Study in Driving Strategy and Analysis of Sustainable and Symbiosis Development Relationship between Agricultural Industrial Clusters and Agricultural Logistics Industry

Abstract

Due to a lack of profound disclosure of the internal mechanism for the symbiotic development of agricultural industrial clusters and agricultural logistics industry, the current study finds it difficult to form specific and implementable driving countermeasures well. Quantitative research on their symbiotic development and evolution is an important method to promote the further development of agricultural industry and agricultural logistics industry. In this paper, the factors affecting the sustainable symbiotic development relationship are analyzed between agricultural industry clusters and agricultural logistics industry with explanatory structural equation, and a system-driving model is constructed for the symbiotic development of agricultural enterprise clusters and agricultural logistics industry. The analysis indicated that, for the symbiotic development of agricultural enterprise clusters and agricultural logistics industry, the macro policy orientation is the fundamental driving force and the symbiotic development effect is its final result. Seven driving paths are refined, and the relevant countermeasures to promote the sustainable development of agricultural industrial clusters and agricultural logistics industry are put forward one at a time.

1. Introduction

As a new agricultural development mode to optimize the layout of agricultural production and enhance the competitiveness of agricultural products, agricultural industrial clusters have attracted more and more attention of scholars. In the past 30 years, there have been a number of agricultural industrial clusters projects that had positive effects on achieving the rural industrialization, improving farmers’ income, and relieving rural poverty in China [1]. As an example, with the active cultivation of agricultural industrial clusters by the local government, Anding County has become one of the largest potato production, distribution, and processing centers in China [2]. However, different from manufacturing and the high-tech industrial clusters, agricultural industrial clusters have the characteristics of being greatly constrained by natural conditions and obvious regional features, limiting further development and wide application [3]. Therefore, it is necessary to strengthen the cooperation of various links and side-related service support systems among agricultural industries for reducing the impact of regionality, seasonality, and periodicity on agricultural industrial clusters, as much as possible.

As an important support of service support systems, agricultural logistics play a vital role in facilitating the development of agricultural industrial clusters. On the one hand, the healthy and stable development of agricultural logistics can effectively ensure the smooth progress of agricultural production and operation, and promote the sustainable development of agricultural industrial clusters economy. On the other hand, the large-scale logistics demand triggered by agricultural industrial clusters has contribute to business growth for the logistics industry and industrial upgrading. Agricultural industry clusters and agricultural logistics industry have thus formed an industrial relationship of matching supply and demand, benefit sharing, and consistent value due to market relations. In general, their rapid and stable development is inseparable from the mutual promotion and symbiosis of each other.

The concept of symbiosis originated from the field of biology, where it was first proposed by German Mycologist Antonde·Bary [4] in 1879 and was mainly used to describe the different organisms that live together under the material link. Symbiosis exists not only in organisms, but also in the social system, because the world as a whole, is mutually related. Currently, most research has transferred from natural science to social science. When symbiosis was introduced into industrial relations in economics, the theory of industrial symbiosis gradually began to develop. The industrial symbiosis theory emphasizes the relationship of interdependence and mutual promotion among industries. By forming the material interaction, information, and energy in a certain region, cooperation is carried out through multiple channels such as resource sharing, complementary advantages, and market dominance, so as to jointly improve the viability, profitability, and influence of enterprises. Therefore, guiding the agricultural industry and logistics industry to realize the symbiotic development for agricultural industry clusters and agricultural logistics industry is an important way to promote further development.

At present, there are many results of single research on agricultural industrial clusters and agricultural logistics, and the research on relevant policies, ideas, and technologies is also more in-depth. For example, in terms of agricultural industrial clusters, Zhao et al. [5] described the effects of local industrial policies on the local industries’ development in the case of tea clusters. The research results showed that when market failure occurred in the development of agricultural clusters, the local government had implemented a series of industrial policies to promote the progress of the tea industry at different development stages. As for the research in agricultural logistics, based on the concept of shared logistics, Cai et al. [6] built an agricultural shared logistics platform via “Internet Plus”, which established the link between the logistics resource supplier and demander, as well as studied the operation mode of agricultural shared storage and agricultural shared transportation. The results indicated that agricultural shared storage and agricultural shared transportation mode can improve the utilization of storage resources and the level of transportation services, and promote the healthy development of agricultural transportation.

However, in the existing work, it is difficult to form specific and implementable driving countermeasures, as there is a lack of profound disclosure of the internal mechanism of the symbiotic development of agricultural industrial clusters and agricultural logistics industry, as well as the quantitative research on the symbiotic development and evolution of the two under the symbiotic relationship.

Therefore, this study takes the symbiosis development of agricultural industrial clusters and agricultural logistics as the research object. Under the guidance of symbiosis theory, through the research on the driving mechanism and countermeasures of the symbiosis development of agricultural industrial clusters, it is expected to provide theoretical support for solving the contradiction between the current agricultural industry logistics demand and the supply capacity of logistics enterprises.

2. Literature Review

2.1. Research on Agricultural Industrial Clusters

The concept of industrial clusters originated from the industrial field, and the related research is much more than that on agricultural industrial clusters. However, agricultural industrial clusters have also gradually begun to receive attention. In general, three aspects, i.e., concept definition, formation mechanism, and empirical research, are mainly included in the research of agricultural industrial clusters.

Firstly, as for the research on concept definition of agricultural industrial clusters, Sosnovskikh [7] pointed out that the concept of industrial clusters was based on the combination of competition within the clusters and the significance of supplier network, geographical particularity, and government policies, leading to innovation and productivity growth. Kulshreshtha et al. [8] believed that the agricultural industrial clusters were the same as the food industrial clusters, which were composed of three sub clusters: agricultural production sub clusters, food primary processing sub clusters, and farm input manufacturing sub clusters. Moreover, Zhang et al. [3] believed that the agricultural industrial clusters were a dense flexible network cooperation group formed by farmers, enterprises, and markets, which was centered on traditional agriculture and supported by a large number of common and complementary professional-related enterprises and institutions.

Secondly, as for research on the formation mechanism of agricultural industrial clusters, Ellison et al. [9] emphasized the importance of geographical location in the formation of agricultural industrial clusters, including the importance of abundant natural resources and convenient transportation conditions. In some specific regions, due to the unique natural resource conditions, a large number of low-cost raw materials are provided for manufacturing and production. The superior terrain conditions reduce the difficulty of transportation infrastructure construction, making the transportation more convenient and the circulation cost lower. Munnich et al. [10] analyzed and found the logic and mechanism of forming agricultural industrial clusters through the construction of the knowledge clusters model. Its evolution and development are the result of the joint action of internal and external factors such as technology, capital, and environment.

Thirdly, as for empirical research of agricultural industrial clusters, Prevezer [11] used the new enterprise entry model to deeply empirically analyze the Xiangbang wine industrial clusters in France, and studied the openness of FA Mei agricultural industrial clusters and the cooperation efficiency among enterprises, which had a significant impact on the cost of new enterprises entering the clusters. In order to find the key influencing factors hindering the development of agricultural industrial clusters and propose the corresponding solutions, Peters et al. [12] empirically analyzed the industrial clusters of agricultural product-processing enterprises with pork as the leading agricultural products, and specifically studied the production enterprises of pig production, processing, wholesale and retail, and export. Meanwhile, Woo et al. [13] also combined analytic hierarchy process and GIS modeling to explore the best location of potential forest industrial clusters.

In the research of agricultural industrial clusters, although the research perspective of agricultural industrial clusters is constantly innovated in combination with current events, the research methods, mainly qualitative analysis, and countermeasures and suggestions, still lack in-depth exploration. After the research on the development of agricultural industrial clusters, the proposed countermeasures and suggestions are more at the macro level and less at the micro level. Moreover, there is a lack of operable and detailed practical suggestions, which could affect the upgrading and transformation of agricultural industrial clusters.

2.2. Research on Logistics Industry

The agricultural logistics industry in this study refers to the logistics industry serving agricultural enterprise clusters. Academic circles pay more attention to agricultural logistics, rural logistics, or agricultural product logistics. Agricultural logistics means that the key areas of logistics are mainly in rural areas, and the main objects of logistics are agricultural products. The studies on rural logistics and agricultural product logistics also have a certain reference value for the research of the logistics industry, serving agricultural industry clusters. The current research on relevant logistics can be summarized into three levels: policy, theory, and application.

At the policy research level, the current agricultural logistics in developing countries have some deficiencies, such as lack of infrastructure, low level of specialization, shortage of professional logistics talents, difficult popularization of informatization, poor sales channels, dispersion of transportation, and so on [3]. In view of these deficiencies and problems, Otsuka et al. [14] put forward corresponding policy suggestions, such as improving the quantity and quality of rural logistics talents, increasing investment in rural logistics infrastructure construction, establishing standardized agricultural product logistics, adjusting agricultural production structure, reducing logistics costs, improving agricultural product sales channels, and implementing preferential policies for rural logistics enterprises, etc.

At the level of theoretical research, researchers mainly study the development mode, platform construction, and system optimization of agricultural logistics and agricultural product logistics. Huang [15] claimed that the construction of rural logistics platform is the key to promote the development of rural logistics, and a perfect logistics platform would help to further improve the accessibility of a rural logistics network, logistics integration, logistics informatization, and network operation efficiency. Mkansi et al. [16] proposed to use network technology to organically integrate agricultural product logistics and agricultural material logistics, so as to realize the smoothness of information channels and the dynamic balance of supply and demand.

At the application research level, the research is relatively mature and in-depth. For location and distribution planning, the models are divided into dynamic, discrete, random, and continuous. Among them, the more classic is the continuous model, which assumes that any location point can be selected as the construction address, and the center of gravity method is generally used to solve the European distance location problem [17]. Yasmine et al. [18] used the multi-agent modeling method to analyze the inventory of agricultural products in the supply chain in detail. Zhang et al. [19] proposed a two-stage layout optimization model of agricultural product joint distribution centers based on the geographical features of remote rural areas.

To sum up, the current academic research of agricultural logistics mainly focuses on the optimization of distribution efficiency, the exploration of development mode, and the construction of transportation platform system. The research on the relationship between agricultural logistics and other industries has not been systematically and perfectly defined.

2.3. Research on Industrial Symbiosis

In the theoretical research of industrial symbiosis, scholars mainly study the concept definition, internal evolution mechanism, relationship, influencing factors, and development model of industrial symbiosis. Renner [20] described the organic relationship between different industries in his article on industrial location. Frosch and Gallopoulos [21] put forward a concept of industrial ecosystem, and they think it should function as an analogue of biological ecosystems and finally form the industrial symbiotic relationship to realize the mutual utilization of resources. Engberg [22] holds the view that the cooperation between different enterprises through mutual utilization is a symbiotic phenomenon. Through this cooperation, the survival and profitability of enterprises can be improved, and at the same time, resources and energy can be utilized efficiently and the environment can be much more easily secured. Rasmussen [23] put forward the industrial symbiosis combination of sharing resources and exchanging by-products among different industries by studying the industrial symbiosis system in Kaldenberg, Denmark. Martin and Sunley [24] think that the growth of a regional economy depends not only on its own basis, but also on the growth of other regions. The essence of this process is the formation and operation of symbiosis. The concept of industrial symbiosis was proposed clearly by Ehrenfeld [25]. Ehrenfeld [25] thinks that enterprises can make use of each other’s waste to reduce the environmental load and waste treatment costs, so as to establish an industrial symbiotic cycle system. Chertow [26] thinks that industrial symbiosis engages traditionally separate industries in a collective approach to competitive advantage involving physical exchange of materials, energy, water, and/or by-products. And with such a perspective, the keys to industrial symbiosis are collaboration and the synergistic possibilities offered by geographic proximity. Lombardi and Layboum [27] proposed an updated definition intended to communicate the essence of industrial symbiosis as a tool for innovative green growth: Industrial symbiosis engages diverse organizations in a network to foster eco-innovation and long-term culture change. Creating and sharing knowledge through the network yields mutually profitable transactions for novel sourcing of required inputs and value-added destinations for non-product outputs, as well as improved business and technical processes.

The origin and development of the above industrial symbiosis theory shows that industrial symbiosis is based on the theory of biological symbiosis and matures with the development of science and technology, economic theory, and industrial practice. Not only that, it is constantly enriched and improved by combining with industrial and economic activities such as enterprise zoning, energy utilization, resource sharing, waste disposal, information service, etc.

2.4. Proposal of Research Questions

According to the literature review, agricultural logistics is an important part of the development of agricultural industrial clusters; the development of agricultural industrial clusters can also help to solve the difficult problems in the development of agricultural logistics and promote the sustainable and stable development of agricultural logistics. However, few scholars have combined the development of agricultural industrial clusters and agricultural logistics, nor have they explored the possibility and impact of their common development as well as the internal relationship between the two industries in symbiosis on the premise of the symbiosis of two industries.

Based on these findings, we try to put forward the hypothesis that they have a close industrial symbiotic relationship and try to answer the following key questions:

▪

How to explore and analyze the symbiotic relationship between agricultural industrial clusters and agricultural logistics industry?

▪

How to put forward the corresponding driving strategies according to the symbiotic relationship between them?

3. Methodology

3.1. Introduction of Research Methods

Interpretive Structural Modelling (ISM) is a method used to analyze a complex system model [28]. ISM is also an application of graph theory, used to solve fuzzy relation by revealing the interrelationships and related structures between the elements of a system [29]. The interpretative structural model method can transform complex and ambiguous situations into intuitive and clear structural models. The basic idea of interpretive structural model method is to regard the research problem as a system. Firstly, sort out the elements of the system and analyze the logical relationship between the elements of the system; secondly, the logical relationship is expressed by a directed relation graph and adjacency matrix; thirdly, the system structure is revealed by Boolean logic operation; and finally, the system structure is presented in the form of directed topology. Whether it is national hot issues, regional economic and social issues, or even specific problems of units and individuals, the interpretative structural model method can be used for analysis. It is suitable for many variables, especially with staggered relationships analysis of problems with unknown levels [30,31,32]. Therefore, in this study, ISM method was chosen to explore the symbiotic relationship between agricultural industrial clusters and agricultural logistics industry.

3.2. Basic Process of ISM Method

The key to explain the structural model method is to show how to build a specific research problem into a structural model, and use the form of hierarchy to reveal and express the relationship between system elements, so as to lay a foundation for further analysis. Constructing a structural model is not only the purpose of interpreting the structural model method, but also the basis of interpreting and analyzing the structural model. As shown in Figure 1, the general process of interpreting the structural model method is: analyze system elements, establish vertex incidence matrix, establish reachable matrix, draw hierarchical topology, and build the system’s structure models and interpretive system structure model.

3.2.1. System Element Analysis

System element analysis is the basis of applying an interpretive structure model method. Scientific and in-depth analysis of specific research problems, combining and analyzing specific problems according to corresponding theories and laws, and decomposing them into different interrelated elements within the same system framework, are the first steps of using an interpretative structure model method. On this basis, the relationship between system elements is further analyzed, the action state between system elements is clarified, and the original relationship matrix is established according to the influence relationship rules of system elements, so as to prepare for the establishment of adjacency matrix in the next step.

3.2.2. Establish Adjacency Matrix

The establishment of the adjacency matrix is the key step in the application of an interpretative structure model method. The essence of its work is to import the original relationship matrix of system elements to complete the adjacency matrix. According to the results of system element analysis, the relationship between elements is expressed by the original relationship matrix, and a_ij is used instead of S_i and S_j. The conversion rules are as follows:

• If S_i has a direct impact on S_j, a_ij = 1;
• If S_i has no direct effect on S_j, a_ij = 0;
• a_ii in adjacency matrix is 1.

3.2.3. Computing Reachability Matrix

The calculation of the reachability matrix is the focus for the application of an interpretative structural model method. There are multiple calculation methods for a reachable matrix. For low-order reachable matrix, Boolean operation rules can be applied and continued multiplication can be used for matrix calculus based on the transition law of reachable matrix.

Boolean operation rules are as follows:

0 + 0 = 0, 0 + 1 = 1, 1 + 0 = 1, 1 + 1 = 1;

0 × 0 = 0, 0 × 1 = 0, 1 × 1 = 1

I is used to refer to the identity matrix. In any identity matrix, the main diagonal elements from the upper left corner to the lower right corner are all “1”, and other elements are “0”. According to this characteristic of the identity matrix, any matrix multiplied by the identity matrix is equal to itself, so the identity matrix has a wide range of applications.

Because the adjacency matrix is a Boolean matrix of “1” or “0”, the multiplication matrix B is obtained by adding the adjacency matrix A and the identity matrix I. Through continuous multiplication of multiplication matrix B, due to the nature of Boolean matrix, its limit matrix will stop changing after a certain multiplication. At this time, the obtained matrix is reachable matrix R.

According to the Boolean operation rules, the solution rules of reachability matrix R are as follows:

B = A₁ = (A + I)

A₂ = (A + I)² = I + A + A²

The derivation shows that: A_k = (A + I)^k = I + A + A² + … + A^K.

For the adjacency matrix with n nodes, the channel length k between nodes does not exceed (n − 1). After sequential operation, when the following situations occur:

A₁ ≠ A₂ ≠ … ≠ A_r−1 = A_r, r ≤ n − 1

Then the reachable matrix R = A_r = A_r−1 = (A + I)^r−1.

For multi-order reachability matrix, Floyd–Warhall algorithm can be used to calculate the reachability matrix through the program.

3.2.4. Draw Hierarchical Topology

Drawing the simplest hierarchical topology of the system is the basic purpose of explaining the structural model method. The analysis of system elements, the establishment of adjacency matrix, and the calculation of reachability matrix are to complete the level extraction and obtain the general skeleton matrix through Boolean operation, so as to draw the simplest level topology map of the system.

Region division and level division are not only a process of continuous screening and reduction, but also a process of mathematical analysis of causes and results among system elements. Based on the analysis results of system elements, further in-depth analysis is carried out from the dimension of causes and results, and the causal relationship between system elements is continuously clarified through mathematical analysis and operation, so as to divide the region and level of system elements, and to find out the initial causes and the last results.

On the basis of reachable matrix R, the reduced point matrix R’ is obtained by reducing point operation; then the edge reduction operation is carried out on the reduced point matrix R’, and the repeated reachability relationship is simplified to obtain the reduced edge matrix S’; then, the simplest daisy chain is used to represent the loop, and the general skeleton matrix S is obtained; the hierarchical topology of the general skeleton matrix S is the hierarchical topology of the whole system.

3.2.5. Build System Structure Model

Constructing a system structure model is the key goal of applying the interpretive structure model method. After the hierarchical topology map is drawn, replace the relationship node with the corresponding system element content according to the corresponding relationship between the system elements and the system-directed relationship map. According to the interaction relationship indicated by the directional edge arrow in the hierarchical topology, the system elements are associated so as to construct the structural model of the system.

3.2.6. Interpretive System Structure Model

The interpretive system structure model is the fundamental purpose of applying an interpretive structure model method. After the construction of the system structure model, we should interpret its interaction according to the relationship between the system elements presented by the structure model, as well as analyze and explain the role of system elements on the overall system from the overall perspective of system. In addition, the function relationship between the internal elements of the whole system and the function mechanism of the whole system should be analyzed, so as to answer and explain the specific research problems.

4. Discussion of Results

4.1. Influence Factors of Sustainable and Symbiotic Development

Herein, the influence factors of sustainable and symbiotic development were analyzed and further used as system elements of ISM. The factors that affect the sustainable development and symbiotic relationship between organic agriculture and logistics industry are the key to study the driving strategy [33,34,35]. Mirata et al. [36] argued that the nature of companies’ operations, industrial history in the regions, the extent of peer pressure, and the positioning of the coordinating body in the region have a major influence on the sustainable development, and the government fiscal policy is an effective driver. Van beers [37] believed that information availability, economic level, regional characteristics, organization level, regulation, and technical issues are the drivers of sustainable development. In Yang and Feng’s view [38], rational production structures, raw materials advantages, technical supports, and correct diversification are the key factors to realize sustainable development. Ashton [39] and Domenech [40] hold that there are differences among the material exchange network, social network, and information network, and social relations are not decisive factors in sustainable development. Deutz et al. [41] discussed the impact on industrial symbiosis from the perspective of producer responsibility and believed that good enterprise cooperation and reasonable government policies are the positive drivers of sustainable development. Salmi et al. [42] believed that under the market-based governance system, a regulatory mechanism is the key factor of sustainable development. The related literature and typical cases regarding the influence factors on the sustainable development and symbiotic relationship between organic agriculture and logistics industry are induced and deduced in this paper. Sixteen core influencing factors are consolidated in

Table 1. The influence factors on the sustainable development and symbiotic relationship between organic agriculture and logistics industry.

Serial Number	Influence Factors	Specific Description
S1	Operating location conditions	Logistics industry; logistics facilities; and urban and rural location, landform, and other conditions where logistics operation is located.
S2	Industrial geographical environment	The geographical environment of the production, processing, sales, and other industrial chains of agricultural industrial clusters.
S3	Enterprise quality control	Product quality, control mechanism, and level of agricultural industrial clusters and agricultural logistics industry.
S4	Enterprise market value	Brand value, brand influence, and market competitiveness of agricultural industrial clusters and agricultural logistics products.
S5	Operation cost investment	Cost of infrastructure investment and enterprise operation in agricultural industrial clusters and agricultural logistics industry.
S6	Enterprise marketing benefits	Profits and benefits of agricultural industrial clusters and agricultural logistics enterprises.
S7	Logistics demand scale	Logistics demand and business volume of agricultural industrial clusters.
S8	Logistics supply quality	Service quality of logistics supply in logistics industry.
S9	Technology R & D and innovation	Agricultural industry clusters; intensive processing of agricultural products and other technology R&D levels.Agricultural logistics industry cold chain logistics and other information and intelligent technology innovation levels.
S10	Talent training measures	Specific training measures for professional talents.
S11	Symbiotic development effect	Effect, benefit, and profit of symbiotic development of agricultural industrial clusters and agricultural logistics industry.
S12	Symbiotic development cognition	Cognition, understanding, trust, support, and guarantee of symbiotic development.
S13	Macro policy orientation	The government’s macro policy guidance for the symbiotic development of agricultural enterprise clusters and logistics industry.
S14	Implementation of policies and measures	The accuracy and implementation of policies to solve specific problems such as finance, taxation, and land.
S15	Market quality supervision	Market monitoring and management of production, processing, and sales of agricultural products in agricultural industrial clusters. Supervision of relevant service procedures and operation status of agricultural logistics industry.
S16	Market management services	Services provided in market management, market order, fair competition, etc.

.

4.2. Analyzing the Driving Mechanism through the ISM Method

4.2.1. Setting Variables

The elements of the adjacency matrix A can be expressed as a_ij, which represents the relationship between the nodes. In this paper, a_ij represents the sustainable development and symbiotic relationship between organic agriculture and the logistics industry. The 16 nodes of the adjacency matrix are S₁, S₂, S₃, S₄, S₅, S₆, S₇, S₈, S₉, S₁₀, S₁₁, S₁₂, S₁₃, S₁₄, S₁₅, and S₁₆, which represent operating location conditions, industrial geographical environment, enterprise quality control, enterprise market value, operation cost input, enterprise marketing benefits, logistics demand scale, logistics supply quality, technology R&D and innovation, talent training measures, symbiotic development effect, symbiotic development cognition, macro policy orientation, implementation of policies and measures, market quality supervision, and market management services, respectively.

4.2.2. Creating an Adjacency Matrix

The system can be represented as an 16 × 16 adjacency matrix A = [a_ij]_{16 × 16}, which is composed of the pairwise relationship between 16 nodes. i represents columns of A, and j represents rows. Two vertices i and j of a directed graph are joined or adjacent if there is an edge from i to j or from j to i, which means node i is affected or restricted by node j, and the value of a_ij is 1, otherwise a_ij = 0.

In terms of the sustainable and symbiotic development system between organic agriculture and logistics industry, the adjacency matrix is shown in

Table 2. The adjacency matrix A.

[aij]16×16	S1	S2	S3	S4	S5	S6	S7	S8	S9	S10	S11	S12	S13	S14	S15	S16
S1	0	0	0	0	1	0	0	1	0	0	0	0	0	0	0	0
S2	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
S3	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0
S4	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0
S5	0	0	1	0	0	1	0	0	1	0	0	0	0	0	0	0
S6	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0
S7	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0
S8	0	0	0	1	0	0	1	0	0	0	0	0	0	0	0	0
S9	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0
S10	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
S11	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
S12	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0
S13	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	1
S14	0	0	0	0	1	1	0	0	0	0	1	0	0	0	0	0
S15	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0
S16	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0

.

4.2.3. Constructing Reachability Matrix

The algorithm proposed by Warshall in 1962 is an effective and widely used algorithm to compute the transitive closure of a relation [33]. The algorithm is summarized in a specific form as follows.

①

Let reachability matrix R = A;

②

Let k = 1;

③

For i = 1 to n

• if R[i,k] = 1
•  for j = 1 to n
•   R[i,j] = R[i,j] ∨ R[k,j];
•   k = k + 1;

④

If k ≤ n, jump to step 3, otherwise stop.

The calculated reachability matrix R is shown in

Table 3. The reachability matrix R.

${\left[{\mathbf{a}}_{\mathbf{i}\mathbf{j}}\right]}_{16\times 16}$	S1	S2	S3	S4	S5	S6	S7	S8	S9	S10	S11	S12	S13	S14	S15	S16
S1	1	0	1	1	1	1	1	1	1	0	1	0	0	0	0	0
S2	0	1	1	1	1	1	1	1	1	0	1	0	0	0	0	0
S3	0	0	1	1	0	1	1	1	0	0	1	0	0	0	0	0
S4	0	0	0	1	0	1	1	1	0	0	1	0	0	0	0	0
S5	0	0	1	1	1	1	1	1	1	0	1	0	0	0	0	0
S6	0	0	0	1	0	1	1	1	0	0	1	0	0	0	0	0
S7	0	0	0	1	0	1	1	1	0	0	1	0	0	0	0	0
S8	0	0	0	1	0	1	1	1	0	0	1	0	0	0	0	0
S9	0	0	1	1	0	1	1	1	1	0	1	0	0	0	0	0
S10	0	0	1	1	0	1	1	1	1	1	1	0	0	0	0	0
S11	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
S12	0	0	1	1	1	1	1	1	1	0	1	1	0	0	0	0
S13	0	0	1	1	1	1	1	1	1	0	1	1	1	0	1	1
S14	0	0	1	1	1	1	1	1	1	0	1	0	0	1	0	0
S15	0	0	1	1	0	1	1	1	0	0	1	0	0	0	1	0
S16	0	0	0	1	0	1	1	1	0	0	1	0	0	0	0	1

.

4.2.4. Hierarchical Partitioning

Drawing a hierarchical topology map is the key to the construction of the symbiotic development driving model of agricultural enterprise clusters and the logistics industry. It is divided into two parts: Firstly, obtain the hierarchical extraction results through the hierarchical extraction process; then the general skeleton matrix is solved and the hierarchical topology is drawn. Through a rotating method between reasons-first and results-first principle, the reachable matrix can be extracted hierarchically.

For reachable matrix R, system elements can be divided into reachable set R(ei), antecedent set Q(ei), and common set T(ei). The reachable set of system elements S_i is a set composed of multiple elements that can be reached from S_i in the reachable matrix; the antecedent set of system elements S_i is a set composed of multiple elements that can reach S_i in the reachability matrix; the common set of system elements S_i is the common part of the reachable set and antecedent set of S_i.

Hierarchical extraction is also called result first hierarchical division. The specific extraction rule is: R(ei) = T(ei). When the reachable set of system elements is the same as the common set, this system element will be extracted; rsepeat this process in turn until all features are extracted. The specific process is shown in

Table 4. Hierarchical partitioning process.

Element Number	R(ei)	Q(ei)	T(ei)
Step one: extract S11
S1	S1, S3, S4, S5, S6, S7, S8, S9, S11	S1	S1
S2	S2, S3, S4, S5, S6, S7, S8, S9, S11	S2	S2
S3	S3, S4, S6, S7, S8, S11	S1, S2, S3, S5, S9, S10, S12, S13, S14, S15	S3
S4	S4, S6, S7, S8, S11	S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, S12, S13, S14, S15, S16	S4, S6, S7, S8
S5	S3, S4, S5, S6, S7, S8, S9, S11	S1, S2, S5, S12, S13, S14	S5
S6	S4, S6, S7, S8, S11	S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, S12, S13, S14, S15, S16	S4, S6, S7, S8
S7	S4, S6, S7, S8, S11	S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, S12, S13, S14, S15, S16	S4, S6, S7, S8
S8	S4, S6, S7, S8, S11	S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, S12, S13, S14, S15, S16	S4, S6, S7, S8
S9	S3, S4, S6, S7, S8, S9, S11	S1, S2, S5, S9, S10, S12, S13, S14	S9
S10	S3, S4, S6, S7, S8, S9, S10, S11	S10	S10
S11	S11	S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, S12, S11, S13, S14, S15, S16	S11
S12	S3, S4, S5, S6, S7, S8, S9, S11, S12	S12, S13	S12
S13	S3, S4, S5, S6, S7, S8, S9, S11, S12, S13, S15, S16	S13	S13
S14	S3, S4, S5, S6, S7, S8, S9, S11, S14	S14	S14
S15	S3, S4, S6, S7, S8, S11, S15	S13, S15	S15
S16	S4, S6, S7, S8, S11, S16	S13, S16	S16
Step two: extract S4, S6, S7 and S8
S1	S1, S3, S4, S5, S6, S7, S8, S9	S1	S1
S2	S2, S3, S4, S5, S6, S7, S8, S9	S2	S2
S3	S3, S4, S6, S7, S8	S1, S2, S3, S5, S9, S10, S12, S13, S14, S15	S3
S4	S4, S6, S7, S8	S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, S12, S13, S14, S15, S16	S4, S6, S7, S8
S5	S3, S4, S5, S6, S7, S8, S9	S1, S2, S5, S12, S13, S14	S5
S6	S4, S6, S7, S8	S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, S12, S13, S14, S15, S16	S4, S6, S7, S8
S7	S4, S6, S7, S8	S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, S12, S13, S14, S15, S16	S4, S6, S7, S8
S8	S4, S6, S7, S8	S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, S12, S13, S14, S15, S16	S4, S6, S7, S8
S9	S3, S4, S6, S7, S8, S9	S1, S2, S5, S9, S10, S12, S13, S14	S9
S10	S3, S4, S6, S7, S8, S9, S10	S10	S10
S12	S3, S4, S5, S6, S7, S8, S9, S12	S12, S13	S12
S13	S3, S4, S5, S6, S7, S8, S9, S12, S13, S15, S16	S13	S13
S14	S3, S4, S5, S6, S7, S8, S9, S14	S14	S14
S15	S3, S4, S6, S7, S8, S15	S13, S15	S15
S16	S4, S6, S7, S8, S16	S13, S16	S16
Step three: extract S3 and S16
S1	S1, S3, S5, S9	S1	S1
S2	S2, S3, S5, S9	S2	S2
S3	S3	S1, S2, S3, S5, S9, S10, S12, S13, S14, S15	S3
S5	S3, S5, S9	S1, S2, S5, S12, S13, S14	S5
S9	S3, S9	S1, S2, S5, S9, S10, S12, S13, S14	S9
S10	S3, S9, S10	S10	S10
S12	S3, S5, S9, S12	S12, S13	S12
S13	S3, S5, S9, S12, S13, S15, S16	S13	S13
S14	S3, S5, S9, S14	S14	S14
S15	S3, S15	S13, S15	S15
S16	S16	S13, S16	S16
Step four: extract S9 and S15
S1	S1, S5, S9	S1	S1
S2	S2, S5, S9	S2	S2
S5	S5, S9	S1, S2, S5, S12, S13, S14	S5
S9	S9	S1, S2, S5, S9, S10, S12, S13, S14	S9
S10	S9, S10	S10	S10
S12	S5, S9, S12	S12, S13	S12
S13	S5, S9, S12, S13, S15	S13	S13
S14	S5, S9, S14	S14	S14
S15	S15	S13, S15	S15
Step five: extract S5 and S10
S1	S1, S5	S1	S1
S2	S2, S5	S2	S2
S5	S5	S1, S2, S5, S12, S13, S14	S5
S10	S10	S10	S10
S12	S5, S12	S12, S13	S12
S13	S5, S12, S13	S13	S13
S14	S5, S14	S14	S14
Step six: extract S1, S2, S12, S14 and S10
S1	S1	S1	S1
S2	S2	S2	S2
S12	S12	S12, S13	S12
S13	S12, S13	S13	S13
S14	S14	S14	S14
Step seven: extract S13
S13	S13	S13	S13

.

After the above seven-step hierarchical extraction process, the hierarchical extraction results are shown in

Table 5. The final hierarchy.

Hierarchy Number	Elements
1	S11
2	S4, S6, S7, S8
3	S3, S16
4	S9, S15
5	S5, S10
6	S1, S2, S12, S14
7	S13

.

Through the work of the hierarchical extraction stage, the system elements are divided into seven layers. Based on the hierarchical extraction principle of result priority, for the system of symbiotic development of agricultural enterprise clusters and logistics industry, S₁₁ is at the lowest level in the final extraction result, indicating that the effect of symbiotic development is the factor in the final result position; S₁₃ is at the highest level, indicating that macro policy orientation is the factor in the initial cause position.

4.2.5. Calculation of General Skeleton Matrix

On the basis of hierarchical extraction, the general skeleton matrix is calculated to clarify the interaction relationship between system elements in each layer. By analyzing the reachability matrix R, it can be seen that each column of S₄, S₆, S₇, and S₈ is the same as each row. Therefore, the reduced point reachability matrix R’ is shown in

Table 6. Reduced point reachability matrix R’.

[aij]13 × 13	S1	S2	S3	S4	S5	S9	S10	S11	S12	S13	S14	S15	S16
S1	1	0	1	1	1	1	0	1	0	0	0	0	0
S2	0	1	1	1	1	1	0	1	0	0	0	0	0
S3	0	0	1	1	0	0	0	1	0	0	0	0	0
S4	0	0	0	1	0	0	0	1	0	0	0	0	0
S5	0	0	1	1	1	1	0	1	0	0	0	0	0
S9	0	0	1	1	0	1	0	1	0	0	0	0	0
S10	0	0	1	1	0	1	1	1	0	0	0	0	0
S11	0	0	0	0	0	0	0	1	0	0	0	0	0
S12	0	0	1	1	1	1	0	1	1	0	0	0	0
S13	0	0	1	1	1	1	0	1	1	1	0	1	1
S14	0	0	1	1	1	1	0	1	0	0	1	0	0
S15	0	0	1	1	0	0	0	1	0	0	0	1	0
S16	0	0	0	1	0	0	0	1	0	0	0	0	1

.

Based on the reduced point reachable matrix R’, the reduced edge reachable matrix s’ can be obtained by further reduced point operation. The operation rule for solving the reduced edge reachable matrix S’ through the reduced point operation is: S’ = R’ − (R’ − I)² − I, and the obtained reduced edge reachable matrix S’ is shown in

Table 7. Reduced edge reachability matrix S’.

[aij]13 × 13	S1	S2	S3	S4	S5	S9	S10	S11	S12	S13	S14	S15	S16
S1	0	0	0	0	1	0	0	0	0	0	0	0	0
S2	0	0	0	0	1	0	0	0	0	0	0	0	0
S3	0	0	0	0	0	0	0	0	0	0	0	0	0
S4	0	0	0	0	0	0	0	1	0	0	0	0	0
S5	0	0	0	0	0	1	0	0	0	0	0	0	0
S9	0	0	1	0	0	0	0	0	0	0	0	0	0
S10	0	0	0	0	0	1	0	0	0	0	0	0	0
S11	0	0	0	0	0	0	0	0	0	0	0	0	0
S12	0	0	0	0	1	0	0	0	0	0	0	0	0
S13	0	0	0	0	0	0	0	0	1	0	0	1	1
S14	0	0	0	0	1	0	0	0	0	0	0	0	0
S15	0	0	1	0	0	0	0	0	0	0	0	0	0
S16	0	0	0	0	0	0	0	0	0	0	0	0	0

.

On this basis, the general skeleton matrix S can be obtained by substituting the simplest daisy chain loop into the reachability matrix R, as shown in

Table 8. General skeleton matrix S.

[aij]16 × 16	S1	S2	S3	S4	S5	S6	S7	S8	S9	S10	S11	S12	S13	S14	S15	S16
S1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
S2	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
S3	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
S4	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0
S5	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
S6	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
S7	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
S8	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
S9	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0
S10	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
S11	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
S12	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
S13	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	1
S14	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
S15	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0
S16	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0

.

The symbiotic development system of agricultural enterprise clusters and logistics industry is stratified by hierarchical extraction. The general skeleton matrix S gives the directional relationship between 16 system elements. On this basis, the hierarchical topology of the elements of the symbiotic development system of agricultural enterprise clusters and logistics industry can be drawn; the hierarchy diagram (Figure 2) is as follows:

The system model of sustainable and symbiotic development between organic agriculture and the logistics industry was drawn as a directed graph (Figure 3) based on the hierarchical graph.

Through the analysis of the driving model of the symbiotic development system of agricultural enterprise clusters and logistics industry, it can be seen that there is a loop in the system, that is, the logistics supply quality, enterprise market value, enterprise marketing benefit, and logistics demand scale constitute a positive cycle-driving loop (hereinafter referred to as the driving loop). By analyzing this drive circuit, the following conclusions can be drawn:

Firstly, the four driving factors that constitute the driving loop are closely related and have a cause and effect cycle. The realization and improvement of logistics supply quality can promote the improvement of enterprise market value; the promotion of enterprise market value can promote the realization and increase of enterprise marketing benefits; the increase of enterprise marketing benefit can promote the continuous expansion of enterprise logistics demand scale; the expansion of logistics demand scale inevitably requires the stability and improvement of logistics supply quality.

Secondly, in the previous analysis of the symbiotic relationship between agricultural enterprise clusters and logistics industry, it is considered that the mutual matching between the high-quality logistics demand of agricultural enterprise clusters and the high-quality logistics supply of the logistics industry are the basis for the formation and continuous development of their symbiotic relationship. In the system-driven model of symbiotic development of agricultural enterprise clusters and logistics industry constructed by grounded theoretical analysis and interpretative structure model, the scale of logistics demand and the quality of logistics supply form a causal cycle and promote each other. The conclusion of the theoretical analysis is consistent with the results of the actual research, which not only explain the scientificity and correctness of the theoretical analysis conclusion, but also explain the rationality and accuracy of the actual research results, and also reflects the rationality and scientificity of the driving model of the symbiotic development system of agricultural enterprise clusters and the logistics industry to a certain extent.

Then, in the driving model of the symbiotic development system of agricultural enterprise clusters and logistics industry, this driving loop is in a very key position. The next link of the driving loop is the effect of symbiotic development. It can be seen that this driving loop is the key to realize the symbiotic development of agricultural enterprise clusters and logistics industry. On the one hand, the symbiotic relationship between agricultural enterprise clusters and logistics industry is formed through high-quality logistics demand and high-quality logistics supply; the realization of enterprise operation benefits and the improvement of enterprise market value will inevitably promote the improvement of logistics demand and logistics supply in quantity and quality. On the other hand, the improvement of the quantity and quality of logistics demand and logistics supply will inevitably promote enterprises to obtain more operational benefits and increase the market value of enterprises. At the same time, the virtuous cycle growth of enterprise business volume, profit value, and value degree formed on the basis of the mutual matching of logistics demand and logistics supply, will inevitably show and enhance the effectiveness of the symbiotic development of agricultural enterprise clusters and the logistics industry, so as to promote the continuous development of the symbiotic relationship between agricultural enterprise clusters and the logistics industry.

Finally, because these four driving factors constitute a positive cycle driving circuit, this driving circuit can be regarded as a whole, playing the driving role of four driving factors: logistics supply quality, enterprise market value, enterprise marketing profit, and logistics demand scale. For other driving factors, any node reaching the driving circuit in the driving path can be regarded as acting with the whole driving circuit.

4.3. Driving Countermeasure Analysis

Through the analysis of the driving model of the symbiotic development system of agricultural industrial clusters and logistics industry, seven driving paths can be obtained. According to the trend of driving paths, the driving paths are explained one by one, and the corresponding theoretical enlightenment is analyzed at the same time. The specific process is as follows.

4.3.1. Drive Path 1

Driving path 1 direction: macro policy orientation → market quality supervision → enterprise quality control → driving loop → symbiotic development effect.

Explanation of driving path 1: The national macro policy supports the symbiotic development of agricultural industrial clusters and logistics industry. The quality of characteristic agricultural products and logistics service quality play an important role in promoting the symbiotic development of both. It is required to strengthen the quality monitoring and guarantee of relevant products and business activities. Under the guidance of macro policy guidance, relevant government departments strengthen market quality supervision; ensure the quality of production and business activities related to production, processing, and sales of agricultural industrial clusters; and strengthen standard management on warehousing, transportation, distribution, and other links involved in the logistics service process of the logistics industry. Under the guidance of national policies and the regulatory actions of government departments, agricultural industrial clusters and logistics industry attach great importance to quality management and control, so as to effectively ensure the quality of logistics supply. It has promoted the continuous improvement of the market value of enterprises of both sides and ensured the continuous performance of symbiotic development.

Driving path 1 enlightenment: Quality is the lifeline of enterprises. Whether it is agricultural enterprise clusters or the logistics industry, we should strengthen quality management in the process of production and operation activities. Only excellent product quality and excellent service quality can realize and ensure the market value of enterprises and realize the continuous growth of enterprise marketing profits, so as to promote the symbiotic development of agricultural enterprise clusters and the logistics industry to a higher and better state while realizing the effectiveness of symbiotic development.

4.3.2. Drive Path 2

Driving path 2 direction: macro policy orientation → symbiotic development cognition → operation cost investment → technology R&D and innovation → enterprise quality control → driving loop → symbiotic development effect.

Explanation of driving path 2: The national macro policy pays attention to and encourages the symbiotic development of agricultural industrial clusters and the logistics industry, and has issued relevant incentives and guarantee measures. Under the dual incentives of macro policy guidance and enterprise development objectives, both sides of symbiotic development gradually deepen their understanding of symbiotic development and establish and consolidate the concept and confidence of symbiotic development. On this basis, the enterprises of both sides have increased their investment in infrastructure construction and upgrading of production and operation equipment, promoted the continuous improvement of the enterprise’s technology R&D and innovation ability, and brought the informatization and intelligence of production and management. The continuous improvement of the informatization and intelligence level of enterprise production and management has effectively promoted the monitoring and guarantee level of enterprise product quality and operation service quality. Thus, it promotes the continuous improvement of enterprise operation quality and product quality, realizes the enterprise market value, and effectively promotes the continuous development of double engine symbiosis.

Enlightenment from driving path 2: The symbiotic development of agricultural enterprise clusters and logistics industry is inseparable from the support of faith and confidence. Both enterprises should have a firm attitude towards symbiotic development, whether from the needs of their own development or from the needs of market competition, so as to promote the common development of both sides through the form of symbiotic development under the guidance of national policies, and to achieve a win-win situation. At the same time, it can be found that the basic conditions and hardware facilities of enterprise production and operation can be continuously improved through certain cost investment. In particular, the enterprise’s product R&D and technological innovation also need capital and resource investment to improve the level and obtain results.

In addition, we should pay attention to the driving effect of technology R&D and innovation on enterprise development. Whether it is the intensive processing of characteristic agricultural products of agricultural enterprise clusters or the cold chain logistics distribution of the logistics industry, we need to improve and progress in continuous technological innovation.

4.3.3. Drive Path 3

Driving path 3 direction: market management service → driving loop → symbiotic development effect.

Explanation of driving path 3: Through reliable product quality and service quality, the benign market competition of dual engine enterprises is promoted and the improvement of enterprise marketing benefits is realized. The continuous development and expansion of the enterprise production scale and the increasing business volume of logistics demand have brought about the continuous improvement of logistics service quality. In this process, the market value of agricultural industrial clusters and the logistics industry is realized, which promotes the continuous improvement of their symbiotic development.

Driving path 3 enlightenment: The characteristic agricultural products of agricultural enterprise clusters should be sold in the market environment, and the logistics services of the logistics industry should also be carried out in the market environment. A good, orderly, and fair competition market environment is a foundation for the continuous development of enterprises. Market supervision departments should attach great importance to the construction of a good market environment. Enterprises of both sides should also participate in market competition fairly and in accordance with regulations, and obtain reputation, win the market, and realize benefits with excellent product quality and reliable service.

4.3.4. Drive Paths 4, 5, and 6

Driving path 4 direction: operation location conditions → operation cost investment → technology R&D and innovation → enterprise quality control → driving loop → symbiotic development effect.

Driving path 5 direction: industrial geographical environment → operation cost investment → technology R&D and innovation → enterprise quality control → driving loop → symbiotic development effect.

Driving path 6 direction: implementation of policies and measures → operation cost investment → technology R&D and innovation → enterprise quality control → driving loop → symbiotic development effect.

Explanation of driving path: except for the difference in the first link, the subsequent driving paths of driving paths 4, 5, and 6 are the same, so they are merged and explained. The operation location conditions of the logistics industry are closely related to the operation cost. The convenient transportation conditions of storage facilities and transfer hubs affect the operation cost investment of logistics enterprises to a considerable extent. To some extent, the geographical environment of agricultural industrial clusters affects the adaptability and satisfaction of the production and processing scale as well as product quality of characteristic agricultural products, and also has an important impact on the operation cost investment of agricultural industrial clusters. The accuracy and effectiveness of the implementation of specific government measures, such as land transfer policies, preferential tax policies, and financial support policies, have an important impact on the operation cost investment of agricultural enterprises and logistics enterprises.

Enlightenment of the driving path: The natural environment where enterprises are located has an important impact on the operation of enterprises. The development of agricultural enterprise clusters is based on regional characteristic agricultural product resources. Whether planting or breeding, the production of raw materials is affected by natural environmental factors. Therefore, the construction site selection planning of raw material origin and processing plant should be scientific and reasonable. We should pay attention to the planning and design of the construction of the logistics distribution system. The logistics distribution system of the logistics industry is greatly affected by regions, and the foundation and conditions of urban and rural logistics infrastructure are inconsistent. We should plan and design the logistics system according to local conditions. Rural road conditions, location of transit stations, urban traffic conditions, and the location of storage facilities should be planned and constructed scientifically and reasonably. The implementation of policies and measures is an important driving factor for the symbiotic development of agricultural enterprise clusters and the logistics industry. The symbiotic development of enterprises of both sides is inseparable from the support of government policies and the directional guidance of national macro policies. It is more necessary for government departments at all levels to introduce specific support and incentive measures according to local conditions and implement them in combination with local conditions, so as to promote the symbiotic development of agricultural enterprise clusters and the logistics industry.

4.3.5. Drive Path 7

Driving path: talent training measures → technology R&D and innovation → enterprise quality control → driving loop → symbiotic development results.

Explanation of driving path 7: agricultural industrial clusters and the logistics industry attach great importance to professional talent training and the formulation and implementation of relevant measures. Professionals are not only the main force of technology R&D and innovation, but also the driving force to promote the improvement of technological level and the progress of innovation achievements. On the basis of the continuous improvement of the enterprise’s technical ability, the enterprise’s quality control level can also be continuously improved, so as to ensure the product quality and service quality, realize the product value and brand value of both enterprises, continuously improve the market competitiveness, and finally promote the symbiotic development of agricultural enterprise clusters and the logistics industry.

Driving path 7 enlightenment: Science and technology is the primary productive force, and talents are the power source of science and technology. Both agricultural enterprises and the logistics industry should pay attention to the cultivation of professional talents, so as to realize the continuous improvement of an enterprise’s technical level and make enterprises adapt to the intelligent development requirements of the information age, so as to promote the symbiotic development of both sides and achieve a win-win situation.

5. Conclusions

This study analyzed the necessity and feasibility of the symbiotic development of agricultural industrial clusters and the logistics industry, together with the theories on the specific content of the symbiotic development of the two. Firstly, using the ISM method, combined with the analysis of the influencing factors on the symbiotic development of agricultural industrial clusters and the logistics industry, this paper revealed the influencing relationship between the 16 important qualities. Later, the hierarchical topology map was drawn by calculating the reachability matrix and hierarchical extraction, and the construction of a symbiotic development driving model for agricultural enterprise clusters and the logistics industry was completed. In addition, through the driving factors, it wass confirmed that the macro policy orientation is the fundamental driving force for the symbiotic development of agricultural enterprise clusters and logistics industry, and the symbiotic development effect is its final result. Finally, through the analysis of the driving mode of the symbiotic development system of agricultural industrial clusters and agricultural logistics industry, seven driving paths were extracted, and the driving paths of the symbiotic development of agricultural industrial clusters and agricultural logistics industry were expounded one-by-one from three aspects: trend, interpretation, and enlightenment.

The existing research has a lack of profound disclosure on the internal mechanism of the symbiotic development of agricultural industrial clusters and the agricultural logistics industry, and quantitative research on the evolution of symbiotic development, so it is difficult to form specific and implementable driving countermeasures. The research results not only make a theoretical contribution to the development of the symbiosis research of agricultural industrial clusters and the agricultural logistics industry, but also provide a contribution to the formulation of policies for the development of agricultural clusters and the agricultural logistics industry by local governments.