Multi-environment trials (MET) are crucial steps in plant breeding programs that aim at increasing crop productivity to ensure global food security. The analysis of MET data requires the combination of several approaches including data manipulation, visualization and modelling. As new methods are proposed, analysing MET data correctly and completely remains a challenge, often intractable with existing tools.Here we describe the metan R package, a collection of functions that implement a workflow-based approach to (a) check, manipulate and summarize typical MET data; (b) analyse individual environments using both fixed and mixed-effect models; (c) compute parametric and nonparametric stability statistics; (d) implement biometrical models widely used in MET analysis and (e) plot typical MET data quickly.In this paper, we present a summary of the functions implemented in metan and how they integrate into a workflow to explore and analyse MET data. We guide the user along a gentle learning curve and show how adding only a few commands or options at a time, powerful analyses can be implemented.metan offers a flexible, intuitive and richly documented working environment with tools that will facilitate the implementation of a complete analysis of MET datasets.
metan: An R package for multi-environment trial analysis
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Decision Letter
2020/02/2525-Feb-2020
MEE-19-11-847 metan: an R package for multi-environment trial analysis
Dear Dr Tiago Olivoto,
It is a pleasure to accept your manuscript entitled "metan: an R package for multi-environment trial analysis" in its current form for publication in Methods in Ecology and Evolution. The comments of the reviewers who reviewed your manuscript are included below. Final instructions for your manuscript, and some promotion options, can be found at the end of this email.
Thank you for your fine contribution. On behalf of all Editors of Methods in Ecology and Evolution, I look forward to your continued contributions to the Journal.
Sincerely,
Professor Robert Freckleton
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Associate Editor Comments to Author:
Associate Editor
Comments to the Author:
Both reviewers for this manuscript were positive, if brief in their comments. I also found this a well written paper on a topic that should be of broad interest, so I recommend it be accepted for publication.Reviewer(s)' Comments to Author:
Reviewer: 1Comments to the Corresponding Author
The manuscript is well written and proposes a new package to perform statistical analysis in various environments. In addition, it provides two new selection indexes proposed by the authors themselves. I believe that this manuscript will be cited a lot due to the importance of the analyzes performed.Reviewer: 2
Comments to the Corresponding Author
Congratulations on the well-designed, documented and useful package and submitted paper.Decision letter by
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Reviewer report
2020/02/14Congratulations on the well-designed, documented and useful package and submitted paper.
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Reviewer report
2020/02/13The manuscript is well written and proposes a new package to perform statistical analysis in various environments. In addition, it provides two new selection indexes proposed by the authors themselves. I believe that this manuscript will be cited a lot due to the importance of the analyzes performed.
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