Business Intelligence Tools Applied in the Assessment of the Genotype X Environment Interaction in Elephant Grass

Objective: This study aimed to present a tool for evaluating the genotype x environment interaction using dashboards developed with Business Intelligence (BI) techniques in multiple elephant grass experiments for bioenergy production. Theoretical Framework: Elephant grass, among the crops dedicated to the production of biomass energy, is considered a prominent alternative. It is widely known to breeders that, when evaluating genotypes in various environments, consistent behavior is not observed. This differentiated response of genotypes to environmental variation is called genotype x environment interaction. As the main purpose of a breeding program is to select genotypes of consistent and high productivity, in the most diverse environments, the interaction of these factors can represent a problem for breeders, as it reduces the precision in the selection from one environment to another. Method: The tool selected for the development and manipulation of dashboards, provided by Google, is called Looker Studio, which allows the unification of several databases in a single environment. This integration facilitates the manipulation, creation and development of interactive management reports, promoting a dynamic and personalized visualization of information, an essential feature for analyzing and interpreting data in complex scenarios such as genotype x environment interaction. Results and Discussion: BI tools prove to be a promising strategy for interpreting the genotype x environment interaction in multiple elephant grass experiments for bioenergy production. These tools allow researchers to quickly and dynamically visualize this phenomenon, providing ease of interpretation of large data sets. Research Implications: The theoretical and practical implications of this research are valuable for evaluating the genotype x environment interaction in elephant grass, as they allow collecting, storing, processing and visualizing large amounts of data efficiently, allowing users to identify patterns and trends in the data that assist in making more assertive decisions. Originality/Value: This study contributes to the evaluation of elephant grass genotypes for biomass energy production.