. In the model, the subsystem index is expressed as a fraction: the numerator is the digital economy score, and the denominator is the agricultural modernization score. We analyzed all 31 provinces in China. Due to space constraints, only the results for 2012, 2014, 2016, 2018, and 2020 are presented in

Table A1. Ratio of digitization and agricultural modernization development score.

Province	2012	2014	2016	2018	2020
Beijing	0.190/0.209	0.347/0.233	0.524/0.329	0.573/0.382	0.602/0.429
Tianjin	0.044/0.184	0.082/0.216	0.102/0.283	0.133/0.304	0.187/0.283
Hebei	0.060/0.299	0.111/0.344	0.164/0.374	0.210/0.358	0.249/0.411
Shanxi	0.035/0.160	0.081/0.186	0.111/0.213	0.134/0.192	0.150/0.243
Inner Mongolia	0.048/0.135	0.082/0.170	0.100/0.214	0.124/0.188	0.149/0.259
Liaoning	0.054/0.204	0.094/0.240	0.122/0.271	0.144/0.241	0.175/0.295
Jilin	0.032/0.139	0.062/0.172	0.087/0.210	0.112/0.186	0.125/0.257
Heilongjiang	0.043/0.136	0.084/0.183	0.104/0.213	0.131/0.185	0.159/0.254
Shanghai	0.161/0.194	0.213/0.287	0.311/0.355	0.339/0.383	0.418/0.418
Jiangsu	0.090/0.374	0.184/0.467	0.283/0.516	0.382/0.510	0.438/0.597
Zhejiang	0.109/0.317	0.235/0.374	0.408/0.447	0.495/0.432	0.555/0.524
Anhui	0.036/0.177	0.072/0.219	0.121/0.254	0.171/0.256	0.215/0.324
Fujian	0.063/0.191	0.117/0.241	0.223/0.276	0.348/0.292	0.302/0.354
Jiangxi	0.029/0.177	0.060/0.203	0.096/0.227	0.135/0.232	0.176/0.292
Shandong	0.082/0.384	0.178/0.431	0.208/0.465	0.262/0.444	0.306/0.491
Henan	0.070/0.276	0.106/0.321	0.186/0.349	0.254/0.341	0.312/0.399
Hubei	0.047/0.186	0.087/0.235	0.143/0.281	0.183/0.266	0.219/0.321
Hunan	0.041/0.230	0.079/0.269	0.133/0.287	0.171/0.290	0.213/0.355
Guangdong	0.148/0.337	0.355/0.404	0.497/0.447	0.616/0.495	0.660/0.579
Guangxi	0.034/0.138	0.068/0.174	0.100/0.215	0.139/0.209	0.180/0.272
Hainan	0.022/0.080	0.046/0.111	0.061/0.147	0.078/0.142	0.091/0.210
Chongqing	0.036/0.122	0.063/0.150	0.097/0.181	0.131/0.176	0.165/0.236
Sichuan	0.067/0.270	0.107/0.313	0.185/0.357	0.252/0.350	0.335/0.387
Guizhou	0.029/0.093	0.048/0.121	0.075/0.163	0.112/0.157	0.151/0.231
Yunnan	0.031/0.113	0.064/0.147	0.093/0.184	0.129/0.193	0.166/0.257
Tibet	0.016/0.142	0.039/0.196	0.054/0.211	0.066/0.235	0.072/0.256
Shaanxi	0.046/0.157	0.075/0.182	0.121/0.217	0.149/0.184	0.196/0.262
Gansu	0.024/0.102	0.047/0.130	0.071/0.164	0.090/0.162	0.105/0.226
Qinghai	0.021/0.105	0.045/0.127	0.057/0.143	0.071/0.134	0.088/0.199
Ningxia	0.019/0.088	0.041/0.116	0.055/0.146	0.077/0.135	0.085/0.202
Xinjiang	0.033/0.136	0.056/0.184	0.075/0.230	0.093/0.189	0.111/0.256

.

The analysis’s findings indicate that there is a closing gap between the level of development of the digital economy and contemporary agriculture. However, on the whole, the growth rate for agricultural modernization is faster than that of digital economy development, which is conducive to the high-order evolution of the coupling system. However, it should also be noted that the overall score for the two subsystems is low, especially that of the digital economic system. From the perspective of space, the development of China’s digital economy and agricultural modernization is imbalanced, and there are significant regional differences. We use ArcGIS10.8 to visualize the spatial characteristics of China’s digital economy and agricultural modernization in 2011 and 2020, as shown in Figure 2 and Figure 3. Since 2011, the eastern region has always been the pilot zone of China’s digital economy and agricultural modernization. The levels of the digital economy and agricultural modernization in China are, from high to low are from the eastern to central to to the northeast, to the to western regions. The eastern region has the first-mover advantage, and the level of the digital economy and agricultural modernization is the highest, significantly ahead of the central, western, and northeastern regions and much higher than the average level of China. The disparity results from regional variations in the infrastructural requirements for the growth of the digital economy and modern agriculture.

The digital economy development score in each province is steadily increasing, but there remain significant inter-provincial differences in both growth rate and scale. By 2020, as shown in Table A1, there are still four provinces—Hainan, Tibet, Qinghai, and Ningxia—in which the digital economy development score remains below 0.1: 0.091, 0.072, 0.088, and 0.085, respectively. Meanwhile, Guangdong’s digital economy development score, which ranked first, was 0.660, more than 10 times that of Tibet, which ranked last. This indicates that there are still immense regional disparities in China’s digital economy development and a large gap in the foundation of the digital economy in various regions, resulting in a gradual increase in the gap over time. There are also several regional disparities in the agricultural modernization development scores. In 2012, the agricultural modernization development scores of three provinces, Hainan, Guizhou, and Ningxia were lower than 0.1. In 2014, the scores for each of these provinces had surpassed 0.1. Guangdong had the highest value on the index (0.404), and Hainan had the lowest (0.111). The difference between the two provinces was nearly fourfold. As of 2020, Jiangsu had the highest agricultural modernization development score (0.597), and the lowest was Qinghai (0.199). The difference between the two provinces was nearly threefold. This indicated that the difference in agricultural modernization between the provinces is declining.

Over the past 10 years, digital economic development in all of China’s provinces has been steadily improving. Analysis of the digital economy and agricultural modernization subsystems reveals that Beijing, Zhejiang, and Guangdong are the three provinces with the highest digital economy development scores, and Guangdong, Jiangsu, and Zhejiang are the three provinces with the highest agricultural modernization scores. This further reflects the regional characteristics of different subsystems in the development process.

4.2. The Degree of Coupling Coordination between Digitization and Agricultural Modernization

Under the same index system, the coupling coordination degreedegree of coupling coordination of the digital economy and agricultural modernization is calculated according to formulas (8)–(10). The results of the degree of coupling coordination are shown in

Table A2. Degree of coupling coordination of digital economy and agricultural modernization.

Province	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020
Beijing	0.446	0.481	0.503	0.534	0.616	0.645	0.666	0.684	0.708	0.696
Tianjin	0.299	0.325	0.351	0.365	0.380	0.413	0.420	0.448	0.461	0.455
Hebei	0.366	0.396	0.421	0.442	0.465	0.498	0.507	0.524	0.556	0.540
Shanxi	0.273	0.297	0.335	0.350	0.369	0.392	0.379	0.400	0.426	0.413
Inner Mongolia	0.283	0.306	0.323	0.343	0.353	0.383	0.380	0.391	0.431	0.410
Liaoning	0.325	0.349	0.371	0.388	0.403	0.427	0.430	0.432	0.470	0.451
Jilin	0.258	0.287	0.308	0.322	0.338	0.368	0.367	0.380	0.413	0.396
Heilongjiang	0.277	0.303	0.334	0.352	0.361	0.386	0.386	0.394	0.429	0.411
Shanghai	0.420	0.427	0.449	0.497	0.549	0.577	0.597	0.600	0.626	0.613
Jiangsu	0.429	0.471	0.504	0.541	0.576	0.618	0.638	0.665	0.705	0.685
Zhejiang	0.431	0.499	0.506	0.544	0.595	0.654	0.660	0.680	0.728	0.704
Anhui	0.282	0.312	0.333	0.354	0.391	0.419	0.434	0.457	0.497	0.477
Fujian	0.332	0.372	0.385	0.410	0.452	0.498	0.548	0.564	0.591	0.577
Jiangxi	0.267	0.297	0.319	0.332	0.361	0.384	0.397	0.421	0.465	0.442
Shandong	0.421	0.441	0.528	0.526	0.532	0.558	0.565	0.584	0.615	0.599
Henan	0.373	0.391	0.405	0.429	0.471	0.505	0.518	0.543	0.581	0.561
Hubei	0.305	0.334	0.353	0.378	0.421	0.448	0.449	0.469	0.510	0.489
Hunan	0.312	0.340	0.364	0.382	0.413	0.443	0.452	0.472	0.520	0.495
Guangdong	0.473	0.535	0.584	0.615	0.645	0.687	0.701	0.743	0.783	0.763
Guangxi	0.262	0.284	0.306	0.330	0.351	0.382	0.389	0.413	0.458	0.435
Hainan	0.204	0.224	0.248	0.268	0.288	0.308	0.309	0.324	0.366	0.345
Chongqing	0.257	0.272	0.291	0.312	0.334	0.364	0.373	0.390	0.433	0.411
Sichuan	0.366	0.386	0.399	0.428	0.472	0.507	0.517	0.545	0.592	0.568
Guizhou	0.228	0.242	0.263	0.277	0.303	0.332	0.343	0.364	0.422	0.392
Yunnan	0.244	0.266	0.292	0.311	0.327	0.362	0.372	0.397	0.440	0.418
Tibet	0.217	0.252	0.273	0.295	0.316	0.326	0.340	0.352	0.362	0.357
Shaanxi	0.292	0.311	0.323	0.341	0.364	0.403	0.399	0.407	0.461	0.433
Gansu	0.223	0.247	0.263	0.279	0.306	0.328	0.332	0.347	0.382	0.364
Qinghai	0.216	0.240	0.265	0.275	0.283	0.300	0.301	0.313	0.347	0.329
Ningxia	0.203	0.224	0.245	0.263	0.278	0.300	0.301	0.319	0.354	0.336
Xinjiang	0.258	0.286	0.305	0.319	0.335	0.362	0.352	0.364	0.402	0.382

. The degree of coupling coordination for China’s overall digital economy and agricultural modernization is between 0.230 and 0.783, which means that there has been an evolution in the coupling and coordination from moderate incoordination to moderate coordination. Hypothesis H1 is proved. By 2020, 31 provinces in China had all left the stage of low coupling coordination, and 10 provinces had reached the stage of high coupling coordination, among which Beijing, Shanghai, and Jiangsu had reached primary coordinated development of digitization and agricultural modernization, while Zhejiang and Guangdong had achieved intermediate coordination. The state of a low degree of coupling and coordination suggests that despite a strong correlation between the digital economy and agricultural modernization, there has not been enough matching between the digital economy’s impact on agriculture and the modernization of agriculture itself. Additionally, the digital transformation of agricultural and rural modernization has made slow progress.

From a regional point of view, as Figure 4 shows, the eastern area has the highest level of coupling coordination between the modernization of agriculture and the digital economy. Meanwhile, the degree of coupling coordination between the two subsystems is low in the western region due to its remote geographical location and underdeveloped economy. This is indicative of a vicious and uncoordinated relationship between the coupling parties. Hypothesis H2 is proved. On the whole, the coupling and coordination of China’s digital economy and agricultural modernization is high in the east and low in the west. Moreover, the western region still needs to strengthen the construction of the digital economy and promote the coordinated development of digitization and agricultural modernization.

In summary, there remain several regional differences in the degree of coupling coordination in China. Because of their advantageous location, the digital economy in eastern cities has progressively accelerated to match the rate of agricultural modernization, whereas the digital economy in western cities has not advanced as far because of infrastructural constraints. Furthermore, the degree of province coupling and coordination is rising annually, indicating the beneficial impact of China’s digital economy for agricultural modernization. Zhejiang and Guangdong have performed well in the coordinated development of the digital economy and agricultural modernization, and have also set a good example for other provinces.

4.3. The Evolution Trend of Coupling Coordination of the Digital Economy and Agricultural Modernization

4.3.1. Kernel Density Analysis

Under the same index system, the kernel density of the degree of coupling coordination of the digital economy and agricultural modernization is calculated according to Formula (11). As Figure 5 shows, from 2011 to 2020, the center point of the kernel curve of the coordination level between the digital economy and agricultural modernization development moved to the right steadily, indicating that the degree of coordination between the digital economy and agricultural modernization in China was constantly improving. The primary peak’s width has increased since 2012, but the kernel curve’s peak value has reduced. This suggests that the degree of degree of coordination variation between various locations is progressively growing. After 2012, the number of peaks gradually decreased, the bimodal form was no longer significant, and there was a transition from bimodal to unimodal, meaning that the multi-level differentiation of coordination levels between regions was weakening gradually. The center points of the kernel curves of the seven geographical regions were gradually moving to the right, and the coordination level between the digital economy and agricultural modernization in all regions was constantly improving. Specifically, the peak value of the kernel curve in each geographical region decreased and the width of the main peak increased steadily, indicating the degree of difference among provinces in these regions was increasing.

4.3.2. Moran Index Analysis

Under the same index system, the global Moran index of the coupling and degree of coordination of the digital economy and farming modernization is calculated according to Formula (12). The results of global spatial autocorrelation tests are shown in

Table 3. Moran I index test results.

Year	I	E(I)	sd(I)	z	p-Value
2011	0.191	−0.033	0.109	2.053	0.020
2012	0.193	−0.033	0.108	2.093	0.018
2013	0.208	−0.033	0.108	2.237	0.013
2014	0.208	−0.033	0.108	2.233	0.013
2015	0.217	−0.033	0.108	2.305	0.011
2016	0.218	−0.033	0.108	2.319	0.010
2017	0.254	−0.033	0.109	2.644	0.004
2018	0.254	−0.033	0.108	2.651	0.004
2019	0.240	−0.033	0.108	2.525	0.006
2020	0.247	−0.033	0.108	2.591	0.005

. The overall Moran index is positive in all years, and with a 5% level of significance, the result is noteworthy. Since 2016, the global Moran index attains significance at the 1% level, which underscores a prominent positive spatial association between China’s digital economy subsystem and its agricultural modernization subsystem spanning the period from 2011 to 2020.

Under the same index system, the local Moran index of the coupling and degree of coordination of the digital economy and agricultural modernization is calculated according to Formula (13). We conducted a local spatial autocorrelation test to present the spatial agglomeration characteristics of the digital economy and agricultural modernization. The Moran scatterplot is shown in Figure 6. On the whole, the provinces with positive spatial correlations in 2011 and 2020 accounted for 68% and 71% of the total sample. This indicates that the local spatial heterogeneity of the coupling and coordination of the digital economy and agricultural modernization in China is decreasing and the degree of spatial agglomeration is increasing. Hypothesis H3 is proved. The provinces are mainly located in the first quadrant (HH) and third quadrant (LL), and high–high agglomeration areas are mainly located in the eastern region and low–low agglomeration areas are located in the central west and northeast region. The results show the agglomeration characteristics of either “low–low” or “high–high” manifest a positive spatial autocorrelation. Compared to 2011, in 2020 there are three high–high agglomeration areas added: Anhui, Hubei and Hunan. The three provinces are near the eastern high–high agglomeration area, where the degree of coupling coordination of the digital economy and agricultural modernization is increasing steadily. There are also two low-low agglomeration areas added: Liaoning and Shanxi. The results show that the eastern region in China has a strong spatial correlation and obvious space spillover effect. In central, west, and northeast regions, the coupling coordination level of the digital economy and agricultural modernization is low and the space spillover effect is not obvious.

4.4. Obstacles to the Coupling and Coordinated Development of the Digital Economy and Agricultural Modernization

Under the same index system, the degree of handicap of the two subsystems is calculated according to Equations (14)–(16). Due to space limitations, we list only the degree of obstacles for the digital economy and agricultural modernization in 2011, 2015, and 2020, as shown in

Table 4. Obstacle factors of the two systems (2011, 2015, and 2020).

Region	Year	The Main Factor of the Digital Economy Is the Degree of Obstacles			The Main Factor of Agricultural Modernization Is the Degree of Obstacles
Eastern	2011	X1 (55.64)	X2 (10.66)	X3 (24.02)	Y1 (38.50)	Y2 (21.16)	Y3 (7.10)	Y4 (7.55)
	2015	X1 (47.98)	X2 (8.70)	X3 (19.50)	Y1 (35.60)	Y2 (19.05)	Y3 (5.13)	Y4 (7.43)
	2020	X1 (42.79)	X2 (6.93)	X3 (12.20)	Y1 (33.02)	Y2 (13.27)	Y3 (3.15)	Y4 (7.61)
	Mean	X1 (48.80)	X2 (8.77)	X3 (18.57)	Y1 (35.71)	Y2 (17.83)	Y3 (5.13)	Y4 (7.53)
Central	2011	X1 (58.96)	X2 (12.06)	X3 (24.70)	Y1 (43.46)	Y2 (20.98)	Y3 (8.22)	Y4 (7.25)
	2015	X1 (55.15)	X2 (10.63)	X3 (23.09)	Y1 (41.96)	Y2 (19.85)	Y3 (6.50)	Y4 (7.17)
	2020	X1 (49.39)	X2 (9.02)	X3 (20.16)	Y1 (41.42)	Y2 (14.06)	Y3 (4.86)	Y4 (7.44)
	Mean	X1 (54.50)	X2 (10.57)	X3 (22.65)	Y1 (42.28)	Y2 (18.30)	Y3 (6.53)	Y4 (7.29)
Western	2011	X1 (59.82)	X2 (12.04)	X3 (24.78)	Y1 (46.44)	Y2 (22.62)	Y3 (8.55)	Y4 (9.04)
	2015	X1 (57.88)	X2 (10.95)	X3 (23.47)	Y1 (45.52)	Y2 (21.18)	Y3 (7.07)	Y4 (8.94)
	2020	X1 (54.39)	X2 (8.66)	X3 (21.93)	Y1 (45.02)	Y2 (15.17)	Y3 (5.49)	Y4 (8.97)
	Mean	X1 (57.36)	X2 (10.55)	X3 (23.39)	Y1 (45.66)	Y2 (19.66)	Y3 (7.04)	Y4 (8.98)
Northeastern	2011	X1 (59.07)	X2 (11.74)	X3 (24.87)	Y1 (44.57)	Y2 (22.67)	Y3 (7.79)	Y4 (9.01)
	2015	X1 (56.71)	X2 (10.52)	X3 (23.39)	Y1 (43.44)	Y2 (21.85)	Y3 (6.20)	Y4 (8.90)
	2020	X1 (54.37)	X2 (8.22)	X3 (22.09)	Y1 (43.96)	Y2 (15.54)	Y3 (4.86)	Y4 (8.77)
	Mean	X1 (56.72)	X2 (10.16)	X3 (23.45)	Y1 (43.99)	Y2 (20.02)	Y3 (6.28)	Y4 (8.89)

.

(1) The main challenges to the digital economy are building digital infrastructure and the digital industrialization of agriculture. The degree of obstacles for digital infrastructure has remained high over the three time points and has been relatively stable. Due to the digital divide between urban and rural areas and between regions, infrastructure support has become a prominent shortcoming restricting the development of digital agriculture in China. Accordingly, accelerating the construction of digital infrastructure has been a point of focus to promote the development of the digital economy. Research and development on cutting-edge digital infrastructure technologies in the agricultural field has been ongoing for a long time. Moreover, the application of some digital infrastructure in the agricultural field has been merely the simple grafting of experience from the industrial and service industries, which cannot meet the actual needs of agricultural operations [57]. For example, remote sensing satellites, the Internet of Things, and intelligent equipment for agricultural machinery still have problems such as low coverage and weak information provision. Therefore, it is necessary to promote the digital and intelligent transformation of regional infrastructure, such as water conservation, highways, and electric power, by promoting the construction of 5G, the Internet of Things, and big data. This will facilitate improvement of the business environment and provide a strong tailwind for the development of smart agriculture and smart supply chains at the industrial level.

(2) The main obstacles to agricultural modernization are the agricultural production capacity, rural infrastructure, and public service modernization. In the three years selected, agricultural production capacity faced a high degree of obstacles. This indicates that promoting the mechanization and intelligence of agricultural production and high-quality agricultural development remain the focus of agricultural modernization. Rural infrastructure and public service modernization ranked second in degree of obstacles for the three years selected, which further showed that the level of agricultural production technology was backward, attention was not paid to the application of scientific and technological, and the unreasonable agricultural industrial structure led to low labor productivity and poor industrial competitiveness.

(3) From a regional perspective, in terms of the digital economy, the obstacles to digital infrastructure and agricultural digital industrialization in the western region are high. This is due to the weak foundation of the digital industry in the western region, the lag in digital technology application and innovation, and the lack of support capacity for development factors. This has slowed the growth of the digital economy in the western area, suggesting that future efforts to build digital infrastructure can still be improved. The western region’s comprehensive agricultural production capacity continues to be a hindrance to its agricultural modernization. There is no variation in the level of challenges facing the northeast and central regions when it comes to modernizing their agricultural governance systems and capacities.

4.5. Discussion

The majority of the research that is now available examines the causal relationship between the growth of agricultural modernization and the digital economy, and it typically views the latter as a tool for the former [2,33,38]. This paper investigates the features of each stage of the coupling and coordination of the digital economy and agricultural modernization construction in China, further enhancing the pertinent literature and bringing them into the same system for comparative analysis, in addition to investigating the short-term factors that have an impact on agricultural modernization and digitization progress.

The digital economy and agricultural modernization construction are long-term and dynamic processes, and the differences in physical and geographic conditions and infrastructure construction have an important impact on the development level of regional digital economies and agricultural modernization. The development process shows a gradually decreasing trend from east to west due to regional differences [15], which further supports the conclusion that the degree of coupling coordination of the two systems is high in the east and low in the west. However, the measurement results of the main obstacles in the two systems are different. The existing studies conclude that the main obstacles to the development level of the digital economy are rural digital infrastructure, digital development, digital innovation, etc., and the main obstacles to agricultural modernization are agricultural supply guarantee modernization, agricultural production system modernization, agricultural operation modernization, etc. The research results are not completely consistent with the conclusion of the obstacle factor measurement in this paper. On the one hand, the indicator system is different, and on the other hand, the selected research scope is different, with most of the research focusing on local areas. For example, Cheng, F.F. and Qian, Y.L. determined that the key obstacles affecting the development of the digital economy were the total extent of telecommunications business and the number of industrial research and development projects above the scale [58], which is different from the conclusion of this paper, i.e., that the main obstacle to the digital economy is digital infrastructure. The main reason is that the research samples are different. The former takes three provinces and one city in the Yangtze River Delta region as the sample, and the latter takes 31 provinces and cities in the whole country as the sample.

5. Conclusions and Limitations

5.1. Conclusion

Through the empirical analysis of the coupling system of the digital economy and agricultural modernization, the following conclusions are obtained. (1) The level of the digital economy and agricultural modernization in China is increasing year by year, the growth rate of the digital economy is faster than that of agricultural modernization, and the development of the two subsystems is imbalanced between regions. (2) The coupled system of China’s digital economy and agricultural modernization is still in the break-in stage of being close to disorder and barely coordinated, but continues to develop with time. Hypothesis H1, that there is a mutually promoting coupling and coordination relationship between the digital economy and agricultural modernization, is proved. (3) There are obvious gaps in the overall degree of coupling coordination between different provinces and cities, showing regional distribution characteristics of high in the east and low in the west and increasing with time. Hypothesis H2, that the digital economy and agricultural modernization have regional heterogeneity in degree of coupling coordination, is proved. (4) The spatial autocorrelation results of degree of coupling coordination show that the coupled coordinated development of the digital economy and agricultural modernization has strong spatial agglomeration and spatial correlation characteristics, and Hypothesis H3 is proved. Moreover, the coupling coordination and spatial linkage pattern in the eastern region are relatively high, while the benign spatial linkage pattern has not yet formed in the central and western regions or the northeast region. (5) The main obstacle affecting the digital economy is digital infrastructure construction, and the main obstacle affecting agricultural modernization is agricultural comprehensive production capacity.

5.2. Research Limitations

This paper discusses the coupling and coordinated development of the digital economy and agricultural modernization and its internal mechanisms, but there are still some limitations. First of all, in recent years, there have been more and more discussions on the evaluation of the development level of the agricultural digital economy and the development level of agricultural and rural modernization. Secondly, this paper uses the entropy method to measure the development level of regional digital economies and agricultural modernization. Different measurement methods may bring different results, e.g., continuously enriching the measurement methods of the two systems, such as using a BP neural network method or efficiency model for measurement and then calculating the degree of coupling coordination between the two systems in order to more accurately understand and grasp the coordinated development of China’s digital economy and agricultural modernization. Finally, at present, the factors affecting the coupling and coordination level of regional digital economies and agricultural modernization are still complex. In the future, horizontal and vertical in-depth research should be carried out to propose specific paradigms for promoting agricultural modernization based on the coupling coordination relationship and degree of obstacles from each index.

6. Practical Implications

In order to improve the level of regional digital economies and agricultural modernization, based on the above research conclusions and the actual situation of each province and city, this paper puts forward relevant suggestions on promoting the coupled and coordinated development of the digital economy and agricultural modernization from the perspectives of government and enterprises, respectively.

First of all, from the perspective of the government, the following aspects should be promoted. (1) Construct more digital infrastructure and data centers, 5G infrastructure, data element markets and similar projects. The limited infrastructure of the digital economy in rural areas in the western region hinders the development of agricultural and rural digitization. Therefore, in order for this region to play a leading role in the digital economy, it is necessary to accelerate the digital transformation of traditional rural infrastructure. A new network infrastructure is needed based on the original 4G network and fiber to the home. We should also use big data to analyze the demand for digital information in rural areas according to local conditions and digitally transform broadband and mobile networks to meet the basic digital needs of local areas. Moreover, it is necessary to integrate digital construction into rural governance, rural medical care, and rural education, and create an application demonstration area and industrial innovation base for the digital economy based on the 5G network. This will further promote agricultural modernization. The eastern region boasts a relatively comprehensive digital economy infrastructure, with farmers exhibiting a robust understanding of the digital economy and possessing a high level of innovative capability. The initial investment in digital infrastructure construction is high and the cost recovery is slow, and the government’s guiding role in it is indispensable. Consequently, the government ought to prioritize the advancement of digital economy products/services, while also focusing on achieving mastery and significant technological breakthroughs in the core capabilities and pivotal technologies that underpin the digital economy. Given the current status and emerging challenges in agricultural and rural economic development, it is imperative to stay abreast of contemporary trends and embrace innovative digital economy products and services, make every effort to break through data barriers in the field of “agriculture, rural areas, and farmers”, drive production flow, logistics, capital flow, and talent flow with data flow, stimulate the power and vitality of innovative elements, and better serve the high-quality development of agriculture. We also should improve the talent training mechanisms in China’s digital agriculture and improve the matching between talent training and social needs. In addition, the relevant departments ought to enhance safeguards for intellectual property rights within the digital economy, improve the system of data standards and norms, and promote the open sharing and transaction of data on the premise of ensuring data security and privacy protection. In this way, we can bridge the data gap between urban and rural areas to adapt to new trends in development. At the same time, we must strengthen the integrated application of modern information technologies such as blockchain, the Internet of Things, big data, and artificial intelligence in rural formats, forms, cultures, ecology, and governance. Moreover, we need suitable pilot demonstrations and must determine the priority of the construction of various scenarios and promote the adaptability of application scenarios.

(2) Enhance the agricultural production capacity, strengthen the construction of agricultural science and technology innovation, and promote the level of agricultural machinery and equipment. Firstly, we can increase investment in agricultural research and accelerate the establishment of an investment system for agricultural research led by the government with broad participation. Secondly, it is necessary to reinforce irrigation and water conservation as well as ecological construction to improve agriculture’s ability to withstand natural disasters while further stabilizing and increasing food supply. Finally, we can develop and train digital talents in agriculture, strengthening publicity, and guidance efforts.

(3) Strengthen regional cooperation and promote regional differentiated and coordinated development. Firstly, it is necessary to build a mechanism for complementary advantages and coordinated development between regions. Additionally, we need to promote comprehensive coordinated development of the digital economy and agricultural modernization. We should also establish regional coordination mechanisms for resource sharing, platform co-construction, talent sharing, and cross-border search. Through policy and capital allocation, we should fully promote the eastern region’s role as the leader in high coupling and coordination, strengthen exchanges and cooperation with provinces with low coupling and coordination, and promote the progression of the digital economy and agricultural modernization in the central and western regions. Secondly, it is necessary to formulate policies for the regulation and control of regional differences, especially for the western region, and strengthen policy support to mitigate imbalanced regional development through the construction of regional development centers [59]. Third, China has a large population and insufficient arable land, and there are large development gaps between urban and rural areas and between regions. Thus, there are many barriers to integrating the digital economy and agriculture. Compared with the industrial and service industries, the overall contribution of the digital economy to China’s agricultural growth has been low, and the overall process of agricultural digital transformation has been slow. Therefore, it is necessary to focus on China’s national conditions and follow a development path with characteristics that are in line with China’s reality.

Secondly, from the perspective of industry and enterprise development, first, agricultural enterprises should develop a clear digital transformation strategy, combining its own characteristics and market demand, and clarify the goals, paths, and key tasks of digital transformation. It is possible to actively introduce and apply cutting-edge technologies like artificial intelligence, big data, and the Internet of Things to enhance the precision and intelligence of agricultural production operations. In addition, a sound data collection, storage, and analysis system has been established to realize real-time monitoring and analysis of data in agricultural production, sales, and other links. Through the use of e-commerce platforms, we need to expand the sales channels of agricultural products and realize the integrated development of online and offline. Second, digital enterprises can strengthen cooperation with the field of agriculture, develop and promote digital products and services applicable to agriculture, such as intelligent agricultural equipment, agricultural information platforms, etc., and apply digital technology to agricultural production, management, sales, and other links to promote the process of agricultural modernization. Secondly, it is necessary to integrate the upstream and downstream resources of the agricultural industry chain, build a digital agricultural ecosystem, realize the synergy and efficiency of agricultural production, and help agricultural enterprises realize digital transformation by providing digital solutions to agricultural enterprises so as to improve the digital level of the whole agricultural industry.