Synergy of National Agricultural Innovation Systems

Abstract

Synergy among the various components of national agricultural innovation systems (AISs) promotes agricultural development. This paper investigated the innovation synergy among the various innovation elements of national AISs. First, we developed a synergy analysis model consisting of three innovation variables (innovation allocation, innovation output, and innovation potentiality) and one control variable (government policy supports). Secondly, a broad set of innovation indicators was selected to describe the innovation variables and the control variable, and the solutions of the order parameter equation were then calculated to investigate the self-organized synergistic patterns of a panel of the Group of Twenty (G20) countries. The empirical results indicated the following. (1) All of the G20 countries’ national AISs had the potential to evolve into more advanced self-organized synergistic states under current government policy support. Furthermore, all of the developing countries were in the active period of synergy, showing stronger synergistic rising powers. However, most of the developed countries were in the stable or general period of synergy, in which synergistic rising powers were relatively weaker; (2) Stronger government policy supports played a positive role in promoting the interaction and collaboration among innovation elements and promoted the national AIS to evolve into a more advanced self-organized synergistic state. This study has important implications for understanding the complex innovation synergy of national AISs, as well as for the design and implementation of agricultural innovation strategies for policy-makers.

Full text

sustainability
Article
Synergy of National Agricultural Innovation Systems
Dan Wang , Xu Du, Jian Sun, Xiangyu Guo * and Yao Chen
Northeast Agricultural University, Harbin 150030, China; wd@neau.edu.cn (D.W.); neau711711@163.com (X.D.);
sunj_neau@126.com (J.S.); cheny_neau@163.com (Y.C.)
*Correspondence: guoxy@neau.edu.cn; Tel.: +86-0451-5519-0558
Received: 13 July 2018; Accepted: 17 September 2018; Published: 21 September 2018


Abstract:
Synergy among the various components of national agricultural innovation systems (AISs)
promotes agricultural development. This paper investigated the innovation synergy among the
various innovation elements of national AISs. First, we developed a synergy analysis model consisting
of three innovation variables (innovation allocation, innovation output, and innovation potentiality)
and one control variable (government policy supports). Secondly, a broad set of innovation indicators
was selected to describe the innovation variables and the control variable, and the solutions of the
order parameter equation were then calculated to investigate the self-organized synergistic patterns of
a panel of the Group of Twenty (G20) countries. The empirical results indicated the following. (1) All
of the G20 countries’ national AISs had the potential to evolve into more advanced self-organized
synergistic states under current government policy support. Furthermore, all of the developing
countries were in the active period of synergy, showing stronger synergistic rising powers. However,
most of the developed countries were in the stable or general period of synergy, in which synergistic
rising powers were relatively weaker; (2) Stronger government policy supports played a positive
role in promoting the interaction and collaboration among innovation elements and promoted the
national AIS to evolve into a more advanced self-organized synergistic state. This study has important
implications for understanding the complex innovation synergy of national AISs, as well as for the
design and implementation of agricultural innovation strategies for policy-makers.
Keywords: agricultural innovation; innovation system; innovation synergy; the Group of Twenty
1. Introduction
Enhancing agricultural innovation has been a permanent preoccupation of public and private
organizations since scientific and technological achievements were used to improve agricultural
productivity and efficiency in the 16th and 17th centuries [
1
]. The innovative developments in
soil fertilization, mechanization, genetic engineering, cultivation techniques, irrigation techniques,
and information technology have been the main drivers behind the increase in agriculture
productivity. Nowadays, food security has become more and more affected by the rapidly growing
food demand, diminishing natural resources, and climate change. How to feed the growing
populations, while simultaneously protecting the environment, remains a complex challenge.
Agricultural innovation needs to be a priority to achieve sustainable productivity growth and address
the global food challenge [2].
Innovation is a complex phenomenon involving the production, diffusion, and translation of
technological knowledge into new products or new processes [
3
]. The multitudinous links among
different innovation stages and the interactions among different actors can lead to knowledge
acquisition and mutual knowledge exchange, which strengthens the innovation capability [
4
,
5
].
The national agricultural innovation is defined as a complex process in which a series of innovative
actors create new knowledge, invent new varieties or new technologies, and popularize them
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Sustainability 2018,10, 3385 2 of 20
in agricultural production through collaborative interaction with each other, so as to reach the
coordination of economic, social, and ecological benefits in one country [6].
As agricultural innovation becomes increasingly viewed as a far more complex and less linear
process, it has become more and more difficult to identify the complex relations that constitute
innovation processes. Recently, the agricultural innovation system (AIS) approach [
5
,
7
] has become
increasingly popular as a tool for analyzing agricultural innovation processes [
8
–
10
]. The AIS approach
evolved from a transition from the simplistic linear or pipeline model to a complex non-linear or
network model of agricultural innovation [
11
,
12
]. The AIS approach provides a conceptual framework
for the integrated analysis of (1) complex agricultural problems [
13
]; (2) innovation capacity in the
agricultural system to solve these problems [
14
]; and (3) the structure and function of the AIS, which
can enhance or constrain innovation capacities in the agricultural system [
8
,
9
,
11
]. Applying the
AIS [
5
,
7
] framework is particularly promising for sustainable agricultural development, because it can
help identify where the most binding constraints on agricultural innovation are located and how better
to target interventions to remove such constraints [15].
Innovation performance depends not only on how the individual component part performs in
isolation, but also on the quality of the interaction and collaboration among the various elements, that is,
the synergies among various elements mark the performance of the innovation system [
16
]. Therefore,
the principal challenge in the agricultural innovation domain using the AIS approach is to understand
how the components synergize to produce innovation. However, with a few exceptions [
17
], there is
still a lack in studying AIS synergy. Especially, the analytical approach to the synergy of national AIS
is a novel feature.
This paper aims to address this gap by undertaking an innovation synergy analysis of the Group
of Twenty (G20) countries’ national AISs. The G20 countries with abundant agricultural resources
and a large agricultural population account for 80% of global food. Therefore, the stable development
of ‘G20 agriculture’ has a significant effect on global food security. In recent years, global warming,
more frequent and severe natural disasters, constraints on land resources, water resources, and other
environmental resources, and the human population growth has caused a tense situation with respect
to the supply and demand of agricultural products. ‘G20 agriculture’ is facing a risk of declining food
production capacity and a lack of guarantee regarding food security. In the meeting of G20 agriculture
ministers on 3 June 2016 in China, ministers discussed how G20 countries can promote food security,
nutrition, sustainable agricultural growth, and rural development worldwide, as well as contribute
toward building an innovative, invigorated, interconnected, and inclusive world economy in order
to fully achieve the 2030 Agenda for Sustainable Development, including eradicating hunger and
extreme poverty. Moreover, the G20 countries have committed to promoting innovation in institutions,
policies, science, and technology in order to increase agricultural productivity in a sustainable manner.
Understanding the mechanisms that enhance synergy in national AISs helps to increase the success
rates in sustainable agricultural productivity.
The key aims of this paper are hence to:
(i) Identify innovation variables based on the existing AIS frameworks and combine these innovation
variables in a synergy analysis model;
(ii) Select a broad set of innovation indicators, which will be used to describe the innovation variables
and their data sources;
(iii)
(Apply the synergy analysis model in the context of G20 countries, with the aim of revealing
where particular strengths and weaknesses exist as regards innovation, as well as the level of
synergy innovation of the G20 countries’ national AISs, and the underlying reasons for these;
(iv)
Inform the national AIS policies for sustainable agricultural development.
Addressing this knowledge gap will have important implications for understanding the complex
innovation synergy of national AISs, as well as for the design and implementation of agricultural
innovation strategies for policy-makers.

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2. Theoretical Background
2.1. Self-Organization Characteristics of National AIS
“Self-organization is the spontaneous and often seemingly purposeful formation of spatial,
temporal, and spatiotemporal structures or functions in systems composed of few or many components”
(Haken, 2008) [
18
]. Self-organization means an enormous reduction of degrees of freedom (entropy)
and an increase of ’order’. National AISs show typical self-organization characteristics [
17
] (as shown
in Figure 1):
(1) Nonlinearity
National agricultural innovation includes a complex set of interactive relationships among
multiple innovation actors (including government, private and public agricultural firms, universities
and research institutions, intermediary service organizations, and farmers). These innovation actors
produce, popularize, and apply various types of technological knowledge. The innovation processes
follow a non-linear path, and are characterized by complicated feedback mechanisms [19].
(2) Openness
National AISs, in the large-scale economic and social systems, have the properties of dissipative
structures that widely exchange material, energy, and information with the external environment.
There are extensive links with the external environment in every stage of research, development, and
the extension of agricultural science and technology. ‘Negative entropy’ flows, which are generated by
exchange with the external environment, reduce the entropy of the national AIS and make the national
AIS form an ordered structure.
(3) Fluctuations and non-equilibrium
In order to achieve sustainable agriculture, the national AISs keep overcoming their own
shortcomings and narrowing the gap between themselves and other advanced countries. The positive
innovation motivation may provoke various changes (fluctuations) in certain innovation elements of
national AISs. A small fluctuation may be enhanced through the interaction of a feedback regulation
mechanism, which could cause the resonance of all of the system elements in responding to the change.
Under these circumstances, the national AIS will be far from equilibrium, and evolve from the original
state to the advanced state.
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2. Theoretical Background
2.1. Self-Organization Characteristics of National AIS
“Self-organization is the spontaneous and often seemingly purposeful formation of spatial,
temporal, and spatiotemporal structures or functions in systems composed of few or many
components” (Haken, 2008) [18]. Self-organization means an enormous reduction of degrees of
freedom (entropy) and an increase of 'order'. National AISs show typical self-organization
characteristics [17] (as shown in Figure 1):
(1) Nonlinearity
National agricultural innovation includes a complex set of interactive relationships among
multiple innovation actors (including government, private and public agricultural firms,
universities and research institutions, intermediary service organizations, and farmers). These
innovation actors produce, popularize, and apply various types of technological knowledge. The
innovation processes follow a non-linear path, and are characterized by complicated feedback
mechanisms [19].
(2) Openness
National AISs, in the large-scale economic and social systems, have the properties of dissipative
structures that widely exchange material, energy, and information with the external
environment. There are extensive links with the external environment in every stage of research,
development, and the extension of agricultural science and technology. ‘Negative entropy’
flows, which are generated by exchange with the external environment, reduce the entropy of
the national AIS and make the national AIS form an ordered structure.
(3) Fluctuations and non-equilibrium
In order to achieve sustainable agriculture, the national AISs keep overcoming their own
shortcomings and narrowing the gap between themselves and other advanced countries. The
positive innovation motivation may provoke various changes (fluctuations) in certain
innovation elements of national AISs. A small fluctuation may be enhanced through the
interaction of a feedback regulation mechanism, which could cause the resonance of all of the
system elements in responding to the change. Under these circumstances, the national AIS will
be far from equilibrium, and evolve from the original state to the advanced state.
Figure 1. The self-organized synergistic evolution process of national agricultural innovation systems
(AISs).
Figure 1.
The self-organized synergistic evolution process of national agricultural innovation
systems (AISs).

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2.2. Synergetics and Belousov-Zhabotinsky (B-Z) Reaction
Synergetics is an interdisciplinary science that was founded by Hermann Haken in the 1970s
and explains the formation and self-organization of patterns and structures in open systems far
from thermodynamic equilibrium [
20
]. The order parameter concept is essential to synergetics, and
is generalized by the enslaving principle. Following the enslaving principle, the emerging order
parameters reduce the degrees of freedom in the behavior of the single parts of a system, and the
dynamics of fast relaxing modes are completely determined by the ‘slow’ dynamics of only a few order
parameters. Synergetics explains the self-organization of patterns in many different systems, such as
that of physics, chemistry, biology, and so on [20].
The Belousov-Zhabotinsky (B-Z) reaction [
21
–
23
], which was discovered in the 1950s by B.
Belousov, is used extensively as a chemical system model in studies on synergetic phenomena [
24
,
25
].
Inspired by the B–Z reaction, this paper established a metaphorical model of the B-Z reaction model to
study the innovation synergy of national AISs with self-organization characteristics.
2.3. Conceptual Frameworks of National AISs
To conduct a synergy analysis from the innovation system perspective, the analytical framework
of this paper was primarily based on the AIS approach of Wang (2018) and Spielman (2008). The two
approaches developed conceptual frameworks that captured the essential elements of national AIS, the
linkages among their components, and the environment that enables innovation. The two frameworks
combined conventional input and output indicators with more process-oriented, systems-specific
indicators [6,9].
This clear division into structural elements provided a practical approach for the synergy analysis
model based on the B-Z reaction. The collaborative relationships among the sub-units of national AISs
were analyzed in the context of the present innovation allocation, innovation output, and the potential
innovation capabilities, which were the metaphors of the three major chemical substances (Br-, HBrO
2
,
and Ce(IV)) of the B-Z reaction model.
3. Methodology
3.1. Selection of State Variables and Determination of Order Parameters
Three innovation variables (innovation allocation variable, innovation output variable, and
innovation potentiality variable) were proposed as state variables of synergy in national AISs.
The innovation allocation state variable was composed of agricultural innovation foundation and
bridging factors. The innovation foundation represented the input and contribution of one country
to agricultural innovation development. A favorable innovation foundation can provide effective
supports for innovation activities. Meanwhile, it is important to recognize that innovation often relies
on a diversity of bridging components facilitating the transfer of knowledge and information among
these diverse domains. Innovation outputs were the achievements and value realization of agricultural
innovation activities. The innovation output state variable was used to represent the quantity and
quality of innovation outcomes. Innovation is a recurring process: these new inputs and outputs go
back to the innovation environment circularly and optimize self-allocation to achieve a higher-level
innovation stage. This process was described by the innovation potentiality state variable.
The order parameter can be interpreted as the amplitude of the unstable modes determining
the macroscopic pattern. From the dynamic perspective, the innovation potentiality state variable
embodied the quality of interaction and collaboration among all of the innovation resources and
determined the future state of the system [
24
]. The innovation potentiality state variable, as the main
driving force in the evolution of national AISs, had similar features to the order parameter. Therefore,
the innovation potentiality state variable was proposed as the order parameter of the synergistic
evolution of national AISs.

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3.2. Synergy Analysis Model Based on the B-Z Reaction
Logistical equations have been widely used for simulating the B-Z reaction model to investigate
synergy evolution [
24
–
26
]. This paper used a set of logistic regression equations, which were revised
from Zhang’s model [
25
], to represent the synergy evolution process of national AISs. The equations
consisted of three state variables (y
1
,y
2,
and y
3
), three adjustment parameters (
α
,
β
,and
γ
), and one
control variable
θ
, which were the metaphors of three major chemical substances (X,Y, and Z), chemical
reaction rates, and control parameters of the B-Z reaction model, respectively. Detailed descriptions of
different types of variables and parameters are presented in Table 1.
Table 1. Variables and parameters of national AISs.
Variables and Parameters Description
Innovation potentiality state variable y1Representing the development speed of national AISs.
Innovation allocation state variable y2Representing the foundation and bridging level of
national AISs.
Innovation output state variable y3Representing the output level of national AISs.
Control variable of government policy θRepresenting the comprehensive index of government
policy supports for the synergy of national AISs.
Innovation potentiality adjustment parameter α
Measuring the development capabilities of national AISs,
obtained through indicators.
Innovation allocation adjustment parameter βMeasuring the foundation and bridging capabilities of
national AISs, obtained through indicators.
Innovation output adjustment parameter γMeasuring the output capabilities of national AISs,
obtained through indicators.
The calculation models of the innovation adjustment parameters (Equation (1)) and control
variable of government policy (Equation (2)) were defined as follows:











α=i
q∏n
i=1
αi
αi(i=1, 2, 3 . . . n)
β=i
r∏n
i=1
βi
βi(i=1, 2, 3 . . . n)
γ=i
q∏n
i=1
γi
γi(i=1, 2, 3 . . . n)
(1)
where
αi
,
βi
, and
γi
are the ith indicator values of the innovation potentiality state variable, the
innovation allocation state variable, and the innovation output state variable, respectively.
αi
,
βi
, and
γiare the average values.
θ=i
s∏n
i=1
θi
θi
(i=1, 2, 3 . . . n)(2)
where
θi
is the ith indicator value of the government policy supports, and
θi
is the average value of all
countries in the ith indicator.
The synergy analysis equations were built as follows. First, the innovation potentiality domain
was represented by Equation (3). The innovation potentiality state variable y
1
is influenced by itself
and the innovation allocation state variable y2in the time dimension (t).
1
α
dy1
dt =θy1+θβ
αy2−βy1y2+η1y2l(3)
where
dy1
dt
is a derivative of y
1
with respect to time, interpreted as the rate of change of y
1
.
θ
y
1
and
θβ
αy2
are the self-influencing factor and the influencing factor of y
2
on y
1
, respectively. Under the influence
of control variable
θ
,
θβ
α
is the influencing coefficient.
−β
y
1
y
2
+
η1
y
2l
is the influencing factor of y
2
on

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y
1
without the influence of
θ
,
η1
y
2l
represents the synergy of the innovation allocation optimization,
and η1and lare synergistic coefficients.
Secondly, Equation (4) describes the impact of y
2
itself, the innovation potentiality state variable
y1, and the innovation output state variable y3on y2.
1
β
dy2
dt =−θy2−αy1y2+γ
βy3(4)
where
dy2
dt
represents the change rate of y
2
, and
−θ
y
2
is the self-influencing factor under the influence
of control variable
θ
. The negative influencing coefficient indicates that the increase of innovation
resources will destroy the original allocation structure and have a negative impact on the current
innovation allocation.
−α
y
1
y
2
is the influencing factor of y
1
on y
2
; the accelerating development of
national AISs will consume more resources, which results in the reduction of the resource flow and
resource sharing among the innovation actors, and furthermore weakens the innovation allocation
capability of national AISs.
γ
βy3
represents the influencing factor of y
3
on y
2
, and the innovation output
shows the effectiveness of the innovation allocation.
Third, the transform process from innovation input to innovation output may be delayed because
of the interaction and influence of various factors in national AISs. Therefore, the innovation input
cannot generate innovation output and profits immediately [
27
]. The innovation output state variable
y
3
is influenced by itself and the innovation potentiality state variable y
1
in the time dimension, as
shown in Equation (5):
1
γ
dy3
dt =−η2y3+η3θα
γy1(5)
where
dy2
dt
represents the change rate of y
3
, and
−η2
y
3
is the self-influencing factor without the
influence of control variable
θ
. With self-consumption, the output of the national AIS will be gradually
reduced.
η3θα
γy1
indicates that the innovation outputs can be greatly improved with the acceleration
of development of national AISs, and
η3
is the potentiality coefficient of a national AIS to develop into
a more ordered state.
In this paper,
η1
= 2 and l= 2 depicts that the synergistic innovation allocation domain can
accelerate the promotion of the agricultural innovation capability.
η2
= 1 indicates that the national
AIS can maintain the current situation without external influence.
η3
= 2 indicates that the national
AIS has a strong potential to develop into a more ordered state [
25
]. The synergy analysis model was
shown in Equation set (6):





dy1
dt =αθy1+βθy2−αβy1y2+2αy22
dy2
dt =−βθy2−αβy1y2+γy3
dy3
dt =−γy3+2αθy1
(6)
3.3. Application of Adiabatic Elimination Principle
The complex phenomena brought about by the collaboration among many subsystems can be
described by a few simple concepts. One of the main concepts is the order parameter; another is the
adiabatic elimination of the subsystem variables, which is based upon a hierarchy of time constants that
is present in most systems [
20
]. Close to the threshold, the dynamics of the system is then governed by,
in general, a few order parameters. That is, the system’s equations are satisfied by the order parameters
of the system. The adiabatic elimination principle allows for the elimination of the fast state variables
and then derives model equations by only including the slow state variables (the order parameters).

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In this paper, the innovation potentiality state variable was set as the order parameter, and the
logistical equation of the order parameter was established by means of the adiabatic elimination
principle. Here, dy2
dt =0 and dy3
dt =0, then the following results were obtained:
(y2=2αθ
β
y1
2y1+θ
y3=2αθ
γy1
(7)
The order parameter equation, as shown in Equation (8), was derived from Equations (6) and (7).
dy1
dt =αθy1+2αθ2y1
αy1+θ−2α2θy12
αy1+θ+8α3θ2
β2
y12
(αy1+θ)2(8)
4. Selection of Indicators and Data Sources
Our empirical analysis made use of the analytical framework presented above, investigating the
synergy of national AISs for the G20 countries, including Argentina, Australia, Brazil, Canada, China,
France, Germany, India, Indonesia, Italy, Japan, Republic of Korea, Mexico, Russian Federation, Saudi
Arabia, South Africa, Turkey, the United Kingdom, and the United States (note: The European Union
(EU) is a political and economic union, and its major member states are already within the G20, so the
EU was not included in this empirical analysis).
Using the analytical framework presented above, this section discussed a broad set of innovation
indicators, which are used to describe the three state variables (innovation allocation variable,
output variable, and potentiality variable) and the control variable, and identified their data sources.
The detailed indicators and their data sources are presented in Tables 2–5.

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Table 2. Index system of innovation allocation of national AISs. R&D: research and development.
Index Sub-Index Indicator Indicator Description and Units of Measure Sources of Data
Allocation of national AISs
Infrastructure
Agricultural machinery Tractors per 100 square kilometers of arable land, total number WB a
Information and
communication technology
Average value of individuals using the internet, fixed-telephone
subscriptions, and mobile-cellular telephone subscriptions per 100
inhabitants, headcounts
ITU b
Transportation infrastructure
Average value of roads, railroad, port, and air transport, index (1–7 scores)
[1 = extremely underdeveloped; 7 = extensive and efficient] WEF-GCR c
Research foundation
Ranking by agricultural sciences
Institutions ranking the world’s top 30 in agricultural sciences, total
number RCCSE d
R&D level of agricultural
enterprises World-famous agricultural enterprises, total number RIIET e
Human capital
Agricultural researchers Number of agricultural researchers (per million rural residents),
headcounts UNESCO-UIS f, FAO g
Peasant cultural quality School life expectancy (primary to tertiary), both sexes (years) UNESCO-UIS f
Collaboration foundation
University–industry
collaboration
To what extent do business and universities collaborate on R&D? Index
(1–7 scores) [1 = do not collaborate at all; 7 = collaborate extensively] WEF-GCR c
State of cluster development
How widespread are well-developed and deep clusters, index (1–7 scores)
[1 = nonexistent; 7 = widespread] WEF-GCR c
Notes:
a
WB: World Bank.
b
ITU: International Telecommunications Union.
c
WEF-GCR: World Economic Forum-Global Competitiveness Report.
d
RCCSE: Research Center for Chinese
Science Evaluation.
e
RIIET: Research Institute of International Economic and Trade (Beijing).
f
UNESCO-UIS: United Nations Educational, Scientific, and Cultural Organization-Institute
for Statistics. gFAO: Food and Agriculture Organization.
Table 3. Index system of innovation output of national AIS.
Index Sub-Index Indicator Indicator Description and Units of Measure Sources of Data
Output of national AISs
Scientific and technological
outcome
Agricultural journal papers
Number of agricultural journal papers (per million rural residents), number
Web of Science
Agricultural patent Number of agricultural patent (per million rural residents), number WIPO h
Plant varieties Number of plant varieties (per million rural residents), number WIPO h
Productivity level
Index of agricultural production
Average value of crop production index, food production index, and
livestock production index, index WB a
Agricultural land productivity Grain yield per unit area, kg/ha WB a
Agricultural labor productivity Agriculture value added per worker, US$ WB a
Economic performance Agricultural value added Agricultural value added, US$ WB a
Notes: aWB: World Bank. hWIPO: World Intellectual Property Organization.

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Table 4. Index system of innovation potentiality of national AISs.
Index Sub-Index Indicator Units of Measure
Potentiality of national AISs
Infrastructure potentiality
Growth rate of agricultural machinery %
Growth rate of information and communication technology
%
Growth rate of transportation infrastructure %
Research foundation potentiality Growth rate of ranking by agricultural sciences %
Growth rate of R&D level of agricultural enterprises %
Human capital potentiality Growth rate of agricultural researchers %
Growth rate of peasant cultural quality %
Collaboration foundation potentiality Growth rate of university–industry collaboration %
Growth rate of cluster development state %
Scientific and technological outcome potentiality
Growth rate of agricultural journal papers %
Growth rate of agricultural patent %
Growth rate of plant varieties %
Productivity level potentiality
Growth rate of index of agricultural production %
Growth rate of agricultural land productivity %
Growth rate of agricultural labor productivity %
Economic performance potentiality Growth rate of added agricultural value %
Table 5. Index system of the control variable of national AISs. ASTI: agricultural science, technology, and innovation.
Index Indicator Indicator Description and Units of Measure Sources of Data
Control variable of national AISs
Political stability Political stability and absence of violence/terrorism, index WB a
Support for ASTI
Agricultural knowledge and innovation system support, %
OECD i
Intellectual property protection Level of intellectual property protection, index WEF-GCR c
Policy rationality Agricultural policy costs, index WEF-GCR c
Notes: aWB: World Bank. cWEF-GCR: World Economic Forum-Global Competitiveness Report. iOECD: Organization for Economic Co-operation and Development.

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4.1. Innovation Allocation Indicators
The main indicators that have been used to describe the innovation allocation domain of
national AISs included various foundation and bridging aspects in national agricultural innovation.
Four sub-indexes were captured: (1) infrastructure; (2) research foundation; (3) human capital; and
(4) collaboration foundation. The first sub-index included agricultural machinery, information and
communication technology (ICT), and transportation infrastructure. They were proxies for enabling
the infrastructure needed to support innovation in the agricultural sector. The second sub-index
captured the country’s level of agricultural research and development (R&D) activities by using (1)
ranking by agricultural discipline and (2) R&D level of agricultural enterprises. The third sub-index
captured human capital investment in agricultural innovation. Innovation in many countries is
constrained by a lack of human capital, particularly in the fields of research and education. Thus,
the number of agricultural researchers was introduced as a proxy for losses of human capital stock
in the agricultural R&D fields. The rural education level is an important determinant of agricultural
innovation capability. Hence, peasant cultural quality was included as an indicator within this domain.
The fourth sub-index captured the importance of linkages in national agricultural innovation. This was
proxied by two indicators: (1) university–industry collaboration, which measured the extent to which
businesses collaborated with universities; and (2) the state of cluster development, which measured
how widespread well-developed and deep clusters were, based on expert opinion polls conducted by
the World Economic Forum (WEF). Many system-oriented indicators were included as the measures
of the degree of integration or connectedness of the AISs. For example, the ICT and transportation
network available to AIS institutions, as well as the collaboration indicators, might measure the extent
of research collaborations among key system actors, primarily public research organizations, public
universities, and private companies.
4.2. Innovation Output Indicators
Indicators that described the nature and performance of the innovation output domain of
national AIS could be divided into three general categories: (1) scientific and technological outcome;
(2) productivity level; and (3) economic performance. The scientific and technological outcome
captured the extent to which a country’s agricultural research and education system actually produced
some type of scientific and technological output. The commonly accepted measures of this were the
agricultural journal publications [
28
], the number of patents [
27
], and plant varieties generated by
research organizations and universities. The productivity level captured the extent to which scientific
and technological changes in agriculture could help foster productivity growth. These indicators were
the index of agricultural production, agricultural land productivity, and agricultural labor productivity.
The added agricultural value indicator was identified to capture the relationship between the national
AIS and its possible economic performance.
4.3. Innovation Potentiality Indicators
Indicators for the innovation potentiality domain were made up of the growth rate, during a
certain period, of all of the indicators from the innovation allocation domain and output domain.
Finally, it should be noted that these indicators combined a range of measures, including inputs
(tools and information used in innovation processes), outputs (knowledge, goods, and performance
resulting from innovation processes), and potentiality (growth rate of all of the above indicators), all of
which were potentially useful for the characterization of AISs.
4.4. Control Variable Indicators
The control variable of government policy represented the comprehensive index of government
policy supports for the synergy of national AISs. The control variable was described by means of
the following indicators: (1) political stability, describing the political environment; (2) support for

Sustainability 2018,10, 3385 11 of 20
agricultural science, technology, and innovation (ASTI), representing the agricultural policy supports
for the national AIS; (3) intellectual property protection, measuring the level of intellectual property
protection; and (4) policy rationality, indicating whether agricultural policy can balance well the
interests of taxpayers, consumers, and producers.
The data of indicators of the innovation allocation domain, innovation output domain, and control
variable of government policy were from 2015. Moreover, the data of indicators of the innovation
potentiality domain were made up of the growth rate, during a 10-year period (2005–2015), of all of
the indicators from the innovation allocation domain and output domain.
5. Empirical Results
5.1. Results of Parameters and the Control Variable
The values of the three adjustment parameters (
α
,
β
,and
γ
) and control variable
θ
were calculated
based on the calculation model [see Equations (1) and (2)]. As shown in Table 6, in 2015, the countries
whose innovation potentiality
α
ranked in 1~10 were China (0.8266), Turkey (0.2682), Brazil (0.2479),
Saudi Arabia (0.0887), Mexico (0.0833), Indonesia (0.0789), Argentina (0.0650), India (0.0635), Italy
(0.0554), and the Russian Federation (0.0516). The countries whose innovation allocation
β
ranked in
1~10 were the United States (1.5388), France (1.2223), Germany (1.2029), Japan (0.5793), Italy (0.3761),
the United Kingdom (0.3565), Australia (0.3308), Canada (0.3166), China (0.2380), and Brazil (0.2215).
The countries whose innovation output
γ
ranked in 1~10 were Japan (1.5783), Australia (1.4291), the
United States (1.2383), the Republic of Korea (1.0960), Canada (1.0305), France (0.9001), Argentina
(0.6604), Germany (0.6325), the United Kingdom (0.5659), and Brazil (0.5543).
Table 6. Results of the adjustment parameters and control variable.
Countries
Item 2015
α β γ θ
Argentina 0.0650 0.0920 0.6604 0.3514
Australia 0.0067 0.3308 1.4291 1.9692
Brazil 0.2479 0.2215 0.5543 0.7306
Canada 0.0065 0.3166 1.0305 1.5734
China 0.8266 0.2380 0.3284 0.7157
France 0.0204 1.2223 0.9001 1.1365
Germany 0.0365 1.2029 0.6325 1.2516
India 0.0635 0.0505 0.0411 0.2887
Indonesia 0.0789 0.0378 0.0645 0.4572
Italy 0.0554 0.3761 0.2594 1.0358
Japan 0.0096 0.5793 1.5783 0.9078
The Republic of Korea 0.0447 0.1501 1.0960 0.8505
Mexico 0.0833 0.1824 0.2498 0.7038
The Russian Federation 0.0516 0.0767 0.3011 0.4555
Saudi Arabia 0.0887 0.0469 0.3174 0.3596
South Africa 0.0341 0.0666 0.2612 1.1839
Turkey 0.2682 0.0985 0.2824 0.1710
The United Kingdom 0.0054 0.3565 0.5659 1.2345
The United States 0.0178 1.5388 1.2383 1.0532
There were obvious differences in the values of current control variables. The values were
divided into three categories: The high-level values (more than 1) included Australia (1.9692), Canada
(1.5734), Germany (1.2516), the United Kingdom (1.2345), South Africa (1.1839), France (1.1365), the
United States (1.0532), and Italy (1.0358); the mid-level values (0.5 to 1) included Japan (0.9078), the
Republic of Korea (0.8505), Brazil (0.7306), China (0.7157), and Mexico (0.7038); and the low-level values

Sustainability 2018,10, 3385 12 of 20
(less than 0.5) included Indonesia (0.4572), the Russian Federation (0.4555), Saudi Arabia (0.3596),
Argentina (0.3514), India (0.2887), and Turkey (0.1710).
5.2. Level of Synergy Innovation of National AISs
In order to identify the level of synergy innovation of the G20 countries’ national AISs
under the current control variables, the solutions of the order parameter equation were calculated
(see Equation (8)] over time (shown in Figure 2). All of the computations were performed with the
help of Matlab 2016a.
Sustainability 2018, 10, x FOR PEER REVIEW 12 of 20
5.2. Level of Synergy Innovation of National AISs
In order to identify the level of synergy innovation of the G20 countries’ national AISs under the
current control variables, the solutions of the order parameter equation were calculated (see Equation (8)]
over time (shown in Figure 2). All of the computations were performed with the help of Matlab 2016a.
As can be seen in Figure 2, all of the evolutionary trajectories of the order parameters of G20
countries under current control variables increased with time. This indicated that the subsystems
matched and synergized with each other, promoting the transformation of the national AIS into a
more advanced self-organized synergistic state. Moreover, the trajectory groups were divided into
three categories. The first-echelon national AISs were in the active period of synergy, the second-
echelon national AISs were in the stable period, and the third-echelon national AISs were in the
general period.
(1) Active synergy
The evolutionary trajectories of the order parameters in the first-echelon national AISs, including
Indonesia (Magenta), Saudi Arabia (Turquoise1), China (Yellow), India (IndianRed1), Brazil
(Blue), South Africa (ForestGreen), the Russian Federation (Firebrick), Mexico (Gold), Argentina
(Red), Turkey (SpringGreen), and the Republic of Korea (NavyBlue) showed a steep rise in a
short amount of time because of the fast-lifting values of the order parameters in the 0–40 period.
The results indicated that the countries in the first echelon had national AISs in the active period
of synergy, and the top five countries expressed especially stronger synergistic rising powers.
(2) Stable synergy
The evolutionary trajectories of the order parameters of the second-echelon national AISs
steadily increased in the 0–70 period, and the rising trend in Italy (Grey) was more significant
than that in Germany (Pink). The second-echelon countries were in the stable period of synergy,
in which synergistic rising powers were relatively weak.
(3) General synergy
A common weakness of the third-echelon national AISs was that there was no significant order
parameter in the 0–40 period, and low increasing evolutionary trajectories of the order
parameters in the 40–70 period. The results might be attributed to the weaker synergistic rising
powers. The third-echelon included France (LightSeaGreen), the United States (DeepPink),
Australia (Green), Canada (Orange), Japan (Black), and the United Kingdom (Purple), which
were in the general period of synergy.
Figure 2. Evolutionary trajectories of the order parameters under the current control variables.
Figure 2. Evolutionary trajectories of the order parameters under the current control variables.
As can be seen in Figure 2, all of the evolutionary trajectories of the order parameters of G20
countries under current control variables increased with time. This indicated that the subsystems
matched and synergized with each other, promoting the transformation of the national AIS into a more
advanced self-organized synergistic state. Moreover, the trajectory groups were divided into three
categories. The first-echelon national AISs were in the active period of synergy, the second-echelon
national AISs were in the stable period, and the third-echelon national AISs were in the general period.
(1) Active synergy
The evolutionary trajectories of the order parameters in the first-echelon national AISs, including
Indonesia (Magenta), Saudi Arabia (Turquoise1), China (Yellow), India (IndianRed1), Brazil (Blue),
South Africa (ForestGreen), the Russian Federation (Firebrick), Mexico (Gold), Argentina (Red), Turkey
(SpringGreen), and the Republic of Korea (NavyBlue) showed a steep rise in a short amount of time
because of the fast-lifting values of the order parameters in the 0–40 period. The results indicated that
the countries in the first echelon had national AISs in the active period of synergy, and the top five
countries expressed especially stronger synergistic rising powers.
(2) Stable synergy
The evolutionary trajectories of the order parameters of the second-echelon national AISs steadily
increased in the 0–70 period, and the rising trend in Italy (Grey) was more significant than that
in Germany (Pink). The second-echelon countries were in the stable period of synergy, in which
synergistic rising powers were relatively weak.
(3) General synergy
A common weakness of the third-echelon national AISs was that there was no significant order
parameter in the 0–40 period, and low increasing evolutionary trajectories of the order parameters in the
40–70 period. The results might be attributed to the weaker synergistic rising powers. The third-echelon
included France (LightSeaGreen), the United States (DeepPink), Australia (Green), Canada (Orange),
Japan (Black), and the United Kingdom (Purple), which were in the general period of synergy.

Sustainability 2018,10, 3385 13 of 20
5.3. Change of Order Parameters under Different Control Variables
The evolutionary trajectories of the order parameters of the G20 countries under different control
variables are shown in Figure 3. The red ‘*’, blue ‘
−
’, and orange ‘+’ represent the simulation results
under the current control variables, which are 0.5 greater than the current value, and one-half of the
current value, respectively.
Sustainability 2018, 10, x FOR PEER REVIEW 13 of 20
5.3. Change of Order Parameters under Different Control Variables
The evolutionary trajectories of the order parameters of the G20 countries under different control
variables are shown in Figure 3. The red ‘*’, blue ‘−’, and orange ‘+’ represent the simulation results
under the current control variables, which are 0.5 greater than the current value, and one-half of the
current value, respectively.
(a) Argentina (b) Australia
(c) Brazil (d) Canada
(e) China (f) France
(g) Germany (h) India
0 100 200 300
0
200
400
600
800
1000
0 500 1000 1500
0
500
1000
1500
0 1020304050
0
50
100
150
200
250
300
0 500 1000 1500 2000
0
500
1000
1500
0 1020304050
0
50
100
150
200
250
300
0 200 400 600 800 1000
0
50
100
150
200
250
300
0 100 200 300 400 500
0
50
100
150
200
0 100 200 300 400 500
0
500
1000
1500
2000
2500
3000
Figure 3. Cont.

Sustainability 2018,10, 3385 14 of 20
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(i) Indonesia (j) Italy
(k) Japan (l) Republic of Korea
(m) Mexico (n) Russian Federation
(o) Saudi Arabia (p) South Africa
(q) Turkey (r) the United Kingdom
0 100 200 300 400 500
0
1000
2000
3000
4000
5000
6000
7000
0 100 200 300 400 500
0
50
100
150
200
0 500 1000 1500 2000
0
100
200
300
400
500
600
0 100 200 300 400
0
200
400
600
800
0 100 200 300
0
100
200
300
400
500
0 100 200 300 400 500
0
500
1000
1500
0 50 100 150 200
0
1000
2000
3000
4000
0 100 200 300
0
1000
2000
3000
4000
0 50 100 150
0
200
400
600
800
0 500 1000 1500 2000 2500
0
500
1000
1500
Figure 3. Cont.

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Sustainability 2018, 10, x FOR PEER REVIEW 15 of 20
(s) the United States (t) average-value of G20
Figure 3. Change trajectories of the order parameters under different control variables.
As shown in Figure 3, on the one hand, when the values of the control variables were greater
than the current values (e.g., 0.5 greater than the current values), the order parameters of national
AISs were at higher levels from the beginning. The greater the values of the control variables, the
higher the order parameters. Nevertheless, when the values of the control variables were lower than
the current values (e.g., one-half of the current values), the order parameters of national AISs were at
poor levels from the start. On the other hand, when the extent of the control variable promotion was
the same (0.5 greater than the current values), the order parameters of some countries (India,
Indonesia, Saudi Arabia, and South Africa) (raised above 1000) improved significantly compared
with others (Brazil, China, France, Germany, Italy, and the United States) (raised below 100). The
results indicated that there were more obstacles for countries without significant improvements to
achieving a more advanced self-organized synergistic state.
6. Discussion
In this section, we carried out the discussion of research results in relation to the synergy of G20
countries’ national AISs, taking into account sustainable agricultural development.
6.1. Optimization of Innovation Indicators
The results of parameters inform us that most developed countries were stronger in innovation
allocation and innovation output, but weaker in innovation potentiality; on the contrary, most
developing countries had comparatively stronger innovation potentialities, but a weaker
performance in innovation allocation and innovation output. Better innovation elements are the
foundation of active synergy innovation. Most of the developed countries that had a relatively higher
innovation allocation and innovation output, but lower innovation potentiality, especially with
weaker synergistic rising powers (e.g., France, the United States, Australia, Canada, Japan, and the
United Kingdom), should focus on strengthening the indicators with a weak innovation potentiality
(as shown in Table 7) while maintaining high standards of innovation allocation and innovation
output. Note: ‘√’ marks the three weakest indicators.
Table 7. Weak potential indicators for the Group of Twenty (G20) developed countries.
Countries
Indicators Australia Canada France Germany Italy Japan
The
Republic of
Korea
The United
Kingdom
The
United
States
Growth rate of agricultural
machinery
Growth rate of information
and communication
technology
Growth rate of transportation
infrastructure √ √ √ √ √ √ √
Growth rate of ranking by
agricultural sciences
0 500 1000 1500
0
50
100
150
200
250
300
0 50 100 150
0
50
100
150
Figure 3. Change trajectories of the order parameters under different control variables.
As shown in Figure 3, on the one hand, when the values of the control variables were greater than
the current values (e.g., 0.5 greater than the current values), the order parameters of national AISs were
at higher levels from the beginning. The greater the values of the control variables, the higher the order
parameters. Nevertheless, when the values of the control variables were lower than the current values
(e.g., one-half of the current values), the order parameters of national AISs were at poor levels from the
start. On the other hand, when the extent of the control variable promotion was the same (0.5 greater
than the current values), the order parameters of some countries (India, Indonesia, Saudi Arabia, and
South Africa) (raised above 1000) improved significantly compared with others (Brazil, China, France,
Germany, Italy, and the United States) (raised below 100). The results indicated that there were more
obstacles for countries without significant improvements to achieving a more advanced self-organized
synergistic state.
6. Discussion
In this section, we carried out the discussion of research results in relation to the synergy of G20
countries’ national AISs, taking into account sustainable agricultural development.
6.1. Optimization of Innovation Indicators
The results of parameters inform us that most developed countries were stronger in innovation
allocation and innovation output, but weaker in innovation potentiality; on the contrary, most
developing countries had comparatively stronger innovation potentialities, but a weaker performance
in innovation allocation and innovation output. Better innovation elements are the foundation of
active synergy innovation. Most of the developed countries that had a relatively higher innovation
allocation and innovation output, but lower innovation potentiality, especially with weaker synergistic
rising powers (e.g., France, the United States, Australia, Canada, Japan, and the United Kingdom),
should focus on strengthening the indicators with a weak innovation potentiality (as shown in Table 7)
while maintaining high standards of innovation allocation and innovation output. Note: ‘
√
’ marks
the three weakest indicators.
In contrast, most of the developing countries with a lower innovation allocation and innovation
output, but higher innovation potentiality, should pay more attention to the promotion of the weaker
indicators of innovation allocation and output (see Tables 8and 9). In addition, attracting more foreign
resources (such as innovation personnel and money) and relying on excellent potentiality advantages
will be a good way to make up for the shortcomings of the developing countries.

Sustainability 2018,10, 3385 16 of 20
Table 7. Weak potential indicators for the Group of Twenty (G20) developed countries.
Indicators
Countries Australia Canada France Germany Italy Japan The Republic
of Korea
The United
Kingdom
The United
States
Growth rate of agricultural
machinery
Growth rate of information
and communication
technology
Growth rate of
transportation infrastructure
√ √ √ √ √ √ √
Growth rate of ranking by
agricultural sciences
Growth rate of R&D level of
agricultural enterprises √
Growth rate of agricultural
researchers
Growth rate of peasant
cultural quality √
Growth rate of
university–industry
collaboration
Growth rate of cluster
development state √ √ √
Growth rate of agricultural
journal papers √ √ √ √ √ √ √
Growth rate of agricultural
patent √ √ √ √
Growth rate of plant
varieties √
Growth rate of index of
agricultural production
Growth rate of agricultural
land productivity
Growth rate of agricultural
labor productivity √ √ √
Table 8. Weak allocation indicators for the G20 developing countries.
Indicators
Countries Argentina Brazil China India Indonesia Mexico The Russian
Federation
Saudi
Arabia
South
Africa Turkey
Agricultural
machinery √ √ √ √ √ √ √ √
Information and
communication
technology
Transportation
infrastructure
Ranking by
agricultural sciences √ √ √ √ √ √ √ √
R&D level of
agricultural enterprises
√ √ √ √ √ √ √ √ √
agricultural
researchers √ √ √ √ √
Peasant cultural
quality
University–industry
collaboration
State of cluster
development

Sustainability 2018,10, 3385 17 of 20
Table 9. Weak output indicators for the G20 developing countries.
Indicators
Countries Argentina Brazil China India Indonesia Mexico The Russian
Federation
Saudi
Arabia
South
Africa Turkey
Agricultural journal
papers √ √ √ √ √
Agricultural patent √ √ √ √ √ √ √ √ √ √
Plant varieties √ √ √ √ √ √
Index of agricultural
production
Agricultural land
productivity
Agricultural labor
productivity √ √ √ √ √ √
Agricultural value
added √ √ √
6.2. Improvement of National AIS
National AIS is the organization and operation carrier of the national ASTI activities [
6
].
The complex structures of the national AIS lead to the formation and development of ASTI capability
through multiple factors. Therefore, the fundamental way to enhance capability is to break through
the bottlenecks that limit synergy by constructing national AISs. Most of the developed countries
have formed relatively perfect national AISs, although they were all in the stable period (including
Italy and Germany) or the general period of synergy (including France, the United States, Australia,
Canada, Japan, and the United Kingdom), in which weaker synergistic rising powers are expressed.
Meanwhile, as a dynamic concept, ASTI has different contents and requirements in different periods.
Therefore, the national AIS also needs to keep pace with the times to achieve continuous improvement.
The developed countries should pay attention to the further optimization of the existing national AIS.
All of the developing countries of the G20 are in the active period of synergy. However, most of them
have not completely set up a national AIS in line with the social and economic development progresses.
In order to achieve sustainable synergetic development, governments should establish or promote
the existing structure of the AIS, based on the current situation of their national AIS and extensive
references to some mature AIS models [
6
,
9
]. The core content of system optimization is to improve the
balance and synergy among the various components, systematize the scattered parts, and integrate the
divergent functions.
6.3. Promotion of Government Policy Supports
The change results of the order parameters under different control variables illustrated that
strong government policy supports played a positive role in promoting interaction and collaboration
among innovation elements. Three suggestions were proposed based on the indicators of the control
variable of government policy (Political stability, Support for ASTI, and Intellectual property protection
and policy rationality). First, political instability is regarded by economists as a serious malaise
that is harmful to economic performance [
29
,
30
]. By increasing uncertainty about the future, it may
lead to a less efficient resource allocation and lower productivity growth, and reduce human capital
accumulation as well as research and development efforts by innovative actors, leading to slower
technological progress [
30
]. Thus, governments should endeavor to build a peaceful and stable
international environment for economic development, including agricultural innovation development.
Second, policy-makers need to pay more attention to agricultural science, technology, and innovation.
They should get rid of the ideological obstacles and institutional barriers in order to improve the
management of innovative personnel, increase the innovative investment, and attach importance to
intellectual property protection. Third, the rationality of the promotion of the agricultural innovation
policy includes: (1) choosing the self-organization development route, which is a highly optimized
way of evolving nature and society over a long period of time [
31
], following the self-organizing rule of
national AISs; (2) strengthening the macro coordination of innovation policies and regulations, as well

Sustainability 2018,10, 3385 18 of 20
as dealing with the central–local government relations, internal relations of science departments, and
science–non-science department relations in order to avoid repeated policy deployments; (3) improving
the efficiency of the formulation of agricultural innovation policy, promoting the innovation policy
implementation, as well as emphasizing policy monitor.
7. Conclusions
The existing literature about AISs has made considerable progress [
32
,
33
], but has failed
to empirically investigate the synergy of national AISs based on the self-organization theory
and methodology.
This paper aimed to explore this new direction in AIS research. First, an analytical model
for the study of the synergy of national AISs based on the B-Z reaction was developed. A set of
logistical regression equations consisting of three state variables (innovation allocation variable,
innovation output variable, and innovation potentiality variable), three adjustment parameters, and
one control variable was used to simulate the process of the synergy evolution of national AISs.
Secondly, our empirical analysis selected a broad set of indicators describing the three state variables
and control variable, calculated the values of the three adjustment parameters corresponding to the
state variables and the control variable for a panel of G20 countries, and then obtained the evolutionary
trajectories of the order parameters under different control variables. The evolutionary trajectories
of the order parameters were used to investigate the self-organized synergistic patterns of the G20
countries. Thirdly, some policy suggestions, simultaneously taking into account the optimization of
the innovation indicators, improvement of the national AISs, and promotion of government policy
supports, were provided.
On the whole, this study contributed to theory-building and policy-making in the following
ways. For theory building, the investigation of the synergy of national AISs could complement
and inform the research of AISs and offer a better insight into the synergistic attributes of systems.
Good synergy among the various innovation elements contributes to the sustainable development of
AISs. Moreover, sustainable AISs help to speed up innovation production, diffusion, and translation
in a sustainable manner. For policy-making, public policies, based on three aspects (optimization of
innovation indicators, improvement of national AISs, and promotion of government policy supports)
should help to promote the rational allocation of agricultural innovation resources, optimize the
synergistic relationship of the multi-components, and thereby achieve the sustainable development
of AISs.
The limitations of our study are certainly interesting and worthy of further exploration. A national
AIS is a complex system whose synergy is driven by various factors. Any given change in one of
the factors composing an AIS has a series of direct and indirect effects on the synergy innovation
process. This study explicitly analyzed and selected a broad set of innovation indicators describing the
three state variables and the control variable, although it was still a relatively simple indicator index
within which some practical complexities were not taken into account. We will attempt to investigate
more detailed indicators for a better simulation of national AISs and develop objectives and specific
strategies for government policy supports in the future.
Author Contributions:
D.W. and X.G. conceived and designed this study. D.W. processed the data, performed the
experiments and wrote the paper. X.D. collected the data. J.S. provided valuable guidance in the different stages of
implementation. Y.C. provided the editing service. All the authors contributed to the analysis and interpretation
of the results. They all read and approved the final manuscript.
Funding:
This research is supported by the Chinese Postdoctoral Science Foundation (2017M621239) and the
Postdoctoral Science Foundation of Heilongjiang Province (LBH-Z17007).
Acknowledgments:
We are grateful for the helpful insights and suggestions from the editor of this journal,
Vanessa Bu, as well as the anonymous referees.
Conflicts of Interest: The authors declare no conflict of interest.

Sustainability 2018,10, 3385 19 of 20
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