Regression models applied to rhizosphere data: A bibliometric review - Batista, Aline Martineli; Prataviera, Fábio (Brazilian Journal of Biometrics, 2024)
The interaction of soil with plant roots in the rhizosphere plays an important role in various ecosystem services and food production, and it has been the focus of numerous studies. In turn, statistical modeling can aid in a more comprehensive understanding of this interaction, such as the application of regression models to rhizosphere data. Thus, the main objective of this work was to develop the first bibliometric analysis on regression models applied to rhizosphere data. Bibliometric data were obtained from Web of Science and Scopus databases. We use the topic retrival as ((“Rhizosphere”) AND (“Regression models” OR “Regression model” OR “Generalized Linear Models” OR “Generalized Linear Model”)) to search for scientific articles that contain these keywords in their title, abstract, or keywords. The search encompassed articles published from 1900 to 2022, resulting in 34 articles, with the earliest record dating back to 1985. While studies of the rhizosphere are increasing, few studies apply regression models to their data. The use of more advanced techniques, such as Generalized Linear Models (MLG), Artificial Neural Network (ANN), Random Forest Model (RFM), Support Vector Machines (SVM), and Generalized Boosted Regression Modeling (GBM), became evident from 2016 onwards, which was associated with the computational advances and the development of artificial intelligence. Some articles demonstrated that the use of more robust models can provide more meaningful results to the researcher. Only one article was published in a journal dedicated to statistics, highlighting the diffusion of regression models into various fields. Collaborations involving co-authorship between researchers from different countries have led to higher citation rates, increasing the importance of the research to the scientific community. Perhaps one of the most notable limitations to increasing research using regression models is the absence of a statistician or researcher within the research groups who is well versed in statistical models and procedures.