Czech J. Anim. Sci., 2025, 70(8):301-318 | DOI: 10.17221/32/2025-CJAS
Visualisation analysis of research frontiers, hotspots and trends in Fleckvieh breeding based on CiteSpace knowledge graphReview
- 1 College of Agriculture, Yanbian University, Yanji, Jilin Province, P.R. China
- 2 Shanshi Farm, Beidahuang Group, Hailin, Heilongjiang Province, P.R. China
- 3 Yanbian Korean Autonomous Prefecture Animal Husbandry General Station, Yanji, Jilin Province, P.R. China
This study investigates advancements and trends in Fleckvieh breeding research using the Web of Science Core Collection database to collate articles published since 1994. Through CiteSpace 6.4.R1, we conducted systematic analyses to visualise research trajectories, identify seminal domains, and delineate emerging patterns. Our methodology encompassed publication profiling, scientific collaboration network mapping, co-citation analysis, and keyword co-occurrence/clustering techniques. The findings reveal that the current research frontiers focus on: Genomic applications in genetic selection, Reproductive efficiency optimisation, Environmental adaptability mechanisms, Thermotolerance management strategies, Cross-disciplinary methodological integration. Germany, Czech Republic, Austria, and Switzerland emerged as leading contributors, with institutions including the Technical University of Munich and University of Natural Resources and Life Sciences, Vienna, demonstrating a particular scholarly influence. Knowledge mapping effectively delineated thematic concentrations and temporal evolution patterns, providing actionable insights for future research prioritisation. The field is progressing towards holistic, interdisciplinary frameworks underpinned by robust international consortia. This work introduces novel analytical perspectives to refine breeding protocols and enhance practical implementation.
Keywords: bibliometric analysis in animal science; climate-resilient breeding; genomic selection application; heat stress mitigation; interdisciplinary breeding strategy; knowledge graph analysis
Received: March 6, 2025; Accepted: July 20, 2025; Prepublished online: August 26, 2025; Published: August 27, 2025 Show citation
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