A Multi-Field Text Mining and Topic Modelling Approach to the Potato Research Journal (1970–2024)

This study systematically examines the thematic and conceptual evolution of the Potato Research journal between 1970 and 2024 using a multi-layered text mining and topic modelling framework. A total of 1967 articles were analyzed across titles, abstracts, and keywords to capture surface-level and latent themes. Word frequency analysis, trend analysis, co-occurrence networks, and thematic mapping were integrated with Latent Dirichlet Allocation (LDA) and Structural Topic Modeling (STM) to identify dominant research themes and to evaluate their temporal dynamics. In addition, Sustainable Development Goals (SDGs) mapping was conducted using three independent SDG identification frameworks to assess the alignment of potato research with global sustainability agendas. The findings reveal a clear transformation in the journal’s scientific orientation, shifting from an early focus on agronomic production and plant pathology toward sustainability-oriented, climate-resilient, and data-intensive research paradigms. LDA identified five core thematic domains, namely post-harvest pathology, genetic resistance and molecular breeding, abiotic stress and physiological responses, plant growth and productivity, and agricultural management, which were further validated through STM-based inferential analysis. Temporal trends indicate statistically significant increases in themes related to climate change, water management, food quality, and analytical modelling, alongside a relative decline in conventional agronomic practices. SDG mapping demonstrates strong alignment with SDG 2 (Zero Hunger), SDG 3 (Good Health and Well-Being), and particularly SDG 13 (Climate Action). The findings highlight the role of Potato Research as both a historical record of disciplinary development and a scientific publishing platform reflecting sustainability-oriented agricultural research.