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Article

Measurement and Dynamic Trend Research on the Development Level of Rural Industry Integration in China

by
Han Hao
,
Chenyang Liu
and
Ling Xin
*
Agricultural Economics and Development Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
*
Author to whom correspondence should be addressed.
Agriculture 2023, 13(12), 2245; https://doi.org/10.3390/agriculture13122245
Submission received: 25 October 2023 / Revised: 21 November 2023 / Accepted: 30 November 2023 / Published: 5 December 2023
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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Abstract

:
China is a traditional agricultural country, and the integration of rural industries has become an essential policy direction for the current strategy of agricultural modernization in China. The purpose of this study is to evaluate the level of integrated rural industry development in China, identify problems in the development process, and combine analysis of related theories and practices of rural industry integration to propose strategies to enhance the level of integrated rural industry development in China. Hence, this paper, rooted in the integration of rural industries, cross-industry, industry extension, industry agglomeration, and industry penetration, sets up a measurement index system. Using provincial panel data from 2011 to 2020, the paper measures the level of integrated development in rural industries across China and its 31 provinces. Research indicates that during the sample period, the overall level of integrated development in rural industries in various provinces in China has shown an upward trend. Development among the eastern, central, and western regions is highly uneven, but this disparity has been gradually narrowing in recent years. Furthermore, the levels of integrated development in different provinces exhibit significant spatial agglomeration effects. The development level of neighboring regions significantly impacts the province’s development status.

1. Introduction

The integrated development of rural industries is an important means to modernize agriculture and plays a crucial role in promoting increased income for farmers and improved efficiency in agriculture [1]. China is a traditional agricultural powerhouse. According to the 2022 Statistical Bulletin on National Economic and Social Development, approximately 490 million people reside in China’s rural areas [2]. The issues related to agriculture, rural areas, and farmers concern the fundamental interests of the vast majority of people. Over the past decade, the government has successively issued a series of policy documents to guide and encourage localities to delve deeper into the multifunctionality of agriculture, continuously expand the agricultural industry value chain, enhance the comprehensive competitiveness of agriculture, and promote farmers’ income growth.
In theoretical research, many scholars have conducted in-depth studies on the connotations, driving mechanisms, model paths, driving factors, domestic and international experiences, and existing problems and countermeasures of rural industry integration. There is a growing consensus on the “core” of rural industry integration; that is, it represents an advanced form of integration, not just a vertical extension of the agricultural industry chain, but also a horizontal expansion [3,4]. It is a multiplicative and composite industry chain development model. International theoretical research on rural industry integration can be traced back to 1963 when the American scholar Rosenberg proposed the concept while studying the machinery equipment industry. The idea was that through scientific and technological progress and the reduction of industry barriers, strengthening cooperation between enterprises across different industries would blur industry boundaries and create new business forms in crossover fields [5]. This concept of industry integration gradually extended to the agricultural sector. Rural industry integration refers to the interaction and recombination between the primary industries, such as agriculture, forestry, animal husbandry, and fisheries, with the secondary industries, such as agricultural product processing, and tertiary industries, such as rural tourism, changing the original rural industrial structure and redistributing benefits. A representative study in this field is the “Sixth Industry” theory, proposed by the Japanese scholar Imamura Narao based on Japan’s rural development [6,7]. The theory suggests that by encouraging agricultural producers to engage in processing, sales, and service activities, it can internalize employment positions and agricultural added value that would typically flow to cities, thereby increasing the actual income of farmers and enhancing the vitality of rural development. The Japanese Sixth Industry theory emphasizes the cooperation and integration between agriculture and other industries, positing that if the output value of one industry in the rural industrial chain is zero, the overall benefit is zero. Therefore, each industry must develop in coordination to achieve overall economic and social benefits in rural areas.
The experience of many developed countries shows that the integrated development of rural industries plays a significant role in raising the income levels of farming households. It also helps narrow the income gap between low-income and high-income agricultural households, achieving common prosperity [8]. The integrated development of the tertiary industries in rural areas represents a higher form of agricultural industrialization. Its core lies in developing the multiple values of agriculture, internalizing the added value profits of agriculture, and creating more job opportunities [9]. The integration of the tertiary industries in rural areas can be divided into two models: the vertical extension model, which improves and perfects the agricultural industry chain, and the horizontal expansion model, which deeply explores the potential value of the agricultural industry, proposing different development strategies for both models [10]. The main models of rural tertiary industry integration, from the perspective of expanding the scope of the agricultural industry, are internal cross-integration within agriculture, extension integration along the agricultural industry chain, multifunctional expansion integration of agriculture, and advanced elements infiltration integration [11]. Whatever model of rural tertiary industry integration is chosen, it should ensure that farmers become the main beneficiaries of shared profits and emphasize the importance of protecting and developing rural natural ecological resources in the development of rural tertiary industry integration. Regarding the methods to evaluate the level of rural industrial integration and the analysis of the measurement results, some scholars have also conducted beneficial explorations. For example, some scholars emphasized the importance of evaluating rural industrial integration based on extending agriculture, industries interacting with agriculture, and expanding agricultural functions [12,13]. Other scholars proposed that the additive effect of rural industrial integration be manifested as the vertical extension of the agricultural industry chain, and the multiplicative effect manifests as the horizontal derivation of new business formats [14,15].
In terms of practical experiences, China’s rural industrial integration has flourished in recent years with the constant expansion and diversification of the agricultural industry chain, emergence of new agricultural industries, and more. For accelerated integration within the agricultural industry, many regions in China have integrated planting, breeding, and animal husbandry, evolving into new industry models, such as ecological, circular, and precision agriculture. In the rapid transformation and upgrading of agricultural product processing, in 2022, over 2.24 million farmer cooperatives were established in China. For continuous innovation in agricultural product marketing models, relying on internet information technology, new rural e-commerce and culturally integrated marketing models have achieved rapid development. In the thriving leisure agriculture and rural tourism, by the end of 2022, there were more than 350,000 leisure agricultural entities in China. For the deep integration and agglomeration of the agricultural industry, the regions of Hubei Province, Hunan Province, and Sichuan Province of China are actively exploring new models of industrial organization [16].
In summary, although significant achievements have been made in both the theoretical research and practical experiences of rural industrial integration in recent years, there remain key issues to be addressed due to regional differences [17]. Accurately and profoundly understanding the inherent characteristics of the integrated development of rural industries in China, establishing a set of scientific and reasonable evaluation indicators that fit China’s rural development status and objectives, and adopting corresponding evaluation methods to judge the level and process of development are of great importance [18]. The purpose of this study is to evaluate the level of integrated rural industry development in China, identify problems in the development process, and combine analysis of related theories and practices of rural industry integration to propose strategies to enhance the level of integrated rural industry development in China. Against this backdrop, this paper aims to grasp the essence of rural industrial integration in China, establish a scientifically sound measurement index system, and use it to evaluate the development level and progress of rural industrial integration across 31 provinces. The ultimate goal is to offer feasible suggestions and countermeasures to promote the high-quality development of modern agriculture in China and achieve the modernization of agriculture and rural areas.

2. Materials and Methods

2.1. Measurement Method

Given that the measurement object is each province in China, and considering the numerous measurement indicators involved, the measurement process should not be overly complicated. Hence, this paper refers to the approaches of Xin Ling, adopting a multi-indicator comprehensive measurement model to assess the level of integrated development of rural industries in China [19]. This method can transform multiple indicators into a single comprehensive index that reflects the overall situation, allowing for a holistic assessment of the research subject and facilitating horizontal or vertical comparisons [20]. The specific process starts by determining the measurement indicator system based on the connotations of modern agricultural development and the objectives of integrated rural industrial development. The entropy method and expert scoring method are then combined to standardize the original measurement indicator data. Finally, the results of each region are formed, and further analysis and discussion are carried out. The calculation formula for the multi-indicator comprehensive measurement model is:
Z is = ∑ i = 1 j w j Y ij
RI i = ∑ s = 1 m Z is
where Z i s is the index of integrated rural industrial development of the i-th province and the s-th subsystem, w j is the weight of the j-th indicator, and Y i j is the standardized value of the j-th indicator of the i-th province. R I i represents the development index of rural industry integration of the i-th province, and m represents the number of subsystems of rural industry integration.

2.2. Gini Coefficient Decomposition Method

For a deeper analysis of spatial heterogeneity, the Gini coefficient decomposition method can be used. According to this method, the total difference, G, can be decomposed into the contribution of intra-regional differences, G w , inter-regional differences, G n b , and the contribution of transvariation density, G t . The transvariation density contribution is the impact difference on the total difference caused by the existence of crossover items when dividing the group. The Dagum Gini coefficient compensates for the shortcomings of other methods used to measure regional disparities, which fail to address the issue of overlapping data in their analysis. It is better equipped to identify the sources of regional disparities [21]. The specific formulas are:
G = G w + G n b + G t
G = 1 2 n 2 μ ∑ j = 1 k ∑ h = 1 k ∑ i = 1 n j ∑ r = 1 n h y j i t − y h r t
where y j i t ( y h r t ) represents the integrated development index of rural industries of the i-th (r-th) measurement object in the j-th (h-th) region in year t, with 31 provinces in total. μ represents the average value of all measurement objects, n is the number of measurement objects, k represents the number of regional divisions, and n j n h is the number of measurement objects in the j-th (h-th) region.
G j j = 1 2 μ j ∑ i = 1 n j ∑ r = 1 n j y j i − y j r n j 2
G w = ∑ j = 1 k G j j P j S j
G j h = ∑ i = 1 n j ∑ r = 1 n j y j i − y j r n j n h ( μ j + μ h )
G n b = ∑ j = 2 k ∑ h = 1 j − 1 G j h ( p j s h + p h s j ) D j h
where G j j and G w represent the Gini coefficient of region j and the intra-regional Gini coefficient, respectively. G j h and G n b represent the Gini coefficient between region j and region h, and the inter-regional Gini coefficient, respectively. D g h represents the relative impact between region j and region h on the development level of rural industry integration.
G t = ∑ j = 2 k ∑ h = 1 j − 1 G h j p j s h + p h s j 1 − D j h
D j h = d j h − p j h d j h + p j h
d j h = ∫ 0 ∞ d F j ( y ) ∫ 0 y ( y − x ) d F h ( y )
p j h = ∫ 0 ∞ d F h ( y ) ∫ 0 y ( y − x ) d F j ( y )
where G t represents the contribution of transvariation density, 1 − D j h represents the transvariation density, and F j ( F h ) represents the distribution function of region j(h).

2.3. Construction of Measurement Index System

In the process of evaluating the level of rural industrial integration, establishing the measurement index system is the most crucial step [22]. The design of the measurement index system hierarchy should comprehensively cover all dimensions of rural industrial development and its integration level. The construction should follow four principles:
Guidance: It should represent the objectives and requirements of rural industrial integration development and should align with international conventions and public perceptions.
Systematic: One must fully consider the profound connotations of rural industrial integration and the strategic goals of China’s rural revitalization.
Scientific: The selected indicators should have both a theoretical foundation and a policy basis. They should also take into account differences in regional resource endowments, development levels, and other real-world situations.
Operability: The selected indicators should be highly representative, targeted, and continuous. There should be connections and mutual validation among the indicators, but each should be independent, avoiding overlaps, to ensure the measurement is straightforward, feasible, and effective.
Considering the above, combined with the main modes of rural industrial integration development in China [23,24,25], this study identified five system-level indicators: agricultural industry development, agricultural chain extension, agricultural industry crossover, agricultural industry agglomeration, and advanced technology penetration.
Agricultural industry development: This primarily examines whether the foundational status of agriculture is solid, thoroughly measuring the production cost of agriculture and the quality of agricultural products [26].
Agricultural chain extension: It mainly examines the degree of connection between “cultivation + breeding” in agriculture and related industries, such as processing, sales, and research [27].
Agricultural industry crossover: This mainly examines the level of coordinated development between other industries closely related to agriculture or those providing services to it [28].
Agricultural industry agglomeration: This mainly examines the excellence of the agricultural industrialization organizational model and whether it has the capability for sustainable integrated development [29].
Advanced technology penetration: This primarily investigates the degree of integration and penetration of new information technologies in various stages of agricultural production, processing, and marketing [30].
Based on the definitions of the system levels for measurement indicators, 18 specific indicators were set at the indicator levels, as shown in Table 1.

2.4. Data

The data for this study originate from public data released by relevant statistical departments, such as the “China Statistical Yearbook”, “China Rural Statistical Yearbook”, and “Rural Business Management Statistical Annual Report”. Considering accessibility, this paper used panel data from 2011 to 2020, spanning 10 years, covering China and its 31 provinces (excluding Hong Kong, Macau, and Taiwan), to evaluate and study the dynamic trends of rural industrial integration development level. To ensure the completeness and comparability of sample data, individual missing values have been filled using the mean method and interpolation.

3. Results

3.1. Measurement Results of Rural Industrial Integration Development Level

Following the measurement index system and measurement method established earlier, the results of evaluating the level of rural industrial integration development in China’s 31 provinces are shown in Figure 1 (for a direct comparison, only the measurement results for the years 2011, 2015, and 2020 are displayed). Observing the overall trend, except for a few provinces (such as Inner Mongolia, Qinghai, etc.) that remained unchanged or slightly declined, the industrial integration development levels of China and its provinces consistently showed an upward trend. From a regional perspective, provinces located in the eastern region (such as Beijing, Zhejiang, Shandong, and Jiangsu) have markedly higher levels of industrial integration development than those in the western region (such as Guizhou, Yunnan, Chongqing, and Tibet). While the central region’s industrial integration level is slightly higher than that of the western region, it is generally lower than the Chinese average. This indicates a significant unevenness in the industrial integration development levels across China’s provinces. The eastern region, due to its locational and economic development advantages, has taken a leading position in promoting rural industrial integration [31]. In contrast, the potential of the central and western regions is yet to be fully exploited. The resource utilization efficiency is not high. However, the measurement results suggested that there is substantial potential for growth in industrial integration development.
To further clarify the development status of each province, this study classified them according to quartile points based on the results of the industrial integration development level measurement. Specifically, those exceeding 75% of the sample measurement scores were categorized as “High Level”, those between 75% and 50% were classified as “Upper-Medium Level”, those between 50% and 25% were categorized as “Lower-Medium Level”, and those below 25% of the sample measurement scores were designated as “Low Level”. The organized results are presented in Table 2.
From Table 2, it is evident that in the eastern region, the provinces that are relatively lagging in development include Hainan, Guangdong, Liaoning, Hebei, and Fujian. Other provinces, such as Beijing, Shanghai, Jiangsu, and Zhejiang, consistently rank at a high level of integrated development. In the western region, most provinces are at a lower-medium development level. Still, leading provinces, such as Shaanxi and Xinjiang, have taken advantage of national policy benefits and their resources to gradually fill their development gaps, joining the ranks of upper-medium-level development [32]. In the central region, leading provinces include Jiangxi, Heilongjiang, and Jilin. These provinces have a robust foundation in agricultural industry development and possess a comparative advantage in promoting rural industrial integration [33]. Overall, the level of rural industrial integration in China shows a clear gradient from northeast (high) to southwest (low), with evident regional disparities.

3.2. Spatial Distribution and Dynamic Trend of Rural Industrial Integration Development

3.2.1. Dynamic Change Trend of Rural Industrial Integration Development

Based on the Gaussian kernel density function, the dynamic distribution of rural industrial integration levels in China and its various regions was analyzed, with results shown in Figure 2. In general, the eastern, central, and western regions have all seen significant improvements in their levels of rural industrial integration. Specifically, in the eastern region, the peak height of the distribution curve in 2011 was relatively high and narrow. Between 2014 and 2020, there was a noticeable shift toward a lower peak and a broader curve. In the central region from 2011 to 2020, the peak height of the distribution curve initially decreased, before slightly rising. In contrast, in the western region, the width of the peak remained relatively stable from 2011 to 2020, but the peak height slightly decreased.
These trends indicate that over the ten years studied, there were no significant disparities in the development levels between the provinces in the western region. In contrast, disparities in the central region initially decreased, and then expanded, while those in the eastern region were significantly reduced. The kernel density curve for China’s overall rural industrial integration level also reveals that the national disparity has slightly decreased over the decade. However, the appearance of two distinct peaks in the kernel density curve in general, especially the tailing on the right, further emphasized the spatial imbalance in China’s rural industrial integration development, with many provinces (such as Qinghai, Guangxi, Yunnan, Gansu, etc.) still lagging in their development levels.

3.2.2. Regional Disparities and Decomposition of Rural Industrial Integration Development

As deduced from the research findings above, China’s rural industrial integration development presents a pattern where the east is strong and the west is relatively weaker, with evident spatial heterogeneity. To further understand the disparities in rural industrial integration development in China, decomposition was carried out, and the results are shown in Table 3.
To make the most of this analysis, it would be useful to know the specific metrics or variables included in Table 3, their values, and any other pertinent details. This way, the analysis could provide a more in-depth understanding of the regional disparities and the reasons behind them, enabling more informed conclusions and recommendations.

3.2.3. Spatial Correlation Analysis of Rural Industrial Integration Development

To further delve into whether the development level of rural industrial integration in each Chinese province has any correlation with its neighboring provinces, this study used a spatial weight matrix and adopted the Moran’s I index to perform a global spatial auto-correlation analysis on the measurement results of the 31 provinces. The outcomes are illustrated in Figure 3.
If the Moran’s I values are close to +1, it suggests a clustered pattern, meaning provinces with similar levels of rural industrial integration development are grouped together. If the values are close to −1, it implies a dispersed pattern, suggesting provinces with similar development levels are spaced apart [34]. A value near 0 would indicate a random pattern, meaning there is no discernible spatial correlation among the provinces.
Upon observing specific Moran’s I values from Figure 3, further insights can be gleaned about how spatially correlated the provinces are in terms of their rural industrial integration development levels. This information can be invaluable for policymakers aiming to implement region-specific development strategies or to understand how changes in one province might impact neighboring provinces [35].
Furthermore, understanding the spatial auto-correlation can provide insights into potential spillover effects, where growth in one province could positively or negatively influence development in adjacent provinces. This is crucial for designing comprehensive and effective regional development policies. The local Moran’s scatterplot was further organized, as shown in Figure 4.
From the spatial auto-correlation types of the rural industrial integration development levels in various provinces of China, the overall local Moran’s scatterplot mainly presented the H-H and L-L distribution features. This indicates that the rural industrial integration development in China had a positive clustering characteristic. There were very few provinces falling into the L-L-type cluster, only appearing in Hainan, Inner Mongolia, and Shanxi. Although there has been a slight increase in provinces in the H-L cluster in recent years, it is still limited to Xinjiang, Shaanxi, and Hunan. Furthermore, eastern provinces, such as Anhui, Beijing, Fujian, Shanghai, and Zhejiang, have always been in the H-H cluster, while western provinces, such as Qinghai, Sichuan, Tibet, Yunnan, and Chongqing, have always been in the L-L cluster. This suggests that the spatial correlation of rural industrial integration development levels is relatively stable.

4. Discussion

4.1. Problems in China’s Rural Industrial Integration Development

The primary issue in the development of rural industrial integration in China is the lack of a sufficient driving force for integration. Based on the experience of developed countries, the main drivers for promoting rural industrial integration come from technology, entities, market, and government [36]. Considering China’s current development status, the driving force from these four areas is still insufficient. From the perspective of technological innovation, there is a significant gap between China’s agricultural science contribution rate and that of developed countries. The application level of new agricultural technologies, agricultural equipment, and modern management methods still needs improvement. In terms of integration entity drive, new business entities, such as leading agricultural enterprises, family farms, farmer cooperatives, and professional households, all face varying degrees of difficulties in funding, land, and talent needs. Coupled with the low efficiency of national policy implementation, it has, to some extent, discouraged the intrinsic driving force of integrated entities. From the market development drive, China’s current agricultural development still has structural contradictions [37,38]. The main function of agriculture is limited to providing factors and products, resulting in low-profit levels and market competitiveness. As for the government policy drive, China mainly adjusts the external environment of rural industrial integration through relevant fiscal and tax policies. However, some regions excessively rely on state policy support without forming planned industrial development goals, greatly increasing the financial pressure on the government.
A secondary issue in the development of rural industrial integration in China is the neglect of the fundamental status of agriculture. The primary purpose of promoting rural industrial integration development is to increase farmers’ income and rural development [39]. Therefore, ensuring the foundational position of agriculture is crucial. If the foundation of agriculture is not solid, downstream high-profit industries can easily squeeze out agriculture, affecting the stability of farmers’ incomes. Due to the low value added of agriculture in the industrial integration development, many external capitals tend to use land for non-agricultural and non-grain purposes after entering, causing a decline in grain production, and endangering national food security [40]. Furthermore, many regions blindly pursue industrial clustering in developing rural industrial integration, with low development levels and shallow resource utilization. This has not genuinely tapped into local agricultural resources, severely restricting the sustainable development of rural industrial integration.
Another issue in the development of rural industrial integration in China is the poor quality of development among the operating entities. The primary entities of integrated rural industry development in China are typically leading enterprises, family farms, and agricultural cooperatives, among others [41]. As of 2022, there were 2.227 million agricultural cooperatives and 3.9 million family farms in China. Although these new types of business entities are numerous, they generally face issues such as small scale and low quality of integrated development, and their capacity to drive the development of the surrounding ordinary farming households needs to be improved. For example, leading agricultural enterprises, despite their financial, technological, and management advantages, have limited radiative driving capability. In some regions, they even dominate the market and become major competitors to surrounding farmers [42,43].
The final issue with the development of rural industrial integration in China is that excellent models of integration are difficult to replicate. Based on practical experience, there are significant disparities in the level and effectiveness of integration across regions [44]. Most provinces are still in the initial stages, indicating substantial room for improvement overall. Generally, there are two types of regions with higher levels of integration and better outcomes. One type is the economically and socially developed provinces and cities in the eastern part of China, such as Zhejiang, Jiangsu, and Shanghai. These regions had already begun to experiment with rural industrial integration through digital agriculture technology, agricultural product processing, and rural e-commerce before the concept of rural industrial integration was introduced nationally. The second type includes some provinces and cities in central and western China rich in cultural and tourism resources, such as Hubei, Sichuan, and Chongqing, which have developed business models for mutual integration of agriculture and other industries, based on years of developing leisure agriculture and rural tourism. The success of these regions is closely related to their unique geographical advantages, tourism resources, and rural culture [45]. Although the current integration outcomes are positive, these successful experiences are not universally applicable or transferable, and attempts by other regions to blindly copy them often backfire.

4.2. Strategies to Enhance the Level of Rural Industrial Integration Development in China

Firstly, to enhance the level of rural industrial integration in China, it is essential to follow the basic laws of industrial integration, improving the driving force of integration from aspects of technology, entities, market, and government [46]. Government departments should accelerate the innovation and application of agricultural technology, establish agricultural technology service platforms, and provide a continuous driving force for rural industrial integration. Furthermore, it is necessary to eliminate various constraints faced by new types of business entities in terms of capital, technology, and talent, enabling various business entities to perform optimally in their areas of advantage and promote coordinated development among different entities [47]. Agricultural enterprises and farmers’ cooperatives should adjust and optimize the agricultural industry structure, develop the production and processing of high-quality agricultural products, and actively guide consumers to form modern green consumption habits, promoting the transformation and upgrading of the consumption structure of agricultural products in the new era.
Secondly, enhancing the level of rural industrial integration development in China requires a clear understanding of the differences in provincial rural industry integration and the implementation of targeted policy measures, choosing the path of integration development according to local conditions. Government departments should base their actions on China’s main functional area planning, scientifically plan the layout of agricultural functional industries, improve policy support, and form a distribution pattern where functions are reasonably positioned, and resource endowments and geographical advantages are fully utilized. Provinces in the eastern region, with high levels of economic development and abundant technology and resources, should play a good demonstrative and leading role in constructing rural industrial integration [48]. The central and western regions, with lower levels of rural industrial integration, should identify their focus areas, strengthen their advantages, and make up for their weaknesses, avoid homogeneity in development with similar regions, establish development models, and better achieve coordinated development of industry integration and rural revitalization [49].
Third, in response to the current issues of non-uniformity and homogenization in the development of rural industrial integration in China, it is necessary to clarify the drivers and obstacles of rural industrial integration in different regions, make up for the shortcomings in modern agricultural development, and harness the spillover effects of core production factors [50]. At the same time, the regions with higher levels of integration should leverage their influence to create inter-regional linkages with surrounding provinces, facilitating technology diffusion, industrial cooperation, and industrial division of labor, and establishing regional industry integration demonstration clusters to achieve high-quality development of rural industrial integration. In the process of China’s rural industrial integration, the foundational status of agriculture in the development should be emphasized, along with a deep exploration of agriculture’s multifunctionality. Provinces should develop industries such as agricultural cultural education, health and wellness, leisure agriculture, and rural tourism based on the agricultural cultural resources of different regions, to realize the harmonious coexistence of humans and nature [51].
Finally, the Chinese government should strengthen the cultivation of various new types of business entities. To promote the development of rural industrial integration, necessary measures include encouraging and supporting various entities to enhance their own strength, improve management levels, and strengthen cooperation to achieve complementary advantages, thus forming a modern agricultural business system with the joint development of diverse business entities [52]. Agricultural enterprises should strive to extend and expand their leading industry chains, enhance the overall supply level, and solidify their leading position in rural industrial integration [53]. Farmers’ professional cooperatives should develop in a standardized and market-oriented way, with homogenous or related cooperatives integrating to capitalize on their advantages in agricultural resource allocation and technical training. Family farms should moderately expand their scale of operation and implement scaled and standardized field management. A good interactive relationship between various business entities and a new model of integrated development should be formed to achieve the docking of elements, products, and services, further solidifying the foundation of agricultural development.

5. Conclusions

This paper established a measurement index system from various dimensions of rural industrial integration development and adopted a multi-index comprehensive measurement method to measure the level of rural industrial integration development in China and its provinces from 2011 to 2020. According to the measurement results, the spatial distribution characteristics and dynamic development trends of the integration level were further analyzed. The research found that the overall level of rural industrial integration development in China and its provinces from 2011 to 2020 showed an upward trend, and some areas have reached a relatively high level of integration. However, there is still a noticeable spatial imbalance in development. Specifically, the gap in the level of rural industrial integration development between the eastern provinces and western regions is significant. Among them, the western region lags the most, followed by the central region. Based on the global Moran’s index changes over the past decade, this gap has been gradually narrowing in recent years.
There is significant homogenization of low-quality development in China’s provincial rural industrial integration, and the level of rural industrial integration development presents a spatial pattern of high in the northeast and low in the southwest. The dominant regional features are “H-H type” and “L-L type” clustering, showing significant positive spatial correlation. Moreover, according to the provincial panel data from 2011 to 2020, the spatial correlation of the integration development level in each province is relatively stable. At the same time, the development of rural industrial integration in most provinces in China is relatively stable, with only a few provinces experiencing upgrades or downgrades. The development level of neighboring areas has a significant impact on the development status of the province. In the process of China’s rural industrial integration and development, it is difficult for each province to quickly improve its own level of rural industrial integration and development, and it is more difficult to achieve leapfrog development. Some provinces are gradually falling behind other regions over time, and most other provinces are more likely to maintain their current status. Very few areas can achieve leapfrog development.
Through research, this paper concluded that there are issues in the development of China’s rural industry integration, such as an insufficient integration driving force, neglecting the fundamental status of agriculture in the integration process, the poor development quality of the main bodies of integration, and the difficulty in replicating excellent integration models. It is proposed that to adhere to the basic laws of industry integration, it is necessary to enhance the integration driving force from aspects such as technology, entities, market, and government, and to advance the in-depth integrated development of rural industries. The differences in the integrated development of rural industries across Chinese provinces should be clarified, and targeted policies and measures should be implemented, choosing integration development paths that are tailored to local conditions. To address the existing issues of regional imbalance and low-quality homogenization, it is crucial to clarify the driving forces and obstacles to rural industry integration in different areas, to fill in the gaps in modern agricultural development, and to harness the spillover effects of core production factors. It is also important to recognize the fundamental status of agriculture in the development of industry integration, to further explore agriculture’s multifunctionality, and to strengthen the cultivation of various new types of business entities as part of the development strategy recommendations.
However, the conclusions of this paper are limited to the measurement of the level of integrated rural industry development in China and its provinces and the dynamic development trend, without further selecting specific cases for analysis or examining specific rural industry integration models and methods. Moreover, due to the availability and comparability of data, there were certain deficiencies in the establishment of the evaluation indicator system in this paper, all of which await further improvement.

Author Contributions

Conceptualization, H.H.; methodology, C.L.; software, H.H.; validation, C.L. and L.X.; formal analysis, H.H.; data curation, H.H. and C.L.; writing—original draft preparation, H.H.; writing—review and editing, C.L. and L.X.; visualization, L.X.; supervision, L.X.; project administration, L.X.; funding acquisition, L.X. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by The Agricultural Science and Technology Innovation Program, grant number 10-IAED-02-2022 and General Project of National Social Science Foundation of China: Research on Accounting Information System of Farmer Cooperatives Based on the Background of High-quality Development, grant number 22BGL083.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The data can be found according to the corresponding data source. Scholars requesting more specific data may email the corresponding author or the first author.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Evaluation index system of rural industry integration development levels.
Figure 1. Evaluation index system of rural industry integration development levels.
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Figure 2. Kernel density curves of the integration levels of rural industries in China.
Figure 2. Kernel density curves of the integration levels of rural industries in China.
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Figure 3. Global Moran’s index of China’s rural industrial integration development levels.
Figure 3. Global Moran’s index of China’s rural industrial integration development levels.
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Figure 4. Spatial auto-correlation distribution types in Chinese provincial regions.
Figure 4. Spatial auto-correlation distribution types in Chinese provincial regions.
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Table 1. Evaluation index system of rural industry integration development levels.
Table 1. Evaluation index system of rural industry integration development levels.
System LevelIndicator LevelCalculation MethodWeight
Agricultural Industry Development(A1) Total agricultural output (in 1373 USD)-0.038
(A2) Agricultural labor productivity (in 1373 USD/person)Added value of agriculture/Number employed in agriculture0.044
(A3) Land yield rate (in 1373 USD/hectare)Added value of agriculture/Rural land area0.050
(A4) Rural fertilizer and pesticide reduction intensity (tons/thousand hectares)Reduced fertilizer and pesticide usage/Total crop-planting area0.063
Agricultural Industry Extension(A5) Proportion of livestock industry output (%)Livestock output/Total agricultural output0.062
(A6) Proportion of agricultural product processing industry output (%)Agricultural product processing output/Total agricultural output0.055
(A7) Proportion of agro-forestry-fishery services output (%)Agro-forestry-fishery services output/Total agricultural output0.087
(A8) Scale of agricultural social services (units/100 households)Number of specialized agricultural machinery service institutions/Number of households in the region0.069
Industrial Crossover Integration(A9) E-commerce transaction amount of agricultural products (in 1373 USD)-0.052
(A10) Ratio of recreational agriculture and rural tourism total output (%)Recreational agriculture and rural tourism total output/Total agricultural output0.077
(A11) Rural logistics penetration rate (km)Total length of rural delivery routes/Total number of households in the rural area0.054
Industry Element Aggregation(A12) Agricultural loan amount (in 1373 USD)-0.069
(A13) Ratio of urban to rural residents’ income (%)Average disposable income of urban residents/Average disposable income of rural residents0.046
(A14) Non-agricultural employment capability of farmersNumber of non-agricultural workers employed in rural areas/Working-age population in rural areas0.054
(A15) Urbanization rate (%)Number of permanent urban residents/Total permanent residents in the region0.023
Industrial Technology Penetration(A16) Comprehensive mechanization rate of crop cultivation and harvesting (%)Ploughing rate × 40% + Planting rate × 30% + Harvesting rate × 30%0.066
(A17) Agricultural disaster prevention rate (%)1 − (Area of crops affected by disaster/Area of crop planting)0.051
(A18) Rural informatization rate (units/100 households)Number of computers owned by rural households/Number of households in the region0.039
Table 2. Level of rural industrial integration development in various provinces by region and year.
Table 2. Level of rural industrial integration development in various provinces by region and year.
Year/RegionLow LevelMedium-Low LevelMedium-High LevelHigh Level
The year 2011
Eastern--Fujian, Hebei, HainanShandong, Jiangsu, Beijing, Tianjin, Guangdong, Liaoning, Zhejiang, Shanghai
CentralShanxiJilin, HeilongjiangJiangxi, Anhui, Hunan, Henan, Hubei-
WesternChongqing, Sichuan, Guangxi, Tibet, Gansu, Yunnan, GuizhouShaanxi, Inner Mongolia, Ningxia, Xinjiang, Qinghai--
The year 2014
Eastern-HainanLiaoning, Guangdong, HebeiShandong, Jiangsu, Tianjin, Beijing, Zhejiang, Fujian, Shanghai
Central-Henan, Hunan, ShanxiHubei, Jilin, Heilongjiang, AnhuiJiangxi
WesternSichuan, Chongqing, Yunnan, Gansu, Qinghai, Tibet, GuizhouXinjiang, Ningxia, Inner Mongolia, GuangxiShaanxi-
The year 2017
Eastern-Guangdong, Liaoning, HainanFujian, HebeiJiangsu, Shandong, Beijing, Shanghai, Zhejiang, Tianjin
CentralShanxiHunanHubei, Jilin, Jiangxi, HenanHeilongjiang, Anhui
WesternChongqing, Yunnan, Guangxi, Tibet, Gansu, GuizhouNingxia, Inner Mongolia, Sichuan, QinghaiShaanxi, Xinjiang-
The year 2020
Eastern-Fujian, Hebei, Guangdong, HainanLiaoningJiangsu, Zhejiang, Shandong, Beijing, Shanghai, Tianjin
Central-ShanxiHenan, Hubei, Jiangxi, Anhui, HunanHeilongjiang, Jilin
WesternChongqing, Qinghai, Guangxi, Yunnan, Gansu, Tibet, GuizhouNingxia, Inner Mongolia, SichuanShaanxi, Xinjiang-
Table 3. Differences and decomposition of China’s rural industrial integration development levels.
Table 3. Differences and decomposition of China’s rural industrial integration development levels.
YearOverallIntra-Regional DifferenceInter-Regional DifferenceContribution Rate (%)
EasternCentralWesternEastern–CentralEastern–WesternCentral–WesternGwGnbGt
20110.1410.0370.0370.1470.1010.2850.20216.4082.291.30
20120.1500.0450.0420.1620.0770.2580.19818.5479.631.81
20130.1550.0550.0460.1450.0970.2710.18717.7981.210.99
20140.1610.0730.0570.1510.1000.2700.19419.8077.682.51
20150.1510.0680.060.1350.0920.2490.19019.7276.184.09
20160.1480.0630.0660.1470.0850.2370.19220.9872.846.16
20170.1430.0640.0790.1380.0840.2220.18521.8970.098.00
20180.1410.0570.0650.1440.0780.2280.18421.0273.485.49
20190.1370.0560.0540.1260.0710.2150.17720.3774.125.49
20200.1320.0550.0550.1330.0700.2240.18620.1674.055.77
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Hao, H.; Liu, C.; Xin, L. Measurement and Dynamic Trend Research on the Development Level of Rural Industry Integration in China. Agriculture 2023, 13, 2245. https://doi.org/10.3390/agriculture13122245

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Hao H, Liu C, Xin L. Measurement and Dynamic Trend Research on the Development Level of Rural Industry Integration in China. Agriculture. 2023; 13(12):2245. https://doi.org/10.3390/agriculture13122245

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Hao, Han, Chenyang Liu, and Ling Xin. 2023. "Measurement and Dynamic Trend Research on the Development Level of Rural Industry Integration in China" Agriculture 13, no. 12: 2245. https://doi.org/10.3390/agriculture13122245

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Hao, H., Liu, C., & Xin, L. (2023). Measurement and Dynamic Trend Research on the Development Level of Rural Industry Integration in China. Agriculture, 13(12), 2245. https://doi.org/10.3390/agriculture13122245

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