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| Visual Analysis of Research Hotspots, Trends and Cooperation Patterns in Global Soybean (Glycine max) Breeding Based on CiteSpace |
| ZHANG Kun-Yue1, LI Xi-Dong2, CHEN Li-Mei2, SHI Yang1, LI Jia-Ming3, YU Xiang1,* |
1 Library of China Agricultural University, Beijing 100083, China;
2 College of Biology, China Agricultural University, Beijing 100193, China;
3 School of Publishing, Beijing Institute of Graphic Communication, Beijing 102600, China |
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Abstract Soybean (Glycine max) is an important grain-oil-forage crop as well as a major source of plant protein. Its varietal improvement is of great significance for ensuring food and edible oil security, enhancing agricultural production resilience, and addressing climate change. With the rapid development of genomics, phenomics, and intelligent algorithms, the knowledge structure and technological pathways of global soybean breeding research are continuously evolving. To systematically reveal the research progress, hotspot evolution, and collaboration characteristics in global soybean breeding, this study used 1 831 English-language publications indexed in the Web of Science Core Collection (WoSCC) from 2016 to 2025 as data sources and applied CiteSpace to conduct a visual bibliometric analysis of collaboration networks, keyword co-occurrence, keyword clustering, and burst terms. The results showed that global soybean breeding research maintained a relatively high level of activity during 2016~2025. The national collaboration network was characterized by the leading roles of China and the United States, with participation from multiple countries, while deeper cross-regional collaboration at the institutional and author levels still needs to be strengthened. Research hotspots were mainly concentrated on QTL mapping, genome-wide association studies, candidate gene mining, gene expression, genetic diversity, yield and quality traits, agronomic traits, stress resistance, and genomic selection. Frontier topics have gradually shifted from genetic linkage maps, molecular markers, and QTL mapping to candidate gene analysis, gene function validation, and machine-learning-assisted breeding. These findings provide references for future research topic selection, international collaboration, and optimization of soybean breeding technical strategies.
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Received: 07 May 2026
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| Fund:农业农村部重大农业科技项目(NK2023080102); 中国农业大学双一流科研基金项目(高产高油大豆专项)(2025AC043) |
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Corresponding Authors:
*mopon@cau.edu.cn
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