Topics and trends in Mountain Livestock Farming research: a text mining approach.

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, of which 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.

Characterisation of terminal sire sheep farm systems, based on a range of environmental factors: a case study in the context of genotype by environment interactions using Charollais lambs.

The objective of this study was to define different terminal sire flock environments, based on a range of environmental factors, and then investigate the presence of genotype by environment interactions (G×E) between the environments identified. Data from 79 different terminal sire flocks (40 Texel, 21 Charollais and 18 Suffolk), were analysed using principal coordinate and non-hierarchical cluster analyses, the results of which identified three distinct environmental cluster groups. The type of grazing, climatic conditions and the use of vitamins and mineral supplements were found to be the most important factors in the clustering of flocks. The presence of G×E was then investigated using data from the Charollais flocks only. Performance data were collected for 12 181 lambs, between 1990 and 2010, sired by 515 different sires. Fifty six of the sires had offspring in at least two of the three different cluster groups and pedigree information was available for a total of 161 431 animals. Traits studied were the 21-week old weight (21WT), ultrasound muscle depth (UMD) and log transformed backfat depth (LogUFD). Heritabilities estimated for each cluster, for each trait, ranged from 0.32 to 0.45. Genetic correlations estimated between Cluster 1 and Cluster 2 were all found to be significantly lower than unity, indicating the presence of G×E. They were 0.31 (±0.17), 0.68 (±0.14) and 0.18 (±0.21) for 21WT, UMD and LogUFD, respectively. Evidence of sires re-ranking across clusters was also observed. Providing a suitable strategy can be identified, there is potential for the optimisation of future breeding programmes, by taking into account the G×E observed. This would enable farmers to identify and select animals with an increased knowledge as to how they will perform in their specific farm environment thus reducing any unexpected differences in performance.