Rainfed rice breeding (RRB) at IRRI started since the establishment of the institute and is continuously committed to innovate and develop improved rice germplasm for improving the livelihood of farmers encountering challenging climates. The International Rice Research Institute (IRRI), established in the 1960s, is the world’s premier research organization dedicated to rice science. Further, this pipeline can be easily implemented by plant breeders or researchers, helping and guiding them in analyzing the breeding trials data in the best possible way. We believe this is a great initiative to modernize the data analysis of IRRI’s RRB program. The analysis workflow and document presented are not limited to IRRI’s RRB program but are applicable to any organization or institute with full-fledged breeding programs. The analytical pipeline source codes and demo data are available at. The analysis is highly reproducible and can be regenerated at any time. Explanatory text with R codes, outputs either in text, tables, or graphics, and interpretation of results are integrated into the unified document. The analysis pipeline shows how to pre-process and check the quality of phenotypic data, perform robust data analysis using modern statistical tools and approaches, and convert it into a reproducible document.

The developed analytical pipeline is open-source, demonstrating how to analyze the phenotypic data in crop breeding programs with step-by-step instructions. We reported the state-of-the-art implementation of the phenotypic data analysis pipeline and workflow embedded into a well-descriptive document.

Here, we provide an overview of how the Rainfed Rice Breeding (RRB) program at IRRI has leveraged R computational power with open-source resource tools like R Markdown, plotly, LaTeX, and HTML to develop an open-source and end-to-end data analysis workflow and pipeline, and re-designed it to a reproducible document for better interpretations, visualizations and easy sharing with collaborators.

Developing a systematic phenotypic data analysis pipeline, creating enhanced visualizations, and interpreting the results is crucial to extract meaningful insights from data in making better breeding decisions.

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