Name: Ivan Ruiz HernandezInstitution: IME, USP, Brazil
Co-authors: Julia Aurora Montano Rivas, Gabriel Diaz Padilla y Andres Rivera Fernandez

Bayesian statistics is not a new approach to statistics, in recent years, through technological advances, its application in research has been increasing. However, despite the advantages over the classic or frequentist approach to statistics (presicion, flexibility, for example), the implementation in agricultural research and biotechnology has been scarce. The objective of this paper is to present an overview of Bayesian statistics in agricultural research and biotechnology, for this purpose, published research of the agricultural area was compiled in the periods 2014-2018, which were analyzed through a textual data mining algorithm. In the case of agricultural research, models are used to analyze spatial distribution as well as regression models, while in biotechnology there is agreater implementation of models for the resolution of complex problems. According to the review of literature in Mexican journals, it was found that the main methodologies used in agricultural research are the experimental designs and their variations, linear regression models and logistic regression (applied to agricultural epidemiology) all through the classic approach. It is important to generate an approach towards Bayesian inference as a better alternative to be
able to solve complex problems, obtaining conclusions with greater reliability that allow us to make better decisions.