Mr.Bean: a comprehensive statistical and visualization application for modeling agricultural field trials data

Johan Aparicio; Salvador A Gezan; Daniel Ariza-Suarez; Bodo Raatz; Santiago Diaz; Ana Heilman-Morales; Juan Lobaton

doi:10.3389/fpls.2023.1290078

Mr.Bean: a comprehensive statistical and visualization application for modeling agricultural field trials data

Front Plant Sci. 2024 Jan 3:14:1290078. doi: 10.3389/fpls.2023.1290078. eCollection 2023.

Authors

Johan Aparicio¹, Salvador A Gezan², Daniel Ariza-Suarez¹, Bodo Raatz¹, Santiago Diaz¹, Ana Heilman-Morales³, Juan Lobaton¹

Affiliations

¹ Bean Program, Crops for Nutrition and Health, Alliance Bioversity-International Center for Tropical Agriculture (CIAT), Cali, Colombia.
² Deparment of Statistical Genetics, InternationalVSN, Hemel Hempstead, United Kingdom.
³ Big Data Pipeline Unit, North Dakota State UniversityAES, Fargo, ND, United States.

Abstract

Crop improvement efforts have exploited new methods for modeling spatial trends using the arrangement of the experimental units in the field. These methods have shown improvement in predicting the genetic potential of evaluated genotypes. However, the use of these tools may be limited by the exposure and accessibility to these products. In addition, these new methodologies often require plant scientists to be familiar with the programming environment used to implement them; constraints that limit data analysis efficiency for decision-making. These challenges have led to the development of Mr.Bean, an accessible and user-friendly tool with a comprehensive graphical visualization interface. The application integrates descriptive analysis, measures of dispersion and centralization, linear mixed model fitting, multi-environment trial analysis, factor analytic models, and genomic analysis. All these capabilities are designed to help plant breeders and scientist working with agricultural field trials make informed decisions more quickly. Mr.Bean is available for download at https://github.com/AparicioJohan/MrBeanApp.

Keywords: breeding; experimental designs; multi-environmental analysis; spatial analysis; trial.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was funded by the Tropical Legumes III-Improving Livelihoods for Smallholder Farmers: Enhanced Grain Legume Productivity and Production in Sub-Saharan Africa and South Asia (OPP1114827), and by the AVISA-Accelerated varietal improvement and seed delivery of legumes and cereals in Africa (OPP1198373) projects funded by the Bill and Melinda Gates Foundation. We would like to thank the USAID for their contributions through the CGIAR Research Program on Grain Legumes and Dryland Cereals.