Author productivity on application of random regression models in animal breeding research through Lotka’s law
K Sakunthala Devi, Kutty Kumar and S Vani
The purpose of this paper is to know whether the author productivity pattern on usage of random regression models in animal breeding research adheres to Lotka’s inverse square law of scientific productivity. Since the law was introduced, it has been tested in various fields of knowledge and results were varied. The data on 236 number of animal breeding research publications where random regression models used were downloaded from the PubMed database to know about the authorship productivity pattern and citations. Data were analyzed by using bibliometric indicators like publication and citation growth, co-authorship pattern, prolific authors and sankey diagram authors, title of research including country where research was carried out. Lotka’s inverse square law was applied to assess authors’ productivity pattern of animal breeding research that used random regression models and further Kolmogorov-Smirnov (K-S) goodness-of-fit test was applied for testing of observed and expected author productivity in the data. Inferences drawn for the set objectives in this study on authorship pattern, collaboration trend and authors’ productivity pattern revealed that, the author “Misztal Ignacy” was found as most prolific author on usage of RRM in animal breeding research. The Chi-square test expected value of Lotka’s law varied significantly from the observed value and K-S goodness-of-fit test revealed that this study does not adhere to Lotka’s inverse square law of scientific productivity.
How to cite this article:
K Sakunthala Devi, Kutty Kumar and S Vani. Author productivity on application of random regression models in animal breeding research through Lotka’s law. The Pharma Innovation Journal. 2023; 12(12S): 546-552.