Pasture-based and small-scale livestock farming systems are the main source of livelihood in the mountain primary sector, ensuring socioeconomic sustainability and biodiversity in rural communities throughout Europe and beyond. Mountain livestock farming (MLF) has attracted substantial research efforts from a wide variety of scientific communities worldwide. In this study, the use of text mining and topic modelling analysis drew a detailed picture of the main research topics dealing with MLF and their trends over the last four decades. The final data corpus used for the analysis counted 2 679 documents, ofwhich 92% were peer-reviewed scientific publications. The number of scientific outputs in MLF doubled every 10 years since 1980. Text mining found that milk, goat and sheep were the terms with the highest weighed frequency in the data corpus. Ten meaningful topics were identified by topic analysis: T1-Livestock management and vegetation dynamics; T2-Animal health and epidemiology; T3-Methodological studies on cattle; T4-Production system and sustainability; T5-Methodological studies; T6-Wildlife and conservation studies; T7-Reproduction and performance; T8- Dairy/meat production and quality; T9-Land use and its change and T10-Genetic/genomic studies. A hierarchical clustering analysis was performed to explore the interrelationships among topics, and three main clusters were identified: the first focused on sustainability, conservation and socioeconomic aspects (T4; T6 and T9), the second was related to food production and quality (T7 and T8) and the last one considered methodological studies on mountain flora and fauna (T1; T2; T3; T5 and T10). The 10 topics identified represent a useful and a starting source of information for further and more detailed analysis (e.g. systematic review) of specific research or geographical areas. A truly holistic and interdisciplinary research approach is needed to identify drivers of change and to understand current and future challenges faced by livestock farming in mountain areas.
