Temporal and spatial evolution of global major grain trade patterns

Ziqi Yin ,Jiaxuan Hu ,Jing Zhang ,Xiangyang Zhou ,Lingling Li ,Jianzhai Wu#

1 Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China

2 Institute of Scientific and Technical Information of China, Beijing 100038, China

3 Zibo Agricultural Science Research Institute, Zibo 255000, China

4 Zibo Institute for Digital Agriculture and Rural Research, Zibo 255000, China

Abstract The complex and volatile international landscape has significantly impacted global grain supply security. This study uses a complex network analysis model to examine the evolution and trends of the global major grain trade from 1990 to 2020,focusing on network topology,centrality ranking,and community structure. There are three major findings. First,the global major grain trade network has expanded in scale,with a growing emphasis on diversification and balance. During the study period,the United States,Canada,China,and Brazil were the core nodes of the network. Grain-exporting countries were mainly situated in Asia,the Americas,and Europe,and importing countries in Asia,Africa,and Europe. Second,a significant increase in the number of high centrality countries with high export capacity occurred,benefiting from natural advantages such as fertile land and favorable climates. Third,the main global grain trade network is divided into four communities,with the Americas-Europe community being the largest and most widespread. The formation of the community pattern was influenced by geographic proximity,driven by the core exporting countries. Therefore,the world needs to enhance the existing trade model,promote the multi-polarization of the grain trade network,and establish a global vision for the future community. Countries and regions should participate actively in global grain trade security governance and institutional reform,expand trade links with other countries,and optimize import and export policies to reduce trade risks.

Keywords: grain trade,pattern evolution,complex network

1.Introduction

With the rapid development of economic globalization,grain trade links among countries have been strengthened(Porkkaet al.2013;Gephartet al.2016;Cottrellet al.2019). From 1990 to 2020,global grain exports increased from 36.48 billion USD to 124.11 billion USD annually.About 80% of the world’s population needs to adjust their grain needs through imports (Porkkaet al.2013),and more than a quarter of grain demand is fulfilledviainternational trade (D’Odoricoet al.2014). Grain trade has become an essential method to balance the regional discrepancy between grain supply and demand (Pumaet al.2015). The world has formed an interconnected trade network,and the evolution and development of the grain trade network have a profound impact on the grain security of countries and regions (Porkkaet al.2013;de Raymondet al.2021).

With the continuous advance of globalization,an increasing number of scholars analyze the global trade pattern. Existing analyses have constructed simulation models using mathematical algorithms and verified their reliability,revealing new characteristics and trends in international trade patterns. For example,Vidmeret al.(2015) analyzed the future development trends of international trade based on the link prediction algorithm and proposed a new method for measuring product similarity;Erokhin and Gao (2020) studied the impact of COVID-19 on grain trade security of developing countries using autoregressive distributed lag and variance analysis methods.

Complex network analysis can quantitatively assess the internal links and characteristics of complex systems and is increasingly used to study trade links of specific industries or products worldwide (O’Bannonet al.2014).Research on complex network theory can be traced back to the “Seven Bridges of Königsberg” problem proposed by mathematician Euler in the 18th century. At the end of the 20th century,breakthrough progress was made in the study of complex network theory,revealing the small-world characteristics and scale-free properties of complex networks and establishing corresponding models to explain their mechanisms (Watts and Strogatz 1998;Barabasiet al.1999). With the deepening of research,scholars have proposed many concepts and measurement methods for characterizing the statistical characteristics of the network topological structure (Newman 2003).Currently,the complex network has developed into an interdisciplinary subject spanning multiple fields and are increasingly being applied to the study of global trade network (Serrano and Boguñá 2003) as well as the trade network of specific industries or bulk commodities such as energy,minerals,crude oil,rare earth resources (Anet al.2014;Wanget al.2016,2020a).

At present,domestic and foreign scholars have applied the complex network theory to the agricultural field to study agricultural trade and grain safety. Ercsey-Ravaszet al.(2012) found that the international agricultural product trade network has evolved into a highly heterogeneous and complex supply chain network,with each country trading with more than 77% of the countries in the world;Torreggianiet al.(2018) studied the community structure of international agricultural trade between 2001 and 2011 and believed that geopolitical and economic factors explained the possibility of coexistence of any two countries in the same community;Ercsey-Ravaszet al.(2012) found that import relation between agricultural trading countries is often closer than the export relation,and agricultural product price shocks play an important role in shaping the community structure of the global grain system. Some scholars also introduced the complex network theory into the study of trade network patterns of specific crops,such as wheat (Fairet al.2017),rice (Udhayakumar and Karunakaran 2020),corn (Wu and Guclu 2013),soybean (Sunet al.2018),or constructed a virtual water network for global agricultural product trade from the perspective of resources and environment to study issues such as water resource security (Carret al.2013;Sartori and Schiavo 2015).

The countries participating in grain trade constitute a complex system. Complex network theory provides an effective quantitative and visual tool for analyzing global grain trade relations and can intuitively express multilateral grain trade relations among countries (Wanget al.2020b). The global grain trade network can be defined as a network representing the multilateral grain trade relations between countries each year.

However,most of the existing studies have a limited time span and a relatively simple overall pattern perspective. Most studies on grain trade are purely from the time dimension,based on short time series to study the evolution of international grain trade,or based on cross-sectional data to compare the horizontal regions,with few studies based on the perspective of time and space over a long-time span and the perspective of the change of the internal structure of mining communities.With the rapid development of globalization and the vicissitudes of the international trade situation in recent years,it is necessary to accurately and comprehensively grasp the changing rules of the grain trade pattern,not only based on the research cycle with a large span but also from the perspective of combining time and space. At the same time,nodes in complex networks show cluster characteristics,reflecting the distribution and interconnection of communities gathered in a large network,which is the cohesive tendency of the network.Considering the specific spatial distribution and dynamic adjustment of different trade community structures can provide a new perspective for international grain trade research. Analyzing the changes in the international grain trade pattern and the trade relations among countries can theoretically further understand the evolution law and development trends of global grain trade,promote research on the mechanism of the new trade situation’s impact on regional grain security,and guide countries or regions to optimize grain imports and exports in practice.

2.Data and methods

2.1.Data and network construction

This study selects wheat,rice,and corn as the representative research objects for grain varieties. In 2020,the trade volume of these three accounted for 85.53% of the total grain trade volume,meeting nearly 50% of the global heat demand,which can represent main grain crops that attract the attention of governments and scholars (Kassayeet al.2021). Grain trade data are derived from the UN Comtrade database and the FAOSTAT detailed trade matrix. The concepts of wheat,rice,and corn varieties refer to the specifications,classifications,and standards of FAOSTAT and the standards of the International Convention on the Harmonized Commodity Description and Coding System.The FAOSTAT grain (product) standard code is used as the unified retrieval condition when downloading data for data retrieval and summary to build a database of grain trade relations from 1990 to 2020. In order to make the network structure clearer and more readable,this study selects trade data with grain trade value greater than or equal to 100 million USD to better analyze major global grain trade relations (Wanget al.2020a). A total of 222 countries’trade data covered by UN Comtrade and FAOSTAT from 1990 to 2020 were collected and collated to analyze the global grain trade situation. The years 1990,2005,and 2020 were selected as specific examination years to analyze the dynamics and spatiotemporal evolution trends of the main grain trade network.

This study constructs a directed weighted network model of global major grain trade. In the network,nodes represent the countries participating in grain trade,edges connecting nodes represent the trade relations between two countries,and the edge direction represents the trade flow,that is,from the exporting country to the importing country. The grain trade volume between countryiand countryjis used to assign values to the edge as the edge weightswi,j. According to UN Comtrade,trade volume in this study is measured by trade value (currency in USD).Each transaction has corresponding exports and imports.In theory,export value should be equal to import value,but in practice,data inconsistencies may occur due to different statistical calibers or reporting biases in exporting and importing countries. For the asymmetry of bilateral trade data,this study adopts the processing method of previous studies (Huet al.2020b;Wanget al.2020b),usingmax{wi,j,wj,i} as the weight of the edge between countriesiandj.

2.2.Basic topology parameters of the network

Centrality reflects the “influence” of a sample in the network,which can be divided into degree centrality,betweenness centrality,and closeness centrality. Degree centrality (Ci) is a core indicator in complex networks that evaluates the position of a node in the network and measures its ability to expand connections with other points. Betweenness centrality (BCi) describes the extent to which a node is “in the middle” of two other nodes and measures its ability to control the connection between them. The formulas for degree centrality and betweenness centrality are as follows:

where,i,j,andkrepresent nodes,Nrepresents the total number of nodes,lijrepresents the connection strength fromitoj,gjkrepresents the number of shortcuts betweenjandk,andgjk(i) represents the number of shortcuts betweenjandkthroughi.

Degree distribution analysis is an overall and direct description of the structural properties and probability distribution of nodes in the network,which is an important part of complex network analysis. Node degree (ki)represents the number of edges connected byi,and the more edges connected,the more connectionsihas to other nodes in the network. In a directed network,node degree is distinguished by out-degree (kiout) and in-degree(kiin),defined as (Wanget al.2020b):

where,k(j,i) represents the variable with the value of 0 or 1,and it is equal to 1 when edge (j,i) exists and 0 otherwise. In-degree represents the nodal ability to engage in import trade,while out-degree represents the nodal ability to engage in export trade.

Average network degreeP(k) is the mean value of all nodes in the network. The more connections between nodes,the higher the average network degree. As scalefree networks are a common type of networks,this study uses ordinary least squares regression to estimate the power-law exponentγof the degree distribution,as shown below (Huet al.2020a):

where,candγare coefficients,withc>0 andγ>0.

Network densityρmeasures the closeness of connections among network nodes. In a directed network,network density represents the ratio of the actual number of nodes connected to the theoretical maximum number of connections. The higher the network density,the more prosperous global grain trade is. It is defined as (Huet al.2020b):

where,Eis the actual number of connections.

Community structure analysis reveals the actual or potential relations between nodes and essentially groups nodes based on connection density,with more connections within groups and sparse connections between groups (Penget al.2020). In this study,the community detection algorithm developed by Blondelet al.(2008) is used to partition the global food trade network into communities.

3.Results

3.1.Analysis of global grain trade networks

Global grain trade networkThe network centrality ranking of countries in 1990,2005,and 2020 are shown in Table 1. For degree centrality,the United States,Canada,China,and Brazil all ranked in the top ten for the three-year period examined,with the United States consistently ranked first as the “center” of the grain trade network. In 2020,Ukraine’s centrality degree rapidly increased. In terms of betweenness centrality,in 1990,only the United States was located on the shortcut between other countries. With the development of grain trade,the number of countries playing a “betweenness”role increased gradually. In 2020,France,Germany,and Italy surpassed the United States,becoming the top three countries with the highest betweenness centrality and could largely influence and control the grain trade relations with other countries,weakening the monopoly position of the United States.

Table 1 Ranking of countries’ centrality of grain trade networks in 1990,2005,and 2020

The top five countries ranked by out-degree and indegree of grain trade networks in 1990,2005,and 2020 are shown in Table 2. The United States consistently ranks first in terms of out-degree. In 2020,Ukraine came in second and Argentina third. Countries with high grain export trade also include Canada,Thailand,and India.Overall,countries with a high out-degree are mainly located in Asia,the Americas,and Europe. In terms of indegree,Japan,China,and Egypt ranked in the top five in all three study periods. In 2020,China became the country with the highest in-degree,importing 22.36 million tons of staple grains,up 113% year-on-year,with a high demand for grain imports. Overall,countries with a high in-degree are mainly located in Asia,Africa,and Europe.

Table 2 Ranking of countries in out-degree and in-degree grain trade networks in 1990,2005,and 2020

The average network degree of the grain trade network gradually increased in 1990,2005,and 2020 (Table 3),indicating that participating countries are trading with more countries,and the whole grain trade network is becoming more interconnected. Simultaneously,the network density gradually increases,which indicates that the distributionof countries in the grain trade network is becoming more compact,and the grain trade links between countries are getting closer.

Table 3 Network degree in 1990,2005,and 2020

In general,the number of nodes in the global grain trade network has increased significantly,and their distribution has become increasingly compact. Moreover,the connections among nodes have become more complex,and the network has shown an increasing number of countries with higher centrality (Fig.1). In 1990,the United States was the center of global grain trade,with trade flows largely exported,with the trade flow mainly oriented towards exports to the Soviet Union,Japan,China,Egypt,and South Korea;Australia and Canada also had relatively high centrality,with Canada exporting more to the Soviet Union and China,while Australia mainly exports to China and other Asian countries. In 2005,the United States remained the center of the grain trade network,with Thailand,China,Canada,and Argentina also having high centrality. China mainly imported from the United States,Canada,and Thailand and exported to Japan and Korea. In 2020,the United States exported more to Japan,Mexico,and China,and it had close bilateral links with Canada. Ukraine occupied an important position in the network,with all trade flows oriented towards exports to China,Indonesia,and Egypt.India,Brazil,Canada,and other countries also had high centrality.

Grain trade network in the United States Considering the importance and unique characteristics of global grain trade,this study analyzes the trade network structure of the top five countries ranked by degree centrality in 2020. As the center of the global grain trade networkover the years,the United States has a complex and interconnected individual network structure (Fig.2). In 2020,the United States exported a greater weight to European and Asian countries such as Mexico,Japan,China,and Korea,while importing from India and Canada.As the world’s largest grain exporter,the United States’agricultural exports amounted to 146 billion USD in 2020,an increase of nearly 7% compared with the previous year,reaching a historic second-high level (second only to 2014). The surge in export value was mainly due to increased exports of soybeans,corn,and pork to China.Among them,the export value of wheat increased by 1%,reaching 6.3 billion USD;the export value of rice increased by 1.5%,reaching 1.9 billion USD;and the export value of sorghum increased 1.6 times,reaching 837 million USD.

During all three study periods,the United States consistently had a relatively developed grain trade network structure and remained in a central position in the network,with many trading partners distributed widely.As time passed,the number of countries and the density of the food trade network in the United States showed a further increasing trend,and the network structure became more complex. In general,the network had a structure that emanated outward from the United States,indicating that most of the United States’ grain trade flows were exports.

The United States has the largest arable land area in the world,and its major grain products include wheat,corn,and soybeans. Most of the United States is a temperate and subtropical climate. With warm and humid weather due to being surrounded by three sides by the sea,the United States is suitable for crop growth.At the same time,the United States government has long focused on supporting agricultural technology,and the huge agricultural research system has produced huge socio-economic benefits. The United States grain yield per unit area is higher than that of other countries and even higher than the world average. In the past fifty years,the United States has been the leader in international agricultural markets,especially in international grain trade,including setting international trade rules as well as dominating grain price changes.Three of the world’s four largest grain merchants are American companies,which control 80% of the global grain trading volume,and the United States controls over half of the world’s grain markets and has significant pricing power over grain.

Fig. 1 Global grain trade networks. Abbreviations in the figure are country codes and the corresponding country names are given in Appendix A.

Fig. 2 Grain trade networks in the United States. Abbreviations in the figure are country codes and the corresponding country names are given in Appendix A.

Grain trade network in UkraineUkraine,as an emerging grain trade power in recent years,did not participate in the grain trade network measured in the first two periods. In 2020,Ukraine had a well-developed and connected individual grain trade network structure that is centered on the country and spreads outward (Fig.3).It indicates that Ukraine’s grain trade has developed rapidly,with all flows being exports,and significant export weights to Asian and European countries such as China,Indonesia,Egypt,Germany,and the Netherlands.

For the global grain trade pattern,Ukraine’s participation is indispensable. In 2020,Ukraine produced 55.26 million tons of grain,with a yield of 4.94 tons per hectare,and exported 46.01 million tons,accounting for 83% of total production. As one of the three largest black soil regions in the world,Ukraine accounts for 40% of the total black soil area in the world. The fertile soil has strengthened Ukraine’s position as the second largest grain exporter and has given it the name of the “grain barn of Europe”. According to the data from Ukraine’s Ministry of Economy,Trade and Agriculture,Ukraine had 42.56 million hectares of agricultural land in 2020,accounting for 70% of the country’s territory. In recent years,Ukraine has been committed to strengthening logistics infrastructure,such as the construction of new grain transshipment terminals in the Black Sea port,which has made it possible to rapidly increase Ukraine’s grain exports.

Fig. 3 Grain trade network in Ukraine in 2020. Abbreviations in the figure are country codes and the corresponding country names are given in Appendix A.

However,the conflict between Russia and Ukraine today has had a strong impact on the global grain trade.The wheat exports of Ukraine and Russia together account for 30% of global exports,and corn exports account for 20% of global exports. More than 95% of Ukraine’s grain,wheat,and corn are exported through the Black Sea. Since the outbreak of the Russia-Ukraine conflict,Ukraine’s Black Sea ports,such as Mariupol and Odessa have been controlled by Russia,forcing most of Ukraine’s grain exports to be interrupted,resulting in a sharp decline in grain exports and a rapid increase in international grain prices,which disrupts the international grain trade market and poses a great threat to grain security in countries that are more dependent on grain imports from Ukraine.

Grain trade network in Argentina Argentina did not have an individual trade network in the first study period.In 2020,Argentina and Brazil formed a more characteristic symmetrical structure of the network,with most countries that imported grain from Argentina also importing from Brazil (Fig.4). Among them,Argentina exports a greater weight to African,American,and Asian countries such as Algeria,Egypt,Brazil,Chile,Peru,Vietnam,and Indonesia.

Argentina’s grain trade has gradually developed over time. In 1990,Argentina’s global grain trade was too small to be included in the grain trade network. In 2005,Argentina increased its grain export capacity to Brazil,Egypt,Peru,Algeria,Saudi Arabia,and Chile. In 2020,Argentina’s grain trade had developed rapidly,with the centrality and network density both significantly improved compared with 2005. The network structure was complex and tight,and its position in the grain trade network was constantly improving.

Fig. 4 Grain trade network in Argentina. Abbreviations in the figure are country codes and the corresponding country names are given in Appendix A.

Argentina is an important producer and exporter of grain in the world,known as the “grain barn of the world”,with a developed agricultural and livestock industry. Most of the country has fertile soil and a subtropical climate,and the eastern and central pampas are famous for their agricultural and pastoral areas,where 70% of the country’s population and 80% of its agriculture are concentrated.Argentina has adopted an externally oriented agricultural development model,actively engaging in the world agricultural market. The country’s agricultural policies are designed to improve the competitiveness of its agriculture,including “loosening the restrictions and reducing the burden” and creating a favorable external environment.In recent years,Argentina has been actively using new technologies to promote agricultural development,with a high level of agricultural mechanization among Latin American countries. At the same time,Argentina attaches importance to crop seed selection,and most of the grain crops produced in the country are genetically modified,including 100% of soybeans,86% of corn,and 99% of cotton.

Grain trade network in lndiaIndia had a developed grain trade network in 2020 (Fig.5),with all flows being exported and a greater weight of exports going to neighboring Asian countries such as Iraq,Nepal,Iran,and Saudi Arabia by virtue of geographical proximity,showing a network structure with India as the center to disperse outward.

In the first two study periods,India’s grain trade volume was small,and the network structure was simple,with trade only with one or two countries. However,the number of trading countries and the volume of India’s grain exports increased significantly in 2020,with a greater weight of exports to nearby Asian countries such as Iraq,Nepal,Iran,and Saudi Arabia. It was also the year when COVID-19 spread around the world,and in an atmosphere of tight internal harvest in the global grain market,India exported 13 million tons of rice,a year-onyear growth rate of 43.5%,accounting for 25% of global rice trade volume.

Fig. 5 Grain trade network in India. Abbreviations in the figure are country codes and the corresponding country names are given in Appendix A.

However,according to the Global Hunger Index released by the International Food Policy Research Institute,India ranked 102nd in the 2019 Hunger Index,classified as a country with “severe” hunger. Up to two million children under the age of 5 die each year in India,and 50% of them die from hunger and malnutrition-related diseases. The root cause of this phenomenon is India’s insufficient per capita grain production. India’s agricultural infrastructure is relatively backward;its agricultural irrigation can only rely on water storage except for the rainy season,and many areas suffer from drought disasters. At the same time,India lacks a sound industrial system,and its industrial level is too low to realize self-production and self-sale. Everything from daily necessities to military equipment needs to be exported.Exporting grains has become the only way for India to earn foreign exchange;as a result,most Indian farmers have to export grains in exchange for the necessities of life.

Grain trade network in BrazilIn 2020,Brazil’s trade network structure was complex and diverse,with grain trade flows including exports and imports (Fig.6). The export of grain to Asian countries such as Vietnam,Iran,Japan,and Egypt had a significant weight,while grain imports came mainly from Argentina,Paraguay,Uruguay,and the United States.

Brazil’s grain trade network has also developed over time. In the first two study periods,Brazil’s trade network had a simple structure with a small number of trading countries. In 1990,it imported grain from the United States and Canada. In 2005,it imported grain from Uruguay and Argentina while exporting to Iran and Korea.In 2020,the number of Brazil’s grain trading partners and network density increased significantly from 2005,with a more diverse and complex network structure and rapid development of grain trade.

Fig. 6 Grain trade network in Brazil in 2020. Abbreviations in the figure are country codes and the corresponding country names are given in Appendix A.

Brazil is rich in arable land resources. The Amazon Plain is located in the northern and western regions of Brazil,covering about one-third of the country’s total area. The Amazon rainforest,the largest in the world,has a moderate and suitable climate,with an average temperature not exceeding 27 degrees Celsius,providing excellent conditions for the development of agriculture and livestock. Brazil also has abundant water,with the Amazon and Parana rivers serving as irrigation sources and producing high-quality land. Brazil has adopted a large estate farming operation model based on local conditions,and the main crops include soybeans,corn,coffee beans,rice,and sugar cane. Farmers can lease large areas of land and use modern agricultural operation models to centralize management,which has led to an increasing level of agricultural mechanization and management in Brazil. Especially in the south of Brazil,most haciendas are operated with capital participation,adopting a centralized management model and macro coordination model,which has resulted in a positive correlation between agricultural input and output.

3.2.Analysis and evolution of community structure

Overview of community structureUsing community structure analysis,all countries covered by UN Comtrade are divided into communities (Appendix B). In 1990,2005,and 2020,those countries were divided into five communities,and the number of communities is relatively stable. In terms of the distribution of community members,there is one large community in 1990,2005,and 2020,whose members are all countries with a grain trade value of less than 100 million USD and,therefore,not present in the grain trade network. The proportion of this kind of community has decreased over time,while the proportion of the other four small independent communities has gradually increased,forming four tighter trading circles. This indicates that more and more countries’ grain trade has been growing and exceeding 100 million USD,and grain trade partnerships among countries within the communities have gradually become stable,showing continuous development and expansion.

Table 4 shows the community structure of the grain trade network in 2020 (countries not present in the network are not listed). In this year,community 1 was mainly composed of European and American countries,community 2 was mainly composed of Asian and African countries,community 3 was mainly composed of European,Asian,and Australian countries,and community 4 was mainly composed of Asian and American countries.

Table 4 Membership of grain trade network communities in 2020

Spatial distribution and evolution of communitystructureFurther comparison of the spatial distribution of global grain trade communities in 1990,2005,and 2020 are grouped into four clusters;each community shows an evolutionary pattern of separation and reorganization:

(1) Evolution of the China-Brazil-Russia community to the Americas-Europe community. In 1990,China,Brazil,and Russia formed the community,and in 2005,China left the community,while countries such as the United States,India,and Saudi Arabia joined,leading to the expansion of the community and the formation of the Americas-Europe community. In 2020,with most countries in East Africa and Western Europe joining,the Americas-Europe community had grown to become the most widespread and largest grain trade group in the world.

(2) Evolution of the South Asia community to the South Asia-Australia-Mediterranean community. In 1990,countries such as Iran,Indonesia,and Thailand formed a community dominated by South Asia,and in 2005,China became a member of this community. In 2020,China broke away from the community,while most of the Mediterranean coastline countries,Australia,and Pakistan joined the South Asia community,forming the South Asia-Australia-Mediterranean community.

(3) Evolution of the United States-Canada-India-Australia community to the Americas-China community.In 1990,the United States,Canada,India,and Australia formed a community,but it no longer existed by 2005. In 2020,China formed a new the Americas-China community with close links to Canada,Brazil,and other countries in the Americas.

(4) Formation of emerging grain trade community. In recent years,Middle Eastern countries,such as Saudi Arabia and Iran,and African coastal countries,such as Somalia,formed a new Africa-Middle East community by virtue of their proximity to maritime transport.

4.Discussion

Studies have shown that over the past 30 years,as economic globalization has deepened,international grain trade has formed a well-connected network system with a diverse and balanced network structure. High centrality nodes have significantly increased,and these nodes all have high export capabilities,as seen in the top five countries ranked by centrality,which is consistent with the views of related network studies (Anet al.2014;Fairet al.2017;Wanget al.2020a). Grain exports are directly dependent on production,and the analysis of high-centrality countries reveals that they mostly have vast territories,abundant arable land resources,and favorable climate conditions for crop growth,providing unique natural advantages. In addition,countries such as the United States,Argentina,and Brazil are committed to strengthening the level of specialization and mechanization of agricultural production to continuously improve agricultural technical efficiency. These countries thus have higher grain output per unit area. Meanwhile,the commercialization and marketization of agriculture have been continuously strengthened,the grain market circulation system has become more mature,and the export channels have become more diverse and complete(Freshwater 2016).

Currently,economic globalization is encountering headwinds,and unilateralism and protectionism are on the rise. Especially since the outbreak of the COVID-19 pandemic,the United States,as the country with the highest centrality in the network,has implemented an infinitely loose monetary policy,leading to a sharp rise in global grain prices and global inflationary pressure,which may easily lead to social unrest and political turmoil in some vulnerable developing countries. At the same time,12 countries have initiated partial grain export restrictions,including Russia,Egypt,Vietnam,India,and others countries,which have increased grain price volatility and heightened fears about world grain security.The ongoing escalation of the conflict between Russia and Ukraine is driving up international grain prices and even posing risks of supply chain disruptions,exerting a strong impact on global grain trade. The development status and trade strategies of core nodes in the grain trade network are highly likely to influence and control global grain trade. The increasingly complex international trade situation and frequent trade shocks are creating unprecedented pressure on safeguarding national grain security (Rosegrant and Cline 2003;Godfrayet al.2010).Therefore,how to ensure national grain security in the new trade situation has become a major issue that must be addressed.

From 1990 to 2020,the position of some countries in global grain trade has been relatively stable. For example,the United States,Canada,China,and Brazil ranked in the top ten,and the United States always ranked first in degree centrality during the three-year study period. However,the status of some countries in grain trade fluctuated and rose significantly over time,such as Argentina,Ukraine,and Russia. Further,based on the net grain trade in 2020,participating countries can be divided into three categories: (1) net grain exporters,such as the United States,France,Canada,Argentina,Australia,and Thailand;(2) countries with volatile grain trade or significant shifts in trade identity,such as Argentina,Ukraine,and Russia;(3) net grain importers,such as Mexico,Iran,Korea,China,and Japan. We find that the global grain trade pattern has changed significantly both in temporal and spatial distribution and geographical patterns. This pattern shift is related to the changes in the grain production capacity of countries on the one hand and the role shift of developed and developing countries in the international grain trade pattern on the other. As for the grain trade volume,although some developing countries have replaced developed countries as the world’s leading grain import and export trade area,this simple quantitative analysis cannot reveal the true economic meaning behind the trade volume from the perspective of the net grain trade. In fact,developed countries run a trade surplus in grain trade and remain net gainers in global grain trade,while developing and low-income countries maintain a chronic grain trade deficit,which continues to expand in some countries,such as China,Korea,and Iran. Developed grain countries prefer to form close grain cooperation relations internally. Thus,from the perspective of economic gains,developed countries are still the biggest winners in international grain trade,and the absolute gap in grain trade among the three categories of countries may continue to widen under the current complex and severe international situation.

5.Conclusion and implications

From 1990–2020,the global major grain trade networks are expanding in size,with more diverse and balanced connections between countries. Countries with significant grain exports are mainly in Asia,the Americas,and Europe;countries with more imports are mainly in Asia,Africa,and Europe. The number of countries with high centrality in the network is increasing,and most of them have natural advantages such as abundant arable land and a suitable climate. Countries such as the United States,Argentina,Brazil,and others have high levels of specialization and mechanization of agricultural production,mature grain market circulation systems,and diverse export channels. From 1990 to 2020,the global major grain trade network can be divided into four communities. The internal structure of the community shows the evolutionary characteristics of separation and reorganization over time. The Americas-Europe community is the largest international grain trade community at present. Geographical proximity is one factor in forming the community pattern,which is dominated by the core exporting countries.

Based on the main conclusions and discussions,the following implications are proposed:

Develop and optimize the existing trade patterns.Intensive attention should be paid to high-centrality countries in global grain trade,such as the United States,Russia,and Argentina. These large trading countries should be urged to reduce trade restrictions,expand producer responsibility,consciously maintain the global grain trade order,and maintain the liquidity of the global grain trade network.

Promote multipolarity in grain trade. More countries should be encouraged to participate in grain trade,promoting a country’s diversified trade network. Countries highly dependent on gain imports,such as Japan and South Africa,should further expand trade ties with other countries to diversify grain trade risks and ensure grain supply.

Countries should establish a global vision of the future community and actively participate in global grain security governance and institutional reform. All countries are in a complex grain trade system with increasing trade agglomeration and dependency. Therefore,in the face of complex domestic and international grain markets,countries should strengthen international cooperation,enhance their collective action capacity,and jointly build a coordinated global grain trade policy to ensure security and stability.

Exploring international grain trade patterns through complex network theory is a research direction for scholars. Although this study deals with international grain trade,the data on wheat,paddy,and corn are only selected as the main representatives of grain,and studies on other grain crops or more comprehensive trade networks are still needed.

Acknowledgements

This research was funded by the National Natural Science Foundation of China (42271313),the Chinese Academy of Agricultural Sciences Innovation Project (CAAS-ASTIP-2021-AII),the Central Public-interest Scientific Institution Basal Research Fund,China (JBYW-AII-2022-06,JBYWAII-2022-40).

Declaration of competing interest

The authors declare that they have no conflict of interest.

Appendicesassociated with this paper are available on https://doi.org/10.1016/j.jia.2023.10.032