USING PEST ANALYSIS TO EXPLORE THE
IMPACT AND MECHANISM OF CHINESE
AND AMERICAN BREEDING PATENTS ON
THE DEVELOPMENT OF AGRICULTURAL
SCIENCE AND TECHNOLOGY
Changyun Lu
Library, China Agricult ural University, Beijing, 100083, China
Information Research Center of China Agricultural University, Beijing, 100083,
China
Longjiao Zhu
Coll Food Sci & Nutr Engn, Beijing Adv Innovat Ctr Food Nutr & Human Hlth,
China Agricultural University, Beijing, 100083, China
Wentao Xu
Coll Food Sci & Nutr Engn, Beijing Adv Innovat Ctr Food Nutr & Human Hlth,
China Agricultural University, Beijing, 100083, China
Yuan Cao*
Library, China Agricultural University, Beijing, 100083, China
Information Research Center of China Agricultural University, Beijing, 100083,
China
E-mail: caoy1004@cau.edu.cn
Reception: 10 January 2024 | Acceptance: 26 January 2024 | Publication: 19 February 2024
Suggested citation:
Lu, C., Zhu, L., Xu, W. and Cao, Y. (2024). Using PEST analysis to explore the
impact and mechanism of Chinese and American breeding patents on
the development of agricultural science and technology. 3C TIC.
Cuadernos de desarrollo aplicados a las TIC, 13(1), 117-137. https://doi.org/
10.17993/3ctic.2024.131.117-137
https://doi.org/10.17993/3ctic.2024.131.117-137
3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529
Ed.44 | Iss.13 | N.1 January - March 2024
127
ABSTRACT
In recent years, PEST analysis and its application in decision-making has become a
hot topic, and this paper proposes a new model of PEST analysis based on neutral
cognitive map static analysis.The proposed framework consists of five activities,
identifying PEST factors and sub-factors, modeling the interrelationships among
PEST factors, calculating centrality measures, categorizing the factors, and ranking
the factors.The results show that the impact factor of the US breeding patent on the
development of agricultural science and technology has reached the level of
interrelationship, and the uncertainty is included in the analysis. Using this model to
analyze the impact of U.S.-China breeding patents on the development of agricultural
science and technology, we ranked the factors according to their interrelationships
and incorporated uncertainty into the analysis. The results showed that the impact
factor of U.S. breeding patents on the development of agricultural science and
technology reached 0.70, and the index of the Gross Agricultural Product (GAP)
caused by patents was greater than 100. The quantitative analysis of the impact of
Sino-US cooperation in agricultural science and technology using PEST analysis has
scientifically and accurately grasped the quantitative law of Sino-US agricultural
science and technology cooperation.
KEYWORDS
Agricultural science and technology; Breeding patents; PEST analysis; Neutral
cognitive map; Breeding technology
https://doi.org/10.17993/3ctic.2024.131.117-137
3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529
Ed.44 | Iss.13 | N.1 January - March 2024
128
INDEX
ABSTRACT .....................................................................................................................2
KEYWORDS ...................................................................................................................2
1. INTRODUCTION .......................................................................................................4
2. LITERATURE REVIEW .............................................................................................5
3. CONSTRUCTING AN IMPACT MECHANISM FOR AGRICULTURAL SCIENCE AND
TECHNOLOGY DEVELOPMENT BASED ON PEST ANALYSIS ...........................6
3.1. PEST analytical framework ................................................................................6
3.2. Impact of U.S.-China Breeding Patents on Agricultural Science and Technology
Development ...........................................................................................................7
3.2.1. Political factors ............................................................................................7
3.2.2. Economic factors .........................................................................................7
3.2.3. Social factors ...............................................................................................8
3.2.4. Technical factors ..........................................................................................9
4. MODELING OF INFLUENCING FACTORS .............................................................9
4.1. Data Acquisition .................................................................................................9
4.2. Model building ..................................................................................................11
4.3. Impact factor metrics ........................................................................................13
4.3.1. Computational centrality measure .............................................................13
4.3.2. Factor classification ...................................................................................14
4.3.3. Ranking factors .........................................................................................14
5. MECHANISM OF ACTION ANALYSIS ...................................................................15
5.1. Performance of Chinese and U.S. Breeding Patents on Different PEST
Dimensions ...........................................................................................................15
5.2. The role of breeding patents in the development of agricultural science and
technology ............................................................................................................17
5.2.1. Changes in the agricultural economy ........................................................17
5.2.2. U.S. Imports of Chinese Agricultural Products ..........................................18
6. CONCLUSION ........................................................................................................19
FUNDING ......................................................................................................................20
REFERENCES ..............................................................................................................20
https://doi.org/10.17993/3ctic.2024.131.117-137
3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529
Ed.44 | Iss.13 | N.1 January - March 2024
129
1. INTRODUCTION
For thousands of years, the development of agricultural science and technology
has contributed to the growth of human population and the expansion of social
complexity [1].In fact, the ability to meet the basic needs of the world's food supply
and to gradually improve the quality of life of the rich people in the employment of
fewer and fewer people is mainly due to the development of agricultural science and
technology. At present, in the context of economic globalization and rapid
development of science and technology, it is important to explore the impact and
mechanism of the Chinese and U.S. patents in the area of agricultural science and
technology development [2]. Currently, in the context of economic globalization and
rapid development of science and technology, it is of great significance to investigate
the impact and mechanism of Sino-US breeding patents on agricultural science and
technology development[2].As the world's two largest economies, the progress of
China and the United States in the field of agricultural science and technology has a
far-reaching impact on the global food security and sustainable agricultural
development[3]. The new technologies created by China and the United States in the
field of agriculture can significantly increase the world's agricultural production and
maintain the sustainability of the world's agricultural development. the promotion of
new disease-resistant hybrid varieties, the reduction of the use of pesticides, the
scientific prevention and control of biological pests, the improvement of cultivation
technology of agricultural products, etc. agricultural science and technology can
increase the production of agricultural products, and promote agricultural
development. Intellectual property management is not only related to the incentive
mechanism of technological innovation, but also affects the competitive pattern of the
global agricultural market [4].In addition, with the global climate change and
population growth brought about by the pressure of food supply, improve crop yield
and adaptability has become an urgent need, which is the core goal of the innovation
of breeding technology [5].Therefore, an in-depth understanding of the development
of the U.S. and Chinese breeding patent situation, the differences in patent policy and
its impact on the development of their respective and global agricultural science and
technology. Therefore, in-depth understanding of the development of Chinese and
American breeding patents, patent policy differences and their impact on the
development of their respective and global agricultural science and technology is the
key to grasp the pulse of the global agricultural science and technology development,
guide the future development of agricultural policy and promote international
cooperation.
This paper utilizes PEST analysis to systematically analyze the impact and
mechanism of agricultural science and technology development, and
comprehensively assesses the factors from political, economic, social and
technological dimensions, so as to provide powerful strategic support for promoting
the healthy development of agricultural science and technology and global agricultural
cooperation. In order to reduce the dependence between the influencing factors, this
paper employs the NCM to model the integrated structure of the sub-factors of
https://doi.org/10.17993/3ctic.2024.131.117-137
3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529
Ed.44 | Iss.13 | N.1 January - March 2024
130
PESTEL, and to stratify and refine the impact of agricultural science and technology
through the measurement of the influencing factors. In order to reduce the
dependence between the influencing factors, this paper adopts NCM to model the
comprehensive structure of PESTEL sub-factors, and to refine the influence of
agricultural science and technology by stratifying the influence of the influencing
factors through the measurement of the influencing factors.In the exploration and
analysis, the performance of Sino-US breeding patents in different PEST dimensions
and the role of breeding patents on the development of agricultural science and
technology are used to validate the reasonableness of the methodology of this paper,
so as to comprehensively and systematically sort out the history of the cooperation
between China and the United States in agricultural science and technology and to
summarize the basic mode of the cooperation in agricultural science and technology
between China and the United States. We quantitatively analyze the influence of Sino-
US agricultural science and technology cooperation.
2. LITERATURE REVIEW
Zhang, F et al. elaborated the mechanism of agricultural science and technology
innovation to promote the green development of agriculture through spatial spillover
from the perspective of multi-dimensional approach, from the perspective of factor
spillover path and product spillover path, and measured the level of China's
agricultural green development by using grey correlation analysis and analyzed the
spatial and temporal evolution of the green development of China's agriculture [6].
Wang, Y proposed a program design for the intelligent platform of agricultural science
and technology park, and listed the basic content of the construction of the intelligent
park. The proposed intelligent platform program design applies new ICT technologies
such as 5G, artificial intelligence, cloud computing, Internet of Things, mobile Internet,
etc., and solves the problems faced by traditional agricultural science and technology
parks for a long time such as lack of experience in service, poor integrated security,
low operational efficiency, high management cost, and difficult business innovation [7].
Mahapatra, B. used autoregressive distributed lag combined with F-test and
investigated the impact of agricultural credit on total cereal, grain and rice production
using ARDL regression modeling framework. The empirical results of bounded F-test
showed that there was a statistically significant relationship between agricultural credit
and total cereal, millet and rice production at 1% level, which verified the long-run
equilibrium relationship in the model [8].Wang, Z designed a set of agricultural digital
greenhouse system based on ZigBee wireless sensor network technology. At the
same time, in the corresponding data acquisition and processing problems, this paper
adopts the PID controller under the particle filtering optimization technology to
optimize the error of the corresponding data acquisition system, eliminate the
corresponding noise and interference, so as to ensure the stability and effectiveness
of the corresponding data acquisition system in the digital greenhouse, reduce the
power consumption of the whole system, and ensure the stable transmission of
data[9].
https://doi.org/10.17993/3ctic.2024.131.117-137
3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529
Ed.44 | Iss.13 | N.1 January - March 2024
131
Aragie, E applied economic modeling in economics to assess the relative efficiency
of alternative investment choices for agricultural performance and household welfare.
To explain the linkages, as well as the direct and indirect impacts of alternative public
expenditure policies, the study used a general equilibrium model of Ethiopia calibrated
according to a well-decomposed socio-accounting matrix representing the structure of
the economy in the year 2010 [10]. Gurnovich, T. The innovation and investment
processes in the agricultural economy were studied by using system analysis
methods taking into account the manifestations of the trend of digitization of the agro-
industry. Structural changes and imbalances in the process of investment and
innovation in the agricultural sector of the economy were revealed. Statistical and
economathematical methods were used to forecast the development of agricultural
production [11].Habtewold, The impact of climate-smart agricultural technology on
multidimensional poverty of rural households in Ethiopia was used by T. To estimate
the impact of the mentioned technology, propensity score matching and endogenous
switching regression methods were used. The increase in income/consumption by
increasing the returns to production reduces the technology-induced multidimensional
poverty. This impact is transmitted more through the non-food expenditure pathway.
Finally, the impact of technology adoption on the Finally, the impact of technology
adoption on multidimensional poverty reduction has also been revealed [12].Treurniet,
M. used exogenous variation in the probability of a baseline survey to estimate the
impact of a baseline survey on the adoption by subsistence farmers of a new
agricultural technology that improves food security, and found that acceptance of the
survey had a large and statistically significant impact [13].
3. CONSTRUCTING AN IMPACT MECHANISM FOR
AGRICULTURAL SCIENCE AND TECHNOLOGY
DEVELOPMENT BASED ON PEST ANALYSIS
3.1. PEST ANALYTICAL FRAMEWORK
PEST analysis is used to assess these four external factors related to the business
situation, when including environmental and legal factors, it is called PESTEL, i.e., the
analysis of political, economic, socio-cultural, technological, environmental, and legal
factors [14]. In the use of PEST analysis to explore the impact and mechanism of the
influence of the Chinese and American breeding patents on the development of
agricultural science and technology, PEST analysis framework provides a
comprehensive method to examine the various external environmental factors. The
PEST analysis framework is shown in Figure 1, and the framework includes four main
dimensions: political, economic, social, and technological. The PEST analysis
framework is shown in Fig. 1, which includes four main dimensions: political,
economic, social and technological. This will help identify opportunities and
challenges, provide decision support for policy makers and the industry, and promote
the healthy and sustainable development of agricultural science and technology.
https://doi.org/10.17993/3ctic.2024.131.117-137
3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529
Ed.44 | Iss.13 | N.1 January - March 2024
132
Figure 1. PEST analysis framework
3.2. IMPACT OF U.S.-CHINA BREEDING PATENTS ON
AGRICULTURAL SCIENCE AND TECHNOLOGY
DEVELOPMENT
3.2.1. POLITICAL FACTORS
The governments of China and the United States influence the protection and
application of breeding patents through the formulation of relevant policies and legal
frameworks. Intellectual property laws, the duration of patent protection, and the
regulation and restriction of biotechnology all have a direct impact on the development
and application of breeding technologies [15-16]. trade agreements and diplomatic
relations between China and the United States also have an impact on breeding
patents. the provisions of trade agreements on agricultural products and
biotechnology affect technology exchanges and market access. The level of
government funding and support for research in agricultural science and technology
has a significant impact on the innovation and application of breeding patents, and
government-sponsored research programs may drive the development of new
varieties and technological innovations. government public policies, including attitudes
toward genetically modified products and consumer right-to-know policies, may also
affect the market acceptance and application of breeding patents. In some cases,
breeding technologies and patents are also linked to national security and food safety
policies, and breeding technologies that improve crop yields and resilience may be
considered part of a national food safety strategy.
3.2.2. ECONOMIC FACTORS
The development of breeding patents requires significant R&D investment, and the
rate of development of new technologies and varieties is determined by the
investment made by companies and research institutions in the U.S. and China. the
https://doi.org/10.17993/3ctic.2024.131.117-137
3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529
Ed.44 | Iss.13 | N.1 January - March 2024
133
patent system provides a level of protection that encourages such investment in the
hope of obtaining a return on that investment through exclusivity during the term of the
patent. the holder of a patented technology can gain a competitive advantage in the
marketplace, influencing the price of seed and the planting choices of farmers. at the
same time, a market with a high degree of patent concentration can lead to market
monopolization and affect fair competition. At the same time, markets with high patent
concentration can lead to market monopolization and affect fair competition in the
marketplace. patent protection for breeding technologies also plays an important role
in international trade. the entry of patented products for export is affected by differing
IPR protection laws and market access rules in different countries, which has a direct
impact on the competitiveness of the U.S. and China in the global marketplace for
agricultural products. patented technologies for breeding affect the cost and efficiency
of agricultural production. New breeding technologies can improve crop yields and
resistance to pests and diseases, reduce the cost of agricultural production, and
increase farmers' income [17].
3.2.3. SOCIAL FACTORS
Public attitudes towards breeding technologies, especially transgenic technologies,
have a direct impact on the market acceptance of breeding patents, and societal
concerns about food safety and environmental impacts may lead to boycotts or
restrictions on certain breeding technologies, thus affecting the commercialization of
these technologies.The degree of societal awareness of intellectual property rights
(IPRs) protection, especially in the agricultural sector, has a significant impact on the
filing and enforcement of breeding patents [18]. In societies with strong awareness of
intellectual property protection, patents are likely to be better defended, thus
stimulating more innovation and investment. breeding patents, especially those
involving gene editing and transgenic technologies, often lead to moral and ethical
debates in society, which may influence the formulation of governmental policies, and
consequently, the development and application of breeding technologies. the degree
of importance that societies attach to education and training in agricultural science
and technology determines the training of human resources in the field of agricultural
science and technology. The level of social emphasis on education and training in
agricultural science and technology determines the cultivation of human resources
and dissemination of technology in the field of agricultural science and technology,
and a high level of scientific education and technical training can accelerate the
progress of agricultural science and technology by facilitating the understanding and
application of breeding technology. changes in consumer demand for food products,
such as the preference for organic and non-GMO products, can affect the commercial
prospects of patents on breeding technology, and the market demand for crops with
specific characteristics, such as higher nutritional value and better taste, can also
drive breeding technology. Market demand for specific crop characteristics, such as
higher nutritional value and better taste, will also drive the direction of breeding
technology.
https://doi.org/10.17993/3ctic.2024.131.117-137
3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529
Ed.44 | Iss.13 | N.1 January - March 2024
134
3.2.4. TECHNICAL FACTORS
The existence of breeding patents encourages technological innovation, particularly
in areas such as gene editing and transgenic technologies, and patent protection
provides inventors with a window of time to recoup their R&D investment, thereby
stimulating additional R&D activities and innovation attempts. patenting standards and
specifications for breeding technologies differ in the U.S. and China, affecting the
direction of the technologies and the scope of their application. the technical
standards in the patenting and approval process determine which innovations can be
protected and disseminated. Breeding patents affect the accessibility and diffusion of
technologies, and the licensing of patented technologies determines how easily they
can be applied and under what conditions they can be used by other researchers and
developers. the patent system facilitates technology transfer and international
cooperation to a certain extent. Through patent licensing and cooperative
agreements, advanced breeding technologies can cross national boundaries and
contribute to the development of global agricultural science and technology [19].The
development of breeding technologies has a direct impact on the efficiency and
sustainability of agricultural production.For example, new drought- or disease-
resistant varieties can increase crop yields and reduce the reliance on chemical
fertilizers and pesticides, thus contributing to the sustainable development of
agriculture.
4. MODELING OF INFLUENCING FACTORS
4.1. DATA ACQUISITION
PEST analysis is a premise analysis whose main function is to identify the
environment in which a company or project operates and to provide data and
information to enable the organization to anticipate new situations and circumstances
[20], Figure 2 shows the process of data collection for agro-technology influencing
factors.
Political factors include government regulation of business, commercial law, labor
legislation, tax legislation, legislation in the field of import and export regulation,
competition protection, consumer protection, and environmental protection laws. It can
be expressed as , , , where
is the
government support index,
is the financial support for agricultural science and
technology from government agricultural policy, and
is the total agricultural science
and technology development budget. is the trade relationship index,
is the
volume of agricultural trade between China and the U.S., and is the total volume of
agricultural trade. is the intellectual property protection index, is the number of
patents on breeding, and
is the total number of patents on agricultural science
and technology.
GSl =G/T
TR I =A/T
IPPI =P/TP
GS
G
T
A
T
IPPI
P
TP
https://doi.org/10.17993/3ctic.2024.131.117-137
3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529
Ed.44 | Iss.13 | N.1 January - March 2024
135
Economic factors include i.e. inflation,
, interest rate, exchange rate,
unemployment etc. as well as ratio, intensity and type of contest between small,
medium and large enterprises, private and state owned property etc. denoted as
, , . Where
is agricultural investment as a percentage of ,
is investment in the agricultural
sector, and is the to the overall state of the country's economy.
is
the price volatility of agricultural products,
is the highest point of the price of key
agricultural products,
is the lowest point of the price of key agricultural products,
is the average price.
is the ratio of agricultural research and
development expenditures,
is the expenditures on agricultural research and
development, is the total agricultural expenditures.
Socio-cultural factors
cover demographic trends including age, sex, number of
people, natural growth rate, birth rate, death rate, population migration, educational
level and social groups in the population, cultural beliefs and values, and people's
individual needs. i.e., ,
, and
. where is the Population Growth Rate,
is the end of the
year population, and is the beginning of the year.
is the Consumer
Concern Index, which is the score of the survey on consumer concern about the
quality and sustainability of agricultural products, and the total survey score.
is
Consumer Concern Survey Score on Quality and Sustainability of Agricultural
Products, is Total Survey Score.
is Rural Education Level, ER is
Percentage of Educated Population in Rural Areas, is Total Population.
The analysis of technological factors covers innovation and creativity, technology
transfer, availability and access to patents, attitudes of researchers toward copyright,
and availability and access to the services of research institutes, which can be
denoted as , , and , where
is the Agricultural Technological Innovation Index,
is the number of new patents in
the field of agriculture, and is the total number of patents.
is the Digital
Agriculture Adoption Rate,
is the number of agricultural operators that have
adopted digital technologies, and
is the total number of agricultural operators;
is the index of cross-country scientific research cooperation,
is the
number of joint research projects between China and the U.S. in the field of
agricultural science and technology, and
is the total number of research
projects.
E
GDP
AIG =I/GDP
PVR = (Pmax Pmin)/Pavg
RDEP =R&D/TA
AI
GDP
I
GDP
GDP
PVR
Pmax
Pmin
Pavg
RDEP
R&D
TA
s
PGR = (Pend Pstart)/Pstar t
CSl =CC/TCC
REI =ER /TR
PGR
Pend
Pstar t
CSI
CC
TCC
REI
TR
ATI =NP/TP
DAI =DA /TA
ICPI =CRC/TRC
ATI
NP
TP
DAI
DA
TA
ICPI
CRC
TRC
https://doi.org/10.17993/3ctic.2024.131.117-137
3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529
Ed.44 | Iss.13 | N.1 January - March 2024
136
Figure 2. Data acquisition process
4.2. MODEL BUILDING
The interdependence between the analyzed factors. In addition, the structure of the
factors and sub-factors is characterized by ambiguity, vagueness and uncertainty.
Therefore, this study proposes a model to solve the problems encountered in the
process of measuring and evaluating the PEST analysis. The composition of the
model is shown in Fig. 3. The interdependence between the sub-factors is also taken
into account. The comprehensive structure of the PESTEL sub-factors is modeled
using the NCM.A quantitative analysis is also carried out on the basis of the static
analysis.
https://doi.org/10.17993/3ctic.2024.131.117-137
3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529
Ed.44 | Iss.13 | N.1 January - March 2024
137
Figure 3. Proposed framework for PEST analysis.
Neutral logic was introduced in 1995 as a generalization of fuzzy logic, especially
intuitionistic fuzzy logic. The logical proposition P is characterized by three neutral
components.
(1)
Where is the degree of truth, is the degree of falsity and
is the degree of
uncertainty.
A neutral matrix is a matrix in which the elements have been replaced by
elements in , where
is a ring of neutral integers. a neutral map is a
map in which at least one of the edges is a neutral edge. a cognitive map is called a
neutral cognitive map if uncertainty is introduced into the cognitive map. the NCM
uses neutral logic to represent the uncertainty and uncertainty in the cognitive map. a
NCM is a directed graph in which at least one edge is uncertainty as represented by
the dashed line, an example of a fuzzy neutral cognitive map is shown in Fig. 4. An
example of a fuzzy neutral cognitive map is shown in Figure 4. The static analysis
results of the mental model in the form of NCM are in the form of neutral numbers.
Finally, a de-neutralization process is applied to give the final ranking values. In this
study, the model is extended and detailed to deal with the classification and
prioritization of factors.
NL(P)=(T,I,F)
T
F
I
a= (aij)
RI
RI
https://doi.org/10.17993/3ctic.2024.131.117-137
3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529
Ed.44 | Iss.13 | N.1 January - March 2024
138
Figure 4. Fuzzy Neutrosophic Cognitive Maps example.
4.3. IMPACT FACTOR METRICS
4.3.1. COMPUTATIONAL CENTRALITY MEASURE
The following metrics are computed using the absolute values of the NCM
adjacency matrix.
1. The out-degree is the row sum of the absolute values of the variables in
the neutral adjacency matrix, showing the cumulative strength of the
connections of the exit variables.
(2)
2. Input is the column sum of the absolute values of the variables, showing
the cumulative strength of the input variables.
(3)
3. The total degree of centrality of a variable, , is the sum of its in- and out-
degrees, which is then calculated as follows.
od(vi)
cij
o
d(vi) =
N
i=1
ci
j
id(vi)
i
d(vi) =
N
i=1
Ci
j
td(vi)
https://doi.org/10.17993/3ctic.2024.131.117-137
3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529
Ed.44 | Iss.13 | N.1 January - March 2024
139
(4)
4.3.2. FACTOR CLASSIFICATION
Factors are categorized according to the following rules.
1. Transmitter variables have positive or uncertain out-degree and zero in-
degree .
2. Receiver variables have positive or uncertain in-degree , and zero out-
degree .
3.
Ordinary variables have non-zero in-degrees, and ordinary variables can be
more or less receiver or transmitter variables, depending on the ratio of their in-
degrees to their out-degrees.
4.3.3. RANKING FACTORS
The process of de-neutralization gives the number of intervals of centrality. this is
based on the maximum and minimum values of I. Neutrality values are transformed in
intervals with two values, the maximum and the minimum [0,1].
The contribution of a variable in a cognitive map can be understood by calculating
its degree centrality, which shows how well the variable is connected to other
variables and what is the cumulative strength of these connections. the median of the
extremes is used to give the centrality value.
(5)
Then:
(6)
Hierarchical refinement of the impact of breeding technology on agricultural science
and technology based on PEST, the variables were ranked as shown in Table 1 for
factor prioritization and/or reduced values.
td(vi)=od(vi)+id(vi)
od(vi)
id(vi)
id(vi)
od(vi)
λ([
a1,a2
])
=
a
1
+a
2
2
A>B
a
1
+a
2
2
>
b
1
+b
2
2
https://doi.org/10.17993/3ctic.2024.131.117-137
3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529
Ed.44 | Iss.13 | N.1 January - March 2024
140
Table 1. The hierarchical model of PEST of breeding patents.
5. MECHANISM OF ACTION ANALYSIS
5.1. PERFORMANCE OF CHINESE AND U.S. BREEDING
PATENTS ON DIFFERENT PEST DIMENSIONS
The data on the political, economic, social and technological dimensions of
breeding patents in China and the United States were collected through the data
collection system, and hypothetical analyses were conducted to investigate the
significance of the impact of these factors on breeding patents.Table 2 shows the
results of the hypothetical analyses.The policy factor represents the impact of the
political environment on breeding patents, including policy support, changes in the
law, etc.The economic impact factor includes R&D investment, market size, etc.The
social impact factor reflects social attitudes, consumer preferences, etc.The
technological impact factor represents technological progress, innovation speed,
etc.The number of breeding patents is the number of applications filed by the country
in a given year. The social influence factor reflects the social attitude and consumer
preference, etc. The technology influence factor represents the technological progress
and innovation speed, and the number of breeding patents is the number of breeding
patent applications in a specific year.The political influence factor of China and the
United States increases year by year from 2019 to 2021, and by 2022, the technology
influence factor reaches 0.70, and the economic influence factor reaches 0.60, which
reflects that the governments of the two countries increase the number of breeding
patents during this period. This reflects that the governments of the two countries
have increased their policy support for agricultural science and technology during this
period, such as providing more R&D funds and optimizing the intellectual property
protection system, etc. This improvement in the policy environment may have played
a positive role in promoting the application and implementation of breeding patents.
Political Economic Social Technology
Political stability
(P1) Labor force level (E1) Entrepreneurial spirit
(S1)
Government
investment measures
(T1)
Intellectual
Property (P2)
Investment incentive
measures (E2) Purchase product (S2)
Government support
for scientific research
(T2)
Environmental
Protection Law
(P3)
National Income (E3)
Citizen attitudes
towards breeding
technology (S3)
Technological
Innovation (T3)
Positive media
promotion (S4)
https://doi.org/10.17993/3ctic.2024.131.117-137
3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529
Ed.44 | Iss.13 | N.1 January - March 2024
141
Table 2. Hypothesis Analysis Results
The NCM was used to identify and model the interdependencies. The weights of
the NCM are shown in Table 3.The weights are all below 0.5.
Table 3. Neutrosophic Adjacency Matrix
Time Country
Political
influence
factors
Economic
impact
factors
Social
impact
factors
Technical
impact
factors
Number
of
breeding
patents
2019 United
States 45 0.60 0.50 0.70 150
2019 China 0.50 0.55 0.45 0.65 130
2020 United
States 0.47 0.62 0.52 0.72 155
2020 China 152 0.58 0.48 0.68 135
2021 United
States 49 0.65 0.55 0.75 160
2022 China 0.55 0.60 0.50 0.70 140
P1 P3 E1 E2 E3 S1 S2 S3 S4 T1 T2 T3 P1
P1 00000000000.70.60
P3 00000.400000001
E1 0000000000000
E2 0000000.6000000
E3 000000.30.4000000
S1 00000000.800000
S2 0000000000000
S3 0000000000000
S4 0000000000000
T1 0000000000000
T2 0000.2000000000
T3 000001100.40.5000
00000000.4000000
https://doi.org/10.17993/3ctic.2024.131.117-137
3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529
Ed.44 | Iss.13 | N.1 January - March 2024
142
5.2. THE ROLE OF BREEDING PATENTS IN THE
DEVELOPMENT OF AGRICULTURAL SCIENCE AND
TECHNOLOGY
5.2.1. CHANGES IN THE AGRICULTURAL ECONOMY
China and the United States agricultural science and technology cooperation,
directly promote China's agricultural science and technology level, and the level of
agricultural science and technology is called the important driving force of the
development of the agricultural economy. therefore, this paper adopts the trade in
agricultural products as a measure of the index to measure the impact of China-US
cooperation in agricultural science and technology on the agricultural economy has a
certain degree of reality and reasonableness. figure 5 for the species of the country's
agricultural output value index in the year 2001-2023 ring changes in the trend of
agricultural output value index, from the From the point of view of agricultural output
value index, after excluding the price fluctuation factor, the output value index of
agriculture, forestry, animal husbandry, fishery, plantation, forestry, pasture and fishery
are all greater than 100 except for a few years, indicating that China's agricultural
output value is still showing an obvious year-on-year growth, which also fully
demonstrates the momentum of China's agricultural development.
From 2001 to 2023, the index of agricultural output value shows an overall trend of
first growth and then decline, and gradually tends to stabilize, in which the index of
total output value and agricultural output value reached the maximum value in 2008,
while the index of forestry, animal husbandry and fishery output value reached the
maximum value in 2007, from the point of view of the stability of the index of recent
years, they are all stable at 105 or above, that is, in recent years, the index of total
output value of agriculture is still showing obvious growth year by year, which also
shows that China's agriculture has a rapid development momentum. From the
perspective of the flat stability of the chain index in recent years, they are all stabilized
at 105 or above, which means that the agricultural industry has shown a coordinated
growth in recent years, and the growth rate of the chain index is around 5%.
https://doi.org/10.17993/3ctic.2024.131.117-137
3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529
Ed.44 | Iss.13 | N.1 January - March 2024
143
Figure 5. 2001-2023 trend of the output value index
5.2.2. U.S. IMPORTS OF CHINESE AGRICULTURAL PRODUCTS
Agricultural products is the final reflection of the results of agricultural science and
technology, China and the United States agricultural trade is an important carrier of
agricultural science and technology cooperation between the U.S. and China. China
and the United States agricultural products there are greater complementarity, in
recent years in the agricultural trade in the cooperation is becoming closer and closer.
figure 6 for the U.S. imports of agricultural products from China's total amount and
share of the 2009 year, the U.S. exports of agricultural products year-on-year will be
reduced by 17 billion U.S. dollars, while the amount of China's exports of agricultural
products to the United States will be Further increase, to 3.42 billion U.S. dollars, the
past four years China's total exports of agricultural products to the U.S. has doubled.
from the development trend, 1990-2008 U.S. imports of agricultural products from
China's total amount and share of the total amount of obvious growth. 1990 U.S.
imports of agricultural products from China's amount of 273 million U.S. dollars,
accounting for the U.S. value of the total value of agricultural products imported
1.19%. 2008 U.S. imports of agricultural products of Chinese origin and share of the
total value of the U.S. value of 1.19%. In 2008, the total value of U.S. imports of
agricultural products of Chinese origin amounted to 3454 million U.S. dollars, an
increase of 11.65 times compared with 1990, with an average annual growth rate of
15.14%. 2008 imports of agricultural products of Chinese origin accounted for the
proportion of U.S. imports of agricultural products to the total value of 4.29%, an
increase of 3.1 percentage points compared with 1990.
https://doi.org/10.17993/3ctic.2024.131.117-137
3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529
Ed.44 | Iss.13 | N.1 January - March 2024
144
Figure 6. Total amount and proportion of U. S. imports of agricultural products from China
6. CONCLUSION
In this paper, the impact and mechanism of U.S. breeding patents on the
development of agricultural science and technology is taken as the target, and the
PEST analysis combined with the static analysis of sexual cognitive map is used to
construct the model of the impact factors and validate it. The conclusions are as
follows.
1. For the performance of Chinese and American breeding patents in different
PEST dimensions, by 2022, the technological impact factor reaches 0.70, and
the economic impact factor reaches 0.60, reflecting that the governments of
the two countries have increased their policy support for agricultural science
and technology during this period, and this improvement in the policy
environment may play a positive role in promoting the application and
implementation of breeding patents.
2. In the analysis of the role of breeding patents on the development of
agricultural science and technology, Chinese and American breeding patents
make China's total agricultural output value is greater than 100, and in 2008,
imports of agricultural products of Chinese origin accounted for the proportion
of total U.S. imports of agricultural products also rose to 4.29%.
https://doi.org/10.17993/3ctic.2024.131.117-137
3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529
Ed.44 | Iss.13 | N.1 January - March 2024
145
FUNDING
This work was supported by Young Elite Scientist Sponsorship Program by BAST
[BYESS2022133], National Key Research and Development Program of China
[2022YFF0607900], 2115 Talent Training Plan of China Agricultural University
[00109012] and Key Research and Development Program of Hebei Province
[21372801D].
REFERENCES
(1) Singh, D., & Kalra, R. (2019). Extent of utilization of services provided by agricultural
technology and information centre (atic). International Journal of Current Microbiology
and Applied Sciences, 8(10).
(2) Correa, C. M., Correa, J. I., & De Jonge, B. (2020). The status of patenting plants in the
global south. The Journal of World Intellectual Property, 23(1/2), 121-146.
(3) Tudi, M., Ruan, H. D. D., Wang, L., Lyu, J., & Phung, D. T. (2023). Agriculture
development, pesticide application and its impact on the environment. International
journal of environmental research and public health, 18(3), 1112.
(4) Delay, N. D., Thompson, N. M., & Mintert, J. R. (2021). Precision agriculture technology
adoption and technical efficiency. Journal of Agricultural Economics, 195-219.
(5) Khan, N., Ray, R. L., Sargani, G. R., Ihtisham, M., Khayyam, M., & Ismail, S. (2021).
Current progress and future prospects of agriculture technology: gateway to sustainable
agriculture. Sustainability, 13.
(6) Zhang, F., Wang, F., Hao, R., & Wu, L. (2022). Agricultural science and technology
innovation, spatial spillover and agricultural green development—taking 30 provinces in
china as the research object. Applied Sciences, 12(2), 845.
(7) Wang, Y., Sun, Z., Wang, X., & Yang, C. (2020). Scheme design of smart platform for the
agricultural science and technology park. Journal of Physics: Conference Series,
1673(1), 012062 (6pp).
(8) Mahapatra, B., & Jena, D. (2023). Impact of agricultural credit disbursement on cereals
yield in odisha. International Social Science Journal, 73(248), 373-391.
(9) Wang, Z. (2021). Greenhouse data acquisition system based on zigbee wireless sensor
network to promote the development of agricultural economy. Environmental Technology
& Innovation, 101689.
(10) Aragie, E., & Jean Balié. (2020). Public spending on agricultural productivity and rural
commercialization: a comparison of impacts using an economy-wide approach.
Development Policy Review, O21-O41.
(11) Gurnovich, T. G., Piterskaya, L. Y., Agarkova, L. V., Buraeva, E. V., & Chistyakova, M. K.
(2021). Trends in the development and financing of investment and innovation activities
in the agricultural sector of the economy. IOP Conference Series: Earth and
Environmental Science, 745(1), 012013 (9pp).
(12) Habtewold, T. M. (2021). Impact of climate-smart agricultural technology on
multidimensional poverty in rural ethiopia. Journal of Integrative Agriculture, 20(4),
1021-1041.
https://doi.org/10.17993/3ctic.2024.131.117-137
3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529
Ed.44 | Iss.13 | N.1 January - March 2024
146
(13) Treurniet, M. (2021). The impact of being surveyed on the adoption of agricultural
technology. Economic Development and Cultural Change, 71, 941 - 962.
(14) Mihailova, M. (2020). The state of agriculture in bulgaria - pestle analysis. Bulgarian
Journal of Agricultural Science, 5(26), 935-943.
(15) Xiaoqing, H. (2020). Analysis of innovation degree of agricultural science and technology
service system based on markov decision process. Dynamic Systems and Applications,
29(5).
(16) Smulders, M. J. M., Wiel, C. C. M. V. D., & Lotz, L. A. P. (2021). The use of intellectual
property systems in plant breeding for ensuring deployment of good agricultural
practices. Agronomy, 11(6), 1163.
(17) Agnès Ricroch, Martin-Laffon, J., Rault, B., Pallares, V., & Kuntz, M. (2022). Next
biotechnological plants for addressing global challenges: the contribution of transgenesis
and new breeding techniques. New Biotechnology, 66, 25-35.
(18) Salzano, A., Cotticelli, A., Marrone, R., D'Occhio, M., D'Onofrio, N., & Neglia, G., et al.
(2021). Effect of breeding techniques and prolonged post dry aging maturation process
on biomolecule levels in raw buffalo meat. Veterinary sciences, 8(4).
(19) Shahzad, R., Jamil, S., Ahmad, S., Nisar, A., Khan, S., & Amina, Z., et al. (2021).
Biofortification of cereals and pulses using new breeding techniques: current and future
perspectives. Frontiers in nutrition, 8, 721728.
(20) Kolomiets, A., Grinchenkov, D., & Vodenko, K. (2019). Pest- and swot-analysis of
university internationalization factors. Journal of Physics Conference Series, 1415,
012003.
https://doi.org/10.17993/3ctic.2024.131.117-137
3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529
Ed.44 | Iss.13 | N.1 January - March 2024
147