Back

Peanut Smut: A scientometric analysis for a pathosystem that concerns the Argentine peanut industry.

Cazon, L. I.; Paredes, J. A.; Miretti, E.; Gonzalez, N. R.; Suarez, L.; Conforto, E. C.; Rago, A. M.

2023-09-07 plant biology
10.1101/2023.09.06.555881 bioRxiv
Show abstract

Since its first report in commercial batches in 1995, the prevalence and yield impact caused by smut disease have increased rapidly in peanut fields. At the same time, various working groups have studied this pathosystem using different approaches, contributing to the scientific knowledge of the disease. By recognizing the importance of a thorough bibliographic review and meticulous organization of information, the process of initiating new research projects becomes more effective. In light of this, the aim of this work was to provide a comprehensive scientometric analysis of the evolution of peanut smut research, spanning from its inception to the current day. For this purpose, we compiled bibliographic data about the disease and extracted information to calculate metrics. We observed that a smaller proportion of the scientific production was presented in peer-reviewed journals, the prevalent topics were epidemiology and breeding, and the collaborative endeavors were crucial for the scientific advancement in the study of this pathosystem. Additionally, the researchers with the most significant presence in the publications, the involved institutions, and the impact of the produced papers, among other trends were identified. Although there have been many scientific-technological advances in peanut smut over the years, this information is not reflected in scientific papers in peer-reviewed journals, which represents a great challenge for researchers involved in this topic. It is crucial to continue generating knowledge that contributes to the integrated management of this complex pathosystem. This will prevent further yield losses and the spread of the pathogen to new production areas.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.