Identification of international trade patterns of agricultural products: the evolution of communities and their core countries

ABSTRACT

As a special branch of global trade, the trade of agricultural products has an important impact on food security and the environment. In this paper, we studied international trade network of agricultural products from 2000 to 2016 as a whole and in part. We explored the overall characteristics of the network, analyzed the evolution of communities and identified core countries of the communities. The results show that the structure of the trade network became increasingly complex and the trade relations became closer over time. There were four major communities in the network, whose primary core countries were Germany, the United States, Brazil, and China. Since 2007, the community represented by China has disappeared, and the community pattern of the network has been in a three-pillar state and basically stable. We discuss the actual roles of certain trading countries, the formation of communities and the impact of economic events on agricultural products trade. This paper reveals the underlying patterns of the agricultural products trade and provides a way to track its evolution over time.

KEYWORDS:

• Complex network
• communities
• core countries
• international trade of agricultural products
• spatial pattern

Acknowledgments

The authors express their gratitude to the Standard Map Service website (http://bzdt.ch.mnr.gov.cn/) and Institute of Geographic Sciences and Natural Resources Research, CAS (https://www.resdc.cn/) for providing the world map.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study were derived from the UN Comtrade and Chatham House Resource Trade Database at https://resourcetrade.earth/. Derived data are available from the corresponding author.

Additional information

Funding

This work is supported by the National Key Research and Development Program of China [grant number 2016YFC0803106] and the National Natural Science Foundation of China [grant number 41571438].

Notes on contributors

Jiaxin Dong

Jiaxin Dong is currently pursuing the PhD degree at the School of Remote Sensing and Information Engineering, Wuhan University. Her main research interests are remote sensing and complex network analysis.

Siwei Li

Siwei Li is a professor at the School of Remote Sensing and Information Engineering, Wuhan University. His main research interests are atmospheric remote sensing and air pollution.

Lina Huang

Lina Huang is an associate professor at the School of Resource and Environmental Sciences, Wuhan University. Her main research interests are geographic information visualization, spatial data multi-representation and complex network.

Jing He

Jing He is a PhD student of School of Resource and Environmental Sciences, Wuhan University. Her research interests focus on data mining, spatial analysis and poverty research.

Wenping Jiang

Wenping Jiang is an associate professor at the School of Resource and Environmental Sciences, Wuhan University. His research interests include digital map, geographic information visualization and spatial analysis.

Fu Ren

Fu Ren is a professor at the School of Resource and Environmental Sciences, Wuhan University. His main research interests are big data analysis and geographic information visualization.

Yujing Wang

Yujing Wang received her PhD degree from the School of Resource and Environmental Sciences, Wuhan University. Her research interests include satellite remote sensing data management and knowledge graph.

Jiang Sun

Jiang Sun is an assistant engineer with Hydrogeology & Geoengineering Investigation Institute of Hubei Province.

Hao Zhang

Hao Zhang received his master’s degree in 2019 from Wuhan University. His research interests include spatio-temporal data mining and mapping.