Trainings

BIOM 101: Introduction to Statistics and Design of Experiments

This course reviews basic statistics concepts and principles of statistical design in order to instill the foundational knowledge essential for understanding how to use statistics to test hypotheses in agricultural research and the importance and features of good experimental design. It is intended for anyone involved in planning, or executing biological or agricultural experiments or breeding trials. Through a series of recorded lectures and quizzes, participants will be able to learn at their own pace and on their own time.
This course introduces the basic principles and concepts of quantitative genetics that are important for understanding genetic improvement in breeding populations and how to make more effective selection decisions. It is intended for anyone with a basic understanding of genetics and statistics who wishes to learn how to utilize quantitative genetic principles to improve the efficiency of plant breeding programs. Through a series of recorded lectures and quizzes, participants will be able to learn at their own pace and on their own time.

BIOM 201: Basic Experimental Design and Data Analysis using STAR

This course is designed to acquaint researchers with the principles of experimental design, basic experimental designs used in crop research, analysis of variance, and correlation and regression analysis. It also introduces Statistical Tool for Agricultural Research (STAR), a user-friendly software that uses GUI created in Java and functions developed in R to assist crop scientists in the design and analysis of data. It is intended for researchers in the Agricultural and Biological Sciences. It employs a combination of lecture, group exercises, and hands-on exercises using STAR.

BIOM 202: Design and Analysis of Breeding Trials using PBTools

This course is designed to spread an awareness of what experimental designs and analysis methods are available, and how they can be used to add value to plant breeding trials and to help address common challenges. Through a combination of lecture and hands-on exercises using the software called Plant Breeding Tools (PBTools), this course will enable participants to apply some common analysis methods and experimental designs.



R is the leading open source statistical and data analysis programming language. Through thousands of useful open source libraries, R empowers its users to solve complex problems, efficiently analyze data and develop powerful visuals. R also enables users to build their own analysis methods and algorithms to solve specific problems. Through the use of an online self-study platform as well as live practice sessions and office-hours offered in-person at International Rice Research Institute (IRRI) Headquarters (HQ) or online via live streaming, this course empowers participants to become proficient in the R programming language so that they can begin to utilize R for whatever purpose they may need. For a more personalized learning experience, in-person attendance at IRRI HQ is recommended, but it is not required.


This course provides researchers with the knowledge and skills to be able to design and analyze agronomic experiments using the R programming language. Through a combination of lecture, group exercises, and hands-on activities, participants will learn the designs commonly used in agricultural research, analysis of variance, and correlation and regression analysis. This course is intended for researchers in agricultural and biological sciences, and is foundational for learning more advanced statistical techniques.


This course provides researchers with the knowledge and skills to be able to design and analyze breeding trial data and estimate breeding values. Through a combination of lecture, group exercises, and hands-on activities in R, participants will learn about designs that are especially useful in plant breeding, analysis methods and techniques for single and multi-trial analysis, and breeding value estimation.  This course is intended for anyone who analyzes or interprets plant breeding data. The knowledge gained in this course is essential for understanding even more advanced methods such as genomic selection.