Economic Management Analysis and Modeling of Rural Economic
Development based on Fuzzy Mathematics Theory
LIANG YIN, MENGZE ZHANG*
Department of Economics,
Sejong University,
Sejong University, 209, Neungdong-ro, Gwangjin-gu, Seoul,
SOUTH KOREA
*Corresponding Author
Abstract: - The importance of agricultural production has gradually increased, and the requirements for
agricultural economic development have become more and more refined. Agricultural economic management
and rural economic development as a complex giant system, and how the two promote each other are related.
Based on the theory of fuzzy mathematics, the interaction between them can be well analyzed and modeled, and
the key factors can be identified. Through scientific agricultural economic management, production factors such
as rural land, water resources, labor, and funds can be reasonably allocated, improving resource utilization
efficiency, reducing waste, and laying a solid foundation for rural economic development. Encourage the
development of modern agricultural technologies, such as smart agriculture, green agriculture, and circular
agriculture, to promote the transformation of the agricultural industry structure from traditional to modern,
enhance the added value of agricultural products, and strengthen market competitiveness. Establishing a sound
agricultural economic management system, including market information monitoring, natural disaster warning,
and response mechanisms, can help farmers respond to market fluctuations and natural risks promptly, ensuring
stable agricultural production.
Key-Words: - fuzzy mathematics, rural economics, agricultural management, conversion index, management
strategies, sustainable development.
Received: April 23, 2024. Revised: October 23, 2024. Accepted: November 24, 2024. Published: December 31, 2024.
1 Introduction
Only by deeply understanding such impact and
optimizing agricultural economic management, can
we really effectively promote rural economic
development. Agricultural economic management is
essentially a social function. At the same time, it
emphasizes government leadership and management,
applies modern agricultural science and economic
principles, and on the basis of following objective
laws, organizes, leads, makes decisions, and
encourages relevant groups and personnel to achieve
the agricultural development goals. For rural
economic development, the development guarantee
provided by agricultural economic management also
needs to be paid attention. This guarantee is mainly
reflected in pointing out the development direction
and standardizing rural economic behavior. The
effect of rural economic development can be
improved accordingly, [1]. Introduce advanced
agricultural technologies and equipment, such as
intelligent irrigation systems, drone spraying,
precision agriculture technology, etc., to reduce labor
costs, and improve production efficiency and crop
yields. At the same time, optimize the planting
structure through technological means and select
high-yield and high-quality varieties that meet
market demand, [2]. The low comprehensive quality
of some management personnel, and the difficulty in
adapting management concepts and strategies to the
requirements of the development of the times.
As the premise of China's rural economy, a
market economy, combined with the agricultural
economic management practice carried out in
various parts of China in recent years, is not difficult
to find that the theoretical guidance it provides plays
an important role. Through an in-depth analysis of
the operating mechanism of the market economy, it is
not difficult to find that the vigorous development of
China's market economy is built on the solid
foundation of active participation and widespread
support from the broad masses of the people. This
kind of enthusiasm for national participation is like
an inexhaustible source of power, which not only
directly promotes high-speed economic growth, but
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also profoundly affects the optimization of social
structure and the significant improvement of public
welfare. In the vast rural world, as the main force of
economic development, the enthusiasm and
creativity of farmers have a decisive significance in
promoting the comprehensive revitalization of
China's rural economy. In this context, the
importance of agricultural economic management as
a bridge connecting agricultural production and
market demand has become increasingly prominent.
It can not only become a key force in stimulating
farmers' enthusiasm and guiding the optimization of
agricultural resource allocation but also play an
irreplaceable role in establishing the strategic
positioning of rural economic development and
consolidating the dominant position of rural market
economy. Under effective guidance, the level and
quality of rural economic development will continue
to improve, and the Rural Revitalization Strategy
will be better implemented, [3].
Agricultural economic management integrates
the theoretical essence of multiple disciplines such as
economics, management, and ecology, providing
scientific theoretical guidance for rural economic
development. These plans and policies are not only
forward-looking and targeted but also effective in
addressing various challenges and problems
encountered in rural economic development, leading
the rural economy towards a healthier and more
sustainable direction. Agricultural economic
management not only improves the quality and safety
of agricultural products but also reduces
environmental pollution and damage caused by
agricultural production. At the same time,
agricultural economic management also focuses on
the improvement of farmland and the development of
circular agriculture. Through scientific and
reasonable rotation, intercropping, and
intercropping, the circular utilization of agricultural
resources and the virtuous cycle of farmland
ecosystems have been achieved. This green
transformation not only promotes the improvement
of agricultural production level but also lays a solid
foundation for the sustainable development of rural
economy. In management practice, relevant
personnel and departments will conduct in-depth
research on the laws of agricultural economic
development, analyze market dynamics and
consumer demand, and based on this, formulate
scientific and reasonable management plans and
policy measures. Driven by agricultural economic
management, the rural economy is gradually
transitioning from traditional models to green
ecological models. By strengthening the supervision
and control of pesticide and fertilizer use, and
promoting green production methods such as organic
agriculture and ecological agriculture, [4].
Given this complexity, traditional precise
mathematical methods are inadequate in handling
information related to agricultural economic
management and rural economic development, as
they often struggle to capture and express vague,
uncertain, or subjective factors. Fuzzy mathematics,
as an emerging branch in the field of mathematics,
has developed to address such fuzzy phenomena and
concepts. Fuzzy mathematics, by introducing
concepts such as fuzzy sets and fuzzy logic, enables
mathematics to be more flexibly applied to describe
and handle practical problems with fuzziness, greatly
expanding the application boundaries of
mathematics. Therefore, introducing fuzzy
mathematics into the modeling process of
agricultural economic management and rural
economic development is not only an innovation and
supplement to traditional management methods but
also an inevitable choice to adapt to the
characteristics of complex economic systems.
Through fuzzy mathematics, we can more
comprehensively and accurately characterize the
fuzzy information in the agricultural economic
system, such as the uncertainty of market demand,
differences in agricultural production conditions, and
lagging policy effects, thus providing strong support
for formulating scientific and reasonable agricultural
economic management strategies.
At present, there are many theoretical analyses of
the two, but there are few empirical studies, and
qualitative analysis is more than quantitative
analysis. How to model and analyze agricultural
economic management and rural economic
development, and put forward targeted suggestions,
have reference significance and reference
significance for realizing the sustainable
development of rural economy and theoretical
breakthrough in China.
The innovation of this article lies in the first
application of fuzzy mathematics theory to the
analysis and modeling of rural economic
development and agricultural economic
management, providing a more accurate and in-depth
description of the interaction between the two;
Secondly, through empirical research, this article
identifies the key factors that affect the mutual
promotion of rural economic development and
agricultural economic management, providing an
important basis for formulating scientific and
reasonable policies and management strategies;
Finally, this article constructs a comprehensive
agricultural economic management system, which
not only covers market information monitoring,
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natural disaster warning and response mechanisms,
but also emphasizes the promotion and application of
modern agricultural technologies such as smart
agriculture, green agriculture, and circular
agriculture, providing strong support for the
sustainable development of rural economy.
2 Overview
Building a scientific and rational agricultural
economic management mechanism is the key to
ensuring the sustainable development of the rural
economy. This mechanism should closely align with
the unique situation of China's agricultural
development, reflecting both the deep
implementation of national agricultural support
policies and the emphasis on improving the
efficiency of rural resource utilization and promoting
the optimal allocation of resources. At the same time,
by implementing regional agricultural planning,
clarifying development goals, guiding farmers and
agricultural enterprises to develop in an orderly
manner, and forming industrial agglomeration
effects, the overall level of the rural economy can be
promoted. In the field of academic research, the issue
of agricultural economic management and rural
economic development has always been of great
concern. Many scholars at home and abroad have
conducted in-depth explorations in this field from
different dimensions and perspectives. Among them,
the research of scholars such as [5] is particularly
noteworthy. They innovatively introduced the fuzzy
linear programming model into the practice of
agricultural economic management and verified the
effectiveness of the model in predicting the resilience
of agricultural economic management through
empirical analysis. This study not only enriches the
theoretical system of agricultural economic
management but also provides powerful tool support
for decision-making in practice. [6], studied the
impact of the perfection of the supply chain of fresh
tomatoes in Argentina on the economic development
of local communities. They found that the unfairness
among farmers was related to the information
distribution mode and the concentration mode. The
uncertainty of the model considered the unfairness
between farmers' income and gross profit rate in the
actual market transactions. With the acceleration of
agricultural modernization, rural economic
management is also facing unprecedented
challenges, especially with financial and ecological
environmental risks becoming increasingly
prominent. The prevention of financial risks requires
the establishment of a sound rural financial system,
enhancing the self-generating ability of the rural
economy, and strengthening financial supervision to
prevent the occurrence of systemic risks, [7].
Ecological and environmental risks require us to pay
more attention to ecological balance and
environmental protection while pursuing economic
benefits, promoting the development of green
agriculture, and achieving sustainable development
of agricultural production. In order to effectively
address these risks, a five-in-one risk response
strategy based on the theory of political, economic,
social, cultural, and ecological complex networks is
particularly important. This strategy emphasizes that
when formulating rural economic management
policies, the interaction and influence between
various factors should be fully considered to form a
synergistic effect, thereby reducing the overall risk
level, [8]. For example, through policy guidance,
promoting the integrated development of agriculture,
tourism, culture, and other industries can not only
enrich the rural industrial structure but also enhance
the rural economy's ability to resist risks. In addition,
the rational utilization and protection of rural land
resources is also an important part of rural economic
management, [9]. The phenomenon of abandoned
rural land and conversion of agricultural land not
only affects the stability of agricultural production
but also exacerbates the pressure on the ecological
environment. Therefore, strengthening rural land
management, promoting land system reform, and
establishing a sound land transfer mechanism are of
great significance for promoting agricultural
economic management. At the same time, the
deepening of rural legal reform also helps to better
control land use changes, safeguard the legitimate
rights and interests of farmers, and promote the
harmony and stability of rural society, [10].
The climate environment has a great impact on
agricultural production, so there is many researches
on climate environment management. For example,
[11] studied the relationship between agricultural
production and flood control reservoirs in Slovenia.
The results show that in areas suffering from floods
and droughts for a long time, the absence of flood
control reservoirs will reduce the crop output in rural
areas by 520 hectares, and the economic loss will
reach 1.7 million euros, and the impact of such loss
will last for 3 to 5 years. Research has shown that
expanding the area of water allocation for
agricultural production can indeed bring significant
production benefits to irrigation-dominated
agricultural regions in the short term. Increasing
irrigation water supply can ensure sufficient water
supply for crops, improve yields, increase farmers'
income, and stimulate agricultural economic growth.
However, the long-term implementation of this
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strategy faces many challenges and potential risks.
Expanding the allocation of water for agricultural
production may exacerbate water resource
competition and conflicts between regions. In arid
regions, water resources are already limited, and the
competition for water resources is particularly fierce
among different regions and industries. When a
region increases water use to expand agricultural
production, it may encroach on the water share of
other regions or industries, causing conflicts and
tensions between regions and affecting social
stability. Overexploitation of agricultural production
water may also have adverse effects on the ecological
environment. The ecological environment in arid
areas is inherently fragile, and excessive reliance on
irrigation may lead to ecological problems such as a
decrease in groundwater levels, wetland drying up,
and reduced river flow, which in turn can affect the
balance and stability of the entire ecosystem. The
deterioration of the ecological environment will in
turn constrain the sustainable development of
agricultural production, forming a vicious cycle,
[12]. Similarly, there is a relationship between power
resource management and rural economic
development. As an important factor of production, it
has an important impact on rural economic
development, [13]. Some regions have carried out
ecological certification to promote the sustainable
development of the rural economy. For example,
through the research on Rwandan coffee producers,
[14] found that rural managers have extended the
agricultural value chain through the certification of
the tropical forest alliance, which uses inverse
probability-weighted regression adjustment to reveal
the relationship between the certification of the
tropical forest alliance and economic sustainability.
Similarly, the research in Canada also has similar
conclusions. For example, the research of [15] found
that the strengthening of environmental management
by departments is conducive to the intensification
and sustainability of agricultural production. When
managing agricultural water resources in arid
regions, it is necessary to adopt more scientific,
rational, and sustainable strategies. On the one hand,
it is necessary to strengthen the efficient utilization
and conservation management of water resources,
and reduce the water demand for agricultural
production through measures such as promoting
water-saving irrigation technology and optimizing
planting structure; On the other hand, it is necessary
to strengthen the coordination and cooperation of
water resources between regions, establish
reasonable water rights allocation and water resource
trading mechanisms, and alleviate water resource
competition and conflicts between regions. At the
same time, attention should also be paid to the
protection and restoration of the ecological
environment, ensuring the coordinated development
of agricultural production and the ecological
environment, [16].
Fuzzy mathematics theory has been widely
discussed in many fields since it came into being. It is
of great significance and profound theoretical value
to use fuzzy mathematics theory to discuss economic
problems and solutions, [17], [18]. It has a set of
fuzzy system theories, which reveals the essence of
fuzzy phenomena and the due laws by mathematical
methods. Fuzzy mathematics has made concrete
breakthroughs in medicine, economic management,
psychology, meteorology, environment, and biology.
The research results found that an app with a quick
start, fast information processing, and consideration
of the psychological factors of the elderly has the
most advantages, [19]. For example, [20] used fuzzy
mathematics theory to test software quality
management. The research results show that the
software quality evaluation system based on fuzzy
mathematics theory can avoid the appearance of local
maxima and improve the accuracy of software
quality evaluation. [21] used the fuzzy mathematics
theory to select the best stock. The method based on
the fuzzy mathematics theory and the random forest
model can reduce the investment risk. The
investment strategy under the combination of the two
models has higher investment and stability. [22]
studied the effects of various threats and network
attacks on the optimization of network securities by
using fuzzy mathematics theory. This method is
more robust and robust than traditional methods. [23]
constructed the credit evaluation index system for
China's household agriculture and animal husbandry
based on the fuzzy mathematics theory and Bayesian
model and solved the actual problem of sample
imbalance under different default conditions. In
addition, fuzzy mathematics theory is also introduced
in higher education teaching evaluation. [24]
integrated fuzzy mathematics theory into machine
learning. When processing complex data and
distinguishing complex rules, they can effectively
simulate expert knowledge and experience, and then
effectively extract teaching quality factors. In the
field of psychological research, fuzzy mathematical
theory is widely used, such as the use of fuzzy
mathematical theory to study synchronicity and
non-local phenomena in clinical work, as well as
consumer sensitivity and preference modeling, [25],
[26].
Scholars from all over the world have used
various methods, which has provided a lot of
reference for this paper, but the above literature
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rarely introduce fuzzy mathematical theory into the
modeling and analysis of agricultural economic
management and rural economic development, [27].
The evaluation thinking process of the fuzzy
mathematics evaluation method is more in line with
the characteristics of human judgment and closer to
the actual situation, [28]. Therefore, this paper
introduces fuzzy mathematics theory into
agricultural economic management and rural
economic development, broadens the research ideas,
and aims at the deviation of results caused by unclear
definitions of concepts, goals, and methods in
agricultural management and rural economic
development. The use of fuzzy mathematics theory
can effectively solve this problem and provide a
reference for related research, [29].
3 Materials and Methods
3.1 Principles of Fuzzy Mathematical
Theory
Fuzzy mathematics theory has significant advantages
in dealing with problems with fuzziness, uncertainty,
and complexity. In agricultural economic
management and rural economic development
evaluation, many indicators (such as agricultural
product quality, ecological balance impact, etc.) are
often difficult to describe in precise mathematical
language and present a vague state. Therefore,
introducing fuzzy mathematics theory and utilizing
tools such as fuzzy sets, fuzzy relationships, and
fuzzy operations can more scientifically and
reasonably handle these fuzzy data, improving the
accuracy and reliability of evaluation. This study
aims to apply fuzzy mathematics theory to the
evaluation of agricultural economic management and
rural economic development. By constructing a
fuzzy comprehensive evaluation model, a
comprehensive evaluation of agricultural production
management, rural economic development, and other
aspects can be achieved. The development of fuzzy
decision models is a multi-step process aimed at
addressing decision-making problems with
uncertainty and fuzziness. Firstly, it is necessary to
clearly define the decision-making problem that
needs to be solved, including the background,
objectives, and constraints of the decision. Based on
the characteristics of the decision-making problem,
select relevant evaluation indicators or factors that
can reflect the key aspects of the problem. Collect
and evaluate data related to indicators from reliable
sources to ensure the accuracy and completeness of
the data. Clean and organize the collected data,
eliminate outliers or missing values, and may require
data conversion or standardization. Define a fuzzy
set for each evaluation indicator, which determines
the fuzzy member functions of input and output
variables, such as the "near", "medium", and "far"
distances, as well as the "left turn", "straight", and
"right turn" of turning actions. Choose appropriate
fuzzy membership functions to describe the
fuzziness of each evaluation indicator, common
membership functions include triangles, trapezoids,
Gaussian functions, etc. According to the fuzzy
evaluation matrix, determine the fuzzy positive ideal
value of each evaluation indicator, which is the set
composed of the maximum values of the fuzzy
indicators in all evaluation indicators. Similarly,
determine the fuzzy negative ideal value of each
evaluation indicator, which is the set composed of the
minimum values of fuzzy indicator values among all
evaluation indicators. The development of fuzzy
decision models is an iterative process that may
require multiple adjustments and optimizations to
achieve satisfactory results. Meanwhile, the accuracy
and reliability of the model depend on multiple
factors, including data accuracy, selection of
evaluation indicators, and determination of fuzzy
membership functions.
The weight set refers to the relative importance
of each evaluation indicator (or index) in the
evaluation process. Due to the varying degrees of
impact of different evaluation indicators on the
evaluation object, it is necessary to determine the
weights of each indicator reasonably based on the
actual situation. The determination of weights can be
done using various methods, such as expert scoring
method, analytic hierarchy process, etc. Through
scientific weight allocation, the objectivity and
impartiality of evaluation results can be ensured. The
index set indicates which aspects of the evaluation
object are evaluated and described, and is
characterized by a set of several factors and
secondary factors. The comment set is actually a
division of the change interval of the thing being
evaluated. The weight set is the influence of the
indicator set, and the appropriateness of the weight
selection is directly related to the success or failure of
the model.
11
1
11 1
1
1
n
n
m mn m m
n
XX
XX
XX
D
X X X X
XX











(1)
According to the hierarchical structure model,
the elements of each layer are based on the adjacent
elements of the previous layer, and the judgment
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matrix D is constructed according to the above
comparison scale.
1
n
i ij
j
MW
(2)
Multiply the elements of the judgment matrix D
by rows to obtain the product Mi of the elements of
each row.
n
ii
WM
(3)
Calculate the nth root of Mi.
(4)
Normalize the
W
vector:
max
1
ni
ii
DW
nW
(5)
Calculate the maximum eigenvalue of the
judgment matrix, i=1,2,..., n.
/
R I I
C C R
(6)
max /1
I
C n n
(7)
The judgment matrix is established by the
analyst based on personal knowledge and experience,
and there are inevitably errors. For quantitative
indicators, the membership function method is
usually used to determine their membership degree.
This method first requires the construction of a target
eigenvalue matrix, which consists of m quantitative
indicator values for n schemes (or objects).
Subsequently, based on the relationship between the
actual value of each indicator and the preset
threshold or standard, the membership degree of each
indicator value for different evaluation levels is
calculated through corresponding membership
functions (such as linear functions, nonlinear
functions, piecewise functions, etc.). The selection
and design of membership functions should be based
on actual situations and evaluation needs to ensure
the accuracy and rationality of the evaluation.
11 1
1
n
mn
m
yy
Y
yy


(8)
Quantitative indicators can be divided into two
categories: profitability indicators and consumption
indicators. For the profitability index, the bigger the
index, the better; For consumption indicators, the
smaller the indicator, the better. The target relative
membership matrix is obtained by normalizing it:
11 1
1
n
m mn
ij
rr
Rr
rr



(9)
Assume the target factor set of the system to be
compared in importance, and conduct the qualitative
arrangement of binary comparison on the importance
of the factors in the target factor set X, thereby
obtaining the binary comparison matrix:
11 1
1
n
m mn
ee
E
ee



(10)
From the evaluation matrix R and factor weight
W, the comprehensive evaluation of scheme set a can
be obtained as:
11 1
1
1 2 3
1 2 3
, , , ,
, , , ,
n
m mn
m
n
rr
B WR W W W W
rr
b b b b


(11)
3.2 Data Source and Processing
In the process of selecting indicators, targeted expert
consultation was conducted simultaneously, and
opinions and suggestions were sought from
authoritative experts in the field in the form of
seminars, telephone consultations, and
questionnaires.
This article further solicited the opinions and
suggestions of experts through telephone
consultation and questionnaire survey and optimized
and improved the indicator system. For quantifiable
indicators such as per capita net income, proportion
of secondary and tertiary industries, labor
productivity, etc., we mainly rely on statistical data
released by official institutions such as the National
Bureau of Statistics and local statistical bureaus.
These data have authority and credibility, and can
accurately reflect the actual situation of agricultural
economic management and rural economic
development. We conducted on-site research on
indicators that are difficult to obtain directly through
official statistical data, such as the number of
technology promotion institutions, the proportion of
scientific researchers, and the number of training
institutions. By visiting relevant institutions and
enterprises, we collected first-hand data to ensure the
authenticity and accuracy of the data.
After collecting the raw data, we conducted data
cleaning to remove duplicates, missing data, and
outliers, ensuring the integrity and consistency of the
data. Due to the different dimensions and ranges of
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values of different indicators, we standardized the
data by converting the values of each indicator to
dimensionless values for subsequent analysis and
comparison. After completing data cleaning and
standardization, we integrated the data of each
indicator to form a complete database, providing a
foundation for subsequent analysis and modeling. In
this paper, the demand side kinetic energy, the supply
side kinetic energy, and the conversion kinetic
energy are regarded as a composite system of
agricultural economic management and rural
economic development kinetic energy conversion
(Table 1, Appendix), which is recorded as Y. Y1, Y2,
and Y3 are regarded as demand side, supply side, and
conversion kinetic energy. The indicators of each
system sub-dimension are set as level 2 indicators
and level 3 indicators respectively, with a total of 14
level 4 indicators. The four-level indicators are all
positive indicators, that is, the better the agricultural
economic management, the more obvious the
promotion effect on the rural economic development,
and the conversion kinetic energy coefficient is
positively correlated with the two.
4 Results and Analysis
4.1 Agricultural Economic Management
Index
In agricultural economic management, many factors
are difficult to describe with precise numerical
values, such as soil fertility, crop growth status,
market demand, etc. Fuzzy sets can be used to
represent these fuzzy concepts, describing the degree
to which elements belong to a certain set through
membership functions. For example, a fuzzy set can
be constructed to represent the levels of soil fertility
and membership functions can be used to describe
the transitions and overlaps between different levels
of soil fertility. Fuzzy logic is a reasoning method
that deals with ambiguity and uncertainty. In
agricultural economic management, fuzzy logic can
be used to construct an inference system that infers
the most suitable agricultural economic management
strategy based on input information such as weather,
soil conditions, market demand, etc. For example, a
fuzzy reasoning system can be designed to
recommend the best irrigation strategy based on the
current weather conditions and crop growth stage.
From the perspective of the agricultural economic
management index (Figure 1, Appendix), in 2020,
the top three provinces with the highest scores were
Chongqing, Inner Mongolia, and Shaanxi, and the
lowest three provinces were Jiangxi, Guizhou, and
Henan. The agricultural economic management
index is closely related to the rural market
development demand space, market potential, and
rural economic vitality. Among them, Chongqing
and Shaanxi are major agricultural provinces, and
Inner Mongolia is also known as the upper reaches of
the Yangtze River. The historical basis, geographical
conditions and resource advantages of agricultural
development are constantly glowing with new
vitality. However, the lower three provinces,
especially Guizhou, are greatly restricted in
agricultural development due to natural reasons such
as terrain. In addition, the agricultural development
of Jiangxi and Henan is mainly based on the grain
industry. The development of the agricultural
industry in these areas is seriously affected by
national policies. Although they have a large amount
of arable land, their agricultural development is also
greatly restricted. As a famous granary of the
country, their agriculture is restricted to grain
production areas. A single agricultural production
system makes it difficult to promote the development
of the rural economy. At the same time, the industrial
development in these areas is relatively backward.
4.2 Rural Economic Development Index
According to the rural Development Index (Figure 2,
Appendix), in 2020, the top three provinces with the
highest scores were Jilin, Guangxi, and Guangdong,
and the lowest three provinces were Xinjiang, Hebei,
and Fujian. The rural economic development index is
related to the per capita net income, the number of
technology promotion institutions, and the level of
comprehensive mechanization, which reflects the
functional and organizational changes in
productivity. Among them, in recent years, Jilin has
vigorously implemented the action plan for
high-quality rural development and promoted the
construction of main functional areas at different
levels. High and new technologies have been widely
applied in the construction of characteristic
agricultural economies. The comprehensive
agricultural productivity has been significantly
improved, and the supply-side kinetic energy has
been orderly improved. The use of agricultural land
in Guangdong and Guangxi is changing to intensive
commercialization, and the combination of
agricultural industrialization continues to inject
vitality into the new driving force of the rural
economy. However, the agricultural production
conditions in Xinjiang, Hebei, and Fujian need to be
improved, the rural scientific and technological
personnel are relatively scarce, and the number of
migrant workers is large. There is still a certain gap
between talent, technology, and management
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knowledge and the national average level.
4.3 Agricultural Economic Management and
Calculation of Rural Economic
Comprehensive Index
The comprehensive index (Figure 3, Appendix) has
increased from 0.409 in 2010 to 0.605 in 2020 and
has leaped from the development stage to the
acceleration stage. From the perspective of
secondary indicators, the demand side kinetic energy
increased most significantly, followed by the supply
side kinetic energy. This shows that the changes in
the transformation of agricultural economic
management and rural economic development
kinetic energy are mainly reflected in the demand
side kinetic energy. In order to accelerate the
transformation of new and old kinetic energy in the
future, we must increase the cultivation of new
talents, help the formation of side supply-side kinetic
energy with talents, constantly improve the dual
financial structure of urban and rural areas, enhance
the level of financial service equalization, and
expand the radiation capacity of rural finance. It is
worth noting that the transformation of kinetic
energy is fluctuating and declining, and its
contribution to rural economic development is
negative. The agricultural modernization index
decreased from 0.214 in 2010 to 0.198 in 2020, and
the transformation and upgrading index decreased
from 0.372 in 2010 to 0.347 in 2020. First, the
national average level of agricultural modernization
has not reached the level of basically realizing
agricultural modernization, especially the per capita
net income and labor productivity need to be
improved. Second, in the process of transforming the
driving force of agricultural economic management
and rural economic development, there are still some
obstacles in ideological understanding, institutional
mechanisms, laws regulations, and policies. The
ecological sensitivity and comprehensive
mechanization level are under great pressure,
resulting in the application level and utilization
efficiency of agricultural mechanization at a low
level, which is incompatible with the transformation
of the driving force. Therefore, the current important
task is to make up for the shortcomings in the kinetic
energy of structural transformation and enhance the
driving force of industrial and product structure
change and upgrading.
4.4 Evaluation of Conversion Index in
Different Provinces
From the perspective of the conversion index (Figure
4, Appendix), from 2010 to 2020, 31 provinces have
passed the initial stage of conversion between
agricultural economic management and rural
economic development. By 2020, there were 20
provinces in the development stage, including Tibet,
Zhejiang, Hebei, Guangxi, Yunnan, Henan,
Chongqing, Hunan, Guizhou, Sichuan, Anhui,
Gansu, Hainan, Fujian, Guangdong, Jiangxi,
Shanghai, Shandong, Jiangsu and Hubei; There are
14 provinces in the acceleration stage, namely, Inner
Mongolia, Heilongjiang, Ningxia, Jilin, Xinjiang,
Tianjin, Beijing, Shanxi, Qinghai, Liaoning and
Shaanxi. In addition, the conversion index of
agricultural economic management and rural
economic development kinetic energy in different
provinces of China does not have the characteristics
of ups and downs but shows a gradual and
sustainable and steady upward trend. Most of the top
provinces in 2010-2015 are still in the forefront in
2015-2020. It can be seen from Figure 5 (Appendix)
that the conversion index of 31 provinces and cities is
quite different, and the difference between the
maximum value and the minimum value is 14.98.
From the perspective of interannual change, Guangxi
Province has the largest change range from 2010 to
2020, with a change value of 0.21. Shaanxi Province
has the smallest change range, with a change value of
0.0048. The main reason is that Guangxi Province
has made great progress in agricultural economic
management and rural economic development
through vigorously developing agricultural
technology promotion in recent years, while Shaanxi
Province has less investment in all aspects, which
makes its rural economic development slow.
4.5 Differences between the EAST, the
Middle, and the West
The accumulation of education, technology, and
talent in the eastern region has also injected a strong
impetus into agricultural economic management and
rural economic development. High-level educational
resources have cultivated a large number of
agricultural technology and management talents,
providing intellectual support for agricultural
technology innovation and the reform of
management models. At the same time, the eastern
region has also attracted a large amount of social
capital and foreign investment, providing solid
financial support for agricultural infrastructure
construction and rural environmental improvement.
The success of agricultural economic management in
the eastern region still relies on its strong policy
support and government guidance. The government
has effectively integrated various resource elements
and promoted the coordinated development of
agriculture and rural economy by formulating
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scientific and reasonable agricultural development
plans and policy measures. At the same time, the
government also focuses on strengthening the
protection and governance of the agricultural
ecological environment, achieving a win-win
situation between the agricultural economy and the
ecological environment. The eastern region has a flat
terrain, many rivers and lakes, abundant water, light,
and heat, and the agricultural production conditions
are optimal. The central and western regions have
regional differences. For example, the Sichuan Basin
and Guanzhong Plain have a rich history of
agricultural development and a vast economic
hinterland. Therefore, agricultural economic
management is quite superior to rural economic
development, and the difference with the eastern
region is small. However, on the whole, the
economic foundation and agricultural management
level in the central and western regions are inferior to
those in the East. The key to reducing regional
differences lies in developing regional characteristic
agriculture according to local conditions. For
example, Xinjiang uses irrigation water to develop
economic crops such as grapes and Hami melon,
which improves farmers' income, promotes the
development of rural areas, and even develops a large
number of agricultural production professional
cooperatives. Figure 6 (Appendix) is the marginal
box diagram of the structural transformation index in
the east, central, and western regions.
4.6 Countermeasures and Suggestions
In the current environment of new rural construction,
China attaches great importance to agriculture and
has invested more energy and time in developing the
agricultural economy. Agricultural economic
management is of great significance to the
organization and development of new rural
construction. However, there are some problems in
agricultural economic management that need to be
paid attention to and solved by relevant departments.
Firstly, strengthening infrastructure construction
and increasing capital and technology investment are
the core paths to enhance the comprehensive
agricultural production capacity. With the rapid
advancement of technology, the modernization
process of agriculture is accelerating. The application
of advanced technology has significantly improved
agricultural production efficiency, enhanced the
ability to resist natural disasters, stabilized
agricultural product yield and quality, and provided
strong support for increasing farmers' income. To
this end, we should continue to increase financial
support, guide social capital to tilt towards the
agricultural sector, focus on the research and
application of key agricultural technologies,
especially the popularization of cutting-edge
technologies such as intelligent agriculture and
precision agriculture, and empower agricultural
transformation and upgrading with technology. At
the same time, we will strengthen the construction of
infrastructure such as agricultural water conservancy,
rural roads, and cold chain logistics, build a sound
rural public service system, and lay a solid
foundation for rural economic development.
Secondly, continuously optimizing the rural
industrial structure and promoting the supply-side
structural reform of agriculture are important tasks
for agricultural economic management. Faced with
changes in market demand and constraints of
resource environment, it is necessary to continuously
adjust and optimize the agricultural industry
structure, and achieve optimal allocation and
efficient utilization of agricultural resources. This
includes promoting the diversification of crop
planting structures, developing characteristic
agriculture, green agriculture, and ecological
agriculture. Promote the integrated development of
agriculture with the secondary and tertiary industries,
extend the industrial chain, enhance the value chain,
and increase the added value of agriculture.
Therefore, in the period of new rural construction, we
should first focus on discussing some negative
situations in the agricultural industrial structure,
consider specific development phenomena, and
create rational, scientific, and highly practical
solutions to make the industrial structure more
planned. It brings important reference for the
efficient development of agricultural economic
management so that farmers can have enough food
and clothing.
The government should develop a policy
framework to support sustainable agricultural
practices, including providing financial subsidies,
tax incentives, and technical support. At the same
time, establish a cross-departmental collaboration
mechanism to ensure the synergy of agriculture,
environment, technology, and other departments in
promoting sustainable agriculture. Encourage
farmers and agricultural enterprises to adopt
sustainable agricultural technologies and
management methods, such as organic farming,
water-saving irrigation, circular agriculture, etc. By
providing training, demonstration projects, and
technical consulting services, we help them
overcome obstacles in the transformation process.
Support research institutions to develop and
innovate sustainable agricultural technologies,
strengthen cooperation with universities, and
cultivate more talents with knowledge of sustainable
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agriculture.
5 Conclusion
In the management of agricultural production, the
information contained in many data presents
ambiguity, such as the quality of agricultural
products and the degree of influence on the
ecological balance. When analyzing and evaluating
this kind of data, since there is no absolutely clear
boundary for each index level or grade of the
evaluated item, a fuzzy matrix can be used to
describe the relationship between them. According to
the effect of each index on the whole, the
corresponding weight coefficient is determined, and
a relatively clear normalized conclusion is drawn
through the compound operation of the weight
coefficient and the fuzzy evaluation matrix.
Therefore, the scientific and rational application of
fuzzy mathematical theory to the modeling work of
agricultural economic management and rural
economic development has scientifically verified the
science and operability of the fuzzy comprehensive
evaluation method in agricultural production
management and provided new ideas and methods
for agricultural production management.
In practical applications, obtaining
comprehensive, accurate, and reflective data on
various aspects of agricultural production
management is a daunting task. The lack,
incompleteness, or error of data may affect the
accuracy of fuzzy comprehensive evaluation. The
determination of weight coefficients is a subjective
and objective process that is influenced by factors
such as the evaluator's experience and knowledge
background. Different evaluators may derive
different weight coefficients, which can affect the
stability and consistency of the evaluation results. In
the future, it is necessary to strengthen the research
and development of data collection and processing
technologies, improve the comprehensiveness,
accuracy, and timeliness of data, and provide more
reliable data support for fuzzy comprehensive
evaluation. Explore more scientific and objective
methods for determining weight coefficients, such
as weight coefficient optimization algorithms based
on data mining, machine learning, and other
technologies, to improve the stability and
consistency of evaluation results.
Declaration of Generative AI and AI-assisted
Technologies in the Writing Process
The authors wrote, reviewed and edited the content
as needed and they have not utilised artificial
intelligence (AI) tools. The authors take full
responsibility for the content of the publication.
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Conflict of Interest
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_US
APPENDIX
Table 1. Agricultural economic management and rural economic development transformation evaluation index
First-level
indicator
Secondary
indicators
Three-level
indicator
Four-level indicator
The promotion
index of
agricultural
economic
management to
rural economic
Demand side
Demand potential
Per capita net income
Engel coefficient
Demand vitality
The proportion of secondary and Tertiary industries
Labor productivity
Supply-side
Technical skills
Number of technology promotion agencies
Proportion of scientific researchers
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development
Management
knowledge
Number of training institutions
Number of management agencies
Farming
modernization
Investment efficiency
Total funding
Convert kinetic
energy
Economic
development
Soil erosion control rate
Fertilizer use intensity
Sustainable
development
Ecological sensitivity
Comprehensive mechanization level
beijng
tianjng
hebei
heilongjiang
jiling
liaoning
neimenggu
shanxi
henan
shanxi
ningxia
gansu
xinjiang
qinghai
xizang
sichuan
chongqing
yunnan
guizhou
hubei
hunan
jiangxi
zhejiang
anhui
jiangsu
shanghai
shandong
fujian
guangdong
guangxi
hainan
0.0
0.2
0.4
0.6
0.8
1.0 2010
2015
2020
Fig. 1: Agricultural economic management index value
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beijng
tianjng
hebei
heilongjiang
jiling
liaoning
neimenggu
shanxi
henan
shanxi
ningxia
gansu
xinjiang
qinghai
xizang
sichuan
chongqing
yunnan
guizhou
hubei
hunan
jiangxi
zhejiang
anhui
jiangsu
shanghai
shandong
fujian
guangdong
guangxi
hainan
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
2010
2015
2020
Fig. 2: Agricultural economic development index value
beijng
tianjng
hebei
heilongjiang
jiling
liaoning
neimenggu
shanxi
henan
shanxi
ningxia
gansu
xinjiang
qinghai
xizang
sichuan
chongqing
yunnan
guizhou
hubei
hunan
jiangxi
zhejiang
anhui
jiangsu
shanghai
shandong
fujian
guangdong
guangxi
hainan
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
2010
2015
2020
Fig. 3: Agricultural economic management and calculation of the rural economic comprehensive index
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beijng
tianjng
hebei
heilongjiang
jiling
liaoning
neimenggu
shanxi
henan
shanxi
ningxia
gansu
xinjiang
qinghai
xizang
sichuan
chongqing
yunnan
guizhou
hubei
hunan
jiangxi
zhejiang
anhui
jiangsu
shanghai
shandong
fujian
guangdong
guangxi
hainan
0.0
0.1
0.2
0.3
0.4
0.5
0.6
2010
2015
2020
Fig. 4: Structural transformation index value
0 5 10 15 20 25 30
0
10
20
30
40
50
CL = 14.98
UCL = 34.18
Range
-7
0
7
14
21
CL = 4.048
LCL = -6.865
UCL = 14.96
Average
Fig. 5: Structural transformation index range diagram
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beijng
tianjng
hebei
zhejiang
jiangsu
shanghai
shandong
fujian
guangdong
hainan
0.2
0.3
0.4
0.5
2010
2015
2020
East
(a)
shanxi henan hubei hunan jiangxi anhui
0.1
0.2
0.3
0.4
0.5
2010
2015
2020
Central
(b)
heilongjiang
jiling
liaoning
neimenggu
shanxi
ningxia
gansu
xinjiang
qinghai
xizang
sichuan
chongqing
yunnan
guizhou
guangxi
0.2
0.4
0.6
2010
2015
2020
West
(c)
Fig. 6: Marginal box diagram of structural transformation index in East, Central, and Western Regions
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